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Author response:

Reviewer #1 (Public review):

Summary:

This work investigated the role of CXXC-finger protein 1 (CXXC1) in regulatory T cells. CXXC1-bound genomic regions largely overlap with Foxp3-bound regions and regions with H3K4me3 histone modifications in Treg cells. CXXC1 and Foxp3 interact with each other, as shown by co-immunoprecipitation. Mice with Treg-specific CXXC1 knockout (KO) succumb to lymphoproliferative diseases between 3 to 4 weeks of age, similar to Foxp3 KO mice. Although the immune suppression function of CXXC1 KO Treg is comparable to WT Treg in an in vitro assay, these KO Tregs failed to suppress autoimmune diseases such as EAE and colitis in Treg transfer models in vivo. This is partly due to the diminished survival of the KO Tregs after transfer. CXXC1 KO Tregs do not have an altered DNA methylation pattern; instead, they display weakened H3K4me3 modifications within the broad H3K4me3 domains, which contain a set of Treg signature genes. These results suggest that CXXC1 and Foxp3 collaborate to regulate Treg homeostasis and function by promoting Treg signature gene expression through maintaining H3K4me3 modification.

Strengths:

Epigenetic regulation of Treg cells has been a constantly evolving area of research. The current study revealed CXXC1 as a previously unidentified epigenetic regulator of Tregs. The strong phenotype of the knockout mouse supports the critical role CXXC1 plays in Treg cells. Mechanistically, the link between CXXC1 and the maintenance of broad H3K4me3 domains is also a novel finding.

Weaknesses:

(1) It is not clear why the authors chose to compare H3K4me3 and H3K27me3 enriched genomic regions. There are other histone modifications associated with transcription activation or repression. Please provide justification.

Thank you for highlighting this important point. We prioritized H3K4me3 and H3K27me3 because they are well-established markers of transcriptional activation and repression, respectively. These modifications provide a robust framework for investigating the dynamic interplay of chromatin states in Treg cells, particularly in regulating the balance between activation and suppression of key genes. While histone acetylation, such as H3K27ac, is linked to enhancer activity and transcriptional elongation, our focus was on promoter-level regulation, where H3K4me3 and H3K27me3 are most relevant. Although other histone modifications could provide additional insights, we chose to focus on these two to maintain clarity and feasibility in our analysis. We are happy to further elaborate on this rationale in the manuscript if necessary.

(2) It is not clear what separates Clusters 1 and 3 in Figure 1C. It seems they share the same features.

We apologize for not clarifying these clusters clearly. Cluster 1 and 3 are both H3K4me3 only group, with H3K4me3 enrichment and gene expression levels being higher in Cluster 1. At first, we divided the promoters into four categories because we wanted to try to classify them into four categories: H3K4me3 only, H3K27me3 only, H3K4me3-H3K27me3 co-occupied, and None. However, in actual classification, we could not distinguish H3K4me3-H3K27me3 co-occupied group. Instead, we had two categories of H3K4me3 only, with cluster 1 having a higher enrichment level for H3K4me3 and gene expression levels.

(3) The claim, "These observations support the hypothesis that FOXP3 primarily functions as an activator by promoting H3K4me3 deposition in Treg cells." (line 344), seems to be a bit of an overstatement. Foxp3 certainly can promote transcription in ways other than promoting H3K3me3 deposition, and it also can repress gene transcription without affecting H3K27me3 deposition. Therefore, it is not justified to claim that promoting H3K4me3 deposition is Foxp3's primary function.

We appreciate the reviewer’s thoughtful observation regarding our claim about FOXP3’s role in promoting H3K4me3 deposition. We acknowledge that FOXP3 is a multifunctional transcription factor with diverse mechanisms of action, including transcriptional activation independent of H3K4me3 deposition and transcriptional repression that does not necessarily involve H3K27me3 deposition.

Our intention was not to imply that promoting H3K4me3 deposition is the exclusive or predominant function of FOXP3 but rather to highlight that this mechanism contributes significantly to its role in regulating Treg cell function. We agree that our wording may have overstated this point, and we will revise the text to provide a more nuanced interpretation. Specifically, we will clarify that our observations suggest FOXP3 can facilitate transcriptional activation, in part, by promoting H3K4me3 deposition, but this does not preclude its other regulatory mechanisms.

