Auhtor response:

Public Reviews:

Reviewer #1 (Public review):

The study analyzes the gastric fluid DNA content identified as a potential biomarker for human gastric cancer. However, the study lacks overall logicality, and several key issues require improvement and clarification. In the opinion of this reviewer, some major revisions are needed:

(1) This manuscript lacks a comparison of gastric cancer patients' stages with PN and N+PD patients, especially T0-T2 patients.

We are grateful for this astute remark. A comparison of gfDNA concentration among the diagnostic groups indicates a trend of increasing values as the diagnosis progresses toward malignancy. The observed values for the diagnostic groups are as follows:

Author response table 1.

The chart below presents the statistical analyses of the same diagnostic/tumor-stage groups (One-Way ANOVA followed by Tukey’s multiple comparison tests). It shows that gastric fluid gfDNA concentrations gradually increase with malignant progression. We observed that the initial tumor stages (T0 to T2) exhibit intermediate gfDNA levels, which in this group is significantly lower than in advanced disease (p = 0.0036), but not statistically different from non-neoplastic disease (p = 0.74).

Author response image 1.

(2) The comparison between gastric cancer stages seems only to reveal the difference between T3 patients and early-stage gastric cancer patients, which raises doubts about the authenticity of the previous differences between gastric cancer patients and normal patients, whether it is only due to the higher number of T3 patients.

We appreciate the attention to detail regarding the numbers analyzed in the manuscript. Importantly, the results are meaningful because the number of subjects in each group is comparable (T0-T2, N = 65; T3, N = 91; T4, N = 63). The mean gastric fluid gfDNA values (ng/µL) increase with disease stage (T0-T2: 15.12; T3-T4: 30.75), and both are higher than the mean gfDNA values observed in non-neoplastic disease (10.81 ng/µL for N+PD and 10.10 ng/µL for PN). These subject numbers in each diagnostic group accurately reflect real-world data from a tertiary cancer center.

(3) The prognosis evaluation is too simplistic, only considering staging factors, without taking into account other factors such as tumor pathology and the time from onset to tumor detection.

Histopathological analyses were performed throughout the study not only for the initial diagnosis of tissue biopsies, but also for the classification of Lauren’s subtypes, tumor staging, and the assessment of the presence and extent of immune cell infiltrates. Regarding the time of disease onset, this variable is inherently unknown--by definition--at the time of a diagnostic EGD. While the prognosis definition is indeed straightforward, we believe that a simple, cost-effective, and practical approach is advantageous for patients across diverse clinical settings and is more likely to be effectively integrated into routine EGD practice.

(4) The comparison between gfDNA and conventional pathological examination methods should be mentioned, reflecting advantages such as accuracy and patient comfort.

We wish to reinforce that EGD, along with conventional histopathology, remains the gold standard for gastric cancer evaluation. EGD under sedation is routinely performed for diagnosis, and the collection of gastric fluids for gfDNA evaluation does not affect patient comfort. Thus, while gfDNA analysis was evidently not intended as a diagnostic EGD and biopsy replacement, it may provide added prognostic value to this exam.

(5) There are many questions in the figures and tables. Please match the Title, Figure legends, Footnote, Alphabetic order, etc.

We are grateful for these comments and apologize for the clerical oversight. All figures, tables, titles and figure legends have now been double-checked.

(6) The overall logicality of the manuscript is not rigorous enough, with few discussion factors, and cannot represent the conclusions drawn.

We assume that the unusual wording remark regarding “overall logicality” pertains to the rationale and/or reasoning of this investigational study. Our working hypothesis was that during neoplastic disease progression, tumor cells continuously proliferate and, depending on various factors, attract immune cell infiltrates. Consequently, both tumor cells and immune cells (as well as tumor-derived DNA) are released into the fluids surrounding the tumor at its various locations, including blood, urine, saliva, gastric fluids, and others. Thus, increases in DNA levels within some of these fluids have been documented and are clinically meaningful. The concurrent observation of elevated gastric fluid gfDNA levels and immune cell infiltration supports the hypothesis that increased gfDNA—which may originate not only from tumor cells but also from immune cells—could be associated with better prognosis, as suggested by this study of a large real-world patient cohort.

In summary, we thank Reviewer #1 for his time and effort in a constructive critique of our work.

Reviewer #2 (Public review):

Summary:

The authors investigated whether the total DNA concentration in gastric fluid (gfDNA), collected via routine esophagogastroduodenoscopy (EGD), could serve as a diagnostic and prognostic biomarker for gastric cancer. In a large patient cohort (initial n=1,056; analyzed n=941), they found that gfDNA levels were significantly higher in gastric cancer patients compared to non-cancer, gastritis, and precancerous lesion groups. Unexpectedly, higher gfDNA concentrations were also significantly associated with better survival prognosis and positively correlated with immune cell infiltration. The authors proposed that gfDNA may reflect both tumor burden and immune activity, potentially serving as a cost-effective and convenient liquid biopsy tool to assist in gastric cancer diagnosis, staging, and follow-up.

Strengths:

This study is supported by a robust sample size (n=941) with clear patient classification, enabling reliable statistical analysis. It employs a simple, low-threshold method for measuring total gfDNA, making it suitable for large-scale clinical use. Clinical confounders, including age, sex, BMI, gastric fluid pH, and PPI use, were systematically controlled. The findings demonstrate both diagnostic and prognostic value of gfDNA, as its concentration can help distinguish gastric cancer patients and correlates with tumor progression and survival. Additionally, preliminary mechanistic data reveal a significant association between elevated gfDNA levels and increased immune cell infiltration in tumors (p=0.001).

Reviewer #2 has conceptually grasped the overall rationale of the study quite well, and we are grateful for their assessment and comprehensive summary of our findings.

Weaknesses:

(1) The study has several notable weaknesses. The association between high gfDNA levels and better survival contradicts conventional expectations and raises concerns about the biological interpretation of the findings.

We agree that this would be the case if the gfDNA was derived solely from tumor cells. However, the findings presented here suggest that a fraction of this DNA would be indeed derived from infiltrating immune cells. The precise determination of the origin of this increased gfDNA remains to be achieved in future follow-up studies, and these are planned to be evaluated soon, by applying DNA- and RNA-sequencing methodologies and deconvolution analyses.

(2) The diagnostic performance of gfDNA alone was only moderate, and the study did not explore potential improvements through combination with established biomarkers. Methodological limitations include a lack of control for pre-analytical variables, the absence of longitudinal data, and imbalanced group sizes, which may affect the robustness and generalizability of the results.

Reviewer #2 is correct that this investigational study was not designed to assess the diagnostic potential of gfDNA. Instead, its primary contribution is to provide useful prognostic information. In this regard, we have not yet explored combining gfDNA with other clinically well-established diagnostic biomarkers. We do acknowledge this current limitation as a logical follow-up that must be investigated in the near future.

Moreover, we collected a substantial number of pre-analytical variables within the limitations of a study involving over 1,000 subjects. Longitudinal samples and data were not analyzed here, as our aim was to evaluate prognostic value at diagnosis. Although the groups are imbalanced, this accurately reflects the real-world population of a large endoscopy center within a dedicated cancer facility. Subjects were invited to participate and enter the study before sedation for the diagnostic EGD procedure; thus, samples were collected prospectively from all consenting individuals.

Finally, to maintain a large, unbiased cohort, we did not attempt to balance the groups, allowing analysis of samples and data from all patients with compatible diagnoses (please see Results: Patient groups and diagnoses).

(3) Additionally, key methodological details were insufficiently reported, and the ROC analysis lacked comprehensive performance metrics, limiting the study's clinical applicability.

We are grateful for this useful suggestion. In the current version, each ROC curve (Supplementary Figures 1A and 1B) now includes the top 10 gfDNA thresholds, along with their corresponding sensitivity and specificity values (please see Suppl. Table 1). The thresholds are ordered from-best-to-worst based on the classic Youden’s J statistic, as follows:

Youden Index = specificity + sensitivity – 1 [Youden WJ. Index for rating diagnostic tests. Cancer 3:32-35, 1950. PMID: 15405679]. We have made an effort to provide all the key methodological details requested, but we would be glad to add further information upon specific request.

Author response:

Reviewer 1:

Summary:

Identifying drugs that target specific disease phenotypes remains a persistent challenge. Many current methods are only applicable to well-characterized small molecules, such as those with known structures. In contrast, methods based on transcriptional responses offer broader applicability because they do not require prior information about small molecules. Additionally, they can be rapidly applied to new small molecules. One of the most promising strategies involves the use of “drug response signatures”-specific sets of genes whose differential expression can serve as markers for the response to a small molecule. By comparing drug response signatures with expression profiles characteristic of a disease, it is possible to identify drugs that modulate the disease profile, indicating a potential therapeutic connection.

This study aims to prioritize potential drug candidates and to forecast novel drug combinations that may be effective in treating triple-negative breast cancer (TNBC). Large consortia, such as the LINCS-L1000 project, offer transcriptional signatures across various time points after exposing numerous cell lines to hundreds of compounds at different concentrations. While this data is highly valuable, its direct applicability to pathophysiological contexts is constrained by the challenges in extracting consistent drug response profiles from these extensive datasets. The authors use their method to create drug response profiles for three different TNBC cell lines from LINCS.

To create a more precise, cancer-specific disease profile, the authors highlight the use of single-cell RNA sequencing (scRNA-seq) data. They focus on TNBC epithelial cells collected from 26 diseased individuals compared to epithelial cells collected from 10 healthy volunteers. The authors are further leveraging drug response data to develop inhibitor combinations.

Strengths:

The authors of this study contribute to an ongoing effort to develop automated, robust approaches that leverage gene expression similarities across various cell lines and different treatment regimens, aiming to predict drug response signatures more accurately. The authors are trying to address the gap that remains in computational methods for inferring drug responses at the cell subpopulation level.

Weaknesses:

One weakness is that the authors do not compare their method to previous studies. The authors develop a drug response profile by summarizing the time points, concentrations, and cell lines. The computational challenge of creating a single gene list that represents the transcriptional response to a drug across different cell lines and treatment protocols has been previously addressed. The Prototype Ranked List (PRL) procedure, developed by Iorio and co-authors (PNAS, 2010, doi:10.1073/pnas.1000138107), uses a hierarchical majority-voting scheme to rank genes. This method generates a list of genes that are consistently overexpressed or downregulated across individual conditions, which then hold top positions in the PRL. The PRL methodology was used by Aissa and co-authors (Nature Comm 2021, doi:10.1038/s41467-021-21884-z) to analyze drug effects on selective cell populations using scRNA-seq datasets. They combined PRL with Gene Set Enrichment Analysis (GSEA), a method that compares a ranked list of genes like PRL against a specific set of genes of interest. GSEA calculates a Normalized Enrichment Score (NES), which indicates how well the genes of interest are represented among the top genes in the PRL. Compared to the method described in the current manuscript, the PRL method allows for the identification of both upregulated and downregulated transcriptional signatures relevant to the drug’s effects. It also gives equal weight to each cell line’s contribution to the drug’s overall response signature.

The authors performed experimental validation of the top two identified drugs; however, the effect was modest. In addition, the effect on TNBC cell lines was cell-line specific as the identified drugs were effective against BT20, whose transcriptional signatures from LINCS were used for drug identification, but not against the other two cell lines analyzed. An incorrect choice of genes for the signature may result in capturing similarities tied to experimental conditions (e.g., the same cell line) rather than the drug’s actual effects. This reflects the challenges faced by drug response signature methods in both selecting the appropriate subset of genes that make up the signature and managing the multiple expression profiles generated by treating different cell lines with the same drug.

We appreciate the reviewer’s thoughtful feedback and their suggestion to refer to the Prototype Ranked List (PRL) manuscript. Unfortunately, since this methodology for the PRL isn’t implemented in an open-source package, direct comparison with our approach is challenging. Nonetheless, we investigated whether using ranks would yield similar results for the most likely active drug pairs identified by retriever. To do this, we calculated and compared the rankings of the average effect sizes provided by retriever. Although the Spearman (ρ \= 0.98) correlation coefficient was high, we observed that key genes are disadvantaged when using ranks compared to effect sizes. This difference is particularly evident in the gene set enrichment analysis, where using average ranks identified only one pathway as statistically significantly enriched. The code to replicate these analyses is available at https://github.com/dosorio/L1000-TNBC/blob/main/Code/.

Author response image 1.

Given the similarity in purpose between retriever and the PRL approach, we have added the following statement to the introduction: “Previously, this goal was approached using a majority-voting scheme to rank genes across various cell types, concentrations, and time points. This approach generates a prototype ranked list (PRL) that represents the consistent ranks of genes across several cell lines in response to a specific drug.”

Regarding the experimental validation, we believe there is a misunderstanding about the evidence we provided. We would like to claridy that we used three different TNBC cell lines: CAL120, BT20, and DU4475. It’s important to note that CAL120 and DU4475 were not included in the signature generation process. Despite this, we observed effects that exceeded the additive effects expectations, particularly in the CAL120 cell line (Figure 5, Panel F).

Reviewer 2:

Summary:

In their study, Osorio and colleagues present ‘retriever,’ an innovative computational tool designed to extract disease-specific transcriptional drug response profiles from the LINCS-L1000 project. This tool has been effectively applied to TNBC, leveraging single-cell RNA sequencing data to predict drug combinations that may effectively target the disease. The public review highlights the significant integration of extensive pharmacological data with high-resolution transcriptomic information, which enhances the potential for personalized therapeutic applications.

Strengths:

A key finding of the study is the prediction and validation of the drug combination QL-XII-47 and GSK-690693 for the treatment of TNBC. The methodology employed is robust, with a clear pathway from data analysis to experimental confirmation.

Weaknesses:

However, several issues need to be addressed. The predictive accuracy of ’retriever’ is contingent upon the quality and comprehensiveness of the LINCS-L1000 and single-cell datasets utilized, which is an important caveat as these datasets may not fully capture the heterogeneity of patient responses to treatment. While the in vitro validation of the drug combinations is promising, further in vivo studies and clinical trials are necessary to establish their efficacy and safety. The applicability of these findings to other cancer types also warrants additional investigation. Expanding the application of ’retriever’ to a broader range of cancer types and integrating it with clinical data will be crucial for realizing its potential in personalized medicine. Furthermore, as the study primarily focuses on kinase inhibitors, it remains to be seen how well these findings translate to other drug classes.

We thank the reviewer for their thoughtful and constructive feedback. We appreciate your insights and agree that several important considerations need to be addressed.

We recognize that the predictive accuracy of retriever depends on the LINCS-L1000 and single-cell datasets. These resources may not fully represent the complete range of transcriptional responses to disease and treatment across different patients. As you mentioned, this is an important limitation. However, we believe that by extrapolating the evaluation of the most likely active compound to each individual patient, we can help address this issue. This approach will provide valuable insights into which patients in the study are most likely to respond positively to treatment.

On the in-vitro validation of drug combinations, we agree that while promising, these results are not sufficient on their own to establish clinical efficacy. Additional in-vivo studies will be essential in assessing the therapeutic potential and safety of these combinations, and clinical trials will be an important next step to validate the translational impact of our findings.

Lastly, we appreciate the reviewer’s comment about the focus of our study on kinase inhibitors. This result was unexpected, as we tested the full set of compounds from the LINCS-L1000 project. We agree that exploring other top candidates, including different drug classes, will be important for assessing how broadly retriever approach can be applied.

Author response:

Public Reviews:

Reviewer #1 (Public review):

Summary:

Pradhan et al investigated the potential gustatory mechanisms that allow flies to detect cholesterol. They found that flies are indifferent to low cholesterol and avoid high cholesterol. They further showed that the ionotropic receptors Ir7g, Ir51b, and Ir56d are important for the cholesterol sensitivity in bitter neurons. The figures are clear and the behavior result is interesting. However, I have several major comments, especially on the discrepancy of the expression of these Irs with other lab published results, and the confusing finding that the same receptors (Ir7g, Ir51b) have been implicated in the detection of various seemingly unrelated compounds.

Strengths:

The results are very well presented, the figures are clear and well-made, text is easy to follow.

Weaknesses:

(1) Regarding the expression of Ir56d. The reported Ir56d expression pattern contradicts multiple previous studies (Brown et al., 2021 eLife, Figure 6a-c; Sanchez-Alcaniz et al., 2017 Nature Communications, Figure 4e-h; Koh et al., 2014 Neuron, Figure 3b). These studies, using three different driver lines, consistently showed Ir56d expression in sweet-sensing neurons and taste peg neurons. Importantly, Sanchez-Alcaniz et al. demonstrated that Ir56d is not expressed in Gr66a-expressing (bitter) neurons. This discrepancy is critical since Ir56d is identified as the key subunit for cholesterol detection in bitter neurons, and misexpression of Ir7g and Ir51b together is insufficient to confer cholesterol sensitivity (Fig.4b,d). Which Ir56d-GAL4 (and Gr66a-I-GFP) line was used in this study? Is there additional evidence (scRNA sequencing, in-situ hybridization, or immunostaining) supporting Ir56d expression in bitter neurons?

We agree that the expression pattern of Ir56d diverges from two prior reports . The studies by Brown et al. and Koh et al. employed the same Ir56d-GAL4 driver line, which exhibited expression in sweet-sensing gustatory receptor neurons (GRNs) and taste peg neurons, but not bitter GRNs (the Sanchez-Alcaniz et al. paper did not use an Ir56d-Gal4).

In our study, we used a Ir56d-GAL4 driver line (KDRC:2307) and the Gr66a-I-GFP reporter line (Weiss et al., 2011 Neuron). This is a crucial distinction, as differences in the regulatory regions used to generate different driver lines are well known to underlie differences in expression patterns. Our double-labeling experiments revealed co-expression of Ir56d with Gr66a-positive bitter GRNs specifically within the S6 and S7 sensilla—types previously shown to exhibit strong electrophysiological responses to cholesterol (Figure 2—figure supplement 1F).

We believe this observation is biologically significant and consistent with our functional data. Specifically, targeted expression of Ir56d in bitter neurons using the Gr33a-GAL4 was sufficient to rescue cholesterol avoidance behavior in Ir56d¹ mutants (Figure 3G). These results demonstrate that Ir56d plays a functional role in bitter GRNs for cholesterol detection. The convergence of genetic, behavioral, and electrophysiological data presented in our study provides compelling support for this previously unappreciated expression pattern and function of Ir56d.

(2) Ir51b has previously been implicated in detecting nitrogenous waste (Dhakal 2021), lactic acid (Pradhan 2024), and amino acids (Aryal 2022), all by the same lab. Additionally, both Ir7g and Ir51b have been implicated in detecting cantharidin, an insect-secreted compound that flies may or may not encounter in the wild, by the same lab. Is Ir51b proposed to be a specific receptor for these chemically distinct compounds or a general multimodal receptor for aversive stimuli? Unlike other multimodal bitter receptors, the expression level of Ir51b is rather low and it's unclear which subset of GRNs express this receptor. The chemical diversity among nitrogenous waste, amino acids, lactic acid, cantharidin, and cholesterol raises questions about the specificity of these receptors and warrants further investigation and at a minimum discussion in this paper. Given the wide and seemingly unrelated sensitivity of Ir51b and Ir7g to these compounds I'm leaning towards the hypothesis that at least some of these is non-specific and ecologically irrelevant without further supporting evidence from the authors.

While it is true that IR51b and IR7g are responsive to a range of compounds, they share chemical features such as nitrogen-containing groups, hydrophobicity, or amphipathic structures suggesting that recognition of these chemicals may be mediated by the same or overlapping domains within the receptor complexes. These features could facilitate binding to a structurally diverse yet chemically related groups of aversive ligands.

In the case of cholesterol, while its sterol ring system is distinct from the other compounds, it shares hydrophobic and amphipathic properties that may enable interaction with these receptors via similar structural motifs. Importantly, our data demonstrates that Ir51b and Ir7g are necessary but not sufficient on their own to confer cholesterol sensitivity, indicating that additional co-factors or receptor subunits are required for full functionality (Figure 4B, D). Furthermore, our dose-response analysis (Figure 3F) shows that Ir7g is particularly important at higher cholesterol concentrations, supporting the idea of graded sensitivity rather than indiscriminate activation. This suggests that these receptors may have evolved to recognize cholesterol and its analogs (e.g., phytosterols such as stigmasterol, yet to be tested), which are naturally found in the fly’s diet (e.g., yeast and plant-derived matter), as ecologically relevant cues signaling microbial contamination, lipid imbalance, or dietary overconsumption.

We acknowledge the reviewer’s concern regarding the relatively low expression levels of Ir51b and Ir7g. However, we note that low transcript abundance does not necessarily equate to diminished physiological relevance. Finally, we agree that the chemical diversity of ligands associated with Ir51b and Ir7g warrants deeper investigation, particularly through structure-function studies aimed at identifying ligand-binding domains and receptor-ligand interactions at atomic resolution.

(3) The Benton lab Ir7g-GAL4 reporter shows no expression in adults. Additionally, two independent labellar RNA sequencing studies (Dweck, 2021 eLife; Bontonou et al., 2024 Nature Communications) failed to detect Ir7g expression in the labellum. This contradicts the authors' previous RT-PCR results (Pradhan 2024 Fig. S4, Journal of Hazardous Materials) showing Ir7g expression in the labellum. Additionally the Benton and Carlson lab Ir51b-GAL4 reporters show no expression in adults as well. Please address these inconsistencies.

With respect to Ir7g, we acknowledge that the Ir7g-GAL4 reporter line from the Benton lab does not exhibit detectable expression in adult labella. Furthermore, two independent transcriptomic studies—Dweck et al., 2021 (eLife) and Bontonou et al., 2024 (Nature Communications) also did not detect Ir7g transcripts in bulk RNA-seq datasets derived from adult labella. However, our previously published RT-PCR data (Pradhan et al., 2024, Journal of Hazardous Materials, Fig. S4) revealed Ir7g expression in labellar tissue, albeit at low levels. Our RT-PCR includes an internal control (tubulin) with the same reaction tube with control and the Ir7g mutant as a negative control. Therefore, we stand behind the findings that Ir7g is expressed in the labellum.

We would like to point out that RT-PCR is more sensitive and better-suited to detect low-abundance transcripts than bulk RNA-seq, which may fail to capture transcripts due to limitations in depth of coverage. Moreover, immunohistochemistry can have limitations in detecting very low expression levels. Costa et al. 2013 (Translational lung cancer research) states that “RNA-Seq technique will not likely replace current RT-PCR methods, but will be complementary depending on the needs and the resources as the results of the RNA-Seq will identify those genes that need to then be examined using RT-PCR methods”.

Similarly, regarding Ir51b, while the GAL4 reporter lines from the Benton and Carlson labs do not show robust adult expression, our RT-PCR and functional data strongly support a role for Ir51b in labellar bitter GRNs. Specifically, Ir51b¹ mutants display electrophysiological deficits in response to cholesterol (Figure 2A–B), and these defects are rescued by expressing Ir51b in Gr33a-positive bitter neurons (Figure 3G), providing functional validation of the RT-PCR expression.

(4) The premise that high cholesterol intake is harmful to flies, which makes sensory mechanisms for cholesterol avoidance necessary, is interesting but underdeveloped. Animal sensory systems typically evolve to detect ecologically relevant stimuli with dynamic ranges matching environmental conditions. Given that Drosophila primarily consume fruits and plant matter (which contain minimal cholesterol) rather than animal-derived foods (which contain higher cholesterol), the ecological relevance of cholesterol detection requires more thorough discussion. Furthermore, at high concentrations, chemicals often activate multiple receptors beyond those specifically evolved for their detection. If the cholesterol concentrations used in this study substantially exceed those encountered in the fly's natural diet, the observed responses may represent an epiphenomenon rather than an ecologically and ethologically relevant sensory mechanism. What is the cholesterol content in flies' diet and how does that compare to the concentrations used in this paper?

Drosophila melanogaster cannot synthesize sterols de novo, and must acquire them from its diet. In natural environments, flies acquire sterols from fermenting fruit, decaying plant matter, and yeast, which contain trace amounts of phytosterols (e.g., stigmasterol, β-sitosterol) and ergosterol. While the exact sterol concentrations in these sources remain uncharacterized, our behavioral assays used concentrations (0.001–0.01% by weight) that align with the low levels expected in such nutrient-limited ecological niches.

In our study, the cholesterol concentrations tested ranged from 0.001% to 0.1%, thereby spanning both the physiologically relevant and slightly elevated range. Importantly, avoidance behaviors and receptor activation were most prominent at 0.1% cholesterol. While it is true that high chemical concentrations may elicit off-target effects via broad receptor activation, our genetic and electrophysiological data indicate that the observed responses are mediated by specific ionotropic receptors (Ir51b, Ir7g, Ir56d) and not merely generalized chemical stress.

Ecologically, elevated sterol levels may also signal conditions unsuitable for egg-laying or larval development. For example, high levels of cholesterol or other sterols may occur in substrates colonized by pathogenic microbes, decaying animal tissue, or in cases of abnormal microbial fermentation, which could represent a nutritional or microbial hazard. The avoidance of cholesterol may help signal the flies to avoid consuming decaying animal tissue. In this context, sensory detection of excessive cholesterol might serve as a protective function.

Reviewer #2 (Public review):

Summary:

In Cholesterol Taste Avoidance in Drosophila melanogaster, Pradhan et al. used behavioral and electrophysiological assays to demonstrate that flies can: (1) detect cholesterol through a subset of bitter-sensing gustatory receptor neurons (GRNs) and (2) avoid consuming food with high cholesterol levels. Mechanistically, they identified five members of the IR family as necessary for cholesterol detection in GRNs and for the corresponding avoidance behavior. Ectopic expression experiments further suggested that Ir7g + Ir56d or Ir51b + Ir56d may function as tuning receptors for cholesterol detection, together with the Ir25a and Ir76b co-receptors.

Strengths:

The experimental design of this study was logical and straightforward. Leveraging their expertise in the Drosophila taste system, the research team identified the molecular and cellular basis of a previously unrecognized taste category, expanding our understanding of gustation. A key strength of the study was its combination of electrophysiological recordings with behavioral genetic experiments.

Weaknesses:

My primary concern with this study is the lack of a systematic survey of the IRs of interest in the labellum GRNs. Consequently, there is no direct evidence linking the expression of putative cholesterol IRs to the B GRNs in the S6 and S7 sensilla.

Specifically, the authors need to demonstrate that the IR expression pattern explains cholesterol sensitivity in the B GRNs of S6 and S7 sensilla, but not in other sensilla. Instead of providing direct IR expression data for all candidate IRs (as shown for Ir56d in Figure 2-figure supplement 1F), the authors rely on citations from several studies (Lee, Poudel et al. 2018; Dhakal, Sang et al. 2021; Pradhan, Shrestha et al. 2024) to support their claim that Ir7g, Ir25a, Ir51b, and Ir76b are expressed in B GRNs (Lines 192-194). However, none of these studies provide GAL4 expression or in situ hybridization data to substantiate this claim.

Without a comprehensive IR expression profile for GRNs across all taste sensilla, it is difficult to interpret the ectopic expression results observed in the B GRN of the I9 sensillum or the A GRN of the L-sensillum (Figure 4). It remains equally plausible that other tuning IRs-beyond the co-receptor Ir25a and Ir76b-could interact with the ectopically expressed IRs to confer cholesterol sensitivity, rather than the proposed Ir7g + Ir56d or Ir51b + Ir56d combinations.

We provide electrophysiological data demonstrating that the S6 and S7 sensilla respond to cholesterol (Figure 1D). This finding is consistent with the hypothesis that these sensilla harbor the complete receptor complexes necessary for cholesterol detection. In our electrophysiological recordings, only those bitter GRNs that co-express Ir56d along with either Ir7g or Ir51b generate action potentials in response to cholesterol. Other S-type sensilla lacking one or more of these subunits remain unresponsive, reinforcing the idea that these components are necessary for receptor function and sensory coding of cholesterol. Moreover, in the cholesterol-insensitive I9 sensillum (based on our mapping results using electrophysiology), co-expression of either Ir7g + Ir56d or Ir51b + Ir56d conferred de novo cholesterol sensitivity (Figure 4B). Importantly, no cholesterol response was observed when any of these IRs was expressed alone or when Ir7g + Ir51b were co-expressed without Ir56d. These findings strongly argue against the possibility that endogenous tuning IRs in I9 sensilla (e.g., Ir25a, Ir76b) are sufficient to generate cholesterol responsiveness.

Furthermore, based on the literature, Ir25a and Ir76b are endogenously expressed in I- and L-type sensilla. Thus, their presence alone is insufficient for cholesterol responsiveness. These data support the model that cholesterol sensitivity depends on a specific, multi-subunit receptor complex (e.g., Ir7g + Ir25a + Ir56d + Ir76b or Ir51b + Ir25a + Ir56d + Ir76b).

In conclusion, while we acknowledge that our data do not provide a full anatomical map of IR expression across all sensilla, our results strongly support the idea that cholesterol sensitivity in S6 and S7 sensilla arises from specific combinations of IRs expressed in the B GRNs.

Reviewer #3 (Public review):

Summary:

Whether and how animals can taste cholesterol is not well understood. The study provides evidence that 1) cholesterol activates a subset of bitter-sensing gustatory receptor neurons (GRNs) in the fly labellum, but not other types of GRNs, 2) flies show aversion to high concentrations of cholesterol, and this is mediated by bitter GRNs, and 3) cholesterol avoidance depends on a specific set of ionotropic receptor (IR) subunits acting in bitter GRNs. The claims of the study are supported by electrophysiological recordings, genetic manipulations, and behavioral readouts.

Strengths:

Cholesterol taste has not been well studied, and the paper provides new insight into this question. The authors took a comprehensive and rigorous approach in several different parts of the paper, including screening the responses of all 31 labellar sensilla, screening a large panel of receptor mutants, and performing misexpression experiments with nearly every combination of the 5 IRs identified. The effects of the genetic manipulations are very clear and the results of electrophysiological and behavioral studies match nicely, for the most part. The appropriate controls are performed for all genetic manipulations.

Weaknesses:

The weaknesses of the study, described below, are relatively minor and do not detract from the main conclusions of the paper.

(1) The paper does not state what concentrations of cholesterol are present in Drosophila's natural food sources. Are the authors testing concentrations that are ethologically Drosophila melanogaster primarily feeds on fermenting fruits and associated microbial communities, especially yeast, which serve as major sources of dietary sterols. These natural food sources are known to contain phytosterols such as stigmasterol and β-sitosterol. One study quantified phytosterols (e.g., stigmasterol, sitosterol) in fruits, reporting concentrations between 1.6–32.6 mg/100 g edible portion (~0.0016–0.0326% wet weight) (Han et al 2008). The range we tested falls within this range. Additionally, ergosterol, the principal sterol in yeast and a structural analog of cholesterol, is present at levels of about 0.005% to 0.02% in yeast-rich environments.

To ensure physiological relevance, we designed our behavioral assays to include a broad concentration range of cholesterol, from 10^-5% to 10^-1%. This spans both physiological levels (0.001–0.01%), which are comparable to those found in the natural diet, and supra-physiological levels (e.g., 0.1%), which exceed natural exposure but help define the threshold for aversive behavior.

Our results demonstrate that flies begin to avoid cholesterol at concentrations ≥10^-3% more (Figure 3A), which falls within the upper physiological range and may reflect the threshold beyond which cholesterol or related sterols become deleterious. At these higher concentrations, excess sterols may disrupt membrane fluidity, interfere with hormone signaling, or promote microbial overgrowth—all of which could compromise fly health.

(2) The paper does not state or show whether the expression of IR7g, IR51b, and IR56d is confined to bitter GRNs. Bitter-specific expression of at least some of these receptors would be necessary to explain why bitter GRNs but not sugar GRNs (or other GRN types) normally show cholesterol responses.

We show the Ir56d-Gal4 is co-expressed with Gr66a-GFP in S6/S7 sensilla, indicating that it is expressed in bitter GRNs (Figure 2—figure supplement 1F). In the case of Ir7g and Ir51b, there are no reporters or antibodies to address expression. However, previously they have been shown to be expressed in bitter GRNs using RT-PCR (Dhakal et al. 2021, Communications Biology; Pradhan et al. 2024, Journal of Hazardous Materials). In addition, we provide functional evidence that bitter GRNs are required for the cholesterol response since silencing bitter GRNs abolishes cholesterol-induced action potentials (Figure 1E–F). Moreover, we showed that we could rescue the Ir7g¹, Ir51b¹ and Ir56d¹ mutant phenotypes only when we expressed the cognate transgenes in bitter GRNs using the Gr33a-GAL4 (Figure 3G). Thus, while Ir7g/Ir51b are not exclusive to bitter GRNs, their functional role in cholesterol detection is bitter-GRN-specific.

(3) The authors only investigated the responses of GRNs in the labellum, but GRN responses in the leg may also contribute to the avoidance of cholesterol feeding. Alternatively, leg GRNs might contribute to cholesterol attraction that is unmasked when bitter GRNs are silenced. In support of this possibility, Ahn et al. (2017) showed that Ir56d functions in sugar GRNs of the leg to promote appetitive responses to fatty acids.

This is an interesting idea. Indeed, when bitter GRNs are hyperpolarized, the flies exhibit a strong attraction to cholesterol. Nevertheless, the cellular basis for cholesterol attraction and whether it is mediated by GRNs in the legs will require a future investigation.

(4) The authors might consider using proboscis extension as an additional readout of taste attraction or aversion, which would help them more directly link the labellar GRN responses to a behavioral readout. Using food ingestion as a readout can conflate the contribution of taste with post-ingestive effects, and the regulation of food ingestion also may involve contributions from GRNs on multiple organs, whereas organ-specific contributions can be dissociated using proboscis extension. For example, does presenting cholesterol on the proboscis lead to aversive responses in the proboscis extension assay (e.g., suppression of responses to sugar)? Does this aversion switch to attraction when bitter GRNs are silenced, as with the feeding assay?

We thank the reviewer for the suggestion regarding the use of the proboscis extension reflex (PER) assay to strengthen the link between labellar GRN activity and behavioral responses to cholesterol.

Author response image 1.

Our PER assay results shown above indicate that cholesterol presentation on the labellum or forelegs leads to an aversive response, as evidenced by a significant reduction in proboscis extension when compared to control stimuli (Author response image 1A. 2% sucrose or 2% sucrose with 10^-1% cholesterol was applied to labellum or forelegs and the percent PER was recorded. n=6. Data were compared using single-factor ANOVA coupled with Scheffe’s post-hoc test. Statistical significance was compared with the control. Means ± SEMs. **p<0.01). This finding supports the idea that cholesterol is detected by labellar and leg GRNs and elicits behavioral avoidance. In contrast, sucrose stimulation robustly induces proboscis extension, as expected for an appetitive stimulus. We confirmed the defects of due to each Ir mutant by presenting the stimuli to the labellum (Author response image 1B). Together, these PER results provide a more direct behavioral correlate of labellar and leg GRN activation and reinforce our conclusion that cholesterol is sensed as an aversive tastant through the labellar bitter GRNs.

(5) The authors claim that the cholesterol receptor is composed of IR25a, IR76b, IR56d, and either IR7g or IR51b. While the authors have shown that IR25a and IR76b are each required for cholesterol sensing, they did not show that both are required components of the same receptor complex. If the authors are relying on previous studies to make this assumption, they should state this more clearly. Otherwise, I think further misexpression experiments may be needed where only IR25a or IR76b, but not both, are expressed in GRNs.

In our study, we relied on prior work demonstrating that Ir25a and Ir76b function as broadly required co-receptors in most IR-dependent chemosensory pathways (Ganguly et al., 2017; Lee et al., 2018). These studies showed that Ir25a and Ir76b are co-expressed in many GRNs across multiple taste modalities. Functional IR complexes often fail to form or signal properly in the absence of these co-receptors. Thus, it is widely accepted in the field that Ir25a and Ir76b function together as a core heteromeric scaffold for diverse IR complexes, akin to co-receptors in other ionotropic glutamate receptor families. We state that while Ir25a and Ir76b are presumed co-receptors in the cholesterol receptor complex based on their conserved roles, their direct physical interaction with Ir7g, Ir51b, and Ir56d remains to be demonstrated.

In support of this model, we note that in our ectopic expression experiments using I9 sensilla, which endogenously express Ir25a and Ir76b, introduction of either Ir7g + Ir56d or Ir51b + Ir56d was sufficient to confer cholesterol sensitivity (Figure 4B). We obtained a similar result in L6 sensilla (Figure 4D), which also endogenously express Ir25a and Ir76b. These findings imply that both co-receptors are already present in these sensilla and are likely part of the functional complex. However, we agree that we have not directly tested the requirement for both co-receptors in a minimal reconstitution context, such as expressing only Ir25a or Ir76b alongside tuning IRs in an otherwise null background. Such an experiment would indeed provide more direct evidence of their joint requirement in the receptor complex. Future studies, including heterologous expression experiments, will be necessary to define the cholesterol-receptor complexes.

Author Response

Reviewer #1 (Public Review):

The authors introduce a computational model that simulates the dendrites of developing neurons in a 2D plane, subject to constraints inspired by known biological mechanisms such as diffusing trophic factors, trafficked resources, and an activity-dependent pruning rule. The resulting arbors are analyzed in terms of their structure, dynamics, and responses to certain manipulations. The authors conclude that 1) their model recapitulates a stereotyped timecourse of neuronal development: outgrowth, overshoot, and pruning 2) Neurons achieve near-optimal wiring lengths, and Such models can be useful to test proposed biological mechanisms- for example, to ask whether a given set of growth rules can explain a given observed phenomenon - as developmental neuroscientists are working to understand the factors that give rise to the intricate structures and functions of the many cell types of our nervous system.

Overall, my reaction to this work is that this is just one instantiation of many models that the author could have built, given their stated goals. Would other models behave similarly? This question is not well explored, and as a result, claims about interpreting these models and using them to make experimental predictions should be taken warily. I give more detailed and specific comments below.

We thank the reviewer for the summary of the work. We find the criticism “that this is one instantiation of many models [we] could have built” can apply to any model. To quote George Box, “all models are wrong, but some models are useful” was the moto that drove our modeling approach. In principle, there are infinitely many possible models. We chose one of the most minimalistic models which implements known biological mechanisms including activity-independent and -dependent phases of dendritic growth, and constrained parameters based on experimental data. We compare the proposed model to other alternatives in the Discussion section, especially to the models of Hermann Cuntz which propose very different strategies for growth.

However, the reviewer is right that within the type of model we chose, we could have more extensively explored the sensitivity to parameters. In the revised manuscript we will investigate the sensitivity of model output to variations of specific parameters, as explained below.

Point 1.1. Line 109. After reading the rest of the manuscript, I worry about the conclusion voiced here, which implies that the model will extrapolate well to manipulations of all the model components. How were the values of model parameters selected? The text implies that these were selected to be biologically plausible, but many seem far off. The density of potential synapses, for example, seems very low in the simulations compared to the density of axons/boutons in the cortex; what constitutes a potential synapse? The perfect correlations between synapses in the activity groups is flawed, even for synapses belonging to the same presynaptic cell. The density of postsynaptic cells is also orders of magnitude of, etc. Ideally, every claim made about the model's output should be supported by a parameter sensitivity study. The authors performed few explorations of parameter sensitivity and many of the choices made seem ad hoc.

It is indeed important to clarify how the model parameters were selected. Here we provide a short justification for some of these parameters, which will be included in the revised manuscript.

1) Potential synapse density: We modelled 1,500 potential synapses in a cortical sheet of size 185x185 microns squared. We used 1 pixel per μm to capture approximately 1 μm thick dendrites. Therefore, we started with initial density of 0.044 potential synapses per μm^2. From Author Response Image 1 we can see that at the end of our simulation time ~1,000 potential synapses remain. So in fact, the density of potential synapses is totally sufficient, since not many potential synapses end up connected. The rapid slowing down of growth in our model is not due to a depletion of potential synaptic partners as the number of potential synapses remains high. Nonetheless, we will explore this in the revised manuscript. (this figure will be included in the revised submission):

2) Stabilized synapse density: Since ~1,000 of the potential synapses in the modeled cortical sheet remain available, ~500 become connected to the dendrites of the 9 somas in the modeled cortical sheet. This means that the density of stable connected synapses is approximately 0.015 synapses per μm^2. This is also the number that is shown in Figure 3b, which is about 60 synapses stabilized per cell. This density is much easier to compare to experimental data, and below we provide some numbers from literature we already cited in the manuscript as well as a recent preprint.

In the developing cortex:

• Leighton, Cheyne and Lohmann 2023 https://doi.org/10.1101/2023.03.02.530772 find up to 0.4 synapses per μm in pyramidal neurons in vivo in the developing mouse visual cortex at P8 to P13. This is almost identical to our value of 0.4 synapses per μm.

• Ultanir et al., 2007 https://doi.org/10.1073/pnas.0704031104 find 0.7 to 1.7 spines per μm in pyramidal neurons in vivo in L2/3 of the developing mouse cortex, at P10 to P20.

• Glynn et al., 2011 https://doi.org/10.1038/nn.2764 find 0.1 to 0.7 spines per μm^2 in pyramidal neurons in vivo and in vitro in L2/3 of the developing mouse cortex, at P8 to P60.

In the developing hippocampus:

• Yang et al., 2014 https://doi.org/10.1016/j.celrep.2014.03.040 find 0.75 to 1 spines per μm in pyramidal neurons in vivo in CA1 of the developing mouse hippocampus, at one month of age.

• Koshimizu et al., 2009 https://doi.org/10.1186/1756-6606-2-27 find 0.1 to 0.4 spines per μm in pyramidal neurons in slice in the developing rat hippocampus, at (Embryonic Day) ED18 to ED20.

• Tyler and Pozzo-Miller, 2001 https://doi.org/10.1523/JNEUROSCI.21-12-04249.2001 find 0.28 to 0.1 spines per μm in slice in the developing mouse hippocampus, at P7 (14 DIV, days in vitro).

Although these values vary somewhat across experiments, in most cases they are in agreement with our chosen values, especially when taking into account that we are modeling development (rather than adulthood).

3) Soma/neuron density: Indeed, we did not exactly mention this number anywhere in the paper. But from the figures we can infer 9 somas growing dendrites on an area of ~34,000 μm^2. Thus, neuron density would be 300 neurons per mm^2. This number seems a bit low after a short search through the literature. For e.g. Keller et al., 2018 https://www.frontiersin.org/articles/10.3389/fnana.2018.00083/full reports about 90,000 neurons per mm^3, albeit in adulthood.

We are also performing a sensitivity analysis where some of these parameters are varied and will include this in the revised manuscript. In particular:

(1) We will vary the nature of the input correlations. In the current model, the synapses in each correlated group receive spike trains with a perfect correlation and there are no correlations across the groups. We will reduce the correlations within group and add non-zero correlations across the groups.

(2) We will vary the density of the neuronal somas. We expect that higher densities of somas will either yield smaller dendritic areas because the different neurons compete more or result in a state where nearby neurons have to complement each other regarding their activity preferences.

(3) We will introduce dynamics in the potential synapses to model the dynamics of axons. We plan to explore several scenarios. We could introduce a gradual increase in the density of potential synapses and implement a cap on the number of synapses that can be alive at the same time, and vary that cap. We could also introduce a lifetime of each synapse (following for example a lognormal distribution). A potential synapse can disappear if it does not form a stable synapse in its lifetime, in which case it could move to a different location.

Point 1.2. Many potentially important phenomena seem to be excluded. I realize that no model can be complete, but the choice of which phenomena to include or exclude from this model could bias studies that make use of it and is worth serious discussion. The development of axons is concurrent with dendrite outgrowth, is highly dynamic, and perhaps better understood mechanistically. In this model, the inputs are essentially static. Growing dendrites acquire and lose growth cones that are associated with rapid extension, but these do not seem to be modeled. Postsynaptic firing does not appear to be modeled, which may be critical to activity-dependent plasticity. For example, changes in firing are a potential explanation for the global changes in dendritic pruning that occur following the outgrowth phase.

As the reviewer concludes, no model can be complete. In agreement with this, here we would like to quote a paragraph from a very nice paper by Larry Abbott (“Theoretical Neuroscience Rising, Neuron 2008 https://www.sciencedirect.com/science/article/pii/S0896627308008921) which although published more than 10 years ago, still applies today:

“Identifying the minimum set of features needed to account for a particular phenomenon and describing these accurately enough to do the job is a key component of model building. Anything more than this minimum set makes the model harder to understand and more difficult to evaluate. The term ‘‘realistic’’ model is a sociological rather than a scientific term. The truly realistic model is as impossible and useless a concept as Borges’ ‘‘map of the empire that was of the same scale as the empire and that coincided with it point for point’’ (Borges, 1975). […] The art of modeling lies in deciding what this subset should be and how it should be described.”

We have clearly stated in the Introduction (e.g. lines 37-75) which phenomena we include in the model and why. The Discussion also compares our model to others (lines 315-373), pointing out that most models either focus on activity-independent or activity-dependent phases. We include both, combining literature on molecular gradients and growth factors, with activity-dependent connectivity refinements instructed by spontaneous activity. We could not think of a more tractable, more minimalist model that would include both activity-independent or activity-dependent aspects. Therefore, we feel that the current manuscript provides sufficient motivation but also a discussion of limitations of the current model.

Regarding including the concurrent development of axons, we agree this is very interesting and currently not addressed in the model. As noted at the bottom of our reply to point 1.1, bullet (3) we are now revising the manuscript to include a simplified form of axonal dynamics by allowing changes in the lifetime and location of potential synapses, which come from axons of presynaptic partners.

Regarding postsynaptic firing, this is indeed super relevant and an important point to consider. In one of our recent publications (Kirchner and Gjorgjieva, 2021 https://www.nature.com/articles/s41467-021-23557-3), we studied only an activity-dependent model for the organization of synaptic inputs on non-growing dendrites which have a fixed length. There, we considered the effect of postsynaptic firing and demonstrated that it plays an important role in establishing a global organization of synapses on the entire dendritic tree of the neuron, and not just local dendritic branches. For example, we showed that could that it could lead to the emergence of retinotopic maps which have been found experimentally (Iacaruso et al., 2017 https://www.nature.com/articles/nature23019). Since we use the same activity-dependent plasticity model in this paper, we expect that the somatic firing will have the same effect on establishing synaptic distributions on the entire dendritic tree. We will make a note of this in the Discussion in the revised paper.

Point 1.3. Line 167. There are many ways to include activity -independent and -dependent components into a model and not every such model shows stability. A key feature seems to be that larger arbors result in reduced growth and/or increased retraction, but this could be achieved in many ways (whether activity dependent or not). It's not clear that this result is due to the combination of activity-dependent and independent components in the model, or conceptually why that should be the case.

We never argued for model uniqueness. There are always going to be many different models (at different spatial and temporal scales, at different levels of abstraction). We can never study all of them and like any modeling study in systems neuroscience we have chosen one model approach and investigated this approach. We do compare the current model to others in the Discussion. If the reviewers have a specific implementation that we should compare our model to as an alternative, we could try, but not if this means doing a completely separate project.

Point 1.4. Line 183. The explanation of overshoot in terms of the different timescales of synaptic additions versus activity-dependent retractions was not something I had previously encountered and is an interesting proposal. Have these timescales been measured experimentally? To what extent is this a result of fine-tuning of simulation parameters?

We found that varying the amount of BDNF controls the timescale of the activity-dependent plasticity (see our Figure 5c). Hence, changing the balance between synaptic additions vs. retractions is already explored in Figure 5e and f. Here we show that the overshoot and retraction does not have to be fine-tuned but may be abolished if there is too much activity-dependent plasticity.

Regarding the relative timescales of synaptic additions vs. retractions: since the first is mainly due to activity-independent factors, and the second due to activity-dependent plasticity, the questions is really about the timescales of the latter two. As we write in the Introduction (lines 60-62), manipulating activity-dependent synaptic transmission has been found to not affect morphology but rather the density and specificity of synaptic connections (Ultanir et al. 2007 https://doi.org/10.1073/pnas.0704031104), supporting the sequential model we have (although we do not impose the sequence, as both activity-independent and activity-dependent mechanisms are always “on”; but note that activity-dependent plasticity can only operate on synapses that have already formed).

Point 1.5. Line 203. This result seems at odds with results that show only a very weak bias in the tuning distribution of inputs to strongly tuned cortical neurons (e.g. work by Arthur Konnerth's group). This discrepancy should be discussed.

First, we note that the correlated activity experienced by our modeled synapses (and resulting synaptic organization) does not necessarily correspond to visual orientation, or any stimulus feature, for that matter.

Nonetheless, this is a very interesting question and there is some variability in what the experimental data show. Many studies have shown that synapses on dendrites are organized into functional synaptic clusters: across brain regions, developmental ages and diverse species from rodent to primate (Kleindienst et al. 2011; Takahashi et al. 2012; Winnubst et al. 2015; Gökçe et al., 2016; Wilson et al. 2016; Iacaruso et al., 2017; Scholl et al., 2017; Niculescu et al. 2018; Kerlin et al. 2019; Ju et al. 2020). Interestingly, some in vivo studies have reported lack of fine-scale synaptic organization (Varga et al. 2011; X. Chen et al. 2011; T.-W. Chen et al. 2013; Jia et al. 2010; Jia et al. 2014), while others reported clustering for different stimulus features in different species. For example, dendritic branches in the ferret visual cortex exhibit local clustering of orientation selectivity but do not exhibit global organization of inputs according to spatial location and receptive field properties (Wilson et al. 2016; Scholl et al., 2017). In contrast, synaptic inputs in mouse visual cortex do not cluster locally by orientation, but only by receptive field overlap, and exhibit a global retinotopic organization along the proximal-distal axis (Iacaruso et al., 2017). We proposed a theoretical framework to reconcile these data: combining activity-dependent plasticity similar to the BDNF-proBDNF model that we used in the current work, and a receptive field model for the different species (Kirchner and Gjorgjieva, 2021 https://www.nature.com/articles/s41467-021-23557-3). We can mention this aspect in the revised manuscript.

Point 1.6. Line 268. How does the large variability in the size of the simulated arbors relate to the relatively consistent size of arbors of cortical cells of a given cell type? This variability suggests to me that these simulations could be sensitive to small changes in parameters (e.g. to the density or layout of presynapses).

As noted at the bottom of our reply to point 1.1, bullet (3) we are now revising the manuscript to include changes in the lifetime and location of potential synapses.

Point 1.7. The modeling of dendrites as two-dimensional will likely limit the usefulness of this model. Many phenomena- such as diffusion, random walks, topological properties, etc - fundamentally differ between two and three dimensions.

The reviewer is right about there being differences between two and three dimensions. But a simpler model does not mean a useless model even if not completely realistic. We have ongoing work that extends the current model to 3D but is beyond the scope of the current paper. In systems neuroscience, people have found very interesting results making such simplified geometric assumptions about networks, for instance the one-dimensional ring model has been used to uncover fundamental insights about computations even though highly simplified and abstracted.

Point 1.8. The description of wiring lengths as 'approximately optimal' in this text is problematic. The plotted data show that the wiring lengths are several deviations away from optimal, and the random model is not a valid instantiation of the 2D non-overlapping constraints the authors imposed. A more appropriate null should be considered.

We did not use the term “optimal” in line with previous literature. We wrongly referred to the minimal wiring length as the optimal wiring length, but neurons can optimize their wiring not only by minimizing their dendritic length (e.g. work of Hermann Cuntz). In the revised manuscript, we will replace the term “optimal wiring” with “minimal wiring”. Then we will compare the wiring length in the model with the theoretically minimal wiring length, the random wiring length and the actual data.

Point 1.9. It's not clear to me what the authors are trying to convey by repeatedly labeling this model as 'mechanistic'. The mechanisms implemented in the model are inspired by biological phenomena, but the implementations have little resemblance to the underlying biophysical mechanisms. Overall my impression is that this is a phenomenological model intended to show under what conditions particular patterns are possible. Line 363, describing another model as computational but not mechanistic, was especially unclear to me in this context.

What we mean by mechanistic is that we implement equations that model specific mechanisms i.e. we have a set of equations that implement the activity-independent attraction to potential synapses (with parameters such as the density of synapses, their spatial influence, etc) and the activity-dependent refinement of synapses (with parameters such as the ratio of BDNF and proBDNF to induce potentiation vs depression, the activity-dependent conversion of one factor to the other, etc). This is a bottom-up approach where we combine multiple elements together to get to neuronal growth and synaptic organization. This approach is in stark contrast to the so-called top-down or normative approaches where the method would involve defining an objective function (e.g. minimal dendritic length) which depends on a set of parameters and then applying a gradient descent or other mathematical optimization technique to get at the parameters that optimize the objective function. This latter approach we would not call mechanistic because it involves an abstract objective function (who could say what a neuron or a circuit should be trying to optimize) and a mathematical technique for how to optimize the function (we don’t know of neurons can compute gradients of abstract objective functions).

Hence our model is mechanistic, but it does operate at a particular level of abstraction/simplification. We don’t model individual ion channels, or biophysics of synaptic plasticity (opening and closing of NMDA channels, accumulation of proteins at synapses, protein synthesis). We do, however, provide a biophysical implementation of the plasticity mechanism though the BDNF/proBDNF model which is more than most models of plasticity achieve, because they typically model a phenomenological STDP or Hebbian rule that just uses activity patterns to potential or depress synaptic weights, disregarding how it could be implemented.

Reviewer #2 (Public Review):

This work combines a model of two-dimensional dendritic growth with attraction and stabilisation by synaptic activity. The authors find that constraining growth models with competition for synaptic inputs produces artificial dendrites that match some key features of real neurons both over development and in terms of final structure. In particular, incorporating distance-dependent competition between synapses of the same dendrite naturally produces distinct phases of dendritic growth (overshoot, pruning, and stabilisation) that are observed biologically and leads to local synaptic organisation with functional relevance. The approach is elegant and well-explained, but makes some significant modelling assumptions that might impact the biological relevance of the results.

Strengths:

The main strength of the work is the general concept of combining morphological models of growth with synaptic plasticity and stabilisation. This is an interesting way to bridge two distinct areas of neuroscience in a manner that leads to findings that could be significant for both. The modelling of both dendritic growth and distance-dependent synaptic competition is carefully done, constrained by reasonable biological mechanisms, and well-described in the text. The paper also links its findings, for example in terms of phases of dendritic growth or final morphological structure, to known data well.

Weaknesses:

The major weaknesses of the paper are the simplifying modelling assumptions that are likely to have an impact on the results. These assumptions are not discussed in enough detail in the current version of the paper.

1) Axonal dynamics.

A major, and lightly acknowledged, assumption of this paper is that potential synapses, which must come from axons, are fixed in space. This is not realistic for many neural systems, as multiple undifferentiated neurites typically grow from the soma before an axon is specified (Polleux & Snider, 2010). Further, axons are also dynamic structures in early development and, at least in some systems, undergo activity-dependent morphological changes too (O'Leary, 1987; Hall 2000). This paper does not consider the implications of joint pre- and post-synaptic growth and stabilisation.

We thank the reviewer for the summary of the strengths and weaknesses of the work. While we feel that including a full model of axonal dynamics is beyond the scope of the current manuscript, some aspects of axonal dynamics can be included. In a revised model, we will introduce a gradual increase in the density of potential synapses and implement a cap on the number of synapses that can be alive at the same time, and vary that cap. We plan to also introduce a lifetime of each synapse (following for example a lognormal distribution). A potential synapse can disappear if it does not form a stable synapse in its lifetime, in which case it could move to a different location. See also our reply to reviewer comment 1.1, bullet (3).

2) Activity correlations

On a related note, the synapses in the manuscript display correlated activity, but there is no relationship between the distance between synapses and their correlation. In reality, nearby synapses are far more likely to share the same axon and so display correlated activity. If the input activity is spatially correlated and synaptic plasticity displays distance-dependent competition in the dendrites, there is likely to be a non-trivial interaction between these two features with a major impact on the organisation of synaptic contacts onto each neuron.

We are exploring the amount of correlation (between and within correlated groups) to include in the revised manuscript (see also our reply to reviewer comment 1.1, bullet (1)).

However, previous experimental work, (Kleindienst et al., 2011 https://doi.org/10.1016/j.neuron.2011.10.015) has provided anatomical and functional analyses that it is unlikely that the functional synaptic clustering on dendritic branches is the result of individual axons making more than one synapse (see pg. 1019).

3) BDNF dynamics

The models are quite sensitive to the ratio of BDNF to proBDNF (eg Figure 5c). This ratio is also activity-dependent as synaptic activation converts proBDNF into BDNF. The models assume a fixed ratio that is not affected by synaptic activity. There should at least be more justification for this assumption, as there is likely to be a positive feedback relationship between levels of BDNF and synaptic activation.

The reviewer is correct. We used the BDNF-proBDNF model for synaptic plasticity based on our previous work: Kirchner and Gjorgjieva, 2021 https://www.nature.com/articles/s41467-021-23557-3.

There, we explored only the emergence of functionally clustered synapses on static dendrites which do not grow. In the Methods section (Parameters and data fitting) we justify the choice of the ratio of BDNF to proBDNF from published experimental work. We also performed sensitivity analysis (Supplementary Fig. 1) and perturbation simulations (Supplementary Fig. 3), which showed that the ratio is crucial in regulating the overall amount of potentiation and depression of synaptic efficacy, and therefore has a strong impact on the emergence and maintenance of synaptic organization. Since we already performed all this analysis, we do not expect there will be any differences in the current model which includes dendritic growth, as the activity-dependent mechanism has such a different timescale.

A further weakness is in the discussion of how the final morphologies conform to principles of optimal wiring, which is quite imprecise. 'Optimal wiring' in the sense of dendrites and axons (Cajal, 1895; Chklovskii, 2004; Cuntz et al, 2007, Budd et al, 2010) is not usually synonymous with 'shortest wiring' as implied here. Instead, there is assumed to be a balance between minimising total dendritic length and minimising the tree distance (ie Figure 4c here) between synapses and the site of input integration, typically the soma. The level of this balance gives the deviation from the theoretical minimum length as direct paths to synapses typically require longer dendrites. In the model this is generated by the guidance of dendritic growth directly towards the synaptic targets. The interpretation of the deviation in this results section discussing optimal wiring, with hampered diffusion of signalling molecules, does not seem to be correct.

We agree with this comment. We had wrongly used the term “optimal wiring” as neurons can optimize their wiring not only by minimizing their dendritic length but other factors as noted by the reviewer. In the revised manuscript will replace the term “optimal wiring” with “minimal wiring” and discuss these differences to previous work.

Reviewer #3 (Public Review):

The authors propose a mechanistic model of how the interplay between activity-independent growth and an activity-dependent synaptic strengthening/weaken model influences the dendrite shape, complexity and distribution of synapses. The authors focus on a model for stellate cells, which have multiple dendrites emerging from a soma. The activity independent component is provided by a random pool of presynaptic sites that represent potential synapses and that release a diffusible signal that promotes dendritic growth. Then a spontaneous activity pattern with some correlation structure is imposed at those presynaptic sites. The strength of these synapses follow a learning rule previously proposed by the lab: synapses strengthen when there is correlated firing across multiple sites, and synapses weaken if there is uncorrelated firing with the relative strength of these processes controlled by available levels of BDNF/proBDNF. Once a synapse is weakened below a threshold, the dendrite branch at that site retracts and loses its sensitivity to the growth signal

The authors run the simulation and map out how dendrites and synapses evolve and stabilize. They show that dendritic trees growing rapidly and then stabilize by balancing growth and retraction (Figure 2). They also that there is an initial bout of synaptogenesis followed by loss of synapses, reflecting the longer amount of time it takes to weaken a synapse (Figure 3). They analyze how this evolution of dendrites and synapses depends on the correlated firing of synapses (i.e. defined as being in the same "activity group"). They show that in the stabilized phase, synapses that remain connected to a given dendritic branch are likely to be from same activity group (Figure 4). The authors systemically alter the learning rule by changing the available concentration of BDNF, which alters the relative amount of synaptic strengthening, which in turn affects stabilization, density of synapses and interestingly how selective for an activity group one dendrite is (Figure 5). In addition the authors look at how altering the activity-independent factors influences outgrowth (Figure 6). Finally, one of the interesting outcomes is that the resulting dendritic trees represent "optimal wiring" solutions in the sense that dendrites use the shortest distance given the distribution of synapses. They compare this distribute to one published data to see how the model compared to what has been observed experimentally.

There are many strengths to this study. The consequence of adding the activity-dependent contribution to models of synapto- and dendritogenesis is novel. There is some exploration of parameters space with the motivation of keeping the parameters as well as the generated outcomes close to anatomical data of real dendrites. The paper is also scholarly in its comparison of this approach to previous generative models. This work represented an important advance to our understanding of how learning rules can contribute to dendrite morphogenesis

We thank the reviewer for the positive evaluation of the work and the suggestions below.

Author response:

Reviewer #1 (Evidence, reproducibility and clarity):

Authors has provided a mechanism by which how presence of truncated P53 can inactivate function of full length P53 protein. Authors proposed this happens by sequestration of full length P53 by truncated P53.

In the study, performed experiments are well described.

My area of expertise is molecular biology/gene expression, and I have tried to provide suggestions on my area of expertise. The study has been done mainly with overexpression system and I have included few comments which I can think can be helpful to understand effect of truncated P53 on endogenous wild type full length protein. Performing experiments on these lines will add value to the observation according to this reviewer.

Major comments:

(1) What happens to endogenous wild type full length P53 in the context of mutant/truncated isoforms, that is not clear. Using a P53 antibody which can detect endogenous wild type P53, can authors check if endogenous full length P53 protein is also aggregated as well? It is hard to differentiate if aggregation of full length P53 happens only in overexpression scenario, where lot more both of such proteins are expressed. In normal physiological condition P53 expression is usually low, tightly controlled and its expression get induced in altered cellular condition such as during DNA damage. So, it is important to understand the physiological relevance of such aggregation, which could be possible if authors could investigate effect on endogenous full length P53 following overexpression of mutant isoforms.

Thank you very much for your insightful comments.

(1) To address “what happens to endogenous wild-type full-length P53 in the context of mutant/truncated isoforms," we employed a human A549 cell line expressing endogenous wild-type p53 under DNA damage conditions such as an etoposide treatment(1). We choose the A549 cell line since similar to H1299, it is a lung cancer cell line (www.atcc.org). For comparison, we also transfected the cells with 2 μg of V5-tagged plasmids encoding FLp53 and its isoforms Δ133p53 and Δ160p53. As shown in Author response image 1A, lanes 1 and 2, endogenous p53 expression, remained undetectable in A549 cells despite etoposide treatment, which limits our ability to assess the effects of the isoforms on the endogenous wild-type FLp53. We could, however, detect the V5-tagged FLp53 expressed from the plasmid using anti-V5 (rabbit) as well as with antiDO-1 (mouse) antibody (Author response image 1). The latter detects both endogenous wildtype p53 and the V5-tagged FLp53 since the antibody epitope is within the Nterminus (aa 20-25). This result supports the reviewer’s comment regarding the low level of expression of endogenous p53 that is insufficient for detection in our experiments.   

In summary, in line with the reviewer’s comment that ‘under normal physiological conditions p53 expression is usually low,’ we could not detect p53 with an anti-DO-1 antibody. Thus, we proceeded with V5/FLAG-tagged p53 for detection of the effects of the isoforms on p53 stability and function. We also found that protein expression in H1299 cells was more easily detectable than in A549 cells (Compare Author response image 1A and B). Thus, we decided to continue with the H1299 cells (p53-null), which would serve as a more suitable model system for this study.  

(2) We agree with the reviewer that ‘It is hard to differentiate if aggregation of full-length p53 happens only in overexpression scenario’. However, it is not impossible to imagine that such aggregation of FLp53 happens under conditions when p53 and its isoforms are over-expressed in the cell. Although the exact physiological context is not known and beyond the scope of the current work, our results indicate that at higher expression, p53 isoforms drive aggregation of FLp53. Given the challenges of detecting endogenous FLp53, we had to rely on the results obtained with plasmid mediated expression of p53 and its isoforms in p53-null cells.

Author response image 1.

Comparative analysis of protein expression in A549 and H1299 cells. (A) A549 cells (p53 wild-type) were treated with etoposide to induce endogenous wild-type p53 expression. To assess the effects of FLp53 and its isoforms Δ133p53 and Δ160p53 on endogenous wild-type p53 aggregation, A549 cells were transfected with 2 μg of V5-tagged p53 expression plasmids, with or without etoposide (20μM for 8h) treatment. Western blot analysis was done with the anti-V5 (rabbit) to detect V5-tagged proteins and anti-DO-1 (mouse), the latter detects both endogenous wild-type p53 and V5-tagged FLp53. The merged image corresponds to the overlay between the V5 and DO1 antibody signals. (B) H1299 cells (p53-null) were transfected with 2 μg V5tagged p53 expression plasmids or the empty vector control pcDNA3.1. Western blot analysis was done with the anti-V5 (mouse) antibody. 

(2) Can presence of mutant P53 isoforms can cause functional impairment of wild type full length endogenous P53? That could be tested as well using similar ChIP assay authors has performed, but instead of antibody against the Tagged protein if the authors could check endogenous P53 enrichment in the gene promoter such as P21 following overexpression of mutant isoforms. May be introducing a condition such as DNA damage in such experiment might help where endogenous P53 is induced and more prone to bind to P53 target such as P21.

Thank you very much for your valuable comments and suggestions. To investigate the potential functional impairment of endogenous wild-type p53 by p53 isoforms, we initially utilized A549 cells (p53 wild-type), aiming to monitor endogenous wild-type p53 expression following DNA damage. However, as mentioned and demonstrated in Author response image 1, endogenous p53 expression was too low to be detected under these conditions, making the ChIP assay for analyzing endogenous p53 activity unfeasible. Thus, we decided to utilize plasmid-based expression of FLp53 and focus on the potential functional impairment induced by the isoforms.

(3) On similar lines, authors described:

"To test this hypothesis, we escalated the ratio of FLp53 to isoforms to 1:10. As expected, the activity of all four promoters decreased significantly at this ratio (Figure 4A-D). Notably, Δ160p53 showed a more potent inhibitory effect than Δ133p53 at the 1:5 ratio on all promoters except for the p21 promoter, where their impacts were similar (Figure 4E-H). However, at the 1:10 ratio, Δ133p53 and Δ160p53 had similar effects on all transactivation except for the MDM2 promoter (Figure 4E-H)."

Again, in such assay authors used ratio 1:5 to 1:10 full length vs mutant. How authors justify this result in context (which is more relevant context) where one allele is Wild type (functional P53) and another allele is mutated (truncated, can induce aggregation). In this case one would except 1:1 ratio of full-length vs mutant protein, unless other regulation is going which induces expression of mutant isoforms more than wild type full length protein. Probably discussing on these lines might provide more physiological relevance to the observed data.

Thank you for raising this point regarding the physiological relevance of the ratios used in our study.

(1) In the revised manuscript (lines 193-195), we added in this direction that “The elevated Δ133p53 protein modulates p53 target genes such as miR‑34a and p21, facilitating cancer development(2, 3). To mimic conditions where isoforms are upregulated relative to FLp53, we increased the ratios to 1:5 and 1:10.” This approach aims to simulate scenarios where isoforms accumulate at higher levels than FLp53, which may be relevant in specific contexts, as also elaborated above.

(2) Regarding the issue of protein expression, where one allele is wild-type and the other is isoform, this assumption is not valid in most contexts. First, human cells have two copies of TPp53 gene (one from each parent). Second, the TP53 gene has two distinct promoters: the proximal promoter (P1) primarily regulates FLp53 and ∆40p53, whereas the second promoter (P2) regulates ∆133p53 and ∆160p53(4, 5). Additionally, ∆133TP53 is a p53 target gene(6, 7) and the expression of Δ133p53 and FLp53 is dynamic in response to various stimuli. Third, the expression of p53 isoforms is regulated at multiple levels, including transcriptional, post-transcriptional, translational, and post-translational processing(8). Moreover, different degradation mechanisms modify the protein level of p53 isoforms and FLp53(8). These differential regulation mechanisms are regulated by various stimuli, and therefore, the 1:1 ratio of FLp53 to ∆133p53 or ∆160p53 may be valid only under certain physiological conditions. In line with this, varied expression levels of FLp53 and its isoforms, including ∆133p53 and ∆160p53, have been reported in several studies(3, 4, 9, 10). 

(3) In our study, using the pcDNA 3.1 vector under the human cytomegalovirus (CMV) promoter, we observed moderately higher expression levels of ∆133p53 and ∆160p53 relative to FLp53 (Author response image 1B). This overexpression scenario provides a model for studying conditions where isoform accumulation might surpass physiological levels, impacting FLp53 function. By employing elevated ratios of these isoforms to FLp53, we aim to investigate the potential effects of isoform accumulation on FLp53.

(4) Finally does this altered function of full length P53 (preferably endogenous one) in presence of truncated P53 has any phenotypic consequence on the cells (if authors choose a cell type which is having wild type functional P53). Doing assay such as apoptosis/cell cycle could help us to get this visualization.

Thank you for your insightful comments. In the experiment with A549 cells (p53 wild-type), endogenous p53 levels were too low to be detected, even after DNA damage induction. The evaluation of the function of endogenous p53 in the presence of isoforms is hindered, as mentioned above. In the revised manuscript, we utilized H1299 cells with overexpressed proteins for apoptosis studies using the Caspase-Glo® 3/7 assay (Figure 7). This has been shown in the Results section (lines 254-269). “The Δ133p53 and Δ160p53 proteins block pro-apoptotic function of FLp53.

One of the physiological read-outs of FLp53 is its ability to induce apoptotic cell death(11). To investigate the effects of p53 isoforms Δ133p53 and Δ160p53 on FLp53-induced apoptosis, we measured caspase-3 and -7 activities in H1299 cells expressing different p53 isoforms (Figure 7). Caspase activation is a key biochemical event in apoptosis, with the activation of effector caspases (caspase-3 and -7) ultimately leading to apoptosis(12). The caspase-3 and -7 activities induced by FLp53 expression was approximately 2.5 times higher than that of the control vector (Figure 7). Co-expression of FLp53 and the isoforms Δ133p53 or Δ160p53 at a ratio of 1: 5 significantly diminished the apoptotic activity of FLp53 (Figure 7). This result aligns well with our reporter gene assay, which demonstrated that elevated expression of Δ133p53 and Δ160p53 impaired the expression of apoptosis-inducing genes BAX and PUMA (Figure 4G and H). Moreover, a reduction in the apoptotic activity of FLp53 was observed irrespective of whether Δ133p53 or Δ160p53 protein was expressed with or without a FLAG tag (Figure 7). This result, therefore, also suggests that the FLAG tag does not affect the apoptotic activity or other physiological functions of FLp53 and its isoforms. Overall, the overexpression of p53 isoforms Δ133p53 and Δ160p53 significantly attenuates FLp53-induced apoptosis, independent of the protein tagging with the FLAG antibody epitope.”

Referees cross-commenting

I think the comments from the other reviewers are very much reasonable and logical.

Especially all 3 reviewers have indicated, a better way to visualize the aggregation of full-length wild type P53 by truncated P53 (such as looking at endogenous P53# by reviewer 1, having fluorescent tag #by reviewer 2 and reviewer 3 raised concern on the FLAG tag) would add more value to the observation.

Thank you for these comments. The endogenous p53 protein was undetectable in A549 cells induced by etoposide (Figure R1A). Therefore, we conducted experiments using FLAG/V5-tagged FLp53.  To avoid any potential side effects of the FLAG tag on p53 aggregation, we introduced untagged p53 isoforms in the H1299 cells and performed subcellular fractionation. Our revised results, consistent with previous FLAG-tagged p53 isoforms findings, demonstrate that co-expression of untagged isoforms with FLAG-tagged FLp53 significantly induced the aggregation of FLAG-FLp53, while no aggregation was observed when FLAG-tagged FLp53 was expressed alone (Supplementary Figure 6). These results clearly indicate that the FLAG tag itself does not contribute to protein aggregation. 

Additionally, we utilized the A11 antibody to detect protein aggregation, providing additional validation (Figure 8 from Jean-Christophe Bourdon et al. Genes Dev. 2005;19:2122-2137). Given that the fluorescent proteins (~30 kDa) are substantially bigger than the tags used here (~1 kDa) and may influence oligomerization (especially GFP), stability, localization, and function of p53 and its isoforms, we avoided conducting these vital experiments with such artificial large fusions. 

Reviewer #1 (Significance):

The work in significant, since it points out more mechanistic insight how wild type full length P53 could be inactivated in the presence of truncated isoforms, this might offer new opportunity to recover P53 function as treatment strategies against cancer.

Thank you for your insightful comments. We appreciate your recognition of the significance of our work in providing mechanistic insights into how wild-type FLp53 can be inactivated by truncated isoforms. We agree that these findings have potential for exploring new strategies to restore p53 function as a therapeutic approach against cancer. 

Reviewer #2 (Evidence, reproducibility and clarity):

The manuscript by Zhao and colleagues presents a novel and compelling study on the p53 isoforms, Δ133p53 and Δ160p53, which are associated with aggressive cancer types. The main objective of the study was to understand how these isoforms exert a dominant negative effect on full-length p53 (FLp53). The authors discovered that the Δ133p53 and Δ160p53 proteins exhibit impaired binding to p53-regulated promoters. The data suggest that the predominant mechanism driving the dominant-negative effect is the coaggregation of FLp53 with Δ133p53 and Δ160p53.

This study is innovative, well-executed, and supported by thorough data analysis. However, the authors should address the following points:

(1) Introduction on Aggregation and Co-aggregation: Given that the focus of the study is on the aggregation and co-aggregation of the isoforms, the introduction should include a dedicated paragraph discussing this issue. There are several original research articles and reviews that could be cited to provide context.

Thank you very much for the valuable comments. We have added the following paragraph in the revised manuscript (lines 74-82): “Protein aggregation has become a central focus of modern biology research and has documented implications in various diseases, including cancer(13, 14, 15). Protein aggregates can be of different types ranging from amorphous aggregates to highly structured amyloid or fibrillar aggregates, each with different physiological implications. In the case of p53, whether protein aggregation, and in particular, co-aggregation with large N-terminal deletion isoforms, plays a mechanistic role in its inactivation is yet underexplored. Interestingly, the Δ133p53β isoform has been shown to aggregate in several human cancer cell lines(16). Additionally, the Δ40p53α isoform exhibits a high aggregation tendency in endometrial cancer cells(17). Although no direct evidence exists for Δ160p53 yet, these findings imply that p53 isoform aggregation may play a major role in their mechanisms of actions.”

(2) Antibody Use for Aggregation: To strengthen the evidence for aggregation, the authors should consider using antibodies that specifically bind to aggregates.

Thank you for your insightful suggestion. We addressed protein aggregation using the A11 antibody which specifically recognizes amyloid-like protein aggregates. We analyzed insoluble nuclear pellet samples prepared under identical conditions as described in Figure 6B. To confirm the presence of p53 proteins, we employed the anti-p53 M19 antibody (Santa Cruz, Cat No. sc-1312) to detect bands corresponding to FLp53 and its isoforms Δ133p53 and Δ160p53. The monomer FLp53 was not detected (Figure 8, lower panel, Jean-Christophe Bourdon et al. Genes Dev. 2005;19:2122-2137), which may be attributed to the lower binding affinity of the anti-p53 M19 antibody to it. These samples were also immunoprecipitated using the A11 antibody (Thermo Fischer Scientific, Cat No. AHB0052) to detect aggregated proteins. Interestingly, FLp53 and its isoforms, Δ133p53 and Δ160p53, were clearly visible with Anti-A11 antibody when co-expressed at a 1:5 ratio suggesting that they underwent co-aggregation. However, no FLp53 aggregates were observed when it was expressed alone (Author response image 2). These results support the conclusion in our manuscript that Δ133p53 and Δ160p53 drive FLp53 aggregation. 

Author response image 2.

Induction of FLp53 Aggregation by p53 Isoforms Δ133p53 and Δ160p53. H1299 cells transfected with the FLAG-tagged FLp53 and V5-tagged Δ133p53 or Δ160p53 at a 1:5 ratio. The cells were subjected to subcellular fractionation, and the resulting insoluble nuclear pellet was resuspended in RIPA buffer. The samples were heated at 95°C until the pellet was completely dissolved, and then analyzed by Western blotting. Immunoprecipitation was performed using the A11 antibody, which specifically recognizes amyloid protein aggregates, and the anti-p53 M19 antibody, which detects FLp53 as well as its isoforms Δ133p53 and Δ160p53. 

(3) Fluorescence Microscopy: Live-cell fluorescence microscopy could be employed to enhance visualization by labeling FLp53 and the isoforms with different fluorescent markers (e.g., EGFP and mCherry tags).

We appreciate the suggestion to use live-cell fluorescence microscopy with EGFP and mCherry tags for the visualization FLp53 and its isoforms. While we understand the advantages of live-cell imaging with EGFP / mCherry tags, we restrained us from doing such fusions as the GFP or corresponding protein tags are very big (~30 kDa) with respect to the p53 isoform variants (~30 kDa).  Other studies have shown that EGFP and mCherry fusions can alter protein oligomerization, solubility and aggregation(18, 19) Moreover, most fluorescence proteins are prone to dimerization (i.e. EGFP) or form obligate tetramers (DsRed)(20, 21, 22), potentially interfering with the oligomerization and aggregation properties of p53 isoforms, particularly Δ133p53 and Δ160p53.

Instead, we utilized FLAG- or V5-tag-based immunofluorescence microscopy, a well-established and widely accepted method for visualizing p53 proteins. This method provided precise localization and reliable quantitative data, which we believe meet the needs of the current study. We believe our chosen method is both appropriate and sufficient for addressing the research question.

Reviewer #2 (Significance):

The manuscript by Zhao and colleagues presents a novel and compelling study on the p53 isoforms, Δ133p53 and Δ160p53, which are associated with aggressive cancer types. The main objective of the study was to understand how these isoforms exert a dominant negative effect on full-length p53 (FLp53). The authors discovered that the Δ133p53 and Δ160p53 proteins exhibit impaired binding to p53-regulated promoters. The data suggest that the predominant mechanism driving the dominant-negative effect is the coaggregation of FLp53 with Δ133p53 and Δ160p53.

We sincerely thank the reviewer for the thoughtful and positive comments on our manuscript and for highlighting the significance of our findings on the p53 isoforms, Δ133p53 and Δ160p53. 

Reviewer #3 (Evidence, reproducibility and clarity):

In this manuscript entitled "Δ133p53 and Δ160p53 isoforms of the tumor suppressor protein p53 exert dominant-negative effect primarily by coaggregation", the authors suggest that the Δ133p53 and Δ160p53 isoforms have high aggregation propensity and that by co-aggregating with canonical p53 (FLp53), they sequestrate it away from DNA thus exerting a dominantnegative effect over it.

First, the authors should make it clear throughout the manuscript, including the title, that they are investigating Δ133p53α and Δ160p53α since there are 3 Δ133p53 isoforms (α, β, γ), and 3 Δ160p53 isoforms (α, β, γ).

Thank you for your suggestion. We understand the importance of clearly specifying the isoforms under study. Following your suggestion, we have added α in the title, abstract, and introduction and added the following statement in the Introduction (lines 57-59): “For convenience and simplicity, we have written Δ133p53 and Δ160p53 to represent the α isoforms (Δ133p53α and Δ160p53α) throughout this manuscript.” 

One concern is that the authors only consider and explore Δ133p53α and Δ160p53α isoforms as exclusively oncogenic and FLp53 dominant-negative while not discussing evidences of different activities. Indeed, other manuscripts have also shown that Δ133p53α is non-oncogenic and non-mutagenic, do not antagonize every single FLp53 functions and are sometimes associated with good prognosis. To cite a few examples:

(1) Hofstetter G. et al. D133p53 is an independent prognostic marker in p53 mutant advanced serous ovarian cancer. Br. J. Cancer 2011, 105, 15931599.

(2) Bischof, K. et al. Influence of p53 Isoform Expression on Survival in HighGrade Serous Ovarian Cancers. Sci. Rep. 2019, 9,5244.

(3) Knezovi´c F. et al. The role of p53 isoforms' expression and p53 mutation status in renal cell cancer prognosis. Urol. Oncol. 2019, 37, 578.e1578.e10.

(4) Gong, L. et al. p53 isoform D113p53/D133p53 promotes DNA doublestrand break repair to protect cell from death and senescence in response to DNA damage. Cell Res. 2015, 25, 351-369.

(5) Gong, L. et al. p53 isoform D133p53 promotes efficiency of induced pluripotent stem cells and ensures genomic integrity during reprogramming. Sci. Rep. 2016, 6, 37281.

(6) Horikawa, I. et al. D133p53 represses p53-inducible senescence genes and enhances the generation of human induced pluripotent stem cells. Cell Death Differ. 2017, 24, 1017-1028.

(7) Gong, L. p53 coordinates with D133p53 isoform to promote cell survival under low-level oxidative stress. J. Mol. Cell Biol. 2016, 8, 88-90.

Thank you very much for your comment and for highlighting these important studies. 

We agree that Δ133p53 isoforms exhibit complex biological functions, with both oncogenic and non-oncogenic potentials. However, our mission here was primarily to reveal the molecular mechanism for the dominant-negative effects exerted by the Δ133p53α and Δ160p53α isoforms on FLp53 for which the Δ133p53α and Δ160p53α isoforms are suitable model systems. Exploring the oncogenic potential of the isoforms is beyond the scope of the current study and we have not claimed anywhere that we are reporting that. We have carefully revised the manuscript and replaced the respective terms e.g. ‘prooncogenic activity’ with ‘dominant-negative effect’ in relevant places (e.g. line 90). We have now also added a paragraph with suitable references that introduces the oncogenic and non-oncogenic roles of the p53 isoforms.

After reviewing the papers you cited, we are not sure that they reflect on oncogenic /non-oncogenic role of the Δ133p53α isoform in different cancer cases.  Although our study is not about the oncogenic potential of the isoforms, we have summarized the key findings below:

(1) Hofstetter et al., 2011: Demonstrated that Δ133p53α expression improved recurrence-free and overall survival (in a p53 mutant induced advanced serous ovarian cancer, suggesting a potential protective role in this context.

(2) Bischof et al., 2019: Found that Δ133p53 mRNA can improve overall survival in high-grade serous ovarian cancers. However, out of 31 patients, only 5 belong to the TP53 wild-type group, while the others carry TP53 mutations.

(3) Knezović et al., 2019: Reported downregulation of Δ133p53 in renal cell carcinoma tissues with wild-type p53 compared to normal adjacent tissue, indicating a potential non-oncogenic role, but not conclusively demonstrating it.

(4) Gong et al., 2015: Showed that Δ133p53 antagonizes p53-mediated apoptosis and promotes DNA double-strand break repair by upregulating RAD51, LIG4, and RAD52 independently of FLp53.

(5) Gong et al., 2016: Demonstrated that overexpression of Δ133p53 promotes efficiency of cell reprogramming by its anti-apoptotic function and promoting DNA DSB repair. The authors hypotheses that this mechanism is involved in increasing RAD51 foci formation and decrease γH2AX foci formation and chromosome aberrations in induced pluripotent stem (iPS) cells, independent of FL p53.

(6) Horikawa et al., 2017: Indicated that induced pluripotent stem cells derived from fibroblasts that overexpress Δ133p53 formed noncancerous tumors in mice compared to induced pluripotent stem cells derived from fibroblasts with complete p53 inhibition. Thus, Δ133p53 overexpression is "non- or less oncogenic and mutagenic" compared to complete p53 inhibition, but it still compromises certain p53-mediated tumor-suppressing pathways. “Overexpressed Δ133p53 prevented FL-p53 from binding to the regulatory regions of p21WAF1 and miR-34a promoters, providing a mechanistic basis for its dominant-negative

inhibition of a subset of p53 target genes.”

(7) Gong, 2016: Suggested that Δ133p53 promotes cell survival under lowlevel oxidative stress, but its role under different stress conditions remains uncertain.

We have revised the Introduction to provide a more balanced discussion of Δ133p53’s dule role (lines 62-73):

“The Δ133p53 isoform exhibit complex biological functions, with both oncogenic and non-oncogenic potentials. Recent studies demonstrate the non-oncogenic yet context-dependent role of the Δ133p53 isoform in cancer development. Δ133p53 expression has been reported to correlate with improved survival in patients with TP53 mutations(23, 24), where it promotes cell survival in a nononcogenic manner(25, 26), especially under low oxidative stress(27). Alternatively, other recent evidences emphasize the notable oncogenic functions of Δ133p53 as it can inhibit p53-dependent apoptosis by directly interacting with the FLp53 (4, 6). The oncogenic function of the newly identified Δ160p53 isoform is less known, although it is associated with p53 mutation-driven tumorigenesis(28) and in melanoma cells’ aggressiveness(10). Whether or not the Δ160p53 isoform also impedes FLp53 function in a similar way as Δ133p53 is an open question. However, these p53 isoforms can certainly compromise p53-mediated tumor suppression by interfering with FLp53 binding to target genes such as p21 and miR-34a(2, 29) by dominant-negative effect, the exact mechanism is not known.” On the figures presented in this manuscript, I have three major concerns:

(1) Most results in the manuscript rely on the overexpression of the FLAGtagged or V5-tagged isoforms. The validation of these construct entirely depends on Supplementary figure 3 which the authors claim "rules out the possibility that the FLAG epitope might contribute to this aggregation. However, I am not entirely convinced by that conclusion. Indeed, the ratio between the "regular" isoform and the aggregates is much higher in the FLAG-tagged constructs than in the V5-tagged constructs. We can visualize the aggregates easily in the FLAG-tagged experiment, but the imaging clearly had to be overexposed (given the white coloring demonstrating saturation of the main bands) to visualize them in the V5-tagged experiments. Therefore, I am not convinced that an effect of the FLAG-tag can be ruled out and more convincing data should be added. 

Thank you for raising this important concern. We have carefully considered your comments and have made several revisions to clarify and strengthen our conclusions.

First, to address the potential influence of the FLAG and V5 tags on p53 isoform aggregation, we have revised Figure 2 and removed the previous Supplementary Figure 3, where non-specific antibody bindings and higher molecular weight aggregates were not clearly interpretable. In the revised Figure 2, we have removed these potential aggregates, improving the clarity and accuracy of the data.

To further rule out any tag-related artifacts, we conducted a coimmunoprecipitation assay with FLAG-tagged FLp53 and untagged Δ133p53 and Δ160p53 isoforms. The results (now shown in the new Supplementary Figure 3) completely agree with our previous result with FLAG-tagged and V5tagged Δ133p53 and Δ160p53 isoforms and show interaction between the partners. This indicates that the FLAG / V5-tags do not influence / interfere with the interaction between FLp53 and the isoforms. We have still used FLAGtagged FLp53 as the endogenous p53 was undetectable and the FLAG-tagged FLp53 did not aggregate alone. 

In the revised paper, we added the following sentences (Lines 146-152): “To rule out the possibility that the observed interactions between FLp53 and its isoforms Δ133p53 and Δ160p53 were artifacts caused by the FLAG and V5 antibody epitope tags, we co-expressed FLAG-tagged FLp53 with untagged Δ133p53 and Δ160p53. Immunoprecipitation assays demonstrated that FLAGtagged FLp53 could indeed interact with the untagged Δ133p53 and Δ160p53 isoforms (Supplementary Figure 3, lanes 3 and 4), confirming formation of hetero-oligomers between FLp53 and its isoforms. These findings demonstrate that Δ133p53 and Δ160p53 can oligomerize with FLp53 and with each other.”

Additionally, we performed subcellular fractionation experiments to compare the aggregation and localization of FLAG-tagged FLp53 when co-expressed either with V5-tagged or untagged Δ133p53/Δ160p53. In these experiments, the untagged isoforms also induced FLp53 aggregation, mirroring our previous results with the tagged isoforms (Supplementary Figure 5). We’ve added this result in the revised manuscript (lines 236-245): “To exclude the possibility that FLAG or V5 tags contribute to protein aggregation, we also conducted subcellular fractionation of H1299 cells expressing FLAG-tagged FLp53 along with untagged Δ133p53 or Δ160p53 at a 1:5 ratio. The results showed (Supplementary Figure 6) a similar distribution of FLp53 across cytoplasmic, nuclear, and insoluble nuclear fractions as in the case of tagged Δ133p53 or Δ160p53 (Figure 6A to D). Notably, the aggregation of untagged Δ133p53 or Δ160p53 markedly promoted the aggregation of FLAG-tagged FLp53 (Supplementary Figure 6B and D), demonstrating that the antibody epitope tags themselves do not contribute to protein aggregation.” 

We’ve also discussed this in the Discussion section (lines 349-356): “In our study, we primarily utilized an overexpression strategy involving FLAG/V5tagged proteins to investigate the effects of p53 isoforms Δ133p53 and Δ160p53 on the function of FLp53. To address concerns regarding potential overexpression artifacts, we performed the co-immunoprecipitation (Supplementary Figure 6) and caspase-3 and -7 activity (Figure 7) experiments with untagged Δ133p53 and Δ160p53. In both experimental systems, the untagged proteins behaved very similarly to the FLAG/V5 antibody epitopecontaining proteins (Figures 6 and 7 and Supplementary Figure 6). Hence, the C-terminal tagging of FLp53 or its isoforms does not alter the biochemical and physiological functions of these proteins.”

In summary, the revised data set and newly added experiments provide strong evidence that neither the FLAG nor the V5 tag contributes to the observed p53 isoform aggregation.

(2) The authors demonstrate that to visualize the dominant-negative effect, Δ133p53α and Δ160p53α must be "present in a higher proportion than FLp53 in the tetramer" and the need at least a transfection ratio 1:5 since the 1:1 ration shows no effect. However, in almost every single cell type, FLp53 is far more expressed than the isoforms which make it very unlikely to reach such stoichiometry in physiological conditions and make me wonder if this mechanism naturally occurs at endogenous level. This limitation should be at least discussed.

Thank you for your insightful comment. However, evidence suggests that the expression levels of these isoforms such as Δ133p53, can be significantly elevated relative to FLp53 in certain physiological conditions(3, 4, 9). For example, in some breast tumors, with Δ133p53 mRNA is expressed at a much levels than FLp53, suggesting a distinct expression profile of p53 isoforms compared to normal breast tissue(4). Similarly, in non-small cell lung cancer and the A549 lung cancer cell line, the expression level of Δ133p53 transcript is significantly elevated compared to non-cancerous cells(3). Moreover, in specific cholangiocarcinoma cell lines, the Δ133p53 /TAp53 expression ratio has been reported to increase to as high as 3:1(9). These observations indicate that the dominant-negative effect of isoform Δ133p53 on FLp53 can occur under certain pathological conditions where the relative amounts of the FLp53 and the isoforms would largely vary. Since data on the Δ160p53 isoform are scarce, we infer that the long N-terminal truncated isoforms may share a similar mechanism.

(3) Figure 5C: I am concerned by the subcellular location of the Δ133p53α and Δ160p53α as they are commonly considered nuclear and not cytoplasmic as shown here, particularly since they retain the 3 nuclear localization sequences like the FLp53 (Bourdon JC et al. 2005; Mondal A et al. 2018; Horikawa I et al, 2017; Joruiz S. et al, 2024). However, Δ133p53α can form cytoplasmic speckles (Horikawa I et al, 2017) when it colocalizes with autophagy markers for its degradation.

The authors should discuss this issue. Could this discrepancy be due to the high overexpression level of these isoforms? A co-staining with autophagy markers (p62, LC3B) would rule out (or confirm) activation of autophagy due to the overwhelming expression of the isoform.

Thank you for your thoughtful comments. We have thoroughly reviewed all the papers you recommended (Bourdon JC et al., 2005; Mondal A et al., 2018; Horikawa I et al., 2017; Joruiz S. et al., 2024)(4, 29, 30, 31). Among these, only the study by Bourdon JC et al. (2005) provided data regarding the localization of Δ133p53(4). Interestingly, their findings align with our observations, indicating that the protein does not exhibit predominantly nuclear localization in the Figure 8 from Jean-Christophe Bourdon et al. Genes Dev. 2005;19:2122-2137. The discrepancy may be caused by a potentially confusing statement in that paper(4).

The localization of p53 is governed by multiple factors, including its nuclear import and export(32). The isoforms Δ133p53 and Δ160p53 contain three nuclear localization sequences (NLS)(4). However, the isoforms Δ133p53 and Δ160p53 were potentially trapped in the cytoplasm by aggregation and masking the NLS. This mechanism would prevent nuclear import. 

Further, we acknowledge that Δ133p53 co-aggregates with autophagy substrate p62/SQSTM1 and autophagosome component LC3B in cytoplasm by autophagic degradation during replicative senescence(33). We agree that high overexpression of these aggregation-prone proteins may induce endoplasmic reticulum (ER) stress and activates autophagy(34). This could explain the cytoplasmic localization in our experiments. However, it is also critical to consider that we observed aggregates in both the cytoplasm and the nucleus (Figures 6B and E and Supplementary Figure 6B). While cytoplasmic localization may involve autophagy-related mechanisms, the nuclear aggregates likely arise from intrinsic isoform properties, such as altered protein folding, independent of autophagy. These dual localizations reflect the complex behavior of Δ133p53 and Δ160p53 isoforms under our experimental conditions.

In the revised manuscript, we discussed this in Discussion (lines 328-335): “Moreover, the observed cytoplasmic isoform aggregates may reflect autophagy-related degradation, as suggested by the co-localization of Δ133p53 with autophagy substrate p62/SQSTM1 and autophagosome component LC3B(33). High overexpression of these aggregation-prone proteins could induce endoplasmic reticulum stress and activate autophagy(34). Interestingly, we also observed nuclear aggregation of these isoforms (Figure 6B and E and Supplementary Figure 6B), suggesting that distinct mechanisms, such as intrinsic properties of the isoforms, may govern their localization and behavior within the nucleus. This dual localization underscores the complexity of Δ133p53 and Δ160p53 behavior in cellular systems.”

Minor concerns:

-  Figure 1A: the initiation of the "Δ140p53" is shown instead of "Δ40p53"

Thank you! The revised Figure 1A has been created in the revised paper.

-  Figure 2A: I would like to see the images cropped a bit higher, so the cut does not happen just above the aggregate bands

Thank you for this suggestion. We’ve changed the image and the new Figure 2 has been shown in the revised paper.

-  Figure 3C: what ratio of FLp53/Delta isoform was used?

We have added the ratio in the figure legend of Figure 3C (lines 845-846) “Relative DNA-binding of the FLp53-FLAG protein to the p53-target gene promoters in the presence of the V5-tagged protein Δ133p53 or Δ160p53 at a 1: 1 ratio.”

-  Figure 3C suggests that the "dominant-negative" effect is mostly senescencespecific as it does not affect apoptosis target genes, which is consistent with Horikawa et al, 2017 and Gong et al, 2016 cited above. Furthermore, since these two references and the others from Gong et al. show that Δ133p53α increases DNA repair genes, it would be interesting to look at RAD51, RAD52 or Lig4, and maybe also induce stress.

