Author Response

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

Reviewer #1:

Drawing on insights from preceding studies, the researchers pinpointed mutations within the spag7 gene that correlate with metabolic aberrations in mice. The precise function of spag7 has not been fully described yet, thereby the primary objective of this investigation is to unravel its pivotal role in the development of obesity and metabolic disease in mice. First, they generated a mice model lacking spag7 and observed that KO mice exhibited diminished birth size, which subsequently progressed to manifest obesity and impaired glucose tolerance upon reaching adulthood. This behaviour was primarily attributed to a reduction in energy expenditure. In fact, KO animals demonstrated compromised exercise endurance and muscle functionality, stemming from a deterioration in mitochondrial activity. Intriguingly, none of these effects was observed when using a tamoxifen-induced KO mouse model, implying that Spag7's influence is predominantly confined to the embryonic developmental phase. Explorations within placental tissue unveiled that mice afflicted by Spag7 deficiency experienced placental insufficiency, likely due to aberrant development of the placental junctional zone, a phenomenon that could impede optimal nutrient conveyance to the developing fetus. Overall, the authors assert that Spag7 emerges as a crucial determinant orchestrating accurate embryogenesis and subsequent energy balance in the later stages of life.

The study boasts several noteworthy strengths. Notably, it employs a combination of animal models and a thorough analysis of metabolic and exercise parameters, underscoring a meticulous approach. Furthermore, the investigation encompasses a comprehensive evaluation of fetal loss across distinct pregnancy stages, alongside a transcriptomic analysis of skeletal muscle, thereby imparting substantial value. However, a pivotal weakness of the study centres on its translational applicability. While the authors claim that "SPAG7 is well-conserved with 97% of the amino acid sequence being identical in humans and mice", the precise role of spag7 in the human context remains enigmatic. This limitation hampers a direct extrapolation of findings to human scenarios. Additionally, the study's elucidation of the molecular underpinnings behind the spag7-mediated anomalous development of the placental junction zone remains incomplete. Finally, the hypothesis positing a reduction in nutrient availability to the fetus, though intriguing, requires further substantiation, leaving an aspect of the mechanism unexplored.

Hence, in order to fortify the solidity of their conclusions, these concerns necessitate meticulous attention and resolution in the forthcoming version of the manuscript. Upon the comprehensive addressing of these aspects, the study is poised to exert a substantial influence on the field, its significance reverberating significantly. The methodologies and data presented undoubtedly hold the potential to facilitate the community's deeper understanding of the ramifications stemming from disruptions during pregnancy, shedding light on their enduring impact on the metabolic well-being of subsequent generations.

Thanks to this reviewer for their thoughtful analysis and commentary. Human mutations in SPAG7 are exceedingly rare (SPAG7 | pLoF (genebass.org)), potentially because of the deleterious effects of SPAG7-deficiency on prenatal development. This makes investigation into the causative effects of SPAG7 in humans challenging. There exist mutations in the SPAG7 region of the genome that are associated with BMI, but no direct coding variants within the spag7 gene itself have been studied.

We agree with the reviewer that the precise role of spag7 in the placenta remains unknown. However, given its robust expression and high protein levels in the placenta, including in key cells, such as the syncytiotrophoblast (https://www.proteinatlas.org/ENSG00000091640-SPAG7/tissue/Placenta), it is highly likely that spag7 is critical for normal placenta development and function. Multiple studies (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9716072/) have recently shown that sperm associated RNAs play a critical role in embryonic and early placenta development. Our findings will provide the basis for future studies that can elucidate the role of spag7 in human placenta.

Reviewer #2:

Summary:

The authors of this manuscript are interested in discovering and functionally characterizing genes that might cause obesity. To find such genes, they conducted a forward genetic screen in mice, selecting strains which displayed increased body weight and adiposity. They found a strain, with germ-line deficiency in the gene Spag7, which displayed significantly increased body weight, fat mass, and adipose depot sizes manifesting after the onset of adulthood (20 weeks). The mice also display decreased organ sizes, leading to decreased lean body mass. The increased adiposity was traced to decreased energy expenditure at both room temperature and thermoneutrality, correlating with decreased locomotor activity and muscle atrophy. Major metabolic abnormalities such as impaired glucose tolerance and insulin sensitivity also accompanied the phenotype. Unexpectedly, when the authors generated an inducible, whole body knockout mouse using a globally expressed Cre-ERT2 along with a globally floxed Spag7, and induced Spag7 knockout before the onset of obesity, none of the phenotypes seen in the original strain were recapitulated. The authors trace this discrepancy to the major effect of Spag7 being on placental development.

Strengths:

Strengths of the manuscript are its inherently unbiased approach, using a forward genetic screen to discover previously unknown genes linked to obesity phenotypes. Another strong aspect of the work was the generation of an independent, complementary, strain consisting of an inducible knockout model, in which the deficiency of the gene could be assessed in a more granular form. This approach enabled the discovery of Spag7 as a gene involved in the establishment of the mature placenta, which determines the metabolic fate of the offspring. Additional strengths include the extensive array of physiological parameters measured, which provided a deep understanding of the whole-body metabolic phenotype and pinpointed its likely origin to muscle energetic dysfunction.

Weaknesses:

Weaknesses that can be raised are the lack of molecular mechanistic understanding of the numerous phenotypic observations. For example, the specific role of Spag7 to promote placental development remains unclear. Also, the reason why placental developmental abnormalities lead to muscle dysfunction, and whether indeed the entire metabolic phenotype of the offspring can be attributed solely to decreased muscle energetics is not fully explored.

Overall, the authors achieved a remarkable success in identifying genes associated with development of obesity and metabolic disease, discovering the role of Spag7 in placental development, and highlighting the fundamental role of in-utero development in setting future metabolic state of the offspring.

We thank this reviewer for their thoughtful analysis and commentary. Significant effort has been made to understand the causes of the metabolic phenotypes observed in SPAG7-deficient mouse models. It is clear that hyperphagia is not the cause and the muscle energetics deficit is likely not the sole cause. We expect that decreased access to nutrition in utero will lead to widespread and varied metabolic adaptation.

We agree with the reviewer that further work can be done to understand the molecular mechanism driving the metabolic phenotypes of SPAG7-deficient animals. We believe that full investigation of the processes behind the developmental abnormalities is beyond the scope of this paper and best to be done under a separate paper.

Reviewer #3:

Summary:

The manuscript by Flaherty III S.E. et al identified SPAG7 gene in their forward mutagenetic screening and created the germline knockout and inducible knockout mice. The authors reported that the SPAG7 germline knockout mice had lower birth weight likely due to intrauterine growth restriction and placental insufficiency. The SPAG7 KO mice later developed obesity phenotype as a result of reduced energy expenditure. However, the inducible SPAG7 knockout mice had normal body weight and composition.

Strengths:

In this reviewer's opinion, this study has high significance in the field of metabolic research for the following reasons.

1) The authors' findings are significant in the field of obesity research, especially from the perspective of maternal-fetal medicine. The authors created and analyzed the SPAG7 KO mice and found that the KO mice had a "thrifty phenotype" and developed obesity.

2) SPAG7 gene function hasn't been thoroughly studied. The reported phenotype will fill the gap of knowledge.

Overall, the authors have presented their results in a clear and logically organized structure, clearly stated the key question to be addressed, used the appropriate methodology, produced significant and innovative main findings.

Weaknesses:

The manuscript can be further strengthened with more clarification on the following points.

1) The germline whole-body KO mice were female mice (Line293), however the inducible knockout mice were male mice (Line549). Sexual dimorphism is often observed in metabolic studies, therefore the metabolic phenotype of both female and male mice needs to be reported for the germline and inducible knockouts in order to make the justified conclusion.

2) SPAG7 has an NLS. Does this protein function in gene expression? Whether the overall metabolic phenotype is the direct cause of SPAG7 ablation is unclear. For example, the Hsd17b10 gene was downregulated in all tissues in the KO mice. Could this have been coincidentally selected for and thus be the cause of the developmental issues and adulthood obesity? Do the iSpag7 mice demonstrate reduced expression of Hsd17b10?

3) Figure 2c should display the energy expenditure normalized to body weight (or lean body mass).

4) Please provide more information for the figure legend, including the statistical test that was conducted for each data set, animal numbers for each genotype and sexes.

5) The authors should report how long after treatment the data was collected for figures 4F-M.

6) The authors should justify ending the data collection after 8 weeks for the iSPAG7 mice in Figures 4C-E. In the WT vs germline KO mice, there was no clear difference in body weight or lean mass at 15 weeks of age.

Response to point #1 (Weakness): We thank the reviewer for their thoughtful analysis and commentary. All inducible KO animals described in the paper are female (the typo in Line 549 has been corrected). We did perform studies in both male and female animals for both of these lines. Males display similar metabolic phenotypes, though not as robustly as the females. A table summarizing key data from male and female germline KO animals and inducible KO animals has been included below.

Author response table 1.

Author response table 2.

Response to point #2 (Weakness): SPAG7 contains an R3H domain, which is predicted to bind polynucleotides, and other proteins that contain R3H domains are known to bind RNA or ssDNA. The iSPAG7 mice do display decreased hsd17b10 expression (to a lesser degree than the germline KOs) in the tissues examined. When we knock-down SPAG7 in specific tissues, we also see hsd17b10 expression decrease specifically in those tissues. These data all suggest that hsd17b10 expression is, at least, linked to spag7 expression. They also raise the question of why these animals have no metabolic phenotype. Some possible explanations are that hsd17b10 expression is essential only during early development, or that the lower magnitude of downregulation of hsd17b10 in the iSPAG7 is insufficient to produce the metabolic phenotypes seen in the germline Kos with higher magnitude of downregulation.

Response to point #3 (Weakness): How best to normalize total energy expenditure data is a subject of debate within the energy expenditure field. As the animals have increased body weight and decreased lean mass, normalizing to either will skew the results in different directions. We have included the data normalized to body weight and to lean mass below. The decrease in total energy expenditure remains significant in either scenario.

Author response image 1.

Response to point #4 (Weakness): The information has been added to all figures.

Response to point #5 (Weakness): Weeks after treatment have been added to the figure legends for Figures 4F-M.

Response to point #6 (Weakness): Highly significant changes in fat mass, glucose tolerance and insulin sensitivity are already present in the germline SPAG7 KO mice at age of 15 week or earlier. Tamoxifen injection effectively induced SPA7 gene KO in less than a week in the iSPAG7 KO mice. Given the absence of significant changes or any trends towards significance in glucose and insulin tolerance test as well as other metabolic testes in the iSPAG7 KO mice at age of 15 week (same age as the germline KO when these changes observed) and 8 week after SPAG7 gene KO, we did not anticipate to see the changes beyond this point and decided to stop the study at 9 weeks after treatment.

Author response:

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

eLife Assessment

This study uses state-of-the-art methods to label endogenous dopamine receptors in a subset of Drosophila mushroom body neuronal types. The authors report that DopR1 and Dop2R receptors, which have opposing effects in intracellular cAMP, are present in axons termini of Kenyon cells, as well as those of two classes of dopaminergic neurons that innervate the mushroom body indicative of autocrine modulation by dopaminergic neurons. Additional experiments showing opposing effects of starvation on DopR1 and DopR2 levels in mushroom body neurons are consistent with a role for dopamine receptor levels increasing the efficiency of learned food-odour associations in starved flies. Supported by solid data, this is a valuable contribution to the field.

We thank the editors for the assessment, but request to change “DopR2” to “Dop2R”. The dopamine receptors in Drosophila have confusing names, but what we characterized in this study are called Dop1R1 (according to the Flybase; aka DopR1, dDA1, Dumb) and Dop2R (ibid; aka Dd2R). DopR2 is the name of a different dopamine receptor.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

This is an important and interesting study that uses the split-GFP approach. Localization of receptors and correlating them to function is important in understanding the circuit basis of behavior.

Strengths:

The split-GFP approach allows visualization of subcellular enrichment of dopamine receptors in the plasma membrane of GAL4-expressing neurons allowing for a high level of specificity.

The authors resolve the presynaptic localization of DopR1 and Dop2R, in "giant" Drosophila neurons differentiated from cytokinesis-arrested neuroblasts in culture as it is not clear in the lobes and calyx.

Starvation-induced opposite responses of dopamine receptor expression in the PPL1 and PAM DANs provide key insights into models of appetitive learning.

Starvation-induced increase in D2R allows for increased negative feedback that the authors test in D2R knockout flies where appetitive memory is diminished.

This dual autoreceptor system is an attractive model for how amplitude and kinetics of dopamine release can be fine-tuned and controlled depending on the cellular function and this paper presents a good methodology to do it and a good system where the dynamics of dopamine release can be tested at the level of behavior.

Weaknesses:

LI measurements of Kenyon cells and lobes indicate that Dop2R was approximately twice as enriched in the lobe as the average density across the whole neuron, while the lobe enrichment of Dop1R1 was about 1.5 times the average, are these levels consistent during different times of the day and the state of the animal. How were these conditions controlled and how sensitive are receptor expression to the time of day of dissection, staining, etc.

To answer this question, we repeated the experiment in two replicates at different times of day and confirmed that the receptor localization was consistent (Figure 3 – figure supplement 1); LI measurements showed that Dop2R is enriched more in the lobe and less in the calyx compared to Dop1R1 (Figure 3D). The states of animals that could affect LI (e.g. feeding state and anesthesia for sorting, see methods) were kept constant. 

The authors assume without discussion as to why and how presynaptic enrichment of these receptors is similar in giant neurons and MB.

In the revision, we added a short summary to recapitulate that the giant neurons exhibit many characteristics of mature neurons (Lines #152-156): "Importantly, these giant neurons exhibit characteristics of mature neurons, including firing patterns (Wu et al., 1990; Yao & Wu, 2001; Zhao & Wu, 1997) and acetylcholine release (Yao et al., 2000), both of which are regulated by cAMP and CaMKII signaling (Yao et al., 2000; Yao & Wu, 2001; Zhao & Wu, 1997)." In addition, we found punctate Brp accumulations localized to the axon terminals of the giant neurons (former Figure 4D and 4E). Therefore, the giant neuron serves as an excellent model to study the presynaptic localization of dopamine receptors in isolated large cells.

Figures 1-3 show the expensive expression of receptors in alpha and beta lobes while Figure 5 focusses on PAM and localization in γ and β' projections of PAM leading to the conclusion that presynaptic dopamine neurons express these and have feedback regulation. Consistency between lobes or discussion of these differences is important to consider.

In the revised manuscript, we show data in the γ KCs (Figure 4C, Figure 5 - figure supplement 1) in addition to α/β KCs, and demonstrate the consistent synaptic localization of Dop1R1 and Dop2R as in α/β KCs (Figure 4B and 5A). 

Receptor expression in any learning-related MBONs is not discussed, and it would be intriguing as how receptors are organized in those cells. Given that these PAMs input to both KCs and MBONs these will have to work in some coordination.

The subcellular localization of dopamine receptors in MBONs indeed provides important insights into the site of dopaminergic signaling in these neurons (Takemura et al., 2017; Pavlowsky et al., 2018; Pribbenow et al., 2022). Therefore, we added new data for Dop1R1 and Dop2R in MBON-γ1pedc>αβ (Figure 6). Interestingly, these receptors are localized to in the dendritic projection in the γ1 compartment as well as presynaptic boutons (Figure 6). 

Although authors use the D2R enhancement post starvation to show that knocking down receptors eliminated appetitive memory, the knocking out is affecting multiple neurons within this circuit including PAMs and KCs. How does that account for the observed effect? Are those not important for appetitive learning? 

In the appetitive memory experiment (Figure 9C), we knocked down Dop2R only in the select neurons of the PPL1 cluster, and this manipulation does not directly affect Dop2R expression in PAMs and KCs.

Starvation-induced enhancement of Dop2R expression in the PPL1 neurons (Figure 8F) would attenuate their outputs and therefore disinhibit expression of appetitive memory in starved flies (Krashes et al., 2009). Consistently, Dop2R knock-down in PPL1 impaired appetitive memory in starved flies (Figure 9C). We revised the corresponding text to make this point clearer (Lines #224227).

The evidence for fine-tuning is completely based on receptor expression and one behavioral outcome which could result from many possibilities. It is not clear if this fine-tuning and presynaptic feedback regulation-based dopamine release is a clear possibility. Alternate hypotheses and outcomes could be considered in the model as it is not completely substantiated by data at least as presented.

The reviewer’s concern is valid, and the presynaptic dopamine tuning by autoreceptors may need more experimental support. We therefore additionally discussed another possibility (Lines #289-291): “Alternatively, these presynaptic receptors could potentially receive extrasynaptic dopamine released from other DANs. Therefore, the autoreceptor functions need to be experimentally clarified by manipulating the receptor expression in DANs.”

Reviewer #2 (Public Review):

Summary:

Hiramatsu et al. investigated how cognate neurotransmitter receptors with antagonizing downstream effects localize within neurons when co-expressed. They focus on mapping the localization of the dopaminergic Dop1R1 and Dop2R receptors, which correspond to the mammalian D1- and D2-like dopamine receptors, which have opposing effects on intracellular cAMP levels, in neurons of the Drosophila mushroom body (MB). To visualize specific receptors in single neuron types within the crowded MB neuropil, the authors use existing dopamine receptor alleles tagged with 7 copies of split GFP to target reconstitution of GFP tags only in the neurons of interest as a read-out of receptor localization. The authors show that both Dop1R1 and Dop2R, with differing degrees, are enriched in axonal compartments of both the Kenyon Cells cholinergic presynaptic inputs and in different dopamine neurons (DANs), which project axons to the MB. Co-localization studies of dopamine receptors with the presynaptic marker Brp suggest that Dop1R1 and, to a larger extent Dop2R, localize in the proximity of release sites. This localization pattern in DANs suggests that Dop1R1 and Dop2R work in dual-feedback regulation as autoreceptors. Finally, they provide evidence that the balance of Dop1R1 and Dop2R in the axons of two different DAN populations is differentially modulated by starvation and that this regulation plays a role in regulating appetitive behaviors.

Strengths:

The authors use reconstitution of GFP fluorescence of split GFP tags knocked into the endogenous locus at the C-terminus of the dopamine receptors as a readout of dopamine receptor localization. This elegant approach preserves the endogenous transcriptional and post-transcriptional regulation of the receptor, which is essential for studies of protein localization.

The study focuses on mapping the localization of dopamine receptors in neurons of the mushroom body. This is an excellent choice of system to address the question posed in this study, as the neurons are well-studied, and their connections are carefully reconstructed in the mushroom body connectome. Furthermore, the role of this circuit in different behaviors and associative memory permits the linking of patterns of receptor localization to circuit function and resulting behavior. Because of these features, the authors can provide evidence that two antagonizing dopamine receptors can act as autoreceptors within the axonal compartment of MB innervating DANs. The differential regulation of the balance of the two receptors under starvation in two distinct DAN innervations provides evidence of the role that regulation of this balance can play in circuit function and behavioral output.

Weaknesses:

The approach of using endogenously tagged alleles to study localization is a strength of this study, but the authors do not provide sufficient evidence that the insertion of 7 copies of split GFP to the C terminus of the dopamine receptors does not interfere with the endogenous localization pattern or function. Both sets of tagged alleles (1X Venus and 7X split GFP tagged) were previously reported (Kondo et al., 2020), but only the 1X Venus tagged alleles were further functionally validated in assays of olfactory appetitive memory. Despite the smaller size of the 7X split-GFP array tag knocked into the same location as the 1X venus tag, the reconstitution of 7 copies of GFP at the C terminus of the dopamine receptor, might substantially increase the molecular bulk at this site, potentially impeding the function of the receptor more significantly than the smaller, single Venus tag. The data presented by Kondo et al. 2020, is insufficient to conclude that the two alleles are equivalent.

In the revision, we validated the function of these engineered receptors by a new set of olfactory learning experiments. Both these receptors in KCs were shown to be required for aversive memory (Kim et al., 2007, Scholz-Kornehl et al., 2016). As in the anatomical experiments, we induced GFP110 expression in KC of the flies homozygous for 7xGFP₁₁-tagged receptors using MB-Switch and 3 days of RU486 feeding o. We confirmed STM performance of these flies were not significantly different from the control (Figure 2 – figure supplement 1). Thus, these fusion receptors are functional.

The authors' conclusion that the receptors localize to presynaptic sites is weak. The analysis of the colocalization of the active zone marker Brp whole-brain staining with dopamine receptors labeled in specific neurons is insufficient to conclude that the receptors are localized at presynaptic sites. Given the highly crowded neuropil environment, the data cannot differentiate between the receptor localization postsynaptic to a dopamine release site or at a presynaptic site within the same neuron. The known distribution of presynaptic sites within the neurons analyzed in the study provides evidence that the receptors are enriched in axonal compartments, but co-labeling of presynaptic sites and receptors in the same neuron or super-resolution methods are needed to provide evidence of receptor localization at active zones.  The data presented in Figures 5K-5L provides compelling evidence that the receptors localize to neuronal varicosities in DANs where the receptors could play a role as autoreceptors.

Given the highly crowded environment of the mushroom body neuropil, the analysis of dopamine receptor localization in Kenyon cells is not conclusive. The data is sufficient to conclude that the receptors are preferentially localizing to the axonal compartment of Kenyon cells, but co-localization with brain-wide Brp active zone immunostaining is not sufficient to determine if the receptor localizes juxtaposed to dopaminergic release sites, in proximity of release sites in Kenyon cells, or both.

To better resolve the microcircuits of KCs, we triple-labeled the plasma membrane and DAR::rGFP in KCs, and Brp, and examined their localizations with high-resolution imaging with  Airyscan. This strategy revealed the receptor clusters associated with Brp accumulation within KCs (Figure 4). To further verify the association of DARs and active zones within KCs, we co-expressed Brp^short::mStraw and GFP_1-10 and confirmed their colocalization (Figure 5A), suggesting presynaptic localization of DARs in KCs. With these additional characterizations, we now discuss the significance of receptors at the presynaptic sites of KCs.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

This is an important and interesting study that uses the split-GFP approach. Localization of receptors and correlating them to function is important in understanding the circuit basis of behavior.

For Figure 1, the authors show PAM, PPL1 neurons, and the ellipsoid body as a validation of their tools (Dop1R1-T2A-GAL4 and Dop2R-T2A-GAL4) and the idea that these receptors are colocalized. However, it appears that the technique was applied to the whole brain so it would be great to see the whole brain to understand how much labelling is specific and how stochastic. Methods could include how dissection conditions were controlled and how sensitive are receptor expression to the time of day of dissection, staining, etc.

The expression patterns of the receptor T2A-GAL4 lines (Figure 1A and 1B) are consistent in the multiple whole brains (Kondo et al., 2020, Author response image 1).

Author response image 1.

The significance of the expression of these two receptors in an active zone is not clearly discussed and presynaptic localization is not elaborated on. Would something like expansion microscopy be useful in resolving this? It would be important to discuss that as giant neurons in culture don't replicate many aspects of the MB system.

In the revised manuscript, we elaborated discussion regarding the function of the two antagonizing receptors at the AZ (Lines #226-275).

Does MB-GeneSwitch > GFP1-1 reliably express in gamma lobes? Most of the figures show alpha/beta lobes.

Yes. MB-GeneSwitch is also expressed in γ KCs, but weakly. 12 hours of RU486 feeding, which we did in the previous experiments, was insufficient to induce GFP reconstitution in the γ KCs. By extending the time of transgene induction, we visualized expression of Dop1R1 and Dop2R more clearly in γ KCs. Their localization is similar to that in the α/β KCs (Figure 4C, Figure 5 - figure supplement 1).

Figure 6, y-axis says protein level. At first, I thought it was related to starvation so maybe authors can be more specific as the protein level doesn't indicate any aspect of starvation.

We appreciate this comment, and the labels on the y-axis were now changed to “rGFP levels” (Figure 8C and 8F, Figure 8 - figure supplement 1B, 1D and 1F).

Reviewer #2 (Recommendations For The Authors):

Title:

The title of the manuscript focuses on the tagging of the receptors and their synaptic enrichment.

Given that the alleles used in the study were generated in a previously published study (Kondo et al, 2020), which describes the receptor tagging and that the data currently provided is insufficient to conclude that the receptors are localizing to synapses, the title should be changed to reflect the focus on localizing antagonistic cognate neurotransmitter receptors in the same neuron and their putative role as autoreceptors in DANs.

Following this advice, we removed the methodology from the title and revised it to “Synaptic enrichment and dynamic regulation of the two opposing dopamine receptors within the same neurons”.

Minor issues with text and figures:

Figure 1

A conclusion from Figure 1 is that the two receptors are co-expressed in Kenyon cells. Please provide panels equivalent to the ones shown in D-G, with Kenyon cells cell bodies, or mark these cells in the existing panels, if present. Line 111 refers to panel 1D as the Kenyon cells panel, which is currently a PAM panel.

We added images for coexpression of these receptors in the cell bodies of KCs (Figure 1 - figure supplement 1) and revised the text accordingly (Lines #89-90).

Given that most of the study centers on visualizing receptor localization, it would benefit the reader to include labels in Figure 1 that help understand that these panels reflect expression patterns rather than receptor localization. For instance, rCD2::GFP could be indicated in the Dop1R1-LexA panels.

As suggested, labels were added to indicate the UAS and lexAop markers (Figure 1D, 1E, 1G-1I and Figure 1 – figure supplement 1).

Given that panels D-E focus on the cell bodies of the neurons, it could be beneficial for the reader to present the ellipsoid body neurons using a similar view that only shows the cell bodies. Similarly, one could just show the glial cell bodies .

We now show the cell bodies of ring neurons (Figure 1G) and ensheathing glia (Figure 1I).

For panel 1E, please indicate the subset of PPL1 neurons that both expressed Dop1R1 and Dop2R, as indicated in the text, as it is currently unclear from the image.

Dop1R1-T2A-LexA was barely detected in all PPL1 (Figure 1E). We corrected the confusing text (Lines #95-96).

Figure 2

The cartoon of the cell-type-specific labeling should show that the tag is 7XFP-11 and the UAScomponent FP-10, as the current cartoon leads the reader to conclude that the receptors are tagged with a single copy of split GFP. The detail that the receptors are tagged with 7 copies of split GFP is only provided through the genotype of the allele in the resource table.  This design aspect should be made clear in the figure and the text when describing the allele and approach used to tag receptors in specific neuron types.

We now added the construct design in the scheme (Figure 2A) and revised the corresponding text (Line #101-103).

Panel A. The arrow representing the endogenous promoter in the yellow gene representation should be placed at the beginning of the coding sequence. Currently, the different colors of what I assume are coding (yellow) and non-coding (white) transcript regions are not described in the legend.  I would omit these or represent them in the same color as thinner boxes if the authors want to emphasize that the tag is inserted at the C terminus within the endogenous locus.

The color scheme was revised to be more consistent and intuitive (Figure 2A).

Figure 3

Labels of the calyx and MB lobes would benefit readers not as familiar with the system used in the study. In addition, it would be beneficial to the reader to indicate in panel A the location of the compartments analyzed in panel H (e.g., peduncle, α3).

Figure 3A was amended to clearly indicate the analyzed MB compartments.

Adding frontal and sagittal to panels B-E, as in Figure 2, would help the reader interpret the data. 

In Figure 3B, “Frontal” and “Sagittal” were indicated.

Panel F-G. A scale bar should be provided for the data shown in the insets. Could the author comment on the localization of Dop1R1 in KCs? The data in the current panel suggests that only a subset of KCs express high levels of receptors in their axons, as a portion of the membrane is devoid of receptor signals. This would be in line with differential dopamine receptor expression in subsets of Kenyon cells, as shown in Kondo et al., 2020, which is currently not commented on in the paper. 

We confirmed that the majority of the KCs express both Dop1R1 and Dop2R genes (Figure 1 - figure supplement 1). LIs should be compared within the same cells rather than the differences of protein levels between cell types as they also reflect the GAL4 expression levels. 

Panel H. Some P values are shown as n.s. (p> 0.05). Other non-significant p values in this panel and in other figures throughout the paper are instead reported (e.g. peduncle P=0.164). For consistency, please report the values as n.s. as indicated in the methods for all non-significant tests in this panel and throughout the manuscript.

We now present the new dataset, and the graph represents the appropriate statistical results (Figure 3D; see the methods section for details).

The methods of labeling the receptors through the expression of the GeneSwitch-controlled GFP1-10 in Kenyon cells induced by RU486 are not provided in the methods. Please provide a description of this as referenced in the figure legend and the genotypes used in the analysis shown in the panels.

The method of RU486 feeding has been added. We apologize for the missing method.

Figure 4

Please provide scale bars for the inset in panels A-B.

Scale bars were added to all confocal images.

The current analysis cannot distinguish between postsynaptic and presynaptic dopamine receptors in KCs, and the figure title should reflect this.

We now present the new data dopamine receptors in KCs and clearly distinguish Brp clusters of the KCs and other cell types (Figure 4, Figure 5).

The reader could benefit from additional details of using the giant neuron model, as it is not commonly used, and it is not clear how to relate this to interpret the localization of dopaminergic receptors within Kenyon cells. The use of the venus-tagged receptor variant should be introduced in the text, as using a different allele currently lacks context. Figures 4F-4J show that the receptor is localizing throughout the neuron. Quantifying the fraction of receptor signal colocalizing with Brp could aid in interpreting the data.  However, it would still not be clear how to interpret this data in the context of understanding the localization of the receptors in neurons within fly brain circuits. In the absence of additional data, the data provided in Figure 4 is inconclusive and could be omitted, keeping the focus of the study on the analysis of the two receptors in DANs. Co-expressing a presynaptic marker in Kenyon cells (e.g., by expressing Brp::SNAP)  in conjunction with rGFP labeled receptor would provide additional evidence of the relationship of release sites in Kenyon cells and tagged dopamine receptors in these same cells and could add evidence in support to the current conclusion.

Following the advice, we added a short summary to recapitulate that the giant neurons exhibit many characteristics of mature neurons (Lines #152-156): "Importantly, these giant neurons exhibit characteristics of mature neurons, including firing patterns (Wu et al., 1990; Yao & Wu, 2001; Zhao & Wu, 1997) and acetylcholine release (Yao et al., 2000), both of which are regulated by cAMP and CaMKII signaling (Yao et al., 2000; Yao & Wu, 2001; Zhao & Wu, 1997)." Therefore, the giant neuron serves as an excellent model to study the presynaptic localization in large cells in isolation.

To clarify polarized localization of Brp clusters and dopamine receptors but not "localizing throughout the neuron", we now show less magnified data (Figure 5C). It clearly demonstrates punctate Brp accumulations localized to the axon terminals of the giant neurons (former Figure 4D and 4E). This is the same membrane segment where Dop1R1 and Dop2R are localized (Figure 5C). Therefore, the association of Brp clusters and the dopamine receptors in the isolated giant neurons suggests that the subcellular localization in the brain neurons is independent of the circuit context. 

As the giant neurons do not form intermingled circuits, venus-tagged receptors are sufficient for this experiment and simpler in genetics.

Following the suggestion to clarify the AZ association of the receptors in KCs, we coexpressed Brpshort-mStraw and GFP1-10 in KCs and confirmed their colocalization (Figure 5A).

Figure 6

The data and analysis show that starvation induces changes in the α3 compartment in PPL1 neurons only, while the data provided shows no significant change for PPL1 neurons innervating other MB compartments. This should be clearly stated in lines 174-175, as it is implied that there is a difference in the analysis for compartments other than α3. Panel L of Figure 6 - supplement 1 shows no significant change for all three compartments analyzed and should be indicated as n.s. in all instances, as stated in the methods. 

We revised the text to clarify that the starvation-induced differences of Dop2R expression were not significant (Lines #217-219). The reason to highlight the α3 compartment is that both Dop1R1 and Dop2R are coexpressed in this PPL1 neuron (Figure 8D).

Additional minor comments:

There are a few typos and errors throughout the manuscript. The text should be carefully proofread to correct these. Here are the ones that came to my attention:

Please reference all figure panels in the text. For instance, Figure 3A is not mentioned and should be revised in line 112 as Figure 3A-E.

Lines 103-104. The sentence "LI was visualized as the color of the membrane signals" is unclear and should be revised. 

Figure 4 legend - dendritic claws should likely be B and C and not B and E.

Lines 147 - Incorrect figure panels, should be 5C-L or 5D-E.

Line 241 - DNAs should be DANs.

Methods - please define what the abbreviation CS stands for.

We really appreciate for careful reading of this reviewer. All these were corrected.

Author response:

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

eLife Assessment

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

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

Public Reviews:

Reviewer #1 (Public review):

Summary:

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

Strengths:

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

We have previously showed that neural replay of MEG activity representing the practiced skill was prominent during rest intervals of early learning, and that the replay density correlated with micro-offline gains (Buch et al., 2021). These findings are consistent with recent reports (from two different research groups) that hippocampal ripple density increases during these inter-practice rest periods, and predict offline learning gains (Chen et al., 2024; Sjøgård et al., 2024). However, decoder performance in our earlier work (Buch et al., 2021) left room for improvement. Here, we reported a strategy to improve decoding accuracy that could benefit future studies of neural replay or BCI using MEG.

Weaknesses:

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

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

Head movement artefacts are highly unlikely to contribute meaningfully to our results for the following reasons. First, in addition to ICA denoising, all “recordings were visually inspected and marked to denoise segments containing other large amplitude artifacts due to movements” (see Methods). Second, the response pad was positioned in a manner that minimized wrist, arm or more proximal body movements during the task. Third, while online monitoring of head position was not performed for this study, it was assessed at the beginning and at the end of each recording. The head was restrained with an inflatable air bladder, and head movement between the beginning and end of each scan did not exceed 5mm for all participants included in the study.

The Reviewer states a concern that “it is conceivable that small head movements would correlate highly with the vigor of individual finger movements”. We agree that despite the steps taken above, it is possible that minor head movements could still contribute to some remaining variance in the MEG data in our study. However, such correlations between small head movements and finger movements could only meaningfully contribute to decoding performance if: (A) they were consistent and pervasive throughout the recording (which might not be the case if the head movements were related to movement vigor and vigor changed over time); and (B) they systematically varied between different finger movements, and also between the same finger movement performed at different sequence locations (see 5-class decoding performance in Figure 4B). The possibility of any head movement artefacts meeting all these conditions is unlikely. Alternatively, for this task design a much more likely confound could be the contribution of eye movement artefacts to the decoder performance (an issue raised by Reviewer #3 in the comments below).

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

Author response image 1.

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

Remember that the task display does not provide explicit feedback related to performance, only information about the present position in the sequence. Thus, it is possible that participants did not actively attend to the feedback. In fact, inspection of the eye position data revealed that on majority of trials, participants displayed random-walk-like gaze patterns around a central fixation point located near the center of the screen. Thus, participants did not attend to the asterisk position on the display, but instead intrinsically generated the action sequence. A similar realworld example would be manually inputting a long password into a secure online application. In this case, one intrinsically generates the sequence from memory and receives similar feedback about the password sequence position (also provided as asterisks) as provided in the study task – feedback which is typically ignored by the user.

The minimal participant engagement with the visual task display observed in this study highlights another important point – that the behavior in explicit sequence learning motor tasks is highly generative in nature rather than reactive to stimulus cues as in the serial reaction time task (SRTT). This is a crucial difference that must be carefully considered when designing investigations and comparing findings across studies.

We observed that initial keypress decoding accuracy was predominantly driven by contralateral primary sensorimotor cortex in the initial practice trials before transitioning to bilateral frontoparietal regions by trials 11 or 12 as performance gains plateaued. The contribution of contralateral primary sensorimotor areas to early skill learning has been extensively reported in humans and non-human animals.(Buch et al., 2021; Classen et al., 1998; Karni et al., 1995; Kleim et al., 1998) Similarly, the increased involvement of bilateral frontal and parietal regions to decoding during early skill learning in the non-dominant hand is well known. Enhanced bilateral activation in both frontal and parietal cortex during skill learning has been extensively reported (Doyon et al., 2002; Grafton et al., 1992; Hardwick et al., 2013; Kennerley et al., 2004; Shadmehr & Holcomb, 1997; Toni, Ramnani, et al., 2001), and appears to be even more prominent during early fine motor skill learning in the non-dominant hand (Lee et al., 2019; Sawamura et al., 2019). The frontal regions identified in these studies are known to play crucial roles in executive control (Battaglia-Mayer & Caminiti, 2019), motor planning (Toni, Thoenissen, et al., 2001), and working memory (Andersen & Buneo, 2002; Buneo & Andersen, 2006; Shadmehr & Holcomb, 1997; Toni, Ramnani, et al., 2001; Wolpert et al., 1998) processes, while the same parietal regions are known to integrate multimodal sensory feedback and support visuomotor transformations (Andersen & Buneo, 2002; Buneo & Andersen, 2006; Shadmehr & Holcomb, 1997; Toni, Ramnani, et al., 2001; Wolpert et al., 1998), in addition to working memory (Grover et al., 2022). Thus, it is not surprising that these regions increasingly contribute to decoding as subjects internalize the sequential task. We now include a statement reflecting these considerations in the revised Discussion.

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

We disagree with the Reviewer’s assertion that the construction of the hybrid-space decoder is circular for the following reasons. First, the base feature set for the hybrid-space decoder constructed for all participants includes whole-brain spatial patterns of MEG source activity averaged within parcels. As stated in the manuscript, these 148 inter-parcel features reflect “lower spatially resolved whole-brain activity patterns” or global brain dynamics. We then independently test how well spatial patterns of MEG source activity for all voxels distributed within individual parcels can decode keypress actions. Again, the testing of these intra-parcel spatial patterns, intended to capture “higher spatially resolved regional brain activity patterns”, is completely independent from one another and independent from the weighting of individual inter-parcel features. These intra-parcel features could, for example, provide additional information about muscle activation patterns or the task environment. These approximately 1150 intra-parcel voxels (on average, within the total number varying between subjects) are then combined with the 148 inter-parcel features to construct the final hybrid-space decoder. In fact, this varied spatial filter approach shares some similarities to the construction of convolutional neural networks (CNNs) used to perform object recognition in image classification applications (Srinivas et al., 2016). One could also view this hybrid-space decoding approach as a spatial analogue to common timefrequency based analyses such as theta-gamma phase amplitude coupling (θ/γ PAC), which assess interactions between two or more narrow-band spectral features derived from the same time-series data (Lisman & Jensen, 2013).

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

Author response image 2.

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

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

We agree with the Reviewer that some inter-parcel features representing neighboring (or spatially contiguous) voxels are likely to be correlated, an important confound in connectivity analyses (Colclough et al., 2015; Colclough et al., 2016), not performed in our investigation.

In our study, correlations between adjacent voxels effectively reduce the dimensionality of the input feature space. However, as long as there are multiple groups of correlated voxels within each parcel (i.e. – the rank is greater than 1), the intra-parcel spatial patterns could meaningfully contribute to the decoder performance, as shown by the following results:

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

Author response image 3.:

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

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

We have already addressed the issue of movement related artefacts in the first response above. With respect to a focus on frequency ranges above movement frequencies, the Reviewer states the “accuracy for those still seems impressive and may provide a slightly more biologically plausible assessment”. First, it is important to note that cortical delta-band oscillations measured with local field potentials (LFPs) in macaques is known to contain important information related to end-effector kinematics (Bansal et al., 2011; Mollazadeh et al., 2011) muscle activation patterns (Flint et al., 2012) and temporal sequencing (Churchland et al., 2012) during skilled reaching and grasping actions. Thus, there is a substantial body of evidence that low-frequency neural oscillatory activity in this range contains important information about the skill learning behavior investigated in the present study. Second, our own data shows (which the Reviewer also points out) that significant information related to the skill learning behavior is also present in higher frequency bands (see Figure 2A and Figure 3—figure supplement 1). As we pointed out in our earlier response to questions about the hybrid space decoder architecture (see above), it is likely that different, yet complimentary, information is encoded across different temporal frequencies (just as it is encoded across different spatial frequencies) (Heusser et al., 2016). Again, this interpretation is supported by our data as the highest performing classifiers in all cases (when holding all parameters constant) were always constructed from broadband input MEG data (Figure 2A and Figure 3—figure supplement 1).

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

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

First, previous fMRI work in humans assessed changes in functional connectivity patterns while participants performed a similar sequence learning task to our present study (Bassett et al., 2011). Using a dynamic network analysis approach, Bassett et al. showed that flexibility in the composition of individual network modules (i.e. – changes in functional brain region membership of orthogonal brain networks) is up-regulated in novel learning environments and explains differences in learning rates across individuals. Thus, consistent with our findings, it is likely that functional brain networks rapidly reconfigure during early learning of novel sequential motor skills.

Second, frontoparietal network activity is known to support motor memory encoding during early learning (Albouy et al., 2013; Albouy et al., 2012). For example, reactivation events in the posterior parietal (Qin et al., 1997) and medial prefrontal (Euston et al., 2007; Molle & Born, 2009) cortex (MPFC) have been temporally linked to hippocampal replay, and are posited to support memory consolidation across several memory domains (Frankland & Bontempi, 2005), including motor sequence learning (Albouy et al., 2015; Buch et al., 2021; F. Jacobacci et al., 2020). Further, synchronized interactions between MPFC and hippocampus are more prominent during early as opposed to later learning stages (Albouy et al., 2013; Gais et al., 2007; Sterpenich et al., 2009), perhaps reflecting “redistribution of hippocampal memories to MPFC” (Albouy et al., 2013). MPFC contributes to very early memory formation by learning association between contexts, locations, events and adaptive responses during rapid learning (Euston et al., 2012). Consistently, coupling between hippocampus and MPFC has been shown during initial memory encoding and during subsequent rest (van Kesteren et al., 2010; van Kesteren et al., 2012). Importantly, MPFC activity during initial memory encoding predicts subsequent recall (Wagner et al., 1998). Thus, the spatial map required to encode a motor sequence memory may be “built under the supervision of the prefrontal cortex” (Albouy et al., 2012), also engaged in the development of an abstract representation of the sequence (Ashe et al., 2006). In more abstract terms, the prefrontal, premotor and parietal cortices support novice performance “by deploying attentional and control processes” (Doyon et al., 2009; Hikosaka et al., 2002; Penhune & Steele, 2012) required during early learning (Doyon et al., 2009; Hikosaka et al., 2002; Penhune & Steele, 2012). The dorsolateral prefrontal cortex DLPFC specifically is thought to engage in goal selection and sequence monitoring during early skill practice (Schendan et al., 2003), all consistent with the schema model of declarative memory in which prefrontal cortices play an important role in encoding (Morris, 2006; Tse et al., 2007). Thus, several prefrontal and frontoparietal regions contributing to long term learning (Berlot et al., 2020) are also engaged in early stages of encoding. Altogether, there is strong biological support for the involvement of bilateral prefrontal and frontoparietal regions to decoding during early skill learning. We now address this issue in the revised manuscript.

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

We agree with the Reviewer that our previous “offline neural representation” nomenclature could be misinterpreted. In the revised manuscript we refer to this difference as the “offline neural representational change”. Please, note that our previous work did link offline neural activity (i.e. – 16-22 Hz beta power (Bonstrup et al., 2019) and neural replay density (Buch et al., 2021) during inter-practice rest periods) to observed micro-offline gains.

Reviewer #2 (Public review):

Summary

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

Strengths

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

Weaknesses

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

The Reviewer raises the possibility that (given the windowing parameters used in the present study) an increase in “contextualization” with learning could simply reflect faster typing speeds as opposed to an actual change in the underlying neural representation.

We now include a new control analysis that addresses this issue as well as additional re-examination of previously reported results with respect to this issue – all of which are inconsistent with this alternative explanation that “contextualization” reflects a change in mixing of keypress related MEG features as opposed to a change in the underlying representations themselves. As correct sequences are generated at higher and higher speeds over training, MEG activity patterns related to the planning, execution, evaluation and memory of individual keypresses overlap more in time. Thus, increased overlap between the “4” and “1” keypresses (at the start of the sequence) and “2” and “4” keypresses (at the end of the sequence) could artefactually increase contextualization distances even if the underlying neural representations for the individual keypresses remain unchanged. One must also keep in mind that since participants repeat the sequence multiple times within the same trial, a majority of the index finger keypresses are performed adjacent to one another (i.e. - the “4-4” transition marking the end of one sequence and the beginning of the next). Thus, increased overlap between consecutive index finger keypresses as typing speed increased should increase their similarity and mask contextualization related changes to the underlying neural representations.

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

We also re-examined our previously reported classification results with respect to this issue. We reasoned that if mixing effects reflecting the ordinal sequence structure is an important driver of the contextualization finding, these effects should be observable in the distribution of decoder misclassifications. For example, “4” keypresses would be more likely to be misclassified as “1” or “2” keypresses (or vice versa) than as “3” keypresses. The confusion matrices presented in Figures 3C and 4B and Figure 3—figure supplement 3A display a distribution of misclassifications that is inconsistent with an alternative mixing effect explanation of contextualization.

Based upon the increased overlap between adjacent index finger keypresses (i.e. – “4-4” transition), we also reasoned that the decoder tasked with separating individual index finger keypresses into two distinct classes based upon sequence position, should show decreased performance as typing speed increases. However, Figure 4C in our manuscript shows that this is not the case. The 2-class hybrid classifier actually displays improved classification performance over early practice trials despite greater temporal overlap. Again, this is inconsistent with the idea that the contextualization effect simply reflects increased mixing of individual keypress features.

In summary, both re-examination of previously reported data and new control analyses all converged on the idea that the proximity between keypresses does not explain contextualization.

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

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

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

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

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

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

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

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

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

The Reviewer suggests that the current data is not enough to show that contextualization occurs during rest and raises two important concerns: 1) the relationship between online contextualization and micro-online gains is not shown, and 2) the online distance was calculated differently from its offline counterpart (i.e. - instead of calculating the distance between last Index_OP5 and first Index_OP1 from a single trial, the distance was calculated for each sequence within a trial and then averaged).

We addressed the first concern by performing individual subject correlations between 1) contextualization changes during rest intervals and micro-offline gains; 2) contextualization changes during practice trials and micro-online gains, and 3) contextualization changes during practice trials and micro-offline gains (Figure 5 – figure supplement 4). We then statistically compared the resulting correlation coefficient distributions and found that within-subject correlations for contextualization changes during rest intervals and micro-offline gains were significantly higher than online contextualization and micro-online gains (t = 3.2827, p = 0.0015) and online contextualization and micro-offline gains (t = 3.7021, p = 5.3013e-04). These results are consistent with our interpretation that micro-offline gains are supported by contextualization changes during the inter-practice rest periods.

With respect to the second concern, we agree with the Reviewer that one limitation of the analysis comparing online versus offline changes in contextualization as presented in the original manuscript, is that it does not eliminate the possibility that any differences could simply be explained by the passage of time (which is smaller for the online analysis compared to the offline analysis). The Reviewer suggests an approach that addresses this issue, which we have now carried out. When quantifying online changes in contextualization from the first Index_OP1 the last Index_OP5 keypress in the same trial we observed no learning-related trend (Figure 5 – figure supplement 5, right panel). Importantly, offline distances were significantly larger than online distances regardless of the measurement approach and neither predicted online learning (Figure 5 – figure supplement 6).

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

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

Reviewer #3 (Public review):

Summary:

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

Strengths:

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

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

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

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

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

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

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

Weaknesses:

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

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

The authors interpret the ordinal position information captured by their decoding approach as a reflection of neural coding dedicated to the local context of a movement (Figure 4). One way to dissociate ordinal position information from information about the moving effectors is to train a classifier on one sequence and test the classifier on other sequences that require the same movements, but in different positions (Kornysheva et al., 2019). In the present study, however, participants trained to repeat a single sequence (4-1-3-2-4). As a result, ordinal position information is potentially confounded by the fixed finger transitions around each of the two critical positions (first and fifth press). Across consecutive correct sequences, the first keypress in a given sequence was always preceded by a movement of the index finger (=last movement of the preceding sequence), and followed by a little finger movement. The last keypress, on the other hand, was always preceded by a ring finger movement, and followed by an index finger movement (=first movement of the next sequence). Figure 4 - Supplement 2 shows that finger identity can be decoded with high accuracy (>70%) across a large time window around the time of the key press, up to at least +/-100 ms (and likely beyond, given that decoding accuracy is still high at the boundaries of the window depicted in that figure). This time window approaches the keypress transition times in this study. Given that distinct finger transitions characterized the first and fifth keypress, the classifier could thus rely on persistent (or "lingering") information from the preceding finger movement, and/or "preparatory" information about the subsequent finger movement, in order to dissociate the first and fifth keypress. Currently, the manuscript provides no evidence that the context information captured by the decoding approach is more than a by-product of temporally extended, and therefore overlapping, but independent neural representations of consecutive keypresses that are executed in close temporal proximity - rather than a neural representation dedicated to context.

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

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

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

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

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

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

Finally, the Reviewer hints that one way to address this issue would be to compare MEG responses before and after learning for sequences typed at a fixed speed. However, given that the speed-accuracy trade-off should improve with learning, a comparison between unlearned and learned skill states would dictate that the skill be evaluated at a very low fixed speed. Essentially, such a design presents the problem that the post-training test is evaluating the representation in the unlearned behavioral state that is not representative of the acquired skill. Thus, this approach would miss most learning effects on a task in which speed is the main learning metrics.

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

The Reviewer argues that the comparison of last finger movement of a trial and the first in the next trial are performed in different circumstances and contexts. This is an important point and one we tend to agree with. For this task, the first sequence in a practice trial is pre-planned before the first keypress is performed. This occurs in a somewhat different context from the sequence iterations that follow, which involve temporally overlapping planning, execution and evaluation processes. The Reviewer is concerned about a difference in the temporal mixing effect issue raised above between the first and last keypresses performed in a trial. Please, note that since neural representations of individual actions are competitively queued during the pre-planning period in a manner that reflects the ordinal structure of the learned sequence (Kornysheva et al., 2019), mixing effects are most likely present also for the first keypress in a trial.

Separately, the Reviewer suggests that contextualization during early learning may reflect preplanning or online planning. This is an interesting proposal. Given the decoding time-window used in this investigation, we cannot dissect separate contributions of planning, memory and sensory feedback to contextualization. Taking advantage of the superior temporal resolution of MEG relative to fMRI tools, work under way in our lab is investigating decoding time-windows more appropriate to address each of these questions.

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

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

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

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

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

Given this high correlation, we firmly agree with the Reviewer that the issue of eye movement related artefacts is still an important one to address. Fortunately, we did collect eye movement data during the MEG recordings so were able to investigate this. As detailed in the response to Reviewer #1 above, we found that gaze positions and eye-movement velocity time-locked to visual display updates (i.e. – a change in asterisk position above the displayed sequence) did not reflect the asterisk location above chance levels (Overall cross-validated accuracy = 0.21817; see Author response image 1). Furthermore, an inspection of the eye position data revealed that most participants on most trials displayed random walk gaze patterns around a center fixation point, indicating that participants did not attend to the asterisk position on the display. This is consistent with intrinsic generation of the action sequence, and congruent with the fact that the display does not provide explicit feedback related to performance. As pointed out above, a similar real-world example would be manually inputting a long password into a secure online application. In this case, one intrinsically generates the sequence from memory and receives similar feedback about the password sequence position (also provided as asterisks), which is typically ignored by the user.

The minimal participant engagement with the visual display in this explicit sequence learning motor task (which is highly generative in nature) contrasts markedly with behavior observed when reactive responses to stimulus cues are needed in the serial reaction time task (SRTT). This is a crucial difference that must be carefully considered when comparing findings across studies using the two sequence learning tasks.

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

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

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

We disagree with this statement. The original (Bonstrup et al., 2019) paper clearly states that micro-offline gains do not necessarily reflect offline learning in some cases and must be carefully interpreted based upon the behavioral context within which they are observed. Further, the paper lays out the conditions under which one can have confidence that micro-offline gains reflect offline learning. In fact, the excellent meta-analysis of (Pan & Rickard, 2015), which re-interprets the benefits of sleep in overnight skill consolidation from a “reactive inhibition” perspective, was a crucial resource in the experimental design of our initial study (Bonstrup et al., 2019), as well as in all our subsequent work. Pan & Rickard state:

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

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

“The traditional design involving 30 s-30 s performance break cycles should be abandoned given the evidence that it results in a reactive inhibition confound, and alternative designs with reduced performance duration per block used instead (Pan & Rickard, 2015 . One promising possibility is to switch to 10 s performance durations for each performance-break cycle Instead (Pan & Rickard, 2015 . That design appears sufficient to eliminate at least the majority of the reactive inhibition effect (Brawn et al., 2010; Rickard et al., 2008 .”

We mindfully incorporated recommendations from (Pan & Rickard, 2015) into our own study designs including 1) utilizing 10s practice trials and 2) constraining our analysis of micro-offline gains to early learning trials (where performance monotonically increases and 95% of overall performance gains occur), which are prior to the emergence of the “scalloped” performance dynamics that are strongly linked to reactive inhibition effects.

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

We strongly disagree with the Reviewer’s assertion that “there is no direct evidence in the literature that micro-offline gains really result from offline learning, i.e., an improvement in skill level.” The initial (Bonstrup et al., 2019) report was followed up by a large online crowd-sourcing study (Bonstrup et al., 2020). This second (and much larger) study provided several additional important findings supporting our interpretation of micro-offline gains in cases where the important behavioral conditions clarified above were met (see Author response image 4 below for further details on these conditions).

Author response image 4.

This Figure shows that micro-offline gains o ser ed in learning and nonlearning contexts are attri uted to different underl ing causes. Micro-offline and online changes relative to overall trial-by-trial learning. This figure is based on data from (Bonstrup et al., 2019). During early learning, micro-offline gains (red bars) closely track trial-by-trial performance gains (green line with open circle markers), with minimal contribution from micro-online gains (blue bars). The stated conclusion in Bönstrup et al. (2019) is that micro-offline gains only during this Early Learning stage reflect rapid memory consolidation (see also (Bonstrup et al., 2020)). After early learning, about practice trial 11, skill plateaus. This plateau skill period is characterized by a striking emergence of coupled (and relatively stable) micro-online drops and micro-offline increases. Bönstrup et al. (2019) as well as others in the literature (Brooks et al., 2024; Gupta & Rickard, 2022; Florencia Jacobacci et al., 2020), argue that micro-offline gains during the plateau period likely reflect recovery from inhibitory performance factors such as reactive inhibition or fatigue, and thus must be excluded from analyses relating micro-offline gains to skill learning. The Non-repeating groups in Experiments 3 and 4 from Das et al. (2024) suffer from a lack of consideration of these known confounds (end of Fig legend).

Evidence documented in that paper (Bonstrup et al., 2020) showed that micro-offline gains during early skill learning were: 1) replicable and generalized to subjects learning the task in their daily living environment (n=389); 2) equivalent when significantly shortening practice period duration, thus confirming that they are not a result of recovery from performance fatigue (n=118); 3) reduced (along with learning rates) by retroactive interference applied immediately after each practice period relative to interference applied after passage of time (n=373), indicating stabilization of the motor memory at a microscale of several seconds consistent with rapid consolidation; and 4) not modified by random termination of the practice periods, ruling out a contribution of predictive motor slowing (N = 71) (Bonstrup et al., 2020). Altogether, our findings were strongly consistent with the interpretation that micro-offline gains reflect memory consolidation supporting early skill learning. This is precisely the portion of the learning curve (Pan & Rickard, 2015) refer to when they state “…rapid learning during that period masks any reactive inhibition effect”.

This interpretation is further supported by brain imaging evidence linking known memory-related networks and consolidation mechanisms to micro-offline gains. First, we reported that the density of fast hippocampo-neocortical skill memory replay events increases approximately three-fold during early learning inter-practice rest periods with the density explaining differences in the magnitude of micro-offline gains across subjects (Buch et al., 2021). Second, Jacobacci et al. (2020) independently reproduced our original behavioral findings and reported BOLD fMRI changes in the hippocampus and precuneus (regions also identified in our MEG study (Buch et al., 2021)) linked to micro-offline gains during early skill learning. These functional changes were coupled with rapid alterations in brain microstructure in the order of minutes, suggesting that the same network that operates during rest periods of early learning undergoes structural plasticity over several minutes following practice (Deleglise et al., 2023). Crucial to this point, Chen et al. (2024) and Sjøgård et al (2024) provided direct evidence from intracranial EEG in humans linking sharp-wave ripple density during rest periods (which are known markers for neural replay (Buzsaki, 2015)) in the human hippocampus (80-120 Hz) to micro-offline gains during early skill learning.

Thus, there is now substantial converging evidence in humans across different indirect noninvasive and direct invasive recording techniques linking hippocampal activity, neural replay dynamics and offline performance gains in skill learning.

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

The recent work of (Gupta & Rickard, 2022, 2024) does not present any data that directly opposes our finding that early skill learning (Bonstrup et al., 2019) is expressed as micro-offline gains during rest breaks. These studies are an extension of the Rickard et al (2008) paper that employed a massed (30s practice followed by 30s breaks) vs spaced (10s practice followed by 10s breaks) experimental design to assess if recovery from reactive inhibition effects could account for performance gains measured after several minutes or hours. Gupta & Rickard (2022) added two additional groups (30s practice/10s break and 10s practice/10s break as used in the work from our group). The primary aim of the study was to assess whether it was more likely that changes in performance when retested 5 minutes after skill training (consisting of 12 practice trials for the massed groups and 36 practice trials for the spaced groups) had ended reflected memory consolidation effects or recovery from reactive inhibition effects. The Gupta & Rickard (2024) follow-up paper employed a similar design with the primary difference being that participants performed a fixed number of sequences on each trial as opposed to trials lasting a fixed duration. This was done to facilitate the fitting of a quantitative statistical model to the data.

To reiterate, neither study included any analysis of micro-online or micro-offline gains and did not include any comparison focused on skill gains during early learning trials (only at retest 5 min later). Instead, Gupta & Rickard (2022), reported evidence for reactive inhibition effects for all groups over much longer training periods than early learning. In fact, we reported the same findings for trials following the early learning period in our original 2019 paper (Bonstrup et al., 2019) (Author response image 4). Please, note that we also reported that cumulative microoffline gains over early learning did not correlate with overnight offline consolidation measured 24 hours later (Bonstrup et al., 2019) (see the Results section and further elaboration in the Discussion). We interpreted these findings as indicative that the mechanisms underlying offline gains over the micro-scale of seconds during early skill learning versus over minutes or hours very likely differ.

In the recent preprint from (Das et al., 2024), the authors make the strong claim that “micro-offline gains during early learning do not reflect offline learning” which is not supported by their own data. The authors hypothesize that if “micro-offline gains represent offline learning, participants should reach higher skill levels when training with breaks, compared to training without breaks”. The study utilizes a spaced vs. massed practice groups between-subjects design inspired by the reactive inhibition work from Rickard and others to test this hypothesis.

Crucially, their design incorporates only a small fraction of the training used in other investigations to evaluate early skill learning (Bonstrup et al., 2020; Bonstrup et al., 2019; Brooks et al., 2024; Buch et al., 2021; Deleglise et al., 2023; F. Jacobacci et al., 2020; Mylonas et al., 2024). A direct comparison between the practice schedule designs for the spaced and massed groups in Das et al., and the training schedule all participants experienced in the original Bönstrup et al. (2019) paper highlights this issue as well as several others (Author response image 5):

Author response image 5.

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

Participants in the original (Bonstrup et al., 2019) experienced 157.14% more practice time and 46.97% less inter-practice rest time than the Spaced group in the Das et al. study (Author response image 5). Thus, the overall amount of practice and rest differ substantially between studies, with much more limited training occurring for participants in Das et al.

In addition, the training interventions (i.e. – the practice schedule differences between the Spaced and Massed groups) were designed in a manner that minimized any chance of effectively testing their hypothesis. First, the interventions were applied over an extremely short period relative to the length of the total training session (5% and 12% of the total training session for Massed and Spaced groups, respectively; see gaps in the red shaded area in Author response image 5). Second, the intervention was applied during a period in which only half of the known total learning occurs. Specifically, we know from Bönstrup et al. (2019) that only 46.57% of the total performance gains occur in the practice interval covered by Das et al Training 1 intervention. Thus, early skill learning as evaluated by multiple groups (Bonstrup et al., 2020; Bonstrup et al., 2019; Brooks et al., 2024; Buch et al., 2021; Deleglise et al., 2023; F. Jacobacci et al., 2020; Mylonas et al., 2024), is in the Das et al experiment amputated to about half.

Furthermore, a substantial amount of learning takes place during Das et al’s Test 1 and Test 2 periods (32.49% of total gains combined). The fact that substantial learning is known to occur over both the Test 1 (18.06%) and Test 2 (14.43%) intervals presents a fundamental problem described by Pan and Rickard (Pan & Rickard, 2015). They reported that averaging over intervals where substantial performance gains occur (i.e. – performance is not stable) inject crucial artefacts into analyses of skill learning:

“A large amount of averaging has the advantage of yielding more precise estimates of each subject’s pretest and posttest scores and hence more statistical power to detect a performance gain. However, calculation of gain scores using that strategy runs the risk that learning that occurs during the pretest and (or posttest periods (i.e., online learning is incorporated into the gain score (Rickard et al., 2008; Robertson et al., 2004 .”

The above statement indicates that the Test 1 and Test 2 performance scores from Das et al. (2024) are substantially contaminated by the learning rate within these intervals. This is particularly problematic if the intervention design results in different Test 2 learning rates between the two groups. This in fact, is apparent in their data (Figure 1C,E of the Das et al., 2024 preprint) as the Test 2 learning rate for the Spaced group is negative (indicating a unique interference effect observable only for this group). Specifically, the Massed group continues to show an increase in performance during Test 2 and 4 relative to the last 10 seconds of practice during Training 1 and 2, respectively, while the Spaced group displays a marked decrease. This post-training performance decrease for the Spaced group is in stark contrast to the monotonic performance increases observed for both groups at all other time-points. One possible cause could be related to the structure of the Test intervals, which include 20 seconds of uninterrupted practice. For the Spaced group, this effectively is a switch to a Massed practice environment (i.e., two 10-secondlong practice trials merged into one long trial), which interferes with greater Training 1 interval gains observed for the Space group. Interestingly, when statistical comparisons between the groups are made at the time-points when the intervention is present (Figure 1E) then the stated hypothesis, “If micro-offline gains represent offline learning, participants should reach higher skill levels when training with breaks, compared to training without breaks”, is confirmed.

In summary, the experimental design and analyses used by Das et al does not contradict the view that early skill learning is expressed as micro-offline gains during rest breaks. The data presented by Gupta and Rickard (2022, 2024) and Das et al. (2024) is in many ways more confirmatory of the constraints employed by our group and others with respect to experimental design, analysis and interpretation of study findings, rather than contradictory. Still, it does highlight a limitation of the current micro-online/offline framework, which was originally only intended to be applied to early skill learning over spaced practice schedules when reactive inhibition effects are minimized (Bonstrup et al., 2019; Pan & Rickard, 2015). Extrapolation of this current framework to postplateau performance periods, longer timespans, or non-learning situations (e.g. – the Nonrepeating groups from Das et al. (2024)), when reactive inhibition plays a more substantive role, is not warranted. Ultimately, it will be important to develop new paradigms allowing one to independently estimate the different coincident or antagonistic features (e.g. - memory consolidation, planning, working memory and reactive inhibition) contributing to micro-online and micro-offline gains during and after early skill learning within a unifying framework.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

(1) I found Figure 2B too small to be useful, as the actual elements of the cells are very hard to read.

We have removed the grid colormap panel (top-right) from Figure 2B. All of this colormap data is actually a subset of data presented in Figure 2 – figure supplement 1, so can still be found there.

Reviewer #2 (Recommendations for the authors):

(1) Related to the first point in my concerns, I would suggest the authors compare decoding accuracy between correct presses followed by correct vs. incorrect presses. This would clarify if the decoder is actually taking the MEG signal for subsequent press into account. I would also suggest the authors use pre-movement MEG features and post-movement features with shorter windows and compare each result with the results for the original post-movement MEG feature with a longer window.

The present study does not contain enough errors to perform the analysis proposed by the Reviewer. As noted above, we did re-examine our data and now report a new control regression analysis, all of which indicate that the proximity between keypresses does not explain contextualization effects.

(2) I was several times confused by the author's use of "neural representation of an action" or "sequence action representations" in understanding whether these terms refer to representation on the level of whole-brain, region (as defined by the specific parcellation used), or voxels. In fact, what is submitted to the decoder is some complicated whole-brain MEG feature (i.e., the "neural representation"), which is a hybrid of voxel and parcel features that is further dimension-reduced and not immediately interpretable. Clarifying this point early in the text and possibly using some more sensible terms, such as adding "brain-wise" before the "sequence action representation", would be the most helpful for the readers.

We now clarified this terminology in the revised manuscript.

(3) Although comparing many different ways in feature selection/reduction, time window selection, and decoder types is undoubtedly a meticulous work, the current version of the manuscript seems still lacking some explanation about the details of these methodological choices, like which decoding method was actually used to report the accuracy, whether or not different decoding methods were chosen for individual participants' data, how training data was selected (is it all of the correct presses in Day 1 data?), whether the frequency power or signal amplitude was used, and so on. I would highly appreciate these additional details in the Methods section.

The reported accuracies were based on linear discriminant analysis classifier. A comparison of different decoders (Figure 3 – figure supplement 4) shows LDA was the optimal choice.

Whether or not different decoding methods were chosen for individual participants' data

We selected the same decoder (LDA) performance to report the final accuracy.

How training data was selected (is it all of the correct presses in Day 1 data?),

Decoder training was conducted as a randomized split of the data (all correct keypresses of Day 1) into training (90%) and test (10%) samples for 8 iterations.

Whether the frequency power or signal amplitude was used

Signal amplitude was used for feature calculation.

(4) In terms of the Methods, please consider adding some references about the 'F1 score', the 'feature importance score,' and the 'MRMR-based feature ranking,' as the main readers of the current paper would not be from the machine learning community. Also, why did the LDA dimensionality reduction reduce accuracy specifically for the voxel feature?

We have now added the following statements to the Methods section that provide more detailed descriptions and references for these metrics:

“The F1 score, defined as the harmonic mean of the precision (percentage of true predictions that are actually true positive) and recall (percentage of true positives that were correctly predicted as true) scores, was used as a comprehensive metric for all one-versus-all keypress state decoders to assess class-wise performance that accounts for both false-positive and false-negative prediction tendencies [REF]. A weighted mean F1 score was then computed across all classes to assess the overall prediction performance of the multi-class model.”

and

“Feature Importance Scores

The relative contribution of source-space voxels and parcels to decoding performance (i.e. – feature importance score) was calculated using minimum redundant maximum relevance (MRMR) and highlighted in topography plots. MRMR, an approach that combines both relevance and redundancy metrics, ranked individual features based upon their significance to the target variable (i.e. – keypress state identity) prediction accuracy and their non-redundancy with other features.”

As stated in the Reviewer responses above, the dimensionality of the voxel-space feature set is very high (i.e. – 15684). LDA attempts to map the input features onto a much smaller dimensional space (number of classes-1; e.g. – 3 dimensions for 4-class keypress decoding). It is likely that the reduction in accuracy observed only for the voxel-space feature was due to the loss of relevant information during the mapping process that resulted in reduced accuracy. This reduction in accuracy for voxel-space decoding was specific to LDA. Figure 3—figure supplement 3 shows that voxel-space decoder performance actually improved when utilizing alternative dimensionality reduction techniques.

(5) Paragraph 9, lines #139-142: "Notably, decoding associated with index finger keypresses (executed at two different ordinal positions in the sequence) exhibited the highest number of misclassifications of all digits (N = 141 or 47.5% of all decoding errors; Figure 3C), raising the hypothesis that the same action could be differentially represented when executed at different learning state or sequence context locations."

This does not seem to be a fair comparison, as the index finger appears twice as many as the other fingers do in the sequence. To claim this, proper statistical analysis needs to be done taking this difference into account.

We thank the Reviewer for bringing this issue to our attention. We have now corrected this comparison to evaluate relative false negative and false positive rates between individual keypress state decoders, and have revised this statement in the manuscript as follows:

“Notably, decoding of index finger keypresses (executed at two different ordinal positions in the sequence) exhibited the highest false negative (0.116 per keypress) and false positive (0.043 per keypress) misclassification rates compared with all other digits (false negative rate range = [0.067 0.114]; false positive rate range = [0.020 0.037]; Figure 3C), raising the hypothesis that the same action could be differentially represented when executed within different contexts (i.e. - different learning states or sequence locations).”

(6) Finally, the authors could consider acknowledging in the Discussion that the contribution of micro-offline learning to genuine skill learning is still under debate (e.g., Gupta and Rickard, 2023; 2024; Das et al., bioRxiv, 2024).

We have added a paragraph in the Discussion that addresses this point.

Reviewer #3 (Recommendations for the authors):

In addition to the additional analyses suggested in the public review, I have the following suggestions/questions:

(1) Given that the authors introduce a new decoding approach, it would be very helpful for readers to see a distribution of window sizes and window onsets eventually used across individuals, at least for the optimized decoder.

We have now included a new supplemental figure (Figure 4 – figure Supplement 2) that provides this information.

(2) Please explain in detail how you arrived at the (interpolated?) group-level plot shown in Figure 1B, starting from the discrete single-trial keypress transition times. Also, please specify what the shading shows.

Instantaneous correct sequence speed (skill measure) was quantified as the inverse of time (in seconds) required to complete a single iteration of a correctly generated full 5-item sequence. Individual keypress responses were labeled as members of correct sequences if they occurred within a 5-item response pattern matching any possible circular shifts of the 5-item sequence displayed on the monitor (41324). This approach allowed us to quantify a measure of skill within each practice trial at the resolution of individual keypresses. The dark line indicates the group mean performance dynamics for each trial. The shaded region indicates the 95% confidence limit of the mean (see Methods).

(3) Similarly, please explain how you arrived at the group-level plot shown in Figure 1C. What are the different colored lines (rows) within each trial? How exactly did the authors reach the conclusion that KTT variability stabilizes by trial 6?

Figure 1C provides additional information to the correct sequence speed measure above, as it also tracks individual transition speed composition over learning. Figure 1C, thus, represents both changes in overall correct sequence speed dynamics (indicated by the overall narrowing of the horizontal speed lines moving from top to bottom) and the underlying composition of the individual transition patterns within and across trials. The coloring of the lines is a shading convention used to discriminate between different keypress transitions. These curves were sampled with 1ms resolution, as in Figure 1B. Addressing the underlying keypress transition patterns requires within-subject normalization before averaging across subjects. The distribution of KTTs was normalized to the median correct sequence time for each participant and centered on the mid-point for each full sequence iteration during early learning.

(4) Maybe I missed it, but it was not clear to me which of the tested classifiers was eventually used. Or was that individualized as well? More generally, a comparison of the different classifiers would be helpful, similar to the comparison of dimension reduction techniques.

We have now included a new supplemental figure that provides this information.

(5) Please add df and effect sizes to all statistics.

Done.

(6) Please explain in more detail your power calculation.

The study was powered to determine the minimum sample size needed to detect a significant change in skill performance following training using a one-sample t-test (two-sided; alpha = 0.05; 95% statistical power; Cohen’s D effect size = 0.8115 calculated from previously acquired data in our lab). The calculated minimum sample size was 22. The included study sample size (n = 27) exceeded this minimum.

This information is now included in the revised manuscript.

(7) The cut-off for the high-pass filter is unusually high and seems risky in terms of potential signal distortions (de Cheveigne, Neuron 2019). Why did the authors choose such a high cut-off?

The 1Hz high-pass cut-off frequency for the 1-150Hz band-pass filter applied to the continuous raw MEG data during preprocessing has been used in multiple previous MEG publications (Barratt et al., 2018; Brookes et al., 2012; Higgins et al., 2021; Seedat et al., 2020; Vidaurre et al., 2018).

(8) "Furthermore, the magnitude of offline contextualization predicted skill gains while online contextualization did not", lines 336/337 - where is that analysis?

Additional details pertaining to this analysis are now provided in the Results section (Figure 5 – figure supplement 4).

(9) How were feature importance scores computed?

We have now added a new subheading in the Methods section with a more detailed description of how feature importance scores were computed.

(10)  Please add x and y ticks plus tick labels to Figure 5 - Figure Supplement 3, panel A

Done

(11) Line 369, what does "comparable" mean in this context?

The sentence in the “Study Participants” part of the Methods section referred to here has now been revised for clarity.

(12) In lines 496/497, please specify what t=0 means (KeyDown event, I guess?).

Yes, the KeyDown event occurs at t = 0. This has now been clarified in the revised manuscript.

(13) Please specify consistent boundaries between alpha- and beta-bands (they are currently not consistent in the Results vs. Methods (14/15 Hz or 15/16 Hz)).

We thank the Reviewer for alerting us to this discrepancy caused by a typographic error in the Methods. We have now corrected this so that the alpha (8-14 Hz) and beta-band (15-24 Hz) frequency limits are described consistently throughout the revised manuscript.

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Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Summary:

In this manuscript, the authors investigate the role of microtubule dynamics and its effects on neuronal aging. Using C. elegans as a model, the authors investigate the role of evolutionarily conserved Hippo pathway in microtubule dynamics of touch receptor neurons (TRNs) in an age-dependent manner. Using genetic, molecular, behavioral, and pharmacological approaches, the authors show that age-dependent loss of microtubule dynamics might underlie structural and functional aging of TRNs. Further, the authors show that the Hippo pathway specifically functions in these neurons to regulate microtubule dynamics. Specifically, authors show that hyperactivation of YAP-1, a downstream component of the Hippo pathway that is usually inhibited by the kinase activity of the upstream components of the pathway, results in microtubule stabilization and that might underlie the structural and functional decline of TRNs with age. However, how the Hippo pathway regulates microtubule dynamics and neuronal aging was not investigated by the authors.

Strengths:

This is a well-conducted and well-controlled study, and the authors have used multiple approaches to address different questions.

Weaknesses:

There are no major weaknesses identified, except that the effect of the Hippo pathway seems to be specific to only a subset of neurons. I would like the authors to address the specificity of the effect of the Hippo pathway in TRNs, in their resubmission.

Although our genetic experiments, including TRNs-specific rescue/overexpression of YAP-1 and knockdown of WTS-1, strongly suggest that a cell-autonomous function of WTS-1-YAP-1 axis in TRNs, the Hpo pathway could have broader roles in neuroprotection. While this pathway may regulate microtubules stability in multiple neurons, other characteristics of TRNs, such as their anatomical localization near the cuticle or their long projections along body axis, could contribute to their susceptibilities to age-related deformation. Otherwise, the Hpo pathway may be truly TRNs-specific. TRNs have unique microtubules in both terms of composition and structure. Among nine α-, six β-tubulin genes in C. elegans, one α-tubulin (mec-12) and one β-tubulin (mec-7) showed highly enriched expression in TRNs [1, 2] and TRNs contain special 15-protofilament microtubule structure, while all other neurons in C. elegans have 11-protofilament microtubules [3]. Transcriptional regulation through YAP-1 may affect the specific microtubule structure of TRNs, leading to premature neuronal deformation. We have included this in the discussion section of the revised manuscript.

Reviewer #2 (Public review):

Summary:

This study examines a novel role of the Hpo signaling pathway, specifically of wts-1/LATS and the downstream regulator of gene expression, yap, in age-related neurodegeneration in C. elegans touch-responsive mechanosensory neurons, ALM and PLM. The study shows that knockdown or deletion of wts-1/LATS causes age-associated morphological abnormalities of these neurons, accompanied by functional loss of touch responsiveness. This is further associated with enhanced, abnormal, microtubule stabilization in these neurons.

Strengths:

This study examines a novel role of the Hpo signaling pathway, specifically of wts-1/LATS and the downstream regulator of gene expression, yap, in age-related neurodegeneration in C. elegans touch-responsive mechanosensory neurons, ALM and PLM. The study shows that knockdown or deletion of wts-1/LATS causes age-associated morphological abnormalities of these neurons, accompanied by functional loss of touch responsiveness. This is further associated with enhanced, abnormal, microtubule stabilization in these neurons. Strong pharmacological and especially genetic manipulations of MT-stabilizing or severing proteins show a strong genetic link between yap and regulation of MTs stability. The study is strong and uses robust approaches, especially strong genetics. The demonstrations on the aging-related roles of the Hpo signaling pathway, and the link to MTs, are novel and compelling. Nevertheless, the study also has mechanistic weaknesses (see below).

Weaknesses:

Specific comments:

(1) The study demonstrates age-specific roles of the Hpo pathway, specifically of wts-1/LATS and yap, specifically in TRN mechanosensory neurons, without observing developmental defects in these neurons, or effects in other neurons. This is a strong demonstration. Nevertheless, the study does not address whether there is a correlation of Hpo signaling pathway activity decline specifically in these neurons, and not other neurons, and at the observed L4 stage and onwards (including the first day of adulthood, 1DA stage). Such demonstrations of spatio-temporal regulation of the Hpo signaling pathway and its activation seem important for linking the Hpo pathway with the observed age-related neurodegeneration. Can this age-related response be correlated to indeed a decline in Hpo signaling during adulthood? Especially at L4 and onwards? It will be informative to measure this by examining the decline in wts1 as well as yap levels and yap nuclear localization.

As described above, we have included possible explanations for the specificity of the Hpo pathway in TRNs. Since components of the Hpo pathway are expressed in various tissues, including the intestine and hypodermis, this pathway could have broader neuroprotective roles across multiple neurons. Alternatively, it could function in TRNs. Given that the TRNs possess unique microtubules in both structure and composition, and that Hpo pathway has crucial roles in microtubule stability regulation, the roles of the Hpo pathway may indeed be TRNs-specific. As we described in the manuscript, our observations, along with those of others, indicate that neuronal deformation of TRNs begins around the 4th day of adulthood. Additionally, the degree of morphological deformation in wts-1 mutants at the L4 stage is comparable to that of aged wild-type worms on the 15th day of adulthood. Therefore, to assess the functional decline of WTS-1 or nuclear localization of YAP-1, observations should begin in 4-day-old animals. Using fluorescence-tagged YAP-1 under the mec-4 promoter, we couldn’t detect a significant increase in nuclear YAP-1 in TRNs of 4-day-old adult. Additionally, we were unable to assess YAP-1 intercellular localization in older animals, such as 10-day-old animals, possibly due to the small cell size of neurons or morphological alteration along with aging of TRNs. Although we did not detect functional decline of WTS-1 or increased nuclear YAP-1 in TRNs, nuclear localization of YAP-1 increases with age in other tissues, such as the intestine and hypodermis (Author response image. 1). This may result from inactivation of the Hippo (Hpo) pathway, an indirect consequence of structural and functional decline—such as tissue stiffness associated with aging—or a combination of both. Additionally, given that morphological deformation of TRNs appears to begin around fourth day of adulthood, nuclear localization of YAP-1 in the intestine and hypodermis seems to have a later onset and be more moderate. It is possible that YAP-1 nuclear localization in TRNs occurs earlier or that other factors contribute early-stage touch neuronal deformation.

Author response image 1.

Quantification of the proportion of worms exhibiting nuclear localization of YAP-1. We used GFP-tagged YAP-1 driven by its own 4 kb promoter. A total of 90 animals were observed each day.

(2) The Hpo pathway eventually activates gene expression via yap. Although the study uses robust genetic manipulations of yap and wts-1/LATS, it is not clear whether the observed effects are attributed to yap-mediated regulation of gene expression (see 3).

Given that the neuronal deformation in the wts-1 mutant was completely restored by the loss of yap-1 or egl-44, it strongly suggests that YAP-TEAD-mediated transcriptional regulation is responsible for the premature neuronal degeneration of the wts-1 mutant. However, in this study, we were unable to identify specific transcriptional target genes associated with these phenomena, which represents a limitation of our research (please see below).

(3) The observations on the abnormal MT stabilization, and the subsequent genetic examinations of MT-stability/severing genes, are a significant strength of the study. Nevertheless, despite the strong genetic links to yap and wts-1/LATS, it is not clear whether MT-regulatory genes are regulated by transcription downstream of the Hpo pathway, thus not enabling a strong causal link between MT regulation and Hpo-mediated gene expression, making this strong part of the study mechanistically circumstantial. Specifically, it will be good to examine whether the genes addressed herein, for example, Spastin, are transcriptionally regulated downstream of the Hpo pathway. This comment is augmented by the finding that in the wts-1/ yap-1 double mutants, MT abnormality, and subsequent neuronal morphology and touch responses are restored, clearly indicating that there is an associated transcriptional regulation

If the target genes of YAP-1 are not identified, it will be difficult to fully understand how YAP-1 regulates microtubule stability. Microtubule-stabilizing genes, whose knockdown alleviates wts-1 mutant neuronal deformation, could be potential transcriptional targets of YAP-1. Among these genes, PTRN-1 and DLK-1 contain MCAT sequences (CATTCCA/T), a well-conserved DNA motif recognized by the TEAD transcription factor, in their promoters near the transcription start site (TSS). We hypothesized that the expression of fluorescence-tagged reporters of promoter regions containing these MCAT sequences would be enhanced in the absence of wts-1 activity. Although both reporters were expressed in TRNs, they did not show significant changes in the wts-1 mutant background. We also focused on spv-1, a worm homolog of ARHGAP29, which negatively regulates RhoA. YAP is known to modulate actin cytoskeleton rigidity through transcriptional regulation of ARHGAP29 [4]. The promoter of spv-1 contains 2 MCAT sequences and loss of spv-1 mitigated neuronal deformation of the wts-1 mutant. However, reporters of promoter regions containing MCAT sequences only weakly expressed in the process of TRNs. More importantly, ectopic expression of dominant-negative form of rho-1/rhoA did not lead to significant deformation of TRNs. While YAP typically functions as a transcriptional co-activator, it has also been reported to repress target gene expression, such as DDIT4 and Trail, in collaborated with TEAD transcriptional factor [5].  As a reviewer pointed out, spas-1 might be transcriptionally repressed by yap-1, given that its loss leads to premature deformation of TRNs. However, since the phenotype of the spas-1 mutant has a later onset than the wts-1 mutant and is relatively restricted to ALM, we excluded it from our candidate gene search. Despite extensive genetic approaches, we were unable to establish a strong causal link between YAP-1 and the regulation of microtubule stability. Unbiased screenings, such as tissue-specific transcriptome analysis, may help address the remaining questions. We have outlined the limitations of this study in the discussion section of the revised manuscript.

Other comments:

(1) The TRN-specific knockdown of wts-1 and yap-1 is a clear strength. Nevertheless, these do not necessarily show cell-autonomous effects, as the yap transcription factor may regulate the expression of external cues, secreted or otherwise, thus generating non-cell autonomous effects. For example, it is known that yap regulates TGF-beat expression and signaling.

In the absence of LATS1/2 activity, activated YAP has been reported to drive biliary epithelial cell lineage specification by directly regulating TGF-β transcription during and after liver development [6]. Even when functioning in an autocrine manner, TGF-β can exhibit non-cell autonomous effects. While it primarily acts on the same cell that secretes it, some molecules may also affect neighboring cells, leading to paracrine effects. Additionally, TGF-β can modify the extracellular matrix (ECM), indirectly affecting surrounding cells. Similarly, if YAP regulates transcription of secretory protein in TRNs, the resulting extracellular factors or surrounding cells may influence touch neuronal microtubules in a non-cell-autonomous manner. Although our genetic data strongly suggest a cell-autonomous function of WTS-1-YAP-1 in TRNs, we could not exclude the possibility that YAP-1 functions non-cell-autonomously, as we were unable to identify its transcriptional targets. We have included this in the discussion section of the revised manuscript.

(2) Continuing from comment (3) above, it seems that many of the MT-regulators chosen here for genetic examinations were chosen based on demonstrated roles in neurodegeneration in other studies. It would be good to show whether these MT-associated genes are directly regulated by transcription by the Hpo pathway.

As we described above, several MT-associated genes­­, such as ptrn-1, dlk-1 and spv-1, contain MCAT sequences in their promoter and their knockdown alleviated wts-1-induced neuronal deformation. These genes were tested to determine whether they were directly regulated by WTS-1-YAP-1. Based on our findings, we concluded that they were unlikely to be regulated by the Hpo pathway in TRNs.

(3) The impairment of the touch response may not be robust: it is only a 30-40% reduction at L4, and even less reduction at 1DA. It would be good to offer possible explanations for this finding.

As pointed out by the reviewer, the impairment of touch responses of wts-1 mutants showed an approximately 33% reduction at both L4 and 1DA compared to age-matched wild-type animals. At the L4 stage, control worms responded to nearly every gentle touch (94%), whereas wts-1 mutants responded to only 60% of stimuli. By 1DA, control worms exhibited slightly decline in touch responses compared to L4 (82.5%), whereas wts-1 mutants displayed more pronounced impairment (55.7%) (Fig 1E). Regarding the severity and frequency of structural degeneration of wts-1 mutant at both stages, it appears to be relatively moderate. As we noted in the manuscript, our observations, along with those of others, indicate that structural abnormalities in ALM and PLM neurons begin to appear around the fourth day of adulthood and progressively worsen as the worms age [7]. In a previous study, Tank et al. categorized day 10-aged worms into two groups based on their movement ability and then assessed structural deformation in each animal to determine whether structural and functional degeneration of TRNs were correlated. In this same group of animals, they examined the gentle touch response and found that animals responded to gentle touch 46 ± 5.1 %, 84 ± 12.2 %, respectively [8]. It could be said that, on average, day 10 animals had 65% touch response on average, which is consistent with our observation in day 10 animals (Fig. 5E, 56.3%). Given these observations, the function of TRNs of wts-1 mutant or aged animals appears to be preserved despite severe structure failures. The gentle touch response evokes an escape behavior in which animals quickly move away from the stimulus; thus proper touch responses are essential for avoiding predators and ensuring survival. It has been reported to be necessary for evading fungal predation, such as escaping from a constricting hyphal ring [9]. Given that the gentle touch response is crucial for survival, its function is likely well preserved despite structural abnormalities, such as age-related deformation.

Reviewer #1 (Recommendations for the authors):

Major comments:

(1) Why is the effect of the Hippo pathway on microtubule dynamics specific to TRNs? Is it the structure of TRNs that makes them prone to the effects of age-dependent decline in microtubule dynamics? The authors are advised to discuss it in their resubmission.

As described above, we have included possible explanations for the tissue specificity of the Hpo pathway in TRNs and the vulnerability of TRNs to age-associated decline in the discussion section of the revised manuscript.

(2) The authors are advised to explain the shorter life span of wts-1; yap-1 double mutants (with restored TRNs) compared to wts-1 single mutants in Figure 2F. The life span of yap-1 single mutants should be included in Figure 2F. Further, based on the data, the shorter lifespan of wts-1 mutants cannot be attributed to abnormal TRNs as the lifespan of wts-1; yap-1 double mutants is even shorter. The authors are advised to explain the shorter life span of wts-1 mutants compared to wild-type controls.

wts-1 is known to be involved in various developmental processes, including the maintenance of apicobasal polarity in the intestine, growth rate control, and dauer formation [10-12]. Since WTS-1 activity is restored in the intestine of the mutant used for lifespan measurement, the shorter lifespan of the wts-1 mutant may result from the loss of WTS-1 in tissues other than the intestine. Although we were unable to include lifespan data for the yap-1 mutant, recent studies indicate that the yap-1(tm1416) mutant or yap-1 RNAi treated worms exhibit a shortened lifespan [13, 14]. Thus, our data showing a slightly shorter lifespan of the wts-1; yap-1 mutant compared with the wts-1 mutant may result from the synergistic action of yap-1 and yap-1-independent downstream factors of wts-1. While this study does not provide an explanation for the shortened lifespan of wts-1 or wts-1; yap-1 mutants, the fact that the wts-1; yap-1 double mutant with restored TRNs still have a shorter lifespan compared with the wts-1 mutant strongly suggests that premature deformation of the wts-1 neurons appear to be a touch neuron-specific event, rather than being associated with whole body, as described in the manuscript..

Minor comments:

(1) In the abstract, please provide definitions for LATS and YAP. Authors can mention that LATS is a kinase and YAP a transcriptional co-activator in the Hippo pathway.

(2) In the last paragraph on page 9, change "these function" to "this function", and change "knock-downed" to "knocked down".

(3) On page 10, paragraph 2, change "regarding the action mechanism" to "regarding the mechanism of action".

(4) On page 11, paragraph 1, change "endogenous WTS-1 could inhibits" to "endogenous WTS-1 could inhibit".

(5) On page 16, paragraph 1, change "consistent to the hypothesis" to "consistent with this hypothesis".

(6) Overall, the paper is well written. However, there is still room to improve the language and diction used by the authors.

We have revised all minor comments suggested by the reviewer in the revised manuscript.

References

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(2) Fukushige T, Siddiqui ZK, Chou M, Culotti JG, Gogonea CB, Siddiqui SS, et al. MEC-12, an alpha-tubulin required for touch sensitivity in C. elegans. J Cell Sci. 1999;112 ( Pt 3):395-403. Epub 1999/01/14. doi: 10.1242/jcs.112.3.395. PubMed PMID: 9885292.

(3) Chalfie M, Thomson JN. Structural and functional diversity in the neuronal microtubules of Caenorhabditis elegans. J Cell Biol. 1982;93(1):15-23. Epub 1982/04/01. doi: 10.1083/jcb.93.1.15. PubMed PMID: 7068753; PubMed Central PMCID: PMCPMC2112106.

(4) Qiao Y, Chen J, Lim YB, Finch-Edmondson ML, Seshachalam VP, Qin L, et al. YAP Regulates Actin Dynamics through ARHGAP29 and Promotes Metastasis. Cell Rep. 2017;19(8):1495-502. Epub 2017/05/26. doi: 10.1016/j.celrep.2017.04.075. PubMed PMID: 28538170.

(5) Kim M, Kim T, Johnson RL, Lim DS. Transcriptional co-repressor function of the hippo pathway transducers YAP and TAZ. Cell Rep. 2015;11(2):270-82. Epub 2015/04/07. doi: 10.1016/j.celrep.2015.03.015. PubMed PMID: 25843714.

(6) Lee DH, Park JO, Kim TS, Kim SK, Kim TH, Kim MC, et al. LATS-YAP/TAZ controls lineage specification by regulating TGFbeta signaling and Hnf4alpha expression during liver development. Nat Commun. 2016;7:11961. Epub 2016/07/01. doi: 10.1038/ncomms11961. PubMed PMID: 27358050; PubMed Central PMCID: PMCPMC4931324.

(7) Toth ML, Melentijevic I, Shah L, Bhatia A, Lu K, Talwar A, et al. Neurite sprouting and synapse deterioration in the aging Caenorhabditis elegans nervous system. J Neurosci. 2012;32(26):8778-90. Epub 2012/06/30. doi: 10.1523/JNEUROSCI.1494-11.2012. PubMed PMID: 22745480; PubMed Central PMCID: PMCPMC3427745.

(8) Tank EM, Rodgers KE, Kenyon C. Spontaneous age-related neurite branching in Caenorhabditis elegans. J Neurosci. 2011;31(25):9279-88. Epub 2011/06/24. doi: 10.1523/JNEUROSCI.6606-10.2011. PubMed PMID: 21697377; PubMed Central PMCID: PMCPMC3148144.

(9) Maguire SM, Clark CM, Nunnari J, Pirri JK, Alkema MJ. The C. elegans touch response facilitates escape from predacious fungi. Curr Biol. 2011;21(15):1326-30. Epub 2011/08/02. doi: 10.1016/j.cub.2011.06.063. PubMed PMID: 21802299; PubMed Central PMCID: PMCPMC3266163.

(10) Cai Q, Wang W, Gao Y, Yang Y, Zhu Z, Fan Q. Ce-wts-1 plays important roles in Caenorhabditis elegans development. FEBS Lett. 2009;583(19):3158-64. Epub 2009/09/10. doi: 10.1016/j.febslet.2009.09.002. PubMed PMID: 19737560.

(11) Kang J, Shin D, Yu JR, Lee J. Lats kinase is involved in the intestinal apical membrane integrity in the nematode Caenorhabditis elegans. Development. 2009;136(16):2705-15. Epub 20090715. doi: 10.1242/dev.035485. PubMed PMID: 19605499.

(12) Lee H, Kang J, Ahn S, Lee J. The Hippo Pathway Is Essential for Maintenance of Apicobasal Polarity in the Growing Intestine of Caenorhabditis elegans. Genetics. 2019;213(2):501-15. Epub 20190729. doi: 10.1534/genetics.119.302477. PubMed PMID: 31358532; PubMed Central PMCID: PMCPMC6781910.

(13) Teuscher AC, Statzer C, Goyala A, Domenig SA, Schoen I, Hess M, et al. Longevity interventions modulate mechanotransduction and extracellular matrix homeostasis in C. elegans. Nat Commun. 2024;15(1):276. Epub 2024/01/05. doi: 10.1038/s41467-023-44409-2. PubMed PMID: 38177158; PubMed Central PMCID: PMCPMC10766642.

(14) Saul N, Dhondt I, Kuokkanen M, Perola M, Verschuuren C, Wouters B, et al. Identification of healthspan-promoting genes in Caenorhabditis elegans based on a human GWAS study. Biogerontology. 2022;23(4):431-52. Epub 2022/06/25. doi: 10.1007/s10522-022-09969-8. PubMed PMID: 35748965; PubMed Central PMCID: PMCPMC9388463.

Author response:

The following is the authors’ response to the original reviews

Public Review:

Reviewer #1 (Public review): 

Summary: 

Odor- and taste-sensing are mediated by two different systems, the olfactory and gustatory systems, and have different behavioral roles. In this study, Wei et al. challenge this dichotomy by showing that odors can activate gustatory receptor neurons (GRNs) in Drosophila to promote feeding responses, including the proboscis extension response (PER) that was previously thought to be driven only by taste. While previous studies suggested that odors can promote PER to appetitive tastants, Wei et al. go further to show that odors alone cause PER, this effect is mediated through sweet-sensing GRNs, and sugar receptors are required. The study also shows that odor detection by bitter-sensing GRNs suppresses PER. The authors' conclusions are supported by behavioral assays, calcium imaging, electrophysiological recordings, and genetic manipulations. The observation that both attractive and aversive odors promote PER leaves an open question as to why this effect is adaptive. Overall, the study sheds new light on chemosensation and multimodal integration by showing that odor and taste detection converge at the level of sensory neurons, a finding that is interesting and surprising while also being supported by another recent study (Dweck & Carlson, Sci Advances 2023).

Strengths: 

(1) The main finding that odors alone can promote PER by activating sweet-sensing GRNs is interesting and novel.

(2) The study uses video tracking of the proboscis to quantify PER rather than manual scoring, which is typically used in the field. The tracking method is less subjective and provides a higherresolution readout of the behavior.

(3) The study uses calcium imaging and electrophysiology to show that odors activate GRNs. These represent complementary techniques that measure activity at different parts of the GRN (axons versus dendrites, respectively) and strengthen the evidence for this conclusion. 

(4) Genetic manipulations show that odor-evoked PER is primarily driven by sugar GRNs and sugar receptors rather than olfactory neurons. This is a major finding that distinguishes this work from previous studies of odor effects on PER and feeding (e.g., Reisenman & Scott, 2019; Shiraiwa, 2008) that assumed or demonstrated that odors were acting through olfactory neurons.

We appreciate the reviewer’s positive assessment of the novelty and significance of our work.

Weaknesses/Limitations: 

(1) The authors may want to discuss why PER to odors alone has not been previously reported, especially as they argue that this is a broad effect evoked by many different odors. Previous studies testing the effect of odors on PER only observed odor enhancement of PER to sugar (Oh et al., 2021; Reisenman & Scott, 2019; Shiraiwa, 2008) and some of these studies explicitly show no effect of odor alone or odor with low sugar concentration; regardless, the authors likely would have noticed if PER to odor alone had occurred. Readers of this paper may also be aware of unpublished studies failing to observe an effect of PER on odor alone (including studies performed by this reviewer and unrelated work by other colleagues in the field), which of course the authors are not expected to directly address but may further motivate the authors to provide possible explanations.

We appreciate the reviewer’s comment. We believe that the difference in genotype is likely the largest reason behind this point. This is because the strength varied widely across genotypes and was quite weak in some strains including commonly used w[1118] empty Gal4 and w[1118] empty spit Gal4 as shown in Figure1- figure supplement 3 (Figure S3 in original submission). However, given that we observed odor-evoked PER in various genotypes (many in main Figures and three in Figure1- figure supplement 3 including Drosophila simulans), the data illustrate that it is a general phenomenon in Drosophila. Indeed, although Oh et al. (2021) did not emphasize it in the text, their Fig. 1E showed that yeast odor evoked PER at a probability of 20%, which is much higher than the rate of spontaneous PER in many genotypes. Therefore, this literature may represent another support for the presence of odor-evoked PER. We have expanded our text in the Discussion to describe these issues.

Another possibility is our use of DeepLabcut to quantitatively track the kinematics of proboscis movement, which may have facilitated the detection of PER.

(2) Many of the odor effects on behavior or neuronal responses were only observed at very high concentrations. Most effects seemed to require concentrations of at least 10-2 (0.01 v/v), which is at the high end of the concentration range used in olfactory studies (e.g., Hallem et al., 2004), and most experiments in the paper used a far higher concentration of 0.5 v/v. It is unclear whether these are concentrations that would be naturally encountered by flies.