(4) For the in vitro suppression assay in Figure S4C, and the Treg transfer EAE and colitis experiments in Figure 4, the Tregs should be isolated from Cxxc1 fl/fl x Foxp3 cre/wt female heterozygous mice instead of Cxxc1 fl/fl x Foxp3 cre/cre (or cre/Y) mice. Tregs from the homozygous KO mice are already activated by the lymphoproliferative environment and could have vastly different gene expression patterns and homeostatic features compared to resting Tregs. Therefore, it's not a fair comparison between these activated KO Tregs and resting WT Tregs.

Thank you for this insightful comment and for pointing out the potential confounding effects associated with using Treg cells from homozygous Foxp3Cre/Cre (or Cre/Y) Cxxc1fl/fl mice. We agree that using Treg cells from _Foxp3_Cre/+ _Cxxc1_fl/fl (referred to as “het-KO”) and their littermate _Foxp3_Cre/+ _Cxxc1_fl/+ (referred to as “het-WT”) female mice would provide a more balanced comparison, as these Treg cells are less likely to be influenced by the activated lymphoproliferative environment present in homozygous KO mice.

To address this concern, we will perform additional experiments using Treg cells isolated from _Foxp3_Cre/+ _Cxxc1_fl/fl (“het-KO”) and their littermate _Foxp3_Cre/+ _Cxxc1_fl/+ (“het-WT”) female mice. We will update the manuscript with these new data to provide a more accurate assessment of the impact of CXXC1 deficiency on Treg cell function.

(5) The manuscript didn't provide a potential mechanism for how CXXC1 strengthens broad H3K4me3-modified genomic regions. The authors should perform Foxp3 ChIP-seq or Cut-n-Taq with WT and Cxxc1 cKO Tregs to determine whether CXXC1 deletion changes Foxp3's binding pattern in Treg cells.

Thank you for your insightful comments and valuable suggestions. We greatly appreciate your recommendation to explore the potential mechanism by which CXXC1 enhances broad H3K4me3-modified genomic regions.

In response, we plan to conduct CUT&Tag experiments for Foxp3 in both WT and Cxxc1 cKO Treg cells.

Reviewer #2 (Public review):

FOXP3 has been known to form diverse complexes with different transcription factors and enzymes responsible for epigenetic modifications, but how extracellular signals timely regulate FOXP3 complex dynamics remains to be fully understood. Histone H3K4 tri-methylation (H3K4me3) and CXXC finger protein 1 (CXXC1), which is required to regulate H3K4me3, also remain to be fully investigated in Treg cells. Here, Meng et al. performed a comprehensive analysis of H3K4me3 CUT&Tag assay on Treg cells and a comparison of the dataset with the FOXP3 ChIP-seq dataset revealed that FOXP3 could facilitate the regulation of target genes by promoting H3K4me3 deposition.

Moreover, CXXC1-FOXP3 interaction is required for this regulation. They found that specific knockdown of Cxxc1 in Treg leads to spontaneous severe multi-organ inflammation in mice and that Cxxc1-deficient Treg exhibits enhanced activation and impaired suppression activity. In addition, they have also found that CXXC1 shares several binding sites with FOXP3 especially on Treg signature gene loci, which are necessary for maintaining homeostasis and identity of Treg cells.

The findings of the current study are pretty intriguing, and it would be great if the authors could fully address the following comments to support these interesting findings.

Major points:

(1) There is insufficient evidence in the first part of the Results to support the conclusion that "FOXP3 functions as an activator by promoting H3K4Me3 deposition in Treg cells". The authors should compare the results for H3K4Me3 in FOXP3-negative conventional T cells to demonstrate that at these promoter loci, FOXP3 promotes H3K4Me3 deposition.

We appreciate the reviewer’s critical observation regarding our claim about FOXP3’s role in promoting H3K4me3 deposition. We acknowledge that FOXP3 is a multifunctional transcription factor with diverse mechanisms of action, including transcriptional activation independent of H3K4me3 deposition and transcriptional repression that does not necessarily involve H3K27me3 deposition.