Thank you for your thoughtful comments and suggestions. In Figure 3C, the presence of Δ133p53 or Δ160p53 only significantly reduced the binding of FLp53 to the p21 promoter. However, isoforms Δ133p53 and Δ160p53 demonstrated a significant loss of DNA-binding activity at all four promoters: p21, MDM2, and apoptosis target genes BAX and PUMA (Figure 3B). This result suggests that Δ133p53 and Δ160p53 have the potential to influence FLp53 function due to their ability to form hetero-oligomers with FLp53 or their intrinsic tendency to aggregate. To further investigate this, we increased the isoform to FLp53 ratio in Figure 4, which demonstrate that the isoforms Δ133p53 and Δ160p53 exert dominant-negative effects on the function of FLp53. 

These results demonstrate that the isoforms can compromise p53-mediated pathways, consistent with Horikawa et al. (2017), which showed that Δ133p53α overexpression is "non- or less oncogenic and mutagenic" compared to complete p53 inhibition, but still affects specific tumor-suppressing pathways. Furthermore, as noted by Gong et al. (2016), Δ133p53’s anti-apoptotic function under certain conditions is independent of FLp53 and unrelated to its dominantnegative effects.

We appreciate your suggestion to investigate DNA repair genes such as RAD51, RAD52, or Lig4, especially under stress conditions. While these targets are intriguing and relevant, we believe that our current investigation of p53 targets in this manuscript sufficiently supports our conclusions regarding the dominant-negative effect. Further exploration of additional p53 target genes, including those involved in DNA repair, will be an important focus of our future studies.

- Figure 5A and B: directly comparing the level of FLp53 expressed in cytoplasm or nucleus to the level of Δ133p53α and Δ160p53α expressed in cytoplasm or nucleus does not mean much since these are overexpressed proteins and therefore depend on the level of expression. The authors should rather compare the ratio of cytoplasmic/nuclear FLp53 to the ratio of cytoplasmic/nuclear Δ133p53α and Δ160p53α.

Thank you very much for this valuable suggestion. In the revised paper, Figure 5B has been recreated.  Changes have been made in lines 214215: “The cytoplasm-to-nucleus ratio of Δ133p53 and Δ160p53 was approximately 1.5-fold higher than that of FLp53 (Figure 5B).” 

Referees cross-commenting

I agree that the system needs to be improved to be more physiological.

Just to precise, the D133 and D160 isoforms are not truncated mutants, they are naturally occurring isoforms expressed in almost every normal human cell type from an internal promoter within the TP53 gene.

Using overexpression always raises concerns, but in this case, I am even more careful because the isoforms are almost always less expressed than the FLp53, and here they have to push it 5 to 10 times more expressed than the FLp53 to see the effect which make me fear an artifact effect due to the overwhelming overexpression (which even seems to change the normal localization of the protein).

To visualize the endogenous proteins, they will have to change cell line as the H1299 they used are p53 null.

Thank you for these comments. We’ve addressed the motivation of overexpression in the above responses. We needed to use the plasmid constructs in the p53-null cells to detect the proteins but the expression level was certainly not ‘overwhelmingly high’. 

First, we tried the A549 cells (p53 wild-type) under DNA damage conditions, but the endogenous p53 protein was undetectable. Second, several studies reported increased Δ133p53 level compared to wild-type p53 and that it has implications in tumor development(2, 3, 4, 9). Third, the apoptosis activity of H1299 cells overexpressing p53 proteins was analyzed in the revised manuscript (Figure 7). The apoptotic activity induced by FLp53 expression was approximately 2.5 times higher than that of the control vector under identical plasmid DNA transfection conditions (Figure 7). These results rule out the possibility that the plasmid-based expression of p53 and its isoforms introduced artifacts in the results. We’ve discussed this in the Results section (lines 254269).

Reviewer #3 (Significance):

Overall, the paper is interesting particularly considering the range of techniques used which is the main strength.

The main limitation to me is the lack of contradictory discussion as all argumentation presents Δ133p53α and Δ160p53α exclusively as oncogenic and strictly FLp53 dominant-negative when, particularly for Δ133p53α, a quite extensive literature suggests a not so clear-cut activity.

The aggregation mechanism is reported for the first time for Δ133p53α and Δ160p53α, although it was already published for Δ40p53α, Δ133p53β or in mutant p53.

This manuscript would be a good basic research addition to the p53 field to provide insight in the mechanism for some activities of some p53 isoforms.

My field of expertise is the p53 isoforms which I have been working on for 11 years in cancer and neuro-degenerative diseases

Thank you very much for your positive and critical comments. We’ve included a fair discussion on the oncogenic and non-oncogenic function of Δ133p53 in the Introduction following your suggestion (lines 62-73). 

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Author Response

Reviewer #1 (Public Review):

Weaknesses:

1) The authors should better review what we know of fungal Drosophila microbiota species as well as the ecology of rotting fruit. Are the microbiota species described in this article specific to their location/setting? It would have been interesting to know if similar species can be retrieved in other locations using other decaying fruits. The term 'core' in the title suggests that these species are generally found associated with Drosophila but this is not demonstrated. The paper is written in a way that implies the microbiota members they have found are universal. What is the evidence for this? Have the fungal species described in this paper been found in other studies? Even if this is not the case, the paper is interesting, but there should be a discussion of how generalizable the findings are.

The reviewer inquires as to whether the microbial species described in this article are ubiquitously associated with Drosophila or not. Indeed, most of the microbes described in this manuscript are generally recognized as species associated with Drosophila spp. For example, species such as Hanseniaspora uvarum, Pichia kluyveri, and Starmerella bacillaris have been detected in or isolated from Drosophila spp. collected in European countries as well as the United States and Oceania (Chandler et al., 2012; Solomon et al., 2019). As for the bacteria, species belonging to the genera Pantoea, Lactobacillus, Leuconostoc, and Acetobacter have also previously been detected in wild Drosophila spp. (Chandler et al., 2011). These elucidations will be incorporated into our revised manuscript.

Nevertheless, the term “core” in the manuscript title may lead to misunderstanding, as the generality does not ensure the ubiquitous presence of these microbial species in every individual fly. Considering this point, we will replace the term with an expression more appropriate to our context.

2) Can the authors clearly demonstrate that the microbiota species that develop in the banana trap are derived from flies? Are these species found in flies in the wild? Did the authors check that the flies belong to the D. melanogaster species and not to the sister group D. simulans?

Can the authors clearly demonstrate that the microbiota species that develop in the banana trap are derived from flies? Are these species found in flies in the wild?

The reviewer asked whether the microbial species identified in the fermented banana samples were derived from flies. To address this question, additional experiments under more controlled conditions, such as the inoculation of specific species of wild flies onto fresh bananas, would be needed. Nevertheless, the microbes may potentially originate from wild flies, as supported by the literature cited in our response to the Weakness 1).

Alternative sources for microbial provenance also merit consideration. For example, microbial entities may be inherently present in unfermented bananas through the infiltration of peel injuries (lines 1141-1142 of the original manuscript). In addition, they could be introduced by insects other than flies, given that both rove beetles (Staphylinidae) and sap beetles (Nitidulidae) were observed in some of the traps. These possibilities will be incorporated into the 'MATERIALS AND METHODS' and 'DISCUSSION' sections of our revised manuscript.

Did the authors check that the flies belong to the D. melanogaster species and not to the sister group D. simulans?

Our sampling strategy was designed to target not only D. melanogaster but also other domestic Drosophila species, such as D. simulans, that inhabit human residential areas. After adult flies were caught in each trap, we identified the species as shown in Table S1, thereby showing the presence of either or both D. melanogaster and D. simulans. We will provide these descriptions in MATERIALS AND METHODS and DISCUSSION.

3) Did the microarrays highlight a change in immune genes (ex. antibacterial peptide genes)? Whatever the answer, this would be worth mentioning. The authors described their microarray data in terms of fed/starved in relation to the Finke article. They should clarify if they observed significant differences between species (differences between species within bacteria or fungi, and more generally differences between bacteria versus fungi).

Did the microarrays highlight a change in immune genes (ex. antibacterial peptide genes)? Whatever the answer, this would be worth mentioning.

Regarding the antimicrobial peptide genes, statistical comparisons of our RNA-seq data across different conditions were impracticable because most of them showed low expression levels (refer to Author response table 1, which exhibits the RNA-seq data of the yeast-fed larvae; similar expression profiles were observed in the bacteria-fed larvae). While a subset of genes exhibited significantly elevated expression in the non-supportive conditions relative to the supportive ones, this can be due to intra-sample variability rather than due to distinct nutritional environments. Therefore, it would be difficult to discuss a change in immune genes in the paper. Additionally, the previous study that conducted larval microarray analysis (Zinke et al., 2002) did not explicitly focus on immune genes.

Author response table 1.

Antimicrobial peptide genes are not up-regulated by any of the microbes. Antimicrobial peptides gene expression profiles of whole bodies of first-instar larvae fed on yeasts. TPM values of all samples and comparison results of gene expression levels in the larvae fed on supportive and non-supportive yeasts are shown. Antibacterial peptide genes mentioned in Hanson and Lemaitre, 2020 are listed. NA or na, not available.

They should clarify if they observed significant differences between species (differences between species within bacteria or fungi, and more generally differences between bacteria versus fungi).

We did not observe significant differences between species within bacteria or fungi, or between bacteria and fungi. For example, the gene expression profiles of larvae fed on the various supporting microbes showed striking similarities to each other, as evidenced by the heat map showing the expression of all genes detected in larvae fed either yeast or bacteria (Author response image 1). Similarities were also observed among larvae fed on distinct non-supporting microbes.

Author response image 1.

Gene expression profiles of larvae fed on the various supporting microbes show striking similarities to each other. Heat map showing the gene expression of the first-instar larvae that fed on yeasts or bacteria. Freshly hatched germ-free larvae were placed on banana agar inoculated with each microbe and collected after 15 h feeding to examine gene expression of the whole body. Note that data presented in Figures 3A and 4C in the original manuscript, which are obtained independently, are combined to generate this heat map. The labels under the heat map indicate the microbial species fed to the larvae, with three samples analyzed for each condition. The lactic acid bacteria (“LAB”) include Lactiplantibacillus plantarum and Leuconostoc mesenteroides, while the lactic acid bacterium (“AAB”) represents Acetobacter orientalis. “LAB + AAB” signifies mixtures of the AAB and either one of the LAB species. The asterisk in the label highlights a sample in a “LAB” condition (Leuconostoc mesenteroides), which clustered separately from the other “LAB” samples. Brown abbreviations of scientific names are for the yeast-fed conditions. H. uva, Hanseniaspora uvarum; K. hum, Kazachstania humilis; M. asi, Martiniozyma asiatica; S. cra, Saccharomycopsis crataegensis; P. klu, Pichia kluyveri; S. bac, Starmerella bacillaris; S. cer, S. cerevisiae BY4741 strain.

Only a handful of genes showed different expression patterns between larvae fed on yeast and those fed on bacteria, without any enrichment for specialized gene functions. Thus, it is challenging to discuss the potential differential impacts, if any, of yeast and bacteria on larval growth.

4) The whole paper - and this is one of its merits - points to a role of the Drosophila larval microbiota in processing the fly food. Are these bacterial and fungal species found in the gut of larvae/adults? Are these species capable of establishing a niche in the cardia of adults as shown recently in the Ludington lab (Dodge et al.,)? Previous studies have suggested that microbiota members stimulate the Imd pathway leading to an increase in digestive proteases (Erkosar/Leulier). Are the microbiota species studied here affecting gut signaling pathways beyond providing branched amino acids?

The whole paper - and this is one of its merits - points to a role of the Drosophila larval microbiota in processing the fly food. Are these bacterial and fungal species found in the gut of larvae/adults? Are these species capable of establishing a niche in the cardia of adults as shown recently in the Ludington lab (Dodge et al.,)?

Although we did not investigate the microbiota in the gut of either larvae or adults, we did compare the microbiota within surface-sterilized larvae or adults with those in food samples. We found that adult flies and early-stage food sources, as well as larvae and late-stage food sources, harbor similar microbial species (Figure 1F). Additionally, previous examinations of the gut microbiota in wild adult flies have identified microbial species or taxa congruent with those we isolated from our foods (Chandler et al., 2011; Chandler et al., 2012). We have elaborated on this in our response to Weakness 1).

While we did not investigate whether these species are capable of establishing a niche in the cardia of adults, we will cite the study by Dodge et al., 2023 in our revised manuscript and discuss the possibility that predominant microbes in adult flies may show a propensity for colonization.

Previous studies have suggested that microbiota members stimulate the Imd pathway leading to an increase in digestive proteases (Erkosar/Leulier). Are the microbiota species studied here affecting gut signaling pathways beyond providing branched amino acids?

The reviewer inquires whether the supportive microbes in our study stimulate gut Imd signaling pathways and induce the expression of digestive protease genes, as demonstrated in a previous study (Erkosar et al., 2015). According to our RNA-seq data, it seems unlikely that the supportive microbes stimulate the signaling pathway. Figures contained in Author response image 2 provide the statistical comparisons of expression levels for seven protease genes between the supportive and the non-supportive conditions. These genes did not exhibit a consistent upregulation in the presence of the supportive microbes (H. uva or K. hum in Author response image 2A; Le mes + A. ori in Author response image 2B). Rather, they exhibited a tendency to be upregulated under the non-supportive microbes (St. bac or Pi. klu in Author response image 2A; La. pla in Author response image 2B).

Author response image 2.

Most of the peptidase genes reported by Erkosar et al., 2015 are more highly expressed under the non-supportive conditions than the supportive conditions. Comparison of the expression levels of seven peptidase genes derived from the RNA-seq analysis of yeast-fed (A) or bacteria-fed (B) first-instar larvae. A previous report demonstrated that the expression of these genes is upregulated upon association with a strain of Lactiplantibacillus plantarum, and that the PGRP-LE/Imd/Relish signaling pathway, at least partially, mediates the induction (Erkosar et al., 2015). H. uva, Hanseniaspora uvarum; K. hum, Kazachstania humilis; P. klu, Pichia kluyveri; S. bac, Starmerella bacillaris; La. pla, Lactiplantibacillus plantarum; Le. mes, Leuconostoc mesenteroides; A. ori, Acetobacter orientalis; ns, not significant.

Reviewer #2 (Public Review):

Weaknesses:

The experimental setting that, the authors think, reflects host-microbe interactions in nature is one of the key points. However, it is not explicitly mentioned whether isolated microbes are indeed colonized in wild larvae of Drosophila melanogaster who eat bananas. Another matter is that this work is rather descriptive and a few mechanical insights are presented. The evidence that the nutritional role of BCAAs is incomplete, and molecular level explanation is missing in "interspecies interactions" between lactic acid bacteria (or yeast) and acetic acid bacteria that assure their inhabitation. Apart from these matters, the future directions or significance of this work could be discussed more in the manuscript.

The experimental setting that, the authors think, reflects host-microbe interactions in nature is one of the key points. However, it is not explicitly mentioned whether isolated microbes are indeed colonized in wild larvae of Drosophila melanogaster who eat bananas.

The reviewer asks whether the isolated microbes were colonized in the larval gut. Previous studies on microbial colonization associated with Drosophila have predominantly focused on adults (Pais et al. PLOS Biology, 2018), rather than larval stages. Developing larvae continually consume substrates which are already subjected to microbial fermentation and abundant in live microbes until the end of the feeding larval stage. Therefore, we consider it difficult to discuss microbial colonization in the larval gut. We will add this point in the DISCUSSION of the revised manuscript.

Another matter is that this work is rather descriptive and a few mechanical insights are presented. The evidence that the nutritional role of BCAAs is incomplete, and molecular level explanation is missing in "interspecies interactions" between lactic acid bacteria (or yeast) and acetic acid bacteria that assure their inhabitation.

While recognizing the importance of comprehensive mechanistic analysis, this study includes all experimentally feasible data. Elucidation of more detailed molecular mechanisms lies beyond the scope of this study and will be the subject of future research.

Regarding the nutritional role of BCAAs, the incorporation of BCAAs enabled larvae fed with the non-supportive yeast to grow to the second instar. This observation suggests that consumption of BCAAs upregulates diverse genes involved in cellular growth processes in larvae. We have discussed the hypothetical interaction between lactic acid bacteria (LAB) and acetic acid bacteria (AAB) in the manuscript (lines 402-405): LAB may facilitate lactate provision to AAB, consequently enhancing the biosynthesis of essential nutrients such as amino acids. To test this hypothesis, future experiments will include the supplementation of lactic acid to AAB culture plates and the co-inoculating LAB mutant strains defective in lactate production with AABs, to assess both larval growth and continuous larval association with AABs. With respect to AAB-yeast interactions, metabolites released from yeast cells might benefit AAB growth, and this possibility will be investigated through the supplementation of AAB culture plates with candidate metabolites identified in the cell suspension supernatants of the late-stage yeasts.

Apart from these matters, the future directions or significance of this work could be discussed more in the manuscript.

We appreciate the reviewer's recommendations and will include additional descriptions regarding these aspects in the DISCUSSION section.

Reviewer #3 (Public Review):

Weaknesses:

Despite describing important findings, I believe that a more thorough explanation of the experimental setup and the steps expected to occur in the exposed diet over time, starting with natural "inoculation" could help the reader, in particular the non-specialist, grasp the rationale and main findings of the manuscript. When exactly was the decision to collect early-stage samples made? Was it when embryos were detected in some of the samples? What are the implications of bacterial presence in the no-fly traps? These samples also harbored complex microbial communities, as revealed by sequencing. Were these samples colonized by microbes deposited with air currents? Were they the result of flies that touched the material but did not lay eggs? Could the traps have been visited by other insects? Another interesting observation that could be better discussed is the fact that adult flies showed a microbiome that more closely resembles that of the early-stage diet, whereas larvae have a more late-stage-like microbiome. It is easy to understand why the microbiome of the larvae would resemble that of the late-stage foods, but what about the adult microbiome? Authors should discuss or at least acknowledge the fact that there must be a microbiome shift once adults leave their food source. Lastly, the authors should provide more details about the metabolomics experiments. For instance, how were peaks assigned to leucine/isoleucine (as well as other compounds)? Were both retention times and MS2 spectra always used? Were standard curves produced? Were internal, deuterated controls used?

When exactly was the decision to collect early-stage samples made? Was it when embryos were detected in some of the samples?

We collected traps and early-stage samples 2.5 days after setting up the traps. This time frame was determined by pilot experiments. A shorter collection time resulted in a greater likelihood of obtaining no-fly traps, whereas a longer collection time caused larval overcrowding, as well as adults’ deaths from drowning in the liquid seeping out of fruits. These procedural details will be delineated in the MATERIALS AND METHODS section of the revised manuscript.

What are the implications of bacterial presence in the no-fly traps? These samples also harbored complex microbial communities, as revealed by sequencing. Were these samples colonized by microbes deposited with air currents? Were they the result of flies that touched the material but did not lay eggs? Could the traps have been visited by other insects?

We assume that the origins of the microbes detected in the no-fly trap foods vary depending on the species. For instance, Colletotrichum musae, the fungus that causes banana anthracnose, may have been present in fresh bananas before trap placement. The filamentous fungi could have originated from airborne spores, but they could also have been introduced by insects that feed on these fungi. We will include these possibilities in the DISCUSSION section of the revised manuscript.

Another interesting observation that could be better discussed is the fact that adult flies showed a microbiome that more closely resembles that of the early-stage diet, whereas larvae have a more late-stage-like microbiome. It is easy to understand why the microbiome of the larvae would resemble that of the late-stage foods, but what about the adult microbiome? Authors should discuss or at least acknowledge the fact that there must be a microbiome shift once adults leave their food source.

We are grateful for the reviewer's insightful suggestions regarding shifts in the adult microbiome. We plan to include in the DISCUSSION section of the revised manuscript the possibility that the microbial composition may change substantially during pupal stages and that microbes obtained after eclosion could potentially form the adult gut microbiota.

Lastly, the authors should provide more details about the metabolomics experiments. For instance, how were peaks assigned to leucine/isoleucine (as well as other compounds)? Were both retention times and MS2 spectra always used? Were standard curves produced? Were internal, deuterated controls used?

We appreciate the reviewer's advice. Detailed methods of the metabolomic experiments will be included in our revised manuscript.

Author Response

We would like to thank the editors and reviewers for their thoughtful comments on our manuscript. Before we can provide a point-by-point response and submit a revised version of the manuscript we would like to provisionally address and alleviate some of their main concerns.

A concern was expressed in the ‘eLife assessment’ and by two of the reviewers that a potential confound between the coding of sensory information and behavior outcome by IC neurons might have been introduced by combining data across different sound levels, which could challenge the conclusions of the study. In addressing this we have carried out the analysis (i.e. averaging the neural activity separately for different sound levels) suggested for distinguishing between the two alternative explanations offered by reviewer #1: That the difference in neural activity between hit and miss trials reflects a) behavior or b) sound level (more precisely: differences in response magnitude arising from a higher proportion of highsound-level trials in the hit trial group than in the miss trial group). If the data favored b), we would expect no difference in activity between hit and miss trials when plotted separately for different sound levels. The figure in Author response image 1 indicates that that is not the case. Hit and miss trial activity are clearly distinct even when plotted separately for different sound levels, confirming that this difference in activity reflects the animals’ behavior rather than sensory information.

Author response image 1.

A related concern was expressed with regards to the decoding analysis. Namely, that differences in the distributions of sound levels in the different trial types could confound the decoding into hit and miss trials and that, consequently, the results of the decoding analysis merely reflect differences in the processing of sound level. Our analysis actually aimed to take this into account but, unfortunately, we failed to include sufficient details in the methods section of the submitted manuscript. Rather than including all the trials in a given session, only trials of intermediate difficulty were used for the decoding analysis. More specifically, we only included trials across five sound levels, comprising the lowest sound level that exceeded a d prime of 1.5 plus the two sound levels below and above that level. That ensured that differences in sound level distributions would be small, while still giving us a sufficient number of trials to perform the decoding analysis. In this context, it is worth bearing in mind that a) the decoding analysis was done on a frame-by-frame basis, meaning that the decoding score achieved early in the trial has no impact on the decoding score at later time points in the trial, b) sound-driven activity can be observed predominantly immediately after stimulus onset and is largely over about 1 s into the trial (see cluster 3, for instance, or average miss trial activity in the plots above), c) decoding performance of the behavioral outcome starts to plateau 5001000 ms into the trial and remains high until it very gradually begins to decline after about 2 s into the trial. In other words, decoding performance remains high far longer than the stimulus would be expected to have an impact on the neurons’ activity. Therefore, we would expect any residual bias due to differences in the sound level distribution that our approach did not control for to be restricted to the very beginning of the trial and not to meaningfully impact the conclusions derived from the decoding analysis.

Another concern expressed in the reviews is that, in relation to the cluster-wise analysis of neural activity, no direct comparison (beyond the pie charts of Figure 5C) was provided between data from lesioned and non-lesioned groups, leaving unclear how similar taskrelevant activity is between these groups. In Author response image 2 we plot, analogous to Figure 5B, the average hit and miss trial activity for the 10 clusters separately for lesioned and non-lesioned mice, illustrating more clearly the high degree of similarity between the two groups.

Author response image 2.

Author response:

Reviewer #1 (Public review):

(1) Some details are not described for experimental procedures. For example, what were the pharmacological drugs dissolved in, and what vehicle control was used in experiments? How long were pharmacological drugs added to cells?

We apologise for the oversight. These details have now been added to the methods section of the manuscript as well as to the relevant figure legends.

Briefly, latrunculin was used at a final concentration of 250 nM and Y27632 at a final concentration of 50 μM. Both drugs were dissolved in DMSO. The vehicle controls were effected with the highest final concentration of DMSO of the two drugs.

The details of the drug treatments and their duration was added to the methods and to figures 6, S10, and S12.

(2) Details are missing from the Methods section and Figure captions about the number of biological and technical replicates performed for experiments. Figure 1C states the data are from 12 beads on 7 cells. Are those same 12 beads used in Figure 2C? If so, that information is missing from the Figure 2C caption. Similarly, this information should be provided in every figure caption so the reader can assess the rigor of the experiments. Furthermore, how heterogenous would the bead displacements be across different cells? The low number of beads and cells assessed makes this information difficult to determine.

We apologise for the oversight. We have now added this data to the relevant figure panels.

To gain a further understanding of the heterogeneity of bead displacements across cells, we have replotted the relevant graphs using different colours to indicate different cells. This reveals that different cells appear to behave similarly and that the behaviour appears controlled by distance to the indentation or the pipette tip rather than cell identity.

We agree with the reviewer that the number of cells examined is low. This is due to the challenging nature of the experiments that signifies that many attempts are necessary to obtain a successful measurement.

The experiments in Fig 1C are a verification of a behaviour documented in a previous publication [1]. Here, we just confirm the same behaviour and therefore we decided that only a small number of cells was needed.

The experiments in Fig 2C (that allow for a direct estimation of the cytoplasm’s hydraulic permeability) require formation of a tight seal between the glass micropipette and the cell, something known as a gigaseal in electrophysiology. The success rate of this first step is 10-30% of attempts for an experienced experimenter. The second step is forming a whole cell configuration, in which a hydraulic link is formed between the cell and the micropipette. This step has a success rate of ~ 50%. Whole cell links are very sensitive to any disturbance. After reaching the whole cell configuration, we applied relatively high pressures that occasionally resulted in loss of link between the cell and the micropipette. In summary, for the 12 successful measurements, hundreds of unsuccessful attempts were carried out.

(3) The full equation for displacement vs. time for a poroelastic material is not provided. Scaling laws are shown, but the full equation derived from the stress response of an elastic solid and viscous fluid is not shown or described.

We thank the reviewer for this comment. Based on our experiments, we found that the cytoplasm behaves as a poroelastic material. However, to understand the displacements of the cell surface in response to localised indentation, we show that we also need to take the tension of the sub membranous cortex into account. In summary, the interplay between cell surface tension generated by the cortex and the poroelastic cytoplasm controls the cell behaviour. To our knowledge, no simple analytical solutions to this type of problem exist.

In Fig 1, we show that the response of the cell to local indentation is biphasic with a short time-scale displacement followed by a longer time-scale one. In Figs 2 and 3, we directly characterise the kinetics of cell surface displacement in response to microinjection of fluid. These kinetics are consistent with the long time-scale displacement but not the short time-scale one. Scaling considerations led us to propose that tension in the cortex may play a role in mediating the short time-scale displacement. To verify this hypothesis, we have now added new data showing that the length-scale of an indentation created by an AFM probe depends on tension in the cortex (Fig S5).

In a previous publication [2], we derived the temporal dynamics of cell surface displacement for a homogenous poroelastic material in response to a change in osmolarity. In the current manuscript, the composite nature of the cell (membrane, cortex, cytoplasm) needs to be taken into account as well as a realistic cell shape. Therefore, we did not attempt to provide an analytical solution for the displacement of the cell surface versus time in the current work. Instead, we turned to finite element modelling to show that our observations are qualitatively consistent with a cell that comprises a tensed sub membranous actin cortex and a poroelastic cytoplasm (Fig 4). We have now added text to make this clearer for the reader.

Reviewer #2 (Public review):

Comments & Questions:

The authors state, "Next, we sought to quantitatively understand how the global cellular response to local indentation might arise from cellular poroelasticity." However, the evidence presented in the following paragraph appears more qualitative than strictly quantitative. For instance, the length scale estimate of ~7 μm is only qualitatively consistent with the observed ~10 μm, and the timescale 𝜏𝑧 ≈ 500 ms is similarly described as "qualitatively consistent" with experimental observations. Strengthening this point would benefit from more direct evidence linking the short timescale to cell surface tension. Have you tried perturbing surface tension and examining its impact on this short-timescale relaxation by modulating acto-myosin contractility with Y-27632, depolymerizing actin with Latrunculin, or applying hypo/hyperosmotic shocks?

Upon rereading our manuscript, we agree with the reviewer that some of our statements are too strong. We have now moderated these and clarified the goal of that section of the text.

The reviewer asks if we have examined the effect of various perturbations on the short time-scale displacements. In our experimental conditions, we cannot precisely measure the time-scale of the fast relaxation because its duration is comparable to the frame rate of our image acquisition. However, we examined the amplitude of the displacement of the first phase in response to sucrose treatment and we have carried out new experiments in which we treat cells with 250nM Latrunculin to partially depolymerise cellular F-actin. Neither of these treatments had an impact on the amplitude of vertical displacements (Author response image 1).

The absence of change in response to Latrunculin may be because the treatment decreases both the elasticity of the cytoplasm E and the cortical tension γ. As the length-scale l of the deformation of the surface scales as , the two effects of latrunculin treatment may therefore compensate one another and result in only small changes in l. We have now added this data to supplementary information and comment on this in the text.

Author response image 1:

Amplitude of the short time-scale displacements of beads in response to AFM indentation at δx=0µm for control cells, sucrose treated cells, and cells treated with Latrunculin B. n indicates the number of cells examined and N the number of beads.

The reviewer’s comment also made us want to determine how cortical tension affects the length-scale of the cell surface deformation created by localised micro indentation. To isolate the role of the cortex from that of cell shape, we decided to examine rounded mitotic cells. In our experiments, we indented a mitotic cell expressing a membrane targeted GFP with a sharp AFM tip (Author response image 2).

In our experiments, we adjusted force to generate a 2μm depth indentation and we imaged the cell profile with confocal microscopy before and during indentation. Segmentation of this data allowed us to determine the cell surface displacement resulting from indentation and measure a length scale of deformation. In control conditions, the length scale created by deformation is on the order of 1.2μm. When we inhibited myosin contractility with blebbistatin, the length-scale of deformation decreased significantly to 0.8 μm, as expected if we decrease the surface tension γ without affecting the cytoplasmic elasticity. We have now added this data to our manuscript.

Author response image 2.

(a) Overlay of the zx profiles of a mitotic cell before (green) and during indentation (red). The cell membrane is labelled with CellMask DeepRed. The arrowhead indicates the position of the AFM tip. Scale bar 10µm. (b) Position of the membrane along the top half of the cell before (green) and during (red) indentation. The membrane position is derived from segmentation of the data in (a). Deformation is highly localised and membrane profiles overlap at the edges. The tip position is marked by an *. (c) The difference in membrane height between pre-indentation and indentation profiles plotted in (b) with the tip located at x=0. (d) Schematic of the cell surface profile during indentation and the corresponding length scale of the deformation induced by indentation. (e) Measured length scale for an indentation ~2µm for DMSO control l=1.2±0.2µm (n=8 cells) and with blebbistatin treatment (100µM) l=0.8±0.4µm (n=9 cells) (p= 0.016

The authors demonstrate that the second relaxation timescale increases (Figure 1, Panel D) following a hyperosmotic shock, consistent with cytoplasmic matrix shrinkage, increased friction, and consequently a longer relaxation timescale. While this result aligns with expectations, is a seven-fold increase in the relaxation timescale realistic based on quantitative estimates given the extent of volume loss?

We thank the reviewer for this interesting question. Upon re-examining our data, we realised that the numerical values in the text related to the average rather than the median of our measurements. The median of the poroelastic time constant increases from ~0.4s in control conditions to 1.4s in sucrose, representing approximately a 3.5-fold increase.

Previous work showed that HeLa cell volume decreases by ~40% in response to hyperosmotic shock [3]. The fluid volume fraction in cells is ~65-75%. If we assume that the water is contained in N pores of volume , we can express the cell volume as with V_s the volume of the solid fraction. We can rewrite with ϕ = 0.42 -0.6. As V_s does not change in response to osmotic shock, we can rewrite the volume change to obtain the change in pore size .

The poroelastic diffusion constant scales as and the poroelastic timescale scales as . Therefore, the measured change in volume leads to a predicted increase in poroelastic diffusion time of 1.7-1.9-fold, smaller than observed in our experiments. This suggests that some intuition can be gained in a straightforward manner assuming that the cytoplasm is a homogenous porous material.

However, the reality is more complex and the hydraulic pore size is distinct from the entanglement length of the cytoskeleton mesh, as we discussed in a previous publication [4]. When the fluid fraction becomes sufficiently small, macromolecular crowding will impact diffusion further and non-linearities will arise. We have now added some of these considerations to the discussion.

If the authors' hypothesis is correct, an essential physiological parameter for the cytoplasm could be the permeability k and how it is modulated by perturbations, such as volume loss or gain. Have you explored whether the data supports the expected square dependency of permeability on hydraulic pore size, as predicted by simple homogeneity assumptions?

We thank the reviewer for this comment. As discussed above, we have explored such considerations in a previous publication (see discussion in [4]). Briefly, we find that the entanglement length of the F-actin cytoskeleton does play a role in controlling the hydraulic pore size but is distinct from it. Membrane bounded organelles could also contribute to setting the pore size. In our previous publication, we derived a scaling relationship that indicates that four different length-scales contribute to setting cellular rheology: the average filament bundle length, the size distribution of particles in the cytosol, the entanglement length of the cytoskeleton, and the hydraulic pore size. Many of these length-scales can be dynamically controlled by the cell, which gives rise to complex rheology. We have now added these considerations to our discussion.

Additionally, do you think that the observed decrease in k in mitotic cells compared to interphase cells is significant? I would have expected the opposite naively as mitotic cells tend to swell by 10-20 percent due to the mitotic overshoot at mitotic entry (see Son Journal of Cell Biology 2015 or Zlotek Journal of Cell Biology 2015).

We thank the reviewer for this interesting question. Based on the same scaling arguments as above, we would expect that a 10-20% increase in cell volume would give rise to 10-20% increase in diffusion constant. However, we also note that metaphase leads to a dramatic reorganisation of the cell interior and in particular membrane-bounded organelles. In summary, we do not know why such a decrease could take place. We now highlight this as an interesting question for further research.

Based on your results, can you estimate the pore size of the poroelastic cytoplasmic matrix? Is this estimate realistic? I wonder whether this pore size might define a threshold above which the diffusion of freely diffusing species is significantly reduced. Is your estimate consistent with nanobead diffusion experiments reported in the literature? Do you have any insights into the polymer structures that define this pore size? For example, have you investigated whether depolymerizing actin or other cytoskeletal components significantly alters the relaxation timescale?

We thank the reviewer for this comment. We cannot directly estimate the hydraulic pore size from the measurements performed in the manuscript. Indeed, while we understand the general scaling laws, the pre-factors of such relationships are unknown.

We carried out experiments aiming at estimating the hydraulic pore size in previous publications [3,4] and others have shown spatial heterogeneity of the cytoplasmic pore size [5]. In our previous experiments, we examined the diffusion of PEGylated quantum dots (14nm in hydrodynamic radius). In isosmotic conditions, these diffused freely through the cell but when the cell volume was decreased by a hyperosmotic shock, they no longer moved [3,4]. This gave an estimate of the pore radius of ~15nm.

Previous work has suggested that F-actin plays a role in dictating this pore size but microtubules and intermediate filaments do not [4].

There are no quantifications in Figure 6, nor is there a direct comparison with the model. Based on your model, would you expect the velocity of bleb growth to vary depending on the distance of the bleb from the pipette due to the local depressurization? Specifically, do blebs closer to the pipette grow more slowly?

We apologise for the oversight. The quantifications are presented in Fig S10 and Fig S12. We have now modified the figure legends accordingly.

Blebs are very heterogenous in size and growth velocity within a cell and across cells in the population in normal conditions [6]. Other work has shown that bleb size is controlled by a competition between pressure driving growth and actin polymerisation arresting it[7]. Therefore, we did not attempt to determine the impact of depressurisation on bleb growth velocity or size.

In experiments in which we suddenly increased pressure in blebbing cells, we did notice a change in the rate of growth of blebs that occurred after we increased pressure (Author response image 3). However, the experiments are technically challenging and we decided not to perform more.

Author response image 3:

A. A hydraulic link is established between a blebbing cell and a pipette. At time t>0, a step increase in pressure is applied. B. Kymograph of bleb growth in a control cell (top) an in a cell subjected to a pressure increase at t=0s (bottom). Top: In control blebs, the rate of growth is slow and approximately constant over time. The black arrow shows the start of blebbing. Bottom: The black arrow shows the start of blebbing. The dashed line shows the timing of pressure application and the red arrow shows the increase in growth rate of the bleb when the pressure increase reaches the bleb. This occurs with a delay δt.

I find it interesting that during depressurization of the interphase cells, there is no observed volume change, whereas in pressurization of metaphase cells, there is a volume increase. I assume this might be a matter of timescale, as the microinjection experiments occur on short timescales, not allowing sufficient time for water to escape the cell. Do you observe the radius of the metaphase cells decreasing later on? This relaxation could potentially be used to characterize the permeability of the cell surface.

We thank the reviewer for this comment.

First, we would like to clarify that both metaphase and interphase cells increase their volume in response to microinjection. The effect is easier to quantify in metaphase cells because we assume spherical symmetry and just monitor the evolution of the radius (Fig 3). However, the displacement of the beads in interphase cells (Fig 2) clearly shows that the cell volume increases in response to microinjection. For both interphase and metaphase cells, when the injection is prolonged, the membrane eventually detaches from the cortex and large blebs form until cell lysis. In contrast to the reviewer’s intuition, we never observe a relaxation in cell volume, probably because we inject fluid faster than the cell can compensate volume change through regulatory mechanisms involving ion channels.

When we depressurise metaphase cells, we do not observe any change in volume (Fig S10). This contrasts with the increase that we observe upon pressurisation. The main difference between these two experiments is the pressure differential. During depressurisation experiments, this is the hydraulic pressure within the cell ~500Pa (Fig 6A); whereas during pressurisation experiments, this is the pressure in the micropipette, ranging from 1.4-10 kPa (Fig 3). We note in particular that, when we used the lowest pressures in our experiments, the increase in volume was very slow (see Fig 3C). Therefore, we agree with the reviewer that it is likely the magnitude of the pressure differential that explains these differences.

I am curious about the saturation of the time lag at 30 microns from the pipette in Figure 4, Panel E for the model's prediction. A saturation which is not clearly observed in the experimental data. Could you comment on the origin of this saturation and the observed discrepancy with the experiments (Figure E panel 2)? Naively, I would have expected the time lag to scale quadratically with the distance from the pipette, as predicted by a poroelastic model and the diffusion of displacement. It seems weird to me that the beads start to move together at some distance from the pipette or else I would expect that they just stop moving. What model parameters influence this saturation? Does membrane permeability contribute to this saturation?

We thank the reviewer for pointing this out. In our opinion, the saturation occurring at 30 microns arises from the geometry of the model. At the largest distance away from the micropipette, the cortex becomes dominant in the mechanical response of the cell because it represents an increasing proportion of the cellular material.

To test this hypothesis, we will rerun our finite element models with a range of cell sizes. This will be added to the manuscript at a later date.

Reviewer #3 (Public review):

Weaknesses: I have two broad critical comments:

(1) I sense that the authors are correct that the best explanation of their results is the passive poroelastic model. Yet, to be thorough, they have to try to explain the experiments with other models and show why their explanation is parsimonious. For example, one potential explanation could be some mechanosensitive mechanism that does not involve cytoplasmic flow; another could be viscoelastic cytoskeletal mesh, again not involving poroelasticity. I can imagine more possibilities. Basically, be more thorough in the critical evaluation of your results. Besides, discuss the potential effect of significant heterogeneity of the cell.

We thank the reviewer for these comments and we agree with their general premise.

Some observations could qualitatively be explained in other ways. For example, if we considered the cell as a viscoelastic material, we could define a time constant with η the viscosity and E the elasticity of the material. The increase in relaxation time with sucrose treatment could then be explained by an increase in viscosity. However, work by others has previously shown that, in the exact same conditions as our experiment, viscoelasticity cannot account for the observations[1]. In its discussion, this study proposed poroelasticity as an alternative mechanism but did not investigate that possibility. This was consistent with our work that showed that the cytoplasm behaves as a poroelastic material and not as a viscoelastic material [4]. Therefore, we decided not to consider viscoelasticity as possibility. We now explain this reasoning better and have added a sentence about a potential role for mechanotransductory processes in the discussion.

(2) The study is rich in biophysics but a bit light on chemical/genetic perturbations. It could be good to use low levels of chemical inhibitors for, for example, Arp2/3, PI3K, myosin etc, and see the effect and try to interpret it. Another interesting question - how adhesive strength affects the results. A different interesting avenue - one can perturb aquaporins. Etc. At least one perturbation experiment would be good.

We agree with the reviewer. In our previous studies, we already examined what biological structures affect the poroelastic properties of cells [2,4]. Therefore, the most interesting aspect to examine in our current work would be perturbations to the phenomenon described in Fig 6G and, in particular, to investigate what volume regulation mechanisms enable sustained intracellular pressure gradients. However, these experiments are particularly challenging and with very low throughput. Therefore, we feel that these are out of the scope of the present report and we mention these as promising future directions.

References:

(1) Rosenbluth, M. J., Crow, A., Shaevitz, J. W. & Fletcher, D. A. Slow stress propagation in adherent cells. Biophys J 95, 6052-6059 (2008). https://doi.org/10.1529/biophysj.108.139139

(2) Esteki, M. H. et al. Poroelastic osmoregulation of living cell volume. iScience 24, 103482 (2021). https://doi.org/10.1016/j.isci.2021.103482

(3) Charras, G. T., Mitchison, T. J. & Mahadevan, L. Animal cell hydraulics. J Cell Sci 122, 3233-3241 (2009). https://doi.org/10.1242/jcs.049262

(4) Moeendarbary, E. et al. The cytoplasm of living cells behaves as a poroelastic material. Nat Mater 12, 253-261 (2013). https://doi.org/10.1038/nmat3517

(5) Luby-Phelps, K., Castle, P. E., Taylor, D. L. & Lanni, F. Hindered diffusion of inert tracer particles in the cytoplasm of mouse 3T3 cells. Proc Natl Acad Sci U S A 84, 4910-4913 (1987). https://doi.org/10.1073/pnas.84.14.4910

(6) Charras, G. T., Coughlin, M., Mitchison, T. J. & Mahadevan, L. Life and times of a cellular bleb. Biophys J 94, 1836-1853 (2008). https://doi.org/10.1529/biophysj.107.113605

(7) Tinevez, J. Y. et al. Role of cortical tension in bleb growth. Proc Natl Acad Sci U S A 106, 18581-18586 (2009). https://doi.org/10.1073/pnas.0903353106

Author Response

eLife assessment

This potentially valuable study uses classic neuroanatomical techniques and synchrotron X-ray tomography to investigate the mapping of the trunk within the brainstem nuclei of the elephant brain. Given its unique specializations, understanding the somatosensory projections from the elephant trunk would be of general interest to evolutionary neurobiologists, comparative neuroscientists, and animal behavior scientists. However, the anatomical analysis is inadequate to support the authors' conclusion that they have identified the elephant trigeminal sensory nuclei rather than a different brain region, specifically the inferior olive.

Comment: We are happy that our paper is considered to be potentially valuable. Also, the editors highlight the potential interest of our work for evolutionary neurobiologists, comparative neuroscientists, and animal behavior scientists. The editors are more negative when it comes to our evidence on the identification of the trigeminal nucleus vs the inferior olive. We have five comments on this assessment. (i) We think this assessment is heavily biased by the comments of referee 2. We will show that the referee’s comments are more about us than about our paper. Hence, the referee failed to do their job (refereeing our paper) and should not have succeeded in leveling our paper. (ii) We have no ad hoc knock-out experiments to distinguish the trigeminal nucleus vs the inferior olive. Such experiments (extracellular recording & electrolytic lesions, viral tracing would be done in a week in mice, but they cannot and should not be done in elephants. (iii) We have extraordinary evidence. Nobody has ever described a similarly astonishing match of body (trunk folds) and myeloarchitecture in the trigeminal system before. (iv) We will show that our assignment of the trigeminal nucleus vs the inferior olive is more plausible than the current hypothesis about the assignment of the trigeminal nucleus vs the inferior olive as defended by referee 2. We think this is why it is important to publish our paper. (v) We think eLife is the perfect place for our publication because the deviating views of referee 2 are published along.

Change: We performed additional peripherin-antibody staining to differentiate the inferior olive and trigeminal nucleus. Peripherin is a cytoskeletal protein that is found in peripheral nerves and climbing fibers. Specifically, climbing fibers of various species (mouse, rabbit, pig, cow, and human; Errante et al., 1998) are stained intensely with peripherin-antibodies. What is tricky for our purposes is that there is also some peripherin-antibody reactivity in the trigeminal nuclei (Errante et al., 1998). Such peripherin-antibody reactivity is weaker, however, and lacks the distinct axonal bundle signature that stems from the strong climbing fiber peripherin-reactivity as seen in the inferior olive (Errante et al., 1998). As can be seen in Author response image 1, we observe peripherin-reactivity in axonal bundles (i.e. in putative climbing fibers), in what we think is the inferior olive. We also observe weak peripherin-reactivity, in what we think is the trigeminal nucleus, but not the distinct and strong labeling of axonal bundles. These observations are in line with our ideas but are difficult to reconcile with the views of the referee. Specifically, the lack of peripherin-reactive axon bundles suggests that there are no climbing fibres in what the referee thinks is the inferior olive.

Errante, L., Tang, D., Gardon, M., Sekerkova, G., Mugnaini, E., & Shaw, G. (1998). The intermediate filament protein peripherin is a marker for cerebellar climbing fibres. Journal of neurocytology, 27, 69-84.

Author response image 1.

The putative inferior olive but not the putative trigeminal nucleus contains peripherin-positive axon bundles (presumptive climbing fibers). (A) Overview picture of a brainstem section stained with anti-peripherin-antibodies (white color). Anti-peripherin-antibodies stain climbing fibers in a wide variety of mammals. The section comes from the posterior brainstem of African elephant cow Bibi; in this posterior region, both putative inferior olive and trigeminal nucleus are visible. Note the bright staining of the dorsolateral nucleus, the putative inferior olive according to Reveyaz et al., and the trigeminal nucleus according to Maseko et al., 2013. (B) High magnification view of the dorsolateral nucleus (corresponding to the upper red rectangle in A). Anti-peripherin-positive axon bundles (putative climbing fibers) are seen in support of the inferior olive hypothesis of Reveyaz et al. (C) High magnification view of the ventromedial nucleus (corresponding to the lower red rectangle in A). The ventromedial nucleus is weakly positive for peripherin but contains no anti-peripherin-positive axon bundles (i.e. no putative climbing fibers) in support of the trigeminal nucleus hypothesis of Reveyaz et al. Note that myelin stripes – weakly visible as dark omissions – are clearly anti-peripherin-negative.

Reviewer #1:

Summary:

This fundamental study provides compelling neuroanatomical evidence underscoring the sensory function of the trunk in African and Asian elephants. Whereas myelinated tracts are classically appreciated as mediating neuronal connections, the authors speculate that myelinated bundles provide functional separation of trunk folds and display elaboration related to the "finger" projections. The authors avail themselves of many classical neuroanatomical techniques (including cytochrome oxidase stains, Golgi stains, and myelin stains) along with modern synchrotron X-ray tomography. This work will be of interest to evolutionary neurobiologists, comparative neuroscientists, and the general public, with its fascinating exploration of the brainstem of an icon sensory specialist.

Comment: We are incredibly grateful for this positive assessment.

Changes: None.

Strengths:

• The authors made excellent use of the precious sample materials from 9 captive elephants.

• The authors adopt a battery of neuroanatomical techniques to comprehensively characterize the structure of the trigeminal subnuclei and properly re-examine the "inferior olive".

• Based on their exceptional histological preparation, the authors reveal broadly segregated patterns of metabolic activity, similar to the classical "barrel" organization related to rodent whiskers.

Comment: The referee provides a concise summary of our findings.

Changes: None.

Weaknesses:

• As the authors acknowledge, somewhat limited functional description can be provided using histological analysis (compared to more invasive techniques).

• The correlation between myelinated stripes and trunk fold patterns is intriguing, and Figure 4 presents this idea beautifully. I wonder - is the number of stripes consistent with the number of trunk folds? Does this hold for both species?

Comment: We agree with the referee’s assessment. We note that cytochrome-oxidase staining is an at least partially functional stain, as it reveals constitutive metabolic activity. A significant problem of the work in elephants is that our recording possibilities are limited, which in turn limits functional analysis. As indicated in Figure 4 for the African elephant Indra, there was an excellent match of trunk folds and myelin stripes. Asian elephants have more, and less conspicuous trunk folds than African elephants. As illustrated in Figure 6, Asian elephants have more, and less conspicuous myelin stripes. Thus, species differences in myelin stripes correlate with species differences in trunk folds.

Changes: We clarify the relation of myelin stripe and trunk fold patterns in our discussion of Figure 6.  

Reviewer #2 (Public Review):

The authors describe what they assert to be a very unusual trigeminal nuclear complex in the brainstem of elephants, and based on this, follow with many speculations about how the trigeminal nuclear complex, as identified by them, might be organized in terms of the sensory capacity of the elephant trunk.

Comment: We agree with the referee’s assessment that the putative trigeminal nucleus described in our paper is highly unusual in size, position, vascularization, and myeloarchitecture. This is why we wrote this paper. We think these unusual features reflect the unique facial specializations of elephants, i.e. their highly derived trunk. Because we have no access to recordings from the elephant brainstem, we cannot back up all our functional interpretations with electrophysiological evidence; it is therefore fair to call them speculative.

Changes: None.

The identification of the trigeminal nuclear complex/inferior olivary nuclear complex in the elephant brainstem is the central pillar of this manuscript from which everything else follows, and if this is incorrect, then the entire manuscript fails, and all the associated speculations become completely unsupported.

Comment: We agree.

Changes: None.

The authors note that what they identify as the trigeminal nuclear complex has been identified as the inferior olivary nuclear complex by other authors, citing Shoshani et al. (2006; 10.1016/j.brainresbull.2006.03.016) and Maseko et al (2013; 10.1159/000352004), but fail to cite either Verhaart and Kramer (1958; PMID 13841799) or Verhaart (1962; 10.1515/9783112519882-001). These four studies are in agreement, but the current study differs.

Comment & Change: We were not aware of the papers of Verhaart and included them in the revised ms.

Let's assume for the moment that the four previous studies are all incorrect and the current study is correct. This would mean that the entire architecture and organization of the elephant brainstem is significantly rearranged in comparison to ALL other mammals, including humans, previously studied (e.g. Kappers et al. 1965, The Comparative Anatomy of the Nervous System of Vertebrates, Including Man, Volume 1 pp. 668-695) and the closely related manatee (10.1002/ar.20573). This rearrangement necessitates that the trigeminal nuclei would have had to "migrate" and shorten rostrocaudally, specifically and only, from the lateral aspect of the brainstem where these nuclei extend from the pons through to the cervical spinal cord (e.g. the Paxinos and Watson rat brain atlases), the to the spatially restricted ventromedial region of specifically and only the rostral medulla oblongata. According to the current paper, the inferior olivary complex of the elephant is very small and located lateral to their trigeminal nuclear complex, and the region from where the trigeminal nuclei are located by others appears to be just "lateral nuclei" with no suggestion of what might be there instead.

Comment: We have three comments here:

1) The referee correctly notes that we argue the elephant brainstem underwent fairly major rearrangements. In particular, we argue that the elephant inferior olive was displaced laterally, by a very large cell mass, which we argue is an unusually large trigeminal nucleus. To our knowledge, such a large compact cell mass is not seen in the ventral brain stem of any other mammal.

2) The referee makes it sound as if it is our private idea that the elephant brainstem underwent major rearrangements and that the rest of the evidence points to a conventional ‘rodent-like’ architecture. This is far from the truth, however. Already from the outside appearance (see our Figure 1B and Figure 6A) it is clear that the elephant brainstem has huge ventral bumps not seen in any other mammal. An extraordinary architecture also holds at the organizational level of nuclei. Specifically, the facial nucleus – the most carefully investigated nucleus in the elephant brainstem – has an appearance distinct from that of the facial nuclei of all other mammals (Maseko et al., 2013; Kaufmann et al., 2022). If both the overall shape and the constituting nuclei of the brainstem are very different from other mammals, it is very unlikely if not impossible that the elephant brainstem follows in all regards a conventional ‘rodent-like’ architecture.

3) The inferior olive is an impressive nucleus in the partitioning scheme we propose (Author response image 1). In fact – together with the putative trigeminal nucleus we describe – it’s the most distinctive nucleus in the elephant brainstem. We have not done volumetric measurements and cell counts here, but think this is an important direction for future work. What has informed our work is that the inferior olive nucleus we describe has the serrated organization seen in the inferior olive of all mammals. We will discuss these matters in depth below.

Changes: None.

Such an extraordinary rearrangement of brainstem nuclei would require a major transformation in the manner in which the mutations, patterning, and expression of genes and associated molecules during development occur. Such a major change is likely to lead to lethal phenotypes, making such a transformation extremely unlikely. Variations in mammalian brainstem anatomy are most commonly associated with quantitative changes rather than qualitative changes (10.1016/B978-0-12-804042-3.00045-2).

Comment: We have two comments here:

1) The referee claims that it is impossible that the elephant brainstem differs from a conventional brainstem architecture because this would lead to lethal phenotypes etc. Following our previous response, this argument does not hold. It is out of the question that the elephant brainstem looks very different from the brainstem of other mammals. Yet, it is also evident that elephants live. The debate we need to have is not if the elephant brainstem differs from other mammals, but how it differs from other mammals.

2). In principle we agree with the referee’s thinking that the model of the elephant brainstem that is most likely correct is the one that requires the least amount of rearrangements to other mammals. We therefore prepared a comparison of the model the referee is proposing (Maseko et al., 2013; see Author response table 1 below) with our proposition. We scored these models on their similarity to other mammals. We find that the referee’s ideas (Maseko et al., 2013) require more rearrangements relative to other mammals than our suggestion.

Changes: Inclusion of Author response table 1, which we discuss in depth below.

The impetus for the identification of the unusual brainstem trigeminal nuclei in the current study rests upon a previous study from the same laboratory (10.1016/j.cub.2021.12.051) that estimated that the number of axons contained in the infraorbital branch of the trigeminal nerve that innervate the sensory surfaces of the trunk is approximately 400 000. Is this number unusual? In a much smaller mammal with a highly specialized trigeminal system, the platypus, the number of axons innervating the sensory surface of the platypus bill skin comes to 1 344 000 (10.1159/000113185). Yet, there is no complex rearrangement of the brainstem trigeminal nuclei in the brain of the developing or adult platypus (Ashwell, 2013, Neurobiology of Monotremes), despite the brainstem trigeminal nuclei being very large in the platypus (10.1159/000067195). Even in other large-brained mammals, such as large whales that do not have a trunk, the number of axons in the trigeminal nerve ranges between 400,000 and 500,000 (10.1007/978-3-319-47829-6_988-1). The lack of comparative support for the argument forwarded in the previous and current study from this laboratory, and that the comparative data indicates that the brainstem nuclei do not change in the manner suggested in the elephant, argues against the identification of the trigeminal nuclei as outlined in the current study. Moreover, the comparative studies undermine the prior claim of the authors, informing the current study, that "the elephant trigeminal ganglion ... point to a high degree of tactile specialization in elephants" (10.1016/j.cub.2021.12.051). While clearly, the elephant has tactile sensitivity in the trunk, it is questionable as to whether what has been observed in elephants is indeed "truly extraordinary".

Comment: These comments made us think that the referee is not talking about the paper we submitted, but that the referee is talking about us and our work in general. Specifically, the referee refers to the platypus and other animals dismissing our earlier work, which argued for a high degree of tactile specialization in elephants. We think the referee’s intuitions are wrong and our earlier work is valid.

Changes: We prepared a Author response image 2 (below) that puts the platypus brain, a monkey brain, and the elephant trigeminal ganglion (which contains a large part of the trunk innervating cells) in perspective.

Author response image 2.

The elephant trigeminal ganglion is comparatively large. Platypus brain, monkey brain, and elephant ganglion. The elephant has two trigeminal ganglia, which contain the first-order somatosensory neurons. They serve mainly for tactile processing and are large compared to a platypus brain (from the comparative brain collection) and are similar in size to a monkey brain. The idea that elephants might be highly specialized for trunk touch is also supported by the analysis of the sensory nerves of these animals (Purkart et al., 2022). Specifically, we find that the infraorbital nerve (which innervates the trunk) is much thicker than the optic nerve (which mediates vision) and the vestibulocochlear nerve (which mediates hearing). Thus, not everything is large about elephants; instead, the data argue that these animals are heavily specialized for trunk touch.

But let's look more specifically at the justification outlined in the current study to support their identification of the unusually located trigeminal sensory nuclei of the brainstem.

(1) Intense cytochrome oxidase reactivity.

(2) Large size of the putative trunk module.

(3) Elongation of the putative trunk module.

(4) The arrangement of these putative modules corresponds to elephant head anatomy.

(5) Myelin stripes within the putative trunk module that apparently match trunk folds.

(6) Location apparently matches other mammals.

(7) Repetitive modular organization apparently similar to other mammals.

(8) The inferior olive described by other authors lacks the lamellated appearance of this structure in other mammals.

Comment: We agree those are key issues.

Changes: None.

Let's examine these justifications more closely.

(1) Cytochrome oxidase histochemistry is typically used as an indicative marker of neuronal energy metabolism. The authors indicate, based on the "truly extraordinary" somatosensory capacities of the elephant trunk, that any nuclei processing this tactile information should be highly metabolically active, and thus should react intensely when stained for cytochrome oxidase. We are told in the methods section that the protocols used are described by Purkart et al (2022) and Kaufmann et al (2022). In neither of these cited papers is there any description, nor mention, of the cytochrome oxidase histochemistry methodology, thus we have no idea of how this histochemical staining was done. To obtain the best results for cytochrome oxidase histochemistry, the tissue is either processed very rapidly after buffer perfusion to remove blood or in recently perfusion-fixed tissue (e.g., 10.1016/0165-0270(93)90122-8). Given: (1) the presumably long post-mortem interval between death and fixation - "it often takes days to dissect elephants"; (2) subsequent fixation of the brains in 4% paraformaldehyde for "several weeks"; (3) The intense cytochrome oxidase reactivity in the inferior olivary complex of the laboratory rat (Gonzalez-Lima, 1998, Cytochrome oxidase in neuronal metabolism and Alzheimer's diseases); and (4) The lack of any comparative images from other stained portions of the elephant brainstem; it is difficult to support the justification as forwarded by the authors. The histochemical staining observed is likely background reactivity from the use of diaminobenzidine in the staining protocol. Thus, this first justification is unsupported.

Comment: The referee correctly notes the description of our cytochrome-oxidase reactivity staining was lacking. This is a serious mistake of ours for which we apologize very much. The referee then makes it sound as if we messed up our cytochrome-oxidase staining, which is not the case. All successful (n = 3; please see our technical comments in the recommendation section) cytochrome-oxidase stainings were done with elephants with short post-mortem times (≤ 2 days) to brain removal/cooling and only brief immersion fixation (≤ 1 day). Cytochrome-oxidase reactivity in elephant brains appears to be more sensitive to quenching by fixation than is the case for rodent brains. We think it is a good idea to include a cytochrome-oxidase staining overview picture because we understood from the referee’s comments that we need to compare our partitioning scheme of the brainstem with that of other authors. To this end, we add a cytochrome-oxidase staining overview picture (Author response image 3) along with an alternative interpretation from Maseko et al., 2013.

Changes: 1) We added details on our cytochrome-oxidase reactivity staining protocol and the cytochrome-oxidase reactivity in the elephant brain in general recommendation.

2) We provide a detailed discussion of the technicalities of cytochrome-oxidase staining below in the recommendation section, where the referee raised further criticisms.

3) We include a cytochrome-oxidase staining overview picture (Author response image 2) along with an alternative interpretation from Maseko et al., 2013.

Author response image 3.

Cytochrome-oxidase staining overview along with the Maseko et al. (2013) scheme Left, coronal cytochrome-oxidase staining overview from African elephant cow Indra; the section is taken a few millimeters posterior to the facial nucleus. Brown is putatively neural cytochrome-reactivity, and white is the background. Black is myelin diffraction and (seen at higher resolution, when you zoom in) erythrocyte cytochrome-reactivity in blood vessels (see our Figure 1E-G); such blood vessel cytochrome-reactivity is seen, because we could not perfuse the animal. There appears to be a minimal outside-in-fixation artifact (i.e. a more whitish/non-brownish appearance of the section toward the borders of the brain). This artifact is not seen in sections from Indra that we processed earlier or in other elephant brains processed at shorter post-mortem/fixation delays (see our Figure 1C). Right, coronal partitioning scheme of Maseko et al. (2013) for the elephant brainstem at an approximately similar anterior-posterior level.

The same structures can be recognized left and right. The section is taken at an anterior-posterior level, where we encounter the trigeminal nuclei in pretty much all mammals. Note that the neural cytochrome reactivity is very high, in what we refer to as the trigeminal-nuclei-trunk-module and what Maseko et al. refer to as inferior olive. Myelin stripes can be recognized here as white omissions.

At the same time, the cytochrome-oxidase-reactivity is very low in what Maseko et al. refer to as trigeminal nuclei. The indistinct appearance and low cytochrome-oxidase-reactivity of the trigeminal nuclei in the scheme of Maseko et al. (2013) is unexpected because trigeminal nuclei stain intensely for cytochrome-oxidase-reactivity in most mammals and because the trigeminal nuclei represent the elephant’s most important body part, the trunk. Staining patterns of the trigeminal nuclei as identified by Maseko et al. (2013) are very different at more posterior levels; we will discuss this matter below.

Justifications (2), (3), and (4) are sequelae from justification (1). In this sense, they do not count as justifications, but rather unsupported extensions.

Comment: These are key points of our paper that the referee does not discuss.

Changes: None.

(4) and (5) These are interesting justifications, as the paper has clear internal contradictions, and (5) is a sequelae of (4). The reader is led to the concept that the myelin tracts divide the nuclei into sub-modules that match the folding of the skin on the elephant trunk. One would then readily presume that these myelin tracts are in the incoming sensory axons from the trigeminal nerve. However, the authors note that this is not the case: "Our observations on trunk module myelin stripes are at odds with this view of myelin. Specifically, myelin stripes show no tapering (which we would expect if axons divert off into the tissue). More than that, there is no correlation between myelin stripe thickness (which presumably correlates with axon numbers) and trigeminal module neuron numbers. Thus, there are numerous myelinated axons, where we observe few or no trigeminal neurons. These observations are incompatible with the idea that myelin stripes form an axonal 'supply' system or that their prime function is to connect neurons. What do myelin stripe axons do, if they do not connect neurons? We suggest that myelin stripes serve to separate rather than connect neurons." So, we are left with the observation that the myelin stripes do not pass afferent trigeminal sensory information from the "truly extraordinary" trunk skin somatic sensory system, and rather function as units that separate neurons - but to what end? It appears that the myelin stripes are more likely to be efferent axonal bundles leaving the nuclei (to form the olivocerebellar tract). This justification is unsupported.

Comment: The referee cites some of our observations on myelin stripes, which we find unusual. We stand by the observations and comments. The referee does not discuss the most crucial finding we report on myelin stripes, namely that they correspond remarkably well to trunk folds.

Changes: None.

(6) The authors indicate that the location of these nuclei matches that of the trigeminal nuclei in other mammals. This is not supported in any way. In ALL other mammals in which the trigeminal nuclei of the brainstem have been reported they are found in the lateral aspect of the brainstem, bordered laterally by the spinal trigeminal tract. This is most readily seen and accessible in the Paxinos and Watson rat brain atlases. The authors indicate that the trigeminal nuclei are medial to the facial nerve nucleus, but in every other species, the trigeminal sensory nuclei are found lateral to the facial nerve nucleus. This is most salient when examining a close relative, the manatee (10.1002/ar.20573), where the location of the inferior olive and the trigeminal nuclei matches that described by Maseko et al (2013) for the African elephant. This justification is not supported.

Comment: The referee notes that we incorrectly state that the position of the trigeminal nuclei matches that of other mammals. We think this criticism is justified.

Changes: We prepared a comparison of the Maseko et al. (2013) scheme of the elephant brainstem with our scheme of the elephant brainstem (see Author response table 1). Here we acknowledge the referee’s argument and we also changed the manuscript accordingly.

(7) The dual to quadruple repetition of rostrocaudal modules within the putative trigeminal nucleus as identified by the authors relies on the fact that in the neurotypical mammal, there are several trigeminal sensory nuclei arranged in a column running from the pons to the cervical spinal cord, these include (nomenclature from Paxinos and Watson in roughly rostral to caudal order) the Pr5VL, Pr5DM, Sp5O, Sp5I, and Sp5C. However, these nuclei are all located far from the midline and lateral to the facial nerve nucleus, unlike what the authors describe in the elephants. These rostrocaudal modules are expanded upon in Figure 2, and it is apparent from what is shown that the authors are attributing other brainstem nuclei to the putative trigeminal nuclei to confirm their conclusion. For example, what they identify as the inferior olive in Figure 2D is likely the lateral reticular nucleus as identified by Maseko et al (2013). This justification is not supported.

Comment: The referee again compares our findings to the scheme of Maseko et al. (2013) and rejects our conclusions on those grounds. We think such a comparison of our scheme is needed, indeed.

Changes: We prepared a comparison of the Maseko et al. (2013) scheme of the elephant brainstem with our scheme of the elephant brainstem (see Author response table 1).

(8) In primates and related species, there is a distinct banded appearance of the inferior olive, but what has been termed the inferior olive in the elephant by other authors does not have this appearance, rather, and specifically, the largest nuclear mass in the region (termed the principal nucleus of the inferior olive by Maseko et al, 2013, but Pr5, the principal trigeminal nucleus in the current paper) overshadows the partial banded appearance of the remaining nuclei in the region (but also drawn by the authors of the current paper). Thus, what is at debate here is whether the principal nucleus of the inferior olive can take on a nuclear shape rather than evince a banded appearance. The authors of this paper use this variance as justification that this cluster of nuclei could not possibly be the inferior olive. Such a "semi-nuclear/banded" arrangement of the inferior olive is seen in, for example, giraffe (10.1016/j.jchemneu.2007.05.003), domestic dog, polar bear, and most specifically the manatee (a close relative of the elephant) (brainmuseum.org; 10.1002/ar.20573). This justification is not supported.

Comment: We carefully looked at the brain sections referred to by the referee in the brainmuseum.org collection. We found contrary to the referee’s claims that dogs, polar bears, and manatees have a perfectly serrated (a cellular arrangement in curved bands) appearance of the inferior olive. Accordingly, we think the referee is not reporting the comparative evidence fairly and we wonder why this is the case.

Changes: None.

Thus, all the justifications forwarded by the authors are unsupported. Based on methodological concerns, prior comparative mammalian neuroanatomy, and prior studies in the elephant and closely related species, the authors fail to support their notion that what was previously termed the inferior olive in the elephant is actually the trigeminal sensory nuclei. Given this failure, the justifications provided above that are sequelae also fail. In this sense, the entire manuscript and all the sequelae are not supported.

Comment: We disagree. To summarize:

(1) Our description of the cytochrome oxidase staining lacked methodological detail, which we have now added; the cytochrome oxidase reactivity data are great and support our conclusions.

(2)–(5)The referee does not really discuss our evidence on these points.

(6) We were wrong and have now fixed this mistake.

(7) The referee asks for a comparison to the Maseko et al. (2013) scheme (agreed, see Author response image 4 4 and Author response table 1).

(8) The referee bends the comparative evidence against us.

Changes: None.

A comparison of the elephant brainstem partitioning schemes put forward by Maseko et al 2013 and by Reveyaz et al.

To start with, we would like to express our admiration for the work of Maseko et al. (2013). These authors did pioneering work on obtaining high-quality histology samples from elephants. Moreover, they made a heroic neuroanatomical effort, in which they assigned 147 brain structures to putative anatomical entities. Most of their data appear to refer to staining in a single elephant and one coronal sectioning plane. The data quality and the illustration of results are excellent.

We studied mainly two large nuclei in six (now 7) elephants in three (coronal, parasagittal, and horizontal) sectioning planes. The two nuclei in question are the two most distinct nuclei in the elephant brainstem, namely an anterior ventromedial nucleus (the trigeminal trunk module in our terminology; the inferior olive in the terminology of Maseko et al., 2013) and a more posterior lateral nucleus (the inferior olive in our terminology; the posterior part of the trigeminal nuclei in the terminology of Maseko et al., 2013).

Author response image 4 gives an overview of the two partitioning schemes for inferior olive/trigeminal nuclei along with the rodent organization (see below).

Author response image 4.

Overview of the brainstem organization in rodents & elephants according to Maseko et. (2013) and Reveyaz et al. (this paper).

The strength of the Maseko et al. (2013) scheme is the excellent match of the position of elephant nuclei to the position of nuclei in the rodent (Author response image 4). We think this positional match reflects the fact that Maseko et al. (2013) mapped a rodent partitioning scheme on the elephant brainstem. To us, this is a perfectly reasonable mapping approach. As the referee correctly points out, the positional similarity of both elephant inferior olive and trigeminal nuclei to the rodent strongly argues in favor of the Maseko et al. (2013), because brainstem nuclei are positionally very conservative.

Other features of the Maseko et al. (2013) scheme are less favorable. The scheme marries two cyto-architectonically very distinct divisions (an anterior indistinct part) and a super-distinct serrated posterior part to be the trigeminal nuclei. We think merging entirely distinct subdivisions into one nucleus is a byproduct of mapping a rodent partitioning scheme on the elephant brainstem. Neither of the two subdivisions resemble the trigeminal nuclei of other mammals. The cytochrome oxidase staining patterns differ markedly across the anterior indistinct part (see our Author response image 4) and the posterior part of the trigeminal nuclei and do not match with the intense cytochrome oxidase reactivity of other mammalian trigeminal nuclei (Referee Figure 3). Our anti-peripherin staining indicates that there probably no climbing fibers, in what Maseko et al. think. is inferior olive; this is a potentially fatal problem for the hypothesis. The posterior part of Maseko et al. (2013) trigeminal nuclei has a distinct serrated appearance that is characteristic of the inferior olive in other mammals. Moreover, the inferior olive of Maseko et al. (2013) lacks the serrated appearance of the inferior olive seen in pretty much all mammals; this is a serious problem.

The partitioning scheme of Reveyaz et al. comes with poor positional similarity but avoids the other problems of the Maseko et al. (2013) scheme. Our explanation for the positionally deviating location of trigeminal nuclei is that the elephant grew one of the if not the largest trigeminal systems of all mammals. As a result, the trigeminal nuclei grew through the floor of the brainstem. We understand this is a post hoc just-so explanation, but at least it is an explanation.

The scheme of Reveyaz et al. was derived in an entirely different way from the Maseko model. Specifically, we were convinced that the elephant trigeminal nuclei ought to be very special because of the gigantic trigeminal ganglia (Purkart et al., 2022). Cytochrome-oxidase staining revealed a large distinct nucleus with an elongated shape. Initially, we were freaked out by the position of the nucleus and the fact that it was referred to as inferior olive by other authors. When we found an inferior-olive-like nucleus at a nearby (although at an admittedly unusual) location, we were less worried. We then optimized the visualization of myelin stripes (brightfield imaging etc.) and were able to collect an entire elephant trunk along with the brain (African elephant cow Indra). When we made the one-to-one match of Indra’s trunk folds and myelin stripes (Figure 4) we were certain that we had identified the trunk module of the trigeminal nuclei. We already noted at the outset of our rebuttal that we now consider such certainty a fallacy of overconfidence. In light of the comments of Referee 2, we feel that a further discussion of our ideas is warranted. A strength of the Reveyaz model is that nuclei look like single anatomical entities. The trigeminal nuclei look like trigeminal nuclei of other mammals, the trunk module has a striking resemblance to the trunk and the inferior olive looks like the inferior olive of other mammals.

We evaluated the fit of the two models in the form of a table (Author response table 1; below). Unsurprisingly, Author response table 1 aligns with our views of elephant brainstem partitioning.

Author response table 1.

Qualitative evaluation of elephant brainstem partitioning schemes

++ = Very attractive; + = attractive; - = unattractive; -- = very unattractive We scored features that are clear and shared by all mammals – as far as we know them – as very attractive. We scored features that are clear and are not shared by all mammals – as far as we know them – as very unattractive. Attractive features are either less clear or less well-shared features. Unattractive features are either less clear or less clearly not shared features.

Author response table 1 suggests two conclusions to us. (i) The Reveyaz et al. model has mainly favorable properties. The Maseko et al. (2013) model has mainly unfavorable properties. Hence, the Reveyaz et al. model is more likely to be true. (ii) The outcome is not black and white, i.e., both models have favorable and unfavorable properties. Accordingly, we overstated our case in our initial submission and toned down our claims in the revised manuscript.

What the authors have not done is to trace the pathway of the large trigeminal nerve in the elephant brainstem, as was done by Maseko et al (2013), which clearly shows the internal pathways of this nerve, from the branch that leads to the fifth mesencephalic nucleus adjacent to the periventricular grey matter, through to the spinal trigeminal tract that extends from the pons to the spinal cord in a manner very similar to all other mammals. Nor have they shown how the supposed trigeminal information reaches the putative trigeminal nuclei in the ventromedial rostral medulla oblongata. These are but two examples of many specific lines of evidence that would be required to support their conclusions. Clearly, tract tracing methods, such as cholera toxin tracing of peripheral nerves cannot be done in elephants, thus the neuroanatomy must be done properly and with attention to detail to support the major changes indicated by the authors.

Comment: The referee claims that Maseko et al. (2013) showed by ‘tract tracing’ that the structures they refer to trigeminal nuclei receive trigeminal input. This statement is at least slightly misleading. There is nothing of what amounts to proper ‘tract tracing’ in the Maseko et al. (2013) paper, i.e. tracing of tracts with post-mortem tracers. We tried proper post-mortem tracing but failed (no tracer transport) probably as a result of the limitations of our elephant material. What Maseko et al. (2013) actually did is look a bit for putative trigeminal fibers and where they might go. We also used this approach. In our hands, such ‘pseudo tract tracing’ works best in unstained material under bright field illumination, because myelin is very well visualized. In such material, we find: (i) massive fiber tracts descending dorsoventrally roughly from where both Maseko et al. 2013 and we think the trigeminal tract runs. (ii) These fiber tracts run dorsoventrally and approach, what we think is the trigeminal nuclei from lateral.

Changes: Ad hoc tract tracing see above.

So what are these "bumps" in the elephant brainstem?

Four previous authors indicate that these bumps are the inferior olivary nuclear complex. Can this be supported?

The inferior olivary nuclear complex acts "as a relay station between the spinal cord (n.b. trigeminal input does reach the spinal cord via the spinal trigeminal tract) and the cerebellum, integrating motor and sensory information to provide feedback and training to cerebellar neurons" (https://www.ncbi.nlm.nih.gov/books/NBK542242/). The inferior olivary nuclear complex is located dorsal and medial to the pyramidal tracts (which were not labeled in the current study by the authors but are clearly present in Fig. 1C and 2A) in the ventromedial aspect of the rostral medulla oblongata. This is precisely where previous authors have identified the inferior olivary nuclear complex and what the current authors assign to their putative trigeminal nuclei. The neurons of the inferior olivary nuclei project, via the olivocerebellar tract to the cerebellum to terminate in the climbing fibres of the cerebellar cortex.

Comment: We agree with the referee that in the Maseko et al. (2013) scheme the inferior olive is exactly where we expect it from pretty much all other mammals. Hence, this is a strong argument in favor of the Maseko et al. (2013) scheme and a strong argument against the partitioning scheme suggested by us.

Changes: Please see our discussion above.

Elephants have the largest (relative and absolute) cerebellum of all mammals (10.1002/ar.22425), this cerebellum contains 257 x109 neurons (10.3389/fnana.2014.00046; three times more than the entire human brain, 10.3389/neuro.09.031.2009). Each of these neurons appears to be more structurally complex than the homologous neurons in other mammals (10.1159/000345565; 10.1007/s00429-010-0288-3). In the African elephant, the neurons of the inferior olivary nuclear complex are described by Maseko et al (2013) as being both calbindin and calretinin immunoreactive. Climbing fibres in the cerebellar cortex of the African elephant are clearly calretinin immunopositive and also are likely to contain calbindin (10.1159/000345565). Given this, would it be surprising that the inferior olivary nuclear complex of the elephant is enlarged enough to create a very distinct bump in exactly the same place where these nuclei are identified in other mammals?

Comment: We agree with the referee that it is possible and even expected from other mammals that there is an enlargement of the inferior olive in elephants. Hence, a priori one might expect the ventral brain stem bumps to the inferior olive, this is perfectly reasonable and is what was done by previous authors. The referee also refers to calbindin and calretinin antibody reactivity. Such antibody reactivity is indeed in line with the referee’s ideas and we considered these findings in our Referee Table 1. The problem is, however, that neither calbindin nor calretinin antibody reactivity are highly specific and indeed both nuclei in discussion (trigeminal nuclei and inferior olive) show such reactivity. Unlike the peripherin-antibody staining advanced by us, calbindin nor calretinin antibody reactivity cannot distinguish the two hypotheses debated.

Changes: Please see our discussion above.

What about the myelin stripes? These are most likely to be the origin of the olivocerebellar tract and probably only have a coincidental relationship with the trunk. Thus, given what we know, the inferior olivary nuclear complex as described in other studies, and the putative trigeminal nuclear complex as described in the current study, is the elephant inferior olivary nuclear complex. It is not what the authors believe it to be, and they do not provide any evidence that discounts the previous studies. The authors are quite simply put, wrong. All the speculations that flow from this major neuroanatomical error are therefore science fiction rather than useful additions to the scientific literature.

Comment: It is unlikely that the myelin stripes are the origin of the olivocerebellar tract as suggested by the referee. Specifically, the lack of peripherin-reactivity indicates that these fibers are not climbing fibers (Referee Figure 1). In general, we feel the referee does not want to discuss the myelin stripes and obviously thinks we made up the strange correspondence of myelin stripes and trunk folds.

Changes: Please see our discussion above.

What do the authors actually have?

The authors have interesting data, based on their Golgi staining and analysis, of the inferior olivary nuclear complex in the elephant.

Comment: The referee reiterates their views.

Changes: None.

Reviewer #3 (Public Review):

Summary:

The study claims to investigate trunk representations in elephant trigeminal nuclei located in the brainstem. The researchers identified large protrusions visible from the ventral surface of the brainstem, which they examined using a range of histological methods. However, this ventral location is usually where the inferior olivary complex is found, which challenges the author's assertions about the nucleus under analysis. They find that this brainstem nucleus of elephants contains repeating modules, with a focus on the anterior and largest unit which they define as the putative nucleus principalis trunk module of the trigeminal. The nucleus exhibits low neuron density, with glia outnumbering neurons significantly. The study also utilizes synchrotron X-ray phase contrast tomography to suggest that myelin-stripe-axons traverse this module. The analysis maps myelin-rich stripes in several specimens and concludes that based on their number and patterning they likely correspond with trunk folds; however, this conclusion is not well supported if the nucleus has been misidentified.

Comment: The referee gives a concise summary of our findings. The referee acknowledges the depth of our analysis and also notes our cellular results. The referee – in line with the comments of Referee 2 – also points out that a misidentification of the nucleus under study is potentially fatal for our analysis. We thank the referee for this fair assessment.

Changes: We feel that we need to alert the reader more broadly to the misidentification concern. We think the critical comments of Referee 2, which will be published along with our manuscript, will go a long way in doing so. We think the eLife publishing format is fantastic in this regard. We will also include pointers to these concerns in the revised manuscript.

Strengths:

The strength of this research lies in its comprehensive use of various anatomical methods, including Nissl staining, myelin staining, Golgi staining, cytochrome oxidase labeling, and synchrotron X-ray phase contrast tomography. The inclusion of quantitative data on cell numbers and sizes, dendritic orientation and morphology, and blood vessel density across the nucleus adds a quantitative dimension. Furthermore, the research is commendable for its high-quality and abundant images and figures, effectively illustrating the anatomy under investigation.

Comment: Again, a very fair and balanced set of comments. We are thankful for these comments.

Changes: None.

Weaknesses:

While the research provides potentially valuable insights if revised to focus on the structure that appears to be the inferior olivary nucleus, there are certain additional weaknesses that warrant further consideration. First, the suggestion that myelin stripes solely serve to separate sensory or motor modules rather than functioning as an "axonal supply system" lacks substantial support due to the absence of information about the neuronal origins and the termination targets of the axons. Postmortem fixed brain tissue limits the ability to trace full axon projections. While the study acknowledges these limitations, it is important to exercise caution in drawing conclusions about the precise role of myelin stripes without a more comprehensive understanding of their neural connections.

Comment: The referee points out a significant weakness of our study, namely our limited understanding of the origin and targets of the axons constituting the myelin stripes. We are very much aware of this problem and this is also why we directed high-powered methodology like synchrotron X-ray tomograms to elucidate the structure of myelin stripes. Such analysis led to advances, i.e., we now think, what looks like stripes are bundles and we understand the constituting axons tend to transverse the module. Such advances are insufficient, however, to provide a clear picture of myelin stripe connectivity.

Changes: We think solving the problems raised by the referee will require long-term methodological advances and hence we will not be able to solve these problems in the current revision. Our long-term plans for confronting these issues are the following: (i) Improving our understanding of long-range connectivity by post-mortem tracing and MR-based techniques such as Diffusion-Tensor-Imaging. (ii) Improving our understanding of mid and short-range connectivity by applying even larger synchrotron X-ray tomograms and possible serial EM.

Second, the quantification presented in the study lacks comparison to other species or other relevant variables within the elephant specimens (i.e., whole brain or brainstem volume). The absence of comparative data for different species limits the ability to fully evaluate the significance of the findings. Comparative analyses could provide a broader context for understanding whether the observed features are unique to elephants or more common across species. This limitation in comparative data hinders a more comprehensive assessment of the implications of the research within the broader field of neuroanatomy. Furthermore, the quantitative comparisons between African and Asian elephant specimens should include some measure of overall brain size as a covariate in the analyses. Addressing these weaknesses would enable a richer interpretation of the study's findings.

Comment: The referee suggests another series of topics, which include the analysis of brain parts volumes or overall brain size. We agree these are important issues, but we also think such questions are beyond the scope of our study.

Changes: We hope to publish comparative data on elephant brain size and shape later this year.  

Author Response

eLife assessment

This study presents a valuable method to visualize the location of the cell types discovered through single-cell RNA sequencing. The evidence supporting the claims is solid, but the inclusion of a larger number of samples would strengthen the study. It would also be helpful to have the methods explained in more detail. The work will be of interest to those seeking to identify new cell types from scRNA-seq and snRNA-seq data.

Response: We are surprised about the editor’s assessment of our paper as a “valuable” method. This is the first Drosophila adult spatial transcriptomics paper. Hence, we would at least consider this being an “important” method. Spatial transcriptomics has thus far only been done in embryos, which are easy to process for FISH for many decades. Integration with single-cell data is also new. We are further surprised that this assessment does not mention the identification of subcellular mRNA patterns in adult muscles as an “important” biological finding of this paper. We are not aware that any localized mRNAs in Drosophila muscles were known prior to our study. This shows the advantage of spatial transcriptomics over single-cell techniques.

The work indeed does not represent a full spatial fly adult atlas – however, a proof of principle study covering both the head and body that we consider at least “important”.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

In this manuscript, Janssens et al. addressed the challenge of mapping the location of transcriptionally unique cell types identified by single nuclei sequencing (snRNA-seq) data available through the Fly Cell Atlas. They identified 100 transcripts for head samples and 50 transcripts for fly body samples allowing the identification of every unique cell type discovered through the Fly Cell Atlas. To map all of these cell types, the authors divided the fly body into head and body samples and used the Molecular Cartography (Resolve Biosciences) method to visualize these transcripts. This approach allowed them to build spatial tissue atlases of the fly head and body, to identify the location of previously unknown cell types and the subcellular localization of different transcripts. By combining snRNA-seq data from the Fly Cell Atlas with their spatially resolved transcriptomics (SRT) data, they demonstrated an automated cell type annotation strategy to identify unknown clusters and infer their location in the fly body. This manuscript constitutes a proof-of-principle study to map the location of the cells identified by ever-growing single-cell transcriptomic datasets generated by others.

Strengths:

The authors used the Molecular Cartography (Resolve Biosciences) method to visualize 100 transcripts for head samples and 50 transcripts for fly body samples in high resolution. This method achieves high resolution by multiplexing a large number of transcript visualization steps and allows the authors to map the location of unique cell types identified by the Fly Cell Atlas.

Response: We thank the reviewer for their comment, but are surprised that this assessment does not mention the identification of subcellular mRNA patterns in adult muscles as an important biological finding of this paper. This might be due to the visualization problem that this reviewer was facing with a greyscale version of the PDF as mentioned in the comments below. We do not know what caused the technical problem for this reviewer (the PDF figures are in color on the eLife website and on bioRxiv). We are surprised that the eLife discussion session did not resolve this issue.

Weaknesses:

Combining single-nuclei sequencing (snRNA-seq) data with spatially resolved transcriptomics (SRT) data is challenging, and the methods used by the authors in this study cannot reliably distinguish between cells, especially in brain regions where the processes of different neurons are clustered, such as in neuropils. This means that a grid that the authors mark as a unique cell may actually be composed of processes from multiple cells.

Response: The size of the fly is one of the most challenging aspects of performing spatial transcriptomics. The small size of the samples led to detachment from the slides, which we solved by coating the slides with gelatin. While the resolution of Molecular Cartography is high (<200nm), in the brain challenges remain as noted by the reviewer. Drosophila neuronal nuclei are notoriously small and cannot be easily resolved with current techniques. We agree that for a full atlas either expansion microscopy, 3D techniques or even higher resolution will be required.

Reviewer #2 (Public Review):

Summary:

The landmark publication of the "Fly Atlas" in 2022 provided a single cell/nuclear transcriptomic dataset from 15 individually dissected tissues, the entire head, and the body of male and female flies. These data led to the annotation of more than 250 cell types. While certainly a powerful and data-rich approach, a significant step forward relies on mapping these data back to the organism in time and space. The goal of this manuscript is to map 150 transcripts defined by the Fly Atlas by FISH and in doing so, provide, for the first time, a spatial transcriptomic dataset of the adult fly. Using this approach (Molecular Cartography with Resolve Biosciences), the authors, furthermore, distinguish different RNA localizations within a cell type. In addition, they seek to use this approach to define previously unannotated clusters found in the Fly Atlas. As a resource for the community at large interested in the computational aspects of their pipeline, the authors compare the strengths and weaknesses of their approach to others currently being performed in the field.

Strengths:

1. The authors use Resolve Biosciences and a novel bioinformatics approach to generate a FISH-based spatial transcriptomics map. To achieve this map, they selected 150 genes (50 body; 100 head) that were highly expressed in the single nuclear RNA sequencing dataset and were used in the 2022 paper to annotate specific cell types; moreover, the authors chose several highly expressed genes characteristic of unannotated cell types. Together, the approach and generated data are important next steps in translating the transcriptomic data to spatial data in the organism.

Response: We thank the reviewer for this comment but would like to add that the statement that we selected “150 genes (50 body; 100 head) that were highly expressed in the single nuclear RNA sequencing dataset” is not correct. We have chosen genes with widely differing expression levels (log-scale range of 3.95 in body, 5.76 in head). Many of the chosen genes are also transcription factors. In fact, the here introduced method is more sensitive than the single cell atlas: the tinman positive cells were readily located (even non-heart cells were found to express tinman), whereas in the single cell FCA data tinman expression is often not detected in the cardiomyocytes (Tinman is detected in 273 cells in the entire FCA (mean expression of 1.44 UMI in positive cells), and in 71 cells out of 273 cardial cells (26%)).

Author response image 1.

Density plots for body (left) and head (right) showing levels of gene expression detected in scRNA-seq (body: Fly Cell Atlas, Li et al. 2022, head: Pech et al. (2023)). Blue: all genes, red: genes used in the spatial study.

1. Working with Resolve, the authors developed a relatively high throughput approach to analyze the location of transcripts in Drosophila adults. This approach confirmed the identification of particular cell types suggested by the FlyAtlas as well as revealed interesting subcellular locations of the transcripts within the cell/tissue type. In addition, the authors used co-expression of different RNAs to unbiasedly identify "new cell types". This pipeline and data provide a roadmap for additional analyses of other time points, female flies, specific mutants, etc.

2. The authors show that their approach reveals interesting patterns of mRNA distribution (e.g alpha- and beta-Trypsin in apical and basal regions of gut enterocytes or striped patterns of different sarcomeric proteins in body muscle). These observations are novel and reveal unexpected patterns. Likewise, the authors use their more extensive head database to identify the location of cells in the brain. They report the resolution of 23 clusters suggested by the single-cell sequencing data, given their unsupervised clustering approach. This identification supports the use of spatial cell transcriptomics to characterize cell types (or cell states).

3. Lastly, the authors compare three different approaches --- their own described in this manuscript, Tangram, and SpaGE - which allow integration of single cell/nuclear RNA-seq data with spatial localization FISH. This was a very helpful section as the authors compared the advantages and disadvantages (including practical issues, like computational time).

Weaknesses:

1. Experimental setup. It is not clear how many and, for some of the data, the sex of the flies that were analyzed. It appears that for the body data, only one male was analyzed. For the heads, methods say male and female heads, but nothing is annotated in the figures. As such, it remains unclear how robust these data are, given such a limited sample from one sex. As such, the claims of a spatial atlas of the entire fly body and its head ("a rosetta stone") are overstated. Also, the authors should clearly state in the main text and figure legends the sex, the age, how many flies, and how many replicates contributed to the data presented (not just the methods). What also adds to the confusion is the use of "n" in para 2 of the results. " ... we performed coronal sections at different depths in the head (n=13)..." 13 sections in total from 1 head or sections from 13 heads? Based on the body and what is shown in the figure, one assumes 13 sections from one head. Please clarify.

Response: While we agree that sex differences present indeed an interesting opportunity to study with spatial transcriptomics, our goal was not to define male/female differences but rather to establish the technology to go into this detail if wanted in the future. In the revised version, we will provide a more detailed description of the sections, including their sex/genotype/age. We would like to point out that we verified the specificity of our FISH method on all the body sections (Figure 2A, TpnC4 & Act88F) and not only on one. Furthermore, we also would like to state that the idea of “a rosetta stone” was mentioned as a future prospect. We will rewrite the discussion to make this more clear.

1. Probes selected: Information from the methods section should be put into the main text so that it is clear what and why the gene lists were selected. The current main text is confusing. If the authors want others to use their approach, then some testing or, at the very least, some discussion of lower expressed genes should be added. How useful will this approach be if only highly expressed genes can be resolved? In addition, while it is understood that the company has a propriety design algorithm for the probes, the authors should comment on whether the probes for individual genes detect all isoforms or subsets (exons and introns?), given the high level of splicing in tissues such as muscle.

Response: As stated above, while there is a slight bias to higher expressed genes (as expected for marker genes), we have also used very low expressed genes like tinman (body) or sens (head). This shows that our method is more sensitive than single-cell data, as ALL cardiomyocytes can be identified by tinman expression and not only some are positive, as is the case in the FCA data. In fact, the method can’t resolve too highly expressed genes due to optical crowding of the signal leading to a worse quantification. For this reason, ninaE was removed from the analysis (as mentioned in Spatial transcriptomics allows the localization of cell types in the head and brain and in Methods).

As mentioned in the Methods, the probes are designed on gene level targeting all isoforms, but favoring principal isoforms (weighted by APPRIS level). The high level of splicing is indeed interesting and we expect that in the future spatial transcriptomics can help to generate more insight in this.

1. Imaging: it isn't clear from the text whether the repeated rounds of imaging impacted data collection. In many of what appear to be "stitched" images, there are gradients of signal (eg, figure 2F); please comment. Also, since this a new technique, could a before and after comparison of the original images and the segmented images be shown in the supplemental data so that the reader can better appreciate how the authors assessed/chose/thresholded their data? More discussion of the accuracy of spot detection would be helpful.

Response: Any high-resolution imaging (pixel size = 138 nm) of a large field of view (>1mm) uses a stitching method to combine several individual images to reconstruct a large field of view. This does not generate signal gradients, apart from lower signal at the extreme edges of each of the individual images. The spot detection algorithm was written and used by Resolve Biosciences and benchmarked for human (Hela) and mouse (NIH-3T3) cell lines in Groiss et al. 2021 (Highly resolved spatial transcriptomics for detection of rare events in cells, biorxiv). The specificity of the decoded probes was found to lie between 99.45 and 99.9% here, matching the results we found for TpnC4 and Act88F (99.4 and 99.8%). We will add their analysis to our discussion.

1. The authors comment on how many RNAs they detected (first paragraph of results). How do these numbers compare to the total mRNA present as detected by single-cell or single-nuclear sequencing?

Response: The total number of mRNAs detected per spatial transcriptomics experiment is much higher for the body samples compared to single-cell experiments (FCA data). In the head it is slightly lower, but here it is important to note that not all cell types are present in each slice in the head (while they are all present in the head scRNA experiments). A comparison on the cell-type level would be more meaningful, and we will investigate this for the revision.

Author response image 2.

Barplots showing total number of mRNA molecules detected in Molecular Cartography (Resolve, spatial spots) and in snRNA-seq data from the Fly Cell Atlas (10x Genomics, UMIs). Individual black dots show individual experiments, counts are only shown for the chosen gene panel for each sample. Bar shows the mean, with error bars representing the standard error.

1. Using this higher throughput method of spatial transcriptomics, the authors discern different cell types and different localization patterns within a tissue/cell type.

a. The authors should comment on the resolution provided by this approach, in terms of the detection of populations of mRNAs detected by low throughput methods, for example, in glia, motor neuron axons, and trachea that populate muscle tissue. Are these found in the images? Please show.

Response: We did not add any markers for trachea in our gene panel, but we do detect sparse spots of repo (glia) and elav/VGlut in the muscle tissues (Gad1/VAChT are hardly detected in the muscle tissue). This is consistent with the glutamatergic nature of motor neurons in Drosophila as described previously (Schuster CM (2006) Glutamatergic synapses of Drosophila neuromuscular junctions: a high-resolution model for the analysis of experience-dependent potentiation. Cell Tissue Res 326: 287–299.)

Author response image 3.

Molecular Cartography zoomed in on indirect flight muscle. Segmented nuclei are shown in white (based on DAPI), scalebars represent 100 μm).

b. The authors show interesting localization patterns in muscle tissue for different sarcomere protein-coding mRNAs, including enrichment of sls in muscle nuclei located near the muscle-tendon attachment sites. As this high throughput approach is newly being applied to the adult fly, it would increase confidence in these data, if the authors would confirm these data using a low throughput FISH technique. For example, do the authors detect such alternating "stripes" ( Act 88F, TpnC4, and Mhc) or enriched localization (sls) using FISH that doesn't rely on the repeated colorization, imaging, decolorization of the probes?

Response: We thank the reviewer for their interest in the localization patterns in muscle tissue. We could confirm localized mRNA in all the sections analyzed, in flight muscles as well as in leg muscles. We furthermore show that Act 88F, TpnC4 are not detected outside of flight muscle cells (99.4% and 99.8% of the single molecular signal in flight muscles only). Hence, we already show the specificity test in a much more quantitative way compared to traditional FISH, which often includes amplification.