We acknowledge that the concentrations used are on the higher side, suggesting that GRNs may need to be stimulated with relatively concentrated odors to induce PER. Although it is difficult to determine the naturalistic range of odor concentration, it is at least widely reported that olfactory neurons including olfactory receptor neurons and projection neurons do not saturate, and exhibit odor identity-dependent responses at the concentration of 10^-2 where odor-evoked PER can be observed. Furthermore, we have shown in Figure 6 that low concentration (10^-4) of banana odor, ethyl butyrate, and 4-methycyclohexanol all significantly increased the rate of odor-taste multisensory PER even in olfactory organs-removed flies, suggesting that low concentration odors can influence feeding behavior via GRNs in a natural context where odors and tastants coexist at food sites. Finally, we note that odors were further diluted by a factor of 0.375 by mixing the odor stream with the main air stream before being applied to the flies as described in Methods.

(3) The calcium imaging data showing that sugar GRNs respond to a broad set of odors contrasts with results from Dweck & Carlson (Sci Adv, 2023) who recorded sugar neurons with electrophysiology and observed responses to organic acids, but not other odors. This discrepancy is not discussed.  

As the reviewer points out, Dweck and Carlson (Sci Adv, 2023) reported using single sensillum electrophysiology (base recording) that sugar GRNs only respond to organic acids whereas we found using calcium imaging from a group of axons and single sensillum electrophysiology (tip recording) that these GRNs respond to a wide variety of odors. Given that we observed odor responses using two methods, the discrepancy is likely due to the differences in genotype examined. We now have discussed this point in the text.

(4) Related to point #1, it would be useful to see a quantification of the percent of flies or trials showing PER for the key experiments in the paper, as this is the standard metric used in most studies and would help readers compare PER in this study to other studies. This is especially important for cases where the authors are claiming that odor-evoked PER is modulated in the same way as previously shown for sugar (e.g., the effect of starvation in Figure S4).

For starved flies, we would like to remind the reviewer that the percentage of trials showing PER is reported in Fig. 1E, which shows a similar trend as the integrated PER duration. For fed flies, we have analyzed the percentage of PER and added the result to Figure 2-figure supplement 1C (Figure S4 in original submission).

(5) Given the novelty of the finding that odors activate sugar GRNs, it would be useful to show more examples of GCaMP traces (or overlaid traces for all flies/trials) in Figure 3. Only one example trace is shown, and the boxplots do not give us a sense of the reliability or time course of the response. A related issue is that the GRNs appear to be persistently activated long after the odor is removed, which does not occur with tastes. Why should that occur? Does the time course of GRN activation align with the time course of PER, and do different odors show differences in the latency of GRN activation that correspond with differences in the latency of PER (Figure S1A)?

Following the reviewer’s suggestion, we now report GCaMP responses for all the trials in all the flies (both Gr5a>GCaMP and Gr66a>GCaMP flies), where the time course and trial-to-trial/animal-toanimal variability of calcium responses can be observed (Figure 3-figure supplement 2).

Regarding the second point, we recorded responses to both sucrose and odors in some flies and found that calcium responses of GRNs are long-lasting not only to odors but also to sucrose, as shown in Author response image 1. This may be due in part to the properties of GCaMP6s and slower decay of intracellular calcium concentration as compared to spikes.

Author response image 1.

Example calcium responses to sucrose and odor (MCH) in the same fly (normalized by the respective peak responses to better illustrate the time course of responses). Sucrose (blue) and odor (orange) concentrations are 100 mM, and 10^-1 respectively. Odor stimulation begins at 5 s and lasts for 2 s. Sucrose was also applied at the same timing for the same duration although there was a limitation in controlling the precise timing and duration of tastant application. Because of this limitation, we did not quantify the off time constant of two responses.

To address whether the time course of GRN activation aligns with the time course of PER, and whether different odors evoke different latencies of GRN activation that correspond to latencies of PER, we plotted the time course of GRN responses and PER, and further compared the response latencies across odors and across two types of responses in Gr5a>GCaMP6s flies. As shown in Author response image 2, no significant differences were found in response latency between the six odors for PER and odor responses. Furthermore, Pearson correlation between GRN response latencies and PER latencies was not significant (r = 0.09, p = 0.872).

Author response image 2.

(A) PER duration in each second in Gr5a-Gal4>UAS-GCaMP6s flies. The black lines indicate the mean and the shaded areas indicate standard error of the mean. n = 25 flies. (B) Time course of calcium responses (ΔF/F) to nine odors in Gr5a GRNs. n = 5 flies. (C) Latency to the first odor-evoked PER in Gr5a-Gal4>UAS-GCaMP6s flies. Green bar indicates the odor application period. p = 0.67, one-way ANOVA. Box plots indicate the median (orange line), mean (black dot), quartiles (box), and 5-95% range (bar). Dots are outliers. (D) Latency of calcium responses (10% of rise to peak time) in Gr5a GRNs. Green bar indicates the odor application period. p = 0.32, one-way ANOVA. Box plots indicate the median (orange line), mean (black dot), quartiles (box), and 5-95% range (bar). Dots are outliers.

(6) Several controls are missing, and in some cases, experimental and control groups are not directly compared. In general, Gal4/UAS experiments should include comparisons to both the Gal4/+ and UAS/+ controls, at least in cases where control responses vary substantially, which appears to be the case for this study. These controls are often missing, e.g. the Gal4/+ controls are not shown in Figure 2C-G and the UAS/+ controls are not shown in Figure 2J-L (also, the legend for the latter panels should be revised to clarify what the "control" flies are). For the experiments in Figure S5, the data are not directly compared to any control group. For several other experiments, the control and experimental groups are plotted in separate graphs (e.g., Figure 2C-G), and they would be easier to visually compare if they were together. In addition, for each experiment, the authors should denote which comparisons are statistically significant rather than just reporting an overall p-value in the legend (e.g., Figure 2H-L).

We thank the reviewer for the input. We have conducted additional experiments for four Gal4/+controls in Figure 2 and added detailed information about control flies in the figure legend (Figure 2C-F).

For the RNAi flies shown in Figure 2 and Figure 2-figure supplement 3, we used the recommended controls suggested by the VDRC. These control flies were crossed with tubulin-Gal4 lines to include both Gal4 and UAS control backgrounds.

Regarding Figure S5 in original submission (current Figure 2-figure supplement 2), we now present the results of statistical tests which revealed that PER to certain odors is statistically significantly stronger than that to the solvent control (mineral oil) for both wing-removed and wing-leg-removed flies.

For Figure 2C-F, we now plot the results for experimental and control groups side by side in each figure.

Regarding the results of statistical tests, we have provided more information in the legend and also prepared a summary table (supplemental table). 

(7) Additional controls would be useful in supporting the conclusions. For the Kir experiments, how do we know that Kir is effective, especially in cases where odor-evoked PER was not impaired (e.g., Orco/Kir)? The authors could perform controls testing odor aversion, for example. For the Gr5a mutant, few details are provided on the nature of the control line used and whether it is in the same genetic background as the mutant. Regardless, it would be important to verify that the Gr5a mutant retains a normal sense of smell and shows normal levels of PER to stimuli other than sugar, ruling out more general deficits. Finally, as the method of using DeepLabCut tracking to quantify PER was newly developed, it is important to show the accuracy and specificity of detecting PER events compared to manual scoring.  

A previous study (Sato, 2023, Front Mol Neurosci) showed that the avoidance to 100 μM 2methylthiazoline was abolished, and the avoidance to 1 mM 2MT was partially impaired in Orco>Kir2.1 flies. However, because Orco-Gal4 does not label all the ORNs and we have more concrete results on flies in which all the olfactory organs are removed as well as specific GRNs and Gr are manipulated, we decided to remove the data for Orco>kir2.1 flies and have updated the text and Figure 2 accordingly.

For the Gr5a mutant and its control, we have added detailed information about the genotype in the figure legend and in the Methods. We have used the exact same lines as reported in Dahanukar et al. (2007) by obtaining the lines from Dr. Dahanukar. Dahanukar et al. has already carefully examined that Gr5a mutant loses responses only to certain types of sugars (e.g. it even retains normal responses to some other sugars), demonstrating that Gr5a mutants do not exhibit general deficits.

As for the PER scoring method, we manually scored PER duration and compared the results with those obtained using DeepLabCut in wild type flies for the representative data. The two results were similar (no statistical difference). We have reported the result in Figure1-figure supplement 1C.

(8) The authors' explanation of why both attractive and aversive odors promote PER (lines 249-259) did not seem convincing. The explanation discusses the different roles of smell and taste but does not address the core question of why it would be adaptive for an aversive odor, which flies naturally avoid, to promote feeding behavior.  

We have extended our explanation in the Discussion by adding the following possibility: “Enhancing PER to aversive odors might also be adaptive as animals often need to carry out the final check by tasting a trace amount of potentially dangerous substances to confirm that those should not be further consumed.”

Reviewer #2 (Public review): 

Summary: 

A gustatory receptor and neuron enhances an olfactory behavioral response, proboscis extension. This manuscript clearly establishes a novel mechanism by which a gustatory receptor and neuron evokes an olfactory-driven behavioral response. The study expands recent observations by Dweck and Carlson (2023) that suggest new and remarkable properties among GRNs in Drosophila. Here, the authors articulate a clear instance of a novel neural and behavioral mechanism for gustatory receptors in an olfactory response.

Strengths: 

The systematic and logical use of genetic manipulation, imaging and physiology, and behavioral analysis makes a clear case that gustatory neurons are bona fide olfactory neurons with respect to proboscis extension behavior.

Weaknesses: 

No weaknesses were identified by this reviewer.  

We appreciate the reviewer’s recognition of the novelty and significance of our work.

Reviewer #3 (Public review): 

Summary: 

Using flies, Kazama et al. combined behavioral analysis, electrophysiological recordings, and calcium imaging experiments to elucidate how odors activate gustatory receptor neurons (GRNs) and elicit a proboscis extension response, which is interpreted as a feeding response. 

The authors used DeepLabCut v2.0 to estimate the extension of the proboscis, which represents an unbiased and more precise method for describing this behavior compared to manual scoring.

They demonstrated that the probability of eliciting a proboscis extension increases with higher odor concentrations. The most robust response occurs at a 0.5 v/v concentration, which, despite being diluted in the air stream, remains a relatively high concentration. Although the probability of response is not particularly high it is higher than control stimuli. Notably, flies respond with a proboscis extension to both odors that are considered positive and those regarded as negative.

The authors used various transgenic lines to show that the response is mediated by GRNs.

Specifically, inhibiting Gr5a reduces the response, while inhibiting Gr66a increases it in fed flies. Additionally, they find that odors induce a strong positive response in both types of GRNs, which is abolished when the labella of the proboscis are covered. This response was also confirmed through electrophysiological tip recordings.

Finally, the authors demonstrated that the response increases when two stimuli of different modalities, such as sucrose and odors, are presented together, suggesting clear multimodal integration.

Strengths: 

The integration of various techniques, that collectively support the robustness of the results.

The assessment of electrophysiological recordings in intact animals, preserving natural physiological conditions.

We appreciate the reviewer’s recognition of the novelty and significance of our work.

Weaknesses: 

The behavioral response is observed in only a small proportion of animals.  

We acknowledge that the probability of odor-evoked PER is lower compared to sucrose-evoked PER, which is close to 100 % depending on the concentration. To further quantify which proportion of animals exhibit odor-evoked PER, we now report this number besides the probability of PER for each odor shown in Fig. 1E. We found that, in wild type Dickinson flies, 73% and 68 % of flies exhibited PER to at least one odor presented at the concentration of 0.5 and 0.1.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors): 

Minor comments/suggestions: 

- Define "MO" in Figure 1D.  

We have defined it as mineral oil in the figure legend.

- Clarify how peak response was calculated for GCaMP traces (is it just the single highest frame per trial?).

We extended the description in the Methods as follows: “The peak stimulus response was quantified by averaging ΔF/F across five frames at the peak, followed by averaging across three trials for each stimulus. Odor stimulation began at frame 11, and the frames used for peak quantification were 12 to 16.” We made sure that information about the image acquisition frame rate was provided earlier in the text.

- Clarify how the labellum was covered in Figure 3 and show that this does not affect the fly's ability to do PER (e.g., test PER to sugar stimulation on tarsus) - otherwise one might think that gluing the labella could affect PER.

In Figure 3, only calcium responses were recorded, and PER was not recorded simultaneously from the same flies. To ensure stable recording from GRN axons in the SEZ, we kept the fly’s proboscis in an extended position as gently as possible using a strip of parafilm. In some of the imaging experiments, we covered the labellum with UV curable glue, whose purpose was not to fix the labellum in an extended position but to prevent the odors from interacting with GRNs on the labellum. We have added a text in the Methods to explain how we covered the labellum.

- Clarify how the coefficients for the linear equation were chosen in Figure 3G.  

We used linear regression (implemented in Python using scikit-learn) to model the relationship between neural activity and behavior, aiming to predict the PER duration based on the calcium responses of two GRN types, Gr5a and Gr66a. The coefficients were estimated using the LinearRegression function. We added this description to the Methods. 

- Typo in "L-type", Figure 4A.  

We appreciate the reviewer for pointing out this error and have corrected it.

- Clarify over what time period ephys recordings were averaged to obtain average responses.

We have modified the description in the Methods as follows: “The average firing rate was quantified by using the spikes generated between 200 and 700 ms after the stimulus contact following the convention to avoid the contamination of motion artifact (Dahanukar and Benton, 2023; Delventhal et al., 2014; Hiroi et al., 2002).

- The data and statistics indicate that MCH does not enhance feeding in Figure 6G, so the text in lines 207-208 is not accurate.

We have modified the text as follows: “A similar result was observed with ethyl butyrate, and a slight, although not significant, increase was also observed with 4-methylcyclohexanol (Figure 6G).”

- P-value for Figure S9 correlation is not reported.  

We appreciate the reviewer for pointing this out. The p-value is 0.00044, and we have added it to the figure legend (current Figure 5-figure supplement 1).

Reviewer #2 (Recommendations for the authors): 

Honestly, I have no recommendations for improvement. The manuscript is extremely well-written and logical. The experiments are persuasive. A lapidary piece of work.

We appreciate the reviewer for the positive assessment of our work.

Reviewer #3 (Recommendations for the authors): 

- I suggest explaining the rationale for selecting a 4-second interval, beginning 1 second after the onset of stimulation.

Integrated PER duration was defined as the sum of PER duration over 4 s starting 1 s after the odor onset. This definition was set based on the following data.

(1) We used a photoionization detector (PID) to measure the actual time that the odor reaches the position of a tethered fly, which was approximately 1.1 seconds after the odor valve was opened. Therefore, we began analyzing PER responses 1 second after the odor onset (valve opening) to align with the actual timing of stimulation.

(2) As shown in Fig.1D and 1F, the majority of PER occurred within 4 s after the odor arrival.

We have now added the above rationale in the Methods.

- I could not find the statistical analysis for Figures 1E and 1G. If these figures are descriptive, I suggest the authors revise the sentences: 'Unexpectedly, we found that the odors alone evoked repetitive PER without an application of a tastant (Figures 1D-1G, and Movie S1). Different odors evoked PER with different probability (Figure 1E), latency (Figure S1A), and duration (Figures 1F, 1G, and S2)'.

We have added the results of statistical analysis to the figure legend.

- In Figure 2, the authors performed a Scheirer-Ray-Hare test, which, to my knowledge, is a nonparametric test for comparing responses across more than two groups with two factors. If this is the case, please provide the p-values for both factors and their interaction

We now show the p-values for both factors, odor and group as well as their interaction in the supplementary table. 

- In line 83, I suggest the authors avoid claiming that 'these data show the olfactory system modulates but is not required for odor-evoked PER,' as they are inhibiting most, but not all olfactory receptor neurons. In this regard, is it possible to measure the olfactory response to odors in these flies?  

We thank the reviewer for the comment. Because Orco-Gal4 does not label all the ORNs and because we have more concrete results on flies in which all the olfactory organs are removed as well as specific GRNs and Gr are manipulated, we decided to remove the data for Orco>kir2.1 flies and have updated the text and Figure 2 accordingly.

- In Figure 2, I wonder if there are differences in the contribution of various receptors in detecting different odors. A more detailed statistical analysis might help address this question.

Although it might be possible to infer the contribution of different gustatory receptors by constructing a quantitative model to predict PER, it is a bit tricky because the activity of individual GRNs and not Grs are manipulated in Figure 2 except for Gr5a. The idea could be tested in the future by more systematically manipulating many Grs that are encoded in the fly genome.

- For Figures 2J-L, please clarify which group serves as the control.  

We have added this information to the legend. 

- In Figure 3, I recommend including an air control in panels D and F to better appreciate the magnitude of the response under these conditions.

The responses to all three controls, air, mineral oil and water, were almost zero. As the other reviewer suggested to present trial-to-trial variability as well, we now show responses to all the controls in all the trials in all the animals tested in Figure 3-figure supplement 2.

- I had difficulty understanding Figure 3G. Could the authors provide a more detailed explanation of the model?

We used linear regression (implemented in Python using scikit-learn) to model the relationship between neural activity and behavior, aiming to predict the PER duration based on the calcium responses of two GRN types, Gr5a and Gr66a. The weights for GRNs were estimated using the LinearRegression function. The weight for Gr5a and Gr66a was positive and negative, respectively, indicating that Gr5a contributes to enhance whereas Gr66a contributes to reduce PER.

To evaluate the model performance, we calculated the coefficient of determination (R²), which was 0.81, meaning the model explained 81% of the variance in the PER data.

The scatter plot in Fig. 3G shows a tight relationship between the predicted PER duration (y-axis) plotted against the actual PER duration (x-axis), demonstrating a strong predictive power of the model.

We added the details to the Methods.

- In Figure S4a, the reported p-value is 0.88, which seems to be a typo, as the text indicates that PER is enhanced in a starved state.

Thank you for pointing this out. We have modified the figure legend to describe that PER was enhanced in a starved state only for the experiments conducted with odors at 10^-1 concentration (current Figure 2-figure supplement 1).

Author Response

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

We thank the editors and reviewers for their tremendously helpful comments. We outline below changes we have made to the manuscript in response to each point. These include new analyses and a substantial rewrite to address the concerns about lack of clarity.

We believe the revisions strengthen the evidence for our conclusion that grid fields can be either anchored to or independent from a task reference frame, and that anchoring is selectively associated with successful path integration-dependent behaviour. Our additional analyses of non-grid cells indicate that while some are coherent with the grid population, many are not, suggesting cell populations within the MEC may implement grid-dependent and grid-independent computations in parallel.

We hope the reviewers will agree that our novel experimental strategy complements and avoids limitations of perturbation-based approaches, and by providing evidence to dissociate the two major hypotheses for whether and when grid cells contribute to behaviour our results are likely to have a substantial impact on the field.

Public Reviews:

Reviewer #1 (Public Review):

In this study, Clark et. al. uncovered an association between the positional encoding of grid cell activity with good performance in spatial navigation tasks that requires path integration, highlighting the contribution of grid firing to behaviour… The conclusions of this paper are mostly well supported by data, the finding about the association between grid cell encoding and behaviour in spatial memory tasks is important. However, some aspects of the analysis need to be clarified or extended.

Thankyou for the overview and constructive comments.

(1) While the current dataset aims to demonstrate a "correlation" between grid cell encoding and task performance, the other variables that could confound this correlation should be carefully examined.

(1.1) The exact breakdown of the fraction of beaconed/non-beaconed/probe trials is never shown. if the session makeup has a significant effect on the coding scheme or other results, this variable should be accounted for.

The lack of information about the trial organisation was a substantial oversight in our preparation of the first version of the manuscript. Session make up can not account for effects on grid stability and its relationship to behavioural outcome but this was not made at all clear.

In all sessions trial types were varied in a fixed repeating sequence. Therefore, continuous blocks of trials on which grid firing is anchored (or independent from) the track can not be explained by the mouse experiencing a particular trial type. We have revised the manuscript to make this clearer, e.g. p 5, ‘These switches could not be explained by variation between trials in the availability of cues or rewards, as these were interleaved in blocks that repeated throughout a session (see Methods), whereas periods in which grid cell activity was in a given mode extended across the repeating blocks (e.g. Figures 3D,E, 4A, 5E,F).’ and methods p 12, ‘Trials were delivered in repeating blocks throughout a recording session…’

(1.2) The manuscript did not provide information about whether individual mice experienced sessions with different combinations of the three trial types, and whether they show different preferences in position or distance encoding even in comparable sessions. This leads to the question of whether different behaviour and activity encoding were dominated by experimental or natural differences between individual mice. Presenting the data per mouse will be helpful.

As we note above, because trial types were interleaved in a fixed sequence, experience of a particular trial type can not account for switching between task-anchored and taskindependent firing modes. This was insufficiently clear in the first version of the manuscript.

We varied the proportions of trials of a particular type between sessions with the aim of maximising the number of non-beaconed and probe trials. This was necessary because we find that if we introduce too high a proportion of these trials early in training then mice appear to ‘lose interest’ in the task and their performance drops off. We therefore used an approach in which we increased the proportions of non-beaconed and probe trials over training days as mice became familiar with the task. This is now described in the methods (p 12).

Because the decision for when to vary the proportion of trial types was based on the previous day’s performance, the experimental design was not optimised for addressing the reviewer’s question about dissociating experimental from natural differences in mice. To provide some initial insight we have analysed the relationship between task anchored coding and proportion of beaconed trials in a session (Figure 3, Figure Supplement 7). While on average there is a higher proportion of trials in which grid fields are task-anchored in sessions with more beaconed trials, this effect is small and most of the variance is independent from the proportion of beaconed trials.

(1.3) Related to the above point, in Figure 5, the mice appeared to behave worse in probe trials than non-beaconed trials. If the mouse did not know if a trial is a probe or a non-beacon trial, they should behave equivalently until the reward location and thus should stop an equal amount. If this difference is because multiple probe trials are placed consecutively, did the mouse learn that it will not get a reward and then stop trying to get rewards? Did this affect switching between position and distance coding?

Thankyou for flagging this. This reflected an inconsistency arising from the way we detected stops that we have now corrected. Briefly, the temporal resolution of the processed location data against which the stop detection threshold was applied was insufficiently high. As a result, stops in the non-beaconed group were picked up, as they tended to be longer because mice remained still to consume rewards, whereas some stops in the probe group were missed because they were relatively short. We have corrected this by repeating the analyses on raw position data at the highest temporal resolution available. This analysis is now clearly described in the Methods (see p13 “A stop was registered in Blender3D if the speed of the mouse dropped below 4.7 cm/s. Speed was calculated on a rolling basis from the previous 100 ms at a rate of 60 Hz.”).

(1.4) It is not shown how the behaviours (e.g., running speed away from the reward zone, licking for reward) in beaconed/non-beaconed/probe trials were different and whether the difference in behaviours led to the different encoding schemes.

Because trial types were interleaved and repeated with a period less than the length of typical trial sequences during which grid cell activity remained either task-anchored or taskindependent, differences between trial types are unlikely to explain use of the different coding schemes. Hopefully, this is clarified by the comments above.

To further describe the relationship between behavioural outcomes, trial types and grid anchoring, we now also show running speed as a function of location for each combination of trial types and trial outcomes (Figure 6, Figure Supplement 1). This illustrates and replicates our previous findings (Tennant et al. 2018) that running speed profiles are similar for a given trial outcome regardless of trial type (Figure 6, Figure Supplement 1A), and further further shows that the behavioural profile for a given trial outcome and trial-type does not differ when grid cells are in task-anchored and task-independent modes (Figure 6, Figure Supplement 1B). This further argues against the possibility that difference in behaviours leads to the different encoding schemes.

(2) Regarding the behaviour and activity encoding on a trial-by-trial basis, did the behavioural change occur first, or did the encoding switch occur first, or did they happen within the same trial? This analysis will potentially determine whether the encoding is causal for the behaviour, or the other way around.

This is a good question but our experimental design lacks sufficient statistical power to address the timing of mode switches within a trial. This is because mode switching is relatively infrequent (so the n for switching is low) and only a subset of trials are uncued (making the relevant n even lower), while at a trial level the behavioural outcome is variable (increasing the required n for adequate power).

(3) The author determined that the grid cell coding schemes were limited to distance encoding and position encoding. However, there could be other schemes, such as switching between different position encodings (with clear spatial fields but at different locations), as indicated by Low et. al., 2021, and switching between different distant encodings (with different distance periods). If these other schemes indeed existed in the data, they might contribute to the variation of the behaviours.

Switching between position encoding schemes appears to be rare within our dataset and unlikely to contribute to variation in behaviour. In most sessions we did not observe switching between grid phases / position encodings (e.g. Figures 2A-B, 3B-E, 4A, 5C-D, F). In one session we found switching between different phases when grid cells were taskanchored. Because the grid period was unchanged, the spatial periodograms remained similar. We report this example in the revised manuscript (Figure 5E).

(4) The percentage of neurons categorised in each coding scheme was similar between nongrid and grid cells. This implies that non-grid cells might switch coding schemes in sync with grid cells, which would mean the whole MEC network was switching between distance and position coding. This raises the question of whether the grid cell coding scheme was important per se, or just the MEC network coding scheme.

We very much appreciate this suggestion. We note first that while the proportion of taskanchored grid and non-grid cells is similar, task-independent periodic firing of non-grid cells is much rarer than for grid cells (Figure 2E), suggesting a dissociation between the populations. To further address the question we have included additional analyses of nongrid cells (Figure 3, Figure Supplement 5). This shows that while some non-grid cells have anchoring that switches coherently with simultaneously recorded grid cells, others do not. Figures 4 and 5 now show examples of non-grid cell activity recorded simultaneously with grid cells.

Together, our data suggest that the MEC implements multiple coding schemes: one that is associated with the grid network and includes some non-grid cells; and one (or more) that can be independent from the grid network. This dissociation adds to the insights into MEC function that are provided by our study and is now highlighted in the abstract and discussion.

(5) In Figure 2 there are several cell examples that are categorised as distance or position coding but have a high fraction of the other coding scheme on a per-trial basis. Given this variation, the full session data in F should be interpreted carefully, since this included all cells and not just "stable" coding cells. It will be cleaner to show the activity comparison only between the stable cells.

We have now included examples in Figure 2A-C where the grid mode is stable throughout a session. As the view of activity at a session level is important, we have not updated Figure 2F, but have clarified the terminology to now clearly refer to classification at either season or trial levels. In addition, we have repeated the analyses shown in Figure 2F but after grouping cells according to whether their firing has a single mode on >85% of the trials (Figure 3 Figure Supplement 4). This analysis supports similar conclusions to those of Figure 2F.

(6) The manuscript is not well written. Throughout the manuscript, there are many unexplained concepts (especially in the introduction) and methods, mis-referenced figures, and unclear labels.

We very much appreciate the feedback and have substantially rewritten the manuscript. We have paid particular attention to explaining key concepts in the introduction and have carefully checked the figures. We welcome further feedback on whether this is now clearer.

Reviewer #2 (Public Review):

Clark and Nolan's study aims to test whether the stability of grid cell firing fields is associated with better spatial behaviour performance on a virtual task… This study is very timely as there is a pressing need to identify/delimitate the contribution of grid cells to spatial behaviours. More studies in which grid cell activity can be associated with navigational abilities are needed.

Thank you for the supportive comments and highlighting the importance of the question.

The link proposed by Clark and Nolan between "virtual position" coding by grid cells and navigational performance is a significant step toward better understanding how grid cell activity might support behaviour. It should be noted that the study by Clark and Nolan is correlative. Therefore, the effect of selective manipulations of grid cell activity on the virtual task will be needed to evaluate whether the activity of grid cells is causally linked to the behavioural performance on this task. In a previous study by the same research group, it was shown that inactivating the synaptic output of stellate cells of the medial entorhinal cortex affected mice's performance of the same virtual task (Tennant et al., 2018). Although this manipulation likely affects non-grid cells, it is still one of the most selective manipulations of grid cells that are currently available.

Again, thank you for the supportive comments. We recognise the previous version of the manuscript did not sufficiently clarify the motivation for our approach, or the benefits of capitalising on behavioural variable variability as a complementary strategy to perturbation approaches. We now make this clearer in the revised introduction (p 2, paragraphs 2 and 3).

When interpreting the "position" and "distance" firing mode of grid cells, it is important to appreciate that the "position" code likely involves estimating distance. The visual cues on the virtual track appear to provide mainly optic flow to the animal. Thus, the animal has to estimate its position on the virtual track by estimating the distance run from the beginning of the track (or any other point in the virtual world).

We appreciate the ambiguity here was confusing. We have re-named the groups to ‘taskanchored’, corresponding to when grid cells encode position on the track (as well as distance as the reviewer correctly points out), and ‘task-independent’, corresponding to the group we previously referred to as distance encoding.

It is also interesting to consider how grid cells could remain anchored to virtual cues. Recent work shows that grid cell activity spans the surface of a torus (Gardner et al., 2022). A run on the track can be mapped to a trajectory on the torus. Assuming that grid cell activity is updated primarily from self-motion cues on the track and that the grid cell period is unlikely to be an integer of the virtual track length, having stable firing fields on the virtual track likely requires a resetting mechanism taking place on each trial. The resetting means that a specific virtual track position is mapped to a constant position on the torus. Thus, the "virtual position" mode of grid cells may involve 1) a trial-by-trial resetting process anchoring the grid pattern to the virtual cues and 2) a path integration mechanism. Just like the "virtual position" mode of grid cell activity, successful behavioural performance on non-beaconed trials requires the animal to anchor its spatial behaviour to VR cues.

Reviewer #3 (Public Review):

This study addresses the major question of 'whether and when grid cells contribute to behaviour'. There is no doubt that this is a very important question. My major concern is that I'm not convinced that this study gives a significant contribution to this question, although this study is well-performed and potentially interesting. This is mainly due to the fact that the relation between grid cell properties and behaviour is exclusively correlative and entirely based on single cell activity, although the introduction mentions quite often the grid cell network properties and dynamics. In general, this study gives the impression that grid cells exclusively support the cognitive processes involved in this task. This problem is in part related to the text.

Thank you for the comments. We recognise now that the previous text was insufficiently clear. We have modified the introduction to clarify the value of an approach that takes advantage of behavioural variability. Importantly, this approach is complementary to perturbation strategies we and others have used previously. In particular it addresses critical limitations of perturbation strategies which can be confounded by off-target effects and possible adaptation, both of which are extremely difficult to fully rule out. We hope that with this additional clarification it is now clear that as for any important question multiple and complementary testing strategies are required to make progres, and second, that our study makes a new and important contribution by introducing a novel experimental approach and by following this up with careful analyses that clearly distinguish competing hypotheses.

However, it would be interesting to look at the population level (even beyond grid cells) to test whether at the network level, the link between behavioural performance and neural activity is more straightforward compared to the single-cell level. This approach could reconcile the present results with those obtained in their previous study following MEC inactivation.

We’re unclear here about what the reviewer means by ‘more straightforward’ as clear relationships between activity of single grid cells and populations of grid cells are well established (Gardner et al., 2021; Waaga et al., 2021; Yoon et al., 2013).

To give a clearer indication of the corresponding population level representations, as mentioned in response to Reviewer #1, we now include additional data showing many simultaneously recorded neurons, and analyses of non-grid as well as grid cells (Figures 4, 5, Figure 5 Figure Supplement 2).

To reconcile results with our previous study of MEC inactivation we have paid additional attention to the roles of non-grid cells (following suggestions by Reviewer #1). We show that while some non-grid cells show transitions between task-anchored and task-independent firing that are coherent with the grid population, many others have more stable firing that is independent of grid representations. This is consistent with the idea that the MEC supports localised behaviour in the cued and uncued versions of the task (Tennant et al., 2018), and suggests that while grid cells preferentially contribute when cues are absent, non-grid cells could also support the cued version. We make this additional implication clear in the revised abstract and discussion.

The authors used a statistical method based on the computation of the frequency spectrum of the spatial periodicity of the neural firing to classify grid cells as 'position-coding' (with fields anchored to the virtual track) and 'distance-coding' (with fields repeating at regular intervals across trials). This is an interesting approach that has nonetheless the default to be based exclusively on autocorrelograms. It would be interesting to compare with a different method based on the similarities between raw maps.

While our main analyses use a periodogram-based method to identify when grid cells are / are not anchored to the task environment, we validate these analyses by examination of the rate maps in each condition (Figures 2-4). For example, when grid cells are task-anchored, according to the periodogram analysis, the rate maps clearly show spatially aligned peaks, whereas when grid cells are not anchored the peaks in their rate maps are not aligned (Figure 2A vs 2B; Figure 3B-E; Figure 4C). We provide further validation by showing that spatial information (in the track reference frame) is substantially higher when grid cell activity is task-anchored vs task-independent (Figures 2F, 3G, 4F and Figure 3 Figure Supplement 4).

To further address this point we have carried out additional complementary analyses in which we identify task anchored vs task independent modes using a template matching method applied to the raw rate maps (Figure 6, Figure Supplement 2). These analyses support similar conclusions to our periodogram-based analyses.

Beyond this minor point, cell categorization is performed using all trial types.

Each trial type (i.e. beacon or non-beacon) is supposed to force mice to use different strategies and should induce different spatial representations within the entorhinal-hippocampal circuit (and not only in the grid cell system). In that context, since all trials are mixed, it is difficult to extrapolate general information.

We recognise that the description of the task design was insufficiently clear but are unsure why ‘it is difficult to extrapolate general information’. Before addressing this point, we should first be clear that mice are not ‘forced’ to adopt any particular strategy. Rather, on uncued trials a path integration strategy is the most efficient way to solve the task. However, mice could instead use a less efficient strategy, for example by stopping at short intervals they still obtain rewards. Detailed behavioural analyses indicate that such random stopping strategies are used by naive mice, while with training mice learn to use spatial stopping strategies (Tennant et al. 2018).

In terms of ‘extracting general information’ from the task, the following findings lead to general predictions: 1) Grid cells can exist in either task-anchored or task-independent periodic firing modes; 2) These modes can be stable across a session, but often modeswitching occurs within a session; 3) While some non-grid cells show task-independent periodic firing, this is much less common than for grid cells, which suggests a model in which many non-grid MEC neurons operate independently from the grid network; 4) When a marker cue is available mice locate a reward equally well when grid cells are in taskanchored versus task-independent modes, which argues against theories in which grid cells are a key part of a general system for localisation; 5) When markers cues are absent taskanchored grid firing is associated with successful reward localisation, which corroborates a key prediction of theories in which grid cells contribute to path integration.

In revising the manuscript we have attempted to improve the writing to make these advances clearer, and have clarified methodological details that made interpretation more challenging than it should have been. For example, as noted in our response to Reviewer #1, we have included additional details to clarify the organisation of trials and relationships between trials, behavioural outcomes and neural codes observed.

On page 5 the authors state that 'Since only position representations should reliably predict the reward location, ..., we reasoned that the presence of positional coding could be used to assess whether grid firing contributes to the ongoing behaviour'. I do not agree with this statement. First of all, position coding should be more informative only in a cue-guided trial. Second, distance coding could be as informative as position coding since at the network level may provide information relevant to the task (such as distance from the reward).

Again, this point perhaps reflects a lack of clarity on our part in writing the manuscript. When grid cells are anchored to the track reference frame (now called ‘tasked anchored’, previously ‘position encoding’), then the location of the rate peaks in grid firing is reliable from trial to trial. This is the case whether or not the trial is cued. When grid cells are independent of the track reference frame (now called ‘task independent’, previously ‘distance encoding’), then the location of the firing rate peaks vary from trial to trial. In the latter case, position can not be read out directly from trial to trial.

In principle, in the task-independent mode track position could be calculated by storing the grid network configuration at the start of the track, which would differ on each trial, and then implementing a mechanism to readout relative distance as mice move along the track. However, if mice do use this computation we would expect them to do so equally well on cued and uncued trials. By contrast, our results clearly show a dissociation between trial types in the relationship between grid firing and behavioural outcome. We highlight and discuss this possibility in the revised manuscript (p 10, ‘Alternatively, mice could in principle estimate track location with a system that utilises information about distance travelled obtained from task-independent grid representations’).

Third, position-coding is interpreted as more relevant because it predominates in correct trials. However, this does not imply that this coding scheme is indeed used to perform correct trials.

We have revised the manuscript to clarify our goal of distinguishing major hypotheses for the roles of grid cells in behaviour (Introduction, ‘On the one hand, theoretical arguments that grid cell populations can generate high capacity codes imply that they could in principle contribute to all spatial behaviours (Fiete et al., 2008; Mathis et al., 2012; Sreenivasan and Fiete, 2011). On the other hand, if the behavioural importance of grid cells follows from their hypothesised ability to generate position representations by integrating self-motion signals (McNaughton et al., 2006), then their behavioural roles may be restricted to tasks that involve path integration strategies.’

By showing that performance on cued trials is similar regardless of whether grid cells are task-anchored or not, we provide strong evidence against the idea that grid firing is in general necessary for location-based behaviours. By showing that task anchoring is associated with successful localisation when cues are absent we corroborate a key prediction of hypothesised roles for grid cells in path integration-dependent behaviour. Therefore, we substantially reduce the space of behaviours to which grid cells might contribute. Importantly, this space is much larger for the MEC, which is required for cued and uncued versions of the task. We have revised the introduction and discussion to make these points clearer.

While we believe our results add a key piece of evidence to the puzzle of when and where grid cells contribute to behaviour, we agree that further work will be required to develop and test more refined hypotheses. Alternative models also remain plausible, for example perhaps the behaviourally relevant computations are implemented elsewhere in the brain with grid anchoring to the track as an indirect consequence. Nevertheless, explanations of this kind are more difficult to reconcile with evidence that inactivation of stellate cells in the MEC impairs learning of the task, and other manipulations that modify grid firing impair performance on similar tasks. We now discuss these possibilities (discussion p 10, ‘mice could in principle estimate track location with a system that utilises information about distance travelled obtained from task-independent grid representations’).

It could be more informative to push forward the correlative analysis by looking at whether behavioural performance can be predicted by the coding scheme on a trial-by-trial basis.

The previous version of the manuscript showed these analyses (now in Figure 6). Thus, task anchored grid firing predicts more successful performance on uncued trials at the session level (Figure 6A-B) and at the trial level (Figure 6C-D).

Reviewer #1 (Recommendations For The Authors):

(1) The author particularly mentioned that the 1D tracks are different from the "cue-rich environments that are typically used to study grid cells". It is not clear what conclusions would hold for a cue-rich environment or a track, which may require relatively less path integration compared to the cue-sparse environment. This point should be discussed.

This is an important point that we did not pay sufficient attention to in the previous version of the manuscript. Our finding of successful localisation in the cued environment when grid cells are not task anchored implies that grid anchoring is not required to solve cued tasks. The implication here is that cue rich environments may then not be the most suitable for investigation of grid roles in behaviour as non-grid mechanisms may suffice, although this does not rule out the possibility that anchored grid codes may play important roles in learning about cue rich environments. We now address this point in the discussion (p 10, ‘An implication of this result is that cue rich tracks often used to investigate grid activity patterns may not engage behaviours that require anchored grid firing.’).

(2) It would be good to see the statistics for the number of different cells (stable position or distance encoding, and unstable cells) identified per mouse/session and the number of grid cells per session.

These are now added to Supplemental Data 2 and will also be accessible through code and datasets that we will make available alongside the version of record.

(3) Figure 2F: any explanation about why AG cells had high spatial information?

Previously the calculation used bits per spike and as aperiodic cells have low firing rates the spatial information was high. We have replaced this with bits per second, which provides a more intuitive measure and no longer implies high spatial information. We have amended this in the methods (p 15, ‘Spatial information was calculated in bits per second…’).

(4) The following methods sections should provide additional details:

(4.1) Details of the training protocol are largely left to reference papers. The reference papers give a general outline of the training protocol, but the details are not completely comparable given the single experiment performed on these mice. More details should be given on training stages and experience at the time of the experiment.

The task is more clearly described in the introduction (p 3), and additional details of the training protocol are now provided in the methods (p 12-13).

(4.2) The methods reference mean speed across sessions, but it is not clear where this was used.

This was very poor wording. We have now changed this to ‘For each session the mean speed was calculated for each trial outcome’.

(4.3) The calculation of the spatial autocorrelogram on a per-trial basis should be more explicitly stated. Is it the average of each 10 cm increment with the centre trial?

We have added additional information to the methods (p 16-18).

(4.4) 1D field detection is not sufficiently explained in Figure 1/S2. This information should also appear in the methods section.

This is now clarified on page 16 in section ‘Analysis of neural activity and behaviour during the location memory task’.

(5) The data in Figure 4A and B only shows speed vs. location for one example mouse. The combined per mouse or per session data should also be shown.

This is now shown in Figure 5A and Figure 5, Figure Supplemental 2

(6) Figure 5 is somewhat confusing. Why are A/B by session and C/D by trial? The methods imply that A/B are originally averaged by cell, but that duplicate cells in the same session are excluded because behaviour versus session type is identical. This method should be valid if all grid cells within a session are all "stable". This is likely given the synchrony of code-switching between grid cells, but not all co-active grid cells behaved identically.

It is understandable that C/D are performed by trial, but it should be made clear that it is not a comparable analysis to A/B. It is unclear what N refers to in C. The figure says by trial, but the legend says the error bar is by cell. If data is calculated by trial and then averaged by cell, this should be more clearly stated.

In Figure 6A/B (previously Figure 5A/B) we focus our analysis on sessions in which the mode of grid firing, either task-anchored or task-independent, was relatively stable on a trialto-trial basis (see Figure 3F for definitions). This enables us to then compare behaviour averaged across each session, with sessions categorised as task-anchored and task independent. This analysis has the advantage that it focuses on large blocks of time (whole sessions) in which the mode of grid firing is unambiguous, but the disadvantage is that it excludes many sessions in which grid firing switches between task-anchored and taskindependent modes.

Figure 6C/D (previously Figure 5C/D) addresses this limitation by carrying out similar analyses with behaviour sorted into task-anchored versus task-independent groups at the level of trials. A potential limitation for this analysis is that grid firing is somewhat variable on a trial-by-trial basis and so some trials may be mis-classified. We don’t expect this to lead to systematic bias, but it may make the data more noisy. Nevertheless, these analyses are important to include as they allow assessment of whether conclusions from 6A/B hold when all sessions are considered.

We have added additional clarification of the rationale for these analyses to the main text (p7-8, ‘’We addressed this by using additional trial-level comparisons’). We have also added clarification in the methods section for categorisation of task-anchored versus taskindependent trials when multiple grid cells were recorded simultaneously (p 17, ‘When assigning a common classification across a group of cells recorded simultaneously...’) and an explanation for the N in the figure legend. We also clarify that the analyses use a nested random effects design to account for dependencies at the levels of sessions and mice (methods, p 20, ‘Random effects had a nested structure to account for animals and sessions…’) .

(7) Panels E and F of Figure 5 are not explained in the main text.

This is now corrected (see p8, ‘Additional analyses…’).

(8) Figure 5: Since stable grid cells and all grid cells are shown, it will be better to show unstable cells, which can be compared with grid cells.

Given that the rationale for differences between Figure 6A/B and C/D (previously Figure 5AD) were not previously clear, the reason for focussing on stable grid cells here was likely also not clear (see point 6 above). We don’t show unstable grid cells in Figure 6A-B as the behaviour averaged at the level of a session would be a mix of trials when they are taskanchored and when they are task-independent. Therefore, the analysis would not test predictions about the relationship between task-anchored vs task-independent modes and behaviour. We hope this is now clear in the manuscript given the revisions introduced to address point 6 above.

(9) The methods describing the statistics for these experiments are also confusing. The methods section should be written more clearly, and it should be made clear in the text or figure legend whether this data is the "original" data or is processed in relation to the model, such as excluding duplicate grid cells within a session. The figure legend should also state that a GLMM was used to calculate the statistics.

We have revised the methods section with the goal of improving clarity, adding detail and removing ambiguity. This includes updates of the methods for the GLMM analysis, which are referred to within the Figure 6 legend. A clear definition of a stable session is now also added to the Figure 6 legend.

Reviewer #2 (Recommendations For The Authors):

When grid fields are anchored to the virtual world (position mode), there is probably small trialto-trial variability in the firing location of the firing fields. Is this trial-to-trial variability related to the variability in the stop location? This would provide a more direct link between path integration in grid cell networks and behaviour that depends on path integration.

When attempting to address this we find that the firing of individual grid cells is too variable to allow sufficiently precise decoding of their fields at a single trial level. This is expected given the Poisson statistics of spike generation and previous evaluations of grid coding (e.g. (Stemmler et al., 2015)).

The conclusion of the abstract is: "Our results suggest that positional anchoring of grid firing enhances the performance of tasks that require path integration." This statement is slightly confusing. The task requires 1) anchoring the behaviour to the visual cues presented at the start of the trial and 2) path integration from thereon to identify the rewarded location. The performance is higher when grid cells anchor to the visual cues presented at the start of the trial. What the results show is that the anchoring of grid firing fields to visual landmarks enhances the performance of tasks that require path integration from visual landmarks (i.e. grid cells being anchored to the reference frame that is behaviorally relevant).

To try to more clearly explain the logic and conclusion we have rewritten the abstract, including the final sentence.

Similar comment for the title of Figure 5: "Positional grid coding is not required for cued spatial localisation but promotes path integration-dependent localisation." Positional coding means that grid cells are anchored to the behaviorally relevant reference frame.

To address the lack of clarity we have modified the little of Figure 6 (previously Figure 5) to read ‘Anchoring of grid firing to the task reference frame promotes localisation by path integration but is not required for cued localisation’.

In Figure 1, there is a wide range of beaconed (40-80%) and non-beaconed (10-60%) trials given. It is not 100% clear whether these refer to the percentage of trials of a given type within the recording sessions. Was the proportion of non-beaconed trials manipulated? If so, was the likelihood of position and distance coding changing according to the percentage of nonbeaconed trials?

The ranges given refer to proportions across different behavioural sessions. Within any given behavioural session the proportion was constant. We now make this clear in the figure legend and in the results and methods sections.

We did not manipulate proportions of trial types during a session. Manipulations betweens sessions were carried out with the goal of maximising the numbers of uncued trials that the mice would carry out (see response to public comments above). While the effect of trial-type at the session level is not relevant to the hypotheses we aim to test here, we have included an additional analysis of the relationship between task anchoring and the proportions of trial types in a session (Figure 3, Figure Supplement 7)(also discussed above). As disentangling the effects of learning and motivation will be complex and likely require new experimental designs we have not drawn strong conclusions or pursued the analysis further..

I was not convinced that the labels "position" and "distance" were appropriate for the two grid cell firing modes. My understanding is that the "position" code also requires the grid cell network to estimate distance. It seems that the main difference between the "position" and "distance" modes is that when in the "position" mode, the activity on the torus is reset to a constant toroidal location when the animal reaches a clearly identifiable location on the virtual track. In the "distance" mode, this resetting does not take place.

As previously mentioned, we agree these terms weren’t the best and have since relabelled these as “task-anchored” and “task-independent”.

There are a few sections in the manuscript that implicitly suggest that a causal link between grid cell activity and behaviour was demonstrated. For instance: "It has been challenging to directly test whether and when grid cells contribute to behaviour.": The assumption here is that the manuscript overcomes this challenge, but the study is correlative.

We have modified the wording to be clear that we are introducing new tests of predictions made by hypotheses about causal relationships between grid coding and behaviour (introduction, p 1-2). We also clarify that our results argue against the hypothesis that grid cells provide a general coded for behaviour, but corroborate predictions of hypotheses in which they are specifically important for path integration (discussion, p 10).

We have modified the title abstract and main text to try to treat claims about causality with care. We now more thoroughly introduce and contrast the approach we report here with previous experiments that use perturbations (introduction, p2). While it is tempting to make stronger claims for causality with these approaches, there are also logical limitations with perturbation-based approaches, for example the challenges of fully excluding off target effects and adaptation. We now explain how these strategies are complementary. Our view is that both strategies will be required to develop strong arguments for whether and when grid cells contribute to behaviour. From this perspective, it is encouraging that our conclusions are in agreement with what are probably the most specific perturbations of grid cells reported to date (Gil et al. 2017), while perturbations that more generally affect MEC function appear to impair cued and path integration-dependent behaviours (Tennant et al. 2018). We now discuss these points more clearly (introduction, p 2).

I am slightly confused by the references to the panels in Figure 4.

"In some sessions, localization of the reward occurred almost exclusively when grid cells were anchored to position and not when they encoded distance (Figure 4C). Figure 4C only shows position coding.

"In other sessions, animals localised the reward when grid firing was anchored to position or distance, but overall performance was improved on positional trials (Figure 4D-E)." The reference should probably point to Figure 4E-F or just to 4E.

"In a few sessions, we observed spatial stopping behaviour comparable to cued trials, even when grid firing almost exclusively encoded distance rather than position (Figure 4F)." From Figure 4F, it seems that the performance on non-beaconed trials is better during "position" coding.

We have now updated Figure 5 (Figure 4 in the original manuscript) and references to the Figure in the text. Now Figure 5 shows the activity of cells recorded in stable and unstable task-anchored and task-independent sessions (see Figure 5C-F).

Minor issues:

Is this correct: (Figure 4A and Figure 4, Figure Supplement 1).

This has been corrected.

Figure 4B: There could be an additional label for position and distance.

Figure 4B from the original manuscript has now been removed.

Figure 4C-F. The panels on the right side should be explained in the Figure Legend.

Legends for Figure 5C-F (previously Figure 4C-F) have now been updated.

Reviewer #3 (Recommendations For The Authors):

Specific questions :

(1) Position coding reflects a coding scheme in which fields are spaced by a fixed distance; previous studies have shown that a virtual track grid map is a slice of the 2D classic grid. In that case, the fields are still anchored to the track but would produce a completely different map. Did the authors check whether it is the case at least for some cells? If not, what could explain such a major difference?

Το avoid confusion we now use the term ‘task-anchored’ rather than ‘position coding’ (see comments above). We should further clarify that our conclusions rest on whether or not the grid fields are anchored to the track. Task anchored firing does not require that grid fields maintain their spacing from 2D environments, only that fields are at the same track position on each trial. Thus, whether the spacing of the fields corresponds to a slice through a 2D grid makes no difference to the hypotheses we test here.

We agree that the relationship between 1D and 2D field organisation could be an interesting future direction, for example anchoring could involve resetting the grid phase while maintaining a stable period, or it could be achieved through local distortions in the grid period. However, since these outcomes would not help distinguish the hypotheses we test here we have not included analyses to address them.

(2) Previous studies have highlighted the role of grid cells in goal coding. Here there is an explicit reward in a particular area. Are there any grid modifications around this area? This question is not addressed in this study.

Again, we note that the hypotheses we test here relate to the firing mode of grid cells - taskanchored or task-independent - and interpretation of our results is independent from the specific pattern of grid fields on the track. This question nevertheless leads to an interesting prediction that if grid fields cluster in the goal area then this clustering should be apparent in the task-anchored but not the task-independent firing mode.

We test this by considering the average distribution of firing fields across all grid cells in each firing mode (Reviewer Figure 1). We find that when grid firing is task-anchored there is a clear peak around the reward zone, which is consistent with previous work by Butler et al. and Boccara et al. Consistent with our other prediction, this peak is reduced when grid cells are in the task-independent mode.

Author response image 1.

Plot shows the grid field distribution during stable grid cell session (> 85 % task-anchored or task-independent) (A) or during task-anchored and task-independent trials (B). Shaded regions in A and B represent standard error of the mean measured across sessions and epochs respectively.

(3) The behavioural procedure during recording is not fully explained. Do trial types alternate within the same session by blocks? How many trials are within a block? Is there any relation between trial alternation and the switch in the coding scheme observed in a large subset of the grid cells?

We agree this wasn’t sufficiently clear in the previous version of the manuscript. Trial types were interleaved in a fixed order within each session. We have updated the results and methods sections to provide details (see responses above).

(4) From the examples in Figure 2 it seems that firing fields tend to shift toward the start position. Is it the case in all cells? Could this reflect some reorganisation at the network level with cells signalling the starting as time progresses?

This is inconsistent between cells. To make this variability clear we have included additional examples of spiking profiles from different grid cells (Figure 2 - 5). Because quantification of the phenomena would not, so far as we can tell, help distinguish our core hypotheses we have not included further analyses here.

(5) Are grid cells with different coding properties recorded in different parts of the MEC? Are there any differences between these cell categories in the 2D map?

The recordings we made are from the dorsal region of the MEC (stated at the start of the results section). We don’t have data to speak to other parts of the MEC.

Minor:

There are very few grid cell examples that repeat in the different figures. I would suggest showing more examples both in the main text and supplementary material.

We have now provided multiple additional examples in Figures 2, 4 and 5. Grid cell examples repeat in the main figures twice, in both cases only when showing additional examples are shown from the same recording session (Figure 2A example #1 with Figure 5C, Figure 3E with Figure 4A). Further similar repeats are found in the supplemental figures (Figure 3D with Figure 5, Figure Supplement 2A, Figure 3C with Figure 5, Figure Supplement 2F).

Fig1 A-B shows the predictions in a 1D track based on distance or position coding. The A inset represents the modification of field distribution from a 2D arena to a 1D track, as performed in this study. The inset B is misleading since it represents the modifications expected from a circular track to a 1D track as in Jacob et al 2019, that is not what the authors studied. It would be better to present either the predictions based on the present study or the prediction based on previous studies. In that case, they should mention the possibility that the 1D map is a slice of the 2D map.

The goal of Figure 1A-B is to illustrate predictions (right) based on conclusions from previous studies (left). Figure 1A shows predicted 1D track firing given anchoring to the environment typically observed in grid cell studies in 2D arenas. Figure 1B shows predicted 1D track firing given the firing shifting firing patterns observed by Jacob et al. in a circular 2D track. To improve clarity, we have modified the legend to make clear that the schematics to the right are predictions given the previous evidence summarised to the left. As we outline above, the critical prediction relates to whether the representations anchor to the track. Whether the 1D representation is a perfect slice isn’t relevant to the hypotheses tested and so isn’t included in the schematic (see comments above).

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”.

Author response:

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

Reviewer #1 (Public Reviews):

Weaknesses: 

Overall I find the data presented compelling, but I feel that the number of observations is quite low (typically n=3-7 neurons, typically one per animal). While I understand that only a few slices can be obtained for the IPN from each animal, the strength of the novel findings would be more convincing with more frequent observations (larger n, more than one per animal). The findings here suggest that the authors have identified a novel mechanism for the normal function of neurotransmission in the IPN, so it would be expected to be observable in almost any animal. Thus,  it is not clear to me why the authors investigated so few neurons per slice and chose to combine different treatments into one group (e.g. Figure 2f), even if the treatments have the same expected effect.  

This is a well taken suggestion. However, we must  point out that we do perform statistical analyses on the original datasets and we believe that our conclusions are justified as acknowledged by the Reviewer. As the Reviewer is aware,  the IPN is a small nucleus and with the slicing protocol used, we typically attain 1-2 slices per mouse that are suitable for recordings. Since most of the experiments in the manuscript deals with some form of pharmacological interrogation, we were reticent to use slices that are not naïve and therefore in general did not perform more than 1 cell recording per slice. Having said this, to comply with the Reviewer’s suggestion we have now performed additional experiments to increase the n number for certain experiments. We have amended all figures and legends to incorporate the additional data. We must point out that during the replotting of the data in the summary Figure 8i (previously Figure 7i) we noticed an error with the data representation of the TAC IPL data and have now corrected this oversight  

Figure 2b,c. 

500nM DAMGO effect on TAC IPL AMPAR EPSC – n increased from 5 to 9

Figure 3g. 

500nM DAMGO effect on CHAT IPR AMPAR EPSC – n increased from 8 to 16 Effect of CTAP on DAMGO on CHAT IPR AMPAR EPSC – n increased from 4 to 7

Figure 3i. 

500nm DAMGO or Met-enk effect in “silent” CHAT IPR AMPAR EPSC – n increased    from 7 to 9

Figure 4e. 

500nM DAMGO effect on ES coupling – Note: in the original version the n number was 5 and not 7 as written in the figure legend. We have now increased the n from 5 – 9.

Figure 5e,f. 

500nM DAMGO effect on TAC IPR AMPAR EPSC – n increased from 5 to 9

Figure 7f.

Effect of DHE on EPSC amplitude after application of DNQX/APV/4-AP or DTX-α – n increased from 7-9.

Figure 7g.

Emergence of nAChR EPSC after DTX – n increased from 4 to 7

Figure 7i. 

Effect of ambenonium on nAChR amplitude and charge – n increased from 4 to 7

Supplementary Figure 3c and h

Effect of DAMGO after DNQX – n increased from 4 to 7

Effect of DNQX after DAMGO mediated potentiation – n increased from 3 to 5.

Throughout the study (Figs. 3i, 7f and 8h in the revised manuscript)  we do indeed pool datasets that were amassed from different conditions since we were not directly investigating the possibility of any deviation in the extent of response between said treatments. For example, and as pointed out by the Reviewer, in Fig. 2F (now Fig. 3i) the use of DAMGO and met-ENK were merely employed to ascertain whether light-evoked synaptic transmission (ChATCre:ai32 mice) in cells that had no measurable EPSC could be pharmacologically “unsilenced” by mOR activation. Thus, the means by which mOR receptor was activated was not relevant to this specific question. Note: 2 more recordings are now added to this dataset (Fig. 3i) that were taken from ChATChR2/SSTCre:ai9 mice in response to the comment by this Reviewer below (“Are there baseline differences in the electrophysiological or morphological properties of these "silent" neurons compared to the responsive neurons?”).  Similarly, in the revised Fig.7f we pooled data investigating the pharmacological block of the EPSC that emerged following application of either DNQX/APV/4-AP or DNQX/APV/DTX. Low concentrations 4-AP or DTX were interchangeably employed to reveal the DNQX-insensitive EPSC that we go on to show is indeed the nAChR response. Finally, in Fig. 8h, we pooled data demonstrating a  lack of effect of DAMGO in potentiating  both the glutamatergic and cholinergic arms of synaptic transmission in the OPRM1 KO mice. Again, here we were only interested in determining whether removal of mOR expression prevented potentiation of transmission mediated by mHB ChAT neurons irrespective of neurotransmitter modality.  Thus, overall we were careful to only pool data in those instances where it  would not change the interpretation and hence conclusions reached. 

There are also significant sex differences in nAChR expression in the IPN that might not be functionally apparent using the low n presented here. It would be helpful to know which of the recorded neurons came from each sex, rather than presenting only the pooled data.  

As the reviewer correctly states there are veins of literature concerning a divergence, based on sex, of not only nicotinic receptor expression but also behaviors associated with nicotine addiction. However, we have reanalyzed our datasets focusing on the extent of the mOR potentiation of glutamatergic and cholinergic transmission mediated by mHB ChAT neurons in IPR  between male and female mice. Please refer to the Author response image 1 below. Although there is a possible trend towards a higher potentiation of nAChR in female mice, this was not found to be of statistical significance (see Author response image 1 below). We therefore chose not to split our data in the manuscript based on gender.

Author response image 1.

Comparison of the mOR (500nM DAMGO) mediated potentiation on evoked (a) AMPAR and (b) nAChR  EPSCs in IPR between male and female mice.  

There are also some particularly novel observations that are presented but not followed up on, and this creates a somewhat disjointed story. For example, in Figure 2, the authors identify neurons in which no response is elicited by light stimulation of ChAT-neurons, but the application of DAMGO (mOR agonist) un-silences these neurons. Are there baseline differences in the electrophysiological or morphological properties of these "silent" neurons compared to the responsive neurons?  

Unfortunately, we did not routinely measure intrinsic properties of the recorded postsynaptic neurons nor systematically recovered biocytin fills to assess morphology. Therefore, it remains unclear whether the  neurons in which there were none or minimal AMPAR-mediated EPSCs are distinct to the ones displaying measurable responses. The IPR is resident to GABAergic SST neurons that comprise the most numerous neuron type in this IPN subdivision. Although heavily outnumbered by the SST neurons there are additionally VGluT3+ glutamatergic neurons in IPN. The Reviewer is likely referring to a recent study investigating synaptic transmission specifically onto  SST+ and VGluT3+ neurons in IPN demonstrating that mHB cholinergic mediated glutamatergic input is “weaker” onto the glutamatergic neurons. Furthermore, in some instances synaptic transmission onto this latter population can be “unsilenced” by GABAB receptor activation in a similar manner to that seen with mOR activation in this manuscript when IPR neurons are blindly targeted(Stinson & Ninan, 2025).  Using a similar strategy as in this recent study(Stinson & Ninan, 2025), we now include experiments in which the ChATChR2 mouse was crossed with  a SSTCre:Ai14. This allowed for recording of postsynaptic EPSCs in directly identified SST IPR neurons. We demonstrate that DAMGO can indeed increase glutamatergic EPSCs and in 2 of the cells where light activation demonstrated no appreciable AMPAR EPSC upon maximal LED light activation, DAMGO clearly “unsilenced” transmission.  Thus, our additional analyses directly demonstrate that our original observations concerning mOR modulation extend to the mHb cholinergic AMPAR mediated input onto IPR SST neurons. This additional data is in the revised manuscript (Figure 3D-F, I). Future experimentation will be required to determine if the propensity of encountering a  “silent” input that can be converted to robust synaptic transmission by mOR differs between these two cell types. Furthermore, it will be of interest to investigate if any differences exist in the magnitude of the cholinergic input or the mOR mediated potentiation of co-transmission between postsynaptic SST GABA and glutamatergic neuronal subtypes. 

Reviewer #2 (Public review)

Weaknesses: 

The genetic strategy used to target the mHb-IPN pathway (constitutive expression in all ChAT+ and Tac1+ neurons) is not specific to this projection.  

This is an important point made. We are acutely aware that the source of the synaptic input in IPN mediated by conditional expression of ChR2 employing  using transgenic cre driver lines does not confer specificity to mHB. This is particularly relevant considering one of the novel observations here relates to  a previously unidentified functional input from TAC1 neurons to the IPR. At this juncture we would like to point the Reviewer to the publicly available Connectivity Atlas provided by the Allen Brain Institute (https://connectivity.brain-map.org/). With reference to mHB TAC1 neuronal output, targeted viral injection into the habenula of Tac1Cre mice allows conditional expression of EGFP to SP neurons as evidenced by the predominant expression of reported fluorescence in dorsal mHB (see Author response image 2 a,b below). Tracing the axonal projections to the IPN clearly demonstrates dense fibers in IPL as expected but also arborization in  IPR (Author response image 2 a,c) . This pattern is reminiscent of that seen in the transgenic Tac1Cre:ai9 or ai32 mice used in the current study (Figs. 1c, 2a, 5c). Closer inspection of the fibers in the IPR reveals putative synaptic bouton like structures as we have shown in Fig. 5a,b (Author response image 2 d below).

Author response image 2.

Sterotaxic viral injection into mHB pf Tac1Cre mice taken from Allen Brain connectivity atlas (Link to Connectivity Atlas for mHb SP neuronal projection pattern)

These anatomical data suggest that part of the synaptic input to the IPR originates from mHB TAC1 neurons although we cannot fully discount additional synaptic input from other brain areas that may impinge on the IPR. Indeed, as the Reviewer points out, it is evident that other regions including the nucleus incertus send outputs to the IPN(Bueno et al., 2019; Liang et al., 2024; Lima et al., 2017). However, it is unclear if neuronal inputs from these alternate sources {Liang, 2024 #123;Lima, 2017 #33}{Bueno, 2019 #178} are glutamatergic in nature AND mediated by a TAC1/OPRM1-expressing neuronal population. Nevertheless, we have now modified text in the discussion to highlight the limitations of using a transgenic strategy (pg 12, para 1).

In addition, a braking mechanism involving Kv1.2 has not been identified.

It is unclear to what the Reviewer is referring to here. Although most of our experiments pertaining to the brake on cholinergic  transmission by potassium channels use low concentrations of 4-AP (50100M) which have been used to block Shaker Kv1 channels there although at these concentrations there are additional action at other K+-channels such as Kv3, for instance. However, we essentially demonstrate that a selective Kv1.1 and Kv1.2 antagonist dendrotoxin replicates the 4-AP effects. We have now also included RNAseq data demonstrating the relative expression levels of Kv1 channel mRNA in mHb ChAT neurons (KCNA1 through KCNA6; Figure 6b). The complete absence of KCNA1 yet a high expression level of KCNA2 transcripts highly suggests a central role of Kv1.2 in unmasking nAChR mediated synaptic transmission. 

Reviewer #3 (Public review)

Weaknesses:  

The significance of the ratio of AMPA versus nACh EPSCs shown in Figure 6 is unclear since nAChR EPSCs measured in the K+ channel blockers are compared to AMPA EPSCs in control (presumably 4-AP would also increase AMPA EPSCs). 

We understand the Reviewer’s concern regarding the calculation of nicotinic/AMPA ratios since they are measured under differing conditions i.e. absence and presence of 4-AP, respectively. As the reviewer correctly points point 4-AP likely increases the amplitude of the AMPA receptor mediated EPSC. However, our intention of calculating this ratio was not to ascertain a measure of relative strengths of fast glutamatergic vs cholinergic transmission onto a given postsynaptic IPN neuron per se. Rather, we used the ratio as a means to normalize the size of the nicotinic receptor EPSC to the strength of the light stimulation (using the AMPA EPSC as the normalizing factor) in each individual recording. This permits a more meaningful comparison across cells/slices/mice . We apologize for the confusion and have amended the text in the results section to reflect this (pg 9; para2).

The mechanistic underpinnings of the most now  results are not pursued. For example, the experiments do not provide new insight into the differential effects of evoked and spontaneous glutamate/Ach release by Gi/o coupled mORs, nor the differential threshold for glutamate versus Ach release. 

Our major goal of the current manuscript was to provide a much-needed roadmap outlining the effects of opioids in the habenulo-interpeduncular axis. Of course, a full understanding of the mechanisms underlying such complex opioid actions at the molecular level will be of great value. We feel that this is beyond the scope of this already quite result dense manuscript but will be essential if directed manipulation of the circuit is to be leveraged to alter maladaptive behaviors associated with addiction/emotion during adolescence and in adult. 

The authors note that blocking Kv1 channels typically enhances transmitter release by slowing action potential repolarization. The idea that Kv1 channels serve as a brake for Ach release in this system would be strengthened by showing that these channels are the target of neuromodulators or that they contribute to activity-dependent regulation that allows the brake to be released. 

The exact mechanistic underpinnings that can potentially titer Kv1.2 availability and hence nAChR transmission would be essential to shed light on potential in vivo conditions under which this arm of neurotransmission can be modulated. However, we feel that detailed mechanistic interrogation constitutes significant work but one that future studies should aim to achieve. Thus, it presently remains unclear under what physiological or pathological scenarios result in attenuation of Kv1.2 to subsequently promote nAChR mediated transmission but as mentioned in the existing discussion future work to decipher such mechanisms would be of great value.

Reviewer #1 (Recommendations for the authors): 

Overall I find this to be a very interesting and exciting paper, presenting novel findings that provide clarity for a problem that has persisted in the IPN field: that of the conundrum that light-evoked cholinergic signaling was challenging to observe despite the abundance of nAChRs in the IPN. 

Major concerns: 

(1) The n is quite low in most cases, and in many instances, data from one figure are replotted in another figure. Given that the findings presented here are expected in the normal condition, it should not be difficult to increase the n. A more robust number of observations would strengthen the novel findings presented here. 

Please refer to the response to the public review above.

(2) In general, I find the organization of the figures somewhat disjointed. Sometimes it feels as if parts of the information presented in the results are split between figures, where it would make more sense to be together in a figure. For example, all the histology for each of the lines is in Figure 1, but only ephys data for one line is included there. It would be more logical to include the histology and ephys data for each line in its own figure. It would also be helpful to show the overlap of mOR expression with Tac1-Cre and ChAT-Cre terminals in the IPN. Likewise, the summarized Tac1Cre:Ai32 IPR data is in Figure 4, but the individual data is in Figure 5. 

We introduce both ChAT and TAC1 cre lines in Figure 1 as an overview particularly for those readers who are not entirely familiar with the distinct afferent systems operating with the habenulointerpeduncular pathway.  However, in compliance with the Reviewer’s suggestion we have now restructured the Figures. In the revised manuscript, the functional data pertaining to the various transmission modalities mediated by the distinct afferent systems impinging on the subdivision of the IPN tested are now split into their own dedicated figure as follows:

Figure 2. 

mOR effect on TAC1neuronal glutamatergic output in IPL.

Figure 3. 

mOR effect on CHAT neuronal glutamatergic output in IPR.

Figure 5. 

mOR effect on TAC1neuronal glutamatergic output in IPR.

Figure 8.

mOR effect on CHAT neuronal cholinergic output in IPC.

Supp. Fig. 1 mOR effect on CHAT neuronal glutamatergic output in IPC.

We thank the Reviewer for their suggestions regarding the style of the manuscript. The restructuring has now resulted in a much better flow of the presented data.

(3) The discussion is largely satisfactory. However, a little more discussion of the integrative function of the IPN is warranted given the opposing effects of MOR activation in the Tac vs ChAT terminals, particularly in the context of both opioids and natural rewards. 

We thank the reviewer for this comment. However, we feel the discussion is rather lengthy as is and therefore we refrained from including additional text.  

Minor concerns: 

(1)  The methods are missing key details. For example, the stock numbers of each of the strains of mice appear to have been left out. This is of particular importance for this paper as there are key differences between the ChAT-Cre lines that are available that would affect observed electrophysiological properties. As the authors indicate, the ChAT-ChR2 mice overexpress VAChT, while the ChAT-IRES-Cre mice do not have this problem. However, as presented it is unclear which mice are being used. 

We apologize for the omission - the catalog numbers of the mice employed have now been included in the methods section.

We have now clearly included in each figure panel (single trace examples and pooled data) from which mice the data are taken from – in some instances the pooled data are from the two CHAT mouse strains employed. Despite the tendency of the ChATChR2 mice to demonstrate more pronounced nAChR mediated transmission (Fig. 7h),  we justify pooling the data since we see no statistical significance in the effect of mOR activation on either potentiating AMPA or nAChR EPSCs (Please refer to response to Reviewer 2, Minor Concern point 2)

(2) Likewise, antibody dilutions used for staining are presented as both dilution and concentration, which is not typical. 

We thank the reviewer for pointing out this inconsistency. We have amended the text in the methods to include only the working dilution for all antibodies employed in the study.

(3) There are minor typos throughout the manuscript. 

All typos have been corrected.

Reviewer #2 (Recommendations for the authors): 

The authors provide a thorough investigation into the subregion, and cell-type effect of mu opioid receptor (MOR) signaling on neurotransmission in the medial habenula to interpeduncular nucleus circuit (mHb-IPN). This circuit largely comprises two distinct populations of neurons: mHb substance P (Tac1+) and cholinergic (ChAT+) neurons. Corroborating prior work, the authors report that Tac1+ neurons preferentially innervate the lateral IPN (IPL) and rostral IPN (IPR), while ChAT+ neurons preferentially innervate the central IPN (IPC) and IPR. The densest expression of MOR is observed in the IPL and MOR agonists produce a canonical presynaptic depression of glutamatergic neurotransmission in this region. Interestingly, MOR signaling in the ChAT+ mHb projection to the IPR potentiates light-evoked glutamate and acetylcholine-mediated currents (EPSC), and this effect is mediated by a MOR-induced inhibition of Kv2.1 channels. 

Major concerns: 

(1) The method used for expressing channelrhodopsin (ChR2) into cholinergic and neurokinin neurons in the mHb (Ai32 mice crossed with Cre-driver lines) has limitations because all Tac1+/ChAT+ inputs to the IPN express ChR2 in this mouse. Importantly, the IPN receives inputs from multiple brain regions besides the IPN-containing neurons capable of releasing these neurotransmitters (PMID: 39270652). Thus, it would be important to isolate the contributions of the mHb-IPN pathway using virally expressed ChR2 in the mHb of Cre driver mice. 

Please refer to the response to the public review above. 

(2) Figure 4: The authors conclude that the sEPSC recorded from IPR originate from Tac1+ mHbIPR projections. However, this cannot be stated conclusively without additional experimentation. For instance, an optogenetic asynchronous release experiment. For these experiments it would also be important to express ChR2 virus in the mHb in Tac1- and ChAT-Cre mice since glutamate originating from other brain regions could contribute to a change in asynchronous EPSCs induced by DAMGO. 

This is a well taken point. The incongruent effect of DAMGO on evoked CHAT neuronal EPSC amplitude and sEPSC frequency prompted us  to consider the the possibility of differing effect of DAMGO on a  secondary input. We agree that we do not show directly if the sEPSCs originate from a TAC1 neuronal population. Therefore, we have tempered our wording with regards the origin of the sEPSCs and  have also restructured the Figure in question moving the sEPSC data into supplemental data (Supplemental Fig. 2) 

(3) Figure 5D: lt would be useful to provide a quantitative measure in a few mice of mOR fluorescence across development (e.g. integrated density of fluorescence in IPR). 

We have now included mOR expression density across development  (Fig. 6). Interestingly, the adult expression levels of mOR in the IPR are essentially reached at a very early developmental age (P10) yet we see stark differences in the role of mOR activation in modulating glutamatergic transmission mediated by mHB cholinergic neurons. Note: since we processed adult tissue (i.e. >p40) for these developmental analyses we utilized these slices to also include an analysis of the relative mOR expression density specifically in adults between the subdivisions of IPN in Fig. 1.

(4) Figure 6B: It would be useful to quantify the expression of Kcna2 in ChAT and Tac1 neurons (e.g. using FISH). 

We thank the Reviewer for this suggestion. We have now included mRNA expression levels available from publicly available 10X RNA sequencing dataset provided by the Allen Brain Institute (Figure 7b).  

(5) It would be informative to examine what the effects of MOR activation are on mHb projections to the (central) . 

In response to this suggestion, we now have included  additional data in the manuscript in putative IPC cells that clearly demonstrate a similar DAMGO elicited potentiation of AMPAR EPSC to that  seen in IPR. These data are now included in the revised manuscript  (Supplemental Fig. 1; Fig. 8i). 

(6) What is the proposed link between MOR activation and the inhibition of Kv1.2 (e.g. beta-Arrestin signaling, G beta-gamma interaction with Kv1.2, PKA inhibition?) 

We apologize for any confusion. We do not directly test whether the potentiation of EPSCs upon mOR activation occurs via inhibition of Kv1.2.Although we have not directly tested this possibility we find it an unlikely underlying cellular mechanism, especially for the potentiation of the cholinergic arm of neurotransmission since in the presence of DNQX/APV, the activation of mOR does not result in any emergence of any nAChR EPSC (see Supplementary Fig. 3a-c)

Minor concerns: 

(1) Methods: Jackson lab ID# for used mouse strains is missing. 

We apologize for this omission and have now included the mouse strain catalog numbers.

(2) The authors use data from both ChAT-Cre x Ai32 and ChAT-ChR2 mice. It would be helpful to show some comparisons between the lines to justify merging data sets for some of the analyses as there appear to be differences between the lines (e.g. Figure 6G). 

This is a well taken point. We have now provided a figure for the Reviewer (see below) that illustrates the lack of  significant difference between the mOR mediated potentiation of both mHB CHAT neuronal AMPAR and nAChR transmission between the two mouse lines employed despite a divergence in the extent of glutamatergic vs cholinergic transmission shown in Fig. 7g (previously Figure 6g). We have chosen not to include this data in the revised manuscript.

Author response image 3.

Comparison of the mOR (500nM DAMGO) mediated potentiation on evoked AMPAR (a) and nAChR (b)EPSCs in IPR between ChATCre:Ai32  and ChATChR2 mice.

(3)  Line 154: How was it determined that the EPSC is glutamatergic? 

We apologize for any confusion. In the revised manuscript we now clearly point to the relevant figures (see Supplementary Figs. 2a and 3) in the Results section (pg. 4, para 2; pg 7, para 1; pg 8, para2) where we determine that both the sEPSCs and ChAT mediated light evoked EPSCs recorded under baseline conditions are totally blocked by DNQX and hence are exclusively AMPAR events 

(4) It would be helpful to discuss the differences between GABA-B mediated potentiation of mHbIPN signaling and the current data in more detail. 

We are unclear as to what differences the Reviewer is referring to. At least from the perspective of ChAT neuronal mediated synaptic transmission, other groups (and in the current study; Fig. 7h) have clearly shown that GABA_B activation markedly potentiates synaptic transmission like mOR activation. Nevertheless, based on our novel findings it would be of interest to determine whether the influence of GABA_B is inhibitory onto the TAC mediated input in IPR and whether there is a developmental regulation of this effect as we demonstrate upon mOR activation. These additional comparisons between the effect of the two Gi-linked receptors may shed light onto the similarity, or lack thereof, regarding the underlying cellular mechanisms. We now have included a few sentences in the discussion to highlight this (pg 11, para 1).

Reviewer #3 (Recommendations for the authors): 

The abstract was confusing at first read due to the complex language, particularly the sentence starting with... Further, specific potassium channels... 

The authors might want to consider simplifying the description of the experiments and the results to clarify the content of the manuscript for readers who many only read the abstract. 

We have altered the wording of the abstract and hope it is now more reader friendly.

The opposite effect of mOR activation on spontaneous EPSCs versus electrical or ChR2-evoked EPSCs is very interesting and raises the issue of which measure is most physiologically relevant. For example, it is unclear whether sEPSCs arise primarily from cholinergic neurons (that are spontaneously active in the slice, Figure 3), and if so, does mOR activation suppress or enhance cholinergic neuron excitability and/or recruitment by ChR2? While a full analysis of this question is beyond the scope of this manuscript, the assumption that glutamate release assayed by electrical/ChR2 evoked transmission is the most physiologically relevant might merit some discussion since sEPSCs presumably also reflect action-potential dependent glutamate release. One wonders whether mORs hyperpolarize cholinergic neurons to reduce spontaneous spiking yet enhance fiber recruitment by ChR2 or an electrical stimulus (i.e. by removing Na channel inactivation). The authors have clearly stated that they do not know where the mORs are located, and that the effects arising from disinhibition are likely complex. But they also might discuss whether glutamate release following synchronous activation of a fiber pathway by ChR2 or electrode is more or less physiologically relevant than glutamate release assayed during spontaneous activity. It seems likely that an equivalent experiment to Figure 3D, E using spontaneous spiking of IPR neurons would show that spiking is reduced by mOR activation. 

We thank the Reviewer for this comment. As pointed it would be of interest to dissect the “network” effect of mOR activation but as the Reviewer acknowledges this is beyond the scope of the current manuscript. The Reviewer is correct in postulating that mOR activation results in hyperpolarization of mHB ChAT neurons.  A recent study(Singhal et al 2025) demonstrate that a subpopulation of ChAT neurons undergoes a reduction in firing frequency following DAMGO application. This is corroborated by our own observations although we chose not to include this data in our current manuscript (but see below).

Additionally, the Reviewer questions whether ChR2/electrical stimulation is physiological. This is a well taken point and of course the simultaneous activation of potentially all possible axonal release sites is not the mode under which the circuit operates. Nevertheless, our data clearly demonstrates the ability of mORs to modulate release under these circumstances that must reflect an impact on spontaneous action potential driven evoked release.  Although the suggested experiment  could shed light on the synaptic outcomes of mOR receptor activation on ES coupling of downstream IPN neurons. Interpretation of the outcome would be confounded by the fact that postsynaptic IPN neurons also express mORs . Thus,  we would not be able to isolate the effects of presynaptic changes in modulating ES coupling from any direct postsynaptic effect on the recorded cell when in current clamp. 

Together these additional sites of action of mOR (i.e. mHB ChAT somatodendritic and postsynaptic IPN neuron) only serve to further highlight the complex nature of the actions of opioids on the habenulo-interpeduncular axis warranting  future work to fully understand the physiological and pathological effects on the habenulo-interpeduncular axis as a whole.

The idea that Kv2.1 channels serve as a brake raises the question of whether they contribute to activity-dependent action potential broadening to facilitate Ach release during trains of stimuli. 

This is an interesting suggestion and one that we had considered ourselves. Indeed, as the Reviewer is likely aware and as mentioned in the manuscript, previous studies have shown nAChR signaling can be revealed under conditions of multiple stimulations given at relatively high frequencies.  We therefore attempted to perform high frequency stimulation (20 stimulations at 25Hz and 50Hz) in the presence of ionotropic glutamatergic receptor antagonists DNQX and APV. We have now included this data in the revised manuscript (Supplementary Fig 3b). As shown, this failed to engage nAChR mediated synaptic transmission in our hands. Interestingly there is evidence from reduced expression systems demonstrating that Kv1.2 channels undergo use-dependent potentiation(Baronas et al., 2015) in contrast to that seen with other K+-channels. Whether this is the case for the axonal Kv1.2 channels on mHB axonal terminals in situ is not known but this may explain the inability to reveal nAChR EPSCs upon delivery of such stimulation paradigms.  

References 

Baronas, V. A., McGuinness, B. R., Brigidi, G. S., Gomm Kolisko, R. N., Vilin, Y. Y., Kim, R. Y., … Kurata, H. T. (2015). Use-dependent activation of neuronal Kv1.2 channel complexes. J Neurosci, 35(8), 3515-3524. doi:10.1523/JNEUROSCI.4518-13.2015

Bueno, D., Lima, L. B., Souza, R., Goncalves, L., Leite, F., Souza, S., … Metzger, M. (2019). Connections of the laterodorsal tegmental nucleus with the habenular-interpeduncular-raphe system. J Comp Neurol, 527(18), 3046-3072. doi:10.1002/cne.24729

Liang, J., Zhou, Y., Feng, Q., Zhou, Y., Jiang, T., Ren, M., … Luo, M. (2024). A brainstem circuit amplifies aversion. Neuron. doi:10.1016/j.neuron.2024.08.010

Lima, L. B., Bueno, D., Leite, F., Souza, S., Goncalves, L., Furigo, I. C., … Metzger, M. (2017). Afferent and efferent connections of the interpeduncular nucleus with special reference to circuits involving the habenula and raphe nuclei. J Comp Neurol, 525(10), 2411-2442. doi:10.1002/cne.24217

Singhal, S. M., Szlaga, A., Chen, Y. C., Conrad, W. S., & Hnasko, T. S. (2025). Mu-opioid receptor activation potentiates excitatory transmission at the habenulo-peduncular synapse. Cell Rep, 44(7), 115874. doi:10.1016/j.celrep.2025.115874

Stinson, H.E., & Ninan, I. (2025). GABA(B) receptor-mediated potentiation of ventral medial habenula glutamatergic transmission in GABAergic and glutamatergic interpeduncular nucleus neurons. bioRxiv doi.10.1101/2025.01.03.631193

Author response:

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

Public Reviews: 

Reviewer #1 (Public Review): 

Summary: 

The authors performed experimental evolution of MreB mutants that have a slow-growing round phenotype and studied the subsequent evolutionary trajectory using analysis tools from molecular biology. It was remarkable and interesting that they found that the original phenotype was not restored (most common in these studies) but that the round phenotype was maintained. 

Strengths: 

The finding that the round phenotype was maintained during evolution rather than that the original phenotype, rod-shaped cells, was recovered is interesting. The paper extensively investigates what happens during adaptation with various different techniques. Also, the extensive discussion of the findings at the end of the paper is well thought through and insighXul. 

Weaknesses: 

I find there are three general weaknesses: 

(1) Although the paper states in the abstract that it emphasizes "new knowledge to be gained" it remains unclear what this concretely is. On page 4 they state 3 three research questions, these could be more extensively discussed in the abstract. Also, these questions read more like genetics questions while the paper is a lot about cell biological findings. 

Thank you for drawing attention to the unnecessary and gratuitous nature of the last sentence of the Abstract. We are in agreement. It has been modified, and we have taken  advantage of additional word space to draw attention to the importance of the two competing (testable) hypotheses laid out in the Discussion. 

As to new knowledge, please see the Results and particularly the Discussion. But beyond this, and as recognised by others, there is real value for cell biology in seeing how (and whether) selection can compensate for effects that are deleterious to fitness. The results will very o_en depart from those delivered from, for example, suppressor analyses, or bottom up engineering. 

In the work recounted in our paper, we chose to focus – by way of proof-of principle – on the most commonly observed mutations, namely, those within pbp1A.  But beyond this gene, we detected mutations  in other components of the cell shape / division machinery whose connections are not yet understood and which are the focus of on-going investigation.  

As to the three questions posed at the end of the Introduction, the first concerns whether selection can compensate for deleterious effects of deleting mreB (a question that pertains to evolutionary aspects); the second seeks understanding of genetic factors; the third aims to shed light on the genotype-to-phenotype map (which is where the cell biology comes into play).  Given space restrictions, we cannot see how we could usefully expand, let alone discuss, the three questions raised at the end of the Introduction in restrictive space available in the Abstract.   

(2) It is not clear to me from the text what we already know about the restoration of MreB loss from suppressors studies (in the literature). Are there suppressor screens in the literature and which part of the findings is consistent with suppressor screens and which parts are new knowledge?  

As stated in the Introduction, a previous study with B. subtilis (which harbours three MreB isoforms and where the isoform named “MreB” is essential for growth under normal conditions), suppressors of MreB lethality were found to occur in ponA, a class A penicillin binding protein (Kawai et al., 2009). This led to recognition that MreB plays a role in recruiting Pbp1A to the lateral cell wall. On the other hand, Patel et al. (2020) have shown that deletion of classA PBPs leads to an up-regulation of rod complex activity. Although there is a connection between rod complex and class A PBPs, a further study has shown that the two systems work semi-autonomously (Cho et al., 2016). 

Our work confirms a connection between MreB and Pbp1A, and has shed new light on how this interaction is established by means of natural selection, which targets the integrity of cell wall. Indeed, the Rod complex and class A PBPs have complementary activities in the building of the cell wall with each of the two systems able to compensate for the other in order to maintain cell wall integrity. Please see the major part of the Discussion. In terms of specifics, the connection between mreB and pbp1A (shown by Kawai et al (2009)) is indirect because it is based on extragenic transposon insertions. In our study, the genetic connection is mechanistically demonstrated.  In addition, we capture that the evolutionary dynamics is rapid and we finally enriched understanding of the genotype-to-phenotype map.

(3) The clarity of the figures, captions, and data quantification need to be improved.  

Modifications have been implemented. Please see responses to specific queries listed below.

Reviewer #2 (Public Review): 

Yulo et al. show that deletion of MreB causes reduced fitness in P. fluorescens SBW25 and that this reduction in fitness may be primarily caused by alterations in cell volume. To understand the effect of cell volume on proliferation, they performed an evolution experiment through which they predominantly obtained mutations in pbp1A that decreased cell volume and increased viability. Furthermore, they provide evidence to propose that the pbp1A mutants may have decreased PG cross-linking which might have helped in restoring the fitness by rectifying the disorganised PG synthesis caused by the absence of MreB. Overall this is an interesting study. 

Queries: 

Do the small cells of mreB null background indeed have have no DNA? It is not apparent from the DAPI images presented in Supplementary Figure 17. A more detailed analysis will help to support this claim. 

It is entirely possible that small cells have no DNA, because if cell division is aberrant then division can occur prior to DNA segregation resulting in cells with no DNA. It is clear from microscopic observation that both small and large cells do not divide. It is, however, true, that we are unable to state – given our measures of DNA content – that small cells have no DNA. We have made this clear on page 13, paragraph 2.

What happens to viability and cell morphology when pbp1A is removed in the mreB null background? If it is actually a decrease in pbp1A activity that leads to the rescue, then pbp1A- mreB- cells should have better viability, reduced cell volume and organised PG synthesis. Especially as the PG cross-linking is almost at the same level as the T362 or D484 mutant.  

Please see fitness data in Supp. Fig. 13. Fitness of ∆mreB ∆pbp1A is no different to that caused by a point mutation. Cells remain round.  

What is the status of PG cross-linking in ΔmreB Δpflu4921-4925 (Line 7)? 

This was not analysed as the focus of this experiment was PBPs. A priori, there is no obvious reason to suspect that ∆4921-25 (which lacks oprD) would be affected in PBP activity.

What is the morphology of the cells in Line 2 and Line 5? It may be interesting to see if PG cross-linking and cell wall synthesis is also altered in the cells from these lines. 

The focus of investigation was restricted to L1, L4 and L7. Indeed, it would be interesting to look at the mutants harbouring mutations in :sZ, but this is beyond scope of the present investigation (but is on-going). The morphology of L2 and L5 are shown in Supp. Fig. 9.

The data presented in 4B should be quantified with appropriate input controls. 

Band intensity has now been quantified (see new Supp. Fig .20). The controls are SBW25, SBW25∆pbp1A, SBW25 ∆mreB and SBW25 ∆mreBpbp1A as explained in the paper.

What are the statistical analyses used in 4A and what is the significance value? 

Our oversight. These were reported in Supp. Fig. 19, but should also have been presented in Fig. 4A. Data are means of three biological replicates. The statistical tests are comparisons between each mutant and SBW25, and assessed by paired t-tests.  

A more rigorous statistical analysis indicating the number of replicates should be done throughout. 

We have checked and made additions where necessary and where previously lacking. In particular, details are provided in Fig. 1E, Fig. 4A and Fig. 4B. For Fig. 4C we have produced quantitative measures of heterogeneity in new cell wall insertion. These are reported in Supp. Fig. 21 (and referred to in the text and figure caption) and show that patterns of cell wall insertion in ∆mreB are highly heterogeneous.

Reviewer #3 (Public Review): 

This paper addresses an understudied problem in microbiology: the evolution of bacterial cell shape. Bacterial cells can take a range of forms, among the most common being rods and spheres. The consensus view is that rods are the ancestral form and spheres the derived form. The molecular machinery governing these different shapes is fairly well understood but the evolutionary drivers responsible for the transition between rods and spheres are not. Enter Yulo et al.'s work. The authors start by noting that deletion of a highly conserved gene called MreB in the Gram-negative bacterium Pseudomonas fluorescens reduces fitness but does not kill the cell (as happens in other species like E. coli and B. subtilis) and causes cells to become spherical rather than their normal rod shape. They then ask whether evolution for 1000 generations restores the rod shape of these cells when propagated in a rich, benign medium. 

The answer is no. The evolved lineages recovered fitness by the end of the experiment, growing just as well as the unevolved rod-shaped ancestor, but remained spherical. The authors provide an impressively detailed investigation of the genetic and molecular changes that evolved. Their leading results are: 

(1) The loss of fitness associated with MreB deletion causes high variation in cell volume among sibling cells a_er cell division. 

(2) Fitness recovery is largely driven by a single, loss-of-function point mutation that evolves within the first ~250 generations that reduces the variability in cell volume among siblings. 

(3) The main route to restoring fitness and reducing variability involves loss of function mutations causing a reduction of TPase and peptidoglycan cross-linking, leading to a disorganized cell wall architecture characteristic of spherical cells. 

The inferences made in this paper are on the whole well supported by the data. The authors provide a uniquely comprehensive account of how a key genetic change leads to gains in fitness and the spectrum of phenotypes that are impacted and provide insight into the molecular mechanisms underlying models of cell shape. 

Suggested improvements and clarifications include: 

(1) A schematic of the molecular interactions governing cell wall formation could be useful in the introduction to help orient readers less familiar with the current state of knowledge and key molecular players. 

We understand that this would be desirable, but there are numerous recent reviews with detailed schematics that we think the interested reader would be better consulting. These are referenced in the text.

(2) More detail on the bioinformatics approaches to assembling genomes and identifying the key compensatory mutations are needed, particularly in the methods section. This whole subject remains something of an art, with many different tools used. Specifying these tools, and the parameter sesngs used, will improve transparency and reproducibility, should it be needed. 

We overlooked providing this detail, which has now been corrected by provision of more information in the Materials and Methods. In short we used Breseq, the clonal option, with default parameters. Additional analyses were conducted using Genieous. The BreSeq output files are provided https://doi.org/10.17617/3.CU5SX1 (which include all read data).

(3) Corrections for multiple comparisons should be used and reported whenever more than one construct or strain is compared to the common ancestor, as in Supplementary Figure 19A (relative PG density of different constructs versus the SBW25 ancestor). 

The data presented in Supp Fig 19A (and Fig 4A) do not involve multiple comparisons. In each instance the comparison is between SBW25 and each of the different mutants. A paired t-test is thus appropriate.

(4) The authors refrain from making strong claims about the nature of selection on cell shape, perhaps because their main interest is the molecular mechanisms responsible. However, I think more can be said on the evolutionary side, along two lines. First, they have good evidence that cell volume is a trait under strong stabilizing selection, with cells of intermediate volume having the highest fitness. This is notable because there are rather few examples of stabilizing selection where the underlying mechanisms responsible are so well characterized. Second, this paper succeeds in providing an explanation for how spherical cells can readily evolve from a rod-shaped ancestor but leaves open how rods evolved in the first place. Can the authors speculate as to how the complex, coordinated system leading to rods first evolved? Or why not all cells have lost rod shape and become spherical, if it is so easy to achieve? These are important evolutionary questions that remain unaddressed. The manuscript could be improved by at least flagging these as unanswered questions deserving of further attention. 

These are interesting points, but our capacity to comment is entirely speculative. Nonetheless, we have added an additional paragraph to the Discussion that expresses an opinion that has yet to receive attention:

“Given the complexity of the cell wall synthesis machinery that defines rod-shape in bacteria, it is hard to imagine how rods could have evolved prior to cocci. However, the cylindrical shape offers a number of advantages. For a given biomass (or cell volume), shape determines surface area of the cell envelope, which is the smallest surface area associated with the spherical shape. As shape sets the surface/volume ratio, it also determines the ratio between supply (proportional to the surface) and demand (proportional to cell volume). From this point of view, it is more efficient to be cylindrical (Young 2006). This also holds for surface attachment and biofilm formation (Young 2006). But above all, for growing cells, the ratio between supply and demand is constant in rod shaped bacteria, whereas it decreases for cocci. This requires that spherical cells evolve complex regulatory networks capable of maintaining the correct concentration of cellular proteins despite changes in surface/volume ratio. From this point of view, rod-shaped bacteria offer opportunities to develop unsophisticated regulatory networks.”

why not all cells have lost rod shape and become spherical.

Please see Kevin Young’s 2006 review on the adaptive significance of cell shape

The value of this paper stems both from the insight it provides on the underlying molecular model for cell shape and from what it reveals about some key features of the evolutionary process. The paper, as it currently stands, provides more on which to chew for the molecular side than the evolutionary side. It provides valuable insights into the molecular architecture of how cells grow and what governs their shape. The evolutionary phenomena emphasized by the authors - the importance of loss-of-function mutations in driving rapid compensatory fitness gains and that multiple genetic and molecular routes to high fitness are o_en available, even in the relatively short time frame of a few hundred generations - are wellunderstood phenomena and so arguably of less broad interest. The more compelling evolutionary questions concern the nature and cause of stabilizing selection (in this case cell volume) and the evolution of complexity. The paper misses an opportunity to highlight the former and, while claiming to shed light on the latter, provides rather little useful insight. 

Thank you for these thoughts and comments. However, we disagree that the experimental results are an overlooked opportunity to discuss stabilising selection. Stabilising selection occurs when selection favours a particular phenotype causing a reduction in underpinning population-level genetic diversity. This is not happening when selection acts on SBW25 ∆mreB leading to a restoration of fitness. Driving the response are biophysical factors, primarily the critical need to balance elongation rate with rate of septation. This occurs without any change in underlying genetic diversity.  

Recommendations for the authors:  

Reviewer 1 (Recommendations for the Authors): 

Hereby my suggestion for improvement of the quantification of the data, the figures, and the text. 

-  p 14, what is the unit of elongation rate?  

At first mention we have made clear that the unit is given in minutes^-1

-  p 14, please give an error bar for both p=0.85 and f=0.77, to be able to conclude they are different 

Error on the probability p is estimated at the 95% confidence interval by the formula:1.96 , where N is the total number of cells. This has been added in the paragraph p »probability » of the Image Analysis section in the Material and Methods. 

We also added errors on p measurement in the main text.

-  p 14, all the % differences need an errorbar 

The error bars and means are given in Fig 3C and 3D.

-  Figure 1B adds units to compactness, and what does it represent? Is the cell size the estimated volume (that is mentioned in the caption)? Shouldn't the datapoints have error bars? 

Compactness is defined in the “Image Analysis” section of the Material and Methods. It is a dimensionless parameter. The distribution of individual cell shapes / sizes are depicted in Fig 1B. Error does arise from segmentation, but the degree of variance (few pixels) is much smaller than the representations of individual cells shown.

-  Figure 1C caption, are the 50.000 cells? 

Correct. Figure caption has been altered.

-  Figure 1D, first the elongation rate is described as a volume per minute, but now, looking at the units it is a rate, how is it normalized? 

Elongation rate is explained in the Materials and Methods (see the image analysis section) and is not volume per minute. It is dV/dt = r*V (the unit of r is min^-1). Page 9 includes specific mention of the unit of r.

-  Figure 1E, how many cells (n) per replicate? 

Our apologies. We have corrected the figure caption that now reads:

“Proportion of live cells in ancestral SBW25 (black bar) and ΔmreB (grey bar) based on LIVE/DEAD BacLight Bacterial Viability Kit protocol. Cells were pelleted at 2,000 x g for 2 minutes to preserve ΔmreB cell integrity. Error bars are means and standard deviation of three biological replicates (n>100).”

-  Figure 1G, how does this compare to the wildtype 

The volume for wild type SBW25 is 3.27µm^3 (within the “white zone”). This is mentioned in the text.

-  Figure 2B, is this really volume, not size? And can you add microscopy images? 

The x-axis is volume (see Materials and Methods, subsection image analysis). Images are available in Supp. Fig. 9.

-  Figure 3A what does L1, L4 and L7 refer too? Is it correct that these same lines are picked for WT and delta_mreB 

Thank you for pointing this out. This was an earlier nomenclature. It was shorthand for the mutants that are specified everywhere else by genotype and has now been corrected. 

-  Figure 3c: either way write out p, so which probability, or you need a simple cartoon that is plotted. 