Our intention was not to imply that promoting H3K4me3 deposition is the exclusive or predominant function of FOXP3 but rather to highlight that this mechanism contributes significantly to its role in regulating Treg cell function. We agree that our wording may have overstated this point, and we will revise the text to provide a more nuanced interpretation. Specifically, we will clarify that our observations suggest FOXP3 can facilitate transcriptional activation, in part, by promoting H3K4me3 deposition, but this does not preclude its other regulatory mechanisms.

We will compare H3K4me3 levels at the promoter loci of interest between FOXP3-negative conventional T cells and FOXP3-positive regulatory T cells. This comparison will help elucidate whether FOXP3 directly promotes H3K4me3 deposition at these loci.

(2) In Figure 3 F&G, the activation status and IFNγ production should be analyzed in Treg cells and Tconv cells separately rather than in total CD4+ T cells. Moreover, are there changes in autoantibodies and IgG and IgE levels in the serum of cKO mice?

We appreciate the reviewer’s constructive feedback on the analyses presented in Figures 3F and 3G and the additional suggestion to investigate autoantibodies and serum immunoglobulin levels.

Regarding Figures 3F and 3G, we agree that separating Treg cells and Tconv cells for analysis of activation status and IFN-γ production would provide a more precise understanding of the cellular dynamics in Cxxc1 cKO mice.

To address this, we will reanalyze the data to examine Treg and Tconv cells independently and include these results in the revised manuscript.

As for the changes in autoantibodies and serum IgG and IgE levels, we acknowledge that these parameters are important indicators of systemic immune dysregulation.

We will now measure serum autoantibodies and immunoglobulin levels in Cxxc1 cKO mice and WT controls.

(3) Why did Cxxc1-deficient Treg cells not show impaired suppression than WT Treg during in vitro suppression assay, despite the reduced expression of Treg cell suppression assay -associated markers at the transcriptional level demonstrated in both scRNA-seq and bulk RNA-seq?

Thank you for your thoughtful question. We appreciate your interest in understanding the apparent discrepancy between the reduced expression of Treg-associated suppression markers at the transcriptional level and the lack of impaired suppression observed in the in vitro suppression assay.

There are several potential explanations for this observation:

(1) Functional Redundancy: Treg cell suppression is a complex, multi-faceted process involving various effector mechanisms such as cytokine production (e.g., IL-10, TGF-β), cell-cell contact, and metabolic regulation. Thus, even though the transcriptional signature of suppression-associated genes is altered, compensatory mechanisms may still allow Cxxc1-deficient Treg cells to retain functional suppression capacity under these specific in vitro conditions.

(2) In Vitro Assay Limitations: The in vitro suppression assay is a simplified model of Treg function that may not capture all the complexities of Treg-mediated suppression in vivo. While we observed altered gene expression in Cxxc1-deficient Treg cells, this might not directly translate to a functional defect under the specific conditions of the assay. In vivo, additional factors such as cytokine milieu, cell-cell interactions, and tissue-specific environments may be required for full suppression, which could be missing in the in vitro assay.

(4) Is there a disease in which Cxxc1 is expressed at low levels or absent in Treg cells? Is the same immunodeficiency phenotype present in patients as in mice?

Thank you for your insightful question regarding the role of CXXC1 in Treg cells and its potential link to human disease. To our knowledge, no specific human disease has been identified where CXXC1 is expressed at low levels or absent specifically in Treg cells. There is currently no direct evidence of an immunodeficiency phenotype in human patients that parallels the one observed in Cxxc1-deficient mice.

Reviewer #3 (Public review):

In the report entitled "CXXC-finger protein 1 associates with FOXP3 to stabilize homeostasis and suppressive functions of regulatory T cells", the authors demonstrated that Cxxc1-deletion in Treg cells leads to the development of severe inflammatory disease with impaired suppressive function. Mechanistically, CXXC1 interacts with Foxp3 and regulates the expression of key Treg signature genes by modulating H3K4me3 deposition. Their findings are interesting and significant. However, there are several concerns regarding their analysis and conclusions.

Major concerns:

(1) Despite cKO mice showing an increase in Treg cells in the lymph nodes and Cxxc1-deficient Treg cells having normal suppressive function, the majority of cKO mice died within a month. What causes cKO mice to die from severe inflammation?