1. The authors developed an unbiased method to identify "new cell types" which relies on co-expression of different transcripts. Are these new cell types or a cell state? While expression is a helpful first step, without any functional data, the significance of what the authors found is diminished. The authors need to soften their statements.

Response: The term “new cell types” only appears in the title. We agree that with the current spatial map we cannot be sure to have found “new cell types”, instead we have shown where unannotated clusters from scRNA-seq map, based on gene expression. Therefore, we will tone down the title in the revised version and thank the reviewer for this valuable suggestion.

Appraisal:

The authors' goal is to map single cell/nuclear RNAseq data described in the 2022 Fly Atlas paper spatially within an organism to achieve a spatial transcriptomic map of the adult fly; no doubt, this is a critical next step in our use of 'omics approaches. While this manuscript does the hard work of trying to take this next step, including developing and testing a new pipeline for high throughput FISH and its analysis, it falls short, in its present form, in achieving this goal. The authors discuss creating a robust spatial map, based on one male fly. Moreover, they do not reveal principles of mRNA localization, as stated in the abstract; they show us patterns, but nothing about the logic or function of these patterns. This same criticism can be said of the identification of "new cell types, just based on RNA colocalization. In both cases (mRNA subcellular localization or cell type identification), further data in the form of validation with traditional low throughput FISH and genetic manipulations to assess the relation to cell function are required for the authors to make such claims.

Response: We have indeed used one male fly for the adult male body data. This is mainly due to the cost of the sample processing. We used 12 individuals for the head samples (from 1 individual we acquired 2 sections, a total of 13 sections). We show that the body samples show a high correlation with each other, while the head samples cover multiple depths of the head. Still, even in the head, we find that sections at similar depths show a high similarity to each other in terms of gene-gene co-expression and expression patterns. Although obtaining more sections would be valuable, we don’t believe it to be necessary for the current goals. Additional replicates beyond the ones we already provide would require significant amounts of extra time and budget, while they would produce similar results as we already show. We are therefore reluctant to repeat the effort again.

The usage of the term “new cell types” is indeed ambiguous and we will tone this down in the revised version. Instead, we meant that unannotated clusters could be mapped to their location. In the text, we further specify that this means that now we only have inferred the location of the nuclei and that for neurons their function/processes are still unknown. As such, our data provides a starting point to identify new cell types since their marker genes and nuclear location are inferred. The next step to identify “new cell types” would indeed be to acquire genetic access to the cell types and characterize them in more detail. This is currently beyond our goals, and therefore we will tone down the title in the revised version and thank the reviewer for this valuable suggestion.

Discussion of likely impact:

If revised, these data, and importantly the approach, would impact those working on Drosophila adults as well as those working in other model systems where single cell/nuclear sequencing is being translated to the spatial localization within the organism. The subcellular localization data - for example, the size of transcripts and how that relates to localization or the patterns of sarcomeric protein localization in muscle - are intriguing, and would likely impact our thinking on RNA localization, transport, etc if confirmed. Lastly, the authors compare their computational approaches to those available in the field; this is valuable as this is a rapidly evolving field and such considerations are critical for those wishing to use this type of approach.

Response: We believe that our manuscript as it stands now is already an “important” paper that will strongly impact the Drosophila community (and beyond the spatial transcriptomics community). As it stands, it provides the groundwork for a full Drosophila adult spatial atlas, similar to how early scRNA-seq datasets provided a framework for the Fly Cell Atlas. In the manuscript we provide both experimental information on how to successfully perform spatial transcriptomics (treating slides for optimal attachment) and the data serves as a benchmark for future experiments to improve upon (similar to how early Drop-seq datasets were compared to later 10x datasets in single-cell transcriptomics). In addition, it also provides proof of principle methods on how to integrate the FCA data with these spatial data and it identifies localized mRNA species in large adult muscle cells, showing the complementarity of spatial techniques with single-cell RNA-seq. To conclude, this is the first spatial adult Drosophila transcriptomics paper, locating 150 mRNA species with easy data access in our user portal (https://spatialfly.aertslab.org/).

Author response:

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

Summary: 

Laura Morano and colleagues have performed a screen to identify compounds that interfere with the formation of TopBP1 condensates. TopBP1 plays a crucial role in the DNA damage response, and specifically the activation of ATR. They found that the GSK-3b inhibitor AZD2858 reduced the formation of TopBP1 condensates and activation of ATR and its downstream target CHK1 in colorectal cancer cell lines treated with the clinically relevant irinotecan active metabolite SN-38. This inhibition of TopBP1 condensates by AZD2858 was independent from its effect on GSK-3b enzymatic activity. Mechanistically, they show that AZD2858 thus can interfere with intra-S-phase checkpoint signaling, resulting in enhanced cytostatic and cytotoxic effects of SN-38 (or SN-38+Fluoracil aka FOLFIRI) in vitro in colorectal carcinoma cell lines. 

Major comments: 

Overall the work is rigorous and the main conclusions are convincing. However, they only show the effects of their combination treatments on colorectal cancer cell lines. I'm worried that blocking the formation of TopB1 condensates will also be detrimental in non-transformed cells. Furthermore it is somewhat disappointing that it remains unclear how AZD2858 blocks selfassembly of TopBP1 condensates, although I understand that unraveling this would be complex and somewhat out-of-reach for now. 

We appreciate your feedback and fully recognize the importance of understanding how AZD2858 blocks the assembly of TopBP1 condensates. While we understand your disappointment, addressing this question remains a key focus for us. Keeping in mind that unravelling such a mechanism in vitro or in vivo is rather challenging, we have consulted an expert who has made efforts to predict the potential docking sites of AZD2858 on TopBP1, which may provide valuable insights for future experimental investigations. Using an AlphaFold model (no crystal or cryo-EM structure available) and looking for suitable pockets or cavities in which AZD2858 could bind, the analyses, though requiring cautious interpretation, suggested that AZD2858 may target the BRCT1 and BRCT8 domains (as shown below, two pockets n°1 and 7 with sufficient volume and surrounded by b-sheets structures like other GSK3 inhibitor) of TopBP1.

However, these are preliminary results that require further exploration and experimental validation to confirm their significance and mechanistic implications.

Author response image 1.

Here are some specific points for improvement: 

(1) The authors conclude that "These data supports [sic] the feasibility of targeting condensates formed in response to DNA damage to improve chemotherapy-based cancer treatments". To support this conclusion the authors need to show that proliferating non-transformed cells (e.g. primary cell cultures or organoids) can tolerate the combination of AZD2858 + SN-38 (or FOLFIRI) better than colorectal cancer cells. 

We would like to thank the reviewer for this vital suggestion to prove that this combination is effective on tumor cells and not very toxic on healthy cells. We therefore used a healthy colon cell line (CCD841) and tested the efficacy of each treatment alone (FOLFIRI and AZD2858) as well as the combination FOLFIRI+AZD2858. We compared the results obtained in the CCD841 cell line with those obtained in the HCT116 colorectal cancer cell line. The results presented below show not only that each treatment alone is much less effective on CCD841 lines, but also that the combination is not synergistic.

Author response image 2.

Page 19 "This suggests that the combination... arrests the cell cycle before mitosis in a DNAPKsc-dependent manner." I find the remark that this arrest would be DNA-PKcs-dependent too speculative. I suppose that the authors base this claim on reference 55 but if they want to support this claim they need to prove this by adding DNA-PKcs inhibitors to their treated cells. 

Thank you for your thoughtful comment. We agree with the reviewer that claiming the G2/M arrest is DNA-PKcs-dependent without direct experimental evidence is speculative. While we initially based this hypothesis on reference 55, we acknowledge that further experiments, such as the use of DNA-PKcs inhibitors, would be necessary to robustly support this claim.

Given that this observation was intended as a potential explanation for the G2/M arrest observed at 6 and 12 hours of treatment with AZD2858 + SN-38 (compared to SN-38 alone), and considering that exploring this pathway is not the primary focus of our study, we have decided to remove this hypothesis from both the figure and the text to avoid any ambiguity.

We appreciate the reviewer’s input and will consider investigating this pathway in future studies.

(2) When discussing Figure S5B the authors claim that SN-38 + AZD2858 progressively increases the fractions of BrdU positive cells, but this is not supported by statistical analysis.

The fractions are still very small, so I would like to see statistics on these data. Alternatively, the authors could take out this conclusion. 

Thank you for your valuable comment. In response, we have conducted a statistical analysis (Mann-Whitney test) on the data, and the results have been added to Figure S5C for the 6-hour time point and Figure S5D for the 12-hour time point, based on three independent biological replicates. We hope this provides the necessary clarification.

Minor comments: 

- Page 5 Materials and methods - Cell culture. Last sentence "Add in what medium you cultured them" looks like an internal review remark and should probably be removed? 

We apologize for this oversight. The medium has now been specified, and the sentence has been removed.

- The numbers in all the synergy matrices (in white font) are extremely small and virtually unreadable, and visually distracting. I recommend taking these out altogether. 

We believe that the reduction in figure quality may be due to the PDF compression, which affected the resolution of the figures. We are happy to provide high-resolution versions of the figures separately for clarity. If the issue persists even with the higher resolution, we will consider removing the numbers, as suggested.

- The legends of the synergy matrices (for example Fig 1D, 4E, 5, 6) are often extremely small, making it difficult to understand them intuitively. Please enlarge them and label them more clearly, and use larger fonts. In the legend of Figure 5D,E a green matrix indicating % live cells is mentioned but I don't see it. Do they mean the grey matrix? 

We have enlarged the figure legends and will provide high-resolution versions of the figures to ensure all details are clearly readable. Regarding Figure 5D,E: we acknowledge that the color may appear differently (more green or gray) depending on the display or printer settings. To avoid any confusion, we have corrected the legend to specify that the color in question is khaki, rather than green. Moreover, following suggestions of the reviewer #2, these figures have been respectively moved to Figure S6B and S6C.

- Figure S2. Perhaps I misunderstand the PML body experiment but the authors seem to use PML body formation to support their idea that AZD2858 blocks TopBP1 condensate formation and not just any condensate formation. However, if this is the case they would need a proper positive control, i.e. an additional experimental condition in which they do see PLM bodies. 

Arsenic is a well-known positive control for experiments involving PML bodies due to its ability to induce specific responses in PML proteins and modify PML nuclear bodies (NBs) structure and function (Jaffray et al., 2023, JCB ; Zhu et al., 1997, PNAS). Thus, we used Arsenic as a positive control and observed a significant increase in PML NBs vs the other conditions (Kruskal-Wallis test) as indicated below. We thus implemented the results in the corresponding figure S2B and text.

Author response image 3.

PML condensates were tested after 2 h of incubation. AZD2858 : 100nM ; SN-38 : 300nM ; Arsenic : 6µM. ****: p<0.0001 (Kruskal-Wallis test).

- The quantification of the flow cytometry data needs to be clarified. I find it strange that in the figures (for example Figure 3A and 3C) representative examples are shown of apparently 3 replicates, and that the percentages shown in these examples are then the given in the text as the overall numbers; for example on page 18 "...BrdU incorporation increased from 16.11% (SN38 alone) to 41.83% (combination)...". This type of description is done in multiple places in the Results section and is confusing. It would be clearer if the authors show proper quantifications (mean +/- sem) of the percentages of (the relevant) gated populations. Besides, I don't think it make a lot of sense to mention in the text the percentages with 2 decimals behind the comma. This suggests a level of precision that does not seem justified in flow cytometry data. Finally, all flow cytometry plots look visually very busy and all the text is crammed in with really small fonts. Cleaning them up and enlarging the fonts of the remaining text/numbers would really improve the readability of the figures. 

Thank you for your helpful comments. We understand your concern regarding the flow cytometry quantification. Indeed, the percentages presented in the figures are derived from representative replicates, and we acknowledge that this presentation could be confusing. To address this, we have included a table summarizing the data from all replicates to improve readability [Table S2 and S3 in the new version]. Second, we specified in the text that the data are representative biological replicates when needed. Third, we have performed statistical analyses on the three replicates when necessary, as shown in Supplementary Figure S5C-F in the new version. The text has been revised to reflect the correct statistical interpretation.

Regarding the use of two decimal, we are unable to remove them due to limitations in the software (Kaluza) used for flow cytometry analysis. However, we agree that this level of precision may not be warranted, and we have revised the text where appropriate to reduce confusion.

- In Figure 5G the authors show that FOLFIRI + AZD2858 are synergistic in two SN-38-resistant cell lines. They conclude that this combination may overcome drug resistance. But tried to figure out the used FOLFIRI concentrations used in these cell lines and they still seem far higher than the SN-38-sensitive HCT116 cell lines, so I would like to see a bit more nuance in their interpretation. I think overcoming drug resistance is an overstatement, and perhaps alleviating would be a better term 

Thank you for highlighting this important point; we have adjusted the text accordingly.

- The legend in Table S2 refers to Figure 5A-B; this should be Figure 4A-B. 

Thank you, this has been corrected and Table S2 is now moved to Table S4 .

Reviewer #1 (Significance (Required)): 

The finding that AZD2858 block TOPbp1 condensate formation via a pleiotropic effect of this compound is interesting and convincing. To my best knowledge it's a novel finding which is interesting to the potential target audience mentioned below. Their findings that inhibition of TOPbp1 condensation and ATR signaling via AZD2858 may synergize with FOLFIRI therapy in colorectal cancer cells are still very preliminary, because the effects on non-cancerous cells are not tested. 

Researchers involved in early cancer drug discovery and cell biologists studying DNA damage responses in cancer cells seem to me typical audience interested and influenced by this paper. 

I'm a cell biologist studying cell cycle fate decisions, and adaptation of cancer cells & stem cells to (drug-induced) stress. My expertise aligns well with the work presented throughout this paper. 

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

The authors have extended their previous research to develop TOPBP1 as a potential drug target for colorectal cancer by inhibiting its condensation. Utilizing an optogenetic approach, they identified the small molecule AZD2858, which inhibits TOPBP1 condensation and works synergistically with first-line chemotherapy to suppress colorectal cancer cell growth. The authors investigated the mechanism and discovered that disrupting TOPBP1 assembly inhibits the ATR/Chk1 signaling pathway, leading to increased DNA damage and apoptosis, even in drug-resistant colorectal cancer cell lines. Addressing the following concerns would enhance clarity and further in vivo work may improve significance: 

(1) How does the optogenetic method for inducing condensates compare to the DNA damage induction mechanism? 

Optogenetics provides a versatile and precise approach for controlling the condensation of scaffold proteins in both space and time. This method enables us to study the role of biomolecular condensates with minute-scale resolution, separating their formation from potentially confounding upstream events, such as DNA damage, and providing valuable insights into their specific function. Importantly, based on our previous publications on TopBP1 or SLX4 optogenetic condensates, we have substantial evidence indicating that light-induced condensates closely mimic those formed in response to DNA damage:

- Functional similarity: Optogenetic condensates recapitulate endogenous condensates formed upon exposure of the cells of DNA damaging agents, and include most known partner proteins involved in the DNA damage response. It was shown for light induced-TopBP1 and SLX4 condensates (1-3).

- Dynamic reversibility: Optogenetic condensates and DNA damage induced condensates are both dynamic and reversible. They dissolve within 15 minutes of light deactivation or after removal of the damaging agent (1,3).

- Chromatin association: Both optogenetic and DNA damage-induced condensates are bound to chromatin or localized at sites of DNA damage (3).

- Regulation: Both types of condensates are regulated similarly, with their formation triggered by the same signaling pathways. ATR basal activity drives the nucleation of opto-TopBP1 condensates and endogenous TopBP1 structures upon light exposure (1). Likewise, sumoylation modifications regulate the formation of opto-SLX4 condensates and endogenous SLX4 condensates (3).

- Structurally: Using super-resolution imaging by stimulation-emission-depletion (STED) microscopy, we observed that endogenous SLX4 nanocondensates formed globular clusters that were indistinguishable from recombinant light induced SLX4 condensates (1,3).  

(1) Frattini C, Promonet A, Alghoul E, Vidal-Eychenie S, Lamarque M, Blanchard MP, et al. TopBP1 assembles nuclear condensates to switch on ATR signaling. Molecular Cell. 18 mars 2021;81(6):1231-1245.e8. 

(2) Alghoul E, Basbous J, Constantinou A. An optogenetic proximity labeling approach to probe the composition of inducible biomolecular condensates in cultured cells. STAR Protocols. 2021;2(3):100677. 

(3) Alghoul E, Basbous J, Constantinou A. Compartmentalization of the DNA damage response: Mechanisms and functions. DNA Repair. août 2023;128:103524.

(2) Why wasn't the initial screen conducted on the HCT116-SN50 resistant cell line? 

Thank you for raising this important question, which we also considered at the outset of the project. After careful consideration, we decided to use the HCT116 WT cells in order to obtain initial data from an unmodified cell line. It is worth mentioning that HCT116-SN50 cells exhibit slower proliferation compared to WT cells, and they also express an efflux pump capable of pumping out SN38. We were concerned that these factors might interfere with the optogenetic assay, which is why we chose to perform the screen using the WT HCT116 cells.

(3) The labels in Fig. 1D are difficult to recognize. 

This issue was also raised by Reviewer #1. We suspect that the PDF conversion may have reduced the resolution of the figures, so we will provide them separately in high resolution. In addition, we have increased the size of some labels to improve their clarity.

The selected cell image in Fig. 2A for SN-38 seems over-representative; unselected cells appear similar to other groups. Why does AZD2858 itself induce TopBP1 condensates in the plot, yet this is not evident in the images? 

Thank you for your comment; we have updated the figure with a more representative image. We indeed observe that AZD2858 alone induces a slight increase in TopBP1 condensates. However, this increase did not lead to the activation of the ATR/Chk1 signaling pathway, as shown by the Western blot data presented in Fig. 2B. In addition, AZD2858 specifically prevents the formation of TopBP1 condensates induced by SN38 treatment, and the level of TopBP1 condensates does not return to the basal levels observed in untreated cells, but rather to those observed with AZD2858 treatment. During the 2-hour AZD2858 treatment, the progression of replication forks was unaffected (Fig. 3A and 3B). However, when AZD2858 was added alone to the Xenopus egg extracts, there was increased recruitment of TopBP1 to the chromatin (Fig. 2E). This result suggests that AZD2858 alone can induce the assembly of TopBP1 on chromatin to initiate DNA replication (a well-established role of TopBP1), but the number and concentration of TopBP1 molecules did not reach levels sufficient to activate the ATR/Chk1 pathway.

(4) In Fig. 3A, despite the drastic change in the FACS plot shape, the quantifications appear quite similar. 

Thank you for this insightful observation. The gates for the S phase were intentionally set wider to avoid biasing the results and inadvertently excluding the population that incorporates BrdU weakly (but still incorporates it) in the SN-38 only condition. As a result, the percentage of cells within this gate remains similar, even though the overall shape of the FACS plot changes, reflecting a shift in the distribution of BrdU incorporation. This point has now been clarified in the legend of the Figure 3A.

This effect can also be attributed to the relatively short treatment time (2 hours), which captures early changes in DNA synthesis. The effect becomes more pronounced at later time points, as shown in Figure 3C. For example, after 6 hours of treatment, the percentage of BrdU-positive cells increases from 15% with SN-38 alone to 41% with the AZD2858 combination, demonstrating a clearer impact on DNA synthesis. A graph summarizing the statistical analysis has been added to Figure S5C for the 6-hour time point and Figure S5D for the 12-hour time point, based on data from three independent biological replicates.

(5) The results section is imbalanced; Figs. 5 and 6 could be combined into one figure. 

We have combined Figures 5 and 6 into a single figure to optimize the presentation of results. To avoid overloading the new figure, some of the data have been moved to supplementary figures, ensuring the main figure remains clear and focused.

(6) An in vivo study is anticipated to assess the drug's efficacy. 

Although AZD2858 was developed a few years ago, there is a limited amount of in vivo data available, which led us to consider potential issues related to the drug's biodistribution or its pharmacokinetics (PK). Despite these concerns, we proceeded with preliminary in vivo studies, testing various diluents and injection routes for AZD2858. However, we observed that the compound was not effective in vivo. Given the strong synergistic effects observed in vitro, we concluded that AZD2858 was likely not being distributed properly in the mice. As a result, we have decided to conduct a more detailed investigation into the pharmacokinetics (PK), pharmacodynamics (PD), and absorption, distribution, metabolism, and excretion (ADME) of AZD2858 to better understand its in vivo behavior and efficacy. Therefore, the in vivo evaluation of AZD2858 will be addressed in a separate study specifically focused on this aspect.

Reviewer #2 (Significance (Required)): 

Addressing the stated concerns would enhance clarity and further in vivo work may improve significance. 

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

Summary 

In 2021 (PMID: 33503405) and 2024 (PMID: 38578830) Constantinou and colleagues published two elegant papers in which they demonstrated that the Topbp1 checkpoint adaptor protein could assemble into mesoscale phase-separated condensates that were essential to amplify activation of the PIKK, ATR, and its downstream effector kinase, Chk1, during DNA damage signalling. A key tool that made these studies possible was the use of a chimeric Topbp1 protein bearing a cryptochrome domain, Cry2, which triggered condensation of the chimeric Topbp1 protein, and thus activation of ATR and Chk1, in response to irradiation with blue light without the myriad complications associated with actually exposing cells to DNA damage. 

In this current report Morano and co-workers utilise the same optogenetic Topbp1 system to investigate a different question, namely whether Topbp1 phase-condensation can be inhibited pharmacologically to manipulate downstream ATR-Chk1 signalling. This is of interest, as the therapeutic potential of the ATR-Chk1 pathway is an area of active investigation, albeit generally using more conventional kinase inhibitor approaches. 

The starting point is a high throughput screen of 4730 existing or candidate small molecule anticancer drugs for compounds capable of inhibiting the condensation of the Topbp1-Cry2mCherry reporter molecule in vivo. A surprisingly large number of putative hits (>300) were recorded, from which 131 of the most potent were selected for secondary screening using activation of Chk1 in response to DNA damage induced by SN-38, a topoisomerase inhibitor, as a surrogate marker for Topbp1 condensation. From this the 10 most potent compounds were tested for interactions with a clinically used combination of SN-38 and 5-FU (FOLFIRI) in terms of cytotoxicity in HCT116 cells. The compound that synergised most potently with FOLFIRI, the GSK3-beta inhibitor drug AZD2858, was selected for all subsequent experiments. 

AZD2858 is shown to suppress the formation of Topbp1 (endogenous) condensates in cells exposed to SN-38, and to inhibit activation of Chk1 without interfering with activation of ATM or other endpoints of damage signalling such as formation of gamma-H2AX or activation of Chk2 (generally considered to be downstream of ATM). AZD2858 therefore seems to selectively inhibit the Topbp1-ATR-Chk1 pathway without interfering with parallel branches of the DNA damage signalling system, consistent with Topbp1 condensation being the primary target. Importantly, neither siRNA depletion of GSK3-beta, or other GSK3-beta inhibitors were able to recapitulate this effect, suggesting it was a specific non-canonical effect of AZD2858 and not a consequence of GSK3-beta inhibition per se. 

To understand the basis for synergism between AZD2858 and SN-38 in terms of cell killing, the effect of AZD2858 on the replication checkpoint was assessed. This is a response, mediated via ATR-Chk1, that modulates replication origin firing and fork progression in S-phase cell under conditions of DNA damage or when replication is impeded. SN-38 treatment of HCT116 cells markedly suppresses DNA replication, however this was partially reversed by co-treatment with AZD2858, consistent with the failure to activate ATR-Chk1 conferring a defect in replication checkpoint function. 

Figures 4 and 5 demonstrate that AZD2858 can markedly enhance the cytotoxic and cytostatic effects of SN-38 and FOLFIRI through a combination of increased apoptosis and growth arrest according to dosage and treatment conditions. Figure 6 extends this analysis to cells cultured as spheroids, sometimes considered to better represent tumor responses compared to single cell cultures. 

Major comments 

Most of the data presented is of good technical quality and supports the conclusions drawn. There are however a small number of instances where this is not true; ie where the data are of insufficient technical quality, or where the description or interpretation of the results is at variance with the data which is presented. Some examples: 

(1) Fig.2E - the claim that "we observed an increase in RPA, Topb1 and Pol-epsilon levels when CPT and AZD2858 were added together" do not seem to be justified by the data provided. It is also unclear what the purpose/ significance of this experiment is. 

Thank you for pointing out the contradiction in Figure 2E. Upon review, we identified an error in the labeling of conditions (CPT and AZD2858 were inadvertently swapped). The corrected figure now clearly shows that, at the 60-minute timepoint after starting replication, the combination of

CPT and AZD2858 results in a greater accumulation of TopBP1, Pol ε, and RPA on chromatin compared to CPT alone. We have revised the sentence to: "Our data demonstrate that combining CPT and AZD2858 earlier enhances the accumulation of replication-related factors (RPA, TopBP1, and Pol ε) on chromatin compared to CPT treatment alone, particularly visible at the 60minute after starting replication."

The significance of this experiment lies in its connection to the earlier observation that AZD2858 restores BrdU incorporation when combined with SN-38, as shown in flow cytometry data (Figure 3A). At a molecular level, this was further supported by DNA fiber assays, which revealed that replication tracks (CldU tracts) were longer in the combination treatment compared to SN-38 alone (Figure 3B).

To strengthen and validate these findings, we chose to employ the Xenopus egg extract system for several reasons. This model provides a highly controlled environment where DNA replication occurs without confounding effects from transcription or translation. Moreover, replication is limited to a single round, offering a unique opportunity to specifically interrogate replication mechanisms. These attributes make the Xenopus model an ideal system to confirm that AZD2858 facilitates replication recovery in the presence of replication stress induced by agents like CPT. This will lead, in longer treatment, to accumulation of DNA damage and apoptosis (Figure 3D-E and Figure 4A-D)

(2) Figs. 3 A and C certainly show that the SN-38-mediated suppression of DNA synthesis is modified and partially alleviated by co-treatment with AZD2858. The statement however that "prolonged co-incubation with AZD2858 for 6 and 12 hours effectively abolished the SN-38 induced S-phase checkpoint" is clearly misleading. If this were true, then the BrdU incorporation profiles of the respective samples would be similar or identical to control, which clearly they are not. Clearly AZD2858 is affecting the imposition of the S-phase checkpoint in some way, but not "abolishing" it. 

We appreciate the reviewer’s detailed observations regarding Figures 3A and 3C and the phrasing in our manuscript. We agree that the term "abolished" is not precise in describing the effects of AZD2858 on the SN-38-induced S-phase checkpoint.

To clarify: our data indicate that co-treatment with AZD2858 modifies and partially alleviates the SN-38-induced suppression of DNA synthesis, as demonstrated by increased BrdU incorporation relative to SN-38 treatment alone. However, as the reviewer correctly points out, the BrdU incorporation profiles of the co-treated samples do not fully return to control non treated cells levels. This suggests that while AZD2858 significantly mitigates the S-phase checkpoint, it does not completely abolish it.

We have revised the statement in the manuscript to better reflect these findings, as follows: "Prolonged co-incubation with AZD2858 for 6 and 12 hours significantly alleviated the SN-38induced S-phase checkpoint, as evidenced by the partially increased BrdU incorporation. However, the population of co-treated cells is heterogeneous: some cells exhibit BrdU incorporation levels similar to those of untreated control cells, while others incorporate BrdU at levels comparable to cells treated with SN-38 alone. This indicates that AZD2858 does not fully restore DNA synthesis to control levels across the entire cell population."

This revised phrasing aligns with the data presented and acknowledges the partial recovery of DNA synthesis observed. Thank you for bringing this to our attention and helping us improve the accuracy of our conclusions.

(3) Fig. 3 E. The western blots of pDNA-PKcs (S2056) and total DNA-PKcs are really not interpretable. It is possible to sympathise that these reagents are probably extremely difficult to work with and obtain clear results, however uninterpretable results are not acceptable. 

We agree that the data presented in the Fig3E are difficult to interpret. As noted by Reviewer 1, we recognize the challenge of obtaining clear and reliable results with these specific reagents. Based on this feedback, and to ensure the robustness of our conclusions, we have decided to exclude these specifics blots from the revised manuscript.

We believe that this adjustment will enhance the clarity and reliability of the manuscript while focusing on the other, more interpretable data presented. Thank you for pointing this out, and we appreciate your understanding.

(4) Fig. 3D. This is a puzzling image. Described as a PFGE assay, it presumably depicts an agarose gel, with intact genomic DNA at the top and a discrete band below representing fragmented genomic DNA. This is a little surprising, as fragmented genomic DNA does not usually appear as a specific band but as a heterogenous population or "smear". Nevertheless, even if one accepts this premise, it is unclear what is meant by "DSBs remained elevated after the combined treatment" when the intensity of this band is equivalent for both SN-38 and SN-38 + AZD2858 treatments. 

We thank the reviewer for his insightful comments regarding the PFGE results in Figure 3D. We agree that the appearance of a discrete band, rather than a heterogeneous smear, is atypical for fragmented genomic DNA in this assay. However, by enhancing the signal intensity (as shown below), the expected smear becomes more appreciable.

Author response image 4.

Regarding the interpretation of the band intensities, we agree that the signals for SN-38 and SN38 + AZD2858 appear similar under these specific conditions. At the relatively high concentration of SN-38 used in this experiment (300 nM), it is indeed challenging to observe a more pronounced effect on DNA breaks. This is why we proposed the "DSBs remained elevated after the combined treatment" because the band intensity of SN-38 single agent treated cells or combined with AZD2858 is comparable. However, we note a slightly more intense γH2AX signal over time when AZD2858 is combined with SN-38 compared to SN-38 alone (Figure 3E). Furthermore, under lower, sub-optimal doses of SN-38 and over extended incubation treatment (48h), we observe a clearer increase in fragmented DNA bands, as demonstrated in Figure 4D.

Minor comments 

(1) Fig. 1. A surprisingly large number of compounds scored positive in the primary screen for inhibition of Topbp1 condensation (>300). Of the 131 of these selected for secondary screening using Chk1 activation (S345 phosphorylation) as a readout approximately 2/3 were negative, implying that a majority of the tested compounds inhibited Topbp1 condensation but not Chk1 activation. What could explain that?

Thank you for this thoughtful comment. The discrepancy between the large number of compounds scoring positive for TopBP1 condensation inhibition and the smaller number inhibiting Chk1 activation (S345 phosphorylation) could be attributed to several factors:

• Different cell lines and induction methods: The initial screen was conducted in HEK293 TrexFlpin cells overexpressing optoTopBP1, while the secondary screen used HCT116 cells. In addition, the methods used to induce the respective pathways were distinct: in the primary screen, we employed a blue light induction of opto-TopBP1 condensates, whereas in the secondary screen, we used an SN-38 treatment to induce DNA replication stress and activate the Chk1 pathway. These differences could account for the varying responses observed in the two screens.

• The compounds that inhibited TopBP1 condensation might not fully block Chk1 activation. While they disrupt TopBP1 condensation, they may still allow for partial activation of Chk1 or Chk1 activation through alternative mechanisms. For instance, Chk1 activation could be mediated by other signaling pathways or molecules, such as ETAA1, a known Chk1 activator (1). Thus, TopBP1 condensation inhibition does not necessarily translate to complete inhibition of Chk1 activation, especially if ETAA1 is employed by cells as a rescue activator.

• Some compounds may affect chromosome dynamics, potentially generating mechanical forces or torsional stress that could activate the ATR/Chk1 pathway independently of TopBP1

(2).

These factors suggest that while the compounds effectively disrupt TopBP1 condensation, they may not always fully inhibit the downstream Chk1 activation, pointing to the complexity of the DNA damage response pathways. 

(1) Bass, T. E. et al. ETAA1 acts at stalled replication forks to maintain genome integrity. Nat Cell Biol 18, 1185–1195 (2016).

(2) Kumar, A. et al. ATR Mediates a Checkpoint at the Nuclear Envelope in Response to Mechanical Stress. Cell 158, 633–646 (2014).

(2) Fig. 2D. The protein-protein interaction assay shown demonstrates that AZD2858 ablates the light-induced auto-interaction between exogenous opto-Topbp1 molecules and ATR plus or minus SN-38, but clearly endogenous Topbp1 molecules do not participate. Why is this? 

The biotin proximity labeling assay was conducted without exposing cells to light, using a TurboID module fused to TopBP1-mCherry-CRY2. Stable cell lines were then generated in HEK293 TrexFlpIn cells, where endogenous TopBP1 is still expressed. Upon adding doxycycline, the recombinant TurboID-TopBP1-mCherry-Cry2 (opto-TopBP1) is induced at levels comparable to endogenous TopBP1 (Fig 2D).

Since the opto-TopBP1 construct exhibits behavior similar to that of endogenous TopBP1 (1), we used it to investigate whether TopBP1 self-assembly and its interaction with ATR are influenced by AZD2858 alone or in combination with SN38. Our results show that treatment with SN38 increases the proximity between opto-TopBP1 and the endogenous TopBP1 (not fused to TurboID). However, AZD2858, either alone or in combination with SN38, disrupts the selfassembly of recombinant TopBP1 with itself as well as its interaction with endogenous TopBP1.

(1) Frattini C, Promonet A, Alghoul E, Vidal-Eychenie S, Lamarque M, Blanchard MP, et al. TopBP1 assembles nuclear condensates to switch on ATR signaling. Molecular Cell. 18 mars 2021;81(6):1231-1245.e8.

Reviewer #3 (Significance (Required)): 

Significance 

Liquid phase separation of protein complexes is increasingly recognised as a fundamental mechanism in signal transduction and other cellular processes. One recent and important example was that of Topbp1, whose condensation in response to DNA damage is required for efficient activation of the ATR-Chk1 pathway. The current study asks a related but distinct question; can protein condensation be targeted by drugs to manipulate signalling pathways which in the main rely on protein kinase cascades? 

Here, the authors identify an inhibitor of GSK3-beta as a novel inhibitor of DNA damage-induced Topbp1 condensation and thus of ATR-Chk1 signalling. 

This work will be of interest to researchers in the fields of DNA damage signalling, biophysics of protein condensation, and cancer chemotherapy.

Author response:

Public Reviews:

Reviewer #1 (Public review):

In this paper by Brickwedde et al., the authors observe an increase in posterior alpha when anticipating auditory as opposed to visual targets. The authors also observe an enhancement in both visual and auditory steady-state sensory evoked potentials in anticipation of auditory targets, in correlation with enhanced occipital alpha. The authors conclude that alpha does not reflect inhibition of early sensory processing, but rather orchestrates signal transmission to later stages of the sensory processing stream. However, there are several major concerns that need to be addressed in order to draw this conclusion.

First, I am not convinced that the frequency tagging method and the associated analyses are adequate for dissociating visual vs auditory steady-state sensory evoked potentials.

Second, if the authors want to propose a general revision for the function of alpha, it would be important to show that alpha effects in the visual cortex for visual perception are analogous to alpha effects in the auditory cortex for auditory perception.

Third, the authors propose an alternative function for alpha - that alpha orchestrates signal transmission to later stages of the sensory processing stream. However, the supporting evidence for this alternative function is lacking. I will elaborate on these major concerns below.

(1) Potential bleed-over across frequencies in the spectral domain is a major concern for all of the results in this paper. The fact that alpha power, 36Hz and 40Hz frequency-tagged amplitude and 4Hz intermodulation frequency power is generally correlated with one another amplifies this concern. The authors are attaching specific meaning to each of these frequencies, but perhaps there is simply a broadband increase in neural activity when anticipating an auditory target compared to a visual target?

We appreciate the reviewer’s insightful comment regarding the potential bleed-over across frequencies in the spectral domain. We fully acknowledge that the trade-off between temporal and frequency resolution is a challenge, particularly given the proximity of the frequencies we are examining.

To address this concern, we performed additional analyses to investigate whether there is indeed a broadband increase in neural activity when anticipating an auditory target as compared to a visual target, as opposed to distinct frequency-specific effects. Our results show that the bleed-over between frequencies is minimal and does not significantly affect our findings. Specifically, we repeated the analyses using the same filter and processing steps for the 44 Hz frequency. At this frequency, we did not observe any significant differences between conditions.

These findings suggest that the effects we report are indeed specific to the 40 Hz frequency band and not due to a general broadband increase in neural activity. We hope this addresses the reviewer’s concern and strengthens the validity of our frequency-specific results.

Author response image 1.

Illustration of bleeding over effects over a span of 4 Hz. A, 40 Hz frequency-tagging data over the significant cluster differing between when expecting an auditory versus a visual target (identical to Fig. 9 in the manuscript). B, 44 Hz signal over the same cluster chosen for A. The analysis was identical with the analysis performed in  A, apart from the frequency for the band-pass filter.

We do, however, not specifically argue against the possibility of a broadband increase when anticipating an auditory compared to a visual target. But even a broadband-increase would directly contradict the alpha inhibition hypothesis, which poses that an increase in alpha completely disengages the whole cortex. We will clarify this point in the revised manuscript.

(2) Moreover, 36Hz visual and 40Hz auditory signals are expected to be filtered in the neocortex. Applying standard filters and Hilbert transform to estimate sensory evoked potentials appears to rely on huge assumptions that are not fully substantiated in this paper. In Figure 4, 36Hz "visual" and 40Hz "auditory" signals seem largely indistinguishable from one another, suggesting that the analysis failed to fully demix these signals.

We appreciate the reviewer’s insightful concern regarding the filtering and demixing of the 36 Hz visual and 40 Hz auditory signals, and we share the same reservations about the reliance on standard filters and the Hilbert transform method.

To address this, we would like to draw attention to Author response image 1, which demonstrates that a 4 Hz difference is sufficient to effectively demix the signals using our chosen filtering and Hilbert transform approach. We believe that the reason the 36 Hz visual and 40 Hz auditory signals show similar topographies lies not in incomplete demixing but rather in the possibility that this condition difference reflects sensory integration, rather than signal contamination.

This interpretation is further supported by our findings with the intermodulation frequency at 4 Hz, which also suggests cross-modal integration. Furthermore, source localization analysis revealed that the strongest condition differences were observed in the precuneus, an area frequently associated with sensory integration processes. We will expand on this in the discussion section to better clarify this point.

(3) The asymmetric results in the visual and auditory modalities preclude a modality-general conclusion about the function of alpha. However, much of the language seems to generalize across sensory modalities (e.g., use of the term 'sensory' rather than 'visual').

We thank the reviewer for pointing this out and agree that in some cases we have not made a good enough distinction between visual and sensory. We will make sure, that when using ‘sensory’, we either describe overall theories, which are not visual-exclusive or refer to the possibility of a broad sensory increase. However, when directly discussing our results and the interpretation thereof, we will now use ‘visual’ in the revised manuscript.

(4) In this vein, some of the conclusions would be far more convincing if there was at least a trend towards symmetry in source-localized analyses of MEG signals. For example, how does alpha power in the primary auditory cortex (A1) compare when anticipating auditory vs visual target? What do the frequency-tagged visual and auditory responses look like when just looking at the primary visual cortex (V1) or A1?

We thank the reviewer for this important suggestion and have added a virtual channel analysis. We were however, not interested in alpha power in primary auditory cortex, as we were specifically interested in the posterior alpha, which is usually increased when expecting an auditory compared to a visual target (and used to be interpreted as a blanket inhibition of the visual cortex). We will improve upon the clarity concerning this point in the manuscript.

We have however, followed the reviewer’s suggestion of a virtual channel analysis, showing that the condition differences are not observable in primary visual cortex for the 36 Hz visual signal and in primary auditory cortex for the 40 Hz auditory signal. Our data clearly shows that there is an alpha condition difference in V1, while there no condition difference for 36 Hz in V1 and for 40 Hz in Heschl’s Gyrus (see Author response image 2).

Author response image 2.

Virtual channels for V1 and Helschl’s gyrus. A, alpha power for the virtual channel created in V1 (Calcerine_L and Calcerine_R from AAL atlas; Tzourio-Mazoyer et al., 2002, NeuroImage). A cluster permutation analysis over time (between -2 and 0) revealed a significant condition difference between ~ -2 and -1.7 s (p = 0.0449). B, 36 Hz frequency-tagging signal for the virtual channel created in V1 (equivalent to the procedure in A). The same cluster permutation as performed in A revealed no significant condition differences. C, 40 Hz frequency-tagging signal for the virtual channel created in Heschl’s gryrus (Heschl_L and Heschl_R from AAL atlas; Tzourio-Mazoyer et al., 2002, NeuroImage). The same cluster permutation as performed in A revealed no significant condition differences.

(5) Blinking would have a huge impact on the subject's ability to ignore the visual distractor. The best thing to do would be to exclude from analysis all trials where the subjects blinked during the cue-to-target interval. The authors mention that in the MEG experiment, "To remove blinks, trials with very large eye-movements (> 10 degrees of visual angle) were removed from the data (See supplement Fig. 5)." This sentence needs to be clarified since eye-movements cannot be measured during blinking. In addition, it seems possible to remove putative blink trials from EEG experiments as well, since blinks can be detected in the EEG signals.

We thank the reviewer for mentioning that we were making this point confusing. From the MEG-data, we removed eyeblinks using ICA. Alone for the supplementary Fig. 5 analysis, we used the eye-tracking data to confirm that participants were in fact fixating the centre of the screen. For this analysis, we removed trials with blinks (which can be seen in the eye-tracker as huge amplitude movements or as large eye-movements in degrees of visual angle; see Author response image 3 below to show a blink in the MEG data and the according eye-tracker data in degrees of visual angle). We will clarify this in the methods section.

As for the concern closed eyes to ignore visual distractors, in both experiments we can observe highly significant distractor cost in accuracy for visual distractors, which we hope will convince the reviewer that our visual distractors were working as intended.

Author response image 3.

Illustration of eye-tracker data for a trial without and a trial with a blink. All data points recorded during this trial are plottet. A, ICA component 1, which reflects blinks and its according data trace in a trial. No blink is visible. B, eye-tracker data transformed into degrees of visual angle for the trial depicted in A. C, ICA component 1, which reflects blinks and its according data trace in a trial. A clear blink is visible. D, eye-tracker data transformed into degrees of visual angle for the trial depicted in C.

(6) It would be interesting to examine the neutral cue trials in this task. For example, comparing auditory vs visual vs neutral cue conditions would be indicative of whether alpha was actively recruited or actively suppressed. In addition, comparing spectral activity during cue-to-target period on neutral-cue auditory correct vs incorrect trials should mimic the comparison of auditory-cue vs visual-cue trials. Likewise, neutral-cue visual correct vs incorrect trials should mimic the attention-related differences in visual-cue vs auditory-cue trials.

We thank the reviewer for this suggestion. We have analysed the neutral cue trials in the EEG dataset (see suppl. Fig. 1) and will expand this figure to show all conditions. There were no significant differences to auditory or visual cues, but descriptively alpha power was higher for neutral cues compared to visual cues and lower for neutral cues compared to auditory cues. While this may suggest that for visual trials alpha is actively suppressed and for auditory trials actively recruited, we do not feel comfortable to make this claim, as the neutral condition may not reflect a completely neutral state. The neutral task can still be difficult, especially because of the uncertainty of the target modality.

As for the analysis of incorrect versus correct trials, we love the idea, but unfortunately the accuracy rate was quite high so that the number of incorrect trials would not be sufficient to perform a reliable analysis.

(7) In the abstract, the authors state that "This implies that alpha modulation does not solely regulate 'gain control' in early sensory areas but rather orchestrates signal transmission to later stages of the processing stream." However, I don't see any supporting evidence for the latter claim, that alpha orchestrates signal transmission to later stages of the processing stream. If the authors are claiming an alternative function to alpha, this claim should be strongly substantiated.

We thank the reviewer for pointing out, that we have not sufficiently explained our case. The first point refers to gain control akin to the alpha inhibition hypothesis, which claims that increases in alpha disengage a whole cortical area. Since we have confirmed the alpha increase in our data to originate from primary visual cortex through source analysis, this should lead to decreased visual processing. The increase in 36 Hz visual processing therefore directly contradicts the alpha inhibition hypothesis. We propose an alternative explanation for the functionality of alpha activity in this task. Through pulsed inhibition, information packages of relevant visual information could be transmitted down the processing stream, thereby enhancing relevant visual signal transmission. We believe the fact that the enhanced visual 36 Hz signal we found correlated with visual alpha power on a trial-by-trial basis, and did not originate from primary visual cortex, but from areas known for sensory integration supports our claim.

We will make this point clearer in our revised manuscript.

Reviewer #2 (Public review):

Brickwedde et al. investigate the role of alpha oscillations in allocating intermodal attention. A first EEG study is followed up with a MEG study that largely replicates the pattern of results (with small to be expected differences). They conclude that a brief increase in the amplitude of auditory and visual stimulus-driven continuous (steady-state) brain responses prior to the presentation of an auditory - but not visual - target speaks to the modulating role of alpha that leads them to revise a prevalent model of gating-by-inhibition.

Overall, this is an interesting study on a timely question, conducted with methods and analysis that are state-of-the-art. I am particularly impressed by the author's decision to replicate the earlier EEG experiment in MEG following the reviewer's comments on the original submission. Evidently, great care was taken to accommodate the reviewer's suggestions.

We thank the reviewer for the positive feedback and expression of interest in the topic of our manuscript.

Nevertheless, I am struggling with the report for two main reasons: It is difficult to follow the rationale of the study, due to structural issues with the narrative and missing information or justifications for design and analysis decisions, and I am not convinced that the evidence is strong, or even relevant enough for revising the mentioned alpha inhibition theory. Both points are detailed further below.

We thank the reviewer for raising this important point. We will revise our introduction and results in line with the reviewer’s suggestions, hoping that our rationale will then be easier to follow and that our evidence will be more convincing.

Strength/relevance of evidence for model revision: The main argument rests on 1) a rather sustained alpha effect following the modality cue, 2) a rather transient effect on steady-state responses just before the expected presentation of a stimulus, and 3) a correlation between those two. Wouldn't the authors expect a sustained effect on sensory processing, as measured by steady-state amplitude irrespective of which of the scenarios described in Figure 1A (original vs revised alpha inhibition theory) applies? Also, doesn't this speak to the role of expectation effects due to consistent stimulus timing? An alternative explanation for the results may look like this: Modality-general increased steady-state responses prior to the expected audio stimulus onset are due to increased attention/vigilance. This effect may be exclusive (or more pronounced) in the attend-audio condition due to higher precision in temporal processing in the auditory sense or, vice versa, too smeared in time due to the inferior temporal resolution of visual processing for the attend-vision condition to be picked up consistently. As expectation effects will build up over the course of the experiment, i.e., while the participant is learning about the consistent stimulus timing, the correlation with alpha power may then be explained by a similar but potentially unrelated increase in alpha power over time.

We thank the reviewer for raising these insightful questions and suggestions.

It is true that our argument rests on a rather sustained alpha effect and a rather transient effect on steady-state responses and a correlation between the two. However, this connection would not be expected under the alpha inhibition hypothesis, which states that alpha activity would inhibit a whole cortical area (when irrelevant to the task), exerting “gain control”. This notion directly contradicts our results of the “irrelevant” visual information a) being transmitted at all and b) increasing.

However, it has been shown on many occasions that alpha activity exerts pulsed inhibition, so we proposed an alternative theory of an involvement in signal transmission. In this case, the cyclic inhibition would serve as an ordering system, which only allows for high-priority information to pass, resulting in higher signa-to-noise. We do not make a claim about how fast or when these signals are transmitted in relation to alpha power. For instance, it could be that alpha power increases as a preparatory state even before signal is actually transmitted.  Zhigalov (2020 Hum. Brain M.) has shown that in V1, frequency-tagging responses were up-and down regulated with attention – independent of alpha activity.

But we do believe that the fact that visual alpha power correlates on a trial-by-trial level with visual 36 Hz frequency-tagging increases and (a relationship which has not been found in V1, see Zhigalov 2020, Hum. Brain Mapp.) suggest a strong connection. Furthermore, the fact that the alpha modulation originates from early visual areas and occurs prior to any frequency-tagging changes, while the increase in frequency-tagging can be observed in areas which are later in the processing stream (such as the precuneus) is strongly indicative for an involvement of alpha power in the transmission of this signal. We cannot fully exclude alternative accounts and mechanisms which effect both alpha power and frequency-tagging responses. 

We do believe that the alternative account described by the reviewer does not contradict our theory, as we do believe that the alpha power modulation may reflect an expectation effect (and the idea that it could be related to the resolution of auditory versus visual processing is very interesting!). It is also possible that this expectation is, as the reviewer suggests, related to attention/vigilance and might result in a modality-general signal increase. And indeed, we can observe an increase in the frequency-tagging response in sensory integration areas. Accordingly, we believe that the alternative explanation provided by the reviewer contradicts the alpha inhibition hypothesis, but not necessarily our alternative theory.

We will revise the discussion, which we hope will make our case stronger and easier to follow. Additionally, we will mention the possibility for alternative explanations as well as the possibility, that alpha networks fulfil different roles in different locations/task environments.

Structural issues with the narrative and missing information: Here, I am mostly concerned with how this makes the research difficult to access for the reader. I list the major points below:

In the introduction the authors pit the original idea about alpha's role in gating against some recent contradictory results. If it's the aim of the study to provide evidence for either/or, predictions for the results from each perspective are missing. Also, it remains unclear how this relates to the distinction between original vs revised alpha inhibition theory (Fig. 1A). Relatedly if this revision is an outcome rather than a postulation for this study, it shouldn't be featured in the first figure.

We agree with the reviewer that we have not sufficiently clarified our goal as well as how different functionalities of alpha oscillations would lead to different outcomes. We will revise the introduction and restructure the results and hope that it will be easier to follow.

The analysis of the intermodulation frequency makes a surprise entrance at the end of the Results section without an introduction as to its relevance for the study. This is provided only in the discussion, but with reference to multisensory integration, whereas the main focus of the study is focussed attention on one sense. (Relatedly, the reference to "theta oscillations" in this sections seems unclear without a reference to the overlapping frequency range, and potentially more explanation.) Overall, if there's no immediate relevance to this analysis, I would suggest removing it.

We thank the reviewer for pointing this out and will add information about this frequency to the introduction part. We believe that the intermodulation frequency analysis is important, as it potentially supports the notion that condition differences in the visual-frequency tagging response are related to downstream processing rather than overall visual information processing in V1. We would therefore prefer to leave this analysis in the manuscript.

Reviewer #3 (Public review):

Brickwedde et al. attempt to clarify the role of alpha in sensory gain modulation by exploring the relationship between attention-related changes in alpha and attention-related changes in sensory-evoked responses, which surprisingly few studies have examined given the prevalence of the alpha inhibition hypothesis. The authors use robust methods and provide novel evidence that alpha likely exhibits inhibitory control over later processing, as opposed to early sensory processing, by providing source-localization data in a cross-modal attention task.

This paper seems very strong, particularly given that the follow-up MEG study both (a) clarifies the task design and separates the effect of distractor stimuli into other experimental blocks, and (b) provides source-localization data to more concretely address whether alpha inhibition is occurring at or after the level of sensory processing, and (c) replicates most of the EEG study's key findings.

We are very grateful to the reviewer for their positive feedback and evaluation of our work.

There are some points that would be helpful to address to bolster the paper. First, the introduction would benefit from a somewhat deeper review of the literature, not just reviewing when the effects of alpha seem to occur, but also addressing how the effect can change depending on task and stimulus design (see review by Morrow, Elias & Samaha (2023).

We thank the reviewer for this suggestion and agree. We will add a paragraph to the introduction which refers to missing correlation studies and the impact of task design.

Additionally, the discussion could benefit from more cautionary language around the revision of the alpha inhibition account. For example, it would be helpful to address some of the possible discrepancies between alpha and SSEP measures in terms of temporal specificity, SNR, etc. (see Peylo, Hilla, & Sauseng, 2021). The authors do a good job speculating as to why they found differing results from previous cross-modal attention studies, but I'm also curious whether the authors think that alpha inhibition/modulation of sensory signals would have been different had the distractors been within the same modality or whether the cues indicated target location, rather than just modality, as has been the case in so much prior work?

We thank the reviewer for suggesting these interesting discussion points and will include a paragraph in our discussion which goes deeper into these topics.

Overall, the analyses and discussion are quite comprehensive, and I believe this paper to be an excellent contribution to the alpha-inhibition literature.

Author Response

Reviewer #1 (Public Review):

The manuscript, "A versatile high-throughput assay based on 3D ring-shaped cardiac tissues generated from human induced pluripotent stem cell-derived cardiomyocytes" developed a unique culture platform with PEG hydrogel that facilitates the in-situ measurement of contractile dynamics of the engineered cardiac rings. The authors optimized the tissue seeding conditions, demonstrated tissue morphology with expressions of cardiac and fibroblast markers, mathematically modeled the equation to derive contractile forces and other parameters based on imaging analysis, and ended by testing several compounds with known cardiac responses.

To strengthen the paper, the following comments should be considered:

1) This paper provided an intriguing platform that creates miniature cardiac rings with merely thousands of CMs per tissue in a 96-well plate format. The shape of the ring and the squeezing motion can recapitulate the contraction of the cardiac chamber to a certain degree. However, Thavandiran et al (PNAS 2013) created a larger version of the cardiac ring and found the electrical propagation revealed spontaneous infinite loop-like cycles of activation propagation traversing the ring. This model was used to mimic a reentrant wave during arrhythmia. Therefore, it presents great concerns if a large number of cardiac tissues experience arrhythmia by geometry-induced re-entry current and cannot be used as a healthy tissue model. It would be interesting to see the impulse propagation/calcium transient on these miniature cardiac rings and evaluate the % of arrhythmia occurrence.

The size is a key factor impacting the electrical propagation within the generated tissues. Our ring-shaped cardiac tissues have a diameter of 360µm, which is largely smaller than other tissues proposed so far, including in Thavandiran et al (PNAS 2013) where circular tissues had a reported size > 1mm. As shown in Figure 4E (and highlighted below in Author response image 1), tissues under basal conditions display regular beating rates without spontaneous arrhythmias. Videos also show that the tissue contraction is homogeneous around the pillar, suggesting that the smaller size favors the electrical propagation and limits the occurrence of spontaneous reentrant waves. Optical mapping measurements will be performed in the future to assess the occurrence of reentrant waves.

**Author response image 1. **

Poincaré plot showing the plots between successive RR intervals (Data from Figure 4E in basal conditions). Linear regression with 95% confidence interval indicates identity.

2) The platform can produce 21 cardiac rings per well in 96-well plates. The throughput has been the highest among competing platforms. The resulting tissues have good sarcomere striation due to the strain from the pillars. Now the emerging questions are culture longevity and reproducibility among tissues. According to Figure 1E, there was uneven ring formation around the pillar, which leads to the tissue thinning and breaking off. There is only 50% survival after 20 days of culture in the optimized seeding group. Is there any way to improve it? The tissues had two compartments, cardiac and fibroblast-rich regions, where fibroblasts are responsible for maintaining the attachment to the glass slides. Do the cardiac rings detach from the glass slides and roll up? The SD of the force measurement is a quarter of the value, which is not ideal with such a high replicate number. As the platform utilizes imaging analysis to derive contractile dynamics, calibration should be done based on the angle and the distance of the camera lens to the individual tissues to reduce the error. On the other hand, how reproducible of the pillars? It is highly recommended to mechanically evaluate the consistency of the hydrogel-based pillars across different wells and within the wells to understand the variance. Figure 2B reports the early results obtained as the system was tested and developed. Since then, we have tested different iPSC lines and confirm that the overall yield is higher (up to 20 tissues at D14 for some cell lines), however dependent of cell lines.

The tissues do not detach from the glass slides. It is very rare to see tissues roll up on the central pillar. As shown in Figure 1B, the pillars have a specific shape to avoid tissues to roll up as they develop and contract.

3) Does the platform allow the observation of non-synchronized beating when testing with compounds? This can be extremely important as the intended applications of this platform are drug testing and cardiac disease modeling. The author should elaborate on the method in the manuscript and explain the obtained results in detail. The arrhythmogenic effect of a drug can be derived from the regularity of the beat-to-beat time. Indeed, we show that dofetilide increases the variability in the beat-to-beat time by plotting for each beat, the beat-to-beat time with the next beat as a function of the beat-to-beat time with the previous beat.

4) The results of drug testing are interesting. Isoproterenol is typically causing positive chronotropic and positive inotropic responses, where inotropic responses are difficult to obtain due to low tissue maturity. It is inconsistent with other reported results that cardiac rings do not exhibit increased beating frequency, but slightly increased forces only. Zhao et al were using electrical pacing at a defined rate during force measurement, whereas the ring constructs are not.

We agree. The difference in the response to isoproterenol with previous papers may be explained by different incubation timing with the drug. In our case, the tissues were incubated for 5 minutes at 37•C before being recorded.

Overall, the manuscript is well written and the designed platform presented the unique advantages of high throughput cardiac tissue culture. Besides the contractile dynamics and IHC images, the paper lacks other cardiac functional evaluations, such as calcium handling, impulse propagation, and/or electrophysiology. The culture reproducibility (high SD) and longevity (<20 days) still remain unsolved.

Since the submission, we have managed to keep some tissues and analyze them up to 32 days. At that time point the tissues are still beating. Nevertheless, a specific study concerning tissue longevity has not been carried out as the tissues were usually fixed after 14 days to be stained and analyze their structure.

Reviewer #2 (Public Review):

The authors should be commended for developing a high throughput platform for the formation and study of human cardiac tissues, and for discussing its potential, advantages and limitations. The study is addressing some of the key needs in the use of engineered cardiac tissues for pharmacological studies: ease of use, reproducible preparation of tissues, and high throughput.

There are also some areas where the manuscript should be improved. The design of the platform and the experimental design should be described in more detail.

It would be of interest to comprehensively document the progression of tissue formation. To this end, it would be helpful to show the changes in tissue structure through a series of images that would correspond to the progression of contractile properties shown in Figure 3.

Our results indicate that the fibroblasts/cardiomyocytes segregation likely happens as soon as the tissue is formed, as the fibroblasts are critical for tissue generation. The change with time in the shape of the contractile ring is reported in Figure 1E, with a series of images which correspond to the timepoints of Figure 3.

The very interesting tissue morphology (separation into the two regions) that was observed in this study is inviting more discussion.

Finally, the reader would benefit from more specific comparisons of the contractile function of cardiac tissues measured in this study with data reported for other cardiac tissue models.

Author Response

We thank the reviewers for truly valuable advice and comments. We have made multiple corrections and revisions to the original pre-print accordingly. Here we address 2 major points.

1) Regarding the genetic association of the common COL11A1 variant rs3753841 (p.(Pro1335Leu)), we do not propose that it is the sole risk variant contributing to the association signal we detected and have clarified this in the manuscript. We concluded that it was worthy of functional testing for reasons described here. Although there were several common variants in the discovery GWAS within and around COL11A1, none were significantly associated with AIS and none were in linkage disequilibrium (R2>0.6) with the top SNP rs3753841. We next reviewed rare (MAF<=0.01) coding variants within the COL11A1 LD region of the associated SNP (rs3753841) in 625 available exomes representing 46% of the 1,358 cases from the discovery cohort. The LD block was defined using Haploview based on the 1KG_CEU population. Within the ~41 KB LD region (chr1:103365089- 103406616, GRCh37) we found three rare missense mutations in 6 unrelated individuals, Author response table 1. Two of them (NM_080629.2: c.G4093A:p.A1365T; NM_080629.2:c.G3394A:p.G1132S), from two individuals, are predicted to be deleterious based on CADD and GERP scores and are plausible AIS risk candidates. At this rate we could expect to find only 4-5 individuals with linked rare coding variants in the total cohort of 1,358 which collectively are unlikely to explain the overall association signal we detected. Of course, there also could be deep intronic variants contributing to the association that we would not detect by our methods. However, given this scenario, the relatively high predicted deleteriousness of rs3753841 (CADD= 25.7; GERP=5.75), and its occurrence in a Gly-X-Y triplet repeat, we hypothesized that this variant itself could be a risk allele worthy of further investigation.

Author response table 1.

We also appreciate the reviewer’s suggestion to perform a rare variant burden analysis of COL11A1. We conducted pilot gene-based analysis in 4534 European ancestry exomes including 797 of our own AIS cases and 3737 controls and tested the burden of rare variants in COL11A1. SKATO P value was not significant (COL11A1_P=0.18) but this could due to lack of power and/or background from rare benign variants that could be screened out using the functional testing we have developed.

2) Regarding functional testing, by knockdown/knockout cell culture experiments, we showed for the first time that Col11a1 negatively regulates Mmp3 expression in cartilage chondrocytes, an AIS-relevant tissue. We then tested the effect of overexpressing the human wt or variant COL11A1 by lentiviral transduction in SV40-transformed chondrocyte cultures. We deleted endogenous mouse Col11a1 by Cre recombination to remove the background of its strong suppressive effects on Mmp3 expression. We acknowledge that Col11a1 missense mutations could confer gain of function or dominant negative effects that would not be revealed in this assay. However as indicated in our original manuscript we have noted that spinal deformity is described in the cho/cho mouse, a Col11a1 loss of function mutant. We also note the recent publication by Rebello et al. showing that missense mutations in Col11a2 associated with congenital scoliosis fail to rescue a vertebral malformation phenotype in a zebrafish col11a2 KO line. Although the connection between AIS and vertebral malformations is not altogether clear, we surmise that loss of the components of collagen type XI disrupt spinal development. in vivo experiments in vertebrate model systems are needed to fully establish the consequences and genetic mechanisms by which COL11A1 variants contribute to an AIS phenotype.

Reviewer #3 (Public review):

Summary:

The authors examine the role of the medial frontal cortex of mice in exploiting statistical structure in tasks. They claim that mice are "proactive": they predict upcoming changes, rather than responding in a "model-free" way to environmental changes. Further, they speculate that the estimation of future change (i.e., prediction of upcoming events, based on learning temporal regularities) might be "a main ... function of dorsal medial frontal cortex (dmFC)." Unfortunately, the current manuscript contains flaws such that the evidence supporting these claims is inadequate.

Strengths:

Understanding the neural mechanisms by which we learn about statistical structure in the world is an important goal. The authors developed an interesting task and used model-based techniques to try to understand the mechanisms by which perturbation of dmFC influenced behavior. They demonstrate that lesions and optogenetic silencing of dmFC influence behavior, showing that this region has a causal influence on the task.

Weaknesses:

I was concerned that the main behavioral effects shown in Figure 1F were a statistical artifact. By requiring the Geometric block length to be preceded by a performance-based block, the authors introduce a dependence that can generate the phenomena they describe as anticipation.

To demonstrate this, I simulated their task with an agent that does not have any anticipation of the change point (Reviewer image 1). The agent repeats the previous action with probability `p(repeat)` (similar to the choice kernel in the author's models). If the agent doesn't repeat then the next choice depends on the previous outcome. If the previous choice was rewarded, it stays with `P(WS)` and chooses randomly with `1-P(WS)`. If the previous choice was unrewarded, it switches with `P(LS)` and chooses randomly with `1-P(LS)`.

Review image 1.

An agent with `P(WS)=P(LS)=P(repeat)=0.85` shows the same phenomena as the mice: a difference in performance before the block switch and "earlier" crossing of the midpoint after the switch. https://imgdrop.io/image/aHn6y. The phenomena go away in the simulations when a fixed block length of 20 trials is followed by a Geometric block length.

The authors did not completely rely on the phenomena of Figure 1F for their conclusions. They did a model comparison to provide evidence that animals are anticipating the switch. Unfortunately, the authors did not use state-of-the-art methods in this section of the paper. In particular, they failed to show that under a range of generative parameters for each model class, the model selection process chooses the correct model class (i.e. a confusion matrix). A more minor point, they used BIC instead of a more robust cross-validated metric for model selection. Finally, instead of comparing their "best" anticipating model to their 2nd best model (without anticipation), they compared their best to their 4th best (Supp Fig 3.5). This seems misleading.

Given all of the the above issues, it is hard to critically evaluate the model-based analysis of the effects of lesions/optogenetics.

Author response:

We thank the reviewers for their thoughtful criticisms.  This provisional response addresses what we consider the central critiques, with a full, point-by-point reply to follow with the revised manuscript.  Central critiques concern 1) providing further clarity about the apportionment cost of time, 2) generality & scope, and 3) clarifying the meaning of key equations.

(1) Apportionment cost

Reviewers commonly identified a need to provide a concise and intuitive definition of apportionment cost, and to explicitly solve and provide for its mathematical expression. 

We will add the following definition of apportionment cost to the manuscript: “Apportionment cost is the difference in reward that can be expected, on average, between a policy of taking versus a policy of not taking the considered pursuit, over a time equal to its duration.”  While this difference is the apportionment cost of time, the amount that would be expected over a time equal to the considered pursuit under a policy of not taking the considered pursuit is the opportunity cost of time.  Together, they sum to Time’s Cost.  The above definition of apportionment cost adds to other stated relationships of apportionment cost found throughout the paper (Lines 434,435,447,450). 

As suggested, we will also add equations of apportionment cost, as below.

(2) Generality & Scope

Generality. We will add further examples in support of the generality of these equations for assessing and thinking about the value of initiating a pursuit.  Specifically, this will include 1) illustrating forgo decision making in a world composed of multiple pursuits, as in prey selection, 2) demonstrating and examining worlds in which a sequence of pursuits compose a considered pursuit’s ‘outside’, and 3) clarifying how our framework does contend with variance and uncertainty in reward magnitude and occurrence.

Scope. In this manuscript, we consider the worth of initiating one or another pursuit having completed a prior one, and not the issue of continuing within a pursuit having already engaged in it.  The worth of continuing a pursuit, as in patch-foraging/give-up tasks, constitutes a third fundamental time decision-making topology which is outside the scope of the current work.  It engages a large and important literature, encompassing evidence accumulation, and requires a paper in its own right that can use the concepts and framework developed here.  We will further consider applying this framework to extant datasets.

(3) Correction of typographical errors and further explanation of equations.   

We would like to redress the two typographical errors identified by the reviewers that appeared in the equations on line 277 and on line 306, and provide further explanation to equations that gave pause to the reviewers.

Typographical errors: 

The first typographical error in the main text regards equation 2 and will be corrected so that equation 2 appears correctly as…

Line 277:  

The second typo regards the definition of the considered pursuit’s reward rate, and will be corrected to appear as…

Line 306:   

Regarding equations:

Cross-reference to equations in the main text refer to equations as they appear in the main text.  Where needed, the appendix in which they are derived is also given.   Equation numbering within the appendices refer to equations as they appear in the appendices.  In the revision, we will refer to all equations that appear in the appendices as Ap.#.#. so as to avoid confusion between referencing equations as they appear in the main text and equations as they appear in the appendices.  

We would also like to clarify that equation 8, , as we derive, is not new, as it is similarly derived and expressed in prior foundational work by McNamara (1982), which is now so properly attributed. 

Equation 1 and Appendix 1

Equation 1 is formulated to calculate the average reward received and average time spent per unit time spent in the default pursuit. So, fi is the encounter rate of pursuit  for one unit of time spent in the default pursuit (lines 259-262). Added to the summation in the numerator, we have the average reward obtained in the default pursuit per unit time and in the denominator we have the time spent in the default pursuit per unit time (1).

Equation 2 and Appendix 2

Eq. 2.4 in Appendix 2 calculates the average time spent outside of the considered pursuit, per encounter with the considered pursuit. Breaking down eq. 2.4, the first term in the numerator,

gives the expected time spent in other pursuits, per unit time spent in the default pursuit, where fi is the encounter rate of pursuit  per unit time spent in the default pursuit, and  is the time required by pursuit i. The second term in the numerator, (1, added outside the summation) simply represents the unit of time spent in the default pursuit, over which the encounter rate of each pursuit is calculated. Together, these represent the total time spent outside the considered pursuit, per unit time spent in the default pursuit. The denominator,

is the frequency with which the considered pursuit is encountered per unit time spent in the default pursuit, so

is the average time spent within the default pursuit, per encounter with the considered pursuit. By multiplying the average time spent outside of the considered pursuit per unit time spent in the default pursuit by the average time spent within the default pursuit per encounter with the considered pursuit, we get eq. 2.4, the average time spent outside of the considered pursuit, per encounter with the considered pursuit, which is equal to tout.

                       (eq. 2.4)

Author response:

To Reviewer #1:

Thank you for your thorough review and comments on our work, which you described as “the role of neuritin in T cell biology studied here is new and interesting.”.  We have summarized your comments into two categories: biology and investigation approach, experimental rigor, and data presentation.

Biology and Investigation approach comments:

(1) Questions regarding the T cell anergy model:

Major point “(4) Figure 1E-H. The authors assume that this immunization protocol induces anergic cells, but they provide no experimental evidence for this. It would be useful to show that T cells are indeed anergic in this model, especially those that are OVA-specific. The lack of IL-2 production by Cltr cells could be explained by the presence of fewer OVA-specific cells, rather than by an anergic status.”

T cell anergy is a well-established concept first described by Schwartz’s group. It refers to the hyporesponsive T cell functional state in antigen-experienced CD4 T cells (Chappert and Schwartz, 2010; Fathman and Lineberry, 2007; Jenkins and Schwartz, 1987; Quill and Schwartz, 1987).  Anergic T cells are characterized by their inability to expand and to produce IL2 upon subsequent antigen re-challenge. In this paper, we have borrowed the existing in vivo T cell anergy induction model used by Mueller’s group for T cell anergy induction (Vanasek et al., 2006).  Specifically, Thy1.1+ Ctrl or Nrn1-/- TCR transgenic OTII cells were co-transferred with the congenically marked Thy1.2+ WT polyclonal Treg cells into TCR-/- mice.  After anergy induction, the congenically marked TCR transgenic T cells were recovered by sorting based on Thy1.1+ congenic marker, and subsequently re-stimulation ex vivo with OVA323-339 peptide. We evaluated the T cell anergic state based on OTII cell expansion in vivo and IL2 production upon OVA323-339 restimulation ex vivo.  

“The authors assume that this immunization protocol induces anergic cells, but they provide no experimental evidence for this.”

Because the anergy model by Mueller's group is well established (Vanasek et al., 2006), we did not feel that additional effort was required to validate this model as the reviewer suggested. Moreover, the limited IL2 production among the control cells upon restimulation confirms the validity of this model.

“The lack of IL-2 production by Cltr cells could be explained by the presence of fewer OVAspecific cells, rather than by an anergic status”.

Cells from Ctrl and Nrn1-/- mice on a homogeneous TCR transgenic (OTII) background were used in these experiments. The possibility that substantial variability of TCR expression or different expression levels of the transgenic TCR could have impacted IL2 production rather than anergy induction is unlikely.

Overall, we used this in vivo anergy model to evaluate the Nrn1-/- T cell functional state in comparison to Ctrl cells under the anergy induction condition following the evaluation of Nrn1 expression, particularly in anergic T cells.  Through studies using this anergy model, we observed a significant change in Treg induction among OTII cells. We decided to pursue the role of Nrn1 in Treg cell development and function rather than the biology of T cell anergy as evidenced by subsequent experiments.

Minor points “(6) On which markers are anergic cells sorted for RNAseq analysis?”

Cells were sorted out based on their congenic marker marking Ctrl or Nrn1-/- OTII cells transferred into the host mice.  We did not specifically isolate anergic cells for sequencing.

(2) Question regarding the validity of iTreg differentiation model.

Major point: “(5) Figure 2A-C and Figure 3. The use of iTregs to try to understand what is happening in vivo is problematic. iTregs are cells that have probably no equivalent in vivo, and so may have no physiological relevance. In any case, they are different from pTreg cells generated in vivo. Working with pTreg may be challenging, that is why I would suggest generating data with purified nTreg. Moreover, it was shown in the article of Gonzalez-Figueroa 2021 that Nrn1-/- nTreg retained a normal suppressive function, which would not be what is concluded by the authors of this manuscript. Moreover, we do not even know what the % of Foxp3 cells is in the iTreg used (after differentiation and 20h of re-stimulation) and whether this % is the same between Ctlr and Nrn1 KO cells.”.

We thank Reviewer #1 for their feedback. While it is true that iTregs made in vitro and in vivo generated pTregs display several distinctions (e. g., differences in Foxp3 expression stability, for example), we strongly disagree with this statement by Revieweer#1 “The use of iTregs to try to understand what is happening in vivo is problematic. iTregs are cells that have probably no equivalent in vivo, and so may have no physiological relevance.” The induced Treg cell (iTreg) model was established over 20 years ago (Chen et al., 2003; Zheng et al., 2002), and the model is widely adopted with over 2000 citations. Further, it has been instrumental in understanding different aspects of regulatory T cell biology (Hurrell et al., 2022; John et al., 2022; Schmitt and Williams, 2013; Sugiura et al., 2022).   

Because we have observed reduced pTreg generation in vivo, we choose to use the in vitro iTreg model system to understand the mechanistic changes involved in Treg cell differentiation and function, specifically, neuritin’s role in this process. We have made no claim that iTreg cell biology is identical to pTreg generated in vivo or nTreg cells. However, the iTreg culture system has proved to be a good in vitro system for deciphering molecular events involved in complex processes. As such, it remains a commonly used approach by many research groups in the Treg cell field (Hurrell et al., 2022; John et al., 2022; Sugiura et al., 2022). Moreover, applying the iTreg in vitro culture system has been instrumental in helping us identify the cell electrical state change in Nrn1-/- CD4 cells and revealed the biological link between Nrn1 and the ionotropic AMPA receptor (AMPAR), which we will discuss in the subsequent discussion. It is technically challenging to use nTreg cells for T cell electrical state studies due to their heterogeneous nature from development in an in vivo environment and the effect of manipulation during the nTreg cell isolation process, which can both affect the T cell electrical state.   

“Moreover, it was shown in the article of Gonzalez-Figueroa 2021 that Nrn1-/- nTreg retained a normal suppressive function, which would not be what is concluded by the authors of this manuscript.” 

We have also carried out nTreg studies in vitro in addition to iTreg cells. Similar to Gonzalez-Figueroa et al.'s findings, we did not observe differences in suppression function between Nrn1-/- and WT nTreg using the in vitro suppression assay. However, Nrn1-/- nTreg cells revealed reduced suppression function in vivo (Fig. 2D-L). In fact, Gonzalez-Figueroa et al. observed reduced plasma cell formation after OVA immunization in Treg-specific Nrn1-/- mice, implicating reduced suppression from Nrn1-/- follicular regulatory T (Tfr) cells. Thus, our observation of the reduced suppression function of Nrn1-/- nTreg toward effector T cell expansion, as presented in Fig. 2D-L, does not contradict the results from Gonzalez-Figueroa et al. Rather, the conclusions of these two studies agree that Nrn1 can play important roles in immune suppression observable in vivo that are not captured readily by the in vitro suppression assay.

“Moreover, we do not even know what the % of Foxp3 cells is in the iTreg used (after differentiation and 20h of re-stimulation) and whether this % is the same between Ctlr and Nrn1 KO cells.”

We have stated in the manuscript on page 7 line 208 that “Similar proportions of Foxp3+ cells were observed in Nrn1-/- and Ctrl cells under the iTreg culture condition, suggesting that Nrn1 deficiency does not significantly impact Foxp3+ cell differentiation”. In the revised manuscript, we will include the data on the proportion of Foxp3+ cells before iTreg restimulation.

(3) Confirmation of transcriptomic data regarding amino acids or electrolytes transport change

Minor point“(3) Would not it be possible to perform experiments showing the ability of cells to transport amino acids or electrolytes across the plasma membrane? This would be a more interesting demonstration than transcriptomic data.”

We appreciate Review# 1’s suggestion regarding “perform experiments showing the ability of cells to transport amino acids or electrolytes across the plasma membrane”.  We have indeed already performed such experiments corroborating the transcriptomics data on differential amino acid and nutrient transporter expression. Specifically, we loaded either iTreg or Th0 cells with membrane potential (MP) dye and measured MP level change after adding the complete set of amino acids (complete AA).  Upon entry, the charge carried by AAs may transiently affect cell membrane potential. Different AA transporter expression patterns may show different MP change patterns upon AA entry, as we showed in Author response image 1. We observed reduced MP change in Nrn1-/- iTreg compared to the Ctrl, whereas in the context of Th0 cells, Nrn1-/- showed enhanced MP change than the Ctrl. We can certainly include these data in the revised manuscript.

Author response image 1.

Membrane potential change induced by amino acids entry. a. Nrn1-/- or WT iTreg cells loaded with MP dye and MP change was measured upon the addition of a complete set of AAs. b. Nrn1-/- or WT Th0 cells loaded with MP dye and MP change was measured upon the addition of a complete set of AAs.

(4) EAE experiment data assessment

Minor point ”(5) Figure 5F. How are cells re-stimulated? If polyclonal stimulation is used, the experiment is not interesting because the analysis is done with lymph node cells. This analysis should either be performed with cells from the CNS or with MOG restimulation with lymph node cells.”

In the EAE study, the Nrn1-/- mice exhibit similar disease onset but a protracted non-resolving disease phenotype compared to the WT control mice.  Several reasons may contribute to this phenotype: 1. Enhanced T effector cell infiltration/persistence in the central nervous system (CNS); 2. Reduced Treg cell-mediated suppression to the T effector cells in the CNS; 3. Protracted non-resolving inflammation at the immunization site has the potential to continue sending T effector cells into CNS, contributing to persistent inflammation. Based on this reasoning, we examined the infiltrating T effector cell number and Treg cell proportion in the CNS.  We also restimulated cells from draining lymph nodes close to the inflammation site, looking for evidence of persistent inflammation.  When mice were harvested around day 16 after immunization, the inflammation at the local draining lymph node should be at the contraction stage.  We stimulated cells with PMA and ionomycin intended to observe all potential T effector cells involved in the draining lymph node rather than only MOG antigen-specific cells.  We disagree with Reviewer #1’s assumption that “This analysis should either be performed with cells from the CNS or with MOG restimulation with lymph node cells.”. We think the experimental approach we have taken has been appropriately tailored to the biological questions we intended to answer.

Experimental rigor and data presentation.

(1) Data labeling and additional supporting data

Major points (2) The authors use Nrn1+/+ and Nrn1+/- cells indiscriminately as control cells on the basis of similar biology between Nrn1+/+ and Nrn1+/- cells at homeostasis. However, it is quite possible that the Nrn1+/- cells have a phenotype in situations of in vitro activation or in vivo inflammation (cancer, EAE). It would be important to discriminate Nrn1+/- and Nrn1+/+ cells in the data or to show that both cell types have the same phenotype in these conditions too.

(3) Figure 1A-D. Since the authors are using the Nrp1 KO mice, it would be important to confirm the specificity of the anti-Nrn1 mAb by FACS. Once verified, it would be important to add FACS results with this mAb in Figures 1A-C to have single-cell and quantitative data as well.

Minor points  

(1) Line 119, 120 of the text. It is said that one of the most up-regulated genes in anergic cells is Nrn1 but the data is not shown.

(2) For all figures showing %, the titles of the Y axes are written in an odd way. For example, it is written "Foxp3% CD4". It would be more conventional and clearer to write "% Foxp3+ / CD4+" or "% Foxp3+ among CD4+".

(4) For certain staining (Figure 3E, H) it would be important to show the raw data, in addition to MFI or % values.

We can adapt the labeling and provide additional data, including Nrn1 staining on Treg cells and flow graphs for pmTOR and pS6 staining (Fig. 3H), as requested by Reviewer #1.

(2) Experimental rigor:

General comments:

“However, it is disappointing that reading this manuscript leaves an impression of incomplete work done too quickly.”

We were discouraged to receive the comment, “this manuscript leaves an impression of incomplete work done too quickly.” Our study of this novel molecule began without any existing biological tools such as antibodies, knockout mice, etc.  Over the past several years, we have established our own antibodies for Nrn1 detection, obtained and characterized Nrn1 knockout mice, and utilized multiple approaches to identify the molecular mechanism of Nrn1 function. Through the use of the in vitro iTreg system described in this manuscript, we identified the association of Nrn1 deficiency with cell electrical state change, potentially connected to AMPAR function. We have further corroborated our findings by generating Nrn1 and AMPAR T cell specific double knockout mice and confirmed that T cell specific AMPAR deletion could abrogate the phenotype caused by the Nrn1 deficiency (see Author response image 2).  We did not include the double knockout data in the current manuscript because AMPAR function has not yet been studied thoroughly in T cell biology, and we feel this topic warrants examination in its own right.  However, the unpublished data support the finding that Nrn1 modulates the T cell electrical state and, consequently, metabolism, ultimately influencing tolerance and immunity.  In its current form, the manuscript represents the first characterization of the novel molecule Nrn1 in anergic cells, Tregs, and effector T cells. While this work has led to several exciting additional questions, we disagree that the novel characterization we have presented Is incomplete. We feel that our present data set, which squarely highlights Nrn1’s role as an important immune regulator while shedding unprecedented light on the molecular events involved, will be of considerable interest to a broad field of researchers.

“Multiple models have been used, but none has been studied thoroughly enough to provide really conclusive and unambiguous data. For example, 5 different models were used to study T cells in vivo. It would have been preferable to use fewer, but to go further in the study of mechanisms.”

We have indeed used multiple in vivo models to reveal Nrn1's function in Treg differentiation, Treg suppression function, T effector cell differentiation and function, and the overall impact on autoimmune disease. Because the impact of ion channel function is often context-dependent, we examined the biological outcome of Nrn1 deficiency in several in vivo contexts.  We would appreciate it if Reviewer#1 would provide a specific example, given the Nrn1 phenotype, of how to proceed deeper to investigate the electrical change in the in vivo models.

“Major points (1) A real weakness of this work is the fact that in most of the results shown, there are few biological replicates with differences that are often small between Ctrl and Nrn1 -/-. The systematic use of student's t-test may lead to thinking that the differences are significant, which is often misleading given the small number of samples, which makes it impossible to know whether the distributions are Gaussian and whether a parametric test can be used. RNAseq bulk data are based on biological duplicates, which is open to criticism.”

We respectfully disagree with Reviewer #1 on the question of statistical power and significance to our work. We have used 5-8 mice/group for each in vivo model and 3-4 technical replicates for the in vitro studies, with a minimum of 2-3 replicate experiments. These group sizes and replication numbers are in line with those seen in high-impact publications. While some differences between Ctrl and Nrn1-/- appear small, they have significant biological consequences, as evidenced by the various Nrn1-/- in vivo phenotypes. Furthermore, we believe we have subjected our data to the appropriate statistical tests to ensure rigorous analysis and representation of our findings.

To Reviewer #2.

We thank Reviewer #2 for the careful review of the manuscript. We especially appreciate the comments that “The characterizations of T cell Nrn1 expression both in vitro and in vivo are comprehensive and convincing. The in vivo functional studies of anergy development, Treg suppression, and EAE development are also well done to strengthen the notion that Nrn1 is an important regulator of CD4 responsiveness.”

“The major weakness of this study stems from a lack of a clear molecular mechanism involving Nrn1. “  

We fully understand this comment from Reviewer #2. The main mechanism we identified contributing to the functional defect of Nrn1-/- T cells involves novel effects on the electric and metabolic state of the cells. Although we referenced neuronal studies that indicate Nrn1 is the auxiliary protein for the ionotropic AMPA-type glutamate receptor (AMPAR) and may affect AMPAR function, we did not provide any evidence in this manuscript as the topic requires further in-depth study.   

For the benefit of this discussion, we include our preliminary Nrn1 and AMPAR double knockout data (Author response image 2), which indicates that abrogating AMPAR expression can compensate for the defect caused by Nrn1 deficiency in vitro and in vivo. This preliminary data supports the notion that Nrn1 modulates AMPAR function, which causes changes in T cell electric and metabolic state, influencing T cell differentiation and function.  

Author response image 2.

Deletion of AMPAR expression in T cells compensates for the defect caused by Nrn1 deficiency. Nrn1-/- mice were crossed with T cell-specific AMPAR knockout mice (AMPARfl/flCD4Cre+) mice. The following mice were generated and used in the experiment: T cell specific AMPAR-knockout and Nrn1 knockout mice (AKONKO), Nrn1 knockout mice (AWTNKO), Ctrl mice (AWTNWT). a. Deletion of AMPAR compensates for the iTreg cell defect observed in Nrn1-/- CD4 cells. iTreg live cell proportion, cell number, and Ki67 expression among Foxp3+ cells 3 days after aCD3 restimulation. b. Deletion of AMPAR in T cells abrogates the enhanced autoimmune response in Nrn1-/- Mouse in the EAE disease model. Mouse relative weight change and disease score progression after EAE disease induction.  

Ion channels can influence cell metabolism through multiple means (Vaeth and Feske, 2018; Wang et al., 2020). First, ion channels are involved in maintaining cell resting membrane potential. This electrical potential difference across the cell membrane is essential for various cellular processes, including metabolism (Abdul Kadir et al., 2018; Blackiston et al., 2009; Nagy et al., 2018; Yu et al., 2022). Second, ion channels facilitate the movement of ions across cell membranes. These ions are essential for various metabolic processes. For example, ions like calcium (Ca2+), potassium (K+), and sodium (Na+) play crucial roles in signaling pathways that regulate metabolism (Kahlfuss et al., 2020). Third, ion channel activity can influence cellular energy balance due to ATP consumption associated with ion transport to maintain ion balances (Erecińska and Dagani, 1990; Gerkau et al., 2019). This, in turn, can impact processes like ATP production, which is central to cellular metabolism. Thus, ion channel expression and function determine the cell’s bioelectric state and contribute to cell metabolism (Levin, 2021).

Because the AMPAR function has not been thoroughly studied using a genetic approach in T cells, we do not intend to include the double knockout data in this manuscript before fully characterizing the T cell-specific AMPAR knockout mice.  

“Although the biochemical and informatics studies are well-performed, it is my opinion that these results are inconclusive in part due to the absence of key "naive" control groups. This limits my ability to understand the significance of these data.

Specifically, studies of the electrical and metabolic state of Nrn1-/- inducible Treg cells (iTregs) would benefit from similar data collected from wild-type and Nrn1-/- naive CD4 T cells.”

We appreciate the reviewer’s comments. This comment reflects two concerns in data interpretation:

(1) Are Nrn1-/- naïve T cells fundamentally different from WT cells? Does this fundamental difference contribute to the observed electrical and metabolic phenotype in iTreg or Th0 cells? This is a very good question we will perform the experiments as the reviewer suggested. While Nrn1 is expressed at a basal (low) level in naïve T cells, deletion of Nrn1 may cause changes in naïve T cell phenotype.   

(2) Is the Nrn1-/- phenotype caused by Nrn1 functional deficiency or due to the secondary effect of Nrn1 deletion, such as non-physiological cell membrane structure changes?

We have done the following experiment to address this concern.  We have cultured WT T cells in the presence of Nrn1 antibody and compared the outcome with Nrn1-/- iTreg cells (Author response image 3). WT iTreg cells under antibody blockade exhibited similar changes as Nrn1-/- iTreg cells, confirming the physiological relevance of the Nrn1-/- phenotype.

Author response image 3.

Nrn1 antibody blockade in WT iTreg cell culture caused similar phenotypic change as in Nrn1-/- iTreg cells. Nrn1-/- and WT CD4 cells were differentiated under iTreg condition in the presence of anti-Nrn1 (aNrn1) antibody or isotype control for 3 days. Cells were restimulated with anti-CD3 and in the presence of aNrn1 or isotype. a. MP measured 18hr after anti-CD3 restimulation. b. live CD4 cell number and proportion of Ki67 expression among live cells three days after restimulation. c. The proportion of Foxp3+ cells among live cells three days after restimulation.  

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Author response:

Reviewer #1 (Public review):

Summary:

Loh and colleagues investigate valence encoding in the mesolimbic dopamine system. Using an elegant approach, they show that sucrose, which normally evokes strong dopamine neuron activity and release in the nucleus accumbens, is made aversive via conditioned taste aversion, the same sucrose stimulus later evokes much less dopamine neuron activity and release. Thus, dopamine activity can dynamically track the changing valence of an unconditioned stimulus. These results are important for helping clarify valence and value related questions that are the matter of ongoing debate regarding dopamine functions in the field.

Strengths:

This is an elegant way to ask this question, the within subject's design and the continuity of the stimulus is a strong way to remove a lot of the common confounds that make it difficult to interpret valence-related questions. I think these are valuable studies that help tie up questions in the field while also setting up a number of interesting future directions. There are number of control experiments and tweaks to the design that help eliminate a number of competing hypotheses regarding the results. The data are clearly presented and contextualized.

Weaknesses for consideration:

The focus on one relatively understudied region of the rat striatum for dopamine recordings could potentially limit generalization of the findings. While this can be determined in future studies, the implications should be further discussed in the current manuscript.

We agree that the manuscript would benefit from providing a stronger rationale for our recording sites and acknowledging the potential for regional differences in dopamine signaling. We have made the following additions to the manuscript:

Added to the Discussion: “Recordings were targeted to the lateral VTA and the corresponding approximate terminal site in the NAc lateral shell (Lammel et al., 2008). Subregional differences in dopamine activity likely contribute to mixed findings on dopamine and affect. For example, dopamine in the NAc lateral shell differentially encodes cues predictive of rewarding sucrose and aversive footshock, which is distinct from NAc medial shell dopamine responses (de Jong et al., 2019). Our findings are similar to prior work from our group targeting recordings to the NAc dorsomedial shell (Hsu et al., 2020; McCutcheon et al., 2012; Roitman et al., 2008): there, intraoral sucrose increased NAc dopamine release while the response in the same rats to quinine was significantly lower.”

Reviewer #2 (Public review):

Summary:

Koh et al. report an interesting manuscript studying dopamine binding in the lateral accumbens shell of rats across the course of conditioned taste aversion. The question being asked here is how does the dopamine system respond to aversion? The authors take advantage of unique properties of taste aversion learning (notably, within-subjects remapping of valence to the same physical stimulus) to address this.

They combine a well controlled behavioural design (including key, unpaired controls) with fibre photometry of dopamine binding via GrabDA and of dopamine neuron activity by gCaMP, careful analyses of behaviour (e.g., head movements; home cage ingestion), the authors show that, 1) conditioned taste aversion of sucrose suppresses the activity of VTA dopamine neurons and lateral shell dopamine binding to subsequent presentations of the sucrose tastant; 2) this pattern of activity was similar to the innately aversive tastant quinine; 3) dopamine responses were negatively correlated with behavioural (inferred taste reactivity) reactivity; and 4) dopamine responses tracked the contingency of between sucrose and illness because these responses recovered across extinction of the conditioned taste aversion.

Strengths:

There are important strengths here. The use of a well-controlled design, the measurement of both dopamine binding and VTA dopamine neuron activity, the inclusion of an extinction manipulation; and the thorough reporting of the data. I was not especially surprised by these results, but these data are a potentially important piece of the dopamine puzzle (e.g., as the authors note, salience-based argument struggles to explain these data).

Weaknesses for consideration:

(1) The focus here is on the lateral shell. This is a poorly investigated region in the context of the questions being asked here. Indeed, I suspect many readers might expect a focus on the medial shell. So, I think this focus is important. But, I think it does warrant greater attention in both the introduction and discussion. We do know from past work that there can be extensive compartmentalisation of dopamine responses to appetitive and aversive events and many of the inconsistent findings in the literature can be reconciled by careful examination of where dopamine is assessed. I do think readers would benefit from acknowledgement this - for example it is entirely reasonable to suppose that the findings here may be specific to the lateral shell.

As with our response to Reviewer 1, we agree that we should provide further rationale for focusing our recordings on the lateral shell and acknowledge potential differences in dopamine dynamics across NAc subregions. In addition to the changes in the Discussion detailed in our response to Reviewer 1, we have made the following additions to the Introduction:

Added to the Introduction: “NAc lateral shell dopamine differentially encodes cues predictive of rewarding (i.e., sipper spout with sucrose) and aversive stimuli (i.e., footshock), which is distinct from other subregions (de Jong et al., 2019). It is important to note that other regions of the NAc may serve as hedonic hotspots (e.g. dorsomedial shell; or may more closely align with the signaling of salience (e.g. ventromedial shell; (Yuan et al., 2021)).”

(2) Relatedly, I think readers would benefit from an explicit rationale for studying the lateral shell as well as consideration of this in the discussion. We know that there are anatomical (PMID: 17574681), functional (PMID: 10357457), and cellular (PMID: 7906426) differences between the lateral shell and the rest of the ventral striatum. Critically, we know that profiles of dopamine binding during ingestive behaviours there can be highly dissimilar to the rest of ventral striatum (PMID: 32669355). I do think these points are worth considering.

There are several reasons why dopamine dynamics were recorded in the NAc lateral shell:

(1) Dopamine neurons in more medial aspects of the VTA preferentially target the NAc medial shell and core whereas dopamine neurons in the lateral VTA – our target for VTA DA recordings – project to the lateral shell of the NAc (Lammel et al., 2008). Thus, our goal was to sample NAc release dynamics in areas that receive projections from our cell body recording sites.

(2) Cues predictive of reward availability (i.e., sipper spout with sucrose) and aversive stimuli (i.e., footshock) are differentially encoded by NAc lateral shell dopamine, which is distinct from NAc ventromedial shell dopamine responses (de Jong et al., 2019). These findings suggest a role for NAc lateral shell dopamine in the encoding of a stimulus’s valence, which made the subregion an area of interest for further examination.

(3) With respect to the medial NAc shell specifically, extensive literature had already shown it to be a ‘hedonic hotspot’ (Morales and Berridge, 2020; Yuan et al., 2021) whereas the ventral portion is more mixed with respect to valence (Yuan et al., 2021). We had previously shown that intraoral infusions of primary taste stimuli of opposing valence (i.e., sucrose and quinine) evoke differential responses in dopamine release within the NAc dorsomedial shell (Roitman et al., 2008). We more recently replicated differential dopamine responses from dopamine cell bodies in the lateral VTA (Hsu et al., 2020) and thus endeavored to the possibility of changing dopamine responses in the lateral VTA to the same stimulus as its valence changes. As a result of these choices, measuring dopamine release in the lateral shell was a logical choice. The field would greatly benefit from continued future work surveying the entirety of the VTA DA projection terminus. 

We have included these points of justification in the Introduction and Discussion sections.

(3) I found the data to be very thoughtfully analysed. But in places I was somewhat unsure:

(a) Please indicate clearly in the text when photometry data show averages across trials versus when they show averages across animals.

We have now explicitly indicated in the figure legends of Figures 1, 3, 5, 7, and 8:

(1) In heat maps, each row represents the averaged (across rats) response on that trial.

(2) Traces below heat maps represent the response to infusion averaged first across trials for each rat and then across all rats.

(3) Insets represent the average z-score across the infusion period averaged first across all trials for each rat and then across all rats.

(b) I did struggle with the correlation analyses, for two reasons.

(i) First, the key finding here is that the dopamine response to intraoral sucrose is suppressed by taste aversion. So, this will significantly restrict the range of dopamine transients, making interpretation of the correlations difficult.