The value p is the probability to proceed to the next generation and is explained in Materials and Methods  subsection image analysis.  We feel this is intuitive and does not require a cartoon. We nonetheless added a sentence to the Materials and Methods to aid clarity.

-  Figure 4B can you add a ladder to the gel? 

No ladder was included, but the controls provide all the necessary information. The band corresponding to PBP1A is defined by presence in SBW25, but absence in SBW25 ∆pbp1A.

-  Figure 4c, can you improve the quantification of these images? How were these selected and how well do they represent the community? 

We apologise for the lack of quantitative description for data presented in Fig 4C. This has now been corrected. In brief, we measured the intensity of fluorescent signal from between 10 and 14 cells and computed the mean and standard deviation of pixel intensity for each cell. To rule out possible artifacts associated with variation of the mean intensity, we calculated the ratio of the standard deviation divided by the square root of the mean. These data reveal heterogeneity in cell wall synthesis and provide strong statistical support for the claim that cell wall synthesis in ∆mreB is significantly more heterogeneous than the control. The data are provided in new Supp. Fig. 21. 

Minor comments: 

-  It would be interesting if the findings of this experimental evolution study could be related to comparative studies (if these have ever been executed).  

Little is possible, but Hendrickson and Yulo published a portion of the originally posted preprint separately. We include a citation to that paper. 

-  p 13, halfway through the page, the second paragraph lacks a conclusion, why do we care about DNA content? 

It is a minor observation that was included by way of providing a complete description of cell phenotype.  

-  p 17, "suggesting that ... loss-of-function", I do no not understand what this is based upon. 

We show that the fitness of a pbp1A deletion is indistinguishable from the fitness of one of the pbp1A point mutants. This fact establishes that the point mutation had the same effects as a gene deletion thus supporting the claim that the point mutations identified during the course of the selection experiment decrease (or destroy) PBP1A function.

-  p 25, at the top of the page: do you have a reference for the statement that a disorganized cell wall architecture is suited to the topology of spherical cells? 

The statement is a conclusion that comes from our reasoning. It stems from the fact that it is impossible to entirely map the surface of a sphere with parallel strands.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

In this study, Basha and colleagues aim to test whether the thalamic nucleus reuniens can facilitate the hippocampus/prefrontal cortex coupling during sleep. Considering the importance of sleep in memory consolidation, this study is important to understand the functional interaction between these three majorly involved regions. This work suggests that the thalamic nucleus reuniens has a functional role in synchronizing the hippocampus and prefrontal cortex.

Strengths:

The authors performed recordings in naturally sleeping cats, and analysed the correlation between the main slow wave sleep oscillatory hallmarks: slow waves, spindles, and hippocampal ripples, and with reuniens' neurons firing. They also associated intracellular recordings to assess the reuniens-prefrontal connectivity, and computational models of large networks in which they determined that the coupling of oscillations is modulated by the strength of hippocampal-thalamic connections.

Thank you for your positive evaluation.

Weaknesses:

The authors' main claim is made on slow waves and spindle coupling, which are recorded both in the prefrontal cortex and surprisingly in reuniens. Known to be generated in the cortex by cortico-thalamic mechanisms, the slow waves and spindles recorded in reuniens show no evidence of local generation in the reuniens, which is not anatomically equipped to generate such activities. Until shown differently, these oscillations recorded in reuniens are most likely volume-conducted from nearby cortices. Therefore, such a caveat is a major obstacle to analysing their correlation (in time or frequency domains) with oscillations in other regions.

(1) We fully agree with the reviewer that reuniens likely does not generate neither slow waves nor spindles. We do not make such claim, which we clearly stated in the discussion (lines 319-324). We propose that Reuniens neurons mediate different forms of activity. In the model, we introduced MD nucleus only because without MD we were unable to generate spindles. While the slow waves and spindles are generated in other thalamocortical regions, the REU neurons show these rhythms due to long-range projections from these regions to REU as has been shown in the model.

(2) Definitely, we cannot exclude some influence of volume conductance on obtained LFP recordings in REU nucleus. However, we show modulation of spiking activity within REU by spindles. Spike modulation cannot be explained by volume conductance but can be explained by either synaptic drive (likely the case here) or some intrinsic neuronal processes (like T-current).

(3) In our REU recordings for spike identification we used tetrode recordings. If slow waves and spindles are volume conducted, then slow waves and spindles recorded with tetrodes should have identical shape. Following reviewer comment, we took these recordings and subtracted one channel from another. The difference in signal during slow waves is in the order 0.1 mV. Considering that the distance between electrodes is in the order of 20 um, such a difference in voltage is major and can only be explained by local extracellular currents, likely due to synaptic activities originating in afferent structures.

Finally, the choice of the animal model (cats) is the best suited one, as too few data, particularly anatomical ones regarding reuniens connectivity, are available to support functional results.

(1) Thalamus of majority of mammals (definitely primates and carnivores, including cats) contain local circuit interneurons (about 30 % of all neurons). A vast majority of studies in rodents (except LGN nucleus) report either absence or extremally low (i.e. Jager P, Moore G, Calpin P, et al. Dual midbrain and forebrain origins of thalamic inhibitory interneurons. eLife. 2021; 10: e59272.) number of thalamic interneurons. Therefore, studies on other species than rodents are necessary, and bring new information, which is impossible to obtain in rodents.

(2) Cats’ brain is much larger than the brain of mice or rats, therefore, the effects of volume conductance from cortex to REU are much smaller, if not negligible. The distance between REU and closest cortical structure (ectosylvian gyrus) in cats is about 15 mm.

(3) Indeed, there is much less anatomical data on cats as opposed to rodents. This is why, we performed experiments shown in the figure 1. This figure contains functional anatomy data. Antidromic responses show that recorded structure projects to stimulated structure. Orthodromic responses show that stimulated structure projects to recorded structure.

Reviewer #2 (Public Review):

Summary:

The interplay between the medial prefrontal cortex and ventral hippocampal system is critical for many cognitive processes, including memory and its consolidation over time. A prominent idea in recent research is that this relationship is mediated at least in part by the midline nucleus reuniens with respect to consolidation in particular. Whereas the bulk of evidence has focused on neuroanatomy and the effects of temproary or permanent lesions of the nucleus reuniens, the current work examined the electrophysiology of these three structures and how they inter-relate, especially during sleep, which is anticipated to be critical for consolidation. They provide evidence from intercellular recordings of the bi-directional functional connectivity among these structures. There is an emphasis on the interactions between these regions during sleep, especially slow-wave sleep. They provide evidence, in cats, that cortical slow waves precede reuniens slow waves and hippocampal sharp-wave ripples, which may reflect prefrontal control of the timing of thalamic and hippocampal events, They also find evidence that hippocampal sharp wave ripples trigger thalamic firing and precede the onset of reuniens and medial prefrontal cortex spindles. The authors suggest that the effectiveness of bidirectional connections between the reuniens and the (ventral) CA1 is particularly strong during non-rapid eye movement sleep in the cat. This is a very interesting, complex study on a highly topical subject.

Strengths:

An excellent array of different electrophysiological techniques and analyses are conducted. The temporal relationships described are novel findings that suggest mechanisms behind the interactions between the key regions of interest. These may be of value for future experimental studies to test more directly their association with memory consolidation.

We thank this reviewer for very positive evaluation of our study.

Weaknesses:

Given the complexity and number of findings provided, clearer explanation(s) and organisation that directed the specific value and importance of different findings would improve the paper. Most readers may then find it easier to follow the specific relevance of key approaches and findings and their emphasis. For example, the fact that bidirectional connections exist in the model system is not new per se. How and why the specific findings add to existing literature would have more impact if this information was addressed more directly in the written text and in the figure legends.

Thank you for this comment. In the revised version, we will do our best to simplify presentation and more clearly explain our findings.

Reviewing Editor (Recommendations for Authors):

Please discuss the ability of reuniens to generate spindles?

We briefly discussed this in previous version. We now extended the discussion (p. 18).

For population data, how many cats were used in acute and chronic experiments, where does the population data originate in Fig. 2? How repeatable were the findings across animals? Was histology verified in each animal?

As previously stated in the beginning of method section we totally used 20 cats: 16 anesthetized (or acute) and 4 non-anesthetized (or chronic). We added number of cats in appropriate places in the result section. Population data in figure 2 comes from 48, 49 or 52 recording sessions (depending on the type of analysis, and indicated in the figure legend) from 4 chronic cats; we clarified this information in the legend. Results were highly repeatable across animals. Histology was verified in all chronic and acute animals, we added a sentence in the method section.

Explanation of figures is very poor, values in figures should be reported in results so they can be compared in the context of the description.

In this revised version, we report most numbers present in figures and their legend to the main text (result section).

The depth of the recording tungsten electrodes are meaningless without the AP and ML coordinates given how heterogenous mPFC is. What is the ventromedial wall of the mPFC in the cat?

We added the ML and AP coordinates in the method section. We corrected ventromedial wall for ventroposterior part of the mPFC.

What are the two vertical lines in 1F?

This was an error while preparing the figure. The panel was corrected.

Line 90 mean +-SD of what? There are no numbers.

Thanks, we now indicate the values.

Panel 2L does not show increased spindling in reuniens prior to PFC as indicated in the results, please explain. It does show SWR in the hippocampus prior to spindles, what is the meaning of such a time relationship?

Panel 2L did show an increased spindling reuniens prior to mPFC, but indeed at the time scale shown, it was not very clear. In this revised manuscript, we added an inset zooming around time zero to make this point clearer.

Panel 2L indeed show an increase in SWR prior to the increase in spindle in both Reuniens and mPFC.

As stated in the discussion, ‘We found that hippocampal SWRs trigger thalamic firing and precede the onset of reuniens and mPFC spindles, which points to SWRs as one of candidate events for spindle initiation.’

It is unclear what the slow waves of PFC mean, these represent filtered PFC lfp, but is this a particular oscillation? They continue to occur during the spindle, while the slow waves supposedly trigger the spindle. Please explain and clarify.

We recently published a review article involving several scientists studying both human and animal sleep that has inserted Box. 1 (Timofeev I, Schoch S, LeBourgeois M, Huber R, Riedner B, Kurth S. Spatio-temporal properties of sleep slow waves and implications for development. Current Opinion in Physiology. 2020; 15: 172–182). In this box among other terms, we provide current definition of slow waves vs slow oscillation. Briefly, if slow waves are repeated with a given rhythm, they typically form slow oscillation. However, if they occur in isolation or are not rhythmic, they remain slow waves, but cannot be called slow oscillation.

Regarding relation of spindles and slow oscillation. We are currently systematically analyzing data on spindles and slow waves obtained from head-restrained and freely behaving cats. One of the main findings is that a majority of ‘cortical’ spindles are local. Local to the extent that spindles can occur in alternation in two neighboring cortical cells. Largely, LFP sleep spindles occur more or less synchronously within suprasylvian gyrus of cats where indeed a large majority of them was triggered by slow waves. The synchrony between LFP spindles in suprasylvian vs other other cortical areas is much less clear. So, it is not surprizing that spindles in one bran region can occur when there is a slow wave present in some other brain region. Something of a kind was also shown in human (Mölle M, Bergmann TO, Marshall L, Born J. Fast and slow spindles during the sleep slow oscillation: disparate coalescence and engagement in memory processing. Sleep. 2011; 34 (10): 1411-1421).

In this regard, we are not ready to include modifications in the manuscript.

Line 134, where is spindle amplitude shown? Plots report power within the spindle frequency band, which obviously captures more than just spindles.

No, plots of figure 3 B, C show the phase-amplitude coupling (PAC) strength. These were calculated with detected spindles, therefore, while we cannot exclude some false spindle detections, we are confident that the false spindle detections are at a negligible level. We modified text and instead of spindle amplitude, we describe SW-spindle amplitude coupling. This reflects our analysis with exactitude.

The discussion must include the medio dorsal nucleus which is the largest thalamic input to the prefrontal cortex and also receives input from the hippocampus. In particular, the case must be made for why reuniens would play a more important or different role than MD? (For example: Occurrence of Hippocampal Ripples is Associated with Activity Suppression in the Mediodorsal Thalamic Nucleus - PMC (nih.gov)).

We cited the suggested study. We cannot say whether reuniens plays a more or less important role. What is clear is that hippocampal ripples at the onset of spindles trigger increased firing in both MD and reuniens. Our extracellular recordings (Fig. 4, K) suggest that the increased firing is associated with spike-bursts. We also have a parallel unpublished study done on anesthetized mice showing SWR triggered inhibitory potentials in both reuniens and MD that reverses around -65mV - -70 mV. Because the majority of SWR occurred at the onset of cortical up state, a relative role of cortico-thalamic vs hippocampo-thalamic drive is not easy to separate. We hope, we will convincingly do this in our forthcoming study, with the limitation that it was done on anesthetized mice.

Reviewer #1 (Recommendations For The Authors):

I strongly encourage the authors to perform current source density analyses on the LFP signals recorded in the nucleus reuniens to make sure that the observed oscillations are indeed locally generated. So far, the anatomical organisation in reuniens cannot support the local generation of oscillations, such as spindles and slow wave. At least in rodents (the cat reuniens does not seem too different, until shown differently), there were no oscillators found in reuniens, and at least not arranged like in cortical areas, allowing the summation in time, and particularly space, of rhythmic input currents. Bipolar recordings with pairs of twisted electrodes might also be useful to assess the local existence of spindles and slow waves.

Current source density calculation is possible when one knows the exact distance between recording sites. As we used tetrodes made with 4 twisted platinum-iridium wires, we know more or less the range of distance between recording sites, but not the exact distance between any given pair of electrodes.

Then, the physical distance between the reuniens and any cortical structure is about 8-9 mm. Therefore, with such distances, volume conductance is expected to be negligible. If slow waves and spindles are volume conducted, then slow waves and spindles recorded with tetrodes should have identical shape. Following reviewer comment, we took these recordings and subtracted one channel from another. The difference in signal during slow waves is in the order 0.1 mV. Considering that the distance between electrodes is in the order of 20 um, such a difference in voltage is major and can only be explained by local extracellular currents, likely due to synaptic activities originating in afferent structures.

Below, we plotted the voltage of one channel of the tetrode versus another channel of the same tetrode. If the signal was simply volume conducted, one would expect to see the vast majority of points on the x=y line (red).

Author response image 1.

Below is a segment of mPFC LFP recording (upper black trace), mPFC LFP filtered for spindle frequency (7-15 Hz) and the spindle detected (black lines above the filtered trace. Then two LFP traces from a tetrode in the Reuniens (orange and light blue) are overlayed. The second trace (Blue) from bottom represents the substraction of Reuniens 1 minus Reuniens 2 channel, and just below (lower Blue trace) is this susbtraction trace filtered for spindle frequency (7-15 Hz) showing clear voltage difference in the spindle range between the two electrodes. Note also that around time 179-179.5 s, there is clear spindle oscillation in the mPFC recording which is not present in the Reuniens recordings.

Author response image 2.

Therefore, we are convinced that in our recordings, volume conductance did not play any significant role.

Another concern regarding delays between events, like slow waves, measured between two regions (as exemplified by Figure 3). It appears that the delays were calculated from the filtered signal. Figure 3G shows a delay between the peak of the mPFC slow wave between the raw and the filtered signal, which might be artifactual of the processing. It is though not (or less) visible for the reuniens recording. Such mismatch might explain the observed differences in delays.

Thanks for this comment. We recomputed the analysis using the original signal (smoothed) and obtained very similar results. Panels H and I of figure 3 were updated using the new analysis performed on original signal.

The overall analyses of LFP-triggered reuniens MUA activity lack of statistics (at least z-scored firing to normalise the firings).

Fig. 2 H and I are representative examples for histograms; statistical data are shown in circular plots as explained in the legend. Fig. 2 L, shows populational data and we provide now standard error. Fig. 4 C and D show individual example. Fig. 4 E shows histograms of activity of all identified putative single units. Units that show significant modulation are displayed above white line. Fig. 4 F shows populational data for significantly modified units.  

A last point of detail in the model, which surprisingly shows reuniens to excitatory hippocampal cells' connectivity. Recent literature reports that reuniens only connect hippocampal interneurons, and not principal cells (at least in rodents, I could not find any report in cats). I wonder how changing this parameter would affect the results of the computational investigation, particularly the results shown in Figure 6.

There are several studies in the literature showing a direct excitation from the Reuniens to pyramidal cells in the CA1, here are three of them:

Goswamee, P., et al. (2021). "Nucleus Reuniens Afferents in Hippocampus Modulate CA1 Network Function via Monosynaptic Excitation and Polysynaptic Inhibition." Frontiers in Cellular Neuroscience 15.

Dolleman-Van der Weel MJ, Lopes da Silva FH, Witter MP (1997) Nucleus Reuniens Thalami Modulates Activity in Hippocampal Field CA1 through Excitatory and Inhibitory Mechanisms. The Journal of Neuroscience 17:5640.

Dolleman-van der Weel MJ, Lopes da Silva FH, Witter MP (2017) Interaction of nucleus reuniens and entorhinal cortex projections in hippocampal field CA1 of the rat. Brain Structure and Function 222:2421-2438.

Because this is not a review paper, we opted to not cite all the papers describing connectivity between mPFC, hippocampus and thalamus.

Reviewer #2 (Recommendations For The Authors):

I respectively suggest that the earlier (public) comments listed above should be addressed. In addition, it would be useful to make it clearer when non-rapid eye movement sleep was being addressed and when rapid eye movement was being addressed. Is it of value to use a single term instead of adding "slow wave sleep" or else clarify when either term is used? The addition of more subheadings might help. Moreover, the relative contribution/value of evidence from these two sleep states was not addressed or was not very clear.

We tried to make it clearer when NREM and when REM was analysed.

We replaced slow-wave sleep with NREM sleep in the figure 5 title.

We added several subheadings in the discussion.

Relative contribution of NREM vs REM sleep was not addressed? Sorry but we do not clearly understand your question. Figs. 2 and 3 deal mainly with NREM sleep (Fig 2.B has an example of REM sleep). Fig. 4 essentially describes results obtained during REM sleep.

I was not sure if the Abstract summarised the key take-home messages from the large amount of evidence provided. Some choices are needed, of course, but "evidence of bidirectional connectivity" struck me as less novel than other evidence provided. Given the huge amount of findings provided, which is commendable, it is still useful to present it perhaps in a more digestible fashion. For example, the headings or the first sentence(s) below headings could indicate the aim or the outcome of the specific method/analysis/findings.

We rewrote abstract and we also added some conclusion to highlight major findings and their meaning.

It is more common to use NRe or Re, rather than REU.

We avoided using RE as, for decades, we used RE to abbreviate the thalamic reticular nucleus in several publications. In this revised version, we spell at full - Reuniens.

Line 49 mentions "short-term" memory. Please specify this more clearly as it is otherwise ambiguous. Also, line 303.

We rephrased the sentence: In particular, the hierarchical coupling of slow waves, spindles and SWRs is thought to play a key role in memory consolidation.

Line 303 was likely about the ventromedial wall: we corrected that sentence.

Line 62: the word, "required" (for memory function) is too strong because there is evidence that it is not always required.

We modified the sentence for plays a major role.

The focus within the medial prefrontal cortex could be specified more clearly / earlier.

The mPFC is mentioned in the second sentence of the abstract and in the first sentence of the introduction.

Line 134: The heading states "determine" and then mentions modulation. These terms may not be interchangeable or they need clarification.

We changed it to slow wave-spindle amplitude coupling. This represents exactly our analysis.

Line 204: Does "cortical network" mean prefrontal cortex network"?

Yes, as described in lines 192-193, the two cortical networks (N1 and N2) of the model represent the mPFC layer 5 and 6 respectively.

Lines 283 to 289: These were not very clear to me.

These lines described the potential mechanisms for the responses to hippocampal and reuniens stimulation recorded intracellularly (results in figure 1). We modified this paragraph for clarity.

Line 296: Specify the "claim".

We modified the sentence for “[…] provides supporting evidence for this claim that nucleus Reuniens might synchronize the activity of ventral hippocampus and mPFC.”

The discussion naturally focuses on the thalamic nucleus reuniens, but also occasionally mentions the thalamic mediodorsal nucleus. The distinction, assuming this is highly relevant, could be expressed more clearly (direct comparison with their previous papers).

We never published a study on the mediodorsal nucleus. We do have some unpublished results from recordings in the MD nucleus and they reveal the presence of an inhibitory component at the beginning of cortical active states, therefore behaving in a similar way to first order nuclei. It is then possible that spindles recorded in the reuniens are actually generated in the MD nucleus and then transmitted to Reuniens through the thalamic reticular nucleus, as both MD and reuniens are connected to the rostral thalamic reticular nucleus. We added some discussion about this.

Figure 1B: Do the authors have any additional evidence of the placements in the reuniens, because the photo provided suggests a large area beyond the reuniens boundary. Also, please confirm is the CEM between Rh and Re in the cat (I think the Rh and Re are adjacent in the rat).

Figure 1B is from an electrolytic lesion, which is necessarily bigger than the tip of the electrode. Therefore the center of the electrolytic lesion indicates where the electrode tip was located which is well within the reuniens nucleus.

Also, yes CE (Nucleus centralis thalami, pars medialis) is located between the reuniens and rhomboid in cats. This can be found in two cat atlas:  

Reinoso-Suárez, F. (1961). Topographischer Hirnatlas der Katze für experimental-physiologische Untersuchungen (Merck).

Berman AL, Jones EG (1982) The Thalamus and Basal Telencephalon of the Cat: A Cytoarchitectonic Atlas with Stereotaxic Coordinates: University of Wisconsin Press.

The first mention of hippocampus in the figure legends should remind the reader by stating "ventral hippocampus".

In this revised version, we added “ventral” in several instances both in the main text and in figure legend.

Figure 2: It seems unusual to mention "unusually short NREM". Presumably, things are the same otherwise - if so, perhaps mention that, especially if some of the effects reflect an "unusual" episode.

We display this particular segment because we want to show continuous recording in which still individual elements characterizing specific states are still visible.

Some effects look like they are strong and others perhaps weaker. If so, how do these impact the final conclusions?

Sorry, we did not understand clearly what is meant here by the reviewer. In general, if any effect has statistically significant difference (old fashion 0.05) we consider it as significant. Any other cases are described on individual basis.

Perhaps "MAD" should be in full on the first occasion, if not already.

It was spelled out at line 659, but we now spell it out also in the results section and in figure 2 legend.

Methods: the key question is the use of rodent recordings to classify cat recordings. It would be good to have a reference indicating that this can be directly used for cats, which may have different sleep cycles and patterns compared to rats.

We did not use rodent recordings to classify cat recordings, however we did used a state detection script that was developed with rodent recordings. As mentioned in the method section, we adapted the script to cat mPFC recordings and then manual corrections were made to correctly detect REM episodes. Respectfully, our lab investigates sleep-wake in non-anesthetized animals for a few decades; we developed state detection algorithm in mice, cats, marmosets when needed (to analyse months of recordings), and we have an extensive expertise in identifying states of vigilance from electrophysiological recordings.

Author response:

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

Reviewer #1 (Public review):

Summary: 

Seon and Chung's study investigates the hypothesis that individuals take more risks when observed by others because they perceive others to be riskier than themselves. To test this, the authors designed an innovative experimental paradigm where participants were informed that their decisions would be observed by a "risky" player and a "safe" player. Participants underwent fMRI scanning during the task. 

Strengths: 

The research question is sound, and the experimental paradigm is well-suited to address the hypothesis. 

Weaknesses:

I have several concerns. Most notably, the manuscript is difficult to read in parts, and I suggest a thorough revision of the writing for clarity, as some sections are nearly incomprehensible. Additionally, key statistical details are missing, and I have reservations about the choice of ROIs.

We appreciate the reviewer’s interest in and positive assessment of our work, and we thank the reviewer for the constructive feedback. In the current revision, we have revised the manuscript for clarity and added previously omitted statistical details. Furthermore, in the response letter, we have also provided additional explanations to clarify our approach, including the rationale for the choice and use of ROIs.

Reviewer #2 (Public review): 

Summary: 

This study aims to investigate how social observation influences risky decision-making. Using a gambling task, the study explored how participants adjusted their risk-taking behavior when they believed their decisions were being observed by either a risk-averse or risk-seeking partner. The authors hypothesized that individuals would simulate the choices of their observers based on learned preferences and integrate these simulated choices into their own decision-making. In addition to behavioral experiments, the study employed computational modeling to formalize decision processes and fMRI to identify the neural underpinnings of risky decision-making under social observation. 

Strengths: 

The study provides a fresh perspective on social influence in decision-making, moving beyond the simple notion that social observation leads to uniformly riskier behavior. Instead, it shows that individuals adjust their choices depending on their beliefs about the observer's risk preferences, offering a more nuanced understanding of how social contexts shape decision-making. The authors provide evidence using comprehensive approaches, including behavioral data based on a well-designed task, computational modeling, and neuroimaging. The three models are well selected to compare at which level (e.g., computing utility, risk preference shift, and choice probability) the social influence alters one's risky decision-making. This approach allows for a more precise understanding of the cognitive processes underlying decision-making under social observation. 

Weaknesses: 

While the neuroimaging results are generally consistent with the behavioral and computational findings, the strength of the neural evidence could be improved. The authors' claims about the involvement of the TPJ and mPFC in integrating social information are plausible, but further analysis, such as model comparisons at the neuroimaging level, is needed to decisively rule out alternative interpretations that other computational models suggest. 

We appreciate the reviewer’s interest in and positive assessment of our work, and we thank the reviewer for the constructive feedback. In the current revision, we have included neural results from additional analyses, which we believe provide stronger support for our proposed computational model.

Reviewer #3 (Public review): 

Summary: 

This is an important paper using a novel paradigm to examine how observation affects the social contagion of risk preferences. There is a lot of interest in the field about the mechanisms of social influence, and adding in the factor of whether observation also influences these contagion effects is intriguing.

Strengths:

(1) There is an impressive combination of a multi-stage behavioural task with computational modelling and neuroimaging.

(2) The analyses are well conducted and the sample size is reasonable. 

Weaknesses: 

(1) Anatomically it would be helpful to more explicitly distinguish between dmPFC and vmPFC. Particularly at the end of the introduction when mPFC and vmPFC are distinguished, as the vmPFC is in the mPFC. 

(2) The authors' definition of ROIs could be elaborated on further. They suggest that peaks are selected from neurosynth for different terms, but were there not multiple peaks identified within a functional or anatomical brain area? This section could be strengthened by confirming with anatomical ROIs where available, such as the atlases here http://www.rbmars.dds.nl/lab/CBPatlases.html and the Harvard-Oxford atlases. 

(3) How did the authors ensure there were enough trials to generate a reliable BOLD signal? The scanned part of the study seems relatively short. 

(4) It would be helpful to add whether any brain areas survived whole-brain correction. 

(5) There is a concern that mediation cannot be used to make causal inferences and much larger samples are needed to support claims of mediation. The authors should change the term mediation in order to not imply causality (they could talk about indirect effects instead) and highlight that the mediation analyses are exploratory as they would not be sufficiently powered (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2843527/). 

(6) The authors may want to speculate on lifespan differences in this susceptibility to risk preferences given recent evidence that older adults are relatively more susceptible to impulsive social influence (Zhu et al, 2024, comms psychology). 

We appreciate the reviewer’s interest in and positive assessment of our work, and we thank the reviewer for the constructive feedback. In the response letter below, we address each of the reviewer’s comments, including clarifications regarding the ROIs and the limitations of the current study in interpreting the results.

Reviewer #1 (Recommendations for the authors):

(1) The neuroimaging hypotheses seem post hoc to me. First, the term "social inference" is used very loosely. In line 103 the authors mentioned that TPJ has been reported to be involved in inferring other's intentions and learning about others. However, in their task, it is not clear where inference is needed. All participants need to do is recall others' "preferences", rather than inferring a hidden variable or hidden intention. In addition, in some of the studies that the authors have cited (e.g., Park et al. 2021), the hippocampus is the focus of the inference, which gets no mention here.

How does solving this task require inference (as defined by the authors: inferring others' intentions)? And why do they choose TPJ while inference is not needed in this task?

We regret any confusion and would like to take this chance to clarify our hypothesis on social inference. As the reviewer pointed out, participants were indeed instructed to predict their choices, through which we expected them to learn the demonstrators’ preferences. Our computational model suggests that during the main phase of the task, i.e., the Observed phase, participants simulated others’ choices based on these previously learned risk preferences of others. The gamble choices they encountered (payoffs and associated probabilities) did not overlap with those in the Learning phase, and therefore, we expected that the cognitive process triggered by the social context involved active simulation—what we describe as making inference about others—rather than simple ‘recall’ of previously learned information. In line with this reasoning, we hypothesized that the TPJ, a brain region previously implicated in simulating others’ actions and intentions, would play a key role during the Observed phase.

Regarding the role of the hippocampus, the paper we cited by BoKyung Park et al. (2021), titled “The role of right temporoparietal junction in processing social prediction error across relationship contexts”, highlights the involvement of the rTPJ but does not mention the hippocampus. We are aware of the study by Seongmin A. Park et al. (2021), “Inferences on a multidimensional social hierarchy use a grid-like code”, which shows the involvement of the hippocampus and entorhinal cortex in making inferences about multidimensional social hierarchies; we believe the reviewer may have mistakenly assumed that we cited this article. As the study showed, the involvement of the hippocampus—and the use of its grid-like representation of social information—is likely tied to the multidimensional nature of task states. In our study, the hippocampus was not included as an ROI because we had no specific rationale to hypothesize that such grid-like representations would be recruited by our task.

(2) Social influence can be motivated informationally (to improve accuracy) or normatively (to be aligned with others). To me, it seems that the authors have studied the latter, because, first, there is no objectively correct response in this task and second, because participants changed their risk preference according to the preference of the observing partner. This distinction has not been made throughout the manuscript. This is important because the two process (information and normative) are supported by different neural processes and it is extremely useful to understand neural basis of which process the authors are studying.

We thank the reviewer for the opportunity to clarify the anticipated role of social influence in our study. As the reviewer pointed out, the gambling task used in our task does not have objectively correct or incorrect answers, and naturally, any social influence present during the task would align with normative social influence. To clarify this point, we have revised the discussion section as follows:

[Page 9, Line 345]

Observational learning and mimicry of others’ behavior are patterns commonly found in social animals, including nonhuman primates (Van de Waal et al., 2013). Such behaviors are thought to be driven either by a motivation to acquire additional information (‘informational conformity’) or by a motivation to align with group norm (‘normative conformity’), even when doing so does not necessarily lead to better outcomes (e.g., higher accuracy) (Cialdini & Goldstein, 2004). Given that there are no objectively correct or incorrect answers in the gambling task used in our study, the observed social influence is more consistent with normative conformity. However, we cannot rule out the possibility that individuals developed false beliefs about a particular observing partner—namely, that the partner had greater control over or insight into the gambling task. Future studies are needed to directly investigate whether individuals’ beliefs about others modulate informational social influence—that is, their motivation to use social information to gain additional insight by inferring others’ potential choices.

(3) From Line 160 onward, the authors report several findings without providing any effect sizes or statistics. Please add effect size and statistics for each finding.

We thank the reviewer for pointing this out. We have now added the corresponding effect sizes and statistical values for the reported findings, beginning from Line 160 in the revised manuscript.

(4) Line 270: "In particular, bilateral TPJ, brain regions not implicated in the Solo phase, positively tracked trial-by-trial model-estimated decision probabilities". How can the authors conclude that TPJ is not involved in the solo phase? As far as I understood from the text, TPJ was not included as one of the ROIs for analysis of the Solo phase. If it was included, it should be mentioned in the text and there should be a direct comparison between the effect sizes of the solo and the observer phase. If not, "not implicated in the Solo phase" is not justified and should be removed.

We apologize for the confusion. As the reviewer correctly pointed out, the TPJ was not included among the ROIs in our analysis of the Solo phase data; therefore, its involvement during the Solo phase was never directly assessed using an ROI-based approach.

To examine brain responses during the Observed phase, we first assessed whether regions that tracked decision probabilities during the Solo phase—vmPFC, vStr, and dACC—were also engaged in the Observed phase. The involvement of the TPJ during the Observed phase was revealed through a subsequent whole-brain analysis. To clarify this point, we now have revised the corresponding part as follows:

[Page 8, Line 276]

In particular, bilateral TPJ positively, brain regions not implicated in the Solo phase, tracked trial-by-trial model-estimated decision probabilities

à Notably, bilateral TPJ showed significant positive tracking of decision probabilities ~

(5) I am a bit puzzled about the PPI analysis. Is the main finding increased connectivity within mPFC in the observing condition? PPI is often done between two separate brain regions. I am not sure what it means that connectivity within mPFC increases in one condition compared to another. What was the motivation for this analysis? Can you also please explain what it means?

As the reviewer noted, psychophysiological interaction (PPI) analyses examine functional connectivity between brain regions as modulated by a psychological factor. To clarify our result, the reported ‘mPFC-mPFC connectivity’ refers to functional connectivity between the mPFC region responsive to the presence of an observing partner and an adjacent, anatomically distinct region within the mPFC. Note that we have revised the manuscript to refer to this region more specifically as the dorsomedial prefrontal cortex (dmPFC). Please see our response to Reviewer 3, Comment 1, for further details.

During the Observed phase of our task, social information was processed at two distinct time points. First, at the beginning of each decision trial, individuals were cued with the presence (or absence) of an observing partner (‘Partner presentation’). Second, the gamble options, as well as the observing partner’s identity, were revealed (‘Options revealed’). Because participants had previously learned about the observing partner’s risk preferences, we expected them to simulate the choice the partner would likely make. We hypothesized that if individuals indeed simulated the partner’s choice and incorporated this information into their decision-making process, the brain region involved in recognizing the partner’s presence (dmPFC_contrast) would be functionally connected to the region responsible for integrating social information into the final decision (TPJ). Our results showed that the two regions were functionally connected via an indirect path through an anatomically adjacent cluster within the mPFC (dmPFC_PPI). Given that the recognition of the partner’s presence and the simulation of their choice occurred at two distinct time points, we interpreted the functional connectivity between the two dmPFC clusters (dmPFC_contrast and dmPFC_PPI) as evidence that the dmPFC_PPI) remained engaged during the decision process to support simulation, rather than being involved solely in the passive recognition of the social context (i.e., observed vs not observed). Note that, consistent with this interpretation, functional connectivity was stronger in individuals who showed greater reliance on social information ('Social reliance' parameter in our model).

To avoid confusion, we have now labeled the two dmPFC clusters as dmPFC_contrast—the seed region identified at partner presentation—and dmPFC_PPI—the target region identified in the PPI analysis.

[Page 8, Line 284]

This cue was intended to dissociate neural responses to the social context per se (i.e., the presence of an observing partner), which we hypothesized would initiate social processing, from the neural processes involved in incorporating this information during the subsequent decision-making phase.

[Page 8, Line 291]

We tested whether the dmPFC was also involved in incorporating social information during the decision process under social observation, particularly among individuals who relied more heavily on simulating others’ behavior.

[Page 8, Line 297]

We confirmed that the functional connectivity between the dmPFC_contrast which is sensitive to cues regarding the presence of an observing partner, and its adjacent, anatomically distinct region within the dmPFC (‘dmPFC_PPI’ hereafter; x = 3, y = 50, z = 5, k_E = .74, cluster-level P_{FWE, SVC} = 0.011; Fig. 4a, b, Table S5) was positively associated with individuals’ social reliance.

(6) In Line 107 the authors say "excitatory stimulation of the TPJ improved social cognition". Improved social cognition is too general and unspecific. Please be more specific.

We agree that the term ‘social cognition’ was too general and unspecific. In the revised manuscript, we have specified that the improvement was observed in tasks specifically involving the control of self-other representation, as demonstrated by Santiesteban et al. (2012).

[Page 4, Line 106]

Corroborating with these neuroimaging data, excitatory stimulation of the TPJ improved social cognition (Santiesteban et al., 2012),~

à Corroborating these neuroimaging findings, excitatory stimulation of the TPJ improved social cognition involving the control of self-other representation (Santiesteban et al., 2012),~

Writing:

We thank the reviewer for their thorough evaluation of our manuscript. We have now made the necessary revisions in accordance with the provided comments.

(7) Line 75: "one risky options" should be one risky option.

[Page 3, Line 74]

between one safe (i.e., guaranteed payoff) and one risky options.

between a safe option (i.e., guaranteed payoff) and a risky option.

(8) Line 82: were given with the same set of gamble should be "were given the same set of gamble".

[Page 3, Line 81]

In the third phase (‘Observed phase’), individuals were given with the same set of gamble choices they faced in the Solo phase,

In the third phase (‘Observed phase’), individuals were given the same set of gamble choices they faced in the Solo phase,~

(9) Line 63: and that the extent of such influence depends on the identity of the observer. It is not clear what the authors mean by the "identity of observer". Does it mean the preference of the observer?

Van Hoorn et al. (2018) showed that the degree of social influence varies depending on whether individuals are being observed by parents or by peers. While one might attribute this difference to divergent preferences typically held by parents and peers, it is important to note that other factors may also differ between these social groups. To avoid overinterpretation while preserving the original meaning, we have revised the sentence as follows:

[Page 3, Line 61]

However, recent studies showed that the unidirectional influence of social others’ presence may be also observed in adults (Otterbring, 2021), and that the extent of such influence depends on the identity of the observer (Van Hoorn et al., 2018).  

However, recent studies showed that the unidirectional influence of social others’ presence can also be observed in adults (Otterbring, 2021), and that the extent of this influence depends on the observer’s identity—specifically, whether the observer is a parent or a peer (Van Hoorn et al., 2018).

(10) Line 103: "including inferring others' intention and in learning about others." An "in" is missing right before inferring.

[Page 4, Line 101]

The temporoparietal junction (TPJ) is another region known to play an important role in social cognitive functions, including inferring others’ intention and in learning about others (Behrens et al., 2008; Boorman et al., 2013; Charpentier et al., 2020; Park et al., 2021; Samson et al., 2004; Saxe & Kanwisher, 2003; Saxe & Kanwisher, 2013; Van Overwalle, 2009; Young et al., 2010).

The temporoparietal junction (TPJ) is another region known to play an important role in a range of social cognitive functions, including simulating others’ intention and choices, as well as learning about others (Behrens et al., 2008; Boorman et al., 2013; Charpentier et al., 2020; Park et al., 2021; Samson et al., 2004; Saxe & Kanwisher, 2003; Saxe & Kanwisher, 2013; Van Overwalle, 2009; Young et al., 2010).

(11) 106: "Corroborating with these neuroimaging data." It should be "corroborating these neuroimaging data".

[Page 4, Line 106]

Corroborating with these neuroimaging data, ~

Corroborating these neuroimaging findings, ~

(12) Lines 113-115. It is not clear what the authors are trying to say here.

We have now revised the sentence as follows:

[Page 4, Line 112]

We hypothesized that even if others’ choices are not explicitly presented, simple presence of social others may trigger inference about others’ potential choices, and the same set of brain regions will play an important role in value-based decision-making.

We hypothesized that, even in the absence of explicit information about others’ choices, the mere presence of social others could lead participants to conform to the option they believe others would choose. To do so, participants would need to simulate others’ potential choices, particularly when option values vary across trials. As a result, we propose that the same brain regions involved in simulating others’ decisions would also be engaged during value-based decision-making in the presence of social observers.

(13) Line 151: This sentence is too long and hard to follow:

We have now revised the sentence as follows:

[Page 5, Line 154]

Furthermore, individuals’ prediction responses on subsequent 10 prediction trials where no feedback was provided (Fig. 2b) as well as self-reports about the perceived riskiness of the partners collected at the end of the Learning phase (Fig. 1d) consistently showed that they were able to distinguish one partner from the other, and correctly estimate the partners’ risk preferences (Predicted risk preference: t(42) = -11.46, P = 1.66e-14; Self-report: t(42) = -35.83, P = 4.10e-33).

Furthermore, individuals’ prediction responses during the subsequent 10 trials without feedback consistently indicated that they could distinguish between the two partners and accurately estimate each partner’s risk preferences (t(42) = -11.46, P = 1.66e-14; Fig. 2b). Self-reported ratings of the partners’ perceived riskiness, collected after the Learning phase, further supported this finding (t(42) = -35.83, P = 4.10e-33; Fig. 1d).

(14) Line 178: This sentence is very hard to follow. I am not sure what the authors were trying to say here. Please clarify.

We have now revised the sentence as follows:

[Page 5, Line 183]

Various previous studies examined the impacts of social context on decision-making processes, but the suggested mechanisms by which individuals were affected by the social information depended on how the information was presented.

à Previous studies have shown that social context can influence decision-making processes. However, the underlying mechanisms proposed have varied depending on how the social information was presented.

(15) Line 183: "when individuals were given with the chances" should be "when individuals were given the chance".

[Page 5, Line 187]

On the contrary, when individuals were given with the chances~

On the contrary, when individuals were given the chances~

(16) Line 192: "are sensitive to the identity of the currently observing partner...". Do the authors mean are sensitive to the preferences of the currently observing partner? If so, please clarify, it is hard to follow.

We have now revised the sentence as follows:

[Page 5, Line 195]

We hypothesized that if individuals are sensitive to the identity of the currently observing partner, they would take into account the learned preferences of others in computing their choices rather than simply in guiding the direction how to change their own preferences.

à We hypothesized that if individuals are sensitive to the learned preferences of the observing partner, they would use this information to simulate the partner’s likely choices, rather than simply aligning their own preferences with those of the partner.

Reviewer #2 (Recommendations for the authors):

(1) The current neuroimaging findings appear to support the decision processes of all three models. I recommend that the authors provide more detailed evidence of model comparisons in the neuroimaging analysis. This should go beyond simply comparing the goodness of fit of neural activity.

We acknowledge that neuroimaging data alone often do not provide conclusive evidence for specific information processing. In our study, we examined brain regions that track decision probabilities and are associated with social cognition, such as simulating others’ choice tendencies. Because these processes are general and not tied to a specific computational model, neural responses supporting the occurrence of such processes cannot be used to rule out alternative decision models. For this reason, our approach prioritized a rigorous behavioral model comparison as a critical first step before probing the neural substrates underlying the proposed mechanism. Our behavioral model comparisons, including both quantitative fit indices and qualitative pattern predictions, indicated that the proposed model best accounted for participants' decision patterns across task conditions.

More importantly, to further validate the model, we conducted a model recovery analysis (see Fig. S2b in SI), which confirmed that our model can be reliably distinguished from alternative accounts even when behavioral differences are subtle. This result suggests that our model captures unique and meaningful characteristics of the decision process that are not equally well explained by competing models.

With this behavioral foundation, our neuroimaging analyses were designed not to serve as independent model arbiters, but rather to examine whether brain activity in regions of interest reflected the computations specified by the best-fitting model. We believe this two-step approach—first establishing behavioral validity, then linking model-derived variables to neural data—offers a principled framework for identifying the cognitive and neural mechanisms of decision-making.

Nevertheless, per the reviewer’s suggestion, we further examined whether there is neural encoding of both the participant’s own utility and the observer’s utility—serving as potential neural evidence to differentiate our model from the two alternative models. Please see below for our response to Reviewer 2’s Comment (2).

(2) Specifically, if participants are combining their own and simulated choices at the level of choice probability, we would expect to see neural encoding of both their own utility and the observer's utility. These may be observed in different areas of the mPFC, as demonstrated by Nicolle et al. (Neuron, 2012). In that study, decisions simulating others' choices were associated with activity in the dorsal mPFC, while one's own decisions were encoded in the vmPFC. On the contrary, if the brain encodes decision values based on the shifted risk preference, rather than encoding each decision's value in separate brain areas, this would support the alternative model.

We thank the reviewer for this constructive comment. In our Social reliance model, we assumed that the decision probability based on an individual’s own risk preferences, as well as that based on the observing partner’s risk preferences, both contribute to the individual’s final choice. As the reviewer suggested, neural evidence that differentiates our model from the two alternative models—the Risk preference change model and the Other-conferred utility model—would involve demonstrating neural encoding of both the participant’s own utility and the observer’s utility.

The utility differences between chosen and unchosen options from the two perspectives—self and observer—were highly correlated, preventing us from including both as regressors in the same design matrix. Instead, we defined ROIs along the ventral-to-dorsal axis of the mPFC, and examined whether each ROI more strongly reflected one’s own utility or that of the observer. Based on the meta-analysis by Clithero and Rangel (2014), we defined the most ventral mPFC ROI (ROI1) as a 10 mm-radius sphere centered at coordinate [x=-3, y=41, z=-7], a region previously associated with subjective value. From this ventral seed, we defined four additional spherical ROIs (10 mm radius each) at 12 mm intervals along the ventral-to-dorsal axis, resulting in five ROIs in total: ROI2 [x=-3, y=41, z=5], ROI3 [x=-3, y=41, z=17], ROI4 [x=-3, y=41, z=29], ROI5 [x=-3, y=41, z=41].

Consistent with Nicolle et al. (2012), the representation of one’s own utility (labelled as ‘Own subjective value’) and that of the observer (‘Observer’s subjective value’) was organized along the ventral-to-dorsal axis of the mPFC. Specifically, utility signals from the participant’s own perspective (SV_{chosen, self} – SV_{unchosen, self}) were most prominently represented in the ventral-most ROIs (blue), whereas utility signals from the observer’s perspective (SV_{chosen, observer} – SV_{unchosen, observer}) were most strongly represented in the dorsal-most ROIs (orange).

(3) Additionally, the authors may be able to detect neural signals related to conflict when the decisions of the individual and the observer differ, compared to when the decisions are congruent. These neural signatures would only be present if social influences are integrated at the choice level, as suggested by the authors.

If individuals simulate the choices that others might make, they may compare them with the choices they would have made themselves. To investigate this possibility, we categorized task trials as Conflict or No-conflict trials based on greedy choice predictions derived from a softmax decision rule. Conflict trials were those in which the choice predicted from the participant’s own risk preference differed from that predicted for the observer, whereas No-conflict trials involved the same predicted choice from both perspectives. A contrast between Conflict and No-conflict trials revealed that the dACC and dlPFC—regions previously associated with conflict monitoring and cognitive control (Shenhav et al., 2013)—were sensitive to differences in choice tendencies between the self and observer perspectives.

Author response image 1.

dACC and dlPFC are associated with the discrepancy between participants’ own choice tendencies and those of observing partners, as estimated based on prior beliefs about the partners’ risk preferences.

As the reviewer suggested, these results provide evidence in support of the Social Reliance model, which posits that participants simulate the observer's choice and integrate it with their own.

(4) Incorporating these additional analyses would provide stronger evidence for distinguishing between the models.

We again thank the reviewer for these constructive suggestions. Based on the new set of analyses and results, we have made the necessary revisions as noted above. We agree that these revisions provide stronger evidence for distinguishing between the models.

Reviewer #3 (Recommendations for the authors):

(1) Anatomically it would be helpful to more explicitly distinguish between dmPFC and vmPFC. Particularly at the end of the introduction when mPFC and vmPFC are distinguished, as the vmPFC is in the mPFC.

We appreciate the reviewer’s suggestion regarding the anatomical distinction between the dmPFC and vmPFC, particularly in relation to our use of the term “mPFC.” We acknowledge that the dmPFC and vmPFC are subregions of the broader mPFC. In our original manuscript, we referred to one region as mPFC in line with prior studies highlighting its role in social cognition and contextual processing (Behrens et al., 2008; Sul et al., 2015; Wittmann et al., 2016). However, in response to the reviewer’s comment and to more clearly distinguish this region from the ventral portion of the mPFC (i.e., vmPFC), which is canonically associated with subjective valuation, we have now revised the manuscript to refer to this region as the dmPFC. This terminology better reflects its association with social cognition, including model-estimated social reliance and sensitivity to social cues in our study.

(2) The authors' definition of ROIs could be elaborated on further. They suggest that peaks are selected from neurosynth for different terms, but were there not multiple peaks identified within a functional or anatomical brain area? This section could be strengthened by confirming with anatomical ROIs where available, such as the atlases here http://www.rbmars.dds.nl/lab/CBPatlases.html and the Harvard-Oxford atlases.

We appreciate the opportunity to clarify how our ROIs were defined. To identify the ROIs, we drew upon both prior literature and results from a term-based meta-analysis using Neurosynth. For each meta-map, we applied an FDR-corrected threshold of p < 0.01 and a cluster extent threshold of k ≥ 100 voxels to identify distinct functional clusters. For each cluster, we constructed a spherical ROI (radius = 10 mm) centered on its center of gravity. Note that for each anatomically distinct brain region, only a single center of gravity was identified and used to define the ROI. The resulting ROIs were subsequently used for small volume correction (SVC) in the second-level fMRI analyses.

For brain regions associated with decision-making processes, we obtained a meta-analytic activation map associated with the term “decision” from Neurosynth. After applying an FDR-corrected threshold of p < 0.001 and a cluster extent threshold of k ≥ 100 voxels, we identified five distinct clusters: vmPFC [x = -3, y = 38, z = -10]; right vStr [x = 12, y = 11, z = -7]; left vStr [x = -12, y = 8, z = -7]; dACC [x = 3, y = 26, z = 44]; and left Insula [x = -30, y = 23, z = -1]. To identify brain regions involved in decision-making under social observation, we used the Neurosynth meta-map associated with the term “social”, applying the same criteria (FDR p < 0.001, k ≥ 100). This analysis revealed several clusters, including bilateral TPJ: right TPJ [x = 51, y = -52, z = 14]; left TPJ [x = -51, y = -58, z = 17]. To isolate brain regions more specifically associated with social processing rather than valuation, we also constructed a conjunction map using the meta-maps for the terms “social” and “value.” We identified clusters present in the “social” map, but not in the “value” map. This analysis yielded, among others, a cluster in the dmPFC [x = 0, y = 50, z = 14].

To clarify our ROI analysis methods, we have now revised the manuscript to include more detailed information about the procedures used, as follows:

[Page 19, Line 746]

Region-of-interest (ROI) analyses. To define ROIs for the neural analyses conducted in the Observed phase, we used significant clusters identified during the Solo phase. Specifically, regions showing significant activation for Prob(chosen) in the DM0 (thresholded at P < 0.001) were selected as ROIs. Three ROI clusters were defined: the vStr (peak voxel at [x = 3, y = 14, z = -10], k_E = 9), vmPFC (peak voxel at [x = –3, y = 62, z = –13], k_E = 99), and dACC (peak voxel at [x = 12, y = 32, z = 29], k_E = 118). These ROIs were then applied in the Observed phase analyses to test whether similar neural representations are also engaged in social contexts.

Term-based meta-analytic maps from Neurosynth for small volume correction. To reduce the likelihood of false positives arising from random significant activations and to enhance sensitivity within regions of theoretical interest, small volume correction (SVC) was applied using term-based meta-analytic maps from Neurosynth. This approach allows for hypothesis-driven correction by restricting statistical testing to anatomically and functionally defined ROI. Specifically, three meta-analytic maps were generated using Neurosynth’s term-based analyses (Yarkoni et al., 2011), with a false discovery rate (FDR) corrected P < 0.01 and a cluster size > 100 voxels. For each resulting cluster, we defined a spherical ROI with a 10 mm radius centered on the cluster’s center of gravity. For each anatomically distinct brain region, only a single center of gravity was identified and used to define the corresponding ROI.

First, to identify regions encoding final decision probabilities during the Solo phase and enhance sensitivity, we used the meta-map associated with the term “decision” to identify neural substrates of value-based decision-making. This yielded three clusters: vmPFC ([x = -3, y = 38, z = -10]), vStr ([x = 12, y = 11, z = -7]), and dACC ([x = 3, y = 26, z = 44]) (Fig. 3a, S7). Second, to examine social processing during the Observed phase, we used the meta-map associated with the term “social” to identify brain regions typically involved in social cognition. This analysis revealed clusters, including the rTPJ ([x = 51, y = -52, z = 14]) and lTPJ ([x = -51, y = -58, z = 17]) (Fig. 3c, S8a). Third, to define an ROI involved in processing social cues independent of valuation, we used a meta-map associated with “social” but excluding “value”, isolating regions specific to non-valuation-related social cognition. This analysis revealed a cluster, including the dmPFC ([x = 0, y = 50, z = 14]) (Fig. 3d, 4a, S8b).

(3) How did the authors ensure there were enough trials to generate a reliable BOLD signal? The scanned part of the study seems relatively short.

We appreciate the reviewer’s concern regarding the number of trials and the potential implications for the reliability of the resulting BOLD signals. While we did not conduct formal statistical tests to determine the optimal number of trials, our task design, in general, followed well-established principles in functional neuroimaging. Specifically, we employed a jittered event-related design and used both temporal and dispersion derivatives in the GLM analyses. These strategies are widely recognized for enhancing the efficiency of BOLD signal deconvolution and improving model fit by accounting for inter-subject and inter-regional variability in the hemodynamic response function (HRF). Furthermore, the number of trials per condition in our study was comparable to those reported in previous publications (20-30 trials) that employed similar gambling paradigms to examine individual differences in the neural substrates of value-based decision-making (Chung et al., 2015; Chung et al., 2020).

(4) It would be helpful to add whether any brain areas survived whole-brain correction.

No brain regions survived whole-brain correction. Nevertheless, as described in the introduction, we had strong a priori hypotheses. Based on these hypotheses, we defined term-based ROIs using Neurosynth, and conducted small volume correction analyses. Per the reviewer’s suggestion, we have added information indicating that no brain regions survived whole-brain correction, as follows:

[Page 8, Line 281]

No additional regions survived whole-brain correction.

(5) There is a concern that mediation cannot be used to make causal inferences and much larger samples are needed to support claims of mediation. The authors should change the term mediation in order to not imply causality (they could talk about indirect effects instead) and highlight that the mediation analyses are exploratory as they would not be sufficiently powered (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2843527/).

We acknowledge the reviewer’s concerns regarding the causal interpretation of mediation analysis results. Per this comment, we have revised the manuscript as follows to avoid overinterpreting these results and to refrain from implying any causal inference.

[Page 9, Line 327]

Given that our sample size is smaller than the recommended threshold for detecting mediation effects (Fritz & MacKinnon, 2007), this significant indirect effect should be interpreted with caution, particularly with respect to causal inference.

(6) The authors may want to speculate on lifespan differences in this susceptibility to risk preferences given recent evidence that older adults are relatively more susceptible to impulsive social influence (Zhu et al, 2024, comms psychology).

We thank the reviewer for the thoughtful suggestion—we believe the referenced work is Zhilin Su et al. (2024). As noted in our manuscript, all participants in the current study were young adults aged between 18 and 29 years. Given this limited age range, our dataset does not provide sufficient variability to directly examine age-related differences across the lifespan. However, we are planning a follow-up study using the same task with older adult participants, which we believe will provide a valuable opportunity to address this important gap in understanding susceptibility to social influence across the lifespan.

Author response:

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

Reviewer #1 (Public Review):

Weaknesses:

INTRODUCTION & THEORY

(1) Can the authors please clarify why the first trial of extinction in a standard protocol does NOT produce the retrieval-extinction effect? Particularly as the results section states: "Importantly, such a short-term effect is also retrieval dependent, suggesting the labile state of memory is necessary for the short-term memory update to take effect (Fig. 1e)." The importance of this point comes through at several places in the paper:

1A. "In the current study, fear recovery was tested 30 minutes after extinction training, whereas the effect of memory reconsolidation was generally evident only several hours later and possibly with the help of sleep, leaving open the possibility of a different cognitive mechanism for the short-term fear dementia related to the retrieval-extinction procedure." ***What does this mean? The two groups in study 1 experienced a different interval between the first and second CS extinction trials; and the results varied with this interval: a longer interval (10 min) ultimately resulted in less reinstatement of fear than a shorter interval. Even if the different pattern of results in these two groups was shown/known to imply two different processes, there is absolutely no reason to reference any sort of cognitive mechanism or dementia - that is quite far removed from the details of the present study.

Indeed, the only difference between the standard extinction paradigm and the retrieval-extinction paradigm is the difference between the first and second CS extinction trials. It has been shown before that a second CS+ presented 1 hour after the initial retrieval CS+ resulted in the dephosphorylation of GluR1 in rats, which was indicative of memory destabilization. The second CS+ presented only 3 minutes after the initial retrieval CS+, as in the standard extinction training, did not cause the GluR1 dephosphorylation effect (Monfils et al., 2009). Therefore, an isolated presentation of the CS+ seems to be important in preventing the return of fear expression. Behaviorally, when the CSs were presented in a more temporally spaced (vs. mass presentation) or a more gradual manner in the extinction training, the fear amnesia effects were more salient (Cain et al., 2003, Gershman et al., 2013). It has also been suggested that only when the old memory and new experience (through extinction) can be inferred to have been generated from the same underlying latent cause, the old memory can be successfully modified (Gershman et al., 2017). On the other hand, if the new experiences are believed to be generated by a different latent cause, then the old memory is less likely to be subject to modification. Therefore, the way the first and 2nd CS are temporally organized (retrieval-extinction or standard extinction) might affect how the latent cause is inferred and lead to different levels of fear expression from a theoretical perspective. These findings, together with studies in both fear and drug memories using the retrieval-extinction paradigm (Liu et al., 2014, Luo et al., 2015, Schiller et al., 2010, Xue et al., 2012), seem to suggest that the retrieval-extinction and the standard extinction procedures engage different cognitive and molecular mechanisms that lead to significant different behavioral outcomes. 

In our study, we focus on the short-term and long-term amnesia effects of the retrieval-extinction procedure but also point out the critical role of retrieval in eliciting the short-term effect.

1B. "Importantly, such a short-term effect is also retrieval dependent, suggesting the labile state of memory is necessary for the short-term memory update to take effect (Fig. 1e)." ***As above, what is "the short-term memory update"? At this point in the text, it would be appropriate for the authors to discuss why the retrieval-extinction procedure produces less recovery than a standard extinction procedure as the two protocols only differ in the interval between the first and second extinction trials. References to a "short-term memory update" process do not help the reader to understand what is happening in the protocol.

Sorry for the lack of clarity here. By short-term memory update we meant the short-term amnesia in fear expression.

(2) "Indeed, through a series of experiments, we identified a short-term fear amnesia effect following memory retrieval, in addition to the fear reconsolidation effect that appeared much later."

***The only reason for supposing two effects is because of the differences in responding to the CS2, which was subjected to STANDARD extinction, in the short- and long-term tests. More needs to be said about how and why the performance of CS2 is affected in the short-term test and recovers in the long-term test. That is, if the loss of performance to CS1 and CS2 is going to be attributed to some type of memory updating process across the retrieval-extinction procedure, one needs to explain the selective recovery of performance to CS2 when the extinction-to-testing interval extends to 24 hours. Instead of explaining this recovery, the authors note that performance to CS1 remains low when the extinction-to-testing interval is 24 hours and invoke something to do with memory reconsolidation as an explanation for their results: that is, they imply (I think) that reconsolidation of the CS1-US memory is disrupted across the 24-hour interval between extinction and testing even though CS1 evokes negligible responding just minutes after extinction.

In our results, we did not only focus on the fear expression related to CS2. In fact, we also demonstrated that the CS1 related fear expression diminished in the short-term memory test but re-appeared in the long-term memory after the CS1 retrieval-extinction training.

The “…recovery of performance to CS2 when the extinction-to-testing interval extends to 24 hours…” is a result that has been demonstrated in various previous studies (Kindt and Soeter, 2018, Kindt et al., 2009, Nader et al., 2000, Schiller et al., 2013, Schiller et al., 2010, Xue et al., 2012). That is, the reconsolidation framework stipulates that the pharmacological or behavioral intervention during the labile states of the reconsolidation window only modifies the fear memory linked to the reminded retrieval cue, but not for the non-reminded CS-US memory expression (but also see (Liu et al., 2014, Luo et al., 2015) for using the unconditioned stimulus as the reminder cue and the retrieval-extinction paradigm to prevent the return of fear memory associated with different CS).  In fact, we hypothesized the temporal dynamics of CS1 and CS2 related fear expressions were due to the interplay between the short-term and long-term (reconsolidation) effects of the retrieval-extinction paradigm in the last figure (Fig. 6). 

(3) The discussion of memory suppression is potentially interesting but, in its present form, raises more questions than it answers. That is, memory suppression is invoked to explain a particular pattern of results but I, as the reader, have no sense of why a fear memory would be better suppressed shortly after the retrieval-extinction protocol compared to the standard extinction protocol; and why this suppression is NOT specific to the cue that had been subjected to the retrieval-extinction protocol.

We discussed memory suppression as one of the potential mechanisms to account for the three characteristics of the short-term amnesia effects: cue-independence, temporal dynamics (short-term) and thought-control-ability relevance. According to the memory suppression theory, the memory suppression effect is NOT specific to the cue and this effect was demonstrated via the independent cue test in a variety of studies (Anderson and Floresco, 2022, Anderson and Green, 2001, Gagnepain et al., 2014, Zhu et al., 2022). Therefore, we suggest in the discussion that it might be possible the CS1 retrieval cue prompted an automatic suppression mechanism and yielded the short-term fear amnesia consistent with various predictions from the memory suppression theory:

“In our experiments, subjects were not explicitly instructed to suppress their fear expression, yet the retrieval-extinction training significantly decreased short-term fear expression. These results are consistent with the short-term amnesia induced with the more explicit suppression intervention (Anderson et al., 1994; Kindt and Soeter, 2018; Speer et al., 2021; Wang et al., 2021; Wells and Davies, 1994). It is worth noting that although consciously repelling unwanted memory is a standard approach in memory suppression paradigm, it is possible that the engagement of the suppression mechanism can be unconscious. For example, in the retrieval-induced forgetting (RIF) paradigm, recall of a stored memory impairs the retention of related target memory and this forgetting effect emerges as early as 20 minutes after the retrieval procedure, suggesting memory suppression or inhibition can occur in a more spontaneous and automatic manner (Imai et al., 2014). Moreover, subjects with trauma histories exhibited more suppression-induced forgetting for both negative and neutral memories than those with little or no trauma (Hulbert and Anderson, 2018). Similarly, people with higher self-reported thought-control capabilities showed more severe cue-independent memory recall deficit, suggesting that suppression mechanism is associated with individual differences in spontaneous control abilities over intrusive thoughts (Küpper et al., 2014). It has also been suggested that similar automatic mechanisms might be involved in organic retrograde amnesia of traumatic childhood memories (Schacter et al., 2012; Schacter et al., 1996).”

3A. Relatedly, how does the retrieval-induced forgetting (which is referred to at various points throughout the paper) relate to the retrieval-extinction effect? The appeal to retrieval-induced forgetting as an apparent justification for aspects of the present study reinforces points 2 and 3 above. It is not uninteresting but needs some clarification/elaboration.

We introduced the retrieval-induced forgetting (RIF) to make the point that RIF was believed to be related to the memory suppression mechanism and the RIF effect can appear relatively early, consistent with what we observed in the short-term amnesia effect. We have re-written the manuscript to make this point clearer:

“It is worth noting that although consciously repelling unwanted memory is a standard approach in memory suppression paradigm, it is possible that the engagement of the suppression mechanism can be unconscious. For example, in the retrieval-induced forgetting (RIF) paradigm, recall of a stored memory impairs the retention of related target memory and this forgetting effect emerges as early as 20 minutes after the retrieval procedure, suggesting memory suppression or inhibition can occur in a more spontaneous and automatic manner (Imai et al., 2014). Moreover, subjects with trauma histories exhibited more suppression-induced forgetting for both negative and neutral memories than those with little or no trauma (Hulbert and Anderson, 2018). Similarly, people with higher self-reported thought-control capabilities showed more severe cue-independent memory recall deficit, suggesting that suppression mechanism is associated with individual differences in spontaneous control abilities over intrusive thoughts (Küpper et al., 2014).”

(4) Given the reports by Chalkia, van Oudenhove & Beckers (2020) and Chalkia et al (2020), some qualification needs to be inserted in relation to reference 6. That is, reference 6 is used to support the statement that "during the reconsolidation window, old fear memory can be updated via extinction training following fear memory retrieval". This needs a qualifying statement like "[but see Chalkia et al (2020a and 2020b) for failures to reproduce the results of 6]."

https://pubmed.ncbi.nlm.nih.gov/32580869/

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7115860/

We have incorporated the reviewer’s suggestion into the revised manuscript in both the introduction:

“Pharmacological blockade of protein synthesis and behavioral interventions can both eliminate the original fear memory expression in the long-term (24 hours later) memory test ( Lee, 2008; Lee et al., 2017; Schiller et al., 2013; Schiller et al., 2010), resulting in the cue-specific fear memory deficit (Debiec et al., 2002; Lee, 2008; Nader, Schafe, & LeDoux, 2000). For example, during the reconsolidation window, retrieving a fear memory allows it to be updated through extinction training (i.e., the retrieval-extinction paradigm (Lee, 2008; Lee et al., 2017; Schiller et al., 2013; Schiller et al., 2010), but also see (Chalkia, Schroyens, et al., 2020; Chalkia, Van Oudenhove, et al., 2020; D. Schiller, LeDoux, & Phelps, 2020)”

And in the discussion:

“It should be noted that while our long-term amnesia results were consistent with the fear memory reconsolidation literatures, there were also studies that failed to observe fear prevention (Chalkia, Schroyens, et al., 2020; Chalkia, Van Oudenhove, et al., 2020; Schroyens et al., 2023). Although the memory reconsolidation framework provides a viable explanation for the long-term amnesia, more evidence is required to validate the presence of reconsolidation, especially at the neurobiological level (Elsey et al., 2018). While it is beyond the scope of the current study to discuss the discrepancies between these studies, one possibility to reconcile these results concerns the procedure for the retrieval-extinction training. It has been shown that the eligibility for old memory to be updated is contingent on whether the old memory and new observations can be inferred to have been generated by the same latent cause (Gershman et al., 2017; Gershman and Niv, 2012). For example, prevention of the return of fear memory can be achieved through gradual extinction paradigm, which is thought to reduce the size of prediction errors to inhibit the formation of new latent causes (Gershman, Jones, et al., 2013). Therefore, the effectiveness of the retrieval-extinction paradigm might depend on the reliability of such paradigm in inferring the same underlying latent cause. Furthermore, other studies highlighted the importance of memory storage per se and suggested that memory retention was encoded in the memory engram cell ensemble connectivity whereas the engram cell synaptic plasticity is crucial for memory retrieval (Ryan et al., 2015; Tonegawa, Liu, et al., 2015; Tonegawa, Pignatelli, et al., 2015). It remains to be tested how the cue-independent short-term and cue-dependent long-term amnesia effects we observed could correspond to the engram cell synaptic plasticity and functional connectivity among engram cell ensembles (Figure 6). This is particularly important, since the cue-independent characteristic of the short-term amnesia suggest that either different memory cues fail to evoke engram cell activities, or the retrieval-extinction training transiently inhibits connectivity among engram cell ensembles. Finally, SCR is only one aspect of the fear expression, how the retrieval-extinction paradigm might affect subjects’ other emotional (such as the startle response) and cognitive fear expressions such as reported fear expectancy needs to be tested in future studies since they do not always align with each other (Kindt et al., 2009; Sevenster et al., 2012, 2013).”

5A. What does it mean to ask: "whether memory retrieval facilitates update mechanisms other than memory reconsolidation"? That is, in what sense could or would memory retrieval be thought to facilitate a memory update mechanism?

It is widely documented in the literatures that memory retrieval renders the old memory into a labile state susceptible for the memory reconsolidation process. However, as we mentioned in the manuscript, studies have shown that memory reconsolidation requires the de novo protein synthesis and usually takes hours to complete. What remains unknown is whether old memories are subject to modifications other than the reconsolidation process. Our task specifically tested the short-term effect of the retrieval-extinction paradigm and found that fear expression diminished 30mins after the retrieval-extinction training. Such an effect cannot be accounted for by the memory reconsolidation effect.

5B. "First, we demonstrate that memory reactivation prevents the return of fear shortly after extinction training in contrast to the memory reconsolidation effect which takes several hours to emerge and such a short-term amnesia effect is cue independent (Study 1, N = 57 adults)."

***The phrasing here could be improved for clarity: "First, we demonstrate that the retrieval-extinction protocol prevents the return of fear shortly after extinction training (i.e., when testing occurs just min after the end of extinction)." Also, cue-dependence of the retrieval-extinction effect was assessed in study 2.

We thank the reviewer and have modified the phrasing of the sentence:

“First, we demonstrate that memory retrieval-extinction protocol prevents the return of fear expression shortly after extinction training and this short-term effect is memory reactivation dependent (Study 1, N = 57 adults).”

5C. "Furthermore, memory reactivation also triggers fear memory reconsolidation and produces cue-specific amnesia at a longer and separable timescale (Study 2, N = 79 adults)." ***In study 2, the retrieval-extinction protocol produced a cue-specific disruption in responding when testing occurred 24 hours after the end of extinction. This result is interesting but cannot be easily inferred from the statement that begins "Furthermore..." That is, the results should be described in terms of the combined effects of retrieval and extinction, not in terms of memory reactivation alone; and the statement about memory reconsolidation is unnecessary. One can simply state that the retrieval-extinction protocol produced a cue-specific disruption in responding when testing occurred 24 hours after the end of extinction.

We have revised the text according to the reviewer’s comment.

“Furthermore, across different timescales, the memory retrieval-extinction paradigm triggers distinct types of fear amnesia in terms of cue-specificity and cognitive control dependence, suggesting that the short-term fear amnesia might be caused by different mechanisms from the cue-specific amnesia at a longer and separable timescale (Study 2, N = 79 adults).”

5D. "...we directly manipulated brain activities in the dorsolateral prefrontal cortex and found that both memory retrieval and intact prefrontal cortex functions were necessary for the short-term fear amnesia."

***This could be edited to better describe what was shown: E.g., "...we directly manipulated brain activities in the dorsolateral prefrontal cortex and found that intact prefrontal cortex functions were necessary for the short-term fear amnesia after the retrieval-extinction protocol."

Edited:

“Finally, using continuous theta-burst stimulation (Study 3, N = 75 adults), we directly manipulated brain activity in the dorsolateral prefrontal cortex, and found that both memory reactivation and intact prefrontal cortex function were necessary for the short-term fear amnesia after the retrieval-extinction protocol.”

5E. "The temporal scale and cue-specificity results of the short-term fear amnesia are clearly dissociable from the amnesia related to memory reconsolidation, and suggest that memory retrieval and extinction training trigger distinct underlying memory update mechanisms."

***The pattern of results when testing occurred just minutes after the retrieval-extinction protocol was different from that obtained when testing occurred 24 hours after the protocol. Describing this in terms of temporal scale is unnecessary, and suggesting that memory retrieval and extinction trigger different memory update mechanisms is not obviously warranted. The results of interest are due to the combined effects of retrieval+extinction and there is no sense in which different memory update mechanisms should be identified with retrieval (mechanism 1) and extinction (mechanism 2).

We did not argue for different memory update mechanisms for the “retrieval (mechanism 1) and extinction (mechanism 2)” in our manuscript. Instead, we proposed that the retrieval-extinction procedure, which was mainly documented in the previous literatures for its association with the reconsolidation-related fear memory retention (the long-term effect), also had a much faster effect (the short-term effect). These two effects differed in many aspects, suggesting that different memory update mechanisms might be involved.

5F. "These findings raise the possibility of concerted memory modulation processes related to memory retrieval..."

***What does this mean?

As we mentioned in our response to the previous comment, we believe that the retrieval-extinction procedure triggers different types of memory update mechanisms working on different temporal scales.

(6) "...suggesting that the fear memory might be amenable to a more immediate effect, in addition to what the memory reconsolidation theory prescribes..."

***What does it mean to say that the fear memory might be amenable to a more immediate effect?

We intended to state that the retrieval-extinction procedure can produce a short-term amnesia effect and have thus revised the text.

(7) "Parallel to the behavioral manifestation of long- and short-term memory deficits, concurrent neural evidence supporting memory reconsolidation theory emphasizes the long-term effect of memory retrieval by hypothesizing that synapse degradation and de novo protein synthesis are required for reconsolidation."

***This sentence needs to be edited for clarity.

We have rewritten this sentence:

“Corresponding to the long-term behavioral manifestation, concurrent neural evidence supporting memory reconsolidation hypothesis emphasizes that synapse degradation and de novo protein synthesis are required for reconsolidation.”

(8) "previous behavioral manipulations engendering the short-term declarative memory effect..."

***What is the declarative memory effect? It should be defined.

We meant the amnesia on declarative memory research, such as the memory deficit caused by the think/no-think paradigms. Texts have been modified for clarity:

“On the contrary, previous behavioral manipulations engendering the short-term amnesia on declarative memory, such as the think/no-think paradigm, hinges on the intact activities in brain areas such as dorsolateral prefrontal cortex (cognitive control) and its functional coupling with specific brain regions such as hippocampus (memory retrieval) (Anderson and Green, 2001; Wimber et al., 2015).”

(9) "The declarative amnesia effect emerges much earlier due to the online functional activity modulation..."

***Even if the declarative memory amnesia effect had been defined, the reference to online functional activity modulation is not clear.

We have rephrased the sentence:

“The declarative amnesia effect arises much earlier due to the more instant modulation of functional connectivity, rather than the slower processes of new protein synthesis in these brain regions.”

(10) "However, it remains unclear whether memory retrieval might also precipitate a short-term amnesia effect for the fear memory, in addition to the long-term prevention orchestrated by memory consolidation."

***I found this sentence difficult to understand on my first pass through the paper. I think it is because of the phrasing of memory retrieval. That is, memory retrieval does NOT precipitate any type of short-term amnesia for the fear memory: it is the retrieval-extinction protocol that produces something like short-term amnesia. Perhaps this sentence should also be edited for clarity.

We have changed “memory retrieval” to “retrieval-extinction” where applicable.

I will also note that the usage of "short-term" at this point in the paper is quite confusing: Does the retrieval-extinction protocol produce a short-term amnesia effect, which would be evidenced by some recovery of responding to the CS when tested after a sufficiently long delay? I don't believe that this is the intended meaning of "short-term" as used throughout the majority of the paper, right?

By “short-term”, we meant the lack of fear expression in the test phase (measured by skin conductance responses) shortly after the retrieval-extinction procedure (30 mins in studies 1 & 2 and 1 hour in study 3). It does not indicate that the effect is by itself “short-lived”.

(11) "To fully comprehend the temporal dynamics of the memory retrieval effect..."<br /> ***What memory retrieval effect? This needs some elaboration.

We’ve changed the phrase “memory retrieval effect” to “retrieval-extinction effect” to refer to the effect of retrieval-extinction on fear amnesia.

(12) "We hypothesize that the labile state triggered by the memory retrieval may facilitate different memory update mechanisms following extinction training, and these mechanisms can be further disentangled through the lens of temporal dynamics and cue-specificities."

***What does this mean? The first part of the sentence is confusing around the usage of the term "facilitate"; and the second part of the sentence that references a "lens of temporal dynamics and cue-specificities" is mysterious. Indeed, as all rats received the same retrieval-extinction exposures in Study 2, it is not clear how or why any differences between the groups are attributed to "different memory update mechanisms following extinction".

As the reviewer mentioned, if only one time point data were collected, we cannot differentiate whether different memory update mechanisms are involved. In study 2, however, the 3 groups only differed on the time onsets the reinstatement test was conducted. Accordingly, our results showed that the fear amnesia effects for CS1 and CS2 cannot be simply explained by forgetting: different memory update mechanisms must be at work to explain the characteristics of the SCR related to both CS1 and CS2 at three different time scales (30min, 6h and 24h). It was based on these results, together with the results from the TMS study (study 3), that we proposed the involvement of a short-term memory update mechanism in addition to the reconsolidation related fear amnesia (which should become evident much later) induced by the retrieval-extinction protocol.

(13) "In the first study, we aimed to test whether there is a short-term amnesia effect of fear memory retrieval following the fear retrieval-extinction paradigm."

***Again, the language is confusing. The phrase, "a short-term amnesia effect" implies that the amnesia itself is temporary; but I don't think that this implication is intended. The problem is specifically in the use of the phrase "a short-term amnesia effect of fear memory retrieval." To the extent that short-term amnesia is evident in the data, it is not due to retrieval per se but, rather, the retrieval-extinction protocol.

We have changed the wordings and replaced “memory retrieval” with “retrieval-extinction” where applicable.

(14) The authors repeatedly describe the case where there was a 24-hour interval between extinction and testing as consistent with previous research on fear memory reconsolidation. Which research exactly? That is, in studies where a CS re-exposure was combined with a drug injection, responding to the CS was disrupted in a final test of retrieval from long-term memory which typically occurred 24 hours after the treatment. Is that what the authors are referring to as consistent? If so, which aspect of the results are consistent with those previous findings? Perhaps the authors mean to say that, in the case where there was a 24-hour interval between extinction and testing, the results obtained here are consistent with previous research that has used the retrieval-extinction protocol. This would clarify the intended meaning greatly.

Our 24 hour test results after the retrieval-extinction protocol was consistent with both pharmacological and behavioral intervention studies in fear memory reconsolidation studies (Kindt and Soeter, 2018, Kindt et al., 2009, Liu et al., 2014, Luo et al., 2015, Monfils et al., 2009, Nader et al., 2000, Schiller et al., 2013, Schiller et al., 2010, Xue et al., 2012) since the final test phase typically occurred 24 hours after the treatment. At the 24-hour interval, the memory reconsolidation effect would become evident either via drug administration or behavioral intervention (extinction training).

DATA

(15) Points about data:

5A. The eight participants who were discontinued after Day 1 in study 1 were all from the no-reminder group. Can the authors please comment on how participants were allocated to the two groups in this experiment so that the reader can better understand why the distribution of non-responders was non-random (as it appears to be)?

15B. Similarly, in study 2, of the 37 participants that were discontinued after Day 2, 19 were from Group 30 min, and 5 were from Group 6 hours. Can the authors comment on how likely these numbers are to have been by chance alone? I presume that they reflect something about the way that participants were allocated to groups, but I could be wrong.

We went back and checked out data. As we mentioned in the supplementary materials, we categorized subjects as non-responders if their SCR response to any CS was less than 0.02  in Day 1 (fear acquisition). Most of the discontinued participants (non-responders) in the no-reminder group (study 1) and the 30min & 24 h groups (study 2) were when the heating seasons just ended or were yet to start, respectively. It has been documented that human body thermal conditions were related to the quality of the skin conductance response (SCR) measurements (Bauer et al., 2022, Vila, 2004). We suspect that the non-responders might be related to the body thermal conditions caused by the lack of central heating.

15C. "Post hoc t-tests showed that fear memories were resilient after regular extinction training, as demonstrated by the significant difference between fear recovery indexes of the CS+ and CS- for the no-reminder group (t26 = 7.441, P < 0.001; Fig. 1e), while subjects in the reminder group showed no difference of fear recovery between CS+ and CS- (t29 = 0.797, P = 0.432, Fig. 1e)."

***Is the fear recovery index shown in Figure 1E based on the results of the first test trial only? How can there have been a "significant difference between fear recovery indexes of the CS+ and CS- for the no-reminder group" when the difference in responding to the CS+ and CS- is used to calculate the fear recovery index shown in 1E? What are the t-tests comparing exactly, and what correction is used to account for the fact that they are applied post-hoc?

As we mentioned in the results section of the manuscript, the fear recovery index was defined as “the SCR difference between the first test trial and the last extinction trial of a specific CS”. We then calculated the “differential fear recovery index” (figure legends of Fig. 1e) between CS+ and CS- for both the reminder and no-reminder groups. The post-hoc t-tests were used to examine whether there were significant fear recoveries (compare to 0) in both the reminder (t₂₉ = 0.797, P = 0.432, Fig. 1e) and no-reminder (t₂₆ = 7.441, P  < 0.001; Fig. 1e) groups. We realize that the description of Bonferroni correction was not specified in the original manuscript and hence added in the revision where applicable.

15D. "Finally, there is no statistical difference between the differential fear recovery indexes between CS+ in the reminder and no reminder groups (t55 = -2.022, P = 0.048; Fig. 1c, also see Supplemental Material for direct test for the test phase)."

***Is this statement correct - i.e., that there is no statistically significant difference in fear recovery to the CS+ in the reminder and no reminder groups? I'm sure that the authors would like to claim that there IS such a difference; but if such a difference is claimed, one would be concerned by the fact that it is coming through in an uncorrected t-test, which is the third one of its kind in this paragraph. What correction (for the Type 1 error rate) is used to account for the fact that the t-tests are applied post-hoc? And if no correction, why not?

We are sorry about the typo.  The reviewer was correct that we meant to claim here that “… there is a significant difference between the differential fear recovery indexes between CS+ in the reminder and no-reminder groups (t₅₅ =- 2.022, P = 0.048; Fig. 1e)”.  Note that the t-test performed here was a confirmatory test following our two-way ANOVA with main effects of group (reminder vs. no-reminder) and time (last extinction trial vs. first test trial) on the differential CS SCR response (CS+ minus CS-) and we found a significant group x time interaction effect (F_1.55 = 4.087, P = 0.048, η² = 0.069). The significant difference between the differential fear recovery indexes was simply a re-plot of the interaction effect mentioned above and therefore no multiple correction is needed. We have reorganized the sequence of the sentences such that this t-test now directly follows the results of the ANOVA:

“The interaction effect was confirmed by the significant difference between the differential fear recovery indexes between CS1+ and CS2+ in the reminder and no-reminder groups (t₅₅ \= -2.022, P \= 0.048; Figure 1E, also see Supplemental Material for the direct test of the test phase).”

15E. In study 2, why is responding to the CS- so high on the first test trial in Group 30 min? Is the change in responding to the CS- from the last extinction trial to the first test trial different across the three groups in this study? Inspection of the figure suggests that it is higher in Group 30 min relative to Groups 6 hours and 24 hours. If this is confirmed by the analysis, it has implications for the fear recovery index which is partly based on responses to the CS-. If not for differences in the CS- responses, Groups 30 minutes and 6 hours are otherwise identical.

Following the reviewer’s comments, we went back and calculated the mean SCR difference of CS- between the first test trial and the last extinction trial for all three studies (see Author response image 1 below). In study 1, there was no difference in the mean CS- SCR (between the first test trial and last extinction trial) between the reminder and no-reminder groups (Kruskal-Wallis test , panel a), though both groups showed significant fear recovery even in the CS- condition (Wilcoxon signed rank test, reminder: P = 0.0043, no-reminder: P = 0.0037). Next, we examined the mean SCR for CS- for the 30min, 6h and 24h groups in study 2 and found that there was indeed a group difference (one-way ANOVA,F_2.76 = 5.3462, P = 0.0067, panel b), suggesting that the CS- related SCR was influenced by the test time (30min, 6h or 24h). We also tested the CS- related SCR for the 4 groups in study 3 (where test was conducted 1 hour after the retrieval-extinction training) and found that across TMS stimulation types (PFC vs. VER) and reminder types (reminder vs. no-reminder) the ANOVA analysis did not yield main effect of TMS stimulation type (F_1.71 = 0.322, P = 0.572) nor main effect of reminder type (F_1.71 = 0.0499, P = 0.824, panel c). We added the R-VER group results in study 3 (see panel c) to panel b and plotted the CS- SCR difference across 4 different test time points and found that CS- SCR decreased as the test-extinction delay increased (Jonckheere-Terpstra test, P = 0.00028). These results suggest a natural “forgetting” tendency for CS- related SCR and highlight the importance of having the CS- as a control condition to which the CS+ related SCR was compared with.

Author response image 1.

15F. Was the 6-hour group tested at a different time of day compared to the 30-minute and 24-hour groups; and could this have influenced the SCRs in this group?

For the 30min and 24h groups, the test phase can be arranged in the morning, in the afternoon or at night. However, for the 6h group, the test phase was inevitably in the afternoon or at night since we wanted to exclude the potential influence of night sleep on the expression of fear memory (see Author response table 1 below). If we restricted the test time in the afternoon or at night for all three groups, then the timing of their extinction training was not matched.

Author response table 1.

Nevertheless, we also went back and examined the data for the subjects only tested in the afternoon or at nights in the 30min and 24h groups to match with the 6h group where all the subjects were tested either in the afternoon or at night. According to Author response table 1 above, we have 17 subjects for the 30min group (9+8),18 subjects for the 24h group (9 + 9) and 26 subjects for the 6h group (12 + 14). As Author response image 2 shows, the SCR patterns in the fear acquisition, extinction and test phases were similar to the results presented in the original figure.

Author response image 2.

15G. Why is the range of scores in "thought control ability" different in the 30-minute group compared to the 6-hour and 24-hour groups? I am not just asking about the scale on the x-axis: I am asking why the actual distribution of the scores in thought control ability is wider for the 30-minute group?

We went back and tested whether the TCAQ score variance was the same across three groups. We found that there was significant difference in the variance of the TCAQ score distribution across three groups (F_2.155 = 4.324, P = 0.015, Levene test). However, post-hoc analyses found that the variance of TCAQ is not significantly different between the 30min and 6h groups (F_26.25 = 0.4788, P = 0.0697), nor between the 30min and 24h groups (i>F_26.25 = 0.4692, P = 0.0625). To further validate our correlational results between the TCAQ score and the fear recovery index, we removed the TCAQ scores that were outside the TCAQ score range of the 6h & 24h groups from the 30min group (resulting in 4 “outliner” TCAQ scores in the 30min group, panel a in Author response image 3 below) and the Levene test confirmed that the variance of the TCAQ scores showed no difference across groups after removing the 4 “outliner” data points in the 30min group (i>F_2.147 = 0.74028, P = 0.4788). Even with the 4 “outliers” removed from the 30min group, the correlational analysis of the TCAQ scores and the fear recovery index still yielded significant result in the 30min group (beta = -0.0148, t = -3.731, P = 0.0006, see panel b below), indicating our results were not likely due to the inclusion of subjects with extreme TCAQ scores.

Author response image 3.

(16) During testing in each experiment, how were the various stimuli presented? That is, was the presentation order for the CS+ and CS- pseudorandom according to some constraint, as it had been in extinction? This information should be added to the method section.

We mentioned the order of the stimuli in the testing phase in the methods section “… For studies 2 & 3, …a pseudo-random stimulus order was generated for fear acquisition and extinction phases of three groups with the rule that no same trial- type (CS1+, CS2+ and CS-) repeated more than twice. In the test phase, to exclude the possibility that the difference between CS1+ and CS2+ was simply caused by the presentation sequence of CS1+ and CS2+, half of the participants completed the test phase using a pseudo-random stimuli sequence and the identities of CS1+ and CS2+ reversed in the other half of the participants.”

(17) "These results are consistent with previous research which suggested that people with better capability to resist intrusive thoughts also performed better in motivated dementia in both declarative and associative memories."

***Which parts of the present results are consistent with such prior results? It is not clear from the descriptions provided here why thought control ability should be related to the present findings or, indeed, past ones in other domains. This should be elaborated to make the connections clear.

In the 30min group, we found that subjects’ TCAQ scores were negatively correlated with their fear recovery indices. That is, people with better capacity to resist intrusive thoughts were also less likely to experience the return of fear memory, which are consistent with previous results. Together with our brain stimulation results, the short-term amnesia is related to subject’s cognitive control ability and intact dlPFC functions. It is because of these similarities that we propose that the short-term amnesia might be related to the automatic memory suppression mechanism originated from the declarative memory research. Since we have not provided all the evidence at this point of the results section, we briefly listed the connections with previous declarative and associative memory research.

Reviewer #2 (Public Review):

The fear acquisition data is converted to a differential fear SCR and this is what is analysed (early vs late). However, the figure shows the raw SCR values for CS+ and CS- and therefore it is unclear whether the acquisition was successful (despite there being an "early" vs "late" effect - no descriptives are provided).

As the reviewer mentioned, the fear acquisition data was converted to a differential fear SCR and we conducted a two-way mixed ANOVA (reminder vs. no-reminder) x time (early vs. late part of fear acquisition) on the differential SCRs. We found a significant main effect of time (early vs. late; F_1.55 = 6.545, P = 0.013, η² = 0.106), suggesting successful fear acquisition in both groups. Fig. 1c also showed the mean differential SCR for the latter half of the acquisition phase in both the reminder and no-reminder groups and there was no significant difference in acquired SCRs between groups (early acquisition: t₅₅ = -0.063, P = 0.950; late acquisition: t₅₅ = -0.318, P = 0.751; Fig. 1c).

In Experiment 1 (Test results) it is unclear whether the main conclusion stems from a comparison of the test data relative to the last extinction trial ("we defined the fear recovery index as the SCR difference between the first test trial and the last extinction trial for a specific CS") or the difference relative to the CS- ("differential fear recovery index between CS+ and CS-"). It would help the reader assess the data if Figure 1e presents all the indexes (both CS+ and CS-). In addition, there is one sentence that I could not understand "there is no statistical difference between the differential fear recovery indexes between CS+ in the reminder and no reminder groups (P=0.048)". The p-value suggests that there is a difference, yet it is not clear what is being compared here. Critically, any index taken as a difference relative to the CS- can indicate recovery of fear to the CS+ or absence of discrimination relative to the CS-, so ideally the authors would want to directly compare responses to the CS+ in the reminder and no-reminder groups. The latter issue is particularly relevant in Experiment 2, in which the CS- seems to vary between groups during the test and this can obscure the interpretation of the result.

In all the experiments, the fear recovery index (FRI) was defined as the SCR difference between the first test trial and the last extinction trial for any CS. Subsequently, the differential fear recovery index (FRI) was defined between the FRI of a specific CS+ and the FRI of the CS-. The differential FRI would effectively remove the non-specific time related effect (using the CS- FRI as the baseline). We have revised the text accordingly.

As we responded to reviewer #1, the CS- fear recovery indices (FIR) for the reminder and no-reminder groups were not statistically different (Kruskal-Wallis test , panel a, Author response image 1), though both groups showed significant fear recovery even in the CS- condition (Wilcoxon signed rank test, reminder: P = 0.0043, no-reminder: P = 0.0037, panel a). Next, we examined the mean SCR for CS- for the 30min, 6h and 24h groups in study 2 and found that there was indeed a group difference (one-way ANOVA,  one-way ANOVA,F_2.76 = 5.3462, P = 0.0067, panel b), suggesting that the CS- SCR was influenced by the test time delay. We also tested the CS- SCR for the 4 groups in study 3 and found that across TMS stimulation types (PFC vs. VER) and reminder types (reminder vs. no-reminder) the ANOVA analysis did not yield main effect of TMS stimulation type (F_1.71 = 0.322, P = 0.572) nor main effect of reminder type (F_1.71 = 0.0499, P = 0.824, panel c). We added the R-VER group results in study 3 (see panel c) to panel b and plotted the CS- SCR difference across 4 different test time points and found that CS- SCR decreased as the test-extinction delay increased (Jonckheere-Terpstra test, P = 0.00028). These results suggest a natural “forgetting” tendency for the CS- fear recovery index and highlight the importance of having the CS- as a control condition to compare the CS+ recovery index with (resulting in the Differential recovery index). Parametric and non-parametric analyses were adopted based on whether the data met the assumptions for the parametric analyses.

In Experiment 1, the findings suggest that there is a benefit of retrieval followed by extinction in a short-term reinstatement test. In Experiment 2, the same effect is observed on a cue that did not undergo retrieval before extinction (CS2+), a result that is interpreted as resulting from cue-independence, rather than a failure to replicate in a within-subjects design the observations of Experiment 1 (between-subjects). Although retrieval-induced forgetting is cue-independent (the effect on items that are suppressed [Rp-] can be observed with an independent probe), it is not clear that the current findings are similar. Here, both cues have been extinguished and therefore been equally exposed during the critical stage.

We appreciate the reviewer’s insight on this issue. Although in the discussion we raised the possibility of memory suppression to account for the short-term amnesia effect, we did not intend to compare our paradigm side-by-side with retrieval-induced forgetting. In our previous work (Wang et al., 2021), we reported that active suppression effect of CS+ related fear memory during the standard extinction training generalized to other CS+, yielding a cue-independent effect. In the current experiments, we did not implement active suppression; instead, we used the CS+ retrieval-extinction paradigm. It is thus possible that the CS+ retrieval cue may function to facilitate automatic suppression. Indeed, in the no-reminder group (standard extinction) of study 1, we did observe the return of fear expression, suggesting the critical role of CS+ reminder before the extinction training. Based on the results mentioned above, we believe our short-term amnesia results were consistent with the hypothesis that the retrieval CS+ (reminder) might prompt subjects to adopt an automatic suppress mechanism in the following extinction training, yielding cue-independent amnesia effects.

The findings in Experiment 2 suggest that the amnesia reported in Experiment 1 is transient, in that no effect is observed when the test is delayed by 6 hours. The phenomena whereby reactivated memories transition to extinguished memories as a function of the amount of exposure (or number of trials) is completely different from the phenomena observed here. In the former, the manipulation has to do with the number of trials (or the total amount of time) that the cues are exposed to. In the current study, the authors did not manipulate the number of trials but instead the retention interval between extinction and test. The finding reported here is closer to a "Kamin effect", that is the forgetting of learned information which is observed with intervals of intermediate length (Baum, 1968). Because the Kamin effect has been inferred to result from retrieval failure, it is unclear how this can be explained here. There needs to be much more clarity on the explanations to substantiate the conclusions.

Indeed, in our studies, we did not manipulate the amount of exposure (or number of trials) but only the retention interval between extinction and test. Our results demonstrated that the retrieval-extinction protocol yielded the short-term amnesia on fear memory, qualitatively different from the reconsolidation related amnesia proposed in the previous literatures. After examining the temporal dynamics, cue-specificity and TCAQ association with the short-term amnesia, we speculated that the short-term effect might be related to an automatic suppression mechanism. Of course, further studies will be required to test such a hypothesis.

Our results might not be easily compared with the “Kamin effect”, a term coined to describe the “retention of a partially learned avoidance response over varying time intervals” using a learning-re-learning paradigm (Baum, 1968, Kamin, 1957). However, the retrieval-extinction procedure used in our studies was different from the learning-re-learning paradigm in the original paper (Kamin, 1957) and the reversal-learning paradigm the reviewer mentioned (Baum, 1968).

There are many results (Ryan et al., 2015) that challenge the framework that the authors base their predictions on (consolidation and reconsolidation theory), therefore these need to be acknowledged. Similarly, there are reports that failed to observe the retrieval-extinction phenomenon (Chalkia et al., 2020), and the work presented here is written as if the phenomenon under consideration is robust and replicable. This needs to be acknowledged.

We thank the reviewer pointing out the related literature and have added a separate paragraph about other results in the discussion (as well as citing relevant references in the introduction) to provide a full picture of the reconsolidation theory to the audience:

“It should be noted that while our long-term amnesia results were consistent with the fear memory reconsolidation literatures, there were also studies that failed to observe fear prevention (Chalkia, Schroyens, et al., 2020; Chalkia, Van Oudenhove, et al., 2020; Schroyens et al., 2023). Although the memory reconsolidation framework provides a viable explanation for the long-term amnesia, more evidence is required to validate the presence of reconsolidation, especially at the neurobiological level (Elsey et al., 2018). While it is beyond the scope of the current study to discuss the discrepancies between these studies, one possibility to reconcile these results concerns the procedure for the retrieval-extinction training. It has been shown that the eligibility for old memory to be updated is contingent on whether the old memory and new observations can be inferred to have been generated by the same latent cause (Gershman et al., 2017; Gershman and Niv, 2012). For example, prevention of the return of fear memory can be achieved through gradual extinction paradigm, which is thought to reduce the size of prediction errors to inhibit the formation of new latent causes (Gershman, Jones, et al., 2013). Therefore, the effectiveness of the retrieval-extinction paradigm might depend on the reliability of such paradigm in inferring the same underlying latent cause. Furthermore, other studies highlighted the importance of memory storage per se and suggested that memory retention was encoded in the memory engram cell ensemble connectivity whereas the engram cell synaptic plasticity is crucial for memory retrieval (Ryan et al., 2015; Tonegawa, Liu, et al., 2015; Tonegawa, Pignatelli, et al., 2015). It remains to be tested how the cue-independent short-term and cue-dependent long-term amnesia effects we observed could correspond to the engram cell synaptic plasticity and functional connectivity among engram cell ensembles (Figure 6). This is particularly important, since the cue-independent characteristic of the short-term amnesia suggest that either different memory cues fail to evoke engram cell activities, or the retrieval-extinction training transiently inhibits connectivity among engram cell ensembles. Finally, SCR is only one aspect of the fear expression, how the retrieval-extinction paradigm might affect subjects’ other emotional (such as the startle response) and cognitive fear expressions such as reported fear expectancy needs to be tested in future studies since they do not always align with each other (Kindt et al., 2009; Sevenster et al., 2012, 2013).”

The parallels between the current findings and the memory suppression literature are speculated in the general discussion, and there is the conclusion that "the retrieval-extinction procedure might facilitate a spontaneous memory suppression process". Because one of the basic tenets of the memory suppression literature is that it reflects an "active suppression" process, there is no reason to believe that in the current paradigm, the same phenomenon is in place, but instead, it is "automatic". In other words, the conclusions make strong parallels with the memory suppression (and cognitive control) literature, yet the phenomena that they observed are thought to be passive (or spontaneous/automatic).

Ultimately, it is unclear why 10 mins between the reminder and extinction learning will "automatically" suppress fear memories. Further down in the discussion, it is argued that "For example, in the well-known retrieval-induced forgetting (RIF) phenomenon, the recall of a stored memory can impair the retention of related long-term memory and this forgetting effect emerges as early as 20 minutes after the retrieval procedure, suggesting memory suppression or inhibition can occur in a more spontaneous and automatic manner". I did not follow with the time delay between manipulation and test (20 mins) would speak about whether the process is controlled or automatic.