Considering the results of Figures 4 and 5, a decrease in Treg cell population due to their reduced proliferative capacity may be one of the causes. It would be informative to analyze the population of tissue Treg cells.

We thank the reviewer for this insightful comment and acknowledge the importance of understanding the causes of severe inflammation and early mortality in cKO mice. Based on our data and previous studies, we propose the following explanations:

(1) Reduced Treg Proliferative Capacity: As shown in Figure 5I, the decreased proportion of FOXP3+Ki67+ Treg cells in cKO mice likely reflects impaired proliferative capacity, which may limit the expansion of functional Treg cells in response to inflammatory cues, particularly in peripheral tissues where active suppression is required.

(2) Altered Treg Function and Activation: Cxxc1-deficient Treg cells exhibit increased expression of activation markers (Il2ra, Cd69) and pro-inflammatory genes (Ifng, Tbx21). This suggests a functional dysregulation that may impair their ability to suppress inflammation effectively, despite their presence in lymphoid organs.

(3) Tissue Treg Populations: Although our study focuses on lymph node-resident Treg cells, tissue-resident Treg cells play a crucial role in maintaining local immune homeostasis. It is plausible that Cxxc1 deficiency compromises the accumulation or functionality of tissue Treg cells, contributing to uncontrolled inflammation in non-lymphoid organs. Unfortunately, we currently lack data on tissue Treg populations, which limits our ability to directly address this hypothesis.

Regarding the suggestion to analyze tissue Treg populations, we agree that this would be an important next step in understanding the cause of the severe inflammation and early mortality in Cxxc1-deficient mice.

We plan to perform detailed analyses of Treg cell populations in various tissues, including the gut, lung, and liver, to determine if there are specific defects in tissue-resident Treg cells that could contribute to the observed phenotype.

(2) In Figure 5B, scRNA-seq analysis indicated that Mki67+ Treg subset are comparable between WT and Cxxc1-deficient Treg cells. On the other hand, FACS analysis demonstrated that Cxxc1-deficient Treg shows less Ki-67 expression compared to WT in Figure 5I. The authors should explain this discrepancy.

Thank you for pointing out the apparent discrepancy between the scRNA-seq and FACS analyses regarding Ki-67 expression in Cxxc1-deficient Treg cells.

In Figure 5B, the scRNA-seq analysis identified the Mki67+ Treg subset as comparable between WT and Cxxc1-deficient Treg cells. This finding reflects the overall proportion of cells expressing Mki67 transcripts within the Treg population. In contrast, the FACS analysis in Figure 5I specifically measures Ki-67 protein levels, revealing reduced expression in Cxxc1-deficient Treg cells compared to WT.

To address this discrepancy more comprehensively, we will further analyze the scRNA-seq data to directly compare Mki67 mRNA expression levels between WT and Cxxc1-deficient Treg cells.

In addition, the authors concluded on line 441 that CXXC1 plays a crucial role in maintaining Treg cell stability. However, there appears to be no data on Treg stability. Which data represent the Treg stability?

We appreciate the reviewer’s observation and recognize that our wording may have been overly conclusive. Our data primarily highlight the impact of Cxxc1 deficiency on Treg cell homeostasis and transcriptional regulation, rather than providing direct evidence for Treg cell stability. Specifically, the downregulation of Treg-specific suppressive genes (Nt5e, Il10, Pdcd1) and the upregulation of pro-inflammatory markers (Gzmb, Ifng, Tbx21) indicate a shift in functional states. While these findings may suggest an indirect disruption in the maintenance of suppressive phenotypes, they do not constitute a direct measure of Treg cell stability.

To address the reviewer’s concern, we will revise our conclusion to more accurately state that our data support a role for CXXC1 in maintaining Treg cell homeostasis and functional balance, without overextending claims about Treg cell stability. Thank you for bringing this to our attention, as it will help us improve the clarity and precision of our manuscript.