The overall hypothesis is that the dopamine response would correlate with the valence of a taste stimulus – even and especially when the stimulus remained constant but its valence changed. We inferred valence from the behavioral reactivity to the stimulus – reasoning that an appetitive taste will evoke minimal movement of the nose and paws (presumably because the animals are primarily engaging in small mouth movements associated with ingestion as shown by the seminal work of Grill and Norgren (1978) and the many studies published by the K.C. Berridge group) whereas an aversive taste will evoke significantly more movement as the rats engage in rejection responses (e.g. forelimb flails, chin rubs, etc.). When we conducted our regression analyses we endeavored to be as transparent as possible and labeled each symbol based on group (Unpaired vs Paired) and day (Conditioning vs Test). Both behavioral reactivity and dopamine responses change – but only for the Paired rats across days. In this sense, we believe the interpretation is clear. However, the Reviewer raises an important criticism that there would essentially be a floor effect with dopamine responses. We believe this is mitigated by data acquired across extinction and especially in Figure 9B. Here, the observations that dopamine responses fall to near zero but return to pre-conditioning levels in the Paired group with strong correlation between dopamine and behavioral reactivity throughout would hopefully partially allay the Reviewer’s concerns. See Part ii below for further support.

(ii) Second, the authors report correlations by combining data across groups/conditions. I understand why the authors have done this, but it does risk obscuring differences between the groups. So, my question is: what happens to this trend when the correlations are computed separately for each group? I suspect other readers will share the same question. I think reporting these separate correlations would be very helpful for the field -

regardless of the outcome.

To address this concern, we performed separate regression analyses for Paired and Unpaired rats and provide the table below to detail results where data were combined across groups or separated. Expectedly, all analyses in Paired rats indicated a significant inverse relationship between dopamine and behavioral reactivity. Afterall, it is only in this group where behavioral reactivity to the taste stimulus changes as function of conditioning. Perhaps even more striking is that in almost all comparisons, even when restricting the regression analysis to Unpaired rats, we still observed a significant inverse relationship between dopamine and behavioral reactivity in most experiments. We have outlined the separated correlations below (asterisks denote slopes significantly different from 0; * p<0.05; ** p<0.01; *** p<0.005; **** p<0.001):

Author response table 1.

(4) Figure 1A is not as helpful as it might be. I do think readers would expect a more precise reporting of GCaMP expression in TH+ and TH- neurons. I also note that many of the nuances in terms of compartmentalisation of dopamine signalling discussed above apply to ventral tegmental area dopamine neurons (e.g. medial v lateral) and this is worth acknowledging when interpreting t

Others have reported (Choi et al., 2020) and quantified (Hsu et al., 2020) GCaMP6f expression in TH+ neurons. While we didn’t report these quantifications, our observations were very much in line with previous quantifications from our laboratory (Hsu et al. 2020).

We agree that we should elaborate on VTA subregional differences and have answered this response above (See responses to Reviewer 1 Weakness #1 and Reviewer 2 Weakness #2).

Reviewer #3 (Public review):

Summary:

This study helps to clarify the mixed literature on dopamine responses to aversive stimuli. While it is well accepted that dopamine in the ventral striatum increases in response to various rewarding and appetitive stimuli, aversive stimuli have been shown to evoke phasic increases or decreasing depending on the exact aversive stimuli, behavioral paradigm, and/or dopamine recording method and location examined. Here the authors use a well-designed set of experiments to show differential responses to an appetitive primary reward (sucrose) that later becomes a conditioned aversive stimulus (sucrose previously paired with lithium chloride in a conditioned taste aversion paradigm). The results are interesting and add valuable data to the question of how the mesolimbic dopamine system encodes aversive stimuli, however, the conclusions are strongly stated given that the current data do not necessarily align with prior conflicting data in terms of recording location, and it is not clear exactly how to interpret the generally biphasic dopamine response to the CTA-sucrose which also evolves over exposures within a single session.

Strengths:

• The authors nicely demonstrate that their two aversive stimuli examined, quinine and sucrose following CTA, evoked aversive facial expressions and paw movements that differed from those following rewarding sucrose to support that the stimuli experienced by the rats differ in valence.

• Examined dopamine responses to the exact same sensory stimuli conditioned to have opposing valences, avoiding standard confounds of appetitive and aversive stimuli being sensed by different sensory modalities (i.e., sweet taste vs. electric shock)

• The authors examined multiple measurements of dopamine activity - cell body calcium (GCaMP6f) in midbrain and release in NAc (Grab-DA2h), which is useful as the prior mixed literature on aversive dopamine responses comes from a variety of recording methods.

• Correlations between sucrose preference and dopamine signals demonstrate behavioral relevance of the differential dopamine signals.

• The delayed testing experiment in Figure 7 nicely controls for the effect of time to demonstrate that the "rewarding" dopamine response to sucrose only recovers after multiple extinction sucrose exposures to extinguish the CTA.

Weaknesses for consideration:

(1) Regional differences in dopamine signaling to aversive stimuli are mentioned in the introduction and discussion. For instance, the idea that dopamine encodes salience is strongly argued against in the discussion, but the paper cited as arguing for that (Kutlu et al. 2021) is recording from the medial core in mice. Given other papers cited in the text about the regional differences in dopamine signaling in the NAc and from different populations of dopamine neurons in midbrain, it's important to mention this distinction wrt to salience signaling. Relatedly, the text says that the lateral NAc shell was targeted for accumbens recordings, but the histology figure looks like the majority of fibers were in the anterior lateral core of NAc. For the current paper to be a convincing last word on the issue, it would be extremely helpful to have similar recordings done in other parts of the NAc to do a more thorough comparison against other studies.

As the Reviewer notes, NAc dopamine recordings were aimed at the lateral NAc shell. It is possible that some dopamine neurons lying within the anterior lateral core were recorded. Fiber photometry and the size of the fiber optics cannot definitively identify the precise location and number of dopamine neurons from which we recorded. Still, recording sites did not systematically differ between groups. Further, the within-subjects design helps to mitigate any potential biases for one subregion over another. The results presented in the manuscript strongly support a valence code. It is difficult to be the ‘last word’ on this topic and we suspect debate will continue. We used taste stimuli for appetitive and aversive stimuli – whereas many in the field will continue to use other noxious stimuli (e.g. foot shock) that likely recruit different circuits en route to the VTA. And there may very well be a different regional profile for dopamine signaling with different noxious stimuli. Moreover, we used intraoral infusion to avoid confounds of stimulus avoidance and competing motivations (e.g. food or fluid deprivation). We believe that this is one of the most important and unique features of our report. Recent work supports a role for phasic increases in dopamine in avoidance of noxious stimuli (Jung et al., 2024) and it will be critical for the field to reflect on the differences between avoidance and aversion. Moreover, in ongoing studies we aspire to fully survey dopamine signaling in conditioned taste aversion across the medial-lateral and dorsal-ventral axes of the VTA and NAc.

(2) Dopamine release in the NAc never dips below baseline for the conditioned sucrose. Is it possible to really consider this as a signal for valence per se, as opposed to it being a weaker response relative to the original sucrose response?

Indeed, NAc dopamine release to intraoral quinine nor aversive sucrose doesn’t dip below baseline but rather dopamine binding doesn’t change from pre-infusion baseline levels. It should be noted that VTA dopamine cell body activity does indeed dip below baseline in response to aversive sucrose. Moreover, using fast-scan cyclic voltammetry, we showed that dopamine release dips below baseline in the NAc dorsomedial shell in response to intraoral quinine (Roitman et al., 2008). The differences across recording sites may reflect regional differences but they may also reflect differences in recording approaches. GrabDA2h, used here, has relatively slow kinetics that may obscure dips below baseline (see response Weakness# 8 below).

(3) Related to this, the main measure of the dopamine signal here, "mean z-score," obscures the temporal dynamics of the aversive dopamine response across a trial. This measure is used to claim that sucrose after CTA is "suppressing" dopamine neuron activity and release, which is true relative to the positive valence sucrose response. However, both GRAB-DA and cell-body GCaMP measurements show clear increases after onset of sucrose infusion before dipping back to baseline or slightly below in the average of all example experiments displayed. One could point to these data to argue either that aversive stimuli cause phasic increases in dopamine (due to the initial increase) or decreases (due to the delayed dip below baseline) depending on the measurement window. Some discussion of the dynamics of the response and how it relates to the prior literature would be useful.

We have used mean z-score to do much of our quantitative analyses but the Reviewer raises the intriguing possibility that we are masking an initial increase in dopamine release and VTA DA activity evoked by aversive taste by doing so. We included the heat maps in the manuscript to be as transparent as possible about the time course of dopamine responses – both within a trial and across trials. The Reviewer’s point prompted us to reflect further on the heat maps and recognize that trials early in the session often showed a brief increase in dopamine for aversive sucrose but this response dissipated (NAc dopamine release) or flipped (VTA DA cell body activity) over trials. We now quantitatively characterize this feature by looking at the timecourse of dopamine responses in each third of the trials (1-10, 11-20, 21-30; see Author response images 1,2 and 3). As we infer the valence of the stimulus from nose and paw movements (behavioral reactivity), it is especially striking that we a similar timecourse for changes in behavior. Collectively, the data may reflect an updating process that is relatively slow and requires experience of the stimulus in a new (aversive) state – that is, a model-free process. While our experiments were not designed to test the updating of dopamine responses and discern their participation in model-based versus model-free learning processes – another debate in the dopamine field (Cone et al., 2016; Deserno et al., 2021)– the data reflect a model-free process. This is further supported in the experiment involving multiple conditioning sessions, where dopamine ‘dips’ are observed in trials 1-10 on Conditioning Day 3 and Extinction Day 1 when the new value of sucrose has been established. Finally, the relatively slow updating of the value of sucrose is reflected in older literature using a continuous intraoral infusion. Using this approach, rats began rejecting the saccharin infusion only after ~2min rather than immediately (Schafe et al., 1998; Schafe and Bernstein, 1996; Wilkins and Bernstein, 2006).   

Author response image 1.

Author response image 2.

Author response image 3.

(4) Would this delayed below-baseline dip be visible with a shorter infusion time?

While our experiments did not explore this parameter, it would be interesting to parametrically vary infusion duration times and examine differences in dopamine responses. However, we believe the most parsimonious explanation is that the ‘dip’ in VTA cell body activity develops as a function of the slow updating of the value of sucrose reflective of a model-free process. We recognize that this is mere speculation.

(5) Does the max of the increase or the dip of the decrease better correlate with the behavioral measures of aversion (orofacial, paw movements) or sucrose preference than "mean z-score" measure used here?

It seems plausible that finding the most extreme value from baseline could better correlate to behavioral measures. Time courses to max increase and max decrease are different. Moreover, with appetitive sucrose, there are often multiple transients that occur throughout a single intraoral infusion. Coupled with a noisy time course for individual components of behavioral reactivity, we determined that averaging data across the whole infusion period (i.e. mean z-score) was the most objective way we could analyze the dopamine and behavioral responses to taste stimuli.

(6) The authors argue strongly in the discussion against the idea that dopamine is encoding "salience." Could this initial peak (also seen in the first few trials of quinine delivery, fig 1c color plot) be a "salience" response?

Our response above to the potential for ‘mixed’ dopamine responses to aversive sucrose led to additional analyses that support a slow updating of both behavior and dopamine to the new, aversive value of sucrose. Quinine is innately aversive and thus the Reviewer rightly points out that even here we observe an increase in dopamine release evoked by quinine on the first few trials (as observed in the heat map). We’d like to note, though, that the order of stimulus exposure was counterbalanced across rats. In those rats first receiving a sucrose session, quinine initially caused a modest increase in dopamine release during the first 10 trials (which is more pronounced in the first 2 trials). In the subsequent 2 blocks of 10 trials, no such increase was observed. Interestingly, in rats for which quinine was their first stimulus, we did not see an increase in dopamine release on the first few trials (see Author response image 4). We speculate that the initial sucrose session required the value of intraoral infusions to be updated when quinine was delivered to these rats and that, once more, the updating process may be slow and akin to a model-free process. This analysis, at present, is underpowered but will direct future attention in follow-up work.

Author response image 4.

(7) Related to this, the color plots showing individual trials show a reduction in the increases to positive valence sucrose across conditioning day trials and a flip from infusion-onset increase to delayed increases across test day trials. This evolution across days makes it appear that the last few conditioning day trials would be impossible to discriminate from the first few test day trials in the CTA-paired. Presumably, from strength of CTA as a paradigm, the sucrose is already aversive to the animals at the first trial of test day. Why do the authors think the response evolves across this session?

As the Reviewer noted, Points 3-7 are related. We have speculated that the evolving dopamine response in Paired rats across test day trials reflects a model-free process. Importantly, as in the manuscript, our additional analyses once again show a tight relationship between behavioral reactivity and the dopamine response across the test session trials. It is important to note, though, that these experiments were not designed to test if responses reflect model-free or model-based processes.

(8) Given that most of the work is using a conditioned aversive stimulus, the comparison to a primary aversive tastant quinine is useful. However, the authors saw basically no dopamine response to a primary aversive tastant quinine (measured only with GRAB-DA) and saw less noticeable decreases following CTA for NAc recordings with GRAB-DA2h than with cell body GCaMP. Given that they are using the high-affinity version of the GRAB sensor, this calls into question whether this is a true difference in release vs. soma activity or issue of high affinity release sensor making decreases in dopamine levels more difficult to observe.

We share the same speculation as the Reviewer. Using fast-scan cyclic voltammetry, albeit measuring dopamine concentration in the dorsomedial shell, we observed a clear decrease from baseline with intraoral infusions of quinine (Roitman et al., 2008). Using fiber photometry here, the Reviewer and we note that GRAB_DA2h is a high-affinity (i.e., EC50: 7nM) dopamine sensor with relatively long off-kinetics (i.e., t1/2 decay time: 7300ms) (Labouesse et al., 2020). It may therefore be much more difficult to observe decreases (below baseline) using this sensor. The publication of new dopamine sensors - with lower affinity, faster kinetics, and greater dynamic range (Zhuo et al., 2024) – introduces opportunities for comparison and the greater potential for capturing decreases below baseline. Due to the poorer kinetics associated with GRAB_DA2h, we would not assert that direct comparisons between the GCaMP- and GRAB-based signals observed here represent true differences between somatic and terminal activity.

References

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Cone JJ, Fortin SM, McHenry JA, Stuber GD, McCutcheon JE, Roitman MF. 2016. Physiological state gates acquisition and expression of mesolimbic reward prediction signals. Proc Natl Acad Sci U S A 113. doi:10.1073/pnas.1519643113

de Jong JW, Afjei SA, Pollak Dorocic I, Peck JR, Liu C, Kim CK, Tian L, Deisseroth K, Lammel S. 2019. A Neural Circuit Mechanism for Encoding Aversive Stimuli in the Mesolimbic Dopamine System. Neuron 101. doi:10.1016/j.neuron.2018.11.005

Deserno L, Moran R, Michely J, Lee Y, Dayan P, Dolan RJ. 2021. Dopamine enhances model-free credit assignment through boosting of retrospective model-based inference. Elife 10. doi:10.7554/eLife.67778

Hsu TM, Bazzino P, Hurh SJ, Konanur VR, Roitman JD, Roitman MF. 2020. Thirst recruits phasic dopamine signaling through subfornical organ neurons. Proc Natl Acad Sci U S A 117:30744–30754. doi:10.1073/PNAS.2009233117/-/DCSUPPLEMENTAL

Jung K, Krüssel S, Yoo S, An M, Burke B, Schappaugh N, Choi Y, Gu Z, Blackshaw S, Costa RM, Kwon HB. 2024. Dopamine-mediated formation of a memory module in the nucleus accumbens for goal-directed navigation. Nat Neurosci. doi:10.1038/s41593-024-01770-9

Labouesse MA, Cola RB, Patriarchi T. 2020. GPCR-based dopamine sensors—A detailed guide to inform sensor choice for in vivo imaging. Int J Mol Sci. doi:10.3390/ijms21218048

Lammel S, Hetzel A, Häckel O, Jones I, Liss B, Roeper J. 2008. Unique Properties of Mesoprefrontal Neurons within a Dual Mesocorticolimbic Dopamine System. Neuron 57. doi:10.1016/j.neuron.2008.01.022

McCutcheon JE, Ebner SR, Loriaux AL, Roitman MF, Tobler PN. 2012. Encoding of aversion by dopamine and the nucleus accumbens. Front Neurosci 6. doi:10.3389/fnins.2012.00137

Morales I, Berridge KC. 2020. ‘Liking’ and ‘wanting’ in eating and food reward: Brain mechanisms and clinical implications. Physiol Behav. doi:10.1016/j.physbeh.2020.113152

Roitman MF, Wheeler RA, Wightman RM, Carelli RM. 2008. Real-time chemical responses in the nucleus accumbens differentiate rewarding and aversive stimuli. Nature Neuroscience 2008 11:12 11:1376–1377. doi:10.1038/nn.2219

Schafe GE, Bernstein IL. 1996. Forebrain contribution to the induction of a brainstem correlate of conditioned taste aversion: I. The amygdala. Brain Res 741. doi:10.1016/S0006-8993(96)00906-7

Schafe GE, Thiele TE, Bernstein IL. 1998. Conditioning method dramatically alters the role of amygdala in taste aversion learning. Learning and Memory 5. doi:10.1101/lm.5.6.481

Wilkins EE, Bernstein IL. 2006. Conditioning method determines patterns of c-fos expression following novel taste-illness pairing. Behavioural Brain Research 169. doi:10.1016/j.bbr.2005.12.006

Yuan L, Dou YN, Sun YG. 2021. Topography of reward and aversion encoding in the mesolimbic dopaminergic system. Journal of Neuroscience 39. doi:10.1523/JNEUROSCI.0271-19.2019

Zhuo Y, Luo B, Yi X, Dong H, Miao X, Wan J, Williams JT, Campbell MG, Cai R, Qian T, Li F, Weber SJ, Wang L, Li B, Wei Y, Li G, Wang H, Zheng Y, Zhao Y, Wolf ME, Zhu Y, Watabe-Uchida M, Li Y. 2024. Improved green and red GRAB sensors for monitoring dopaminergic activity in vivo. Nat Methods 21. doi:10.1038/s41592-023-02100-w

Author Response:

We greatly appreciate invaluable and constructive comments from Editors and Reviewers. We also thank for their time and patience. We are pleased for our manuscript to have been assessed valuable and solid.

One of most critical concerns was a possible involvement of Ca2+ channel inactivation in the strong paired pulse depression (PPD). Meanwhile, we have already measured total (free plus buffered) calcium increments induced by each of first four APs in a 40 Hz train at axonal boutons of prelimbic layer 2/3 pyramidal cells. We found that first four Ca2+ increments were not different each other, arguing against possible contribution of Ca2+ channel inactivation to PPD. Please see our reply to the 2nd issue in the Weakness section of Reviewer #3.

The second critical issue was on the definition of ‘vesicular probability’. Previously, vesicular probability (pv) has been used with reference to the releasable vesicle pool which includes not only tightly docked vesicles but also reluctant vesicles. On the other hand, the meaning of pv in the present study was release probability of tightly docked vesicles. We clarified this point in our replies to the 1st issues in the Weakness sections of Reviewer #2 and Reviewer #3.

To other Reviews’ comments, we below described our point-by-point replies.

Reviewer #2 (Public review):

Summary:

Shin et al aim to identify in a very extensive piece of work a mechanism that contributes to dynamic regulation of synaptic output in the rat cortex at the second time scale. This mechanism is related to a new powerful model is well versed to test if the pool of SV ready for fusion is dynamically scaled to adjust supply demand aspects. The methods applied are state-of-the-art and both address quantitative aspects with high signal to noise. In addition, the authors examine both excitatory output onto glutamatergic and GABAergic neurons, which provides important information on how general the observed signals are in neural networks, The results are compellingly clear and show that pool regulation may be predominantly responsible. Their results suggests that a regulation of release probability, the alternative contender for regulation, is unlikely to be involved in the observed short term plasticity behavior (but see below). Besides providing a clear analysis pof the underlying physiology, they test two molecular contenders for the observed mechanism by showing that loss of Synaptotagmin7 function and the role of the Ca dependent phospholipase activity seems critical for the short term plasticity behavior. The authors go on to test the in vivo role of the mechanism by modulating Syt7 function and examining working memory tasks as well as overall changes in network activity using immediate early gene activity. Finally, they model their data, providing strong support for their interpretation of TS pool occupancy regulation.

Strengths:

This is a very thorough study, addressing the research question from many different angles and the experimental execution is superb. The impact of the work is high, as it applies recent models of short term plasticity behavior to in vivo circuits further providing insights how synapses provide dynamic control to enable working memory related behavior through nonpermanent changes in synaptic output.

Weaknesses:

While this work is carefully examined and the results are presented and discussed in a detailed manner, the reviewer is still not fully convinced that regulation of release provability is not a putative contributor to the observed behavior. No additional work is needed but in the moment I am not convinced that changes in release probability are not in play. One solution may be to extend the discussion of changes in rules probability as an alternative.

Quantal content (m) depends on n * pv, where n = RRP size and pv =vesicular release probability. The value for pv critically depends on the definition of RRP size. Recent studies revealed that docked vesicles have differential priming states: loosely or tightly docked state (LS or TS, respectively). Because the RRP size estimated by hypertonic solution or long presynaptic depolarization is larger than that by back extrapolation of a cumulative EPSC plot (Moulder & Mennerick, 2005; Sakaba, 2006) in glutamatergic synapses, the former RRP (denoted as RRPhyper) may encompass not only AP-evoked fast-releasing vesicles (TS vesicle) but also reluctant vesicles (LS vesicles). Because we measured pv based on AP-evoked EPSCs such as strong paired pulse depression (PPD) and associated failure rates, pv in the present study denotes vesicular fusion probability of TS vesicles not that of LS plus TS vesicles.

Recent studies suggest that release sites are not fully occupied by TS vesicles in the baseline (Miki et al., 2016; Pulido and Marty, 2018; Malagon et al., 2020; Lin et al., 2022). Instead the occupancy (pocc) by TS vesicles is subject to dynamic regulation by reversible rate constants (denoted by k1 and b1, respectively). The number of TS vesicles (n) can be factored into the number of release sites (N) and pocc, among which N is a fixed parameter but pocc depends on k1/(k1+b1) under the framework of the simple refilling model (see Methods). Because these refilling rate constants are regulated by Ca2+ (Hosoi, et al., 2008), pocc is not a fixed parameter. Therefore, release probability should be re-defined as pocc x pv. In this regard, the increase in release probability is a major player in STF. Our study asserts that STF by 2.3 times can be attributed to an increase in pocc rather than pv, because pv is close to unity (Fig. S8). Moreover, strong PPD was observed not only in the baseline but also at the early and in the middle of a train (Fig. 2 and 7) and during the recovery phase (Fig. 3), arguing against a gradual increase in pv of reluctant vesicles.

If the Reviewer meant vesicular release or fusion probability (pv) by ‘release provability’, pv (of TS vesicles) is not a major player in STF, because the baseline pv is already higher than 0.8 even if it is most parsimoniously estimated (Fig. 2). Moreover, considering very high refilling rate (23/s), the high double failure rate cannot be explained without assuming that pv is close to unity (Fig. S8).

Conventional models for facilitation assume a post-AP residual Ca2+-dependent step increase in pv of RRP (Dittman et al., 2000) or reluctant vesicles (Turecek et al., 2016). Given that pv of TS vesicles is close to one, an increase in pv of TS vesicles cannot account for facilitation. The possibility for activity-dependent increase in fusion probability of LS vesicles (denoted as pv,LS) should be considered in two ways depending on whether LS and TS vesicles reside in distinct pools or in the same pool. Notably, strong PPD at short ISI implies that pv,LS is near zero at the resting state. Whereas LS vesicles do not contribute to baseline transmission, short-term facilitation (STF) may be mediated by cumulative increase in pv, LS that reside in a distinct pool. Because the increase in pv,LS during facilitation recruits new release sites (increase in N), the variance of EPSCs should become larger as stimulation frequency increases, resulting in upward deviation from a parabola in the V-M plane, as shown in recent studies (Valera et al., 2012; Kobbersmed et al., 2020). This prediction is not compatible with our results of V-M analysis (Fig. 3), showing that EPSCs during STF fell on the same parabola regardless of stimulation frequencies. Therefore, it is unlikely that an increase in fusion probability of reluctant vesicles residing in a distinct release pool mediates STF in the present study.

For the latter case, in which LS and TS vesicles occupy in the same release sites, it is hard to distinguish a step increase in fusion probability of LS vesicles from a conversion of LS vesicles to TS. Nevertheless, our results do not support the possibility for gradual increase in pv,LS that occurs in parallel with STF. Strong PPD, indicative of high pv, was consistently found not only in the baseline (Fig. 2 and Fig. S6) but also during post-tetanic augmentation phase (Fig. 3D) and even during the early development of facilitation (Fig. 2D-E and Fig. 7), arguing against gradual increase in pv,LS. One may argue that STF may be mediated by a drastic step increase of pv,LS from zero to one, but it is not distinguishable from conversion of LS to TS vesicles.

To address the reviewer’s concern, we will incorporate these perspectives into the discussion and further clarify the reasoning behind our conclusions.

<References>

Moulder KL, Mennerick S (2005) Reluctant vesicles contribute to the total readily releasable pool in glutamatergic hippocampal neurons. J Neurosci 25:3842–3850.

Sakaba, T (2006) Roles of the fast-releasing and the slowly releasing vesicles in synaptic transmission at the calyx of Held. J Neurosci 26(22): 5863-5871.

Fig 3 I am confused about the interpretation of the Mean Variance analysis outcome. Since the data points follow the curve during induction of short term plasticity, aren't these suggesting that release probability and not the pool size increases? Related, to measure the absolute release probability and failure rate using the optogenetic stimulation technique is not trivial as the experimental paradigm bias the experiment to a given output strength, and therefore a change in release probability cannot be excluded.

Under the recent definition of release probability, it can be factored into pv and pocc, which are fusion probability of TS vesicles and the occupancy of release sites by TS vesicles, respectively. With this regard, our interpretation of the Variance-Mean results is consistent with conventional one: different data points along a parabola represent a change in release probability (= pocc x pv). Our novel finding is that the increase in release probability should be attributed to an increase in pocc, not to that in pv.

Fig4B interprets the phorbol ester stimulation to be the result of pool overfilling, however, phorbol ester stimulation has also been shown to increase release probability without changing the size of the readily releasable pool. The high frequency of stimulation may occlude an increased paired pulse depression in presence of OAG, which others have interpreted in mammalian synapses as an increase in release probability.

To our experience in the calyx of Held synapses, OAG, a DAG analogue, increased the fast releasing vesicle pool (FRP) size (Lee JS et al., 2013), consistent with our interpretation (pool overfilling). Once the release sites are overfilled in the presence of OAG, it is expected that the maximal STF (ratio of facilitated to baseline EPSCs) becomes lower as long as the number of release sites (N) are limited. As aforementioned, the baseline pv is already close to one, and thus it cannot be further increased by OAG. Instead, the baseline pocc seems to be increased by OAG.

<Reference>

Lee JS, et al., Superpriming of synaptic vesicles after their recruitment to the readily releasable pool. Proc Natl Acad Sci U S A, 2013. 110(37): 15079-84.

The literature on Syt7 function is still quite controversial. An observation in the literature that loss of Syt7 function in the fly synapse leads to an increase of release probability. Thus the observed changes in short term plasticity characteristics in the Syt7 KD experiments may contain a release probability component. Can the authors really exclude this possibility? Figure 5 shows for the Syt7 KD group a very prominent depression of the EPSC/IPSC with the second stimulus, particularly for the short interpulse intervals, usually a strong sign of increased release probability, as lack of pool refilling can unlikely explain the strong drop in synaptic output.

The reviewer raises an interesting point regarding the potential link between Syt7 KD and increased initial pv, particularly in light of observations in Drosophila synapses (Guan et al., 2020; Fujii et al., 2021), in which Syt7 mutants exhibited elevated initial pv. However, it is important to note that these findings markedly differ from those in mammalian systems, where the role of Syt7 in regulating initial pv has been extensively studied. In rodents, consistent evidence indicates that Syt7 does not significantly affect initial pv, as demonstrated in several studies (Jackman et al., 2016; Chen et al., 2017; Turecek and Regehr, 2018). Furthermore, in our study of excitatory synapses in the mPFC layer 2/3, we observed an initial pv already near its maximal level, approaching a value of 1. Consequently, it is unlikely that the loss of Syt7 could further elevate the initial pv. Instead, such effects are more plausibly explained by alternative mechanisms, such as alterations in vesicle replenishment dynamics, rather than a direct influence on pv.

<References>

Chen, C., et al., Triple Function of Synaptotagmin 7 Ensures Efficiency of High-Frequency Transmission at Central GABAergic Synapses. Cell Rep, 2017. 21(8): 2082-2089.

Fujii, T., et al., Synaptotagmin 7 switches short-term synaptic plasticity from depression to facilitation by suppressing synaptic transmission. Scientific reports, 2021. 11(1): 4059.

Guan, Z., et al., Drosophila Synaptotagmin 7 negatively regulates synaptic vesicle release and replenishment in a dosage-dependent manner. Elife, 2020. 9: e55443.

Jackman, S.L., et al., The calcium sensor synaptotagmin 7 is required for synaptic facilitation. Nature, 2016. 529(7584): 88-91.

Turecek, J. and W.G. Regehr, Synaptotagmin 7 mediates both facilitation and asynchronous release at granule cell synapses. Journal of Neuroscience, 2018. 38(13): 3240-3251.

Reviewer #3 (Public review):

Summary:

The report by Shin, Lee, Kim, and Lee entitled "Progressive overfilling of readily releasable pool underlies short-term facilitation at recurrent excitatory synapses in layer 2/3 of the rat prefrontal cortex" describes electrophysiological experiments of short-term synaptic plasticity during repetitive presynaptic stimulation at synapses between layer 2/3 pyramidal neurons and nearby target neurons. Manipulations include pharmacological inhibition of PLC and actin polymerization, activation of DAG receptors, and shRNA knockdown of Syt7. The results are interpreted as support for the hypothesis that synaptic vesicle release sites are vacant most of the time at resting synapses (i.e., p_occ is low) and that facilitation (and augmentation) components of short-term enhancement are caused by an increase in occupancy, presumably because of acceleration of the transition from not-occupied to occupied. The report additionally describes behavioural experiments where trace fear conditioning is degraded by knocking down syt7 in the same synapses.

Strengths:

The strength of the study is in the new information about short-term plasticity at local synapses in layer 2/3, and the major disruption of a memory task after eliminating short-term enhancement at only 15% of excitatory synapses in a single layer of a small brain region. The local synapses in layer 2/3 were previously difficult to study, but the authors have overcome a number of challenges by combining channel rhodopsins with in vitro electroporation, which is an impressive technical advance.

Weaknesses:

The question of whether or not short-term enhancement causes an increase in p_occ (i.e., "readily releasable pool overfilling") is important because it cuts to the heart of the ongoing debate about how to model short term synaptic plasticity in general. However, my opinion is that, in their current form, the results do not constitute strong support for an increase in p_occ, even though this is presented as the main conclusion. Instead, there are at least two alternative explanations for the results that both seem more likely. Neither alternative is acknowledged in the present version of the report.

The evidence presented to support overfilling is essentially two-fold. The first is strong paired pulse depression of synaptic strength when the interval between action potentials is 20 or 25 ms, but not when the interval is 50 ms. Subsequent stimuli at frequencies between 5 and 40 Hz then drive enhancement. The second is the observation that a slow component of recovery from depression after trains of action potentials is unveiled after eliminating enhancement by knocking down syt7. Of the two, the second is predicted by essentially all models where enhancement mechanisms operate independently of release site depletion - i.e., transient increases in p_occ, p_v, or even N - so isn't the sort of support that would distinguish the hypothesis from alternatives (Garcia-Perez and Wesseling, 2008, https://doi.org/10.1152/jn.01348.2007).

The apparent discrepancy in interpretation of post-tetanic augmentation between the present and previous papers [Sevens Wesseling (1999), Garcia-Perez and Wesseling (2008)] is an important issue that should be clarified. We noted that different meanings of ‘vesicular release probability’ in these papers are responsible for the discrepancy. We will add an explanation to Discussion on the difference in the meaning of ‘vesicular release probability’ between the present study and previous studies [Sevens Wesseling (1999), Garcia-Perez and Wesseling (2008)]. In summary, the pv in the present study was used for vesicular release probability of TS vesicles, while previous studies used it as vesicular release probability of vesicles in the RRP, which include LS and TS vesicles. Accordingly, pocc in the present study is occupancy of release sites by TS vesicles.

Not only double failure rate but also other failure rates upon paired pulse stimulation were best fitted at pv close to 1 (Fig. S8 and associated text). Moreover, strong PPD, indicating release of vesicles with high pv, was observed not only at the beginning of a train but also in the middle of a 5 Hz train (Fig. 2D), during the augmentation phase after a 40 Hz train (Fig 3D), and in the recovery phase after three pulse bursts (Fig. 7). Given that pv is close to 1 throughout the EPSC trains and that N does not increase during a train (Fig. 3), synaptic facilitation can be attained only by the increase in pocc (occupancy of release sites by TS vesicles). In addition, it should be noted that Fig. 7 demonstrates strong PPD during the recovery phase after depletion of TS vesicles by three pulse bursts, indicating that recovered vesicles after depletion display high pv too. Knock-down of Syt7 slowed the recovery of TS vesicles after depletion of TS vesicles, highlighting that Syt7 accelerates the recovery of TS vesicles following their depletion.

As addressed in our reply to the first issue raised by Reviewer #2 and the third issue raised by Reviewer #3, our results do not support possibilities for recruitment of new release sites (increase in N) having low pv or for a gradual increase in pv of reluctant vesicles during short-term facilitation.  

<Following statement will be added to _Discussion_ in the revised manuscript>

Previous studies suggested that an increase in pv is responsible for post-tetanic augmentation (Stevens and Wesseling, 1999; Garcia-Perez and Wesseling, 2008) by observing invariance of the RRP size after tetanic stimulation. In these studies, the RRP size was estimated by hypertonic sucrose solution or as the sum of EPSCs evoked 20 Hz/60 pulses train (denoted as ‘RRPhyper’). Because reluctant vesicles (called LS vesicles) can be quickly converted to TS vesicles (16/s) and are released during a train (Lee et al., 2012), it is likely that the RRP size measured by these methods encompasses both LS and TS vesicles. In contrast, we assert high pv based on the observation of strong PPD and failure rates upon paired stimulations at ISI of 20 ms (Fig. 2 and Fig. S8). Given that single AP-induced vesicular release occurs from TS vesicles but not from LS vesicles, pv in the present study indicates the fusion probability of TS vesicles. From the same reasons, pocc denotes the occupancy of release sites by TS vesicles. Note that our study does not provide direct clue whether release sites are occupied by LS vesicles that are not tapped by a single AP, although an increase in the LS vesicle number may accelerate the recovery of TS vesicles. As suggested in Neher (2024), even if the number of LS plus TS vesicles are kept constant, an increase in pocc (occupancy by TS vesicles) would be interpreted as an increase in ‘vesicular release probability’ as in the previous studies (Stevens and Wesseling (1999); Garcia-Perez and Wesseling (2008)) as long as it was measured based on RRPhyper.

Regarding the paired pulse depression: The authors ascribe this to depletion of a homogeneous population of release sites, all with similar p_v. However, the details fit better with the alternative hypothesis that the depression is instead caused by quickly reversing inactivation of Ca2+ channels near release sites, as proposed by Dobrunz and Stevens to explain a similar phenomenon at a different type of synapse (1997, PNAS,<br /> https://doi.org/10.1073/pnas.94.26.14843). The details that fit better with Ca2+ channel inactivation include the combination of the sigmoid time course of the recovery from depression (plotted backwards in Fig1G,I) and observations that EGTA (Fig2B) increases the paired-pulse depression seen after 25 ms intervals. That is, the authors ascribe the sigmoid recovery to a delay in the activation of the facilitation mechanism, but the increased paired pulse depression after loading EGTA indicates, instead, that the facilitation mechanism has already caused p_r to double within the first 25 ms (relative to the value if the facilitation mechanism was not active). Meanwhile, Ca2+ channel inactivation would be expected to cause a sigmoidal recovery of synaptic strength because of the sigmoidal relationship between Ca2+-influx and exocytosis (Dodge and Rahamimoff, 1967, https://doi.org/10.1113/jphysiol.1967.sp008367).

The Ca2+-channel inactivation hypothesis could probably be ruled in or out with experiments analogous to the 1997 Dobrunz study, except after lowering extracellular Ca2+ to the point where synaptic transmission failures are frequent. However, a possible complication might be a large increase in facilitation in low Ca2+ (Fig2B of Stevens and Wesseling, 1999, https://doi.org/10.1016/s0896-6273(00)80685-6).

We appreciate the reviewer's thoughtful comment regarding the potential role of Ca2+ channel inactivation in the observed paired-pulse depression (PPD). As noted by the Reviewer, the Dobrunz and Stevens (1997) suggested that the high double failure rate at short ISIs in synapses exhibiting PPD can be attributed to Ca2+ channel inactivation. This interpretation seems to be based on a premise that the number of RRP vesicles are not varied trial-by-trial. The number of TS vesicles, however, can be dynamically regulated depending on the parameters k1 and b1, as shown in Fig. S8, implying that the high double failure rate at short ISIs cannot be solely attributed to Ca2+ channel inactivation. Nevertheless, we acknowledge the possibility that Ca2+ channel inactivation may contribute to PPD, and therefore, we have further investigated this possibility. Specifically, we measured action potential (AP)-evoked Ca2+ transients at individual axonal boutons of layer 2/3 pyramidal cells in the mPFC using two-dye ratiometry techniques. Our analysis revealed no evidence for Ca2+ channel inactivation during a 40 Hz train of APs. This finding indicates that voltage-gated Ca2+ channel inactivation is unlikely to contribute to the pronounced PPD.

Author response image 1 below shows how we measured the total Ca2+ increments at axonal boutons. First we estimated endogenous Ca2+-binding ratio from analyses of single AP-induced Ca2+ transients at different concentrations of Ca2+ indicator dye (panels A to E). And then, using the Ca2+ buffer properties, we converted free [Ca2+] amplitudes to total calcium increments for the first four AP-evoked Ca2+ transients in a 40 Hz train (panels G-I). We will incorporate these results into the revised version of reviewed preprint to provide evidence against the Ca2+ channel inactivation.

Author response image 1.

On the other hand, even if the paired pulse depression is caused by depletion of release sites rather than Ca2+-channel inactivation, there does not seem to be any support for the critical assumption that all of the release sites have similar p_v. And indeed, there seems to be substantial emerging evidence from other studies for multiple types of release sites with 5 to 20-fold differences in p_v at a wide variety of synapse types (Maschi and Klyachko, eLife, 2020, https://doi.org/10.7554/elife.55210; Rodriguez Gotor et al, eLife, 2024, https://doi.org/10.7554/elife.88212 and refs. therein). If so, the paired pulse depression could be caused by depletion of release sites with high p_v, whereas the facilitation could occur at sites with much lower p_v that are still occupied. It might be possible to address this by eliminating assumptions about the distribution of p_v across release sites from the variance-mean analysis, but this seems difficult; simply showing how a few selected distributions wouldn't work - such as in standard multiple probability fluctuation analyses - wouldn't add much.

We appreciate the reviewer’s insightful comments regarding the potential increase in pfusion of reluctant vesicles. It should be noted, however, that Maschi and Klyachko (2020) showed a distribution of release probability (pr) within a single active zone rather than a heterogeneity in pfusion of individual docked vesicles. Therefore both pocc and pv of TS vesicles would contribute to the pr distribution shown in Maschi and Klyachko (2020). 

The Reviewer’s concern aligns closely with the first issue raised by Reviewer #2, to which we addressed in detail. Briefly, new release site may not be recruited during facilitation or post-tetanic augmentation, because variance of EPSCs during and after a train fell on the same parabola (Fig. 3). Secondly, strong PPD was observed not only in the baseline but also during early and late phases of facilitation, indicating that vesicles with very high pv contribute to EPSC throughout train stimulations (Fig. 2, 3, and 7). These findings argue against the possibilities for recruitment of new release sites harboring low pv vesicles and for a gradual increase in fusion probability of reluctant vesicles.

To address the reviewers’ concern, we will incorporate the perspectives into Discussion and further clarify the reasoning behind our conclusions.

In any case, the large increase - often 10-fold or more - in enhancement seen after lowering Ca2+ below 0.25 mM at a broad range of synapses and neuro-muscular junctions noted above is a potent reason to be cautious about the LS/TS model. There is morphological evidence that the transitions from a loose to tight docking state (LS to TS) occur, and even that the timing is accelerated by activity. However, 10-fold enhancement would imply that at least 90 % of vesicles start off in the LS state, and this has not been reported. In addition, my understanding is that the reverse transition (TS to LS) is thought to occur within 10s of ms of the action potential, which is 10-fold too fast to account for the reversal of facilitation seen at the same synapses (Kusick et al, 2020, https://doi.org/10.1038/s41593-020-00716-1).

As the reviewer suggested, low external Ca2+ concentration can lower release probability (pr). Given that both pv and pocc are regulated by [Ca2+]i, low external [Ca2+] may affect not only pv but also pocc, both of which would contribute to low pr. Under such conditions, it would be plausible that the baseline pr becomes much lower than 0.1 due to low pv and pocc (for instance, pv decreases from 1 to 0.5, and pocc from 0.3 to 0.1, then pr = 0.05), and then pr (= pv x pocc) has a room for an increase by a factor of ten (0.5, for example) by short-term facilitation as cytosolic [Ca2+] accumulates during a train.

If pv is close to one, pr depends pocc, and thus facilitation depends on the number of TS vesicles just before arrival of each AP of a train. Thus, post-train recovery from facilitation would depend on restoration of equilibrium between TS and LS vesicles to the baseline. Even if transition between LS and TS vesicles is very fast (tens of ms), the equilibrium involved in de novo priming (reversible transitions between recycling vesicle pool and partially docked LS vesicles) seems to be much slower (13 s in Fig. 5A of Wu and Borst 1999). Thus, we can consider a two-step priming model (recycling pool -> LS -> TS), which is comprised of a slow 1st step (-> LS) and a fast 2nd step (-> TS). Under the framework of the two-step model, the slow 1st step (de novo priming step) is the rate limiting step regulating the development and recovery kinetics of facilitation. Given that on and off rate for Ca2+ binding to Syt7 is slow, it is plausible that Syt7 may contribute to short-term facilitation (STF) by Ca2+-dependent acceleration of the 1st step (as shown in Fig. 9). During train stimulation, the number of LS vesicles would slowly accumulate in a Syt7 and Ca2+-dependent manner, and this increase in LS vesicles would shift LS/TS equilibrium towards TS, resulting in STF. After tetanic stimulation, the recovery kinetics from facilitation would be limited by slow recovery of LS vesicles.

<Reference>

Wu, L.-G. and Borst J.G.G. (1999) The reduced release probability of releasable vesicles during recovery from short-term synaptic depression. Neuron, 23(4): 821-832.

Individual points:

(1) An additional problem with the overfilling hypothesis is that syt7 knockdown increases the estimate of p_occ extracted from the variance-mean analysis, which would imply a faster transition from unoccupied to occupied, and would consequently predict faster recovery from depression. However, recovery from depression seen in experiments was slower, not faster. Meanwhile, the apparent decrease in the estimate of N extracted from the mean-variance analysis is not anticipated by the authors' model, but fits well with alternatives where p_v varies extensively among release sites because release sites with low p_v would essentially be silent in the absence of facilitation.

Slower recovery from depression observed in the Syt7 knockdown (KD) synapses (Fig. 7) may results from a deficiency in activity-dependent acceleration of TS vesicle recovery. Although basal occupancy was higher in the Syt7 KD synapses, this does not indicate a faster activity-dependent recovery.

Higher baseline occupancy does not always imply faster recovery of PPR too. Actually PPR recovery was slower in Syt7 KD synapses than WT one (18.5 vs. 23/s). Under the framework of the simple refilling model (Fig. S8Aa), the baseline occupancy and PPR recovery rate are calculated as k1 / (k1 + b1) and (k1 + b1), respectively. The baseline occupancy depends on k1/b1, while the PPR recovery on absolute values of k1 and b1. Based on pocc and PPR recovery time constant of WT and KD synapses, we expect higher k1/b1 but lower values for (k1 +b1) in Syt7 KD synapses compared to WT ones.

Lower release sites (N) in Syt7-KD synapses was not anticipated. As you suggested, such low N might be ascribed to little recruitment of release sites during a train in KD synapses. But our results do not support this model. If silent release sites are recruited during a train, the variance should upwardly deviate from the parabola predicted under a fixed N (Valera et al., 2012; Kobbersmed et al. 2020). Our result was not the case (Fig. 3). In the first version of Ms, we have argued against this possibility in line 203-208.

As discussed in both the Results and Discussion sections, the baseline EPSC was unchanged by KD (Fig. S3) because of complementary changes in the number of docking sites and their baseline occupancy (Fig. 6). These findings suggest that Syt7 may be involved in maintaining additional vacant docking sites, which could be overfilled during facilitation. It remains to be determined whether the decrease in docking sites in Syt7 KD synapses is related to its specific localization of Syt7 at the plasma membrane of active zones, as proposed in previous studies (Sugita et al., 2001; Vevea et al., 2021).

(2) Figure S4A: I like the TTX part of this control, but the 4-AP part needs a positive control to be meaningful (e.g., absence of TTX).

The reason why we used 4-AP in the presence of TTX was to increase the length constant of axon fibers and to facilitate the conduction of local depolarization in the illumination area to axon terminals. The lack of EPSC in the presence of 4-AP and TTX indicates that illumination area is distant from axon terminals enough for optic stimulation-induced local depolarization not to evoke synaptic transmission. This methodology has been employed in previous studies including the work of Little and Carter (2013).

<Reference>

Little JP and Carter AG (2013) Synaptic mechanisms underlying strong reciprocal connectivity between the medial prefrontal cortex and basolateral amygdala. J Neurosci, 33(39): 15333-15342.

(3) Line 251: At least some of the previous studies that concluded these drugs affect vesicle dynamics used logic that was based on some of the same assumptions that are problematic for the present study, so the reasoning is a bit circular.

(4) Line 329 and Line 461: A similar problem with circularity for interpreting earlier syt7 studies.

(Reply to #3 and #4) We selected the target molecules as candidates based on their well-characterized roles in vesicle dynamics, and aimed to investigate what aspects of STP are affected by these molecules in our experimental context. For example, we could find that the baseline pocc and short-term facilitation (STF) are enhanced by the baseline DAG level and train stimulation-induced PLC activation, respectively. Notably, the effect of dynasore informed us that slow site clearing is responsible for the late depression of 40 Hz train EPSC. The knock-down experiments also provided us with information on the critical role of Syt7 in replenishment of TS vesicles. These approaches do not deviate from standard scientific reasoning but rather builds upon prior knowledge to formulate and test hypotheses.

Importantly, our conclusions do not rely solely on the assumption that altering the target molecule impacts synaptic transmission. Instead, our conclusions are derived from a comprehensive analysis of diverse outcomes obtained through both pharmacological and genetic manipulations. These interpretations align closely with prior literature, further validating our conclusions.

Therefore, the use of established studies to guide candidate selection and the consistency of our findings with existing knowledge do not represent a logical circularity but rather a reinforcement of the proposed mechanism through converging lines of evidence.

Author response:

We were pleased to read the positive comments regarding our manuscript and thank the reviewers and editors for the constructive feedback which we believe will be very helpful to improve the current version of the manuscript.

Prior to addressing all comments in a full response, we provide a response to three issues that were raised in this provisional plan for revision: validation of the tracking algorithm, biological replicates, and mosquito survival.

(1) Validation of the tracking algorithm:

Reviewer 2 mentions that there is "No external validation for the flight tracking algorithm using manual annotation". We will address this comment in our full response by creating a manually labelled dataset to validate our detection algorithm.

However, we would like to point out two important points:

i) Quantifying the accuracy of a detection algorithm using a manually annotated set is indeed common practice in deep/machine learning algorithms in which manually annotated data are used to train the algorithm, and another set of manually annotated data is used to validate it. However, our detection and tracking algorithm is based on conventional computer vision techniques (not using any deep learning) that have been in use for several decades. Given that these algorithms are completely transparent and deterministic (as opposed to deep learning algorithms that are difficult to dissect and are created using partly stochastic processes) it is not common practice to use human annotations for validation. However, to address Reviewer 2's comment we will provide validation metrics in our full response.

ii) We furthermore would like to note that our main metrics of interest (e.g. fraction of mosquitoes flying) only depends on accurately detecting mosquitoes and quantifying movement, its accuracy is not affected by potential identity swaps (the typical bottleneck in tracking algorithms).

(2) Replicates:

Reviewer 3 states that "Most experiments are only done with single replicates". This statement is not accurate: In Figure 2 we used 3 independent biological replicates for 4 colonies, 2 of which are Aaa and 2 are Aaf. We indeed provide additional data for 6 more colonies using a single replicate. Combined this data set comprises 588 days of continuous recordings. For Figures 3 and 4 we have 2 replicates for each perturbation experiment. For Figure 5 we provided 3 replicates for the host-seeking experiments. As outlined, the vast majority of our experiments has multiple replicates. We realize this may not have been described clearly enough in the manuscript, we will clarify this in the revised manuscript.

(3) Mosquito survival:

Below we provide survival data for the data shown in Figures 1 - 4, we will include this data as supplementary material. Overall we note here that mortality for all experiments was similar to the 'baseline' mortality we observe in our standard colony maintenance procedures. After three weeks, we typically observed that 70% of mosquitoes were still alive.

Author response image 1.

Survival curves for the data presented in Figures 1 - 4 of the main text. Day 0 indicates the day on which the BuzzWatch experiment started

Author response:

Reply to Reviewer #1 (Public Review):

The post-processing increases number of putative neoantigens. As shown in Author response image 1, this is done through data augmentation or “mutations” of individual amino acids in a sequence by their most similar amino acid in the BLOSUM62 embedding. If most of the mutations result in a positive prediction (which we binarize through a >0.5 score) the sequence changes its prediction.

Author response image 1.

Post-processing pipeline to increase the number of putative neoantigens. Sequences can either be predicted using the forward method, for which a raw score is produced, or it can be introduced to a majority-vote prediction of the ensemble prediction of similar protein sequences.

In this article, we obtain the following candidates after post-processing.

Author response table 1.

As mentioned, the prediction column shows a binary label. The full list contained 402 sequences did not include any other sequences that met the majority vote criteria.

As noted by the reviewer, the Table 3 of our original paper includes the scores of the direct prediction, which has four sequences in common with the post-processing criteria (*Pnp, *Adar, *Lrrc28 and *Nr1h2). * indicates the mutated form of the peptide, i.e neoantigen.

We selected the top 4 predicted antigens (present both by direct prediction and after post-processing; (*Pnp, *Adar, *Lrrc28 and *Nr1h2) (Wert-Carvajal et al. 2021), but we encountered difficulty in synthesizing, *Nr1h2 (Mutated Nr1h2), and thus it could not be included in the study.

We also decided to evaluate the immunogenicity of *Wiz, which was identified as a potential TNA only after postprocessing. *Wiz exhibited lower levels of immunogenicity compared to *Pnp, *Adar, and *Lrrc28. However, unlike these, *Wiz is highly expressed in the tumor, and vaccination with *Wiz provided the strongest protection levels. These findings led us to incorporate post-processingg into the NAP-CNB platform.

We chose *Herc6 as a mutated antigen predicted not to be a TNA over other candidates because its expression in the tumor was similar to that of *Wiz.

Depending on the experiment we used 4 or 5 animals per group (this will be clarify in the revised version)

The software used for statistical analysis was GraphPad Prism.

Reply to Reviewer #2 (Public Review):

This is true, binding affinity does not always predict immune responses but in most cases, high affinity peptides are immunogenic. There are of course other parameters that drive the effective priming of tumor-reactive CD8+ T cells through antigen cross-presentation, but the mechanisms of antigen presentation are yet not completely understood. High affinity peptides are desirable as good candidates in neoantigen-based vaccines.

Author Response

Reviewer #1 (Public Review):

The authors report a new bioinformatics pipeline ("SPICE") to predict pairwise cooperative binding-sites based on input ChIP-seq data for transcription factor (TF)-of-interest, analyzed against DNA-binding sites (DNA motifs) in a database (HOCOMOCO). The pipeline also predicts the optimal distance between the paired binding sites. The pipeline correctly predicted known/reported transcription factor cooperations, and also predicted new cooperations, not yet reported in literature. The authors choose to follow up on the predicted interaction between Ikaros and Jun. Using ChIP-seq in mouse B cells, they show extensive overlap in binding regions between Ikaros and Jun in LPS+IL21 stimulated cells. In a human B-lineage cell line (MINO) they show that anti-Ikaros Ab can co-immunoprecipitate Jun protein, and that the MINO cell extracts contain protein(s) that can bind to the CNS9 probe (conserved region upstream of IL10 gene), and that binding is lost upon mutation of two basepairs in the AP1 binding motif, and reduced upon mutation of two basepairs in the non-canonical Ikaros binding motif. Part of this protein complex is super-shifted with an anti-Jun antibody, and more DNA is shifted with addition of an anti-Ikaros antibody.

The authors perform EMSA showing that recombinant Jun can bind to the tested DNA-region (IL10 CNS9) and that addition of recombinant Ikaros (or anti-Ikaros antibody in Fig 3E) can enhance binding (increase amount of DNA shifted). The authors lastly show that the IL10 CNS9 DNA region can enhance transcription in B- and T-cells with a luciferase reporter assay, and that 2 bp mutation of the Ikaros or Jun DNA motifs greatly reduce or abolish this activity.

This is interesting work, with two main contributions: The SPICE pipeline (if made available to the scientific community), and the report of interaction between Ikaros and Jun. However, the distinction between DNA motifs, and the proteins actually binding and having a biological function, should be made clear consistently throughout the manuscript. The same DNA motifs can be bound by multiple factors, for instance within transcription factor families with highly homology in the DNA-binding regions of the proteins.

The reviewer has correctly assessed the content of our manuscript.

Some specific points:

SPICE: It is unclear if this is uploaded somewhere to be available to the scientific community.

Thanks for this comment. We will upload the SPICE pipeline and its associated scripts (R and shell) via GitHub.

It was unclear if Ikaros-Jun interaction was initially found from primary Jun ChIP-seq (and secondary Ikaros motif from HOCOMOCO) or from primary Ikaros CHIP-seq (and secondary Jun motif from HOCOMOCO). And - what were the two DNA motifs (primary and secondary, and their distance) from the SPICE analysis?

The IKZF1-JUN interaction was found from primary JUN ChIP-seq data and searching for secondary IKZF1 motifs identified in the HOCOMOCO database. We will provide the primary and secondary motifs in our revised manuscript.

Authors have mostly careful considerations and statements. One additional comment is that binding does not equal function (Fig 2D), and that opening of chromatin (by any other factor(s)) can give DNA-binding factors (like Ikaros and Jun) the opportunity to bind, without functional consequence for the biological process studied.

We appreciate that the reviewer believes our considerations and statement are careful. We agree that opening of chromatin can give the opportunity of factors to bind, and we now make this point in the manuscript.

Figure 2E: Ikaros is reported to be expressed at baseline in murine B cells, yet the Ikaros ChIP-seq in unstimulated cells had what looks to be no significant or low peaks. LPS stimulation induced strong Ikaros ChIP-seq signal. A western blot showing the Ikaros protein levels in the 3 conditions could help understand if the binding pattern is due to protein expression level induction. Similar for Jun (western in the 3 conditions), which seemed to mainly bind in the LPS+IL21 condition. Furthermore, as also suggested below, tracks showing Ikaros and Jun binding from all conditions (unstimulated, LPS only and LPS+IL21 stimulated cells), at select genomic loci, would be helpful in illustrating this difference in signal between the different cell conditions. This is relevant in regards to the point of cooperativity of binding.

The main point of the paper was showing functional cooperation and proximity of binding. However, the use of purified JUN and Ikaros protein suggest cooperative binding. Exhaustive evaluation of the JUN-Ikaros association is left for future studies.

ChIP-seq in mouse B cells showed that Ikaros bound strongly in LPS stimulated cells, in the (relative) absence of Jun binding (Fig. 2C). However, in EMSA (Fig 3C), there is no binding when the AP1 site is mutated, and the authors describe this as Ikaros binding site. What does the Ikaros binding look like at this genomic location in LPS (only) stimulated cells? The authors could show the same figure as in Fig 2F but show Ikaros and Jun ChIP-seq tracks at IL10 CNS9 locus from all conditions to compare binding in unstimulated, LPS and LPS+IL21 cells.

As requested, we now show Ikaros and Jun ChIP-seq tracks from unstimulated, LPS-treated, and LPS + IL21-treated cells. Both Ikaros and cJUN were bound to the Il10 upstream CNS9 region with LPS treatment of cells (see Author response image 1, highlighted in red box), but binding was weaker than that observed with LPS + IL21.

Author response image 1.

Also: How does this reconcile with the luciferase assay in Fig 4E, where LPS (only) stimulation is used, which in Fig 2E only/mainly induced Ikaros, and not Jun ChIP-seq signal (while EMSA indicate Ikaros cannot bind the site alone, but can enhance Jun-dependent binding).

As shown above, in the LPS (only) condition, both IKZF1 (Ikaros) and cJUN bind to Il10 CNS9 locus. Thus, this is not in conflict with our luciferase assay data in Fig. 4E, which showed Ikaros is dependent on AP-1 binding. Moreover, the AP-1 site in Fig. 4D and 4E can be bound by other AP-1 factors as well, such as JUND, JUNB, BATF, etc. These points can be made in the manuscript. These factors potentially can compete with cJUN binding and their roles remain to be explored.

Comment on statements in results section: The luciferase assays in B and T cells do not demonstrate the role of the proteins Ikaros or Jun directly (page 10, lines 208 and surrounding text). The assay measures an effect of the DNA sequences (implying binding of some transcription factor(s)), but does not identify which protein factors bind there.

We agree with the reviewer. It is reasonable and even likely that different family members may be partially redundant. This point is now made on our revised manuscript.

Lastly, the authors only discuss Ikaros (using the term IKZF1 which is the gene symbol for the Ikaros protein). There are other Ikaros family members that have high homology and that are reported to bind similar DNA sequences (for instance Aiolos and Helios), which are expressed in B-cells and T-cells. A discussion of this is of relevance, as these are different proteins, although belonging to the same family (the Ikaros-family) of transcription factors. For instance, western for Aiolos and Helios will likely detect Aiolos in the B cells used, and Helios in the T cells used.

We agree with the reviewer. As requested, we now discuss the possibility that Aiolos or Helios may also contribute.

Reviewer #2 (Public Review):

The study is performed with old tool Spamo (12 year ago), source data from Encode (2010-2012), even peak caller tool version MACS is old ~ 2013. De novo motif search tool is old too (new one STREME is not mentioned). Any composite element search tool published for the recent 12 years are not cited, there are some issues in data analysis in presentation. Almost all references are from about 8-10 year ago (the most recent date is 2019)

The title is misleading

Instead of “A new pipeline SPICE identifies novel JUN-IKZF1 composite elements”

It should be written as “Application of SpaMo tool identifies novel JUN-IKZF1 composite elements”

It reflects the pipeline better but honestly shows that the novelty is missed.

Regarding the above two points, we respectfully disagree with the reviewer. Although SpaMo was used, the pipeline we developed is new and our findings are distinctive. The pipeline can systematically screen and predict novel protein-protein binding complex, and our discovery related to IKZF1-JUN composite element is new and the biological findings and validation are distinctive. This point is now made in the revised manuscript. As requested, we have added some additional references.

The study was performed on too old data from ENCODE, authors mentioned 343 Encode ChIP-Seq libraries, but authors even did not care even about to set for each library the name of target TF (Figure 1E, Figure S2, Table 2).

Although we used ENCODE data, which was in part when we initially developed the algorithm, those data are valid and using them allowed us to demonstrate the functionality of SPICE, which is versatile and can be used on datasets of one’s choice as well. As requested, in the revised manuscript we have added the names of the TFs in Figs, Fig. S2, and Table 1.

Reviewer #3 (Public Review):

The authors of this study have designed a novel screening pipeline to detect DNA motif spacing preferences between TF partners using publicly available data. They were able to recapitulate previously known composite elements, such as the AP-1/IRF4 composite elements (AICE) and predict many composite elements that are expected to be very useful to the community of researchers interested in dissecting the regulatory logic of mammalian enhancers and promoters. The authors then focus on a novel, SPICE predicted interaction between JUN and IKZF1, and show that under LPS and IL-21 treatment, JUN and IKZF1 in B cells have significant overlap in their genomic localization. Next, to know whether the two TFs physically interact, a co-immunoprecipitation experiment was performed. While JUN immunoprecipitated with an anti-IKZF1 antibody, curiously IKZF1 did not immunoprecipitate with an anti-JUN antibody. Finally, EMSA and luciferase experiments were performed to show that the two TFs bind cooperatively at an IL20 upstream probe.

The reviewer has described the basic results of the study.

Major strengths:

1) SPICE was able to recapitulate previously known composite elements, such as the AP-1/IRF4 composite elements (AICE).

2) Under LPS and IL-21 treatment, JUN and IKZF1 in B cells have significant overlap in their genomic localization. This is very good supporting evidence for the efficacy of SPICE in detecting TF partners.

We are glad that the reviewer believes that SPICE is effective in detecting TF partners.

Major weaknesses:

1) The authors fail to convincingly show that IKZF1 and Jun physically interact. A quantitative measurement of their interaction strength would have been ideal.

We agree that it is not conclusive that the factors interact directly as opposed to binding to nearby sites on DNA, which is what SPICE was intended to detect. We never intended to claim that we established a definite physical interaction. The coIP worked in one direction, but not reliably in the other, even though we have tried a total of four different antibodies. We now mention in the revised manuscript that we have tried the additional anti-JUN antibodies, cJun (60A8, CST) and JunD (D17G2, CST).

2) The super-shift experiment to show that the proteins bound to their EMSA probe were indeed IKZF1 and JUN are not very convincing and would benefit from efforts to quantify the shift (Figure 3E). Nuclear extracts from cells with single or double CRISPR knock outs of the two TFs would have been ideal.

We agree that using single or double knockouts would be helpful, but other Ikaros family or Jun family members could be involved, so such studies might not be definitive. That is why we used purified proteins to show apparent cooperative binding (Figure 4C).

3) There is a second band beneath the more prominent band in the EMSA experiment with recombinant IKZF1 and JUN (Figure 4C). This second band is most probably bound by IKZF1 because it becomes weaker when the IKZF1 site is mutated and is completely absent when only JUN is added. This is completely ignored by the authors. Therefore, experiments with EMSA fail to convincingly show that IKZF1 and Jun bind cooperatively. They could just as well bind independently to the two sites.

The second band has a faster mobility and might relate to IKZF1, although this is difficult to know. We comment on this band on revised manuscript. As noted above, the purified protein experiments do suggest cooperativity. However, our overall intent was to identify factors binding in proximity, which SPICE has successfully done, even if the binding was “independent”.

Author response:

Public Reviews:

Reviewer #1 (Public Review):

(1) It is a nice study but lacks some functional data required to determine how useful these alleles will be in practice, especially in comparison with the figure line that stimulated their creation.

We are grateful for this comment. For the usefulness of these alleles, figure 3 shows that specific and efficient genetic manipulation of one cell subpopulation can be achieved by mating across the DreER mouse strain to the rox-Cre mouse strain. In addition, figure 6 shows that R26-loxCre-tdT can effectively ensure Cre-loxP recombination on some gene alleles and for genetic manipulation. The expression of the tdT protein is aligned with the expression of the Cre protein (Alb roxCre-tdT and R26-loxCre-tdT, figure 2 and figure 5), which ensures the accuracy of the tracing experiments. We believe more functional data can be shown in future articles that use mice lines mentioned in this manuscript.

(2) The data in Figure 5 show strong activity at the Confetti locus, but the design of the newly reported R26-loxCre line lacks a WPRE sequence that was included in the iSure-Cre line to drive very robust protein expression.

Thank you for coming up with this point in the manuscript. In the R26-loxCre-tdT mice knock-in strategy, the WPRE sequence is added behind the loxCre-P2A-tdT sequence.

(3) the most valuable experiment for such a new tool would be a head-to-head comparison with iSure (or the latest iSure version from the Benedito lab) using the same CreER and target foxed allele. At the very least a comparison of Cre protein expression between the two lines using identical CreER activators is needed.

According to the reviewer’s suggestion, we will compare iSuRe-Cre with R26-loxCre-tdT by using Alb-CreER and target R26-Confetti in the revised manuscript.

(4) Why did the authors not use the same driver to compare mCre 1, 4, 7, and 10? The study in Figure 2 uses Alb-roxCre for 1 and 7 and Cdh5-roxCre for 4 and 10, with clearly different levels of activity driven by the two alleles in vivo. Thus whether mCre1 is really better than mCre4 or 10 is not clear.

Thank you for raising this concern. After screening out four robust versions of mCre, we generated these four roxCre knock-in mice. It is unpredictable for us which is the most robust mCre in vivo. It might be one or two mCre versions that work efficiently. For example, if Alb-mCre1 was competitive with Cdh5-mCre10, we can use them for targeting genes in different cell types, broadening the potential utility of these mice.

(5) Technical details are lacking. The authors provide little specific information regarding the precise way that the new alleles were generated, i.e. exactly what nucleotide sites were used and what the sequence of the introduced transgenes is. Such valuable information must be gleaned from schematic diagrams that are insufficient to fully explain the approach.

Thank you for your careful suggestions.

We will provide schematic figures as well as nucleotide sequences for mice generation in the revised manuscript.

Reviewer #2 (Public Review):

(1) The scenario where the lines would demonstrate their full potential compared to existing models has not been tested.

We are grateful for this suggestion. We will compare iSuRe-Cre with R26-loxCre-tdT by using Alb-CreER and target R26-Confetti in the revised manuscript.

(2) The challenge lies in performing such experiments, as low doses of tamoxifen needed for inducing mosaic gene deletion may not be sufficient to efficiently recombine multiple alleles in individual cells while at the same time accurately reporting gene deletion. Therefore, a demonstration of the efficient deletion of multiple floxed alleles in a mosaic fashion would be a valuable addition.

Thank you for your constructive comments. Mosaic analysis using sparse labeling and efficient gene deletion would be our future direction using roxCre and loxCre strategies. We will include some discussion of using such strategy in the revised manuscript.

(3) When combined with the confetti line, the reporter cassette will continue flipping, potentially leading to misleading lineage tracing results.

Thank you for your professional comments. Indeed, the confetti used in this study can continue flipping, which would lead to potentially misleading lineage tracing results. Our use of R26-Confetti is to demonstrate the robustness of mCre for recombination. Some multiple-color mice lines that don’t flip have been published, for example, R26-Confetti2(10.1038/s41588-019-0346-6) and Rainbow (10.1161/CIRCULATIONAHA.120.045750). These reporters could be used for tracing Cre-expressing cells, without concerns of flipping of reporter cassettes.

(4) Constitutive expression of Cre is also associated with toxicity, as discussed by the authors in the introduction.

Thank you for your professional comments. The toxicity of constitutive expression of Cre and the toxicity associated with tamoxifen treatment in CreER mice line (10.1038/s44161-022-00125-6) are known to the field. This study can’t solve the toxicity of the constitutive expression of Cre in this work. Many mouse lines with constitutive Cre driven by different promoters are present across various fields, representing similar toxicity. To solve this issue, it would be possible to construct a new strategy that enables the removal of Cre after its expression.

Reviewer #3 (Public Review):

(1) Although leakiness is rather minor according to the original publication and the senior author of the study wrote in a review a few years ago that there is no leakiness(https://doi.org/10.1016/j.jbc.2021.100509).

Thank you so much for your careful check. In this review (https://doi.org/10.1016/j.jbc. 2021.100509), the writer’s comments on iSuRe-Cre are on the reader's side, and all summary words are based on the original published paper (10.1038/s41467-019-10239-4). Currently, we have tested iSuRe-Cre in our hands. We did detect some leakiness in the heart and muscle, but hardly in other tissues as shown in the following figure.

Author response image 1.

Leakiness in Alb CreER;iSuRe-Cre mouse line. Pictures are representative results for 5 mice. Scale bars, white 100 µm.

(2) I would have preferred to see a study, which uses the wonderful new tools to address a major biological question, rather than a primarily technical report, which describes the ongoing efforts to further improve Cre and Dre recombinase-mediated recombination.

We gratefully appreciate your valuable comment. The roxCre and loxCre mice mentioned in this study provide more effective methods for inducible genetic manipulation in studying gene function. We hope that the application of our new genetic tools could help address some major biological questions in different biomedical fields in the future.

(3) Very high levels of Cre expression may cause toxic effects as previously reported for the hearts of Myh6-Cre mice. Thus, it seems sensible to test for unspecific toxic effects, which may be done by bulk RNA-seq analysis, cell viability, and cell proliferation assays. It should also be analyzed whether the combination of R26-roxCre-tdT with the Tnni3-Dre allele causes cardiac dysfunction, although such dysfunctions should be apparent from potential changes in gene expression.

We are sorry that we mistakenly spelled R26-loxCre-tdT into R26-roxCre-tdT in our manuscript. We have not generated R26-roxCre-tdT mouse line. We also thank the reviewer for concerns about the toxicity of high Cre expression. The toxicity of constitutive expression of Cre and the toxicity of tamoxifen treatment of CreER mice line (10.1038/s44161-022-00125-6) are known to the field. This study can’t solve the toxicity of the constitutive expression of Cre in this work. Many mouse lines with constitutive Cre driven by different promoters are present across various fields, representing similar toxicity. To solve this issue, it would be possible to construct a new strategy that enables the removal of Cre after its expression.

(4) Is there any leakiness when the inducible DreER allele is introduced but no tamoxifen treatment is applied? This should be documented. The same also applies to loxCre mice.

In this study, we come up with new mice tool lines, including Alb roxCre1-tdT, Cdh5 roxCre4-tdT, Alb roxCre7-GFP, Cdh5 roxCre10-GFP and R26-loxCre-tdT. As the data shown in supplementary figure 1, supplementary figure 2, and figure 4D, Alb roxCre1-tdT, Cdh5 roxCre4-tdT, Alb roxCre7-GFP, Cdh5 roxCre10-GFP and R26-loxCre-tdT are not leaky. Therefore, if there is any leakiness driven by the inducible DreER or CreER allele, the leakiness is derived from the DreER or CreER. We will supplement relevant experimental data in the revision.

(5) It would be very helpful to include a dose-response curve for determining the minimum dosage required in Alb-CreER; R26-loxCre-tdT; Ctnnb1flox/flox mice for efficient recombination.

Thank you for your suggestion. We understand the reviewer’s concern. We can do a dose-response curve in the revision work.

(6) In the liver panel of Figure 4F, tdT signals do not seem to colocalize with the VE-cad signals, which is odd. Is there any compelling explanation?

As the file-loading website has a file size limitation, the compressed image results in some signal unclear. The following are the zoom-out figures. The staining in Figure 4F will be optimized and high-resolution images will be provided in the revision.

Author response image 2.

(7) The authors claim that "virtually all tdT+ endothelial cells simultaneously expressed YFP/mCFP" (right panel of Figure 5D). Well, it seems that the abundance of tdT is much lower compared to YFP/mCFP. If the recombination of R26-Confetti was mainly triggered by R26-loxCre-tdT, the expression of tdT and YFP/mCFP should be comparable. This should be clarified.

Thank you so much for your careful check. We checked these signals carefully and didn't find the “much lower” tdT signal. As the file-loading website has a file size limitation, the compressed image results in some signal unclear. We attached clear high resolution images here. The following figure shows how we split the tdT signal and compared it with YFP/mCFP.

Author response image 3.

(8) In several cases, the authors seem to have mixed up "R26-roxCre-tdT" with "R26-loxCre-tdT". There are errors in #251 and #256.Furthermore, in the passage from line #278 to #301. In the lines #297 and #300 it should probably read "Alb-CreER; R26-loxCretdT;Ctnnb1flox/flox"" rather than "Alb-CreER;R26-tdT2;Ctnnb1flox/flox".

We are grateful for these careful observations. We have corrected these typos accordingly.

Author Response

Reviewer #1 (Public Review):

We thank the Reviewer for their comments.

Reviewer #2 (Public Review):

1) In Figure 4, the authors injected a retrograde tracer in the NA and an anterograde tracer in DCN to find potential "nodes" of overlap. From this experiment, the authors identify the VTA and regions of the thalamus as potential areas of tracer overlap, but it is unclear how many other brain regions were examined. Did the authors jump straight to likely locations of overlap based on previous findings, or were large swaths of the brain examined systematically? If other brain regions were examined, which regions and how was this done? A table listing which brain regions were examined and the presence/intensity of ctb-Alexa568 and GFP fluorescence would be helpful.

We thank the Reviewer for their comments. Exhaustive characterizations of inputs into nucleus accumbens (NAc) as well as of direct outputs of the deep cerebellar nuclei (DCN) have appeared elsewhere (e.g, Ma et al., 2020 doi: 10.3389/fnsys.2020.00015; Novello et al., 2022 doi: 10.1007/s12311-022-01499-w). Our anatomical investigations with retrograde and anterograde tracers were focused on putative intermediary nodal regions with robust inputs from the DCN, clear outputs to NAc, and limbic functionality. Only a handful of brain regions fulfill these criteria, and from those, we chose to target the VTA and intralaminar thalamus based on the observation that cerebellar activation induces dopamine release in the NAc medial shell and core (Holloway et al., 2019 doi: 10.1007/s12311-019-01074-w; Low et al., 2021 10.1038/s41586-021-04143-5) and on our previous work on DCN projections to the midline thalamus (Jung et al., 2022 doi: 10.3389/fnsys.2022.879634).

2) In Figure 5, the authors inject AAV1-Cre in DCN and AAV-FLEX-tdTomato in VTA or thalamus. This is an interesting experiment, but controls are missing. An important control is to inject AAV-FLEX-tdTomato in the VTA or thalamus in the absence of AAV1-Cre injection in DCN. Cre-independent expression of tdTomato should be assessed in the VTA/thalamus and the NA.

We thank the reviewer for bringing up this important control. We routinely perform this control experiment to test for any “leakiness” of floxed vectors prior to use but we did not include it in the paper. In response to the Reviewer’s comment, we show results from this control below. Briefly, we performed a large injection (300 nl) of AAV-FLEX-tdTomato in the thalamus area together with AAV-EGFP (to visualize the injection). No Cre-expressing virus was injected anywhere in the brain. PFA-fixed brain slices were obtained at 3 weeks post-injection and imaged for EGFP and tdTomato. Author Response Figure 1 shows images of the injected thalamus area. No tdTomato expression (Fig. 1C, red) was observed despite abundant EGFP expression (Fig. 1B, green), which confirms that in the absence of Cre the floxed construct does not “leak”.

Author response image 1.

(related to Fig. 5 of manuscript). Control experiment for “leakiness” of floxed tdTomato. A, Epifluorescence image of thalamus region in brain slice with EGFP (green) and tdTomato (red) channels merged. Gain settings in the red channel were increased intentionally, to ensure detection of any “leaky” cells. B, Cellular EGFP expression marks successful viral injection. C, No cellular expression of tdTomato without Cre. Note diffuse red signal from background fluorescence.

Reviewer #3 (Public Review):

1) The novelty of this paper lies in the mapping of projections from the interposed and the lateral nuclei of the cerebellum, as the authors themselves mention. However, in some of the experiments the medial nucleus is also clearly injected (Fig. 4B and 6B). In those experiments, it is impossible to distinguish which nucleus these projections come from, and they could be the ones from the medial nucleus that were previously described (see above).

We thank the Reviewer for their comments. As stated in the Results and in the legend of Fig. 4, in addition to experiments with injections in all DCN (Fig. 4B-D), we also performed experiments with injections in only the lateral nucleus (Fig. 4E and F). The results of these experiments support the existence of lateral DCN projections that overlap with NAc-projecting neurons in VTA and thalamus. This finding is further corroborated by our transsynaptic experiments with lateral DCN-only injections (Fig. 5E,F). Regarding the optophysiological experiments, as mentioned in the Results, all DCN were injected (Fig. 6B) in order to maximize ChR2 expression and the chances of successful stimulation of projections.

2) A strength of the paper is the use of both electrical and optogenetic stimulation. However, the responses to the two in the NAc are very different - electrical stimulation results in both excitation and inhibition, whereas opto stimulation mostly results in only excitation.

We thank the Reviewer for this comment. At least two different explanations could account for the observed differences in the prevalence of inhibitory responses elicited by optogenetic vs electrical stimulation: i) inhibitory response prevalence is sensitive to stimulation intensity (Table 1 and Fig. 2B). Because of inherent differences between optogenetic and electrical stimulation, it is not possible to directly compare intensities between the two modalities in order to determine at which intensities, if at all, the prevalence of responses should match. The observation that, at least in the VTA, the prevalence of inhibitory responses elicited by 1 mW light intensity (the lowest intensity that we tested) was comparable to the prevalence of inhibitory responses elicited by 100 µA electrical stimulation is in line with this explanation; ii) DCN electrical stimulation is not node-specific. To our knowledge, there is currently no evidence to support mesoscale topographic organization in lateral and interposed DCN that is node-specific. Consequently, electrical stimulation of DCN could, in principle, result in NAc responses through various polysynaptic pathways and/or nodes. This possibility would still exist even if electrical stimulation had limited spread of a few hundred microns (as in our experiments) and is at least partly supported by the observed long latencies of electrically-evoked inhibitory responses (NAcCore: 268 ± 25 ms (10th percentile: 42 ms), NAcMed: 259 ± 14 ms (10th percentile: 60 ms). Our recognition of this intrinsic limitation of DCN electrical stimulation was the impetus behind the node-specific optogenetic experiments.

3) The stimulation frequency at which the electrical stimulation in Fig 1 is done to identify responses in the NAc is 200 Hz for 25 ms. Is this physiological? In addition, responses in the NAc are measured for 500 ms after, which is a very long response time.

Regarding stimulation frequency, DCN neurons readily reach firing rates between 100-200 Hz in vivo and ex vivo (e.g., Beekhof et al., 2021 doi.org/10.3390/cells10102686; Sarnaik & Raman, 2018 doi:10.7554/eLife.29546; Canto et al., 2016 doi:10.1371/journal.pone.0165887). Regarding the duration of the response window, at the outset of our experiments we were agnostic to the type of responses that our stimulation protocols would evoke in NAc. We therefore established a response time window that would allow us to capture both fast neurotransmitter-mediated responses as well as neuromodulatory (e.g., dopaminergic) responses, which are known to occur at hundred-millisecond latencies or longer (Wang et al., 2017 doi.org/10.1016/j.celrep.2017.02.062; Chuhma et al., 2014 doi:10.1016/j.neuron.2013.12.027; Gonon, 1997). A posteriori analysis indicated that even if we reduced the response time window by 50%, the ratio of DCN-evoked excitatory vs inhibitory responses in NAc would not change substantially (E/I500: 4.3 vs E/I250: 5).

4) Previous studies have described how different cell types within the DCN have different downstream projections (Fujita et al. 2020). However, the experiments here bundle together all this known heterogeneity.

We agree with the Reviewer that dissecting the contributions of specific DCN cell types to NAc circuits is an important next step, which we are excited to undertake in future studies. Here we have broken new ground by identifying for the first time nodes and functional properties of DCN-NAc connectivity. Performing these studies with DCN cell type-specificity was not justified or feasible, given that the molecular identity of participating DCN neurons is currently unknown.

5) Previous studies have also highlighted the importance of different cell types within the NAc and how input streams are differentially targeted to them. Here, that heterogeneity is also obscured.

Along the same lines as #4, we agree with the Reviewer about the importance of examining the cellular identity of NAc neurons that participate in DCN-NAc circuitry. We are excited to undertake these examinations using ex vivo approaches, which are well suited to dissect cellular and molecular mechanisms.

6) In Fig. 4C, E and F, the experiments on overlap between anterograde and retrograde tracers are not particularly convincing - it's hard to see the overlap.

We thank the reviewer for this comment and have included revised figure panels 4C5, E3, Author response image 1 and Figure 2 below. Single optical sections with altered color scheme and orthogonal confocal views are presented in order to show the overlap between DCN projections and NAc-projecting nodal neurons more clearly. The findings of these imaging experiments are corroborated by the results of our transsynaptic labeling approach (Fig. 5), which we have validated elsewhere (Jung et al., 2022 doi:10.3389/fnsys.2022.879634; and Author response image 1).

Author response image 2.

(related to Fig. 4 of manuscript). Co-localization of NAc-projecting neurons with DCN axonal projections in VTA and thalamus. A-D, Single optical sections and orthogonal views are shown. Green: EGFP-expressing DCN axons; white: ctb- Alexa 568; red: anti-TH (A-B; VTA) or NeuN (C-D; thalamus). White arrows in orthogonal views point to regions of overlap.

Author Response

Reviewer #1 (Public Review):

Comment 1:

The pharmacological tools used in this study are highly non-selective. Gd3+, used here to block NALCN is actually more commonly used to block TRP channels. 2-APB inhibits not only TRPC channels, but also TRPM and IP3 receptors while stimulating TRPV channels (Bon and Beech, 2013), while FFA actually stimulates TRPC6 channels while inhibiting other TRPCs (Foster et al., 2009).

We agree with the reviewer that the substances mentioned are not specific. Although we performed shRNA experiments against NALCN and TRPC6, we do plan to use more specific pharmacological modulators for these two channels; for this, L703,606 (the antagonist of NALCN) [1] and larixyl acetate (a potent TRPC6 inhibitor) [2] will be used. Actually, we have completed experiments of using larixyl acetate and the results are shown in Author response image 1.

Author response image 1.

Example time-course (A), traces (B) and the summaried data (C) for the effect of larixyl acetate (LA), the antagonist of TRPC6 channel, on the spontaneous firing activity of VTA DA neurons. Paired-sample T test, ** P < 0.01. n is number of neurons recorded and N is number of mice used

Comment 2:

The multimodal approach including shRNA knockdown experiments alleviates much of the concern about the non-specific pharmacological agents. Therefore, the author's claim that NALCN is involved in VTA dopaminergic neuron pacemaking is well-supported.

However, the claim that TRPC6 is the key TRPC channel in VTA spontaneous firing is somewhat, but not completely supported. As with NALCN above, the pharmacology alone is much too non-specific to support the claim that TRPC6 is the TRP channel responsible for pacemaking. However, unlike the NALCN condition, there is an issue with interpreting the shRNA knockdown experiments. The issue is that TRPC channels often form heteromers with TRPC channels of other types (Goel, Sinkins and Schilling, 2002; Strübing et al., 2003). Therefore, it is possible that knocking down TRPC6 is interfering with the normal function of another TRPC channel, such as TRPC7 or TRPC4.

According with your advice, we plan to perform single-cell qPCR experiments to check the expression level of other TRPC channels, after selective knockdown of TRPC6 in VTA DAT+ neurons, results will be shown later in the revised version. From our single-cell RNA-seq results, TRPC7 and TRPC4 are found not to be present broadly like TRPC6 in the VTA DA neurons, therefore it is possible that knocking down TRPC6 maybe not interfering with the normal function of another TRPC channel, such as TRPC7 or TRPC4.

Comment 3:

The claim that TRPC6 channels in the VTA are involved in the depressive-like symptoms of CMUS is supported.

However, the connection between the mPFC-projecting VTA neurons, TRPC6 channels, and the chronic unpredictable stress model (CMUS) of depression is not well supported. In Figure 2, it appears that the mPFC-projecting VTA neurons have very low TRPC6 expression compared to VTA neurons projecting to other targets. However, in figure 6, the authors focus on the mPFC-projecting neurons in their CMUS model and show that it is these neurons that are no longer sensitive to pharmacological agents non-specifically blocking TRPC channels (2-APB, see above comment). Finally, in figure 7, the authors show that shRNA knockdown of TRPC6 channels (in all VTA dopaminergic neurons) results in depressive-like symptoms in CMUS mice. Due to the low expression of TRPC6 in mPFC-projecting VTA neurons, the author's claims of "broad and strong expression of TRPC6 channels across VTA DA neurons" is not fully supported. Because of the messy pharmacological tools used, it cannot be clamed that TRPC6 in the mPFC-projecting VTA neurons is altered after CMUS. And because the knockdown experiments are not specific to mPFC-projecting VTA neurons, it cannot be claimed that reducing TRPC6 in these specific neurons is causing depressive symptoms.

The reason we focused on the mPFC-projecting VTA DA neurons is that this pathway is indicated in depressive-like behaviors of the CMUS model[3-5]. Although mPFC-projecting VTA DA neurons seem have lower level of TRPC6, we reason they are still functional there. However, we do agree with the reviewer that the statement “broad and strong expression of TRPC6 channels across VTA DA neurons" is not fully supported. We have changed the statements based on the reviewer suggestion. Furthermore, we also plan to selectively knockdown TRPC6 in the mPFC-projecting VTA DA neurons, and then study the behavior.

Comment 4:

It is important to note that the experiments presented in Figure 1 have all been previously performed in VTA dopaminergic neurons (Khaliq and Bean, 2010) including showing that low calcium increases VTA neuron spontaneous firing frequency and that replacement of sodium with NMDG hyperpolarizes the membrane potential.

We agree with reviewer that similar experiments have been performed previously [6]for the flow of our manuscript and for general readers.

Comment 5:

The authors explanation for the increase in firing frequency in 0 calcium conditions is that calcium-activated potassium channels would no longer be activated. However, there is a highly relevant finding that low calcium enhances the NALCN conductance through the calcium sensing receptor from Dejian Ren's lab (Lu et al., 2010) which is not cited in this paper. This increase in NALCN conductance with low calcium has been shown in SNc dopaminergic neurons (Philippart and Khaliq, 2018), and is likely a factor contributing to the low-calcium-mediated increase in spontaneous VTA neuron firing.

We agree with the reviewer and thanks for the suggestions. A discussion for this has been added.

Comment 6:

One of the only demonstrations of the expression and physiological significance of TRPCs in VTA DA neurons was published by (Rasmus et al., 2011; Klipec et al., 2016) which are not cited in this paper. In their study, TRPC4 expression was detected in a uniformly distributed subset of VTA DA neurons, and TRPC4 KO rats showed decreased VTA DA neuron tonic firing and deficits in cocaine reward and social behaviors.

We thank the reviewer for the suggestion.The references and a discussion for this has been added.

Comment 7:

Out of all seven TRPCs, TRPC5 is the only one reported to have basal/constitutive activity in heterologous expression systems (Schaefer et al., 2000; Jeon et al., 2012). Others TRPCs such as TRPC6 are typically activated by Gq-coupled GPCRs. Why would TRPC6 be spontaneously/constitutively active in VTA DA neurons?

In a complex neuronal environment where VTA DA neurons are located, multiple modulatory factors including the GPCRs could be dynamically active, this could lead to the activation of TRP channels including TRPC6.

Comment 8:

A new paper from the group of Myoung Kyu Park (Hahn et al., 2023) shows in great detail the interactions between NALCN and TRPC3 channels in pacemaking of SNc DA neurons.

The reference mentioned has been added. We thank the reviewer.

Reviewer #2 (Public Review):

Comment 1:

These results do not show that TRPC6 mediates stress effects on depression-like behavior. As stated by the authors in the first sentence of the final paragraph, "downregulation of TRPC6 proteins was correlated with reduced firing activity of the VTA DA neurons, the depression-like behaviors, and that knocking down of TRPC6 in the VTA DA neurons confer the mice with depression behaviors." Therefore, the results show associations between TRPC6 downregulation and stress effects on behavior, occlusion of the effects of one by the other on some outcome measures, and cell manipulation effects that resemble stress effects. There is no experiment that shows reversal of stress effects with cell/circuit-specific TRPC6 manipulations. Please adjust the title, abstract and interpretation accordingly.

We agree with the reviewer’s suggestion. The title was changed to ‘’The cation channel mechanisms of subthreshold inward depolarizing currents in the VTA dopaminergic neurons and their roles in the chronic stress-induced depression-like behavior” and the abstract and interpretation were also adjusted accordingly.

Comment 2:

Statistical tests and results are unclear throughout. For all analyses, please report specific tests used, factors/groups, test statistic and p-value for all data analyses reported. In some cases, the chosen test is not appropriate. For example, in Figure 6E, it is not clear how an experiment with 2 factors (stress and drug) can be analyzed with a 1-way RM ANOVA. The potential impact of inappropriate statistical tests on results makes it difficult to assess the accuracy of data interpretation.

We have redone the statistical analysis as suggested by the reviewer and added specific tests used, factors/groups, test statistic and p-value for all data analyses into the revised manuscript.

Comment 3:

Why were only male mice used? Please justify and discuss in the manuscript. Also, change the title to reflect this.

Although most similar previous studies used male mice or rats[7, 8], we do agree with the reviewer that the female animals should also be tested, in consideration possible role of sex hormones, as such we plan to repeat some key experiments on female mice.

Comment 4:

Number of recorded cells is very low in Figure 1. Where in VTA did recordings occur? Given the heterogeneity in this brain region, this n may be insufficient. Additional information (e.g., location within VTA, criteria used to identify neurons) should be included. Report the number of mice (i.e., n = 6 cells from X mice) in all figures.

Yes indeed, the number here is not high. More experiments will be performed to increase the N/n number. And the location of recorded cells in VTA and the number of used mice are now shown in all figures; criteria to identify neurons is stated in the Methods- Identification of DA neurons and electrophysiological recordings. At the end of electrophysiological recordings, the recorded VTA neurons were collected for single-cell PCR. VTA DA neurons were identified by single-cell PCR for the presence of TH and DAT.

Comment 5:

Authors refer to VTA DA neurons as those that are DAT+ in line 276, although TH expression is considered the standard of DAergic identity, and studies (e.g., Lammel et al, 2008) have shown that a subset of VTA DA neurons have low levels of DAT expression. Authors should reword/clarify that these are DAT-expressing VTA DA neurons.

The study published by Lammel[9] in 2015 has shown the low dopamine specificity of transgene expression in ventral midbrain of TH-Cre mice; on the other hand, DAT-Cre mice exhibit dopamine-specific Cre expression patterns, although DAT-Cre mice are likely to suffer from their own limitations (for example, low DAT expression in mesocortical DA neurons may make it difficult to target this subpopulation, see Lammel et al., 2008[10]). Hence, in our study, the DAT was used as criteria to identify DAT neurons. Of course, TH and DAT were all tested in single-cell PCR to identify whether the recorded cells were DA neurons.

Comment 6:

Neuronal subtype proportions should be quantified and reported (Fig. 1Aii).

Neuronal subtype proportions are now quantified and reported in Fig. 1Aii.

Comment 7:

In addition to reporting projection specificity of neurons expressing specific channels, it would be ideal to report these data according to spatial location in VTA.

The spatial location of recorded cells in VTA are now shown in all figures.

Comment 8:

The authors state that there are a small number of Glut neurons in VTA, then they state that a "significant proportion" of VTA neurons are glutamatergic.

Thanks, “a significant proportion of neurons” has been changed to “ less than half of sequenced DA neurons”.

Comment 9:

It is an overstatement that VTA DA neurons are the key determinant of abnormal behaviors in affective disorders.

Thanks, we have amended the statement to that “Dopaminergic (DA) neurons in the ventral tegmental area (VTA) play an important role in mood, reward and emotion-related behaviors”.

Reviewer #3 (Public Review):

Comment 1:

The authors of this study have examined which cation channels specifically confer to ventral tegmental area dopaminergic neurons their autonomic (spontaneous) firing properties. Having brought evidence for the key role played by NALCN and TRPC6 channels therein, the authors aimed at measuring whether these channels play some role in so-called depression-like (but see below) behaviors triggered by chronic exposure to different stressors. Following evidence for a down-regulation of TRPC6 protein expression in ventral tegmental area dopaminergic cells of stressed animals, the authors provide evidence through viral expression protocols for a causal link between such a down-regulation and so-called depression-like behaviors. The main strength of this study lies on a comprehensive bottom-up approach ranging from patch-clamp recordings to behavioral tasks. However, the interpretation of the results gathered from these behavioral tasks might also be considered one main weakness of the abovementioned approach. Thus, the authors make a confusion (widely observed in numerous publications) with regard to the use of paradigms (forced swim test, tail suspension test) initially aimed (and hence validated) at detecting the antidepressant effects of drugs and which by no means provide clues on "depression" in their subjects. Indeed, in their hands, the authors report that stress elicits changes in these tests which are opposed to those theoretically seen after antidepressant medication. However, these results do not imply that these changes reflect "depression" but rather that the individuals under scrutiny simply show different responses from those seen in nonstressed animals. These limits are even more valid in nonstressed animals injected with TRPC6 shRNAs (how can 5-min tests be compared to a complex and chronic pathological state such as depression?). With regard to anxiety, as investigated with the elevated plus-maze and the open field, the data, as reported, do not allow to check the author's interpretation as anxiety indices are either not correctly provided (e.g. absolute open arm data instead of percents of open arm visits without mention of closed arm behaviors) or subjected to possible biases (lack of distinction between central and peripheral components of the apparatus).

We agree with the reviewer that behavior tests we used here is debatable whether they represent a real depression state, and this is an open question that could be discussed from different respective. Since these testes (forced swimming and tail suspension), as the reviewer noted, were “widely observed in numerous publications”, we used these seemly only options to reflect a “depression-like” state. One could argue that since these testes were initially used for testing antidepressants (“validated”), with decreased immobility time as indications of anti-depressive effects, why not an increased immobility time reflect a “depression-like” state. As for anxiety tests, both absolute time in open and closed arms are now provided.

Author response:

Responses to Editors:

We appreciate Reviewer 1’s first concern regarding the difficulty of disentangling the contributions of tightly-coupled brain regions to the speech-gesture integration process—particularly due to the close temporal and spatial proximity of the stimulation windows and the potential for prolonged disruption. We would like to provide clarification and evidence supporting the validity of our methodology.

Our previous study (Zhao et al., 2021, J. Neurosci) employed the same experimental protocol—using inhibitory double-pulse transcranial magnetic stimulation (TMS) over the inferior frontal gyrus (IFG) and posterior middle temporal gyrus (pMTG) in one of eight 40-ms time windows. The findings from that study demonstrated a time-window-selective disruption of the semantic congruency effect (i.e., reaction time costs driven by semantic conflict), with no significant modulation of the gender congruency effect (i.e., reaction time costs due to gender conflict). This result establishes that double-pulse TMS provides sufficient temporal precision to independently target the left IFG and pMTG within these 40-ms windows during gesture-speech integration. Importantly, by comparing the distinctively inhibited time windows for IFG and pMTG, we offered clear evidence of distinct engagement and temporal dynamics between these regions during different stages of gesture-speech semantic processing.