In our previous research, we showed that the memory suppression instruction together with the extinction procedure successfully prevented the return of fear expression in the reinstatement test trials 30mins after the extinction training (Wang et al., 2021). In the current experiments, we replaced the suppression instruction with the retrieval cue before the extinction training (retrieval-extinction protocol) and observed similar short-term amnesia effects. These results prompted us to hypothesize in the discussion that the retrieval cue might facilitate an automatic suppression process. We made the analogy to RIF phenomenon in the discussion to suggest that the suppression of (competing) memories could be unintentional and fast (20 mins), both of which were consistent with our results. We agree with the reviewer that this hypothesis is more of a speculation (hence in the discussion), and more studies are required to further test such a hypothesis. However, what we want to emphasize in this paper is the report of the short-term amnesia effects which were clearly not related to the memory reconsolidation effect in a variety of aspects.

Among the many conclusions, one is that the current study uncovers the "mechanism" underlying the short-term effects of retrieval extinction. There is little in the current report that uncovers the mechanism, even in the most psychological sense of the mechanism, so this needs to be clarified. The same applies to the use of "adaptive".

Whilst I could access the data on the OFS site, I could not make sense of the Matlab files as there is no signposting indicating what data is being shown in the files. Thus, as it stands, there is no way of independently replicating the analyses reported.

We have re-organized data on the OFS site, and they should be accessible now.

The supplemental material shows figures with all participants, but only some statistical analyses are provided, and sometimes these are different from those reported in the main manuscript. For example, the test data in Experiment 1 is analysed with a two-way ANOVA with the main effects of group (reminder vs no-reminder) and time (last trial of extinction vs first trial of the test) in the main report. The analyses with all participants in the sup mat used a mixed two-way ANOVA with a group (reminder vs no reminder) and CS (CS+ vs CS-). This makes it difficult to assess the robustness of the results when including all participants. In addition, in the supplementary materials, there are no figures and analyses for Experiment 3.

We are sorry for the lack of clarity in the supplementary materials. We have supplementary figures Fig. S1 & S2 for the data re-analysis with all the responders (learners + non-learners). The statistical analyses performed on the responders in both figures yielded similar results as those in the main text. For other analyses reported in the supplementary materials, we specifically provided different analysis results to demonstrate the robustness of our results. For example, to rule out the effects we observed in two-way ANOVA in the main text may be driven by the different SCR responses on the last extinction trial, we only tested the two-way ANOVA for the first trial SCR of test phase and these analyses provided similar results. Please note we did not include non-learners in these analyses (the texts of the supplementary materials).

Since we did not exclude any non-learners in study 3, all the results were already reported in the main text.

One of the overarching conclusions is that the "mechanisms" underlying reconsolidation (long term) and memory suppression (short term) phenomena are distinct, but memory suppression phenomena can also be observed after a 7-day retention interval (Storm et al., 2012), which then questions the conclusions achieved by the current study.

As we stated before, the focus of the manuscript was to demonstrate a novel short-term fear amnesia effect following the retrieval-extinction procedure. We discussed memory suppression as one of the potential mechanisms for such a short-term effect. In fact, the durability of the memory suppression effect is still under debate. Although Storm et al. (2012) suggested that the retrieval-induced forgetting can persist for as long as a week, other studies, however, failed to observe long-term forgetting (after 24 hrs; (Carroll et al., 2007, Chan, 2009). It is also worth noting that Storm et al. (2012) tested RIF one week later using half of the items the other half of which were tested 5 minutes after the retrieval practice. Therefore, it can be argued that there is a possibility that the long-term RIF effect is contaminated by the test/re-test process on the same set of (albeit different) items at different time onsets (5mins & 1 week).

Reviewer #3 (Public Review):

(1) The entire study hinges on the idea that there is memory 'suppression' if (1) the CS+ was reminded before extinction and (2) the reinstatement and memory test takes place 30 minutes later (in Studies 1 & 2). However, the evidence supporting this suppression idea is not very strong. In brief, in Study 1, the effect seems to only just reach significance, with a medium effect size at best, and, moreover, it is unclear if this is the correct analysis (which is a bit doubtful, when looking at Figure 1D and E). In Study 2, there was no optimal control condition without reminder and with the same 30-min interval (which is problematic, because we can assume generalization between CS1+ and CS2+, as pointed out by the authors, and because generalization effects are known to be time-dependent). Study 3 is more convincing, but entails additional changes in comparison with Studies 1 and 2, i.e., applications of cTBS and an interval of 1 hour instead of 30 minutes (the reason for this change was not explained). So, although the findings of the 3 studies do not contradict each other and are coherent, they do not all provide strong evidence for the effect of interest on their own.

Related to the comment above, I encourage the authors to double-check if this statement is correct: "Also, our results remain robust even with the "non-learners" included in the analysis (Fig. S1 in the Supplemental Material)". The critical analysis for Study 1 is a between-group comparison of the CS+ and CS- during the last extinction trial versus the first test trial. This result only just reached significance with the selected sample (p = .048), and Figures 1D and E even seem to suggest otherwise. I doubt that the analysis would reach significance when including the "non-learners" - assuming that this is what is shown in Supplemental Figure 1 (which shows the data from "all responded participants").

Our subjects were categorized based on the criteria specified in supplementary table S1. More specifically, we excluded the non-responders (Mean CS SCR < 0.02 uS  in the fear acquisition phase), and non-learners and focused our analyses on the learners. Non-responders were dismissed after day 1 (the day of fear acquisition), but both learners and non-learners finished the experiments. This fact gave us the opportunity to examine data for both the learners and the responders (learners + non-learners). What we showed in fig. 1D and E were differential SCRs (CS+ minus CS-) of the last extinction trials and the differential fear recovery indices (CS+ minus CS-), respectively. We have double checked the figures and both the learners (Fig. 1) and the responders (i.e. learners and non-learners, supplementary Fig. 1) results showed significant differences between the reminder and no-reminder groups on the differential fear recovery index.

Also related to the comment above, I think that the statement "suggesting a cue-independent short-term amnesia effect" in Study 2 is not correct and should read: "suggesting extinction of fear to the CS1+ and CS2+", given that the response to the CS+'s is similar to the response to the CS-, as was the case at the end of extinction. Also the next statement "This result indicates that the short-term amnesia effect observed in Study 2 is not reminder-cue specific and can generalize to the non-reminded cues" is not fully supported by the data, given the lack of an appropriate control group in this study (a group without reinstatement). The comparison with the effect found in Study 1 is difficult because the effect found there was relatively small (and may have to be double-checked, see remarks above), and it was obtained with a different procedure using a single CS+. The comparison with the 6-h and 24-h groups of Study 2 is not helpful as a control condition for this specific question (i.e., is there reinstatement of fear for any of the CS+'s) because of the large procedural difference with regard to the intervals between extinction and reinstatement (test).

In Fig. 2e, we showed the differential fear recovery indices (FRI) for the CS+ in all three groups. Since the fear recovery index (FRI) was calculated as the SCR difference between the first test trial and the last extinction trial for any CS, the differential fear recovery indices (difference between CS+ FRI and CS- FRI) not significantly different from 0 should be interpreted as the lack of fear expression in the test phase. Since spontaneous recovery, reinstatement and renewal are considered canonical phenomena in demonstrating that extinction training does not really “erase” conditioned fear response, adding the no-reinstatement group as a control condition would effectively work as the spontaneous recovery group and the comparison between the reinstatement and no-instatement groups turns into testing the difference in fear recovery using different methods (reinstatement vs. spontaneous recovery).

(2) It is unclear which analysis is presented in Figure 3. According to the main text, it either shows the "differential fear recovery index between CS+ and CS-" or "the fear recovery index of both CS1+ and CS2+". The authors should clarify what they are analyzing and showing, and clarify to which analyses the ** and NS refer in the graphs. I would also prefer the X-axes and particularly the Y-axes of Fig. 3a-b-c to be the same. The image is a bit misleading now. The same remarks apply to Figure 5.

We are sorry about the lack of clarity here. Figures 3 & 5 showed the correlational analyses between TCAQ and the differential fear recovery index (FRI) between CS+ and CS-. That is, the differential FRI of CS1+ (CS1+ FRI minus CS- FRI) and the differential FRI of CS2+ (CS2+ FRI minus CS- FRI).

We have rescaled both X and Y axes for figures 3 & 5 (please see the revised figures). 

(3) In general, I think the paper would benefit from being more careful and nuanced in how the literature and findings are represented. First of all, the authors may be more careful when using the term 'reconsolidation'. In the current version, it is put forward as an established and clearly delineated concept, but that is not the case. It would be useful if the authors could change the text in order to make it clear that the reconsolidation framework is a theory, rather than something that is set in stone (see e.g., Elsey et al., 2018 (https://doi.org/10.1037/bul0000152), Schroyens et al., 2022 (https://doi.org/10.3758/s13423-022-02173-2)).

In addition, the authors may want to reconsider if they want to cite Schiller et al., 2010 (https://doi.org/10.1038/nature08637), given that the main findings of this paper, nor the analyses could be replicated (see, Chalkia et al., 2020 (https://doi.org/10.1016/j.cortex.2020.04.017; https://doi.org/10.1016/j.cortex.2020.03.031).

We thank the reviewer’s comments and have incorporated the mentioned papers into our revised manuscript by pointing out the extant debate surrounding the reconsolidation theory in the introduction:

“Pharmacological blockade of protein synthesis and behavioral interventions can both eliminate the original fear memory expression in the long-term (24 hours later) memory test ( Lee, 2008; Lee et al., 2017; Schiller et al., 2013; Schiller et al., 2010), resulting in the cue-specific fear memory deficit (Debiec et al., 2002; Lee, 2008; Nader, Schafe, & LeDoux, 2000). For example, during the reconsolidation window, retrieving a fear memory allows it to be updated through extinction training (i.e., the retrieval-extinction paradigm (Lee, 2008; Lee et al., 2017; Schiller et al., 2013; Schiller et al., 2010), but also see (Chalkia, Schroyens, et al., 2020; Chalkia, Van Oudenhove, et al., 2020; D. Schiller, LeDoux, & Phelps, 2020). ”

As well as in the discussion:

“It should be noted that while our long-term amnesia results were consistent with the fear memory reconsolidation literatures, there were also studies that failed to observe fear prevention (Chalkia, Schroyens, et al., 2020; Chalkia, Van Oudenhove, et al., 2020; Schroyens et al., 2023). Although the memory reconsolidation framework provides a viable explanation for the long-term amnesia, more evidence is required to validate the presence of reconsolidation, especially at the neurobiological level (Elsey et al., 2018). While it is beyond the scope of the current study to discuss the discrepancies between these studies, one possibility to reconcile these results concerns the procedure for the retrieval-extinction training. It has been shown that the eligibility for old memory to be updated is contingent on whether the old memory and new observations can be inferred to have been generated by the same latent cause (Gershman et al., 2017; Gershman and Niv, 2012). For example, prevention of the return of fear memory can be achieved through gradual extinction paradigm, which is thought to reduce the size of prediction errors to inhibit the formation of new latent causes (Gershman, Jones, et al., 2013). Therefore, the effectiveness of the retrieval-extinction paradigm might depend on the reliability of such paradigm in inferring the same underlying latent cause. Furthermore, other studies highlighted the importance of memory storage per se and suggested that memory retention was encoded in the memory engram cell ensemble connectivity whereas the engram cell synaptic plasticity is crucial for memory retrieval (Ryan et al., 2015; Tonegawa, Liu, et al., 2015; Tonegawa, Pignatelli, et al., 2015). It remains to be tested how the cue-independent short-term and cue-dependent long-term amnesia effects we observed could correspond to the engram cell synaptic plasticity and functional connectivity among engram cell ensembles (Figure 6). This is particularly important, since the cue-independent characteristic of the short-term amnesia suggest that either different memory cues fail to evoke engram cell activities, or the retrieval-extinction training transiently inhibits connectivity among engram cell ensembles. Finally, SCR is only one aspect of the fear expression, how the retrieval-extinction paradigm might affect subjects’ other emotional (such as the startle response) and cognitive fear expressions such as reported fear expectancy needs to be tested in future studies since they do not always align with each other (Kindt et al., 2009; Sevenster et al., 2012, 2013).”

Relatedly, it should be clarified that Figure 6 is largely speculative, rather than a proven model as it is currently presented. This is true for all panels, but particularly for panel c, given that the current study does not provide any evidence regarding the proposed reconsolidation mechanism.

We agree with the reviewer that Figure 6 is largely speculative. We realize that there are still debates regarding the retrieval-extinction procedure and the fear reconsolidation hypothesis. We have provided a more elaborated discussion and pointed out that figure 6 is only a working hypothesis and more work should be done to test such a hypothesis:

“Although mixed results have been reported regarding the durability of suppression effects in the declarative memory studies (Meier et al., 2011; Storm et al., 2012), future research will be needed to investigate whether the short-term effect we observed is specifically related to associative memory or the spontaneous nature of suppression (Figure 6C).”

Lastly, throughout the paper, the authors equate skin conductance responses (SCR) with fear memory. It should at least be acknowledged that SCR is just one aspect of a fear response, and that it is unclear whether any of this would translate to verbal or behavioral effects. Such effects would be particularly important for any clinical application, which the authors put forward as the ultimate goal of the research.

Again, we agree with the reviewer on this issue, and we have acknowledged that SCR is only one aspect of the fear response and caution should be exerted in clinical application:

“Finally, SCR is only one aspect of the fear expression, how the retrieval-extinction paradigm might affect subjects’ other emotional (such as the startle response) and cognitive fear expressions such as reported fear expectancy needs to be tested in future studies since they do not always align with each other (Kindt et al., 2009; Sevenster et al., 2012, 2013).”

(4) The Discussion quite narrowly focuses on a specific 'mechanism' that the authors have in mind. Although it is good that the Discussion is to the point, it may be worthwhile to entertain other options or (partial) explanations for the findings. For example, have the authors considered that there may be an important role for attention? When testing very soon after the extinction procedure (and thus after the reminder), attentional processes may play an important role (more so than with longer intervals). The retrieval procedure could perhaps induce heightened attention to the reminded CS+ (which could be further enhanced by dlPFC stimulation)?

We thank the reviewer for this suggestion and have added more discussion on the potential mechanisms involved. Unfortunately, since the literature on attention and fear recovery is rather scarce, it is even more of a speculation given our study design and results are mainly about subjects’ skin conductance responses (SCR).

(5) There is room for improvement in terms of language, clarity of the writing, and (presentation of the) statistical analyses, for all of which I have provided detailed feedback in the 'Recommendations for the authors' section. Idem for the data availability; they are currently not publicly available, in contrast with what is stated in the paper. In addition, it would be helpful if the authors would provide additional explanation or justification for some of the methodological choices (e.g., the 18-s interval and why stimulate 8 minutes after the reminder cue, the choice of stimulation parameters), and comment on reasons for (and implications of) the large amount of excluded participants (>25%).

We have addressed the data accessibility issue and added the justifications for the methodological choices as well as the excluded participants. As we mentioned in the manuscript and the supplementary materials, adding the non-learners into data analysis did not change the results. Since the non-responders discontinued after Day 1 due to their non-measurable spontaneous SCR signals towards different CS, it’s hard to speculate whether or how the results might have changed. However, participants’ exclusion rate in the SCR studies were relatively high (Hu et al., 2018, Liu et al., 2014, Raio et al., 2017, Schiller et al., 2010, Schiller et al., 2012, Wang et al., 2021). The non-responders were mostly associated with participants being tested in the winter in our tasks. Cold weather and dry skins in the winter are likely to have caused the SCR hard to measure (Bauer et al., 2022, Vila, 2004). Different intervals between the reinstating US (electric shock) and the test trials were used in the previous literature such as 10min (Schiller et al., 2010, Schiller et al., 2013) and 18 or 19s (Kindt and Soeter, 2018, Kindt et al., 2009, Wang et al., 2021). We stuck with the 18s reinstatement interval in the current experiment. For the cTBS stimulation, since the stimulation itself lasted less than 2mins, we started the cTBS 8min after the onset of reminder cue to ensure that any effect caused by the cTBS stimulation occurred during the hypothesized time window, where the old fear memory becomes labile after memory retrieval. All the stimulation parameters were determined based on previous literature, which showed that with the transcranial magnetic stimulation (TMS) on the human dorsolateral prefrontal cortex could disrupt fear memory reconsolidation (Borgomaneri et al., 2020, Su et al., 2022).

Finally, I think several statements made in the paper are overly strong in light of the existing literature (or the evidence obtained here) or imply causal relationships that were not directly tested.

We have revised the texts accordingly.

Reviewer #2 (Recommendations For The Authors):

On numerous occasions there are typos and the autocorrect has changed "amnesia" for "dementia".

We are sorry about this mistake and have revised the text accordingly.

Reviewer #3 (Recommendations For The Authors):

*"Neither of the studies reported in this article was preregistered. The data for both studies are publicly accessible at https://osf.io/9agvk". This excerpt from the text suggests that there are 2 studies, but there are 3 in the paper. Also, the data are only accessible upon request, not publicly available. I haven't requested them, as this could de-anonymize me as a reviewer.

We are sorry for the accessibility of the link. The data should be available to the public now.

*Please refrain from causal interpretations when they are not supported by the data:

- Figure 3 "thought-control ability only affected fear recovery"; a correlation does not provide causal evidence.

- "establishing a causal link between the dlPFC activity and short-term fear amnesia." I feel this statement is too strong; to what extent do we know for sure what the applied stimulation of (or more correct: near) the dlPFC does exactly?

We thank the reviewer for the suggestion and have changed the wording related to figure 3. On the other hand, we’d like to argue that the causal relationship between the dlPFC activity and short-term fear amnesia is supported by the results from study 3. Although the exact functional role of the TMS on dlPFC can be debated, the fact that the TMS stimulation on the dlPFC (compared to the vertex group) brought back the otherwise diminished fear memory expression can be viewed as the causal evidence between the dlPFC activity and short-term fear amnesia.

*The text would benefit from language editing, as it contains spelling and grammar mistakes, as well as wording that is vague or inappropriate. I suggest the authors check the whole text, but below are already some excerpts that caught my eye:

"preludes memory reconsolidation"; "old fear memory can be updated"; "would cause short-term memory deficit"; "the its functional coupling"; "Subjects (...) yielded more severe amnesia in the memory suppression tasks"; "memory retrieval might also precipitate a short-term amnesia effect"; "more SEVERE amnesia in the memory suppression tasks"; "the effect size of reinstatement effect"; "the previous literatures"; "towards different CS"; "failed to show SCR response to the any stimuli"; "significant effect of age of TMS"; "each subject' left hand"; "latter half trials"; "Differntial fear recovery"; "fear dementia"; "the fear reinstatement effects at different time scale is related to"; "fear reocery index"; "thought-control abiliites"; "performed better in motivated dementia"; "we tested that in addition to the memory retrieval cue (reminder), whether the"; "during reconsolidation window"; "consisitent with the short-term dementia"; "low level of shock (5v)"

We thank the reviewer for thorough reading and sorry about typos in the manuscript. We have corrected typos and grammar mistakes as much as we can find.

*In line with the remark above, there are several places where the text could still be improved.

- The last sentence of the Abstract is rather vague and doesn't really add anything.

- Please reword or clarify: "the exact functional role played by the memory retrieval remains unclear".

- Please reword or clarify: "the unbinding of the old memory trace".

- "suggesting that the fear memory might be amenable to a more immediate effect, in addition to what the memory reconsolidation theory prescribes" shouldn't this rather read "in contrast with"?

We have modified the manuscript.

- In the Introduction, the authors state: "Specifically, memory reconsolidation effect will only be evident in the long-term (24h) memory test due to its requirement of new protein synthesis and is cue-dependent". They then continue about the more immediate memory update mechanisms that they want to study, but it is unclear from how the rationale is presented whether (and why (not)) they also expect this mechanism to be cue-dependent.

Most of the previous studies on the fear memory reconsolidation using CS as the memory retrieval cues have demonstrated that the reconsolidation effect is cue-dependent (Kindt and Soeter, 2018, Kindt et al., 2009, Monfils et al., 2009, Nader et al., 2000, Schiller et al., 2013, Schiller et al., 2010, Xue et al., 2012). However, other studies using unconditioned stimulus retrieval-extinction paradigm showed that such protocol was able to prevent the return of fear memory expression associated with different CSs (Liu et al., 2014, Luo et al., 2015). In our task, we used CS+ as the memory retrieval cues and our results were consistent with results from previous studies using similar paradigms.

- "The effects of cTBS over the right dlPFC after the memory reactivation were assessed using the similar mixed-effect four-way ANOVA". Please clarify what was analyzed here.<br /> - "designing novel treatment of psychiatric disorders". Please make this more concrete or remove the statement.

This sentence was right after a similar analysis performed in the previous paragraph. While the previous graph focused on how the SCRs in the acquisition phase were modulated by factors such as CS+ (CS1+ and CS2+), reminder (reminder vs. no-reminder), cTBS site (right dlPFC vs. vertex) and trial numbers, this analysis focused instead on the SCR responses in the extinction training phase. We have made the modifications as the reviewer suggested.

*I have several concerns related to the (presentation) of the statistical analyses/results:<br /> - Some statistical analyses, as well as calculation of certain arbitrary indices (e.g., differential fear recovery index) are not mentioned nor explained in the Methods section, but only mentioned in the Results section.

We have added the explanation of the differential fear recovery index into the methods section:

“To measure the extent to which fear returns after the presentation of unconditioned stimuli (US, electric shock) in the test phase, we defined the fear recovery index as the SCR difference between the first test trial and the last extinction trial for a specific CS for each subject. Similarly, in studies 2 and 3, differential fear recovery index was defined as the difference between fear recovery indices of CS+ and CS- for both CS1+ and CS2+.”

- Figure 1C-E: It is unclear what the triple *** mean. Do they have the same meaning in Figure 1C and Figure 1E? I am not sure that that makes sense. The meaning is not explained in the figure caption (I think it is different from the single asterisk*) and is not crystal clear from the main text either.

We explained the triple *** in the figure legend (Fig. 1): ***P < 0.001. The asterisk placed within each bar in Figure 1C-E indicates the statistical results of the post-hoc test of whether each bar was significant. For example, the *** placed inside bars in Figure 1E indicates that the differential fear recovery index is statistically significant in the no-reminder group (P < 0.001).

- Supplemental Figure 1: "with all responded participants" Please clarify how you define 'responded participants' and include the n's.

We presented the criteria for both the responder/non-responder and the learner/non-learner in the table of the supplementary materials and reported the number of subjects in each category (please see supplement Table 1).

- "the differential SCRs (difference between CS+ and CS-) for the CS+". Please clarify what this means and/or how it is calculated exactly.

Sorry, it means the difference between the SCRs invoked by CS+ and CS- for both CS1+ (CS1+ minus CS-) and CS2+ (CS2+ minus CS-).

*I suggest that the authors provide a bit more explanation about the thought-control ability questionnaire. For example, the type of items, etc, as this is not a very commonly used questionnaire in the fear conditioning field.

We provided a brief introduction to the thought-control ability questionnaire in the methods section:

“The control ability over intrusive thought was measured by the 25-item Thought-Control Ability Questionnaire (TCAQ) scle(30). Participants were asked to rate on a five-point Likert-type scale the extent to which they agreed with the statement from 1 (completely disagree) to 5 (completely agree). At the end of the experiments, all participants completed the TCAQ scale to assess their perceived control abilities over intrusive thoughts in daily life(17).”

We have added further description of the item types to the TCAQ scale.

*The authors excluded more than 25% of the participants. It would be interesting to hear reasons for this relatively large number and some reflection on whether they think this selection affects their results (e.g., could being a (non)responder in skin conductance influence the susceptibility to reactivation-extinction in some way?).

Participants exclusion rate in the SCR studies were relatively high (Hu et al., 2018, Liu et al., 2014, Raio et al., 2017, Schiller et al., 2010, Schiller et al., 2012, Wang et al., 2021). The non-responders were mostly associated with participants being tested in the winter in our tasks. Cold weather and dry skins in the winter are likely to have caused the SCR hard to measure (Bauer et al., 2022, Vila, 2004).

*Minor comments that the authors may want to consider:

- Please explain abbreviations upon first use, e.g., TMS.

- In Figure 6, it is a bit counterintuitive that the right Y-axis goes from high to low.

We added the explanation of TMS:

“Continuous theta burst stimulation (cTBS), a specific form of repetitive transcranial magnetic stimulation (rTMS)…”

We are sorry and agree that the right Y-axis was rather counterintuitive. However, since the direction of the fear recovery index (which was what we measured in the experiment) and the short/long-term amnesia effect are of the opposite directions, plotting one index from low to high would inevitably cause the other index to go from high to low.

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Author Response

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

Public Reviews:

Reviewer #1 (Public Review):

Many drugs have off-target effects on the gut microbiota but the downstream consequences for drug efficacy and side effect profiles remain unclear. Herein, Wang et al. use a mouse model of liver injury coupled to antibiotic and microbiota transplantation experiments. Their results suggest that metformin-induced shifts in gut microbial community structure and metabolite levels may contribute to drug efficacy. This study provides valuable mechanistic insights that could be dissected further in future studies, including efforts to identify which specific bacterial species, genes, and metabolites play a causal role in drug response. Importantly, although some pilot data from human subjects is shown, the clinical relevance of these findings for liver disease remain to be determined.

Thank you for reviewing our manuscript. We appreciate your valuable feedback. We agree that the downstream consequences of off-target effects on the gut microbiota by various drugs remain unclear. Our study aimed to shed light on this aspect by utilizing a mouse model of liver injury and conducting antibiotic and microbiota transplantation experiments. Our findings suggest that shifts in the structure and metabolite levels of the gut microbial community induced by metformin play a role in the drug’s efficacy. We believe that these mechanistic insights provide a strong foundation for further investigations. Specifically, future studies could focus on identifying the specific bacterial species, genes, and metabolites that have a causal role in drug response. While we have included some pilot data from human subjects, we acknowledge that the clinical relevance of our findings in the context of liver disease still requires further determination. In fact, we focused on the alteration of microbiota and metabolism caused by metformin in human bodies, which could capture the characteristics of changes in a more composite clinical direction, elucidating the potential role of metformin. We appreciate your attention to this aspect and thank you again for your thoughtful review and valuable suggestions.

The major strength of this work is its scope, including detailed mouse phenotyping, inter-disciplinary methods, and numerous complementary experiments. The antibiotic depletion and FMT experiments provide support for a role of the gut microbiota in this mouse model.

A major limitation is the lack of studies narrowing down which microbes are responsible. Sequencing data is shown, but no follow-up studies are done with bacterial isolates or defined communities.

We acknowledge the limitation of our study in not narrowing down the specific microbes responsible for the observed effects. We hold the opinion that metformin exerts its effects through modulation of specific metabolic pathways unique to the microbial community. Previous study has shown that metformin can inhibit microbial folate metabolism, leading to longevity-promoting effects that are not attributed to a single colony or strain[1]. Similarly, the impact of metformin on amino acid metabolism in the microbial community appears to be widespread. While further investigations with bacterial isolates or defined communities are needed, our findings suggest that metformin's effects on microbial metabolism are complex and involve multiple members of the microbial community.

The link to GABA is also somewhat tenuous. While it does match the phenotypic data, there are no targeted experiments in which GABA producing microbial communities/strains are compared to a control community/strain. As such, it seems difficult to know how much of the effects in this model are due to GABA vs. other metabolites.

We agree with your point regarding the tenuous link to GABA in our study. While we did observe an increase in GABA as the only amino acid following metformin treatment, and this finding has not been reported previously, we acknowledge the need for targeted experiments comparing GABA-producing microbial communities/strains to control communities/strains. Previous literatures suggest that metformin's modulation of the microbiota can vary significantly depending on the disease context, with different microbial populations exhibiting differential responses[2-4]. Given this complexity, we opted to study the overall microbial community response to metformin rather than focusing on specific strains. Additionally, our detection of key enzymes involved in GABA synthesis at the community level further supports our findings.

My major recommendation would be to revise the title, abstract, and discussion to provide more qualification and to consider alternative interpretations.

We appreciate your feedback and understand your concern regarding the need for more qualification and consideration of alternative interpretations. We hope to have more specific and detailed suggestions you may have to enhance the clarity and qualification of our title and abstract. Furthermore, we have tried to revise discussion in order to enhance the scientific rigor and logical coherence of our study. If you have any specific recommendations or insights, we would be more than willing to make further revisions to address those concerns.

Some key controls are also missing, which could be addressed by repeat experiments in the mouse model.

We appreciate your suggestion to include additional key controls in the mouse model experiments. We have conducted repeat experiments to test the effect of antibiotics in the absence of metformin to differentiate between the effects of the model itself and the interaction of metformin with antibiotics. As results of liver injury indicators shown, there were no significance among Control, Control+Met, Control+FMT and Control+Abx groups, revealing that metformin and its treated feces, and antibiotics had no effect on liver function in normal mice (Figure 1).

Author response image 1.

Figure1 a: Liver MDA detection; b: Serum ALT level; c: Serum AST level.

The antibiotic depletion experiment would be improved by testing the effect of antibiotics in the absence of metformin, to see if the effect is just driven by the model itself as opposed to an interaction between metformin and antibiotics.

For the antibiotic depletion experiment, we had used antibiotics (Abx) for the mice of modeling, and the survival rate and liver function detection suggested that Abx had no extra effect on liver, which demonstrated that the effect is just driven by the model itself as opposed to an interaction between metformin and antibiotics (Figure 2).

Author response image 2.

Figure2 a: Survival rate between IR and IR + Abx group; b: Serum ALT level; c: Serum AST level.

References

[1] CABREIRO F, AU C, LEUNG K Y, et al. Metformin Retards Aging in C. elegans by Altering Microbial Folate and Methionine Metabolism [J]. Cell, 2013, 153(1): 228-39.

[2] LIANG H, SONG H, ZHANG X, et al. Metformin attenuated sepsis-related liver injury by modulating gut microbiota [J]. Emerg Microbes Infect, 2022, 11(1): 815-28.

[3] SUN L, XIE C, WANG G, et al. Gut microbiota and intestinal FXR mediate the clinical benefits of metformin [J]. Nat Med, 2018, 24(12): 1919-29.

[4] ZHAO H Y, LYU Y J, ZHAI R Q, et al. Metformin Mitigates Sepsis-Related Neuroinflammation via Modulating Gut Microbiota and Metabolites [J]. Frontiers in Immunology, 2022, 13:797312.

Reviewer #2 (Public Review):

The authors examine the use of metformin in the treatment of hepatic ischemia/reperfusion injury (HIRI) and suggest the mechanism of action is mediated in part by the gut microbiota and changes in hepatic ferroptosis. While the concept is intriguing, the experimental approaches are inadequate to support these conclusions.

The histological and imaging studies were considered a strength and reveal a significant impact of metformin post-HIRI.

Thank you for reviewing our paper titled “Gut microbiota-derived gamma-aminobutyric acid from metformin treatment reduces hepatic ischemia/reperfusion injury through inhibiting ferroptosis”. We appreciate your insightful comments and suggestions, which have provided valuable insights into improving the quality and credibility of my research. We agree with your assessment that the experimental approaches used in this study may have limitations in supporting the conclusions drawn, and we appreciate your recognition of the strength of our histological and imaging studies, which clearly demonstrate the impact of metformin post-HIRI.

Weaknesses largely stem from the experimental design. First, use of the iron chelator DFO would be strengthened using the ferroptosis inhibitor, liproxstatin.

Your suggestion to employ the ferroptosis inhibitor, liproxstatin, in addition to the iron chelator DFO is well-taken. Incorporating liproxstatin into our experimental setup would provide a more comprehensive understanding of the involvement of hepatic ferroptosis in the mechanism of action of metformin. Therefore, we employed liproxstatin to inhibit HIRI and detected some core indicators of liver injury. As figure 3 shown, liproxstatin can reduce liver injury, restore liver GSH level and inhibit Fe accumulation, suggesting that ferroptosis plays an important role in HIRI. We hope this modification will enhance the credibility of our conclusions.

Author response image 3.

Figure3 a: Liver MDA detection; b: Serum ALT level; c: Serum AST level; d: Liver GSH level; e: Liver Fe level.

Second, the impact of metformin on the microbiota is profound resulting in changes in bile acid, lipid, and glucose homeostasis. Throughout the manuscript no comparisons are made with metformin alone which would better capture the metformin-specific effects.

Thank you for raising an important point regarding the impact of metformin on the microbiota and its potential effects on bile acid, lipid, and glucose homeostasis. It has well known that that the effects of metformin on normal blood glucose and lipid metabolism are minimal. Metformin primarily exerts its effects in cases of impaired glucose tolerance, which is why it is widely used for non-diabetic conditions. Regarding the changes in bile acid metabolism and chronic cholesterol and lipid elevation, these associations are typically observed in chronic liver disease models. Since our study focuses on an acute model of HIRI, we did not specifically investigate these changes.

Lastly, the absence of proper controls including germ free mice, metformin treated mice, FMT treated mice, etc make it difficult to understand the outcomes and to properly reproduce the findings in other labs.

Lastly, we acknowledge your concern regarding the absence of proper controls, including germ-free mice, metformin-treated mice, and FMT -treated mice. We understand that these controls are essential for robustly interpreting and reproducing our findings. Therefore, we have added a batch of experiments for verification. As results shown, there were no significance among Control, Control+Met, Control+FMT and Control+Abx groups, revealing that metformin and its treated feces, and antibiotics had no effect on liver function in normal mice (Figure 1). We hope the result of these controls could address your valid point and provide a more comprehensive framework for understanding the outcomes.

Author response image 4.

Figure1 a: Liver MDA detection; b: Serum ALT level; c: Serum AST level.

Overall, while the concept is interesting and has the potential to better understand the pleiotropic functions of metformin, the limitations with the experimental design and lack of key controls make it challenging to support the conclusions.

We genuinely appreciate your constructive criticism and the time you have taken to evaluate my work. Your feedback has shed light on the limitations of our experimental design and the need for key controls, which we have addressed in revised manuscript. If you have any further recommendations or concerns, we would be more than willing to incorporate them into my future work.

Reviewer #3 (Public Review):

The study presented in this paper explores the role of gut microbiota in the therapeutic effect of metformin on HIRI, as supported by fecal microbiota transplantation (FMT) experiments. Through high throughput sequencing and HPLC-MS/MS, the authors have successfully demonstrated that metformin administration leads to an increase in GABA-producing bacteria. Moreover, the study provides compelling evidence for the beneficial impact of GABA on HIRI.

Thank you for your valuable feedback on our paper exploring the role of gut microbiota in the therapeutic effect of metformin on hepatic ischemia-reperfusion injury (HIRI). We appreciate your positive remarks and suggestions for improvement. In response to your comments, we have revised the manuscript accordingly. We have included additional details on the high throughput sequencing and HPLC-MS/MS methods used to analyze the gut microbiota and GABA levels. This should provide readers with a clearer understanding of our experimental approach and the evidence supporting our findings.

Regarding your suggestion to further investigate the mechanisms underlying the beneficial impact of GABA on HIRI, we agree that this is an important direction for future research. We plan to conduct additional studies to explore the specific mechanisms by which GABA exerts its protective effects on HIRI in the future. We also supplemented discussion of potential therapeutic strategies targeting GABAergic pathways in the discussion section.

Thank you once again for your insightful comments. We believe that these revisions have strengthened the manuscript and improved its scientific rigor. We hope that you find the revised version to be satisfactory and look forward to your further feedback.

Reviewer #1 (Recommendations For The Authors):

The writing could be improved. Multiple typos are found throughout and there is an overuse of adverbs like "expectedly". You should let the reader decide what is or is not expected. Try to avoid terms like "confirmed" or "validated", which only applies if you knew the result a priori. Remove underscores in species names. The Results section is also very difficult to interpret given the lack of explanation of experimental design. For example, the human study is only briefly mentioned within a larger paragraph on mouse data, without any explanation as to the study design. Similar issues are true for the transcriptomics and amplicon sequencing - it would help the reader to explain what samples were processed, the timepoints, etc.

Thank you for your valuable feedback on our manuscript entitled “Gut microbiota-derived gamma-aminobutyric acid from metformin treatment reduces hepatic ischemia/reperfusion injury through inhibiting ferroptosis” We appreciate your constructive comments and insightful suggestions for improvement.

We have carefully reviewed your comments and have made several revisions to enhance the clarity and readability of the manuscript. We have addressed the issue of multiple typos and have removed the overuse of adverbs, such as “expectedly,” to allow readers to draw their own conclusions from the results. Additionally, we have eliminated terms like “confirmed” or “validated” that may imply a priori knowledge of the results.

We apologize for the lack of clarity regarding the experimental design in the Results section. We have now provided a more detailed explanation of the study design for the human study, transcriptomics, and amplicon sequencing experiments. This includes information on the samples processed, timepoints, and other relevant details, to aid readers in understanding the experimental procedures.

In response to your comment about removing underscores in species names, we have revised the text accordingly to ensure consistency and accuracy in the species nomenclature used throughout the manuscript.

Once again, we sincerely appreciate your valuable input, which has helped us improve the quality of our manuscript. We hope that the revised version now meets your expectations and look forward to any further feedback you may have.

Thank you for your time and attention.

Line 53 - prebiotics aren't "microbial agents"

We apologize for this error, which we have corrected. (line 55: “Microbial agents, such as synbioticsprebiotics and probiotics…”)

Line 88 - sequencing doesn't "verify the critical role of gut microbiota"

We apologize for this error, which we have corrected. (line 90: “In order to verifyclarify the critical role of gut microbiota in the pleiotropic actions of metformin,22-24 fecal samples were collected from the mice to perform 16S rRNA sequencing.

Line 92 - missing a citation for the "microbiota-gut-liver axis theory"

We have corrected it in manuscript. (line 93: “Next, as the microbiota-gut-liver axis theory indicates,25 HIRI-induced dysfunction of the gut barrier may aggravate liver damage by disrupting the gut microbiota.”)

Line 112 - it's very surprising to me that FMT led to lower alpha diversity, which seems impossible.

We understand your surprise regarding the observed decrease in alpha diversity after FMT. Our findings indeed deviate from the commonly observed pattern of increased alpha diversity post-FMT. We have carefully re-examined our data and conducted additional analyses to ensure the accuracy of our results. After thorough investigation, we have identified a potential reason for this unexpected outcome, which we believe could shed light on this phenomenon. We hypothesize that the lower alpha diversity observed in our study might be attributed to the specific characteristics of the donor microbiota used for FMT. While the donor microbiota exhibited certain beneficial properties associated with the therapeutic effect on HIRI, it could have presented a limited diversity compared to the recipient’s original gut microbiota. This discrepancy in diversity could have contributed to the observed decrease in alpha diversity following FMT.

To further support our hypothesis, we have included a discussion on this unexpected finding in the revised manuscript. We believe that this addition will provide a more comprehensive understanding of the results and help contextualize the observed decrease in alpha diversity following FMT.

Line 117 - Antibiotics don't "identify the function of gut microbes." Need to specify which antibiotics were used and for how long.

We have corrected it in manuscript. (line 119: “To further identify the function of gut microbes, experiments were designed, and combination treatment of antibiotics (1 mg/mL penicillin sulfate, 1 mg/mL neomycin sulfate, 1 mg/mL metronidazole and 0.16 mg/mL gentamicin) and metformin were employed for 1 week before IR treated.”)

Line 120 - this experiment shows that the gut microbiota (or antibiotics more precisely) matters, not the "reshaped gut microbiota"

We have corrected it in manuscript. (line 124: “The results confirmed that reshaped gut microbiota is critical for the effect of metformin against HIRI.”)

Line 122 - need to reword this subheading and the concluding sentence. The main takeaway is that the FMT improved markers of ferroptosis, but no additional causal links are provided here.

We have revised in manuscript. (line 125: “FMT alleviates HIRI-induced ferroptosis through reshaped fecal microbiota.”)

Line 141 - need to explain what transcriptomics data was generated and how it was analyzed.

We have revised in manuscript. (line 144: “To elucidate the molecular mechanisms through which pathway participates metformin-treated IR injury, we analysed gene expression profiles of each group mice. Transcriptome sequencing analysis revealed that 9697 genes were in common among four groups (Supplementary Figure 6). Therefore, we used these common genes for KEGG analysis, showing that The transcriptome analysis of liver tissues showed that similar mRNA changes between Met group and FMT group are mainly concentrated in the three top pathways: lipid metabolism, carbohydrate metabolism, and amino acid metabolism (Fig 4a).”)

Line 150 - change to "16S rRNA gene sequencing". Typo: "mice microbes".

We have revised in manuscript. (line 156: “Moreover, it was observed that the genus of Bacteroides had a significant increase based on the 16s rRNA gene sequencing of metformin-treated mice microbes.”)

Line 152 - upregulated refers to gene expression, change to enriched.

We have revised in manuscript. (line 171: “Detailedly, the species of Bacteroides containing Bacteroides thetaiotaomicron, Bacteroides unifomis, and Bacteroides salyersiae, were enriched in human gut after metformin administration (Fig. 4i).”)

Line 159 - typo: "prokaryotes"

We have revised in manuscript. (line 165: “In order to further identify the increased GABA originates from gut microbiota, two key enzymes of prokaryotes protokaryotic GABA synthesis, GAD and PAT, were detected on DNA level, finding that both of them are significantly increased in the feces from IR+Met and IR+FMT groups (Fig. 4h).”)

Line 161 - the human study should be under a new sub-heading and provide more details.

We have revised in manuscript. (line 168: In order to clarify the specific effects of metformin on microbiota, given the big safety margin, healthy volunteers were recruited for a 1 week of daily oral 500mg dose of metformin trial. Fecal samples were collected before and after oral administration of metformin for metagenomic analysis .”)

Line 197 - It's unclear why the current study conflicts with prior literature. Is it due to the disease model, the starting microbiota, something else? Please add more discussion.

Thank you for bringing this important point to our attention, and we appreciate your valuable input. We agree that it is important to discuss the potential reasons for the discrepancy between our findings and prior literature on metformin-reshaped microbiota. In our study, we used a disease model of HIRI, which may have unique characteristics compared to other disease models. It is possible that the specific disease model influenced the response of the gut microbiota. Additionally, the starting microbiota of the recipients and the characteristics of the donor microbiota used for FMT could also play a role in the disparity. We have expanded the discussion section of our revised manuscript to further address these potential factors and their implications. We hope that this additional information will provide a more comprehensive explanation for the discrepancy between our study and prior literature.

Figure 1a - change to Kaplan Meier not ANOVA. Specify the contrast - which groups are being compared?

We have revised in Figure 1a.

Figure 1e, alpha diversity - relabel "sobs" with "observed OTUs". Change to 3 bars with error and add statistics.

We have revised in Figure 1e.

Figure 1e, PCA - this should be a separate panel (1f). Color of big red circle doesn't match the points. Add PERMANOVA p-value/R2. Change to OTUs not genera. Better yet, use amplicon sequence variants from DADA2.

We have revised in Figure 1e..

Figure 2a - Change to Kaplan Meier. Also, it's unclear if residual metformin could be in the donor samples.

We have revised in Figure 2a.

Figure 2f, alpha diversity - relabel "sobs" with "observed OTUs". Change to 3 bars with error and add statistics.

We have revised in Figure 2f.

Figure 2f, PCA - this should be a separate panel (2g). Color of big orange circle doesn't match the points. Add PERMANOVA p-value/R2. Change to OTUs not genera. Better yet, use amplicon sequence variants from DADA2.

We have revised in Figure 2f.

Figure 4b - check units, shouldn't this be ng/mg (i.e. weight not volume).

We have revised in Figure 4b.

Figure 4c,d - need more explanation in the legend and Results as to what is shown here.

We have revised in Figure 4c,d.

Figure 4d - unclear why only Bacteroides are shown here or if the p-values are adjusted for multiple comparisons.

Thank you for your comment regarding Figure 4d in our manuscript. We apologize for the confusion caused. The reason why only Bacteroides is shown in Figure 4d is because we specifically wanted to investigate the changes in Bacteroides abundance following metformin treatment.

In the mouse experiments, we observed a significant increase in Bacteroides after metformin treatment. To investigate if a similar change occurs in healthy volunteers, we examined the levels of Bacteroides in fecal samples before and after oral administration of metformin. We found that the abundance of Bacteroides also increased in the human gut after metformin administration, consistent with the results from the animal experiments. Regarding the p-values, we apologize for not mentioning whether they were adjusted for multiple comparisons in the figure legend. In our revised manuscript, we have provided a clarification stating that the p-values were adjusted using the appropriate method. We appreciate your feedback and hope that this explanation clarifies the rationale behind Figure 4d. Thank you for your valuable input.

Reviewer #2 (Recommendations For The Authors):

Below I've listed several suggestions to improve the paper.

1. Controls - the authors should include metformin only treated mice, FMT only treated mice, etc. Additionally, germ free mice treated with metformin and HIRI would be helpful to better implicate the gut microbiome in these beneficial effects.

Thank you for your suggestion regarding the inclusion of additional control groups in our study. We agree that including metformin only treated mice, FMT only treated mice, and germ-free mice treated with metformin and HIRI would provide valuable insights into the role of the gut microbiome in the observed beneficial effects.

Therefore, we have included metformin only treated mice, FMT only treated mice and Abx only treated mice as supplement to better assess the specific contribution to the observed effects. As results shown, there were no significance among Control, Control+Met, Control+FMT and Control+Abx groups, revealing that metformin and its treated feces, and antibiotics had no effect on liver function in normal mice (figure1).

We appreciate your input and believe that the inclusion of these additional control groups will strengthen our study and provide a more comprehensive understanding of the role of the gut microbiome in the therapeutic effects observed.

Author response image 5.

Figure1 a: Liver MDA detection; b: Serum ALT level; c: Serum AST level.

1. More thorough characterization of metabolite pools. Metformin is known to influence many pathways including bile acids and lipids. These important molecules should be measures as they likely play a key role in the observed protective effect. In fact, many of the key changes displayed in Figure 3H are involved in lipid metabolism.

Thank you for your valuable feedback regarding the characterization of metabolite pools in our study. We appreciate your suggestion to measure the influence of metformin on bile acids and lipid metabolism, as they are crucial pathways that may play a significant role in the observed protective effect.

Regarding bile acids, we agree that they are important in the context of metformin’s influence on metabolic pathways. However, it is important to note that the impact of metformin on bile acids appears to be more prominent in chronic liver disease models. In our acute model, the changes in bile acids were not as significant. Instead, our results primarily indicate a close association between lipid changes and hepatic ferroptosis. Metformin significantly modulates lipid metabolism, thereby alleviating liver ferroptosis.

Additionally, we have conducted metagenomic sequencing on the gut microbiota of healthy volunteers before and after oral administration of metformin. While analyzing the data, we did not observe significant changes in key genes involved in regulating bile acid variations. This might be attributed to the healthy volunteers used in our study, where significant changes in bile acids were not induced.

We appreciate your insightful comments and suggestions, which have shed light on the importance of characterizing bile acids and lipid metabolism in our study. While the impact of bile acids may be more evident in chronic liver disease models, our findings highlight the significant influence of metformin on lipid metabolism, closely related to hepatic ferroptosis. We will take your suggestions into account for future studies to further explore the role of bile acids and their regulation by metformin.

1. Imaging of lipid ROS is not quantitative. The authors should conduct more standard assays with BODIPY 581/591 C11 using cell lysates.

We appreciate your suggestion to conduct more standard assays using BODIPY 581/591 C11 with cell lysates.

We would like to clarify that we did indeed utilize assays with BODIPY 581/591 C11 to detect and measure lipid ROS in our study. The detailed description of these assays can be found in the Methods section of our paper. We followed established protocols and guidelines to ensure accurate and reliable measurements of lipid ROS levels.

We acknowledge that imaging techniques may have limitations in providing quantitative data. However, we employed BODIPY 581/591 C11 assays as a widely accepted and commonly used method to assess lipid ROS levels. This allowed us to obtain qualitative and semi-quantitative information on the changes in lipid ROS levels in response to metformin treatment.

1. Liproxstatin may be a better drug choice or at the very least should be used to compare with the DFO data

Thank you for your suggestion. We have taken your advice into consideration and conducted an evaluation of Liproxstatin as a ferroptosis inhibitor. Our findings indicate that Liproxstatin significantly improves HIRI (Figure C). We believe that incorporating Liproxstatin in our research will provide valuable insights and allow for a comprehensive comparison with the DFO data.

Author response image 6.

Figure3 a: Liver MDA detection; b: Serum ALT level; c: Serum AST level; d: Liver GSH level; e: Liver Fe level.

1. The rationale for how GABA was selected is not clear. I am surprised that there were not more significant metabolite changes. It might be better to show a volcano plot of heatmap of the significantly changed features.

Thank you for raising an important question regarding the rationale for selecting GABA as the focus metabolite in our study. Initially, we also had concerns about the limited number of significant metabolite changes observed. However, through our comprehensive metabolomic profiling, we identified GABA as the most significantly altered metabolite following HIRI.

It is worth noting that we specifically focused on the measurement of 22 essential amino acids in our analysis. While it is possible that changes in non-essential amino acids may have occurred, we did not examine them in this study. Nevertheless, we have since used additional methods to validate the upregulation of GABA levels, and the biological effects observed support the specific role of GABA in protecting against HIRI. Based on the fact that GABA was the only significant amino acid, the volcano plot was of little significance, so we did not supplement this plot.

We appreciate your valuable input and thank you for bringing up this important issue.

1. The manuscript needs to be proofread and edited. There are a variety of typos and grammar issues throughout.

Thank you for your feedback. We acknowledge that the manuscript requires proofreading and editing, as we have identified several typos and grammar issues. We will try to ensure that the necessary revisions are made to improve the overall quality of the manuscript.

Reviewer #3 (Recommendations For The Authors):

However, I have some major concerns for the manuscript.

1. Line 26 16S rRNA and metagenomic sequencing alone can't accurately confirm the improvement effect of GABA producing bacteria on HIRI. In fact, transcriptome analysis, HPLC-MS/MS and other methods were also used in this paper, so the language expression here is not appropriate

Thank you for pointing out the language expression issue in line 26 of the manuscript. We apologize for any confusion caused. You are correct in stating that 16S rRNA and metagenomic sequencing alone may not accurately confirm the improvement effect of GABA-producing bacteria on HIRI. In our study, we employed a combination of multiple methods, including transcriptome analysis, HPLC-MS/MS, especially detection of bacteria GABA key synthetases, PAT and GAD, to comprehensively investigate the impact of GABA-producing bacteria on HIRI.

We have revised the language in line 26 to reflect the broader range of methods used in our study to support the conclusions regarding the improvement effect of GABA-producing bacteria on HIRI.

1. The Introduction section needs to add a description of the previous research on the association between HIRI and ferroptosis

Thank you for your suggestion regarding the inclusion of a description of the association between HIRI and ferroptosis in the Introduction section. We agree that this is an important aspect to address. However, upon further consideration, we have decided to move the discussion of ferroptosis and its potential role in HIRI to the Discussion section, as it aligns better with the logical flow of the manuscript. This allows us to discuss the potential implications and future directions in a more organized and coherent manner.

1. Authors should provide quantified figure or table next to the results of western blot that are more convenient to understand.

We have revised in manuscript. (See sfigure 7)

1. In this paper, FMT experiments are used to verify that metformin remodeled gut microbiota can play a role in improving HIRI. The operation steps of FMT should be described more specifically in the method part

*What is the fecal donor information for FMT?

*Line272 Did the IR + FMT group put the transplanted microbiota of FMT directly into the drinking water like the other treatment groups? Will such an operation affect the quality and quantification of the transplanted microbiota and lead to the loss of microbiota species? It is crucial for the authors to provide a clear and thorough clarification regarding these matters within the context of their FMT experiment.

Thank you for your feedback regarding the need for a more detailed description of the fecal microbiota transplantation (FMT) procedure and clarification regarding the IR + FMT group in our manuscript. We appreciate your suggestions and we have taken them into consideration.

In our study, the fecal donor for FMT was obtained from mice that had been orally administered metformin. The fecal microbiota was collected and processed to remove any residual metformin before transplantation. Specifically, the microbiota for the IR + FMT group was administered through gavage, as stated in line 272. This method does not affect the quality or quantity of the transplanted microbiota, nor does it lead to a loss of microbiota species. We understand the importance of providing clear and thorough clarification regarding these matters. Therefore, we have included additional specific details of the FMT procedure in the revised version of the manuscript. We hope that this clarification addresses your concerns and provides a more comprehensive understanding of our FMT experiment.

1. The presentation of transcriptomic analysis results in the manuscript is insufficiently comprehensive and specific, as they are solely depicted through Fig 4a. Relying solely on Fig 4a is inadequate to establish the definitive roles of the met group and FMT group in ferroptosis compared to other groups. Therefore, the authors should provide additional transcriptomic analysis results to ascertain the specific effects of the met group and FMT group in ferroptosis, as well as their comparison with other groups.

Thank you for your feedback regarding the comprehensiveness of our transcriptomic analysis results in the manuscript. We understand your concerns and appreciate your suggestion. In our study, we have provided additional data beyond Fig 4a to support the specific effects of the met group and FMT group in ferroptosis, as well as their comparison with other groups. Specifically, in Figure 3, we have included Western blot (WB) and quantitative real-time polymerase chain reaction (qRT-PCR) data to confirm the involvement of ferroptosis in HIRI and the role of metformin in attenuating ferroptosis. Moreover, we have presented transcriptomic analysis results in Figure 3h, which includes a heatmap of genes related to lipid metabolism. These findings can strengthen our conclusions regarding the importance of ferroptosis in HIRI and the protective effects of metformin against ferroptosis. We hope that these data address your concerns and provide a more comprehensive understanding of our research findings.

Author response:

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

Public Reviews:

Summary:

The authors examine the eigenvalue spectrum of the covariance matrix of neural recordings in the whole-brain larval zebrafish during hunting and spontaneous behavior. They find that the spectrum is approximately power law, and, more importantly, exhibits scale-invariance under random subsampling of neurons. This property is not exhibited by conventional models of covariance spectra, motivating the introduction of the Euclidean random matrix model. The authors show that this tractable model captures the scale invariance they observe. They also examine the effects of subsampling based on anatomical location or functional relationships. Finally, they briefly discuss the benefit of neural codes which can be subsampled without significant loss of information.

Strengths:

With large-scale neural recordings becoming increasingly common, neuroscientists are faced with the question: how should we analyze them? To address that question, this paper proposes the Euclidean random matrix model, which embeds neurons randomly in an abstract feature space. This model is analytically tractable and matches two nontrivial features of the covariance matrix: approximate power law scaling, and invariance under subsampling. It thus introduces an important conceptual and technical advance for understanding large-scale simultaneously recorded neural activity.

Weaknesses:

The downside of using summary statistics is that they can be hard to interpret. Often the finding of scale invariance, and approximate power law behavior, points to something interesting. But here caution is in order: for instance, most critical phenomena in neural activity have been explained by relatively simple models that have very little to do with computation (Aitchison et al., PLoS CB 12:e1005110, 2016; Morrell et al., eLife 12, RP89337, 2024). Whether the same holds for the properties found here remains an open question.

We are grateful for the thorough and constructive feedback provided on our manuscript. We have addressed each point raised by you.

Regarding the main concern about power law behavior and scale invariance, we would like to clarify that our study does not aim to establish criticality. Instead, we focus on describing and understanding a specific scale-invariant property in terms of collapsed eigenspectra in neural activity. We tested Morrell et al.’s latent-variable model (eLife 12, RP89337, 2024, [1]), where a slowly varying latent factor drives population activity. Although it produces a seemingly power-law-like spectrum, random sampling does not replicate the strict spectral collapse observed in our data (second row in Fig. S23). This highlights that simply adding latent factors does not fully recapitulate the scale invariance we measure, suggesting richer or more intricate processes may be involved in real neural recordings.

Specifically, we have incorporated five key revisions.

• As mentioned, we evaluated the latent variable model proposed by Morrell et al., and found that they fail to reproduce the scale-invariant eigenspectra observed in our data; these results are now presented in the Discussion section and supported by a new Supplementary Figure (Fig. S23).

• We included a comparison with the findings of Manley et al. (2024 [2]) regarding the issue of saturating dimension in the Discussion section, highlighting the methodological differences and their implications.

• We added a new mathematical derivation in the Methods section, elucidating the bounded dimensionality using the spectral properties of our model. • We have added a sentence in the Discussion section to further emphasize the robustness of our findings by demonstrating their consistency across diverse datasets and experimental techniques.

• We have incorporated a brief discussion on the implications for neural coding (lines 330-332). In particular, Fisher information can become unbounded when the slope of the power-law rank plot is less than one, as highlighted in the recent work by Moosavi et al. (bioRxiv 2024.08.23.608710, Aug, 2024 [3]).

We believe these revisions address the concerns raised during the review process and collectively strengthen our manuscript to provides a more comprehensive and robust understanding of the geometry and dimensionality of brain-wide activity. We appreciate your consideration of our revised manuscript and look forward to your feedback.

Recommendations for the authors:

In particular, in our experience replies to the reviewers are getting longer than the paper, and we (and I’m sure you!) want to avoid that. Maybe just reply explicitly to the ones you disagree with? We’re pretty flexible on our end.

(1) The main weakness, from our point of view, is whether the finding of scale invariance means something interesting, or should be expected from a null model. We can suggest such model; if it is inconsistent with the data, that would make the results far more interesting.

Morrell et al. (eLife 12, RP89337,2024 [1]) suggest a very simple model in which the whole population is driven by a slowly time-varying quantity. It would be nice to determine whether it matched this data. If it couldn’t, that would add some evidence that there is something interesting going on.

We appreciate your insightful suggestion to consider the model proposed by Morrell et al. (eLife 12, RP89337, 2024 [1]), where a slowly time-varying quantity drives the entire neural population. We conducted simulations using parameters from Morrell et al. [4, 1], as detailed below.

Our simulations show that Morrell’s model can replicate a degree of scaleinvariance when using functional sampling or RG as referred to in Morrell et al, 2021, PRL [4] (FSap, Fig.S23A-D, Author response image 1). However, it fails to fully capture the scale-invariance of collapsing spectra we observed in data under random sampling (RSap, Fig.S23E-H). This discrepancy suggests that additional dynamics or structures in the neural activity are not captured by this simple model, indicating the presence of potentially novel and interesting features in the data that merit further investigation.

Unlike random sampling, the collapse of eigenspectra under functional sampling does not require a stringent condition on the kernel function f(x) in our ERM theory (see Discussion line 269-275), potentially explaining the differing results between Fig.S23A-D and Fig.S23E-H.

We have incorporated these findings into the Result section 2.1 (lines 100-101) and Discussion section (lines 277-282, quoted below):

“Morrell et al. [4, 1] suggested a simple model in which a slow time-varying factor influences the entire neural population. To explore the effects of latent variables, we assessed if this model explains the scale invariance in our data. The model posits that neural activity is primarily driven by a few shared latent factors. Simulations showed that the resulting eigenspectra differed considerably from our findings (Fig. S23). Although the Morrell model demonstrated a degree of scale invariance under functional sampling, it did not align with the scale-invariant features under random sampling observed in our data, suggesting that this simple model might not capture all crucial features in our observations.”

Author response image 1:

Morrell’s latent model. A: We reproduce the results as presented in Morrell et al., PRL 126(11), 118302 (2021) [4]. Parameters are same as Fig. S23A. Sampled 16 to 256 neurons. Unlike in our study, the mean eigenvalues are not normalized to one. Dashed line: eigenvalues fitted to a power law. See also Morrell et al. [4] Fig.1C. Parameters are same as Author response image 1. µ is the power law exponent (black) of the fit, which is different from the µ parameter used to characterize the slow decay of the spatial correlation function, but corresponds to the parameter α in our study.

(2) The quantification of the degree of scale invariance is done using a ”collapse index” (CI), which could be better explained/motivated. The fact that the measure is computed only for the non-leading eigenvalues makes sense but it is not clear when originally introduced. How does this measure compare to other measures of the distance between distributions?

We thank you for raising this important point regarding the explanation and motivation for our Collapse Index (CI). We defined the Collapse Index (CI) instead of other measures of distance between distributions for two main reasons. First, the CI provides an intuitive quantification of the shift of the eigenspectrum motivated by our high-density theory for the ERM model (Eq. 3, Fig. 4A). This high-density theory is only valid for large eigenvalues excluding the leading ones, and hence we compute the CI measure with a similar restriction of the range of area integration. Second, when using distribution to assess the collapse (e.g., we can use kernel density method to estimate the distribution of eigenvalues and then calculate the KL divergence between the two distributions), it is necessary to first estimate the distributions. This estimation step introduces errors, such as inaccuracies in estimating the probability of large eigenvalues.

We agree that a clearer explanation would enhance the manuscript and thus have made modifications accordingly. The CI is now introduced more clearly in the Results section (lines 145-148) and further detailed in the Methods section (lines 630-636). We have also revised the CI diagram in Fig. 4A to better illustrate the shift concept using a more intuitive cartoon representation.

(3) The paper focuses on the case in which the dimensionality saturates to a finite value as the number of recorded neurons is increased. It would be useful to contrast with a case in which this does not occur. The paper would be strengthened by a comparison with Manley et al. 2024, which argued that, unlike this study, dimensionality of activity in spontaneously behaving head-fixed mice did not saturate.

Thank you for highlighting this comparison. We have included a discussion (lines 303-309) comparing our approach with Manley et al. (2024) [2]. While Manley et al. [2] primarily used shared variance component analysis (SVCA) to estimate neural dimensionality, they observed that using PCA led to dimensionality saturation (see Figure S4D, Manley et al. [2]), consistent with our findings (Fig. 2D). We acknowledge the value of SVCA as an alternative approach and agree that it is an interesting avenue for future research. In our study, we chose to use PCA for several reasons. PCA is a well-established and widely trusted method in the neuroscience community, with a proven track record of revealing meaningful patterns in neural data. Its mathematical properties are well understood, making it particularly suitable for our theoretical analysis. While we appreciate the insights that newer methods like SVCA can provide, we believe PCA remains the most appropriate tool for addressing our specific research questions.

(4) More importantly, we don’t understand why dimensionality saturates. For the rank plot given in Eq. 3,

where k is rank. Using this, one can estimate sums over eigenvalues by integrals. Focusing on the N-dependence, we have

This gives

We don’t think you ever told us what mu/d was (see point 13 below), but in the discussion you implied that it was around 1/2 (line 249). In that case, D_PR should be approximately linear in N. Could you explain why it isn’t?

Thank you for your careful derivation. Along this line of calculations you suggested, we have now added derivations on using the ERM spectrum to estimate the upper bound of the dimension in the Methods (section 4.14.4). To deduce D_PR from the spectrum, we focus on the high-density region, where an analytical expression for large eigenvalues λ is given by:

Here, d is dimension of functional space, L is the linear size of functional space, ρ is the neuron density and γ is the coefficient in Eq. (3), which only depends on d, µ and E(σ²). The primary difference between your derivation and ours is that the eigenvalue λ_r decays rapidly after the threshold r \= β(N), which significantly affects the summations and . Since we did not discuss the small eigenvalues in the article, we represent them here as an unknown function η(r,N,L).

The sum is the trace of the covariance matrix C. As emphasized in the Methods section, without changing the properties the covariance spectrum, we always consider a normalized covariance matrix such that the mean neural activity variance E(σ²) = 1. Thus

rather than

The issue stems from overlooking that Eq. (3) is valid only for large eigenvalues (λ > 1).

Using the Cauchy–Schwarz inequality, we have a upper bound of

Conversely, provides a lower bound of :

As a result, we must have

In random sampling (RSap), L is fixed. We thus must have a bounded dimensionality that is independent of N for our ERM model. In functional sampling (FSap), L varies while the neuronal density ρ is fixed, leading to a different scaling relationship of the upper bound, see Methods (section 4.14.4) for further discussion.

(5) The authors work directly with ROIs rather than attempting to separate the signals from each neuron in an ROI. It would be worth discussing whether this has a significant effect on the results.

We appreciate your thoughtful question on the potential impact of using ROIs. The use of ROIs likely does not impact our key findings since they are validated across multiple datasets with various recording techniques and animal models, from zebrafish calcium imaging to mouse brain multi-electrode recordings (see Figure S2, S24). The consistency of the scale-invariant covariance spectrum in diverse datasets suggests that ROIs in zebrafish data do not significantly alter the conclusions, and they together enhance the generalizability of our results. We highlight this in the Discussion section (lines 319-323).

(6) Does the Euclidean random matrix model allow the authors to infer the value of D or µ? Since the measured observables only depend on µ/D it seems that one cannot infer the latent dimension where distances between neurons are computed. Are there any experiments that one could, in principle, perform to measure D or mu? Currently the conclusion from the model and data is that D/µ is a large number so that the spectrum is independent of neuron density rho. What about the heterogeneity of the scales σ_i, can this be constrained by data?

Measuring d and µ in the ERM Model

We agree with you that the individual values of d and µ cannot be determined separately from our analysis. In our analysis using the Euclidean Random Matrix (ERM) model, we fit the ratio µ/d, rather than the individual values of d (dimension of the functional space) or µ (exponent of the distance-dependent kernel function). This limitation is inherent because the model’s predictions for observable quantities, such as the distribution of pairwise correlation, are dependent solely on this ratio.

Currently there are no directly targeted experiments to measure d. The dimensions of the functional space is largely a theoretical construct: it could serve to represent latent variables encoding cognitive factors that are distributed throughout the brain or specific sensory or motor feature maps within a particular brain region. It may also be viewed as the embedding space to describe functional connectivity between neurons. Thus, a direct experimental measurement of the dimensions of the functional space could be challenging. Although there are variations in the biological interpretation of the functional space, the consistent scale invariance observed across various brain regions indicates that the neuronal relationships within the functional space can be described by a uniform slowly decaying kernel function.

Regarding the Heterogeneity of σ_i

The heterogeneity of neuronal activity variances ( σ_i) is a critical factor in our analysis. Our findings indicate that this heterogeneity:

(1) Enhances scale invariance: The covariance matrix spectrum, which incorporates the heterogeneity of , exhibits stronger scale invariance compared to the correlation matrix spectrum, which imposes for all neurons. This observation is supported by both experimental data and theoretical predictions from the ERM model, particularly in the intermediate density regime.

(2) Can be constrained by data: We fit a log-normal distribution to the experimentally observed σ² values to capture the heterogeneity in our model which leads to excellent agreement with data (section 4.8.1). Figure S10 provides evidence for this by directly comparing the eigenspectra obtained from experimental data (Fig S10A-F) with those generated by the fitted ERM model (Fig S10M-R). These results suggest that the data provides valuable information about the distribution of neuronal activity variances.

In conclusion, the ERM model and our analysis cannot separately determine d and µ. We also highlight that the neuronal activity variance heterogeneity, constrained by experimental data, plays a crucial role in improving the scale invariance.

(7) Does the fitting procedure for the positions x in the latent space recover a ground truth in your statistical regime (for the number of recorded neurons)? Suppose you sampled some neurons from a Euclidean random matrix theory. Does the MDS technique the authors use recover the correct distances?

While sampling neurons from a Euclidean random matrix model, we demonstrated numerically that the MDS technique can accurately recover the true distances, provided that the true parameter f(x) is known. To quantify the precision of recovery, we applied the CCA analysis (Section 4.9) and compared the true coordinates from the original Euclidean random matrix with the fitted coordinates obtained through our MDS procedure. The CCA correlation between the true and fitted coordinates in each spatial dimension is nearly 1 (the difference from 1 is less than 10⁻⁷). When fitting with experimental data, one source of error arises from parameter estimation. To evaluate this, we assess the estimation error of the fitted parameters. When we choose µ \= 0_.5 in our ERM model and then fit the distribution of the pairwise correlation (Eq. 21), the estimated parameter is = 0.503 ± 0._007 (standard deviation). Then, we use the MDS-recovered distances to fit the coordinates with the fitted kernel function , which is determined by the fitted parameter . The CCA correlation between the true and fitted coordinates in each direction remains nearly 1 (the difference from 1 is less than 10⁻⁵).

(8) l. 49: ”... both the dimensionality and covariance spectrum remain invariant ...”. Just to be clear, if the spectrum is invariant, then the dimensionality automatically is too. Correct?

Thanks for the question. In fact, there is no direct causal relationship between eigenvalue spectrum invariance and dimensionality invariance as we elaborate below and added discussions in lines 311-317. For eigenvalue spectrum invariance, we focus on the large eigenvalues, whereas dimensionality invariance considers the second order statistics of all eigenvalues. Consequently, the invariance results for these two concepts may differ. And dimensional and spectral invariance have different requirements:

(1) The condition for dimensional saturation is finite mean square covariance

The participation ratio D_PR for random sampling (RSap) is given by Eq. 5:

This expression becomes invariant as N → ∞ if the mean square covariance is finite. In contrast, neural dynamics models, such as the balanced excitatory-inhibitory (E-I) neural network [5], exhibit a different behavior, where , leading to unbounded dimensionality (see discussion lines 291-295, section 6.9 in SI).

(2) The requirements for spectral invariance involving the kernel function

In our Euclidean Random Matrix (ERM) model, the eigenvalue distribution follows:

For spectral invariance to emerge: (1) The eigenvalue distribution must remain unchanged after sampling. (2) Since sampling reduces the neuronal density ρ. (3) The ratio µ/d must approach 0 to maintain invariance.

We can also demonstrate that D_PR is independent of density ρ in the large N limit (see the answer of question 4).

In conclusion, there is no causal relationship between spectral invariance and dimensionality invariance. This is also the reason why we need to consider both properties separately in our analysis.

(9) In Eq. 1, the exact expression, which includes i=j, isn’t a lot harder than the one with i=j excluded. So why i≠j?

The choice is for illustration purposes. In Eq. 1, we wanted to demonstrate that the dimension saturates to a value independent of N. When dividing the numerator and denominator of this expression by N², the term is independent of the neuron number N, but the term associated with the diagonal entries is of order O(1_/N_) and can be ignored for large N.

(10) Fig. 2D: Could you explain where the theory line comes from?

We first estimate ] from all neurons, and then compute D_PR for different neuron numbers N using Eq.5 (). This is further clarified in lines 511-512.

(11) l 94-5: ”It [scale invariance] is also absent when replacing the neural covariance matrix eigenvectors with random ones, keeping the eigenvalues identical (Fig. 2H).” If eigenvalues are identical, why does the spectrum change?

The eigenspectra of the covariance matrices in full size are the same by construction, but the eigenspectra of the sampled covariance matrices are different because the eigenvectors affect the sampling results. Please also refer to the construction process described in section 4.3 where this is also discussed: “The composite covariance matrix with substituted eigenvectors in (Fig. 2H) was created as described in the following steps. First, we generated a random orthogonal matrix U<sub>r<.sup> (based on the Haar measure) for the new eigenvectors. This was achieved by QR decomposition A=U_rR of a random matrix A with i.i.d. entries A_ij ∼ N(0_,1/N_). The composite covariance matrix C_r was then defined as, where Λ is a diagonal matrix that contains the eigenvalues of C. Note that since all the eigenvalues are real and U_r is orthogonal, the resulting C_r is a real and symmetric matrix. By construction, C_r and C have the same eigenvalues, but their sampled eigenspectra can differ.”

(12) Eq 3: There’s no dependence on the distribution of sigma. Is that correct?

Indeed, this is true in the high-density regime when the neuron density ρ is large. The p(λ) depends only on E(σ²) rather than the distribution of σ (see Eq. 8). However, in the intermediate density regime, p(λ) depends on the distribution of σ (see Eq.9 and Eq.10). In our analysis, we consider E(σ⁴) as a measure of heterogeneity.

(13) Please tell us the best fit values of µ/d.

This information now is added in the figure caption of Fig S10: µ/d \= [0_.456,0.258,0.205,0.262,0.302,0._308] in fish 1-6.

(14) l 133: ”The eigenspectrum is rho-independent whenever µ/d ≈ 0.”

It looks to me like rho sets the scale but not the shape. Correct? If so, why do we care about the overall scale – isn’t it the shape that’s important?

Yes, our study focuses on the overall scale not only the shape, because many models, such as the ERM with other kernel functions, random RNNs, Morrell’s latent model [4, 1], can exhibit a power-law spectrum. However, these models do not exhibit scale-invariance in terms of spectrum curve collapsing. Therefore, considering the overall scale reveal additional non-trivial phenomenon.

(15) Figs. 3 and 4: Are the grey dots the same as in previous figures? Either way, please specify what they are in the figure caption.

Yes, they are the same, and thank you for pointing it out. It has been specified in the figure caption now.

(16) Fig. 4B: Top is correlation matrix, bottom is covariance matrix, correct? If so, that should be explicit. If not, it should be clear what the plots are.

That is correct. Both matrices (correlation - top, covariance - bottom) are labeled in the figure caption and plot (text in the lower left corner).

(17) l 158: ”First, the shape of the kernel function f(x) over a small distance ...”. What does ”over a small distance” mean?

We thank you for seeking clarification on this point. We understand that the phrase ”over a small distance” could be made clearer. We made a revised explanation in lines 164-165 Here, “over a small distance” refers to modifications of the particular kernel function f(x) we use Eq. 11 near x \= 0 in the functional space, while preserving the overall power-law decay at larger distances. The t-distribution based f(x) (Eq. 11) has a natural parameter ϵ that describes the transition to near 0. So we modified f(x) in different ways, all within this interval of |x| ≤ ϵ, and considered different values of ϵ. Table S3 and Figure S7 provide a summary of these modifications. Figure S7 visually compares these modifications to the standard power-law kernel function, highlighting the differences in shape near x \= 0.

Our findings indicate that these alterations to the kernel function at small distances do not significantly affect the distribution of large eigenvalues in the covariance spectrum. This supports our conclusion that the large eigenvalues are primarily determined by the slow decay of the kernel function at larger distances in the functional space, as this characteristic governs the overall correlations in neural activity.

(18) l390 . This x_i is, we believe, different from the x_i which is position in feature space. Given the difficulty of this paper, it doesn’t help to use the same symbol to mean two different things. But maybe we’re wrong?

Thank you for your careful reading and suggestion. Indeed here x_i was representing activity rather than feature space position. We have thus revised the notation (Line 390 has been updated to line 439 as well.):

In this revised notation: a_i(t) represents the neural activity of neuron i at time t (typically the firing rate we infer from calcium imaging). is simply the mean activity of neuron i across time. Meanwhile, we’ll keep x_i exclusively for denoting positions in the functional space.

This change should make it much easier to distinguish between neural activity measurements and spatial coordinates in the functional space.

(19) Eq. 19: is it correct that g(u) is not normalized to 1? If so, does that matter?

It is correct that the approximation of g(u) is not normalized to 1, as Eq. 19 provides an approximation suitable only for small pairwise distances (i.e., large correlation). Therefore, we believe this does not pose an issue. We have newly added this note in lines 691-693.

(20) I get a different answer in Eq. 20:

Whereas in Eq. 20,

µ

Which is correct?

Thank you for your careful derivation. We believe the difference arises in the calculation of g(u).In our calculations:

,

(Your first equation seems to missed an 1_/µ_ in R’s exponent.)

,

That is, Eq. 20 is correct. From these, we obtain

rather than

We hope this clarifies the question.

(21) I’m not sure we fully understand the CCA analysis. First, our guess as to what you did: After sampling (either Asap or Fsap), you used ERM to embed the neurons in a 2-D space, and then applied canonical correlation analysis (CCA). Is that correct? If so, it would be nice if that were more clear.

We first used ERM to embed all the neurons in a 2-D functional space, before any sampling. Once we have the embedding, we can quantify how similar the functional coordinates are with the anatomical coordinates using R_CCA (section 2.4). We can then use the anatomical and functional coordinates to perform ASap and FSap, respectively. Our theory in section 2.4 predicts the effect on dimension under these samplings given the value of R_CCA estimated earlier (Fig. 5D). The detailed description of the CCA analysis is in section 4.9, where we explain how CCA is used to find the axes in both anatomical and functional spaces that maximize the correlation between projections of neuron coordinates.

As to how you sampled under Fsap, I could not figure that out – even after reading supplementary information. A clearer explanation would be very helpful.

Thank you for your feedback. Functional sampling (FSap) entails the expansion of regions of interest (ROIs) within the functional space, as illustrated in Figure 5A, concurrently with the calculation of the covariance matrix for all neurons contained within the ROI. Technically, we implemented the sampling using the RG approach [6], which is further elaborated in Section 4.12 (lines 852-899), quoted below.

Stage (i): Iterative Clustering We begin with N</sub>0</sub> neurons, where N</sub>0</sub> is assumed to be a power of 2. In the first iteration, we compute Pearson’s correlation coefficients for all neuron pairs. We then search greedily for the most correlated pairs and group the half pairs with the highest correlation into the first cluster; the remaining neurons form the second cluster. For each pair (a,b), we define a coarse-grained variable according to:

,

Where normalizes the average to ensure unit nonzero activity. This process reduces the number of neurons to N₁ = N₀/2. In subsequent iterations, we continue grouping the most correlated pairs of the coarse-grained neurons, iteratively reducing the number of neurons by half at each step. This process continues until the desired level of coarse-graining is achieved.

When applying the RG approach to ERM, instead of combining neural activity, we merge correlation matrices to traverse different scales. During the _k_th iteration, we compute the coarse-grained covariance as:

and the variance as:

Following these calculations, we normalize the coarse-grained covariance matrix to ensure that all variances are equal to one. Note that these coarse-grained covariances are only used in stage (i) and not used to calculate the spectrum.

Stage (ii): Eigenspectrum Calculation The calculation of eigenspectra at different scales proceeds through three sequential steps. First, for each cluster identified in Stage (i), we compute the covariance matrix using the original firing rates of neurons within that cluster (not the coarse-grained activities). Second, we calculate the eigenspectrum for each cluster. Finally, we average these eigenspectra across all clusters at a given iteration level to obtain the representative eigenspectrum for that scale.

In stage (ii), we calculate the eigenspectra of the sub-covariance matrices across different cluster sizes as described in [6]. Let N₀ = 2<sup>n</sub> be the original number of neurons. To reduce it to size N \= N₀/2^k = 2^n-k, where k is the kth reduction step, consider the coarse-grained neurons in step n − k in stage (i). Each coarse-grained neuron is a cluster of 2^n-k neurons. We then calculate spectrum of the block of the original covariance matrix corresponding to neurons of each cluster (there are 2^k such blocks). Lastly, an average of these 2^k spectra is computed.

For example, when reducing from N₀ = 2³ = 8 to N \= 2³⁻¹ = 4 neurons (k \= 1), we would have two clusters of 4 neurons each. We calculate the eigenspectrum for each 4x4 block of the original covariance matrix, then average these two spectra together. To better understand this process through a concrete example, consider a hypothetical scenario where a set of eight neurons, labeled 1,2,3,...,7,8, are subjected to a two-step clustering procedure. In the first step, neurons are grouped based on their maximum correlation pairs, for example, resulting in the formation of four pairs: {1,2},{3,4},{5,6}, and {7,8} (see Fig. S22). Subsequently, the neurons are further grouped into two clusters based on the results of the RG step mentioned above. Specifically, if the correlation between the coarse-grained variables of the pair {1,2} and the pair {3,4} is found to be the largest among all other pairs of coarse-grained variables, the first group consists of neurons {1,2,3,4}, while the second group contains neurons {5,6,7,8}. Next, take the size of the cluster N = 4 for example. The eigenspectra of the covariance matrices of the four neurons within each cluster are computed. This results in two eigenspectra, one for each cluster. The correlation matrices used to compute the eigenspectra of different sizes do not involve coarse-grained neurons. It is the real neurons 1,2,3,...,7,8, but with expanding cluster sizes. Finally, the average of the eigenspectra of the two clusters is calculated.

(22) Line 37: ”even if two cell assemblies have the same D_PR, they can have different shapes.” What is meant by shape here isn’t clear.

Thank you for pointing out this potential ambiguity. The “shape” here refers to the geometric configuration of the neural activity space characterized as a highdimensional ellipsoid by the covariance. Specifically, if we denote the eigenvalues of the covariance matrix as λ₁,λ₂,...,λ_N, then corresponds to the length of the i-th semi-axis of this ellipsoid (Figure 1B). As shown in Figure 1C, two neural populations with the same dimensionality (D_PR = 25/11 ≈ 2.27) exhibit different eigenvalue spectra, leading to differently shaped ellipsoids. This clarification is now included in lines 39-40.

(23) Please discuss if any information about the latent dimension or kernel function can be inferred from the measurements.

Same as comment(6): we would like to clarify that in our analysis using the Euclidean Random Matrix (ERM) model, we fit the ratio µ/d, rather than the individual values of d (dimension of the functional space) or µ (exponent of the distancedependent kernel function). This limitation is inherent because the model’s predictions for observable quantities, such as the eigenvalue spectrum of the covariance matrix, are dependent solely on this ratio.

For the kernel function, once the d is chosen, we can infer the general shape of the kernel function from data (Figs S12 and S13), up to a certain extent (see also lines 164-166). In particular, we can compare the eigenspectrum of the simulation results for different kernel functions with the eigenspectrum of our data. This allows us to qualitatively exclude certain kernel functions, such as the exponential and Gaussian kernels (Fig. S4), which show clear differences from our data.

References

(1) M. C. Morrell, I. Nemenman, A. Sederberg, Neural criticality from effective latent variables. eLife 12, RP89337 (2024).

(2) J. Manley, S. Lu, K. Barber, J. Demas, H. Kim, D. Meyer, F. M. Traub, A. Vaziri, Simultaneous, cortex-wide dynamics of up to 1 million neurons reveal unbounded scaling of dimensionality with neuron number. Neuron (2024).

(3) S. A. Moosavi, S. S. R. Hindupur, H. Shimazaki, Population coding under the scale-invariance of high-dimensional noise (2024).

(4) M. C. Morrell, A. J. Sederberg, I. Nemenman, Latent dynamical variables produce signatures of spatiotemporal criticality in large biological systems. Physical Review Letters 126, 118302 (2021).

(5) A. Renart, J. De La Rocha, P. Bartho, L. Hollender, N. Parga, A. Reyes, K. D. Harris, The asynchronous state in cortical circuits. science 327, 587–590 (2010).

(6) L. Meshulam, J. L. Gauthier, C. D. Brody, D. W. Tank, W. Bialek, Coarse graining, fixed points, and scaling in a large population of neurons. Physical Review Letters 123, 178103 (2019).

Author response:

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

Reviewer #1 (Public Review):

Summary:

This study explores the sequence characteristics and features of high-occupancy target (HOT) loci across the human genome. The computational analyses presented in this paper provide information into the correlation of TF binding and regulatory networks at HOT loci that were regarded as lacking sequence specificity.

By leveraging hundreds of ChIP-seq datasets from the ENCODE Project to delineate HOT loci in HepG2, K562, and H1-hESC cells, the investigators identified the regulatory significance and participation in 3D chromatin interactions of HOT loci. Subsequent exploration focused on the interaction of DNA-associated proteins (DAPs) with HOT loci using computational models. The models established that the potential formation of HOT loci is likely embedded in their DNA sequences and is significantly influenced by GC contents. Further inquiry exposed contrasting roles of HOT loci in housekeeping and tissue-specific functions spanning various cell types, with distinctions between embryonic and differentiated states, including instances of polymorphic variability. The authors conclude with a speculative model that HOT loci serve as anchors where phase-separated transcriptional condensates form. The findings presented here open avenues for future research, encouraging more exploration of the functional implications of HOT loci.

Strengths:

The concept of using computational models to define characteristics of HOT loci is refreshing and allows researchers to take a different approach to identifying potential targets. The major strengths of the study lies in the very large number of datasets analyzed, with hundreds of ChIP-seq data sets for both HepG2 and K562 cells as part of the ENCODE project. Such quantitative power allowed the authors to delve deeply into HOT loci, which were previously thought to be artifacts.

Weaknesses:

While this study contributes to our knowledge of HOT loci, there are critical weaknesses that need to be addressed. There are questions on the validity of the assumptions made for certain analyses. The speculative nature of the proposed model involving transcriptional condensates needs either further validation or be toned down. Furthermore, some apparent contradictions exist among the main conclusions, and these either need to be better explained or corrected. Lastly, several figure panels could be better explained or described in the figure legends.

We thank the reviewer for their valuable comments.

- We have extended the study and included a new chapter focusing on the condensate hypothesis, added more supporting evidence (including the ones suggested by the reviewer), and made explicit statements on the speculative nature of this model.

- We have restructured the text to remove the sentences which might be construed as contradictory.

Reviewer #2 (Public Review):

Summary:

The paper 'Sequence characteristic and an accurate model of abundant hyperactive loci in human genome' by Hydaiberdiev and Ovcharenko offers comprehensive analyses and insights about the 'high-occupancy target' (HOT) loci in the human genome. These are considered genomic regions that overlap with transcription factor binding sites. The authors provided very comprehensive analyses of the TF composition characteristics of these HOT loci. They showed that these HOT loci tend to overlap with annotated promoters and enhancers, GC-rich regions, open chromatin signals, and highly conserved regions, and that these loci are also enriched with potentially causal variants with different traits.

Strengths:

Overall, the HOT loci' definition is clear and the data of HOT regions across the genome can be a useful dataset for studies that use HepG2 or K562 as a model. I appreciate the authors' efforts in presenting many analyses and plots backing up each statement.

Weaknesses:

It is noteworthy that the HOT concept and their signature characteristics as being highly functional regions of the genome are not presented for the first time here. Additionally, I find the main manuscript, though very comprehensive, long-winded and can be put in a shorter, more digestible format without sacrificing scientific content.

The introduction's mention of the blacklisted region can be rather misleading because when I read it, I was anticipating that we are uncovering new regulatory regions within the blacklisted region. However, the paper does not seem to address the question of whether the HOT regions overlap, if any, with the ENCODE blacklisted regions afterward. This plays into the central assessment that this manuscript is long-winded.

The introduction also mentioned that HOT regions correspond to 'genomic regions that seemingly get bound by a large number of TFs with no apparent DNA sequence specificity' (this point of 'no sequence specificity' is reiterated in the discussion lines 485-486). However, later on in the paper, the authors also presented models such as convolutional neural networks that take in one-hot-encoded DNA sequence to predict HOT performed really well. It means that the sequence contexts with potential motifs can still play a role in forming the HOT loci. At the same time, lines 59-60 also cited studies that "detected putative drive motifs at the core segments of the HOT loci". The authors should edit the manuscript to clarify (or eradicate) contradictory statements.

We thank the reviewer for their valuable comments. Below are our responses to each paragraph in the given order:

We added a statement in the commenting and summarizing other publications that studied the functional aspects of HOT loci with the following sentence in the introduction part:

“Other studies have concluded that these regions are highly functionally consequential regions enriched in epigenetic signals of active regulatory elements such as histone modification regions and high chromatin accessibility”.

We significantly shortened the manuscript by a) moving the detailed analyses of the computational model to the supplemental materials, and b) shortening the discussions by around half, focusing on core analyses that would be most beneficial to the field.

Given that the ENCODE blacklisted regions are the regions that are recommended by the ENCODE guidelines to be avoided in mapping the ChIP-seq (and other NGS), we excluded them from our analyzed regions before mapping to the genome. Instead, we relied on the conclusions of other publications on HOT loci that the initial assessments of a fraction of HOT loci were the result of factoring in these loci which later were included in blacklisted regions.

We addressed the potential confusion by using the expression of “no sequence specificity” by a) changing the sentence in the introduction by adding a clarification as “... with no apparent DNA sequence specificity in terms of detectible binding motifs of corresponding motifs” and b) removing that part from the sentence in the discussions.

Reviewer #3 (Public Review):

Summary:

Hudaiberdiev and Ovcharenko investigate regions within the genome where a high abundance of DNA-associated proteins are located and identify DNA sequence features enriched in these regions, their conservation in evolution, and variation in disease. Using ChIP-seq binding profiles of over 1,000 proteins in three human cell lines (HepG2, K562, and H1) as a data source they're able to identify nearly 44,000 high-occupancy target loci (HOT) that form at promoter and enhancer regions, thus suggesting these HOT loci regulate housekeeping and cell identity genes. Their primary investigative tool is HepG2 cells, but they employ K562 and H1 cells as tools to validate these assertions in other human cell types. Their analyses use RNA pol II signal, super-enhancer, regular-enhancer, and epigenetic marks to support the identification of these regions. The work is notable, in that it identifies a set of proteins that are invariantly associated with high-occupancy enhancers and promoters and argues for the integration of these molecules at different genomic loci. These observations are leveraged by the authors to argue HOT loci as potential sites of transcriptional condensates, a claim that they are well poised to provide information in support of. This work would benefit from refinement and some additional work to support the claims.

Comments:

(1) Condensates are thought to be scaffolded by one or more proteins or RNA molecules that are associated together to induce phase separation. The authors can readily provide from their analysis a check of whether HOT loci exist within different condensate compartments (or a marker for them). Generally, ChIPSeq signal from MED1 and Ronin (THAP11) would be anticipated to correspond with transcriptional condensates of different flavors, other coactivator proteins (e.g., BRD4), would be useful to include as well. Similarly, condensate scaffolding proteins of facultative and constitutive heterochromatin (HP1a and EZH2/1) would augment the authors' model by providing further evidence that HOT Loci occur at transcriptional condensates and not heterochromatin condensates. Sites of splicing might be informative as well, splicing condensates (or nuclear speckles) are scaffolded by SRRM/SON, which is probably not in their data set, but members of the serine arginine-rich splicing factor family of proteins can serve as a proxy-SRSF2 is the best studied of this set. This would provide a significant improvement to their proposed model and be expected since the authors note that these proteins occur at the enhancers and promoter regions of highly expressed genes.

(2) It is curious that MAX is found to be highly enriched without its binding partner Myc, is Myc's signal simply lower in abundance, or is it absent from HOT loci? How could it be possible that a pair of proteins, which bind DNA as a heterodimer are found in HOT loci without invoking a condensate model to interpret the results?

(3) Numerous studies have linked the physical properties of transcription factor proteins to their role in the genome. The authors here provide a limited analysis of the proteins found at different HOT-loci by employing go terms. Is there evidence for specific types of structural motifs, disordered motifs, or related properties of these proteins present in specific loci?

(4) Condensates themselves possess different emergent properties, but it is a product of the proteins and RNAs that concentrate in them and not a result of any one specific function (condensates can have multiple functions!)

(5) Transcriptional condensates serve as functional bodies. The notion the authors present in their discussion is not held by practitioners of condensate science, in that condensates exist to perform biochemical functions and are dissolved in response to satisfying that need, not that they serve simply as reservoirs of active molecules. For example, transcriptional condensates form at enhancers or promoters that concentrate factors involved in the activation and expression of that gene and are subsequently dissolved in response to a regulatory signal (in transcription this can be the nascently synthesized RNA itself or other factors). The association reactions driving the formation of active biochemical machinery within condensates are materially changed, as are the kinetics of assembly. It is unnecessary and inaccurate to qualify transcriptional condensates as depots for transcriptional machinery.

6) This work has the potential to advance the field forward by providing a detailed perspective on what proteins are located in what regions of the genome. Publication of this information alongside the manuscript would advance the field materially.

We thank the reviewer for constructive comments and suggestions. Below are our point-by-point responses:

(1) We added a new short section “Transcriptional condensates as a model for explaining the HOT regions” with additional support for the condensate hypothesis, wherein some of the points raised here were addressed. Specifically, we used a curated LLPS proteins (CD-CODE) database and provided statistics of those annotation condensate-related DAPs.

Regarding the DAPs mentioned in this question, we observed that the distributions corresponding ChIP-seq peaks confirm the patterns expected by the reviewer (Author response image 1). Namely:

- MED1 and Ronin (THAP11) are abundant in the HOT loci, being present 67% and 64% of HOT loci respectively.

- While the BRD4 is present in 28% of the HOT loci, we observed that the DAPs with annotated LLPS activity ranged from 3% to 73%, providing further support for the condensate hypothesis.

- ENCODE database does not contain ChIP-seq dataset for HP1A. EZH2 peaks were absent in the HOT loci (0.4% overlap), suggesting the lack of heterochromatin condensate involvement.

- Serine-rich splicing factor family proteins were present only in 7.7% of the HOT loci, suggesting the absence or limited overlap with splicing condensates or nuclear speckles.

Author response image 1.

(2) In this study we selected the TF ChIP-seq datasets with stringent quality metrics, excluding those which had attached audit warning and errors. As a result, the set of DAPs analyzed in HepG2 did not include MYC, since the corresponding ChIP-seq dataset had the audit warning tags of "borderline replicate concordance, insufficient read length, insufficient read depth, extremely low read depth". Analyses in K562 and H1 did include MYC (alongside MAX) ChIP-seq dataset.

To address this question, we added the mentioned ChIP-seq dataset (ENCODE ID: ENCFF800JFG) and analyzed the colocalization patterns of MYC and MAX. We observed that the MYC ChIP-seq peaks in HepG2 display spurious results, overlapping with only 5% of HOT loci. Meanwhile in K562 and H1, MYC and MAX are jointly present in 54% and 44% of the HOT loci, respectively (Author response image 2).

Author response image 2.

These observations were also supported by Jaccard indices between the MYC and MAX ChIP-seq peaks. To do this analysis, we calculated the pairwise Jaccard indices between MYC and MAX and divided them by the average Jaccard indices of 2000 randomly selected DAP pairs. In K562 and H1, the Jaccard indices between MYC and MAX are 5.72x and 2.53x greater than the random background, respectively. For HepG2, the ratio was 0.21x, clearly indicating that HepG2 MYC ChIP-seq dataset is likely erroneous.

Author response image 3.

(3) Despite numerous publications focusing on different structural domains in transcription factors, we could not find an extensive database or a survey study focusing on annotations of structural motifs in human TFs. Therefore, surveying such a scale would be outside of this study’s scope. We added only the analysis of intrinsically disordered regions, as it pertains to the condensate hypothesis. To emphasize this shortcoming, we added the following sentence to the end of the discussions section.

“Further, one of the hallmarks of LLPS proteins that have been associated with their abilities to phase-separate is the overrepresentation of certain structural motifs, which we did not pursue due to size limitations.”

(4, 5) We agree with these statements and thank the reviewer for pointing out this faulty statement. We modified the sections in the discussions related to the condensates and removed the part where we implied that the condensate model could be because of mostly a single function of TF reservoir.

(6) We added a table to the supplemental materials (Zenodo repository) with detailed annotation of HOT and non-HOT DAP-bound loci in the genome.

Recommendations for the authors:

Reviewing Editor (Recommendations For The Authors):

The clause with "inadequate" would be dropped if the authors sufficiently address reviewer concerns about clarity of writing, including:

(1) Editing the title to better reflect the findings of the paper.

(2) Making clear that the condensate model is speculative and not explicitly tested in this study (and may be better described as a hypothesis).

(3) Resolving apparent contradictions regarding DNA sequence specificity and the interpretation of ChIP-seq signal intensity.

(4) Better specifying and justifying model parameters, thresholds, and assumptions.

(5) Shortening the manuscript to emphasize the main, well-supported claims and to enhance readability (especially the discussion section).

We thank the Editor for their work. We followed their advice and implemented changes and additions to address all 5 points.

Reviewer #1 (Recommendations For The Authors):

(1) The title "Sequence characteristics and an accurate model of abundant hyperactive loci in the human genome" does not accurately reflect the findings of the paper. We are unclear as to what the 'accurate model' refers to. Is it the proposed model 'based on the existence of large transcriptional condensates' (abstract)? If so, there are concerns below regarding this statement (see comment 2). If the authors are referring to the computational modeling presented in Figure 5, it is unclear that any one of them performed that much better than the others and the best single model was not identified. Furthermore, the models being developed in the study constitute only a portion of the paper and lacked validation through additional datasets. Additionally, sequence characteristics were not a primary focus of the study. Only figure 5 talks about the model and sequence characteristics, the rest of the figures are left out of the equation.

We agree with and thank the reviewer for this idea of clarifying the intended meaning.

(1) We changed the title and clarified that the computational model is meant:

“Functional characteristics and a computational model of abundant hyperactive loci in the human genome”.

(2) Shortened the part of the manuscript discussing the computational models and pointed out the CNNs as “the best single model”.

(2) The abstract and discussion (and perhaps the title) propose a model of transcriptional condensates in relation to HOT loci. However, there is no data provided in the manuscript that relates to condensates. Therefore, anything relating to condensates is primarily speculative. This distinction needs to be properly made, especially in the abstract (and cannot be included in the title). Otherwise, these statements are misleading. Although the field of transcriptional condensates is relatively new, there have been several factors studied. The authors could include in Figure 2d which factors have been shown to form transcriptional condensates. This might provide some support for the model, though it would still largely remain speculative unless further testing is done.

We added a new short chapter “Transcriptional condensates as a model for explaining the HOT regions”,  with additional analyses testing the condensates hypothesis. We provided supportive evidence by analyzing the metrics used as hallmarks of condensates including the distributions of annotated condensate-related proteins, nascent transcription, and protein-RNA interaction levels in HOT loci. Still, we acknowledge that this is a speculative hypothesis and we clarified that with the following statement in the discussions:

“It is important to note here that our proposed condensate model is a speculative hypothesis. Further experimental studies in the field are needed to confirm or reject it.”

(3) Several apparent contradictions exist throughout the manuscript. For example, "HOT locus formation are likely encoded in their DNA sequences" (lines 329-330) vs the proposed model of formation through condensates (abstract). These two statements do not seem compatible, or at the very least, the authors can explain how they are consistent with each other. Another example: "ChIP-seq signal intensity as a proxy for... binding affinity" (line 229) vs. "ChIP-seq signal intensities do not seem to be a function of the DNA-binding properties of the DAPs" (lines 259-260). The first statement is the assumption for subsequent analyses, which has its own concerns (see comment 4). But the conclusion from that analysis seems to contradict the assumption, at least as it is stated.