(3) The authors found that Cxxc1-deficient Treg cells exhibit weaker H3K4me3 signals compared to WT in Figure 7. This result suggests that Cxxc1 regulates H3K4me3 modification via H3K4 methyltransferases in Treg cells. The authors should clarify which H3K4 methyltransferases contribute to the modulation of H3K4me3 deposition by Cxxc1 in Treg cells.

Thank you for pointing out the need to clarify the role of H3K4 methyltransferases in the modulation of H3K4me3 deposition by CXXC1 in Treg cells.

In our study, we found that Cxxc1-deficient Treg cells exhibit reduced H3K4me3 levels, as shown in Figure 7. CXXC1 has been previously reported to function as a non-catalytic component of the Set1/COMPASS complex, which contains H3K4 methyltransferases such as SETD1A and SETD1B. These methyltransferases are the primary enzymes responsible for H3K4 trimethylation.

References:

(1) Lee J.H., Skalnik D.G. CpG-binding protein (CXXC finger protein 1) is a component of the mammalian Set1 histone H3-Lys4 methyltransferase complex, the analogue of the yeast Set1/COMPASS complex. J. Biol. Chem. 2005; 280:41725–41731.

(2). J. P. Thomson, P. J. Skene, J. Selfridge, T. Clouaire, J. Guy, S. Webb, A. R. W. Kerr, A. Deaton, R. Andrews, K. D. James, D. J. Turner, R. Illingworth, A. Bird, CpG islands influence chromatin structure via the CpG-binding protein Cfp1. Nature 464, 1082–1086 (2010).

(3) Shilatifard, A. 2012. The COMPASS family of histone H3K4 methylases: mechanisms of regulation in development and disease pathogenesis. Annu. Rev. Biochem. 81:65–95.

(4) Brown D.A., Di Cerbo V., Feldmann A., Ahn J., Ito S., Blackledge N.P., Nakayama M., McClellan M., Dimitrova E., Turberfield A.H. et al. The SET1 complex selects actively transcribed target genes via multivalent interaction with CpG Island chromatin. Cell Rep. 2017; 20:2313–2327.

Furthermore, it would be important to investigate whether Cxxc1-deletion alters Foxp3 binding to target genes.

Thank you for this important suggestion regarding the impact of Cxxc1 deletion on FOXP3 binding to target genes. We agree that understanding whether Cxxc1 deficiency affects FOXP3’s ability to bind to its target genes would provide valuable insight into the regulatory role of CXXC1 in Treg cell function.

To address this, we plan to perform CUT&Tag experiments to assess FOXP3 binding profiles in Cxxc1-deficient versus wild-type Treg cells. These experiments will allow us to determine if Cxxc1 loss disrupts FOXP3’s occupancy at key regulatory sites, which may contribute to the observed functional impairments in Treg cells.

(4) In Figure 7, the authors concluded that CXXC1 promotes Treg cell homeostasis and function by preserving the H3K4me3 modification since Cxxc1-deficient Treg cells show lower H3K4me3 densities at the key Treg signature genes. Are these Cxxc1-deficient Treg cells derived from mosaic mice? If Cxxc1-deficient Treg cells are derived from cKO mice, the gene expression and H3K4me3 modification status are inconsistent because scRNA-seq analysis indicated that expression of these Treg signature genes was increased in Cxxc1-deficient Treg cells compared to WT (Figure 5F and G).

Thank you for the insightful comment. To clarify, the Cxxc1-deficient Treg cells analyzed for H3K4me3 modification in Figure 7 were indeed derived from Cxxc1 conditional knockout (cKO) mice, not mosaic mice.

The scRNA-seq analysis presented in Figures 5F and G revealed an upregulation of Treg signature genes in Cxxc1-deficient Treg cells. This finding suggests that the loss of Cxxc1 drives these cells toward a pro-inflammatory, activated state, underscoring the pivotal role of CXXC1 in maintaining Treg cell homeostasis and suppressive function.

Regarding the apparent discrepancy between the reduced H3K4me3 levels and the increased expression of these genes, it is important to note that H3K4me3 primarily functions as an epigenetic mark that facilitates chromatin accessibility and transcriptional regulation, acting as an upstream modulator of gene expression. However, gene expression levels are also influenced by downstream compensatory mechanisms and complex inflammatory environments. In this context, the reduction in H3K4me3 likely reflects the direct role of CXXC1 in epigenetic regulation, whereas the upregulation of gene expression in Cxxc1-deficient Treg cells may result as a side effect of the inflammatory environment.