Furthermore, we reviewed prior studies utilizing double-pulse TMS on structurally and functionally connected brain regions to explore neural contributions across timescales as brief as 3–60 ms. These studies, which encompass areas from the tongue and lip areas of the primary motor cortex (M1) to high-level semantic regions such as the pMTG and ATL (Author response table 1), consistently demonstrate the methodological rigor and precision of double-pulse TMS in disentangling the neural dynamics of different regions within these short temporal windows.

Author response table 1.

Double-pulse TMS studies on brain regions over 3-60 ms time interval

Response to Reviewer #1:

(1) For concern on the difficulty of disentangling the contributions of tightly-coupled brain regions to the speech-gesture integration process:

We trust that the explanation provided above has clarified this issue.

(2) For concern on the rationale for delivering HD-tDCS/TMS in set time windows for each region, as well as how these time windows were determined and how the current results compare to our previous studies from 2018 and 2023:

The current study builds on a series of investigations that systematically examined the temporal and spatial dynamics of gesture-speech integration. In our earlier work (Zhao et al., 2018, J. Neurosci), we demonstrated that interrupting neural activity in the IFG or pMTG using TMS selectively disrupted the semantic congruency effect (reaction time costs due to semantic incongruence), without affecting the gender congruency effect (reaction time costs due to gender incongruence). These findings identified the IFG and pMTG as critical hubs for gesture-speech integration. This informed the brain regions selected for subsequent studies.

In Zhao et al. (2021, J. Neurosci), we employed a double-pulse TMS protocol, delivering stimulation within one of eight 40-ms time windows, to further examine the temporal involvement of the IFG and pMTG. The results revealed time-window-selective disruptions of the semantic congruency effect, confirming the dynamic and temporally staged roles of these regions during gesture-speech integration.

In Zhao et al. (2023, Frontiers in Psychology), we investigated the semantic predictive role of gestures relative to speech by comparing two experimental conditions: (1) gestures preceding speech by a fixed interval of 200 ms, and (2) gestures preceding speech at its semantic identification point. We observed time-window-selective disruptions of the semantic congruency effect in the IFG and pMTG only in the second condition, leading to the conclusion that gestures exert a semantic priming effect on co-occurring speech. These findings underscored the semantic advantage of gesture in facilitating speech integration, further refining our understanding of the temporal and functional interplay between these modalities.

The design of the current study—including the choice of brain regions and time windows—was directly informed by these prior findings. Experiment 1 (HD-tDCS) targeted the entire gesture-speech integration process in the IFG and pMTG to assess whether neural activity in these regions, previously identified as integration hubs, is modulated by changes in informativeness from both modalities (i.e., entropy) and their interactions (mutual information, MI). The results revealed a gradual inhibition of neural activity in both areas as MI increased, evidenced by a negative correlation between MI and the tDCS inhibition effect in both regions. Building on this, Experiments 2 and 3 employed double-pulse TMS and event-related potentials (ERPs) to further assess whether the engaged neural activity was both time-sensitive and staged. These experiments also evaluated the contributions of various sources of information, revealing correlations between information-theoretic metrics and time-locked brain activity, providing insights into the ‘gradual’ nature of gesture-speech integration.

We acknowledge that the rationale for the design of the current study was not fully articulated in the original manuscript. In the revised version, we will provide a more comprehensive and coherent explanation of the logic behind the three experiments, ensuring clear alignment with our previous findings.

(3) For concern about the use of Pearson correlation and the normality of EEG data.

We appreciate the reviewer’s thoughtful consideration. In Figure 5 of the manuscript, we have already included normal distribution curves that illustrate the relationships between the average ERP amplitudes within each ROI or elicited clusters and the three information models. Additionally, multiple comparisons were addressed using FDR correction, as outlined in the manuscript.

To further clarify the data, we will calculate the Shapiro-Wilk test, a widely accepted method for assessing bivariate normality, for both the MI/entropy and averaged ERP data. The corresponding p-values will be provided in the following-up point-to-point responses.

(4) For concern about the ROI selection, and the suggestion of using whole-brain electrodes to build models of different variables (MI/entropy) to predict neural responses:

For the EEG data, we conducted both a traditional region-of-interest (ROI) analysis, with ROIs defined based on a well-established work (Habets et al., 2011), and a cluster-based permutation approach, which utilizes data-driven permutations to enhance robustness and address multiple comparisons. The latter method complements the hypothesis-driven ROI analysis by offering an exploratory, unbiased perspective. Notably, the results from both approaches were consistent, reinforcing the reliability of our findings.

To make the methods more accessible to a broader audience, we will provide a clear description of the methods used and how they relate to each other in the revised manuscript.

Reference:

Habets, B., Kita, S., Shao, Z.S., Ozyurek, A., and Hagoort, P. (2011). The Role of Synchrony and Ambiguity in Speech-Gesture Integration during Comprehension. J Cognitive Neurosci 23, 1845-1854. 10.1162/jocn.2010.21462

(5) For concern about the median split of the data:

To identify ERP components or spatiotemporal clusters that demonstrated significant semantic differences, we split each model into higher and lower halves, focusing on indexing information changes reflected by entropy or mutual information (MI). To illustrate the gradual activation process, the identified components and clusters were further analyzed for correlations with each information matrix. Remarkably, consistent results were observed between the ERP components and clusters, providing robust evidence that semantic information conveyed through gestures and speech significantly influenced the amplitude of these components or clusters. Moreover, the semantic information was shown to be highly sensitive, varying in tandem with these amplitude changes.

We acknowledge that the rationale behind this approach may not have been sufficiently clear in the initial manuscript. In our revision, we will ensure a more detailed and precise explanation to enhance the clarity and coherence of this logical framework.

Response to Reviewer #2:

We greatly appreciate Reviewer2 ’s concern regarding whether "mutual information" adequately captures the interplay between the meanings of speech and gesture. We would like to clarify that the materials used in the present study involved gestures performed without actual objects, paired with verbs that precisely describe the corresponding actions. For example, a hammering gesture was paired with the verb “hammer”, and a cutting gesture was paired with the verb “cut”. In this design, all gestures conveyed redundant meaning relative to the co-occurring speech, creating significant overlap between the information derived from speech alone and that from gesture alone.

We understand the reviewer’s concern about cases where gestures and speech may provide complementary rather than redundant information. To address this, we have developed an alternative metric for quantifying information gains contributed by supplementary multisensory cues, which will be explored in a subsequent study. However, for the present study, we believe that the observed overlap in information serves as an indicator of the degree of multisensory convergence, a central focus of our investigation.

Regarding the reviewer’s concern about how the neural processes of speech-gesture integration may change with variations in the relative timing between speech and gesture stimuli, we would like to highlight findings from our previous study (Zhao, 2023, Frontiers in Psychology). In that study, we explored the semantic predictive role of gestures relative to speech under two conditions: (1) gestures preceding speech by a fixed interval of 200 ms, and (2) gestures preceding speech of its semantic identification point. Interestingly, only in the second condition did we observe time-window-selective disruptions of the semantic congruency effect in the IFG and pMTG. This led us to conclude that gestures play a semantic priming role for co-occurring speech. Building on this, we designed the present study with gestures preceding speech of its semantic identification point to reflect this semantic priming relationship. Additionally, ongoing research is exploring gesture and speech interactions in natural conversational settings to investigate whether the neural processes identified here are consistent across varying contexts.

To prevent any similar concerns from causing doubt among the audience and to ensure clarity regarding the follow-up study, we will provide a detailed discussion of the two issues in the revised manuscript.

Response to Reviewer #3:

The primary aim of this study is to investigate whether the degree of activity in the established integration hubs, IFG and pMTG, is influenced by the information provided by gesture-speech modalities and/or their interactions. While we provided evidence for the differential involvement of the IFG and pMTG by delineating their dynamic engagement across distinct time windows of gesture-speech integration and associating these patterns with unisensory information and their interaction, we acknowledge that the mechanisms underlying these dynamics remain open to interpretation. Specifically, whether the observed effects stem from difficulties in semantic control processes, as suggested by Reviewer 3, or from resolving information uncertainty, as quantified by entropy, falls outside the scope of the current study. Importantly, we view these two interpretations as complementary rather than mutually exclusive, as both may be contributing factors. Nonetheless, we agree that addressing this question is a compelling avenue for future research. In the revised manuscript, we will include an exploratory analysis to investigate whether the confounding difficulty, stemming from the number of lexical or semantic representations, is limited to high-entropy items. Additionally, we will address and discuss alternative interpretations.

Regarding the concern of conceptual equivocation, we would like to emphasize that this study represents the first attempt to focus on the relationship between information quantity and neural engagement. In our initial presentation, we inadvertently conflated the commonly used term "graded hub," which refers to anatomical distribution, with its usage in the present context. We sincerely apologize for this oversight and are grateful for the reviewer’s careful critique. In the revised manuscript, we will clearly articulate the study’s objectives, clarify the representations of entropy and mutual information, and accurately describe their association with neural engagement.

Reference

Teige, C., Mollo, G., Millman, R., Savill, N., Smallwood, J., Cornelissen, P. L., & Jefferies, E. (2018). Dynamic semantic cognition: Characterising coherent and controlled conceptual retrieval through time using magnetoencephalography and chronometric transcranial magnetic stimulation. Cortex, 103, 329-349.

Amemiya, T., Beck, B., Walsh, V., Gomi, H., & Haggard, P. (2017). Visual area V5/hMT+ contributes to perception of tactile motion direction: a TMS study. Scientific reports, 7(1), 40937.

Muessgens, D., Thirugnanasambandam, N., Shitara, H., Popa, T., & Hallett, M. (2016). Dissociable roles of preSMA in motor sequence chunking and hand switching—a TMS study. Journal of Neurophysiology, 116(6), 2637-2646.

Vernet, M., Brem, A. K., Farzan, F., & Pascual-Leone, A. (2015). Synchronous and opposite roles of the parietal and prefrontal cortices in bistable perception: a double-coil TMS–EEG study. Cortex, 64, 78-88.

Pitcher, D. (2014). Facial expression recognition takes longer in the posterior superior temporal sulcus than in the occipital face area. Journal of Neuroscience, 34(27), 9173-9177.

Bardi, L., Kanai, R., Mapelli, D., & Walsh, V. (2012). TMS of the FEF interferes with spatial conflict. Journal of cognitive neuroscience, 24(6), 1305-1313.

D’Ausilio, A., Bufalari, I., Salmas, P., & Fadiga, L. (2012). The role of the motor system in discriminating normal and degraded speech sounds. Cortex, 48(7), 882-887.

Pitcher, D., Duchaine, B., Walsh, V., & Kanwisher, N. (2010). TMS evidence for feedforward and feedback mechanisms of face and body perception. Journal of Vision, 10(7), 671-671.

Gagnon, G., Blanchet, S., Grondin, S., & Schneider, C. (2010). Paired-pulse transcranial magnetic stimulation over the dorsolateral prefrontal cortex interferes with episodic encoding and retrieval for both verbal and non-verbal materials. Brain Research, 1344, 148-158.

Kalla, R., Muggleton, N. G., Juan, C. H., Cowey, A., & Walsh, V. (2008). The timing of the involvement of the frontal eye fields and posterior parietal cortex in visual search. Neuroreport, 19(10), 1067-1071.

Pitcher, D., Garrido, L., Walsh, V., & Duchaine, B. C. (2008). Transcranial magnetic stimulation disrupts the perception and embodiment of facial expressions. Journal of Neuroscience, 28(36), 8929-8933.

Author response:

Reviewer #1 (Public review):

Summary:

This is an interesting study on the role of FGF signaling in the induction of primitive streak-like cells (PS-LC) in human 2D-gastruloids. The authors use a previously characterized standard culture that generates a ring of PS-LCs (TBXT+) and correlate this with pERK staining. A requirement for FGF signaling in TBXT induction is demonstrated via pharmacological inhibition of MEK and FGFR activity. A second set of culture conditions (with no exogenous FGFs) suggests that endogenous FGFs are required for pERK and TBXT induction. The authors then characterize, via scRNA-seq, various components of the FGF pathway (genes for ligands, receptors, ERK regulators, and HSPG regulation). They go on to characterize the pFGFR1, receptor isoforms, and polarized localization of this receptor. Finally, they perform FGF4 inhibition and use a cell line with a limited FGF17 inactivation (heterozygous null) and show that loss of these FGFs reduces PS-LC and derivative cell types.

Strengths:

(1) As the authors point out, the role of FGF signaling in gastrulation is less well understood than other signaling pathways. Hence this is a valuable contribution to that field.

(2) The FGF4 and FGF17 loss-of-function experiments in Figure 5 are very intriguing. This is especially so given the intriguing observation that these FGFs appear to be dominating in this model of human gastrulation, in contrast to what FGFs dominate in mice, chicks, and frogs.

(3) In general this paper is valuable as a further development of the Human gastruloid system and the role of FGF signaling in the induction of PS-CLs. The wide net that the authors cast in characterizing the FGF ligand gene, receptor isoforms, and downstream components provides a foundation for future work. As the authors write near the beginning of the Discussion "Many questions remain."

We thank the reviewer for these positive comments.

Weaknesses:

(1) FGFs are cell survival factors in various aspects of development. The authors fail to address cell death due to loss of FGF signaling in their experiments. For example, in Figure 1E (which requires statistical analysis) and 1G (the bottom FGFRi row), there appears to be a significant amount of cell loss. Is this due to cell death? The authors should address the question of whether the role of FGF/ERK signaling is to keep the cells alive.

Indeed, FGF also strongly affects cell number and it is an interesting question to what extent this depends on ERK. Our manuscript focuses instead on the role of FGF/ERK signaling in cell fate patterning. However, as mentioned in our discussion, figure 1de show that doxycycline induced pERK leads to more TBXT+ cells than the control without restoring cell number, suggesting the role of FGF in controlling cell number is independent of the requirement for FGF/ERK in PS-LC differrentiation. Unpublished data below showing a MEK inhibitor dose response further supports this: low doses of MEKi are sufficient to inhibit differentiation without affecting cell number. To address the reviewer’s question we will include this data in the revised manuscript and perform several additional experiments to determine in more detail how cell death and proliferation depend on FGF.

Author response image 1.

MEK affects differentiation and cell number at different doses. a-c) control and MEKi (0.3uM) treated colonies with similar cell number but different TBXT expression. d-f) quantification of cell number per colonies (d), percentage of TBXT-positive cell per colony (e), and the distribution of pERK intensities for different doses of MEK inhibitor (f). N>6 colonies per condition. MEKi = PD0325901. Scalebar = 50 micron.

(2) Regarding the sparse cells in 1G, is there a reduction in cell number only with FGFRi and not MEKi? Is this reproducible? Gattiglio et al (Development, 2023, PMID: 37530863) present data supporting a "community effect" in the FGF-induced mesoderm differentiation of mouse embryonic stem cells. Could a community effect be at play in this human system (especially given the images in the bottom row of 1G)? If the authors don't address this experimentally they should at least address the ideas in Gattoglio et al.

Indeed, FGFRi reproducibly affects cell number more than MEKi, in line with the fact that pathways downstream of FGF other than MAPK/ERK (e.g. PI3K) play important roles in cell survival and growth. We think the lack of differentiation in MEKi and FGFRi in Fig.1g cannot be attributed to a loss of cells combined with a community effect. This is because without FGFRi or MEKi cells also differentiate to primitive streak at much lower densities than those shown, consistent with the data we show above in response to (1), which argue against a primarily indirect effect of FGF on PS-LC differentiation through cell density. In the context of directed differentiation (rather than 2D gastruloids), we will show this in a controlled manner by repeating the experiment in Fig.1g while adjusting cell seeding densities to obtain similar final cell densities in all three conditions. We will also include Gattoglio et al. in our revised discussion.

(3) Do the FGF4 and FGF17 LOF experiments in Figure 5 affect cell numbers like FGFRi in Figure 1?

It seems the effect on cell number is small but we will analyze this carefully and include it in the revised manuscript. A small effect would be consistent with our unpublished data below showing a near uniform proliferation rate. This in turn suggests that low levels of pERK in the center are sufficient to maintain proliferation there while the much higher pERK levels in the PS-LC ring (that we think depend on FGF4 and FGF17) do not signifcantly increase the proliferation rate (see Fig.1 in the manuscript for the pERK pattern). Thus, loss of high pERK in PS-LC ring while maintaining low pERK throughout would not be expected to have a major impact on cell number but would impact differentiation. In contrast, loss of all FGF signaling through FGFRi does dramatically affect cell number. This is again consistent with the data provided in response to (1) showing that ERK levels can be reduced to a point where PS-LC differentiation is lost without significantly affecting cell number. We will include the data below in the revised manuscript.

Author response image 2.

Why examine PS-LC induction only in FGF17 heterozygous cells and not homozygous FGF17 nulls?

We were unable to obtain homozygous FGF17 nulls, it is not clear if there is a reason for this. We will try again and otherwise attempt to corroborate our findings with further knockdown data.

(4) The idea that FGF8 plays a dominant role during gastrulation of other species but not humans is so intriguing it warrants deeper testing. The authors dismiss FGF8 because its mRNA "...levels always remained low." (line 363) as well as the data published in Zhai et al (PMID: 36517595) and Tyser et al (PMID: 34789876). But there are cases in mouse development where a gene was expressed at levels so low, that it might be dismissed, and yet LOF experiments revealed it played a role or even was required in a developmental process. The authors should consider FGF8 inhibition or inactivation to explore its potential role, despite its low levels of expression.

We agree with the reviewer that FGF8 is worth investigating further and we will now pursue this.

(5) Redundancy is a common feature in FGF genetics. What is the effect of inhibiting FGF4 in FGF17 LOF cells?

We will attempt to do the experiment the reviewer suggests.

(6) I suggest stating that the authors take more caution in describing FGF gradients. For example, in one Results heading they write "Endogenous FGF4 and FGF17 gradients underly the ERK activity pattern.", implying an FGF protein gradient. However, they only present data for FGF mRNA , not protein. This issue would be clarified if they used proper nomenclature for gene, mRNA (italics), and protein (no italics) throughout the paper.

We will edit the paper to more clearly distinguish protein and mRNA.

Reviewer #2 (Public review):

Summary:

The role of FGFs in embryonic development and stem cell differentiation has remained unclear due to its complexity. In this study, the authors utilized a 2D human stem cell-based gastrulation model to investigate the functions of FGFs. They discovered that FGF-dependent ERK activity is closely linked to the emergence of primitive streak cells. Importantly, this 2D model effectively illustrates the spatial distribution of key signaling effectors and receptors by correlating these markers with cell fate markers, such as T and ISL1. Through inhibition and loss-of-function studies, they further corroborated the needs of FGF ligands. Their data shows that FGFR1 is the primary receptor, and FGF2/4/17 are the key ligands for primitive streak development, which aligns with observations in primate embryos. Additional experiments revealed that the reduction of FGF4 and FGF17 decreases ERK activity.

Strengths:

This study provides comprehensive data and improves our understanding of the role of FGF signaling in primate primitive streak formation. The authors provide new insights related to the spatial localization of the key components of FGF signaling and attempt to reveal the temporal dynamics of the signal propagation and cell fate decision, which has been challenging.

Weaknesses:

Given the solid data, the work only partially clarifies the complex picture of FGF signaling, so details remain somewhat elusive. The findings lack a strong punchline, which may limit their broader impact.

We thank this reviewer for their valuable feedback and the compliment on the solidity of our data. The punchline of our work is that FGF4- and FGF17-dependent ERK signaling plays a key role in human PS-LC differentiation, and that these are different FGFs than those thought to drive mouse gastrulation. A second key point is that like BMP and TGFβ signaling, FGF signaling is restricted to the basolateral sides of pluripotent stem cell colonies due to polarized receptor expression, which is crucial for understanding the response to exogenous ligands added to the cell medium. Indeed, many facets of FGF signaling remain to investigated in the future, such as how FGF regulates and is regulated by other signals, which we will dedicate a different manuscript to.

Reviewer #3 (Public review):

Jo and colleagues set out to investigate the origins and functions of localized FGF/ERK signaling for the differentiation and spatial patterning of primitive streak fates of human embryonic stem cells in a well-established micropattern system. They demonstrate that endogenous FGF signaling is required for ERK activation in a ring-domain in the micropatterns, and that this localized signaling is directly required for differentiation and spatial patterning of specific cell types. Through high-resolution microscopy and transwell assays, they show that cells receive FGF signals through basally localized receptors. Finally, the authors find that there is a requirement for exogenous FGF2 to initiate primitive streak-like differentiation, but endogenous FGFs, especially FGF4 and FGF17, fully take over at later stages.

Even though some of the authors' findings - such as the localized expression of FGF ligands during gastrulation and the importance of FGF/ERK signaling for cell differentiation in the primitive streak - have been reported in model organisms before, this is one of the first studies to investigate the role of FGF signaling during primitive streak-like differentiation of human cells. In doing so, the paper reports a number of interesting and valuable observations, namely the basal localization of FGF receptors which mirrors that of BMP and Nodal receptors, as well as the existence of a positive feedback loop centered on FGF signaling that drives primitive-streak differentiation. The authors also perform a comparison of the role of different FGFs across species and try to assign specific functions to individual FGFs. In the absence of clean genetic loss-of-function cell lines, this part of the work remains less strong.

We thank the reviewer for emphasizing the value of our findings in a human model for gastrulation. We agree more loss-of-function experiments would provide further insight into the role of different FGFs, and we plan to provide additional data along these lines in the revised manuscript.

Author Response

We thank the reviewers and editorial team for the positive reaction to our paper and for the constructive recommendations and comments on our work. Here we provide a brief provisional response to key points that were identified. We will give a detailed point-by-point response with highlighted changes in our manuscript when we upload the revised version of our paper.

Reviewer 1:

Statistical evaluation of the null

In Experiment 2, we inferred the existence of a null effect of image category on suppression depth based on frequentist statistics. At the reviewer’s suggestion we performed a statistical evaluation of the evidence in favour of the null effect using a Bayesian repeated measures ANOVA implemented in JASP. That analysis provides strong evidence for the null (BF01= 20.38) and will be included in the final version of the paper.

Likelihood of exceptional cases

We acknowledge that our selection of categories is only a sampling of possible categories to which our novel tCFS method can be applied for deriving suppression depth. Other possibilities that come to mind include objects that emerge from specific configurations of simple 'tokens' such as dots (such as actions defined by biological motion (Watson et al., 2004)) or different shaped tokens configured to generate pareidolia faces (Zhou et al., 2021). We will expand on the possibility of these exceptional cases impacting bCFS and reCFS thresholds in the discussion of our revised manuscript.

Reviewer 2:

In response to the claim “the paper overreaches by claiming breakthrough thresholds are insufficient for drawing certain conclusions about subconscious processing.”

We agree that breakthrough thresholds can provide useful information to draw conclusions about unconscious processing – as our procedure is predicated on breakthrough thresholds. Our key point is that breakthrough provides only half of the needed information and will amend our manuscript accordingly. In so doing, we will also shift our focus toward the influence of semantics and low-level factors, including discussion of the possibility that suppression depth and bCFS thresholds could be driven by statistically orthogonal factors.

Reviewer 3:

On the appropriateness of log-transformed contrast

Our motivation to quantify suppression depth after log-transform to decibel scale was two-fold. First, we recognised that the traditional use of a linear contrast ramp in bCFS is at odds with the well-characterised profile of contrast discrimination thresholds which obey a power law (Legge, 1981) and the observations that neural contrast response functions show the same compressive non-linearity in many different cortical processing areas (e.g.: V1, V2, V3, V4, MT, MST, FST, TEO. See Ekstrom et al., 2009). Increasing contrast in linear steps could thus lead to a rapid saturation of the response function, which may account for the overshoot that has been reported in many canonical bCFS studies. For example, in Jiang et al. (2007), target contrast reached 100% after 1 second, yet average suppression times for faces and inverted faces were 1.36 and 1.76 seconds respectively. As contrast response functions in visual neurons saturate at high contrast, the upper levels of a linear contrast ramp have less and less effect on the target's strength. This approach to response asymptote may have exaggerated small differences between stimulus conditions and may have inflated some previously reported differences. In sum, the use of a log-transformed contrast ramp allows finer increments in contrast to be explored before saturation, a simple manipulation which we hope will be adopted by our field.

Second, by quantifying suppression depth as a decibel change, we enable the comparison of suppression depth between experiments and laboratories, which inevitably differ in presentation environments. As a comparison, a reaction-time for bCFS of 1.36 s cannot easily be compared without access to near-identical stimulation and testing environments. In addition, once ramp contrast is log-transformed it effectively linearises the neural contrast response function. This means that different studies that use different contrast levels for masker or target can be directly compared because a given suppression depth (for example, 15 dB) is the same proportionate difference between bCFS and reCFS regardless of the contrasts used in the particular study.

We also acknowledge that different stimulus categories may engage neural and visual processing associated with different contrast gain values (e.g., magno- vs parvo-mediated processing). But the breaks and returns to suppression of a given stimulus category would be dependent on the same contrast gain function appropriate for that stimulus which thus permits their direct comparison. Indeed, this is why our novel approach offers a promising technique for comparing suppression depth associated with various stimulus categories (a point mentioned above). Viewed in this way, differences in actual durations of break times (such as we report in our paper) may tell us more about differences in gain control within neural mechanisms responsible for processing of those categories.

Consider that preferential processing could shift both bCFS and reCFS thresholds together

This is related to the point raised in the previous comment. A stimulus that is preferentially processed (such as a face) could have lower bCFS and reCFS thresholds than other stimuli such that it emerges into awareness at a lower contrast but also remains visible at lower contrasts. We plan to address this interpretation of our data in our revised discussion and highlight that this type of preferential processing could well occur, and yet could still produce the same uniform suppression depth.

Can the effect of contrast ramp be explained by slower RTs?

A 500 ms reaction time estimate would not account for the magnitude of the changes observed in Experiment 3. Suppression depths in our slow, medium, and fast contrast ramps were 9.64 dB, 14.64 dB and 18.97 dB, respectively (produced by step sizes of .035, .07 and .105 dB per video frame at 60 fps). At each rate, assuming a 500 ms reaction time for both thresholds (1 second total) would capture a change of 2.1 dB, 4.2 dB, 6.3 dB. This difference cannot account for the size of the effects observed between our different ramp speeds.

Non-zero switch rate probability affecting ramping

We agree that for a given ramp speed there is a variable probability of a switch in perceptual state for both bCFS and reCFS portions of the trial. To put it in other words, for a given ramp speed and a given observer the distribution of durations at which transitions occur will exhibit variance. We see that variance in our data (just as it’s present in conventional binocular rivalry duration histograms), as a non-zero probability of switches at very short durations (for example). One might surmise that slower ramp speeds would afford more opportunity for stochastic transitions to occur and that the measured suppression depths for slow ramps are underestimates of the suppression depth produced by contrast adaptation. Yet by the same token, the same underestimation would occur during fast ramp speeds, indicating that that difference may be even larger than we reported. In our revision we will spell this out in more detail, and indicate that a non-zero probability of switches at any time may lead to an underestimation of all recorded suppression depths.

In our data, we believe the contribution of these stochastic switches are minimal. Our current Supplementary Figure 1(d) indicates that there is a non-zero probability of responses early in each ramp (e.g. durations < 2 seconds), yet these are a small proportion of all percept durations. This small proportion is clear in the empirical cumulative density function of percept durations, which we include in Author response image 1, and will address in our detailed response. Notably, during slow-ramp conditions, average percept durations actually increased, implying a resistance to any effect of early stochastic switching. We plan to expand on our analysis of these reaction-time differences in our revised manuscript.

Author response image 1.

The specificity of the DHO fit

In our revised manuscript we will increase the justification for this model, and plan to include a comparison of model fits over time (as opposed to response number in the current manuscript).

References

Ekstrom, L. B., Roelfsema, P. R., Arsenault, J. T., Kolster, H., & Vanduffel, W. (2009). Modulation of the contrast response function by electrical microstimulation of the macaque frontal eye field. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience, 29(34), 10683–10694.

Jiang, Y., Costello, P., & He, S. (2007). Processing of invisible stimuli: advantage of upright faces and recognizable words in overcoming interocular suppression. Psychological Science, 18(4), 349–355.

Legge, G. E. (1981). A power law for contrast discrimination. Vision Research, 21(4), 457–467.

Watson, T. L., Pearson, J., & Clifford, C. W. G. (2004). Perceptual grouping of biological motion promotes binocular rivalry. Current Biology: CB, 14(18), 1670–1674.

Zhou, L.-F., Wang, K., He, L., & Meng, M. (2021). Twofold advantages of face processing with or without visual awareness. Journal of Experimental Psychology. Human Perception and Performance, 47(6), 784–794.

Author response:

Data replicability

There are no replicates contained in the manuscript. (Reviewer #1)

We respectfully disagree with this statement. In this manuscript, we included both cell and animal replicates. For cell replicates, we analyzed over 50.000 cells using RNAscope and over 10.000 cells using RNAseq, employing two independent methods on different animals. We believe this extensive analysis is sufficient by any standards. Regarding animal replicates, we generated four different transgenic lines (two knockin lines and two BAC transgenic lines), which is an uncommon and rigorous effort. We analyzed dozens of animals, consistently observing the expression pattern of Smim32 and its derived transgenes across multiple experiments, including crosses between transgenics and various reporter lines, which is again an uncommon and rigorous effort. These experiments were conducted on animals from different litters to ensure robustness. Additionally, our longitudinal study, which includes 13 animals harvested at two-day intervals from E16 to P20, provides further consistency of our data. 

However, to underscore the consistency of endogenous Smim32 expression, when submitting a revised manuscript, we will present Smim32 expression levels across individuals in single-cell RNA-seq data. Furthermore, we will pool data from different transgenic animals to demonstrate interindividual variability in the claustrum of adult animals. 

Additional examples of female mice should also be included and separately quantified. (Reviewer #1)

We initially analyzed both males and females for one line (the Smim32-Cre knock-in line). Since we observed no differences between males and females (which we will note in the revised manuscript), we subsequently limited our analyses to males to minimize the use of animals. 

Claustrum definition

Weaknesses lie in poor anatomical definitions of the claustrum (and endopiriform nucleus). (Reviewer #2)

No other orthogonal approaches were used to define the claustrum, such as retrograde neuroanatomical tracing from cortex. (Reviewer #3)

We share the reviewers’ opinion that the claustrum (CLA) and endopiriform nucleus (EN) are poorly defined anatomically in rodent brains due to the limited development of white matter tracts. This ambiguity has led to many conflicting descriptions of CLA/EN boundaries in various papers and atlases, including those by Paxinos and the Allen Brain Institute. Notably, the Allen Institute frequently updates the shape and anatomical location of the CLA/EN in their reference atlas, resulting in different websites displaying various versions (as illustrated in rebuttal figure 1 at comparable levels of the anteroposterior brain axis). It remains uncertain which version would most effectively satisfy the entire scientific community, if any. Indeed, after many years of working on these structures and surveying the literature, we regret to note that there is currently no consensus on the anatomical definition of the CLA and EN, even among expert laboratories using tracing or staining methods. At one end of the spectrum, some authors define the CLA as a small nucleus that could be, for example, characterized by the PVrich plexus. At the other end, other authors consider it part of a larger complex that includes the EN and extends dorsally to the S2 cortex. Additionally, differing definitions of the core and shell regions, as well as the precise anteroposterior extent of the nucleus, further complicate the issue.

Author response image 1.

Comparison of CLA and EN shapes in two recent versions of the Allen brain atlas

Given this lack of consensus, we deliberately opted for a molecular definition of the claustrum and its projection neurons. We used a set of well-documented canonical markers for the claustrum and neighboring neurons to determine the expression pattern of Smim32. The claustrum-specific markers we selected (Nr4a2, Lxn, Gnb4, Car3, etc.) have been extensively studied and allow us to distinguish claustrum projection neurons from neighboring and intermingled populations. Although none of these individual markers are exclusively specific to CLA and EN neurons, the combined expression of these markers provides greater confidence in identifying the different neuronal populations in space.

Smim32 expression is used to define claustrum anatomical boundaries, rather than first using several structural, molecular, and connectivity lines of evidence to define the claustrum anatomically and then to assess whether Smim32 expression fits within this anatomical definition. (Reviewer #2)

Contrary to the reviewer's suggestion, we do not define the claustrum based on Smim32 expression. Instead, Figures 1 and 2 demonstrate that Smim32 expression is highly correlated with the expression of known claustrum markers (Nr4a2, Lxn, Gnb4, Car3, etc.), both regionally and at the cellular level. As suggested by Peng et al. (2021, Fig. 4 and Extended Data Fig. 11), this population of cells, which includes the claustrum, a specific subset of cells in cortical layer 6, and the dorsal endopiriform nucleus, forms a discrete group of neurons sharing the same transcriptomic identity. Given what is known about the connectivity of claustrum and endopiriform nucleus projection neurons, this population obviously includes neurons projecting to various areas, likely fulfilling distinct functions. Whether these cells should be subdivided based on projection area, developmental origin, or structural features is beyond the scope of this article.

Specificity issues

Cre/Flp expression driven by the Smim32 promoter is present in non-claustrum regions, including the neighboring cortex, striatum, and endopiriform nucleus as well as the more distant thalamic reticular nucleus. (Reviewer #2)

The Smim32 gene is not specific to the claustrum. (Reviewer #3)

We do not claim that endogenous Smim32 expression is exclusive to the claustrum or that the knock-in lines, by themselves, are sufficient to isolate claustrum neurons without combined approaches based on the transgenic lines presented here. However, there are significant differences in the expression pattern between endogenous Smim32 and the expression of Cre in the various derived transgenic lines, which might not have been clear in the current manuscript. Notably, there is no expression of Cre in the striatum and the thalamic reticular nucleus, and only sparse expression in the endopiriform nucleus in Tg61(Smim32-cre). Each transgenic line provides different levels of overlap with the endogenous Smim32 expression, with the Tg61(Smim32-cre)  line allowing for the most specific genetic access to claustrum neurons. Again, for greater specificity, any of these lines could be used in combined approaches, such as viral targeting (as shown in Figure 6A and B) or using transgenic intersectional (dual recombinase) approaches based on Cre- and Flp-expressing mice with an overlap in the claustrum, leading to circuit-specific and/or claustrum-only labeling.

This means that our claims are supported by the observed data. However, we acknowledge that we may not have clearly explained the specificity of the random transgenes, which could have led some reviewers to believe that « the data do not support the claims ».

We will clarify these points in the revised manuscript and include additional examples and quantifications to highlight the differences between endogenous Smim32 expression and Cre expression in the transgenic Tg61(Smim32-cre)  line.

Regarding Cre-expressing cells in the neighboring cortex (layer 6 projection neurons), these cells are genetically distinct from other layer 6 cortical neurons and express the same canonical markers as claustrum projection neurons, likely sharing also the same transcriptomic identity. We will provide a more detailed characterization of these cells in the revised manuscript.

Since Smim32 driven recombinase (in 61 or 62lrod) is not exclusively expressed in the claustrum, it is not clear how Smim32 is an advantage over possible Nr4a2 or, the more selective, GNB4 Cre driver lines. (Reviewer #2)

Over the years, we have found a limited number of Cre lines used in the literature for targeting claustrum neurons. These include Gnb4-cre, Slc17a6-cre (also known as Vglut2-cre), Egr2-cre, Tg(Tbx21-cre), Ntng2-cre, Cux2-cre and Esr2-cre lines. We have not found any study describing and/or using an Nr4a2-cre line. Although a Nr4a2-Dre line exists (that we have studied in our laboratories), caution is warranted in its use, as it lacks the complete coding sequence of the Nr4a2 gene.

One problem with Nr4a2 is its documented expression in the adjacent Layer 6b cortical neurons, which discards it as a suitable candidate to selectively target the claustrum. Furthermore, Nr4a2 is also expressed in a majority of the endopiriform nucleus neurons, whereas endogenous Smim32 is expressed in a smaller proportion of these cells, and is restricted mainly to the dorsal endopiriform nucleus. These reasons led us to select Smim32 over Nr4a2.

Author response image 2.

(A) In situ hybridization for various CLA/EN marker genes. (B) Developmental recombination observed outside the CLA/EN in various cre lines (all data from the Allen brain databases)

What are the advantages of using the different Smim32-cre lines over the existing Cre lines mentioned above?

Let’s first consider the Gnb4-cre line, which is considered one of the best available. Although the endogenous Gnb4 gene appears to have a similar expression pattern to Nr4a2, Slc17a6, and Smim32 in the striato-claustro-insular region of adult mice (Rebuttal Figure 2A), the results observed with the Gnb4-cre line either shows otherwise, or indicate that the Cre line does not fully recapitulate Gnb4 endogenous expression (Rebuttal Figure 3). Indeed some neurons in the insular cortex, piriform cortex, and putamen express the Cre recombinase (possibly due to low Gnb4 expression not detected in the in situ hybridization data of the Allen brain institute or due to nonspecific transgene expression) and will recombine viral vectors injected in adult mice (Rebuttal Figure 3). Therefore, this Cre expression outside the CLA/EN neurons in the Gnb4-cre line presents complications for data interpretation, depending on the viral injection coordinates and the quantity of injected vectors. 

Author response image 3.

Specificity of the Gnb4-Cre line tested with viral transduction in adult mice (all data from the Allen Brain Institute database). The top and middle rows display the same data but with different scaling of the lookup tables to highlight either the patterns of axonal projections (top) or the infected neurons themselves (middle). The bottom row shows a higher magnification of the infection site. Note that individual neurons cannot be resolved in experiment 485903475 due to signal saturation.  

Cre expression in the CLA appears more specific in the various Smim32-cre transgenic lines than in many of the lines mentioned above. Although we have no doubt that the different existing transgenic lines can target CLA neurons, the selectivity of the targeting (for example, the fraction and types of CLA neurons versus potential non-CLA neurons) remains to be fully described for most of the lines. It is particularly true in the case of Tbx21 and Esr2 (used as drivers for the Tg(Tbx21-cre) and Esr2-cre transgenic lines). Tbx21 is not endogenously expressed in adult CLA neurons (evaluated by in situ and RNAseq data) and Egr2, if expressed in the claustrum, is not restricted to CLA neurons as it is an immediate early gene expressed in recently active neurons (Rebuttal Figure 2A). 

Cre expression in the EN is observed in all Cre-expressing transgenic lines used to target the claustrum (with the exception of Slc17a6-cre). This can naturally be problematic for some approaches. Luckily, the random integrant Tg61(Smim32-cre) we describe in our manuscript shows a strong expression in the claustrum, and very limited expression outside the CLA (a very weak activity in the EN), representing a novel tool with improved claustrum selectivity. An advantage of the Tg61(Smim32-cre) over the Slc17a6-cre is that more CLA neurons can be targeted with the Tg61(Smim32-cre) line. 

Another advantage of our four transgenic lines is their versatility; they can be used to recombine reporter lines as well as FRT-floxed and loxP-floxed knockouts in limited neuronal populations. They will be employed in the future for intersectional genetics to exclusively target CLA neurons. Existing transgenic lines cannot offer these possibilities because their marker genes are broadly expressed in the brain during embryogenesis, leading to the impact on a large number of non-CLA/EN neurons. This is evident in the Gnb4-cre and Slc17a6-cre lines crossed with the Ai14 reporter line expressing the fluorescent protein tomato (Rebuttal Figure 2B, right panels). Similar observations have been made for the Ntng2-Cre and Cux2-cre lines (see the Allen Brain Institute database for these data). Alternatively, inducible recombinase systems, such as the Gnb4-IRES2CreERT2-D line, could be used. However, the Gnb4-IRES2-CreERT2-D line requires tamoxifen to induce Cre recombination, which can be problematic depending on the research context, as well as recombinations in the absence of tamoxifen treatment (see experiments 560948627 and 560948194 in the Allen Brain database).

It is unclear how Smim32 relates to claustrum in other mammalian species (e.g. primates) (Reviewer #3)

As mentioned in the last paragraph of the introduction of the initial manuscript, Smim32 is specifically expressed in the claustrum of a primate species, Homo sapiens (reference 37 of the initially submitted manuscript).

Availability of the transgenic mice

These mice should be made available to the community through commercial vendors. (Reviewer #1 and #2 in private comments)

We are pleased to see that two of the three reviewers would like to see these mice available. These mice will not be kept for ourselves, and we will distribute them at some point in time, but this will naturally occur after the publication of the revised manuscript.

Critical comments on discussion and other topics

A clear description of the search in the Allen Mouse Brain Atlas is missing. A search for Smim32 in the ISH mouse atlas did not provide any hits and so it would be useful to include in the methods or results section the exact query used for examination of Smim32 expression as well as other genes identified in this process. (Reviewer #2)

Smim32 has been referred to by different names in various versions of the mouse genome. For the readers not versed in navigating genomes and annotations, before being officially named Smim32, this gene was originally called Gm6753 (as noted in the Allen Brain Institute database, see Rebuttal Figure 2A for an example of their in situ data) and later Gm45623.

Several sentences highlighting the shortfalls of other approaches are overstated and should be toned down. (Reviewer #1)

Very concerning is problematic language in the abstract and introduction sections that diminish the impact of several published studies (not cited) that have led to important findings regarding claustrum function. The authors create an argument that all the research performed thus far on the claustrum is unreliable because targeting the structure has been sub-optimal. (Reviewer #2)

A more balanced discussion of the strengths and weaknesses of these mice should be included. (Reviewer #1)

We regret if our choice of language inadvertently appeared to undermine the contributions of our colleagues; that was certainly not our intention. The paragraph in question was meant to address certain studies that we believe have led to inconsistent findings and unreliable data due to a lack of rigorous methodology in targeting claustrum projection neurons. To avoid singling out specific works, we chose not to cite them directly. We understand that some colleagues whose research does not fall under the “various cases” mentioned may feel unfairly targeted by this statement. We will revise this section to better clarify our intent and ensure it is respectful of all contributions. We will rephrase passages in the abstract, introduction, and discussion to provide a balanced view of the strengths and weaknesses of these mice.

Our main goal is to provide tools to specifically target claustrum cells based on their transcriptomic identity, which we believe is the best means to assess the function of any neuronal population. Due to the intermingling of claustrum neurons with neighboring populations, employing stereotaxic injections in the claustrum without genetic segregation will always infect and label physically adjacent cells that do not belong to the claustrum, ontologically and functionally speaking. 

Similarly, targeting claustrum neurons retrogradely by injecting into claustrum projection sites likely labels neurons from different populations. For instance, as reviewer 1 mentions Erwin et al. (2021), infecting retrosplenial projections without genetic specificity labels many claustrum Synpr+ neurons (considered the claustrum core), a small proportion of claustrum Nnat+ neurons (considered the claustrum shell by some, and non-claustrum neurons by others), and some neighboring cortical L6b neurons. These three populations have very different transcriptomic identities, connectivity patterns, and likely distinct functions.

Thus, we believe that genetic specificity provides an important added value for selectively targeting the claustrum or claustro-insular complex.

A better characterization of all data should be undertaken. (Reviewer #1)

Having generated hundreds of transgenic lines over the years, we have never performed a more thorough analysis of transgenic lines, nor have a recollection of reading a publication evaluating at such a precise level the expression pattern of transgenes in mice. We, therefore, do not see exactly what the reviewer means by this remark. It is possible, not being native English speakers, that we did not grasp a certain form of joke.

Author response:

Public Reviews:

Reviewer #1 (Public review):

Summary:

In this manuscript, Paturi et.al. presents a detailed structural and mechanistic study of the DRB7.2:DRB4 complex in plants, focusing on its role in sequestering endogenous inverted-repeat dsRNA precursors and inhibiting Dicer-like protein 3 (DCL3) activity. By truncating the two proteins, they systematically identify the domains involved in direct interaction between DRB7.2 and DRB4 and study the interactions between the two using biophysical techniques (ITC and NMR). They show using NMR that the interacting domains between the two proteins are likely partially unfolded or aggregated in the absence of the binding partner and determining the NMR structure of the individual interacting domains in the presence of the isotopically unlabelled partner using sparse restrain data combined with Rosetta. They also determine the complex structure of the interacting DRB7.2 dsRBD domain and the DRB4 D3 domain using X-ray crystallography.

Strengths:

Overall, the manuscript is well written, provides molecular details at high resolution between the interaction of DRB7.2 and DRB4, and the data in the manuscript strongly supports the proposed model where DRB7.2:DRB4 complex sequesters the DCL3 substrates inhibiting its function of producing epigenetically activated siRNAs.

Weaknesses:

Major comments:

(1) The manuscript, unfortunately, completely lacks functional validation of the determined DRB7.2:DRB4 complex structure, which is required for the rigorous validation of the proposed model. For functional validation of the determined structures, the author should at least present the mutational analysis (impact on complex formation, RNA affinity) of the point mutants derived from the structure of the DRB7.2:DRB4 complex.

We thank the reviewer for pointing out a crucial aspect that is missed out in our manuscript. With the inputs and experiments proposed above, we would certainly like to perform additional mutational analysis to determine the impact on the heterodimeric complex formation and identify the key essential residues involved in the RNA binding.

We expect that we can accomplish this study in the next ~ 4-6 months as we may have to create a combination of mutations for residues involved in the dimerization interface, namely, T131, V132, E134, F136, W156, and V161 on DRB7.2M. Having said that, the disruption of the heterodimer interface would probably lead to DRB7.2M and DRB4D3 returning to their fast-intermediate timescale exchanging native homo-oligomeric state/partially folded state.

For dsRNA binding, six residues (i.e., A85 and K86 (a1), H112 and K114 (b1-b2 loop), and K142 and K144 (a2)) involved in the RNA binding interface and a few other residues based on the mutational data will be considered.

(2) The proposed model shows the DRB7.2M and DRB4D3 as partially folded/aggregated proteins in the absence of the complex, understandably from the presented NMR data of the individual domains. However, in the cellular context, when the RNAs are present, especially DRB7.2M might be properly folded/not aggregated. Could the authors support or negate this by showing the ¹⁵N HSQC spectrum of DRB7.2M in complex with the 13 bp dsRNA?

While we have no direct proof that the DRB7.2M might be folded/not aggregated in the presence of RNAs in the cellular context, the in vitro NMR-based titration studies of alone DRB7.2 (Author response image 1A) with two molar equivalence of 13 bp dsRNA (Author response image 1B and R1C) indicate that there is no change in overall spectral pattern (except for the apparent chemical shift perturbations as expected from fast-intermediate exchange timescale binding of DRB7.2M with 13 bp dsRNA), implying that the dsRNA alone is neither necessary nor sufficient to disrupt the native fast exchange oligomeric states sampled by individual DRB7.2 and DRB7.2M.

Author response image 1.

DRB7.2M binding interaction with 13bp dsRNA (A) 1H-15N TROSY-HSQC of U[15N, 2H] DRB7.2M. (B) 1H-15N TROSY-HSQC of U[15N, 2H] DRB7.2M in the presence of 13 bp dsRNA with 1:2 molar equivalence. (C) An overlay of (A) and (B) indicates no evident changes in the broadening of resonances. (D) The 15N linewidth analysis of unbound (red) and bound (green) forms of U[15N, 2H] DRB7.2M resonances for which the assignment could be traced from the assignments of the DRB7.2M:DRB4D3 complex.

Furthermore, the line-width analysis, shown in Author response image 1D, implies that the ~R₂ rates are roughly identical in the presence of dsRNA, indicating that the native oligomeric state of DRB7.2M remains unperturbed by the presence of dsRNA. Our observation also corroborates with the crystal structure presented in the manuscript, where we have observed that the hetero-dimeric interface lies on the opposite side of the dsRNA binding interface of the DRB7.2M:DRB4D3 complex.

Therefore, the dsRNA substrate does not have any role in the native partially folded/oligomeric state of DRB7.2M.

(3) It remains unclear from the manuscript if DRB7.1 will have a similar or different mechanism of interaction with DRB4. Based on the sequence comparisons of the two proteins, the authors should comment on this in the discussion section.

Pairwise sequence alignment of full-length DRB7.2 and DRB7.1 reveals 50.7% similarity and a 33.2% identity derived from EMBOSS Needle (Author response image 2).

Author response image 2.

ClustalW alignment of full-length DRB7.2 and DRB7.1. The secondary structure elements are derived from the crystal structure of DRB7.2M (PDB ID: 8IGD). Identical residues are marked with red highlights, whereas similar residues are marked with yellow highlights, and the consensus residues (> 50%) are annotated below the sequence alignment.

As expected, for the dsRBD region (corresponding to DRB7.2M), we observe a much higher degree of alignment with a 76.7% similarity with a 54.7% identity (Author response image 3).

Author response image 3.

ClustalW alignment of the dsRBD region of DRB7.2 and DRB7.1. The secondary structure elements are derived from the crystal structure of DRB7.2M (PDB ID: 8IGD). Identical residues are marked with red highlights, whereas similar residues are marked with yellow highlights, and the consensus residues (> 50%) are annotated below the sequence alignment.

Moreover, the residues involved in the heterodimerization interface in DRB7.2M are identical to those in DRB7.1. As a matter of fact, the residues involved in the dimerization interface, namely, T131, V132, E134, F136, W156, and V161 in DRB7.2M are unchanged in DRB7.1, suggesting that DRB7.1M may interact with DRB4D3 using a similar manner as illustrated for DRB7.2M:DRB4D3 in the manuscript.

Future studies will shed more light on the binding preference of DRB4D3 with DRB7.1 versus DRB7.2. One interesting thing to note is that DRB7.2 is exclusively present in the nucleus, whereas DRB7.1 is observed to localize in the nucleus as well as the cytoplasm. Therefore, spatial restriction may be one of the mechanisms that bring exclusivity to the interaction partner despite having a conserved interaction interface.

Minor comments:

(1) There are no errors for the N, dH, and dS values of the ITC measurements in Table 1. Also, it seems that the measurements are done only once. Values derived from at least triplicates should be presented. This would be helpful to increase confidence in the values derived from ITC, especially for the titration between DRB7.2, DRB4C, and DRB4D3, as the N value there is substantially lower than 1, which does not agree with the other data.

We plan to estimate the errors as proposed by the reviewer in the revised manuscript to ensure that the presented data is of high confidence.

Reviewer #2 (Public review):

Summary:

The manuscript by Paturi and colleagues uses an approach that combines structural biology and biochemistry to probe protein-protein and protein-RNA interactions for two protein factors related to the dsRNA pathway in plants.

Strengths:

A key finding in the research is the direct demonstration of the ability of the single dsRBD (double-strand RNA binding domain) of DRB7.2 to interact simultaneously with dsRNA as well as the C-terminal domain of DRB4. The heterodimerization of DRB7.2 and DRB4 is demonstrated to make a high-affinity complex with dsRNA, and it is proposed that this atypical use of the dsRBD domain to bridge the protein and RNA may contribute to the ability to prevent cleavage that would otherwise occur for dsRNA. The primary results for the interactions are generally well-supported by the data, and the conclusions are taken from the available results without excessive speculation.

Weaknesses:

There is a need for some statistical repeats, as well as a suggested movement of many protein characterization findings in the solution state to support data or to better indicate how these properties could play a role in the final proposed mechanism. There is also the need for certain measurement replicates, such as for the ITC data, which are derived from single measurements and lack sufficient estimates of error.

We plan to restructure the manuscript on the lines proposed by the reviewer in the revised version. Moreover, as mentioned in the response to the comments of Reviewer 1, we suggest estimating the errors to ensure that the presented data is of high confidence in the revised version.

Author Response

We thank the reviewers for their useful and constructive comments. In this provisional response, we will address a few of the major issues and plan to submit a detailed, point-by-point response along with the revised manuscript.

1. Robustness of activated combination of neurons (the ‘activated ensemble’).

The reviewers have asked for additional analyses and visualization of the group of neurons activated and a classification analysis to illustrate the point that the activated set of neurons would allow discrimination between different concentrations even after the spiking activity reduced significantly in the later trials. We relied on visualization using PCA (Manuscript Fig. 4) and quantification using correlation analysis (Manuscript Fig. 5a and Manuscript Supplementary Figure 2). But this point can be easily amplified further to support our conclusions and address a major concern raised by the reviewers.

Visualization of neural responses across trials and odorants: As recommended, we followed the procedures used in Stopfer et al., 2003 (Fig. 6c) and Miura et al., 2012(Fig. 3C) to image neural responses across recorded PNs as a matrix (Author response image 1).

Author response image 1.

Author response image 1: Spike counts averaged over the entire 4s odor presentation window across all recorded neurons are shown as a function of trial number (columns). The sorting is same across different panels. Note that there are 80 neurons whose response was monitored for hexanol and octanol responses (Dataset 1; first row of panels), and 81 neurons whose response was monitored for isoamyl acetate and benzaldehyde (Dataset 2; second row of panels). As can be noted, across the 25 trials the pattern of activation remains consistent. Also, the activated combination of neurons varied robustly with odor identity and intensity.

Classification analysis: To illustrate that there is enough information to recognize an odorant and discriminate between different intensities, we performed a leave-one-trial-out classification analysis. The left-out trial was assigned the class label of its nearest neighbor (using correlation distance metric). The results from this classification analysis are shown below in Author response image 2. As a control, we shuffled the odor class labels and repeated the leave-one-trial-out classification analysis.

Author response image 2.

Author response image 2: Results from classification analysis are shown for the two datasets: hexanol–octanol at different concentrations (dataset 1; 80 PNs), and isoamyl acetate and benzaldehyde (dataset 2; 81 PNs). We did a leave-onetrial-out validation. The true odor label is shown along the x-axis and the predicted odor label is shown along the yaxis. As can be noted, the class labels for every single trial were correctly predicted in both datasets. The result after class labels were shuffled is also shown for comparison. These results strongly support our conclusion that odor intensity information is preserved and odor concentration can be recognized independent of adaptation.

Correlation with the first trial:

We had shown the correlation across odorants and concentrations as a function of the trial (manuscript Figure 5A). To complement these analyses, here we focus on the correlations with the response evoked in the first trial of each odorant at high and low concentrations and plot this information as a function of trial number (Author response image 3, 4). As can be noted, the correlation across different trials of a given odorant at specific concentrations remains much higher than all other conditions.

Author response image 3.

Author response image 3: (top-left) Correlation between 80-dimensional neural responses (averaged over the entire 4s odor presentation window) with the first trial of hexanol at high intensity (hex-H; 1% v/v) is plotted as a function of trial number. (top-right) similar plots but correlation computed with neural responses evoked during the first trial of octanol at high intensity (oct-H; 1% v/v). (bottom-left) similar plots but correlation computed with neural responses evoked in the first trial of hexanol at low intensity (hex-L; 1% v/v). (bottom-right) similar plots but correlation computed with neural responses evoked in the first trial of octanol at low intensity (oct-L; 1% v/v).

Author response image 4.

Author response image 4: (top-left) Correlation between 81-dimensional neural responses (averaged over the entire 4s odor presentation window) with the first trial of isoamyl acetate at high intensity (iaa-H; 1% v/v) is plotted as a function of trial number. (top-right) similar plots but correlation computed with neural responses evoked in the first trial of benzaldehyde at a high intensity (bza-H; 1% v/v). (bottom-left) similar plots but correlation computed with neural responses evoked in the first trial of isoamyl acetate at low intensity (iaa-L; 1% v/v). (bottom-right) similar plots but correlation computed with neural responses evoked in the first trial of benzaldehyde at low intensity (bza-L; 1% v/v).

Behavioral significance and dynamics: The reviewers had wondered about the relevance of the behavior to the organism. The maxillary palps are sensory organs close to the mouth parts that are used to grab food and help with the feeding process. In our previous studies, we had shown that these palpopening responses are innately triggered by many ‘appetitive odorants.’ However, the probability of palp opening varied across different odorants (Chandak and Raman, 2023). Some odorants evoked higher palp-opening responses and others reduced the probability of palp-opening response (below the median value across odorants). Since all other parameters (such as the clicking sound of valves, and mechanical cues due to airflow during odor presentation), are the same across these different odorants, these observed differences in palp-opening response probability are attributed to the identity of the odorants presented.

Author response image 5.

Author response image 5: Preference indices were calculated for all odors tested and are shown as a bar plot (n = 26 locusts). Blue bars indicate odors classified as appetitive, gray bars indicate neutral odors and red bars indicate unappetitive odors. Locusts with a significant deviation from the median response (one-sided binomial test, P < 0.1, were classified as either being appetitive or unappetitive; P < 0.1, P < 0.05, **P < 0.01). Error bars indicate s.e.m. [Replotted Fig 1.c from Chandak and Raman, 2023].

We had also shown that we could train locusts to have stereo-typed palp-opening responses using the classical conditioning approach (odor – odor-conditioned stimulus and food reward – unconditioned stimulus; Video: https://static- content.springer.com/esm/art%3A10.1038%2Fncomms7953/MediaObjects/41467_2015_BFncomms7953 _MOESM483_ESM.mov; Saha et al., 2015). The dynamics of those conditioned palp-opening responses have been well characterized.

We will use similar tracking procedures to monitor and quantify the dynamics of innate palp-opening responses as well. We will add supplementary videos to fully capture this behavior.

Early vs. late neural responses:

Since behavioral responses are more likely to start as soon as the odorant is presented, the reviewers wondered whether there are differences in the observed findings if we focus only on the early neural activity (as it might be more important to triggering behavior). Note that the median response time for conditioned palp-opening responses is less than 750 ms (Saha et al., 2015, Chandak and Raman, 2023). Hence, we divided the neural dataset and analyzed the neural response patterns during these early (0-750 ms after onset) and late (2-4 s after odor onset) time windows. In both these epochs, we found that the total spike counts across neurons reduced as a function of trial number or repetition and the combination of neuron activated remained robust (Author response images 6-11). Hence, we conclude that while the neural responses in different time windows would be important for shaping other parameters of behavioral response dynamics, the overall behavioral response probability that we used in our analysis had a similar relationship with early, late, or total neural activity during the entire odor presentation (i.e. time-window of the neural response did not matter for the analyses presented in the manuscript).

Author response image 6.

Author response image 6: Total spike counts reduced as a function of trial number. This reduction was observed for the total spike counts during the entire odor presentation window and during both the early (0-750 ms) and late (2-4 s) response time windows. Dataset 1: 80 PNs, hexanol, and octanol odorants.

Author response image 7.

Author response image 7: Total spike counts reduced as a function of trial number. This reduction was observed for the total spike counts during the entire odor presentation window and during both the early (0-750 ms) and late (2-4 s) response time windows. Dataset 2: 81 PNs, isoamyl acetate, and benzaldehyde odorants.

Author response image 8.

Author response image 8: Similar plots as in Figures 3 and 4 but analyzing 80-dimensional spike count vectors calculated using only the first 750 ms of odor-evoked response. Note that the correlation with the odor evoked response in the first trial remains high across trials. But between different odorants or different intensities of the same odorant, the response correlation drops significantly. Dataset 1: 80 PNs, hexanol, and octanol odorants.

Author response image 9.

Author response image 9: Similar plots as in Figures 3 and 4 but analyzing 80-dimensional spike count vectors calculated using only the last 2 seconds of odor-evoked response. Note that the correlation with the odor evoked response in the first trial remains high across trials. But between different odorants or different intensities of the same odorant, the response correlation drops significantly. Dataset 1: 80 PNs, hexanol, and octanol odorants.

Author response image 10.

Author response image 10: Similar plots as in Figures 3 and 4 but analyzing 80-dimensional spike count vectors calculated using only the first 750 ms of odor-evoked response. Note that the correlation with the odor evoked response in the first trial remains high across trials. But between different odorants or different intensities of the same odorant, the response correlation drops significantly. Dataset 2: 81 PNs, isoamyl acetate, and benzaldehyde odorants.

Author response image 11.

Author response image 11: Similar plots as in Figures 3 and 4 but analyzing 80-dimensional spike count vectors calculated using only the last 2 seconds of odor-evoked response. Note that the correlation with the odor evoked response in the first trial remains high across trials. But between different odorants or different intensities of the same odorant, the response correlation drops significantly. Dataset 2: 81 PNs, isoamyl acetate, and benzaldehyde odorants.

Other Statistical Tests:

The reviewers felt that in many analyses, we did not include error bars to indicate the sample size, SEM, or SD. We will fix this by adding the sample size information to each panel and as appropriate. However, we would also like to point out that many of the analyses are done in a trial-by-trial fashion (e.g. Manuscript Figures 3 – 6). For these analyses, it would not be possible to add SEM or SD. One condition (hex -H or iaa-H) was repeated in each dataset, and we have added them in the results shown in this response letter to demonstrate repeatability. We will strive our best to add these statistics as would be appropriate, but this cannot be done for the trial-by-trial analyses.

References:

Stopfer M, Jayaraman V, Laurent G. Intensity versus identity coding in an olfactory system. Neuron. 2003 Sep 11;39(6):991-1004. doi: 10.1016/j.neuron.2003.08.011. PMID: 12971898.

Miura K, Mainen ZF, Uchida N. Odor representations in olfactory cortex: distributed rate coding and decorrelated population activity. Neuron. 2012 Jun 21;74(6):1087-98. doi: 10.1016/j.neuron.2012.04.021. PMID: 22726838; PMCID: PMC3383608.

Chandak, R., Raman, B. Neural manifolds for odor-driven innate and acquired appetitive preferences. Nat Commun 14, 4719 (2023). https://doi.org/10.1038/s41467-023-40443-2

Saha, D., Li, C., Peterson, S. et al. Behavioural correlates of combinatorial versus temporal features of odour codes. Nat Commun 6, 6953 (2015). https://doi.org/10.1038/ncomms7953

Author response:

We thank the reviewers for their thoughtful comments and constructive suggestions. We describe how we will address each point below and are grateful for the guidance on areas where our work could be clarified or expanded. In particular, we note the following:

Selection scan summary statistics: In our revised manuscript, we will include summary statistics from the selection scans. We believe this addition will enhance transparency and provide additional context for readers.

Reporting of outliers: As highlighted by the editor, the reviewers expressed differing views on the most appropriate way to report outliers. To provide a comprehensive and balanced presentation, we will report both the empirical selection statistics and the corresponding converted p-values. This dual approach will allow readers to fully interpret the results under both perspectives.

Methodological considerations: We have carefully considered the reviewers' methodological suggestions and will incorporate them into our revisions where possible. These changes strengthen the rigor and clarity of the analyses.

Public Reviews:

Reviewer #1 (Public review):

Summary:

The paper reports an analysis of whole-genome sequence data from 40 Faroese. The authors investigate aspects of demographic history and natural selection in this population. The key findings are that the Faroese (as expected) have a small population size and are broadly of Northwest European ancestry. Accordingly, selection signatures are largely shared with other Northwest European populations, although the authors identify signals that may be specific to the Faroes. Finally, they identify a few predicted deleterious coding variants that may be enriched in the Faroes.

Strengths:

The data are appropriately quality-controlled and appear to be of high quality. Some aspects of the Faroese population history are characterized, in particular, by the relatively (compared to other European populations) high proportion of long runs of homozygosity, which may be relevant for disease mapping of recessive variants. The selection analysis is presented reasonably, although as the authors point out, many aspects, for example differences in iHS, can reflect differences in demographic history or population-specific drift and thus can't reliably be interpreted in terms of differences in the strength of selection.

Weaknesses:

The main limitations of the paper are as follows:

(1) The data are not available. I appreciate that (even de-identified) genotype data cannot be shared; however, that does substantially reduce the value of the paper. Minimally, I think the authors should share summary statistics for the selection scans, in line with the standard of the field.

We agree with the reviewer that sharing the selection scan results is important, so in the next revision of this manuscript we will make the selection scan summary statistics publicly available, and clearly lay out the guidelines and research questions for which the data can be accessed.

(2) The insight into the population history of the Faroes is limited, relative to what is already known (i.e., they were settled around 1200 years ago, by people with a mixture of Scandinavian and British ancestry, have a small effective population size, and any admixture since then comes from substantially similar populations). It's obvious, for example, that the Faroese population has a smaller bottleneck than, say, GBR.

More sophisticated analyses (for example, ARG-based methods, or IBD or rare variant sharing) would be able to reveal more detailed and fine-scale information about the history of the populations that is not already known. PCA, ADMIXTURE, and HaplotNet analysis are broad summaries, but the interesting questions here would be more specific to the Faroes, for example, what are the proportions of Scandinavian vs Celtic ancestry? What is the date and extent of sex bias (as suggested by the uniparental data) in this admixture? I think that it is a bit of a missed opportunity not to address these questions.

We clarify that we did quantify the proportions of various ancestry components as estimated by HaploNet in main text Figure 5 and supplemental figures S5 and S6. In our revisions, we will include the average global ancestry of the various components in the Main Text so that this result is more clear.

We agree that more fine-scale demographic analyses would be informative. We have begun working on an estimation of the admixture date, for example, but have encountered problems with using different standard date estimation software, which give very inconsistent and unstable results. We suspect this might be due to the strong bottleneck experienced in the history of the Faroe Islands breaking one or more of the assumptions of these methods. We will continue working on this problem in coming months, possibly using simulations to assess where the problem might be. We recognize that our relatively small sample size places limits on the fine-scale demographic analyses that can be performed. We are addressing this in ongoing work by generating a larger cohort, which we hope will enable more detailed inference in the future.

(3) I don't really understand the rationale for looking at HLA-B allele frequencies. The authors write that "ankylosing spondylitis (AS) may be at a higher prevalence in the Faroe Islands (unpublished data), however, this has not been confirmed by follow-up epidemiological studies". So there's no evidence (certainly no published evidence) that AS is more prevalent, and hence nothing to explain with the HLA allele frequencies?

We agree that no published studies have confirmed a higher prevalence of ankylosing spondylitis (AS) in the Faroe Islands. Our recruitment data suggest that AS might be more common than in other European populations, but we understand that this is only based on limited, unpublished observations and what we are hearing from the community. We emphasized in our original manuscript that this is based on observational evidence from the FarGen project. However, as this reviewer pointed out, we can be more clear that this prevalence has not been formally studied.

In our next revision we will clarify in the text that our recruitment data suggest a higher prevalence of AS may be possible, but more formal epidemiological studies are needed to confirm this observation. The reason we study HLA-B allele frequencies is to see if the genetic background of the Faroese population could help explain this possible difference, since HLA-B27 is already known to play a strong role in AS.

Reviewer #2 (Public review):

In this paper, Hamid et al present 40 genomes from the Faroe Islands. They use these data (a pilot study for an anticipated larger-scale sequencing effort) to discuss the population genetic diversity and history of the sample, and the Faroes population. I think this is an overall solid paper; it is overall well-polished and well-written. It is somewhat descriptive (as might be expected for an explorative pilot study), but does make good use of the data.

The data processing and annotation follows a state-of-the-art protocol, and at least I could not find any evidence in the results that would pinpoint towards bioinformatic issues having substantially biased some of the results, and at least preliminary results lead to the identification of some candidate disease alleles, showing that small, isolated cohorts can be an efficient way to find populations with locally common, but globally rare disease alleles.

I also enjoyed the population structure analysis in the context of ancient samples, which gives some context to the genetic ancestry of Faroese, although it would have been nice if that could have been quantified, and it is unfortunate that the sampling scheme effectively precludes within-Faroes analyses.

We note that although the ancestry proportions are not specified in the main text, we did quantify ancestry proportions in the modern Faroese individuals and other ancient samples, and we visualized these proportions in Figure 5 and Supplementary Figures S5 and S6. As stated in our response to Reviewer #1, in our revisions, we will more clearly state the average global ancestry of the various components in the Main Text.

I am unfortunately quite critical of the selection analysis, both on a statistical level and, more importantly, I do not believe it measures what the authors think it does.

Major comments:

(1) Admixture timing/genomic scaling/localization:

As the authors lay out, the Faroes were likely colonized in the last 1,000-1,500 years, i.e., 40-60 generations ago. That means most genomic processes that have happened on the Faroese should have signatures that are on the order of ~1-2cM, whereas more local patterns likely indicate genetic history predating the colonization of the islands. Yet, the paper seems to be oblivious to this (to me) fascinating and somewhat unique premise. Maybe this thought is wrong, but I think the authors miss a chance here to explain why the reader should care beyond the fact that the small populations might have high-frequency risk alleles and the Faroes are intrinsically interesting, but more importantly, it also makes me think it leads to some misinterpretations in the selection analysis

See response to point #3

(2) ROH:

Would the sampling scheme impact ROH? How would it deal with individuals with known parental coancestry? As an example of what I mean by my previous comment, 1MB is short enough in that I would expect most/many 1MB ROH-tracts to come from pedigree loops predating the colonization of the Faroes. (i.e, I am actually quite surprised that there isn't much more long ROH, which makes me wonder if that would be impacted by the sampling scheme).

The sampling scheme was designed to choose 40 Faroese individuals that were representative of the different regions and were minimally related. There were no pairs of third-degree relatives or closer (pi-hat > 0.125) in either the Faroese cohort or the reference populations. It is possible that this sampling scheme would reduce the amount of longer ROHs in the population, but we should still be able to see overall patterns of ROH reflective of bottlenecks in the past tens of generations. Additionally, based on this reviewer's earlier comment, 1 Mb ROHs would still be relevant to demographic events in the last 40-60 generations given that on average 1 cM corresponds to 1 Mb in humans, though we recognize that is not an exact conversion.

That said, the “sum total amount of the genome contained in long ROH” as we described in the manuscript includes all ROHs greater than 1Mb. Although we group all ROHs longer than 1Mb into one category in the current manuscript, we can look more specifically at the distribution of the longer ROH in future revisions and add discussion into what this might tell us about the timing of bottlenecks. 

For now, we share a plot of the distribution in ROH lengths across all individuals for each cohort. As this plot shows, there certainly are ROHs longer than 1Mb in the Faroese cohort, and on average there is a higher proportion of long ROH particularly in the 5-15 Mb range in the Faroese cohort relative to the other cohorts.

Author response image 1.

(3) Selection scan:

We are talking about a bottlenecked population that is recently admixed (Faroese), compared to a population (GBR) putatively more closely related to one of its sources. My guess would be that selection in such a scenario would be possibly very hard to detect, and even then, selection signals might not differentiate selection in Faroese vs. GBR, but rather selection/allele frequency differences between different source populations. I think it would be good to spell out why XP-EHH/iHS measures selection at the correct time scale, and how/if these statistics are expected to behave differently in an admixed population.

The reviewer brings up good points about the utility of classical selection statistics in populations that are admixed or bottlenecked, and whether the timescale at which these statistics detect selection is relevant for understanding the selective history of the Faroese population. We break down these concerns separately.

(1) Bottlenecks: Recent bottlenecks result in higher LD within a population. However, demographic events such as bottlenecks affect global genomic patterns while positive selection is expected to affect local genomic patterns. For this reason, iHS and XP-EHH statistics are standardized against the genome-wide background, to account for population-specific demographic history.

(2) Admixture: The term “admixture” has different interpretations depending on the line of inquiry and the populations being studied. Across various time and geographic scales, all human populations are admixed to some degree, as gene flow between groups is a common fixture throughout our history. For example,

even the modern British population has “admixed” ancestry from North / West European sources as well, dating to at least as recently as the Medieval & Viking periods (Gretzinger et al. 2022, Leslie et al. 2015), yet we do not commonly consider it an “admixed” population, and we are not typically concerned about applying haplotype-based statistics in this population. This is due to the low divergence between the source populations. In the case of the Faroe Islands, we believe admixture likely occurred on a similar timescale. We see low variance in ancestry proportions estimated by HaploNet, both from the historical Faroese individuals (250BP) and the modern samples. This indicates admixture predating the settlement of the Faroe Islands, where recombination has had time to break up long ancestry tracts and the global ancestry proportions have reached an equilibrium. That is, these ancestry patterns suggest that the modern Faroese are most likely descended from already admixed founders. We mention this as a likely possibility in the main text: “This could have occurred either via a mixture of the original “West Europe” ancestry with individuals of predominantly “North Europe” ancestry, or a by replacement with individuals that were already of mixed ancestry at the time of arrival in the islands (the latter are not uncommon in Viking Age mainland Europe).” And, as with the case of the British population, the closely-related ancestral sources for the Faroese founders were likely not so diverged as to have differences in allele frequencies and long-range haplotypes that would disrupt signals of selection from iHS or XP-EHH.

(3) Time scale: It is certainly possible, and in fact likely, that iHS measures selection older than the settlement of the Faroe Islands. In our manuscript, we calculated iHS in both the Faroese and the closely related British cohort, and we highlight in the main Main Text that the top signals, with the exception of LCT, are shared between the two cohorts, indicative of selection that began prior to the population split. iHS is a commonly calculated statistic, and it is often calculated in a single population without comparing to others, so we feel it is important to show our result demonstrating these shared selection signals. In future revisions, we will emphasize in the main text that we are not claiming to have identified selection that occurred in the Faroese population post-settlement with the iHS statistic. As far as XP-EHH, it is a statistic designed to identify differentiated variants that are fixed or approaching fixation in one population but not others. The time-scale of selection that XP-EHH can detect would therefore be dependent on the populations used for comparison. As XP-EHH has the best power to identify alleles that are fixed or approaching fixation in one population but not others, it is less likely to detect older selection events / incomplete sweeps from the source populations.

In our next revision, we will more clearly state limitations of these statistics under various population histories, and clarify the time-scale at which we are detecting selection for iHS vs XP-EHH.

(4) Similarly, for the discussion of LCT, I am not convinced that the haplotypes depicted here are on the right scale to reflect processes happening on the Faroes. Given the admixture/population history, it at the very least should be discussed in the context of whether the 13910 allele frequency on the Faroes is at odds with what would be expected based on the admixture sources.

We agree that more investigation into the LCT allele frequency in the other ancient samples may provide some insight into the selection history, particularly in light of ancient admixture. Please note, we did look at the allele frequency of the LCT allele rs4988235 and stated in the main text that it was present at high frequencies in the historical (250BP) Faroese samples. The frequency of this allele in the imputed historical Faroese samples is 82% while the allele is present at ~74% frequency in modern samples. We did not report the exact percentage in the main text because the sample size of the historical samples (11 individuals) is small and coverage of ancient samples is low, leading to potential errors in imputation. However, we can try to calculate the LCT allele frequency in other ancient samples, and assuming that we have good proxies for the sources at the time of admixture, we may calculate the expected allele frequency in the admixed ancestors of the Faroese founders in the next revision.

(5) I am lacking information to evaluate the procedure for turning the outliers into p-values. Both iHS and XP-EHH are ratio statistics, meaning they might be heavy-tailed if one is not careful, and the central limit theorem may not apply. It would be much easier (and probably sufficient for the points being made here) to reframe this analysis in terms of empirical outliers.

Given that there are disagreements on the best approach to reporting selection scan results from the reviewers, in our revision, we can additionally supply both the standardized iHS / XP-EHH values in the supplementary information as well as these values transformed to p-values. As the p-values are derived from the empirical distribution, the “significant” p-values are also empirical outliers from the empirical distribution, so the conclusions of the manuscript do not change. We found that the p-value approach and controlling for FDR is more conservative, with fewer signals reaching “significance” than are considered empirical outliers based on common approaches such as IQR or arbitrary percentile cutoffs.

(6) Oldest individual predating gene flow: It seems impossible to make any statements based on a single individual. Why is it implausible that this person (or their parents), e.g., moved to the Faroes within their lifetime and died there?

We agree with the reviewer that this is a plausible explanation, and in future revisions we will update the main text to acknowledge this possibility.

Author response:

Reviewer #1 (Public review):

Summary:

Mast cells have previously been reported to play an important role in bacterial immune defense and act protectively in sepsis. However, many of these findings were based on studies using Kit mutant mice. In this study, the authors conducted a detailed investigation using mast cell-deficient Cpa3 Cre-Master mice. As a result, the authors found that the Cpa3 Cre-Master mice exhibited responses similar to wild-type mice in terms of bacterial immune defense. This suggests that the observed phenotype is not due to mast cell-dependent bacterial immune defense, but rather is associated with dysbiosis of the gut microbiota.

Strengths:

Mast cells have long been reported to play an important role in the protective response against sepsis, and their function in infection defense has been demonstrated. However, Kit mutant mice have been reported to exhibit impaired peristalsis, and several mast cell-specific genetically modified mouse lines have since been developed and examined in detail. This study presents an important finding by logically demonstrating that the exacerbation of sepsis in Kit mice is due to alterations in the gut microbiota, and that the phenotype previously thought to be mast cell-dependent was, in fact, not.

In addition, the experiments were carefully designed using mice with matched genetic backgrounds. These findings underscore the importance of microbiota composition in interpreting immune phenotypes and highlight the need for co-housing controls in mutant mouse studies.

A major strength of this work is the robustness of the CLP data, generated over eight years by three independent researchers across two institutions with large sample sizes, lending strong support to the conclusions.

Weaknesses:

The study assesses only a limited subset of gut bacterial species, leaving the extent to which E. coli expansion contributes to the observed phenotype unclear.

We will add new data based on 16S rRNA sequencing to the revised version.

Moreover, in the cohousing experiments, there is no evidence provided to confirm successful microbiota normalization between groups.

We note that co-housing is a generally accepted method for microbiota equalization or conversion (Caruso et al., Cell Rep. 2019, Ridaura et al., Science 2013, and reviewed in Moore et al., Clin. Transl. Immunol. 2016). In any case, Kit^W/Wv mutants were made resistant to CLP by co-housing. Similar microbiota sequencing results between groups,while useful, would again only be correlative.

A more detailed analysis of the microbial composition would be necessary to strengthen the reliability of the findings.

See above

It is also important to note that Cpa3-deficient mice exhibit not only mast cell depletion but also defects in basophils and T cells. These additional immunological alterations may counterbalance one another, potentially masking phenotypic changes and complicating interpretation.

Regarding basophils in Cpa3^Cre mice, compared to wild-type mice, basophils are reduced to about 39% of normal (Feyerabend et al., Immunity 2011). In Kit^W/Wv mice, compared to wild-type mice, basophils are reduced to about 11% of normal. To our knowlegde, there has been no phenotype reported in which a reduction in basophils compensates for the loss for mast cells. Given that Kit^W/Wv mice have about threefold lower numbers of basophils, and are highly susceptible to sepsis, there is no evidence that a reduction in basophils is protective in mast cell-deficient mice. On the contrary, mice that were normal for mast cells but had their basophils depleted were more susceptible to sepsis (Piliponsky et al., Nat. Immunol. 2019). Hence, basophils appear to be protective, and their reduction increases susceptibility. In light of these data and considerations, there is no evidence for a reduction in basophils to counterbalance the loss of mast cells in Cpa3^Cre mice.

Regarding T cells, there is no evidence, and there are no reports, that Cpa3^Cre mice have defects in T cells (Feyerabend et al., Immunity 2011, Feyerabend et al., Cell Metabolism 2016). Cpa3 is weakly and transiently expressed early in the T cell lineage (Feyerabend et al., Immunity 2009; for expression levels in T cells versus mast cells, see below figure from the Immgen Database). In summary, in contrast to the reviewer's claim, there are no known defects in T cell development or functions in Cpa3^Cre mice.

Author response image 1.

Generated from the Immgen database. Shown are RNAseq gene expression levels of diverse T-cell and mast cell populations.

Furthermore, it remains to be determined whether the altered gut microbiota observed in Kit^W/Wv mice is a consequence of impaired intestinal motility, whether a similar phenotype is observed in KitW-sh/W-sh mice, and whether comparable results occur in SCF-deficient models. Addressing these questions would provide greater clarity on the contribution of mast cells versus secondary factors in the observed phenotypes.

Mice without mast cells (Cpa3^Cre mice) are as resistant to sepsis as wild-type mice. Hence, mast cells are not involved in the immunity against sepsis, and 'secondary factors' are not involved in this simple experiment (both groups of mice, wild type and Cpa3^Cre mice, were on the idential genetic background). Second, Kit^W/Wv mice are also as resistant to sepsis as wild-type mice when confronted with the identical intestinal slurry. Therefore, Kit^W/Wv mice have no immune deficit in response to sepsis. Hence, in our view, the underlying immunological question regarding the role of mast cells in sepsis has been conclusively addressed by our data. Future studies may address the mechanism that causes dysbiosis in Kit^W/Wv mice, and other Kit mutants and steel mutants could be examined as well. These questions are, however, unrelated to the role of mast cells in sepsis, or the response of Kit^W/Wv mice to sepsis, and would therefore not affect the central conclusion of our manuscript ("Susceptibility of Kit-mutant mice to sepsis caused by enteral dysbiosis, not mast cell deficiency").

Given that Kit^W/Wv mice exhibit impaired peristalsis, is the observed increase in E. coli a consequence of this dysfunction?

See above

Previous studies with BMMC reconstitution experiments have indicated that mast cells are a source of TNF - how does this align with the current findings?

It is possible that cultured and transplanted mast cells (BMMC) produce TNF. Given that we did not find a reduction in TNF levels in the peritoneal lavage or serum in mice without mast cells undergoing sepsis, under physiological conditions mast cell-derived TNF does not seem to have a measuable impact on total TNF levels.

Reviewer #2 (Public review):

Summary:

This study presents a useful finding that the high susceptibility to CLP sepsis of Kit-mutant mice is not due to mast cell deficiency, but to dysbiosis.

However, the present data are insufficient and incomplete to support the conclusion, and would benefit from more rigorous approaches. With the mechanism part strengthened, this paper would be of interest to researchers on mast cell biology and mucosal immunology.

We disagree with this view that our data are insufficient and incomplete. Our results demonstrate that mice lacking mast cells (Cpa3^Cre mice) are as resistant to sepsis as wild-type mice, indicating that mast cells do not play a detectable role in immunity against sepsis. Additionally, we show that Kit^W/Wv mice exhibit the same resistance to sepsis as wild-type mice when confronted with the identical intestinal slurry. This finding demonstrates that Kit^W/Wv mice have no immune deficit in response to sepsis. These central data are both sufficient and complete, given that our data fully address the immunological questions regarding the role of mast cells in sepsis. Our study aimed to investigate the role of mast cells in sepsis, not to examine the mechanisms of dysbiosis or associated pathological phenotypes in Kit mutant controls.

Recommendations:

(1) The authors showed that E. coli increases in the cecum of Kit-mutant mice, which causes high CLP susceptibility. However, they did not provide any evidence E. coli is responsible for the high susceptibility.

We showed that E. coli CFUs were increased in the cecum of Kit-mutant mice, but we did not state that this causes CLP susceptibility. We wrote: 'Hence, Kit^W/Wv microbiota contains high levels of E. coli, which may underlie the observed pathogenicity'. We demonstrated that intestinal slurry from Kit^W/Wv mice is more pathogenic compared to intestinal slurry from wild-type mice. However, we did not search for, or identify the bacterial species that causes this increased pathogenicity because we were adressing the role of mast cell in sepsis. 

In the Figure 3 experiments, the authors administered the same number of cecal bacteria and did not show the number of E. coli after the administration.

The samples were split and one aliquot was analysed by microbiology and the other aliquot was injected intraperitoneally. Fig. 3d shows the colony forming units (for Lactobacilli and E coli) from aliquots of cecal slurry used in the intraperitoneal injection experiments shown in Fig. 3a-c. Hence, our data show the colony forming units that were injected into the mice. It is unclear to us why this is not the key information rather than 'the number of E. coli after the administration'.

The authors should provide evidence showing that depletion of E. coli decreases susceptibility.

See response to point 1 above.

(2) The author should provide direct evidence of dysbiosis by, for example, shotgun sequencing of cecal and fecal contents.

The large increase in E coli counts in Kit^W/Wv is evidence of dysbiosis. To obtain data beyond classical microbiology, we also performed 16S rRNA sequencing which will be included in the revision.

(3) In case the authors find dysbiosis, they should analyze the mechanisms by which Kit mutation causes dysbiosis.

The mechanism that causes dysbiosis in Kit^W/Wv mice (which emerged from our work) belongs to other research areas that address the role of Kit in intestinal pathophysiology. These questions are unrelated to the role of mast cells in sepsis, or the response of Kit^W/Wv mice to sepsis. Regardless of the results of such experiments, the conclusion ("Susceptibility of Kit-mutant mice to sepsis caused by enteral dysbiosis, not mast cell deficiency") remains unaffected. In brief, further explorations of pathological phenotypes of a control mutant will not add to the core message. Along these lines, the review process and the revision shall center on making the core of a paper as conclusive as possible, and not widen a paper by requests 'tangential to the main conclusion' (Kaelin Jr. Nature 2017).

References

Caruso, R., Ono, M., Bunker, M. E., Núñez, G. & Inohara, N. Dynamic and Asymmetric Changes of the Microbial Communities after Cohousing in Laboratory Mice. Cell Rep. 27, 3401-3412.e3 (2019).

Feyerabend, T. B. et al. Deletion of Notch1 Converts Pro-T Cells to Dendritic Cells and Promotes Thymic B Cells by Cell-Extrinsic and Cell-Intrinsic Mechanisms. Immunity 30, 67–79 (2009).

Feyerabend, T. B. et al. Cre-Mediated Cell Ablation Contests Mast Cell Contribution in Models of Antibody- and T Cell-Mediated Autoimmunity. Immunity 35, 832–844 (2011).

Feyerabend, T. B., Gutierrez, D. A. & Rodewald, H.-R. Of Mouse Models of Mast Cell Deficiency and Metabolic Syndrome. Cell Metab 24, 1–2 (2016).

Kaelin Jr, W. G. Publish houses of brick, not mansions of straw. Nature 545, 387–387 (2017).

Moore, R. J. & Stanley, D. Experimental design considerations in microbiota/inflammation studies. Clin. Transl. Immunol. 5, e92 (2016).

Piliponsky, A. M. et al. Basophil-derived tumor necrosis factor can enhance survival in a sepsis model in mice. Nat. Immunol. 20, 129–140 (2019).

Ridaura, V. K. et al. Gut Microbiota from Twins Discordant for Obesity Modulate Metabolism in Mice. Science 341, 1241214 (2013).

Author response:

Reviewer #1 (Public review):

Wang et al., recorded concurrent EEG-fMRI in 107 participants during nocturnal NREM sleep to investigate brain activity and connectivity related to slow oscillations (SO), sleep spindles, and in particular their co-occurrence. The authors found SO-spindle coupling to be correlated with increased thalamic and hippocampal activity, and with increased functional connectivity from the hippocampus to the thalamus and from the thalamus to the neocortex, especially the medial prefrontal cortex (mPFC). They concluded the brain-wide activation pattern to resemble episodic memory processing, but to be dissociated from task-related processing and suggest that the thalamus plays a crucial role in coordinating the hippocampal-cortical dialogue during sleep.

The paper offers an impressively large and highly valuable dataset that provides the opportunity for gaining important new insights into the network substrate involved in SOs, spindles, and their coupling. However, the paper does unfortunately not exploit the full potential of this dataset with the analyses currently provided, and the interpretation of the results is often not backed up by the results presented. I have the following specific comments.

Thank you for your thoughtful and constructive feedback. We greatly appreciate your recognition of the strengths of our dataset and findings Below, we address your specific comments and provide responses to each point you raised to ensure our methods and results are as transparent and comprehensible as possible. We hope these revisions address your comments and further strengthen our manuscript. Thank you again for the constructive feedback.

(1) The introduction is lacking sufficient review of the already existing literature on EEG-fMRI during sleep and the BOLD-correlates of slow oscillations and spindles in particular (Laufs et al., 2007; Schabus et al., 2007; Horovitz et al., 2008; Laufs, 2008; Czisch et al., 2009; Picchioni et al., 2010; Spoormaker et al., 2010; Caporro et al., 2011; Bergmann et al., 2012; Hale et al., 2016; Fogel et al., 2017; Moehlman et al., 2018; Ilhan-Bayrakci et al., 2022). The few studies mentioned are not discussed in terms of the methods used or insights gained.

We acknowledge the need for a more comprehensive review of prior EEG-fMRI studies investigating BOLD correlates of slow oscillations and spindles. However, these articles are not all related to sleep SO or spindle. Articles (Hale et al., 2016; Horovitz et al., 2008; Laufs, 2008; Laufs, Walker, & Lund, 2007; Spoormaker et al., 2010) mainly focus on methodology for EEG-fMRI, sleep stages, or brain networks, which are not the focus of our study. Thank you again for your attention to the comprehensiveness of our literature review, and we will expand the introduction to include a more detailed discussion of the existing literature, ensuring that the contributions of previous EEG-fMRI sleep studies are adequately acknowledged.

Introduction, Page 4 Lines 62-76

“Investigating these sleep-related neural processes in humans is challenging because it requires tracking transient sleep rhythms while simultaneously assessing their widespread brain activation. Recent advances in simultaneous EEG-fMRI techniques provide a unique opportunity to explore these processes. EEG allows for precise event-based detection of neural signal, while fMRI provides insight into the broader spatial patterns of brain activation and functional connectivity (Horovitz et al., 2008; Huang et al., 2024; Laufs, 2008; Laufs, Walker, & Lund, 2007; Schabus et al., 2007; Spoormaker et al., 2010). Previous EEG-fMRI studies on sleep have focused on classifying sleep stages or examining the neural correlates of specific waves (Bergmann et al., 2012; Caporro et al., 2012; Czisch et al., 2009; Fogel et al., 2017; Hale et al., 2016; Ilhan-Bayrakcı et al., 2022; Moehlman et al., 2019; Picchioni et al., 2011). These studies have generally reported that slow oscillations are associated with widespread cortical and subcortical BOLD changes, whereas spindles elicit activation in the thalamus, as well as in several cortical and paralimbic regions. Although these findings provide valuable insights into the BOLD correlates of sleep rhythms, they often do not employ sophisticated temporal modeling (Huang et al., 2024), to capture the dynamic interactions between different oscillatory events, e.g., the coupling between SOs and spindles.”

(2) The paper falls short in discussing the specific insights gained into the neurobiological substrate of the investigated slow oscillations, spindles, and their interactions. The validity of the inverse inference approach ("Open ended cognitive state decoding"), assuming certain cognitive functions to be related to these oscillations because of the brain regions/networks activated in temporal association with these events, is debatable at best. It is also unclear why eventually only episodic memory processing-like brain-wide activation is discussed further, despite the activity of 16 of 50 feature terms from the NeuroSynth v3 dataset were significant (episodic memory, declarative memory, working memory, task representation, language, learning, faces, visuospatial processing, category recognition, cognitive control, reading, cued attention, inhibition, and action).

Thank you for pointing this out, particularly regarding the use of inverse inference approaches such as “open-ended cognitive state decoding.” Given the concerns about the indirectness of this approach, we decided to remove its related content and results from Figure 3 in the main text and include it in Supplementary Figure 7. We will refocus the main text on direct neurobiological insights gained from our EEG-fMRI analyses, particularly emphasizing the hippocampal-thalamocortical network dynamics underlying SO-spindle coupling, and we will acknowledge the exploratory nature of these findings and highlight their limitations.

Discussion, Page 17-18 Lines 323-332

“To explore functional relevance, we employed an open-ended cognitive state decoding approach using meta-analytic data (NeuroSynth: Yarkoni et al. (2011)). Although this method usefully generates hypotheses about potential cognitive processes, particularly in the absence of a pre- and post-sleep memory task, it is inherently indirect. Many cognitive terms showed significant associations (16 of 50), such as “episodic memory,” “declarative memory,” and “working memory.” We focused on episodic/declarative memory given the known link with hippocampal reactivation (Diekelmann & Born, 2010; Staresina et al., 2015; Staresina et al., 2023). Nonetheless, these inferences regarding memory reactivation should be interpreted cautiously without direct behavioral measures. Future research incorporating explicit tasks before and after sleep would more rigorously validate these potential functional claims.”

(3) Hippocampal activation during SO-spindles is stated as a main hypothesis of the paper - for good reasons - however, other regions (e.g., several cortical as well as thalamic) would be equally expected given the known origin of both oscillations and the existing sleep-EEG-fMRI literature. However, this focus on the hippocampus contrasts with the focus on investigating the key role of the thalamus instead in the Results section.

We appreciate your insight regarding the relative emphasis on hippocampal and thalamic activation in our study. We recognize that the manuscript may currently present an inconsistency between our initial hypothesis and the main focus of the results. To address this concern, we will ensure that our Introduction and Discussion section explicitly discusses both regions, highlighting the complementary roles of the hippocampus (memory processing and reactivation) and the thalamus (spindle generation and cortico-hippocampal coordination) in SO-spindle dynamics.

Introduction, Page 5 Lines 87-103

“To address this gap, our study investigates brain-wide activation and functional connectivity patterns associated with SO-spindle coupling, and employs a cognitive state decoding approach (Margulies et al., 2016; Yarkoni et al., 2011)—albeit indirectly—to infer potential cognitive functions. In the current study, we used simultaneous EEG-fMRI recordings during nocturnal naps (detailed sleep staging results are provided in the Methods and Table S1) in 107 participants. Although directly detecting hippocampal ripples using scalp EEG or fMRI is challenging, we expected that hippocampal activation in fMRI would coincide with SO-spindle coupling detected by EEG, given that SOs, spindles, and ripples frequently co-occur during NREM sleep. We also anticipated a critical role of the thalamus, particularly thalamic spindles, in coordinating hippocampal-cortical communication.

We found significant coupling between SOs and spindles during NREM sleep (N2/3), with spindle peaks occurring slightly before the SO peak. This coupling was associated with increased activation in both the thalamus and hippocampus, with functional connectivity patterns suggesting thalamic coordination of hippocampal-cortical communication. These findings highlight the key role of the thalamus in coordinating hippocampal-cortical interactions during human sleep and provide new insights into the neural mechanisms underlying sleep-dependent brain communication. A deeper understanding of these mechanisms may contribute to future neuromodulation approaches aimed at enhancing sleep-dependent cognitive function and treating sleep-related disorders.”

Discussion, Page 16-17 Lines 292-307

“When modeling the timing of these sleep rhythms in the fMRI, we observed hippocampal activation selectively during SO-spindle events. This suggests the possibility of triple coupling (SOs–spindles–ripples), even though our scalp EEG was not sufficiently sensitive to detect hippocampal ripples—key markers of memory replay (Buzsáki, 2015). Recent iEEG evidence indicates that ripples often co-occur with both spindles (Ngo, Fell, & Staresina, 2020) and SOs (Staresina et al., 2015; Staresina et al., 2023). Therefore, the hippocampal involvement during SO-spindle events in our study may reflect memory replay from the hippocampus, propagated via thalamic spindles to distributed cortical regions.

The thalamus, known to generate spindles (Halassa et al., 2011), plays a key role in producing and coordinating sleep rhythms (Coulon, Budde, & Pape, 2012; Crunelli et al., 2018), while the hippocampus is found essential for memory consolidation (Buzsáki, 2015; Diba & Buzsá ki, 2007; Singh, Norman, & Schapiro, 2022). The increased hippocampal and thalamic activity, along with strengthened connectivity between these regions and the mPFC during SO-spindle events, underscores a hippocampal-thalamic-neocortical information flow. This aligns with recent findings suggesting the thalamus orchestrates neocortical oscillations during sleep (Schreiner et al., 2022). The thalamus and hippocampus thus appear central to memory consolidation during sleep, guiding information transfer to the neocortex, e.g., mPFC.”