In this study, we argue that the two statements may not necessarily contradict each other. We aimed to a) demonstrate that the observed intensity of DAP-DNA interactions as measured by ChIP-seq experiments at HOT loci cannot be explained with direct DNA-binding events of the DAPs alone and b) propose a hypothesis that this observation can be at least partially explained if the HOT loci have the propensity to either facilitate or take part in the formation of transcriptional condensates.

One of the conditions for condensates to form at enhancers was shown to be the presence of strong binding sites of key TFs (Shrinivas et al. 2019 “Enhancer features that drive the formation of transcriptional condensates”), where the study was conducted using only one TF (OCT4) and one coactivator (MED1). To the best of our knowledge, no such study has been conducted involving many TFs and cofactors simultaneously. We also know that the factors that lead to liquid-to-liquid phase separation include weak multivalent IDR-IDR, IDR-DNA, and IDR-RNA interactions. As a result, the observed total sum of ChIP-seq peaks in HOT loci is the direct DNA-binding events combined with the indirect DAP-DNA interactions, some of which may be facilitated by condensates. And, the fact that CNNs can recognize the HOT loci with high accuracy suggests that there must be an underlying motif grammar specific to HOT loci.

We emphasized this conclusion in the discussions.

The comment on using the ChIP-seq signal as a proxy for DNA-binding affinity is addressed under comment 4.

(4) In lines 229-230, the authors used "the ChIP-seq signal intensity as a proxy for the DAP binding affinity." What is the basis for this assumption? If there is a study that can be referenced, it should be added. However, ChIP-seq signal intensity is generally regarded as a combination of abundance, frequency, or percentage of cells with binding. RNA Pol2 is a good example of this as it has no specific binding affinity but the peak heights indicate level of expression. Therefore, the analyses and conclusions in Figure 4, particularly panel A, are problematic. In addition, clarification from lines 258-260 is needed as it contradicts the earlier premise of the section (see comment 3).

We thank the reviewer for pointing out this error. The main conclusion of the paragraph is that the average ChIP-seq signal values at HOT loci do not correlate well with the sequence-specificity of TFs. We reworded the paragraph stating that we are analyzing the patterns of ChIP-seq signals across the HOT loci, removing the part that we use them as a proxy for sequence-specific binding affinity.

(5) In Figure 1A, the authors show that "the distribution of the number of loci is not multimodal, but rather follows a uniform spectrum, and thus, this definition of HOT loci is ad-hoc" (lines 92-95). The threshold to determine how a locus is considered to be HOT is unclear. How did the authors decide to use the current threshold given the uniform spectrum observed? How does this method of calling HOT loci compare to previous studies? How much overlap is there in the HOT loci in this study versus previous ones?

We moved the corresponding explanation from the supplemental methods to the main methods section of the manuscript.

Briefly, our reasoning was as follows: assuming that an average TFBS is 8bp long and given that we analyze the loci of length 400bp, we can set the theoretical maximum number of simultaneous binding events to be 50. Hence, if there are >50 TF ChIP-seq peaks in a given 400bp locus, it is highly unlikely that the majority of ChIP-seq peaks can be explained by direct TF-DNA interactions. The condition of >50 TFs corresponded to the last four bins of our binning scale, which was used as an operational definition for HOT loci.

We have compared our definition of HOT loci to those reported in previous studies by Remaker et al. and Boyle et al. The results of our analyses are in lines 147-154.

(6) In Figure 3B, the authors state that of "the loop anchor regions with >3 overlapping loops, 51% contained at least one HOT locus, suggesting an interplay between chromatin loops and HOT loci." However, it is unclear how "51%" is calculated from the figure. Similarly, in the following sentence, "94% of HOT loci are located in regions with at least one chromatin interaction". It is unclear as to how the number was obtained based on the referenced figure.

Initially, the x-axis on the Figure 3B was missing, making it hard to understand what we meant. We added the x-axis numbers and changed the “51%” to “more than half”. We intend to say that, of the loci with 4 and 5 overlapping loops, exactly 50% contain at least one HOT locus. However, since for x=6 the percentage is 100% (since there’s only one such locus), the percentage is technically “more than half”.

The percentage of HOT loci engaging in chromatin interaction regions (91%) was calculated by simply overlapping the HOT regions with Hi-C long-range contact anchors. The details of extracting these regions using FitHiChip are described in Supplemental Methods 1.3.

(7) While we have a limited basis to evaluate computational models, we would like to see a clearer explanation of the model set-up in terms of the number of trained vs. test datasets. In addition, it would be interesting to see if the models can be applied to data from different cell lines.

We added the table with the sizes of the datasets used for classification in Supplemental Methods 1.6.1.

Evaluating the models trained on the HOT loci of HepG2 and K562 on other cell lines would pose challenges since the number of available ENCODE TF ChIP-seq datasets is significantly less compared to the mentioned cell lines. Therefore, we conducted the proposed analysis between the studied cell lines. Specifically, we used the CNN models trained on HOT and regular enhancers of HepG2 and K562. Then, we evaluated each model on the test sets of each classification experiment (Author response image 4). We observed that the classification results of the HOT loci demonstrated a higher level of tissue-specificity compared to the same classification results of the regular enhancers.

Author response image 4.

(8) Lines 349-351. The significance of highly expressed genes being more prone to having multiple HOT loci, and vice versa, appears conventional and remains unclear. Intuitively, it makes sense for higher expressed genes to have more of the transcriptional machinery bound, and would bias the analysis. One way to circumvent this is to only analyze sequence-specific TFs and remove ones that are directly related to transcription machinery.

We thank the reviewer for this suggestion. Our attempt to re-annotate the HOT loci with only sequence-specific TFs led to a significantly different set of loci, which would not be strictly comparable to the HOT loci defined by this study. Analyzing these new sets of loci would create a noticeable departure from the flow of the manuscript and further extend the already long scope of the study.

Moreover, numerous studies have shown that super-enhancers recruit large numbers of TFs via transcriptional condensates (Boija et al., 2018; Cho et al., 2018; Sabari et al., 2018). We hope that our results can serve as data-driven supportive evidence for those studies.

(9) Lines 393-396. We would like to see a reference to the models shown in the figures, if these models have been published previously.

We could not understand the question. The lines 393-396 contains the following sentence:

“However, many of the features of the loci that we’ve analyzed so far demonstrated similar patterns (GC contents, target gene expressions, ChIP-seq signal values etc.) when compared to the DAP-bound loci in HepG2 and K562, suggesting that albeit limited, the distribution of the DAPs in H1 likely reflects the true distribution of HOT loci.”

In case the question was about the models that we trained to classify the HOT loci, we included the models and codebase to Zenodo and GitHub repository.

(10) Values in Figure 7D are not reflected in the text. Specifically, the text states "Average ... phastCons of the developmental HOT loci are 1.3x higher than K562 and HepG2 HOT loci (Figure 7D)" (lines 408-409). Figure 7D shows conservation scores between HOT enhancers vs promoters for each cell line, and does not seem to reflect the text.

We modified the figure to reflect the statement appropriately.

(11) Methodology should include a justification for the use of the Mann-Whitney U-test (non-parametric) over other statistical tests.

We added the following description to the methods section:

“For calculating the statistical significance, we used the non-parametric Mann-Whitney U-test when the compared data points are non-linearly correlated and multi-modal. When the data distributions are bell-curve shaped, the Student’s t-test was used.“

Minor:

(1) Figure 2b was never mentioned in the paper. This can be added alongside Figure S6C, line 148.

Indeed, Figure 2B was supposed to be listed together with Figure S6C, which was omitted by mistake. It was corrected.

(2) Supplementary Figure 8 has two Cs. Needs to be corrected to D.

Fixed.

(3) Figure 3B is missing labels on the x-axis.

Fixed.

(4) The horizontal bar graph on the bottom left of Figure 1E needs to be described in the figure legend.

Description added to the figure caption.

(5) Line 345, Fig 15A should be Fig S15A.

Corrected.

Reviewer #2 (Recommendations For The Authors):

I listed all my concerns about the paper in the public comments. I think the manuscript is very comprehensive and it is valuable, but it should be cut short and presented in a more digestible way.

We thank the reviewer for their valuable comments and suggestions. We addressed all the concerns listed in the public comments. We shortened the manuscript by reducing the paragraph that focuses on computational classification models and reduced the discussions by about half in length.

Line 55: What are chromatin-associated proteins, i.e. are they histone modifications?

To clarify the definition used from the citation we changed the sentence to the following:

“For instance, Partridge et al. studied the HOT loci in the context of 208 proteins including TFs, cofactors, and chromatin regulators which they called chromatin-associated proteins.”

Though most of the paper can be cut short to avoid analysis paralysis for readers, there are details that still need filling in. For example, how did the authors perform PCA analysis, i.e. what are the features of each data point in the PCA analysis? Lines 214-215: How do we calculate the number of multi-way contacts in Hi-C data?

We added clarifying descriptions and changed the mentioned sentences to the following:

PCA:

“To analyze the signatures of unique DAPs in HOT loci, we performed a PCA analysis where each HOT locus is represented by a binary (presence/absence) vector of length equal to the total number of DAPs analyzed.”

Multi-way contacts on loop anchors:

“To investigate further, we analyzed the loop anchor regions harboring HOT loci and observed that the number of multi-way contacts on loop anchors (i.e. loci which serve as anchors to multiple loops) correlates with the number of bound DAPs (rho=0.84 p-value<10E-4; Pearson correlation). “

- Lines 251-252: How did the referenced study categorize DAPs? It is important for any manuscript to be self-contained.

We added the explanation and changed the sentence to the following:

“To test this hypothesis, we classified the DAPs into those two categories using the definitions provided in the study (Lambert et al. 2018) 28, where the TFs are classified by manual curation through extensive literature review and supported by annotations such as the presence of DNA-binding domains and validated binding motifs. Based on this classification, we categorized the ChIP-seq signal values into these two groups.“

- Lines 181-185, sentences starting with 'To test' can be moved to the methods, leaving only brief mentions of the statistic tests if needed.

We removed the mentioned sentence and moved to the supplemental methods (1.4).

- Lines 217-220: I find this sentence extremely redundant unless it can offer more specific insights about a particular set of DAPs or if the DAPs are closer/or a proven distal enhancer to a confirmed causal gene.

We removed the mentioned sentence from the text.

- Lines 243-246: How did the authors determine the set DAPs that have stabilizing effects, and how exactly are the 'stabilizing effects' observed/measured?

We added explanations to Supplemental Methods 3.1 and Fig S18, S19.

While addressing this comment we realized that the reported value of the ratio is 1.91x, not 1.7x. We corrected that value in the main text and added the p-value.

- When discussing the phastCons scores analyses, such as in lines 268-271, how did the authors calculate the relationship between phastCons scores and HOT loci, i.e. was the score averaged across the 400-bp locus to obtain a locus-specific conservation score?

Yes, per-locus conservation scores were averaged over the bps of loci. We added this clarification to the methods.

- Line 311: What is the role of the 'control sets' in the analyses of the sequence's relationship with HOT?

In this specific case, the control sets are used as background or negative sets to set up the classification tasks. In other words, we are asking, whether the HOT loci can be distinguished when compared to random chromatin-accessible regions, promoters, or regular enhancers. We clarified this in the text.

- I also find the discussion about different machine learning methods that classify HOT loci based on sequence contexts quite redundant UNLESS the authors decide to go further into the features' importance (such as motifs) in the models that predict/ are associated with HOT loci, which in itself can constitute another study.

We agree with the reviewer, and shortened the part with the discussions of models by limiting it to only 3 main models and moved the rest to the supplemental materials.

- Can the authors clarify where they obtain data on super-enhancers?

We obtained the super-enhancer definitions from the original study (Hnisz et al. 2013, PMID: 24119843) where the super-enhancers were defined for multiple cell lines. We clarified this in the methods.

- Figure 1B, the x and y axis should be clarified.

We clarified it by using MAX as an example case in the figure caption as follows:

“Prevalence of DAPs in HOT loci. Each dot represents a DAP. X-axis: percentage of HOT loci in which DAP is present (e.g. MAX is present in 80% of HOT loci). Y-axis: percentage of total peaks of DAPs that are located in HOT loci (e.g. 45% of all the ChIP-seq peaks of MAX is located in the HOT loci). Dot color and size are proportional to the total number of ChIP-seq peaks of DAP.”

Reviewer #3 (Recommendations For The Authors):

The list of proteins associated with different types of genomic loci at a meta level (enhancers, promoters, and gene body etc.), and an annotation of the genome at the specific loci level.

The authors use a wide range of acronyms throughout the text and figure legends, they do a reasonably good job, but the main text section "HOT-loci are enriched in causal variants" and Figure 8 would be materially improved if they held it to the same standard.

Size is a physical property and not a physicochemical property.

We thank the reviewer for their comments and suggestions. We added a table to supplemental files with detailed annotations of analyzed loci.

We reviewed the section “HOT loci are enriched in causal variants” and corrected a few mismatches in the acronyms.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary: 

In this paper, Kalidindi and Crevecoeur ask why sequential movements are sometimes coarticulated. To answer this question, first, they modified a standard optimal controller to perform consecutive reaches to two targets (T1 and T2). They investigated the optimal solution with and without a constraint on the endpoint's velocity in the via target (T1). They observed that the controller coarticulates the movements only when there is no constraint on the speed at the via-point. They characterized coarticulation in two ways: First, T2 affected the curvature of the first reach in unperturbed reaches. Second, T2 affected corrective movements in response to a mechanical perturbation of the first reach. 

Parallel to the modeling work, they ran the same experiment on human participants. The participants were instructed to either consider T1 as via point (go task) or to slow down in T1 and then continue to T2 (stop task). Mirroring the simulation results, they observed coarticulation only in the go task. Interestingly, in the go task, when the initial reach was occasionally perturbed, the long-latency feedback responses differed for different T2 targets, suggesting that the information about the final target was already present in the motor circuits that mediate the long-latency response. In summary, they conclude that coarticulation in sequential tasks depends on instruction, and when coarticulation happens, the corrections in earlier segments of movement reflect the entirety of the coarticulated sequence.

Evaluation 

Among many strengths of this paper, most notably, the results and the experiment design are grounded in, and guided by the optimal control simulation. The methods and procedures are appropriate and standard. The results and methods are explained sufficiently and the paper is written clearly. The results on modulation of long-latency response based on future goals are interesting and of broad interest for future experiments on motor control in sequential movement. However, I find the authors' framing of these results, mostly in the introduction section, somewhat complicated.

The current version of the introduction motivates the study by suggesting that "coarticulation and separation of sub-movement [in sequential movements] have been formulated as distinct hypotheses" and this apparent distinction, which led to contradictory results, can be resolved by Optimal Feedback Control (OFC) framework in which task-optimized control gains control coarticulation. This framing seems complicated for two main reasons. First, the authors use chunking and coarticulation interchangeably. However, as originally proposed by (Miller 1956), the chunking of the sequence items may fully occur at an abstract level like working memory, with no motoric coarticulation of sequence elements at the level of motor execution. In this scenario, sequence production will be faster due to the proactive preparation of sequence elements. This simple dissociation between chunking and coarticulation may already explain the apparent contradiction between the previous works mentioned in the introduction section. Second, the authors propose the OFC as a novel approach for studying neural correlates of sequence production. While I agree that OFC simulations can be highly insightful as a normative model for understanding the importance of sequence elements, it is unclear to me how OFCs can generate new hypotheses regarding the neural implementation of sequential movements. For instance, if the control gains are summarizing the instruction of the task and the relevance of future targets, it is unclear in which brain areas, or how these control gains are implemented. I believe the manuscript will benefit from making points more clear in the introduction and the discussion sections. 

We agree that chunking may occur at different levels that do not necessarily involve motor coarticulation. We clarified that our contribution is towards answering why sequence movements sometimes coarticulate, and how the way sequences are executed influences the representation of future goals in the sensorimotor system.

To address this point, we made the following modifications in the introduction:

Line 44:

“It remains unclear how future goals are integrated in the sensorimotor system. For rapid execution of a sequence, one possible solution is to represent multiple goals within low-level control circuits (3, 16), enabling the execution of several elements as a single entity, called “motor chunk”. Note that chunking can also occur at a higher level such as in working memory-guided sequences, which in this case may or may not involve the production of a movement (17, 18).”

Lines 50:

“Recent neural recordings in the primary motor cortex (M1) have shown no specific influence of future goals on the population responses governing ongoing action (19, 20). Specifically, Zimnik and Churchland (20) observed in a two-reach sequence task that, there was no coarticulation in sub-movement kinematics although the execution got faster with practice. Notably, M1 displayed separate phases of execution related activity for each sub-movement. Using a neural network model, they interpreted that sequence goals could be separated and serially specified to the controller from regions upstream of M1 (Figure 1A). These findings contrast with earlier studies showing coarticulation of sub-movements and whole sequence representations in M1 (21–23). As a result, it has been suggested that coarticulation and separation in rapid sequences may involve distinct computations: coarticulation possibly involves replacing sub-movements with a motor chunk, while separation possibly indicates independent control of each sub-movement with chunking at a higher-level (4, 20).  Thus, there are unresolved questions regarding why sequential movements sometimes coarticulate, and how the representation of future goals in the sensorimotor system influences the way sequences are executed.”

With respect to the second part of your concern about OFC, we agree that this framework does not make direct prediction about the neural implementation and our statements required clarifications. The first link between the model and prediction about neural data follows from the observation that long-latency circuits participate in task-dependent sequence production, thus indicating that transcortical pathways must express this task dependency. The second link between our work and neural activities is by providing a counter argument to previous interpretation: indeed, Zimnik and Churchland argued that independent or “holistic” sequence production should be associated with different representations in monkey’s brain. In contrast we suggest that the same controller can flexibly generate both kinds of sequences, without implying a different structure in the controller, only a different cost-function. We thus refine the expectation about neural correlates of sequence representations by showing that it potentially relates to the encoding of task constraints.

To address this point, we added the following changes in the introduction and discussion:

Line 69 in Introduction: 

“The theory of optimal feedback control (OFC) has been particularly useful in predicting the influence of numerous task parameters on the controller (27–34), thus reproducing goal-directed motor commands during both unperturbed movements and feedback responses to disturbances (30). OFC has been used in numerous studies to interpret flexible feedback responses occurring in the long-latency response period (30, 35).” 

Line 454 in Discussion:

“Although OFC has been predominantly used as a behavioral level framework agnostic to neural activity patterns, it can shed light on the planning, state estimation and execution related computations in the transcortical feedback pathway (Takei et al.,). Using OFC, our study proposes a novel and precise definition of the difference to expect in neural activities in order to identify coarticulated versus independent sequence representations from a computational point of view. Because each condition (i.e., overlapping versus non-overlapping controllers as in Figure 2) was associated with different cost-functions and time-varying control gains, it is the process of deriving these control gains, using the internal representation of the task structure, that may differ across coarticulated and separated sequence conditions. To our knowledge, how and where this operation is performed is unknown. A corollary of this definition is that the preparatory activity (20, 50) may not discern independently planned or coarticulated sequences because these situations imply different control policies (and cost functions), as opposed to different initial states. Moreover, the nature of the sequence representation is potentially not dissociable from its execution for the same reason.”

Reviewer #2 (Public Review):

Summary: 

In this manuscript, the authors examine the question of whether discrete action sequences and coarticulated continuous sequential actions can be produced from the same controller, without having to derive separate control policies for each sequential movement. Using modeling and behavioral experiments, the authors demonstrate that this is indeed possible if the constraints of the policy are appropriately specified. These results are of interest to those interested in motor sequences, but it is unclear whether these findings can be interpreted to apply to the control of sequences more broadly (see weaknesses below). 

Strengths: 

The authors provide an interesting and novel extension of the stochastic optimal control model to demonstrate how different temporal constraints can lead to either individual or coarticulated movements. The authors use this model to make predictions about patterns of behavior (e.g., in response to perturbations), which they then demonstrate in human participants both by measuring movement kinematics as well as EMG. Together this work supports the authors' primary claims regarding how changes in task instructions (i.e., task constraints) can result in coarticulated or separated movement sequences and the extent to which the subsequent movement goal affects the planning and control of the previous movement. 

Weaknesses: 

I reviewed a prior version of this manuscript, and appreciate the authors addressing many of my previous comments. However, there are some concerns, particularly with regard to how the authors interpret their findings. 

We thank the reviewer for their continued assessment of our work and for helping us to improve the paper. We are convinced that this and the previous review helped us clarifying our work considerably.

(1) It would be helpful for the authors to discuss whether they think there is a fundamental distinction between a coarticulated sequence and a single movement passing through a via point (or equivalently, avoiding an obstacle). The notion of a coarticulated sequence brings with it the notion of sequential (sub)movements and temporal structure, whereas the latter can be treated as more of a constraint on the production of a single continuous movement. If I am interpreting the authors' findings correctly it seems they are suggesting that these are not truly different kinds of movements at the level of a control policy, but it would be helpful for the authors to clarify this claim. 

Indeed, this is our interpretation of the results/simulations. This suggestion can also be observed in Ramkumar et al., article on chunking. To clarify this, we added a statement in the discussion as follows: 

Line 449: 

“Notably, in the framework of optimal feedback control, an intermediate goal is equivalent to a via-point that constrains the execution of the sequence (similar to (13)). It is thus possible that coarticulation in motor systems be processed similarly as other kinds of movement constraints, such as via-points, avoiding obstacles, or changes in control policies.”

(2) The authors' model clearly shows that each subsequent target only influences the movement of one target back, but not earlier ones (page 7 lines 199-204). This stands in contrast to the paper they cite from Kashefi 2023, in which those authors clearly show that people account for at least 2 targets in the future when planning/executing the current movement. It would be useful to know whether this distinction arises because of a difference in experimental methodology, or because the model is not capturing something about human behavior.  

Thank you for raising this point. There are some differences between the study of Kashefi and colleagues (2023), and ours. Both studies looked into planning of more than one reach. In the study of Kashefi et al., the results of Figure 6 showed that in H2 condition, there was no significant curvature, and the curvature increases in H3 and H4 conditions (only in the 75ms dwell-time scenario). Note that H2 condition in their work meant the presentation of +2 target after the initiation of +1 reach. Hence, we think the GO task in our case should be compared to the H3 condition, resulting in similar curvature as in our study. These authors also showed that curvature increased even in the H4 condition (75 ms dwell). OFC also accommodates this observation, if we consider the relationship between the cost of intermediate goals and spatial location of the targets (see figure below, also added to Supplementary Figure 4). To see this, we performed additional 3 target simulations where the constraint on intermediate goal velocity (at T1 and T2) was varied to achieve similar dwell velocity at the intermediate targets (Supplementary Figure 4C). In this case, the hand curvature of the first reach differed while the dwell velocity was similar across T3 up and T3 down conditions, as may be instructed experimentally. Again, the task instructions and the spatial location of the future goals together determine how much the first reach components are influenced by the next ones, and this may impact several reaches ahead. 

We added the following clarification in the result to describe this. 

Line 199:

“It is worth noting that the OFC model can be generalized to longer sequences (10) through the incorporation of additional cost terms (in Equation 10 of Methods) and targets, enabling simultaneous planning for more than two targets. Simulations of a sample three-reach sequence (Supplementary Figure S4) revealed that, varying the cost of dwell velocity at intermediate targets (w2 and w3 parameters in Methods) caused a variation in control gains. Different amount of change in control gains can be expected for intermediate versus late targets (Supplementary Figure 4A). Notably, even when we used the same dwell velocity cost (w2 = w3 = 0), the observed velocity profiles were different between the two sequences towards different final targets (T3 up and T3 down) (Supplementary Figure 4B). We tested a condition in which both sequence reaches were forced to have similar dwell velocity profiles by increasing the dwell velocity costs in the sequence towards one of the targets (T3 down), while leaving this parameter unchanged for the other target (T3 up). In this scenario, T3 up sequence had the parameters (w2, w3) = (0, 0), while T3 down sequence had the parameters (0.8, 0.8). In this case, the curvature of the first reach was different, and predominantly occurred due to differences in K2 between the two sequence reaches (Supplementary Figure S4C). These simulations highlight that, planning for a longer horizon sequence can indirectly influence the curvature of early reaches, due to the interaction between intermediate dwell constraints, spatial arrangement of targets, and sequence horizon in a task dependent manner.”

(3) In my prior review I raised a concern that the authors seem to be claiming that because they can use a single control policy for both coarticulated and separated movement sequences, there need not be any higher-level or explicit specification of whether the movements are sequential. While much of that language has been removed, it still appears in a few places (e.g., p. 13, lines 403-404). As previously noted, the authors' control policy can generate both types of movements as long as the proper constraints are provided to the model. However, these constraints must be specified somewhere (potentially explicitly, as the authors do by providing them as task instructions). Moreover, in typical sequence tasks, although some movements become coarticulated, people also tend to form chunks with distinct chunk boundaries, which presumably means that there is at least some specification of the sequential ordering of these chunks that must exist (otherwise the authors' model might suggest that people can coarticulate forever without needing to exhibit any chunk boundaries). Hence the authors should limit themselves to the narrow claim that a single control policy can lead to separated or coarticulated movements given an appropriate set of constraints, but acknowledge that their work cannot speak to where or how those constraints are specified in humans (i.e., that there could still be an explicit sequence representation guiding coarticulation). 

We thank the reviewer for raising this point. We do not dispute the statement that the controller needs to be set dependent on the constraints of the task that must be specified somewhere. In our view, this problem is similar to the question of how a cost-function (or a task representation) is transformed into a control policy in the brain, which is unknown in general. In the earlier version, our intention was to stress that separation can occur without necessarily implying that the goals be processed independently (as in Figure 1A and Zimnik 2021). To avoid confusion on this point, we modified this statement in the new version as follows:

Line 405: 

“A straightforward interpretation could be that the stopping at the first target invoked a completely different strategy in which the control of the two reaches was performed independently (Figure 1A), effectively separating the two movements, whereas executing them rapidly could produce the merging of the two sub-movements into a coarticulated sequence. While this is conceptually valid, it is not necessary and the model provides a more nuanced view: both apparent separation or coarticulation of the two motor patterns can be explained within the same framework of flexible feedback control. These different modes of sequence execution still require proper specification of the task constraints in the model, such as number of intermediate steps, dwell-time, or velocity limit. Such specifications must be considered as input to the controller.”

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors): 

Line 57: Distinct hypotheses. 

Line 209, The term "planned holistically" is confusing here. Seems like the authors suggest that the sequence is "planned holistically" as long as all sequence elements are given during the optimization process. 

We changed the sentence as follows.

Line 218: 

“Overall, the model predicted that even if a feedback control policy was computed by optimizing the whole sequence over a long time-horizon, the requirements associated with intermediate goals determine how early in the sequence the second (future) target can influence the feedback controller”

Line 336, It was not clear to me why the authors explained "the weak significant" results of PEC shortening in R0 given the nonsignificant values in R1. 

We wanted to be transparent about whether changing the statistical analysis will lead to different interpretations, such as the sequence encoding even before long latency epochs. But we realized that it could lead to confusion and we deleted this sentence in the updated manuscript.

Reviewer #2 (Recommendations For The Authors): 

About Weakness #2, to clarify this point the authors should either model and discuss what it would take for their model to account for multiple targets ahead, or else run a study to show that in this task people indeed only ever plan 1 target ahead.  

Please see our response above (in Weakness #2).

I am still puzzled by why people would resist the perturbation more when they eventually have to move in the direction of the perturbation (e.g., p 10 lines 313-314). Perhaps this is simply due to the geometry of the task, but it could also depend on what participants were trying to accomplish in the experiment. To help clarify this, the authors should report exactly what instructions were given to participants in each task condition.  

The simulations suggest that the observed perturbation movements are an optimal way to perform the task given the task constraints on accuracy, control effort and constraints at intermediate goals. The intuition is that modulating the acceleration at the intermediate goal is preferred rather than missing it. This however depends on the cost parameter. 

Below, in Author response figure 1, we show the simulations by varying the accuracy requirements at intermediate goal and the total motor cost parameters. Clearly, as expected, increasing the cost on accuracy of the intermediate reach, or decreasing the cost on motor output modulated the hand deviation (simulations not included in the article).

Author response image 1.

Impact of movement costs (motor effort and intermediate goal reach errors) on the hand path following a mechanical perturbation   

Our observation suggests that participants’ behaviour agreed with the interpretation that can result from the model. We clarified the exact instructions in the methods section. Note that the instructions were given at the beginning of the task and did not differ across the different conditions involving changes in the location of T2 or perturbation direction:

Line 594:

Participants were given the following instructions verbally: “Wait in the starting circle until you receive a GO signal, where the target circles turn red and you will simultaneously hear a beep sound. When the circles turn red, react quickly, move as soon, and as straight as possible to target 1 and then move to target 2. You will get two points at the end of the trial if you reach T1 in the prescribed time window and then move to T2, and in all other cases you will not receive any points. Importantly, once you reach T1 you should try to come out of it quickly. If you stay in T1 for more than 150 ms then T2 will disappear and you will receive only one point. Additionally, in some trials, a force will perturb your hand towards the right or left direction randomly while moving towards T1. The instructions remain the same in the presence of perturbations. Try to score as many points as you can.”

Additionally, we added the following lines in the results description:

Line 284:

“The influence of second target on the lateral hand deviation was qualitatively similar to that observed in model simulations, and counterintuitive to what we might expect without the help of the model simulations. As observed in the model simulations (see also Supplementary Figure S2), lateral hand deviation was smaller when the perturbation was in the direction of the second target (T2) and vice-versa. This was consistent for both rightward and leftward perturbation conditions. Both the model and humans expressed this strategy that can be seen as an emergent feature of efficient feedback control during production of movement sequences. Additionally, even though behavior was reproduced in simulations, changing the cost on control effort and/or accuracy of intermediate reaches could modulate the sequencedependent changes in curvature.”

I am not sure if "the data and code for simulations can be provided by the corresponding author" satisfies the eLife/PLoS software guidelines (i.e., that it be deposited in a public repository).

Thank you for pointing this out. This sentence was added by mistake.

We modified this statement in the updated manuscript. 

“The data and code from simulations and experiments is available in the public repository ‘figshare’ in the following link (https://figshare.com/s/865a8b77c264ef17a181).”

Author Response

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

Reviewer #1

Recommendation 1: The authors reasoned upon the presence of a differential basal hydraulic stress in waves' valleys vs hills at first from the observation of "domes" formation upon 48h cultivation. I suggest performing a quantification to support the statement as a good scientific practice. Furthermore, it would strengthen the concept when the formation of domes was compared between the waves' dimensions as a different grade of cell extrusion was quantified. i.e., 50, 100, and 200 µm.

Response 1: Upon seeing the phenomenon (Author response image 1 A), we performed a count for domes on the 100 µm and saw a significant effect. We refrained from including the results as it is the subject of ongoing research in our lab. In response to the reviewer’s suggestion, we have included a graph (Author response image 1 B) showing the increasing number of domes over 48 hours from three 100 µm wave samples.

We have updated Figure 2A and B in the manuscript to include the new graph.

Author response image 1.

(A) shows dome (white arrows) over a 100 µm wave substrate. (B) is the number of accumulated domes in valley and hill regions, for 3 independent samples, over 48 hours.

Recommendation 2: Using RICM microscopy to quantify the cell basal separation with the substrate and hydraulic stress is very clever. Nevertheless, I am in doubt if the different intensity reported for the hills vs valley (Fig. 2G and H) is a result of the signal reduction at deeper Z levels. Since there is no difference in extrusion and forces between valleys and hills in the 200 µm waves but only in 50µm and 100µm, I would add this to the quantification. I would expect no intensity difference from RICM for the 200 µm sample if this is not an artefact of imaging.

Response 2: We performed additional experiments on blank wave substrates (both 100 and 200 µm) to ascertain the extent of reflection intensity drop (Author response image 2A). And, as correctly pointed out by Reviewer #1, there was a drop in intensity even without cells. On the 100 µm waves, hill reflections are on average ~27 % dimmer than valley reflections. Whereas, on the 200 µm waves, hill reflections are on average ~39 % dimmer.

Using this information, we performed a calibration on the RICM results obtained from both the 100 and 200 µm waves (Author response image 3B). The calibrated 100 µm data showed residual signatures of difference, whereas the calibrated 200 µm distributions appeared very similar. We noticed large cross- sample variations in the registered intensities, which will negatively impact effect size if not accounted for. To do this, we subsequently normalized both hill and valley intensities against planar region intensities for each sample. As shown by the final output (Author response image 3C), we were able to remove the skewness in the distributions. Moreover, 1-way ANOVA followed by a post hoc analysis with BH correction revealed a significant reduction in 100 µm hill/flat intensity ratio compared to 100 µm valley/flat intensity ratios (Δ~-23 %). Conversely, no significance was observed for the same comparison on the 200 µm waves.

Author response image 2.

(A). RICM from blank wave samples reveal a reduction in reflection intensity in hill regions compared to flat and valley regions.

Author response image 3.

(B) shows the RICM intensities after adjusting for the inherent reflection intensity drop shown in (A). (C) show the RICM intensities after normalization against planar region signals; this removes cross-sample variations and improve effect size of differences.

We have updated the manuscript Figure 2I and text accordingly. The blank wave results are included in Figure 2-figure supplement 1 along with updated text and summary data table in Supplementary File 4.

Recommendation 3: To measure 3D forces on top of the hills and valleys, the use of PAA gels is necessary. Since in Fig 3B, the authors show a difference in cell extrusion number between substrates and stiffnesses, I think it is necessary to confirm the presence of more extrusion in valleys vs hills on PAA gels. This would ensure the conclusion between normal forces and extrusion.

Response 3: We do have time-lapse data with monolayers on the PAA waves. However, we felt results from the flat regions were sufficient in supporting the point being made in the text. Specifically, our original intention with PAA gels was to show that the extrusion reductions seen in osmotic perturbations were by virtue of removing basal stress and not some cryptic osmotic response. Hydrogels were chosen because they can effectively dilute basal solute concentration and thereby reduce the osmotically induced water transport. Moreover, as fluid could freely move within the gel, the fluid stress can quickly equilibrate across the basal surface. In contrast, poorly water/solute permeable substrates could lead to localized spikes in solute concentration and transient basal regions with high fluid stress.

To get a sense of the potential difference in basal solute concentration between the two materials, we can do a quick hand-waving estimation. For monolayers on non-water/solute permeable PDMS of 20x20 mm and using the laser wavelength (640 nm) for RICM as an extreme estimate of basal separation, we should expect ~0.25 µl of total basal water content. On the other hand, we typically produce our PAM gel slabs using ~150 µl of precursor solutions. This means that, given similar amounts of solute, PAM gels will lead to monolayer basal osmolarity that is around 3 orders of magnitude lower than monolayers on PDMS, producing significantly lower osmotic potential. This implies from the outset that we should expect high survivability of cells on these substrates irrespective of curvature domains. Indeed, later immunoblotting experiments showed MDCKs exhibiting hyper activated FAK and Akt on PAM gels.

In response to Reviewer #1’s suggestion then, we have added another supporting time-lapse (Video 19) showing typical response of MDCK monolayers on 100 µm PAA waves (Author response image 4). Evident from the time-lapses, like the planar regions, cell extrusions were very rare. This supports the idea that on PAM gels the effects of basal hydraulic stress and asymmetric forces are marginal against the strong survival signals. And the response is similar to hyper-osmotic perturbations; there, we did not see a significant difference between valley and hill extrusions.

Author response image 4.

Time-lapse snapshot showing negligible MDCK extrusions 24 hours after confluency over PAM gel wave substrates.

Recommendation 4: Before proceeding with the FAK inhibitor experiment, the authors should better justify why the 4.1 wt % sucrose vs DMSO or NaCl is the most inert treatment. This can be done by citing relevant papers or showing time-lapses (as it is done for the higher FAKI14 dose).

Response 4: Although some cells have recently been shown to be able to transport and utilize sucrose, mammalian cells generally cannot directly take up polysaccharides for metabolism and this is frequently mentioned in literature: see (Ref. R1) for example. Without special enzymes to break sucrose down into monosaccharides, such as sucrase found in the gut, the sugars should remain spectators in the culture medium, contributing only to osmotic effects.

DMSO on the other hand, besides changing osmolarity, can also be integrated into cell membrane and pass through cells over time. It has been reported to chronically affect cell membrane properties and gene expressions (Ref. R2).

Finally, it is well known that both sodium and chloride ions are readily taken up and transported by cells (Ref R3). They help to regulate the transmembrane potential, which in turn can affect membrane bound proteins and biochemical reactions within a cell.

Hence, comparing the 3 hyper-osmotic perturbations, adding sucrose should have the least off- target effects on both the inhibitor study and the subsequent immunoblotting. And, in response to the reviewer’s recommendation, we have updated the text accordingly and included new references to support our statement.

Ref R1. H. Meyer, O. Vitavska, H. Wieczorek; Identification of an animal sucrose transporter. Journal of Cell Science 124, 1984–1991 (2011). Doi: 10.1242/jcs.082024

Ref R2. B. Gironi, Z. Kahveci, B. McGill, B.-D. Lechner, S. Pagliara, J. Metz, A. Morresi, F. Palombo, P. Sassi, P. G. Petrov; Effect of DMSO on the Mechanical and Structural Properties of Model and Biological Membranes. Biophysical Journal 119, 274-286 (2020). Doi: doi.org/10.1016/j.bpj.2020.05.037

Ref R3. X. Zhang, H. Li; Interplay between the electrostatic membrane potential and conformational changes in membrane proteins. Protein Science 28, 502-512 (2019). Doi: 10.1002/pro.3563

Recommendation 5: The data showing a FAK-dependent phosphorylation of AKT responsible for a higher cell survival rate in the hills is not yet completely convincing. Please show a reduced AKT phosphorylation level after FAK inhibition in high osmolarity levels. Furthermore, the levels of AKT activation seem to increase slightly upon substrate softening independently of FAK activation or osmotic pressure (i.e., Fig. 4E, Soft PDMS). The authors should comment on this in connection with the results shown for PAA gels.

Response 5: For the additional immunoblotting experiments, work is currently underway. We could not, however, complete these experiments in time for this revision, as both Cheng-Kuang and Xianbin will shortly be taking on new jobs elsewhere. David will continue with the immunoblotting studies and should be able to include the results in an update in the coming months. As for the apparent elevated levels of AKT seen on soft silicones, we speculate that it is because we cannot immunoblot cells that have died and were inevitably washed out at the start of the procedure. Inferring from the higher extrusion rates on these soft substrates, we could be missing a significant portion of stats. Specifically, we are missing all the cells that would have lowered AKT activation but died, and had we been able to collect those statistics, perhaps both the FAK and AKT should have shown lower levels. We risk committing survival bias on the results if we read too much into the data as is.

Alternatively, another explanation could be that, by virtue of survival of the fittest, we might have effectively selected a subpopulation of cells that were able to survive on lower FAK signals, or completely irrespectively of it.

At any rate, to prove our foregoing hypothesis would require us to perform comprehensive immunoblotting and total transcriptome analysis over different duration conditions. Unfortunately, we do not have the time to do that for the current article, but it could be developed into a stand-alone molecular biology investigation in future. We have included similar discussion in the main text.

Recommendation 6: In the discussion, the authors suggest the reported findings be especially relevant for epithelia that significantly separate compartments and regulate water and soluble transport. These are for example kidney epithelia (i.e., MDCK is the best experimental choice), retinal epithelium or intestinal epithelium. I would suggest that some proof-of-concept experiments could be done to support this concept. For example, I would expect keratinocytes (i.e., HaCaT) not to show a strong difference in extrusion rate between valleys and hills since the monolayer is not so sealed as kidney epithelium. In general, this kind of experiment would significantly strengthen the finding of this work.

Response 6: As recommended, we tracked the behavior of retina pigment epithelial cells (hTERT RPE-1 from ATCC) which do not form tight monolayers like MDCKs (Ref. R4). We did not detect extrusion events occurring from monolayers of these cells (Author response image 5). This is true even for portions of monolayers over waved regions.

Author response image 5.

Time-lapse snapshot showing non-existent o cell extrusions from RPE monolayers confluent for over 21 hours.

We have updated these findings in the main text discussions and included a new supporting time- lapse (Video 15) in our article.

Ref R4 F. Liu, T. Xu, S. Peng, R. A. Adelman, L. I. Rizzolo; Claudins regulate gene and protein expression of the retinal pigment epithelium independent of their association with tight junctions. Experimental Eye Research 198, 108157 (2020). Doi: 10.1016/j.exer.2020.108157

Recommendation 7 (minor point): Figure S1 needs to have clear notes indicating in each step what is what. i.e., where is glass, PDMS, NOA73, etc? A more detailed caption will help the figure's comprehension. Also "Cy52" should be changed to "soft silicone" to be consistent with the text (or Cy52 should be mentioned in the text).

Response 7 (minor point): Changes were made to Figure 1-figure supplement 1 to improve comprehension accordingly. CY52 was added to the main-text, next to the first appearance of the word soft silicone, to be consistent with the figures.

Recommendation 8 (minor point): The authors often mentioned that epithelial monolayers are denser on PAA gels. Please add a reference(s) to this statement.

Response 8 (minor point): The statement is an inference from visually comparing monolayers on PAM gels and PDMS. The difference is quite evident (Author response image 6). The density difference is in spite of the fact that the substrates share similar starting cell numbers.

To address the reviewer’s comment, we have combined time-lapses of monolayers on silicones and PAM gels side-by-side in Video 17 to facilitate convenient comparisons.

Author response image 6.

Time-lapse snapshot at 24 hours after confluence, showing conspicuously higher density of MDCK monolayers on PAM gel compared to those on silicon elastomer.

Reviewer #2

Recommendation 1: The sinusoidal wavy substrate that the authors use in their investigation is interesting and relevant, but it is important to realize that this is a single-curved surface (also known as a developable surface). This means that the Gaussian curvature is zero and that monolayers need to undergo (almost) no stretching to conform to the curvature. The authors should at least discuss other curved surfaces as an option for future research, and highlight how the observations might change. Convex and concave hemispherical surfaces, for example, might induce stronger differences than observed on the sinusoidal substrates, due to potentially higher vertical resultant forces that the monolayer would experience. The authors could discuss this geometry aspect more in their manuscript and potentially link it to some other papers exploring cell-curvature interactions in more complex environments (e.g. non-zero Gaussian curvature).

Response 1: In response to reviewer #2’s recommendation we have highlighted in the discussion of our text that our waves constitute a developable surface and that cells will experience little stretching for the most part. Based on our knowledge of how curvature can modulate forces and thus osmotic effects, we included some rudimentary analysis of what one would expect on hemispherical surfaces of two types: one that is periodic and contiguous (Ref. R5), and another with delineating flat regions (Ref. R6).

For epithelial monolayers in the first scenario, and on poorly solute/water permeable substrates, we should also expect to see a relatively higher likelihood of extrusions from concave regions compared to convex ones. Moreover, as the surfaces are now curved in both principal directions (producing larger out-of-plane forces), we should see the onset of differential extrusions seen in this study, but at larger length scales. For example, the effects seen on 100 µm hemicylindrical waves might now happen at larger feature size for hemispherical waves. Furthermore, as this kind of surface would invariably contain hyperbolic regions (saddle points), we might expect an intermediate response from these locations. If the forces in both principal directions offset each other, the extrusion response may parallel planar regions. On the other hand, if one dominates over the other, we may see extrusion responses tending to the dominating curvature (concave of convex).

On the other hand, on curved landscapes with discrete convex or concave regions, we should expect, within the curved surface, extrusion behaviors paralleling findings in this study. What would be interesting would be to see what happens at the rims (or skirt regions) of the features. At these locations we effectively have hyperbolically curved surfaces, and like before, we should expect some sort of competing effect between the forces generated from the principal directions. So, for dome skirts, we should see fewer extrusions when the domes are small, and vice versa, when they are larger. Meanwhile, for pit rims, we should see a reversed behavior. It should also be noted that the transitioning curvature between convex/concave and planar regions would also modulate the effect.

These effects might have interesting developmental implications. For instance, in developing pillar like tissues (e.g., villi) structures, the strong curvatures of nascent lumps would favor accumulation of cell numbers. However, once the size of the lumps reaches some critical value, epithelial cell extrusions might begin to appear at the roots of the developing structures, offsetting cell division, and eventually halting growth.

Ref R5. L. Pieuchot, J. Marteau, A. Guignandon, T. Dos Santos, I. Brigaud, P. Chauvy, T. Cloatre, A. Ponche, T. Petithory, P. Rougerie, M. Vassaux, J. Milan, N. T. Wakhloo, A. Spangenberg, M. Bigerelle, K. Anselme, Curvotaxis directs cell migration through cell-scale curvature landscapes. Nature Communications 9, 3995 (2018). Doi: 10.1038/s41467-018-06494-6

Ref R6. M. Werner, S. B.G. Blanquer, S. P. Haimi, G. Korus, J. W. C. Dunlop, G. N. Duda, D. W. Grijpma, A. Petersen, Surface curvature differentially regulates stem cell migration and differentiation via altered attachment morphology and nuclear deformation. Advanced Science 4, 1–11 (2017). Doi: 10.1002/advs.201600347

Recommendation 2: The discussion of the experiments on PAM gels is rather limited. The authors describe that cells on the PAM gels experience fewer extrusions than on the PDMS substrates, but this is not discussed in sufficient detail (e.g. why is this the case). Additionally, the description of the 3D traction force microscopy and its validation is quite limited and should be extended to provide more convincing evidence that the measured force differences are not an artefact of the undulations of the surface.

Response 2: We first saw a significant reduction in cell extrusions when we performed hyper-osmotic perturbations, and to eliminate possible off-target effects of the compounds used to increase osmolarity, we used three different compounds to be sure. In spite of this, we felt it would further support our argument, that basal accumulation of fluid stress was responsible for the extrusions, if we had some other independent means of removing fluid stress without directly tuning osmolarity through addition of extraneous solutes. We hence thought of culturing MDCK monolayers on hydrogels.

Hydrogels were chosen because they can effectively dilute basal solute concentration (for reference ions (Na+) are continuously pumped out basally by the monolayer) and thereby reduce the associated osmotically induced water transport. Moreover, as fluid could freely move within the gel, the fluid stress can quickly equilibrate across the basal surface. In contrast, poorly water/solute permeable substrates will lead to localized spikes in solute concentration and transient basal regions with high fluid stress.

To get a sense of the extent of difference in basal solute concentration between the two materials, we can do a quick hand-waving estimation. For monolayers on non-water-permeable PDMS of 20x20 mm, and using the laser wavelength (640 nm) for RICM as an extreme estimate of basal separation, we should expect ~0.25 µl of total basal water content. On the other hand, we typically produce our PAM gel slabs using ~150 µl of precursor solutions. This means that, given similar amounts of solute, PAM gels will lead to monolayer basal osmolarity that is around 3 orders of magnitude lower than monolayers on PDMS, producing significantly lower osmotic potential. This implies from the outset that we should expect high survivability of cells on these substrates. Indeed, later immunoblotting experiments showed MDCKs exhibiting hyper activated FAK and Akt on PAM gels.

As for the 3D TFM used in this study, it is actually implemented from a well-established finite element method to solve inverse problems in engineering and has been repeatedly validated in larger scale engineering contexts (Ref. R7). The novelty and contribution of our article is in its adaptation to reconstruct cellular forces at microscopic scales.

In brief, soft materials, such as hydrogels used in our case, are doped with fluorescent particles, coated with ECM, and then seeded with cells. The cells would exert forces that deform the soft substrate, thereby displacing the fluorescent particles from their equilibrium positions. This particle displacement can be extracted by producing an image pair with microscopy; first one with the cells, and subsequent one of relaxed gel after removal of cells with acutely cytotoxic reagents, such as SDS. There are several ways in which the displacement field can be extracted from the image pair. These include particle tracking velocimetry, particle image velocimetry, digital volume correlation, and optical flow.

We employed 3D Farneback optical flow in our study for its superior computational performance. The method was validated using synthetically generated images from Sample 14 of the Society for Experimental Mechanics DIC challenge. The accuracy of the calculated displacements using the 3D Farneback optical flow was then compared to the provided ground truth displacements. For the highest frequency displacement image pairs, an x-component root-mean-square-error (RMSE) value of 0.0113 was observed. This was lower than the 0.0141 RMSE value for the Augmented Lagrangian Digital Volume Correlation method. This suggested that the 3D Farneback optical flow is capable of accurately calculating the displacement between two bead images.

The displacement fields are then fed into a finite element suite (ANSYS in our case) along with the model and mesh of the underlying substrate structure to obtain node specific displacements. This is required because mech nodes do not typically align with voxel positions of displacements. With these node specific displacements, we subsequently solve the inverse problem for the forces using Tikhonov regularization (Ref. R8). The outcome is a vector of node specific forces.

In light of the above, to physically validate the method in our context would require the generation of a known ground truth force on the scale of pico- to nano-newtons and subsequently image the particle displacements from this force using confocal microscopy. The force must then be released in situ in order for the relaxed gel to be imaged again. This is not a straightforward feat at this scale, and a method that immediately springs to mind is magnetic tweezers. Unfortunately, this is a tool that we cannot develop within reasonable timeframes, as the method will have to be seamlessly integrated with our spinning-disk confocal. However, as a compromise, we have included an in-silico validation with our revised manuscript.

Specifically, given a finite element model with a predefined curvature, a known force was applied to the surface of the model (Author response image 7A). The resulting displacements were then calculated from the finite element solution. A 10% random noise is then added to the resulting displacement. The traction force recovery (Fig. R2-1 B) was then performed using the in-silico noisy displacements. To evaluate the accuracy of the recovery, the cosine similarity along with the mean norm of the force vectors were calculated. A value closer to 1 for both evaluation metrics indicates a more accurate reconstruction of the simulated traction force. The cosine similarity of the recovered traction forces to the original applied force was 0.977±0.056 while the norm of the recovered traction forces as a proportion of the original applied force was 1.016±0.165. As both values are close to 1 (i.e., identical), this suggested that the traction forces could be satisfactorily recovered using the finite-element based method.

In response to the reviewer’s recommendations then, additional content has been included in the main text to explain the use of PAM gels and the workings of our 3D TFM pipeline.

Ref R7. James F. Doyle, Modern Experimental Stress Analysis: Completing the Solution of Partially Specified Problems (John Wiley & Sons, Chichester, 2004).

Ref R8. Per Christian Hansen, Discrete Inverse Problems: Insight and Algorithms (siam, Philadelphia, 2010).

Author response image 7.

(A) shows simulated force field to generate simulated displacements. (B) shows force field reconstructed from simulated displacements with noise.

Recommendation 3: The authors show nuclear deformation on the hills and use this as evidence for a resultant downward-pointing force vector. This has, indeed, also been observed in other works referenced by the authors (e.g. Werner et al.), and could be interesting evidence to support the current observations, provided the authors also show a nuclear shape on the concave and flat regions. The authors could potentially also characterize this shape change better using higher-resolution data.

Response 3: We characterized nucleus deformation using Hoechst-stained samples as per recommendation. The deformation is estimated by dividing segmented nuclei volumes by best-fit ellipsoid volumes of same objects. In this way, objects exhibiting minimal bending will lead to values close to 1.0. The obtained graph is shown in figure Author response image 8B (and manuscript Figure 3D).

Author response image 8.

(A) an example of deformed nuclei on 50 µm wave hill region. (B) a Violin plot of calculated nuclear deformations across dimensions and features using segmented volume normalized against best-fit ellipsoid volume.

Our quantifications show a statistically significant difference in nuclei deformation measure medians between hill and valley cells on the 50 µm (0.973 vs 0.982) and 100 µm (0.971 vs 0.979) waves; this indicates that cells on the hills tend to have more deformed nuclei compared to cells in the valleys. Meanwhile, no significant difference was found for a similar comparison on 200 µm (0.978 vs 0.978) samples. For reference, the median found for cells pooled from planar regions was 0.975.

In response to the reviewer’s suggestions Figure 3 of our manuscript has been updated to include the new results on nuclei deformation. The text has also been updated to account for the new information to support our claims. The statistics are included in a new summary data table in Supplementary File 6.

Recommendation 4: The U-net for extrusion detection is a central tool used within this study, though the explanation and particularly validation of the tool are somewhat lacking. More clarity in the explanation and more examples of good (or bad) detections would help establish this tool as a more robust component of the data collection (on all geometries).

Response 4: The architecture of the neural network used in this study is outlined in supplementary figure S5a. To validate the performance of the model, a test dataset consisting of 200 positive examples and 100 negative examples were fed into the network and the resulting prediction was obtained from model. The confusion matrix of the model is shown in supplementary figure S5c. The weighted precision and recall of the model are 0.958 and 0.953 respectively.

Additionally, we have included examples of false positive and false negative detections in Figure 1-figure supplement 5 (Author response image 8). For false positive detections, these were typically observed to be extrusions that were labelled to have occurred the frame prior to the frame of interest (Author response image 9 bottom sequence). However, as the extrusion process is incomplete in the prior frame, there are still changes in the extruded cell body and the network falsely predicts this as a detection.

Author response image 9.

Examples of false negative and false positive extrusions registration.

Recommendation 5: The authors study the involvement of FAK in the observed curvature-dependent and hydraulic stress-dependent spatial regulation of cell extrusion. In one of the experiments, the authors supplement the cell medium with FAK inhibitors, though only in a hyper-osmotic medium. They show that FAK inhibition counteracts the extrusion-suppressing effect of a hyper-osmotic medium. However, no data is shown on the effect of FAK inhibitors within the control medium. Would the extrusion rates be even higher then?

Response 4: We proceeded, as suggested by the reviewer, to explore the effects of the FAK inhibitor on MDCK monolayers in our control medium. The results revealed that, at the 3 µM FAK concentration, where cells in sucrose media showed an elevated extrusion rate, monolayers in control medium quickly suffered massive cell death (Author response image 10) similar to what was seen when 6 µM FAK was introduced to sucrose medium.

This finding suggests that osmolarity protects against FAK inhibitors in a dose dependent manner. Moreover, as cell extrusions require an intact monolayer, its rates cannot increase indefinitely: a point will be reached where an intact monolayer can no longer be maintained.

We have updated the main text of our article to mention this observation, and also included a new time-lapse (Video 22) to demonstrate the effect.

Author response image 10.

Timelapse snapshot of MDCK monolayers over waves 4 hours after inclusion of focal adhesion kinase inhibitor.

Recommendation 6: The supplementary videos show two fields of view next to each other, which is not immediately clear to the viewer. I strongly advise the authors to add a clear border between the two panels, so that it is clear that the cells from one panel are not migrating into the next panel.

Response 6: A distinctive border has been added to the movies to separate panels showing different focal planes of the same stack.

Recommendation 7: The general quality and layout of the figures could be improved. Some figures would benefit from higher-resolution or larger cell images (e.g. Figure 2A, C, D), and the organisation of subpanels could be improved (e.g. especially in Figure 2). The box plots and bar graphs are also not consistent throughout the manuscript in terms of colouring and style, which should be improved.

Response 7: We have enlarged the figures in question accordingly, at the cost of reducing some information. However, the full scope of the sub-figures remains accessible in the supplementary movies. We have also tried to change the placement of the panels to improve readability. We have also adjusted the valley, hill, and flat coloring scheme for the extrusion boxplots in Figures 1 and 2 to make them consistent.

Recommendation 8: The graphs in Figures 3E and F are confusing and difficult to interpret. The x-axis states "Position along curve in radians" but it is unclear how to relate this to the position on the wavy substrate. The graphs also have a second vertical axis on the right ("valley-interface-hill"), which adds to the confusion. I would recommend the authors provide more explanation and consider a different approach of plotting this.

Response 8: We have removed the confusing plot of cross-sectional profile from the force graphs. To indicate positions on the waves, we have augmented radian values with Hill, Interface, and Valley accordingly.

Recommendation 9: Specify which silicone was used for the low-stiffness silicone substrates in the methods and in the main text.

Response 9: CY52 has been added to the main-text, next to the first appearance of the word soft silicone, to be consistent with the figures.

Recommendation 10: The flow lines that are plotted over the RICM data make it difficult to see the underlying RICM images. I would advise to also show the RICM images without the flow lines.

Response 10: The original movie S15 (now Video 16) showing the RICM overlapped with optical flow paths has now been replaced by a movie showing the same, but with the flow paths and RICM in separate panels.

Recommendation 11: In the first paragraph of the discussion, the authors write: "And this difference was both dependent on the sense (positive or negative)...". This is superfluous since the authors already mentioned earlier in the paragraph that the convex and concave regions (i.e. different signs of curvature) show differences in extrusion rates.

Response 11: The sentence has been changed to “And this difference was also dependent on the degree of curvature.”

Recommendation 12: In the second paragraph of the discussion, the authors mention that "basal fluid spaces under monolayers in hill regions were found consistently smaller than those in valley regions". Is this data shown in the figures of the manuscript? If so, a reference should be made because it was unclear to me.

Response 12: This statement is an inference from the comparison of the hill and valley RICM grey values. Specifically, RICM intensities are direct surrogates for basal separations (i.e., fluid space (as there cannot be a vacuum)) by virtue of the physics underlying the effect. To be more precise then, “inferred from RICM intensity differences (Figure 2I)” has been added to support the statement.

Recommendation 13: On page 7 of the discussion, the authors talk about positively and negatively curved surfaces. This type of description should be avoided, as this depends on the definition of the surface normal (i.e. is positive convex or concave?). Rather use convex and concave in this context.

Response 13: The wording has been changed accordingly.

Recommendation 14: The label of Table 8 reads "Table 2".

Response 14: The error has been corrected.

Reviewer #3

Recommendation 1: The central finding seems to be opposite to an earlier report (J Cell Sci (2019) 132, jcs222372), where MDCK cells in curved alginate tubes exhibit increased extrusion on a convex surface. I suggest that you comment on possible explanations for the different behaviors.

Response 1: The article in question primarily reported the phenomenon of MDCK and J3B1A monolayers detaching from the concave alginate tube walls coated with Matrigel. The authors attributed this to the curvature induced out-of-plane forces towards the center of the tubes. Up to this point, the findings and interpretation are consistent with our current study where we also find a similar force trend in concave regions.

To further lend support to the importance of curvature in inducing detachment, the authors cleverly bent the tubes to introduce asymmetry in curvature between outer and inner surfaces. Specifically, the outside bend is concave in both principal directions, whereas the inside bend is convex in one of its principal directions. As expected, the authors found that detachment rates from the outer surface were much larger compared to the inner one. Again, the observations and interpretations are consistent with our own findings; the convex direction will generate out-of-plane forces pointing into the surface, serving to stabilize the monolayer against the substrate. It should be noted however, since the inner-side tube is characterized by both convex and concave curvatures in its two principal directions, the resulting behavior of overlaying monolayers will depend on which of the two resulting forces become dominant. So, for gradual bends, one should expect the monolayers to still be able to detach from the inner tube surface. This is what was reported in their findings.

For their extrusion observations, I am surprised. Because their whole material (hydrogels) is presumably both solute and water permeable, I would be more inclined to expect very few extrusions irrespective of curvature. This is indeed the case with our study of MDCKs on PAM hydrogels, where the hydrogel substrate effectively buffers against the quick build-up of solute concentration and basal hydraulic stress. Without the latter, concave monolayer forces alone are unlikely to be able to disrupt cell focal adhesions. Indeed, the detachments seen in their study are more likely by exfoliation of Matrigel rather than pulling cells off Matrigel matrix entirely.

My guess is that the extrusions seen in their study are solely of the canonical crowding effect. If this was the case, then the detached monolayer on the outside bend could buffer against crowding pressure by buckling. Meanwhile, the monolayer on the inside bend, being attached to the surface, can only regulate crowding pressure by removing cells through extrusions. This phenomenon should be particular to soft matrices such as Matrigel. Using stiffer and covalently bonded ECM should be sufficient to prevent monolayers from detaching, leading to similar extrusion behaviors. In response to the reviewer’s recommendation then, we have included a short paragraph to state the points discussed in this response.

Recommendation 2: Fig 3E, F: The quantities displayed on the panels are not forces, but have units of pressure (or stress).

Response 2: we have changed “force” to “stress” according to the reviewer’s suggestion. The reason we kept the use of force in the original text was due to the fact that we were reconstructing forces. Due to discretization, the resulting forces will inevitably be assigned to element nodes. In between the nodes, in the faces, there will be no information. So, in order to have some form of continuity to plot, the face forces are obtained by averaging the 4 nodes around the element face. Unfortunately, element face areas are not typically of the same size, therefore the average forces obtained needs to be further normalized against the face area, leading to a quantity that has units of stress.

Recommendation 3: Fig 2D: Asterisks are hard to see.

Response 3: the color of the asterisks has been changed to green for better clarity against a B&W background.

Recommendation 4: p 19, l 7: Word missing in "the of molding"

Response 4: the typo has been amended to “the molding of”.

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:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

Wang et al. generate XAP5 and XAP5L knockout mice and find that they are male infertile due to meiotic arrest and reduced sperm motility, respectively. RNA-Seq was subsequently performed and the authors concluded that XAP5 and XAP5L are antagonistic transcription factors of cilliogenesis (in XAP5-KO P16 testis: 554 genes were unregulated and 1587 genes were downregulated; in XAP5L-KO sperm: 2093 genes were unregulated and 267 genes were downregulated).

We are grateful for the comprehensive summary.

Strengths:

Knockout mouse models provided strong evidence to indicate that XAP5 and XAP5L are critical for spermatogenesis and male fertility.

Thank you for your positive comment.

Weaknesses:

The key conclusions are not supported by evidence. First, the authors claim that XAP5 and XAP5L transcriptionally regulate sperm flagella development; however, detailed molecular experiments related to transcription regulation are lacking. How do XAP5 and XAP5L regulate their targets? Only RNA-Seq is not enough. Second, the authors declare that XAP5 and XAP5L are antagonistic transcription factors; however, how do XAP5 and XAP5L regulate sperm flagella development antagonistically? Only RNA-Seq is not enough. Third, I am concerned about whether XAP5 really regulates sperm flagella development. XAP5 is specifically expressed in spermatogonia and XAP5-cKO mice are in meiotic arrest, indicating that XAP5 regulates meiosis rather than sperm flagella development.

Thank you for the critical comments. To strengthen our conclusions, we have included XAP5/XAP5L CUT&Tag data in our revised manuscript. This highly sensitive method has allowed us to identify direct target genes of XAP5 and XAP5L (Table S1, Figure S6). Notably, our results demonstrate that both FOXJ1 and RFX2 are occupied by XAP5 (Figure 4G). Additionally, real-time PCR validation confirmed that RFX2 is also associated with XAP5L, even though enriched peaks for the RFX2 gene were not detected in the initial CUT&Tag data (Figure 4G). These findings indicate that XAP5 and XAP5L regulate the expression of FOXJ1 and RFX2 by directly binding to these genes. De novo motif analyses revealed that XAP5 and XAP5L shared a conserved binding sequence (CCCCGCCC/GGGCGGGG) (Figure S6C), and the bound regions of FOXJ1 and RFX2 contain this sequence. Further analysis shows that many XAP5L target genes are also targets of XAP5 (Figure S6G), despite the limited number of identified XAP5L target genes. This differential binding and regulation of shared target genes underscore the antagonistic relationship between XAP5 and XAP5L. Collectively, these findings provide additional support for the idea that XAP5 and XAP5L function as antagonistic transcription factors, acting upstream of transcription factor families, including FOXJ1 and RFX factors, to coordinate ciliogenesis during spermatogenesis.

While we agree that XAP5 primarily regulates meiosis during spermatogenesis, our data also indicate that many cilia-related genes, including key transcription regulators of spermiogenesis such as RFX2 and SOX30, are downregulated in XAP5-cKO mice and are bound by XAP5 (Figure 4, Figures S4 and S6). It is important to note that genes coding for flagella components are expressed sequentially and in a germ cell-specific manner during development. When we refer to "regulating sperm flagella development", we mean the spatiotemporal regulation. We have revised the manuscript to clarify this point.

Reviewer #2 (Public Review):

In this study, Wang et al., report the significance of XAP5L and XAP5 in spermatogenesis, involved in transcriptional regulation of the ciliary gene in testes. In previous studies, the authors demonstrate that XAP5 is a transcription factor required for flagellar assembly in Chlamydomonas. Continuing from their previous study, the authors examine the conserved role of the XAP5 and XAP5L, which are the orthologue pair in mammals.

XAP5 and XAP5L express ubiquitously and testis specifically, respectively, and their absence in the testes causes male infertility with defective spermatogenesis. Interestingly, XAP5 deficiency arrests germ cell development at the pachytene stage, whereas XAP5L absence causes impaired flagellar formation. RNA-seq analyses demonstrated that XAP5 deficiency suppresses ciliary gene expression including Foxj1 and Rfx family genes in early testis. By contrast, XAP5L deficiency abnormally remains Foxj1 and Rfx genes in mature sperm. From the results, the authors conclude that XAP5 and XAP5L are the antagonistic transcription factors that function upstream of Foxj1 and Rfx family genes.

This reviewer thinks the overall experiments are performed well and that the manuscript is clear. However, the current results do not directly support the authors' conclusion. For example, the transcriptional function of XAP5 and XAP5L requires more evidence. In addition, this reviewer wonders about the conserved XAP5 function of ciliary/flagellar gene transcription in mammals - the gene is ubiquitously expressed despite its functional importance in flagellar assembly in Chlamydomonas. Thus, this reviewer thinks authors are required to show more direct evidence to clearly support their conclusion with more descriptions of its role in ciliary/flagellar assembly.

Thank you for your thoughtful review of our work. We appreciate your positive feedback on the overall quality of the experiments and the clarity of the manuscript. In response to your concerns, we have included new experimental data and made revisions to the manuscript (lines 193-217) to better support our conclusions, particularly regarding the transcriptional function of XAP5 and XAP5L. Additionally, we have expanded on the role of XAP5 in ciliary and flagellar assembly to provide more direct evidence for its functional importance. Thank you for your insights.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

The title (Control of ciliary transcriptional programs during spermatogenesis by antagonistic transcription factors) is not specific and does tend to exaggerate.

Thank you for the comment, and we appreciate the opportunity to clarify the appropriateness of the title. Our paper extensively investigates the transcriptional regulation of ciliary genes during spermatogenesis. It demonstrates that XAP5/XAP5L are key transcription factors involved in this process. The title reflects our primary focus on the transcriptional programs that govern ciliary gene expression. Moreover, our paper shows that XAP5 positively regulates the expression of ciliary genes, particularly during the early stages of spermatogenesis, while XAP5L negatively regulates these genes. This antagonistic relationship is a crucial aspect of the study and is effectively conveyed in the title. In addition, our revised paper provides detailed insights into how XAP5/XAP5L control ciliary gene expression during spermatogenesis.

Figure 4C: FOXJ1 and RFX2 are absent in sperm from WT mice. Are you sure? They are highly expressed in WT testes.

Thank you for your careful review. While FOXJ1 and RFX2 are indeed highly expressed in the testes of wild-type (WT) mice, our data show that they are not detectable in mature sperm. This observation is consistent with published single-cell RNA-seq data(Jung et al., 2019), which indicate that FOXJ1 and RFX2 are primarily expressed in spermatocytes but not in spermatids (Figure S7). This expression pattern aligns with that that of IFT-particle proteins, which are essential for the formation but not the maintenance of mammalian sperm flagella(San Agustin, Pazour, & Witman, 2015).

XAP5 is specifically expressed in spermatogonia and XAP5-cKO mice are in meiotic arrest, indicating that XAP5 regulates meiosis rather than sperm flagella development.

We appreciate your insightful comments. As mentioned above, we agree that XAP5 primarily regulates meiosis during spermatogenesis. When we mentioned "regulating sperm flagella development," we were referring to the spatiotemporal regulation of these processes. We have revised the manuscript to clarify this distinction. Thank you for your understanding.

The title of Figure 2 (XAP5L is required for normal sperm formation) is not accurate because the progress of spermatogenesis and sperm count is normal in XAP5L-KO mice (only sperm motility is reduced).

We apologize for any confusion caused by the previous figure. It did not accurately convey the changes in sperm count. In the revised Figure 2B, we clearly demonstrate that the sperm count in XAP5L-KO mice is indeed lower than that in WT mice. This revision aims to provide a more accurate representation of the effects of XAP5L deficiency on spermatogenesis. Thank you for bringing this to our attention.

Reviewer #2 (Recommendations For The Authors):

(1) Although XAP5 and XAP5L deficiency alters the transcription of Foxj1 and Rfx family genes, which are the essential transcription factors for the ciliogenesis, current data do not directly support that XAP5 and XAP5L are the upstream transcription factors. The authors need to show more direct evidence such as CHIP-Seq data.

Thank you for your valuable feedback! In this revised manuscript, we have included data identifying candidate direct targets of XAP5 and XAP5L using the highly sensitive CUT&Tag method (Kaya-Okur et al., 2019). Our results show that XAP5 occupies both FOXJ1 and RFX2 (Figure 4G). Furthermore, real-time PCR validation of the CUT&Tag experiments confirmed that RFX2 is also occupied by XAP5L (Figure 4G), despite the initial CUT&Tag data not revealing enriched peaks for the RFX2 gene (Table S1). Unfortunately, the limited number of enriched peaks identified for XAP5L (Table S1) suggests that the XAP5L antibody used in the CUT&Tag experiment might have suboptimal performance, which prevented us from detecting occupancy on the FOXJ1 promoter. Nevertheless, these additional data provide strong evidence that XAP5 and XAP5L function as upstream transcription factors for FOXJ1 and RFX family genes, supporting their essential roles in ciliogenesis.

(2) Shared transcripts that are altered by the absence of either XAP5 or XAP5L do not clearly support they are antagonistic transcription factors.

Thank you for your insightful comment. In our revised manuscript, we performed CUT&Tag analysis to identify target genes of XAP5 and XAP5L. Motif enrichment analysis revealed conserved binding sequences for both factors (Figures S6C), indicating a subset of shared downstream genes between XAP5 and XAP5L. Among the downregulated genes in XAP5 cKO germ cells, 891 genes were bound by XAP5 (Figure S6D). Although the number of enriched peaks identified for XAP5L was limited, 75 of the upregulated genes in XAP5L KO sperm were bound by XAP5L (Figure S6E). Importantly, of these 75 XAP5L target genes, approximately 30% (22 genes) were also identified as targets of XAP5 (Figure S6G), further support the idea that XAP5 and XAP5L function as antagonistic transcription factors.

(3) XAP5 seems to be an ancient transcription factor for cilia and flagellar assembly. However, XAP5 expresses ubiquitously in mice. How can this discrepancy be explained? Is it also required for primary cilia assembly? Are their expression also directly linked to ciliogenesis in other types of cells?

Thank you for the thoughtful questions. The ubiquitous expression of XAP5 in mice can be understood in light of its role as an ancient transcription factor for cilia and flagellar assembly. Given that cilia are present on nearly every cell type in the mammalian body (O'Connor et al., 2013), this broad expression pattern makes sense. In fact, XAP5 serves not only as a master regulator of ciliogenesis but also as a critical regulator of various developmental processes (Kim et al., 2018; Lee et al., 2020; Xie et al., 2023).

Our current unpublished work demonstrates that XAP5 is essential for primary cilia assembly in different cell lines. The loss of XAP5 protein results in abnormal ciliogenesis, further supporting its vital role in ciliary formation across different cell types.

We believe that the widespread expression of XAP5 reflects its fundamental importance in multiple cellular processes, including ciliogenesis, development, and potentially other cellular functions yet to be discovered.

(4) XAP5L causes impairs flagellar assembly. Have the authors observed any other physiological defects in the absence of XAP5L in mouse models? Such as hydrocephalus and/or tracheal defects?

Thank you for the questions. We have carefully examined XAP5L KO mice for other physiological defects. To date, we have not observed any additional physiological abnormalities. Specifically, we assessed the condition of tracheal cilia in XAP5L KO mice and found no significant differences compared to wild-type (WT) mice, as illustrated in Author response image 1 below.

Author response image 1.

References

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Kaya-Okur, H. S., Wu, S. J., Codomo, C. A., Pledger, E. S., Bryson, T. D., Henikoff, J. G., . . . Henikoff, S. (2019). CUT&Tag for efficient epigenomic profiling of small samples and single cells. Nat Commun, 10(1), 1930. doi:10.1038/s41467-019-09982-5

Kim, Y., Hur, S. W., Jeong, B. C., Oh, S. H., Hwang, Y. C., Kim, S. H., & Koh, J. T. (2018). The Fam50a positively regulates ameloblast differentiation via interacting with Runx2. J Cell Physiol, 233(2), 1512-1522. doi:10.1002/jcp.26038

Lee, Y.-R., Khan, K., Armfield-Uhas, K., Srikanth, S., Thompson, N. A., Pardo, M., . . . Schwartz, C. E. (2020). Mutations in FAM50A suggest that Armfield XLID syndrome is a spliceosomopathy. Nature Communications, 11(1). doi:10.1038/s41467-020-17452-6

O'Connor, A. K., Malarkey, E. B., Berbari, N. F., Croyle, M. J., Haycraft, C. J., Bell, P. D., . . . Yoder, B. K. (2013). An inducible CiliaGFP mouse model for in vivo visualization and analysis of cilia in live tissue. Cilia, 2(1), 8. doi:10.1186/2046-2530-2-8

San Agustin, J. T., Pazour, G. J., & Witman, G. B. (2015). Intraflagellar transport is essential for mammalian spermiogenesis but is absent in mature sperm. Mol Biol Cell, 26(24), 4358-4372. doi:10.1091/mbc.E15-08-0578

Xie, X., Li, L., Tao, S., Chen, M., Fei, L., Yang, Q., . . . Chen, L. (2023). Proto-Oncogene FAM50A Can Regulate the Immune Microenvironment and Development of Hepatocellular Carcinoma In Vitro and In Vivo. Int J Mol Sci, 24(4). doi:10.3390/ijms24043217

Author response:

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

Joint Public Review:

Previously, this group showed that Tgfbr1 regulates the reorganization of the epiblast and primitive streak into the chordo-neural hinge and tailbud during the trunk-to-tail transition. Gdf11 signaling plays a crucial role in orchestrating the transition from trunk to tail tissues in vertebrate embryos, including the reallocation of axial progenitors into the tailbud and Tgfbr1 plays a key role in mediating its signaling activity. Progenitors that contribute to the extension of the neural tube and paraxial mesoderm into the tail are located in this region. In this work, the authors show that Tgfbr1 also regulates the reorganization of the posterior primitive streak/base of allantois and the endoderm as well. 

By analyzing the morphological phenotypes and marker gene expression in Tgfbr1 mutant mouse embryos, they show that it regulates the merger of somatic and splanchnic layers of the lateral plate mesoderm, the posterior streak derivative. They also present evidence suggesting that Tgfbr1 acts upstream of Isl1 (key effector of Gdf11 signaling for controlling differentiation of lateral mesoderm progenitors) and regulates the remodelling of the major blood vessels, the lateral plate mesoderm and endoderm associated with the trunk-to-tail transition. Through a detailed phenotypic analysis, the authors observed that, similarly to Isl1 mutants, the lack of Tgfbr1 in mouse embryos hinders the activation of hindlimb and external genitalia maker genes and results in a failure of lateral plate mesoderm layers to converge during tail development. As a result, they interpret that ventral lateral mesoderm, which generates the peri cloacal mesenchyme and genital tuberculum, fails to specify. 

They also show defects in the morphogenesis of the dorsal aorta at the trunk/tail juncture, resulting in an aberrant embryonic/extraembryonic vascular connection. Endoderm reorganization defects following abnormal morphogenesis of the gut tube in the Tgfbr1 mutants cause failure of tailgut formation and cloacal enlargement. Thus, Tgfbr1 activity regulates the morphogenesis of the trunk/tail junction and the morphogenetic switch in all germ layers required for continuing post-anal tail development. Taken together with the previous studies, this work places Gdf11/8 - Tgfbr1 signaling at the pivot of trunk-to-tail transition and the authors speculate that critical signaling through Tgfbr1 occurs in the posterior-most part of the caudal epiblast, close to the allantois. 

Strengths: 

The data shown is solid with excellent embryology/developmental biology. This work demonstrates meticulous execution and is presented in a comprehensive and coherent manner. Although not completely novel, the results/conclusions add to the known function of Gdf11 signaling during the trunk-to-tail transition. 

Weaknesses: 

The authors rely on the expression of a small number of key regulatory genes to interpret the developmental defects. The alternative possibilities remain to be ruled out thoroughly. The manuscript is also quite descriptive and would benefit from more focused highlighting of the novelty regarding the absence of Tgfbr1 in the mouse embryo. They should also strengthen some of their conclusions with more details in the results.

Although we used a limited number of key regulatory genes to interpret the phenotype, these genes were carefully chosen to focus on specific processes involving the lateral mesoderm, its derivatives, and the endoderm. In addition to these markers, we included references to other relevant markers that were previously analyzed and initially led us to examine the lateral plate mesoderm and tail gut in Tgfbr1 mutants. To strengthen our analysis, we have now incorporated additional data to clarify specific phenotypes. For instance, in situ hybridization (ISH) for Shh further confirms abnormalities at the caudal end of the endoderm in mutant embryos, while no endodermal defects are observed in the trunk region. We also included an analysis of the intermediate mesoderm, which shows abnormalities at the same level as those found in the lateral plate mesoderm and endoderm of Tgfbr1 mutants.

It’s important to note that using additional markers to assess the epiblast/primitive streak of Tgfbr1 mutants at E7.5–E8.5, as suggested by a reviewer, is unlikely to yield new insights. At these early stages, Tgfbr1 mutant embryos do not display observable phenotypes in the main body axis. Data in this manuscript already demonstrate the absence of abnormalities at this stage, as shown in Figure 3 and Supplementary Figure 6. Additionally, the expression of certain genes showing abnormalities when the embryo would enter tail development, in the trunk their expression remains unaffected, indicating that trunk extension is not significantly impacted by Tgfbr1 deficiency. While transcriptomic analysis of these Tgfbr1 mutants could provide interesting insights, it would be more appropriate to focus on later developmental stages, which would be beyond the scope of the current study.

The second major critique was that the manuscript is primarily descriptive. We disagree with this assessment. Several hypotheses were rigorously tested using genetic approaches, including Isl1 knockout experiments, cell tracing from the primitive streak with a newly generated Cre driver to activate a reporter from the ROSA26 locus, and assessment of extraembryonic endoderm fate in Tgfbr1 mutants by introducing the Afp-GFP transgene into the Tgfbr1 mutant background. Additionally, we conducted tracing analyses of tail bud cell contributions to the tail gut via DiI injection and embryo incubation. To address potential concerns regarding this experiment, we have included data showing the DiI position immediately after injection to confirm that it does not contact the tail gut. We also considered and accounted for potential DiI leakage into neuromesodermal progenitors to clarify the endodermal results.

Our genetic and DiI experiments were specifically designed to differentiate between alternative hypotheses and to confirm hypotheses generated from other analyses. Additionally, improvements in some of the imaging data have helped address remaining concerns.

Reviewer #1 (Recommendations For The Authors): 

I have listed my suggestions as queries. The authors may perform experiments or clarify by editing the text to address them. 

The authors state on Page 11 and elsewhere that the ventral lateral mesoderm is absent in the Tgfbr1 mutant. What is the basis for this conclusion? Are there specific markers for PCM or GT primordium? 

The specific marker of PCM and GT primordium is Isl1. The absence of this marker in the Tgfbr1 mutants is shown in (Dias et al, 2020). The reference is introduced in the manuscript.

A schematic illustrating the VLM and the expression patterns of Tgfbr1, Gdf11, etc., would be helpful. 

Characterization of Gdf11 expression has been previously reported (e.g. McPherron et al 1999, cited in our manuscript). It is expressed in the region containing of axial progenitors before the trunk to tail transition and not expressed in the VLM. As for Tgfbr1 expression is hard to detect, likely because it is ubiquitously expressed at low level. We include in this document some pictures of an ISH, including a control using the Tgfbr1 mutants to illustrate that the staining resembling background actually represents Tgfbr1 expression. If the reviewers find it important, we can also incorporate these data into the manuscript. Under these circumstances, we feel that a schematic might not be very informative.

Author response image 1.

Image showing an example of an ISH procedure with a probe against Tgfbr1, showing widespread and low expression. The lower picture shows a ventral view of a stained wild type E10.5 embryo.

Foxf1+ cells in the 'extended LPM' of Tgfbr1 mutants suggest fate transformation, or does it indicate the misexpression of marker gene otherwise suppressed by Tgfbr1 activity? The authors suggest that Foxf1+ cells are VLM progenitors from posterior PS trapped in the extended LPM. Do they continue to express PS markers? 

The observation that both in wild type and Tgfbr1 mutant embryos Foxf1 expression in the trunk is restricted to the splanchnic LPM indicates that the absence of this marker in the somatic LPM is not the result of a suppression of its expression by Tgfbr1. In wild type embryos Foxf1 is also expressed in the posterior PS, regulated independently of its expression in the LPM (i.e. Shh-independent) and later in the pericloacal mesoderm (our supplementary figure 2). As Foxf1 expression in the posterior PS was not suppressed in the Tgfbr1 mutants, together with the absence of pericloacal mesoderm, we interpret that the Foxf1-positive cells in the two layers around the extended celomic cavity in the posterior end of the mutant embryos derived from the posterior PS, resulting from the absence of its normal progression through the embryonic tissues.

We did not find expression of PS markers giving rise to paraxial mesoderm, like Tbxt, further suggesting that those cells could derive from the restricted set of cells within the posterior PS that contribute to the pericloacal mesoderm

For example, the misexpression of Apela is interpreted as mis-localized endoderm cells. They show scattered Keratin 8 misexpression to support the interpretation. It would be more convincing if the authors tested the expression of other endoderm markers. 

As indicated in the manuscript, we suggest that these cells are endoderm progenitors (p. 13), like those present at the posterior end of the gut tube at E9.5 and E10.5, that are unable to incorporate into the gut tube. Apela is not a general endodermal marker: it is expressed in the foregut pocket and the nascent cells of the hindgut/tail gut, becoming down regulated as cells take typical endodermal signatures. The presence of ectopic Apela expression in the extended LPM of the mutant embryos might indeed indicate the presence of progenitors that failed to downregulate Apela resulting from the lack differentiation-associated downregulation. This would also implicate the absence of definitive endodermal markers.

The Nodal signaling pathway in the anterior PS drives endoderm development. It acts through Alk7. Does Tgfbr1 (Alk5) mutation impact endoderm development, in general? It isn't easy to assess this from the Foxa2 in situ RNA hybridization shown in Figures 6A and B. It would be helpful for the readers if the authors clarified this point. 

In the pictures shown in Figure 7D-D’ it is already shown that the endoderm is mostly preserved until the region of the trunk to tail transition. The presence of a rather normal endoderm in the embryonic trunk can also be seen with Shh, a figure added as Supplementary Fig.5.

Reviewer #2 (Recommendations For The Authors): 

The authors mention two interesting novel points which they should develop in the discussion, and probably also in the results. 

(1) The authors speculate about the possible involvement of the posterior PS as a mediator of Gdf11/Tgfbr1 signaling activity. However, as mentioned in the manuscript, their experiments do not allow regional sublocalization within the PS... Here it would be important to assess/discuss in more detail which progenitors respond to this signaling activity and when they do it. At the very least, the authors should provide high-resolution spatiotemporal data of the expression of Tgfbr1 in the PS. 

Tgfbr1 expression at this embryonic stage does not give clear differential patterns. The data reported for this expression in Andersson et al 2006 is very low quality and we have not been able to reproduce the reported pattern. On the contrary, all our efforts over the years provided a very general staining that could even be interpreted as background. When we now included Tgfbr1 mutants as controls, it became clear that the ubiquitous and low level signal observed in wild type embryos indeed represent Tgfbr1 expression pattern: low level and ubiquitous. We are attaching a figure to this document illustrating these observations. If required, this can also be included in the manuscript as a supplementary figure. 

Also, the work of Wymeersch et al., 2019 regarding the lateral plate mesoderm progenitors (LPMPs) should be referred to and discussed here. 

This was now added in the results (page 11) and in discussion (page 16). 

For instance, are the LPMP transcriptomic differences detected between E7.5 and E8.5 caused by Tgfbr1 signaling activity? This question could be easily answered through a comparative bulk RNAseq analysis of the posterior-most region of the PS of mutant and WT embryos. The possible colocalization of Tgfb1 (Wymeersch et al., 2019) and Tgfbr1 in the LPMPs should also be addressed. 

We agree with the suggestion that RNA-seq in the posterior PS of WT and mutant embryos might be informative. However, it is very likely that within the proposed timeframe (E7.5 to E8.5) that there are no significant differences between the wild type and the Tgfbr1 mutant embryos because there is no apparent axial phenotype in Tgfbr1 mutant embryos before the trunk to tail transition. Therefore, at this stage, we think that this experiment is out of the scope of the present manuscript. 

(2) The activity of Tgfbr1 during the trunk-to-tail transition is critical for the development of tail endodermal tissues. Here the authors suggest again the involvement of the posterior PS/allantois region, but a similar phenotype can also be observed for instance in the absence of Snai1 in the caudal epiblast (Dias et al., 2020)... It would be important to assess/discuss the origin of those morphogenetic problems in the gut. Is it due to the reallocation of NMC cells into the CNH? The tailbud-EMT process? LPMPs specification?... Regional mutations or gain of functions of Snai1 or Tgfbr1 in the caudal epiblast would help answer the question.  

The endodermal phenotype in the Snai1 mutants is different to that observed in the Tgfbr1 mutants. As can be observed in Figures 3, 4 and 5 of Dias et al. the absence of tailbud is replaced by a structure that extends the epiblast. As a consequence, the endoderm finishes at the base of that structure, even expanding to make a structure resembling the cloaca, which is different to what is seen in the Tgfbr1 mutants. In this case, the lack of tail gut is likely to result either from the lack of formation of the progenitors of the gut endoderm or from the dissociation of what would be the tail bud from the LPM. Actually, hindlimb/pericloacal mesoderm markers, like Tbx4, are preserved in the Snai1 mutant. As for the gain of function of Snai1 experiment, already reported also in Dias et al 2020, the destiny of these cells is not clear. The ISH for Foxa2 showed extra signals but as it is not an exclusive marker for endoderm it is not possible to know whether any of these signals correspond to endodermal tissues.

Regarding the development of tail endodermal tissues, the authors suggest that it occurs from a structure derived from the PS that is located posteriorly, in the tailbud, after the tip of the growing gut. This is an important and novel point as it suggests that the primordia of the endoderm is not wholly specified during gastrulation. So the observation should be well supported. How can Anastasiia et al. distinguish such "structure" from the actual developing gut? Does it have a distinct molecular signature or any morphological landmark that enables its separation from the actual gut? The data suggests that the region highlighted in Supplementary Figure 4Ab contains part of the actual gut tube (the same is suggested in Figure 5B). If the authors think otherwise, they must characterize that region of the tailbud by doing a thorough morphological and gene/protein expression analysis and assess its potency, via transplantation experiments. Also, the authors' claim mostly relies on the DiI experiments and those have three problems: #1 Anastasiia et al. assess "tail" endodermal growth at E9.5 when the correct stage to do it is after E10.5 (after tailbud formation). 2# Incongruencies, low number (only three embryos), and diversity in the results shown in Figure 8 and Supplementary Figure 4. For instance, despite similar staining at 0h, the extension and amount of DiI present in the gut tube after 20h varies significantly amongst the differently labeled embryos. A possible explanation lies in the abnormal leakiness of the DiI labelings and that is confirmed by the observations shown in Supplementary Figure 4M-O; the same for Supplementary Figure 4G, which shows a substantial amount of DiI in the neural tube. 3# The authors must provide high-quality data showing which tissues/regions were labelled at time 0h, including transversal and sagittal sections as they did for the 20h time-point. Additionally, it is important to re-orient the sagittal optical sections to a position that also shows the neural tube (like a mid-sagittal section) and include information concerning the AP/DV axis, as well as the location of the transversal optical sections in the sagittal image. 

As described in the reply to reviewer 1, Apela is expressed in the nascent tail gut endoderm but not in more anterior areas except for a foregut pocket, and becomes downregulated as the tube acquires endodermal signatures. Therefore, the structure to which the reviewer refers to might indeed represent a group of progenitors that extend the tail gut. And the observation that this property is observed only in the tail gut as it grows, already separates this region of the gut, which in the end do not contribute to mature organs, from more anterior areas of the endoderm (essentially anterior to the cloaca) that will become a relevant tissue of the intestinal organs. Our DiI labelling experiment was aimed to test whether this pool of cells contributes to the gut but does not allow to determine the nature of those cells, a question that will require further research (discussed on p. 17) and we think is beyond the scope of the present manuscript.

Regarding the labelling at E10.5, we agree that the tail bud in terms of NMCs is not completely formed, for example, at E9.5 the neuropore is not yet closed. However, we are more interested in regression of the epiblast, which is complete by E9.5. Injecting at E9.5 also has technical advantages for us, first, because in our hands earlier embryos grow better in culture, and second, because it is easier to inject in the tailbud at E9.5 because it is a little bit bigger than at E10.5. Therefore, injecting at E9.5 is less prone to technical artifacts due to injection inaccuracy and compromised growth in culture.

We agree that the injected DiI could also leak into NMPs, which might be located in the same area. However, while this could result in labeling of the neural tube, it would not affect the interpretation of the finding of labeled cells in the tail gut. Indeed, the presence of this label in the gut epithelium indicates the presence of progenitors in the injected region of the tail gut. We added some considerations of this the possible leakage into the results section of the manuscript (p. 15). We thank the reviewer for drawing our attention to this issue. 

We also now provide high quality data showing labelled tissue at 0h in Supplementary figure 8A-c’, higher magnification images in Fig. 8, and reoriented optical sections in Fig.6 and in Supplementary Fig. 7, including axis and location of the sections as suggested by the reviewer.

Minor concerns/comments: 

(1) The abstract is quite long, though this might be fine for this journal. 

(2) In relation to the comment on the abstract, the manuscript needs an initial Figure descrbing the events that are described in the introduction. Otherwise, the manuscript will only be accessible to mouse embryologists.

We have a figure summarizing the results at the end of the manuscript, we think that including similar figure in the beginning might be redundant. What we could do, if required, is to include this type of schematic as a graphical abstract.

(3) The authors need to clarify what they mean when they use the following expressions "PS fate" and "fate of the posterior PS".

I do not think that we have used such expressions. Indeed, they did not come out when we run a “find” in the word document. However, they would mean the tissue that would come out from them at later developmental stages.

(4) The assessment of Isl1 expression in Tgfbr1 mutant and transgenic mouse embryos would be better indicative of their molecular relationship than a comparative phenotypic analysis. 

These data have been reported in Dias et al 2020 and Jurberg et al 2013, both cited in the manuscript.  

(5) The authors should explain or discuss what the upregulation of Foxa2 in the posterior end of Tgfbr1 mutants means.

While an upregulation is apparent in the figure, looking at other pictures we cannot be sure of this being a significantly quantifiable up-regulation. We therefore removed the statement from the text.

(6) What happens to the intermediate mesoderm during the trunk-to-tail transition? Is Tgfbr1 involved in the regulation of its development?

We have tested this using Pax2 and added the relevant data in Supplementary Fig. 1 and described in the results.

(7) The term "potential" should not be used during the description of DiI labeling experiments as this technique only assesses cell fate.

Corrected

(8) Some figures lack AP/DV axis information (e.g. Figures 6, C, and D).

Corrected

Author Response

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

We would like to extend our gratitude to the reviewers for their meticulous analysis and constructive feedback on our manuscript. We have revised our paper based on the suggestions regarding supporting literature and the theory behind CAPs along with detailed insights regarding our methods. Their suggestions have been extremely useful in strengthening the clarity and rigor of our manuscript.

Reviewer #1 (Recommendations For The Authors):

(1) There are no obvious problems with this paper and it is relatively straightforward. There are some challenges that I would like to suggest. These variants have multiple mutations, so it would be interesting if you could drill down to find out which mutation is the most important for the collective changes reported here. I would like to see a sequence alignment of these variants, perhaps in the supplemental material, just to get some indication of the extent of mutations involved.

Finding the most important mutation within a set is a tricky question, as each mutation changes the way future mutations will affect function due to epistasis. Indeed, this is what we aim to explore in this work. To illustrate this point, we included a new supplementary figure S5A. Three critical mutations that emerged quickly, and were frequently observed in other dominant variants, were S477N, T478K, and N501Y. Thus, we computed the EpiScore values of these three mutations, with several critical residues contributing to hACE2 binding. The EpiScore distribution indicates that residues 477, 478, and 501 have strong epistatic (i.e., non-additive) interactions, as indicated by EpiScore values above 2.0.

To further investigate these epistatic interactions, we first conducted MD simulations and computed the DFI profile of these three single mutants. We analyzed how different the DFI scores of the hACE2 binding interface residues of the RBD are, across three single mutants with Omicron, Delta, and Omicron XBB variants (Fig S5B). Fig S5B shows how mutations at these particular sites affect the binding interface DFI in various backgrounds, as the three mutations are also observed in the Omicron, XBB, and XBB 1.5 variants. If the difference in the DFI profile of the mutant and the given variant is close to 0, then we could safely state that this mutation affected the variant the most. However, what we observe is quite the opposite: the DFI profile of the mutation is significantly different in different variant backgrounds. While these mutations may change overall behavior, their individual contributions to overall function are more difficult to pin down because overall function is dependent on the non-additive interactions between many different residues.

Author response image 1.

(A) Three critical mutations that emerged quickly, and were frequently observed in other dominant variants, were S477N, T478K, and N501Y. EpiScores of sites 477, 478, and 501 with one another are shown with k = the binding interface of the open chain. These residues are highly epistatic, producing higher responses than expected when perturbed together. (B) The difference in the dynamic flexibility profiles between the single mutants and the most common variants for the hACE2 binding residues of the RBD. DFI profiles exhibit significant variation from zero, and also show different flexibility in each background variant, highlighting the critical non-additive interactions of the other mutation in the given background variant. Thus, these three critical mutations, impacting binding affinity, do not solely contribute to the binding. There are epistatic interactions with the other mutations in VOCs that shape the dynamics of the binding interface to modulate binding affinity with hACE2.

As we discussed above, while the epistatic interactions are crucial and the collective impact of the mutations shape the mutational landscape of the spike protein, we would like note that mutation S486P is one of the critical mutations we identify, modulating both antibody and hACE2 binding and our analysis reveals the strong non-additive interactions with the other mutational sites. This mutational site appears in both XBB1.5 and earlier Omicron strains which highlights its importance in functional evolution of the spike protein. CAPs 346R, 486F, and 498Q also may be important, as they have a high EpiScore, indicating critical epistatic interaction with many mutation sites.

Regarding to the suggestion about presenting the alignment of the different variants, we have attached a mutation table, highlighting the mutated residues for each strain compared to the reference sequence as supplemental Figure S1 along with the full alignment file.

(2) Also, I am wondering if it would be possible to insert some of these flexibilities and their correlations directly into the elastic network models to enable a simpler interpretation of these results. I realize this is beyond the scope of the present work, but such an effort might help in understanding these relatively complex effects.

This is great suggestion. A similar analysis has been performed for different proteins by Mcleash (See doi: 10.1016/j.bpj.2015.08.009) by modulating the spring constants of specific position to alter specific flexibility and evaluate change in elastic free energy to identify critical mutation (in particular, allosteric mutation) sites. We will be happy to pursue this as future work.

Minor

(3) 1 typo on line 443 - should be binding instead of biding.

Fixed, thanks for spotting that.

(4) The two shades of blue in Fig. 4B were not distinguishable in my version.

To fix this, we have changed the overlapping residues between Delta and Omicron to a higher contrast shade of blue.

(5) Compensatory is often used in an entirely different way - additional mutations that help to recover native function in the presence of a deleterious mutation.

Although our previous study (Ose et al. 2022, Biophysical Journal) shows that compensatory mutations were generally additive, the two ideas are not one and the same. We thank the reviewer for pointing this out. Therefore, to clarify, we have now described our results in terms of dynamic additivity, rather than compensation.

Reviewer #2 (Recommendations For The Authors):

(1) The authors note that the identified CAPs overlap with those of others (Cagliani et al. 2020; Singh and Yi 2021; Starr, Zepeda, et al. 2022). In itself, this merits a deeper discussion and explicit indication of which positions are not identified. However, there is one point that I believe may represent a fundamental flaw in this study in that the calculation of EP from the alignment of S proteins ignores entirely the differences in the interacting interface with which S for different coronaviruses in the alignment interact in the different receptors in each host species. This may be the reason why so many "CAPs" are in the RBD. The authors should at the very least make a convincing case of why they are not simply detecting constraints imposed by the different interacting partners, at least in the case of positions within the RBD interface with ACE2. Another point that the authors should discuss is that ACE2 is not the only receptor that facilitates infection, TMPRSS2 and possibly others have been identified as well. The results should be discussed in light of this.