To further substantiate our findings, we performed RNA-seq analysis on Treg cells from Foxp3_Cre/+ _Cxxc1_fl/fl (“het-KO”) and their littermate _Foxp3_Cre/+ _Cxxc1_fl/+ (“het-WT”) female mice, as presented in Figure S6C. This analysis revealed a notable reduction in the expression of key Treg signature genes, including _Icos, Ctla4, Tnfrsf18, and Nt5e, in het-KO Treg cells. Importantly, the observed changes in gene expression were consistent with the altered H3K4me3 modification status, further supporting the epigenetic regulatory role of CXXC1. These results further emphasize the critical role of CXXC1 promotes Treg cell homeostasis and function by preserving the H3K4me3 modification.

Reviewer #2 (Public review):

FOXP3 has been known to form diverse complexes with different transcription factors and enzymes responsible for epigenetic modifications, but how extracellular signals timely regulate FOXP3 complex dynamics remains to be fully understood. Histone H3K4 tri-methylation (H3K4me3) and CXXC finger protein 1 (CXXC1), which is required to regulate H3K4me3, also remain to be fully investigated in Treg cells. Here, Meng et al. performed a comprehensive analysis of H3K4me3 CUT&Tag assay on Treg cells and a comparison of the dataset with the FOXP3 ChIP-seq dataset revealed that FOXP3 could facilitate the regulation of target genes by promoting H3K4me3 deposition.

Moreover, CXXC1-FOXP3 interaction is required for this regulation. They found that specific knockdown of Cxxc1 in Treg leads to spontaneous severe multi-organ inflammation in mice and that Cxxc1-deficient Treg exhibits enhanced activation and impaired suppression activity. In addition, they have also found that CXXC1 shares several binding sites with FOXP3 especially on Treg signature gene loci, which are necessary for maintaining homeostasis and identity of Treg cells.

The findings of the current study are pretty intriguing, and it would be great if the authors could fully address the following comments to support these interesting findings.

Major points:

(1) There is insufficient evidence in the first part of the Results to support the conclusion that "FOXP3 functions as an activator by promoting H3K4Me3 deposition in Treg cells". The authors should compare the results for H3K4Me3 in FOXP3-negative conventional T cells to demonstrate that at these promoter loci, FOXP3 promotes H3K4Me3 deposition.

(2) In Figure 3 F&G, the activation status and IFNγ production should be analyzed in Treg cells and Tconv cells separately rather than in total CD4+ T cells. Moreover, are there changes in autoantibodies and IgG and IgE levels in the serum of cKO mice?

(3) Why did Cxxc1-deficient Treg cells not show impaired suppression than WT Treg during in vitro suppression assay, despite the reduced expression of Treg cell suppression assay -associated markers at the transcriptional level demonstrated in both scRNA-seq and bulk RNA-seq?

(4) Is there a disease in which Cxxc1 is expressed at low levels or absent in Treg cells? Is the same immunodeficiency phenotype present in patients as in mice?

Author response:

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

eLife Assessment

This important study addresses how 3' splice site choice is modulated by the conserved spliceosome-associated protein Fyv6. The authors provide compelling evidence Fyv6 functions to enable selection of 3' splice sites distal to a branch point and in doing so antagonizes more proximal, suboptimal 3' splice sites. The study would be improved through a more nuanced discussion of alternative possibilities and models, for instance in discussing the phenotypic impact of Fyv6 deletion.