(4) The study included an impressive number of 107 subjects. It is surprising though that only 31 subjects had to be excluded under these difficult recording conditions, especially since no adaptation night was performed. Since only subjects were excluded who slept less than 10 min (or had excessive head movements) there are likely several datasets included with comparably short durations and only a small number of SOs and spindles and even less combined SO-spindle events. A comprehensive table should be provided (supplement) including for each subject (included and excluded) the duration of included NREM sleep, number of SOs, spindles, and SO+spindle events. Also, some descriptive statistics (mean/SD/range) would be helpful.

We appreciate your recognition of our sample size and the challenges associated with simultaneous EEG-fMRI sleep recordings. We acknowledge the importance of transparently reporting individual subject data, particularly regarding sleep duration and the number of detected SOs, spindles, and SO-spindle events. To address this, we will provide comprehensive tables in the supplementary materials, contains descriptive information about sleep-related characteristics (Table S1), as well as detailed information about sleep waves at each sleep stage for all 107 subjects(Table S2-S4), listing for each subject:(1)Different sleep stage duration; (2)Number of detected SOs; (3)Number of detected spindles; (4)Number of detected SO-spindle coupling events; (5)Density of detected SOs; (6)Density of detected spindles; (7)Density of detected SO-spindle coupling events.

However, most of the excluded participants were unable to fall asleep or had too short a sleep duration, so they basically had no NREM sleep period, so it was impossible to count the NREM sleep duration, SO, spindle, and coupling numbers.

Supplementary Materials, Page 42-54, Table S1-S4

(Consider of the length, we do not list all the tables here. Please refer to the revised manuscript.)

(5) Was the 20-channel head coil dedicated for EEG-fMRI measurements? How were the electrode cables guided through/out of the head coil? Usually, the 64-channel head coil is used for EEG-fMRI measurements in a Siemens PRISMA 3T scanner, which has a cable duct at the back that allows to guide the cables straight out of the head coil (to minimize MR-related artifacts). The choice for the 20-channel head coil should be motivated. Photos of the recording setup would also be helpful.

Thank you for your comment regarding our choice of the 20-channel head coil for EEG-fMRI measurements. We acknowledge that the 64-channel head coil is commonly used in Siemens PRISMA 3T scanners; however, the 20-channel coil was selected due to specific practical and technical considerations in our study. In particular, the 20-channel head coil was compatible with our EEG system and ensured sufficient signal-to-noise ratio (SNR) for both EEG and fMRI acquisition. The EEG electrode cables were guided through the lateral and posterior openings of the head coil, secured with foam padding to reduce motion and minimize MR-related artifacts. Moreover, given the extended nature of nocturnal sleep recordings, the 20-channel coil allowed us to maintain participant comfort while still achieving high-quality simultaneous EEG-fMRI data.

We have made this clearer in the revised manuscript.

Methods, Page 20 Lines 385-392

“All MRI data were acquired using a 20-channel head coil on a research-dedicated 3-Tesla Siemens Magnetom Prisma MRI scanner. Earplugs and cushions were provided for noise protection and head motion restriction. We chose the 20-channel head coil because it was compatible with our EEG system and ensured sufficient signal-to-noise ratio (SNR) for both EEG and fMRI acquisition. The EEG electrode cables were guided through the lateral and posterior openings of the head coil, secured with foam padding to reduce motion and minimize MR-related artifacts. Moreover, given the extended nature of nocturnal sleep recordings, the 20-channel coil helped maintain participant comfort while still achieving high-quality simultaneous EEG-fMRI data.”

(6) Was the EEG sampling synchronized to the MR scanner (gradient system) clock (the 10 MHz signal; not referring to the volume TTL triggers here)? This is a requirement for stable gradient artifact shape over time and thus accurate gradient noise removal.

Thank you for raising this important point. We confirm that the EEG sampling was synchronized to the MR scanner’s 10 MHz gradient system clock, ensuring a stable gradient artifact shape over time and enabling accurate artifact removal. This synchronization was achieved using the standard clock synchronization interface of the EEG amplifier, minimizing timing jitter and drift. As a result, the gradient artifact waveform remained stable across volumes, allowing for more effective artifact correction during preprocessing. We appreciate your attention to this critical aspect of EEG-fMRI data acquisition.

We have made this clearer in the revised manuscript.

Methods, Page 19-20 Lines 371-383

“EEG was recorded simultaneously with fMRI data using an MR-compatible EEG amplifier system (BrainAmps MR-Plus, Brain Products, Germany), along with a specialized electrode cap. The recording was done using 64 channels in the international 10/20 system, with the reference channel positioned at FCz. In order to adhere to polysomnography (PSG) recording standards, six electrodes were removed from the EEG cap: one for electrocardiogram (ECG) recording, two for electrooculogram (EOG) recording, and three for electromyogram (EMG) recording. EEG data was recorded at a sample rate of 5000 Hz, the resistance of the reference and ground channels was kept below 10 kΩ, and the resistance of the other channels was kept below 20 kΩ. To synchronize the EEG and fMRI recordings, the BrainVision recording software (BrainProducts, Germany) was utilized to capture triggers from the MRI scanner. The EEG sampling was synchronized to the MR scanner’s 10 MHz gradient system clock, ensuring a stable gradient artifact shape over time and enabling accurate artifact removal. This was achieved via the standard clock synchronization interface of the EEG amplifier, minimizing timing jitter and drift.”

(7) The TR is quite long and the voxel size is quite large in comparison to state-of-the-art EPI sequences. What was the rationale behind choosing a sequence with relatively low temporal and spatial resolution?

We acknowledge that our chosen TR and voxel size are relatively long and large compared to state-of-the-art EPI sequences. This decision was made to optimize the signal-to-noise ratio (SNR) and reduce susceptibility-related distortions, which are particularly critical in EEG-fMRI sleep studies where head motion and physiological noise can be substantial. A longer TR allowed us to sample whole-brain activity with sufficient coverage, while a larger voxel size helped enhance BOLD sensitivity and minimize partial volume effects in deep brain structures such as the thalamus and hippocampus, which are key regions of interest in our study. We appreciate your concern and hope this clarification provides sufficient rationale for our sequence parameters.

We have made this clearer in the revised manuscript.

Methods, Page 20-21 Lines 398-408

“Then, the “sleep” session began after the participants were instructed to try and fall asleep. For the functional scans, whole-brain images were acquired using k-space and steady-state T2*-weighted gradient echo-planar imaging (EPI) sequence that is sensitive to the BOLD contrast. This measures local magnetic changes caused by changes in blood oxygenation that accompany neural activity (sequence specification: 33 slices in interleaved ascending order, TR = 2000 ms, TE = 30 ms, voxel size = 3.5 × 3.5 × 4.2 mm³, FA = 90°, matrix = 64 × 64, gap = 0.7 mm). A relatively long TR and larger voxel size were chosen to optimize SNR and reduce susceptibility-related distortions, which are critical in EEG-fMRI sleep studies where head motion and physiological noise can be substantial. The longer TR allowed whole-brain coverage with sufficient temporal resolution, while the larger voxel size helped enhance BOLD sensitivity and minimize partial volume effects in deep brain structures (e.g., the thalamus and hippocampus), which are key regions of interest in this study.”

(8) The anatomically defined ROIs are quite large. It should be elaborated on how this might reduce sensitivity to sleep rhythm-specific activity within sub-regions, especially for the thalamus, which has distinct nuclei involved in sleep functions.

We appreciate your insight regarding the use of anatomically defined ROIs and their potential limitations in detecting sleep rhythm-specific activity within sub-regions, particularly in the thalamus. Given the distinct functional roles of thalamic nuclei in sleep processes, we acknowledge that using a single, large thalamic ROI may reduce sensitivity to localized activity patterns. To address this, we will discuss this limitation in the revised manuscript, acknowledging that our approach prioritizes whole-structure effects but may not fully capture nucleus-specific contributions.

Discussion, Page 18 Lines 333-341

“Despite providing new insights, our study has several limitations. First, our scalp EEG did not directly capture hippocampal ripples, preventing us from conclusively demonstrating triple coupling. Second, the combination of EEG-fMRI and the lack of a memory task limit our ability to parse fine-grained BOLD responses at the DOWN- vs. UP-states of SOs and link observed activations to behavioral outcomes. Third, the use of large anatomical ROIs may mask subregional contributions of specific thalamic nuclei or hippocampal subfields. Finally, without a memory task, we cannot establish a direct behavioral link between sleep-rhythm-locked activation and memory consolidation. Future studies combining techniques such as ultra-high-field fMRI or iEEG with cognitive tasks may refine our understanding of subregional network dynamics and functional significance during sleep.”

(9) The study reports SO & spindle amplitudes & densities, as well as SO+spindle coupling, to be larger during N2/3 sleep compared to N1 and REM sleep, which is trivial but can be seen as a sanity check of the data. However, the amount of SOs and spindles reported for N1 and REM sleep is concerning, as per definition there should be hardly any (if SOs or spindles occur in N1 it becomes by definition N2, and the interval between spindles has to be considerably large in REM to still be scored as such). Thus, on the one hand, the report of these comparisons takes too much space in the main manuscript as it is trivial, but on the other hand, it raises concerns about the validity of the scoring.

We appreciate your concern regarding the reported presence of SOs and spindles in N1 and REM sleep and the potential implications. Our detection method for detecting SO, spindle, and coupling were originally designed only for N2&N3 sleep data based on the characteristics of the data itself, and this method is widely recognized and used in the sleep research (Hahn et al., 2020; Helfrich et al., 2019; Helfrich et al., 2018; Ngo, Fell, & Staresina, 2020; Schreiner et al., 2022; Schreiner et al., 2021; Staresina et al., 2015; Staresina et al., 2023). While, because the detection methods for SO and spindle are based on percentiles, this method will always detect a certain number of events when used for other stages (N1 and REM) sleep data, but the differences between these events and those detected in stage N23 remain unclear. We will acknowledge the reasons for these results in the Methods section and emphasize that they are used only for sanity checks.

Methods, Page 25 Lines 515-524

“We note that the above methods for detecting SOs, spindles, and their couplings were originally developed for N2 and N3 sleep data, based on the specific characteristics of these stages. These methods are widely recognized in sleep research (Hahn et al., 2020; Helfrich et al., 2019; Helfrich et al., 2018; Ngo, Fell, & Staresina, 2020; Schreiner et al., 2022; Schreiner et al., 2021; Staresina et al., 2015; Staresina et al., 2023). However, because this percentile-based detection approach will inherently identify a certain number of events if applied to other stages (e.g., N1 and REM), the nature of these events in those stages remains unclear compared to N2/N3. We nevertheless identified and reported the detailed descriptive statistics of these sleep rhythms in all sleep stages, under the same operational definitions, both for completeness and as a sanity check. Within the same subject, there should be more SOs, spindles, and their couplings in N2/N3 than in N1 or REM (see also Figure S2-S4, Table S1-S4).”

(10) Why was electrode F3 used to quantify the occurrence of SOs and spindles? Why not a midline frontal electrode like Fz (or a number of frontal electrodes for SOs) and Cz (or a number of centroparietal electrodes) for spindles to be closer to their maximum topography?

We appreciate your suggestion regarding electrode selection for SO and spindle quantification. Our choice of F3 was primarily based on previous studies (Massimini et al., 2004; Molle et al., 2011), where bilateral frontal electrodes are commonly used for detecting SOs and spindles. Additionally, we considered the impact of MRI-related noise and, after a comprehensive evaluation, determined that F3 provided an optimal balance between signal quality and artifact minimization. We also acknowledge that alternative electrode choices, such as Fz for SOs and Cz for spindles, could provide additional insights into their topographical distributions.

(11) Functional connectivity (hippocampus -> thalamus -> cortex (mPFC)) is reported to be increased during SO-spindle coupling and interpreted as evidence for coordination of hippocampo-neocortical communication likely by thalamic spindles. However, functional connectivity was only analysed during coupled SO+spindle events, not during isolated SOs or isolated spindles. Without the direct comparison of the connectivity patterns between these three events, it remains unclear whether this is specific for coupled SO+spindle events or rather associated with one or both of the other isolated events. The PPIs need to be conducted for those isolated events as well and compared statistically to the coupled events.

We appreciate your critical perspective on our functional connectivity analysis and the interpretation of hippocampus-thalamus-cortex (mPFC) interactions during SO-spindle coupling. We acknowledge that, in the current analysis, functional connectivity was only examined during coupled SO-spindle events, without direct comparison to isolated SOs or isolated spindles. To address this concern, we have conducted PPI analyses for all three ROIs(Hippocampus, Thalamus, mPFC) and all three event types (SO-spindle couplings, isolated SOs, and isolated spindles). Our results indicate that neither isolated SOs nor isolated Spindles yielded significant connectivity changes in all three ROIs, as all failed to survive multiple comparison corrections. This suggests that the observed connectivity increase is specific to SO-spindle coupling, rather than being independently driven by either SOs or spindles alone.

Results, Page 14 Lines 248-255

“Crucially, the interaction between FC and SO-spindle coupling revealed that only the functional connectivity of hippocampus -> thalamus (ROI analysis, t₍₁₀₆₎ = 1.86, p = 0.0328) and thalamus -> mPFC (ROI analysis, t₍₁₀₆₎ = 1.98, p = 0.0251) significantly increased during SO-spindle coupling, with no significant changes in all other pathways (Fig. 4e). We also conducted PPI analyses for the other two events (SOs and spindles), and neither yielded significant connectivity changes in the three ROIs, as all failed to survive whole-brain FWE correction at the cluster level (p < 0.05). Together, these findings suggest that the thalamus, likely via spindles, coordinates hippocampal-cortical communication selectively during SO-spindle coupling, but not isolated SOs or spindle events alone.”

(12) The limited temporal resolution of fMRI does indeed not allow for easily distinguishing between fMRI activation patterns related to SO-up- vs. SO-down-states. For this, one could try to extract the amplitudes of SO-up- and SO-down-states separately for each SO event and model them as two separate parametric modulators (with the risk of collinearity as they are likely correlated).

We appreciate your insightful comment regarding the challenge of distinguishing fMRI activation patterns related to SO-up vs. SO-down states due to the limited temporal resolution of fMRI. While our current analysis does not differentiate between these two phases, we acknowledge that separately modeling SO-up and SO-down states using parametric modulators could provide a more refined understanding of their distinct neural correlates. However, as you notes, this approach carries the risk of collinearity, and there is indeed a high correlation between the two amplitudes across all subjects in our results (r=0.98). Future studies could explore more on leveraging high-temporal-resolution techniques. While implementing this in the current study is beyond our scope, we will acknowledge this limitation in the Discussion section.

Discussion, Page 17 Lines 308-322

“An intriguing aspect of our findings is the reduced DMN activity during SOs when modeled at the SO trough (DOWN-state). This reduced DMN activity may reflect large-scale neural inhibition characteristic of the SO trough. The DMN is typically active during internally oriented cognition (e.g., self-referential processing or mind-wandering) and is suppressed during external stimuli processing (Yeshurun, Nguyen, & Hasson, 2021). It is unlikely, however, that this suppression of DMN during SO events is related to a shift from internal cognition to external responses given it is during deep sleep time. Instead, it could be driven by the inherent rhythmic pattern of SOs, which makes it difficult to separate UP- from DOWN-states (the two temporal regressors were highly correlated, and similar brain activation during SOs events was obtained if modelled at the SO peak instead, Fig. S5). Since the amplitude at the SO trough is consistently larger than that at the SO peak, the neural activation we detected may primarily capture the large-scale inhibition from DOWN-state. Interestingly, no such DMN reduction was found during SO-spindle coupling, implying that coupling may involve distinct neural dynamics that partially re-engage DMN-related processes, possibly reflecting memory-related reactivation. Future research using high-temporal-resolution techniques like iEEG could clarify these possibilities.”

Discussion, Page 18 Lines 333-341

“Despite providing new insights, our study has several limitations. First, our scalp EEG did not directly capture hippocampal ripples, preventing us from conclusively demonstrating triple coupling. Second, the combination of EEG-fMRI and the lack of a memory task limit our ability to parse fine-grained BOLD responses at the DOWN- vs. UP-states of SOs and link observed activations to behavioral outcomes. Third, the use of large anatomical ROIs may mask subregional contributions of specific thalamic nuclei or hippocampal subfields. Finally, without a memory task, we cannot establish a direct behavioral link between sleep-rhythm-locked activation and memory consolidation. Future studies combining techniques such as ultra-high-field fMRI or iEEG with cognitive tasks may refine our understanding of subregional network dynamics and functional significance during sleep.”

(13) L327: "It is likely that our findings of diminished DMN activity reflect brain activity during the SO DOWN-state, as this state consistently shows higher amplitude compared to the UP-state within subjects, which is why we modelled the SO trough as its onset in the fMRI analysis." This conclusion is not justified as the fact that SO down-states are larger in amplitude does not mean their impact on the BOLD response is larger.

We appreciate your concern regarding our interpretation of diminished DMN activity reflecting the SO down-state. We acknowledge that the current expression is somewhat misleading, and our interpretation of it is: it could be driven by the inherent rhythmic pattern of SOs, which makes it difficult to separate UP- from DOWN-states (the two temporal regressors were highly correlated, and similar brain activation during SOs events was obtained if modelled at the SO peak instead). Since the amplitude at the SO trough is consistently larger than that at the SO peak, the neural activation we detected may primarily capture the large-scale inhibition from DOWN-state. And we will make this clear in the Discussion section.

Discussion, Page 17 Lines 308-322

“An intriguing aspect of our findings is the reduced DMN activity during SOs when modeled at the SO trough (DOWN-state). This reduced DMN activity may reflect large-scale neural inhibition characteristic of the SO trough. The DMN is typically active during internally oriented cognition (e.g., self-referential processing or mind-wandering) and is suppressed during external stimuli processing (Yeshurun, Nguyen, & Hasson, 2021). It is unlikely, however, that this suppression of DMN during SO events is related to a shift from internal cognition to external responses given it is during deep sleep time. Instead, it could be driven by the inherent rhythmic pattern of SOs, which makes it difficult to separate UP- from DOWN-states (the two temporal regressors were highly correlated, and similar brain activation during SOs events was obtained if modelled at the SO peak instead, Fig. S5). Since the amplitude at the SO trough is consistently larger than that at the SO peak, the neural activation we detected may primarily capture the large-scale inhibition from DOWN-state. Interestingly, no such DMN reduction was found during SO-spindle coupling, implying that coupling may involve distinct neural dynamics that partially re-engage DMN-related processes, possibly reflecting memory-related reactivation. Future research using high-temporal-resolution techniques like iEEG could clarify these possibilities.”

(14) Line 77: "In the current study, while directly capturing hippocampal ripples with scalp EEG or fMRI is difficult, we expect to observe hippocampal activation in fMRI whenever SOs-spindles coupling is detected by EEG, if SOs- spindles-ripples triple coupling occurs during human NREM sleep". Not all SO-spindle events are associated with ripples (Staresina et al., 2015), but hippocampal activation may also be expected based on the occurrence of spindles alone (Bergmann et al., 2012).

We appreciate your clarification regarding the relationship between SO-spindle coupling and hippocampal ripples. We acknowledge that not all SO-spindle events are necessarily accompanied by ripples (Staresina et al., 2015). However, based on previous research, we found that hippocampal ripples are significantly more likely to occur during SO-spindle coupling events. This suggests that while ripple occurrence is not guaranteed, SO-spindle coupling creates a favorable network state for ripple generation and potential hippocampal activation. To ensure accuracy, we will revise the manuscript to delete this misleading sentence in the Introduction section and acknowledge in the Discussion that our results cannot conclusively directly observe the triple coupling of SO, spindle, and hippocampal ripples.

Discussion, Page 18 Lines 333-341

“Despite providing new insights, our study has several limitations. First, our scalp EEG did not directly capture hippocampal ripples, preventing us from conclusively demonstrating triple coupling. Second, the combination of EEG-fMRI and the lack of a memory task limit our ability to parse fine-grained BOLD responses at the DOWN- vs. UP-states of SOs and link observed activations to behavioral outcomes. Third, the use of large anatomical ROIs may mask subregional contributions of specific thalamic nuclei or hippocampal subfields. Finally, without a memory task, we cannot establish a direct behavioral link between sleep-rhythm-locked activation and memory consolidation. Future studies combining techniques such as ultra-high-field fMRI or iEEG with cognitive tasks may refine our understanding of subregional network dynamics and functional significance during sleep.”

Reviewer #2 (Public review):

In this study, Wang and colleagues aimed to explore brain-wide activation patterns associated with NREM sleep oscillations, including slow oscillations (SOs), spindles, and SO-spindle coupling events. Their findings reveal that SO-spindle events corresponded with increased activation in both the thalamus and hippocampus. Additionally, they observed that SO-spindle coupling was linked to heightened functional connectivity from the hippocampus to the thalamus, and from the thalamus to the medial prefrontal cortex-three key regions involved in memory consolidation and episodic memory processes.

This study's findings are timely and highly relevant to the field. The authors' extensive data collection, involving 107 participants sleeping in an fMRI while undergoing simultaneous EEG recording, deserves special recognition. If shared, this unique dataset could lead to further valuable insights. While the conclusions of the data seem overall well supported by the data, some aspects with regard to the detection of sleep oscillations need clarification.

The authors report that coupled SO-spindle events were most frequent during NREM sleep (2.46 [plus minus] 0.06 events/min), but they also observed a surprisingly high occurrence of these events during N1 and REM sleep (2.23 [plus minus] 0.09 and 2.32 [plus minus] 0.09 events/min, respectively), where SO-spindle coupling would not typically be expected. Combined with the relatively modest SO amplitudes reported (~25 µV, whereas >75 µV would be expected when using mastoids as reference electrodes), this raises the possibility that the parameters used for event detection may not have been conservative enough - or that sleep staging was inaccurately performed. This issue could present a significant challenge, as the fMRI findings are largely dependent on the reliability of these detected events.

Thank you very much for your thorough and encouraging review. We appreciate your recognition of the significance and relevance of our study and dataset, particularly in highlighting how simultaneous EEG-fMRI recordings can provide complementary insights into the temporal dynamics of neural oscillations and their associated spatial activation patterns during sleep. In the sections that follow, we address each of your comments in detail. We have revised the text and conducted additional analyses wherever possible to strengthen our argument, clarify our methodological choices. We believe these revisions improve the clarity and rigor of our work, and we thank you for helping us refine it.

We appreciate your insightful comments regarding the detection of sleep oscillations. Our methods for detecting SOs, spindles, and their couplings were originally developed for N2 and N3 sleep data, based on the specific characteristics of these stages. These methods are widely recognized in sleep research (Hahn et al., 2020; Helfrich et al., 2019; Helfrich et al., 2018; Ngo, Fell, & Staresina, 2020; Schreiner et al., 2022; Schreiner et al., 2021; Staresina et al., 2015; Staresina et al., 2023). However, because this percentile-based detection approach will inherently identify a certain number of events if applied to other stages (e.g., N1 and REM), the nature of these events in those stages remains unclear compared to N2/N3. We nevertheless identified and reported the detailed descriptive statistics of these sleep rhythms in all sleep stages, under the same operational definitions, both for completeness and as a sanity check. Within the same subject, there should be more SOs, spindles, and their couplings in N2/N3 than in N1 or REM. We will acknowledge the reasons for these results in the Methods section and emphasize that they are used only for sanity checks.

Regarding the reported SO amplitudes (~25 µV), during preprocessing, we applied the Signal Space Projection (SSP) method to more effectively remove MRI gradient artifacts and cardiac pulse noise. While this approach enhances data quality, it also reduces overall signal power, leading to systematically lower reported amplitudes. Despite this, our SO detection in NREM sleep (especially N2/N3) remain physiologically meaningful and are consistent with previous fMRI studies using similar artifact removal techniques. We appreciate your careful evaluation and valuable suggestions.

In addition, we will provide comprehensive tables in the supplementary materials, contains descriptive information about sleep-related characteristics (Table S1), as well as detailed information about sleep waves at each sleep stage for all 107 subjects(Table S2-S4), listing for each subject:(1)Different sleep stage duration; (2)Number of detected SOs; (3)Number of detected spindles; (4)Number of detected SO-spindle coupling events; (2)Density of detected SOs; (3)Density of detected spindles; (4)Density of detected SO-spindle coupling events.

Methods, Page 25 Lines 515-524

“We note that the above methods for detecting SOs, spindles, and their couplings were originally developed for N2 and N3 sleep data, based on the specific characteristics of these stages. These methods are widely recognized in sleep research (Hahn et al., 2020; Helfrich et al., 2019; Helfrich et al., 2018; Ngo, Fell, & Staresina, 2020; Schreiner et al., 2022; Schreiner et al., 2021; Staresina et al., 2015; Staresina et al., 2023). However, because this percentile-based detection approach will inherently identify a certain number of events if applied to other stages (e.g., N1 and REM), the nature of these events in those stages remains unclear compared to N2/N3. We nevertheless identified and reported the detailed descriptive statistics of these sleep rhythms in all sleep stages, under the same operational definitions, both for completeness and as a sanity check. Within the same subject, there should be more SOs, spindles, and their couplings in N2/N3 than in N1 or REM (see also Figure S2-S4, Table S1-S4).”

Supplementary Materials, Page 42-54, Table S1-S4

(Consider of the length, we do not list all the tables here. Please refer to the revised manuscript.)

Reviewer #3 (Public review):

Summary:

Wang et al., examined the brain activity patterns during sleep, especially when locked to those canonical sleep rhythms such as SO, spindle, and their coupling. Analyzing data from a large sample, the authors found significant coupling between spindles and SOs, particularly during the upstate of the SO. Moreover, the authors examined the patterns of whole-brain activity locked to these sleep rhythms. To understand the functional significance of these brain activities, the authors further conducted open-ended cognitive state decoding and found a variety of cognitive processing may be involved during SO-spindle coupling and during other sleep events. The authors next investigated the functional connectivity analyses and found enhanced connectivity between the hippocampus, the thalamus, and the medial PFC. These results reinforced the theoretical model of sleep-dependent memory consolidation, such that SO-spindle coupling is conducive to systems-level memory reactivation and consolidation.

Strengths:

There are obvious strengths in this work, including the large sample size, state-of-the-art neuroimaging and neural oscillation analyses, and the richness of results.

Weaknesses:

Despite these strengths and the insights gained, there are weaknesses in the design, the analyses, and inferences.

Thank you for your detailed and thoughtful review of our manuscript. We are delighted that you recognize our advanced analysis methods and rich results of neuroimaging and neural oscillations as well as the large sample size data. In the following sections, we provide detailed responses to each of your comments. And we have revised the text and conducted additional analyses to strengthen our arguments and clarify our methodological choices. We believe these revisions enhance the clarity and rigor of our work, and we sincerely appreciate your thoughtful feedback in helping us refine the manuscript.

(1) A repeating statement in the manuscript is that brain activity could indicate memory reactivation and thus consolidation. This is indeed a highly relevant question that could be informed by the current data/results. However, an inherent weakness of the design is that there is no memory task before and after sleep. Thus, it is difficult (if not impossible) to make a strong argument linking SO/spindle/coupling-locked brain activity with memory reactivation or consolidation.

We appreciate your suggestion regarding the lack of a pre- and post-sleep memory task in our study design. We acknowledge that, in the absence of behavioral measures, it is hard to directly link SO-spindle coupling to memory consolidation in an outcome-driven manner. Our interpretation is instead based on the well-established role of these oscillations in memory processes, as demonstrated in previous studies. We sincerely appreciate this feedback and will adjust our Discussion accordingly to reflect a more precise interpretation of our findings.

Discussion, Page 18 Lines 333-341

“Despite providing new insights, our study has several limitations. First, our scalp EEG did not directly capture hippocampal ripples, preventing us from conclusively demonstrating triple coupling. Second, the combination of EEG-fMRI and the lack of a memory task limit our ability to parse fine-grained BOLD responses at the DOWN- vs. UP-states of SOs and link observed activations to behavioral outcomes. Third, the use of large anatomical ROIs may mask subregional contributions of specific thalamic nuclei or hippocampal subfields. Finally, without a memory task, we cannot establish a direct behavioral link between sleep-rhythm-locked activation and memory consolidation. Future studies combining techniques such as ultra-high-field fMRI or iEEG with cognitive tasks may refine our understanding of subregional network dynamics and functional significance during sleep.”

(2) Relatedly, to understand the functional implications of the sleep rhythm-locked brain activity, the authors employed the "open-ended cognitive state decoding" method. While this method is interesting, it is rather indirect given that there were no behavioral indices in the manuscript. Thus, discussions based on these analyses are speculative at best. Please either tone down the language or find additional evidence to support these claims.

Moreover, the results from this method are difficult to understand. Figure 3e showed that for all three types of sleep events (SO, spindle, SO-spindle), the same mental states (e.g., working memory, episodic memory, declarative memory) showed opposite directions of activation (left and right panels showed negative and positive activation, respectively). How to interpret these conflicting results? This ambiguity is also reflected by the term used: declarative memory and episodic memories are both indexed in the results. Yet these two processes can be largely overlapped. So which specific memory processes do these brain activity patterns reflect? The Discussion shall discuss these results and the limitations of this method.

We appreciate your critical assessment of the open-ended cognitive state decoding method and its interpretational challenges. Given the concerns about the indirectness of this approach, we decided to remove its related content and results from Figure 3 in the main text and include it in Supplementary Figure 7.

Due to the complexity of memory-related processes, we acknowledge that distinguishing between episodic and declarative memory based solely on this approach is not straightforward. We will revise the Supplementary Materials to explicitly discuss these limitations and clarify that our findings do not isolate specific cognitive processes but rather suggest general associations with memory-related networks.

Discussion, Page 17-18 Lines 323-332

“To explore functional relevance, we employed an open-ended cognitive state decoding approach using meta-analytic data (NeuroSynth: Yarkoni et al. (2011)). Although this method usefully generates hypotheses about potential cognitive processes, particularly in the absence of a pre- and post-sleep memory task, it is inherently indirect. Many cognitive terms showed significant associations (16 of 50), such as “episodic memory,” “declarative memory,” and “working memory.” We focused on episodic/declarative memory given the known link with hippocampal reactivation (Diekelmann & Born, 2010; Staresina et al., 2015; Staresina et al., 2023). Nonetheless, these inferences regarding memory reactivation should be interpreted cautiously without direct behavioral measures. Future research incorporating explicit tasks before and after sleep would more rigorously validate these potenial functional claims.”

(3) The coupling strength is somehow inconsistent with prior results (Hahn et al., 2020, eLife, Helfrich et al., 2018, Neuron). Specifically, Helfrich et al. showed that among young adults, the spindle is coupled to the peak of the SO. Here, the authors reported that the spindles were coupled to down-to-up transitions of SO and before the SO peak. It is possible that participants' age may influence the coupling (see Helfrich et al., 2018). Please discuss the findings in the context of previous research on SO-spindle coupling.

We appreciate your concern regarding the temporal characteristics of SO-spindle coupling. We acknowledge that the SO-spindle coupling phase results in our study are not identical to those reported by Hahn et al. (2020); Helfrich et al. (2018). However, these differences may arise due to slight variations in event detection parameters, which can influence the precise phase estimation of coupling. Notably, Hahn et al. (2020) also reported slight discrepancies in their group-level coupling phase results, highlighting that methodological differences can contribute to variability across studies. Furthermore, our findings are consistent with those of Schreiner et al. (2021), further supporting the robustness of our observations.

That said, we acknowledge that our original description of SO-spindle coupling as occurring at the "transition from the lower state to the upper state" was not entirely precise. The -π/2 phase represents the true transition point, while our observed coupling phase is actually closer to the SO peak rather than strictly at the transition. We will revise this statement in the manuscript to ensure clarity and accuracy in describing the coupling phase.

Discussion, Page 16 Lines 283-291

“Our data provide insights into the neurobiological underpinnings of these sleep rhythms. SOs, originating mainly in neocortical areas such as the mPFC, alternate between DOWN- and UP-states. The thalamus generates sleep spindles, which in turn couple with SOs. Our finding that spindle peaks consistently occurred slightly before the UP-state peak of SOs (in 83 out of 107 participants), concurs with prior studies, including Schreiner et al. (2021). Yet it differs from some results suggesting spindles might peak right at the SO UP-state (Hahn et al., 2020; Helfrich et al., 2018). Such discrepancies could arise from differences in detection algorithms, participant age (Helfrich et al., 2018), or subtle variations in cortical-thalamic timing. Nonetheless, these results underscore the importance of coordinated SO-spindle interplay in supporting sleep-dependent processes.”

(4) The discussion is rather superficial with only two pages, without delving into many important arguments regarding the possible functional significance of these results. For example, the author wrote, "This internal processing contrasts with the brain patterns associated with external tasks, such as working memory." Without any references to working memory, and without delineating why WM is considered as an external task even working memory operations can be internal. Similarly, for the interesting results on SO and reduced DMN activity, the authors wrote "The DMN is typically active during wakeful rest and is associated with self-referential processes like mind-wandering, daydreaming, and task representation (Yeshurun, Nguyen, & Hasson, 2021). Its reduced activity during SOs may signal a shift towards endogenous processes such as memory consolidation." This argument is flawed. DMN is active during self-referential processing and mind-wandering, i.e., when the brain shifts from external stimuli processing to internal mental processing. During sleep, endogenous memory reactivation and consolidation are also part of the internal mental processing given the lack of external environmental stimulation. So why during SO or during memory consolidation, the DMN activity would be reduced? Were there differences in DMN activity between SO and SO-spindle coupling events?

We appreciate your concerns regarding the brevity of the discussion and the need for clearer theoretical arguments. We will expand this section to provide more in-depth interpretations of our findings in the context of prior literature. Regarding working memory (WM), we acknowledge that our phrasing was ambiguous. We will modify this statement in the Discussion section.

For the SO-related reduction in DMN activity, we recognize the need for a more precise explanation. This reduced DMN activity may reflect large-scale neural inhibition characteristic of the SO trough. The DMN is typically active during internally oriented cognition (e.g., self-referential processing or mind-wandering) and is suppressed during external stimuli processing (Yeshurun, Nguyen, & Hasson, 2021). It is unlikely, however, that this suppression of DMN during SO events is related to a shift from internal cognition to external responses given it is during deep sleep time. Instead, it could be driven by the inherent rhythmic pattern of SOs, which makes it difficult to separate UP- from DOWN-states (the two temporal regressors were highly correlated, and similar brain activation during SOs events was obtained if modelled at the SO peak instead). Since the amplitude at the SO trough is consistently larger than that at the SO peak, the neural activation we detected may primarily capture the large-scale inhibition from DOWN-state.

To address your final question, we have conducted the additional post hoc comparison of DMN activity between isolated SOs and SO-spindle coupling events. Our results indicate that

DMN activation during SOs was significantly lower than during SO-spindle coupling (t₍₁₀₆₎ = -4.17, p < 1e-4). This suggests that SO-spindle coupling may involve distinct neural dynamics that partially re-engage DMN-related processes, possibly reflecting memory-related reactivation. We appreciate your constructive feedback and will integrate these expanded analyses and discussions into our revised manuscript.

Results, Page 11 Lines 199-208

“Spindles were correlated with positive activation in the thalamus (ROI analysis, t₍₁₀₆₎ = 15.39, p < 1e-4), the anterior cingulate cortex (ACC), and the putamen, alongside deactivation in the DMN (Fig. 3c). Notably, SO-spindle coupling was linked to significant activation in both the thalamus (ROI analysis, t₍₁₀₆₎ \= 3.38, p = 0.0005) and the hippocampus (ROI analysis, t₍₁₀₆₎ \= 2.50, p = 0.0070, Fig. 3d). However, no decrease in DMN activity was found during SO-spindle coupling, and DMN activity during SO was significantly lower than during coupling (ROI analysis, t₍₁₀₆₎ \= -4.17, p < 1e-4). For more detailed activation patterns, see Table S5-S7. We also varied the threshold used to detect SO events to assess its effect on hippocampal activation during SO-spindle coupling and observed that hippocampal activation remained significant when the percentile thresholds for SO detection ranged between 71% and 80% (see Fig. S6).”

Discussion, Page 17-18 Lines 308-332

“An intriguing aspect of our findings is the reduced DMN activity during SOs when modeled at the SO trough (DOWN-state). This reduced DMN activity may reflect large-scale neural inhibition characteristic of the SO trough. The DMN is typically active during internally oriented cognition (e.g., self-referential processing or mind-wandering) and is suppressed during external stimuli processing (Yeshurun, Nguyen, & Hasson, 2021). It is unlikely, however, that this suppression of DMN during SO events is related to a shift from internal cognition to external responses given it is during deep sleep time. Instead, it could be driven by the inherent rhythmic pattern of SOs, which makes it difficult to separate UP- from DOWN-states (the two temporal regressors were highly correlated, and similar brain activation during SOs events was obtained if modelled at the SO peak instead, Fig. S5). Since the amplitude at the SO trough is consistently larger than that at the SO peak, the neural activation we detected may primarily capture the large-scale inhibition from DOWN-state. Interestingly, no such DMN reduction was found during SO-spindle coupling, implying that coupling may involve distinct neural dynamics that partially re-engage DMN-related processes, possibly reflecting memory-related reactivation. Future research using high-temporal-resolution techniques like iEEG could clarify these possibilities.

To explore functional relevance, we employed an open-ended cognitive state decoding approach using meta-analytic data (NeuroSynth: Yarkoni et al. (2011)). Although this method usefully generates hypotheses about potential cognitive processes, particularly in the absence of a pre- and post-sleep memory task, it is inherently indirect. Many cognitive terms showed significant associations (16 of 50), such as “episodic memory,” “declarative memory,” and “working memory.” We focused on episodic/declarative memory given the known link with hippocampal reactivation (Diekelmann & Born, 2010; Staresina et al., 2015; Staresina et al., 2023). Nonetheless, these inferences regarding memory reactivation should be interpreted cautiously without direct behavioral measures. Future research incorporating explicit tasks before and after sleep would more rigorously validate these potential functional claims.”

Reviewing Editor Comment:

The reviewers think that you are working on a relevant and important topic. They are praising the large sample size used in the study. The reviewers are not all in line regarding the overall significance of the findings, but they all agree the paper would strongly benefit from some extra work, as all reviewers raise various critical points that need serious consideration.

We appreciate your recognition of the relevance and importance of our study, as well as your acknowledgment of the large sample size as a strength of our work. We understand that there are differing perspectives regarding the overall significance of our findings, and we value the constructive critiques provided. We are committed to addressing the key concerns raised by all reviewers, including refining our analyses, clarifying our interpretations, and incorporating additional discussions to strengthen the manuscript. Below, we address your specific recommendations and provide responses to each point you raised to ensure our methods and results are as transparent and comprehensible as possible. We believe that these revisions will significantly enhance the rigor and impact of our study, and we sincerely appreciate your thoughtful feedback in helping us improve our work.

Reviewer #1 (Recommendations for the authors):

(1) The phrase "overnight sleep" suggests an entire night, while these were rather "nocturnal naps". Please rephrase.

Thank you for pointing this out. We have revised the phrasing in our manuscript to "nocturnal naps" instead of "overnight sleep" to more accurately reflect the duration of the sleep recordings.

(2) Sleep staging results (macroscopic sleep architecture) should be provided in more detail (at least min and % of the different sleep stages, sleep onset latency, total sleep duration, total recording duration), at least mean/SD/range.

Thank you for this suggestion. We will provide comprehensive tables in the supplementary materials, contains descriptive information about sleep-related characteristics. This information will help provide a clearer overview of the macroscopic sleep architecture in our dataset.

Supplementary Materials, Page 42, Table S1

Author response table 1.

Descriptive results of demographic information and sleep characteristics. Note: The total recorded time is equal to the awake time plus the total sleep time. The sleep onset latency is the time taken to reach the first sleep epoch. The Sleep Efficiency is the ratio of actual sleep time to total recording time.

Reviewer #2 (Recommendations for the authors):

In order to allow for a better estimation of the reliability of the detected sleep events, please:

(1) Provide densities and absolute numbers of all detected SOs and spindles (N1, NREM, and REM sleep).

Thank you for pointing this out. We will provide comprehensive tables in the supplementary materials, contains detailed information about sleep waves at each sleep stage for all 107 subjects (Table S2-S4), listing for each subject:1) Different sleep stage duration; 2) Number of detected SOs; 3) Number of detected spindles; 4) Number of detected SO-spindle coupling events; 5) Density of detected SOs; 6) Density of detected spindles; 7) Density of detected SO-spindle coupling events.

Supplementary Materials, Page 43-54, Table S2-S4

(Consider of the length, we do not list all the tables here. Please refer to the revised manuscript.)

(2) Show ERPs for all detected SOs and spindles (per sleep stage).

Thank you for the suggestion. We will provide ERPs for all detected SOs and spindles, separated by sleep stage (N1, N2&N3, and REM) in supplementary Fig. S2-S4. These ERP waveforms will help illustrate the characteristic temporal profiles of SOs and spindles across different sleep stages.

Methods, Page 25, Line 525-532

“Event-related potentials (ERP) analysis. After completing the detection of each sleep rhythm event, we performed ERP analyses for SOs, spindles, and coupling events in different sleep stages. Specifically, for SO events, we took the trough of the DOWN-state of each SO as the zero-time point, then extracted data in a [-2 s to 2 s] window from the broadband (0.1–30 Hz) EEG and used [-2 s to -0.5 s] for baseline correction; the results were then averaged across 107 subjects (see Fig. S2a). For spindle events, we used the peak of each spindle as the zero-time point and applied the same data extraction window and baseline correction before averaging across 107 subjects (see Fig. S2b). Finally, for SO-spindle coupling events, we followed the same procedure used for SO events (see Fig. 2a, Figs. S3–S4).”

Supplementary Materials, Page 36-38, Fig. S2-S4

Author response image 1.

ERPs of SOs and spindles coupling during different sleep stages across all 107 subjects. a. ERP of SOs in different sleep stages using the broadband (0.1–30 Hz) EEG data. We align the trough of the DOWN-state of each SO at time zero (see Methods for details). The orange line represents the SO ERP in the N1 stage, the black line represents the SO ERP in the N2&N3 stage, and the green line represents the SO ERP in the REM stage. b. ERP of spindles in different sleep stages using the broadband (0.1–30 Hz) EEG data. We align the peak of each spindle at time zero (see Methods for details). The color scheme is the same as in panel a.

Author response image 2.

ERP and time-frequency patterns of SO-spindle coupling in the N1 stage. The averaged temporal frequency pattern and ERP across all instances of SO-spindle coupling, computed over all subjects, following the same procedure as in Fig. 2a, but for N1 stage.

Author response image 3.

ERP and time-frequency patterns of SO-spindle coupling in the REM stage. The averaged temporal frequency pattern and ERP across all instances of SO-spindle coupling, computed over all subjects, again following the same procedure as in Fig. 2a, but for REM stage.

(3) Provide detailed info concerning sleep characteristics (time spent in each sleep stage etc.).

Thank you for this suggestion. Same as the response above, we will provide comprehensive tables in the supplementary materials, contains descriptive information about sleep-related characteristics.

Supplementary Materials, Page 42, Table S1 (same as above)

(4) What would happen if more stringent parameters were used for event detection? Would the authors still observe a significant number of SO spindles during N1 and REM? Would this affect the fMRI-related results?

Thank you for this suggestion. Our methods for detecting SOs, spindles, and their couplings were originally developed for N2 and N3 sleep data, based on the specific characteristics of these stages. These methods are widely recognized in sleep research (Hahn et al., 2020; Helfrich et al., 2019; Helfrich et al., 2018; Ngo, Fell, & Staresina, 2020; Schreiner et al., 2022; Schreiner et al., 2021; Staresina et al., 2015; Staresina et al., 2023). However, because this percentile-based detection approach will inherently identify a certain number of events if applied to other stages (e.g., N1 and REM), the nature of these events in those stages remains unclear compared to N2/N3. We nevertheless identified and reported the detailed descriptive statistics of these sleep rhythms in all sleep stages, under the same operational definitions, both for completeness and as a sanity check. Within the same subject, there should be more SOs, spindles, and their couplings in N2/N3 than in N1 or REM (see also Figure S2-S4, Table S1-S4).

Furthermore, in order to explore the impact of this on our fMRI results, we conducted an additional sensitivity analysis by applying different detection parameters for SOs. Specifically, we adjusted amplitude percentile thresholds for SO detection (the parameter that has the greatest impact on the results). We used the hippocampal activation value during N2&N3 stage SO-spindle coupling as an anchor value and found that when the parameters gradually became stricter, the results were similar to or even better than the current results. However, when we continued to increase the threshold, the results began to gradually decrease until the threshold was increased to 80%, and the results were no longer significant. This indicates that our results are robust within a specific range of parameters, but as the threshold increases, the number of trials decreases, ultimately weakening the statistical power of the fMRI analysis.

Thank you again for your suggestions on sleep rhythm event detection. We will add the results in Supplementary and revise our manuscript accordingly.

Results, Page 11, Line 199-208

“Spindles were correlated with positive activation in the thalamus (ROI analysis, t₍₁₀₆₎ = 15.39, p < 1e-4), the anterior cingulate cortex (ACC), and the putamen, alongside deactivation in the DMN (Fig. 3c). Notably, SO-spindle coupling was linked to significant activation in both the thalamus (ROI analysis, t₍₁₀₆₎ \= 3.38, p = 0.0005) and the hippocampus (ROI analysis, t₍₁₀₆₎ \= 2.50, p = 0.0070, Fig. 3d). However, no decrease in DMN activity was found during SO-spindle coupling, and DMN activity during SO was significantly lower than during coupling (ROI analysis, t₍₁₀₆₎ \= -4.17, p < 1e-4). For more detailed activation patterns, see Table S5-S7. We also varied the threshold used to detect SO events to assess its effect on hippocampal activation during SO-spindle coupling and observed that hippocampal activation remained significant when the percentile thresholds for SO detection ranged between 71% and 80% (see Fig. S6).”

Supplementary Materials, Page 40, Fig. S6

Author response image 4.

Influence of the percentile threshold for SO detection on hippocampal activation (ROI) during SO-spindle coupling. We changed the percentile threshold for SO event detection in the EEG data analysis and then reconstructed the GLM design matrix based on the SO events detected at each threshold. The brain-wide activation pattern of SO-spindle couplings in the N2/3 stage was extracted using the same method as shown in Fig. 3. The gray horizontal line represents the significant range (71%–80%). * p < 0.05.

Finally, we sincerely thank all again for your thoughtful and constructive feedback. Your insights have been invaluable in refining our analyses, strengthening our interpretations, and improving the clarity and rigor of our manuscript. We appreciate the time and effort you have dedicated to reviewing our work, and we are grateful for the opportunity to enhance our study based on your recommendations.

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Author response:

Public Reviews:

Reviewer #1 (Public Review):

The authors describe a massively parallel reporter assays (MPRA) screen focused on identifying polymorphisms in 5' and 3' UTRs that affect translation efficiency and thus might have a functional impact on cells. The topic is of timely interest, and indeed, several related efforts have recently been published and preprinted (e.g., https://pubmed.ncbi.nlm.nih.gov/37516102/ and https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10635273/). This study has several major issues with the results and their presentation.

Major comments:

(1) The main issue is that it appears that the screen has largely failed, yet the reasons for that are unclear, which makes it difficult to interpret. The authors start with a library that includes approximately 6,000 variants, which makes it a medium-sized MPRA. But then, only 483 pairs of WT/mutated UTRs yield high-confidence information, which is already a small number for any downstream statistical analysis, particularly since most don't actually affect translation in the reporter screen setting (which is not unexpected). It is unclear why >90% of the library did not give highconfidence information. The profiles presented as base-case examples in Figure 2B don't look very informative or convincing. All the subsequent analysis is done on a very small set of UTRs that have an effect, and it is unclear to this reviewer how these can yield statistically significant and/or biologically relevant associations.

To make sure our final results are technically and statistically sound, we applied stringent selection criteria and cutoffs in our analytics workflow. First, from our RNA-seq dataset, we filtered the UTRs with at least 20 reads in a polysome profile across all three repeated experiments. Secondly, in the following main analysis using a negative binomial generalized linear model (GLM), we further excluded the UTRs that displayed batch effect, i.e. their batch-related main effect and interaction are significant. We believe our measure has safeguarded the filtered observations (UTRs) from the (potential) high variation of our massively parallel translation assays and thus gives high confidence to our results.

Regarding the interpretation of Figure 2B, since we aimed to identify the UTRs whose interaction term of genotype and fractions is significant in our generalized linear model, it is statistically conventional to double-check the interaction of the two variables using such a graph. For instance, in the top left panel of Figure 2B (5'UTR of ANK2:c.-39G>T), we can see that read counts of WT samples congruously decreased from Mono to Light, whereas the read counts of mutant samples were roughly the same in the two fractions – the trend is different between WT and mutant. Ergo, the distinct distribution patterns of two genotypes across three fractions in Figure 2B offer the readers a convincing visual supplement to our statistics from GLM.

In contrast to Figure 2B, the graphs of nonsignificant UTRs (shown below) reveal that the trends between the two genotypes are similar across the 'Mono and Light' and 'Light and Heavy' polysome fractions. Importantly, our analysis remains unaffected by differential expression levels between WT and mutant, as it specifically distinguishes polysome profiles with different distributions. This consistent trend further supports the lack of interaction between genotype and polysome fractions for these UTRs.

Author response image 1.

Figure: Examples of non-significant UTR pairs in massively parallel polysome profiling assays.

(2) From the variants that had an effect, the authors go on to carry out some protein-level validations and see some changes, but it is not clear if those changes are in the same direction as observed in the screen.

To infer the directionality of translation efficiency from polysome profiles, a common approach involves pooling polysome fractions and comparing them with free or monosome fractions to identify 'translating' fractions. However, this method has two major potential pitfalls: (i) it sacrifices resolution and does not account for potential bias toward light or heavy polysomes, and (ii) it fails to account for discrepancies between polysome load and actual protein output (as discussed in https://doi.org/10.1016/j.celrep.2024.114098 and https://doi.org/10.1038/s41598-019-47424-w). Therefore, our analysis focused on the changes within polysome profiles themselves. 'Significant' candidates were identified based on a significant interaction between genotype and polysome distribution using a negative binomial generalized linear model, without presupposing the direction of change on protein output. 

(3) The authors follow up on specific motifs and specific RBPs predicted to bind them, but it is unclear how many of the hits in the screen actually have these motifs, or how significant motifs can arise from such a small sample size.

We calculated the Δmotif enrichment in significant UTRs versus nonsignificant UTRs using Fisher’s exact test. For example, the enrichment of the Δ‘AGGG’ motif in 3’ UTRs is shown below:

Author response table 1.

This test yields a P-value of 0.004167 by Fisher’s exact test. The P-values and Odds ratios of Δmotifs in relation to polysome shifting are included in Supplementary Table S4, and we will update the detailed motif information in the revised Supplementary Table S4.

(4) It is particularly puzzling how the authors can build a machine learning predictor with >3,000 features when the dataset they use for training the model has just a few dozens of translation-shifting variants.

We understand the concern regarding the relatively small number of translation-shifting variants compared to the large number of features. To address this, we employed LASSO regression, which, according to The Elements of Statistical Learning by Hastie, Tibshirani, and Friedman, is particularly suitable for datasets where the number of features 𝑝𝑝 is much larger than the number of samples 𝑁𝑁. LASSO effectively performs feature selection by shrinking less important coefficients to zero, allowing us to build a robust and generalizable model despite the limited number of variants.

(5) The lack of meaningful validation experiments altering the SNPs in the endogenous loci by genome editing limits the impact of the results.

We plan to assess the endogenous effect by generating CRISPR knock-in clones carrying the UTR variant.

Reviewer #2 (Public Review):

Summary:

In their paper "Massively Parallel Polyribosome Profiling Reveals Translation Defects of Human Disease‐Relevant UTR Mutations" the authors use massively parallel polysome profiling to determine the effects of 5' and 3' UTR SNPs (from dbSNP/ClinVar) on translational output. They show that some UTR SNPs cause a change in the polysome profile with respect to the wild-type and that pathogenic SNPs are enriched in the polysome-shifting group. They validate that some changes in polysome profiles are predictive of differences in translational output using transiently expressed luciferase reporters. Additionally, they identify sequence motifs enriched in the polysome-shifting group. They show that 2 enriched 5' UTR motifs increase the translation of a luciferase reporter in a proteindependent manner, highlighting the use of their method to identify translational control elements.

Strengths:

This is a useful method and approach, as UTR variants have been more difficult to study than coding variants. Additionally, their evidence that pathogenic mutations are more likely to cause changes in polysome association is well supported.

Weaknesses:

The authors acknowledge that they "did not intend to immediately translate the altered polysome profile into an increase or decrease in translation efficiency, as the direction of the shift was not readily evident. Additionally, sedimentation in the sucrose gradient may have been partially affected by heavy particles other than ribosomes." However, shifted polysome distribution is used as a category for many downstream analyses. Without further clarity or subdivision, it is very difficult to interpret the results (for example in Figure 5A, is it surprising that the polysome shifting mutants decrease structure? Are the polysome "shifts" towards the untranslated or heavy fractions?)

Our approach, combining polysome fractionation of the UTR library with negative binomial generalized linear model (GLM) analysis of RNA-seq data, systematically identifies variants that affect translational efficiency. The GLM model is specifically designed to detect UTR pairs with significant interactions between genotype and polysome fractions, relying solely on changes in polysome profiles to identify variants that disrupt translation. Consequently, our analytical method does not determine the direction of translation alteration.

Following the massively parallel polysome profiling, we sought to understand how these polysomeshifting variants influence the translation process. To do this, we examined their effects on RNA characteristics related to translation, such as RBP binding and RNA structure. In Figure 5A, we observed a notable trend in significant hits within 5’ UTRs—they tend to increase ΔG (weaker folding energy) in response to changes in polysome profiles, regardless of whether protein production increases or decreases (Fig. 3).

Author Response:

Reviewer #1 (Public Review):

Despite numerous studies on quinidine therapies for epilepsies associated with GOF mutant variants of Slack, there is no consensus on its utility due to contradictory results. In this study Yuan et al. investigated the role of different sodium selective ion channels on the sensitization of Slack to quinidine block. The study employed electrophysiological approaches, FRET studies, genetically modified proteins and biochemistry to demonstrate that Nav1.6 N- and C-tail interacts with Slack's C-terminus and significantly increases Slack sensitivity to quinidine blockade in vitro and in vivo. This finding inspired the authors to investigate whether they could rescue Slack GOF mutant variants by simply disrupting the interaction between Slack and Nav1.6. They find that the isolated C-terminus of Slack can reduce the current amplitude of Slack GOF mutant variants co-expressed with Nav1.6 in HEK cells and prevent Slack induced seizures in mouse models of epilepsy. This study adds to the growing list of channels that are modulated by protein-protein interactions, and is of great value for future therapeutic strategies.

I have a few comments with regard to how Nav1.6 sensitize Slack to block by quinidine.

(1) It is not clear to me if the Slack induced current amplitude varies depending on the specific Nav subtype. To this end, it would be valuable to test if Slack open probability is affected by the presence of specific Nav subtypes. Nav induced differences in Slack current amplitude and open probability could explain why individual Nav subtypes show varied ability to sensitize Slack to quinidine blockade.

We appreciate the reviewer for raising this point. In order to address whether the whole-cell current amplitudes of Slack varies depending on the specific NaV subtype, we examined Slack current amplitudes upon co-expression of Slack with specific NaV subtypes in HEK293 cells. The results have shown that there are no significant differences in Slack current amplitudes upon co-expression of Slack with different NaV channel subtypes (Author response image 1), suggesting whole-cell Slack current amplitudes cannot explain the varied ability of NaV subtypes to sensitize Slack to quinidine blockade. To investigate the effect of different NaV channel subtypes on Slack open probability, we will perform the single-channel recordings in the future studies.

Author response image 1.

The amplitudes of Slack currents upon co-expression of Slack with specific NaV subtypes in HEK293 cells. ns, p > 0.05, one-way ANOVA followed by Bonferroni’s post hoc test.

(2) It has previously been shown that INaP (persistent sodium current) is important for inducing Slack currents. Here the authors show that INaT (transient sodium current) of Nav1.6 is necessary for the sensitization of Slack to quinidine block whereas INaP surprisingly has no effect. The authors then show that the N-tail together with C-tail of Nav1.6 can induce same effect on Slack as full-length Nav1.6 in presence of high intracellular concentrations of sodium. However, it is not clear to me how the isolated N- and C-tail of Nav1.6 can induce sensitization of Slack to quinidine by interacting with C-terminus of Slack, while sensitization also is dependant on INaT. The authors speculate on different slack open conformation, but one could speculate if there is a missing link, such as an un-identified additional interacting protein that causes the coupling.

We fully agree the importance of investigating the detailed mechanism underlying the sensitization of Slack to quinidine blockade mediated by the N- and C-termini of NaV1.6. Regarding the possibility of additional interacting proteins (“missing link”) that mediate the coupling between Slack and NaV1.6, our GST-pull down assays involving Slack and the N- and C-termini of NaV1.6 (Fig. S7) suggest a direct interaction between Slack and NaV1.6 channels. This finding leads us to consider the possibility of additional interacting proteins might be excluded. In order to further address these questions, we plan to employ structural biological methods, such as cryo-electron microscopy (cryo-EM).

Reviewer #2 (Public Review):

This is a very interesting paper about the coupling of Slack and Nav1.6 and the insight this brings to the effects of quinidine to treat some epilepsy syndromes.

Slack is a sodium-activated potassium channel that is important to hyperpolarization of neurons after an action potential. Slack is encoded by KNCT1 which has mutations in some epilepsy syndromes. These types of epilepsy are treated with quinidine but this is an atypical antiseizure drug, not used for other types of epilepsy. For sufficient sodium to activate Slack, Slack needs to be close to a channel that allows robust sodium entry, like Na channels or AMPA receptors. but more mechanistic information is not available. Of particular interest to the authors is what allows quinidine to be effective in reducing Slack.

In the manuscript, the authors show that Nav, not AMPA receptors are responsible for Slack activation, at least in cultured neurons (HeK293, primary cortical neurons). Most of the paper focuses on the evidence that Nav1.6 promotes Slack sensitivity to quinidine.

(1) The paper is very well written although there are reservations about the use of non-neuronal cells or cultured primary neurons rather than a more intact system.

We appreciate the reviewer's positive evaluation of our work. We acknowledge that utilizing a more intact system would provide valuable insights into the inhibitory effect of quinidine on Slack-NaV1.6. However, there are certain challenges associated with studying Slack currents in their entirety.

First, in our experiments, isolating Slack currents from Na+-activated K+ currents in an intact system is challenging as selective inhibitors for Slick are currently unavailable. To address this, we propose using Slick gene knockout mice to specifically measure Slack currents under physiological conditions in the future investigations. Second, we have observed that the interaction between Slack and NaV1.6 primarily occurs at the axon initial segment of neurons. This poses a difficulty when using brain slices for measurements, as employing the whole-cell voltage-clamp technique to assess Slack at the axon initial segment may introduce systemic errors.

We believe that testing the pharmacological effects of quinidine on Slack-NaV1.6 in primary neurons remains the optimal approach. Although non-neuronal cells or cultured primary neurons may not fully replicate the complexity of an intact system, they still provide valuable insights into the interactions between Slack and NaV1.6, and the effects of quinidine.

(2) I also have questions about the figures.

We will make the necessary modifications and clarifications based on the reviewer's comments:

(3) Finally, riluzole is not a selective drug, so the limitations of this drug should be discussed.

We thank the reviewer for raising this point. We will discuss the limitations of riluzole in our revised version of the manuscript.

(4) On a minor point, the authors use the term in vivo but there are no in vivo experiments.

We thanks the reviewer for raising this point. In our experiments, although we did not conduct experiments directly in living organisms, our results demonstrated the co-immunoprecipitation of NaV1.6 with Slack in homogenates from mouse cortical and hippocampal tissues (Fig. 3C). This result may support that the interaction between Slack and NaV1.6 occurs in vivo.

Reviewer #3 (Public Review):

Yuan et al., set out to examine the role of functional and structural interaction between Slack and NaVs on the Slack sensitivity to quinidine. Through pharmacological and genetic means they identify NaV1.6 as the privileged NaV isoform in sensitizing Slack to quinidine. Through biochemical assays, they then determine that the C-terminus of Slack physically interacts with the N- and C-termini of NaV1.6. Using the information gleaned from the in vitro experiments the authors then show that virally-mediated transduction of Slack's C-terminus lessens the extent of SlackG269S-induced seizures. These data uncover a previously unrecognized interaction between a sodium and a potassium channel, which contributes to the latter's sensitivity to quinidine.

The conclusions of this paper are mostly well supported by data, but some aspects of functional and structural studies in vivo as well as physically interaction need to be clarified and extended.

(1) Immunolabeling of the hippocampus CA1 suggests sodium channels as well as Slack colocalization with AnkG (Fig 3A). Proximity ligation assay for NaV1.6 and Slack or a super-resolution microscopy approach would be needed to increase confidence in the presented colocalization results. Furthermore, coimmunoprecipitation studies on the membrane fraction would bolster the functional relevance of NaV1.6-Slac interaction on the cell surface.

We thank the reviewer for good suggestions. We acknowledge that employing proximity ligation assay and high-resolution techniques would significantly enhance our understanding of the localization of the Slack-NaV1.6 coupling.

At present, the technical capabilities available in our laboratory and institution do not support high-resolution testing. However, we are enthusiastic about exploring potential collaborations to address these questions in the future. Furthermore, we fully recognize the importance of conducting co-immunoprecipitation (Co-IP) assays from membrane fractions. While we have already completed Co-IP assays for total protein and quantified the FRET efficiency values between Slack and NaV1.6 in the membrane region, the Co-IP assays on membrane fractions will be conducted in our future investigations.

(2) Although hippocampal slices from Scn8a+/- were used for studies in Fig. S8, it is not clear whether Scn8a-/- or Scn8a+/- tissue was used in other studies (Fig 1J & 1K). It will be important to clarify whether genetic manipulation of NaV1.6 expression (Fig. 1K) has an impact on sodium-activated potassium current, level of surface Slack expression, or that of NaV1.6 near Slack.

We thank the reviewer for pointing this out. In Fig. 1G,J,K, primary cortical neurons from homozygous NaV1.6 knockout (Scn8a-/-) mice were used. We will clarify this information in the revised manuscript. In terms of the effects of genetic manipulation of NaV1.6 expression on IKNa and surface Slack expression, we compared the amplitudes of IKNa measured from homozygous NaV1.6 knockout (NaV1.6-KO) neurons and wild-type (WT) neurons. The results showed that homozygous knockout of NaV1.6 does not alter the amplitudes of IKNa (Author response image 2). The level of surface Slack expression will be tested further.

Author response image 2.

The amplitudes of IKNa in WT and NaV1.6-KO neurons (data from manuscript Fig. 1K). ns, p > 0.05, unpaired two-tailed Student’s t test.

(3) Did the epilepsy-related Slack mutations have an impact on NaV1.6-mediated sodium current?

We thank the reviewer’s question. We examined the amplitudes of NaV1.6 sodium current upon expression alone or co-expression of NaV1.6 with epilepsy-related Slack mutations (K629N, R950Q, K985N). The results showed that the tested epilepsy-related Slack mutations do not alter the amplitudes of NaV1.6 sodium current (Author response image 3).

Author response image 3.

The amplitudes of NaV1.6 sodium currents upon co-expression of NaV1.6 with epilepsy-related Slack mutant variants (SlackK629N, SlackR950Q, and SlackK985N). ns, p>0.05, one-way ANOVA followed by Bonferroni’s post hoc test.

4) Showing the impact of quinidine on persistent sodium current in neurons and on NaV1.6-expressing cells would further increase confidence in the role of persistent sodium current on sensitivity of Slack to quinidine.

We appreciate the reviewer’s question. Previous studies have shown that quinidine can inhibit persistent sodium currents at low concentrations1. In our experiments, blocking persistent sodium currents by application of riluzole in the bath solution showed no significant effects on the sensitivity of Slack to quinidine blockade upon co-expression of Slack with NaV1.6 (Fig. 2F,H). This result suggested that persistent sodium currents were not involved in the sensitization of Slack to quinidine blockade.

1. Ju YK, Saint DA, Gage PW. Effects of lignocaine and quinidine on the persistent sodium current in rat ventricular myocytes. Br J Pharmacol. Oct 1992; 107(2):311-6. doi:10.1111/j.1476-5381.1992.tb12743.x

Author response:

Public Reviews:

Reviewer #1 (Public review):

Summary

The authors describe a method for gastruloid formation using mouse embryonic stem cells (mESCs) to study YS and AGM-like hematopoietic differentiation. They characterise the gastruloids during nine days of differentiation using a number of techniques including flow cytometry and single-cell RNA sequencing. They compare their findings to a published data set derived from E10-11.5 mouse AGM. At d9, gastruloids were transplanted under the adrenal gland capsule of immunocompromised mice to look for the development of cells capable of engrafting the mouse bone marrow. The authors then applied the gastruloid protocol to study overexpression of Mnx1 which causes infant AML in humans.

In the introduction, the authors define their interpretation of the different waves of hematopoiesis that occur during development. 'The subsequent wave, known as definitive, produces: first, oligopotent erythro-myeloid progenitors (EMPs) in the YS (E8-E8.5); and later myelo-lymphoid progenitors (MLPs - E9.5-E10), multipotent progenitors (MPPs - E10-E11.5), and hematopoietic stem cells (HSCs - E10.5-E11.5), in the aorta-gonadmesonephros (AGM) region of the embryo proper.' Herein they designate the yolk sac-derived wave of EMP hematopoiesis as definitive, according to convention, although paradoxically it does not develop from intraembryonic mesoderm or give rise to HSCs.

The apparent perplexity of the Reviewer with our definition of primitive and definitive waves is somewhat surprising, as it is widely used in the field (e.g. PMID: 18204427; PMID: 28299650; PMID: 33681211). Definitive haematopoiesis, encompassing EMP, MLP, MPP and HSC, highlights their origin from haemogenic hendothelium, generation of mature cells with adult characteristics from progenitors with multilineage potential and direct and indirect developmental contributions to the intra-embryonic and time-restricted generation of HSCs.

General comments

The authors make the following claims in the paper:

(1) The development of a protocol for hemogenic gastruloids (hGx) that recapitulates YS and AGM-like waves of blood from HE.

(2) The protocol recapitulates both YS and EMP-MPP embryonic blood development 'with spatial and temporal accuracy'.

(3) The protocol generates HSC precursors capable of short-term engraftment in an adrenal niche.

(4) Overexpression of MNX1 in hGx transforms YS EMP to 'recapitulate patient transcriptional signatures'.

(5) hGx is a model to study normal and leukaemic embryonic hematopoiesis.

There are major concerns with the manuscript. The statements and claims made by the authors are not supported by the data presented, data is overinterpreted, and the conclusions cannot be justified. Furthermore, the data is presented in a way that makes it difficult for the reader to follow the narrative, causing confusion. The authors have not discussed how their hGx compares to the previously published mouse embryoid body protocols used to model early development and hematopoiesis. the data is presented in a way that makes it difficult for the reader to follow the narrative, causing confusion. The authors have not discussed how their hGx compares to the previously published mouse embryoid body protocols used to model early development and hematopoiesis.

Specific points

(1) It is claimed that HGxs capture cellularity and topography of developmental blood formation. The hGx protocol described in the manuscript is a modification of a previously published gastruloid protocol (Rossi et al 2022). The rationale for the protocol modifications is not fully explained or justified. There is a lack of novelty in the presented protocol as the only modifications appear to be the inclusion of Activin A and an extension of the differentiation period from 7 to 9 days of culture. No direct comparison has been made between the two versions of gastruloid differentiation to justify the changes.

The Reviewer paradoxically claims that the protocol is not novel and that it differs from a previous publication in at least 2 ways – the patterning pulse and the length of the protocol. Of these, the patterning pulse is key. As documented in Fig. S1, we cannot obtain Flk1-GFP expression in the absence of Activin A. Expression of Flk1 is a fundamental step in haemato-endothelial specification and, accordingly, we do not see CD41 or CD45+ cells in the absence of Activin A. Also, in our hands, there is a clear time-dependent progression of marker expression, with sequential acquisition of CD41 and CD45, with the latter not detectable until 192h (Fig. 1C-D), another key difference relative to the Rossi et al (2022) protocol. The 192h-timepoint, we argue in the manuscript, and present further evidence for in this rebuttal, corresponds to the onset of AGM-like haematopoiesis. We have empirically extended the protocol to maximise the CD45+ cell output (Fig. S1B-D).

The inclusion of Activin A at high concentration at the beginning of differentiation would be expected to pattern endoderm rather than mesoderm. BMP signaling is required to induce Flk1+ mesoderm, even in the presence of Wnt.

Again, we call the Reviewer’s attention to Fig. S1 which clearly shows that Activin A (with no BMP added) is required for induction of Flk1 expression, in the presence of Wnt. Activin A in combination with Wnt, is used in other protocols of haemato-endothelial differentiation from pluripotent cells, with no BMP added in the same step of patterning and differentiation (PMID: 39227582; PMID: 39223325). In the latter protocol, we also call the Reviewer’s attention to the fact that a higher concentration of Activin A precludes the need for BMP4 addition. Finally, one of us has recently reported that Activin A, on its own, will induce FLK1, as well as other anterior mesodermal progenitors (https://www.biorxiv.org/content/10.1101/2025.01.11.632562v1)..) In addressing the Reviewer’s concerns with the dose of Activin A used, we titrated its concentration against activation of Flk1, confirming optimal Flk1-GFP expression at the 100ng/ml dose used in the manuscript.

Author response image 1.

Dose-dependent requirement of Activin A for induction of Flk1 expression in haemogenic gastruloids. Composite GFP and brightfield live imaging of Flk1-GFP haemogenic gastruloids at 96h. Images were acquired using a Cytation5 instrument (Thermo). Images are representative of 12 gastruloids per condition.

FACS analysis of the hGx during differentiation is needed to demonstrate the co-expression of Flk1-GFP and lineage markers such as CD34 to indicate patterning of endothelium from Flk1+ mesoderm. The FACS plots in

Fig. 1 show c-Kit expression but very little VE-cadherin which suggests that CD34 is not induced. Early endoderm expresses c-Kit, CXCR4, and Epcam, but not CD34 which could account for the lack of vascular structures within the hGx as shown in Fig. 1E.

We were surprised by the Reviewer’s comment that there are no endothelial structures in our gastruloids. The presence of a Flk1-GFP+ network is visible in the GFP images in Fig.1B, from 144h onwards, also shown in Author response image 2A. In addition, our single-cell RNA-seq data, included in the manuscript, confirms the presence of endothelial cells with a developing endothelial, including arterial, programme. This can be seen in Fig. 2B, F of the manuscript and is represented in Author response image 2B. In contrast with the Reviewer’s claims that no endothelial cells are formed, the data show that Kdr (Flk1)+ cells co-express Cdh5/VE-Cadherin and indeed Cd34, attesting to the presence of an endothelial programme. Arterial markers Efnb2, Flt1, and Dll4 are present. A full-blown programme, which also includes haemogenic markers including Sox17, Esam, Cd44 and Mecom is clear at early (144h) and, particularly at late (192h) timepoints in cells sorted on detection of surface c-Kit (Author response image 2B). Further to the data shown in B, already present in the manuscript, we also document co-expression of Flk1-GFP and CD34 by flow cytometry (Author response image 2C).

Author response image 2.

Haemogenic gastruloids have a branched vascular network. A. Whole-mount confocal imaging of 144h-haemogenic gastruloids. B. Differentiation of an arterial endothelial programme in haemogenic gastruloids; singlecell RNA-seq data of differentiating haemogenic gastruloids, sorted on cell surface expression of c-Kit at 144 and 192h; gene expression colour scale from yellow (low) to orange (high); grey = no detectable expression. C. Flow cytometry plots of 216h-haemogenic gastruloids showing detection of haemato-endothelial marker CD34.

(2) The protocol has been incompletely characterised, and the authors have not shown how they can distinguish between either wave of Yolk Sac (YS) hematopoiesis (primitive erythroid/macrophage and erythro-myeloid EMP) or between YS and intraembryonic Aorta-Gonad-Mesonephros (AGM) hematopoiesis. No evidence of germ layer specification has been presented to confirm gastruloid formation, organisation, and functional ability to mimic early development. Furthermore, differentiation of YS primitive and YS EMP stages of development in vitro should result in the efficient generation of CD34+ endothelial and hematopoietic cells. There is no flow cytometry analysis showing the kinetics of CD34 cell generation during differentiation. Benchmarking the hGx against developing mouse YS and embryo data sets would be an important verification.

The Reviewer is correct that we have not provided detailed characterisation of the different germ layers, as this was not the focus of the study. In that context, we were surprised by the earlier comment assuming co-expression of c-kit, Cxcr4 and Epcam, which we did not show, while overlooking the endothelial programme reiterated above, which we have presented.

Given our focus on haemato-endothelial specification, we have started the single-cell RNA-seq characterisation of the haemogenic gastruloid at 120h and have not looked specifically at earlier timepoints of embryo patterning.

This said, we show the presence of neuroectodermal cells in cluster 9; on the other hand, cluster 7 includes hepatoblast-like cells, denoting endodermal specification. We are happy to include this characterisation, to the extent that it is present, in a revised version of the manuscript. However, in the absence of earlier timepoints and given the bias towards mesodermal specification, we expect that specification of ectodermal and endodermal programmes may be incomplete.

In respect of the contention regarding the capture of YS-like and AGM-like haematopoiesis, we have presented evidence in the manuscript that haemogenic cells generated during gastruloid differentiation, particularly at late 192h and 216h timepoints project onto highly purified c-Kit+ CD31+ Gfi1-expressing cells from mouse AGM (PMID: 38383534), providing support for the recapitulation of the corresponding developmental stage. In distinguishing between YS-like and AGM-like haematopoiesis, we call the Reviewer’s attention to the replotting of the single-cell RNA-seq data already in the manuscript, which we provided in response to point 1 (Author response image 2B), which highlights an increase in Sox17, but not Sox18, expression in the 192h haemogenic endothelium, which suggests an association with AGM haematopoiesis (PMID: 20228271). A significant association of Cd44 and Procr expression with the same time-point (Fig. 2F in the manuscript), further supports an AGM-like endothelial-to-haematopoietic transition at the 192h timepoint.

Following on the Reviewer’s comments about CD34, we also inspected co-expression of CD34 with CD41 and CD45, the latter co-expression present in, although not necessarily exclusive to, AGM haematopoiesis.

Reassuringly, we observed clear co-expression with both markers (Author response image 3), in addition to a CD41+CD34-population, which likely reflects YS EMP-independent erythropoiesis. Interestingly, marker expression is responsive to the levels of Activin A used in the patterning pulse, with the 100ng/ml Activin A used in our protocol superior to 75ng/ml.

Author response image 3.

Association of CD34 with CD41 and CD45 expression is Activin A-responsive and supports the presence of definitive haematopoiesis. A. Flow cytometry analysis of CD34 and CD41 expression in 216h-haemogenic gastruloids; two doses of Activin A were used in the patterning pulse with CHI99021 between 48-72h. FMO controls shown. B. Flow cytometry analysis of CD34 and CD45 at 216h in the same experimental conditions.

We agree that it remains challenging to identify markers exclusive to AGM haematopoiesis, which is operationally equated with generation of transplantable haematopoietic stem cells. While HSC generation is a key event characteristic of the AGM, not all AGM haematopoiesis corresponds to HSCs, an important point in evaluating the data presented in the manuscript, and indeed acknowledged by us.

Author response image 4.

Clustering of haemogenic gastruloid cells sorted on the basis of haemato-endothelial surface markers CD41, C-Kit and CD45. A. Leiden clustering to single-cell RNA-seq data. B. Time stamps of sorted haemogenic gastruloid cells in A. C. Surface marker stamps of cells in A.

Given the centrality of this point in comments by all the Reviewers, we have conducted projections of our single-cell RNA-seq data against two studies which (1) capture arterial and haemogenic specification in the para-splanchnopleura (pSP) and AGM region between E8.0 and E11 (Hou et al, PMID: 32203131), and (2) uniquely capture YS, AGM and FL progenitors and the AGM endothelial-to-haematopoietic transition (EHT) in the same scRNA-seq dataset (Zhu et al, PMID: 32392346).

Focusing the analysis on the subsets of haemogenic gastruloid cells sorted as CD41+ (144h) CKit+ (144h and 192h) and CD45+ (192h and 216h) (Author response image 4AC), we show:

(1) That a subset of haemato-endothelial cells from haemogenic gastruloids at 144h to 216h project onto intra-embryonic cells spanning E8.25 to E10 (Author response image 5A-B). This is in agreement with our interpretation that 216h are no later than the MPP/pre-HSC state of embryonic development, requiring further maturation to generate long-term engrafting HSC.

(2) That haemogenic gastruloids contain YS-like (including EMP-like) and AGM-like haematopoietic cells (Author response image 6A-B). Significantly, some of the cells, particularly c-Kit-sorted cells with a candidate endothelial and HE-like signature project onto AGM pre-HE and HE, as well as IAHC, and later, predominantly 216h cells, have characteristics of MPP/LMPP-like cells from the FL.

Altogether, the data support the notion that haemogenic gastruloids capture YS and AGM haematopoiesis until E10, as suggested by us in the manuscript. We thought it was important to share this preliminary data with the Editors at an early stage, and we will incorporate a deeper analysis in a revised version of the manuscript.

Single-cell RNA sequencing was used to compare hGx with mouse AGM. The authors incorrectly conclude that ' ..specification of endothelial and HE cells in hGx follows with time-dependent developmental progression into putative AGM-like HE..' And, '...HE-projected hGx cells.......expressed Gata2 but not Runx1, Myb, or Gfi1b..' Hemogenic endothelium is defined by the expression of Runx1 and Gfli1b is downstream of Runx1.

As a hierarchy of regulation, Gata2 precedes and drives Runx1 expression at the specification of HE (PMID: 17823307; PMID: 24297996), while Runx1 drives the EHT, upstream of Gfi1b in haematopoietic clusters (PMID: 34517413).

Author response image 5.

Projection of sorted haemogenic gastruloid cells onto Hou et al dataset (PMID: 32203131) analysing development of mouse intra-embryonic haematopoiesis. A. Time signatures of Hou et al data. B. Projection of Leiden clusters in Author response image 4A. Methodology as described in our manuscript; 68% gastruloid cells projected.

Author response image 6.

Projection of sorted haemogenic gastruloid cells onto Zhu et al dataset (PMID: 32392346), capturing arterial endothelial and haemogenic endothelial development, in reference to YS, AGM and FL haematopoietic progenitors. A. Functional cluster classification as per Zhu et al. B. Projection of Leiden clusters in Author response image 4A. Methodology as detailed in our manuscript; 58% gastruloid cells projected. Haematopoietic clusters annotated as in A.

(3) The hGx protocol 'generates hematopoietic SC precursors capable of short-term engraftment' is not supported by the data presented. Short-term engraftment would be confirmed by flow cytometric detection of hematopoietic cells within the recipient bone marrow, spleen, thymus, and peripheral blood that expressed the BFP transgene. This analysis was not provided. PCR detection of transcripts, following an unspecified number of amplification cycles, as shown in Figure 3G (incorrectly referred to as Figure 3F in the legend) is not acceptable evidence for engraftment.

We provide the full flow cytometry analysis of spleen engraftment in the 5 mice which received implantation of 216h-haemogenic gastruloids in the adrenal gland; an additional (control) animal received adrenal injection of PBS (Author response image 7). The animals were analysed at 4 weeks. In this experiment, the bone marrow collection was limiting, and material was prioritised for PCR.

We had previously provided only representative plots of flow cytometry analysis of bone marrow and spleen in Fig. S4E, which we described as low-level engraftment. The analysis was complemented with genomic DNA PCR, where detection was present in only some of the replicates tested per animal. We confirm that PCR analysis used conventional 40 cycles; the sensitivity was shown in Fig. S4F. As shown in Fig. 3 A-C, no more than 7 CD45+CD144+ multipotent cells are present per haemogenic gastruloid, with 3 haemogenic gastruloids implanted in the adrenal gland of each transplanted animal. We argue that the low level of cytometric and molecular engraftment at 4 weeks, from haemogenic gastruloid-derived progenitors that have not progressed beyond a stage equivalent to E10 Author response image 5A-B) and that we have described as requiring additional maturation in vivo, are not surprising.

Author response image 7.

BFP engraftment of Nude recipient mice 4 weeks after unilateral adrenal implantation of 216h-haemogenic gastruloids. Flow cytometry analysis of spleen engraftment. Genomic PCR analysis is shown in Fig. 3G of the manuscript.

Transplanted hGx formed teratoma-like structures, with hematopoietic cells present at the site of transplant only analysed histologically. Indeed, the quality of the images provided does not provide convincing validation that donor-derived hematopoietic cells were present in the grafts.

As stated in the text, the images mean to illustrate that the haemogenic gastruloids developed in situ. The observation of donor-derived blood cells in the implanted haemogenic gastruloids would not correspond to engraftment, as we have amply demonstrated that they have generated blood cells in vitro. There is no evidence that there are remaining pluripotent cells in the haemogenic gastruloid after 9 days of differentiation, and it is therefore not clear that these are teratomas

There is no justification for the authors' conclusion that '... the data suggest that 216h hGx generate AGM-like pre-HSC capable of at least short-term multilineage engraftment upon maturation...'. Indeed, this statement is in conflict with previous studies demonstrating that pre-HSCs in the dorsal aorta of the mouse embryo are immature and actually incapable of engraftment.

We have clearly stated that we do not see haematopoietic engraftment through transplantation of dissociated haemogenic gastruloids, which reach the E10 state containing pre-HSC (Author response image 5). Instead, we observed rare myelo-erythroid (in the manuscript) and myelo-lymphoid (Author response image 9 below, in response to Reviewer 2) engraftment upon in vivo maturation of haemogenic gastruloids with preserved 3D organisation. These statements are not contradictory.

The statement '...low-level production of engrafting cells recapitulates their rarity in vivo, in agreement with the embryo-like qualities of the gastruloid system....' is incorrect. Firstly, no evidence has been provided to show the hGx has formed a dorsal aorta facsimile capable of generating cells with engrafting capacity. Secondly, although engrafting cells are rare in the AGM, approximately one per embryo, they are capable of robust and extensive engraftment upon transplantation.

We are happy to rephrase the statement to simply say that “…the data suggest that 216h haemogenic gastruloids contain candidate AGM-like progenitors with some short-term engraftment potential but incomplete functional maturation.” To be clear, with our existing statement we meant to highlight that the production of definitive AGM-like haematopoietic progenitors (not all of which are engrafting) in haemogenic gastruloids does not correspond to non-physiological single-lineage programming. We did not claim that we achieved production of HSC, which would be long-term engrafting.

(4) Expression MNX1 transcript and protein in hematopoietic cells in MNX1 rearranged acute myeloid leukaemia (AML) is one cause of AML in infants. In the hGX model of this disease, Mnx1 is overexpressed in the mESCs that are used to form gastruloids. Mnx1 overexpression seems to confer an overall growth advantage on the hGx and increase the serial replating capacity of the small number of hematopoietic cells that are generated. The inefficiency with which the hGx model generates hematopoietic cells makes it difficult to model this disease. The poor quality of the cytospin images prevents accurate identification of cells. The statement that the kit-expressing cells represent leukemic blast cells is not sufficiently validated to support this conclusion. What other stem cell genes are expressed? Surface kit expression also marks mast cells, frequently seen in clonogenic assays of blood cells. Flow cytometric and gene expression analyses using known markers would be required.

The haemogenic gastruloid model generates haematopoietic and haemato-endothelial cells. MNX1 expands Kit+ cells at 144h, which we show to have a haemato-endothelial signature (manuscript Fig. 2B, which we replotted in Author response image 2B).

Serial replating of CFC assays is a conventional in vitro assay of leukaemia transformation. Critically, colony replating is not maintained in EV control cells, attesting to the transformation potential of MNX1.

Although we have not fully-traced the cellular hierarchy of MNX1-driven transformation in the haemogenic gastruloid system, the in vitro replating expands a Kit+ cell (Fig. 5E), which reflects the surface phenotype of the leukaemia, also recapitulated in the mouse model initiated by MNX1-overexpressing FL cells. Importantly, it recapitulates the transcriptional profile of MNX1-leukaemia patients (Fig. 6C), which is uniquely expressed by MNX1144h and replated colony cells, but not to MNX1 216h gastruloid cells, arguing against a generic signature of MNX1 overexpression (Fig. 6B). Importantly, the MNX1-transformation of haemogenic gastruloid cells is superior to the FL leukaemia model at capturing the unique transcriptional features of MNX1-driven leukaemia, distinct from other forms of AML in the same age group (Fig S7). It is possible that this corresponds to a preleukaemia event, and we will explore this in future studies, which are beyond the proof-of-principle nature of this paper.

(5) In human infant MNX1 AML, the mutation is thought to arise at the fetal liver stage of development. There is no evidence that this developmental stage is mimicked in the hGx model.

We never claim that the haemogenic gastruloid model mimics the foetal liver. We propose that susceptibility to MNX1 is at the HE-to-EMP transition. Moreover, and importantly, contrary to the Reviewer’s statement, there is no evidence in the literature that the mutation arises in the foetal liver stage, just that the mutation arises before birth (PMID: 38806630), which is different. In a mouse model of MNX1 overexpression, the authors achieve leukaemia engraftment upon MNX1 overexpression in foetal liver, but not in bone marrow cells (PMID: 37317878). This is in agreement with a vulnerability of embryonic / foetal, but not adult cells to the MNX1 expression caused by the translocation. However, haematopoietic cells in the foetal liver originate from YS and AGM precursors, so the origin of the MNX1-susceptible cells can be in those locations, rather than the foetal liver itself.

Reviewer #2 (Public review):<br /> Summary:<br /> In this manuscript, the authors develop an exciting new hemogenic gastruloid (hGX) system, which they claim reproduces the sequential generation of various blood cell types. The key advantage of this cellular system would be its potential to more accurately recapitulate the spatiotemporal emergence of hematopoietic progenitors within their physiological niche compared to other available in vitro systems. The authors present a large set of data and also validate their new system in the context of investigating infant leukemia.<br /> Strengths:<br /> The development of this new in vitro system for generating hematopoietic cells is innovative and addresses a significant drawback of current in vitro models. The authors present a substantial dataset to characterize this system, and they also validate its application in the context of investigating infant leukemia.<br /> Weaknesses:<br /> The thorough characterization and full demonstration that the cells produced truly represent distinct waves of hematopoietic progenitors are incomplete. The data presented to support the generation of late yolk sac (YS) progenitors, such as lymphoid cells, and aortic-gonad-mesonephros (AGM)-like progenitors, including pre-hematopoietic stem cells (pre-HSCs), by this system are not entirely convincing. Given that this is likely the manuscript's most crucial claim, it warrants further scrutiny and direct experimental validation. Ideally, the identity of these progenitors should be further demonstrated by directly assessing their ability to differentiate into lymphoid cells or fully functional HSCs. Instead, the authors primarily rely on scRNA-seq data and a very limited set of markers (e.g., Ikzf1 and Mllt3) to infer the identity and functionality of these cells. Many of these markers are shared among various types of blood progenitors, and only a well-defined combination of markers could offer some assurance of the lymphoid and pre-HSC nature of these cells, although this would still be limited in the absence of functional assays.<br /> The identification of a pre-HSC-like CD45⁺CD41⁻/lo c-Kit⁺VE-Cadherin⁺ cell population is presented as evidence supporting the generation of pre-HSCs by this system, but this claim is questionable. This FACS profile may also be present in progenitors generated in the yolk sac such as early erythro-myeloid progenitors (EMPs). It is only within the AGM context, and in conjunction with further functional assays demonstrating the ability of these cells to differentiate into HSCs and contribute to long-term repopulation, that this profile could be strongly associated with pre-HSCs. In the absence of such data, the cells exhibiting this profile in the current system cannot be conclusively identified as true pre-HSCs.

At this preliminary response stage, we present 2 additional pieces of evidence to support our claims that we capture YS and AGM stages of haematopoietic development. In future experiments, we can complement these with functional assays, including co-culture with OP9 and OP9-DL stroma.

Author response image 8.

EZH2 inhibition affects CD41+ cellular output in haemogenic gastruloids at 144, but not 216h. A. Flow cytometry analysis of CD41 expression in 144h-haemogenic gastruloid treated with 0.5μM EZH2 inhibitor GSK126 from 120h. DMSO (0.05%), vehicle. 1 of 2 independent experiments (average CD41+: DMSO, 21.20%; GSK126, 12.10%; CD45 not detected). B. Flow cytometry analysis of CD41 and CD45 expression in 216h gastruloids, treated with DMSO or GSK216. (DMSO: average CD41+, 15.28%; average CD45+ 0.46%. GSK126: average CD41+, 23.78%; average CD45+, 2.08%).

In Author response images 5 and 6, we project our single-cell RNA-seq data onto (1) developing intra-embryonic pSP and AGM between E8 and E11 (Author response image 5) and (2) a single-cell RNA-seq study of HE development which combines haemogenic and haematopoietic cells from the YS, the developing HE and IAHC in the AGM, and FL (Author response image 6). Our data maps E8.25-E10 (Author response image 5) and captures YS EMP and erythroid and myeloid progenitors, as well as AGM pre-HE, HE and IAHC, with some cells matching HSPC and LMPP (Author response image 6), as suggested by the projection onto the Thambyrajah et al data set (Fig. S3 in the manuscript).

Given the difficulty in finding markers that specifically associate with AGM haematopoiesis, we inspected the possibility of capturing different regulatory requirements at different stages of gastruloid development mirroring differential effects in the embryo. Polycomb EZH2 is specifically required for EMP differentiation in the YS, but does not affect AGM-derived haematopoiesis; it is also not required for primitive erythroid cells (PMID: 29555646; PMID: 34857757). We treated haemogenic gastruloids from 120h onwards with either DMSO (0.05%) or GSK126 (0.5μM), and inspected the cellularity of gastruloids at 144h, which we equate with YS-EMP, and 216h – putatively AGM haematopoiesis (Author response image 8). We show that EZH2 inhibition / GSK126 treatment specifically reduces %CD41+ cells at 144h (Author response image 8A), but does not reduce %CD41+ or %CD45+ cells at 216h (Author response image 8B).

Although preliminary, these data, together with the scRNA-seq projections described, provide evidence to our claim that 144h haemogenic gastruloids capture YS EMPs, while CD41+ and CD45+ cells isolated at 216h reflect AGM progenitors. We cannot conclude as to the functional nature of the AGM cells from this experiment.

The engraftment data presented are also not fully convincing, as the observed repopulation is very limited and evaluated only at 4 weeks post-transplantation. The cells detected after 4 weeks could represent the progeny of EMPs that have been shown to provide transient repopulation rather than true HSCs.

We clearly state that there is low level engraftment and do not claim to have generated HSC. We describe cells with short-term engraftment potential. Although the cells we show in the manuscript at 4 weeks could be EMPs (Author response image 7 and Fig. 3 and S3), we now have 8-week analysis of implant recipients, in which we observed, again low-level, engraftment of the recipient bone marrow in 1:3 animals (Author response image 9). This engraftment is myeloid-lymphoid and therefore likely to have originated in a later progenitor. To be clear, we do not claim that this corresponds to the presence of HSC. It nevertheless supports the maturation of progenitors with engraftment potential.

Author response image 9.

Flow cytometry BFP engraftment of recipient bone marrow 8-weeks post implantation of 216hhaemogenic gastruloids in the adrenal gland of Nude mice. 1:3 animals show BFP CD45+ engraftment in the myeloid (Mac1+) and B-lymphoid (B220+) lineages. 3 haemogenic gastruloids were implanted unilaterally in the adrenal gland of each animal. A. Engrafted animal, showing CD45+ BFP cells of myeloid (CD11b) and B-lymphoid affiliation (B220). B. Non-engrafted mouse recipient of haemogenic gastruloid implants.

Reviewer #3 (Public review):<br /> In this study, the authors employ a mouse ES-derived "hemogenic gastruloid" model which they generated and which they claim to be able to deconvolute YS and AGM stages of blood production in vitro. This work could represent a valuable resource for the field. However, in general, I find the conclusions in this manuscript poorly supported by the data presented. Importantly, it isn't clear what exactly are the "YS" and the "AGM"-like stages identified in the culture and where is the data that backs up this claim. In my opinion, the data in this manuscript lack convincing evidence that can enable us to identify what kind of hematopoietic progenitor cells are generated in this system. Therefore, the statement that "our study has positioned the MNX1-OE target cell within the YS-EMP stage (line 540)" is not supported by the evidence presented in this study. Overall, the system seems to be very preliminary and requires further optimization before those claims can be made.<br /> Specific comments below:<br /> (1) The flow cytometric analysis of gastruloids presented in Figure 1 C-D is puzzling. There is a large % of c-Kit+ cells generated, but few VE-Cad+ Kit+ double positive cells. Similarly, there are many CD41+ cells, but very few CD45+ cells, which one would expect to appear toward the end of the differentiation process if blood cells are actually generated. It would be useful to present this analysis as consecutive gating (i.e. evaluating CD41 and CD45 within VE-Cad+ Kit+ cells, especially if the authors think that the presence of VE-Cad+ Kit+ cells is suggestive of EHT). The quantification presented in D is misleading as the scale of each graph is different.

Fig. 1C-D provide an overview of haemogenic markers during the timecourse of haemogenic gastruloid differentiation, and does indeed show a late up-regulation of CD45, as the Reviewer points out would be expected. The %CD45+ cells is indeed low. However, we should point out that the haemogenic gastruloid protocol, although biased towards mesodermal outputs, does not aim to achieve pure haematopoietic specification, but rather place it in its embryo-like context. Consecutive gating at the 216h-timepoint is shown and quantified in Fig. 3A-B. We refute that the scale is misleading. It is a necessity to represent the data in a way that is interpretable by the reader: the gates (in C) are truly representative and annotated, as are the plot axes (in D).

(2) The imaging presented in Figure 1E is very unconvincing. C-Kit and CD45 signals appear as speckles and not as membrane/cell surfaces as they should. This experiment should be repeated and nuclear stain (i.e. DAPI) should be included.

We include the requested images below (Author response image 10).

Author response image 10.

Confocal images of haematopoietic production in haemogenic gastruloids. Wholemount, cleared haemogenic gastruloids were stained for CD45 (pseudo-coloured red) and c-Kit antigens (pseudo-coloured yellow) with indirect staining, as described in the manuscript. Flk1-GFP signal is shown in green. Nuclei are contrasted with DAPI. (A) 192h. (B) 216h.

(3) Overall, I am not convinced that hematopoietic cells are consistently generated in these organoids. The authors should sort hematopoietic cells and perform May-Grunwald Giemsa stainings as they did in Figure 6 to confirm the nature of the blood cells generated.