To begin with, we have now explicitly noted (on line 135) that “sites 478, 486, 498, and 681 have already been implicated in SARS-CoV-2 evolution, leaving the remaining 11 CAPs as undiscovered candidate sites for adaptation.” Evolutionary analyses are done using orthologous protein sequences, so there is no way to integrate information on different receptors in each host species in the calculation of EPs. However, we appreciate that the preponderance of CAPs in the RBD is likely due to different binding environments. We have added the following text (on line 83) to clarify our point: “Adaptation in this case means a virus which can successfully infect human hosts. As CAPs are unexpected polymorphisms under neutral theory, their existence implies a non-neutral effect. This can come in the form of functional changes (Liu et al. 2016) or compensation for functional changes (Ose et al. 2022). Therefore, we suspect that these CAPs, being unexpected changes from coronaviruses across other host species with different binding substrates, may be partially responsible for the functional change of allowing human infection.” This hypothesis is supported by the overlap of CAPs we identified with the positions identified in other studies (e.g., 478, 486, 498, and 681). Binding to TMPRSS2 and other substrates are also covered by this analysis as it is a measure of overall evolutionary fitness, rather than binding to any specific substrate. Our paper does focus on discussing hACE2 binding and mentions furin cleavage, but indeed lacks discussion on the role of TMPRSS2. We have added the following text to line 157: “Another host cell protease, TMPRSS2, facilitates viral attachment to the surface of target cells upon binding either to sites Arg815/Ser816, or Arg685/Ser686 which overlaps with the furin cleavage site 676-689, further emphasizing the importance of this area (Hoffmann et al. 2020b; Fraser et al. 2022).”

(2) Turning now to the computational methods utilized to study dynamics, I have serious reservations about the novelty of the results as well as the validity of the methodology. First of all, the authors mention the work of Teruel et al. (PLOS Comp Bio 2021) in an extremely superficial fashion and do not mention at all a second manuscript by Teruel et al. (Biorxiv 2021.12.14.472622 (2021)). However, the work by Teruel et al. identifies positions and specific mutations that affect the dynamics of S and the evolution of the SARS-CoV-2 virus in light of immune escape, ACE2 binding, and open and closed state dynamics. The specific differences in approach should be noted but the results specifically should be compared. This omission is evident throughout the manuscript. Several other groups have also published on the use of nomal-mode analysis methods to understand the Spike protein, among them Verkhivker et al., Zhou et al., Majumder et al., etc.

Thank you for your suggestions. Upon further examination of the listed papers, we have added citations to other groups employing similar methods. However, it's worth noting that the results of Teruel et al.'s studies are generally not directly comparable to our own. Particularly, they examine specific individual mutations and overall dynamical signatures associated with them, whereas our results are always considered in the context of epistasis and joint effects with CAPs, and all mutations belong to the common variants. Although important mutations may be highlighted in both cases, it is for very different reasons. Nevertheless, we provide a more detailed mention of the results of both studies. See lines 178, 255, and 393.

(3) The last concern that I have is with respect to the methodology. The dynamic couplings and the derived index (DCI) are entirely based on the use of the elastic network model presented which is strictly sequence-agnostic. Only C-alpha positions are taken into consideration and no information about the side-chain is considered in any manner. Of course, the specific sequence of a protein will affect the unique placement of C-alpha atoms (i.e., mutations affect structure), therefore even ANM or ENM can to some extent predict the effect of mutations in as much as these have an effect on the structure, either experimentally determined or correctly and even incorrectly modelled. However, such an approach needs to be discussed in far deeper detail when it comes to positions on the surface of a protein such that the reader can gauge if the observed effects are the result of modelling errors.

We would like to clarify that most of our results do not involve simulations of different variants, but rather how characteristic mutation sites for those variants contribute to overall dynamics. For the full spike, we operate on only two simulations: open and closed. When we do analyze different variants, starting on line 438, the observed difference does not come from the structure, but from the covariance matrix obtained from molecular dynamics (MD) simulations, which are sensitive to single amino acid changes.

Reviewer #3 (Recommendations For The Authors):

(1) On line 99 there is a misspelling, 'withing'.

It has been fixed. Thanks for spotting that.

(2) Some graphical suggestions to make the figures easier to read:

In Figure 1C, a labeled circle around the important sites, the receptor binding domain, and the Furin cleavage site, would help the reader orient themselves. Moreover, it would make clear which CAPs are NOT in the noteworthy sites described in the text.

Good idea. We have added transparent spheres and labels to show hACE2 binding sites and Furin cleavage sites.

In Figure 2C the colors are a bit low contrast; moreover, there are multiple text sizes on the same figure which should perhaps be avoided to ensure legibility.

We have made yellow brighter and standardized font sizes.

Figure 3 is a bit dry, perhaps indicating in which bins the 'interesting' sites could be informative.

Thank you for the suggestion, but the overall goal of Figure 3 is to illustrate that the mutational landscape is governed by the equilibrium dynamics in which flexible sites undergo more mutations during the evolution of the CoV2 spike protein. Therefore, adding additional positional information may complicate our message.

Figure 4, the previous suggestions about readability apply.

We ensured same sized text and higher contrast colors.

Figure 5B, the residue labels are too small.

We increased the font size of the residue labels.

In Figure 8 maybe adding Delta to let the reader orient themselves would be helpful to the discussion.

Unfortunately, there is no single work that has experimentally quantified binding affinities towards hACE2 for all the variants. When we conducted the same analysis for the Delta variant in Figure 8, the experimental values were obtained from a different source (doi: 10.1016/j.cell.2022.01.001) and the values were significantly different from the experimental work we used for Omicron (Yue et al. 2023). When we could adjust based on the difference in experimentally measured binding affinity values of the original Wuhan strain in these two separate studies, we observed a similar correlation, as seen below. However, we think this might not be a proper representation. Therefore, we chose to keep the original figure.

Author response image 2.

The %DFI calculations for variants Delta, Omicron, XBB, and XBB 1.5. (A) %DFI profile of the variants are plotted in the same panel. The grey shaded areas and dashed lines indicate the ACE2 binding regions, whereas the red dashed lines show the antibody binding residues. (B) The sum of %DFI values of RBD-hACE2 interface residues. The trend of total %DFI with the log of Kd values overlaps with the one seen with the experiments. (C) The RBD antibody binding residues are used to calculate the sum of %DFI. The ranking captured with the total %DFI agrees with the susceptibility fold reduction values from the experiments.

(3) Replicas of the MD simulations would make the conclusions stronger in my opinion.

We ran a 1µs long simulation and performed convergence analysis for the MD simulations using the prior work (Sawle L, Ghosh K. 2016.) More importantly, we also evaluated the statistical significance of computed DFI values as explained in detail below (Please see the answer to question 3 of Reviewer #3 (Public Review):)

Reviewer #3 (Public Review):

(1) A longer discussion of how the 19 orthologous coronavirus sequences were chosen would be helpful, as the rest of the paper hinges on this initial choice.

The following explanation has been added on line 114: EP scores of the amino acid variants of the S protein were obtained using a Maximum Likelihood phylogeny (Kumar et al. 2018) built from 19 orthologous coronavirus sequences. Sequences were selected by examining available non-human sequences with a sequence identity of 70% or above to the human SARS CoV-2’s S protein sequence. This cutoff allows for divergence over evolutionary history such that each amino acid position had ample time to experience purifying selection, whilst limiting ourselves to closely related coronaviruses. (Figure 1A).

(2) The 'reasonable similarity' with previously published data is not well defined, nor there was any comment about some of the residues analyzed (namely 417-484). We have revised this part of the manuscript and add to the revised version.

We removed the line about reasonable similarity as it was vague, added a line about residues 417-484, and revised the text accordingly, starting on line 354.

(3) There seem to be no replicas of the MD simulations, nor a discussion of the convergence of these simulations. A more detailed description of the equilibration and production schemes used in MD would be helpful. Moreover, there is no discussion of how the equilibration procedure is evaluated, in particular for non-experts this would be helpful in judging the reliability of the procedure.

We opted for a single, extended equilibrium simulation to comprehensively explore the longterm behavior of the system. Given the specific nature of our investigation and resource constraints, a well-converged, prolonged simulation was deemed a practical and scientifically valid approach, providing a thorough understanding of the system's dynamics. (doi: 10.33011/livecoms.1.1.5957, https://doi.org/10.1146/annurev-biophys-042910-155255 )

We updated our methods section starting on line 605 with extended information about the MD simulations and the converge criteria for the equilibrium simulations. We also added a section that explains our analysis to check statistical significance of obtained DFI values.

Author response:

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

Reviewer #1 (Recommendations for authors):

(1) Motivation for studying SUL1 in RLS

Considering that the regulation of cellular metabolism in response to nutrient availability is crucial for cell survival and lifespan, and several organic nutrient transporters have also been implicated in the mediation of aging, we believe that transporters of specific nutrients can transduce the signal downstream to control genes responsible for survival. However, the impact of inorganic nutrient transporters, including phosphate and sulfate, on longevity remains largely unexplored. And another work of our group utilized a LASSO model derived from multi-omics data related to yeast aging, identifying SUL1 as a key candidate for regulating lifespan, which aroused our interest.

(2) Discrepancy with prior RLS data (PMID: 26456335)​​

Previous literature (PMID: 26456335) reported a limited number of experimental cells (n=25), which may have contributed to the observed variability in results. To enhance the reliability of our work, we have expanded the number of experimental cells for the sul1Δ strain to 400 (see Figure 1A). In contrast, the lifespan data for other mutant strains have been increased to 200 (see Figure 1B). This confirms the reproducibility of the lifespan extension observed in the sul1Δ strain.

(3) Mechanistic link between sulfate transport and lifespan​​

Sulfate absorption assays were performed on the WT, SUL1Δ, SUL2Δ, and SUL1^E427Q strains (Figure 1C). Compared to the wild type (WT), the SUL1Δ, SUL2Δ, and SUL1^E427Q strains exhibited delayed sulfate intracellular transportation. However, there was no significant difference in the final concentration of intracellular sulfur ions among all groups. This result reinforces our conclusion that the extended lifespan of SUL1Δ is not associated with sulfate transport.

(4) Testing the RLS of SUL1ΔMSN4Δ double mutants​​

The replicative lifespan data for the SUL1ΔMSN4Δ double mutant were further analyzed (shown in the following supplementary figure). It was observed that the extension of the SUL1Δ lifespan was not rescued by the knockout of MSN4, supporting the hypothesis that MSN2 may serve as the downstream transcription factor responsible for the increased lifespan of SUL1Δ.

Author response image 1.

Replicative life span of MSN4 deletion mutants in WT and SUL1Δ strains.

Reviewer #2 (Recommendations for authors):

(1) Inconsistent WT lifespan in Figure 1B

All measurements of life expectancy were conducted under controlled conditions (30°C, 2% glucose). The revised Figure 1C illustrates that across three independent experiments (n=200 cells), the average lifespan of wild-type (WT) cells was 29.1 generations, which is comparable to the average lifespan of 25.6 generations reported in Figure 1A after data expansion (n=400 cells). This similarity may be attributed to experimental variability arising from multiple trials; however, it does not compromise the validity of our conclusions.

(2) Sulfate level measurements​​

Intracellular sulfate levels were measured by quantitatively assessing the sulfate concentrations in wild-type (WT), SUL1Δ, SUL2Δ, and SUL^E427 cells, as detailed in the methods section (Figure 1C). The results indicated that all mutant strains showed a delayed sulfur uptake process, but there was no significant difference in the final concentration of intracellular sulfur ions in all groups.

(3) RNA-seq for non-lifespan-extending mutants​​

RNA-seq data for the SUL2Δ and SULE427 mutants can be found in Supplementary Figure 1. These mutants do not exhibit a significant upregulation of stress-response genes, such as HSP12 and TPS1, which reinforces the specificity of the pathways induced by SUL1Δ.

(4) Improved Msn2/4 imaging​​

Figure 3C and supplementary Figure 4A present high-resolution confocal images (using a 63× objective lens) of cell nuclei labeled with MSN2-GFP and DAPI. The GFP intensity within the nucleus was normalized against the DAPI signal to account for differences in nuclear size.​​

​​Reviewer #3 (Recommendations for authors):

(1) Nuclear size normalization​​

The verification data for MSN2 and MSN4 were re-evaluated through DAPI signal normalization. The revised figures are presented in Figure 3C and Supplementary Figure 4A.

(2) Strain nomenclature​​

All strain names (e.g., SUL1Δ) were updated to follow SGD guidelines.

(3) Grammar and formatting​​

We have carefully revised the text to improve readability. And the manuscript was proofread by a native English speaker. Citations (e.g., "trehalose (Lillie and Pringle, 1980)") and spacing errors were corrected.

(4) Microscopy resolution​​

In the revised figures (Figures 3C, 3E, 4B, 4E, Supplementary Figure 3A, 4A, 4C), all fluorescence images are displayed as separate channels (EGFP, DAPI, BF). The scale and arrows have been added to the figure for clarity.

Author response:

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

We greatly appreciate the recommendations of the reviewers and have performed further analyses with existing data where requested. 

Below are our responses to each of the individual points. 

Reviewer #1 (Recommendations For The Authors):

(1) P11 mouse retina is still quite young, would MG isolated from adult retina be more interesting and relevant to disease-oriented cell replacement therapy? How efficiently would the sci-Plex system work for in vitro screen of mature murine MG?

Thank you for bringing this up. While a protocol for the conversion of MG to neurons with adult mice in vivo exists, it has proven to be more difficult to maintain adult MG in dissociated cell cultures, due to their more limited proliferation in vitro. This makes it difficult to use the sci-Plex assay, since cell number is limiting for treatment conditions. Therefore, we have chosen the strategy of screening on P11, where MG undergo proliferative cell divisions in dissociated cultures, allowing us to grow the millions of cells needed for this assay, and then to test the efficacy of the compounds we find from the screen with an adult in vivo assay.

(2) The study identified and tested the compounds individually, how would a combination of the compounds work in vivo? It would be interesting to examine how different combinations may affect the reprogramming efficiency and neuronal compositions.

We agree that this would be very interesting to investigate.  However, the number of treatment conditions then expands beyond the scale of the current sci-Plex technology with the number of MG that we are able to collect.  We instead adopted the strategy of casting a very wide net to identify additional molecular pathways that might be important in the reprogramming process.

(3) In-depth mechanistic and/or functional studies of the reprogrammed MG are highly desirable to improve the quality and significance of the study and to better understand how the compounds may influence the signaling and the reprogramming process.

While we agree that this would strengthen the study, this would increase the scope of the required revisions considerably. We are very interested in following up on some of the hits and look forward to providing additional details of mechanisms in future publications.  However, we feel that reporting this method and the results will stimulate those interested in reprogramming glia in other areas of the nervous system to test the compounds we identified in this assay.

Reviewer #2 (Recommendations For The Authors):

(1) The authors employed two protocols to initiate direct reprogramming of MG into retinal neurons in vitro. These protocols, referred to as "Timecourse" and "Pulse," involved short-term treatments lasting no more than 5 days. However, the findings obtained indicate that these brief treatments were insufficient to achieve a stable conversion. This conclusion is supported by the comparison between the "4 days (Timecourse)" and "4 days (Pulse)" conditions, as depicted in Figure 1 (D and E). In this set of experiments, labeling cells that express specific neuronal markers as neurons raises concerns, as these cells may have multiple fates, either died, reverted, arrested in certain intermediate stages, or converted to functional neurons. It is thus critical to determine whether the conversion to functional neurons is enhanced.

We thank you for your concern about this. We aimed to be very careful in our naming. In our naming scheme for this figure, we only consider the small number of cells with specific Bipolar markers (Trpm1, Grm6, Capb5, Otx2) neurons based on previous publications ((Jorstad et al. 2017; Todd et al. 2021; Todd et al. 2022; Todd et al. 2020)). The other cells that have some neuronal markers are identified as neuronal precursors (NeuPre) and are, as you mentioned, not necessarily mature/functional. While these NeuPre cells may eventually have multiple fates/may die/may revert to more ProL cells at some rate we believe it’s fair to define them as Neuronal Precursors due to the genes they are expressing (Dcx, Snap25, Elavl3, Gap43) at the moment of collection.  

Furthermore, your statement indicating that “the findings obtained indicate that these brief treatments were insufficient to achieve a stable conversion” is not what we intended to demonstrate. The text will be reworked to reflect what we hoped to convey. We acknowledge that 1) the majority cells are not stably converted, and 2) the levels of NeuPre cells are lower in the Pulse experiment overall, but this is true even at Day 5 when the conditions should be the same across experiments. The Pulse and Timecourse experiments were done on different days, and having previously found that there are differences in MG to BP conversion rate from experiment to experiment, these results were not unexpected. Of more note to us was that while ProL cells, Transition cells, and MG have very different patterns of abundance across time when comparing the experiments, the NeuPre cells accumulate at a similar time and pattern across the two experiments. This indicated to us that they uniquely have some amount of Ascl1 independent stability in their cell fate even when exposed to Ascl1 for as little as 3 days. See Author response image 1 below. This plot will be added to Fig. S1.

Author response image 1.

(2) The authors made a claim that a pseudo time value of 15 represents a crucial timepoint where the transition in cell fate becomes stable and ceases to rely on ectopic Ascl1 expression. However, it is essential to provide concrete evidence to substantiate this assertion. It is prudent to perform quantitative analyses rather than relying solely on the deduced trajectory to make this claim.

This is a fair point, the value of 15 was estimated by eye. We have returned to the data and estimated a density function for the pseudotime scores of the cells from the 1, 2, 3, and 4 day conditions in both the Pulse and Timecourse experiments (Author response image 2A-B below). We then calculated 16 to be the local minima between the pseudotime values of 10-20 for the Pulse experiment (Blue line). When comparing the two experiments, it’s apparent that there is a massive accumulation of cells with a pseudotime value just lower than 16 in the Timecouse experiment (values 10-15), and very few cells across the same region for the Pulse experiment, indicating some dependence on continued Ascl1 expression for the cell fate that exists from pseudotime 10-16 (mostly ProL cells). To the contrary, cells with greater pseudotime values exist across both experiments at similar levels.

We have also looked at the expression of Ascl1 along the pseudotime trajectory in the Timecourse experiment. Interestingly, and consistent with experiments in previous studies, both in vitro and in vivo (Todd et al. 2021; Todd et al. 2022; Todd et al. 2020), we see a decrease in Ascl1 expression as the cells move towards the end of the pseudotime trajectory (C below). It’s intriguing to us that the downregulation also happens right after a pseudotime value of 16. The temporal coalescence of the loss of Ascl1 expression in the Timecourse experiment with the persistence of cells with pseudotime values > 16 in the Pulse experiment provides strong evidence that we have identified the point at which cells stop expressing Ascl1 while maintaining more mature cell fates. The plots below will be added to the manuscript.

Author response image 2.

(3) It is intriguing to observe that the expression of Ascl1 was down-regulated in both neuronal precursors and bipolar cells in the mouse retina following tamoxifen and NMDA treatment (refer to Fig. 3C). However, the expression of ectopical Ascl1 should have been constitutively activated by tamoxifen. Therefore, if the GFP+ bipolar cells and neuronal precursors were indeed converted from Müller cells, we would expect to capture a high level of Ascl1 expression. How to account for this discrepancy? How is the expression exogenous Ascl1 expressed from a constitutive promoter attenuated?

As discussed above, this has been observed previously. Ascl1 driven from the TTA transgenic mouse line is high in the MG, but declines as these cells are reprogrammed into neurons in vivo or in vitro.  One possibility is that the TTA is not as active in neurons as in MG, but in other lines of transgenic mice, eg. TRE-Atoh1 mice, the transgene continues to be expressed at a high level even in the differentiating neurons, so this downregulation appears to be unique to Ascl1.  We do not understand why Ascl1 levels decline in the differentiating neurons, but this has been a consistent finding across several studies of in vivo and in vitro reprogramming.

(4) Exogenous Ascl1 was shut down after other neuronal specific genes were induced during MG reprogramming in vitro. Is this also the case during Ascl1-mediated reprogramming in vivo? If so, do converting cells show a distinct gene expression program if exogenous Ascl1 is constitutively overexpressed?

Yes, as can be seen in Fig 3C Ascl1 expression is high in the MG and Transition cell populations, but decreases in the NeuPre and Bipolar cells. As stated above, continued high Ascl1 expression keeps cells in a more progenitor-like state. This is true in vivo and in vitro. It has been more clearly addressed upon revision.  

(5) As previously documented in their Science Advances publication, the authors have established the requirement of NMDA injury for facilitating the successful induction of neuronal conversion through Ascl1 over-expression. Why is injury required for MG conversion in vivo, but not in vitro? This is related to question #1 above that certain signals may be required for the full conversion process, not just the initial induction of a few neuronal specific genes.

While the in vitro and in vivo systems share similarities, there are key differences, which affect what must be done to the cells in order to produce converted neurons. In our initial publication demonstrating that Ascl1 can reprogram mouse MG to a neurogenic state, we carried out our experiments in dissociated cell cultures (Pollak et al 2013) like those described in this report.  At that time, we did not need to add either NMDA or TSA to the cultures to induce neurogenesis from Ascl1.  However, when we attempted the reprogramming in vivo, we found that after postnatal day 8, injury and TSA were required in vivo (Ueki et al; Jorstad et al). We surmise that the massive neuronal loss that occurs in establishing dissociated MG cultures replaces the NMDA injury we carry out in vivo.   

To your second point about the requirement for more than “just the initial induction of a few neuronal specific genes”. This is definitely true. When we carry out reprogramming in vivo with Ascl1 or other transcription factors, the MG-derived neurons acquire neuronal morphology, develop neuron-like electrophysiological properties, integrate into the retinal circuit and respond to light stimulus; however, they are still not identical in gene expression or morphology to normal retinal neurons. This  is why we are continuously looking for more compounds or conditions that can help improve the process.

(6) The discovery that Metformin acts as a stimulator for MG-to-neuron conversion is interesting.

However, before drawing definitive conclusions, several questions need to be addressed:

(a) As specific small molecules have been identified to change cell fates, the question is whether Metformin and other effective compounds can function alone or have to effect in conjunction with Ascl1? This can and should be tested in vitro by simply treating MG with Metformin but not doxycycline.

To our knowledge there are no convincing in vivo trials in which neurons have been generated from MG using only combinations of small molecules. Because Metformin was identified in vitro due to the increase in recovered cells and not an increase in % neurons, we especially doubt it would have the desired increase in neurons without expression of a transcription factor.  

(b) Metformin is known to target AMPK, but this is unlikely the only target of the drug. Does AMPK knockdown have the same enhancement effect?

In the drug screen, we also tested the AMPK inhibitor Dorsomorphin dihydrochloride, but it didn’t have any effect. However, Metformin is an activator, so it would be interesting to see in future studies if Dorsomorphin dihydrochloride could inhibit the effect of Metformin or if the enhancement is acting independently.  

(c) Is the effect of Metformin specific for Ascl1 or any TF(s) that stimulates MG-to-neuron conversion?

We would like to follow up with this in future.

Author Response

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

eLife assessment:

This important study advances the understanding of physiological mechanisms in deep-sea Planctomycetes bacteria, revealing unique characteristics such as the only known Phycisphaerae using a budding mode of division, extensive involvement in nitrate assimilation and release phage particles without cell death. The study uses convincing evidence, based on experiments using growth assays, phylogenetics, transcriptomics, and gene expression data. The work will be of interest to bacteriologists and microbiologists in general.

Response: Thanks for the Editor’s and Reviewers’ positive comments, which help us improve the quality of our manuscript entitled “Physiological and metabolic insights into the first cultured anaerobic representative of deep-sea Planctomycetes bacteria” (paper#eLife-RP-RA-2023-89874). The comments are all valuable, and we have studied the comments carefully and have made corresponding revisions according to the suggestions. Revised portions are marked in blue in the modified manuscript.

Please find the detailed responses as following.

Public Reviews:

Reviewer #1 (Public Review):

The authors of the manuscript cultivated a Planctomycetes strain affiliated with Phycisphaerae. The strain was one of the few Planctomycetes from deep-sea environments and demonstrated several unique characteristics, such as being the only known Phycisphaerae using a budding mode of division, extensive involvement in nitrate assimilation, and being able to release phage particles without cell death. The manuscript is generally well-written. However, a few issues need to be more clearly addressed, especially regarding the identification and characterization of the phage.

Response: Thanks for your positive comments. Please find the detailed responses as following.

Reviewer #1 (Recommendations For The Authors):

• Line 75-77, add a reference for this statement.

Response: Thanks for your suggestion. We have added a reference (Fuerst and Sagulenko, 2011) for this statement in the revised manuscript (Line 77).

References related to this response:

Fuerst, J.A., and Sagulenko, E. Beyond the bacterium: planctomycetes challenge our concepts of microbial structure and function. Nat Rev Microbiol. 2011;9:403-413.

• Line 124-134, add key statistics (such as ANI) of strain ZRK32 and KS4 to this section.

Response: Thanks for your suggestion. We added the key statistics of strain ZRK32 and KS4, and described as “Based on the 16S rRNA sequence of strain ZRK32, a sequence similarity calculation using the NCBI server indicated that the closest relatives of strain ZRK32 were Poriferisphaera corsica KS4T (98.06%), Algisphaera agarilytica 06SJR6-2T (88.04%), Phycisphaera mikurensis NBRC 102666T (85.28%), and Tepidisphaera mucosa 2842T (82.94%). Recently, the taxonomic threshold for species based on 16S rRNA gene sequence identity value was 98.65% (Kim et al., 2014). Based on these criteria, we proposed that strain ZRK32 might be a novel representative of the genus Poriferisphaera. In addition, to clarify the phylogenetic position of strain ZRK32, the genome relatedness values were calculated by the average nucleotide identity (ANI), the tetranucleotide signatures (Tetra), and in silico DNA-DNA similarity (isDDH), against the genomes of strains ZRK32 and KS4. The ANIb, ANIm, Tetra, and isDDH values were 72.89%, 85.34%, 0.97385, and 20.90%, respectively (Table S1). These results together demonstrated the strain ZRK32 genome to be obviously below established ‘cut-off’ values (ANIb: 95%, ANIm: 95%, Tetra: 0.99, isDDH: 70%) for defining bacterial species, suggesting strain ZRK32 represents a novel strain within the genus Poriferisphaera.” in the revised manuscript (Lines 124-139).

• Fig. 2A missing description for figure key.

Response: Thanks for your comments. We modified the Figure 2A, shown as below:

Author response image 1.

Figure. 2. Growth assay and transcriptomic analysis of P. heterotrophicis ZRK32 strains cultivated in basal medium and rich medium.

• Regarding the page released, could this be a membrane vesicle-engulfed phage? I would recommend checking "Spontaneous Prophage Induction Contributes to the Production of Membrane Vesicles by the Gram-Positive Bacterium Lacticaseibacillus casei BL23" and "Chronic Release of Tailless Phage Particles from Lactococcus lactis" for further references.

Response: Thanks for your valuable comments. We carefully read these two papers and found that phage ZRK32 is most likely a membrane vesicle-engulfed phage. We added the corresponding description as “Moreover, it has recently been reported that the tailless Caudoviricetes phage particles are enclosed in lipid membrane and are released from the host cells by a nonlytic mechanism (Liu et al., 2022), and the prophage induction contributes to the production of membrane vesicles by Lacticaseibacillus casei BL23 during cell growth (da Silva Barreira et al., 2022). Considering that strain ZRK32 has a large number of membrane vesicles during cell growth (Figure S9), we speculated that Phage-ZRK32 might be a membrane vesicle-engulfed phage and its release should be related to membrane vesicles.” in the revised manuscript (Lines 381-388).

References related to this response:

Liu Y, Alexeeva S, Bachmann H, Guerra Martníez J.A, Yeremenko N, Abee T et al. Chronic release of tailless phage particles from Lactococcus lactis. Appl Environ Microbiol. 2022; 88: e0148321.

Silva Barreira, D., Lapaquette, P., Novion Ducassou, J., Couté, Y., Guzzo, J., and Rieu, A. Spontaneous prophage induction contributes to the production of membrane vesicles by the gram-positive bacterium Lacticaseibacillus casei BL23. mBio. 2022;13:e0237522.

• How were the reference sequences for Fig. S10-S13 retrieved, was it by blasting the phage gene against the entire NCBI database, or only the virus sequence within the NCBI? Please clarify this.

Response: Thanks for your comments. The reference sequences for Fig. S10-S13 were retrieved by blasting the phage gene against the entire NCBI database. We clarified this as “The reference sequences of four AMGs encoding amidoligase, glutamine amidotransferase, gamma-glutamylcyclotransferase, and glutathione synthase were retrieved by blasting the phage gene against the entire NCBI database, respectively.” in the revised manuscript (Lines 444-447).

Reviewer #2 (Public Review):

Summary:

Planctomycetes encompass a group of bacteria with unique biological traits, the compartmentalized cells make them appear to be organisms in between prokaryotes and eukaryotes. However, only a few of the Planctomycetes bacteria are cultured thus far, and this hampers insight into the biological traits of these evolutionarily important organisms. This work reports the methodology details of how to isolate the deep-sea bacteria that could be recalcitrant to laboratory cultivation, and further reveals the distinct characteristics of the new species of a deep-sea Planctomycetes bacterium, such as the chronic phage release without breaking the host and promote the host and related bacteria in nitrogen utilization. Therefore, the finding of this work is of importance in extending our knowledge of bacteria.

Response: Thanks for your positive comments.

Strengths:

Through the combination of microscopic, physiological, genomics, and molecular biological approaches, this reports the isolation and comprehensive investigation of the first anaerobic representative of the deep-sea Planctomycetes bacterium, in particular in that of the budding division, and release phage without lysis of the cells. Most of the results and conclusions are supported by the experimental evidence.

Response: Thanks for your positive comments.

Weaknesses:

1. While EMP glycolysis is predicted to be involved in energy conservation, no experimental evidence indicated any sugar utilization by the bacterium.

Response: Thanks for your comments. We have previously tested the sugar utilization of strain ZRK32, and now added this description as “Consistent with the presence of EMP glycolysis pathway in strain ZRK32, we found that it could use a variety of sugars including glucose, maltose, fructose, isomaltose, galactose, D-mannose, and rhamnose (Table S2).” in the revised manuscript (Lines 281-284).

1. "anaerobic representative" is indicated in the Title, the contrary, TCA in energy metabolism is predicted by the bacterium.

Response: Thanks for your valuable comments. Currently, anaerobic microorganisms can use other alternative electron acceptors (such as sulfate reducers, nitrate reducers, iron reducers, etc) in place of oxygen for the TCA cycle. For example, Proteus mirabilis uses the whole oxidative TCA cycle without using oxygen as the final electron acceptor when it performs multicellular swarming (Alteri et al., 2012). In this study, all the genes involved in the TCA cycle were present in anaerobic strain ZRK32 and most of them are upregulated, thus we speculate that it might function through the complete TCA metabolic pathway to obtain energy. We added the related description as “Notably, when growing in the rich medium, the expressions of most genes involved in the TCA cycle and EMP glycolysis pathway in strain ZRK32 were upregulated (Figure 2B-D, Figure S5B and Figure S6), suggesting that strain ZRK32 might function through the complete TCA metabolic pathway and EMP glycolysis pathway to obtain energy for growth (Figure S8) (Zheng et al., 2021b). Consistent with the presence of EMP glycolysis pathway in strain ZRK32, we found that it could use a variety of sugars including glucose, maltose, fructose, isomaltose, galactose, D-mannose, and rhamnose (Table S2). As for the presence of TCA cycle in the anaerobic strain ZRK32, we propose that it might use other alternative electron acceptors (such as sulfate reducers, nitrate reducers, iron reducers, etc) in place of oxygen for the TCA cycle, as shown in other anaerobic bacteria (Alteri et al., 2012).” in the revised manuscript (Lines 277-287).

References related to this response:

Alteri CJ, Himpsl SD, Engstrom MD, Mobley HL. Anaerobic respiration using a complete oxidative TCA cycle drives multicellular swarming in Proteus mirabilis. mBio. 2012; 3(6): e00365-12.

1. The possible mechanisms of the chronic phage release without breaking the host are not discussed.

Response: Thanks for your valuable comments. The possible mechanism of the chronic phage release without breaking the host might be that it was enclosed in lipid membrane and released from the host cells by a nonlytic mechanism. We added the corresponding description as “Moreover, it has recently been reported that the tailless Caudoviricetes phage particles are enclosed in lipid membrane and are released from the host cells by a nonlytic mechanism (Liu et al., 2022), and the prophage induction contributes to the production of membrane vesicles by Lacticaseibacillus casei BL23 during cell growth (da Silva Barreira et al., 2022). Considering that strain ZRK32 has a large number of membrane vesicles during cell growth (Figure S9), we speculated that Phage-ZRK32 might be a membrane vesicle-engulfed phage and its release should be related to membrane vesicles.” in the revised manuscript (Lines 381-388).

References related to this response:

Liu Y, Alexeeva S, Bachmann H, Guerra Martníez J.A, Yeremenko N, Abee T et al. Chronic release of tailless phage particles from Lactococcus lactis. Appl Environ Microbiol. 2022; 88: e0148321. da Silva Barreira, D., Lapaquette, P., Novion Ducassou, J., Couté, Y., Guzzo, J., and Rieu, A. Spontaneous prophage induction contributes to the production of membrane vesicles by the gram-positive bacterium Lacticaseibacillus casei BL23. mBio. 2022;13:e0237522.

Reviewer #2 (Recommendations For The Authors):

• Have you tested whether strain ZRK32 uses any sugars? If not, why it uses EMP pathway to obtain energy?

Response: Thanks for your comments. We have previously tested the sugar utilization of strain ZRK32, and now added this description as “Consistent with the presence of EMP glycolysis pathway in strain ZRK32, we found that it could use a variety of sugars including glucose, maltose, fructose, isomaltose, galactose, D-mannose, and rhamnose (Table S2).” in the revised manuscript (Lines 281-284).

• Further discussion on possible mechanisms of the chronic phage release without breaking the host is expected.

Response: Thanks for your valuable comments. The possible mechanism of the chronic phage release without breaking the host might be that it was enclosed in lipid membrane and released from the host cells by a nonlytic mechanism. We added the corresponding description as “Moreover, it has recently been reported that the tailless Caudoviricetes phage particles are enclosed in lipid membrane and are released from the host cells by a nonlytic mechanism (Liu et al., 2022), and the prophage induction contributes to the production of membrane vesicles by Lacticaseibacillus casei BL23 during cell growth (da Silva Barreira et al., 2022). Considering that strain ZRK32 has a large number of membrane vesicles during cell growth (Figure S9), we speculated that Phage-ZRK32 might be a membrane vesicle-engulfed phage and its release should be related to membrane vesicles.” in the revised manuscript (Lines 381-388).

References related to this response:

Liu Y, Alexeeva S, Bachmann H, Guerra Martníez J.A, Yeremenko N, Abee T et al. Chronic release of tailless phage particles from Lactococcus lactis. Appl Environ Microbiol. 2022; 88: e0148321.

da Silva Barreira, D., Lapaquette, P., Novion Ducassou, J., Couté, Y., Guzzo, J., and Rieu, A. Spontaneous prophage induction contributes to the production of membrane vesicles by the gram-positive bacterium Lacticaseibacillus casei BL23. mBio. 2022;13:e0237522.

• It is recommended that the writing is improved, including presentation style and grammar.

Response: Thanks for your comments. We have invited an English native speaker (Dr. Diana Walsh from Life Science Editors, USA) to revise our manuscript, which we hope to meet your approval.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

In this study, Millard and colleagues investigated if the analgesic effect of nicotine on pain sensitivity, assessed with two pain models, is mediated by Peak Alpha Frequency (PAF) recorded with resting state EEG. The authors found indeed that nicotine (4 mg, gum) reduced pain ratings during phasic heat pain but not cuff pressor algometry compared to placebo conditions. Nicotine also increased PAF (globally). However, mediation analysis revealed that the reduction in pain ratings elicited by the phasic heat pain after taking nicotine was not mediated by the changes in PAF. Also, the authors only partially replicated the correlation between PAF and pain sensitivity at baseline (before nicotine treatment). At the group-level no correlation was found, but an exploratory analysis showed that the negative correlation (lower PAF, higher pain sensitivity) was present in males but not in females. The authors discuss the lack of correlation.

In general, the study is rigorous, methodology is sound and the paper is well-written. Results are compelling and sufficiently discussed.

Strengths:

Strengths of this study are the pre-registration, proper sample size calculation, and data analysis. But also the presence of the analgesic effect of nicotine and the change in PAF.

Weaknesses:

It would even be more convincing if they had manipulated PAF directly.

We thank Reviewer #1 for their positive and constructive comments regarding our study. We appreciate the view that the study was rigorous and methodologically sound, that the paper was well-written, and that the strengths included our pre-registration, sample size calculation, and data analysis.

In response to the reviewer's comment about more directly manipulating Peak Alpha Frequency (PAF), we agree that such an approach could provide a more direct investigation of the role of PAF in pain processing. We chose nicotine to modulate PAF as the literature suggested it was associated with a reliable increase in PAF speed. As mentioned in our Discussion, there are several alternative methods to manipulate PAF, such as non-invasive brain stimulation techniques (NIBS) like transcranial alternating current stimulation (tACS) or neurofeedback training. These approaches could help clarify whether a causal relationship exists between PAF and pain sensitivity. Although methods such as NIBS still require further investigation as there is little evidence for these approaches changing PAF (Millard et al., 2024).

Reviewer #2 (Public Review):

Summary:

The study by Millard et al. investigates the effect of nicotine on alpha peak frequency and pain in a very elaborate experimental design. According to the statistical analysis, the authors found a factor-corrected significant effect for prolonged heat pain but not for alpha peak frequency in response to the nicotine treatment.

Strengths:

I very much like the study design and that the authors followed their research line by aiming to provide a complete picture of the pain-related cortical impact of alpha peak frequency. This is very important work, even in the absence of any statistical significance. I also appreciate the preregistration of the study and the well-written and balanced introduction. However, it is important to give access to the preregistration beforehand.

Weaknesses:

The weakness of the study revolves around three aspects:

(1) I am not entirely convinced that the authors' analysis strategy provides a sufficient signal-tonoise ratio to estimate the peak alpha frequency in each participant reliably. A source separation (ICA or similar) would have been better suited than electrode ROIs to extract the alpha signal. By using a source separation approach, different sources of alpha (mu, occipital alpha, laterality) could be disentangled.

(2) Also, there's a hint in the literature (reference 49 in the manuscript) that the nicotine treatment may not work as intended. Instead, the authors' decision to use nicotine to modulate the peak alpha frequency and pain relied on other, not suitable work on chronic pain and permanent smokers. In the present study, the authors use nicotine treatment and transient painful stimulation on nonsmokers.

(3) In my view, the discussion could be more critical for some aspects and the authors speculate towards directions their findings can not provide any evidence. Speculations are indeed very important to generate new ideas but should be restricted to the context of the study (experimental pain, acute interventions). The unfortunate decision to use nicotine severely hampered the authors' aim of the study.

Impact:

The impact of the study could be to show what has not worked to answer the research questions of the authors. The authors claim that their approach could be used to define a biomarker of pain. This is highly desirable but requires refined methods and, in order to make the tool really applicable, more accurate approaches at subject level.

We thank reviewer #2 for their recognition of the study’s design, the importance of this research area, and the pre-registration of our study. In response to the weaknesses highlighted:

(1) We appreciate the reviewer’s suggestion to improve the signal-to-noise ratio by applying source separation techniques, such as ICA, which have now been performed and incorporated into the manuscript. Our original decision to use sensor-level ROIs followed the precedent set in previous studies, our rationale being to improve reproducibility and avoid  biases from picking individual electrodes or manually picking sources. We have  added analyses using an automated pipeline that selects components based on the presence of a peak in the alpha range and alignment with a predefined template topography representing sensorimotor sites. Here again we found no significant differences in the mediation results that used a sensor space sensorimotor ROI, further supporting the robustness of the chosen approach. ICA could still potentially disentangle different sources of alpha, such as occipital alpha and mu rhythm, and provide new insights into the PAF-pain relationship. We have now added a discussion in the manuscript about the potential advantages of source separation techniques and suggest that the possible contributions of separate alpha sources be investigated and compared to sensor space PAF as a direction for future research.

(2) We recognise the reviewer's concern regarding our choice of nicotine as a modulator of pain and alpha peak frequency (PAF). The meta-analysis by Ditre et al. (2016) indeed points to small effect sizes for nicotine's impact on experimental pain and highlights the potential for publication bias. However, our decision to use nicotine in this study was not primarily based on its direct analgesic effects, but rather on its well-documented ability to modulate PAF, in smoking and non-smoker populations, as outlined in our study aims.

In this regard, the intentional use of nicotine was to assess whether changes in PAF could mediate alterations in pain. This approach aligns with the broader concept that a direct effect of an intervention is not necessary to observe indirect effects (Fairchild & McDaniel, 2017). We have, however, revised our introduction to further clarify this rationale, highlighting that nicotine was used as a tool for PAF modulation, not solely for its potential analgesic properties.

(3) We agree with the reviewer’s observation that certain aspects of the Discussion could be more cautious, particularly regarding speculations about nicotine’s effects and PAF as a biomarker of pain. We have revised the Discussion to ensure that our interpretations are better grounded in the data from this study, clearly stating the limitations and avoiding overgeneralization. This revision focuses on a more critical evaluation of the potential relationships between PAF, nicotine, and pain sensitivity based solely on our experimental context.

Finally, We also apologize for not providing access to the preregistration earlier. This was an oversight on our end, and we will ensure that future preregistrations are made available upfront.

Reviewer #3 (Public Review):

In this manuscript, Millard et al. investigate the effects of nicotine on pain sensitivity and peak alpha frequency (PAF) in resting state EEG. To this end, they ran a pre-registered, randomized, double-blind, placebo-controlled experiment involving 62 healthy adults who received either 4 mg nicotine gum (n=29) or placebo (n=33). Prolonged heat and pressure were used as pain models. Resting state EEG and pain intensity (assessed with a visual analog scale) were measured before and after the intervention. Additionally, several covariates (sex at birth, depression and anxiety symptoms, stress, sleep quality, among others) were recorded. Data was analyzed using ANCOVAequivalent two-wave latent change score models, as well as repeated measures analysis of variance. Results do not show *experimentally relevant* changes of PAF or pain intensity scores for either of the prolonged pain models due to nicotine intake.

The main strengths of the manuscript are its solid conceptual framework and the thorough experimental design. The researchers make a good case in the introduction and discussion for the need to further investigate the association of PAF and pain sensitivity. Furthermore, they proceed to carefully describe every aspect of the experiment in great detail, which is excellent for reproducibility purposes. Finally, they analyse the data from almost every possible angle and provide an extensive report of their results.

The main weakness of the manuscript is the interpretation of these results. Even though some of the differences are statistically significant (e.g., global PAF, pain intensity ratings during heat pain), these differences are far from being experimentally or clinically relevant. The effect sizes observed are not sufficiently large to consider that pain sensitivity was modulated by the nicotine intake, which puts into question all the answers to the research questions posed in the study.

We would like to express our gratitude to Reviewer #3 for their thoughtful and constructive review, including the positive feedback on the strengths of our study's conceptual framework, experimental design, and thorough methodological descriptions.

We acknowledge the concern regarding the experimental and clinical relevance of some statistically significant results (e.g., global PAF and pain intensity during heat pain) and agree that small effect sizes may limit their practical implications. However, our primary goal was to assess whether nicotine-induced changes in PAF mediate pain changes, rather than to demonstrate large direct effects on pain sensitivity. Nicotine was chosen for its known ability to modulate PAF, and our focus was on the mechanistic role of PAF in pain perception. To clarify this, we have revised the discussion to better differentiate between statistical significance, experimental relevance, and clinical applicability. We emphasize that this study represents a preliminary step towards understanding PAF’s mechanistic role in pain, rather than a direct clinical application.

We appreciate the suggestion to refine our interpretation. We have adjusted our language to ensure it aligns with the effect sizes observed and made recommendations for future research, such as testing different nicotine doses, to potentially uncover stronger or more clinically relevant effects.

Although modest, we believe these findings offer valuable insights into the potential mechanisms by which nicotine affects alpha oscillations and pain. We have also discussed how these small effects could become more pronounced in different populations (e.g., chronic pain patients) and over time, offering guidance for future research on PAF modulation and pain sensitivity.

Recommendations for the authors:

Reviewer #2 (Recommendations For The Authors):

I have a number of points that the authors may want to consider for this or future work.

(1) By reviewing the literature provided by the authors in the introduction I think that using nicotine as a means to modulate pain and alpha peak frequency was a mistake. The only work that may give a hint on whether nicotine can modulate experimental pain is the meta-analysis by Ditre and colleagues (2016). They suggest that their small effect may contain a publication bias. I think the other "large body of evidence" is testing something else than analgesia.

Thank you for your consideration of our choice of nicotine in the study. The meta-analysis by Ditre and colleagues (2016) suggests small effect sizes for nicotine's impact on experimental pain, compared to the moderate effects claimed in some papers, especially when accounting for the potential publication bias you mentioned. However, our selection of nicotine was primarily driven by its documented ability to modulate PAF rather than its direct analgesic effects, as clearly stated in our aims. Therefore, we do not view our decision to use nicotine as a mistake; instead, it was aligned with our goal of assessing whether changes in PAF mediate alterations in pain and thus served as a valuable tool. This perspective aligns with the broader concept that a direct effect is not a prerequisite for observing indirect effects of an intervention on an outcome (Fairchild &

McDaniel, 2017). To further enhance clarity, we've revised the introduction to emphasize the role of nicotine in manipulating PAF in relation to our study's aims.

Previously we wrote: “A large body of evidence suggests that nicotine is an ideal choice for manipulating PAF, as both nicotine and smoking increase PAF speed [37,40–47] as well as pain thresholds and tolerance [48–52].” This has been changed to read: “Because evidence suggests that nicotine can modulate PAF, where both nicotine and smoking increase PAF speed [37,40–47], we chose nicotine to assess our aim of whether changes in PAF mediate changes in pain in a ‘mediation by design’ approach [48]. In addition, given evidence that nicotine may increase experimental pain thresholds and tolerance [49–53], nicotine could also influence pain ratings during tonic pain.”

(2) As mentioned above, the OSF page is not accessible.

We apologise for this. We had not realised that the pre-registration was under embargo, but we have now made it available.

(3) I generally struggle with the authors' approach to investigating alpha. With the approach the authors used to detect peak alpha frequency it might be that the alpha signal may just show such a low amplitude that it is impossible to reliably detect it at electrode level. In my view, the approach is not accurate enough, which can be seen by the "jagged" shape of the individual alpha peak frequency. In my view, a source separation technique would have been more useful. I wonder which of the known cortical alphas contributes to the effects the authors have reported previously: occipital, mu rhythms projections or something else? A source separation approach disentangles the different alphas and will increase the SNR. My suggestion would be to work on ICA components or similar approaches. The advantage is that the components are almost completely free of any artefacts. ICAs could be run on the entire data or separately for each individual. In the latter case, it might be that some participants do not exhibit any alpha component.

We appreciate your thoughtful consideration of our approach to investigating alpha. The calculation of PAF involves various methods and analysis steps across the literature (Corcoran et al., 2018; Gil Avila et al., 2023; McLain et al., 2022). Your query about which known cortical alphas contribute to reported effects is important. Initially focusing on a sensorimotor component from an ICA in Furman et al., 2018, subsequent work from our labs suggested a broader relationship between PAF and pain across the scalp (Furman et al., 2019; Furman et al., 2020; Millard et al., 2022), and a desire to conduct analyses at the sensor level in order to improve the reproducibility of the methods (Furman et al., 2020). However, based on your comment we have made several additions to the manuscript, including: explaining why we did not use manual ICA methods, suggest this for future research, and added an exploratory analysis using a recently developed automated pipeline that selects components based on the presence of a peak in the alpha range and alignment with a predefined template topography representing activity from occipital or motor sites.

While we acknowledge that ICA components can offer a better signal-to-noise ratio (SNR) and possibly smoother spectral plots, we opted for our chosen method to avoid potential bias inherent in deciding on a component following source separation. The desire for a quick, automated, replicable, and unbiased pipeline, crucial for potential clinical applications of PAF as a biomarker, influenced this decision. At the time of analysis registration, automated methods for deciding which alpha components to extract following ICA were not apparent. We have now added this reasoning to Methods.

“Contrary to some previous studies that used ICA to isolate sensory region alpha sources (Furman et al., 2018; De Martino et al., 2021; Valentini et al., 2022), we used pre-determined sensor level ROIs to improve reproducibility and reduce the potential for bias when individually selecting ICA components. Using sensor level ROIs may decrease the signal-to-noise ratio of the data; however, this approach has still been effective for observing the relationship between PAF and experimental pain (Furman et al., 2019; Furman et al., 2020).”

We have also added use of ICA and development of methods as a suggestion for future research in the discussion:

“Additionally, the use of global PAF may have introduced mediation measurement error into our mediation analysis. The spatial precision used in the current study was based on previous literature on PAF as a biomarker of pain sensitivity, which have used global and/or sensorimotor ROIs (Furman et al., 2018; Furman et al., 2020). Identification and use of the exploratory electrode clusters found in this study could build upon the current work (e.g., Furman et al., 2021). However, exploratory analysis of the clusters found in the present analysis demonstrated no influence on mediation analysis results (Supplementary Materials 3.8-3.10). Alternatively, independent component analysis (ICA) could be used to identify separate sources of alpha oscillations (Choi et al., 2005), as used in other experimental PAF-pain studies (Furman et al., 2018; Valentini et al., 2022), which could aid to disentangle the potential relevance of different alpha sources in the PAFpain relationship. Although this comes with the need to develop more reproducible and automated methods for identifying such components.”

The specific location or source of PAF that relates to pain remains unclear. Because of this, we did employ an exploratory cluster-based permutation analysis to assess the potential for variations in the presence of PAF changes across the scalp at sensor level, and emphasise that location of PAF change could be explored in future. However, we have now conducted the mediation analysis (difference score 2W-LCS model) using averages from the data-driven parietal cluster, frontal cluster, and both clusters together. For these we see a stronger effect of gum on PAF change, which was expected given the data driven approach of picking electrodes. There was still a total and direct effect of nicotine on pain during the PHP model, but still no indirect effect via change in PAF. For the CPA models, there were still no significant total, direct, or indirect effects of nicotine on CPA ratings. Therefore, using these data-driven clusters did not alter results compared to the model using the global PAF variable.

The reader has been directed to this supplementary material so:

“The potential mediating effect of this change in PAF on change in PHP and CPA was explored (not pre-registered) by averaging within each cluster (central-parietal: CP1, CP2, Cpz, P1, P2, P3, P4, Pz, POz; right-frontal: F8, FT8, FT10) and across both clusters. This averaging across electrodes produced three new variables, each assessed in relation to mediating effects on PHP and CPA ratings. The resulting in six exploratory mediation analysis (difference score 2W-LCS) models demonstrated minimal differences from the main analysis of global PAF (8-12 Hz), except for the

expected stronger effect of nicotine on change in PAF (bs = 0.11-0.14, ps < .003; Supplementary

Materials 3.8-3.10).”

Moreover, our team has been working on an automated method for selecting ICA components, so in response to your comment we assessed whether using this method altered the results of the current analysis. The in-depth methodology behind this new automatic pipeline will be published with a validation from some co-authors in the current collaboration in due course. At present, in summary, this automatic pipeline conducts independent component analysis (ICA) 10 times for each resting state, and selects the component with the highest topographical correlation to a template created of a sensorimotor alpha component from Furman et al., (2018). 

The results of the PHP or CPA mediation models were not substantially different using the PAF calculated from independent components than that using the global PAF. For the PHP model, the total effect (b = -0.648, p \= .033) and direct effects (b = -0.666, p \= .035) were still significant, and there was still no significant indirect effect (b = 0.018, p \= .726). The general fit was reduced, as although the CFI was above 0.90, akin to the original model, the RMSEA and SRMR were not below 0.08, unlike the original models (Little, 2013). For the CPA model, there were still no significant total (b = -0.371, p \= .357), direct (b = -0.364, p \= .386), or indirect effects (b = -0.007, p \= .906), and the model fit also decreased, with CFI below 0.90 and RMSEA and SRMR above 0.08. See supplementary material (3.11). Note that still no correlations were seen between this IC sensorimotor PAF and pain (PHP: r = 0.11, p = .4; CPA: r \= -0.064, p = .63).

Interestingly, in both models, there was now no longer a significant a-path (PHP: b = 0.08, p =

0.292; CPA: b = 0.039, p = 0.575), unlike previously observed (PHP: b = 0.085, p = 0.018; CPA: b = 0.089, p = 0.011). We interpret this as supporting the previously highlighted difference between finding an effect on PAF globally but not in a sensorimotor ROI (and now a sensorimotor IC), justifying the exploratory CBPA and the suggestion in the discussion to explore methodology.

We understand that this analysis does not fully uncover the reviewer’s question in which they wondered which of the known cortical alphas contributes to the effects reported in our previous work. However, we consider this exploration to be beyond the scope of the current paper, as it would be more appropriately addressed with larger datasets or combinations of datasets, potentially incorporating MEG to better disentangle oscillatory sources. The highlighted differences seen between global PAF, sensorimotor ROI PAF, sensorimotor IC PAF, as well as the CBPA of PAF changes provide ample directions for future research to build upon: 1) which alpha (sensor or source space) are related to pain, 2) how are these alpha signals represented robustly in a replicable way, and 3) which alpha (sensor or source space) are manipulable through interventions. These are all excellent questions for future studies to investigate.

The below text has been added to the Discussion:

In-house code was developed to compare a sensorimotor component to the results presented in this manuscript (Supplementary Material 3.11), showing similar results to the sensorimotor ROI mediation analysis presented here. However, examination of which alpha - be it sensor or source space - are related to pain, how they can be robustly represented, and how they can be manipulated are ripe avenues for future study.

(4) I have my doubts that you can get a reliable close to bell-shaped amplitude distribution for every participant. The argument that the peak detection procedure is hampered by the high-amplitude lower frequency can be easily solved by subtracting the "slope" before determining the peak. My issue is that the entire analysis is resting on the assumption that each participant has a reliable alpha effect at electrode level. This is not the case. Non-alpha participants can severely distort the statistics. ICA-based analyses would be more sensitive but not every participant will show alpha. You may want to argue with robust group effects but In my view, every single participant counts, particularly for this type of data analysis, where in the case of a low SNR the "peak" can easily shift to the extremes. In case there is an alpha effect for a specific subject, we should see a smooth bump in the frequency spectrum between 8 and 12 12Hz. Anything beyond that is hard to believe. The long stimulation period allows a broad FFT analysis window with a good frequency resolution in order to detect the alpha frequency bump.

The reviewer is correct that non-alpha participants can distort the statistics. We did visually assess the EEG of each individual’s spectra at baseline to establish the presence of global peaks, as we believe this is good practice to aid understanding of the data. Please see Author response image 1 for individual spectra seen at baseline. Although not all participants had a ‘smooth bump in the frequency spectrum between 8 and 12 Hz’, we prefer to not apply/necessitate this assumption to our data. Chiang et al., (2011) suggest that ~3% of individuals do not have a discernible alpha peak, and in our data we observed only one participant without a very obvious spectral peak (px-39). But, this participant does have enough activity within the alpha range to identify PAF by the CoG method (i.e. not just flat spectra and activity on top of 1/f characteristics). Without a pre-registered and standardised decision process to remove such a participant in place, we opted to not remove any participants to avoid curation of our data.

Author response image 1.

(5) I find reports on frequent channel rejections reflect badly on the data quality. Bad channels can be avoided with proper EEG preparation. EEG should be continuously monitored during recording in order to obtain best data quality. Have any of the ROI channels been rejected?

We appreciate your attention to the channel rejection. We believe that the average channels removed (0.94, 0.98, 0.74, and 0.87 [range: 0-4] for each of the four resting states out of 64 channels) does not suggest overly frequent rejection, as it was less than one electrode on average and the numbers are below the accepted number of bad channels to remove/interpolate (i.e. 10%) in EEG pipelines (Debnath et al., 2020; Kayhan et al., 2022). To maintain data quality, consistently poor channels were identified and replaced over time. We hope you will accept our transparency on this issue and note that by stating how channel removal decisions were made (i.e. 8 or more deviations) and reporting the number of channels removed, we adhere to the COBIDAS guidelines (Pernet et al., 2018; 2020).

During analysis, cases of sensorimotor ROI channels being rejected were noted and are now specified in our manuscript. “Out of 248 resting states recorded, 14 resting states had 4 ROI channels instead of 5. Importantly, no resting state had fewer than 4 channels for the sensorimotor ROI.”

Note, we also realised that we had not specified that we did interpolate channels for the cluster based permutation analysis. This has been corrected with the following sentence:

“Removed channels were not interpolated for the pre-registered global and sensorimotor ROI averaged analyses, but were interpolated for an exploratory cluster based permutation analysis using the nearest neighbour average method in `Fieldtrip`.”

(6) I have some issues buying the authors' claims that there is an effect of nicotine on prolonged pain. By looking at the mean results for the nicotine and placebo condition, this can not be right. What was the point in including the variables in the equation? In my view, in this within-subject design the effect of nicotine should be universal, no matter what gender, age, or depression. The unconditional effect of nicotine is close to zero. I can not get my head around how any of the variables can turn the effects into significance. There must be higher or lower variable scores that might be related to a higher or lower effect on nicotine. The question is not to consider these variables as a nuisance but to show how they modulate the pain-related effect of nicotine treatment. Still, the overall nicotine effect of the entire group is basically zero.

Another point is that for within-subject analyses even tiny effects can become statistically significant if they are systematically in one direction. This might be the case here. There might be a significant effect of nicotine on pain but the actual effect size (5.73 vs. 5.78) is actually not interpretable. I think it would be interesting for the reader how (in terms of pain rating difference) each of the variables can change the effect of nicotine.

Thank you for your comments. We recognize the concern about interpreting the effect of nicotine on prolonged pain solely based on mean results, and in fact wish to discourage this approach. It's crucial to note that both PAF and pain are highly individual measures (i.e. high inter-individual variance), necessitating the use of random intercepts for participants in our analyses to acknowledge the inherent variability at baseline across participants. Including random intercepts rather than only considering the means helps address the heterogeneity in baseline levels among participants. We also recognise that displaying the mean PHP ratings for all participants in Table 2 could be misleading, firstly because these means do not have weight in an analysis that takes into account a random-effects intercept for participants, and secondly because two participants (one from each group) did not have post-gum PHP assessments and were not included in the mediation analysis due to list-wise deletion of missing data. Therefore, to reduce the potential for misinterpretation, we have added extra detail to display both the full sample and CPA mediation analysis (i.e. N=62) and the data used for PHP mediation analysis (i.e. n=60) in Table 2. We hope that the extra details added to this table will help the readers interpretation of results.

In light of this, we have also altered the PAF Table 3 to reflect both the pre-post values used for the CPA mediation and baseline correlations with CPA and PHP pain (i.e. N=62), and the pre-post values used for the PHP mediation (i.e. n=60).

It is inherently difficult to visualise the findings of a mediation analysis with confounding variables that also used latent change scores (LCS) and random-effect intercepts for participants. LCS was specifically used because of issues of regression to the mean that occur if you calculate a straightforward ‘difference-score’, therefore calculating the difference in order to demonstrate the results of the statistical model in a figure, for example, does not provide a full description of the data assessed (Valente & McKinnon, 2017). Nevertheless, if we look at the data descriptively with this in mind, then calculating the change in PHP ratings does indicate that, for the nicotine group, the mean change in PHP ratings was -0.047 (SD = 1.05, range: -4.13, 1.45). Meanwhile, for the placebo group the mean change in PHP ratings was 0.33 (SD = 0.75, range: -1.37, 1.66). Therefore suggesting a slight decrease in pain ratings on average for the nicotine group compared to a slight increase on average for the placebo group. With control for pre-determined confounders, we found that the latent change score was -0.63 lower for the nicotine group compared to the control group (i.e. the direct effect of nicotine on change in pain).

If the reviewer is only discussing the effect of nicotine on pain, we do not believe that this effect ‘should be universal’. There is clear evidence that effects of nicotine on other measures can vary greatly across individuals (Ettinger et al., 2009; Falco & Bevins, 2015; Pomerleau et al., 1995). Our intention would not be to propose a universal effect but to understand how these variables may influence nicotine's impact on pain for individuals. Here we focus on the effects of nicotine on PAF and pain sensitivity, but attempted to control for the potential influence of these other confounding factors. Therefore, our statistical approach goes beyond mean values, incorporating variables like sex at birth, age, and depression to control for and explore potential modulating factors. Control for confounding factors is an important aspect of mediation analysis (Lederer et al., 2019; VanderWeele, 2019).

Regarding the seemingly small effect size, we understand your concern. Indeed ‘tiny effects can become statistically significant if they are systematically in one direction’, which may be what we see in this analysis. We do not agree that the effect is ‘not interpretable’, rather that it should be interpreted in light of its small effect size (effect size being the beta coefficient in our analysis, rather than the mean group difference). We agree on the importance of considering practical significance alongside statistical significance and hope to conduct additional experiments and analyses in future to elucidate the contribution of each variable to the subtle and therefore not entirely conclusive overall effect you mention.

Your feedback on this is valuable, and we have ensured a more detailed discussion in the revised manuscript on how these factors should be interpreted alongside some additional post-hoc analyses of confounding factors that were significant in our mediation, with the note that investigation of these interactions is exploratory. We had already discussed the potential contribution of sex on the effect of nicotine on PAF, with exploratory post-hoc analysis on this included in supplementary materials. In addition, we have now added an exploratory post-hoc analysis on the potential contribution of stress on the effect of nicotine on pain. This then shows the stratified effects by the covariates that our model suggest are influencing change in PAF and pain.

Results edits:

“There was also a significant effect of perceived stress at baseline on change in PHP ratings when controlling for group allocation and other confounding variables (b = -0.096, p = .048, bootstrapped 95% CI: [-0.19, -0.000047]), where higher perceived stress resulted in larger decreases in PHP ratings (see Supplementary Material 3.3 for post-hoc analysis of stress).”

Supplementary material addition:

“3.3 Exploratory analysis of the influence of perceived stress on the effects of nicotine on change in PHP ratings “

“Due to the significant estimated effects of perceived stress on change in PHP ratings in the 2WLCS mediation model, we also explored post-hoc effects of stress on change in PHP ratings. We found that there is strong evidence for a negative correlation between stress and change in PHP rating within the nicotine group (n = 28, r = −0.39, BF10 = 13.65; Figure 3) that is not present in the placebo group, with equivocal evidence (n = 32, r = −0.14, BF10 = 0.46). This suggests that those with higher baseline stress who had nicotine gum experienced greater decreases in PHP ratings. Note that there was less, but still sufficient evidence for this relationship within the nicotine group when the participant who was a potential outlier for change in PHP rating was removed (n = 27, r = −0.32, BF10 = 1.45). “

Author response image 2.

Spearman correlations od baseline perceived stress with the change in phasic heat pain (PHP) ratings, suggest strong evidence for a negative relationship for the nicotine gum groupin orange (n=28; BF₁₀=13.65) but not for the placebo group in grey (n=32; BF₁₀=0.46). Regression lines and 95% confidence intervals.

Discussion edits:

“For example, in addition to the effect of nicotine on prolonged heat pain ratings, our results suggest an effect of stress on changes in heat pain ratings, with those self-reporting higher stress at baseline having greater reductions in pain. Our post-hoc analysis suggested that this relationship between higher stress and larger decrease in PHP ratings was only present for the nicotine group (Supplementary Material 3.3). As stress is linked to nicotine use [69,70] and pain [71–73], these interactions should be explored in future.”

(7) Is the differential effect of nicotine vs. placebo based on the pre vs. post treatment effect of the placebo condition or on the pre vs. post effect of the nicotine treatment? Can the mediation model be adapted and run for each condition separately? The placebo condition seems to have a stronger effect and may have driven the result.

Thank you for your comments. In our mediation analysis, the differential effect of nicotine vs. placebo is assessed as a comparison between the pre-post difference within each condition. A latent change score (i.e. pre-post) is calculated for each condition (nicotine and placebo), and then the effect of being in the nicotine group (dummy coded as 1) is compared to being in the placebo group (dummy coded as 0). The comparison between conditions is needed for this model (Valente & MacKinnon, 2017), as we are assessing the change in PAF and pain in the nicotine group compared to the change in the placebo group.

However, to address your response, it is possible to simplify and assess the relationship between the change in peak alpha frequency (PAF) and change in pain within each gum group (nicotine and placebo) independently, without including the intervention as a factor. To do this, the mediation model can be simplified to regression analysis with latent change scores that focus purely on these relationships. The results of this can help to understand whether change in PAF influences change in pain within each group separately. As with the main analysis, we see no significant influence of change in PAF on change in pain while controlling for the same confounding variables within the nicotine group (Beta = -0.146 +/- 1.105, p = 0.895, 95% CI: -2.243, 2.429) or the placebo group (Beta = 0.730 +/- 2.061, p = 0.723, 95% CI: -4.177, 3.625).

When suggesting that the “the placebo condition seems to have a stronger effect and may have driven the result”, we believe you are referring to the increase in mean PHP ratings within the placebo group from pre (5.51 +/- 2.53) to post-placebo gum (5.84 +/- 2.67). Indeed there was a significant increase in pain ratings pre to post chewing placebo gum (t(31) = -2.53, p = 0.0165, 95% CI: -0.603, -0.0653), that was not seen after chewing nicotine gum (t(27) = 0.237, p = 0.81, 95% CI: -0.358, 0.452). In lieu of a control where no gum was chewed (i.e. simply a second pain assessment ~30 minutes after the first), we assume the gum without nicotine is a good reference that controls for the effect of time plus expectation of chewing nicotine gum. With this in mind, as we describe in our results, the change in PHP ratings is reduced in the nicotine group compared to the placebo group. Note that this phrasing keeps the effect of placebo on pain as our reference from which to view the effect of nicotine on pain. However, you are correct that we need to ensure we emphasise that the change in pain in the PHP group is reduced in comparison to the change seen after placebo.

We have not included these extra statistics in our revised manuscript, but hope that they aid the your understanding and interpretation of the included analyses and have highlighted these nuances in the discussion.

“However, we note that the observed effect of nicotine on pain was small in magnitude, and most prominent in comparison to the effect of placebo, where pain ratings increased after chewing, which brings into question whether this reduction in pain is meaningful in practice.”

(8) I would not dare to state that nicotine can function as an acute analgesic. Acute analgesics need to work for everyone. The average effect here is close to zero.

In light of your feedback, we have refined our language to avoid a sweeping assertion of universal analgesic effects and emphasize individual variability. Nicotine's role as a coping strategy for pain is acknowledged in the literature (Robinson et al., 2022), with the meta-analysis by Ditre et al. (2016) discussing its potential as an acute analgesic in humans, along with some evidence from animal research (Zhang et al., 2020). Our revised discussion underscores the need for further exploration into factors influencing nicotine's potential impact on pain. We have also specified the short-term nature of nicotine use in this context to distinguish acute effects from potential opposing effects after long-term use (Zhang et al., 2020).

“Short-term nicotine use is thought to have acute analgesic properties in experimental settings, with a review reporting that nicotine increased pain thresholds and pain tolerance [49]. In addition, research in a rat model suggests analgesic effects on mechanical thresholds after short-term nicotine use (Zhang et al., 2020). However, previous research has not assessed the acute effects of nicotine on prolonged experimental pain models. The present study found that 4 mg of nicotine reduced heat pain ratings during prolonged heat pain compared to placebo for our human participants, but that prolonged pressure pain decreased irrespective of which gum was chewed. Our findings are thus partly consistent with the idea that nicotine may have acute analgesic properties [49], although further research is required to explore factors that may influence nicotine’s potential impact on a variety of prolonged pain models. We further advance the literature by reporting this effect in a

model of prolonged heat pain, which better approximates the experience of clinical pain than short lasting models used to assess thresholds and tolerance [50]. However, we note that the observed effect of nicotine on pain was small in magnitude, and most prominent in comparison to the effect of placebo, where pain ratings increased after chewing, which brings into question whether this reduction in pain is meaningful in practice. Future research should examine whether effects on pain increase in magnitude with different nicotine administration regimens (i.e. dose and frequency).”

(9) Figures 2E and 2F are not particularly intuitive. Usually, the colour green in "jet" colour coding is being used for "zero" values. I would suggest to cut off the blue and use only the range between red green and red.

We have chosen to retain the current colour scale for several reasons. In our analysis, green represents the middle of the frequency range (approx 10 Hz in this case), and if we were to use green as zero, it would effectively remove both blue and green from the plot, resulting in only red shades. Additionally, we have provided a clear colour scale for reference next to the plot, which allows readers to interpret the data accurately. Our intention is to maintain clarity and precision in representing the data, rather than conforming strictly to conventional practices in color coding.

We believe that the current representation effectively conveys the results of our study while allowing readers to interpret the data within the context provided. Thank you again for your suggestion, and we hope you understand our reasoning in this matter.

(10) Did the authors do their analysis on the parietal ROI or on the pre-registerred ROI?

The analysis was conducted on the pre-registered sensorimotor ROI and on the global values. We have now also conducted the analysis with the regions suggested with the cluster based permutation analysis as requested by reviewer 2, comment 3.

(11) Point 3.2 in the discussion. I would be very cautious to discuss smoking and chronic pain in the context of the manuscript. The authors can not provide any additional knowledge with their design targeting non-smokers, acute nicotine and experimental pain. The information might be interesting in the introduction in order to provide the reader with some context but is probably misleading in the discussion.

We appreciate your perspective and agree with your caution regarding the discussion of smoking and chronic pain. While our study specifically targets non-smokers and focuses on acute nicotine effects in experimental pain, we understand the importance of contextual clarity. We have removed these points from the discussion to not mislead the reader.

Previously we wrote, and have removed: “For those with chronic pain, smoking and nicotine use is reported as a coping strategy for pain [52]; abstinence can increase pain sensitivity [48,50], and pain is thus seen as a barrier to smoking cessation due to fear of worsening pain [51,52]. Therefore, continued understanding of the acute effects of nicotine on models of prolonged pain could improve understanding of the role of nicotine and smoking use in chronic pain [49,51,52].”

(12) I very much appreciate section 3.3 of the discussion. I would not give up on PAF as a target to modulate pain. A modulation might not be possible in such a short period of experimental intervention. PAF might need longer and different interventions to gradually shift in order to attenuate the intensity of pain. As discussed by the authors themselves, I would also consider other targets for alpha analysis (as mentioned above not other electrodes or ROIs but separated sources.)

Thank you for your comments on section 3.3. We appreciate your recognition of the potential significance of PAF as a target for pain modulation. Your insights align with our considerations that the experimental intervention duration or type might be a limiting factor in observing substantial shifts in PAF to attenuate pain intensity. We had mentioned the use of the exploratory electrode clusters in future work, but have now also mentioned that the use of ICA to identify separate ICA sources may provide an alternative approach. See responses to your previous ICA comment regarding separate sources.

REFERENCES for responses to reviewer 2

Chiang, A. K. I., Rennie, C. J., Robinson, P. A., Van Albada, S. J., & Kerr, C. C. (2011). Age trends and sex differences of alpha rhythms including split alpha peaks. Clinical Neurophysiology, 122(8), 1505-1517.

Debnath, R., Buzzell, G. A., Morales, S., Bowers, M. E., Leach, S. C., & Fox, N. A. (2020). The Maryland analysis of developmental EEG (MADE) pipeline. Psychophysiology, 57(6), e13580.

Ettinger, U., Williams, S. C., Patel, D., Michel, T. M., Nwaigwe, A., Caceres, A., ... & Kumari, V. (2009). Effects of acute nicotine on brain function in healthy smokers and non-smokers: estimation of inter-individual response heterogeneity. Neuroimage, 45(2), 549-561.

Falco, A. M., & Bevins, R. A. (2015). Individual differences in the behavioral effects of nicotine: a review of the preclinical animal literature. Pharmacology Biochemistry and Behavior, 138, 80-90.

Kayhan, E., Matthes, D., Haresign, I. M., Bánki, A., Michel, C., Langeloh, M., ... & Hoehl, S. (2022). DEEP: A dual EEG pipeline for developmental hyperscanning studies. Developmental cognitive neuroscience, 54, 101104.

Lederer, D. J., Bell, S. C., Branson, R. D., Chalmers, J. D., Marshall, R., Maslove, D. M., ... & Vincent, J. L. (2019). Control of confounding and reporting of results in causal inference studies. Guidance for authors from editors of respiratory, sleep, and critical care journals. Annals of the American Thoracic Society, 16(1), 22-28.

Little TD. Longitudinal structural equation modeling. Guilford press; 2013.

Pernet, C., Garrido, M., Gramfort, A., Maurits, N., Michel, C. M., Pang, E., ... & Puce, A. (2018). Best practices in data analysis and sharing in neuroimaging using MEEG.

Pernet, C., Garrido, M. I., Gramfort, A., Maurits, N., Michel, C. M., Pang, E., ... & Puce, A. (2020). Issues and recommendations from the OHBM COBIDAS MEEG committee for reproducible EEG and MEG research. Nature neuroscience, 23(12), 1473-1483.

Pomerleau, O. F. (1995). Individual differences in sensitivity to nicotine: implications for genetic research on nicotine dependence. Behavior genetics, 25(2), 161-177.

Robinson, C. L., Kim, R. S., Li, M., Ruan, Q. Z., Surapaneni, S., Jones, M., ... & Southerland, W. (2022). The Impact of Smoking on the Development and Severity of Chronic Pain. Current Pain and Headache Reports, 26(8), 575-581.

Xia, J., Mazaheri, A., Segaert, K., Salmon, D. P., Harvey, D., Shapiro, K., ... & Olichney, J. M. (2020). Event-related potential and EEG oscillatory predictors of verbal memory in mild cognitive impairment. Brain communications, 2(2), fcaa213.

VanderWeele, T. J. (2019). Principles of confounder selection. European journal of epidemiology, 34, 211-219.

Valente, M. J., & MacKinnon, D. P. (2017). Comparing models of change to estimate the mediated effect in the pretest–posttest control group design. Structural Equation Modeling: A Multidisciplinary Journal, 24(3), 428-450.

Vimolratana, O., Aneksan, B., Siripornpanich, V., Hiengkaew, V., Prathum, T., Jeungprasopsuk, W., ... & Klomjai, W. (2024). Effects of anodal tDCS on resting state eeg power and motor function in acute stroke: a randomized controlled trial. Journal of NeuroEngineering and Rehabilitation, 21(1), 1-15.

Zhang, Y., Yang, J., Sevilla, A., Weller, R., Wu, J., Su, C., ... & Candiotti, K. A. (2020). The mechanism of chronic nicotine exposure and nicotine withdrawal on pain perception in an animal model. Neuroscience letters, 715, 134627.

Reviewer #3 (Recommendations For The Authors):

Introduction

(1) Rationale and link to chronic pain. I am not sure I agree with the statement "The ability to identify those at greater risk of developing chronic pain is limited". I believe there is an abundance of literature associating risk factors with the different instances of chronic pain (e.g., Mills et al., 2019). The fact that the authors cite studies involving potential neuroimaging biomarkers leads me to believe that they perhaps did not intend to make such a broad statement, or that they wanted to focus on individual prediction instead of population risk.

We thank the reviewer for the thought put into this comment. We did indeed wish to refer to individual prediction, but also realise that the focus on predicting pain might not be the most appropriate opening for this manuscript. Therefore, we have adjusted the below sentence to refer to the need to identify modifiable factors rather than the need to predict pain.

“Identifying modifiable factors that influence pain sensitivity could be a key step in reducing the presence and burden of chronic pain (van der Miesen et al., 2019; Davis et al., 2020; Tracey et al., 2021).”

(2) The statement "Individual peak alpha frequency (PAF) is an electro-physiological brain measure that shows promise as a biomarker of pain sensitivity, and thus may prove useful for predicting chronic pain development" is a non sequitur. PAF may very well be a biomarker of pain sensitivity, but the best measures of pain sensitivity we have (selfreported pain intensity ratings) in general are not in themselves predictive of the development of chronic pain. Conversely, features that are not related to pain sensitivity could be useful for predicting chronic pain (e.g., Tanguay-Sabourin et al., 2023).

We agree that it is essential to acknowledge that self-reported pain intensity ratings alone are not definitive predictors of chronic pain development. To align with this, we have revised the sentence, removing the second clause to avoid overstatement. The adjusted sentence now reads, "Individual peak alpha frequency (PAF) is an electrophysiological brain measure that shows promise as a biomarker of pain sensitivity."

(3) Finally, some of the statements in the discussion comparing a tonic heat pain model with chronic neuropathic pain might be an overstatement. Whereas it is true that some of the descriptors are similar, the time courses and mechanisms are vastly different.

We appreciate this comment, and agree that it is difficult to compare the heat pain model used to clinical neuropathic pain. This was an oversight and with further understanding we have removed this comment from the introduction and the discussion:

“In parallel, we saw no indication of a relationship between PAF and pain ratings during CPA. The introduction of the CPA model, specifically calibrated to a moderate pain threshold, provides further support for the notion that the relationship between PAF and pain is specific to certain pain types [17,28]. Prolonged heat pain was pre-dominantly described as moderate/severe shooting, sharp, and hot pain, whereas prolonged pressure pain was predominantly described as mild/moderate throbbing, cramping, and aching in the present study. It is possible that the PAF–pain relationship is specific to particular pain models and protocols [12,17].”

Methodology

(4) or the benefit of good science. However, I am compelled to highlight that I could not access the preregistered files, even though I waited for almost two weeks after requesting permission to do so. This was a problem on two levels: the main one is that I could not check the hypothesized effect sizes of the sample size estimation, which are not only central to my review, and in general negate all the benefits that should go with preregistration (i.e., avoiding phacking, publication bias, data dredging, HARKing, etc.). The second one is that I had to provide an email address to request access. This allows the authors to potentially identify the reviewers. Whereas I have no issues with this and I support transparent peer review practices (https://elifesciences.org/inside-elife/e3e90410/increasingtransparency-in-elife-s-review-process), I also note that this might condition other reviewers.

We apologise for this. We had not realised that the pre-registration was under embargo, but we have now made it available.

Interpretation of results

(5)To be perfectly clear, I trust the results of this study more than some of the cited studies regarding nicotine and pain because it was preregistered, the sample size is considerably larger, and it seems carefully controlled. I just do not agree with the interpretation of the results, stated in the first paragraph of the Discussion. Quoting J. Cohen, "The primary product of a research inquiry is one or more measures of effect size, not P values" (Cohen, 1990). As I am sure the authors are aware of, even tiny differences between conditions, treatments or groups will eventually be statistically significant given arbitrarily large sample sizes. What really matters then is the magnitude of these differences. In general, the authors hypothesize on why there were no differences on the pressure pain model, and why decreases in heat pain were not mediated by PAF, but do not seem to consider the possibility that the intervention just did not cause the intended effect on the nociceptive system, which would be a much more straightforward explanations for all observations.

While acknowledging and agreeing with the concern that 'even tiny differences between conditions, treatments, or groups will eventually be statistically significant given arbitrarily large sample sizes,' it's crucial to clarify that our sample size of N=62 does not fall into the category of arbitrarily large. We carefully considered the observed outcomes in the pressure pain model and the lack of PAF mediation in heat pain, as dictated by our statistical approach and the obtained results.

The suggestion of a straightforward explanation aligning with the intervention not causing the intended effect on the nociceptive system is a valid consideration. We did contemplate the possibility of a false positive, emphasising this in the limitations of our findings and the need for replication to draw stronger conclusions to follow up this initial study.

(6) In this regard, I do not believe that an average *increase* of 0.05 / 10 (Nicotine post - pre) can be considered a "reduction of pain ratings", regardless of the contrast with placebo (average increase of 0.24 / 10). This tiny effect size is more relevant in the context of the considerable inter-individual variation, in which subjects scored the same heat pain model anywhere from 1 to 10, and the same pressure pain model anywhere from 1 to 8.5. In this regard, the minimum clinically or experimentally important differences (MID) in pain ratings varies from study to study and across painful conditions but is rarely below 1 / 10 in a VAS or NRS scale, see f. ex. (Olsen et al., 2017). It is not my intention to question whether nicotine can function as an acute analgesic in general (as stated in the Discussion), but instead, if it worked as such under these very specific experimental conditions. I also acknowledge that the authors note this issue in two lines in the Discussion, but I believe that this is not weighed properly.

We appreciate your perspective on the interpretation of the effect size, and we understand the importance of considering it in the context of individual variation.

As also discussed in response to comment 6 From reviewer 2, we recognize the concern about interpreting the effect of nicotine on prolonged pain solely based on mean results, and in fact wish to discourage this approach. It's crucial to note that both PAF and pain are highly individual measures (i.e. high inter-individual variance), necessitating the use of random intercepts for participants in our analyses to acknowledge the inherent variability at baseline across participants. Including random intercepts rather than only considering the means helps address the heterogeneity in baseline levels among participants. We also recognise that displaying the mean PHP ratings for all participants in Table 2 could be misleading, firstly because these means do not have weight in an analysis that takes into account a random-effects intercept for participants, and secondly because two participants (one from each group) did not have post-gum PHP assessments and were not included in the mediation analysis due to list-wise deletion of missing data. Therefore, to reduce the potential for misinterpretation, we have added extra detail to display both the full sample and CPA mediation analysis (i.e. N=62) and the data used for PHP mediation analysis (i.e. n=60) in Table 2. We hope that the extra details added to this table will help the readers interpretation of results.

Moreover, we have made sure refer to the comparison with the placebo group when discussing the reduction or decrease in pain seen in the nicotine group, for example:

“2) nicotine reduced prolonged heat pain intensity but not prolonged pressure pain intensity compared to placebo gum;”

“The nicotine group had a decrease in heat pain ratings compared to the placebo group and increased PAF speed across the scalp from pre to post-gum, driven by changes at central-parietal and right-frontal regions.”

We have kept our original comment of whether this effect on pain is meaningful in practice to refer to the minimum clinically or experimentally important differences in pain ratings as highlighted by Olsen et al., 2017.

“While acknowledging the modest effect size, it’s essential to consider the broader context of our study’s focus. Assessing the clinical relevance of pain reduction is pertinent in applications involving the use of any intervention for pain management [69]. However, from a mechanistic standpoint, particularly in understanding the implications of and relation to PAF, the specific magnitude of the pain effect becomes less pivotal. Nevertheless, future research should examine whether effects on pain increase in magnitude with different nicotine administration regimens (i.e. dose and frequency).”

(7) In line with the topic of effect sizes, average effect sizes for PAF in the study cited in the manuscript range from around 1 Hz (Boord et al., 2008; Wydenkeller et al., 2009; Lim et al., 2016), to 2 Hz (Foulds et al., 1994), compared with changes of 0.06 Hz (Nicotine post - pre) or -0.01 Hz (Placebo post - pre). MIDs are not so clearly established for peak frequencies in EEG bands, but they should be certainly larger than some fractions of a Hertz (which is considerably below the reliability of the measurement).

We appreciate your care of these nuances. We acknowledge the differences in effect sizes between our study and those referenced in the manuscript. Given the current state of the literature, it's noteworthy that ‘MIDs’ for peak frequencies in EEG bands, particularly PAF changes, are not clearly established, other than a recent publication suggesting that even small changes in PAF are reliable and meaningful (Furman et al., 2021). In light of this, we have addressed the uncertainty around the existence and determination of MIDs in our revision, highlighting the need for further research in this area.

In addition, our study employed a greater frequency resolution (0.2 Hz) compared to some of the referenced studies, with approximately 0.5 Hz resolution (Boord et al., 2008; Wydenkeller et al., 2009; Foulds et al., 1994). This improved resolution allows for a more precise measurement of changes in PAF. Considering this, it is plausible that studies with lower resolution might have conflated increases in PAF, and our higher resolution contributes to a more accurate representation of the observed changes.

We have also incorporated this insight into the manuscript, emphasising the methodological advancements in our study and their potential impact on the interpretation of PAF changes. Thank you for your thoughtful feedback.

“The ability to detect changes in PAF can be considerably impacted by the frequency resolution used during Fourier Transformations, an element that is overlooked in recent methodological studies on PAF calculation [16,95]. Changes in PAF within individuals might be obscured or conflated by lower frequency resolutions, which should be considered further in future research.”

(8) The authors also ran alternative statistical models to analyze the data and did not find consistent results in terms of PHP ratings (PAF modulation was still statistically significantly different). The authors attribute this to the necessity of controlling for covariates. Now, considering the effects sizes, aren't these statistically significant differences just artifacts stemming from the inclusion of too many covariates (Simmons et al., 2011)? How much influence should be attributable to depression and anxiety symptoms, stress, sleep quality and past pain, considering that these are healthy volunteers? Should these contrasting differences call the authors to question the robustness of the findings (i.e., whether the same data subjected to different analysis provides the same results), particularly when the results do not align with the preregistered hypothesis (PAF modulation should occur on sensorimotor ROIs)?

Thank you for your comments on our alternative statistical models. By including these covariates, we aim to provide a more nuanced understanding of the complexities within our data by considering their potential impact on the effects of interest. The decision to include covariates was preregistered (apologies again that this was not available) and made with consideration of balancing model complexity and avoiding potential confounding. Moreover, we hope that the insights gained from these analyses will offer valuable information about the behaviour of our data and aid future research in terms of power calculations, expected variance, and study design.

(9) Beyond that, I believe in some cases that the authors overreach in an attempt to provide explanations for their results. While I agree that sex might be a relevant covariate, I cannot say whether the authors are confirming a pre-registered hypothesis regarding the gender-specific correlation of PAF and pain, or if this is just a post hoc subgroup analysis. Given the large number of analyses performed (considering the main document and the supplementary files), caution should be exercised on the selective interpretation of those that align with the researchers' hypotheses.

We chose to explore the influence of sex on the correlation between PAF and pain, because this has also been investigated in previous publications of the relationship (Furman et al., 2020).  We state that the assessment by sex is exploratory in our results on p.17: “in an exploratory analysis of separate correlations in males and females (Figure 5, plot C)”. For clarity regarding whether this was a pre-registered exploration or not, we have adjusted this to be: “in an exploratory analysis (not pre-registered) of separate correlations in males and females (Figure 5, plot C), akin to those conducted in previous research on this topic (Furman et al., 2020),

We have made sure to state this in the discussion also. Therefore, when we previously said on p.22:

“Regarding the relationship between PAF and pain at baseline, the negative correlation between PAF and pain seen in previous work [7–11,15] was only observed here for male participants during the PHP model for global PAF.” We have now changed this to: “Regarding the relationship between PAF and pain at baseline, the negative correlation between PAF and pain seen in previous work [7– 11,15] was only observed here for male participants during the PHP model for global PAF in an exploratory analysis.”

Please also note that we altered the colour and shape of points on the correlation plot (Figure 5 in initial submission), the male brown was changed to a dark brown as we realised that the light brown colour was difficult to read. The shape was then changed for male points so that the two groups can be distinguished in grey-scale.

Overall, your thoughtful feedback is instrumental in refining the interpretation of our findings, and we look forward to presenting a more comprehensive and nuanced discussion. Thank you for your comments.

REFERENCES for responses to reviewer 3

Arendt-Nielsen, L., & Yarnitsky, D. (2009). Experimental and clinical applications of quantitative sensory testing applied to skin, muscles and viscera. The Journal of Pain, 10(6), 556-572.

Chowdhury, N. S., Skippen, P., Si, E., Chiang, A. K., Millard, S. K., Furman, A. J., ... & Seminowicz, D. A. (2023). The reliability of two prospective cortical biomarkers for pain: EEG peak alpha frequency and TMS corticomotor excitability. Journal of Neuroscience Methods, 385, 109766.

Fishbain, D. A., Lewis, J. E., & Gao, J. (2013). Is There Significant Correlation between SelfReported Low Back Pain Visual Analogue Scores and Low Back Pain Scores Determined by Pressure Pain Induction Matching?. Pain practice, 13(5), 358-363.

Furman, A. J., Prokhorenko, M., Keaser, M. L., Zhang, J., Chen, S., Mazaheri, A., & Seminowicz, D. A. (2021). Prolonged pain reliably slows peak alpha frequency by reducing fast alpha power.

bioRxiv, 2021-07.

Heitmann, H., Ávila, C. G., Nickel, M. M., Dinh, S. T., May, E. S., Tiemann, L., ... & Ploner, M. (2022). Longitudinal resting-state electroencephalography in patients with chronic pain undergoing interdisciplinary multimodal pain therapy. Pain, 163(9), e997.

McLain, N. J., Yani, M. S., & Kutch, J. J. (2022). Analytic consistency and neural correlates of peak alpha frequency in the study of pain. Journal of neuroscience methods, 368, 109460.

Ngernyam, N., Jensen, M. P., Arayawichanon, P., Auvichayapat, N., Tiamkao, S., Janjarasjitt, S., ... & Auvichayapat, P. (2015). The effects of transcranial direct current stimulation in patients with neuropathic pain from spinal cord injury. Clinical Neurophysiology, 126(2), 382-390.

Parker, T., Huang, Y., Raghu, A. L., FitzGerald, J., Aziz, T. Z., & Green, A. L. (2021). Supraspinal effects of dorsal root ganglion stimulation in chronic pain patients. Neuromodulation: Technology at the Neural Interface, 24(4), 646-654.

Petersen-Felix, S., & Arendt-Nielsen, L. (2002). From pain research to pain treatment: the role of human experimental pain models. Best Practice & Research Clinical Anaesthesiology, 16(4), 667680.

Sarnthein, J., Stern, J., Aufenberg, C., Rousson, V., & Jeanmonod, D. (2006). Increased EEG power and slowed dominant frequency in patients with neurogenic pain. Brain, 129(1), 55-64.

Sato, G., Osumi, M., & Morioka, S. (2017). Effects of wheelchair propulsion on neuropathic pain and resting electroencephalography after spinal cord injury. Journal of Rehabilitation Medicine, 49(2), 136-143.

Sufianov, A. A., Shapkin, A. G., Sufianova, G. Z., Elishev, V. G., Barashin, D. A., Berdichevskii, V. B., & Churkin, S. V. (2014). Functional and metabolic changes in the brain in neuropathic pain syndrome against the background of chronic epidural electrostimulation of the spinal cord. Bulletin of experimental biology and medicine, 157(4), 462-465.

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public review):

Summary:

This study probes the role of the NF-κB inhibitor IκBa in the regulation of pluripotency in mouse embyronic stem cells (mESCs). It follows from previous work that identified a chromatin-specific role for IκBa in the regulation of tissue stem cell differentiation. The work presented here shows that a fraction of IκBa specifically associates with chromatin in pluripotent stem cells. Using three Nfkbia-knockout lines, the authors show that IκBa ablation impairs the exit from pluripotency, with embryonic bodies (an in vitro model of mESC multi-lineage differentiation) still expressing high levels of pluripotency markers after sustained exposure to differentiation signals. The maintenance of aberrant pluripotency gene expression under differentiation conditions is accompanied by pluripotency-associated epigenetic profiles of DNA methylation and histone marks. Using elegant separation of function mutants identified in a separate study, the authors generate versions of IκBa that are either impaired in histone/chromatin binding or NF-κB binding. They show that the provision of the WT IκBa, or the NF-κB-binding mutant can rescue the changes in gene expression driven by loss of IκBa, but the chromatin-binding mutant can not. Thus the study identifies a chromatin-specific, NF-κB-independent role of IκBa as a regulator of exit from pluripotency.

Strengths:

The strengths of the manuscript lie in: (a) the use of several orthogonal assays to support the conclusions on the effects of exit from pluripotency; (b) the use of three independent clonal Nfkbia-KO mESC lines (lacking IκBa), which increase confidence in the conclusions; and (c) the use of separation of function mutants to determine the relative contributions of the chromatin-associated and NF-κB-associated IκBa, which would otherwise be very difficult to unpick.

Weaknesses:

In this reviewer's view, the term "differentiation" is used inappropriately in this manuscript. The data showing aberrant expression of pluripotency markers during embryoid body formation are supported by several lines of evidence and are convincing. However, the authors call the phenotype of Nfkbia-KO cells a "differentiation impairment" while the data on differentiation markers are not shown (beyond the fact that H3K4me1, marking poised enhancers, is reduced in genes underlying GO processes associated with differentiation and organ development). Data on differentiation marker expression from the transcriptomic and embryoid body immunofluorescent experiments, for example, should be at hand without the need to conduct many more experiments and would help to support the conclusions of the study or make them more specific. The lack of probing the differentiation versus pluripotency genes may be a missed opportunity in gaining in-depth understanding of the phenotype associated with loss of the chromatin-associated function of IκBa.

Reviewer #2 (Public review):

Summary:

This manuscript investigates the role of IκBα in regulating mouse embryonic stem cell (ESC) pluripotency and differentiation. The authors demonstrate that IκBα knockout impairs the exit from the naïve pluripotent state during embryoid body differentiation. Through mechanistic studies using various mutants, they show that IκBα regulates ESC differentiation through chromatin-related functions, independent of the canonical NFκB pathway.

Strengths:

The authors nicely investigate the role of IκBα in pluripotency exit, using embryoid body formation and complementing the phenotypic analysis with a number of genome-wide approaches, including transcriptomic, histone marks deposition, and DNA methylation analyses. Moreover, they generate a first-of-its-kind mutant set that allows them to uncouple IκBα's function in chromatin regulation versus its NF-κB-related functions. This work contributes to our understanding of cellular plasticity and development, potentially interesting a broad audience including developmental biologists, chromatin biology researchers, and cell signaling experts.

Weaknesses:

- The study's main limitation is the lack of crucial controls using bona fide naïve cells across key experiments, including DNA methylation analysis, gene expression profiling in embryoid bodies, and histone mark deposition. This omission makes it difficult to evaluate whether the observed changes in IκBα-KO cells truly reflect naïve pluripotency characteristics.

- Several conclusions in the manuscript require a more measured interpretation. The authors should revise their statements regarding the strength of the pluripotency exit block, the extent of hypomethylation, and the global nature of chromatin changes. - From a methodological perspective, the manuscript would benefit from additional orthogonal approaches to strengthen the knockout findings, which may be influenced by clonal expansion of ES cells.

Overall, this study makes an important contribution to the field. However, the concerns raised regarding controls, data interpretation, and methodology should be addressed to strengthen the manuscript and support the authors' conclusions.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

I have the following comments and suggestions for the authors to consider:

(1) Fig, 1D: the number of replicates for this experiment is not mentioned. It would be good to see if the apparent accumulation of IκBa on chromatin of S/L cells is reproducible. If it is, does the accumulation of IκBa "prime" chromatin for differentiation?

We apologize for missing this information in the figure legend. We have repeated the experiment two independent times, and confirmed the localization of IκBα in the chromatin fraction of mESCs cultured in Serum/LIF (S/L). We have included the information in the figure legend.

Regarding the second question, we do believe that the presence of IκBα primes mESCs to exit from differentiation. Previous data from the lab (Mulero et al Cancer Cell 2012; Marruecos et al EMBO Reports 2020) demonstrated that IκBα regulates important developmental genes (Hox genes and differentiation-related genes), which become dysregulated upon IκBα depletion. Based on those previous results, together with our results that demonstrated that lack of IκBα hyperactivates the pluripotency network, we conclude that IκBα is a crucial element to attenuate pluripotency programs, allowing a successful exit from naïve pluripotency and differentiation.

(2) Fig. 1E: From what is shown, Rela doesn't agree (i.e. no enrichment in EpiSCs in the Atlasi data). Are the culture conditions in Atlasi 2020 the same as in this paper (base medium etc.)? Also, why not label all genes/proteins that are shown in 1C?

Differences observed between our data and the in-silico data might be due to differences in culture conditions used in Atlasi and colleagues. In particular, Atlasi et al. cultured the mESCs in 2i/LIF for 2 consecutive months, whereas we induced ground state of naïve pluripotency (2i/LIF) for only 96h. In the case of EpiSC differentiation, similar protocols are used in both our work and in Atlasi et al. Nevertheless, despite existing differences, in both studies IκBα is enriched in the ground state of naive pluripotency. 

The reason why some proteins that are missing in Figure 1E but appearing in Figure 1C is because they are not detected in the mass spectrometry experiment.

(3) Fig. 1F: The word "clustering" here is misleading. While Nfkbia shows similar dynamics as pluripotency genes, clustering should not be used unless clusters of genes are shown in the same heatmap (and the transcripts naturally cluster together). The figure would be even more informative if all the genes from the 4 different categories were presented on the same heatmap.

As suggested by the reviewer, we have generated a heatmap where the  genes from the different four categories (Figure 1F) are displayed  and clustered together:

Author response image 1.

Heatmap including all the genes from Figure 1F of the manuscript and clustering is simultaneously conducted over the four categories.

As shown in previous heatmap, we can confirm that most of the Nf-kB genes (except for Nfkbia and Nfkbid) clustered together with differentiation markers.   

Nonetheless, to be more conservative with original Figure 1F and for clarity upon gene categories,  we have updated the figure  with a combined heatmap, sliced by gene categories.  In this updated version, we can observe how IkBα gene, though classified by the biological process where it classically belongs (NF-kB pathway), is higher at pluripotency, whereas it decreases upon differentiation induction, similarly as most of the pluripotency genes.

We have also changed the text accordingly and have added the following sentences in the main text (lines 121-125): “The expression pattern of Nfkbia was similar to the pluripotency genes whereas most of the NF-κB genes were upregulated upon differentiation, showing an analogous expression dynamics as developmental genes, as previously described”.

(4) This reviewer felt that the statement "Notably, several polycomb elements were highly expressed in mESCs, consistent with the possibility that chromatin-bound IκBα modulates PRC2 activity in the pluripotent state" (p.5, lines 125-127) is premature here. While similar expression dynamics may be consistent with a linked function, they in no way suggest this. This can be more accurately stated to point out that Nfkbia shows similar expression dynamics in pluripotency and differentiation as Polycomb component      genes.

We agree that the statement is premature and we have changed it by: “Previous reports have demonstrated that chromatin-bound IκBα modulates PRC2 activity in different adult stem cell models [27]. Interestingly, we observed that most of the Polycomb target genes follow a similar expression pattern of Nfkbia and pluripotency, with higher expression in mESCs (Figure 1F).” (lines 125-128 in the manucript).