We thank the editors and reviewers for their supportive comments and assessment of this manuscript. We have improved the discussion at several points as suggested by the reviewers to include discussion of alternative possibilities.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

A key challenge at the second chemical step of splicing is the identification of the 3' splice site of an intron. This requires recruitment of factors dedicated to the second chemical step of splicing and exclusion of factors dedicated to the first chemical step of splicing. Through the highest resolution cyroEM structure of the spliceosome to-date, the authors show the binding site for Fyv6, a factor dedicated to the second chemical step of splicing, is mutually exclusive with the binding site for a distinct factor dedicated to the first chemical step of splicing, highlighting that splicing factors bind to the spliceosome at a specific stage not only by recognizing features specific to that stage but also by competing with factors that bind at other stages. The authors further reveal that Fyv6 functions at the second chemical step to promote selection of 3' splice sites distal to a branch point and thereby discriminate against proximal, suboptimal 3' splice site. Lastly, the authors show by cyroEM that Fyv6 physically interacts with the RNA helicase Prp22 and by genetics Fyv6 functionally interacts with this factor, implicating Fyv6 in 3'SS proofreading and mRNA release from the spliceosome. The evidence for this study is robust, with the inclusion of genomics, reporter assays, genetics, and cyroEM. Further, the data overall justify the conclusions, which will be of broad interest.

Strengths:

(1) The resolution of the cryoEM structure of Fyv6-bound spliceosomes at the second chemical step of splicing is exceptional (2.3 Angstroms at the catalytic core; 3.0-3.7 Angstroms at the periphery), providing the best view of this spliceosomal intermediate in particular and the core of the spliceosome in general.

(2) The authors observe by cryoEM three distinct states of this spliceosome, each distinguished from the next by progressive loss of protein factors and/or RNA residues. The authors appropriately refrain from overinterpreting these states as reflecting distinct states in the splicing cycle, as too many cyroEM studies are prone to do, and instead interpret these observations to suggest interdependencies of binding. For example, when Fyv6, Slu7, and Prp18 are not observed, neither are the first and second residues of the intron, which otherwise interact, suggesting an interdependence between 3' splice site docking on the 5' splice site and binding of these second step factors to the spliceosome.

(3) Conclusions are supported from multiple angles.

(4) The interaction between Fyv6 and Syf1, revealed by the cyroEM structure, was shown to account for the temperature-sensitive phenotypes of a fyv6 deletion, through a truncation analysis.

(5) Splicing changes were observed in vivo both by indirect copper reporter assays and directly by RT-PCR.

(6) Changes observed by RNA-seq are validated by RT-PCR.

(7) The authors go beyond simply observing a general shift to proximal 3'SS usage in the fyv6 deletion by RNA-seq by experimentally varying branch point to 3' splice site distance experimentally in a reporter and demonstrating in a controlled system that Fyv6 promotes distal 3' splice sites.

(8) The importance of the Fyv6-Syf1 interaction for 3'SS recognition is demonstrated by truncations of both Fyv6 and of Syf1.

(9) In general, the study was executed thoroughly and presented clearly.

We thank the reviewer for their recognition of the strengths of our multi-faceted approach that led to highly supported conclusions.

Weaknesses:

(1) Despite the authors restraint in interpreting the three states of the spliceosome observed by cyroEM as sequential intermediates along the splicing pathway, it would be helpful to the general reader to explicitly acknowledge the alternative possibility that the difference states simply reflect decomposition from one intermediate during isolation of the complex (i.e., the loss of protein is an in vitro artifact, if an informative one).

We thank the reviewer for noticing our restraint in interpreting these structures, and we agree that the scenario described by the reviewer is a possibility. We have now explicitly mentioned this in the Discussion on lines 755-757.

(2) The authors acknowledge that for prp8 suppressors of the fyv6 deletion, suppression may be indirect, as originally proposed by the Query and Konarska labs - that is, that defects in the second step conformation of the spliceosome can be indirectly suppressed by compensating, destabilizing mutations in the first step spliceosome. Whereas some of the other suppressors of the fyv6 deletion can be interpreted as impacting directly the second step spliceosome (e.g., because the gene product is only present in the second step conformation), it seems that many more suppressors beyond prp8 mutants, especially those corresponding to bulky substitutions, which would more likely destabilize than stabilize, could similarly act indirectly by destabilization of first step conformation. The authors should acknowledge this where appropriate (e.g., for factors like Prp8 that are present in both first and second step conformations).

We agree that this is also a possibility and have now included this on lines 480-486.