It is factual that the data are reproducible and complemented by functional assays shown in Fig. 3, which clearly demonstrate haematopoietic output. The single-cell RNA-seq data also show expression of a haematopoietic programme. Nevertheless, we include Giemsa-Wright’s stained cytospins obtained at 216h to illustrate haematopoietic output (Reviewer Fig. 11). Inevitably, the cytospins will be inconclusive as to the presence of endothelial-to-haematopoietic transition or the generation of haematopoietic stem/progenitor cells, as these cells do not have a distinctive morphology.

Author response image 11.

Cytospin of dissociated haemogenic gastruloids at 216h. Cytospins were stained with Giemsa-Wright’s stain and are visualised with a 40x objective. Annotated are cells in the monocytic (dashed open arrow), granulocytic (solid open arrow), megakaryocytic (solid arrow) and erythroid (asterisk) lineages; arrowheads indicate cells with a non-specific blast-like morphology. Representative image.

(4) The scRNAseq in Figure 2 is very difficult to interpret. Specific points related to this:<br /> - Cluster annotation in Figure 2a is missing and should be included.<br /> - Why do the heatmaps show the expression of genes within sorted cells? Couldn't the authors show expression within clusters of hematopoietic cells as identified transcriptionally (which ones are they? See previous point)? Gene names are illegible.<br /> - I see no expression of Hlf or Myb in CD45+ cells (Figure 2G). Hlf is not expressed by any of the populations examined (panels E, F, G). This suggests no MPP or pre-HSC are generated in the culture, contrary to what is stated in lines 242-245. (PMID 31076455 and 34589491).<br /> Later on, it is again stated that "hGx cells... lacked detection of HSC genes like Hlf, Gfi1, or Hoxa9" (lines 281-283). To me, this is proof of the absence of AGM-like hematopoiesis generated in those gastruloids.

Author response image 12.

Expression of endothelial, haemogenic and haematopoietic genes in haemogenic gastruloid cells sorted at 144h, 192h and 216h. UMAP as in Author response image 4. Pecam (CD31) and CD34 represent endothelial genes also detected in haemogenic endothelium. CD44 is specifically enriched at the endothelial-to-haemogenic transition. Mecom is detected in haemogenic endothelium and haematopoietic progenitors. Mllt3 and Runx1 are haematopoietic markers. Hoxa9 and Hlf are associated with haematopoietic stem and progenitor cells and their detection is rare in haemogenic gastruloids at 216h.

For a combination of logistic and technical reasons, we performed single-cell RNA-seq using the Smart-Seq2 platform, which is inherently low throughput. We overcame the issue of cell coverage by complementing whole-gastruloid transcriptional profiling at successive time-points with sorting of subpopulations of cells based on individual markers documented in Fig. 1. We clearly stated which platform was used as well as the number and type of cells profiled (Fig. S2A and lines 172-179 of the manuscript), and our approach is standard. We will review our representation of the data in a revised manuscript. Nevertheless, at this stage, we provide plots of the expression of key haematopoietic markers over UMAPs of haemogenic gastruloid timecourse (Author response image 12). We also show preliminary qRT-PCR data with increased Hlf expression upon extension of the protocol to 264h (Author response image 13), further confirming haematopoietic specification, including of candidate definitive progenitor cells, in the haemogenic gastruloid model.

Author response image 13.

Hlf expression is up-regulated in late stage haemogenic gastruloids. Quantitative RT-PCR analysis of Hlf expression in unfractionated haemogenic gastruloids cultured for 264h. From 168h onwards, haemogenic gastruloids were cultured in N2B27 in the presence of VEGF, SCF, FLT3L and TPO, all recombinant mouse cytokines, as described in the manuscript. Shown are mean±standard deviation of n=5 replicates from 2 mouse ES cell lines, respectively Flk1-GFP and Rosa26-BFP::Flk1-GFP, reported in the manuscript; 2-tailed unpaired t-test with Welch correction.

(5) Mapping of scRNA-Seq data onto the dataset by Thambyrajah et al. is not proof of the generation of AGM HE. The dataset they are mapping to only contains AGM cells, therefore cells do not have the option to map onto something that is not AGM. The authors should try mapping to other publicly available datasets also including YS cells.

We have done this and the data are presented in Author response image 5 and 6. As detailed in response to Reviewer 1, we have conducted projections of our single-cell RNA-seq data against two studies which (1) capture arterial and haemogenic specification in the para-splanchnopleura (pSP) and AGM region between E8.0 and E11 (Hou et al, PMID: 32203131) (Author response image 5), and (2) uniquely capture YS, AGM and FL progenitors and the AGM endothelial-to-haematopoietic transition (EHT) in the same scRNA-seq dataset (Zhu et al, PMID: 32392346) (Author response image 6). Specifically in answering the Reviewers’ point, we show that different subsets of haemogenic gastruloid cells sorted on haemogenic surface markers c-Kit, CD41 and CD45 cluster onto pre-HE and HE, intra-aortic clusters and FL progenitor compartments, and to YS EMP and erythroid and myeloid progenitors. This lends support to our claim that the haemogenic gastruloid system specifies both YS-like and AGM-like cells.

(6) Conclusions in Figure 3, named "hGx specify cells with preHSC characteristics" are not supported by the data presented here. Again, I am not convinced that hematopoietic cells can be efficiently generated in this system, and certainly not HSCs or pre-HSCs.

We have provided evidence, both in the manuscript and in this response to Reviewers, that there is haematopoietic specification, including of progenitor cells, in the haemogenic gastruloid system (Fig. 3 and Author response image 7,9). We have added data in this response that supports the specification of YS-like and AGM-like cells (Author response image 5-6, 8). Importantly, we have never claimed that haemogenic gastruloids generate HSC. We accept the Reviewer’s comment that we have not provided sufficient evidence for the specification of pre-HSC-like cells. We will re-phrase Fig. 3 conclusion as “Haemogenic gastruloids specify cells with characteristics of definitive haematopoietic progenitors”.

- FACS analysis in 3A is again very unconvincing. I do not think the population identified as c-Kit+ CD144+ is real. Also, why not try gating the other way around, as commonly done (e.g. VE-Cad+ Kit+ and then CD41/CD45)?

There is nothing unconventional about our gating strategy, which was done from a more populated gate onto the less abundant one to ensure that the results are numerically more robust. In the case of haemogenic gastruloids, unlike the AGM preparations the Reviewer may be referring to, CD41 and CD45+ cells are more abundant as there is no circulation of more differentiated haematopoietic cells away from the endothelial structures. This said, we did perform the gating as suggested (Author response image 14), indeed confirming that most VE-cad+ Kit+ cells are CD45+. Interestingly VE-cad+Kit- are predominantly CD41+, reinforcing the true haematopoietic nature of these cells.

Author response image 14.

Flow cytometry analysis of VE-cadherin+ cells in haemogenic gastruloids at 216h of the differentiation protocol, probing co-expression of CD45, CD41 and c-Kit.

- The authors must have tried really hard, but the lack of short- or long-engraftment in a number of immunodeficient mouse models (lines 305-313) really suggests that no blood progenitors are generated in their system. I am not familiar with the adrenal gland transplant system, but it seems like a very non-physiological system for trying to assess the maturation of putative pre-HSCs. The data supporting the engraftment of these mice, essentially seen only by PCR and in some cases with a very low threshold for detection, are very weak, and again unconvincing. It is stated that "BFP engraftment of the Spl and BM by flow cytometry was very low level albeit consistently above control (Fig. S4E)" (lines 337-338). I do not think that two dots in a dot plot can be presented as evidence of engraftment.

We have presented the data with full disclosure and do not deny that the engraftment achieved is low-level and short-term, indicating incomplete maturation of definitive haematopoietic progenitors in the current haemogenic gastruloid system. However, we call the Reviewer’s attention to the fact that detection of BFP+ cells by PCR and flow cytometry in the recipient animals at 4 weeks is consistent between the 2 methods (Author response image 7).

Furthermore, we have now also been able to detect low-level myelo-lymphoid engraftment in the bone marrow 8 weeks after adrenal implantation, again suggesting the presence of a small number of definitive haematopoietic progenitors that potentially mature from the 3 haemogenic gastruloids implanted (Author response image 9).

(7) Given the above, I find that the foundations needed for extracting meaningful data from the system when perturbed are very shaky at best. Nevertheless, the authors proceed to overexpress MNX1 by LV transduction, a system previously shown to transform fetal liver cells, mimicking the effect of the t(7;12) AML-associated translocation. Comments on this section:<br /> - The increase in the size of the organoid when MNX1 is expressed is a very unspecific finding and not necessarily an indication of any hematopoietic effect of MNX1 OE.

We agree with the Reviewer on this point; it is nevertheless a reproducible observation which we thought relevant to describe for completeness and data reproducibility.

- The mild increase of cKit+ cells (Figure 4E) at the 144hr timepoint and the lack of any changes in CD41+ or CD45+ cells suggests that the increase in Kit+ cells % is not due to any hematopoietic effect of MNX1 OE. No hematopoietic GO categories are seen in RNA seq analysis, which supports this interpretation. Could it be that just endothelial cells are being generated?

The Reviewer is correct that the MNX1-overexpressing cells have a strong endothelial signature, which is present in the patients (Fig. 4A). We investigated a potential link with c-Kit by staining cells from the replating colonies during the process of in vitro transformation with CD31. We observed that 40-50% of c-Kit+ cells (20-30% total colony cells) co-expressed CD31(Author response image 15), at least at early plating. These cells co-exist with haematopoietic cells, namely Ter119+ cells, as expected from the YS-like erythroid and EMP-like affiliation of haematopoietic output from 144h-haemogenic gastruloids (Fig. 5F).

Author response image 15.

Endothelial affiliation of MNX1-oe replating cells from haemogenic gastruloid. A. Representative flow cytometry plot of plate 1 CFC from MNX1-overexpressing haemogenic gastruloids at 144h. B. Quantification of the proportion of CD31+c-Kit+ cells in plates 1 and 2 of MNX1-oe-driven in vitro transformation.

(8) There seems to be a relatively convincing increase in replating potential upon MNX1-OE, but this experiment has been poorly characterized. What type of colonies are generated? What exactly is the "proportion of colony forming cells" in Figures 5B-D? The colony increase is accompanied by an increase in Kit+ cells; however, the flow cytometry analysis has not been quantified.

Given the inability to replate control EV cells, there is not a population to compare with in terms of quantification. The level of c-Kit+ represented in Fig. 5E is achieved at plate 2 or 3 (depending on the experiment), both of which are significantly enriched for colony-forming cells relative to control (Fig. 5B, D).

(9) Do hGx cells engraft upon MNX1-OE? This experiment, which appears not to have been performed, is essential to conclude that leukemic transformation has occurred.

For the purpose of this study, we are satisfied with confirmation of in vitro transformation potential of MNX1 haemogenic gastruloids, which can be used for screening purposes. Although interesting, in vivo leukaemia engraftment from haemogenic gastruloids is beyond the scope of this study.

Author response:

We kindly thank the senior editor, the reviewing editor, and the esteemed reviewers for their invaluable insights in enhancing our manuscript. The assessment and feedback, particularly on the role of directly released bacterial ATP versus OMV-delivered bacterial ATP and its role on neutrophils, addressing study limitations, and discussing our models is highly appreciated.

The points you raised let us critically rethink our approach, our results, and our conclusions. Furthermore, it gave us the chance to elaborate on some critical aspects that you mentioned. With your help, we will make clarifications throughout the manuscript, and we will add the data about neutrophil numbers in the different organs (reviewer #1, weaknesses #3).

Reviewer #1 (Public Review):

Summary:

• Extracellular ATP represents a danger-associated molecular pattern associated to tissue damage and can act also in an autocrine fashion in macrophages to promote proinflammatory responses, as observed in a previous paper by the authors in abdominal sepsis. The present study addresses an important aspect possibly conditioning the outcome of sepsis that is the release of ATP by bacteria. The authors show that sepsis-associated bacteria do in fact release ATP in a growth dependent and strain-specific manner. However, whether this bacterial derived ATP play a role in the pathogenesis of abdominal sepsis has not been determined. To address this question, a number of mutant strains of E. coli has been used first to correlate bacterial ATP release with growth and then, with outer membrane integrity and bacterial death. By using E. coli transformants expressing the ATP-degrading enzyme apyrase in the periplasmic space, the paper nicely shows that abdominal sepsis by these transformants results in significantly improved survival. This effect was associated with a reduction of peritoneal macrophages and CX3CR1+ monocytes, and an increase in neutrophils. To extrapolate the function of bacterial ATP from the systemic response to microorganisms, the authors exploited bacterial OMVs either loaded or not with ATP to investigate the systemic effects devoid of living microorganisms. This approach showed that ATP-loaded OMVs induced degranulation of neutrophils after lysosomal uptake, suggesting that this mechanism could contribute to sepsis severity.

Strengths:

• A strong part of the study is the analysis of E. coli mutants to address different aspects of bacterial release of ATP that could be relevant during systemic dissemination of bacteria in the host.

We want to thank the reviewer for recognizing this important aspect of our experimental approach.

Weaknesses:

• As pointed out in the limitations of the study whether ATP-loaded OMVs provide a mechanistic proof of the pathogenetic role of bacteria-derived ATP independently of live microorganisms in sepsis is interesting but not definitively convincing. It could be useful to see whether degranulation of neutrophils is differentially induced by apyrase-expressing vs control E. coli transformants.

We thank the reviewer for raising several important points. In our study, we assessed local and systemic effects of released bacterial ATP. The consequences of local bacterial ATP release were assessed using an apyrase-expressing E. coli transformant. Locally, bacterial ATP resulted in a decrease in neutrophil numbers and we hypothesize that directly released bacterial ATP either leads to neutrophil death (e.g. via P2X7 receptor (Proietti et al., 2019)) or interferes with the recruitment of neutrophils (e.g. via P2Y receptors (Junger, 2011)).

The systemic consequences were assessed using ATP-loaded and empty OMV. We have shown that degranulation is induced by OMV-derived bacterial ATP. ATP-containing OMV are engulfed by neutrophils, reach its endolysosomal compartment and might activate purinergic receptors, which then lead to aberrant degranulation. This concept, that needs to be explored in future studies, is fundamentally different from classical purinergic signaling via directly released bacterial ATP into the extracellular space.

It is possible that neutrophil degranulation is also modulated by directly released bacterial ATP. We agree that this should be assessed in future studies. Also, the role of OMV-derived bacterial ATP should be assessed locally as well as the importance of directly released vs. OMV-mediated bacterial ATP dissected locally. Based on our measurements (Figure 4-figure supplement 1A and Figure 5C), we estimate that the effect of OMV-derived bacterial ATP might be much smaller than the effects of directly released bacterial ATP. Thus, direct ATP release might predominate locally. However, we fully agree that this has to be investigated in a future study to reconcile the different aspects of bacterial ATP signaling. A paragraph will be added to the manuscript, in which we discuss this particular issue.

• Also, the increase of neutrophils in bacterial ATP-depleted abdominal sepsis, which has better outcomes than "ATP-proficient" sepsis, seems difficult to correlate to the hypothesized tissue damage induced by ATP delivered via non-infectious OMVs.

We fully acknowledge the mentioned discrepancy. What we propose is that bacterial ATP exhibits different functions that are dependent on the release mechanism (see above). Locally, in the peritoneal cavity, neutrophil numbers are decreased by directly released bacterial ATP. Remotely, ATP is delivered via OMV and impacts on neutrophil function. We agree that, in particular, in the peritoneal cavity, both effects may play a role. However, the impact of directly released bacterial ATP seems to be dominant (see above).

We propose that neutrophils are decreased locally because of directly released bacterial ATP, which prevents efficient infection control and, therefore, impairs sepsis survival. In addition, these fewer neutrophils might even be dysregulated by the engulfment of bacterial ATP delivered via OMV, which leads to an upregulated and possibly aberrant degranulation process worsening local and remote tissue damage. We agree that in addition to neutrophil numbers, the function of local neutrophils should be assessed with and without the influence of OMV-delivered bacterial ATP. This could be done by RNA sequencing of primary neutrophils from the peritoneal cavity or neutrophil cell lines as well as degranulation assays.

• Are the neutrophils counts affected by ATP delivered via OMVs?

This is difficult to show in the peritoneal cavity where we have both, directly released bacterial ATP and OMV-derived bacterial ATP. We assessed such putative difference, however, for the systemic organs and the blood, where we did not find any differences in neutrophil numbers. We will include the figure in the revised manuscript as Figure 6-figure supplement 3C.

Author response image 1.

• A comparison of cytokine profiles in the abdominal fluids of E. coli and OMV treated animals could be helpful in defining the different responses induced by OMV-delivered vs bacterial-released ATP. The analyses performed on OMV treated versus E. coli infected mice are not closely related and difficult to combine when trying to draw a hypothesis for bacterial ATP in sepsis.

We fully agree that there are several open questions that remain to be elucidated, in particular, to differentiate the local role of directly released versus OMV-delivered bacterial ATP. In this study, we laid the foundation for future in vivo research to examine the specific role of bacterial ATP in sepsis. Such future research avenues might be to investigate the local effects of OMV-delivered bacterial ATP, and how neutrophil migration, apoptosis and degranulation are altered. We agree that exploration of the local secretory immune response and cytokine profiles are relevant to understand the different mechanisms of how bacterial ATP alters sepsis. However, such experiments should be ideally performed in systems where the source and the delivery of ATP can be modulated locally.

• Also it was not clear why lung neutrophils were used for the RNAseq data generation and analysis.

Thank you for this remark. We have chosen primary lung neutrophils for four reasons:

(1) Isolation of primary lung neutrophils allowed us to assess an in vivo response that would not have been possible with cell lines.

(2) The lung and the respiratory system are among the clinically most important organs affected during sepsis resulting in a significant cause of mortality.

(3) We show in Figure 6C that specifically in the lung, OMV are engulfed by neutrophils, which shows the relevance of the lung also in our study context.

(4) And finally, lung neutrophils were chosen to examine specifically distant and not local effects.

Reviewer #2 (Public Review):

Summary:

• In their manuscript "Released Bacterial ATP Shapes Local and Systemic Inflammation during Abdominal Sepsis", Daniel Spari et al. explored the dual role of ATP in exacerbating sepsis, revealing that ATP from both host and bacteria significantly impacts immune responses and disease progression.

Strengths:

• The study meticulously examines the complex relationship between ATP release and bacterial growth, membrane integrity, and how bacterial ATP potentially dampens inflammatory responses, thereby impairing survival in sepsis models. Additionally, this compelling paper implies a concept that bacterial OMVs act as vehicles for the systemic distribution of ATP, influencing neutrophil activity and exacerbating sepsis severity.

We thank the reviewer for mentioning these key points and supporting the relevance of our study.

Weaknesses:

(1) The researchers extracted and cultivated abdominal fluid on LB agar plates, then randomly picked 25 colonies for analysis. However, they did not conduct 16S rRNA gene amplicon sequencing on the fluid itself. It is worth noting that the bacterial species present may vary depending on the individual patients. It would be beneficial if the authors could specify whether they've verified the existence of unculturable species capable of secreting high levels of Extracellular ATP.

Most septic complications are caused by a limited spectrum of bacteria, belonging mainly either to the Firmicutes or the Proteobacteria phyla, including E. coli, K. pneumoniae, S. aureus or E. faecalis (Diekema et al., 2019; Mureșan et al., 2018). We validated this well documented existing evidence by randomly assessing 25 colonies. For the planned experiments, it was crucial to work with culturable bacteria; otherwise, ATP measurements, the modulation of ATP generation or loading of OMV would not have been possible. Using such culturable bacteria allowed us to describe mechanisms of ATP release.

We fully agree that hard-to-culture or unculturable bacteria might contribute significantly to septic complications. This, however, would need to be explored in future studies using extensive culturing methods (Cheng et al., 2022).

(2) Do mice lacking commensal bacteria show a lack of extracellular ATP following cecal ligation puncture?

ATP is typically secreted by many cells of the host in active and passive manners in the case of any injury, including cecal ligation and puncture (Burnstock, 2016; Dosch et al., 2018; Eltzschig et al., 2012; Idzko et al., 2014). We hypothesize that bacterial ATP is a potential priming agent at early stages of sepsis, and indeed, at such early time points, a comparison of peritoneal ATP levels between germfree and colonized mice could support our hypothesis. Future studies addressing this question must, however, correct for the different immune responses between germ-free and colonized mice. This is of utmost importance, especially for the cecal ligation and puncture model, since the cecum of germ-free mice is extremely large, making such experiments hard to control.

(3) The authors isolated various bacteria from abdominal fluid, encompassing both Gram-negative and Gram-positive types. Nevertheless, their emphasis appeared to be primarily on the Gram-negative E. coli. It would be beneficial to ascertain whether the mechanisms of Extracellular ATP release differ between Gram-positive and Gram-negative bacteria. This is particularly relevant given that the Gram-positive bacterium E. faecalis, also isolated from the abdominal fluid, is recognized for its propensity to release substantial amounts of Extracellular ATP.

We fully agree with this comment. In this paper, we used E. coli as our model organism to determine the principles of sepsis-associated bacterial ATP release and therefore focused on gram-negative bacteria. In addition to the direct, growth-dependent release, we found a relevant impact of OMV-delivered bacterial ATP. For this latter purpose, a gram-negative strain, in which OMV generation has been well described (Schwechheimer & Kuehn, 2015), was chosen. Recently, gram-positive bacteria have been shown to secrete ATP and OMV as well (Briaud & Carroll, 2020; Hironaka et al., 2013; Iwase et al., 2010). Given the fundamental differences in the structure of the cell wall of gram-positive bacteria and the mechanisms of OMV generation and release, future studies are required to assess the relevance of directly released and OMV-delivered ATP in gram-positive bacteria.

(4) The authors observed changes in the levels of LPM, SPM, and neutrophils in vivo. However, it remains uncertain whether the proliferation or migration of these cells is modulated or inhibited by ATP receptors like P2Y receptors. This aspect requires further investigation to establish a convincing connection.

We fully agree with this comment. The decrease in LPM and the consequential predomination of SPM have been well described after inflammatory stimuli in the context of the macrophage disappearance reaction (Ghosn et al., 2010). Also, it has been shown that purinergic signaling modulates infiltration of neutrophils and can lead to cell death as a consequence of P2Y and P2X receptor activation (Junger, 2011; Proietti et al., 2019). In our study, we propose that intracellular purinergic receptors contribute to neutrophil function during sepsis. After introducing the general principles and fundaments of bacterial ATP with our studies, we fully agree that additional experiments need to address downstream purinergic receptor activation. That, however, would go beyond the scope of our study.

(5) Additionally, is it possible that the observed in vivo changes could be triggered by bacterial components other than Extracellular ATP? In this research field, a comprehensive collection of inhibitors is available, so it is desirable to utilize them to demonstrate clearer results.

This question is of utmost importance and defined the choice of our model and experimental approach. When we started the project, we used two different E. coli mutants that release low (ompC) and high (eaeH) amounts of ATP. However, the limitation of this approach is that these are different bacteria, which may also differ in the components they secrete or the surface proteins they express. We, therefore, decided against that approach. With the approach we finally used (same bacterium, just with and without ATP), we aimed to minimize the influence of non-ATP bacterial components.

(6) Have the authors considered the role of host-derived Extracellular ATP in the context of inflammation?

Yes, the role of host-derived extracellular ATP in inflammation and sepsis is well-established with contradictory results (Csóka et al., 2015; Ledderose et al., 2016). This conflicting data was the rationale to test the relevance of bacterial ATP. We suggest that bacterial ATP is essential in the early phase of sepsis when bacteria invade the sterile compartment and before efficient host response, including the eukaryotic release of ATP, is established.

(7) The authors mention that Extracellular ATP is rapidly hydrolyzed by ectonucleotases in vivo. Are the changes of immune cells within the peritoneal cavity caused by Extracellular ATP released from bacterial death or by OMVs?

This is a relevant question that was also asked by reviewer #1, and we answered it in detail above (weaknesses comment #1 and #2). From our ATP measurements (Figure 4-figure supplement 1A and Figure 5C), we conclude that locally, the role of directly released bacterial ATP (extracellular) predominates over OMV-derived bacterial ATP. Furthermore, the mechanisms between directly released and OMV-derived bacterial ATP (within OMV, engulfed and transported to the endolysosomal compartment) are different, and especially extracellular ATP has been described to lead to apoptosis via P2X7 signaling.

(8) In the manuscript, the sample size (n) for the data consistently remains at 2. I would suggest expanding the sample size to enhance the robustness and rigor of the results.

Two biological replicates (independent cultures) were only used for the bacteria cultures in Figure 1, Figure 2, and Figure 3, which achieved similar results and the standard deviation remained very small, indicating its robustness. In the in vitro experiments in Figure 5 we used a sample size of 6 (three biological replicates measured in technical duplicates), since we saw bigger deviations in our measurements. For the in vivo experiments, we always used 5 or more animals in at least two independent experiments.

References

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Author response:

Public Reviews:

Reviewer #1 (Public review):

Summary:

This work made a lot of efforts to explore the multifaceted roles of the inferior colliculus (IC) in auditory processing, extending beyond traditional sensory encoding. The authors recorded neuronal activitity from the IC at single unit level when monkeys were passively exposed or actively engaged in behavioral task. They concluded that 1）IC neurons showed sustained firing patterns related to sound duration, indicating their roles in temporal perception, 2) IC neuronal firing rates increased as sound sequences progress, reflecting modulation by behavioral context rather than reward anticipation, 3) IC neurons encode reward prediction error and their capability of adjusting responses based on reward predictability, 4) IC neural activity correlates with decision-making. In summary, this study tried to provide a new perspective on IC functions by exploring its roles in sensory prediction and reward processing, which are not traditionally associated with this structure.

Strengths:

The major strength of this work is that the authors performed electrophysiological recordings from the IC of behaving monkeys. Compared with the auditory cortex and thalamus, the IC in monkeys has not been adequately explored.

We appreciate the reviewer’s acknowledgment of the efforts and strengths of our study. Indeed, our goal was to provide a comprehensive exploration of the multifaceted roles of the inferior colliculus (IC) in auditory processing and beyond, particularly in sensory prediction and reward processing. The use of electrophysiological recordings in behaving monkeys was central to our approach, as we sought to uncover the underexplored aspects of IC function in these complex cognitive domains. We are pleased that the reviewer recognizes the value of investigating the IC, a structure that has not been adequately explored in primates compared to other auditory regions like the cortex and thalamus. This feedback reinforces our belief that our work contributes significantly to advancing the understanding of the IC's roles in cognitive processing.

We look forward to addressing any further points the reviewers may have and refining our manuscript accordingly. Thank you for your constructive feedback and for recognizing the strengths of our research approach.

Weaknesses:

(1) The authors cited several papers focusing on dopaminergic inputs in the IC to suggest the involvement of this brain region in cognitive functions. However, all those cited work were done in rodents. Whether monkey's IC shares similar inputs is not clear.

We appreciate the reviewer's insightful comment on the limitations of extrapolating findings from rodent models to monkeys, particularly concerning dopaminergic inputs to the Inferior Colliculus (IC). While it is true that most studies on dopaminergic inputs to the IC have been conducted in rodents, to our knowledge, no studies have been conducted specifically in primates. To address the reviewer's concern, we have added a statement in both the introduction and discussion sections of our manuscript:

- Introduction: " However, these studies were conducted in rodents, and the existence and role of dopaminergic inputs in the primate IC remain underexplored."

- Discussion: " However, the exact mechanisms and functions of dopamine modulation in the inferior colliculus are still not fully understood, particularly in primates. "

(2) The authors confused the two terms, novelty and deviation. According to their behavioral paradigm, deviation rather than novelty should be used in the paper because all the stimuli have been presented to the monkeys during training. Therefore, there is actually no novel stimuli but only deviant stimuli. This reflects that the author has misunderstood the basic concept.

We appreciate the reviewer's clarification regarding the distinction between "novelty" and "deviation" in the context of our behavioral paradigm. We agree that, given the nature of our experimental design where all stimuli were familiar to the monkeys during training, the term "deviation" more accurately describes the stimuli used in our study rather than "novelty."

To address this, we have revised the manuscript to replace the term "novelty" with "deviation" wherever applicable. This change has been made to ensure accurate terminology is used throughout the paper, thereby eliminating any potential misunderstanding of the concepts involved in our study.

We thank the reviewer for pointing out this important distinction, which has improved the clarity and precision of our manuscript.

(3) Most of the conclusions were made based on correlational analysis or speculation without providing causal evidences.

We appreciate the reviewer’s concern regarding the reliance on correlational analyses in our study. Indeed, we acknowledge that the conclusions drawn primarily reflect correlations between neuronal activity and behavioral outcomes, rather than direct causal evidence. This limitation is inherent to many electrophysiological studies, particularly those conducted in behaving primates, where direct manipulation of specific neural circuits to establish causality is often challenging.

This limitation becomes even more complex when considering the IC’s role as a key lower-level relay station in the auditory pathway. Manipulating IC activity could potentially affect auditory responses in downstream pathways, which, in turn, may influence sensory prediction and decision-making processes. Moreover, we hypothesize that the sensory prediction and reward signals observed in the IC may not have direct causal effects but may instead be driven by top-down projections from higher cognitive regions. However, it is important to emphasize that our study provides novel evidence that the IC may exhibit multiple facets of cognitive signaling, which could inspire future research into the underlying mechanisms and broader functional implications of these signals.

To address this, we have taken the following steps in our revised manuscript:

(1) Clarified the Scope of Conclusions: We have revised the language in the Results and Discussion sections to explicitly state that our findings represent correlational relationships rather than causal mechanisms. For example, we now refer to the associations observed between IC activity and behavioral outcomes as "correlational" and have refrained from making definitive causal claims without supporting experimental evidence.

(2) Proposed Future Directions: In the Discussion section, we have included suggestions for future studies to directly test the causality of the observed relationships. We acknowledge the need for further investigation to substantiate the causal links between IC activity and cognitive functions such as sensory prediction, decision-making, and reward processing.

We believe these revisions provide a more balanced interpretation of our findings while emphasizing the importance of future research to build on our results and establish causal relationships. Thank you for raising this critical point, which has led to a more rigorous and transparent presentation of our study.

(4) Results are presented in a very "straightforward" manner with too many detailed descriptions of phenomena but lack of summary and information synthesis. For example, the first section of Results is very long but did not convey clear information.

We appreciate the reviewer’s feedback regarding the presentation of our results. We understand that the detailed descriptions of phenomena may have made it difficult to discern the key findings and overarching themes in the study. We recognize the importance of balancing detailed reporting with clear summaries and synthesis to effectively communicate our findings.

To address this concern, we have made the following revisions to the manuscript:

(1) Condensed and Synthesized Key Findings: We have streamlined the presentation of the Results section by condensing overly detailed descriptions and focusing on the most critical aspects of the data. Key findings are now summarized at the end of each subsection to ensure that the main points are clearly conveyed.

(2) Enhanced Section Summaries: We have added summary statements at the end of each major results section to synthesize the findings and highlight their significance. This should help guide the reader through the narrative and emphasize the key takeaways from each part of the study.

(3) Improved Flow and Clarity: We have revised the structure and organization of the Results section to improve the flow of information. By rearranging certain paragraphs and refining the language, we aim to present the results in a more cohesive and coherent manner.

We believe these changes will make the Results section more accessible and informative, allowing readers to more easily grasp the significance of our findings. Thank you for your valuable suggestion, which has significantly improved the clarity and impact of our manuscript.

(5) The logic between different sections of Results is not clear.

We appreciate the reviewer’s observation regarding the lack of clear logical connections between different sections of the Results. We acknowledge that a coherent flow is essential for effectively communicating the progression of findings and their implications.

To address this concern, we have made the following revisions:

(1) Enhanced Transitions Between Sections: We have introduced clearer transitional statements between sections of the Results. These transitions explicitly state how each new section builds upon or relates to the previous findings, creating a more cohesive narrative.

(2) Integration of Findings: In several places within the Results, we have added brief synthesis paragraphs that integrate findings across sections. These integrative summaries help to tie together the different aspects of our study, demonstrating how they collectively contribute to our understanding of the Inferior Colliculus’s (IC) role in sensory prediction, decision-making, and reward processing.

(3) Clarified Rationale: At the beginning of each major section, we have clarified the rationale behind why certain experiments were conducted, connecting them more clearly to the overarching goals of the study. This should help the reader understand the purpose of each set of results in the context of the broader research objectives.

We believe these changes improve the overall coherence and readability of the Results section, allowing readers to better follow the logical progression of our study. We are grateful for this constructive feedback and believe it has significantly enhanced the manuscript.

(6) In the Discussion, there is excessive repetition of results, and further comparison with and discussion of potentially related work are very insufficient. For example, Metzger, R.R., et al. (J Neurosc, 2006) have shown similar firing patterns of IC neurons and correlated their findings with reward.

We appreciate the reviewer's insightful critique regarding the excessive repetition in the Discussion and the lack of sufficient comparison with related work. We acknowledge that a well-balanced Discussion should not only interpret findings but also place them in the context of existing literature to highlight the novelty and significance of the study.

To address these concerns, we have made the following revisions:

(1) Reduction of Repetition: We have carefully revised the Discussion to minimize redundant repetition of the Results. Instead of restating the findings, we now focus more on their implications, limitations, and how they advance the current understanding of the Inferior Colliculus (IC) and its broader cognitive roles.

(2) Incorporation of Related Work: We have expanded the Discussion to include a more comprehensive comparison with existing literature, specifically highlighting studies that have reported similar findings. For example, we now discuss the work by Metzger et al. (2006), which demonstrated similar firing patterns of IC neurons and correlated these with reward-related processes. This comparison helps contextualize our results and emphasizes the novel contributions our study makes to the field.

We believe these revisions have significantly improved the quality of the Discussion by reducing unnecessary repetition and providing a more thorough engagement with the relevant literature. We are grateful for the reviewer's valuable feedback, which has helped us refine and strengthen the manuscript.

Reviewer #2 (Public review):

Summary:

The inferior colliculus (IC) has been explored for its possible functions in behavioral tasks and has been suggested to play more important roles rather than simple sensory transmission. The authors revealed the climbing effect of neurons in IC during decision-making tasks, and tried to explore the reward effect in this condition.

Strengths:

Complex cognitive behaviors can be regarded as simple ideals of generating output based on information input, which depends on all kinds of input from sensory systems. The auditory system has hierarchic structures no less complex than those areas in charge of complex functions. Meanwhile, IC receives projections from higher areas, such as auditory cortex, which implies IC is involved in complex behaviors. Experiments in behavioral monkeys are always time-consuming works with hardship, and this will offer more approximate knowledge of how the human brain works.

We greatly appreciate the reviewer's positive summary of our work and recognition of the effort involved in conducting experiments on behaving monkeys. We agree with the reviewer that the inferior colliculus (IC) plays a significant role beyond mere sensory transmission, particularly in integrating sensory inputs with higher cognitive functions. Our study aims to shed light on these complex functions by revealing the climbing effect of IC neurons during decision-making tasks and exploring how reward influences this dynamic.

We are encouraged that the reviewer acknowledges the importance of investigating the IC's role within the broader framework of complex cognitive behaviors and appreciates the hierarchical nature of the auditory system. The reviewer's comments reinforce the value of our research in contributing to a more nuanced understanding of how the IC might contribute to sensory-cognitive integration.

We thank the reviewer for highlighting the significance of using behavioral monkey models to approximate human brain function. We are hopeful that our findings will serve as a stepping stone for further research exploring the multifaceted roles of the IC in cognition and behavior.

We will now proceed to address the specific concerns and suggestions provided by the reviewer in the following sections.

Weaknesses:

These findings are more about correlation but not causality of IC function in behaviors. And I have a few major concerns.

We appreciate the reviewer’s concern regarding the reliance on correlational analyses in our study. We acknowledge the importance of distinguishing between correlation and causality. As detailed in our response to Question 3 from Reviewer #1, we recognize the limitations of relying on correlational data and the challenges of establishing direct causal links in electrophysiological studies involving behaving primates.

We have taken steps to clarify this distinction throughout our manuscript. Specifically, we have revised the Results and Discussion sections to ensure that the findings are presented as correlational, not causal, and we have proposed future studies utilizing more direct manipulation techniques to assess causality. We hope these revisions adequately address your concerns.

Comparing neurons' spike activities in different tests, a 'climbing effect' was found in the oddball paradigm. The effect is clearly related to training and learning process, but it still requires more exploration to rule out a few explanations. First, repeated white noise bursts with fixed inter-stimulus-interval of 0.6 seconds was presented, so that monkeys might remember the sounds by rhymes, which is some sort of learned auditory response. It is interesting to know monkeys' responses and neurons' activities if the inter-stimuli-interval is variable. Second, the task only asked monkeys to press one button and the reward ratio (the ratio of correct response trials) was around 78% (based on the number from Line 302). so that, in the sessions with reward, monkeys had highly expected reward chances, does this expectation cause the climbing effect?

We thank the reviewer for raising these insightful points regarding the 'climbing effect' observed in the oddball paradigm and its potential relationship with training, learning processes, and reward expectation. Below, we address each of the reviewer's specific concerns:

(1) Inter-Stimulus Interval (ISI) and Rhythmic Auditory Response:

The reviewer suggests that the fixed inter-stimulus interval (ISI) of 0.6 seconds might lead to a rhythmic auditory response, where monkeys could anticipate the sounds. We appreciate this perspective. However, we believe that rhythm is unlikely to play a significant role in the 'climbing effect' for the following reason: The 'climbing effect' starts from the second sound in the block (Fig.2D and Fig.3B), before any rhythm or pattern could be fully established, as a rhythm generally requires at least three repetitions to form. Unfortunately, we did not explore variable ISIs in the current study, so we cannot directly address this concern with the data at hand.

(2) Reward Expectation and Climbing Effect:

The reviewer raises an important concern about whether the 'climbing effect' could be influenced by the monkeys' high reward expectation, especially given the high reward ratio (~78%) in the sessions. While it is plausible that reward expectation could contribute to the observed increase in neuronal firing rates, we believe the results from our reward experiment (Fig. 4) suggest otherwise. In this experiment, even though reward expectation was likely formed due to the consistent pairing of sounds with rewards (100%), we did not observe a climbing effect in the auditory response. The presence of reward prediction error (Fig. 4D) further suggests that while the monkeys may form reward expectations, these expectations do not directly drive the climbing effect.

To clarify this point, we have added sentences in the revised manuscript to explicitly discuss the relationship between reward expectation and the climbing effect, emphasizing that our findings indicate the climbing effect is not primarily due to reward expectation.

We believe these revisions provide a clearer understanding of the factors contributing to the climbing effect and address the reviewer's concerns effectively. Thank you for these valuable suggestions.

"Reward effect" on IC neurons' responses were showed in Fig. 4. Is this auditory response caused by physical reward action or not? In reward sessions, IC neurons have obvious response related to the onset of water reward. The electromagnetic valve is often used in water-rewarding system and will give out a loud click sound every time when the reward is triggered. IC neurons' responses may be simply caused by the click sound if the electromagnetic valve is used. It is important to find a way to rule out this simple possibility.

We appreciate the reviewer’s concern regarding the potential confounding factor introduced by the electromagnetic valve’s click sound during water reward delivery, which could be misinterpreted as an auditory response rather than a response to the reward itself. Anticipating this possibility, we took measures to eliminate it by placing the electromagnetic valve outside the soundproof room where the neuronal recordings were performed.

To address your concern more explicitly, we have added sentences in the Methods section of the revised manuscript detailing this setup, ensuring that readers are aware of the steps we took to eliminate this potential confound. By doing so, we believe that the observed reward-related neural activity in the IC is attributable to the reward processing itself rather than an auditory response to the valve click. We appreciate you bringing this important aspect to our attention, and we hope our clarification strengthens the interpretation of our findings.

Reviewer #3 (Public review):

Summary:

The authors aimed to investigate the multifaceted roles of the Inferior Colliculus (IC) in auditory and cognitive processes in monkeys. Through extracellular recordings during a sound duration-based novelty detection task, the authors observed a "climbing effect" in neuronal firing rates, suggesting an enhanced response during sensory prediction. Observations of reward prediction errors within the IC further highlight its complex integration in both auditory and reward processing. Additionally, the study indicated IC neuronal activities could be involved in decision-making processes.

Strengths:

This study has the potential to significantly impact the field by challenging the traditional view of the IC as merely an auditory relay station and proposing a more integrative role in cognitive processing. The results provide valuable insights into the complex roles of the IC, particularly in sensory and cognitive integration, and could inspire further research into the cognitive functions of the IC.

We appreciate the reviewer’s positive summary of our work and recognition of its potential impact on the field. We are pleased that the reviewer acknowledges the significance of our findings in challenging the traditional view of the Inferior Colliculus (IC) as merely an auditory relay station and in proposing its integrative role in cognitive processing.

Our study indeed aims to provide new insights into the multifaceted roles of the IC, particularly in the context of sensory and cognitive integration. We believe that this research could pave the way for future studies that further explore the cognitive functions of the IC and its involvement in complex behavioral processes.

We are encouraged by the reviewer’s positive assessment and are committed to continuing to refine our work in response to the constructive feedback provided. We hope that our findings will contribute to advancing the understanding of the IC’s role in the broader context of neuroscience.

We will now proceed to address the specific concerns and suggestions provided by the reviewer in the following sections.

Weaknesses:

Major Comments:

(1) Structural Clarity and Logic Flow:

The manuscript investigates three intriguing functions of IC neurons: sensory prediction, reward prediction, and cognitive decision-making, each of which is a compelling topic. However, the logical flow of the manuscript is not clearly presented and needs to be well recognized. For instance, Figure 3 should be merged into Figure 2 to present population responses to the order of sounds, thereby focusing on sensory prediction. Given the current arrangement of results and figures, the title could be more aptly phrased as "Beyond Auditory Relay: Dissecting the Inferior Colliculus's Role in Sensory Prediction, Reward Prediction, and Cognitive Decision-Making."

We appreciate the reviewer’s detailed feedback on the structural clarity and logical flow of the manuscript. We understand the importance of presenting our findings in a clear and cohesive manner, especially when addressing multiple complex topics such as sensory prediction, reward prediction, and cognitive decision-making.

To address the reviewer's concerns, we have made the following revisions:

(1) Reorganization of Figures and Results:

We agree with the suggestion to merge Figure 3 into Figure 2. By doing so, we can present the population responses to the order of sounds more effectively, thereby streamlining the focus on sensory prediction. This will allow readers to more easily follow the progression of the results related to this key function of the IC.

We have reorganized the Results section to ensure a smoother transition between the different aspects of IC function that we are investigating. The new structure will better guide the reader through the narrative, aligning with the themes of sensory prediction, reward prediction, and cognitive decision-making.

(2) Revised Title:

In line with the reviewer's suggestion, we have revised the title to "Beyond Auditory Relay: Dissecting the Inferior Colliculus's Role in Sensory Prediction, Reward Prediction, and Cognitive Decision-Making." We believe this title more accurately reflects the scope and focus of our study, as it highlights the three core functions of the IC that we are investigating.

(3) Improved Logic Flow:

We have added introductory statements at the beginning of each section within the Results to clarify the rationale behind the experiments and the logical connections between them. This should help to improve the overall flow of the manuscript and make the progression of our findings more intuitive for readers.

We believe these changes significantly enhance the clarity and logical structure of the manuscript, making it easier for readers to understand the sequence and importance of our findings. Thank you for your valuable suggestion, which has led to a more coherent and focused presentation of our work.

(2) Clarification of Data Analysis:

Key information regarding data analysis is dispersed throughout the results section, which can lead to confusion. Providing a more detailed and cohesive explanation of the experimental design would significantly enhance the interpretation of the findings. For instance, including a detailed timeline and reward information for the behavioral paradigms shown in Figures 1C and D would offer crucial context for the study. More importantly, clearly presenting the analysis temporal windows and providing comprehensive statistical analysis details would greatly improve reader comprehension.

We appreciate the reviewer’s insightful comment regarding the need for clearer and more cohesive explanations of the data analysis and experimental design. We recognize that a well-structured presentation of this information is essential for the reader to fully understand and interpret our findings. To address this, we have made the following revisions:

(1) Detailed Explanation of Experimental Design:

We have included a more detailed explanation of the experimental design, particularly for the behavioral paradigms shown in Figures 1C and 1D. This includes a comprehensive timeline of the experiments, along with explicit information about the reward structure and timing. By providing this context upfront, we aim to give readers a clearer understanding of the conditions under which the neuronal recordings were obtained.

(2) Cohesive Presentation of Data Analysis:

Key information regarding data analysis, which was previously dispersed throughout the Results section, has been consolidated and moved to a dedicated subsection within the Methods. This subsection now provides a step-by-step description of the analysis process, including the temporal windows used for examining neuronal activity, as well as the specific statistical methods employed.

We have also ensured that the temporal windows used for different analyses (e.g., onset window, late window, etc.) are clearly defined and consistently referenced throughout the manuscript. This will help readers track the use of these windows across different figures and analyses.

(3) Enhanced Statistical Analysis Details:

We have expanded the description of the statistical analyses performed in the study, including the rationale behind the choice of tests, the criteria for significance, and any corrections for multiple comparisons. These details are now presented in a clear and accessible format within the Methods section, with relevant information also highlighted in the Result section or the figure legends to facilitate understanding.

We believe these changes will significantly improve the clarity and comprehensibility of the manuscript, allowing readers to better follow the experimental design, data analysis, and the conclusions drawn from our findings. Thank you for this valuable feedback, which has helped us to enhance the rigor and transparency of our presentation.

(3) Reward Prediction Analysis:

The conclusion regarding the IC's role in reward prediction is underdeveloped. While the manuscript presents evidence that IC neurons can encode reward prediction, this is only demonstrated with two example neurons in Figure 6. A more comprehensive analysis of the relationship between IC neuronal activity and reward prediction is necessary. Providing population-level data would significantly strengthen the findings concerning the IC's complex functionalities. Additionally, the discussion of reward prediction in lines 437-445, which describes IC neuron responses in control experiments, does not sufficiently demonstrate that IC neurons can encode reward expectations. It would be valuable to include the responses of IC neurons during trials with incorrect key presses or no key presses to better illustrate this point.

We deeply appreciate the detailed feedback provided regarding the conclusions on the inferior colliculus (IC)'s role in reward prediction within our manuscript. We acknowledge the importance of a robust and comprehensive presentation of our findings, particularly when discussing complex neural functionalities.

In response to the reviewers' concerns, we have made the following revisions to strengthen our manuscript:

(1) Inclusion of Population-Level Data for IC Neurons:

In the revised manuscript, we have included population-level results for IC neurons in a supplementary figure. Initially, we focused on two example neurons that did not exhibit motor-related responses to key presses to isolate reward-related signals. However, most IC neurons exhibit motor responses during key presses (as indicated in Fig.7), which can complicate distinguishing between reward-related activity and motor responses. This complexity is why we initially presented neurons without motor responses. To clarify this point, we have added sentences in the Results section to explain the rationale behind our selection of neurons and to address the potential overlap between motor and reward responses in the IC.

(2) Addition of Data on Key Press Errors and No-Response Trials:

In response to the reviewer’s suggestion, we have demonstrated Peri-Stimulus Time Histograms (PSTHs) for two example neurons during error trials as below, including incorrect key presses and no-response trials. Given that the monkeys performed the task with high accuracy, the number of error trials is relatively small, especially for the control condition (as shown in the top row of the figure). While we remain cautious in drawing definitive conclusions from this limited trials, we observed that no clear reward signals were detected during the corresponding window (typically centered around 150 ms after the end of the sound). It is important to note that the experiment was initially designed to explore decision-making signals in the IC, rather than focusing specifically on reward processing. However, the data in Fig. 6 demonstrated intriguing signals of reward prediction error, which is why we believe it is important to present them.

When combined with the results from our reward experiment (Fig. 5), we believe these findings provide compelling evidence of reward prediction errors being processed by IC neurons. Additionally, we observed that the reward prediction error in the IC appears to be signed, meaning that IC neurons showed robust responses to unexpected rewards but not to unexpected no-reward scenarios. However, the sign of the reward prediction error should be explored in greater depth with specifically designed experiments in future studies.

Author response image 1.

(A) PSTH of the neuron from Figure 6a during a key press trial under control condition. The number in the parentheses in the legend represents the number of trials for control condition. (B) PSTHs of the neuron from Figure 6a during non-key press trials under experimental conditions. The numbers in the parentheses in the legend represent the number of trials for experimental conditions. (C-D) Equivalent PSTHs as in A-B but from the neuron in Figure 6b.

We are grateful for the reviewer's insightful suggestions, which have allowed us to improve the depth and rigor of our analysis. We believe these revisions significantly enhance our manuscript's conclusions regarding the complex functionalities of IC.

Reviewer #2 (Public Review):

Patterns scored into or painted on durable media have long been considered important markers of the cognitive capabilities of hominins. More specifically, the association of such markers with Homo sapiens has been used to argue that our evolutionary success was in part shaped by our unique ability to code, store and convey information through abstract conventions.

That singularity of association has been cast into doubt in the last decade with finds of designs apparently painted or carved by Neanderthals, and potentially by even earlier hominins. Even allowing for these developments, however, extending the capability to generate putatively abstract designs to a relatively small-brained hominin like Homo naledi is contentious. The evidential bar for such claims is necessarily high, and I don't believe that it has been cleared here.

The central issue is that the engravings themselves are not dated. As the authors themselves note, the minimum age constraint provided by U/Th on flowstone does not necessarily relate to the last occupation of the Dinaledi cave system, as the earlier ESR age on teeth does not necessarily document first use of the cave. The authors state that "At present we have no evidence limiting the time period across which H. naledi was active in the cave system". On those grounds though, assigning the age range of presently dated material within the cave system to the engravings - as the current title unambiguously does - is not justifiable.

Because we don't know when they were made, the association between the engravings and Homo naledi rests on the assertion that no humans entered and made alterations to the cave system between its last occupation by Homo naledi, and its recent scientific recording. This is argued on page 6 with the statement that "No physical or cultural evidence of any other hominin population occurs within this part of the cave system".

There is an important contrast between the quotes I have referred to in the last two paragraphs. In the earlier quote, the absence of evidence for Homo naledi in the cave system >335 ka and <241 ka is not considered evidence for their absence before or after these ages. Just because we have no evidence that Homo naledi was in the cave at 200 ka doesn't mean they weren't there, which is an argument I think most archaeologists would accept. When it comes to other kinds of humans, though - per the latter quote - the opposite approach is taken. Specifically, the present lack of physical evidence of more recent humans in the cave is considered evidence that no such humans visited the cave until its exploration by cavers 40 years ago. I don't think many archaeologists would consider that argument compelling. I can see why the authors would be drawn to make that assertion, but an absence of evidence cannot be used to argue in one way for use of the cave by Homo naledi and in another way for use of the cave by all other humans.

A second problem is with what Homo naledi might have made engravings. The authors state that "The lines appear to have been made by repeatedly and carefully passing a pointed or sharp lithic fragment or tool into the grooves". The authors then describe one rock with superficial similarities to a flake from the more recent site of Blombos to suggest that sharp-edge stones with which to make the engravings were available to Homo naledi. Blombos is considered relevant here presumably because it has evidence for Middle Stone Age engravings. The authors do not, however, demonstrate any usewear on that stone object such as might be expected if it was used to carve dolomite. Given that it is presented as the only such find in the cave system so far, this seems important.

My greater concern is that the authors did not compare the profile morphology of the Dinaledi engravings with the extensive literature on the morphology of scored lines caused by sharp-edge stone implements (e.g., Braun et al. 2016, Pante et al. 2017). I appreciate that the research group is reticent to undertake any invasive work until necessary, but non-destructive techniques could have been used to produce profiles with which to test the proposition that the engravings were made with a sharp edge stone.

One thing I noticed in this respect is that the engravings seem very wide, both in absolute terms and relative to their depth. The data I collected from the Middle Stone Age engraved ochre from Klein Kliphuis suggested average line widths typically around 0.1-0.2 mm (Mackay and Welz 2008). The engraved lines at Dinaledi appear to be much wider, perhaps 2-5 mm. This doesn't discount the possibility that the engravings in the Dinaledi system were carved with a sharp edge stone - the range of outcomes for such engravings in soft rock can be quite variable (Hodgskiss 2010) - only that detailed analysis should precede rather than follow any assertion about their mode of formation.

None of this is to say that the arguments mounted here are wrong. It should be considered possible that Homo naledi made the engravings in the Dinaledi cave system. The problem is that other explanations are not precluded.

As an example, the western end of the Dinaledi subsystem has a particular geometry to the intersection of its passages, with three dominant orientations, one vertical (which is to say, north-south), and two diagonal (Figure 1). The major lines on Panel A have one repeated vertical orientation and two repeated diagonal orientations (Figure 16), particularly in the upper area not impacted by stromatolites. The lines in both the cave system and engravings in Panel A appear to intersect at similar angles. Several of the cave features appear, superficially at least, to be replicated. In fact, scaled, rotated, and super-imposed, Figure 16 is a plausible 'mud map' of the western end of the Dinaledi system carved incrementally by people exploring the caves. A figure showing this is included here:

Of course, there are problems with this suggestion. The choice of the upper part of Panel A is selective, the similarity is superficial, and the scales are not necessarily comparable. (Note, btw, that all of those caveats hold equally well for the comparison the authors make between the unmodified rock from Dinaledi and the flake from Blombos in Figure 19). However, the point is that such a 'mud map hypothesis' is, as with the arguments mounted in this paper, both plausible and hard to prove.

Having read this paper a few times, I am intrigued by the engravings in the Dinaledi system and look forward to learning more about them as this research unfolds. Based on the evidence presently available, however, I feel that we have no robust grounds for asserting when these engravings were made, by whom they were made, or for what reason they were made.

References:

• Braun, D. R., et al. (2016). "Cut marks on bone surfaces: influences on variation in the form of traces of ancient behaviour." Interface Focus 6: 20160006.

• Hodgskiss, T. (2010). "Identifying grinding, scoring and rubbing use-wear on experimental ochre pieces." Journal of Archaeological Science 37: 3344-3358.

• Mackay, A. & A. Welz (2008). "Engraved ochre from a Middle Stone Age context at Klein Kliphuis in the Western Cape of South Africa." Journal of Archaeological Science 35: 1521-1532.

• Pante, M. C., et al. (2017). "A new high-resolution 3-D quantitative method for identifying bone surface modifications with implications for the Early Stone Age archaeological record." J Hum Evol 102: 1-11.

Author Response:

Points from reviewer 1 (Public Review):

In this manuscript, Yong and colleagues link perturbations in lysosomal lipid metabolism with the generation of protein aggregates resulting from proteosome inhibition.

We apologize for any confusion in the explanation of the results. We found that both proteasome inhibition and, independently, perturbations to lysosomal lipid metabolism lead to accumulation of protein aggregates in the lysosome. There was no evidence of proteasome inhibition in the context of lysosomal lipid perturbations (Figure 4J).

Despite using various tools of lysosomal function, acidity, permeability, etc, the authors couldn't identify the link between lysosomal lipid metabolism and protein aggregate formation.

Indeed, despite testing numerous mechanistic hypotheses, we have yet to explain how perturbation of lysosomal lipid metabolism causes protein aggregates. However, we have demonstrated that lipids are both necessary (via epistasis and serum delipidation) and sufficient (media supplementation) to drive these phenotypes.

Although this work is interesting and thought-provoking, their approach to identify novel pathways involved in proteostasis is limited and this weakens the contribution of the paper in its current form.

We are glad the reviewer found the work to be thought-provoking. As a fundamental cellular process critical for longevity, we agree that the connections made here between lipids, lysosomes and protein aggregates are interesting and broaden the impact of cellular health on proteostasis. Though we have falsified multiple hypotheses for how perturbation of lysosomal lipid metabolism could influence protein aggregation, we agree that a major weakness of the current work is our limited mechanistic understanding of this process. We hope that by engaging the thoughtful and creative eLife readership, novel mechanistic hypotheses will emerge.

Points from reviewer 2 (Public Review):

This might be too much of an ask, but they should go further in excluding one very attractive alternative model: effects on proteasome activity. This explanation should be addressed definitively because the transcription factor that regulates proteasome subunit gene expression (Nrf1/NFE2L1) is processed in the ER and is therefore well placed to be influenced by membrane conditions, and because it is shown here that proteasome inhibition increase ProteoStat puncta.

We appreciate the constructive suggestion to examine loss of proteasome expression as a relevant mechanism linking cellular dyslipidemia with proteostasis impairment. We analyzed the genome-wide perturb-seq data from Replogle et al. [1], which was performed in K562 cells cultured under similar conditions to our screen. As expected, perturbation of Nrf1/NFE2L1 reduced expression of proteasome subunits, whereas perturbation of proteasome subunits that increased proteostat staining (e.g. PSMD2, PSMD13) homeostatically increased expression of multiple proteasome subunits. In contrast, other top hits, including those related to lipid-related perturbations (e.g. MYLIP, PSAP) did not reduce the expression of genes encoding the proteasome (Author response image 1).

Author response image 1.

The relative expression of genes encoding proteasomal subunits for representative genes was re-plotted from genome-wide perturb-seq data in K562 cells [1]. Shown are hit genes that increase Proteostat staining along with non-targeting controls and the positive control gene NFE2L1. Proteasome expression was induced by proteasome impairment (PSMD2 and PSMD13) and repressed by NFE2L1 knockdown. Other hit genes related to lipid metabolism and lysosome function did not consistently impact the expression of proteasome subunits.

The authors address proteasome activity only by using a dye that is not referenced. Here a much more solid answer is needed.

We thank Reviewer #2 for bringing to our attention the missing reference for the proteasome activity probe we used (Me4BodipyFL-Ahx3Leu3VS). Both this probe [2] and its close derivative [3], BodipyFL-Ahx3Leu3VS, were fully characterized previously. We’ll include these references in the revision. In our hands, this probe behaved as expected under MG132 and Bortezomib treatment when quantified by flow cytometry (Fig. 4I), and by in-blot fluorescence scan (data will be included as supplementary in the revision). We further observed that HMGCR KD increased proteasome activity, consistent with what’s suggested by current literature. This validated our use of this probe and strongly suggested that proteasome activity was not perturbed by impaired lipid homeostasis.

In general, most conclusions in the paper rely essentially solely on ProteoStat assays. The entire study would be greatly strengthened if the authors incorporated biochemical or other modalities to substantiate their results.

We agree that orthogonal characterization of proteostasis impairment would be valuable. We chose the ProteoStat stain as a reporter of proteostasis because it is capable of integrating the aggregation states of multiple endogenously expressed proteins, and in the absence of exogenous stressors such as the overexpression of aggregation-prone proteins. With aging, a context where ProteoStat staining increases, hundreds of proteins exhibit reduced solubility [4], thus motivating the focus on endogenously expressed proteins. Despite the biochemical limitations, we think our work is differentiated from published screens focused on specific metastable proteins by our focus on regulators of endogenous proteostasis.

The presentation would be improved greatly if the authors provided diagrams illustrating the pathways implicated in their results, as well as their models.

We thank Reviewer #2 for the helpful suggestion. We have provided the suggested diagrams below (Author response image 2).

Author response image 2.

Mechanistic models linking screen hits to accrual of lysosomal protein aggregates, related to Figure 4. Perturbations that increased cholesterol and sphingolipid levels were evaluated for effects on lysosomal pH, lysosomal proteolytic capacity, lysosomal membrane permeability, lipid peroxidation and proteasome activity. None of these mechanisms appear to play a causal role in protein aggregation in response to elevated lipids.

Author Response References

1. Replogle, J. M. et al. Mapping information-rich genotype-phenotype landscapes with genome-scale Perturb-seq. Cell 185, 2559-2575.e28 (2022).

2. Berkers, C. R. et al. Probing the Specificity and Activity Profiles of the Proteasome Inhibitors Bortezomib and Delanzomib. Mol Pharmaceut 9, 1126–1135 (2012).

3. Berkers, C. R. et al. Profiling Proteasome Activity in Tissue with Fluorescent Probes. Mol. Pharmaceutics 4, 739–748 (2007).

4. David, D. C. et al. Widespread Protein Aggregation as an Inherent Part of Aging in C. elegans. Plos Biol 8, e1000450 (2010).

Author Response

We would like to thank the reviewers for providing constructive feedback on the manuscript. To address the weaknesses identified, we are performing additional experiments and generating additional data, to be added to the updated manuscript.

(1) The utility of a pipeline depends on the generalization properties.

While the proposed pipeline seems to work for the data the authors acquired, it is unclear if this pipeline will actually generalize to novel data sets possibly recorded by a different microscope (e.g. different brand), or different imagining conditions (e.g. illumination or different imagining artifacts) or even to different brain regions or animal species, etc.

The authors provide a 'black-box' approach that might work well for their particular data sets and image acquisition settings but it is left unclear how this pipeline is actually widely applicable to other conditions as such data is not provided.

In my experience, without well-defined image pre-processing steps and without training on a wide range of image conditions pipelines typically require significant retraining, which in turn requires generating sufficient amounts of training data, partly defying the purpose of the pipeline. It is unclear from the manuscript, how well this pipeline will perform on novel data possibly recorded by a different lab or with a different microscope.

To address generalizability, we are performing several validation experiments with data from different 1) channels, 2) species (rat), and 3) microscopes, to highlight the robustness of our deep learning (DL) segmentation model to out-of-distribution data with different characteristics and acquisition protocols. We first used our model to segment three images (507x507 x&y, 250-170 um z) from three C57BL/6 mice acquired on the same two-photon fluorescent microscope following the same imaging protocol. The vasculature was labelled with the Texas Red dextran, as in the current experiment. In place of the EYFP signal from pyramidal neurons (2nd channel), gaussian noise was generated with a mean and standard deviation identical to the acquired vascular channel. A second set of two images(507x507 x&y, 300-400 um z) from two Fischer rats with Alexa680-dextran label in the plasma; these rats were imaged on the same two-photon fluorescence microscope, but with galvano scanners (instead of resonant scanners). A second channel of random Gaussian noise was also added here. Finally, an image of vasculature from a ex-vivo cleared mouse brain (1665x1205x780 um) imaged on a light sheet fluorescence microscope (Miltenyi UltraMicroscope Blaze) was also segmented with our model. Lectin-DyLight 649 was used to label the vasculature in this cohort. The Dice Score, Precision, Recall, Hausdorff 95%, and Mean surface distance will be reported for all of these additional image segmentations, upon generation of ground truth images. Finally, examples of the generated segmentation masks are presented in Author response image 1 for visual comparison. Of final note, should the segmentation results on a new data set be unsatisfactory, the methods downstream from segmentation are still applicable and the model can be further fine-tuned on other out-of-distribution data.

Author response image 1.

Examples of the deep learning model output on out of distribution data from a different mouse strain, from a different species (Fischer rat), and on a different microscope using a different imaging modality.

(2) Some of the chosen analysis results seem to not fully match the shown data, or the visualization of the data is hard to interpret in the current form.

We are updating the visualizations to make them more accessible and we will ensure matching between tables and figures.

(3) Additionally, some measures seem not fully adapted to the current situation (e.g. the efficiency measure does not consider possible sources or sinks). Thus, some additional analysis work might be required to account for this.

Thank you for your comment. The efficiency metric was selected as it does not consider sources or sinks. We do agree that accounting for vessel subtypes in the analysis (thus classifying larger vessels as either supplying or draining) would be uniquely useful: notwithstanding, it is extremely laborious. We are therefore leveraging machine learning in a parallel project to afford vessel classification by subtype. The source/sink analysis is also confounded by the small field-of-view of in situ 2PFM. Future work will investigate network remodelling across the whole brain with ex-vivo light sheet fluorescence microscopy.

(4) The authors apply their method to in vivo data. However, there are some weaknesses in the design that make it hard to accept many of the conclusions and even to see that the method could yield much useful data with this type of application. Primarily, the acquisition of a large volume of tissue is very slow. In order to obtain a network of vascular activity, large volumes are imaged with high resolution. However, the volumes are scanned once every 42 seconds following stimulation. Most vascular responses to neuronal activation have come and gone in 42 seconds so each vessel segment is only being sampled at a single time point in the vascular response. So all of the data on diameter changes are impossible to compare since some vessels are sampled during the initial phase of the vascular response, some during the decay, and many probably after it has already returned to baseline. The authors attempt to overcome this by alternating the direction of the scan (from surface to deep and vice versa). But this only provides two sample points along the vascular response curve and so the problem still remains.

We thank the Reviewer for bringing up this important point.

Although vessels can show relatively rapid responses to perturbation, vascular responses to photostimulation of ChannelRhodopsin-2 in neighbouring neurons are typically long lasting: they do not come and go in 42 seconds. To demonstrate this point, we acquired higher temporal-resolution images of smaller volumes of tissue over 5 minutes preceding and following the 5-s photoactivation with the original parameters. Imaging protocol was different in that we utilized a piezoelectric motor, smaller field of view, and only 3x frame averaging, resulting in a temporal resolution of 1.57-2.63 seconds. This acquisition was repeated at 4 different cortical depths (325 um, 250 um, 150um, and 40 um) in a single mouse.The vascular radii were estimated using our presented pipeline. Raw data and LOESS fits are shown in Author response image 2 (below). Vessels shorter than 20 um in length were excluded from the analysis. A video of one of the acquisitions is shown along with the timecourses of select vessels’ caliber changes in Author response image 3. The vascular caliber changes following photostimulation persisted for several minutes, consistent with earlier observations by us and others1–4. These higher temporal-resolution scans of smaller tissue volumes will be repeated in two more mice; we will therein assess the repeatability of individual vessel responses to repeated stimulations.

Author response image 2.

A. The vascular radii of multiple vessels were imaged at 4 different cortical depths, each within a 507 x (75-150) x (30-45)um tissue volume. Baseline scanning lasted for 5 minutes, followed by 5 seconds of blue or green light stimulation at 4.3 mW/mm2, and culminating in 5 minutes of post-stimulation scanning. B. LOESS fits of the vessel radius estimates for each vessel segment identified.

Author response image 3.

Estimated vascular radius at each timepoint for select vessels from the imaging stack shown in the following video: https://flip.com/s/kB1eTwYzwMJE

(5) A second problem is the use of optogenetic stimulation to activate the tissue. First, it has been shown that blue light itself can increase blood flow (Rungta et al 2017). The authors note the concern about temperature increases but that is not the same issue. The discussion mentions that non-transgenic mice were used to control for this with "data not shown". This is very important data given these earlier reports that have found such effects and so should be included.

We will update the manuscript to incorporate the data on volumetric scanning in nontransgenic C57BL/6 mice undergoing blue light stimulation, with identical parameters as those used in Thy-ChR2 mice. As before, responders were identified as vessels that following blue light stimulation show a radius change greater than 2 standard deviations of their baseline radius standard deviation: their estimated radii changes are shown in Author response image 4 below. There were no statistical difference between radii distributions of any of the photostimulation conditions and pre-photostimulation baseline. A comparison of this with the transgenic THY1-ChR2-EYFP mice will be included in manuscript updates.

Author response image 4.

Radius change measurements for responding vessels from the Thy1-ChR2 mice described in the manuscript (top row) vs. 4 wild-type C57BL6/J mice (bottom row). Response to photostimulation was defined as a change above twice their baseline standard deviation. 458nm light was applied at 1.1 mW/mm^2 and 4.3 mW/mm^2; while 552 nm light was applied at 4.3 mW/mm^2. No statistically significant differences were observed between the radii distributions in any condition, Wilcoxon test, Bonferroni correction.

(6) Secondly, there doesn't seem to be any monitoring of neural activity following the photo-stimulation. The authors repeatedly mention "activated" neurons and claim that vessel properties change based on distance from "activated" neurons. But I can't find anything to suggest that they know which neurons were active versus just labeled. Third, the stimulation laser is focused at a single depth plane. Since it is single-photon excitation, there is likely a large volume of activated neurons. But there is no way of knowing the spatial arrangement of neural activity and so again, including this as a factor in the analysis of vascular responses seems unjustified.

Given the high fidelity of Channel-Rhodpsin2 activation with blue light, we assume that all labeled neurons within the volume of photostimulation are being activated. Depending on their respective connectivities, their postsynaptic neurons (whether or not they are labelled) are also activated. We indeed agree with the reviewer that the spatial distribution of neuronal activation is not well defined. We will revise the manuscript to update the terminology from activated to labeled neurons and stress in the Discussion that the motivation for assessing the distance to the closest labelled neuron as one of our metrics is purely to demonstrate the possibility of linking vascular response to activations in some of their neighbouring neurons and including morphological metrics in the computational pipeline. Of final note, the depth-dependence of the distance between labelled neurons and responding vessels can also readily be assessed using our computational pipeline.

(7) The study could also benefit from more clear illustration of the quality of the model's output. It is hard to tell from static images of 3-D volumes how accurate the vessel segmentation is. Perhaps some videos going through the volume with the masks overlaid would provide some clarity. Also, a comparison to commercial vessel segmentation programs would be useful in addition to benchmarking to the ground truth manual data.

We generated a video demonstrating the deep-learning model outputs and have made the video available here: https://flip.com/s/_XBs4yVxisNs Additional videos will be uploaded.

(8) Another useful metric for the model's success would be the reproducibility of the vessel responses. Seeing such a large number of vessels showing constrictions raises some flags and so showing that the model pulled out the same response from the same vessels across multiple repetitions would make such data easier to accept.

We have generated a figure demonstrating the repeatability of the vascular responses following photoactivation in a volume, and presented them next to the corresponding raw acquisitions for visual inspection. It is important to note that there is a significant biological variability in vessels’ responses to repeated stimulation, as described previously 2,5. Constrictions have been reported in the literature by our group and others 1,3,4,6,7, though their prevalence has not been systematically studied to date. Concerning the reproducibility of our analysis, we will demonstrate model reproducibility (as a metric of its success) in the updated manuscript.

Author response image 5.

Registered acquisitions of the vasculature before and after optogenetic stimulation for 5 scan pairs over 3 different stimulation conditions. The estimated radii along vessel segments are presented.

Author response image 6.

Sample capillaries constrictions from maximum intensity projections at repeated timepoints following optogenetic stimulation. Baseline (pre-stimulation) image is shown on the left and the post-stimulation image, on the right, with the estimated radius changes listed to the left.

(9) A number of findings are questionable, at least in part due to these design properties. There are unrealistically large dilations and constrictions indicated. These are likely due to artifacts of the automated platform. Inspection of these results by eye would help understand what is going on.

Some of the dilations were indeed large in magnitude. We present select examples of large dilations and constrictions ranging in magnitude from 2.08 to 10.80 um for visual inspection (for reference, average, across vessel and stimuli, magnitude of radius changes were 0.32 +/- 0.54 um). Diameter changes above 5 um were visually inspected.

Author response image 7.

Additional views of diameter changes in maximum intensity projections ranging in magnitude from 2.08 um to 10.80 um.

(10) In Figure 6, there doesn't seem to be much correlation between vessels with large baseline level changes and vessels with large stimulus-evoked changes. It would be expected that large arteries would have a lot of variability in both conditions and veins much less. There is also not much within-vessel consistency. For instance, the third row shows what looks like a surface vessel constricting to stimulation but a branch coming off of it dilating - this seems biologically unrealistic.

We now plot photostimulation-elicited vesselwise radius changes vs. their corresponding baseline radius standard deviations (Author response image 8 below). The Pearson correlation between the baseline standard deviation and the radius change was 0.08 (p<1e-5) for 552nm 4.3 mW/mm^2 stimulation, -0.08 (p<1e-5) for 458nm 1.1 mW/mm^2 stimulation, and -0.04 (p<1e-5) for 458nm 4.3 mW/mm^2 stimulation. For non-control (i.e. blue) photostimulation conditions, the change in the radius is thus negatively correlated to the vessel’s baseline radius standard deviation. The within-vessel consistency is explicitly evaluated in Figure 8 of the manuscript. As for the instance of a surface vessel constricting while a downstream vessel dilates, it is important to remember that the 2PFM FOV restricts us to imaging a very small portion of the cortical microvascular network (one (among many) daughter vessels showing changes in the opposite direction to the parent vessel is not violating the conservation of mass).

Author response image 8.

A plot of the vessel radius change elicited by photostimulation vs. baseline radius standard deviation.

(11) As mentioned, the large proportion of constricting capillaries is not something found in the literature. Do these happen at a certain time point following the stimulation? Did the same vessel segments show dilation at times and constriction at other times? In fact, the overall proportion of dilators and constrictors is not given. Are they spatially clustered? The assortativity result implies that there is some clustering, and the theory of blood stealing by active tissue from inactive tissue is cited. However, this theory would imply a region where virtually all vessels are dilating and another region away from the active tissue with constrictions. Was anything that dramatic seen?

The kinetics of the vascular responses are not accessible via the current imaging protocol and acquired data; however, this computational pipeline can readily be adapted to test hypotheses surrounding the temporal evolution of the vascular responses, as shown in Author response image 2 (with higher temporal-resolution data). Some vessels dilate at some time points and constrict at others as shown in Author response image 2. As listed in Table 2, 4.4% of all vessels constrict and 7.5% dilate for 452nm stimulation at 4.3 mW/mm^2. There was no obvious spatial clustering of dilators or constrictors: we expect such spatial patterns to more likely result from different modes of stimulation and/or in the presence of a pathology. The assortativity peaked at 0.4 (i.e. is quite far from 1 where each vessel’s response exactly matches that of its neighbour).

(12) Why were nearly all vessels > 5um diameter not responding >2SD above baseline? Did they have highly variable baselines or small responses? Usually, bigger vessels respond strongly to local neural activity.

In Author response image 9, we now present the stimulation-induced radius changes vs. baseline radius variability across vessels with a radius greater than 5 um. The Pearson correlation between the radius change and the baseline radius standard deviation was 0.04 (p=0.5) for 552nm 4.3 mW/mm^2 stimulation, -0.26 (p<1e-5) for 458nm 1.1 mW/mm^2 stimulation, and -0.24 (p<1e-5) for 458nm 4.3 mW/mm^2 stimulation. We will incorporate an additional analysis to address this issue by identifying responding vessels as those showing supra-threshold percent change in their radius (instead of SD).

Author response image 9.

A plot of the vessel radius change elicited by photostimulation vs. baseline radius standard deviation in vessels with a baseline radius greater than 5 um.

References

(1) Alarcon-Martinez L, Villafranca-Baughman D, Quintero H, et al. Interpericyte tunnelling nanotubes regulate neurovascular coupling. Nature. 2020;kir 2.1(7823):91-95. doi:10.1038/s41586-020-2589-x

(2) Mester JR, Bazzigaluppi P, Weisspapir I, et al. In vivo neurovascular response to focused photoactivation of Channelrhodopsin-2. NeuroImage. 2019;192:135-144. doi:10.1016/j.neuroimage.2019.01.036

(3) O’Herron PJ, Hartmann DA, Xie K, Kara P, Shih AY. 3D optogenetic control of arteriole diameter in vivo. Nelson MT, Calabrese RL, Nelson MT, Devor A, Rungta R, eds. eLife. 2022;11:e72802. doi:10.7554/eLife.72802

(4) Hartmann DA, Berthiaume AA, Grant RI, et al. Brain capillary pericytes exert a substantial but slow influence on blood flow. Nat Neurosci. Published online February 18, 2021:1-13. doi:10.1038/s41593-020-00793-2

(5) Mester JR, Bazzigaluppi P, Dorr A, et al. Attenuation of tonic inhibition prevents chronic neurovascular impairments in a Thy1-ChR2 mouse model of repeated, mild traumatic brain injury. Theranostics. 2021;11(16):7685-7699. doi:10.7150/thno.60190

(6) Mester JR, Rozak MW, Dorr A, Goubran M, Sled JG, Stefanovic B. Network response of brain microvasculature to neuronal stimulation. NeuroImage. 2024;287:120512. doi:10.1016/j.neuroimage.2024.120512

(7) Hall CN, Reynell C, Gesslein B, et al. Capillary pericytes regulate cerebral blood flow in health and disease. Nature. 2014;508(7494):55-60. doi:10.1038/nature13165

Author Response

We thank both the editors and the Reviewers for their thoughtful comments and recommendations, that will certainly help us improve the manuscript. Below we address in a brief format some of the comments made, and then outline the changes to the manuscript that we plan to implement in the revision.

We see three interrelated issues in the comments of the Reviewers:

• the length and complexity of the manuscript;

• the link to previously proposed formalisms;

• the impact of adopting the proposed information-theoretic framework.

With regard to all of these issues, we would first like to highlight that the overall goal of our effort was to integrate con tributions to understanding the mechanisms underlying cognitive control across multiple different disciplines, using the information theoretic framework as a common formalism, while respecting and building on prior efforts as much as possible. Accordingly, we sought to be as explicit as possible about how we bridge from prior work using information theory, as well as neural networks and dynamical systems theory, which contributed to length of the original manuscript. While we continue to consider this an important goal, we will do our best to shorten and clarify the main exposition by reorganizing the manuscript as suggested by Reviewer #1 (i.e., in a way that is similar to what we did in our previous Nature Physics paper on multitasking). Specifically, we will move a substantially greater amount of the bridging material to the Supple mentary Information (SI), including the detailed discussion of the Stroop task, and the description of the link to Koechlin & Summerfield’s [L1] information theory formalism. We will also now include an outline of the full model at the beginning of the manuscript, that includes control and learning, and then more succinctly describe simplifications that focus on specific issues and applications in the remainder of the document.

Along similar lines, we will revise and harmonize our presentation of the formalism and notations, to make these more consistent, clearer and more concise throughout the document. Again, some of the inconsistencies in notation arose from our initial description of previous work, and in particular that of Koechlin & Summerfield[L1] that was an important inspiration for our work but that used slightly different notations. An important motivation for our introduction of new notation was that their formulation focused on the performance of a single task at a time, whereas a primary goal of our work was to extend the information theoretic treatment to simultaneous performance of multiple tasks. That is, in focusing on single tasks, Koechlin & Summerfield could refer to a task simply as a direct association between stimuli and responses, whereas we required a way of being able to refer to sets of tasks performed at once (”multitasks”), which in turn required specification of internal pathways. Moreover, they do not provide a mechanism to compute the conditional information Q(a|s) of a response/action s conditioned to a stimulus s does not provide a way to compute it explicitly. Our formalism instead provides a way to explicitly unpack this expression in terms of the efficacies –automatic (Eq. 5) or controlled (Eq. 15)– which can also account for the competition between different stimuli {s1, s2, . . . sn}. It also describes explicitly the competition between multiple tasks (Eq. 18, and Eq. 25 for multiple layers), because different ways of processing schemes for the same combinations of stimuli/responses can incur different levels of internal dependencies and thus require different control strategies.

To mitigate any confusion over terminology we will, as noted above, move a detailed discussion of Koechlin & Summer- field’s formulation, and how it maps to the one we present, to the SI, while taking care to introduce ours clearly at the beginning of the main document, and use it consistently throughout the remainder of the document. We will also make an important distinction – between informational and cognitive costs – more clearly, that we did not do adequately in the original manuscript.

Finally, to more clearly and concretely convey what we consider to be the most important contributions, we will restrict the number of examples we present to ones that relate most directly to the central points (e.g., the effect and limits of control in the presence of interference, and the differences in control strategy under limited temporal horizons). Accompanying our revision, we will also provide a full point-by-point response to the comments and questions raised by the Reviewers. We summarize some the key points we will address below.

PRELIMINARY REPLY TO THE REPORT OF REVIEWER #1

We want to thank the Reviewer for the time and effort put into reviewing our paper and constructive feedback that was provided. We also thank the Reviewer for recognizing the need for a clear computational account of how ”control” manages conflicts by scheduling tasks to be executed in parallel versus serially, and for the positive evaluation on our “efforts of the authors to give these intuitions a more concrete computational grounding.”. As noted in the general reply above, we regret the lack of clarity in several parts of the manuscript and in our introduction and use of the formalism. We consider the following to be the main points to be addressed:

• the role of task graphs and their mapping to standard neural architectures

• the description of entropy and related information-theoretic concepts;

• confusing choice of symbols in our notation between stimuli/responses and serialization/reconfiguration costs;

• missing definition of response time;

Regarding the first part point, we acknowledge that the network architectures we focus on do not draw direct inspiration from conventional machine learning models. Instead, our approach is rooted in the longstanding tradition of using (often simpler, but also more readily interpretable) neural network models to address human cognitive function and how this may be implemented in the brain [L2]; and, in particular, the mechanisms underlying cognitive control (e.g., [L3, L4]). In this context, we emphasize that, for analytical clarity, we deliberately abstract away from many biological details, in an effort to identify those principles of function that are most relevant to cognitive function. Nevertheless, our network architecture is inspired by two concepts that are central to neurobiological mechanisms of control: inhibition and gain modulation. Specifi- cally, we incorporate mutual inhibition among neural processing units, a feature represented by the parameter β. This aspect of our model is consistent with biologically inspired frameworks of neural processing, such as those discussed by Munakata et al. (2011)[L5], reflecting the competitive dynamics observed in neural circuits. Moreover, we introduce the parameter ν to represent a strictly modulatory form of control, akin to the role of neuromodulators in the brain. This modulatory control adjusts the sensitivity of a node to differences among its inputs (e.g., Servan-Schreiber, Printz, & Cohen, (1990)[L6]; Aston-Jones & Cohen (2005)[L7]). Finally, as the Reviewer notes, additional hidden layers can improve expressivity in neural networks, enabling the efficient implementation of more complex tasks, and are a universal feature of biological and artificial neural systems. We thus examined multitasking capability under the assumption that multiple hidden layers are present in a network; irrespective of whether they are needed to implement the corresponding tasks.

Regarding the second point, as noted above, we believe that the confusion arose from our review of the work by Koechlin & Summerfield. In their formalism, in which an action a is chosen (from a set of potential actions) with probability p(a), the cost of choosing that action is − log p(a). This is usually referred to as the information content or, alternatively, the localized entropy [L8]. As the Reviewer correctly observed, the canonical (Shannon) entropy is actually the expectation lEa[− log p(a)] over the localized entropies of a set of actions. In summarizing their formulation, we misleadingly stated that ”they used standard Shannon entropy formalism as a measure of the information required to select the action a.” We will now correct this to state: “[..] they used local entropy (− log p(a)) as a measure of the information required to select the action a, that can be treated as the cost of choosing that action.” We follow this formulation in our own, referring to informational cost as Ψ, and generalizing this to include cases in which more than one action may be chosen to perform at a time.

Regarding the third point, the confusion is due to our use of the letters S and R for both the stimulus and response units (in Sec. II.B) and then serialization and reconstruction costs (in eqs 31-33). We will fix this by renaming the serialization and reconstruction costs more explicitly as S er and Rec.

Finally, we realized we never explicitly stated the expression of the response time we used, but only pointed to it in the literature. In the manuscript we used the expression given in Eq. 53 of [L9], which provides response times as function of the error rates ER and the number of options .

PRELIMINARY REPLY TO THE REPORT OF REVIEWER #2

We want to thank the Reviewer for recognizing our effort to ”rigorously synthesize ideas about multi-tasking within an information-theoretic framework” and its potential. We also thank the Reviewer for the careful comments.

To our best understanding, and similarly to Reviewer #1, the main comments of the Reviewer are on:

• the length and density of the paper;

• the presentation of the Koechlin & Summerfield’s formalism, and the mismatch/lack of clarity of ours in certain points;

• the added value of the information theoretic formalism.

Regarding the first two points, which are common to Reviewer #1, we plan to move a significant part of the manuscript to the Supplementary Information, both to improve readability and make the manuscript shorter, as well as to provide one consistent and cleaner formalism (in particular with regards to the typos and errors highlighted by the Reviewer). In par- ticular, with respect to the comment on Eq. 4-5-6, we will clarify that the probability p[ fi j] is the probability that a certain input dimension (i in this case) is selected by on node j to produce its response (averaged over the individual inputs in each input dimension). We will also take care to make sure that the definition and domain of the various probabilities and probability distributions we use are clearly delineated (e.g. where the costs computed for tasks and task pathways come from).

Regarding the third point, we hope that our work offers value in at least two ways: i) it helps bring unity to ideas and descriptions about the capacity constraints associated with cognitive control that have previously been articulated in different forms (viz., neural networks, dynamical systems, and statistical mechanical accounts); and ii) doing so within an information theoretic framework not only lends rigor and precision to the formulation, but also allows us to cast the allocation of control in normative form – that is, as an optimization problem in which the agent seeks to minimize costs while maximizing gains. While we do not address specific empirical phenomena or datasets in the present treatment, we have done our best to provide examples showing that: a) our information theoretic formulation aligns with treatments using other formalisms that have been used to address empirical phenomena (e.g., with neural network models of the Stroop task); and b) our formulation can be used as a framework for providing a normative approach to widely studied empirical phenomena (e.g., the transition from control-dependent to automatic processing during skill acquisition) that, to date, have been addressed largely from a descriptive perspective; and that it can provide a formally rigorous approach to addressing such phenomena.

[L1] E. Koechlin and C. Summerfield, Trends in cognitive sciences 11, 229 (2007).

[L2] J. L. McClelland, D. E. Rumelhart, P. R. Group, et al., Explorations in the Microstructure of Cognition 2, 216 (1986).

[L3] J. D. Cohen, K. Dunbar, and J. L. McClelland, Psychological Review 97, 332 (1990).

[L4] E. K. Miller and J. D. Cohen, Annual review of neuroscience 24, 167 (2001).

[L5] Y. Munakata, S. A. Herd, C. H. Chatham, B. E. Depue, M. T. Banich, and R. C. O’Reilly, Trends in cognitive sciences 15, 453 (2011).

[L6] D. Servan-Schreiber, H. Printz, and J. D. Cohen, Science 249, 892 (1990).

[L7] G. Aston-Jones and J. D. Cohen, Annu. Rev. Neurosci. 28, 403 (2005).

[L8] T. F. Varley, Plos one 19, e0297128 (2024).

[L9] T. McMillen and P. Holmes, Journal of Mathematical Psychology 50, 30 (2006).

Author response:

We would like to thank the three reviewers for the careful review and thoughtful comments on our manuscript. In addition to providing useful suggestions, they uncovered some embarrassing oversights on our part, related to experimental details including number of embryos, and quantification of variance in the observed changes for some of the experiments, which were inadvertently omitted in the submission. We provide below an initial response to the reviewer’s public reviews and expect to submit a revised manuscript comprehensively addressing all their concerns.

I would like to start by addressing some of their most critical comments related to validation of the tools used to reduce soxB1 gene family function in the embryo.  In the absence of the critical supplementary data that we inadvertently failed to include, the reviewers were left with an understandable, but we feel erroneous impression, that there was insufficient validation of mutant and knockdown tools. 

Reviewer #2 says “The sox2y589 mutant line is not properly verified in this manuscript, which could be done by examining ant-Sox2 antibody labeling, Western blot analysis or…”

This validation, which had been performed previously both with antibody staining and with western blot analysis, was inadvertently omitted from the supplementary data submitted with the paper. The western blot data is shown here.

Author response image 1.

Validation of sox2 mutant phenotype with Western blot.

Lysates were prepared from 25 embryos selected as wild type or potentially mutant based on the “loss of L1” phenotype at 6 dpf. This polyclonal antibody recognizes within the last 16 amino acids of the C-terminal.

Author response image 2.

Validation of sox2 mutant phenotype with antibody staining.

Though in this experiment there was considerable background in the red channel, and it shows the lateral line nerve, loss of nuclear Sox2 expression is evident in the deposited neuromast of an embryo identified as a mutant based on its delayed deposition of the L1 neuromast.

This data and a repeat of the antibody staining showing the primordium with loss of Sox2 will be included in a revised manuscript.

Furthermore, Reviewer #2 comments “the authors show that the anti-Sox2 and antiSox3 antibody labeling is reduced but not absent in sox2 MO1 and sox3 MO-injected embryos, but do not show antibody labeling of the sox2 MO and sox3 MO-double injected embryos to determine if there is an additional knockdown”

This will be included in a revised manuscript.

Reviewer #2:

The authors acknowledge that the sox2 MO1 used in this manuscript also alters sox3 function, but do not redo the experiments with a specific sox2 MO

This is not exactly true. Having discovered sox2 MO1 simultaneously reduces sox2 and sox3 function, three new morpholinos were obtained based on another paper (Kamachi et al 2008), which had quantitatively assessed efficacy of three sox2 specific morpholinos (sox2 MO2, sox2 MO3, and sox2 MO4). The effects of these morpholinos on the pattern of L1 deposition was compared to that of sox2 MO1. This comparison was shown in supplementary Figure 2 and is included below. It shows that the sox2 specific morpholinos resulted in a poorly penetrant delay in deposition of L1, comparable to that of a sox2 mutant, which was quantified in supplementary Figure 3B. The observations with these three sox2 specific morpholinos independently supported the observations made with the sox2 mutant that reduction of sox2 on its own results in a delay in deposition of the first neuromast with low penetrance and that to effectively examine the role of these SoxB1 genes in the primordium their function needs to be compromised in a combinatorial manner. A conclusion that was independently supported by observations made by crossing sox1a, sox2 and sox3 mutants (Figure 3 and Supplementary Figure 3). Therefore, even though the initial use of a sox2 morpholino, which simultaneously knocks down sox3, was unintentional, its use turned out to be useful. It allowed us to examine effects of knocking down sox2 and sox3 with a single morpholino. Furthermore, though this project was initiated more than 15 years ago to specifically understand sox2 function, our focus had shifted to understanding the role of soxB1 family members sox1a, sox2 and sox3 functioning together as an interacting system that regulates Wnt activity in the primordium. Considering this broader focus, reflected in the title of the paper, it was not a priority to repeat every experiment previously done with the sox2MO1 with the new sox2 specific morpholinos. Instead, having acknowledged the “limitations” of sox2MO1, we used it to better understand effects of combinatorial reduction of SoxB1 function.

Reviewer #1:

It is not exactly clear what underlies the apparent redundancy. It would be helpful if the soxb gene family member expression was reported after loss of each.

As suggested by reviewer #1, we had previously looked changes in expression of each of the soxB1 factors following loss of individual soxB1 factors but not included it in the supplementary data with the original submission. Independent of a reproducible and consistent expansion sox1a expression into the trailing zone, following loss of sox2 function, which is reported in the paper and quantified here where 10/10 mutant embryos showed the expansion (compare region within bracket in WT and sox2^-/-), no consistent changes in the expression of other soxB1 family members was observed as part of a mechanism that might account for compensation when function of a particular soxB1 factor is soxB1 factor is lost. The data shown above together with more extensive quantification of changes will be included in a revised version of the manuscript. At this time the only consistent change was the expansion of sox1a to the trailing zone when lost. The data trailing zone when sox2 function is lost. This change reflects dependence of sox1a on Wnt activity and the fact that Wnt activity expands into the trailing zone when sox2 function is lost.  

Author response image 3.

Reviewer #3:

Given that the expression patterns of Sox1a and Sox3 are not merely different but are largely reciprocal, the mechanistic basis of their very similar double mutant phenotypes with Sox2 remains opaque.

The simplest way to think about compensation for gene function in a network is to think of it being determined by expression of a homolog or another gene with a similar function being expressed in a similar or overlapping domain.  However, it is more useful to think of Sox2 function in the primordium as part of a interacting network of SoxB1 factors whose differential regulatory mechanisms create a robust system that simultaneously regulates two key aspects of Wnt activity in the primordium; how high Wnt activity is allowed to get in the leading zone and how effectively it is shut off to facilitate protoneuromast maturation in the trailing zone. These features of Wnt activity influence both when and where nascent protoneuromasts will form in the wake of a progressively shrinking Wnt system and where they undergo effective maturation and stabilization prior to deposition. Changes in individual SoxB1 expression patterns provide some hints about how some SoxB1 factors may compensate when function of one or more of these factors is compromised. However, a deeper understanding of robustness and “compensation” will require a systems level understanding of this gene regulatory network with computational models, which we are currently working on in our group. It remains possible, for example, that how far into the trailing zone the Wnt activity has an influence is regulated at least in part by how high it is allowed to get in the leading zone by sox1a. Conversely, how high Wnt activity gets in the leading zone may be influenced by how effectively it is shut off in the trailing zone by sox2 and sox3, as this influences the size of the Wnt system, which in turn can influence the overall level of Wnt activity. In this manner Sox1a may cooperate with Sox2 and Sox3 to limit both how high Wnt activity is allowed to get in the primordium and to effectively shut it off in the trailing zone.

Reviewer #3:

Related to this, the authors discuss that Sox1a/Sox2 double knockdown produces a more severe phenotype than Sox2/Sox3 double knockdown, yet this difference is not obviously reflected in the data.

The severity of the sox1a/sox2 double mutant phenotype compared to that of the sox2/sox3 double mutant is shown in Figure 3 K and N, and quantified in Supplementary Figure 3A. Simultaneous loss of sox2 and sox3 results in a small but relatively penetrant delay in where the first stable neuromast is deposited (Figure 2 N). By contrast, loss of sox2 and sox1a together consistently results in a longer delay in deposition of the first stable (Figure 2 K). A new graph, shown below, which will be incorporated in the revised paper, shows that there is a significant difference in the pattern of L1 deposition in sox1a^-/-, sox2^-/- and sox2^-/-, sox3^-/- double mutants. 

Author response image 4.

All 3 datasets found to be normally distributed by Shapiro-Wilk test. 1-way ANOVA showed significance (<0.0001), with Tukey’s multiple comparisons test showing significant difference between all 3 conditions. (***p=0.0008, ****p<0.0001)

Reviewer #1:

It would be good to more clearly state why sox3 is not regulated by Wnt given its expression is inhibited by the delta TCF construct (Figure 2M).

The explanation for why we believe sox3 expression is determined by Fgf signaling, and not Wnt activity requires integrating what is observed both with induction of the delta TCF construct and the dominant negative Fgf receptor (DN FgfR). Loss of sox3 expression with induced expression of the delta TCF construct could result from loss of Wnt activity or the downstream loss of Fgf activity, which is ultimately dependent on Fgfs secreted by Wnt active cells in the leading domain. Distinguishing between these possibilities is based on inhibition of FGF signaling with the DN FgfR, described in the next paragraph. Heat Shock induced expression of DN FgfR expression results in loss of FGF signaling and the simultaneous expansion of Wnt activity into the trailing zone. As explained in the original text, loss of sox3 expression in this context, rather than its expansion, suggests its expression is determined by Fgf signaling not Wnt activity. We will emphasize that its loss, rather than its expansion, following induction of DN FgfR, indicates its expression is determined by Fgf signaling not Wnt activity.

Reviewer #2:

The manuscript lacks quantification of many of the experiments, making it difficult to conclude their significance.

One of the biggest inadvertent omissions of the paper was the inadequate quantification of some of the results. Quantification of results with considerable variation in the outcome, like the pattern of L1 deposition,  was provided following manipulations where various combinations of sox1a, sox2, and sox3 function was lost (Figures 3, supplementary Figures 2 and 3) or where sox2MO1/sox3MO was used with or without IWR (Figure 5 and Figure 6). However, numbers for the experiments in Figures 2 were omitted in the Figure legend, where typically about 10 embryos for each manipulation were photographed, scored, and a representative image was used to make the figure. In these experiments  there was a very consistent result with 100% of the embryos showing changes represented by each panel in Figure 2. The only exception was Figure 2Y where 9/10 embryos showed the described change. Similarly in Figure 4 there was a consistent result and 100% of embryos showed the change shown. Numbers and statistics for these results will be included in a revised manuscript.