(5) Top of p. 6: the results are mis-attributed to Fig. 1, it should be Fig. 2.

We thank the reviewer for this observation. We have corrected it in the main text.

(6) Fig. 1B and Fig. 5I: the images of the AP stains are very difficult to see, better resolution images should be used.

We have increased both the resolution and the size of the AP colonies.

(7) Line 142 (p.6): Fig. S1B should be S1C. In general the manuscript would benefit from review of the order and labeling of the figure panels as there are a number of inconsistencies.

We have better organized the figures in the new version of the manuscript. In particular, we have reorganized the Figure S1 to have a more logical order. We have done the same for the Figure 2 and Figure 5 and they are updated in the new version of the reviewed manuscript.

(8) The authors call the phenotype of Nfkbia-KO cells a "differentiation impairment". Do the EBs shown in Fig. 2 also express differentiation markers? Do they fail to up-regulate those markers or just fail to down-regulate pluripotency markers? At the transcriptomic level the Nfkbia-KO cells still change significantly upon provision of differentiation signals (Fig. 2C), what types of gene processes underlie the differences between WT and KO cells and which processes are common? Also, based on this figure, the phenotype looks to be more of a delay than a failure in differentiation, as the cells still follow the same trajectory but lag behind the WT cells. It is difficult to discern whether this is the case based on Fig. 2E-G as we don't see the later time point (up to Day 9).

In general, with the data presented in Fig. 2C and Fig. S1, the authors show that many of the hallmarks of exit from pluripotency are impaired in Nfkbia-KO cells, as well as the general "transcriptional status" of the cells, but they don't show differentiation markers (which would be necessary to conclude an impairment in differentiation). The data should be readily available in the datasets that are in the manuscript already and it will be informative to extract and present them. The data are not currently publicly accessible (unavailable until July 2025) so it was not possible to mine them.

We appreciate the observation, and we have included more data to confirm that the IκBα-KO cells show a differentiation impairment. In the first version of the manuscript, differentiation markers are displayed from Figures 2E-G, where genes from the three germ layers (ectoderm, mesoderm and endoderm) are not activated in IκBα-KO EBs at 48h and 96h. Moreover, the volcano plot displayed in Figure S1F of the first version clearly shows a downregulation of important differentiation genes such as a T, Eomes, Lhx1 and Foxa2. We agree that 96h EBs is an early time point to talk about differentiation impairment. For that reason, we have also included the same pluripotent and differentiation genes in 216h EBs (Figures S1F-G of the newer version of the manuscript). It is clearly observed that IκBα-KO 216h EBs maintain an upregulation of pluripotency programs which negatively correlate with a lower differentiation capability. Moreover, the impairment in the differentiation with a higher expression of pluripotency markers is confirmed by the presence of high SSEA-1 expression in IκBα-KO 216h EBs (Figure S1C of the manuscript) and alkaline phosphatase (AP) staining (Figure 2C of the manuscript). Lastly, the fact that IκBα-KO teratomas contain higher proportion of OCT3/4+ cells further confirming that IκBα-KO cells cannot differentiate because of the inability to exit from pluripotency.

Finally, generated data (and deposited in GEO repository with SuperSeries id GSE239565) is already publicly available. 

(9) Fig. 5A: even if there are no global changes in NF-κB target genes, could a small subset of NF-κB target genes still mediate the IκBa effects?

We have analyzed the whole NF-κB signature, and we have identified a small cluster of genes that are differentially expressed at 96h EBs between IκBα-KO and IκBα-WT (Author response image 2). Interestingly, what we observed is the opposite as expected since we see un downregulation of that subset in the IκBα-KO 96h EBs (Author response image 3). For that reason, detected changes in the NF-κB target gene expression after deletion of Nfkbia do not support an NF-κB inhibitory role for IkBa in pluripotent ESC.

Author response image 2.

Heatmap of NF-κB genes expression at the different time points of differentiation (mESCs, 48h EBs, 96h EBs). Highlighted region marks the genes that are differentially expressed between both genotypes at 96h EBs.

Author response image 3.

Violin plot of genes from the NF-κB pathway which are differentially expressed at 96h EBs.

(10) Lines 233-238, the part of the text is repeated.

We appreciate the observation and have deleted the repeated part.

(11) The data in Fig. 5D-E make it difficult to be sure whether the conclusions on the relative subcellular localisations of the different mutants are accurate, as the chromatin-binding mutant seems to be less abundant than the other mutants (judging from the Input in Fig. 5C and also from the tubulin loading controls in Fig. 5D-E). Showing the IκBa levels in total extracts would make the interpretation of these data more robust. The authors do mention that the chromatin-binding mutant IκBa protein is consistently expressed at lower levels but they do not comment on how this may affect the data interpretation - could the lack of rescue be due to lower levels of the chromatin-binding mutant IκBa relative to the wild-type IκBa? This should be addressed in the Discussion, if not tested formally by normalising the expression levels of the different forms of IκBa in the rescue experiments.

Although protein stability is different among the SOF mutants, IκBα^ΔChromatin is exclusively detected in the cytoplasm, with lack of detection in the chromatin compartment (Figures 5D-E of the reviewed manuscript). For this reason, we believe that the quantitative differences in protein levels of the different mutants cannot explain the subcellular localization differences and the phenotype observed.

Nonetheless, we cannot discard that differences in the protein levels between SOF mutants can affect the rescue phenotype, and we have specified so in the discussion section of the manuscript. 

(12) Lines 260-261: "Induction of i-IκBαWT and i-IκBαΔNF-κB reduced the expression levels of the naive pluripotent genes Zfp42, Klf2, Sox2 and Tbx3, which were increased by i-IκBαΔChromatin (Figure 5F)." This is not an accurate statement. The expression was not reduced by the ΔChrom mutant in the same way as it was by the WT and the ΔNF-κB mutant, but it was not increased.

We have better specified the description of the results displayed in Figure 5F (lines 258-261 of the main manuscript):

“Induction of i-IκBα^WT and i-IκBα^ΔNF-κB reduced the expression levels of the naïve pluripotent genes Zfp42, Klf2, Sox2 and Tbx3. On the other hand, the same genes either do not change their expression (Zfp42, Sox2, Klf2) or increase their levels (Tbx3) upon i-IκBα^ΔChromatin  induction (Figure 5F).”

(13) In Fig. 5J the images will ideally be shown before and after Doxycycline treatment, to better support the conclusions.

We have included a new panel in Figure S4 (Figure S4E in the reviewed manuscript) where the No doxycycline control 216 EBs between the different conditions (i-IκBα^WT, i-IκBα^ΔChrom and i-IκBα^ΔNF-κB) are included.

Reviewer #2 (Recommendations for the authors):

- The PCA analysis in Figure 2 appears to contradict the authors' conclusions about global transcriptome changes in KO cells. Furthermore, there is a discrepancy between immunofluorescence data showing near-complete methylation loss and the methylation array analysis results.

Although there is a differentiation block in the IkBa KO EBs, this is not complete and they show some differentiation trend after 96h (Fig 2C), moreover, acquisition of differentiation genes from all three germ layers is strongly affected (Figure 2E of the reviewed manuscript) and these programs remain downregulated and pluripotency genes are still expressed in IκBα-KO EBs at later time points (216h) (Fig 2B). Altogether demonstrates that the lack of IκBα impairs differentiation and the silencing of the pluripotency network.

Discrepancies between methylation array and immunofluorescence are expected since immunofluorescence is not quantitative and the methylation array is very precise.  

- The authors should revise their statements regarding the strength of the pluripotency exit block, the extent of hypomethylation, and the global nature of chromatin changes. For example, the observed chromatin changes, including H3K27ac modifications, appear relatively modest and should be described as such. - The manuscript would benefit from additional orthogonal approaches to strengthen the knockout findings, which may be influenced by clonal expansion of ES cells. Additionally, the emphasis on overlapping H3K4me3 and H3K27me3 regions should be reduced, as these represent a minor fraction of the affected regions (only 41 regions).

We have revised the text and have included it in the discussion section the following text (lines 327-331 in the reviewed manuscript):

“Although IκBα KO  mESCs  exhibit a transcriptional phenotype and hypomethylation state  that resembles the ground state of naïve pluripotency, there are only modest changes on histone marks associated to enhancers (H3K27Ac) or gene regulation (H3K4me3 and H3K27me3). Altogether indicates that further experiments are required to fully elucidate the effect of chromatin IκBα.”

We have also included Fig S3E-S3F to show that similar differences as WT and KO in H3K4me3 and H3K27me3 are observed in a serum/LIF and 2i conditions, further supporting the fact that KO cells in Serum/LIF resemble WT cells in 2i condition.

Author response:

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

eLife Assessment

In an important fMRI study with an elegant experimental design and rigorous cross-decoding analyses, this work shows a solid dissociation between two parietal regions in visually processing actions. Specifically, aIPL is found to be sensitive to the causal effects of observed actions, while SPL is sensitive to the patterns of body motion involved in those actions. Additional analysis and explanation would help to determine the strength of evidence and the mechanistic underpinnings would benefit from closer consideration. Nevertheless, the work will be of broad interest to cognitive neuroscientists, particularly vision and action researchers.

We thank the editor and the reviewers for their assessment and their excellent comments and suggestions. We really believe they helped us to provide a stronger and more nuanced paper. In our revision, we addressed all points raised by the reviewers. Most importantly, we added a new section on a series of analyses to characterize in more detail the representations isolated by the action-animation and action-PLD cross-decoding. Together, these analyses strengthen the conclusion that aIPL and LOTC represent action effect structures at a categorical rather than specific level, that is, the type of change (e.g., of location or configuration) rather than the specific effect type (e.g. division, compression). SPL is sensitive to body-specific representations, specifically manuality (unimanual vs. bimanual) and movement kinematics. We also added several other analyses and addressed each point of the reviewers. Please find our responses below.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The authors report a study aimed at understanding the brain's representations of viewed actions, with a particular aim to distinguish regions that encode observed body movements, from those that encode the effects of actions on objects. They adopt a cross-decoding multivariate fMRI approach, scanning adult observers who viewed full-cue actions, pantomimes of those actions, minimal skeletal depictions of those actions, and abstract animations that captured analogous effects to those actions. Decoding across different pairs of these actions allowed the authors to pull out the contributions of different action features in a given region's representation. The main hypothesis, which was largely confirmed, was that the superior parietal lobe (SPL) more strongly encodes movements of the body, whereas the anterior inferior parietal lobe (aIPL) codes for action effects of outcomes. Specifically, region of interest analyses showed dissociations in the successful cross-decoding of action category across full-cue and skeletal or abstract depictions. Their analyses also highlight the importance of the lateral occipito-temporal cortex (LOTC) in coding action effects. They also find some preliminary evidence about the organisation of action kinds in the regions examined.

Strengths:

The paper is well-written, and it addresses a topic of emerging interest where social vision and intuitive physics intersect. The use of cross-decoding to examine actions and their effects across four different stimulus formats is a strength of the study. Likewise, the a priori identification of regions of interest (supplemented by additional full-brain analyses) is a strength.

Weaknesses:

I found that the main limitation of the article was in the underpinning theoretical reasoning. The authors appeal to the idea of "action effect structures (AES)", as an abstract representation of the consequences of an action that does not specify (as I understand it) the exact means by which that effect is caused, nor the specific objects involved. This concept has some face validity, but it is not developed very fully in the paper, rather simply asserted. The authors make the claim that "The identification of action effect structure representations in aIPL has implications for theories of action understanding" but it would have been nice to hear more about what those theoretical implications are. More generally, I was not very clear on the direction of the claim here. Is there independent evidence for AES (if so, what is it?) and this study tests the following prediction, that AES should be associated with a specific brain region that does not also code other action properties such as body movements? Or, is the idea that this finding -- that there is a brain region that is sensitive to outcomes more than movements -- is the key new evidence for AES?

Thank you for raising this important issue. We reasoned that AES should exist to support the recognition of perceptually variable actions, including those that we have never experienced before. To the best of our knowledge, there is only indirect evidence for the existence of AES, namely that humans effortlessly and automatically recognize actions (and underlying intentions and feelings) in movements of abstract shapes, as in the famous Heider and Simmel (1949) animations. As these animations do not contain any body posture or movement information at all, the only available cues are the spatiotemporal relations between entities and entity parts in the perceived scene. We think that the effortless and automatic attribution of actions to these stimuli points toward an evolutionary optimized mechanism to capture action effect structures from highly variable action instantiations (so general that it even works for abstract animations). Our study thus aimed to test for the existence of such a level of representation in the brain. We clarified this point in the introduction.

In our revised manuscript, we also revised our discussion of the implications of the finding of AES representations in the brain:

"The identification of action effect structure representations in aIPL and LOTC has implications for theories of action understanding: Current theories (see for review e.g. Zentgraf et al., 2011; Kemmerer, 2021; Lingnau and Downing, 2024) largely ignore the fact that the recognition of many goal-directed actions requires a physical analysis of the action-induced effect, that is, a state change of the action target. Moreover, premotor and inferior parietal cortex are usually associated with motor- or body-related processing during action observation. Our results, together with the finding that premotor and inferior parietal cortex are similarly sensitive to actions and inanimate object events (Karakose-Akbiyik et al., 2023), suggest that large parts of the 'action observation network' are less specific for body-related processing in action perception than usually thought. Rather, this network might provide a substrate for the physical analysis and predictive simulation of dynamic events in general (Schubotz, 2007; Fischer, 2024). In addition, our finding that the (body-independent) representation of action effects substantially draws on right LOTC contradicts strong formulations of a 'social perception' pathway in LOTC that is selectively tuned to the processing of moving faces and bodies (Pitcher and Ungerleider, 2021). The finding of action effect representation in right LOTC/pSTS might also offer a novel interpretation of a right pSTS subregion thought to specialized for social interaction recognition: Right pSTS shows increased activation for the observation of contingent action-reaction pairs (e.g. agent A points toward object; agent B picks up object) as compared to two independent actions (i.e., the action of agent A has no effect on the action of agent B) (Isik et al., 2017). Perhaps the activation reflects the representation of a social action effect - the change of an agent's state induced by someone else's action. Thus, the representation of action effects might not be limited to physical object changes but might also comprise social effects not induced by a physical interaction between entities. Finally, not all actions induce an observable change in the world. It remains to be tested whether the recognition of, e.g., communication (e.g. speaking, gesturing) and perception actions (e.g. observing, smelling) similarly relies on structural action representations in aIPL and LOTC"

On a more specific but still important point, I was not always clear that the significant, but numerically rather small, decoding effects are sufficient to support strong claims about what is encoded or represented in a region. This concern of course applies to many multivariate decoding neuroimaging studies. In this instance, I wondered specifically whether the decoding effects necessarily reflected fully five-way distinction amongst the action kinds, or instead (for example) a significantly different pattern evoked by one action compared to all of the other four (which in turn might be similar). This concern is partly increased by the confusion matrices that are presented in the supplementary materials, which don't necessarily convey a strong classification amongst action kinds. The cluster analyses are interesting and appear to be somewhat regular over the different regions, which helps. However: it is hard to assess these findings statistically, and it may be that similar clusters would be found in early visual areas too.

We agree that in our original manuscript, we did not statistically test what precisely drives the decoding, e.g., specific actions or rather broader categories. In our revised manuscript, we included a representational similarity analysis (RSA) that addressed this point. In short, we found that the action-animation decoding was driven by categorical distinctions between groups of actions (e.g. hit/place vs. the remaining actions) rather than a fully five-way distinction amongst all action kinds. The action-PLD decoding was mostly driven by , specifically manuality (unimanual vs. bimanual)) and movement kinematics; in left and right LOTC we found additional evidence for action-specific representations.

Please find below the new paragraph on the RSA:

"To explore in more detail what types of information were isolated by the action-animation and action-PLD cross-decoding, we performed a representational similarity analysis.

We first focus on the representations identified by the action-animation decoding. To inspect and compare the representational organization in the ROIs, we extracted the confusion matrices of the action-animation decoding from the ROIs (Fig. 5A) and compared them with different similarity models (Fig. 5B) using multiple regression. Specifically, we aimed at testing at which level of granularity action effect structures are represented in aIPL and LOTC: Do these regions encode the broad type of action effects (change of shape, change of location, ingestion) or do they encode specific action effects (compression, division, etc.)? In addition, we aimed at testing whether the effects observed in EVC can be explained by a motion energy model that captures the similarities between actions and animations that we observed in the stimulus-based action-animation decoding using motion energy features. We therefore included V1 in the ROI analysis. We found clear evidence that the representational content in right aIPL and bilateral LOTC can be explained by the effect type model but not by the action-specific model (all p < 0.005; two-sided paired t-tests between models; Fig. 5C). In left V1, we found that the motion energy model could indeed explain some representational variance; however, in both left and right V1 we also found effects for the effect type model. We assume that there were additional visual similarities between the broad types of actions and animations that were not captured by the motion energy model (or other visual models; see Supplementary Information). A searchlight RSA revealed converging results, and additionally found effects for the effect type model in the ventral part of left aIPL and for the action-specific model in the left anterior temporal lobe, left dorsal central gyrus, and right EVC (Fig. 5D). The latter findings were unexpected and should be interpreted with caution, as these regions (except right EVC) were not found in the action-animation cross-decoding and therefore should not be considered reliable (Ritchie et al., 2017). The motion energy model did not reveal effects that survived the correction for multiple comparison, but a more lenient uncorrected threshold of p = 0.005 revealed clusters in left EVC and bilateral posterior SPL.

To characterize the representations identified by the action-PLD cross-decoding, we used a manuality model that captures whether the actions were performed with both hands vs. one hand, an action-specific model as used in the action-animation RSA above, and a kinematics model that was based on the 3D kinematic marker positions of the PLDs (Fig. 6B). Since pSTS is a key region for biological motion perception, we included this region in the ROI analysis. The manuality model explained the representational variance in the parietal ROIs, pSTS, and LOTC, but not in V1 (all p < 0.002; two-sided paired t-tests between V1 and other ROIs; Fig. 6C). By contrast, the action-specific model revealed significant effects in V1 and LOTC, but not in pSTS and parietal ROIs (but note that effects in V1 and pSTS did not differ significantly from each other; all other two-sided paired t-tests between mentioned ROIs were significant at p < 0.0005). The kinematics model explained the representational variance in all ROIs. A searchlight RSA revealed converging results, and additionally found effects for the manuality model in bilateral dorsal/medial prefrontal cortex and in right ventral prefrontal cortex and insula (Fig. 6D).”

We also included an ROI covering early visual cortex (V1) in our analysis. While there was significant decoding for action-animation in V1, the representational organization did not substantially match the organization found in aIPL and LOTC: A cluster analysis revealed much higher similarity between LOTC and aIPL than between these regions and V1:

(please note that in this analysis we included the action-PLD RDMs as reference, and to test whether aIPL shows a similar representational organization in action-anim and action-PLD; see below)

Given these results, we think that V1 captured different aspects in the action-animation cross-decoding than aIPL and LOTC. We address this point in more detail in our response to the "Recommendations for The Authors".

Reviewer #2 (Public Review):

Summary:

This study uses an elegant design, using cross-decoding of multivariate fMRI patterns across different types of stimuli, to convincingly show a functional dissociation between two sub-regions of the parietal cortex, the anterior inferior parietal lobe (aIPL) and superior parietal lobe (SPL) in visually processing actions. Specifically, aIPL is found to be sensitive to the causal effects of observed actions (e.g. whether an action causes an object to compress or to break into two parts), and SPL to the motion patterns of the body in executing those actions.

To show this, the authors assess how well linear classifiers trained to distinguish fMRI patterns of response to actions in one stimulus type can generalize to another stimulus type. They choose stimulus types that abstract away specific dimensions of interest. To reveal sensitivity to the causal effects of actions, regardless of low-level details or motion patterns, they use abstract animations that depict a particular kind of object manipulation: e.g. breaking, hitting, or squashing an object. To reveal sensitivity to motion patterns, independently of causal effects on objects, they use point-light displays (PLDs) of figures performing the same actions. Finally, full videos of actors performing actions are used as the stimuli providing the most complete, and naturalistic information. Pantomime videos, with actors mimicking the execution of an action without visible objects, are used as an intermediate condition providing more cues than PLDs but less than real action videos (e.g. the hands are visible, unlike in PLDs, but the object is absent and has to be inferred). By training classifiers on animations, and testing their generalization to full-action videos, the classifiers' sensitivity to the causal effect of actions, independently of visual appearance, can be assessed. By training them on PLDs and testing them on videos, their sensitivity to motion patterns, independent of the causal effect of actions, can be assessed, as PLDs contain no information about an action's effect on objects.

These analyses reveal that aIPL can generalize between animations and videos, indicating that it is sensitive to action effects. Conversely, SPL is found to generalize between PLDs and videos, showing that it is more sensitive to motion patterns. A searchlight analysis confirms this pattern of results, particularly showing that action-animation decoding is specific to right aIPL, and revealing an additional cluster in LOTC, which is included in subsequent analyses. Action-PLD decoding is more widespread across the whole action observation network.

This study provides a valuable contribution to the understanding of functional specialization in the action observation network. It uses an original and robust experimental design to provide convincing evidence that understanding the causal effects of actions is a meaningful component of visual action processing and that it is specifically localized in aIPL and LOTC.

Strengths:

The authors cleverly managed to isolate specific aspects of real-world actions (causal effects, motion patterns) in an elegant experimental design, and by testing generalization across different stimulus types rather than within-category decoding performance, they show results that are convincing and readily interpretable. Moreover, they clearly took great care to eliminate potential confounds in their experimental design (for example, by carefully ordering scanning sessions by increasing realism, such that the participants could not associate animation with the corresponding real-world action), and to increase stimulus diversity for different stimulus types. They also carefully examine their own analysis pipeline, and transparently expose it to the reader (for example, by showing asymmetries across decoding directions in Figure S3). Overall, this is an extremely careful and robust paper.

Weaknesses:

I list several ways in which the paper could be improved below. More than 'weaknesses', these are either ambiguities in the exact claims made, or points that could be strengthened by additional analyses. I don't believe any of the claims or analyses presented in the paper show any strong weaknesses, problematic confounds, or anything that requires revising the claims substantially.

(1) Functional specialization claims: throughout the paper, it is not clear what the exact claims of functional specialization are. While, as can be seen in Figure 3A, the difference between action-animation cross-decoding is significantly higher in aIPL, decoding performance is also above chance in right SPL, although this is not a strong effect. More importantly, action-PLD cross-decoding is robustly above chance in both right and left aIPL, implying that this region is sensitive to motion patterns as well as causal effects. I am not questioning that the difference between the two ROIs exists - that is very convincingly shown. But sentences such as "distinct neural systems for the processing of observed body movements in SPL and the effect they induce in aIPL" (lines 111-112, Introduction) and "aIPL encodes abstract representations of action effect structures independently of motion and object identity" (lines 127-128, Introduction) do not seem fully justified when action-PLD cross-decoding is overall stronger than action-animation cross-decoding in aIPL. Is the claim, then, that in addition to being sensitive to motion patterns, aIPL contains a neural code for abstracted causal effects, e.g. involving a separate neural subpopulation or a different coding scheme. Moreover, if sensitivity to motion patterns is not specific to SPL, but can be found in a broad network of areas (including aIPL itself), can it really be claimed that this area plays a specific role, similar to the specific role of aIPL in encoding causal effects? There is indeed, as can be seen in Figure 3A, a difference between action-PLD decoding in SPL and aIPL, but based on the searchlight map shown in Figure 3B I would guess that a similar difference would be found by comparing aIPL to several other regions. The authors should clarify these ambiguities.

We thank the reviewer for this careful assessment. The observation of action-PLD cross-decoding in aIPL is indeed not straightforward to interpret: It could mean that aIPL encodes both body movements and action effect structures by different neural subpopulations. Or it could mean that representations of action effect structures were also activated by the PLDs, which lead to successful decoding in the action-PLD cross-decoding. Our revision allows a more nuanced view on this issue:

First, we included the results of a behavioral test show that PLDs at least weakly allow for recognition of the specific actions (see our response to the second comment), which in turn might activate action effect structure representations. Second, the finding that also the cross-decoding between animations and PLDs revealed effects in left and right aIPL (as pointed out by the reviewer in the second comment) supports the interpretation that PLDs have activated, to some extent, action effect structure representations.

On the other hand, if aIPL encodes only action-effect-structures, that were also captured in the action-PLD cross-decoding, we would expect that the RDMs in aIPL are similar for the action-PLD and action-animation cross-decoding. However, the cluster analysis (see our response to Reviewer 1 above) does not show this; rather, all action-PLD RDMs are representationally more similar with each other than with action-animation RDMs, specifically with regard to aIPL. In addition, the RSA revealed sensitivity to manuality and kinematics also in aIPL. This suggests that the action-PLD decoding in aIPL was at least partially driven by representations related to body movements.

Taken together, these findings suggest that aIPL encodes also body movements. In fact, we didn't want to make the strong claim that aIPL is selectively representing action effect structures. Rather, we think that our results show that aIPL and SPL are disproportionally sensitive to action effects and body movements, respectively. We added this in our revised discussion:

"The action-PLD cross-decoding revealed widespread effects in LOTC and parietal cortex, including aIPL. What type of representation drove the decoding in aIPL? One possible interpretation is that aIPL encodes both body movements (isolated by the action-PLD cross-decoding) and action effect structures (isolated by the action-animation cross-decoding). Alternatively, aIPL selectively encodes action effect structures, which have been activated by the PLDs. A behavioral test showed that PLDs at least weakly allow for recognition of the specific actions (Tab. S2), which might have activated corresponding action effect structure representations. In addition, the finding that aIPL revealed effects for the cross-decoding between animations and PLDs further supports the interpretation that PLDs have activated, at least to some extent, action effect structure representations.  On the other hand, if aIPL encodes only action effect structures, we would expect that the representational similarity patterns in aIPL are similar for the action-PLD and action-animation cross-decoding. However, this was not the case; rather, the representational similarity pattern in aIPL was more similar to SPL for the action-PLD decoding, which argues against distinct representational content in aIPL vs. SPL isolated by the action-PLD decoding. In addition, the RSA revealed sensitivity to manuality and kinematics also in aIPL, which suggests that the action-PLD decoding in aIPL was at least partially driven by representations related to body movements. Taken together, these findings suggest that aIPL encodes not only action effect structures, but also representations related to body movements. Likewise, also SPL shows some sensitivity to action effect structures, as demonstrated by effects in SPL for the action-animation and pantomime-animation cross-decoding. Thus, our results suggest that aIPL and SPL are not selectively but disproportionally sensitive to action effects and body movements, respectively."

A clarification to the sentence "aIPL encodes abstract representations of action effect structures independently of motion and object identity": Here we are referring to the action-animation cross decoding only; specifically, the fact that because the animations did not show body motion and concrete objects, the representations isolated in the action-animation cross decoding must be independent of body motion and concrete objects. This does not rule out that the same region encodes other kinds of representations in addition.

And another side note to the RSA: It might be tempting to test the "effects" model (distinguishing change of shape, change of location and ingest) also in the action-PLD multiple regression RSA in order to test whether this model explains additional variance in aIPL, which would point towards action effect structure representations. However, the "effect type" model is relatively strongly correlated with the "manuality" model (VIF=4.2), indicating that multicollinearity might exist. We therefore decided to not include this model in the RSA. However, we nonetheless tested the inclusion of this model and did not find clear effects for the "effects" model in aIPL (but in LOTC). The other models revealed largely similar effects as the RSA without the "effects" model, but the effects appeared overall noisier. In general, we would like to emphasize that an RSA with just 5 actions is not ideal because of the small number of pairwise comparisons, which increases the chance for coincidental similarities between model and neural RDMs. We therefore marked this analysis as "exploratory" in the article.

(2) Causal effect information in PLDs: the reasoning behind the use of PLD stimuli is to have a condition that isolates motion patterns from the causal effects of actions. However, it is not clear whether PLDs really contain as little information about action effects as claimed. Cross-decoding between animations and PLDs is significant in both aIPL and LOTC, as shown in Figure 4. This indicates that PLDs do contain some information about action effects. This could also be tested behaviorally by asking participants to assign PLDs to the correct action category. In general, disentangling the roles of motion patterns and implied causal effects in driving action-PLD cross-decoding (which is the main dependent variable in the paper) would strengthen the paper's message. For example, it is possible that the strong action-PLD cross-decoding observed in aIPL relies on a substantially different encoding from, say, SPL, an encoding that perhaps reflects causal effects more than motion patterns. One way to exploratively assess this would be to integrate the clustering analysis shown in Figure S1 with a more complete picture, including animation-PLD and action-PLD decoding in aIPL.

With regard to the suggestion to behaviorally test how well participants can grasp the underlying action effect structures: We indeed did a behavioral experiment to assess the recognizability of actions in the PLD stick figures (as well as in the pantomimes). In short, this experiment revealed that participants could not well recognize the actions in the PLD stick figures and often confused them with kinematically similar but conceptually different actions (e.g. breaking --> shaking, hitting --> swiping, squashing --> knitting). However, the results also show that it was not possible to completely eliminate that PLDs contain some information about action effects.

Because we considered this behavioral experiment as a standard assessment of the quality of the stimuli, we did not report them in the original manuscript. We now added an additional section to the methods that describes the behavioral experiments in detail:

"To assess how much the animations, PLD stick figures, and pantomimes were associated with the specific action meanings of the naturalistic actions, we performed a behavioral experiment. 14 participants observed videos of the animations, PLDs (without stick figures), and pantomimes in three separate sessions (in that order) and were asked to describe what kind of actions the animations depict and give confidence ratings on a Likert scale from 1 (not confident at all) to 10 (very confident). Because the results for PLDs were unsatisfying (several participants did not recognize human motion in the PLDs), we added stick figures to the PLDs as described above and repeated the rating for PLD stick figures with 7 new participants, as reported below.

A general observation was that almost no participant used verb-noun phrases (e.g. "breaking a stick") in their descriptions for all stimulus types. For the animations, the participants used more abstract verbs or nouns to describe the actions (e.g. dividing, splitting, division; Tab. S1). These abstract descriptions matched the intended action structures quite well, and participants were relatively confident about their responses (mean confidences between 6 and 7.8). These results suggest that the animations were not substantially associated with specific action meanings (e.g. "breaking a stick") but captured the coarse action structures. For the PLD stick figures (Tab. S2), responses were more variable and actions were often confused with kinematically similar but conceptually different actions (e.g. breaking --> shaking, hitting --> turning page, squashing --> knitting). Confidence ratings were relatively low (mean confidences between 3 and 5.1). These results suggest that PLD stick figures, too, were not substantially associated with specific action meanings and additionally did not clearly reveal the underlying action effect structures. Finally, pantomimes were recognized much better, which was also reflected in high confidence ratings (mean confidences between 8 and 9.2; Tab. S3). This suggests that, unlike PLD stick figures, pantomimes allowed much better to access the underlying action effect structures."

We also agree with the second suggestion to investigate in more detail the representational profiles in aIPL and SPL. We think that the best way to do so is the RSA that we reported above. However, to provide a complete picture of the results, we also added the whole brain maps and RDMs for the animation-pantomime, animation-PLD, pantomime-PLD, and action-pantomime to the supplementary information.

(3) Nature of the motion representations: it is not clear what the nature of the putatively motion-driven representation driving action-PLD cross-decoding is. While, as you note in the Introduction, other regions such as the superior temporal sulcus have been extensively studied, with the understanding that they are part of a feedforward network of areas analyzing increasingly complex motion patterns (e.g. Riese & Poggio, Nature Reviews Neuroscience 2003), it doesn't seem like the way in which SPL represents these stimuli are similarly well-understood. While the action-PLD cross-decoding shown here is a convincing additional piece of evidence for a motion-based representation in SPL, an interesting additional analysis would be to compare, for example, RDMs of different actions in this region with explicit computational models. These could be, for example, classic motion energy models inspired by the response characteristics of regions such as V5/MT, which have been shown to predict cortical responses and psychophysical performance both for natural videos (e.g. Nishimoto et al., Current Biology 2011) and PLDs (Casile & Giese Journal of Vision 2005). A similar cross-decoding analysis between videos and PLDs as that conducted on the fMRI patterns could be done on these models' features, obtaining RDMs that could directly be compared with those from SPL. This would be a very informative analysis that could enrich our knowledge of a relatively unexplored region in action recognition. Please note, however, that action recognition is not my field of expertise, so it is possible that there are practical difficulties in conducting such an analysis that I am not aware of. In this case, I kindly ask the authors to explain what these difficulties could be.

Thank you for this very interesting suggestion. We conducted a cross-decoding analysis that was based on the features of motion energy models as described in Nishimoto et al. (2011). Control analyses within each stimulus type revealed high decoding accuracies (animations: 100%, PLDs: 100%, pantomimes: 65%, actions: 55%), which suggests that the motion energy data generally contains information that can be detected by a classifier. However, the cross-decoding between actions and PLDs was at chance (20%), and the classification matrix did not resemble the neural RDMs. We also tested optical flow vectors as input to the decoding, which revealed similarly high decoding for the within-stimulus-type decoding (animations: 75%, PLDs: 100%, pantomimes: 65%, actions: 40%), but again at-chance decoding for action-PLD (20%), notably with a very different classification pattern:

Author response image 1.

Given these mixed results, we decided not to use these models for a statistical comparison with the neural action-PLD RDMs.

It is notable that the cross-decoding worked generally less well for decoding schemes that involve PLDs, which is likely due to highly different feature complexity of actions and PLDs: Naturalistic actions have much richer visual details, texture, and more complex motion cues. Therefore, motion energy features extracted from these videos likely capture a mixture of both fine-grained and broad motion information across different spatial frequencies. By contrast, motion energy features of PLDs are sparse and might not match the features of naturalistic actions. In a way, this was intended, as we were interested in higher-level body kinematics rather than lower-level motion features. We therefore decided to use a different approach to investigate the representational structure found in the action-PLD cross-decoding: As the PLDs were based on kinematic recordings of actions that were carried out in exactly the same manner as the naturalistic actions, we computed the dissimilarity of the 5 actions based on the kinematic marker positions. Specifically, we averaged the kinematic data across the 2 exemplars per PLD, vectorized the 3D marker positions of all time points of the PLDs (3 dimensions x 13 markers x 200 time points), computed the pairwise correlations between the 5 vectors, and converted the correlations into dissimilarity values by subtracting 1 - r. This RDM was then compared with the neural RDMs extracted from the action-PLD cross-decoding. This was done using a multiple regression RSA (see also our response to Reviewer 1's public comment 2), which allowed us to statistically test the kinematic model against other dissimilarity models: a categorical model of manuality (uni- vs. bimanual) and an action-specific model that discriminates each specific action from each other with equal distance.

This analysis revealed interesting results: the kinematic model explained the representational variance in bilateral SPL and (particularly right) pSTS as well as in right fusiform cortex and early visual cortex. The action-specific model revealed effects restricted to bilateral LOTC. The manuality model revealed widespread effects throughout the action observation network but not in EVC.

(4) Clustering analysis: I found the clustering analysis shown in Figure S1 very clever and informative. However, there are two things that I think the authors should clarify. First, it's not clear whether the three categories of object change were inferred post-hoc from the data or determined beforehand. It is completely fine if these were just inferred post-hoc, I just believe this ambiguity should be clarified explicitly. Second, while action-anim decoding in aIPL and LOTC looks like it is consistently clustered, the clustering of action-PLD decoding in SPL and LOTC looks less reliable. The authors interpret this clustering as corresponding to the manual vs. bimanual distinction, but for example "drink" (a unimanual action) is grouped with "break" and "squash" (bimanual actions) in left SPL and grouped entirely separately from the unimanual and bimanual clusters in left LOTC. Statistically testing the robustness of these clusters would help clarify whether it is the case that action-PLD in SPL and LOTC has no semantically interpretable organizing principle, as might be the case for a representation based entirely on motion pattern, or rather that it is a different organizing principle from action-anim, such as the manual vs. bimanual distinction proposed by the authors. I don't have much experience with statistical testing of clustering analyses, but I think a permutation-based approach, wherein a measure of cluster robustness, such as the Silhouette score, is computed for the clusters found in the data and compared to a null distribution of such measures obtained by permuting the data labels, should be feasible. In a quick literature search, I have found several papers describing similar approaches: e.g. Hennig (2007), "Cluster-wise assessment of cluster stability"; Tibshirani et al. (2001) "Estimating the Number of Clusters in a Data Set Via the Gap Statistic". These are just pointers to potentially useful approaches, the authors are much better qualified to pick the most appropriate and convenient method. However, I do think such a statistical test would strengthen the clustering analysis shown here. With this statistical test, and the more exhaustive exposition of results I suggested in point 2 above (e.g. including animation-PLD and action-PLD decoding in aIPL), I believe the clustering analysis could even be moved to the main text and occupy a more prominent position in the paper.

With regard to the first point, we clarified in the methods that we inferred the 3 broad action effect categories after the stimulus selection: "This categorization was not planned before designing the study but resulted from the stimulus selection."

Thank you for your suggestion to test more specifically the representational organization in the action-PLD and action-animation RDMs. However, after a careful assessment, we decided to replace the cluster analysis with an RSA. We did this for two reasons:

First, we think that RSA is a better (and more conventional) approach to statistically investigate the representational structure in the ROIs (and in the whole brain). The RSA allowed us, for example, to specifically test the mentioned distinction between unimanual and bimanual actions, and to test it against other models, i.e., a kinematic model and an action-specific model. This indeed revealed interesting distinct representational profiles of SPL and LOTC.

Second, we learned that the small number of items (5) is generally not ideal for cluster analyses (absolute minimum for meaningful interpretability is 4, but to form at least 2-3 clusters a minimum of 10-15 items is usually recommended). A similar rule of thumb applies to methods to statistically assess the reliability of cluster solutions (e.g., Silhouette Scores, Cophenetic Correlation Coefficient, Jaccard Coefficient). Finally, the small number of items is not ideal to run a permutation test because the number of unique permutations (for shuffling the data labels: 5! = 30) is insufficient to generate a meaningful null distribution. We therefore think it is best to discard the cluster analysis altogether. We hope you agree with this decision.

(5) ROI selection: this is a minor point, related to the method used for assigning voxels to a specific ROI. In the description in the Methods (page 16, lines 514-24), the authors mention using the MNI coordinates of the center locations of Brodmann areas. Does this mean that then they extracted a sphere around this location, or did they use a mask based on the entire Brodmann area? The latter approach is what I'm most familiar with, so if the authors chose to use a sphere instead, could they clarify why? Or, if they did use the entire Brodmann area as a mask, and not just its center coordinates, this should be made clearer in the text.

We indeed used a sphere around the center coordinate of the Brodmann areas. This was done to keep the ROI sizes / number of voxels constant across ROIs. Since we aimed at comparing the decoding accuracies between aIPL and SPL, we thereby minimized the possibility that differences in decoding accuracy between ROIs are due to ROI size differences. The approach of using spherical ROIs is a quite well established practice that we are using in our lab by default (e.g. Wurm & Caramazza, NatComm, 2019; Wurm & Caramazza, NeuroImage, 2019; Karakose, Caramazza, & Wurm, NatComm, 2023). We clarified that we used spherical ROIs to keep the ROI sizes constant in the revised manuscript.

Reviewer #3 (Public Review):

This study tests for dissociable neural representations of an observed action's kinematics vs. its physical effect in the world. Overall, it is a thoughtfully conducted study that convincingly shows that representations of action effects are more prominent in the anterior inferior parietal lobe (aIPL) than the superior parietal lobe (SPL), and vice versa for the representation of the observed body movement itself. The findings make a fundamental contribution to our understanding of the neural mechanisms of goal-directed action recognition, but there are a couple of caveats to the interpretation of the results that are worth noting:

(1) Both a strength of this study and ultimately a challenge for its interpretation is the fact that the animations are so different in their visual content than the other three categories of stimuli. On one hand, as highlighted in the paper, it allows for a test of action effects that is independent of specific motion patterns and object identities. On the other hand, the consequence is also that Action-PLD cross-decoding is generally better than Action-Anim cross-decoding across the board (Figure 3A) - not surprising because the spatiotemporal structure is quite different between the actions and the animations. This pattern of results makes it difficult to interpret a direct comparison of the two conditions within a given ROI. For example, it would have strengthened the argument of the paper to show that Action-Anim decoding was better than Action-PLD decoding in aIPL; this result was not obtained, but that could simply be because the Action and PLD conditions are more visually similar to each other in a number of ways that influence decoding. Still, looking WITHIN each of the Action-Anim and Action-PLD conditions yields clear evidence for the main conclusion of the study.

The reviewer is absolutely right: Because the PLDs are more similar to the actions than the animations, a comparison of the effects of the two decoding schemes is not informative. As we also clarified in our response to Reviewer 2, we cannot rule out that the action-PLD decoding picked up information related to action effect structures. Thus, the only firm conclusion that we can draw from our study is that aIPL and SPL are disproportionally sensitive to action effects and body movements, respectively. We clarified this point in our revised discussion.

(2) The second set of analyses in the paper, shown in Figure 4, follows from the notion that inferring action effects from body movements alone (i.e., when the object is unseen) is easier via pantomimes than with PLD stick figures. That makes sense, but it doesn't necessarily imply that the richness of the inferred action effect is the only or main difference between these conditions. There is more visual information overall in the pantomime case. So, although it's likely true that observers can more vividly infer action effects from pantomimes vs stick figures, it's not a given that contrasting these two conditions is an effective way to isolate inferred action effects. The results in Figure 4 are therefore intriguing but do not unequivocally establish that aIPL is representing inferred rather than observed action effects.

We agree that higher decoding accuracies for Action-Pant vs. Action-PLD and Pant-PLD could also be due to visual details (in particular of hands and body) that are more similar in actions and pantomimes relative to PLDs. However, please note that for this reason we included also the comparison of Anim-Pant vs. Anim-PLD. For this comparison, visual details should not influence the decoding. We clarified this point in our revision.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

It struck me that there are structural distinctions amongst the 5 action kinds that were not highlighted and may have been unintentional. Specifically, three of the actions are "unary" in a sense: break(object), squash(object), hit(object). One is "binary": place(object, surface), and the fifth (drink) is perhaps ternary - transfer(liquid, cup, mouth)? Might these distinctions be important for the organization of action effects (or actions generally)?

This is an interesting aspect that we did not think of yet. We agree that for the organization of actions (and perhaps action effects) this distinction might be relevant. One issue we noticed, however, is that for the animations the suggested organization might be less clear, in particular for "drink" as ternary, and perhaps also for "place" as binary. Thus, in the action-animation cross-decoding, this distinction - if it exists in the brain - might be harder to capture. We nonetheless tested this distinction. Specifically, we constructed a dissimilarity model (using the proposed organization, valency model hereafter) and tested it in a multiple regression RSA against an effect type model and two other models for specific actions (discriminating each action from each other with the same distance) and motion energy (as a visual control model). This analysis revealed no effects for the "valency" model in the ROI-based RSA. Also a searchlight analysis revealed no effects for this model. Since we think that the valency model is not ideally suited to test representations of action effects (using data from the action-animation cross-decoding) and to make the description of the RSA not unnecessarily complicated, we decided to not include this model in the final RSA reported in the manuscript.

In general, I found it surprising that the authors treated their LOTC findings as surprising or unexpected. Given the long literature associating this region with several high-level visual functions related to body perception, action perception, and action execution, I thought there were plenty of a priori reasons to investigate the LOTC's behaviour in this study. Looking at the supplementary materials, indeed some of the strongest effects seem to be in that region.

(Likewise, classically, the posterior superior temporal sulcus is strongly associated with the perception of others' body movements; why not also examine this region of interest?)

One control analysis that would considerably add to the strength of the authors' conclusions would be to examine how actions could be cross-decoded (or not) in the early visual cortex. Especially in comparisons of, for example, pantomime to full-cue video, we might expect a high degree of decoding accuracy, which might influence the way we interpret similar decoding in other "higher level" regions.

We agree that it makes sense to also look into LOTC and pSTS, and also EVC. We therefore added ROIs for these regions: For EVC and LOTC we used the same approach based on Brodmann areas as for aIPL and SPL, i.e., we used BA 17 for V1 and BA 19 for LOTC. For pSTS, we defined the ROI based on a meta analysis contrast for human vs. non-human body movements (Grobras et al., HBM 2012). Indeed we find that the strongest effects (for both action effect structures and body movements) can be found in LOTC. We also found effects in EVC that, at least for the action-animation cross-decoding, are more difficult to interpret. To test for a coincidental visual confound between actions and animations, we included a control model for motion energy in the multiple regression RSA, which could indeed explain some of the representational content in V1. However, also the effect type model revealed effects in V1, suggesting that there were additional visual features that caused the action-animation cross-decoding in V1. Notably, as pointed out in our response to the Public comments, the representational organization in V1 was relatively distinct from the representational organization in aIPL and LOTC, which argues against the interpretation that effects in aIPL and LOTC were driven by the same (visual) features as in V1.

Regarding the analyses reported in Figure 4: wouldn't it be important to also report similar tests for SPL?

In the analysis of implied action effect structures, we focused on the brain regions that revealed robust effects for action-animation decoding in the ROI and the searchlight analysis, that is, aIPL and SPL. However, we performed a whole brain conjunction analysis to search for other brain regions that show a profile for implied action effect representation. This analysis (that we forgot to mention in our original manuscript; now corrected) did not find evidence for implied action effect representations in SPL.

However, for completeness, we also added a ROI analysis for SPL. This analysis revealed a surprisingly complex pattern of results: We observed stronger decoding for Anim-Pant vs. Anim-PLD, whereas there were no differences for the comparisons of Action-Pant with Action-PLD and Pant-PLD:

This pattern of results is not straightforward to explain: First, the equally strong decoding for Action-Pant, Action-PLD, and Pant-PLD suggests that SPL is not substantially sensitive to body part details. Rather, the decoding relied on the coarse body part movements, independently of the specific stimulus type (action, pantomime, PLD). However, the stronger difference between Anim-Pant and Anim-PLD suggests that SPL is also sensitive to implied AES. This appears unlikely, because no effects (in left aIPL) or only weak effects (in right SPL) were found for the more canonical Action-Anim cross-decoding. The Anim-Pant cross-decoding was even stronger than the Action-Anim cross-decoding, which is counterintuitive because naturalistic actions contain more information than pantomimes, specifically with regard to action effect structures. How can this pattern of results be interpreted? Perhaps, for pantomimes and animations, not only aIPL and LOTC but also SPL is involved in inferring (implied) action effect structures. However, for this conclusion, also differences for the comparison of Action-Pant with Action-PLD and for Action-Pant with Pant-PLD should be found. Another non-mutually exclusive interpretation is that both animations and pantomimes are more ambiguous in terms of the specific action, as opposed to naturalistic actions. For example, the squashing animation and pantomime are both ambiguous in terms of what is squashed/compressed, which might require additional load to infer both the action and the induced effect. The increased activation of action-related information might in turn increase the chance for a match between neural activation patterns of animations and pantomimes.

In any case, these additional results in SPL do not question the effects reported in the main text, that is, disproportionate sensitivity for action effect structures in right aIPL and LOTC and for body movements in SPL and other AON regions. The evidence for implied action effect structures representation in SPL is mixed and should be interpreted with caution.

We added this analysis and discussion as supplementary information.

Statistical arguments that rely on "but not" are not very strong, e.g. "We found higher cross-decoding for animation-pantomime vs. animation-PLD in right aIPL and bilateral LOTC (all t(23) > 3.09, all p < 0.0025; one-tailed), but not in left aIPL (t(23) = 0.73, p = 0.23, one-tailed)." Without a direct statistical test between regions, it's not really possible to support a claim that they have different response profiles.

Absolutely correct. Notably, we did not make claims about different profiles of the tested ROIs with regard to implied action effect representations. But of course it make sense to test for differential profiles of left vs. right aIPL, so we have added a repeated measures ANOVA to test for an interaction between TEST (animation-pantomime, animation-PLD) and ROI (left aIPL, right aIPL), which, however, was not significant (F(1,23)=3.66, p = 0.068). We included this analysis in the revised manuscript.

Reviewer #2 (Recommendations for The Authors):

(1) I haven't found any information about data and code availability in the paper: is the plan to release them upon publication? This should be made clear.

Stimuli, MRI data, and code are deposited at the Open Science Framework (https://osf.io/am346/). We included this information in the revised manuscript.

(2) Samples of videos of the stimuli (or even the full set) would be very informative for the reader to know exactly what participants were looking at.

We have uploaded the full set of stimuli on OSF (https://osf.io/am346/).

(3) Throughout the paper, decoding accuracies are averaged across decoding directions (A->B and B->A). To my knowledge, this approach was proposed in van den Hurk & Op de Beeck (2019), "Generalization asymmetry in multivariate cross-classification: When representation A generalizes better to representation B than B to A". I believe it would be fair to cite this paper.

Absolutely, thank you very much for the hint. We included this reference in our revised manuscript.

(4) Page 3, line 70: this is a very nitpicky point, but "This suggests that body movements and the effects they induce are at least partially processed independently from each other." is a bit of an inferential leap from "these are distinct aspects of real-world actions" to "then they should be processed independently in the brain". The fact that a distinction exists in the world is a prerequisite for this distinction existing in the brain in terms of functional specialization, but it's not in itself a reason to believe that functional specialization exists. It is a reason to hypothesize that the specialization might exist and to test that hypothesis. So I think this sentence should be rephrased as "This suggests that body movements and the effects they induce might be at least partially processed independently from each other.", or something to that effect.

Your reasoning is absolutely correct. We revised the sentence following your suggestion.

(5) Page 7, line 182: the text says "stronger decoding for action-animation vs. action-PLD" (main effect of TEST), which is the opposite of what can be seen in the figure. I assume this is a typo?

Thanks for spotting this, it was indeed a typo. We corrected it: “…stronger decoding for action-PLD vs. action-animation cross-decoding..”

(6) Page 7, Figure 3B: since the searchlight analysis is used to corroborate the distinction between aIPL and SPL, it would be useful to overlay the contours of these ROIs (and perhaps LOTC as well) on the brain maps.

We found that overlaying the contours of the ROIs onto the decoding searchlight maps would make the figure too busy, and the contours would partially hide effects. However, we added a brain map with all ROIs in the supplementary information.

(7) Page 9, Figure 4A: since the distinction between the significant difference between anim-pant and anim-PLD is quite relevant in the text, I believe highlighting the lack of difference between the two decoding schemes in left aIPL (for example, by writing "ns") in the figure would help guide the reader to see the relevant information. It is generally quite hard to notice the absence of something.

We added “n.s.” to the left aIPL in Fig. 4A.

(8) Page 11, line 300: "Left aIPL appears to be more sensitive to the type of interaction between entities, e.g. how a body part or an object exerts a force onto a target object" since the distinction between this and the effect induced by that interaction" is quite nuanced, I believe a concrete example would clarify this for the reader: e.g. I guess the former would involve a representation of the contact between hand and object when an object is pushed, while the latter would represent only the object's displacement following the push?

Thank you for the suggestion. We added a concrete example: “Left aIPL appears to be more sensitive to the type of interaction between entities, that is, how a body part or an object exerts a force onto a target object (e.g. how a hand makes contact with an object to push it), whereas right aIPL appears to be more sensitive to the effect induced by that interaction (the displacement of the object following the push).”

(9) Page 12, line 376: "Informed consent, and consent to publish, was obtained from the participant in Figure 2." What does this refer to? Was the person shown in the figure both a participant in the study and an actor in the stimulus videos? Since this is in the section about participants in the experiment, it sounds like all participants also appeared in the videos, which I guess is not the case. This ambiguity should be clarified.

Right, the statement sounds misleading in the “Participants” section. We rephrased it and moved it to the “Stimuli” section: “actions…were shown in 4 different formats: naturalistic actions, pantomimes, point light display (PLD) stick figures, and abstract animations (Fig. 2; informed consent, and consent to publish, was obtained from the actor shown in the figure).”

(10) Page 15, line 492: Here, "within-session analyses" are mentioned. However, these analyses are not mentioned in the text (only shown in Figure S2) and their purpose is not clarified. I imagine they were a sanity check to ensure that the stimuli within each stimulus type could be reliably distinguished. This should be explained somewhere.

We clarified the purpose of the within session decoding analyses in the methods section: "Within-session decoding analyses were performed as sanity checks to ensure that for all stimulus types, the 5 actions could be reliably decoded (Fig. S2)."

(11) Page 20, Figure S1: I recommend using the same color ranges for the two decoding schemes (action-anim and action-PLD) in A and C, to make them more directly comparable.

Ok, done.

Reviewer #3 (Recommendations For The Authors):

(1) When first looking at Figure 1B, I had a hard time discerning what action effect was being shown (I thought maybe it was "passing through") Figure 2 later clarified it for me, but it would be helpful to note in the caption that it depicts breaking.

Thank you for the suggestion. Done.

(2) It would be helpful to show an image of the aIPL and SPL ROIs on a brain to help orient readers - both to help them examine the whole brain cross-decoding accuracy and to aid in comparisons with other studies.

We added a brain map with all ROIs in the supplementary information.

(3) Line 181: I'm wondering if there's an error, or if I'm reading it incorrectly. The line states "Moreover, we found ANOVA main effects of TEST (F(1,24)=33.08, p=7.4E-06), indicating stronger decoding for action-animation vs. action-PLD cross-decoding..." But generally, in Figure 3A, it looks like accuracy is lower for Action-Anim than Action-PLD in both hemispheres.

You are absolutely right, thank you very much for spotting this error. We corrected the sentence: “…stronger decoding for action-PLD vs. action-animation cross-decoding..”

(4) It might be useful to devote some more space in the Introduction to clarifying the idea of action-effect structures. E.g., as I read the manuscript I found myself wondering whether there is a difference between action effect structures and physical outcomes in general... would the same result be obtained if the physical outcomes occurred without a human actor involved? This question is raised in the discussion, but it may be helpful to set the stage up front.

We clarified this point in the introduction:

In our study, we define action effects as induced by intentional agents. However, the notion of action effect structures might be generalizable to physical outcomes or object changes as such (e.g. an object's change of location or configuration, independently of whether the change is induced by an agent or not).

(5) Regarding my public comment #2, it would perhaps strengthen the argument to run the same analysis in the SPL ROIs. At least for the comparison of Anim-Pant with Anim-PLD, the prediction would be no difference, correct?

The prediction would indeed be that there is no difference for the comparison of Anim-Pant with Anim-PLD, but also for the comparison of Action-Pant with Action-PLD and for Action-Pant with Pant-PLD, there should be no difference. As explained in our response to the public comment #2, we ran a whole brain conjunction (Fig. 4B) to test for the combination of these effects and did not find SPL in this analysis. However, we did found differences for Anim-Pant vs. Anim-PLD, which is not straightforward to interpret (see our response to your public comment #2 for a discussion of this finding).

Author response:

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

Reviewer #1 (Public Review):

Weaknesses:

The weaknesses of the study include the following. 

(1) It remains unclear whether the function described for CDK2 is regulatory, that is, it affects TBK1 levels during physiological responses such as viral infection or cell cycle progression, or if it is homeostatic, governing the basal abundance of TBK1 but not responding to signaling.

The regulation of TBK1 by CDK2 described in this article occurs during viral infection. Simultaneously, we also investigated the effects of CDK2 overexpression and knockdown on TBK1 levels under non-infected state and observed a slight reduction, as shown in Figure 4K and 4L. Thus, we speculate that the regulation of TBK1 by CDK2 serves, on one hand, to maintain cellular homeostasis and, on the other hand, to respond to signaling triggered by viral infection.

(2) The authors have not explored whether the catalytic activity of CDK2 is required for TBK1 ubiquitinoylation and, if so, what its target specificity is.

We found that the ubiquitination modification of TBK1 was not affected by treatment with a CDK2 kinase activity inhibitor (SNS-032), as demonstrated in the results below (Author response image 1).

Author response image 1.

(3) Given the multitude of CDK isoforms in fish, it remains unexplored whether the identified fish CDK2 homolog is a requisite cell cycle regulator or if its action in the cell cycle is redundant with other CDKs.

A comparison of the protein sequences of fish CDK2 and human CDK2 revealed a 90% similarity (Author response image 2). It has also been reported that the kinase activity of goldfish CDK2 significantly increases during oocyte maturation (ref. 1). Furthermore, UHRF1 phosphorylation by cyclin A2/CDK2 is crucial for zebrafish embryogenesis (ref. 2). Additionally, Red grouper nervous necrosis virus (RGNNV) infection activated the p53 pathway, leading to the upregulation of p21 and downregulation of cyclin E and CDK2, which forces infected cells to remain in the G1/S replicative phase (ref. 3). All these evidences suggest that fish CDK2 plays a vital role in cell cycle regulation, and there have been no reports of other CDKs demonstrating CDK2-like functions.

References:

(1) Hirai T, et al. (1992) Isolation and Characterization of Goldfish Cdk2, a Cognate Variant of the Cell-Cycle Regulator Cdc2. Developmental biology 152(1):113-120.

(2) Chu J, et al. (2012) UHRF1 phosphorylation by cyclin A2/cyclin-dependent kinase 2 is required for zebrafish embryogenesis. Molecular biology of the cell 23(1):59-70. 

(3) Mai WJ, Liu HX, Chen HQ, Zhou YJ, & Chen Y (2018) RGNNV-induced cell cycle arrest at G1/S phase enhanced viral replication via p53-dependent pathway in GS cells. Virus Res 256:142-152.

Author response image 2.

Reviewer #2 (Public Review):

Weaknesses:

(1) While the study focuses on fish, the broader implications for other lower vertebrates and higher vertebrates are not extensively discussed.

Thanks to your comment, we have added a paragraph to the Discussion section of the manuscript regarding the implications of the negative regulation of IFN expression by fish CDK2 for other vertebrates (lines 398-403). The details are as follows: first, we selected representative species from each of the six major vertebrate groups and compared their CDK2 protein sequences, finding that they are over 90% similar to one another (Author response image 3). This suggests that the function of CDK2 may be conserved to some extent across vertebrates. Additionally, CDK2 inhibition has been shown to enhance anti-tumor immunity by increasing the IFN response to endogenous retroviruses (ref. 1). Our studies provide evidence that fish CDK2 inhibits the IFN response by promoting the ubiquitination and degradation of TBK1, strongly supporting the role of CDK2 in the regulation of the immune response.

Reference:

(1) Chen Y, et al. (2022) CDK2 Inhibition Enhances Antitumor Immunity by Increasing IFN Response to Endogenous Retroviruses. Cancer Immunol Res 10(4):525-539.

Author response image 3.

(2) The study heavily relies on specific fish models, which may limit the generalizability of the findings across different species.

Thank you for your comment. First, we compared the amino acid sequences of CDK2 proteins from fish and other vertebrates, which show over 90% similarity. Moreover, the small size, low cost, and external development of zebrafish make it an excellent model for vertebrate developmental biology. It has been reported that due to the high genomic and molecular similarities between zebrafish and other vertebrates, including humans, many significant discoveries in zebrafish development are relevant to humans (ref. 2). Our study concentrated on CDK2 in zebrafish, and the findings should be valuable for other vertebrates.

Reference:

(2) Veldman MB & Lin S (2008) Zebrafish as a Developmental Model Organism for Pediatric Research. Pediatr Res 64(5):470-476.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

The following additional data/discussion could improve the manuscript.

(1) Investigate whether the catalytic activity of CDK2 is required to regulate TBK1 abundance. It is common for E3 ligases to be directed towards phosphorylated substrates, so it would be of interest to know if CDK2 phosphorylates TBK1 to facilitate its recognition for ubiquitinylation.

We examined the effect of CDK2 on the TBK1 protein after inhibiting its kinase activity with SNS-032 treatment and found that it could still affect TBK1 expression, as shown in the results below (Figure R4). Our previous experiments investigating the effect of CDK2 on TBK1 did not show that CDK2 caused the migration of TBK1 bands (typically, proteins that undergo phosphorylation exhibit band migration). Furthermore, in this study, CDK2 did not function as an E3 ligase; instead, it recruited the E3 ligase Dtx4 to ubiquitinate TBK1.

Author response image 4.

(2) Investigate how CDK2 abundance is regulated by viral infection and whether viral infection impacts cell cycle progression in a CDK2-dependent manner.

In fact, as illustrated in Figure 1, we investigated the changes in CDK2 at both the mRNA and protein levels following viral infection. Our findings revealed that SVCV infection resulted in an increase in CDK2 mRNA and protein expression. Additionally, our earlier reports have indicated that SVCV infection can induce alterations in the cell cycle, resulting in a notable increase in the S phase (Figure 1 of ref. 1). However, whether SVCV infection impacts cell cycle progression in a CDK2dependent manner will be explored in our upcoming study.

Reference:

(1) Li S, et al. Spring viraemia of carp virus modulates p53 expression using two distinct mechanisms. PLoS Pathog 15, e1007695 (2019).

(3) Provide data/discussion concerning the role of fish CDK2 in the regulation of cell cycle progression and whether this process is impacted by viral infection (part 1). Are TBK1 abundance and interferon production differentially regulated across the cell cycle due to the action of CDK2 (part 2).

Thank you for your advice. This concern is addressed in two parts, as follows: 

For part 1: To date, there has been limited research conducted on fish CDK2 in the regulation of cell cycle progression. The details are as follows: It has been reported that the kinase activity of goldfish CDK2 significantly increases during oocyte maturation (ref. 1). Furthermore, UHRF1 phosphorylation by cyclin A2/CDK2 is crucial for zebrafish embryogenesis (ref. 2). Additionally, a novel CDK2 homolog has been identified in Japanese lamprey, which plays a crucial role in apoptosis (ref. 3). Red grouper nervous necrosis virus (RGNNV) infection activates the p53 pathway, leading to the upregulation of p21 and downregulation of cyclin E and CDK2, which forces infected cells to remain in the G1/S replicative phase (ref. 4). All this evidence suggests that fish CDK2 plays a vital role in cell cycle regulation, and this process is also impacted by viral infection. Relevant content has been added to the Discussion section in the revised manuscript (lines 389-398).

References:

(1) Hirai T, et al. (1992) Isolation and Characterization of Goldfish Cdk2, a Cognate Variant of the Cell-Cycle Regulator Cdc2. Developmental biology 152(1):113-120.

(2) Chu J, et al. (2012) UHRF1 phosphorylation by cyclin A2/cyclin-dependent kinase 2 is required for zebrafish embryogenesis. Molecular biology of the cell 23(1):5970.

(3) Xu Y, Tian Y, Zhao H, Zheng N, Ren KX, Li QW. A novel CDK-2 homolog identified in lamprey, with roles in apoptosis. Fish Physiol Biochem 47, 189-189 (2021). 

(4) Mai WJ, Liu HX, Chen HQ, Zhou YJ, & Chen Y (2018) RGNNV-induced cell cycle arrest at G1/S phase enhanced viral replication via p53-dependent pathway in GS cells. Virus Res 256:142-152.

For part 2: TBK1 plays a crucial role in regulating IFN production. Variations in CDK2 activity during different phases of the cell cycle may lead to changes in the expression and function of TBK1. Our findings suggest that heightened CDK2 activity may suppress TBK1 expression, thereby hindering the cell's capacity to produce IFN. Conversely, during the late phase of the cell cycle or in an inhibited state, TBK1 expression may rise, enhancing IFN synthesis and release. In summary, CDK2 is involved in intracellular signaling by modulating TBK1 levels and IFN production, affecting the cellular immune response and cycle regulation—two processes that are notably distinct at various stages of the cell cycle. Relevant content has been added to the Discussion section in the revised manuscript (lines 377-384).

Minor suggestions:

(1) The authors introduce their study with the consideration that knowledge of fish signaling pathways can inform mammalian biology because mammals evolved from fish. This is not strictly true, since mammals and fish both evolved from an ancient common ancestor and the diversification of signaling in each species likely occurred in response to distinct evolutionary selective pressures.

Thank you for your suggestion. We have revised the statement in the manuscript to eliminate the notion that mammals evolved from fish (lines 98-99). The immune systems of higher vertebrates (e.g., humans) and lower vertebrates (e.g., fish) generally exhibit some consistency, although there are notable differences.

(2) On line 210 and line 276, the authors appear to have misstated the data. CDK2 knockout increases not decreases TBK1 and Dtx4 knockdown abrogated rather than restored CDK2 suppression of TBK1.

Thanks for your reminder, I jumped to the wrong conclusions in these two places (line 204 and line 267) and have changed them as you suggested.

Reviewer #2 (Recommendations For The Authors):

The manuscript has some shortcomings that, if addressed, could improve the overall quality of the article.

(1) Line 63-72, line 77-79, line 88-90- please add additional references for these sentences.

Thanks to your comment, we have added references for these sentences (Line 63-72, line 77-79, line 88-90).

(2) It is of the utmost importance to quantify the data presented in Figures 4J and 5D, as this will facilitate the visualization of the immunoblot.

Thank you for your comment. We have quantified the data presented in Figures 4J and 5D to enhance the clarity of the immunoblot.

(3) The scale in Figure 4E is difficult to discern.

Thanks for your comment. To improve the visual clarity of the image, we have enlarged the scale label in Figure 4E.

(4) In Figure 3B, shCDK2 is shown in italics, preferably in line with other standards such as Figures 3C and 3F.

Thank you for your comment. We have revised the shCDK2 in Figure 3B.

(5) The functions of CDK family members in immunity are hoped to be discussed.

Thanks for your suggestion. We have discussed the functions of CDK family members in immunity (lines 363-387). The details are as follows: Recent studies have demonstrated that CDK activity is crucial for virus-induced innate immune responses. Reports indicate that CDKs are involved in the Toll-like receptor (TLR) signaling pathway, the nuclear factor-κB (NF-κB) signaling pathway, and the JAK-STAT signaling pathway. For instance, CDK8 and/or CDK19 enhanced the transcription of inflammatory genes, such as IL-8 and IL-10, in cells following TLR9 stimulation. CDKs and NF-κB establish a remarkable paradigm where CDKs can act directly on substrate proteins rather than depending solely on transcriptional control. It has been reported that CDK1 serves as a positive regulator of the IFN-I signaling pathway, facilitating STAT1 phosphorylation, which subsequently boosts the expression of ISGs. Furthermore, inhibiting CDK activity has been shown to obstruct STAT phosphorylation, proinflammatory gene activation, and ISG mRNA induction in response to SeV infection. It is important to note that no evidence suggests the involvement of CDKs in RLR signaling pathways. This study has shown that fish CDK2 functions as a negative regulator of the key kinase TBK1, which is involved in the RLR signaling pathway. A better understanding of the relationship between CDK2 and RLR signaling pathways will enhance our grasp of the regulatory mechanisms of CDKs in antiviral innate immunity.

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public review):

Summary:

Amaral et al. presents a study investigating the mesoscale modelling and dynamics of bolalipids.

Strengths:

The figures in this paper are exceptional. Both those to outline and introduce the lipid types, but also the quality and resolution of the plots. The data held within also appears to be outstanding and of significant (hopefully) general interest.

We thank the reviewer for their kind words and the appreciation of our work.

Weaknesses:

In the introduction, I would like to have read more specifics on the biological role of bolalipids. Archaea are mentioned, but this kingdom is huge - there must be specific species that can be discussed where bolalipids are integral to archaeal life. The authors should go beyond ’extremophiles’. In short, they should unpack why the general audience should be interested in these lipids, within a subset of organisms that are often forgotten about.

Following the reviewer’s advice we have revised the introduction of the manuscript, in which we now discuss specific species (Sulfolobus acidocaldarius and Thermococcus kodakarensis) and how in these species bolalipids are integral to archaeal life. We explain that the ratio between bilayer and bolalipids, and the number of cyclopentane rings contained within bolalipids can change to adapt to the environment. The revised parts of the introduction read (p.1 ):

“Like for bacteria and eukaryotes, archaea must keep their lipid membranes in a fluid state (homeoviscous adaptation). This is important even under extreme environmental conditions, such as hot and cold temperatures, or high and low pH values [7]. Because of this, many archaea adapt to changes in their environment by tuning the lipid composition of their membranes: altering the ratio between bola- and bilayer lipids in their membranes [8, 9] and/or by changing the number of cyclopentane rings in their lipid tails, which are believed to make lipid molecules more rigid [5]. For example, Thermococcus kodakarensis increases its tetraether bolalipid ratio from around 50% to over 80% when the temperature of the environment increases from 60 to 85 C [10]. Along the same lines, the cell membrane of Sulfolobus acidocaldarius, can contain over 90 % of bolalipids with up to 8 cyclopentane rings at 70 C and pH 2.5 [5, 11]. It is worth mentioning that in exceptional cases bacteria also synthesise bolalipids in response to high temperatures [12], highlighting that the study of bolalipid membranes is relevant not only for archaeal biology but also from a general membrane biophysics perspective.”

Reviewer #2 (Public review):

Summary:

The authors aimed to understand the biophysical properties of archeal membranes made of bolalipids. Bacterial and eukaryotic membranes are made of lipids that self-assemble into bilayers. Archea, instead, use bolalipids, lipids that have two headgroups and can span the entire bilayer. The authors wanted to determine if the unique characteristics of archaea, which are often extremophiles, are in part due to the fact that their membranes contain bolalipids.

The authors develop a minimal computational model to compare the biophysics of bilayers made of lipids, bolalipids, and mixtures of the two. Their model enables them to determine essential parameters such as bilayer phase diagrams, mechanical moduli, and the bilayer behaviour upon cargo inclusion and remodelling.

The author demonstrates that bolalipid bilayers behave as binary mixtures, containing bolalipids organized either in a straight conformation, spanning the entire bilayer, or in a u-shaped one, confined to a single leaflet. This dynamic mixture allows bolalipid bilayers to be very sturdy but also provides remodelling. However, remodelling is energetically more expensive than with standard lipids. The authors speculate that this might be why lipids were more abundant in the evolutionary process. Strengths:

This is a wonderful paper, a very fine piece of scholarship. It is interesting from the point of view of biology, biophysics, and material science. The authors mastered the modelling and analysis of these complex systems. The evidence for their findings is really strong and complete. The paper is written superbly, the language is precise and the reading experience is very pleasant. The plots are very well-thought-out.

Weaknesses:

I would not talk about weaknesses, because this is really a nice paper. If I really had to find one, I would have liked to see some clear predictions of the model expressed in such a way that experimentalists could design validation experiments.

We thank the reviewer for their very kind assessment. We incorporated their recommendations regarding experimental validation in the discussion section, as follows (p.14):

“Our model makes a number of predictions that could be tested by experiment either in cells or in vitro. First, it predicts that a small increase in the fraction of archaeal bilayer lipids should be sufficient to soften a bolalipid-rich membrane. While this could be tested in the future, so far only very few studies have yet reported experimental analysis of archaeal membrane mixtures [18, 50]. Second, we observed that membranes with moderate bolalipid molecular rigidity k_bola exhibit curvature-dependent bending rigidity. To experimentally verify this, one could extrude membrane tethers from cells while controlling for membrane tension. Finally, to get to the core mechanism underlying our findings, it will be important to develop experimental methods that will allow the fraction of U-shaped bolalipid conformers per leaflet to be imaged and measured.”

Reviewer #3 (Public review):

Summary:

The authors have studied the mechanics of bolalipid and archaeal mixed-lipid membranes via comprehensive molecular dynamics simulations. The Cooke-Deserno 3-bead-per-lipid model is extended to bolalipids with 6 beads. Phase diagrams, bending rigidity, mechanical stability of curved membranes, and cargo uptake are studied. Effects such as the formation of U-shaped bolalipids, pore formation in highly curved regions, and changes in membrane rigidity are studied and discussed. The main aim has been to show how the mixture of bolalipids and regular bilayer lipids in archaeal membrane models enhances the fluidity and stability of these membranes.

Strengths:

The authors have presented a wide range of simulation results for different membrane conditions and conformations. For the most part, the analyses and their results are presented clearly and concisely. Figures, supplementary information, and movies very well present what has been studied. The manuscript is well-written and is easy to follow.

We thank the reviewer for the detailed assessment of our work and their constructive feedback.

Major issues

R3.Q1: The Cooke-Deserno model, while very powerful for biophysical analysis of membranes at the mesoscale, is very much void of chemical information. It is parametrized such that it is good in producing fluid membranes and predicting values for bending rigidity, compressibility, and even thermalexpansioncoefficientfallingintheacceptedrangeofvaluesforbilayermembranes. But it still represents a generic membrane. Now, the authors have suggested a similar model for the archaeal bolalipids, which have chemically different lipids (the presence of cyclopentane rings for one), and there is no good justification for using the same pairwise interactions between their representative beads in the coarse-grained model. This does not necessarily diminish the worth of all the authors’ analyses. What is at risk here is the confusion between ”what we observe this model of bolalipidor mixed-membranes do” and ”how real bolalipid-containing archaeal membranes behave at these mechanical and thermal conditions.”.

As the reviewer correctly notes, Cooke and Deserno used a minimal model, devoid of chemical detail, to represent fluid lipid membranes composed of bilayer lipids. Indeed archaeal lipids are chemically different compared to non-archaeal lipids, but just like non-archaeal lipids, they can be very different from one another. Given the chemical diversity of bolalipids between each other, instead of representing their complexity in a complicated model with many experimentally unconstrained parameters, we here defined a minimal model for bolalipids. The power of this minimal model is to represent the key physical/geometrical characteristics of archaeal membranes, namely the fact that lipid heads on two sides of the membrane are often connected, that bolalipids can exhibit a conformational change, and that bolalipids mix with some percentage of bilayer molecules. We then ask a general question: how do these unique geometrical characteristics of archaeal membranes influence their mechanics and reshaping? The reviewer is however right in pointing out that a model, regardless of its level of details (atomistic, coarse-grained, minimal), is still a model.

Our approach of extending an established coarse-grained model for bilayer lipids to bolalipids is further supported by experimental observations, which report that archaeal bilayer lipids can form membranes of comparable bending rigidity to those of non-archaeal bilayer membranes [53]. Hence, different lipid linkages (archaeal vs. non-archaeal) give rise to fluid, deformable membranes of not too dissimilar rigidities, suggesting that both archaeal and non-archaeal bilayer lipids can be represented by a similar minimal coarse-grained model for the purpose of mesoscopic biophysical investigations. Since archaeal bolalipids have the same core chemical structure as two archaeal bilayer lipids joined by their tail ends, similarly we model a bolalipid by joining two bilayer lipids. Such an approach also efficiently enables us to compare bolalipid with bilayer membranes, and connect to the large body of knowledge on the physics of bilayer membranes.

To conclude, our coarse-grained model is indeed intended to capture the main physical properties of bolalipid membranes, and not their chemical diversity.

R3.Q2: Another more specific, major issue has to do with using the Hamm-Kozlov model for fitting the power spectrum of thermal undulations. The 1/q² term can very well be attributed to membrane tension. While a barostat is indeed used, have the authors made absolutely sure that the deviation from 1/q⁴ behaviour does not correspond to lateral tension?

To the casual observer, any 1/q² trend might point at membrane tension. However, the precise functional form is relevant as it determines whether the 1/q² dominates the 1/q⁴ trend for small or large values of the wave number q in the fitted power spectrum.

The first model (including lipid tilt) exhibits the functional form 1/(kq⁴) + 1/(kq²). In contrast, the second model (including membrane tension) exhibits the functional form 1/(kq⁴ + ∑q²). Importantly, the two models obey a different functional form. Here k and k_θ, are the bending and tilt moduli, which are assumed positive, and ∑ is the membrane tension, which can be either positive or negative. For the first model (with tilt), while for small q the amplitude is proportional to q^-4, for large q the amplitude is proportional to q^-2. In contrast, for the second model (with positive tension) while for small q the amplitude is proportional to q^-2, for large q the amplitude is proportional to q^-4. If membrane tension were to be negative in the second model, the slope would cross from negative infinity for small q to -4 for large q. The functional dependencies are summarized in Author response image 1A.

For rigid bolalipid membranes, it is clearly visible that the slope of the power spectrum plotted against the wave number q decreases with increasing q (Author response image 1B). While the slope initially assumes a value close to 4, it gradually approaches 2 for larger values of q. We conclude that only the model including lipid tilt can fit the power spectrum of membrane fluctuations appropriately (solid-dashed line), whereas the model with tension fails to fit the data (dashed line). We note that the combined model containing both lipid tilt and membrane tension does not give a better fit (dotted line).

To demonstrate that the tension model cannot fit the data, we included the best fits for both models for rigid bolalipid membranes in the new SI section 16 (p. S22) and show that only the tilt model leads to acceptable fits. We also measured the projected membrane tension - , where P_x,P_y are respectively the pressure in x and y direction and  L_z is the dimension of the simulation box in z axis. We found the projected membrane tension to give a negligible value similarly to the one that we indirectly measured by fitting a combined model with both tension and tilt, further confirming our conjecture.

Author response image 1.

(A) Schematic showing the decay of the power spectrum as a function of the wave number q in the tilt model (top), in the tension model with positive membrane tension (middle), and in the tension model with negative membrane tension (bottom). (B) Fitted power spectrum as a function of q for rigid bolalipid membranes (k_bola=5k_BT). The fit shows that while the model with tension (dashed line) cannot fit the data, the model with tilt nicely fits the spectrum (solid-dashed line). The combined model including both tension and tilt does not fit the spectrum any better (dotted line).

R3.Q3: I got more worried when I noticed in the SI that the simulations had been done with combined ”fix langevin” and ”fix nph” LAMMPS commands. This combination does not result in a proper isothermal-isobaric ensemble. The importance of tilt terms for bolalipids is indeed very interesting, but I believe more care is needed to establish that.

In what follows, we show that there is no reason to worry. First of all we want to clarify that the physical setup we simulate is that of a membrane contained in a heat bath under negligible tension with correct diffusional dynamics. To achieve this physical setup, for which we use a Langevin thermostat combined with pressure control via an overdamped barostat, which we implement in LAMMPS by combining ”fix langevin” and ”fix nph”.

In more detail: we simulated particles in an implicit solvent, for which we use a Langevin thermostat to get the right diffusional dynamics. To apply the theory of fitting fluctuation spectrums the simulation box length needs to be (near) constant. However, simulating membranes at a fixed box size results in an average non-zero membrane tension, making it hard to measure bending rigidity. The reason is that the effect of membrane tension is most influential on the largest wavelength modes, which are also most decisive when determining mechanical membrane properties like membrane rigidity. To minimize the effect of tension, we perform our simulation with an overdamped barostat (𝜏_baro = 10 𝜏 _langevin), which keeps the membrane near tensionless, as also done before [32]. In the revised manuscript, we have clarified the statement on the physical ensemble used (p.S2):

“For simulating flat membrane patches of bolalipids, we combined the previously used Langevin thermostat with relaxation time of 1𝜏 with a Nosé–Hoover barostat with relaxation time of 10𝜏. In LAMMPS this amounts to combining the commands ’fix langevin’ with ’fix nph’. We configured the barostat to set lateral pressure P_xy to zero by re-scaling the simulation box in the x-y plane. We compare this setup to a fixed box length setup, and an NPT ensemble setup, in SI section 17.”

To connect our results with statistical mechanics ensemble theory we tested alternative setups. Similar setups, including the formal isothermal-isobaric ensemble, where N,P,T are kept constant using Nose-Hoover style equations for thermostating and barostating with modern corrections [34], which the reviewer refers to, result in very similar fluctuation spectrums. Consequently, our measurements of bending and tilt modulus hold true regardless of the integration scheme. However, such a setup does not correctly capture implicit solvent and diffusional dynamics.

In even more detail: we tested our setup (implemented via ”fix langevin”+”fix nph”) versus a isothermal-isobaric ensemble (implemented via ”fix npt”). We measured volume mean and standard deviation, and found them matching for a reference LJ gas.

To be completely sure, and to please the reviewer, we have performed additional verifications in the new SI section 17, which we summarize in the following. We simulated three representative membranes with different integration schemes: ”fix npt”, ”fix langevin”+”fix nph”, and ”fix langevin” (Langevin dynamics with projected area fixed at the average value obtained from a ”langevin+nph”). We checked that the ”fix nph” barostat is merely equilibrating the membrane to a tensionless configuration, after which the projected membrane area (A_p = L_xL<sub<y</sub>) is practically constant. Consequently, the different schemes resulted in minor changes in the longest wavelength modes that we tracked down to small changes in the negligible tension. The resulting measurements of bending modulus change by less than 10%, and our main text conclusions do not change. Author response image 2 compares the fluctuation spectrums for the different integration schemes.

Author response image 2.

Height fluctuation spectrum, for a bilayer membrane at T_eff =1.1, simulated with Langevin dynamics (pink, ‘langevin‘), our setup (purple, ‘nph+langevin‘), and under an isothermal-isobaric ensemble (blue, ‘npt‘); fits are shown as dotted lines.

R3.Q4: This issue is reinforced when considering Figure 3B. These results suggest that increasing the fraction of regular lipids increases the tilt modulus, with the maximum value achieved for a normal Cooke-Deserno bilayer void of bolalipids. But this is contradictory. For these bilayers, we don’t need the tilt modulus in the first place.

We understand the concern why this might be counter-intuitive, and we thank the reviewer for pointing it out. We first want to stress that the tilt modulus can also be measured for bilayer membranes even if it is not needed to fit the fluctuation spectrum. If we measure the tilt modulus for a bilayer membrane, we obtain a value similar to the previously measured one [36]. Importantly, here we also report measurements for the tilt modulus for bolalipid membranes.

To understand the seemingly contradictory behaviour of the tilt modulus, it is insightful to rewrite the expression for the fluctuation spectrum as done in Eq. (1):

where is a characteristic length scale related to tilt, which we call the tilt persistence length. From the last equation it is easy to see that the tilt modulus 𝜅_𝜃 becomes relevant for the fluctuation spectrum if the tilt persistence length l_𝜃  is not negligible. In other words, this means that we have to consider the tilt modulus 𝜅_𝜃 as relevant, if it is sufficiently small compared to the bending rigidity 𝜅.

However, this is not only counter-intuitive, but also difficult to communicate graphically. Per the excellent reviewer’s suggestion, to make the interpretation more accessible, we converted in the main text and its figures the tilt modulus to the more directly interpretable tilt persistence length l_𝜃, as this is small when tilt is irrelevant (for bilayer lipids and flexible bolalipids) and large otherwise (for rigid bolalipids). This includes changes to the main text on p.6 and p.8 , and to the insets in Figs. 2C and 3B. We note that for completeness we also report the tilt modulus 𝜅_𝜃  in the SI.

R3.Q5: Also, from the SI, I gathered that the authors have neglected the longest wavelength mode because it is not equilibrated. If this is indeed the case, it is a dangerous thing to do, because with a small membrane patch, this mode can very well change the general trend of the power spectrum. As a lot of other analyses in the manuscript rely on these measurements, I believe more elaboration is in order.

We thank the reviewer for the careful examination of our supplementary material. For each fluctuation spectrum measurement, we ran multiple replicas. We observed that the largest wavelength modes were not fully equilibrated. In the simulations the first mode of the fluctuation spectrum is probed at different amplitudes and phases. We thus expected the potential systematic error would show up clearly when comparing spectrums of the different replicas. As we saw no correlation in these systematic offsets between replicas, we concluded that the simulations are sufficiently equilibrated and we could safely exclude the first mode of the fluctuation spectrum from our analysis.

To show without doubt that this procedure does not randomly bias our results, we also ran simulations for three representative membranes until all modes were equilibrated. On the modes previously equilibrated, the resulting spectrums agree with our previous shorter simulations. On the largest wavelength modes that were previously not fully equilibrated, we noticed a small deviation from theory, specifically for flexible membranes (small bending modulus). These small deviations can be explained by including a negligible negative tension. Importantly, however, the resulting bending modulus σ stays nearly the same. We note that the small negative tension disappears when we halve the timestep (see Author response image 3). This verification is shown in SI section 17.

R3.Q6: The authors have found that ”there is a strong dependency of the bending rigidity on the membrane mean curvature of stiffer bolalipids.” The effect is negative, with the membrane becoming less stiff at higher mean curvatures. Why is that? I would assume that with more flexible bolalipids, the possibility of reorganization into U-shaped chains should affect the bending rigidity more (as Figure 2E suggests). While for a stiff bolalipid, not much would change if you increase the mean curvature. This should be either a tilt effect, or have to do with asymmetry between the leaflets. But on the other hand, the tilt modulus is shown to decrease with increasing bolalipid rigidity. The authors get back to this issue only on page 10, when they consider U-shaped lipids in the inner and outer leaflets and write, ”this suggested that an additional membrane-curving mechanism must be involved.” But then again, in the Discussion, the authors write, ”It is striking that membranes made from stiffer bolalipids showed a curvature-dependent bending modulus, which is a clear signature that bolalipid membranes exhibit plastic behaviour during membrane reshaping,” adding to the confusion.

Author response image 3.

Height fluctuation spectrum, for a bilayer membrane at T_eff =1.1, as simulated in the main text (grey, for 60⇥10³τ), for longer duration (1_.44⇥10⁶τ) (pink), and with the longer duration and halved timestep =0.005_τ(purple); fits are shown as dotted lines (tension and tilt) or dash-dot lines (tilt only).

We thank the reviewer for asking this important question. Membrane bending rigidity in bolalipid membranes decreases dramatically once a small fraction of U-shapes is allowed to form, but then plateaus once this U-shape fraction reaches 20%. In a curved bolalipid membrane, U-shapes must accumulate in the outer leaflet to accommodate for area difference. Together, the bending rigidity non-linear dependence on U-shape fraction, and the promotion of U-shapes by curvature, explain why in a membrane made of moderately stiff bolalipids (k_bola = 1k_BT), which contain very few U-shapes in the flatstate, the bending rigidity of the membrane decreases as curvature increases. While in a membrane made of flexible bolalipid molecules (k_bola = 0), where many U-shapes are present in the flat membrane, the bending rigidity does not change with curvature.

Bending rigidity 𝜅 in flat membranes composed of bolalipids decreases dramatically once a small fraction of U-shapes is allowed to form, but plateaus once more than 20% of U-shaped bolalipids are present. In details, our data shows that with an increasing bolalipid molecular rigidity k_bola, both the number of U-shaped bolalipids decreases (Fig. 2B) and the membrane rigidity 𝜅 increases (Fig. 2C). Thus, the correlation suggests that U-shaped bolalipids soften the membrane, in a non-linear way where most of the change in membrane bending rigidity happens for U-shaped bolalipid fraction < 20% (Figure S11).

Separately, membrane curvature affects the area difference between curved membrane leaflets and thus drives U-shape accumulation. To be specific, a cylindrical membrane with area A, mean curvature H and thickness h has the outer leaflet with area A(1 + Hh) and the inner leaflet with smaller area A(1 Hh). This can be large, in our simulations up to an area change of Hh \= 25%. For pure bolalipid membranes, straight bolalipids occupy the same space in each leaflet. Area difference can then be achieved only by having a different amount of U-shaped bolalipids in each leaflet, which can result in a different U-shape fraction between leaflets and thus ’asymmetry between leaflets’. Figure S10 confirms U-shape head fraction asymmetry that increases with curvature, for both flexible (k_bola = 0) and moderately stiff bolalipids (k_bola = 1k_BT).

Together, these two effects result in membrane softening under curvature for the moderately stiff bolalipids, but constant rigidity for flexible bolalipids (Fig. 2F). In details: for membranes composed of moderately stiff bolalipid molecules (k_bola = 1k_BT), the U-shape bolalipid head fraction only increases in the outer leaflet, goingfrom10to20%(Figure S10). This is in the high sensitivity region where the bending rigidity is expected to change the most (Figure S11). We hypothesize that the molecular rigidity of a U-shaped bolalipid creates compression on the outer leaflet that stabilizes the membrane curvature and thus causes membrane softening. We suspect that for membranes composed of rigid bolalipids (k > 1k_BT), the effect is likely not present due to the absence of U-shape formation even under strong bending.

By contrast, for membranes composed of flexible bolalipids (k = 0), the U-shaped bolalipid head fraction changes relatively little from its value for flat membranes (from 50% to respectively 60 and 40% for the outer and inner leaflet, Figure S10). This is in the region where the membrane bending rigidity is expected to respond weakly to U-shape fraction (Figure S11). Additionally, the change is symmetric, so presumably the outer leaflet becomes softer as the inner leaflet becomes stiffer, thus creating opposing effects and only weakly affecting the membrane bending rigidity as a whole. We note that the distinction between the U-shape head fraction that we plot (Figure S10) and U-shape fraction (Figure S11) matters little for this analysis.

We have added this deduction and its plots to SI section 8, and revised the corresponding statement in the main text accordingly (p.7 ).

“Changing membrane curvature alters the area differently in the two membrane leaflets. To adapt to the area difference, we thus expect the fraction of U-shaped bolalipids to change as the membrane curvature changes. Moreover, the results of Fig. 2B and Fig. 2C showed that the U-shaped bolalipid fraction and the membrane bending rigidity are correlated. As a result, we predict that the fraction of straight versus U-shaped bolalipids in a membrane will change in response to membrane bending, in a way that makes the bending rigidity of a bolalipid membrane curvature dependent.”

R3.Q7: This issue is repeated when the authors study nanoparticle uptake. They write: ”to reconcile these seemingly conflicting observations we reason that the bending rigidity, similar to Figure 2F, is not constant but softens upon increasing membrane curvature, due to dynamic change in the ratio between bolalipids in straight and U-shaped conformation. Hence, bolalipid membranes show stroking plastic behaviour as they soften during reshaping.” But the softening effect that they refer to, as shown in Figure 4B, occurs for very stiff bolalipids, for which not much switching to U-shaped conformation should occur.

We thank the reviewer for locating a particularly dense sentence. We changed the text to explicitly refer to the range k 2 [0,2] k_BT for which there is significant change in U-shape fraction (p.8 ):

“To reconcile these seemingly conflicting observations we reason that the bending rigidity κ, similar to Fig. 2F, is not constant but softens in the range k 2 [0,2] k_BT, upon increasing membrane curvature. This is due to the dynamic change in the ratio between bolalipids in straight and U-shaped conformation.”

As for Fig. 4B, for k > 2k_BT, pores form thus explaining the plateau in adsorption energy.

R3.Q8: Another major issue is with what the authors refer to as the ”effective temperature”. While plotting phase diagrams for kT/eps value is absolutely valid, I’m not a fan of calling this effective temperature. It is a dimensionless quantity that scales linearly with temperature, but is not a temperature. It is usually called a ”reduced temperature”. Then the authors refer to their findings as studying the stability of archaeal membranes at high temperatures. I have to disagree because eps is not the only potential parameter in the simulations (there are at least space exclusion and angle-bending stiffnesses) so one cannot identify changing eps with changing the global simulation temperature. This only works when you have one potential parameter, like an LJ gas.

We indeed thought about this before and found that it makes little difference in our set-up. To thoroughly show that the distinction matters very little, per reviewer’s question, we computed our phase diagrams by scaling temperature T explicitly (and not lipid tail interactions T_eff = k_BT /ϵ_p). We added these results to the SI section 14 and found no significant difference when comparing scaling tail interactions (Figure S15A) with scaling temperature explicitly (Figure S15B).

We also computed Fig. 2A-C for scaling interactions (Figure S17A) and scaling temperature explicitly (Figure S17B). We found a slightly increased U-shaped bolalipid fraction for low k when comparing scaling interactions (Figure S17A) with temperature scaling (Figure S17B). The reason is that the U-shaped fraction depends on temperature, as with higher temperature bolalipids can easier transition into the U-shape. Most importantly, however, we found no qualitative changes on the liquid region or the mechanical membrane properties when we compared the different scaling variants.

The reason why both scaling variants match so well can be understood easily. All pair potentials, including volume exclusion interactions between head beads and other membrane beads, were also scaled in the same manner as tail-to-tail interactions, as described in the SI. In contrast, the energy scales for maintaining the lipid bonds, the bilayer lipid angles and the bolalipid angles are relatively large compared to the energy scales involved in tail-to-tail interactions. This separation of energy scales guarantees that there will be little effect when increasing global temperature. Regarding nomenclature, we take the reviewer’s advice and have added ’reduced temperature’ as an alias for T_eff in the main text.

In the revised version of the manuscript, we mention these observations in the SI section 14 and point towards these results in the main text (p.4 ):

“This interaction strength governs the membrane phase behaviour and can be interpreted as the effective temperature or reduced temperature T_eff = k_BT /ϵ_p. As the distinction between scaling interactions (T_eff) or temperature (T) is not important for our analysis (see Supplemental Information (SI) section 14), for simplicity we refer to T_eff as temperature in the following.”

Minor issues

R3.Q9: As the authors have noted, the fact that the membrane curvature can change the ratio of U-shaped to straight bolalipids would render the curvature elasticity non-linear (though the term ”plastic” should not be used, as this is still structurally reversible when the stress is removed. Technically, it is hypoelastic behaviour, possibly with hysteresis.) With this in mind, when the authors use essentially linear elastic models for fluctuation analysis, they should make a comparison of maximum curvatures occurring in simulations with a range that causes significant changes in bolalipid conformational ratios.

We thank the reviewer for their suggestion on calling the non-linear behaviour of the curvature elasticity hypoelastic. We have edited the main text accordingly (p.8 ):

“In an elastic material, the strain modulus holds constant and deformation is reversible. For bolalipid membranes at k = 1k_BT, however, the bending modulus decreases when deformation increases, rendering bolalipid membranes hypoelastic.”

Moreover, regarding the maximum curvatures occurring in the fluctuation simulations: We first note that the ensemble average of the mean curvature H from the fluctuation measurements is indicated as a vertical line in Fig. 2F. As the average value is nearly zero, the membrane can be considered as flat in good approximation. To investigate the question in more detail, we extended the SI with a careful analysis of the validity of the maximum membrane curvature and the validity of the Monge gauge approximation (SI section 15).

In short, we found that the involved membrane curvatures are small and therefore are unlikely to trigger any significant changes of the bending modulus. Moreover, since we are dealing with two bolalipid conformations, we also tested the homogeneity of the membrane. In our simulations of flat membrane patches we did not observe clustering or phase separation between the two bolalipid conformations beyond the [2,3]σ range. Furthermore, we get good agreement between our fluctuation measurement and the cylinder simulations in Fig. 2F. We now mention this verification in the revised version of the manuscript (p.8 ):

“Fortunately, this dependency on curvature does not invalidate our fluctuation results, where the curvature is small enough that its effect on the bending modulus is negligible (SI section 15).”

Last but least, simulating bending/unbending cycles of an arc-shaped membrane (frozen endpoints) shows agreement with cylinder membrane simulations, and no hysteresis at the rates of deformation employed (cf. M. Amaral’s thesis [54], soon to be out of the embargo period).

R3.Q10: The Introduction section of the manuscript is written with a biochemical approach, with very minor attention to the simulation works on this system. Some molecular dynamics works are only cited as existing previous work, without mentioning what has already been studied in archaeal membranes. While some information, like the binding of ESCRT proteins to archaeal membranes, though interesting, helps little to place the study within the discipline. The Introduction should be revised to show what has already been studied with simulations (as the authors mention in the Discussion) and how the presented research complements it.

The present research for the first time covers archaeal membranes with a single coarse-grained model capable of assuming both bolalipid in-membrane conformations and sweeps through temperature, membrane composition, and molecular rigidity. The work shows the first curvature dependent bending modulus for pure bolalipid membranes. It also investigates systematically bending modulus and Gaussian modulus, and tests the model in an all-encompassing budding simulation that incorporates topology changes. Existing atomistic or coarse-grained MD simulations (MARTINI or similar force fields) are limited to small patches of membrane, with no study of large-scale deformations or topology changes; plus, they rely on force fields that were parametrized for bilayer membranes.

To give a comprehensive overview of the field, we revised the introduction section of the manuscript, in which we now discuss previous computational work investigating membrane diffusivity, U-shaped lipid fraction, and bending rigidity (p.3 ):

“By contrast, only a few studies have investigated bolalipid membranes applying computational or theoretical tools [24, 25]. Specifically, the pore closure time in bolalipid membranes, and the role of cyclopentane rings for membrane properties has been investigated using all-atom simulations, showing decreased lateral mobility, reduced permeability to water, and increased lipid packing [26–28]. Moreover, using coarse-grained simulations, it was suggested that bolalipid membranes are thicker [29], exhibit a gel-to-liquid phase transition at higher temperature [30], and exhibit a reduced diffusivity [31]. However, little research has been devoted to investigating mechanics and reshaping of bolalipid membranes at the mesoscale despite the obvious importance of this question from evolutionary, biophysics, and biotechnological perspectives and although different membrane physics is expected to manifest.”

Following the reviewer’s advice and to keep the introduction concise and focused on bolalipid membranes, we have removed the paragraph on ESCRT-III proteins in the revised manuscript.

R3.Q11: The authors have been a bit loose with using the term ”stability”. I’d like to see the distinction in each case, as in ”chemical/thermal/mechanical/conformational stability”.

We have clarified when applicable the type of stability throughout the manuscript. In all other instances, if not clear from context, we mean simply that the membrane persists being a membrane. At our coarse-grained level, this means the membrane does not disassemble into a gas phase.

R3.Q12: In the original Cooke-Deserno model, a so-called ”poorman’s angle-bending term” is used, which is essentially a bond-stretching term between the first and third particle. However, I notice the authors using the full harmonic angle-bending potential. This should be mentioned.

This is made clear in the SI (Eq. (S3)). Cooke and Deserno mention the harmonic angle potential as a valid alternative in their original publication. We now also added this detail to the main text (p.3 ):

“The angle formed by the chain of three beads is kept near 180° via an angular potential with strength k₀, instead of the approximation by a bond between end beads of the original model [32].”