Reviewer #2:

The statistical analysis in Figure 5 and Supplementary Figures 2 and 3 should be one-way ANOVA or Kruskal-Wallis with a Dunn's multiple comparisons test rather than pair-wise comparisons.

The analysis has been re-done following the reviewer’s suggestions. The analysis confirms the primary conclusions of the original submission, and this analysis will be incorporated in a revised manuscript. However, to improve the power of the analysis, experiments with low numbers of embryos will be repeated.

See redone graphs in Figure 5 and supplementary Figure 2 and 3.

Author response:

Public Reviews:

Reviewer #1 (Public Review):

Summary:

In their manuscript entitled 'The domesticated transposon protein L1TD1 associates with its ancestor L1 ORF1p to promote LINE-1 retrotransposition', Kavaklıoğlu and colleagues delve into the role of L1TD1, an RNA binding protein (RBP) derived from a LINE1 transposon. L1TD1 proves crucial for maintaining pluripotency in embryonic stem cells and is linked to cancer progression in germ cell tumors, yet its precise molecular function remains elusive. Here, the authors uncover an intriguing interaction between L1TD1 and its ancestral LINE-1 retrotransposon.

The authors delete the DNA methyltransferase DNMT1 in a haploid human cell line (HAP1), inducing widespread DNA hypo-methylation. This hypomethylation prompts abnormal expression of L1TD1. To scrutinize L1TD1's function in a DNMT1 knock-out setting, the authors create DNMT1/L1TD1 double knock-out cell lines (DKO). Curiously, while the loss of global DNA methylation doesn't impede proliferation, additional depletion of L1TD1 leads to DNA damage and apoptosis.

To unravel the molecular mechanism underpinning L1TD1's protective role in the absence of DNA methylation, the authors dissect L1TD1 complexes in terms of protein and RNA composition. They unveil an association with the LINE-1 transposon protein L1-ORF1 and LINE-1 transcripts, among others.

Surprisingly, the authors note fewer LINE-1 retro-transposition events in DKO cells than in DNMT1 KO alone.

Strengths:

The authors present compelling data suggesting the interplay of a transposon-derived human RNA binding protein with its ancestral transposable element. Their findings spur interesting questions for cancer types, where LINE1 and L1TD1 are aberrantly expressed.

Weaknesses:

Suggestions for refinement:

The initial experiment, inducing global hypo-methylation by eliminating DNMT1 in HAP1 cells, is intriguing and warrants a more detailed description. How many genes experience misregulation or aberrant expression? What phenotypic changes occur in these cells?

The transcriptome analysis of DNMT1 KO cells showed hundreds of deregulated genes upon DNMT1 ablation. As expected, the majority were up-regulated and gene ontology analysis revealed that among the strongest up-regulated genes were gene clusters with functions in “regulation of transcription from RNA polymerase II promoter” and “cell differentiation” and genes encoding proteins with KRAB domains. In addition, the de novo methyltransferases DNMT3A and DNMT3B were up-regulated in DNMT1 KO cells suggesting the set-up of compensatory mechanisms in these cells. We will include this data set in the revised version of the manuscript.

Why did the authors focus on L1TD1? Providing some of this data would be helpful to understand the rationale behind the thorough analysis of L1TD1.

We have previously discovered that conditional deletion of the maintenance DNA methyltransferase DNMT1 in the murine epidermis results not only in the up-regulation of mobile elements, such as IAPs but also the induced expression of L1TD1 ((Beck et al, 2021), Suppl. Table 1 and Author response image 1). Similary, L1TD1 expression was induced by treatment of primary human keratinocytes or squamous cell carcinoma cells with the DNMT inhibitor aza-deoxycytidine (Author response image 2 and 3). These finding are in accordance with the observation that inhibition of DNA methyltransferase activity by azadeoxycytidine in human non-small cell lung cancer cells (NSCLCs) results in upregulation of L1TD1 (Altenberger et al, 2017). Our interest in L1TD1 was further fueled by reports on a potential function of L1TD1 as prognostic tumor marker. We will include this information in the revised manuscript.

Author response image 1.

RT-qPCR of L1TD1 expression in cultured murine control and Dnmt1 Δ/Δker keratinocytes. mRNA levels of L1td1 were analyzed in keratinocytes isolated at P5 from conditional Dnmt1 knockout mice (Beck et al., 2021). Hprt expression was used for normalization of mRNA levels and wildtype control was set to 1. Data represent means ±s.d. with n=4. **P < 0.01 (paired t-test).

Author response image 2.

RT-qPCR analysis of L1TD1 expression in primary human keratinocytes. Cells were treated with 5-aza-2-deoxycidine for 24 hours or 48 hours, with PBS for 48 hours or were left untreated. 18S rRNA expression was used for normalization of mRNA levels and PBS control was set to 1. Data represent means ±s.d. with n=3. **P < 0.01 (paired t-test).

Author response image 3.

Induced L1TD1 expression upon DNMT inhibition in squamous cell carcinoma cell lines SCC9 and SCCO12. Cells were treated with 5-aza-2-deoxycidine for 24 hours, 48 hours or 6 days. (A) Western blot analysis of L1TD1 protein levels using beta-actin as loading control. (B) Indirect immunofluorescence microscopy analysis of L1TD1 expression in SCC9 cells. Nuclear DNA was stained with DAPI. Scale bar: 10 µm. (C) RT-qPCR analysis of L1TD1 expression in primary human keratinocytes. Cells were treated with 5-aza-2deoxycidine for 24 hours or 48 hours, with PBS for 48 hours or were left untreated. 18S rRNA expression was used for normalization of mRNA levels and PBS control was set to 1. Data represent means ±s.d. with n=3. P < 0.05, *P < 0.01 (paired t-test).

The finding that L1TD1/DNMT1 DKO cells exhibit increased apoptosis and DNA damage but decreased L1 retro-transposition is unexpected. Considering the DNA damage associated with retro-transposition and the DNA damage and apoptosis observed in L1TD1/DNMT1 DKO cells, one would anticipate the opposite outcome. Could it be that the observation of fewer transposition-positive colonies stems from the demise of the most transposition-positive colonies? Further exploration of this phenomenon would be intriguing.

This is an important point and we were aware of this potential problem. Therefore, we calibrated the retrotransposition assay by transfection with a blasticidin resistance gene vector to take into account potential differences in cell viability and blasticidin sensitivity. Thus, the observed reduction in L1 retrotransposition efficiency is not an indirect effect of reduced cell viability.

Based on previous studies with hESCs, it is likely that, in addition to its role in retrotransposition, L1TD1 has additional functions in the regulation of cell proliferation and differentiation. L1TD1 might therefore attenuate the effect of DNMT1 loss in KO cells generating an intermediate phenotype (as pointed out by Reviewer 2) and simultaneous loss of both L1TD1 and DNMT1 results in more pronounced effects on cell viability.

Reviewer #2 (Public Review):

In this study, Kavaklıoğlu et al. investigated and presented evidence for the role of domesticated transposon protein L1TD1 in enabling its ancestral relative, L1 ORF1p, to retrotranspose in HAP1 human tumor cells. The authors provided insight into the molecular function of L1TD1 and shed some clarifying light on previous studies that showed somewhat contradictory outcomes surrounding L1TD1 expression. Here, L1TD1 expression was correlated with L1 activation in a hypomethylation-dependent manner, due to DNMT1 deletion in the HAP1 cell line. The authors then identified L1TD1-associated RNAs using RIP-Seq, which displays a disconnect between transcript and protein abundance (via Tandem Mass Tag multiplex mass spectrometry analysis). The one exception was for L1TD1 itself, which is consistent with a model in which the RNA transcripts associated with L1TD1 are not directly regulated at the translation level. Instead, the authors found the L1TD1 protein associated with L1-RNPs, and this interaction is associated with increased L1 retrotransposition, at least in the contexts of HAP1 cells. Overall, these results support a model in which L1TD1 is restrained by DNA methylation, but in the absence of this repressive mark, L1TD1 is expressed and collaborates with L1 ORF1p (either directly or through interaction with L1 RNA, which remains unclear based on current results), leads to enhances L1 retrotransposition. These results establish the feasibility of this relationship existing in vivo in either development, disease, or both.

Author Response

We are grateful for the constructive comments of the reviewers and for the succinct assessment of our work by the editors. Here we provide a brief summary of our response to answer the major criticism of our reviewers. We will give a detailed point-to-point response soon when we upload a revision of our paper.

1) The MATLAB code for the spatial autocorrelation analysis is now freely available at the following site: : https://github.com/dcsabaCD225/Moran_Matlab/blob/main/moran_local.m If any question arises during its implementation, please contact Csaba Dávid (david.csaba@koki.hu)

2) Concerning the computer resources and times required to perform Moran’s I image analysis, here we provide a brief description of the hardware and the calculations for images with different sizes.

Hardware used for performing the analysis:

Intel(R) Xeon(R) Silver 4112 CPU @ 2.60GHz, 2594 Mhz, 4 kernel CPU, 64GB RAM, NVIDIA GeForce GTX 1080 graphic card.

MATLAB R2021b software was used for implementation.

Computation times are shown in Author response table 1.

Author response table 1.

3) In response to the comment:

“While the method's avoidance of AI training appeals to those lacking computational know-how and shows improved accuracy over basic threshold-based techniques, there are valid concerns regarding its performance in comparison to advanced methodologies”.

Comparison of Moran’s I image analysis with AI based segmentations raises conceptual problems which will be addressed in detail in the revised version. Briefly, the basis of AI based analyses is that the ground truth is known and using a large teaching set AI learns to extract the relevant information for image segmentation. In several cases, however (like protein distribution in the membrane) the ground truth is not known and cannot be easily determined by any single observer. Defining spatial inhomogeneities in protein distribution, differentiating proteins involved vs not involved in clusters is highly subjective. Indeed, our analysis showed the 23 expert human observers varied hugely in establishing the boundaries of a protein cluster. As a consequence, establishing and using a teaching set would be highly contentious in these cases. In an average laboratory setting generating a teaching set using hundreds of images examined by two dozen people would not be impossible but not really plausible. The beauty of Moran’n I analysis is that it is able to extract the relevant signals from an image generated in different, often noisy condition using a simple algorithm that allows quantitative characterization and identification of changes in many biological and non-biological samples.

Author response:

We deeply appreciate the editors’ and reviewers’ invaluable time and effort. We would also like to extend our gratitude to eLife for its unwavering commitment to a transparent review and publication model. Below, we present our point-by-point responses to the comments.  

Besides the WT allele, equivalent to the mouse TMEM173 gene, the human TMEM173 gene has two common alleles: the HAQ and AQ alleles carried by billions of people. The main conclusions and interpretation, summarized in the Title and Abstract, are (i) Different from the WT TMEM173 allele, the HAQ or AQ alleles are resistant to STING activation-induced cell death; (ii) STING residue 293 is critical for cell death; (iii) HAQ, AQ alleles are dominant to the SAVI allele; iv) One copy of the AQ allele rescues the SAVI disease in mice. We propose that STING research and STING-targeting immunotherapy should consider human TMEM173 heterogeneity. These interpretations and conclusions were based on Data and Logic. We welcome alternative, logical interpretations from our peers and potential collaborations to advance the human TMEM173 research.  

Reviewer #1 (Public Review):

Responses to Comment 1: We greatly appreciate Reviewer 1's insights. We will change the “lymphocytes” to “splenocytes” (line 134) as suggested. We respectfully disagree with Reviewer 1’s comments on TBK1 (lines 129 – 134). First, we used two different TBK1 inhibitors: BX795 and GSK8612. Second, because BX795 also inhibits PDK1, we used a PDK1 inhibitor GSK2334470; Third, both BX795 and GSK8612 completely inhibited diABZI-induced splenocyte cell death (Figure 1B). The logical conclusion is “TBK1 activation is required for STING-mediated mouse spleen cell death ex vivo”. (line 118). 

This manuscript uncovers a significant aspect of the interplay between the common human TMEM173 alleles and the rare SAVI mutation (lines 23-26). Our discovery that the common human TMEM173 alleles are resistant to STING activation-induced cell death is a substantial finding. It further strengthens the argument that the HAQ and AQ alleles are functionally distinct from the WT allele 1-3. We wish to underscore the crucial message of this study-that 'STING research and STING-targeting immunotherapy should consider TMEM173 heterogeneity in humans' (line 37), which has been largely overlooked in current STING clinical trials 4.  

Regarding STING-Cell death, as we stated in the Introduction (lines 62-79). (i) STING-mediated cell death is cell type-dependent 5-7 and type I IFNs-independent 5,7,8. (ii) The in vivo biological significance of STING-mediated cell death is not clear 7,8. (iii) The mechanisms of STING-Cell death remain controversial. Multiple cell death pathways, i.e., apoptosis, necroptosis, pyroptosis, ferroptosis, and PANoptosis, are proposed 7,9,10. SAVI patients (WT/SAVI) and mouse models had CD4 T cellpenia 8,11. SAVI/HAQ, SAVI/AQ restored T cells in mice. Thus, the manuscript provides some answers to the biological significance of STING-cell death. Next, splenocytes from Q293/Q293 mice are resistant to STING cell death. The logical conclusion is that the amino acid 293 is critical for STING cell death. How aa293 mediates this function needs future investigation. Similarly, how TBK1 mediates STING cell death, independent of type I IFNs and NFκB induction, needs future investigation.

Responses to Comment 2: These are all very interesting questions that we will address in future studies. This manuscript, titled “The common TMEM173 HAQ, AQ alleles rescue CD4 T cellpenia, restore T-regs, and prevent SAVI (N153S) inflammatory disease in mice” does not focus on Q293 mice. We have been researching the common human TMEM173 alleles since 2011 from the discovery12 , mouse model1,3, human clinical trial2, and human genetics studies 3. This manuscript is another step towards understanding these common human TMEM173 alleles with the new discovery that HAQ, AQ are resistant to STING cell death. 

Responses to Comment 3: We aim to address these worthy questions in future studies. In this manuscript, Figure 6 shows AQ/SAVI had more T-regs than HAQ/SAVI (lines 246 – 256). In our previous publication on HAQ, AQ knockin mice, we showed that AQ T-regs have more IL-10 and mitochondria activity than HAQ T-regs 3. We propose that increased IL-10+

Tregs in AQ mice may contribute to an improved phenotype in AQ/SAVI compared to

HAQ/SAVI. However, we are not excluding other contributions (e.g. metabolic difference) by the AQ allele. We will explore these possibilities in future research.   

Responses to Comment 4: Figure 2 is necessary because it reveals the difference between mouse and human STING cell death. Figure 2A-2B showed that STING activation killed human CD4 T cells, but not human CD8 T cells or B cells. This observation is different from Figure 1A, where STING activation killed mouse CD4, CD8 T cells, and CD19 B cells, revealing the species-specific STING cell death responses. Regarding human CD8 T cells, as we stated in the Discussion (lines 318-320), human CD8 T cells (PBMC) are not as susceptible as the CD4 T cells to STING-induced cell death 8. We used lung lymphocytes that showed similar observations (Figure 2A). For Figure 2C, we used 2 WT/HAQ and 3 WT/WT individuals (lines 738-739). We generate HAQ, AQ THP-1 cells in STING-KO THP-1 cells (Invivogen,, cat no. thpd-kostg) (lines 740-741). 

A recent study found that STING agonist SHR1032 induces cell death in STING-KO THP-1 cells expressing WT(R232) human STING 10 (line 182) independent of type I IFNs. SHR1032 suppressed THP1-STING-WT(R232) cell growth at GI50: 23 nM while in the parental THP1STING-HAQ cells, the GI50 of SHR1032 was >103 nM 10. Cytarabine was used as an internal control where SHR1032 killed more robustly than cytarabine in the THP1-STING-WT(R232) cells but much less efficiently than cytarabine in the THP-1-STING-HAQ cells 10.   

This manuscript rigorously uses mouse splenocytes, human lung lymphocytes, THP-1 reconstituted with HAQ, AQ, and HAQ/SAVI, AQ/SAVI mice, to demonstrate that the common human HAQ, AQ alleles are resistant to STING cell death in vitro and in vivo.

We agree with reviewer 1 that STING-mediated cell death mechanisms in myeloid and lymphoid cells may be different and likely contribute to the different mechanisms proposed in STING cell death research 7,9,10. Our study focuses on the in vivo mechanism of T cellpenia.  

Responses to Comment 5: We stated in the Introduction that “AQ responds to CDNs and produce type I IFNs in vivo and in vitro 3,13,14 ”(line 94, 95). We reported that the AQ knock in mice responded to STING activation 3. We previously showed that there was a negative natural selection on the AQ allele in individuals outside of Africa 3. 28% of Africans are WT/AQ but only 0.6% East Asians are WT/AQ 3. Future research on the AQ allele will address this interesting question that may shed new mechanistic light on STING action.

Responses to Comment 6: The comment here is similar to comment 3. In this manuscript, Figure 6 shows AQ/SAVI had more T-regs than HAQ/SAVI (lines 246 – 256). In our previous publication on HAQ, AQ knockin mice, we showed that AQ T-regs have more IL-10 and mitochondria activity than HAQ T-regs 3. We propose that increased IL-10+ Tregs in AQ mice may contribute to an improved phenotype in AQ/SAVI compared to HAQ/SAVI. However, we are not excluding other contributions (e.g. metabolic difference) by the AQ allele.

Responses to Comment 7: Both radioresistant parenchymal and/or stromal cells and hematopoietic cells influence SAVI pathology in mice 15,16. Nevertheless, the lack of CD 4 T cells, including the anti-inflammatory T-regs, likely contributes to the inflammation in SAVI mice and patients. We characterized lung function, lung inflammation (Figure 4), lung neutrophils, and inflammatory monocyte infiltration (Figure S4). 

Responses to Comment 8: Several publications have linked STING to HIV pathogenesis 17-22  (line 271). The manuscript studies STING activation-induced cell death. It is not stretching to ask, for example, does preventing STING cell death, without affecting type I IFNs production, restore CD4 T cell counts and improve care for AIDS patients?

Reviewer #2 (Public Review):

Response to Comment 1: Please see the Figure below for cell death by diABZI, DMXAA in Splenocytes from WT/WT, WT/HAQ, HAQ/SAVI, AQ/SAVI mice. The HAQ/SAVI and AQ/SAVI splenocytes showed similar partial resistance to STING activationinduced cell death. 

Responses to Comment 2: We examined HAQ, AQ mouse splenocytes, HAQ human lung lymphocytes, THP-1 reconstituted with HAQ, AQ, and HAQ/SAVI, AQ/SAVI mice, to demonstrate that the common human HAQ, AQ alleles are resistant to STING cell death in vitro and in vivo. Additional human T cell line work does not add too much. 

Responses to Comment 3: This is possibly a misunderstanding. We use BMDM for the purpose of comparing STING signaling (TBK1, IRF3, NFκB, STING activation) by WT/SAVI, HAQ/SAVI, AQ/SAVI. Ideally, we would like to compare STING signaling in CD4 T cells from WT/SAVI to HAQ/SAVI, AQ/SAVI mice. However, WT/SAVI has no CD4 T cells. Here, we are making the assumption that the basic STING signaling (TBK1, IRF3, NFκB, STING activation) is conserved between T cells and macrophages. 

Responses to Comment 4: Reviewer 2 suggests looking for evidence of inflammation and STING activation in the lungs of HAQ/SAVI, AQ/SAVI. We would like to elaborate further. First, anti-inflammatory treatments, e.g. steroids, DMARDs, IVIG, Etanercept, rituximab, Nifedipine, amlodipine, et al., all failed in SAVI patients 11. Second, Figure S4 examined lung neutrophils and inflammatory monocyte infiltration. Interestingly, while AQ/SAVI mice had a better lung function than HAQ/SAVI mice (Figure 4D, 4E vs 4H, 4I), HAQ/SAVI and AQ/SAVI lungs had comparable neutrophils and inflammatory monocyte infiltration. Last, SAVI is classified as type I interferonopathy 11, but the lung diseases of SAVI are mainly independent of type I IFNs 23-26. The AQ allele suppresses SAVI in vivo.  Understanding the mechanisms by which AQ rescues SAVI can generate curative care for SAVI patients.  

Author response image 1.

(A-B). Flow cytometry of HAQ/SAVI, AQ/SAVI, WT/WT or WT/HAQ splenocytes treated with diABZI (100ng/ml) or DMXAA (20µg/ml) for 24hrs. Cell death was determined by PI staining. Data are representative of three independent experiments. Graphs represent the mean with error bars indication s.e.m. p values are determined by one-way ANOVA Tukey’s multiple comparison test. * p<0.05. n.s: not significant.

References.

(1)             Patel, S. et al. The Common R71H-G230A-R293Q Human TMEM173 Is a Null Allele. J Immunol 198, 776-787 (2017). 

(2)             Sebastian, M. et al. Obesity and STING1 genotype associate with 23-valent pneumococcal vaccination efficacy. JCI Insight 5 (2020). 

(3)             Mansouri, S. et al. MPYS Modulates Fatty Acid Metabolism and Immune Tolerance at Homeostasis Independent of Type I IFNs. J Immunol 209, 2114-2132 (2022). 

(4)             Sivick, K. E. et al. Comment on "The Common R71H-G230A-R293Q Human TMEM173 Is a Null Allele". J Immunol 198, 4183-4185 (2017). 

(5)             Gulen, M. F. et al. Signalling strength determines proapoptotic functions of STING. Nat Commun 8, 427 (2017). 

(6)             Kabelitz, D. et al. Signal strength of STING activation determines cytokine plasticity and cell death in human monocytes. Sci Rep 12, 17827 (2022). 

(7)             Murthy, A. M. V., Robinson, N. & Kumar, S. Crosstalk between cGAS-STING signaling and cell death. Cell Death Differ 27, 2989-3003 (2020). 

(8)             Kuhl, N. et al. STING agonism turns human T cells into interferon-producing cells but impedes their functionality. EMBO Rep 24, e55536 (2023). 

(9)             Li, C., Liu, J., Hou, W., Kang, R. & Tang, D. STING1 Promotes Ferroptosis Through MFN1/2-Dependent Mitochondrial Fusion. Front Cell Dev Biol 9, 698679 (2021). 

(10)         Song, C. et al. SHR1032, a novel STING agonist, stimulates anti-tumor immunity and directly induces AML apoptosis. Sci Rep 12, 8579 (2022). 

(11)         Liu, Y. et al. Activated STING in a vascular and pulmonary syndrome. N Engl J Med 371, 507-518 (2014). 

(12)         Jin, L. et al. Identification and characterization of a loss-of-function human MPYS variant. Genes Immun 12, 263-269 (2011). 

(13)         Yi, G. et al. Single nucleotide polymorphisms of human STING can affect innate immune response to cyclic dinucleotides. PLoS One 8, e77846 (2013). 

(14)         Patel, S. et al. Response to Comment on "The Common R71H-G230A-R293Q Human TMEM173 Is a Null Allele". J Immunol 198, 4185-4188 (2017). 

(15)         Gao, K. M. et al. Endothelial cell expression of a STING gain-of-function mutation initiates pulmonary lymphocytic infiltration. Cell Rep 43, 114114 (2024). 

(16)         Gao, K. M., Motwani, M., Tedder, T., Marshak-Rothstein, A. & Fitzgerald, K. A. Radioresistant cells initiate lymphocyte-dependent lung inflammation and IFNgammadependent mortality in STING gain-of-function mice. Proc Natl Acad Sci U S A 119, e2202327119 (2022). 

(17)         Monroe, K. M. et al. IFI16 DNA sensor is required for death of lymphoid CD4 T cells abortively infected with HIV. Science 343, 428-432 (2014). 

(18)         Doitsh, G. et al. Cell death by pyroptosis drives CD4 T-cell depletion in HIV-1 infection. Nature 505, 509-514 (2014). 

(19)         Jakobsen, M. R., Olagnier, D. & Hiscott, J. Innate immune sensing of HIV-1 infection. Curr Opin HIV AIDS 10, 96-102 (2015). 

(20)         Silvin, A. & Manel, N. Innate immune sensing of HIV infection. Curr Opin Immunol 32, 54-60 (2015). 

(21)         Altfeld, M. & Gale, M., Jr. Innate immunity against HIV-1 infection. Nat Immunol 16, 554-562 (2015). 

(22)         Krapp, C., Jonsson, K. & Jakobsen, M. R. STING dependent sensing - Does HIV actually care? Cytokine Growth Factor Rev 40, 68-76 (2018). 

(23)         Luksch, H. et al. STING-associated lung disease in mice relies on T cells but not type I interferon. J Allergy Clin Immunol 144, 254-266 e258 (2019). 

(24)         Stinson, W. A. et al. The IFN-gamma receptor promotes immune dysregulation and disease in STING gain-of-function mice. JCI Insight 7 (2022). 

(25)         Warner, J. D. et al. STING-associated vasculopathy develops independently of IRF3 in mice. J Exp Med 214, 3279-3292 (2017). 

(26)         Fremond, M. L. et al. Overview of STING-Associated Vasculopathy with Onset in Infancy (SAVI) Among 21 Patients. J Allergy Clin Immunol Pract 9, 803-818 e811 (2021).

Author Response:

Reviewer #1 (Public Review):

Force sensing and gating mechanisms of the mechanically activated ion channels is an area of broad interest in the field of mechanotransduction. These channels perform important biological functions by converting mechanical force into electrical signals. To understand their underlying physiological processes, it is important to determine gating mechanisms, especially those mediated by lipids. The authors in this manuscript describe a mechanism for mechanically induced activation of TREK-1 (TWIK-related K+ channel. They propose that force induced disruption of ganglioside (GM1) and cholesterol causes relocation of TREK-1 associated with phospholipase D2 (PLD2) to 4,5-bisphosphate (PIP2) clusters, where PLD2 catalytic activity produces phosphatidic acid that can activate the channel. To test their hypothesis, they use dSTORM to measure TREK-1 and PLD2 colocalization with either GM1 or PIP2. They find that shear stress decreases TREK-1/PLD2 colocalization with GM1 and relocates to cluster with PIP2. These movements are affected by TREK-1 C-terminal or PLD2 mutations suggesting that the interaction is important for channel re-location. The authors then draw a correlation to cholesterol suggesting that TREK-1 movement is cholesterol dependent. It is important to note that this is not the only method of channel activation and that one not involving PLD2 also exists. Overall, the authors conclude that force is sensed by ordered lipids and PLD2 associates with TREK-1 to selectively gate the channel. Although the proposed mechanism is solid, some concerns remain.

1) Most conclusions in the paper heavily depend on the dSTORM data. But the images provided lack resolution. This makes it difficult for the readers to assess the representative images.

The images were provided are at 300 dpi. Perhaps the reviewer is referring to contrast in Figure 2? We are happy to increase the contrast or resolution.

As a side note, we feel the main conclusion of the paper, mechanical activation of TREK-1 through PLD2, depended primarily on the electrophysiology in Figure 1b-c, not the dSTORM. But both complement each other.

2) The experiments in Figure 6 are a bit puzzling. The entire premise of the paper is to establish gating mechanism of TREK-1 mediated by PLD2; however, the motivation behind using flies, which do not express TREK-1 is puzzling.

The fly experiment shows that PLD mechanosensitivity is more evolutionarily conserved than TREK-1 mechanosensitivity. We should have made this clearer.

-Figure 6B, the image is too blown out and looks over saturated. Unclear whether the resolution in subcellular localization is obvious or not.

Figure 6B is a confocal image, it is not dSTORM. There is no dSTORM in Figure 6. This should have been made clear in the figure legend. For reference, only a few cells would fit in the field of view with dSTORM.

-Figure 6C-D, the differences in activity threshold is 1 or less than 1g. Is this physiologically relevant? How does this compare to other conditions in flies that can affect mechanosensitivity, for example?

Yes, 1g is physiologically relevant. It is almost the force needed to wake a fly from sleep (1.2-3.2g). See ref 33. Murphy Nature Pro. 2017.

3) 70mOsm is a high degree of osmotic stress. How confident are the authors that a. cell health is maintained under this condition and b. this does indeed induce membrane stretch? For example, does this stimulation activate TREK-1?

Yes, osmotic swell activates TREK1. This was shown in ref 19 (Patel et al 1998). We agree the 70 mOsm is a high degree of stress. This needs to be stated better in the paper.

Reviewer #2 (Public Review):

This manuscript by Petersen and colleagues investigates the mechanistic underpinnings of activation of the ion channel TREK-1 by mechanical inputs (fluid shear or membrane stretch) applied to cells. Using a combination of super-resolution microscopy, pair correlation analysis and electrophysiology, the authors show that the application of shear to a cell can lead to changes in the distribution of TREK-1 and the enzyme PhospholipaseD2 (PLD2), relative to lipid domains defined by either GM1 or PIP2. The activation of TREK-1 by mechanical stimuli was shown to be sensitized by the presence of PLD2, but not a catalytically dead xPLD2 mutant. In addition, the activity of PLD2 is increased when the molecule is more associated with PIP2, rather than GM1 defined lipid domains. The presented data do not exclude direct mechanical activation of TREK-1, rather suggest a modulation of TREK-1 activity, increasing sensitivity to mechanical inputs, through an inherent mechanosensitivity of PLD2 activity. The authors additionally claim that PLD2 can regulate transduction thresholds in vivo using Drosophila melanogaster behavioural assays. However, this section of the manuscript overstates the experimental findings, given that it is unclear how the disruption of PLD2 is leading to behavioural changes, given the lack of a TREK-1 homologue in this organism and the lack of supporting data on molecular function in the relevant cells.

We agree, the downstream effectors of PLD2 mechanosensitivity are not known in the fly. Other anionic lipids have been shown to mediate pain see ref 46 and 47. We do not wish to make any claim beyond PLD2 being an in vivo contributor to a fly’s response to mechanical force.

That said we do believe we have established a molecular function at the cellular level. We showed PLD is robustly mechanically activated in a cultured fly cell line (BG2-c2) Figure 6a of the manuscript. And our previous publication established mechanosensation of PLD (Petersen et. al. Nature Com 2016) through mechanical disruption of the lipids. At a minimum, the experiments show PLDs mechanosensitivity is evolutionarily better conserved across species than TREK1.

This work will be of interest to the growing community of scientists investigating the myriad mechanisms that can tune mechanical sensitivity of cells, providing valuable insight into the role of functional PLD2 in sensitizing TREK-1 activation in response to mechanical inputs, in some cellular systems.

The authors convincingly demonstrate that, post application of shear, an alteration in the distribution of TREK-1 and mPLD2 (in HEK293T cells) from being correlated with GM1 defined domains (no shear) to increased correlation with PIP2 defined membrane domains (post shear). These data were generated using super-resolution microscopy to visualise, at sub diffraction resolution, the localisation of labelled protein, compared to labelled lipids. The use of super-resolution imaging enabled the authors to visualise changes in cluster association that would not have been achievable with diffraction limited microscopy. However, the conclusion that this change in association reflects TREK-1 leaving one cluster and moving to another overinterprets these data, as the data were generated from static measurements of fixed cells, rather than dynamic measurements capturing molecular movements.

When assessing molecular distribution of endogenous TREK-1 and PLD2, these molecules are described as "well correlated: in C2C12 cells" however it is challenging to assess what "well correlated" means, precisely in this context. This limitation is compounded by the conclusion that TREK-1 displayed little pair correlation with GM1 and the authors describe a "small amount of TREK-1 trafficked to PIP2". As such, these data may suggest that the findings outlined for HEK293T cells may be influenced by artefacts arising from overexpression.

The changes in TREK-1 sensitivity to mechanical activation could also reflect changes in the amount of TREK-1 in the plasma membrane. The authors suggest that the presence of a leak currently accounts for the presence of TREK-1 in the plasma membrane, however they do not account for whether there are significant changes in the membrane localisation of the channel in the presence of mPLD2 versus xPLD2. The supplementary data provide some images of fluorescently labelled TREK-1 in cells, and the authors state that truncating the c-terminus has no effect on expression at the plasma membrane, however these data provide inadequate support for this conclusion. In addition, the data reporting the P50 should be noted with caution, given the lack of saturation of the current in response to the stimulus range.

We thank the reviewer for his/her concern about expression levels. We did test TREK-1 expression. mPLD decreases TREK-1 expression ~two-fold (see Author response image 1). We did not include the mPLD data since TREK-1 was mechanically activated with mPLD. For expression to account for the loss of TREK-1 stretch current (Figure 1b), xPLD would need to block surface expression of TREK-1. The opposite was true, xPLD2 increased TREK-1 expression increased (see Figure S2c). Furthermore, we tested the leak current of TREK-1 at 0 mV and 0 mmHg of stretch. Basal leak current was no different with xPLD2 compared to endogenous PLD (Figure 1d; red vs grey bars respectively) suggesting TREK-1 is in the membrane and active when xPLD2 is present. If anything, the magnitude of the effect with xPLD would be larger if the expression levels were equal.

Author response image 1.

TREK expression at the plasma membrane. TREK-1 Fluorescence was measured by GFP at points along the plasma membrane. Over expression of mouse PLD2 (mPLD) decrease the amount of full-length TREK-1 (FL TREK) on the surface more than 2-fold compared to endogenously expressed PLD (enPLD) or truncated TREK (TREKtrunc) which is missing the PLD binding site in the C-terminus. Over expression of mPLD had no effect on TREKtrunc.

Finally, by manipulating PLD2 in D. melanogaster, the authors show changes in behaviour when larvae are exposed to either mechanical or electrical inputs. The depletion of PLD2 is concluded to lead to a reduction in activation thresholds and to suggest an in vivo role for PA lipid signaling in setting thresholds for both mechanosensitivity and pain. However, while the data provided demonstrate convincing changes in behaviour and these changes could be explained by changes in transduction thresholds, these data only provide weak support for this specific conclusion. As the authors note, there is no TREK-1 in D. melanogaster, as such the reported findings could be accounted for by other explanations, not least including potential alterations in the activation threshold of Nav channels required for action potential generation. To conclude that the outcomes were in fact mediated by changes in mechanotransduction, the authors would need to demonstrate changes in receptor potential generation, rather than deriving conclusions from changes in behaviour that could arise from alterations in resting membrane potential, receptor potential generation or the activity of the voltage gated channels required for action potential generation.

We are willing to restrict the conclusion about the fly behavior as the reviewers see fit. We have shown PLD is mechanosensitivity in a fly cell line, and when we knock out PLD from a fly, the animal exhibits a mechanosensation phenotype.

This work provides further evidence of the astounding flexibility of mechanical sensing in cells. By outlining how mechanical activation of TREK-1 can be sensitised by mechanical regulation of PLD2 activity, the authors highlight a mechanism by which TREK-1 sensitivity could be regulated under distinct physiological conditions.

Reviewer #3 (Public Review):

The manuscript "Mechanical activation of TWIK-related potassium channel by nanoscopic movement and second messenger signaling" presents a new mechanism for the activation of TREK-1 channel. The mechanism suggests that TREK1 is activated by phosphatidic acids that are produced via a mechanosensitive motion of PLD2 to PIP2-enriched domains. Overall, I found the topic interesting, but several typos and unclarities reduced the readability of the manuscript. Additionally, I have several major concerns on the interpretation of the results. Therefore, the proposed mechanism is not fully supported by the presented data. Lastly, the mechanism is based on several previous studies from the Hansen lab, however, the novelty of the current manuscript is not clearly stated. For example, in the 2nd result section, the authors stated, "fluid shear causes PLD2 to move from cholesterol dependent GM1 clusters to PIP2 clusters and this activated the enzyme". However, this is also presented as a new finding in section 3 "Mechanism of PLD2 activation by shear."

For PLD2 dependent TREK-1 activation. Overall, I found the results compelling. However, two key results are missing. 1. Does HEK cells have endogenous PLD2? If so, it's hard to claim that the authors can measure PLD2-independent TREK1 activation.

Yes, there is endogenous PLD (enPLD). We calculated the relative expression of xPLD2 vs enPLD. xPLD2 is >10x more abundant (Fig. S3d of Pavel et al PNAS 2020, ref 14 of the current manuscript). Hence, as with anesthetic sensitivity, we expect the xPLD to out compete the endogenous PLD, which is what we see. This should have been described more carefully in this paper and the studies pointed out that establish this conclusion.

1. Does the plasma membrane trafficking of TREK1 remain the same under different conditions (PLD2 overexpression, truncation)? From Figure S2, the truncated TREK1 seem to have very poor trafficking. The change of trafficking could significantly contribute to the interpretation of the data in Figure 1.

If the PLD2 binding site is removed (TREK-1trunc), yes, the trafficking to the plasma membrane is unaffected by the expression of xPLD and mPLD (Figure R1 above). For full length TREK1 (FL-TREK-1), co-expression of mPLD decreases TREK expression (Figure R1) and co-expression with xPLD increases TREK expression (Figure S2). This is exactly opposite of what one would expect if surface expression accounted for the change in pressure currents. Hence, we conclude surface expression does not account for loss of TREK-1 mechanosensitivity with xPLD2.

For shear-induced movement of TREK1 between nanodomains. The section is convincing, however I'm not an expert on super-resolution imaging. Also, it would be helpful to clarify whether the shear stress was maintained during fixation. If not, what is the time gap between reduced shear and the fixed state. lastly, it's unclear why shear flow changes the level of TREK1 and PIP2.

Shear was maintained during the fixing. We do not know why shear changes PIP2 and TREK-1 levels. Presumably endocytosis and or release of other lipid modifying enzymes affect the system. The change in TREK-1 levels appears to be directly through an interaction with PLD as TREKtrunc is not affected by over expression of xPLD or mPLD.

For the mechanism of PLD2 activation by shear. I found this section not convincing. Therefore, the question of how does PLD2 sense mechanical force on the membrane is not fully addressed. Particularly, it's hard to imagine an acute 25% decrease cholesterol level by shear - where did the cholesterol go? Details on the measurements of free cholesterol level is unclear and additional/alternative experiments are needed to prove the reduction in cholesterol by shear.

The question “how does PLD2 sense mechanical force on the membrane” we addressed and published in Nature Comm. In 2016. The title of that paper is “Kinetic disruption of lipid rafts is a mechanosensor for phospholipase D” see ref 13 Petersen et. al. PLD is a soluble protein associated to the membrane through palmitoylation. There is no transmembrane domain, which narrows the possible mechanism of its mechanosensation to disruption.

The Nature Comm. reviewer identified as “an expert in PLD signaling” wrote the following of our data and the proposed mechanism:

"This is a provocative report that identifies several unique properties of phospholipase D2 (PLD2). It explains in a novel way some long established observations including that the enzyme is largely regulated by substrate presentation which fits nicely with the authors model of segregation of the two lipid raft domains (cholesterol ordered vs PIP2 containing). Although PLD has previously been reported to be involved in mechanosensory transduction processes (as cited by the authors) this is the first such report associating the enzyme with this type of signaling... It presents a novel model that is internally consistent with previous literature as well as the data shown in this manuscript. It suggests a new role for PLD2 as a force transduction tied to the physical structure of lipid rafts and uses parallel methods of disruption to test the predictions of their model."

Regarding cholesterol. We use a fluorescent cholesterol oxidase assay which we described in the methods. This is an appropriate assay for determining cholesterol levels in a cell which we use routinely. We have published in multiple journals using this method, see references 28, 30, 31. Working out the metabolic fate of cholesterol after sheer is indeed interesting but well beyond the scope of this paper. Furthermore, we indirectly confirmed our finding using dSTORM cluster analysis (Figure 3d-e). The cluster analysis shows a decrease in GM1 cluster size consistent with our previous experiments where we chemically depleted cholesterol and saw a similar decrease in cluster size (see ref 13). All the data are internally consistent, and the cholesterol assay is properly done. We see no reason to reject the data.

Importantly, there is no direct evidence for "shear thinning" of the membrane and the authors should avoid claiming shear thinning in the abstract and summary of the manuscript.

We previously established a kinetic model for PLD2 activation see ref 13 (Petersen et al Nature Comm 2016). In that publication we discussed both entropy and heat as mechanisms of disruption. Here we controlled for heat which narrowed that model to entropy (i.e., shear thinning) (see Figure 3c). We provide an overall justification below. But this is a small refinement of our previous paper, and we prefer not to complicate the current paper. We believe the proper rheological term is shear thinning. The following justification, which is largely adapted from ref 13, could be added to the supplement if the reviewer wishes.

Justification: To establish shear thinning in a biological membrane, we initially used a soluble enzyme that has no transmembrane domain, phospholipase D2 (PLD2). PLD2 is a soluble enzyme and associated with the membrane by palmitate, a saturated 16 carbon lipid attached to the enzyme. In the absence of a transmembrane domain, mechanisms of mechanosensation involving hydrophobic mismatch, tension, midplane bending, and curvature can largely be excluded. Rather the mechanism appears to be a change in fluidity (i.e., kinetic in nature). GM1 domains are ordered, and the palmate forms van der Waals bonds with the GM1 lipids. The bonds must be broken for PLD to no longer associate with GM1 lipids. We established this in our 2016 paper, ref 13. In that paper we called it a kinetic effect, however we did not experimentally distinguish enthalpy (heat) vs. entropy (order). Heat is Newtonian and entropy (i.e., shear thinning) is non-Newtonian. In the current study we paid closer attention to the heat and ruled it out (see Figure 3c and methods). We could propose a mechanism based on kinetic disruption, but we know the disruption is not due to melting of the lipids (enthalpy), which leaves shear thinning (entropy) as the plausible mechanism.

The authors should also be aware that hypotonic shock is a very dirty assay for stretching the cell membrane. Often, there is only a transient increase in membrane tension, accompanied by many biochemical changes in the cells (including acidification, changes of concentration etc). Therefore, I would not consider this as definitive proof that PLD2 can be activated by stretching membrane.

Comment noted. We trust the reviewer is correct. In 1998 osmotic shock was used to activate the channel. We only intended to show that the system is consistent with previous electrophysiologic experiments.

Author response:

Reviewer #1 (Public Review):

Summary:

For many years, there has been extensive electrophysiological research investigating the relationship between local field potential patterns and individual cell spike patterns in the hippocampus. In this study, using state-of-the-art imaging techniques, they examined spike synchrony of hippocampal cells during locomotion and immobility states. In contrast to conventional understanding of the hippocampus, the authors demonstrated that hippocampal place cells exhibit prominent synchronous spikes locked to theta oscillations.

Strengths:

The voltage imaging used in this study is a highly novel method that allows recording not only suprathreshold-level spikes but also subthreshold-level activity. With its high frame rate, it offers time resolution comparable to electrophysiological recordings. Moreover, it enables the visualization of actual cell locations, allowing for the examination of spatial properties (e.g., Figure 4G).

We thank the reviewer for pointing out the technical novelty of this work.

Weaknesses:

There is a notable deviation from several observations obtained through conventional electrophysiological recordings. Particularly, as mentioned below in detail, the considerable differences in baseline firing rates and no observations of ripple-triggered firing patterns raise some concerns about potential artifacts from imaging and analysis, such as cell toxicity, abnormal excitability, and false detection of spikes. While these findings are intriguing if the validity of these methods is properly proven, accepting the current results as new insights is challenging.

We appreciate the reviewer’s insightful comments regarding the intriguing aspect of our findings. Indeed, the emergence of a novel form of CA1 population synchrony presents exciting implications for hippocampal memory research and beyond.

While we acknowledge the deviations from conventional electrophysiological recordings, we respectfully contend that these differences do not necessarily imply methodological flaws. All experiments and analyses were conducted with meticulous adherence to established standards in the field.

Regarding the observed variations in averaging firing rates, it is important to note the well-documented heterogeneity in CA1 pyramidal neuron firing rates, spanning from 0.01 to 10 Hz, with a skewed distribution toward lower frequencies (Mizuseki et al., 2013). Our exclusion criteria for neurons with low estimated firing rates may have inadvertently biased the selection towards more active neurons. Moreover, prior research has indicated that averaging firing rates tend to increase during exposure to novel environments (Karlsson et al., 2008), and among deep-layer CA1 pyramidal neurons (Mizuseki et al., 2011). Given our recording setup in a highly novel environment and the predominance of deep CA1 pyramidal neurons in our sample, the observed higher averaging firing rates could be influenced by these factors. Considering these points, our mean firing rates (3.2 Hz) are reasonable estimations compared to previously reported values obtained from electrophysiological recordings (2.1 Hz in McHugh et al., 1996 and 2.4-2.6 Hz in Buzsaki et al., 2003).

Regarding concerns about potential cell toxicity, previous studies have shown that Voltron expression and illumination do not significantly alter membrane resistance, membrane capacitance, resting membrane potentials, spike amplitudes, and spike width (see Abdelfattah 2019, Science, Supplementary Figure 11 and 12). In our recordings, imaged neurons exhibit preserved membrane and dendritic morphology during and after experiments (Author response image 1), supporting the absence of significant toxicity.

Author response image 1.

Voltron-expressing neurons exhibit preserved membrane and dendritic morphology. (A) Images of two-photon z-stack maximum intensity projection showing Voltron-expressing neurons taken after voltage image experiments in vivo. (B) Post-hoc histological images of neurons being voltage-imaged.

Regarding spike detection, we use validated algorithms (Abdelfattah et al., 2019 and 2023) to ensure robust and reliable detection of spikes. Spiking activity was first separated from slower subthreshold potentials using high-pass filtering. This way, a slow fluorescence increase will not be detected as a spike, even if its amplitude is large. We benchmarked the detection algorithm in computer simulation. The sensitivity and specificity of the algorithm exceed 98% at the level of signal-to-noise ratio of our recordings. While we acknowledge that a small number of spikes, particularly those occurring later in a burst, might be missed due to their smaller amplitudes (as illustrated in Figure 1 and 2 of the manuscript), we anticipate that any missed spikes would lead to a decrease rather than an increase in synchrony between neurons. Overall, we are confident that spike detection is performed in a rigorous and robust manner.

To further strengthen these points, we will include the following in the revision:

(1) Histological images of recorded neurons during and after experiments.

(2) Further details regarding the validation of spike detection algorithms.

(3) Analysis of publicly available electrophysiological datasets.

(4) Discussion regarding the reasons behind the novelty of some of our findings compared to previous observations.

In conclusion, we assert that our experimental and analysis approach upholds rigorous standards. We remain committed to reconciling our findings with previous observations and welcome further scrutiny and engagement from the scientific community to explore the intriguing implications of our findings.

Reviewer #2 (Public Review):

Summary:

This study employed voltage imaging in the CA1 region of the mouse hippocampus during the exploration of a novel environment. The authors report synchronous activity, involving almost half of the imaged neurons, occurred during periods of immobility. These events did not correlate with SWRs, but instead, occurred during theta oscillations and were phased-locked to the trough of theta. Moreover, pairs of neurons with high synchronization tended to display non-overlapping place fields, leading the authors to suggest these events may play a role in binding a distributed representation of the context.

We thank the reviewer for a thorough and thoughtful review of our paper.

Strengths:

Technically this is an impressive study, using an emerging approach that allows single-cell resolution voltage imaging in animals, that while head-fixed, can move through a real environment. The paper is written clearly and suggests novel observations about population-level activity in CA1.

We thank the reviewer for pointing out the technical strength and the novelty of our observations.

Weaknesses:

The evidence provided is weak, with the authors making surprising population-level claims based on a very sparse data set (5 data sets, each with less than 20 neurons simultaneously recorded) acquired with exciting, but less tested technology. Further, while the authors link these observations to the novelty of the context, both in the title and text, they do not include data from subsequent visits to support this. Detailed comments are below:

We understand the reviewer’s concerns regarding the size of the dataset. Despite this limitation, it is important to note that synchronous ensembles beyond what could be expected from chance (jittering) were detected in all examined data. In the revision, we plan to add more data, including data from subsequent visits, to further strengthen our findings.

(1) My first question for the authors, which is not addressed in the discussion, is why these events have not been observed in the countless extracellular recording experiments conducted in rodent CA1 during the exploration of novel environments. Those data sets often have 10x the neurons simultaneously recording compared to these present data, thus the highly synchronous firing should be very hard to miss. Ideally, the authors could confirm their claims via the analysis of publicly available electrophysiology data sets. Further, the claim of high extra-SWR synchrony is complicated by the observation that their recorded neurons fail to spike during the limited number of SWRs recorded during behavior- again, not agreeing with much of the previous electrophysiological recordings.

We understand the reviewer’s concern. We will examine publicly available electrophysiology datasets to gain further insights into any similarities and differences to our findings. Based on these results, we will discuss why these events have not been previously observed/reported.

(2) The authors posit that these events are linked to the novelty of the context, both in the text, as well as in the title and abstract. However, they do not include any imaging data from subsequent days to demonstrate the failure to see this synchrony in a familiar environment. If these data are available it would strengthen the proposed link to novelty if they were included.

We thank the reviewer’s constructive suggestion. We will acquire more datasets from subsequent visits to gain further insights into these synchronous events.

3) In the discussion the authors begin by speculating the theta present during these synchronous events may be slower type II or attentional theta. This can be supported by demonstrating a frequency shift in the theta recording during these events/immobility versus the theta recording during movement.

We thank the reviewer’s constructive suggestion. We did demonstrate a frequency shift to a lower frequency in the synchrony-associated theta during immobility than during locomotion (see Fig. 4B, the red vs. blue curves). We will enlarge this panel and specifically refer to it in the corresponding discussion paragraph.

(4) The authors mention in the discussion that they image deep-layer PCs in CA1, however, this is not mentioned in the text or methods. They should include data, such as imaging of a slice of a brain post-recording with immunohistochemistry for a layer-specific gene to support this.

We thank the reviewer’s constructive suggestion. We do have images of brain slices post-recordings (Author response image 2). Imaged neurons are clearly located in the deep CA1 pyramidal layer. We will add these images and quantification in the revised manuscript.

Author response image 2.

Imaged neurons are located in the deep pyramidal layer of the dorsal hippocampal CA1 region.

Reviewer #3 (Public Review):

Summary:

In the present manuscript, the authors use a few minutes of voltage imaging of CA1 pyramidal cells in head-fixed mice running on a track while local field potentials (LFPs) are recorded. The authors suggest that synchronous ensembles of neurons are differentially associated with different types of LFP patterns, theta and ripples. The experiments are flawed in that the LFP is not "local" but rather collected in the other side of the brain, and the investigation is flawed due to multiple problems with the point process analyses. The synchrony terminology refers to dozens of milliseconds as opposed to the millisecond timescale referred to in prior work, and the interpretations do not take into account theta phase locking as a simple alternative explanation.

We genuinely appreciate the reviewer’s feedback and acknowledge the concerns raised. However, we believe these concerns can be effectively addressed without undermining the validity of our conclusions. With this in mind, we respectfully disagree with the assessment that our experiments and investigation are flawed. Please allow us to address these concerns and offer additional context to support the validity of our study.

Weaknesses:

The two main messages of the manuscript indicated in the title are not supported by the data. The title gives two messages that relate to CA1 pyramidal neurons in behaving head-fixed mice: (1) synchronous ensembles are associated with theta (2) synchronous ensembles are not associated with ripples.

There are two main methodological problems with the work:

(1) Experimentally, the theta and ripple signals were recorded using electrophysiology from the opposite hemisphere to the one in which the spiking was monitored. However, both signals exhibit profound differences as a function of location: theta phase changes with the precise location along the proximo-distal and dorso-ventral axes, and importantly, even reverses with depth. And ripples are often a local phenomenon - independent ripples occur within a fraction of a millimeter within the same hemisphere, let alone different hemispheres. Ripples are very sensitive to the precise depth - 100 micrometers up or down, and only a positive deflection/sharp wave is evident.

We appreciate the reviewer’s consideration regarding the collection of LFP from the contralateral hemisphere. While we acknowledge the limitation of this design, we believe that our findings still offer valuable insights into the dynamics of synchronous ensembles. Despite potential variations in theta phases with recording locations and depth, we find that the occurrence and amplitudes of theta oscillations are generally coordinated across hemispheres (Buzsaki et al., Neurosci., 2003). Therefore, the presence of prominent contralateral LFP theta around the times of synchronous ensembles in our study (see Figure 4A of the manuscript) strongly supports our conclusion regarding their association with theta oscillations, despite the collection of LFP from the opposite hemisphere.

In addition, in our manuscript, we specifically mentioned that the “preferred phases” varied from session to session, likely due to the variability of recording locations (see Line 254-256). Therefore, we think that the reviewer’s concern regarding theta phase variability has already been addressed in the present manuscript.

Regarding ripple oscillations, while we recognize that they can sometimes occur locally, the majority of ripples occur synchronously in both hemispheres (up to 70%, see Szabo et al., Neuron, 2022; Buzsaki et al., Neurosci., 2003). Therefore, using contralateral LFP to infer ripple occurrence on the ipsilateral side has been a common practice in the field, employed by many studies published in respectable journals (Szabo et al., Neuron, 2022; Terada et al., Nature, 2021; Dudok et al., Neuron, 2021; Geiller et al., Neuron, 2020). Furthermore, our observation that 446 synchronous ensembles during immobility do not co-occur with contralateral ripples, and the remaining 313 ensembles during locomotion are not associated with ripples, as ripples rarely occur during locomotion. Therefore, our conclusion that synchronous ensembles are not associated with ripple oscillations is supported by data.

(2) The analysis of the point process data (spike trains) is entirely flawed. There are many technical issues: complex spikes ("bursts") are not accounted for; differences in spike counts between the various conditions ("locomotion" and "immobility") are not accounted for; the pooling of multiple CCGs assumes independence, whereas even conditional independence cannot be assumed; etc.

We acknowledge the reviewer’s concern regarding spike train analysis. Indeed, complex bursts or different behavioral conditions can lead to differences in spike counts that could potentially affect the detection of synchronous ensembles. However, our jittering procedure (see Line 121-132) is designed to control for the variation of spike counts. Importantly, while the jittered spike trains also contain the same spike count variations, we found 7.8-fold more synchronous events in our data compared to jitter controls (see Figure 1G of the manuscript), indicating that these factors cannot account for the observed synchrony.

To explicitly demonstrate that complex bursts cannot account for the observed synchrony, we have performed additional analysis to remove all latter spikes in bursts and only count the single and the first spikes of bursts. Importantly, we found that this procedure did not change the rate and size of synchronous ensembles, nor did it significantly alter the grand-average CCG (see Author response image 3). The results of this analysis explicitly rule out a significant effect of complex spikes on the analysis of synchronous ensembles.

Author response image 3.

Population synchrony remains after the removal of spikes in bursts. (A) The grand-average cross correlogram (CCG) was calculated using spike trains without latter spikes in bursts. The gray line represents the mean grand average CCG between reference cells and randomly selected cells from different sessions. (B) Pairwise comparison of the event rates of population synchrony between spike trains containing all spikes and spike trains without latter spikes in bursts. Bar heights indicate group means (n=10 segments, p=0.036, Wilcoxon signed-rank test). (C) Histogram of the ensemble sizes as percentages of cells participating in the synchronous ensembles.

Beyond those methodological issues, there are two main interpretational problems: (1) the "synchronous ensembles" may be completely consistent with phase locking to the intracellular theta (as even shown by the authors themselves in some of the supplementary figures).

We agree with the reviewer that the synchronous ensembles are indeed consistent with theta phase locking. However, it is important to note that theta phase locking alone does not necessarily imply population synchrony. In fact, theta phase locking has been shown to “reduce” population synchrony in a previous study (Mizuseki et al., 2014, Phil. Trans. R. Soc. B.). Thus, the presence of theta phase locking cannot be taken as a simple alternative explanation of the synchronous ensembles.

To directly assess the contribution of theta phase locking to synchronous ensembles, we have performed a new analysis to randomize the specific theta cycles in which neurons spike, while keeping the spike phases constant. This manipulation disrupts spike co-occurrence while preserving theta phase locking, allowing us to test whether theta phase locking alone can explain the population synchrony, or whether spike co-occurrence in specific cycles is required. The grand-average CCG shows a much smaller peak compared to the original peak (Author response image 4A). Moreover, synchronous event rates show a 4.5-fold decrease in the randomized data compared to the original event rates (Author response image 4B). Thus, the new analysis reveals theta phase locking alone cannot account for the population synchrony.

Author response image 4.

Drastic reduction of population synchrony by randomizing spikes to other theta cycles while preserving the phases. (A) The grand-average cross correlogram (CCG) was calculated using original spike trains (black) and randomized spike trains where theta phases of the spikes are kept the same but spike timings were randomly moved to other theta cycles (red). (B) Pairwise comparison of the event rates of population synchrony between the original spike trains and randomized spike trains (n=10 segments, p=0.002, Wilcoxon signed-rank test). Bar heights indicate group means. ** p<0.01

(2) The definition of "synchrony" in the present work is very loose and refers to timescales of 20-30 ms. In previous literature that relates to synchrony of point processes, the timescales discussed are 1-2 ms, and longer timescales are referred to as the "baseline" which is actually removed (using smoothing, jittering, etc.).

Regarding the timescale of synchronous ensembles, we acknowledge that it varies considerably across studies and cell types. However, it is important to note that a timescale of dozens, or even hundreds of milliseconds is common for synchrony terminology in CA1 pyramidal neurons (see Csicsvari et al., Neuron, 2000; Harris et al., Science, 2003; Malvache et al., Science, 2016; Yagi et al., Cell Reports, 2023). In fact, a timescale of 20-30 ms is considered particularly important for information transmission and storage in CA1, as it matches the membrane time constant of pyramidal neurons, the period of hippocampal gamma oscillations, and the time window for synaptic plasticity. Therefore, we believe that this timescale is relevant and in line with established practices in the field.

Author response:

eLife Assessment

This useful study integrates experimental methods from materials science with psychophysical methods to investigate how frictional stabilities influence tactile surface discrimination. The authors argue that force fluctuations arising from transitions between frictional sliding conditions facilitate the discrimination of surfaces with similar friction coefficients. However, the reliance on friction data obtained from an artificial finger, together with the ambiguous correlative analyses relating these measurements to human psychophysics, renders the findings incomplete.

Our main goal with this paper was to show that the most common metric, i.e. average friction coefficient—widely used in tactile perception and device design—is fundamentally unsound, and to offer a secondary parameter that is compatible with the fact that human motion is unconstrained, leading to dynamic interfacial mechanics. In contrast with the summary assessment, we also note that the average friction coefficients in our study were not particularly similar, ranging from differences of 0.4 – 1, a typical range seen in most studies. We believe some of the comments originate from a misinterpretation of our statistically significant, but negative correlation between human results and friction coefficients – which leads to the spurious conclusion that nearly identical objects should be very easy to tell apart, thus supporting our central argument for the need of an alternative. We understand the Reviewers wanting to see that we can demonstrate that humans using instabilities in situ. This is seemingly reasonable, but we explain the significant challenges and fundamental unknowns to those experiments. However, we modified our title to reflect our focus on offering an alternative to the average coefficient of friction.

We do not think it was feasible, at this stage, to demonstrate that humans use friction instabilities through direct manipulation and observation in human participants. In short, there are still several fundamental unknowns: (1) a decision-making model would need to be created, but it is unknown if tactile decision making follows other models, (2) it is further unknown what constitutes “tactile evidence”, though at our manuscript’s conclusion, we propose that friction instabilities are better suited for to be tactile evidence than the averaging of friction coefficients from a narrow range of human exploration (3) in the design of samples, from a friction mechanics and materials perspective, it is not at this point, possible to pre-program surfaces a priori to deliver friction instabilities and instead must be experimentally determined – especially when attempting to achieve this in controlled surfaces that do not create other overriding tactile cues, like macroscopic bumps or large differences in surface roughness. (4) Given that the basis for tactile percepts, like which object feels “rougher” or “smoother” is not sufficiently established and we have seen leads to confusion, it is necessary to use a 3-alternative forced choice task which avoids asking objects along a preset perceptual dimension – a challenge recognized by Reviewer 3. However, this would bring in issues of memory in the decision-making model. (5) The prior points are compounded by the fact that, we believe, tactile exploration must be performed in an unconstrained manner, i.e., without an apparatus generating motion onto a stationary finger. Work by Liu et al. (IEEE ToH, 2024) showed that recreating friction obtained during free exploration onto a stationary finger was uninterpretable by the participants, hinting at the importance of efference copies(1). We believe that each of the above-mentioned issues constitutes a significant advance in knowledge and would require discussion and dissemination with the community. Finally, one of our overarching goals is to create a consistent method to characterize surfaces, and given individual variability in human fingers and motion, a machine-based method that can rapidly, consistently, and sufficiently replicate tactile exploration is needed.

Finally, we also justify our use of a mock finger to provide a method to characterize surfaces in tactile studies that other researchers could reasonably recreate, without creating a standard around individual humans, considering the variability in finger shape and motion during exploration. We do not believe this is an “either-or” argument, but rather that standardized methods to characterize surfaces and devices are greatly needed in the field. From these standardized methods, like surface roughness, some tabulated values of friction coefficient, or surface energy, etc., the current metrics to parameterize results are largely incapable of capturing the dynamic changes in forces expected during human tactile exploration.

Our changes to the manuscript (Page 1 & SI Page 1, Title)

“Alternatives to Friction Coefficient: Role of Frictional Instabilities for Fine Touch Perception”

Reviewer 1 (Public review):

Summary:

In this paper, Derkaloustian et. al look at the important topic of what affects fine touch perception. The observations that there may be some level of correlation with instabilities are intriguing. They attempted to characterize different materials by counting the frequency (occurrence #, not of vibration) of instabilities at various speeds and forces of a PDMS slab pulled lengthwise over the material. They then had humans make the same vertical motion to discriminate between these samples. They correlated the % correct in discrimination with differences in frequency of steady sliding over the design space as well as other traditional parameters such as friction coefficient and roughness. The authors pose an interesting hypothesis and make an interesting observation about the occurrences of instability regimes in different materials while in contact with PDMS, which is interesting for the community to see in the publication. It should be noted that the finger is complex, however, and there are many factors that may be quite oversimplified with the use of the PDMS finger, and the consideration and discounting of other parameters are not fully discussed in the main text or SI. Most importantly, however, the conclusions as stated do not align with the primary summary of the data in Figure 2.

Strengths:

The strength of this paper is in its intriguing hypothesis and important observation that instabilities may contribute to what humans are detecting as differences in these apparently similar samples.

We thank Reviewer 1 for their time on the manuscript, recognizing the approach we took, and offering constructive feedback. We believe that our conclusions, in fact, are supported by the primary summary of the data in Figure 2 but we believe that our use of R² could have led to misinterpretation. The trend with friction coefficient and percent correct was indeed statistically significant but was spurious because the slope was negative. In the revision, we add clarifying comments throughout, change from R² to r as to highlight the negative trend, and adjust the figures to better focus on friction coefficient.

Finally, we added a new section to discuss the tradeoffs between using a real human finger versus a mock finger, and which situations may warrant the use of one or the other. In short, for our goal of characterizing surfaces to be used in tactile experiments, we believe a mock finger is more sustainable and practical than using real humans because human fingers are unique per participant, humans move their fingers at constantly changing pressures and velocities, and friction generated during free exploring human cannot be satisfactorily replicated by moving a sample onto a stationary finger. But, we do not disagree that for other types of experiments, characterizing a human participant directly may be more advantageous.

Weaknesses:

Comment 1 - The most important weakness is that the findings do not support the statements of findings made in the abstract. Of specific note in this regard is the primary correlation in Figure 2B between SS (steady sliding) and percent correct discrimination. Of specific note in this regard is the primary correlation in Figure 2B between SS (steady sliding) and percent correct discrimination. While the statistical test shows significance (and is interesting!), the R-squared value is 0.38, while the R-squared value for the "Friction Coefficient vs. Percent Correct" plot has an R-squared of 0.6 and a p-value of < 0.01 (including Figure 2B). This suggests that the results do not support the claim in the abstract: "We found that participant accuracy in tactile discrimination was most strongly correlated with formations of steady sliding, and response times were negatively correlated with stiction spikes. Conversely, traditional metrics like surface roughness or average friction coefficient did not predict tactile discriminability."

We disagree that the trend with friction coefficient suggests the results do not support the claim because the correlation was found to be negative. However, we could have made the comparison more apparent and expanded on this point, given its novelty.

While the R² value corresponding to the “Friction Coefficient vs. Percent Correct” plot is notably higher, our results show that the slope is negative, which would be statistically spurious. This is because a negative correlation between percent correct (accuracy in discriminating surfaces) and difference in friction coefficient means that the more similar two surfaces are (by friction coefficient), the easier it would be for people to tell them apart. That is, it incorrectly concludes that two identical surfaces would be much easier to tell apart than two surfaces with greatly different friction coefficients.

This is counterintuitive to nearly all existing results, but we believe our samples were well-positioned to uncover this trend by minimizing variability, by controlling multiple physical parameters in the samples, and that the friction coefficient — typically calculated in the field as an average friction coefficient — ignores all the dynamic changes in forces present in elastic systems undergoing mesoscale friction, i.e., human touch, as seen in Fig. 1 in a mock finger and Fig. 3 in a real finger. By demonstrating this statistically spurious trend, we believe this strongly supports our premise that an alternative to friction coefficient is needed in the design of tactile psychophysics and haptic interfaces.

We believe that this could have been misinterpreted, so we took several steps to improve clarity, given the importance of this finding: we separated the panel on friction coefficient to its own panel, we changed from R² to r throughout, and we added clarifying text. We also added a small section focusing on this spurious trend.

Our changes to the manuscript (Page 10)

“To compare the value of looking at frictional instabilities, we also performed GLMM fits on common approaches in the field, like a friction coefficient or material property typically used in tactile discrimination, shown in Fig. 2D-E. Interestingly, in Fig. 2D, we observed a spurious, negative correlation between friction coefficient (typically and often problematically simplified as across all tested conditions) and accuracy (r = -0.64, p < 0.01); that is, the more different the surfaces are by friction coefficient, the less people can tell them apart. This spurious correlation would be the opposite of intuition, and further calls into question the common practice of using friction coefficients in touch-related studies. The alternative, two-term model which includes adhesive contact area for friction coefficient(29) was even less predictive (see Fig. S6A of SI). We believe such a correlation could not have been uncovered previously as our samples are minimal in their physical variations. Yet, the dynamic changes in force even within a single sample are not considered, despite being a key feature of mesoscale friction during human touch.

We investigate different material properties in Fig. 2E. Differences in average roughness R_a (or other parameters, like root mean square roughness R_rms (Fig. S6A of SI) did not show a statistically significant correlation to accuracy. Though roughness is a popular parameter, correlating any roughness parameter to human performance here could be moot: the limit of detecting roughness differences has previously been defined as 13 nm on structured surfaces33 and much higher for randomly rough surfaces,(46) all of which are magnitudes larger than the roughness differences between our surfaces. The differences in contact angle hysteresis – as an approximation of the adhesion contributions(47) – do not present any statistically significant effects on performance.”

Comment 2, Part 1

Along the same lines, other parameters that were considered such as the "Percent Correct vs. Difference in Sp" and "Percent Correct vs. Difference in SFW" were not plotted for consideration in the SI. It would be helpful to compare these results with the other three metrics in order to fully understand the relationships.

We have added these plots to the SI. We note that we had checked these relationships and discussed them briefly, but did not include the plot. The plots show that the type of instability was not as helpful as its presence or absence.

Our changes to the manuscript (Page 9)

“Furthermore, a model accounting for slow frictional waves alone specifically shows a significant, negative effect on performance (p < 0.01, Fig. S5 of SI), suggesting that in these samples and task, the type of instability was not as important.”

Added (SI Page 4)

“and no correlation between accuracy and stiction spikes (Fig. S5).”

Comment 2, Part 2

Other parameters such as stiction magnitude and differences in friction coefficient over the test space could also be important and interesting.

We agree these are interesting and have thought about them. We are aware that others, like Gueorguiev et al., have studied stiction magnitudes, and though there was a correlation, the physical differences in surface roughness (glass versus PMMA) investigated made it unclear if these could be generalized further(2). We are unsure how to proceed here with a satisfactory analysis of stiction magnitude, given that stiction spikes are not always generated. In fact, Fig. 1 shows that for many velocities and pressures, they do not form. However, we offer some speculation on why stiction spikes may be overrepresented in the literature because:

(1) They are prone to being created if the finger was loaded for a long time onto a surface prior to movement, thus creating adhesion by contact aging which is unlike active human exploration. We avoid this by discarding the first pull in our measurements, and is a standard practice in mechanical characterization if contact aging needs to be avoided.

(2) The ranges of velocities and pressures explored were small.

(3) In an effort to generate strong tactile stimuli, highly adhesive or rough surfaces are used.

(4) They are visually distinctive on a plot, but we are unaware of any mechanistic reason that mechanoreceptors would be extremely sensitive to this low frequency event over other signals.

In ongoing work, however, we are always cognizant that if stiction spikes are a dominant factor, then a secondary analysis on their magnitude would be important.

We interpret “difference in friction coefficient over the test space” to be, for a single surface, like C4, to find the highest average friction for a condition of single velocity and mass and subtract that from the lowest average friction for a condition of single velocity and mass. We calculated the difference in friction coefficient in the typical manner of the field, by averaging all data collected at all velocities and masses and assigning a single value for all of a surface, like C4. We had performed this, and have the data, but we are wary of overinterpreting secondary and tertiary metrics because they do not have any fundamental basis in traditional tribology, and this value, if used by humans, would suggest that they rapidly explore a large parameter space to find a “maximum” and “minimum” friction. Furthermore, the range in friction across the test space, after averaging, may in fact, be smaller than the range of friction in a single measurement. For example, in Fig. 1B, the friction coefficient can be calculated by dividing the data by the normal force ([applied mass + 6 g finger] × gravity). The friction coefficient in a single run varies widely, as expected.

Fig. 2D shows a GLMM fit between percent correct responses across our pairs and the differences in friction coefficient for each pair, where we see a spurious negative correlation. As we had the data of all average friction coefficients for each condition for a given material, we also looked at the difference in maximum and minimum friction coefficients. For our tested pairs, these differences also lined up on a statistically significant, negative GLMM fit (r = -0.86, p < 0.005). However, the values for a given surface can vary drastically, with an interquartile range of 1.20 to 2.09 on a single surface. We fit participant accuracy to the differences in these IQRs across pairs. This also led to a negative GLMM fit (r = -0.65, p < 0.05). However, we are hesitant to add this to the manuscript for the reasons stated previously.

Comment 3, Part 1

Beyond this fundamental concern, there is a weakness in the representativeness of the PDMS finger, the vertical motion, and the speed of sliding to real human exploration.

Overall, this is a continuous debate that we think offers two solutions. There is always a tradeoff between using a synthetic model of a finger versus a real human finger, and there is a place for both models. That is, while our mock finger will be more successful the closer it is to a human finger, it is not our goal to fully replace a human finger, rather our goal is to provide a method of characterizing surfaces that is indeed relevant on the length scale of human touch.

The usefulness of the mock finger is in isolating the features of each surface that is independent of human variability, i.e., instabilities that form without changing loading conditions between sliding motions or even within one sliding motion. Of course, with this method, we still require confirmation of these features still forming during human exploration, which we show in Fig. 3.

We believe that this method of characterizing surfaces at the mesoscale will ultimately lead to more successful human studies on tactile perception. Currently, and as shown in the paper, characterizing surfaces through traditional techniques, such as a commercial tribometer (friction coefficient, using a steel or hard metal ball), roughness (via atomic force microscopy or some other metrology), surface energy are less predictive. Thus, we believe this mock finger is stronger than the current state-of-the-art characterizing surfaces (we are also aware of a commercial mock finger company, but we were unable to purchase or obtain an evaluation model).

One of the main – and severe – limitations of using a human finger is that all fingers are different, meaning any study focusing on a particular user may not apply to others or be recreated easily by other researchers. We cannot set a standard for replication around a real human finger as that participant may no longer be available, or willing to travel the world as a “standard”. Furthermore, the method in which changes their pressures and velocities is different. We note that this is a challenge unique to touch perception – how an object is touched changes the friction generated, and thus the tactile stimulus generated, whereas a standardized stimulus is more straightforward for light or sound.

However, we do emphasize that we have strongly considered the balance between feasibility and ecological validity in the design of a mock finger. We have a mock finger, with the three components of stiffness of a human finger (more below). Furthermore, we have also successfully used this mock finger in correlations with human psychophysics in previous work, where findings from our mechanical experiments were predictive of human performance(3-6).

Our changes to the manuscript Added (Page 2-3)

“Mock finger as a characterization tool

In this work, we use a mechanical setup with a PDMS mock finger to derive tactile predictors from controlled friction traces alternative to average friction coefficients. While there is a tradeoff in selecting a synthetic finger over a more accurate, real human finger in modeling touch, our aim to design a method of mesoscale surface characterization for more successful studies on tactile perception cannot be fulfilled using one human participant as a standard. We believe that with sufficient replication of surface and bulk properties as well as contact geometry, and controlled friction measurements collected at loading conditions observed during a tactile discrimination task, we can isolate unique frictional features of a set of surfaces that do not arise from human-to-human variability.

The major component of a human finger, by volume, is soft tissue (~56%)(22), resulting in an effective modulus close to 100 kPa(23,24). In order to achieve this same softness, we crosslink PDMS in a 1×1×5 cm mold at a 30:1 elastomer:crosslinker ratio. However, two more features impart increased stiffness in a human finger. Most of this added rigidity is derived from the bone at the fingertip, the distal phalanx(23–25), which we mimic with an acrylic bone within our PDMS network. The stratum corneum, the stiffer, glassier outer layer of skin(26), is replicated with the surface of the mock finger glassified, or further crosslinked, after 8 hours of UV-Ozone treatment(27). This treatment also modifies the surface properties of the native PDMS to align with those of a human finger more closely. It minimizes the viscoelastic tack at the surface, resulting in a comparable non-sticky surface. At least one day after treatment, the finger surface returns to moderate hydrophilicity (~60º), as is typically observed for a real finger(28).

The initial contact area formed before a friction trace is collected is a rectangle of 1×1 cm. While this shape is not entirely representative of a human finger with curves and ridges, human fingers flatten out enough to reduce the effects of curvature with even very light pressures(28–30). This implies that regardless of finger pressure, the contact area is largely load-independent, which is more accurately replicated with a rectangular mock finger. It is still a challenge to control pressure distribution with this planar interface, but non-uniform pressures are also expected during human exploration.

Lastly, we consider fingerprints vs. flat fingers. A key finding of our previous work is that while fingerprints enhanced frictional dynamics at certain conditions, key features were still maintained with a flat finger.7 Furthermore, for some loading conditions, the more amplified signals could also result in more similar friction traces for different surfaces. We have continued to use flat fingers in our mechanical experiments, and have observed good agreement between these friction traces and human experiments(7,8,21,31).”

(Page 3-4, Materials and Methods)

“Mock Finger Preparation

Friction forces across all six surfaces were measured using a custom apparatus with a polydimethylsiloxane (PDMS, Dow Sylgard 184) mock finger that mimics a human finger’s

mechanical properties and contact mechanics while exploring a surface relatively closely(7,8). PDMS and crosslinker were combined in a 30:1 ratio to achieve a stiffness of 100 kPa comparable to a real finger, then degassed in a vacuum desiccator for 30 minutes. We are aware that the manufacturer recommended crosslinking ratio for Sylgard 184 is 10:1 due to potential uncrosslinked liquid residues(32), but further crosslinking concentrated at the surface prevents this. The prepared PDMS was then poured into a 1×1×5 cm mold also containing an acrylic 3D-printed “bone” to attach applied masses on top of the “fingertip” area contacting a surface during friction testing. After crosslinking in the mold at 60ºC for 1 hour, the finger was treated with UV-Ozone for 8 hours out of the mold to minimize viscoelastic tack.

Mechanical Testing

A custom device using our PDMS mock finger was used to collect macroscopic friction force traces replicating human exploration(7,8). After placing a sample surface on a stage, the finger was lowered at a slight angle such that an initial 1×1 cm rectangle of “fingertip” contact area could be established. We considered a broad range of applied masses (M \= 0, 25, 75, and 100 g) added onto the deadweight of the finger (6 g) observed during a tactile discrimination task. The other side of the sensor was connected to a motorized stage (V-508 PIMag Precision Linear Stage, Physikinstrumente) to control both displacement (4 mm across all conditions) and sliding velocity (v \= 5, 10, 25, and 45 mm s^-1). Forces were measured at all 16 combinations of mass and velocity via a 250 g Futek force sensor (k \= 13.9 kN m^-1) threaded to the bone, and recorded at an average sampling rate of 550 Hz with a Keithley 7510 DMM digitized multimeter. Force traces were collected in sets of 4 slides, discarding the first due to contact aging. Because some mass-velocity combinations were near the boundaries of instability phase transitions, not all force traces at these given conditions exhibited similar profiles.

Thus, three sets were collected on fresh spots for each condition to observe enough occurrences of multiple instabilities, at a total of nine traces per combination for each surface.”

Added References (Page 13)

M. Murai, H.-K. Lau, B. P. Pereira and R. W. H. Pho, J. Hand Surg., 1997, 22, 935–941.

A. Abdouni, M. Djaghloul, C. Thieulin, R. Vargiolu, C. Pailler-Mattei and H. Zahouani, R. Soc. Open Sci., DOI:10.1098/rsos.170321.

P.-H. Cornuault, L. Carpentier, M.-A. Bueno, J.-M. Cote and G. Monteil, J. R. Soc. Interface, DOI:10.1098/rsif.2015.0495.

K. Qian, K. Traylor, S. W. Lee, B. Ellis, J. Weiss and D. Kamper, J. Biomech., 2014, 47, 3094– 3099.

Y. Yuan and R. Verma, Colloids Surf. B Biointerfaces, 2006, 48, 6–12.

Y.-J. Fu, H. Qui, K.-S. Liao, S. J. Lue, C.-C. Hu, K.-R. Lee and J.-Y. Lai, Langmuir, 2010, 26, 4392–4399.

Comment 3, Part 2

“The real finger has multiple layers with different moduli. In fact, the stratum corneum cells, which are the outer layer at the interface and determine the friction, have a much higher modulus than PDMS. The real finger has multiple layers with different moduli. In fact, the stratum corneum cells, which are the outer layer at the interface and determine the friction, have a much higher modulus than PDMS.

We have approximated the softness of the finger with 100 kPa crosslinked PDMS, which is close to what has been reported for the bulk of a human fingertip(8,9). However, as mentioned in the Materials and Methods, there are two additional features of the mock finger that impart greater strength. The PDMS surrounds a rigid, acrylic bone comparable to the distal phalanx, which provides an additional layer of higher modulus(10). Additionally, the 8-hour UV-Ozone treatment decreases the viscoelastic tack of the pristine PDMS by glassifying, or further crosslinking the surface of the finger(11), therefore imparting greater stiffness at the surface similar to the contributions of the stratum corneum, along with a similar surface energy(12). This technique is widely used in wearables(13), soft robotics(14), and microfluidics(15) to induce both these material changes. Additionally, the finger is used at least a day after UV-Ozone treatment is completed in order for the surface to return to moderate hydrophilicity, similar to the outermost layer of human skin(16).

Comment 3, Part 3

In addition, the slanted position of the finger can cause non-uniform pressures across the finger. Both can contribute to making the PDMS finger have much more stick-slip than a real finger.

To ensure that there is minimal contribution from the slanted position of the finger, an initial contact area of 1×1 cm is established before sliding and recording friction measurements. As the PDMS finger is a soft object, the portion in contact with a surface flattens and the contact area remains largely unchanged during sliding. Any additional stick-slip after this alignment step is caused by contact aging at the interface, but the first trace we collect is always discarded to only consider stick-slip events caused by surface chemistry. We recognize that it is difficult to completely control the pressure distribution due to the planar interface, but this is also expected when humans freely explore a surface.

Comment 3, Part 4

In fact, if you look at the regime maps, there is very little space that has steady sliding. This does not represent well human exploration of surfaces. We do not tend to use a force and velocity that will cause extensive stick-slip (frequent regions of 100% stick-slip) and, in fact, the speeds used in the study are on the slow side, which also contributes to more stick-slip. At higher speeds and lower forces, all of the materials had steady sliding regions.

We are not aware of published studies that extensively show that humans avoid stickslip regimes. In fact, we are aware familiar with literature where stiction spike formation is suppressed – a recent paper by AliAbbasi, Basdogan et. al. investigates electroadhesion and friction with NaCl solution-infused interfaces, resulting in significantly steadier forces(17). We also directly showed evidence of instability formation that we observed during human exploration in Fig. 3B-C. These dynamic events are common, despite the lack of control of normal forces and sliding velocities. We also note that Reviewer 1, Comment 2, was suggesting that we further explore possible trends from parameterizing the stiction spike.

We note that many studies have often not gone at the velocities and masses required for stiction spikes – even though these masses and velocities would be routinely seen in free exploration – this is usually due to constraints of equipment(18). Sliding events during human free exploration of surfaces can exceed 100 mm/s for rapid touches. However, for the surfaces investigated here, we observe that large regions of stick-slip can emerge at velocities as low as 5 mm/s depending on the applied load. The incidence of steady sliding appears more dependent on the applied mass, with almost no steady sliding observed at or above 75 g. Indeed, the force categorization along our transition zones is the main point of the paper.

Comment 3, Part 5

Further, on these very smooth surfaces, the friction and stiction are more complex and cannot dismiss considerations such as finger material property change with sweat pore occlusion and sweat capillary forces. Also, the vertical motion of both the PDMS finger and the instructed human subjects is not the motion that humans typically use to discriminate between surfaces.

We did not describe the task sufficiently. Humans were only given the instruction to slide their finger along a single axis from top to bottom of a sample, not vertical as in azimuthal to gravity. We have updated our wording in the manuscript to reflect this.

Our changes to the manuscript (Page 4)

“Participants could touch for as long as they wanted, but were asked to only use their dominant index fingers along a single axis to better mimic the conditions for instability formation during mechanical testing with the mock finger.”

(Page 11)

“The participant was then asked to explore each sample simultaneously, and ran over each surface in strokes along a single axis until the participant could decide which of the two had “more friction”.”

Comment 3, Part 6

Finally, fingerprints may not affect the shape and size of the contact area, but they certainly do affect the dynamic response and detection of vibrations.

We are aware of the nuance. Our previous work on the role of fingerprints on friction experienced by a PDMS mock finger showed enhanced signals with the incorporation of ridges on the finger and used a rate-and-state model of a heterogenous, elastic body to find corresponding trends (though there is no existing model of friction that can accurately model experiments on mesoscale friction)(7). The key conclusion was that a flat finger still preserved key dynamic features, and the presence of stronger or more vibrations could result in more similar forces for different surfaces depending on the sliding conditions.

This is also in the context that we are seeking to provide a reasonable and experimentally accessible method to characterize surfaces, which will always be better as we get closer in replicating a true human finger. But our goal here was to replicate the finger sufficiently for use in human studies. We believe the more appropriate metric of success is if the mock finger is more successful than replacing traditional characterization experiments, like friction coefficient, roughness, surface energy, etc.

Comment 4

This all leads to the critical question, why are friction, normal force, and velocity not measured during the measured human exploration and in a systematic study using the real human finger? The authors posed an extremely interesting hypothesis that humans may alter their speed to feel the instability transition regions. This is something that could be measured with a real finger but is not likely to be correlated accurately enough to match regime boundaries with such a simplified artificial finger.

We are excited that our manuscript offers a tractable manner to test the hypothesis that tactile decision-making models use friction instabilities as evidence. However, we lay out the challenges and barriers, and how the scope of this paper will lead us in that direction. We also clarify that our goals are to provide a method to characterize samples to better design tactile interfaces in haptics or in psychophysical experiments and raise awareness that the common methods of sample characterization in touch by an average friction coefficient or roughness is fundamentally unsound.

In short, in our view, to further support our findings on instabilities would require answering:

(1) Which one, or combination of, of the multiple swipes that people make responsible for a tactile decision? (The need for a decision-making model)

(2) Establish what is, or may be, tactile evidence.

(3) Establish tactile decision-making models are similar or different than existing decision-making models.

(4) Test the hypothesis, in these models, that friction instabilities are evidence, and not some other unknown metric. This requires design samples that vary in the amount of evidence generated, but this evidence cannot be controlled directly. Rather, the samples indirectly vary evidence by how likely it is for a human to generate different types of friction instabilities during standard exploration.

(5) Design a task that does not require the use of subjective tactile descriptors, like “which one feels rougher”, which we see cause confusion in participants, which will likely require accounting for memory effects.

We elaborate these points below:

To successfully perform this experiment, we note that freely exploring humans make multiple strokes on a surface. Therefore, we would need to construct a decision-making model. It has not yet been demonstrated whether tactile decision making follows visual decision making, but perhaps to start, we can assume it does. Then, in the design of our decision-making paradigm, we immediately run into the problem: What is tactile evidence?

From Fig. 3C, we already can see that identifying evidence is challenging. Prior to this manuscript, people may have chosen the average force, or the highest force. Or we may choose the average friction force. Then, after deciding on the evidence, we need to find a method to manipulate the evidence, i.e., create samples or a machine that causes high friction, etc. We show that during the course of human touch, due to the dynamic nature of friction, the average can change a large amount and sample design becomes a central barrier to experiments. Others may suggest immobilizing the finger and applying a known force, but given how much friction changes with human exploration, there is no known method to make a machine recreate temporally and spatially varying friction forces during sliding onto a stationary finger. Finally, perhaps most importantly, in addition to mechanical challenges, a study by Liu, Colgate et al. showed that even if they recorded the friction (2D) of a finger exploring a surface and then replicated the same friction forces onto a finger, the participant could not determine which surface the replayed friction force was supposed to represent.1 This supports that the efference copy is important, that the forces in response to expected motion are important to determine friction. Finally, there is no known method to design instabilities a priori. They must be found through experiments. Especially since if we were to introduce, say a bump or a trough, then we bring in confounding variables to how participants tell surfaces apart.

Furthermore, even if we had some consistent method to create tactile “evidence”, the paradigm also deserves some consideration. In our experience, the 3-AFC task we perform is important because the vocabulary for touch has not been established. That is, in 3-AFC, by asking to determine which one sample is unlike the others, we do not have to ask the participant questions like “which one is rougher” or “which one has less friction”. In contrast, 2-AFC, which is better for decision-making models because it does not include memory, requires the asking of a perceptual question like: “which one is rougher?”. In our ongoing work, taking two silane coatings, we found that participants could easily identify which surface is unlike the others above chance in a 3-AFC, but participants, even within their own trials, could not consistently identify one silane as perceptually “rougher” by 2-AFC. To us, this calls into question the validity of tactile descriptors, but is beyond the scope of this manuscript.

This is not our only goal, but in the context of human exploration, in this manuscript here, we believed it was important to identify a mechanical parameter that was consistent with how humans explore surfaces, but was also a parameter that could characterize to some consistent property of a surface – irrespective of whether a human was touching it. We thought that designing human decision-making models and paradigms around the friction coefficient would not be successful.

Given the scope of these challenges, we do not think it would be possible to establish these conceptual sequences in a single manuscript.

Reviewer 2 (Public review):

Summary:

In this paper, the authors want to test the hypothesis that frictional instabilities rather than friction are the main drivers for discriminating flat surfaces of different sub-nanometric roughness profiles.

They first produced flat surfaces with 6 different coatings giving them unique and various properties in terms of roughness (picometer scale), contact angles (from hydrophilic to hydrophobic), friction coefficient (as measured against a mock finger), and Hurst exponent.

Then, they used those surfaces in two different experiments. In the first experiment, they used a mock finger (PDMS of 100kPA molded into a fingertip shape) and slid it over the surfaces at different normal forces and speeds. They categorized the sliding behavior as steady sliding, sticking spikes, and slow frictional waves by visual inspection, and show that the surfaces have different behaviors depending on normal force and speed. In a second experiment, participants (10) were asked to discriminate pairs of those surfaces. It is found that each of those pairs could be reliably discriminated by most participants.

Finally, the participant's discrimination performance is correlated with differences in the physical attributes observed against the mock finger. The authors found a positive correlation between participants' performances and differences in the count of steady sliding against the mock finger and a negative correlation between participants' reaction time and differences in the count of stiction spikes against the mock finger. They interpret those correlations as evidence that participants use those differences to discriminate the surfaces.

Strengths:

The created surfaces are very interesting as they are flat at the nanometer scale, yet have different physical attributes and can be reliably discriminated.”

We thank Reviewer 2 for their notes on our manuscript. The responses below address the reviewer’s comments and recommendations for revised work.

Weaknesses:

Comment 1

In my opinion, the data presented in the paper do not support the conclusions. The conclusions are based on a correlation between results obtained on the mock finger and results obtained with human participants but there is no evidence that the human participants' fingertips will behave similarly to the mock finger during the experiment. Figure 3 gives a hint that the 3 sliding behaviors can be observed in a real finger, but does not prove that the human finger will behave as the mock finger, i.e., there is no evidence that the phase maps in Figure 1C are similar for human fingers and across different people that can have very different stiffness and moisture levels.

The mechanical characterization conducted with the mock finger seeks to extract significant features of friction traces of a set of surfaces to use as predictors of tactile discriminability. The goal is to find a consistent method to characterize surfaces for use in tactile experiments that can be replicated by others and used prior to any human experiments. However, in the overall response and in a response to a similar comment by Reviewer 1, we also explain why we believe experiments on humans to establish this fact is not yet reasonable.

Comment 2

I believe that the authors collected the contact forces during the psychophysics experiments, so this shortcoming could be solved if the authors use the actual data, and show that the participant responses can be better predicted by the occurrence of frictional instabilities than by the usual metrics on a trial by trial basis, or at least on a subject by subject basis. I.e. Poor performers should show fewer signs of differences in the sliding behaviors than good performers.

To fully implement this, a decision-making model is necessary because, as a counter example, a participant could have generated 10 swipes of SFW and 1 swipe of a Sp, but the Sp may have been the most important event for making a tactile decision. This type of scenario is not compatible with the analysis suggested — and similar counterpoints can be made for other types of seemingly straightforward analysis.

While we are interested and actively working on this, the study here is critical to establish types of evidence for a future decision-making model. We know humans change their friction constantly during real exploration, so it is unclear which of these constantly changing values we should input into the decision making model, and the future challenges we anticipate are explained in Comment 1.

Comment 3

The sample size (10) is very small.

We recognize that, with all factors being equal, this sample size is on the smaller end. However, we emphasize the degree of control of samples is far above typical, with minimal variations in sample properties such as surface roughness, and every sample for every trial was pristine. Furthermore, the sample preparation (> 300 individual wafers were used) and cost became a factor. Although not typically appropriate, and thus not included in the manuscript, a post-hoc power analysis for our 100 trials of our pair that was closest to chance, P4, (53%, closest to chance at 33%) showed a power of 98.2%, suggesting that the study was appropriately powered.

Reviewer 2 (Recommendations for the authors):

Comment 1

Differences in SS and Sp (Table 2) are NOT physical or mechanical differences but are obtained by counting differences in the number of occurrences of each sliding behavior. It is rather a weird choice.

We disagree that differences in SS and Sp are not physical or mechanical, as these are well-established phenomena in the soft matter and tribology literature(19-21). These are known as “mechanical instabilities” and generated due to the effects of two physical phenomena: the elasticity of the finger (which is constant in our mechanical testing) and the friction forces present (which change per sample type). The motivation behind using these different shapes is that the instabilities, in some conditions, can be invariant to external factors like velocity. This would be quite advantageous for human exploration because, unlike friction coefficient, which changes with nearly any factor, including velocity and mass, the instabilities being invariant to velocity would mean that we are accurately characterizing a unique identifier of the surface even though velocity may be variable.

This “weird choice” is the central innovation of this paper. This choice was necessary because we demonstrated that the common usage of friction coefficient is fundamentally flawed: we see that friction coefficient suggests that surface which are more different would feel more similar – indeed the most distinctive surfaces would be two surfaces that are identical, which is clearly spurious. One potential explanation for why we were able to see this is effect is because our surfaces have similar (< 0.6 nm variability) roughness, removing potential confounding factors, and this type of low roughness control has not been used in tactile studies to the best of our knowledge.

Comment 2

Figures 2B-C: why are the x-data different than Table 2?

The x-data in Fig. 2B-C are the absolute differences in the number of occurrences measured for a given instability type or material property out of 144 pulls. Modeling the human participant results in our GLMMs required the independent variables to be in this form rather than percentages. We initially chose to list percent differences in Table 2 to highlight the ranges of differences instead of an absolute value, but have added both for clarity.

Our changes to the manuscript (Page 7)

“To determine if humans can detect these three different instabilities, we selected six pairs of surfaces to create a broad range of potential instabilities present across all three types. These are summarized in Table 2, where the first column for each instability is the difference in occurrence of that instability formed between each pair, and the second is the percent difference.”

Comment 3

"We constructed a set of coated surfaces with physical differences which were imperceptible by touch but created different types of instabilities based on how quickly a finger is slid and how hard a human finger is pressed during sliding." Yet, in your experiment, participants could discriminate them, so this is incoherent.

To clarify the point, macroscopic objects can differ in physical shape and in chemical composition. What we meant was that the physical differences, i.e., roughness, were below a limit (Skedung et al.) that participants, without a coating, would not be able to tell these apart(22). Therefore, the reason people could tell our surfaces apart was due to the chemical composition of the surface, and not any differences in roughness or physical effects like film stiffness (due to the molecular-scale thinness of the surface coatings, they are mechanically negligible). However, we concede that at the molecular scale, the traditional macroscopic distinction between physical and chemical is blurred.

We have made minor revisions to the wording in the abstract. We clarify that the surface coatings had physical differences in roughness that were smaller than 0.6 nm, which based purely on roughness, would not be expected to be distinguishable to participants. Therefore, the reason participants can tell these surfaces apart is due to differences in friction generated by chemical composition, and we were able to minimize contributions from physical differences in the sample our study.

Our changes to the manuscript (Page 1, Abstract)

“We constructed a set of coated surfaces with minimal physical differences that by themselves, are not perceptible to people, but instead, due to modification in surface chemistry, the surfaces created different types of instabilities based on how quickly a finger is slid and how hard a human finger is pressed during sliding.”

Reviewer 3 (Public review):

Strengths:  

The paper describes a new perspective on friction perception, with the hypothesis that humans are sensitive to the instabilities of the surface rather than the coefficient of friction. The paper is very well written and with a comprehensive literature survey.

One of the central tools used by the author to characterize the frictional behavior is the frictional instabilities maps. With these maps, it becomes clear that two different surfaces can have both similar and different behavior depending on the normal force and the speed of exploration. It puts forward that friction is a complicated phenomenon, especially for soft materials.

The psychophysics study is centered around an odd-one-out protocol, which has the advantage of avoiding any external reference to what would mean friction or texture for example. The comparisons are made only based on the texture being similar or not.

The results show a significant relationship between the distance between frictional maps and the success rate in discriminating two kinds of surface.”