R3.Q13: The analysis of energy of U-shaped lipids with the linear model E \= c₀ + c₁k is indeed very interesting. I am curious, can this also be corroborated with mean energy measurements? The minor issue is calling the source of the favorability of U-shaped lipids ”entropic”, while clearly an energetic contribution is found. The two conformations, for example, might differ in the interactions with the neighbouring lipids.

We were also curious and thank the reviewer for the suggestion of mean energy measurements. We concluded that there must be either an entropic contribution to the free energy or an intermolecular interaction energy favouring U-shaped bolalipids. We have now included these measurements in SI section 6 (p.S5 ):

“By splitting the average potential energy between an internal contribution (bonds, angles and pair interactions between particles in the same molecule) and an external contribution (pair interactions between a molecule and its neighbours), we determined the transition energy from straight to U-shaped bolalipids in detail. We found that this transition lowers the internal potential energy of the bolalipid while increasing its interaction energy. In total, we obtained an energy barrier for the transition of ΔE_s→u = 0.79±0.01k_BT. Since the fit indicates, however, that the U-shaped bolalipid conformation is preferred over the straight conformation, we conclude that there must be either an entropic contribution to the free energy or an intermolecular interaction energy favouring U-shaped bolalipids.”

We refer to these measurements in the main text (p.6 ):

“For the fit it appears that c₀ < 0, which implies that bolalipids in U-shape conformation are slightly favoured over straight bolalipids at k = 0 (explored in SI section 6).”

R3.Q14: The authors write in the Discussion, ”In any case, our results indicate that membrane remodelling, such as membrane fission during membrane traffic, is much more difficult in bolalipid membranes [34].” Firstly, I’m not sure if studying the dependence of budding behaviour on adhesion energy with nanoparticles is enough to make claims about membrane fission. Secondly, why is the 2015 paper by Markus Deserno cited here?

We thank the reviewer for giving us the opportunity to clarify. We make an energetic argument on membrane fission based on the observed difference in the ratio of .

Splitting a spherical membrane vesicle into two spherical vesicles (fission) increases the bending energy by 8𝜋𝜅 and decreases the energy related to the Gaussian bending modulus by . The second part of the argument is given for example in the review by Markus Deserno (p.23, right column), that’s why we cite the paper here. Together, this gives an energy barrier, required for membrane fission in the considered geometry of ∆E_fission = . We found that is around 0.5 for bolalipid membranes and around 1 for bilayer membranes. Since 𝜅 was typically larger in bolalipid membranes we thus expect the energy barrier for fission ∆E_fission to be larger for bolalipid membranes. We therefore predict that membrane remodelling, such as membrane fission during membrane trafficking, is harder in bolalipid membranes. We explain our reasoning in the discussion of the revised manuscript (p.13 ):

“Membrane remodelling, such as the fission of one spherical vesicle into two, increases the bending energy by 8πκ but decreases the energy related to the Gaussian modulus by – [39], giving rise to a fission energy barrier of ∆E_fission = . Our results indicated that while in bolalipid membranes 𝜅 is larger, is smaller compared to bilayer membranes. Our results thus predict a larger energy barrier for membrane fission ∆E_fission in bolalipid membranes compared to bilayer membranes.”

R3.Q15: In the SI, where the measurement of the diffusion coefficient is discussed, the expression for D is missing the power 2 of displacement.

We thank the reviewer for spotting this oversight. We corrected it in the revised version of the SI (p.S5 ).

R3.Q16: Where cargo uptake is discussed, the term ”adsorption energy” is used. I think the more appropriate term would be ”adhesion energy”.

For the sake of simplicity, we changed the term to adhesion energy (caption of Fig. 4, and p.10). We do not have a strong opinion on this, but we believe that adsorption energy would be equally correct as we describe the adsorption of many lipid head beads to a nanoparticle.

R3.Q17: Typos:

Page 1, paragraph 2: Adaption → Adaptation. Page 10, paragraph 1: Stroking → Striking.

We thank the reviewer for spotting these typos which we have corrected in the revised version of the manuscript.

Recommendations for the authors

Reviewer #1 (Recommendations for the authors):

A few thoughts (likely out of the scope of this paper but possibly to consider upon revision):

R1.Q1: Do bolalipids always have the same headgroup? I don’t recall reading this in the introduction/discussion. R1 and R2 are in Figure 1, but I don’t know whether there are standard types. Could this be expanded upon? Is the model able to take these differences into account?

We thank the reviewer for raising this important question. Similar to bacteria and eukaryotes, in archaea there is a huge variety in terms of the different head groups that lipids can contain and thus also lipid variety. Most archaeal lipids have head groups that contain either phosphate groups or sugar residues. Typically, archaeal bolalipids are asymmetric and contain a phosphatidyl and a sugar moiety at the two ends of the lipid molecule. Within the membrane the lipid is oriented such that the phosphatidyl moiety points towards the interior of the cell whereas the sugar moiety points towards the outside of the cell as it occupies more space [5].

In our computational model, however, we consider symmetric bolalipids for the sake of simplicity and to decouple the role of ”connected geometry” from other effects. In principle, we could investigate the effect of lipid asymmetry by increasing the size of one of the lipid head beads. However, this investigation exceeds the scope of the present study and therefore requires future work.

In the revised version of the manuscript, we now clarify that bolalipids can have different headgroups (p.1 and the caption of Fig. 1):

“The hydrophilic heads can be composed of different functional groups with phosphatidyl and sugar being the most relevant moieties. For bolalipids the two head groups at either end of the molecule are typically distinct (Fig. 1A right) [5].”

“The hydrophilic head of a bolalipid can be composed of different functional groups represented by R1 and R2 (right).”

We also explicitly state that we neglect lipid head group asymmetry for the sake of simplicity (p.4 ):

“To decouple the effect of the connected geometry of the bolalipids from that of lipid asymmetry, we assume both head beads of a bolalipid to share the same properties.”

R1.Q2: Is it possible to compare the mesoscale models to either Coarse-grained or even all-atom lipid models? Have simulations previously been performed for bolalipids at those levels of description?

A few studies have investigated bolalipids membranes in simulations previously. These studies either used all-atom or coarse-grained simulations. However, none of these studies investigated how bolalipids respond to membrane deformations. Therefore, it is currently not possible to directly compare our results to studies in the literature. However, to recapitulate our predictions experimentally is certainly something that could and should be done in the future. As a reply to this reviewer and reviewer 3, we discuss the current state of modelling bolalipid membranes in simulations in the revised version of the manuscript (p.3 ):

“By contrast, only a few studies have investigated bolalipid membranes applying computational or theoretical tools [24, 25]. Specifically, the pore closure time in bolalipid membranes, and the role of cyclopentane rings for membrane properties has been investigated using all-atom simulations, showing decreased lateral mobility, reduced permeability to water, and increased lipid packing [26–28]. Moreover, using coarse-grained simulations, it was suggested that bolalipid membranes are thicker [29], exhibit a gel-to-liquid phase transition at higher temperature [30], and exhibit a reduced diffusivity [31]. However, little research has been devoted to investigating mechanics and reshaping of bolalipid membranes at the mesoscale despite the obvious importance of this question from evolutionary, biophysics, and biotechnological perspectives and although different membrane physics is expected to manifest.”

We want to mention, however, that we do compare membrane diffusivity, U-shaped lipid fraction, and bending rigidity to the behaviour and values that have been previously measured in simulations in the discussion section. In general, we find good agreement between our results and previously reported behaviour/values (p.13 ):

“While flexible bolalipid membranes are liquid under the same conditions as bilayer membranes, we found that stiff bolalipids form membranes that operate in the liquid regime at higher temperatures. These results agree well with previous molecular dynamics simulations that suggested that bolalipid membranes are more ordered and have a reduced diffusivity compared to bilayer membranes [24, 29]. In our simulations, this is due to the fact that completely flexible bolalipids molecules adopt both straight (transmembrane) as well as the U-shaped (loop) conformation with approximately the same frequency. In contrast, stiff bolalipids typically only take on the straight conformation when assembled in a membrane. These results agree with the previous coarse-grained molecular dynamics simulations using the MARTINI force field which showed that the ratio of straight to U-shaped bolalipids increased upon stiffening the linker between the lipid tails [29].

[...]

When we determined the bending rigidity of bolalipid membranes by measuring their response to thermal fluctuations, we found that membranes made from flexible bolalipids are only slightly more rigid than bilayer membranes. This result is consistent with previous atomistic simulations, which showed that the membrane rigidity was similar for membranes composed of bilayer lipids and flexible synthetic bolalipids [45].”

R1.Q3: How would membrane proteins alter the behaviour of bolalipids? Either those integral to the membrane or those binding peripherally?

The reviewer asks an important question. However, the question is difficult to answer due to its scope and the gaps in the current literature. Important examples of integral or peripheral membrane proteins that alter the behaviour of bolalipids and archaeal bolalipid membranes are involved in cell homeostasis, cell division, membrane trafficking, and lipid synthesis.

The cells of many archaeal species are enclosed in a paracrystalline protein layer called the Slayer, which is attached to the lipid membrane [4, 55]. The main function of the S-layer is to keep the cell’s shape and to protect it against osmotic stress. Due to the embedding of the S-layer in the membrane at specific locations, it is to be expected that the membrane properties are influenced by the S-layer. Furthermore, archaea execute cell division by locally reshaping the membrane using FtsZ and ESCRT-III proteins [56]. While Asgard archaeal genomes encode proteins with homology to those regulating aspects of eukaryotic membrane remodelling and trafficking [57], they have yet to be observed undergoing a process like endocytosis [58]. In addition, it has been speculated that the proteins that drive the synthesis of two diether lipids into a tetraether lipid are either membrane associated or integral membrane proteins [59].

However, to the best of our knowledge it is not known how membrane proteins specifically alter the behaviour of bolalipids. Future work will need to be executed to answer this question. Following the advice of reviewer 3 and to keep the introduction concise and focused on bolalipid membranes, we do not mention these observations in the revised manuscript.

R1.Q4: Is there a mechanism in cells to convert or switch bolalipids from a straight to a u-shaped description? Does this happen spontaneously or are there enzymes responsible for this?

We thank the reviewer for bringing up this important point. Despite the relevance of the question, little is currently known about the mechanism that make bolalipids transition between a straight and a U-shaped configuration mainly because there is to date no established experimental method.

Besides our own results, most of what we know comes from coarse-grained molecular dynamics simulations, which showed that bolalipids can spontaneously transition between the straight and U-shaped configuration [29]. In addition, by using comparative genomic analysis, it has been predicted that many archaeal species contain flippases, i.e., membrane proteins that are able, upon the consumption of energy, to transfer (flipflop) bilayer lipids between the two membrane leaflets [43]. Moreover, it has been shown that Halobacterium salinarum (an archaeon with a bilayer lipid membrane) [44] contains scramblases, which are membrane proteins that passively transfer bilayer lipids from one membrane leaflet to the other. It is therefore tempting to speculate that similar proteins might exist for bolalipids which could facilitate the straight to U-shaped transition.

In addition, it has been reported that vesicles composed of bolalipid membranes can undergo fusion with enveloped influenza viruses [17]. In this context, it has been suggested that the influenza fusion protein hemagglutinin may locally induce U-shaped bolalipids to facilitate membrane fusion. However, all these hints are by far no proof of a mechanism that can drive the straight to U-shaped bolalipid transition, and further work needs to be done to investigate this question in detail.

In the revised version of the manuscript, we now discuss what is known about potential mechanisms to facilitate the straight to U-shaped transition in the discussion section (p.13 ):

“While previous coarse-grained simulations predicted that bolalipids spontaneously transition between the straight and U-shaped conformations [29], how this happens in archaeal membranes and whether membrane proteins are involved in this conformational transition needs to be clarified in the future. Experimental studies suggest that archaeal membranes contain flippases and scramblases for the transitioning of bilayer lipids between membrane leaflets [43, 44], raising the possibility that similar proteins could also facilitate conformational transitions in bolalipids. In addition, it has been suggested that the viral fusion protein hemagglutinin could cause a transition from straight to U-shaped bolalipid conformation during the fusion of bolalipid vesicles with influenza viruses [17]. However, future investigation is required.”

R1.Q5: Ideally, coordinates and any parameter files required to run the molecular simulations should be included for reproducibility.

We absolutely share the reviewer’s concern with reproducibility and as such have included in the original submission as part of our data availability section a link to a code repository (available at: https://doi.org/10.5281/zenodo.13934991 [51]) that allows initializing and simulating flat membrane patches, with user control of the parameters explored in this paper (𝜔,T_eff,k_bola,f^bi).

Reviewer #2 (Recommendations for the authors):

This is a great paper and I congratulate the authors for writing such a fine piece of scholarship. The only nitty-gritty feedback that I have is summarized in the following three points:

R2.Q1: In the introduction the authors talk about archaea adapting their membrane to retain membrane fluidity. However, homeoviscous adaptation is also fundamental in bacteria and eukaryotes.

The reviewer is correct, like archaea the membranes of bacteria and eukaryotes must balance between flexibility and stability. Moreover, the cell membranes in all 3 domains of life need to maintain membrane fluidity and provide mobility to the embedded lipids and membrane proteins (homeoviscous adaptation). The general idea is that these organisms change the ratio of different lipids to change membrane properties and thereby optimally adapt to their environments [10]. Importantly, however, there are differences of how homeoviscous adaptation is maintained across the different domains of life. As a reply to this reviewer and reviewer 3, we now discuss the underlying mechanisms in the revised parts of the introduction (p.1 ):

“Like for bacteria and eukaryotes, archaea must keep their lipid membranes in a fluid state (homeoviscous adaptation). This is important even under extreme environmental conditions, such as hot and cold temperatures, or high and low pH values [7]. Because of this, many archaea adapt to changes in their environment by tuning the lipid composition of their membranes: altering the ratio between bola- and bilayer lipids in their membranes [8, 9] and/or by changing the number of cyclopentane rings in their lipid tails, which are believed to make lipid molecules more rigid [5]. For example, Thermococcus kodakarensis increases its tetraether bolalipid ratio from around 50% to over 80% when the temperature of the environment increases from 60 to 85 C [10]. Along the same lines, the cell membrane of Sulfolobus acidocaldarius, can contain over 90 % of bolalipids with up to 8 cyclopentane rings at 70 C and pH 2.5 [5, 11]. It is worth mentioning that in exceptional cases bacteria also synthesise bolalipids in response to high temperatures [12], highlighting that the study of bolalipid membranes is relevant not only for archaeal biology but also from a general membrane biophysics perspective.”

R2.Q2: Uncertainties in Gaussian rigidity modulus estimates are not properly reported.

The large uncertainties in the Gaussian rigidity modulus were due to the fact how they were calculated. In short, is determined in cap folding simulations [41] (SI section 9), by using the measured values of the dimensionless parameter 𝜉, related to the folding probability, the bending modulus 𝜅, the membrane line tension , and the cap radius R. In our case, the main source of uncertainty for determining comes from the uncertainty in the measurement of the bending rigidity 𝜅. To obtain 𝜅, previously, we fitted fluctuation spectra for different seeds and only then averaged the obtained values. In the revised version of the manuscript, we now first pool the fluctuation spectra of the different simulation seeds before we fit all spectra at the same time. This new approach results in smaller uncertainties for the bending rigidity 𝜅 and also the Gaussian rigidity modulus .

As a consistency check, in addition to the simulations that we previously performed at T_eff = 1.3, we have repeated the cap folding and line tension simulations at T_eff = 1.2, resulting in similar values for . In the revised version of the manuscript, we report the newly calculated values and uncertainties for at T_eff  = 1.2 in the main text (p.8 ):

“At T_eff  = 1.2, we obtained = 4.30±0.22kBT and thus a ratio of = 0.89±0.04 for bilayer membranes, similar to what has been reported previously [41]. For flexible bolalipid membranes, we got a slightly smaller value for = 5.04 ± 0.37kBT. Due to the larger bending modulus, however, flexible bolalipid membranes show a significantly smaller ratio = 0.64± 0.04 (k = 0). At larger temperature (Teff = 1.3), the ratio can be even smaller = 0.45 ± 0.07 (see SI section 9).”

In addition, we report the values at T_eff = 1.3 and T_eff = 1.2 in the SI (p.S15 , Tabl. S4):

We have also adapted the discussion of the Gaussian bending modulus accordingly (p.13 ):

“Another marked difference between bilayer and flexible bolalipid membranes is the ratio of the Gaussian rigidity to the bending modulus. Instead of being around 1 as for bilayer membranes [41], it is around 1/2 and therefore only half of that of bilayer lipids.”

Reviewer #3 (Recommendations for the authors):

While I think the bulk of the work presented is useful, some of the issues that I raised in my review are indeed major. Without properly addressing them, it is hard to accept the conclusions of the manuscript. I hope the authors can address them by revising their analysis.

We thank the reviewer for their constructive feedback, which helped us to improve the manuscript. We have addressed all points raised by the reviewer in our detailed point-by-point response to the reviewer (see above). We hope the reviewer will now find it easier to accept our conclusions.

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Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

This manuscript by Liu et al explores the role of the UPR and immune regulators in the evaluation of nutritional quality in C. elegans. They identify neuronal UPR activation and the MAPK PMK-1 as key responders to low food quality. In particular, the data suggest that these pathways are activated by low levels of vitamin C synthesis that result from the low sugar levels present in heat-killed E. coli.

Strengths:

The results are intriguing and expand our understanding both of physiological food evaluation systems, and of the known roles of stress response pathways in organismal physiology. The authors use a range of techniques, encompassing imaging, metabolomic analysis, gene expression analysis, and behavioural assays, to support their claims.

Thank you for your thorough review and acknowledgment of the strengths of our study.

Weaknesses:

There is limited mechanistic analysis in the study. In particular, how does low vitamin C trigger UPR activation? This is an intriguing finding that, if followed up, could potentially reveal a novel mechanism of UPR activation. In addition, how is the activation of the PMK-1 pathway driven by/coordinated with UPR activation? The data in some figures is not as convincing as it could be: the magnitude of the effect size is small in the supplementation experiments, and the statistical tests used are not always appropriate to enable multiple comparisons.

(1) There is limited mechanistic analysis in the study. In particular, how does low vitamin C trigger UPR activation? This is an intriguing finding that, if followed up, could potentially reveal a novel mechanism of UPR activation. 

Thank you for highlighting the need for further mechanistic analysis in our study. We appreciate the opportunity to clarify the process by which low vitamin C triggers UPR activation.

Our investigation revealed that the vitamin C content in heat-killed E. coli (HK-E. coli) is comparable to that of live E. coli or HK-yfbR mutant E. coli (Figure 4-figure supplement 1A), indicating that the induction of unfolded protein response (UPR) in C. elegans by HK-E. coli is not solely attributed to low vitamin C levels but rather involves other unidentified factors.

Through metabolomic analysis, we observed significant decreases in sugar levels, including lactose, D-(+)-sucrose, and D-(+)-glucose, in HK-E. coli (Figure 3B, Table S1). Notably, supplementing D-(+)-glucose effectively inhibited UPRER, immune response, and avoidance behavior induced by HK-E. coli (Figure 3E-H). These findings suggest that the deficiency in sugars in HK-E. coli triggers a stress response and avoidance behavior in animals, which can be alleviated by D-(+)-glucose supplementation.

Furthermore, when comparing heat-killed E. coli mutant yfbR (HK-yfbR) to HK-E. coli, we observed significantly higher sugar levels, including lactose and D-(+)-sucrose, in HK-yfbR (Figure 3B). This was accompanied by reduced UPRER in animals feeding on HK-yfbR (Figure 3-figure supplement 1B), indicating that higher sugar levels may inhibit the induction of UPRER by low-quality food.

Considering that the synthesis of vitamin C (VC) occurs through the glucuronate pathway, utilizing D-glucose as a precursor 1, 2 (Figure 4A), we investigated whether the vitamin C biosynthesis pathway is involved in evaluating low-quality food using D-glucose. Contrary to our initial hypothesis, animals fed live E. coli did not exhibit higher glucose levels compared to those fed low-quality food (HK_-E. coli_). Our results indicate that animals maintain similar VC levels when fed ideal food (live E. coli) compared to low-quality food (HK-E. coli) (Figure 4B), suggesting that animals do not stimulate VC biosynthesis under favorable food conditions. However, supplementation of D-GlcA or E. coli-yfbR mutation in HK-E. coli significantly improved VC levels when animals were fed low-quality food (HK-OP50) (Figure 4B, 4C). Moreover, VC or D-glucuronate (D-GlcA) supplementation inhibited HK-E. coli-induced UPRER (Figure 4D), indicating that glucose boosts the animal's ability to adapt to unfavorable food environments by increasing VC levels, thereby inhibiting UPRER, but not under favorable food conditions.

These findings shed light on the complex interplay between vitamin C, sugar levels, and UPR activation, providing valuable insights into the mechanisms underlying food evaluation and stress response pathways in organisms.

Overall, we are grateful for the reviewer's constructive feedback, which motivates us to continue our efforts to understanding how the UPR response contributes to the complexities of food evaluation and behavioral responses in organisms.

(2) In addition, how is the activation of the PMK-1 pathway driven by/coordinated with UPR activation?

Thank you for your insightful inquiry. In our discussion section, we have addressed this question by integrating new data and discussion to provide insights into the coordination between PMK-1 pathway activation and UPR activation.

Previous studies have demonstrated that activating innate immunity, specifically the PMK-1 MAPK pathway, results in a reduction in translation3, as well as a shutdown of food digestion in animals4, likely aimed at reducing protein translation and cellular metabolism. To further investigate this relationship, we measured the translation level of animals fed with heat-killed E. coli (HK-E. coli) and found a significant reduction in total translation ability in these animals (Figure 5-figure supplement 1D). This observation suggests that activating innate immunity through the PMK-1 MAPK pathway may serve as a mechanism to slow down translation progress, thereby alleviating the pressure on the unfolded protein response (UPR) and preventing excessive UPRER activation.

By integrating these findings, we propose a model wherein activation of the PMK-1 pathway coordinates with UPR activation to regulate translation and cellular metabolism in response to low-quality food. This coordinated response likely serves to maintain cellular homeostasis and prevent detrimental effects associated with excessive UPRER activation.

These insights contribute to our understanding of the intricate interplay between innate immunity, cellular stress responses, and metabolic regulation in organisms facing nutritional challenges.

(3) The data in some figures is not as convincing as it could be: the magnitude of the effect size is small in the supplementation experiments, and the statistical tests used are not always appropriate to enable multiple comparisons.

We appreciate the reviewers' concerns regarding the data presentation and statistical analyses in some of our figures. In response to this feedback, we have made revisions to improve the robustness and clarity of our statistical methods.

All statistical analyses were conducted using GraphPad Prism 8.0 software. Specifically, a two-tailed unpaired t-test was employed for the statistical analysis of two groups of samples, while one-way or two-way ANOVA was utilized for the statistical analysis of more than two groups of samples. These adjustments ensure appropriate statistical comparisons and enhance the reliability of our findings.

Reviewer #2 (Public Review):

Summary:

In this work, the authors aim to better understand how C. elegans detects and responds to heat-killed (HK) E. coli, a low-quality food. They find that HK food activates two canonical stress pathways, ER-UPR, and innate immunity, in the nervous system to promote food aversion. Through the creative use of E. coli genetics and metabolomics, the authors provide evidence that the altered carbohydrate content of HK food is the trigger for the activation of these stress responses and that supplementation of HK food with sugars (or their biosynthetic product, vitamin C), reduces stress pathway induction and food avoidance. This work makes a valuable addition to the literature on metabolite detection as a mechanism for the evaluation of nutritional value; it also provides some new insight into the physiologically relevant roles of well-known stress pathways in modulating behavior.

Strengths:

-The work addresses an important question by focusing on understanding how the nervous system evaluates food quality and couples this with behavioral change. -The work takes full advantage of the tools available in this powerful system and builds on extensive previous studies on feeding behavior and stress responses in C. elegans.

-Creative use of E. coli genetics and metabolite profiling enabled the identification of carbohydrate metabolism as a candidate source of food-quality signals.

-For the most part, the studies are rigorous and logically designed, providing good support for the authors' model.

We deeply appreciate the reviewer's insightful assessment of our study's strengths. 

Weaknesses:

-It is not clear how the mechanism identified here is connected to previously described, related processes. In particular, it is not clear whether this mechanism has a role in the detection of other low-quality foods. Further, the specificity of the ability of sugar/vitamin C to suppress stress pathway induction is unclear (i.e., does sugar/vitamin C have any effect on the activation of these pathways through other means?). Additionally, the relationship of this pathway to the vitamin B2-sensing mechanism previously described by the senior author is unclear. These issues do not weaken confidence in the authors' conclusions, but they do reduce the potential significance of the work.

(1) In particular, it is not clear whether this mechanism has a role in the detection of other low-quality foods. 

Thank you for your valuable feedback. In response to your inquiry, we investigated whether the UPRER (IRE-1/XBP-1) - Innate immunity (PMK-1/p38 MAPK) axis is specific to evaluating low-quality food (HK-E. coli) or if it plays a broader role in food detection.

We conducted behavioral assays using N2, pmk-1, and xbp-1 mutant animals fed with normal E. coli food, inedible food (Saprophytic staphylococci)4, and pathogenic food (Pseudomonas aeruginosa-PA14)5. We found that N2, pmk-1, and xbp-1 mutant worms did not exhibit avoidance behavior when presented with normal food (OP50). However, both N2 and xbp-1 mutant worms were able to escape from inedible food (N2 was predominantly found on the border areas of the bacterial lawn and xbp-1 mutant worms on border and in), Saprophytic staphylococci, whereas pmk-1 mutant worms did not exhibit this avoidance behavior. Notably, N2 and xbp-1 mutant worms exhibited even more pronounced avoidance behavior when exposed to Pseudomonas aeruginosa, whereas pmk-1 mutant worms were more susceptible to infection by this pathogen (Figure 2-figure supplement 2C). These findings suggest that the UPR-Immunity pathway plays a crucial role in helping animals avoid low-quality food (HK-E. coli) by triggering an avoidance response. In contrast, the Innate immunity pathway, mediated by PMK-1/p38 MAPK, appears to play a key role in evaluating unfavorable food sources, such as HK-E. coli, Saprophytic staphylococci, and Pseudomonas aeruginosa, and helping animals avoid these environments.

(2) Further, the specificity of the ability of sugar/vitamin C to suppress stress pathway induction is unclear (i.e., does sugar/vitamin C have any effect on the activation of these pathways through other means?). 

Thank you for your inquiry regarding the specificity of the ability of sugar/vitamin C to suppress stress pathway induction. We aimed to address this question by investigating whether high levels of VC inhibit other stress-induced UPRER pathways.

Previous studies have shown that both Tunicamycin6 and pathogenic bacteria, such as Pseudomonas aeruginosa-PA145, induce UPRER in C. elegans. In response to your query, we conducted experiments to examine whether VC supplementation inhibits UPRER induced by these stressors. Our findings indicate that VC supplementation does not inhibit UPRER induced by either Tunicamycin or PA14 (Author response image 1).

These results suggest that while sugar/vitamin C may suppress stress pathway induction in the context of low-quality food, its effects may not extend to other stressors that induce UPRER through different mechanisms. This insight helps clarify the specificity of sugar/vitamin C's role in modulating stress pathway activation, contributing to a better understanding of the broader regulatory networks involved in stress response in C. elegans.

Author response image 1.

VC supplementation does not inhibit Tunicamycin or PA14-induced UPRER.

(3) Additionally, the relationship of this pathway to the vitamin B2-sensing mechanism previously described by the senior author is unclear.

In response to your comment, we would like to clarify the relationship of our pathway to the previously described vitamin B2-sensing mechanism we found. Previous studies have demonstrated that heat-killed E. coli (HK-E. coli) serves as a low-quality food source incapable of supporting the growth of C. elegans larvae, whereas supplementation with vitamin B2 (VB2) can restore animal growth7

This study investigates the role of sugar deficiency in HK-E. coli, which induces the UPRER-immune response and avoidance behavior in C. elegans. Surprisingly, our findings indicate that supplementing HK-E. coli with carbohydrates such as D-Glc and D-GlcA does not promote animal development (Figure 3-figure supplement 2G), suggesting that carbohydrates are not essential for supporting animal growth on this food source. However, we did observe that carbohydrates play a critical role in inhibiting the UPRER-immune response induced by sugar deficiency in HK-E. coli.

-The authors claim that the induction of the innate immune pathway reporter irg-5::GFP is "abolished" in pmk-1(RNAi) animals, but Figure S2K seems to show a clear GFP signal when these animals are fed HK-OP50. Similarly, the claim that feeding WT animals HK-OP50 enriches phospho-PMK-1 levels (Fig 2E) is unconvincing - only one western blot is shown, with no quantification, and there is a smear in the critical first lane.

(1) The authors claim that the induction of the innate immune pathway reporter irg-5::GFP is "abolished" in pmk-1(RNAi) animals, but Figure S2K seems to show a clear GFP signal when these animals are fed HK-OP50. 

We sincerely appreciate the reviewer's attention. To address this concern, we have replaced the images with higher resolution, larger ones in Figure 2-figure supplement 1-I. These updated images provide a clearer representation of the data, ensuring that all details are readily visible and enabling a more accurate interpretation of the results.

(2) Similarly, the claim that feeding WT animals HK-OP50 enriches phospho-PMK-1 levels (Fig 2E) is unconvincing - only one western blot is shown, with no quantification, and there is a smear in the critical first lane.

Thank you, following reviewer’s suggestion, we also repeated some of the western. We now replace the Figure 2E and quantified relative intensity of pPMK-1/tublin. We also provide the uncropped western blots images as source data ( “raw-data WB” file). 

-The rationales for some of the paper's hypotheses could be improved. For example, the rationale for screening the E. coli mutant library is that some mutants, when heat-killed, may be missing a metabolite that induces the ER-UPR. A more straightforward hypothesis might be that some mutant E. coli strains aberrantly induce the ER-UPR when *not* heat-killed, because they are missing a metabolite that prevents stress pathway induction. This is not in itself a major concern, but it would be useful for the authors to provide a rationale for their hypothesis.

Thank you for the insightful suggestion. We acknowledge the importance of providing a clear rationale for our hypotheses in the paper. In response to this feedback, we have enhanced the discussion section to better elucidate the rationale behind our hypotheses.

One limitation of our study is the lack of explanation for why HK-E. coli activates UPRER and immunity. We hypothesized that when heat-killed, HK-E. coli may lack or contain altered levels of certain metabolites that either activate or inhibit UPRER and immunity, respectively. Additionally, we speculated that E. coli mutants killed by heat may lack metabolites that activate UPRER and immunity, or conversely, have increased levels of metabolites that inhibit these pathways.

Fortunately, our investigation led to the discovery of the E. coli mutant yfbR, which inhibits UPRER and immunity by increasing carbohydrates that aid in resisting these stress pathways. Moving forward, we intend to further explore the intricate relationship between HK-E. coli and UPRER-immunity. This will be a key focus of our future research efforts.

-The authors do not provide any explanation for some unexpected results from the E. coli screen. Earlier in the paper, the authors found that innate immune signaling is downstream of ER-UPR activation. However, of the 20 E. coli mutants that, when heat-killed, "did not induce... the UPR-ER reporter," 9 of them still activate the innate immune response. This seems at odds with the authors' simple model since it suggests that low-quality food can induce innate immune signaling independently of the ER-UPR. Further, only one of the 9 has an effect on behavior, even though failure to activate the innate immune pathway might be expected to lead to a behavioral defect in all of these.

Thank you for your understanding, and we apologize for any confusion caused by our earlier statement. To provide clarification, our study revealed that out of the 20 E. coli mutants examined, none activated the UPRER. Among these mutants, 9 did not induce immunity, and interestingly, one out of these 9 mutants demonstrated the ability to inhibit avoidance behavior.

This diversity in phenotypic outcomes can be attributed to the varied metabolites present in different E. coli mutants. To thoroughly evaluate the effects of these mutants, we conducted a comprehensive three-step screening process, utilizing UPRER marker, immunity marker, and avoidance behavior assays.

Through this rigorous approach, we identified the E. coli mutant, yfbR, which exhibited the desired inhibitory effects on UPRER, immunity, and avoidance behavior.

Subsequently, we conducted a metabolomics analysis of various food qualities (HK-K12, HK-yfbR, and Live-K12). Our findings revealed higher sugar levels in

HK-yfbR and Live-K12 compared to HK-K12 (Figure 3B, Figure 3-figure supplement 2A, and Table S1), indicating that sugar deficiency might trigger the UPRER, immunity responses, and subsequent avoidance behavior. 

-In a number of places, the writing style can make the authors' arguments difficult to follow.

Thanks for the reviewer’s efforts. We changed all of these errors and polish the language of this paper. 

-Some of the effect sizes observed by the authors are exceedingly small (e.g, the suppression of hsp-4::gfp induction by sugar supplementation in Figs 3C-E), raising some concern about the biological significance of the effect.

Thank you for your feedback. In response to your concern, we have included additional clarification in the manuscript.

We have added the following statement: “While sugar effectively inhibits the HK-E. coli-induced UPRER and immune response, it does not fully suppress it to the extent observed with live-E. coli (Figure 3C-F). This implies that additional nutrients present in live-E. coli might also contribute to the inhibition of UPRER and immune response.”

This addition helps to address the observation that some effect sizes appear small, providing context and suggesting potential factors that may influence the outcomes. 

-In some cases, there is a discrepancy between the fluorescence images and their quantitation (e.g., Figure 3E, where the effect of glucose on GFP fluorescence seems much stronger in the image than in the graph).

Thank you for your valuable suggestion. In response, we have revised our image selection process to ensure impartiality. We now randomly select images to ensure they accurately represent the quantified data without bias. More details regarding this update can be found in Author response image 2.

Author response image 2.

More original picture corresponding to Figure 3E 

Reviewer #3 (Public Review):

Summary:

Animals can evaluate food quality in many ways. In contrast to the rapid sensory evaluation with smell and taste, the mechanism of slow nutrient sensation and its impact on food choice is unexplored. The authors utilize C. elegans larvae and their bacterial food as an elegant model to tackle this question and reveal the detailed molecular mechanism to avoid nutrient-poor foods.

Strengths:

The strength of this study is that they identified the molecular identities of the critical players in bacterial food and C. elegans using unbiased approaches, namely metabolome analysis, E. coli mutant screening, and RNA sequencing. Furthermore, they strengthen their findings by thorough experiments combining multiple methods such as genetics, fluorescent reporter analysis, and Western blot.

Thank you for highlighting the strengths of our study. 

Weaknesses:

The major caveat of this study is the reporter genes. The transcriptional reporters were used to monitor the UPRER and immune responses in the intestine of C. elegans.

However, their tissue-specific rescue experiments suggest that the genes in the UPRER and immune response function in the neurons. Thus, we should carefully interpret the results of the reporter genes.

Thank you for your insightful comment. We appreciate the opportunity to address your concerns regarding the interpretation of our reporter gene data.

Upon reevaluation, we observed strong induction of the UPRER reporter

(Phsp-4::GFP)8 and immunity reporter (Pirg-5::GFP)9 both in the intestine (Figure 1F-G) and in neurons (Figure 1-figure supplement 2A) in response to feeding unfavorable food (HK-E. coli). This suggests that both the UPRER and immune pathways may indeed respond to low-quality food (HK-E. coli) in multiple tissues of C. elegans. While we acknowledge that our tissue-specific rescue experiments suggest a role for these pathways in neurons, the intestinal fluorescence of Phsp-4::GFP or Pirg-5::GFP is easily observable and scorable. Therefore, we chose to focus our further analyses on the intestine for practical reasons.

Overall, this work provides convincing data to support their model. In the C. elegans field, the behaviors of larvae are not well studied compared to adults. This work will pose an interesting question about the difference between larvae and adults in nutrition sensing in C. elegans and provide a framework and candidate molecules to be studied in other organisms.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Major suggestions:

(1) My major overall comment is that the paper would be substantially strengthened by more mechanistic analysis. In particular, how does low vitamin C trigger UPR activation? This is an intriguing finding and it would be important to see it more fully explored.  

Our study revealed that the vitamin C content in HK_-E. coli_ is comparable to that of live E. coli or HK-yfbR (Figure 4-figure supplement 1A), suggesting that the induction of unfolded protein response (UPR) in C. elegans by HK-E. coli is not attributed to low vitamin C levels, but rather to unknown factors.

Metabolomic analysis showed that the sugar levels, including lactose, D-(+)-sucrose, and D-(+)-glucose, were significantly decreased in HK-E. coli (Figure 3B, Table S1).

Furthermore, we found that supplementing D-(+)-glucose effectively inhibited UPRER (Figure 3E), immune response (Figure 3F, 3G, and Figure 3-figure supplement 2D), and avoidance behavior (Figure 3H) induced by HK-E. coli. Our findings suggest that the deficiency in sugars in HK-E. coli triggers a stress response and avoidance behavior in animals, which can be alleviated by D-(+)-glucose supplementation.

Notably, when E. coli was heat-killed, we observed that the sugar levels, including lactose and D-(+)-sucrose, were significantly higher in the heat-killed E. coli mutant yfbR (HK-yfbR) compared to HK-E. coli (Figure 3B). Moreover, we found that UPRER was reduced in animals feeding HK-yfbR (Figure 3-figure supplement 1B), indicating that higher sugar levels may inhibit the induction of UPRER by low-quality food.

The synthesis of vitamin C (VC) occurs through the glucuronate pathway, utilizing D-glucose as a precursor 1, 2 (Figure 4A). This led us to investigate whether the vitamin C biosynthesis pathway is involved in evaluating low-quality food by using D-glucose. In this study, we found that animals feeding live E. coli, which should produce more VC, exhibit higher glucose levels. However, our results show that animals maintain similar VC levels when fed ideal food (live E. coli) compared to low-quality food (HK-E. coli) (Figure 4B), suggesting that animals do not stimulate VC biosynthesis under favorable food conditions. In contrast, when animals are fed low-quality food (HK-OP50), we found that supplementing D-GlcA (Figure 4C) or E. coli-yfbR mutation (Figure 4B) in HK-E. coli can improve VC levels. Moreover, we found that VC or D-glucuronate (D-GlcA) supplementation inhibited HK-E. coli induced UPRER (Figure 4D). These data indicate that glucose boosts the animal's ability to adapt to unfavorable food environments by increasing VC levels, thereby inhibiting UPRER, but not in favorable food conditions.

In addition,we asked whether high level of VC inhibits other stress induced UPRER. Previous study shown that Tunicamycin6 and pathogenic bacteria-Pseudomonas aeruginosa-PA145 induce UPRER in C. elegans. We found that VC supplementation does not inhibit Tunicamycin or PA14-induced URPER (Author response image 3). 

Author response image 3.

VC supplementation does not inhibit Tunicamycin or PA14-induced UPRER.

In addition, how is the activation of the PMK-1 pathway driven by/coordinated with UPR activation? 

If the authors do not want to pursue these directions experimentally in this study, the discussion would be strengthened by considering these questions and identifying candidate regulatory mechanisms for further exploration.

In this study, we found that heat-killed E. coli (HK-E. coli), a low-sugar food, triggers cellular unfolded protein response (UPRER) and immune response. We also demonstrated that 1) the activation of UPRER by low-quality food depends on the IRE-1/XBP-1, 2) activation of immune response (PMK-1) is downstream of XBP-1 in responding to low-quality food.

how is the activation of the PMK-1 pathway driven by/coordinated with UPR activation? 

In our discussion part, we added new data and discussion to answer reviewer’s question. 

A previous study has shown that activating innate immunity (PMK-1 MAPK) leads to a reduction in translation 3. Our own previous research has also demonstrated that PMK-1 activation causes a shutdown of food digestion in animals4, likely to reduce protein translation and cellular metabolism. To investigate this further, we measured the translation level of animals fed with HK-E. coli and found that total translation ability is significantly reduced in these animals (Figure 5-figure supplement 1D). This finding suggests that activating innate immunity (PMK-1 MAPK) may serve as a mechanism to slow down translation progress, thereby alleviating the pressure on the unfolded protein response (UPR) and preventing excessive UPRER activation.

(2) Figure 2C: The data shows that xbp-1 mutants are significantly more likely to leave heat-killed E. coli. However, no other conditions are examined. Is this avoidance defect specific to heat-killed E. coli, or is it a more general effect of xbp-1 mutants - that is, are other conditions that evoke avoidance also affected by mutation of xbp-1? Is feeding behavior on regular E. coli altered in this background? The finding would be more relevant if the authors could clarify or provide more context for their claims here.

We then asked whether UPRER (IRE-1/XBP-1) - Innate immunity (PMK-1/p38 MAPK) axis is specific to evaluate low-quality food (HK-E. coli). We examined the avoidance behavior phenotype of wild-type and mutant L1 animals by placing them on various food conditions, including normal E. coli food, inedible food (Saprophytic staphylococci) and pathogenic food (Pseudomonas aeruginosa-PA14), for a 24-hour period. We found that N2, pmk-1, and xbp-1 mutant worms did not exhibit avoidance behavior when presented with normal food (OP50). However, both N2 and xbp-1 mutant worms were able to escape from inedible food, Saprophytic staphylococci, whereas pmk-1 mutant worms did not show this avoidance. Notably, xbp-1 mutant worms exhibited even more pronounced avoidance behavior when exposed to Pseudomonas aeruginosa, whereas pmk-1 mutant worms were more susceptible to infection by this pathogen (Figure 2-figure supplement 2C). These findings suggest that the UPR-Immunity pathway plays a crucial role in helping animals avoid low-quality food by triggering an avoidance response. In contrast, the Innate immunity pathway, which is mediated by PMK-1/p38 MAPK, appears to play a key role in evaluating unfavorable food sources, such as HK-E. coli, Saprophytic staphylococci, and Pseudomonas aeruginosa, and helping animals avoid these environments.

(3) Figure 3C-F: The magnitude of the changes between conditions shown in these panels is small. To what extent does this supplementation represent a full rescue? The findings would be strengthened if figures/images for the control condition (non-HK E. coli) were shown for comparison to allow the reader to assess the extent to which UPR/PMK-1 activation is rescued.

In response to a reviewer's suggestion, we included live-E. coli as a control in our study. Notably, our data revealed that the addition of lactose, D-(+)-sucrose, and D-(+)-glucose partially inhibited the HK-E. coli-induced unfolded protein response (UPRER) and immune response, suggesting that other nutrients present in live-E. coli may also play a role in inhibiting UPRER.

We added this in manuscript: “While sugar effectively inhibits the HK-E. coli-induced UPRER and immune response, it does not fully suppress it to the extent observed with live-E. coli (Figure 3C-F). This implies that additional nutrients present in live-E. coli might also contribute to the inhibition of UPRER and immune response.” 

(4) Figure 5B-D: The magnitude of changes shown between conditions here again appear to be very small, even those labelled as statistically significant. It is important to ensure that the correct statistical tests have been used to assess the significance of these differences (see below).

All statistical analyses were performed in Graphpad prism 8.0. Two-tailed unpaired t test was used for statistical analysis of two groups of samples，one-way or two-way ANOVA was used for statistical analysis of more than two groups of samples.

(5) Methods: In the "Statistical analysis" section, the authors state that "All statistical analyses were performed using Student's t-test". However, this is not the appropriate test to use in experiments where multiple comparisons are made, which is true in several instances across the paper. In these cases, a more appropriate statistical test should be used.

All statistical analyses were performed in Graphpad prism 8.0. Two-tailed unpaired t test was used for statistical analysis of two groups of samples，one-way or two-way ANOVA was used for statistical analysis of more than two groups of samples.

Minor suggestions:

(1) Figure S2: RNAi is usually delivered in a different E. coli strain, HT115. Is this the case with the RNAi knockdowns in Figure S2, and given that diet can influence UPR activation, is it possible that this different diet could change the phenotypes observed?

This should be clarified by the authors.

In this study, all RNAi experiments involved bleaching adult animals under RNAi strain culture conditions to obtain L1 animals. Subsequently, L1 animals were transferred to HK-E. coli OP50 for phenotype analysis. In response to a reviewer's suggestion, we observed that L1 animals obtained from mothers fed E. coli strains OP50, HT115, or K12 exhibited similar UPR induction under HK-E. coli OP50 feeding conditions (Author response image 4). These findings suggest that variations in diet did not alter the UPR phenotypes.

Author response image 4.

L1 animals obtained from mothers fed E. coli strains OP50, HT115, or K12 exhibited similar UPR induction under HK-E. coli OP50 feeding conditions 

Reviewer #2 (Recommendations For The Authors):

Line 182: "irg-5::GFP" should be "hsp-4::gfp".

Thanks for the reviewer’s efforts. We have changed this error.

Reviewer #3 (Recommendations For The Authors):

Major comments:

(1) The reporter genes of UPRER and immune response were analyzed in the intestine throughout the study. On the other hand, their rescue experiments suggest that these pathways function in the neurons. They should provide the fluorescence data in the neurons at least for Figures 1F and 1G to confirm that the intestinal response matches the neuronal response and mention that further analyses were done in the intestine for easy scoring.

Consistent with the results of the RNA sequencing (RNA-seq) analysis, the UPRER reporter (Phsp-4::GFP)8 and immunity reporter (Pirg-5::GFP)9 were strongly induced in intestinal (Figure 1F-G) and neurons (Figure 1-figure supplement 2A) by feeding unfavorable food (HK-E. coli), suggesting that UPRER and immune pathways may respond to low-quality food (HK-E. coli). As intestinal fluorescence (Phsp-4::GFP or Pirg-5::GFP) is easy observation and scoring, the further analyses were done in the intestine. 

(2) I have concerns about the interpretation of the p-PMK-1 data. Although the authors described that "p-PMK-1 is prominently increased" in the text (Line 150), it is unclear on the data (Figure 2E). Similarly, the authors' statement "p-PMK-1 is decreased in animals with D-GlcA (F).." was not fully supported by the data in Figure 4F. The experiment should be repeated and quantified. Moreover, pPMK-1 showed single bands in Figure 2E, but double bands in Figure 3G, 4F, and 4G. The authors should explain why that is the case and which band we should look at for Figures 3G, 4F, and 4G.

As reviewer’s suggestion, we also repeated some of the western. We found that after longer expose, there are two bands for pPMK-1 (Figure 2E, new data; and “raw-data WB” file). The VHP-1 phosphatase is known to inhibit PMK-13. In our previous study, we found that worms treated with vhp-1(RNAi), which hyperactivates p-PMK-1 (lower band) 4. In contrast, the two bands are disappeared in pmk-1 mutant (Author response image 5). Thus, the lower band indicates the pPMK-1. We now replace the Figure 2E and quantified relative intensity of pPMK-1/tublin. We also provide the uncropped western blots images as source data ( “raw-data WB” file). 

Author response image 5.

In our previous study, we found that worms treated with vhp-1(RNAi), which hyperactivates p-PMK-1 (lower band) 4. In contrast, the two bands are disappeared in pmk-1 mutant. These pictures are extracted from our previous study4.

(3) Heat-killed E. coli (HK-E. coli) is low-quality because the lack of sugar cannot support the growth of C. elegans larvae (Qi and Han, Cell, 2018). Thus, animals do not show the UPRER-immune response and avoidance when HK-E. coli is supplemented with sugars such as glucose (Line 225-227). If these sugars are the key, C. elegans larvae should be able to grow better with HK-E. coli supplemented with glucose. Authors should address this possibility.

Previous studies have shown that heat-killed E. coli (HK-E. coli) is a low-quality food source that cannot support the growth of C. elegans larvae7. Here, we found that sugar deficiency in HK-E. coli induces the UPRER-immune response and avoidance behavior in C. elegans. Given this, we investigated whether sugar supplementation could promote animal growth when fed HK-E. coli. To our surprise, supplementing HK-E. coli with carbohydrates (D-Glc, D-GlcA) did not support animal development (Figure 3-figure supplement 2G), suggesting that carbohydrates are not essential for supporting animal growth on this food source. However, we did find that carbohydrates are critical for inhibiting the UPRER-immune response induced by sugar deficiency in HK-E. coli.

(4) Line 884: Instead of the Student's t-test, the ANOVA should be used for multiple comparisons.

All statistical analyses were performed in Graphpad prism 8.0. Two-tailed unpaired t test was used for statistical analysis of two groups of samples，one-way or two-way ANOVA was used for statistical analysis of more than two groups of samples.

(5) Although the results are interesting and convincing, the manuscript needs some careful editing and proofreading. As far as I could catch, there are more than 100 errors and typos, as I summarized in minor comments. I recommend the authors proofread thoroughly to make this work easier to read.

Thanks for the reviewer’s efforts. We changed all of these errors and polish the language of this paper. 

Minor comments:

(1) Line 30: nature -> natural

(2) Line 86: elegnas -> elegans

(3) Line 93: the17h -> the 17h

(4) Line 97: response -> respond

(5) Line106: responded -> respond

(6) Lien 107-109: Add references for the three reporters

(7) Line 114: immune -> immune pathway

(8) Line 118: immune depended -> immune-dependent

(9) Line 128, 594, 596: deferentially -> differentially

(10) Line 131: Explain what IRE-1-mediated splicing of xbp-1 with references

(11) Line 170: XPB-1 -> XBP-1

(12) Line 179: URP -> UPR

(13) Line 181: hsp-4::GFP -> Phsp-4::GFP

(14) Line 183: Italicize E. coli; mutant -> mutants

(15) Line 184: irg-5::GFP -> Pirg-5::GFP (2 places)

(16) Line 197, 203, 206, 207: Lactose -> lactose

(17) Line 206, 209, 217, 225, 228, 232, 237, 262, 442, 445, 604, 739: Glucose -> glucose

(18) Line 218: Sugars deficiency -> sugar deficiency

(19) Line 229: found contribute to -> found to contribute to

(20) Line 235, 537, 539, 587, 599, 642, 855: Italicize E. coli

(21) Line 236: same -> the same

(22) Line 239: I recommend adding "in C. elegans". This study uses both E. coli and C.

elegans genetics. Sometimes, it is confusing which organism was mentioned. It should be applied where it is necessary.

(23) Line 240: additional -> addition

(24) Line 339, 642: Italicize kgb-1

(25) Line 390: Italicize Pseudomonas aeruginosa, Bacillus thuringiensis,

Staphylococcus aureus, and Serratia marcescens

(26) Line 394: wiht -> with

(27) Line 400, 550: Change ER to superscript; Italicize ire-1, xbp-1, and pmk-1

(28) Line 415: xpb-1 -> xbp-1

(29) Line 460, 525, 531, 532, 617, 655: Italicize yfbR

(30) Line 457, 468, 472, 475, 482, 497, 513, 624, 629, 633, 733. 758: Vitamin -> vitamin

(31) Line 459: Make it clear what is the relationship between vitamin C and TAA

(32) Line 527: Do not italicize mutant

(33) Line 538: Phsp-6:GFP -> Phsp-6::GFP (to match other descriptions)

(34) Line 540: Phsp-4:GFP -> Phsp-4::GFP (to match other descriptions)

(35) Line 540: Italicize hsp-4

(36) Line 543: Pirg-5:GFP -> Pirg-5::GFP (to match other descriptions) and italicize irg-5

(37) Line 550, 881: Innate -> innate

(38) Line 557, 560, 564, 838: Do not italicize HK

(39) Line 561: Remove the extra space before "three"

(40) Line 575, 577: Reporter -> reporter

(41) Line 575, 607: Italicize Phsp-4::GFP

(42) Line 577: immunity -> Immunity; Italicize Pirg-5::GFP

(43) Line 585, 653: keio -> Keio

(44) Line 586: hsp-4::GFP -> Phsp-4::GFP

(45) Line 586, 589 (2 places): irg-5::GFP -> Pirg-5::GFP

(46) Line 597: Remove "all"

(47) Line 600: Trehalose -> trehalose

(48) Line 609: Italicize Pirg-5::GFP

(49) Line 615: critically -> critical

(50) Line 636: Remove "+"

(51) Line 656 (2 places), 682: Do not italicize OP50

(52) Line 664: Lead -> lead

(53) Line 681: Describe the composition of NGM or show the reference. Since this paper examines nutrition, the composition of the medium is crucial.

(54) Line 686-706: Italicize all allele names. Be consistent with how to write the promoter to avoid confusion (e.g., ttx-3p -> Pttx-3). Be consistent with how to describe the transgene (e.g., Phsp-4::GFP(zcIs4) -> zcIs4[Phsp-4::GFP])

(55) Line 710: Describe the composition of LB or show the reference. Since this paper examines nutrition, the composition of the medium is crucial.

(56) Line 709, 856 (2 places), 858: Do not italicize K12 to make it consistent

(57) Line 719: Podr-1p:RFP -> Podr-1::RFP

(58) Line 722, 724: Italicize ges-1 and xbp-1

(59) Line 723: Pges-1:xbp-1::GFP -> Pges-1::xbp-1::GFP

(60) Line 735: Glucuronic -> glucuronic

(61) Line 748: I believe it is 5 mm instead of 0.5 mm

(62) Line 750: The equation should be (5 mm)2/(17.5 mm)2

(63) Line 759: Remove the period after "pattern".

(64) Line 766: Describe how they were synchronized

(65) Line 774: Italicize Psysm-1p::GFP

(66) Line 785: Insert a space before "until"

(67) Line 787: the mutant -> mutant

(68) Line 789, 792, 793, 795 (2 places): GPF -> GFP

(69) Line 791: next -> Next; an -> a

(70) Line 799: Remove a space before "MRC".

(71) Line 804: I do not understand what "until adulthood" means in this context;

Remove a space before "by". (I recommend searching double space and correcting it.)

(72) Line 853: Metabolome -> metabolome

(73) Line 893-1082: Species and gene names should be italicized in Reference

(74) Figures 1F, 1G, S2F, S2G: The panels' order should match the bar graphs' order. The apparent difference in the representative data does not match the marginal difference in the bar graph in Fig. 1G. The authors should double-check the results.

(75) Figure 1F, 2A, 2B, 3C, 3D, 3E, 4D, 4I, S1J, S2A, S2B, S2I, S3B, S3F, S3H: hsp-4::GFP -> Phsp-4::GFP

(76)  Figure 1G, 2D, 3F, 4E, 4J, S1K, S2H, S3C, S3I: irg-5::GFP -> Pirg-5::GFP

(77)  Figure 6: Liquids -> Lipids; Italicize ire-1, xbp-1, pmk-1

(78)  Figure S1I: hsp-6::GFP -> Phsp-6::GFP

(79)  In the legend for Figure S1 after Figure S1, (A), (B)... were duplicated. It is OK in the corresponding main text (Line 530)

(80)  Figure S2F, S3G, S4C, S4D: sysm-1::GFP -> Psysm-1::GFP

(81)  Figure S2G: irg-1::GFP -> Pirg-1::GFP

(82)  Figure S3H and S3I: Describe which ones are Glu + conditions

References: 

(1) Patananan AN, Budenholzer LM, Pedraza ME, Torres ER, Adler LN, Clarke SG. The invertebrate Caenorhabditis elegans biosynthesizes ascorbate. Arch Biochem Biophys 569, 32-44 (2015).

(2) Yabuta Y_, et al. L-Ascorbate Biosynthesis Involves Carbon Skeleton Rearrangement in the Nematode Caenorhabditis elegans. _Metabolites 10,  (2020).

(3) Weaver BP, Weaver YM, Omi S, Yuan W, Ewbank JJ, Han M. Non-Canonical Caspase Activity Antagonizes p38 MAPK Stress-Priming Function to Support Development. Dev Cell 53, 358-369 e356 (2020).

(4) Geng S_, et al. Gut commensal E. coli outer membrane proteins activate the host food digestive system through neural-immune communication. _Cell Host Microbe 30, 1401-1416 e1408 (2022).

(5)  Richardson CE, Kooistra T, Kim DH. An essential role for XBP-1 in host protection against immune activation in C. elegans. Nature 463, 1092-1095 (2010).

(6) Harding HP_, et al. An Integrated Stress Response Regulates Amino Acid Metabolism and Resistance to Oxidative Stress. _Molecular Cell 11, 619-633 (2003).

(7) Qi B, Kniazeva M, Han M. A vitamin-B2-sensing mechanism that regulates gut protease activity to impact animal’s food behavior and growth. eLife 6, e26243 (2017).

(8) Calfon M_, et al. IRE1 couples endoplasmic reticulum load to secretory capacity by processing the XBP-1 mRNA. _Nature 415, 92-96 (2002).

(9) Bolz DD, Tenor JL, Aballay A. A Conserved PMK-1/p38 MAPK Is Required in Caenorhabditis elegans Tissue-specific Immune Response to Yersinia pestis Infection*. The Journal of Biological Chemistry 285, 10832 - 10840 (2010).

Author response:

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

Public Reviews: 

Reviewer #1 (Public Review): 

Summary: 

In Ryu et al., the authors use a cortical mouse astrocyte culture system to address the functional contribution of astrocytes to circadian rhythms in the brain. The authors' starting point is transcriptional output from serum-shocked culture, comparative informatics with existing tools and existing datasets. After fairly routine pathway analyses, they focus on the calcium homeostasis machinery and one gene, Herp, in particular. They argue that Herp is rhythmic at both mRNA and protein levels in astrocytes. They then use a calcium reporter targeted to the ER, mitochondria, or cytosol and show that Herp modulates calcium signaling as a function of circadian time. They argue that this occurs through the regulation of inositol receptors. They claim that the signaling pathway is clock-controlled by a limited examination of Bmal1 knockout astrocytes. Finally, they switch to calcium-mediated phosphorylation of the gap junction protein Connexin 43 but do not directly connect HERP-mediated circadian signaling to these observations. While these experiments address very important questions related to the critical role of astrocytes in regulating circadian signaling, the mechanistic arguments for HERP function, its role in circadian signaling through inositol receptors, the connection to gap junctions, and ultimately, the functional relevance of these findings is only partially substantiated by experimental evidence. 

Strengths: 

- The paper provides useful datasets of astrocyte gene expression in circadian time. 

- Identifies HERP as a rhythmic output of the circadian clock. 

- Demonstrates the circadian-specific sensitivity of ATP -> calcium signaling. 

- Identifies possible rhythms in both Connexin 43 phosphorylation and rhythmic movement of calcium between cells. 

Weaknesses: 

- It is not immediately clear why the authors chose to focus on Ca2+ homeostasis or Herp from their initial screens as neither were the "most rhythmic" pathways in their primary analyses. 

We appreciate the reviewer’s comment. We chose to focus on Ca2+ homeostasis processes because intracellular Ca2+ signaling plays crucial role in numerous astrocyte functions and is notably associated with sleep/wake status of animals, which is our primary interest (Bojarskaite et al., 2020; Ingiosi et al., 2020; Blum et al., 2021; Szabó et al., 2017). Among the genes involved in calcium ion homeostasis, Herp exhibited the most robust rhythmicity (supplementary table 1). The rationale for our focus on Ca2+ homeostasis and Herp is explained in the results section (line 143-150). We hope this provides a clear justification for our focus.

- It would have been interesting (and potentially important) to know whether various methods of cellular synchronization would also render HERP rhythmic (e.g., temperature, forskolin, etc). If Herp is indeed relatively astrocyte-specific and rhythmic, it should be easy to assess its rhythmicity in vivo. 

Thank you for the reviewer’s insightful comment. In response, we examined HERP expression in cultured astrocytes synchronized using either Dexamethasone or Forskolin treatment. We found that Herp exhibited rhythmic expression at both the the mRNA and protein levels under these conditions. These results have been added to Figure S3 and are explained in the manuscript (lines 173-175).

Additionally, we measured HERP levels in the prefrontal cortex of mice at CT58 and CT70 and found no rhythmicity, as shown in Author response image 1. Given that Herp is expressed in various brain cell types, including microglia, endothelial cells, neurons, oligodendrocytes, and the astrocytes- with the highest expression in microglia(Cahoy et al., 2008), we reason that the potential rhythmic expression of HERP in astrocytes might be masked by its continuous expression in other cell types. Nonetheless, to assess HERP rhythmicity specifically in astrocytes in vivo, we attempted immunostaining using several anti-HERP antibodies, but none were successful. Consequently, we were unable to determine whether HERP exhibits rhythmic expression in astrocytes in vivo.

Author response image 1.

HERP levels were constant at CT58 and CT70. (A, B) Mice were entrained under 12h:12h LD cycle and maintained in constant dark. Prefrontal cortices were harvested at indicated time and processed for Western blot analysis. Representative image shows three independent samples. (B) Quantification of HERP levels normalized to VINCULIN. Values in graphs are mean ± SEM (*p < 0.05, **p < 0.005, ***p < 0.0005, and ****p < 0.00005; t-test)

- The authors show that Herp suppression reduces ATP-mediated suppression of calcium whereas it initially increases Ca2+ in the cytosol and mitochondria and then suppresses it. The dynamics of the mitochondrial and cytosolic responses are not discussed in any detail and it is unclear what their direct relationship is to Herp-mediated ER signaling. What is the explanation for Herp (which is thought to be ER-specific) to calcium signaling in other organelles? 

Our examination of cytosolic and mitochondrial Ca2+ responses was aimed at corroborating HERP’s effect on ER Ca2+ response. Upon ATP stimulation, Ca2+ is released from the ER via IP3R receptors (IP3Rs) and subsequently transmitted to other organelles including mitochondria (Carreras-Sureda et al., 2018; Giorgi et al., 2018). Ca2+ is directly transferred to the cytosol by IP3Rs located on the ER membrane, and to the mitochondria through a complex formed by IP3R and the voltage-dependent anion channel (VDAC) on the mitochondria (Giorgi et al., 2018).  Consistent with previous reports, we observed an increase of cytosolic and mitochondrial Ca2+ levels accompanied by decrease in ER Ca2+ levels following ATP treatment (See Fig. 3B, E, H, control siRNA). The ATP-stimulated ER Ca2+ release was enhanced by Herp knockdown. We reasoned that if Ca2+ release was enhanced, then cytosolic and mitochondrial Ca2+ uptakes would also be enhanced. The results were consistent with our hypothesis (See Fig. 3B, E, H, Herp siRNA). These observations are described in the Results section (lines 202-208) and in the Discussion (lines 333-348). We hope this explanation clarifies the relationship between Herp-mediated ER Ca2+ response and Ca2+ response in other organelles. Thank you for your consideration.

- What is the functional significance of promoting ATP-mediated suppression of calcium in ER? 

In astrocytes, intracellular Ca2+ plays crucial role in regulating several processes. In this study, among various downstream effects of intracellular Ca2+, we examined the gap junction channel (GJC) conductance, which affects astrocytic communication. As discussed in the manuscript (lines 357-381), circadian variation in HERP results in rhythmic Cx43 (S368) phosphorylation linked with GJC conductance. We propose that during the subjective night phase, heightened ATP induced ER Ca2+ release reduces GJC conductance, uncoupling astrocytes from the syncytium, making them better equipped for localized response. On the other hand, during the subjective day phase, increased GJC conductance may allow astrocytes to control a larger area for synchronous neuronal activity which is a key feature of sleep.

- The authors then nicely show that the effect of ATP is dependent on intrinsic circadian timing but do not explain why these effects are antiphase in cytosol or mitochondria.

Moreover, the ∆F/F for calcium in mitochondria and cytosol both rise, cross the abscissa, and then diminish - strongly suggesting a biphasic signaling event. Therefore, one wonders whether measuring the area under the curve is the most functionally relevant measurement of the change. 

We appreciate the reviewer’s insightful comments. As explained in our previous response, Ca2+ released from the ER is transferred to the cytosol and mitochondria. This transfer explains why the fluorescent intensities of cytosolic and mitochondrial Ca2+ indicators show anti-phasic responses to those of the ER.

We agree that cytosolic and mitochondrial Ca2+ responses may be biphasic. The decrease below the abscissa in mitochondria and cytosol likely reflects Ca2+ extrusion from these organelles. However, our primary focus was on the initial uptake of Ca2+ following ER Ca2+ release. Thus, when calculating the area under the curve (AUC), we measured the area between the ∆F/F graph and the y=0 (X-axis) for both mitochondria and cytosol. We reason that the measuring the area under the curve (above the abscissa) fits with our objective.

While addressing your concerns, we noticed errors in the Y-axis labels of Fig. 3C, 4D, and 5C. For the ER Ca2+ dynamics, we measured the area above curve. These mistakes have now been corrected.

- Why are mitochondrial and cytosolic calcium not also demonstrated for Bmal1 KO astrocytes? 

In two sets of experiments (Fig. 3 and Fig. 4), we demonstrated that the increase in cytosolic and mitochondrial Ca2+ aligns with ER Ca2+ release. Since there were no circadian time differences in ER Ca2+ release in the Bmal1 KO cultures, we concluded that it was unnecessary to measure Ca2+ levels in the mitochondria and cytosol. Additionally, our primary focus is on the ER Ca2+ response rather than the Ca2+ dynamics in subcellular organelles. We hope this clarifies our rationale and maintains the focus of our study.

- The authors claim that Herp acts by regulating the degradation of ITPRs but this hypothesis - rather central to the mechanisms proposed in this study - is not experimentally substantiated. 

We appreciate the reviewer’s insightful comments regarding the role of HERP in the degradation of IP3Rs. In the original manuscript, we demonstrated that treating cells with Herp siRNA leads to an increase in the levels of ITPR1 and ITPR2, suggesting that HERP might be involved in the regulation of IP3Rs stability. This observation is consistent with previous studies, which showed that Herp siRNA treatment increases ITPR levels in HeLa and cardiac cells (Paredes et al., 2016; Torrealba et al., 2017). Torrealba et al. also showed that HERP regulates the polyubiquitination of IP3Rs. Based on our results and previous reports, we hypothesized that HERP similarly regulates ITPR degradation in cultured astrocytes.

However, as the reviewer rightly pointed out, further evidence is needed to confirm that HERP specifically regulates ITPR degradation. To address this, we conducted new experiments examining the effect of XesC, an inhibitor of IP3Rs, on ER Ca2+ release. The treatment of XesC reduced the ER Ca2+ release and abolished the enhancement of ER Ca2+ release by Herp KD. These results demonstrated that HERP influences ER Ca2+ response through IP3Rs. These new findings have been added to Fig. 3N – 3P and explained in the Results section (lines 217-221).

We believe these additional experiments and clarifications strengthen our hypothesis that HERP regulates IP3R degradation, thereby modulating ER Ca2+ responses.

- There is no clear demonstration of the functional relevance of the circadian rhythms of ATP-mediated calcium signaling.

As mentioned in the previous response, we examined Cx43 phosphorylation linked with GJC conductance in the context of ATP-mediated Ca2+ signaling. Our results demonstrated circadian variations in Cx43 Ser368 phosphorylation leading to variations of gap junction channel (GJC) conductance (Fig. 6C – F and Fig. 7D - I). We have discussed the significance of this circadian rhythm in ATP driven ER Ca2+ signaling concerning astrocytic function during sleep/wake states in the manuscript (lines 357 – 382) as follows.

“ATP-stimulated Cx43 (S368) phosphorylation is higher at 30hr (subjective night phase) than at 42hr (subjective day phase) (Fig. 6C and 6D.), a finding further supported by in vivo experiments showing higher pCx43(S368) levels in the prefrontal cortex during the subjective night than during the day (Fig. 6E and 6F). What are the implications of this day/night variation in Cx43 (S368) phosphorylation? We reasoned that the circadian variation in Cx43 phosphorylation could significantly impact astrocyte functionality within the syncytium. Indeed, our cultured astrocytes exhibited circadian phase-dependent variation in gap junctional communication (Fig.7D – 7F). Astrocytes influence synaptic activity through the release of gliotransmitters such as glutamate, GABA, D-serine, and ATP, triggered by increases in intracellular Ca2+ in response to the activity of adjacent neurons and astrocytes (Verkhratsky & Nedergaard, 2018). Importantly, this increase in Ca2+ spreads to adjacent astrocytes through GJCs (Fujii et al., 2017), influencing a large area of the neuronal network. Considering that Cx43 Ser368 phosphorylation occurs to uncouple specific pathways in the astrocytic syncytium to focus local responses (Enkvist & McCarthy, 1992), our findings suggest that astrocytes better equipped for localized responses when presented with a stimulus during the active phase in mice. Conversely, during the rest period, characterized by more synchronous neuronal activity across broad brain areas (Vyazovskiy et al., 2009) higher GJC conductance might allow astrocytes to exert control over a larger area. In support of this idea, recent study showed that synchronized astrocytic Ca2+ activity advances the slow wave activity (SWA) of the brain, a key feature of non-REM sleep (Szabó et al., 2017). Blocking GJC was found to reduce SWA, further supporting this interpretation. However, conflicting findings have also been reported. For instance, Ingiosi et al. (Ingiosi et al., 2020) found that astrocytic synchrony was higher during wakefulness than sleep in the mouse frontal cortex. Whether these differing results in astrocyte synchrony during resting and active periods are attributable to differences in experimental context (e.g., brain regions, sleep-inducing condition) remains unclear. Indeed, astrocyte Ca2+ dynamics during wakefulness/sleep vary according to brain regions (Tsunematsu et al., 2021). While the extent of astrocyte synchrony might differ depending on brain region and/or stimulus, on our results suggest that the baseline state of astrocyte synchrony, which is affected by GJC conductance, varies with the day/night cycle.”

Reviewer #2 (Public Review): 

Summary: 

The article entitled "Circadian regulation of endoplasmic reticulum calcium response in mouse cultured astrocytes" submitted by Ryu and colleagues describes the circadian control of astrocytic intracellular calcium levels in vitro. 

Strengths: 

The authors used a variety of technical approaches that are appropriate 

We appreciate the reviewer’s acknowledgement of the strengths of our manuscript.

Weaknesses: 

Statistical analysis is poor and could lead to a misinterpretation of the data 

Thank you for the comment. We have carefully reviewed our statistical analyses and applied appropriate methods where necessary. Please see below for the specific revisions and improvements made.

For Fig. 2D-E, we initially used a t-test. However, after adding more replicates and conducting a normality test, we found that the data did not follow a normal distribution. Therefore, we switched to the Mann-Whitney U test. In Fig. 5D-E, we originally used a repeated measures two-way ANOVA, but we have now changed it to a standard two-way ANOVA. For Fig. 7C and I, we also observed non-normal distribution in the normality test and consequently replaced the t-test with the Mann-Whitney U test. For other analyses not specifically mentioned, normality tests confirmed normal distribution, allowing us to use t-tests or ANOVA as appropriate for statistical analysis.

Several conceptual issues have been identified. 

We have addressed the reviewer’s concerns. Please see our detailed point-by-point responses below.

Overinterpretation of the data should be avoided. This is a mechanistic paper done completely in vitro, all references to the in vivo situation are speculative and should be avoided. 

We appreciate the reviewer’s insightful comment. Following the reviewer’s suggestion, we have removed the interpretations of GO pathways in the context of in vivo situation.

Reviewer #3 (Public Review): 

Astrocyte biology is an active area of research and this study is timely and adds to a growing body of literature in the field. The RNA-seq, Herp expression, and Ca2+ release data across wild-type, Bmal1 knockout, and Herp knockdown cellular models are robust and lend considerable support to the study's conclusions, highlighting their importance. Despite these strengths, the manuscript presents a gap in elucidating the dynamics of HERP and the involvement of ITPR1/2 in modulating Ca2+ release patterns and their circadian variations, which remains insufficiently supported and characterized. While the Connexin data underscore the importance of rhythmic Ca2+ release triggered by ATP, the relationship here appears correlational and the role of HERP and ITPR in Cx function remains to be characterized. Moreover, enhancing the manuscript's clarity and readability could significantly benefit the presentation and comprehension of the findings. 

We appreciate the reviewer’s acknowledgement of the strengths of our manuscript. Regarding the identified gaps, we have conducted several new experiments to clearly demonstrate the HERP-ITPR-Cx phosphorylation axis. Please see our detailed point-by-point responses below.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors): 

- While HERP appears to be a clock-controlled gene and its protein levels appear to demonstrate rhythmicity as well, the data quality of the western blotting in Bmal1 knockout raises some concern about the accuracy of HERP protein quantification. 

We understand the reviewer’s concern regarding the proximity of the HERP band to a nonspecific band in the Western blotting for the Bmal1 knockout. However, we took great care to ensure the accuracy of our HERP band quantification. We meticulously selected only the specific HERP band, excluding nonspecific band. Therefore, we are confident in the accuracy of our HERP protein measurements.

- If HERP is rhythmic and ITPRs are not, if their model is correct, might we expect HERP suppression to result in 'unmasking' an ITPR rhythm? 

Our model suggests that both HERP and ITPRs are rhythmic, with HERP regulating the degradation of ITPR proteins and driving their rhythms. Consistent with this, we observed that day/night variations in ITPR2 levels (Fig. 4N and 4O). Therefore, we concluded that circadian variations in HERP are sufficient to drive ITPR2 rhythms. We have explained this in detail in the Result section (lines 236-241) and the Discussion section (lines 324-332).

- The authors make a rather abrupt switch to examining gap junctions and connexin 43 phosphorylation. While the data demonstrating that the phosphorylation of S368 may indeed be rhythmic - the authors do not connect these data to the rest of the manuscript by showing a connection to HERP-mediated calcium signaling, limiting the coherence of the narrative. 

Thank you for the reviewer’s insightful comments. To address the reviewer's concern regarding the connection between Herp and the phosphorylation of CX43 at S368, we have conducted new experiments to test whether KD of Herp abolishes the rhythms of Cx43 phosphorylation at S368. We found that the phosphorylation of Cx43 at S368 is significantly enhanced at 30hrs post sync compared with 42hrs post sync in control siRNA-treated astrocytes consistent with our previous results (Fig. 6C & 6D). On the other hand, this circadian phase dependent difference in phosphorylation was abolished in Herp siRNA treated astrocytes. These results clearly indicate that circadian variations in Cx43 phosphorylation are driven by the HERP. These new results are now included in Fig. 6G and 6H and explained in the Results section (lines 276-281).

- Comment on data presentation: the authors repeatedly present histograms with attached lines between data points - from my understanding of the experiments, this is inappropriate unless these were repeated measures from the same cells. Otherwise, the lines connecting one data point to another between different conditions (e.g., Ctrl or Herp knockdown) are arbitrary and possibly misleading (i.e., Figure 3K, 3M, 4L, 6D). 

Thank you for the reviewer’s comment. We have updated the figures by removing the lines connecting data points in the relevant figures (Fig.3K, M, Fig4.N and Fig.6D)).

Reviewer #2 (Recommendations For The Authors): 

Most of the suggestions of this reviewer are related to the conceptual interpretation and presentation of the data and to the statistical analysis 

In Figure 1 the authors analyzed the rhythmic transcriptome of cortical astrocytes synchronized with a serum shock in two different ways. The authors need to discuss what is the difference between the two methods used to detect rhythmic transcripts and make sense of them. 

Following the reviewer’s suggestion, we have provided a more detailed explanation about MetaCycle and BioCycle, as well as the rationale for using both packages in our analysis as follows: “Various methods have been used to identify periodicity in time-series data, such as Lomb-Scargle (Glynn et al., 2006), JTK_CYCLE (Hughes et al., 2010) and ARSER (Yang & Su, 2010), each with distinct advantages and limitations. MetaCycle, integrates these three methods, facilitating the evaluation of periodicity in time-series data without requiring the selection of an optimal algorithm (Wu et al., 2016). Additionally, BioCycle has been developed using a deep neural network trained with extensive synthetic and biological time series datasets (Agostinelli et al., 2016). Because MetaCycle and Biocycle identify periodic signal based on different algorithms, we applied both packages to identify periodicity in our time-series transcriptome data. BioCycle and MetaCycle analyses detected 321 and 311 periodic transcripts, respectively (FDR corrected, q-value < 0.05) (Fig. 1B). Among these, 220 (53.4%) were detected by both methods, but many transcripts did not overlap. MetaCycle is known for its inability to detect asymmetric waveforms (Mei et al., 2020). In our analysis, genes with increasing waveforms like Adora1 and Mybph were identified as rhythmic only by BioCycle, while Plat and Il34 were identified as rhythmic only by MetaCycle (Fig. S1C). Despite these discrepancies, the clear circadian rhythmic expression profiles of these genes led us to conclude that using the union of the two lists compensates for the limitations of each algorithm.”

Please refer to lines 105-117 in the Results section.

The reasoning for comparing CT0 with the phase of the clock 8 hs after SS needs to be explained. Circadian time (CT) conceptually refers to the clock phase in the absence of entrainment cues in vivo, the direct transformation of "time after synchronization" in vitro to CT is misleading. 

Thank you for the reviewer’s insightful comments. Initially, we believed that transforming TASS to CT, despite being in vitro data, might provide a more intuitive and physiologically relevant interpretation of our results. However, we agree that this approach might be misleading. Following the reviewer’s suggestion, we have revised our terminology by changing “CT” to “Time post sync (hr)”. Nonetheless, in Fig. 1F for circular peak phase map, we set 8hrs post sync to ZT0 based on a phase comparison result in Fig. 1D for physiologically relevant interpretation. We hope these revisions clarify our approach.

Moreover, also by definition a CT cannot be defined in terms of "dark" or "light". Figure 6M needs to be changed. 

Following the reviewer’s suggestion, we removed the labels CT22 and CT34. Instead. we have labeled the respective periods as “30hr post sync” and “42hr post sync”.

In Figure 1D, the authors present a gene ontology analysis that is certainly interesting, however, it should not be overinterpreted when trying to explain processes that take place only in vivo (e.g. wound repair). 

Thank you for the insightful comment. Following the reviewer’s feedback, we have removed the paragraph interpreting the cell migration process in relation to wound repair and have focused instead on Ca2+ ion homeostasis.

In Figure 2A the relative expression of clock genes and Herp is again misleading by a white/grey shading indicating subjective night and subjective day when the system under study is a cell culture. 

We understand the reviewer’s concern that a cell culture system is not equivalent to light/dark entrainment condition. However, we apply time-synchronizing stimuli to recapitulate in vivo entrainment. In addition, by comparing our data with CircaDB, we defined 8hrs post sync as corresponding to ZT0, thus aligning it with the beginning of the day. We have retained the shading to facilitate easier interpretation of our data in relation to in vivo situations. However, in response to the reviewer’s concern, we have revised the shading from white/grey to light grey/dark grey. We hope this adjustment addresses the reviewer’s concern, but if the reviewer still believes it is inappropriate, please let us know, we will gladly update it.

In the Figure 2A legend, it is indicated that rhythmicity is assessed using MetaCycle with mean values obtained from n=2. The authors need to make clear whether this n=2 mean: 2 biological replicates or 2 technical replicates. This difference is relevant because it would make the analysis statistically valid or invalid, respectively. 

Thank you for your feedback. n=2 refers to 2 biological replicates. Therefore, the analysis is statistically valid.

In Figures 2C and D the authors applied a T-test, a parametric statistical test for one-to-one comparison that requires normality distribution of the data to be tested first. To test normality, the authors need at least 4 biological replicates. The suggestion of this reviewer is that these experiments have to be repeated and proper statistics applied. 

Thank you for your feedback. In response to the reviewer's suggestion, we conducted additional experiments to increase the number of biological replicates to 4. After verifying the normality of the data, we applied a t-test for Figure 2C and a Mann-Whitney test for Figure 2D and 2E. These tests confirmed significant statistical difference between groups.

Further evidence of Bmal1-dependent control of HERP circadian expression authors could check the presence of E-Box elements in the Herp promoter. 

Thank you for the reviewer’s insightful comment. In the original version of our manuscript's Discussion section, we mentioned the absence of a canonical E-Box in the upstream of Herp gene. However, following the reviewer’s suggestion and considering the potential role of non-canonical E-Boxes, we conducted an additional analysis. This analysis identified several non-canonical E-Boxes within the 6 kb upstream region of the Herp gene (Table S2). Notably, we found one non-canonical E-Box, “CACGTT,” known to regulate circadian expression (Yoo et al., 2005) is close to the transcription start site (chr8:94386194-94386543). Moreover, this element is evolutionarily conserved across various mammals, including humans, rats, mice, dogs, and opossums (See Author response image 2). Therefore, we reasoned that these non-canonical E boxes might drive the CLOCK/BMAL1 dependent expression of Herp. We have updated the Discussion to reflect these findings in lines 315-319.

Author response image 2.

The calcium experiments shown in Figures 3A-I, could be more convincing if the authors showed that the different Ca2+ sensors are compartment-specific by showing co-localization with a subcellular marker. In the pictures shown it is not even possible to recognize the cell dimensions. 

Following the reviewer’s suggestion, we performed co-staining experiments with organelle specific Ca2+ indicators and organelle markers. First, astrocytes were co-transfected with G-CEPIA1er, an ER specific Ca2+ indicator and ER targeted DsRed2 (with Calreticulin signal sequence). Live imaging analysis showed that the fluorescent intensities of G-CEPIA1er and DsRed2-ER-5 significantly overlapped in co-transfected cells. Secondly, astrocytes were transfected with Mito-R-GECO1 and Mitotracker, a cell permeable mitochondria dye, was applied. The fluorescent intensities of Mito-R-GECO1 and Mitotracker also significantly overlapped. These new data are included in Figure S4 and explained in the Result section (lines 194-195).

Data analysis in Figure 3 K and M is misleading. According to the explanations of the results, each of the experiments to assess ITRP1 or 2 is run independently. Then it is not clear why the relative levels obtained with control or Herp siRNA are plotted as pairs. Same comment as above for Figure 4L and Figure 6D. 

Thank you for the reviewer’s insightful comments. Reviewer1 raised similar issues. Following the reviewers’ suggestions, we have removed the lines connecting the data points in Fig. 3K, 3M, 4L, and 6D.

In Figure 5E the authors need to explain why they consider that repeated measures 2-way ANOVA is the right statistical test to apply. According to the explained experimental design, cells transfected, synchronized, and then harvested independently at the indicated time after synchronization. 

Thank you for the reviewer’s insightful comment. Upon reviewing the statistical methods as suggested, we have revised our approach. Instead of using repeated measures 2-way ANOVA, we have now applied a standard 2-way ANOVA, which is more appropriate given the experimental procedures were independent, as the reviewer pointed out.

The English language needs to be revised throughout the text. 

We have thoroughly revised the English language throughout the text.

Reviewer #3 (Recommendations For The Authors): 

(1) Figure 3. Clarify the physiological importance of 100 µM ATP. Would the Herp rhythm warrant Ca2+ release rhythms under basal conditions? In 3J-K, the relatively weak effect of Herp knockdown on ITPR1/2 levels, albeit statistically significant, may not be physiologically significant. This calls into question the claimed Herp-ITPR axis that underlies the Ca2+ release phenotype. Further, the correlation certainly exists but further characterization of Herp KD cells would be required to address the mechanism. 

As previously reported, a broad range of ATP concentrations can induce Ca2+ activity in the astrocytes (Neary et al., 1988). Originally, we conducted an ATP dose-response analysis to observe ER Ca2+ release in our primary astrocyte culture. Our results show that ER Ca2+ release begins at 50 µM ATP and plateaus at 500 µM. Please refer to Author response image 3. We selected 100µM ATP for our experiments because it induces a medium level of ER Ca2+ response. Importantly, although measuring ATP concentrations at the synapse in vivo is challenging(Tan et al., 2017), estimates suggest synaptic ATP concentrations range from 5-500 µM (Pankratov et al., 2006). Thus, 100µM ATP is a physiologically relevant concentration that can affect nearby cells, including astrocytes, in the nervous system.

Author response image 3.

Cultured astrocytes were transfected with G-CEPIA1er ER and at 48hrs post transfection, cultured astrocytes were treated with various concentrations of ATP and Ca2+ imaging analysis was performed. (A) ΔF/F0 values over time following ATP application. (B) Area above curve values. Values in graphs are mean ± SEM (*p < 0.05, **p < 0.005, ***p < 0.0005, and ****p < 0.00005; one-way ANOVA).

Regarding the comment on Ca2+ release rhythms under basal conditions, we interpret this as referring Ca2+ release in the absence of a stimulus. We typically observe Ca2+ release only upon stimulation, such as ATP treatment. However, we acknowledge that the modest effects of HERP knockdown on ITPR1/2 levels could question the HERP-ITPR axis’s role in ER Ca2+ release.

To address this, we analyzed whether Herp KD induced increases in ER Ca2+ release were mediated through ITPRs by treating cells with Xestospongin C (XesC), an IP3R inhibitor. XesC treatment reduced ATP-induced ER Ca2+ release and eliminated the differences in ER Ca2+ release between control and Herp KD astrocytes (Fig. 3N – 3P). These results clearly indicate that HERP-ITPR axis plays critical role in controlling ER Ca2+ release. These new experiments have been included in Fig. 3 and explained in the result section (lines 217-221).

Furthermore, following the reviewer’s suggestion, we examined whether HERP rhythms underlie the rhythms of ER Ca2+ response by analyzing ER Ca2+ response in Herp KD astrocyte in two different times following synchronization. In control astrocytes, ATP-induced ER Ca2+ responses vary depending on time, whereas these time-dependent variations were abolished in Herp KD astrocytes. These new experiments have been included in Fig. 4K – 4M and explained in the Results section (lines 232-235).

Collectively, these results indicate that HERP rhythms lead to time-dependent differences in ER Ca2+ response through ITPRs.

(2) Figure 4K-L. As data suggested the involvement of ITPR1 and ITPR2 (circadian effect), a reasonable next step is to determine their involvement, but the study did not pursue the hypothesis. 

Thank you for your insightful comment. Our results indeed suggest that rhythms in ITPR2 levels may drive the time-dependent variations in ATP-induced ER Ca2+ release following synchronization. The newly conducted experiments demonstrated that treatment with the ITPR inhibitor XesC suppressed ATP-induced ER Ca2+ release at both control and Herp siRNA treatment conditions (Fig. 3). Based on these findings, we now further confirm that rhythms of ITPR levels, specifically ITPR2 underlie the circadian variations in ER Ca2+ release. While examining the effect of ITPR2 siRNA would directly prove the involvement of ITPR2, we have decided to pursue this experiment in the future studies.

(3) Figure 5A-C. Data from WT cells should be included side by side with Bmal1-/- cells for comparison which is expected to be consistent with the HERP levels as in 5D-E. Again, the role of ITPR2 is suggested but not demonstrated. 

Following the reviewer's suggestion, we conducted additional experiments including both WT and Bmal1-/- cultured astrocytes side-by-side. The results were consistent with our previous findings: WT astrocytes showed rhythms of ER Ca2+ release while Bmal1-/- astrocytes did not. We have updated the Figure 5A to 5C and the corresponding Results section in lines 242-245 accordingly.<br /> Regarding second comment, as mentioned in our previous response, we plan to examine the role of ITPR2 in further studies.

(4) Figure 6. The Connexin data seems an addon and is correlative with the Ca2+ release. The role of Herp and Itpr in Connexin function is not addressed. Figure 6E-F was not called out in the results section. Suggest providing additional data to support the role of the HERP-ITPR axis in regulating Ca2+ release and Connexin activity. 

We agree that additional data are needed to support the role of HERP in regulating CX43 phosphorylation. Therefore, we have conducted further experiments to determine whether rhythms of Cx43 phosphorylation are regulated by HERP. In the control astrocytes, ATP treatment induced time-dependent variations in Cx43 phosphorylation. However, these rhythms were abolished in Herp KD astrocytes. These results indicate that rhythms in HERP levels contribute to the time-dependent variations in Cx43 phosphorylation. These new experiments have included in Fig. 6G and 6H and explained in the results section (lines 276-281).

Regarding second comment, we have corrected our oversight by properly referencing figures 6E-F in the results section. Please refer to lines 357-359 for clarification.

(5) Discussion. This section should focus on noteworthy points to discuss, not repeating the results. 

Based on the reviewer's valuable suggestions, we have revised the Discussion section to minimize repetition of the results. Thank you for your guidance.

(6) The manuscript exhibits numerous grammatical and textual inaccuracies that necessitate careful revision by the authors. My observations here are confined to the title and the abstract alone. I recommend altering the title from "mouse cultured astrocytes" to "cultured mouse astrocytes" for clarity and grammatical correctness. The abstract, meanwhile, needs enhancements both in terms of its content and language. It should incorporate the results of the partitioning among the ER, cytoplasm, and mitochondria, and provide clear definitions for some of the critical terms used. It's worth noting that the abstract's second sentence contains a grammatical error. 

Thank you for the reviewer’s valuable feedback. We have carefully revised the title, abstract, and main text to address the grammatical and textual issues. The title has been changed to “cultured mouse astrocytes”. Additionally, the abstract now includes results related to cytoplasmic Ca2+ dynamics and has been revised in several places. We appreciate your insights and have worked to enhance the content and language accordingly.

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Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

Yang, Hu et al. examined the molecular mechanisms underlying astrocyte activation and its implications for multiple sclerosis. This study shows that the glycolytic enzyme PKM2 relocates to astrocyte nuclei upon activation in EAE mice. Inhibiting PKM2's nuclear import reduces astrocyte activation, as evidenced by decreased proliferation, glycolysis, and inflammatory cytokine release. Crucially, the study identifies TRIM21 as pivotal in regulating PKM2 nuclear import via ubiquitination. TRIM21 interacts with PKM2, promoting its nuclear translocation and enhancing its activity, affecting multiple signaling pathways. Confirmatory analyses using single-cell RNA sequencing and immunofluorescence demonstrate TRIM21 upregulation in EAE astrocytes. Modulating TRIM21 expression in primary astrocytes impacts PKM2-dependent glycolysis and proliferation. In vivo experiments targeting this mechanism effectively mitigate disease severity, CNS inflammation, and demyelination in EAE.

The authors supported their claims with various experimental approaches, however, some results should be supported with higher-quality images clearly depicting the conclusions and additional quantitative analyses of Western blots.

Thanks for the reviewer’s comments. We agree with the reviewer and have added higher magnification images, for example Fig.2A to better visualize the localization of PKM2 in DASA-treated conditions, and Fig. 3A and Fig.3B to better visualize the pSTAT3 and pp65. Moreover, we have added quantitative analyses of Western blots for some key experiments, for example quantitative results for Fig.2D is added in Fig.S3 to show the change of PKM2 and p-c-myc in DASA-58-treated conditions and quantitative results for Fig. 3D are added in Fig.S4B and S4C to show the change of nuclear and cytoplasmic PKM2, STAT3 and NF-κB in different conditions.

Strength:

This study presents a comprehensive investigation into the function and molecular mechanism of metabolic reprogramming in the activation of astrocytes, a critical aspect of various neurological diseases, especially multiple sclerosis. The study uses the EAE mouse model, which closely resembles MS. This makes the results relevant and potentially translational. The research clarifies how TRIM21 regulates the nuclear import of PKM2 through ubiquitination by integrating advanced techniques. Targeting this axis may have therapeutic benefits since lentiviral vector-mediated knockdown of TRIM21 in vivo significantly reduces disease severity, CNS inflammation, and demyelination in EAE animals.

We thank the reviewer for their positive and constructive comments on the manuscript.

Weaknesses:

The authors reported that PKM2 levels are elevated in the nucleus of astrocytes at different EAE phases compared to cytoplasmic localization. However, Figure 1 also shows elevated cytoplasmic expression of PKM2. The authors should clarify the nuclear localization of PKM2 by providing zoomed-in images. An explanation for the increased cytoplasmic PKM2 expression should provided. Similarly, while PKM2 translocation is inhibited by DASA-58, in addition to its nuclear localization, a decrease in the cytoplasmic localization of PKM2 is also observed. This situation brings to mind the possibility of a degradation mechanism being involved when its nuclear translocation of PKM2 is inhibited.

According to the results of immunofluorescence staining of PKM2 in spinal cord of EAE mice and in cultured primary astrocytes, in addition to the observation of PKM2 nuclear translocation in EAE conditions, we showed an elevated expression of PKM2 in astrocytes, including the cytoplasmic and nuclear expression. In neurological diseases, various studies showed consistent results, for example, following spinal cord injury (SCI), not only the upregulated expressing of PKM2 but also nuclear translocation was observed in astrocytes (Zhang et al., 2015). In EAE conditions, CNS inflammation is elevated and several proinflammatory cytokines and chemokines might contribute to the upregulated expression of PKM2 in astrocytes. We have tested TNFα and IL-1β, which are recognized to play important roles in EAE and MS (Lin and Edelson, 2017, Wheeler et al., 2020), and results from western blots showed the increased expression of PKM2 upon stimulation with TNFα and IL-1β (Author response image 1). Moreover, according to the reviewer’s suggestions, we have added zoomed-in images for figure 2A.

Additionally, the reviewer has noted the decrease in the cytoplasmic PKM2 level, degradation-related mechanism and other mechanisms might be involved in this process.

Author response image 1.

Upregulated expression of PKM2 in astrocytes following stimulation with TNF-α and IL-1β. Primary astrocytes were stimulated with TNF-α and IL-1β (50 ng/mL) for 48 h and western blotting analysis were performed.

In Figure 3D, the authors claim that PKM2 expression causes nuclear retention of STAT3, p65, and p50, and inhibiting PKM2 localization with DASA-58 suppresses this retention. The western blot results for the MOG-stimulated group show high levels of STAT3, p50, and p65 in nuclear localization. However, in the MOG and DASA-58 treated group, one would expect high levels of p50, p65, and STAT3 proteins in the cytoplasm, while their levels decrease in the nucleus. These western blot results could be expanded. Additionally, intensity quantification for these results would be beneficial to see the statistical difference in their expressions, especially to observe the nuclear localization of PKM2.

We agree with the reviewer’s comments and we have incorporated the quantification of STAT3，p50 and p65 for Fig.3D and Fig.S4B and Fig.S4C. Nevertheless, given that DASA-58 did not trigger a notable increase in the cytoplasmic level of PKM2, we did not detect an upregulation of STAT3, p50, or p65 in the cytoplasm of the MOG and DASA-58-treated groups. With the quantification results, it is more obvious to see the changes of these proteins in different conditions.

The discrepancy between Figure 7A and its explaining text is confusing. The expectation from the knocking down of TRIM21 is the amelioration of activated astrocytes, leading to a decrease in inflammation and the disease state. The presented results support these expectations, while the images showing demyelination in EAE animals are not highly supportive. Clearly labeling demyelinated areas would enhance readers' understanding of the important impact of TRIM21 knockdown on reducing the disease severity.

Thank you for pointing this out. We sincerely apologize for our carelessness. Based on your comments, we have made the corrections in the manuscript. As there is indeed a statistical difference in the mean clinical scores between shTRIM21-treated group and shVec group, we have accordingly revised the sentence for Figure 7A to state, “At the end time point at day 22 p.i., shTRIM21-treated group showed reduced disease scores compared to control groups (Fig. 7A).” .

Additionally, we have added the whole image of the spinal cord for MBP in Author Response image 2. Moreover, we have labelled the demyelinated areas to facilitate readers’ understanding.

Author response image 2.

MBP staining of the whole spinal cord in EAE mice from shVec and shTRIM21 group. Scale bar: 100 μm. Demyelinated areas are marked with dashed lines.

Reviewer #2 (Public Review):

This study significantly advances our understanding of the metabolic reprogramming underlying astrocyte activation in neurological diseases such as multiple sclerosis. By employing an experimental autoimmune encephalomyelitis (EAE) mouse model, the authors discovered a notable nuclear translocation of PKM2, a key enzyme in glycolysis, within astrocytes.

Preventing this nuclear import via DASA 58 substantially attenuated primary astrocyte activation, characterized by reduced proliferation, glycolysis, and inflammatory cytokine secretion.<br /> Moreover, the authors uncovered a novel regulatory mechanism involving the ubiquitin ligase TRIM21, which mediates PKM2 nuclear import. TRIM21 interaction with PKM2 facilitated its nuclear translocation, enhancing its activity in phosphorylating STAT3, NFκB, and c-myc. Single-cell RNA sequencing and immunofluorescence staining further supported the upregulation of TRIM21 expression in astrocytes during EAE.

Manipulating this pathway, either through TRIM21 overexpression in primary astrocytes or knockdown of TRIM21 in vivo, had profound effects on disease severity, CNS inflammation, and demyelination in EAE mice. This comprehensive study provides invaluable insights into the pathological role of nuclear PKM2 and the ubiquitination-mediated regulatory mechanism driving astrocyte activation.

The author's use of diverse techniques, including single-cell RNA sequencing, immunofluorescence staining, and lentiviral vector knockdown, underscores the robustness of their findings and interpretations. Ultimately, targeting this PKM2-TRIM21 axis emerges as a promising therapeutic strategy for neurological diseases involving astrocyte dysfunction.

While the strengths of this piece of work are undeniable, some concerns could be addressed to refine its impact and clarity further; as outlined in the recommendations for the authors.

Thanks for the reviewer’s comment and positive evaluation of our present work. We have further answered each question in recommendations section.

Reviewer #3 (Public Review):

Summary:

Pyruvate kinase M2 (PKM2) is a rate-limiting enzyme in glycolysis and its translocation to the nucleus in astrocytes in various nervous system pathologies has been associated with a metabolic switch to glycolysis which is a sign of reactive astrogliosis. The authors investigated whether this occurs in experimental autoimmune encephalomyelitis (EAA), an animal model of multiple sclerosis (MS). They show that in EAA, PKM2 is ubiquitinated by TRIM21 and transferred to the nucleus in astrocytes. Inhibition of TRIM21-PKM2 axis efficiently blocks reactive gliosis and partially alleviates symptoms of EAA. Authors conclude that this axis can be a potential new therapeutic target in the treatment of MS.

Strengths:

The study is well-designed, controls are appropriate and a comprehensive battery of experiments has been successfully performed. Results of in vitro assays, single-cell RNA sequencing, immunoprecipitation, RNA interference, molecular docking, and in vivo modeling etc. complement and support each other.

Weaknesses:

Though EAA is a valid model of MS, a proposed new therapeutic strategy based on this study needs to have support from human studies.