Reviewer #2 (Public Review):

In this manuscript, Senn, Lipinski, and colleagues report on the structure and function of the conserved spliceosomal protein Fyv6. Pre-mRNA splicing is a critical gene expression step that occurs in two steps, branching and exon ligation. Fyv6 had been recently identified by the Hoskins' lab as a factor that aids exon ligation (Lipinski et al., 2023), yet the mechanistic basis for Fyv6 function was less clear. Here, the authors combine yeast genetics, transcriptomics, biochemical assays, and structural biology to reveal the function of Fyv6. Specifically, they describe that Fyv6 promotes the usage of distal 3'SSs by stabilizing a network of interactions that include the RNA helicase PRP22 and the spliceosome subunit SYF1. They discuss a generalizible mechanism for splice site proofreading by spliceosomsal RNA helicases that could be modulated by other, regulatory splicing factors.

This is a very high quality study, which expertly combines various approaches to provide new insights into the regulation of 3'SS choice, docking, and undocking. The cryo-EM data is also of excellent quality, which substantially extends on previous yeast P complex structures. This is also supported by the authors use of the latest data analysis tools (Relion-5, AlphaFold2 multimer predictions, Modelangelo). The authors re-evaluate published EM densities of yeast spliceosome complexes (B*, C,C*,P) for the presence or absence of Fyv6, substantiate Fyv6 as a 2nd step specific factor, confirm it as the homolog of the human protein FAM192A, and provide a model for how Fyv6 may fit into the splicing pathway. The biochemical experiments on probing the splicing effects of BP to 3'SS distances after Fyv6 KO, genetic experiments to probe Fyv6 and Syf1 domains, and the suppressor screening add substantially to the study and are well executed. The manuscript is clearly written and we particularly appreciated the nuanced discussions, for example for an alternative model by which Prp22 influences 3'SS undocking. The research findings will be of great interest to the pre-mRNA splicing community.

We thank the reviewer for their positive comments on our manuscript.

We have only few comments to improve an already strong manuscript.

Comments:

(1) Can the authors comment on how they justify K+ ion positions in their models (e.g. the K+ ion bridging G-1 and G+1 nucleotides)? How do they discriminate e.g. in the 'G-1 and G+1' case K+ from water?

The assignment of K+ at this position is justified by both longer coordination distances and relatively high cryo-EM density compared to structured water molecules in the same vicinity. We have added a panel to figure3-figure supplement 4C to show the density for the G-1/G+1 bridging K+ ion and to show the adjacent density for putative water molecules which coordinate the ion. The K+ ion density is larger and has stronger signal than the adjacent water molecules. The coordination distances are also longer than would be expected for a Mg2+. For these reasons and because K+ was present in the purification buffer, we modelled the density as K+.

(2) The authors comment on Yju2 and Fyv6 assignments in all yeast structures except for the ILS. Can the authors comment on if they have also looked into the assignment of Yju2 in the yeast ILS structure in the same manner? While it is possible that Fyv6 could dissociate and Yju2 reassociate at the P to ILS transition, this would merit a closer look given that in the yeast P complex Yju2 had been misassigned previously.

We thank the reviewer for pointing out this very interesting topic! We have used ModelAngelo to analyze the S. cerevisiae ILS structure for support of density assignment as Yju2 (and not Fyv6). This analysis supports the assignment as Yju2 in this structure and we have no evidence to doubt its presence in those particular purified spliceosomes. We have updated Figure 4- figure supplement 1B accordingly.

That being said, we do think that this issue should be studied more carefully in the future. The S. cerevisiae ILS structure (5Y88) was determined by purifying spliceosome complexes with a TAP-tag on Yju2. So the conclusion that Yju2 is part of the ILS spliceosome involves some circular logic: Yju2 is part of ILS spliceosome complexes because it is present in ILS complexes purified with Yju2. We also note that Yju2 was absent in ILS complexes recently determined from metazoans by the Plaschka group.  We have added some additional nuance to the Discussion to raise this important mechanistic point at lines 711-718.

(3) For accessibility to a general reader, figures 1c, d, e, 2a, b, would benefit from additional headings or labels, to immediately convey what is being displayed. It is also not clear to us if Fig 1e might fit better in the supplement and be instead replaced by Supplementary Figure 1a (wt) , b (delta upf1), and a new c (delta fyv6) and new d (delta upf1, delta fyv6). This may allow the reader to better follow the rationale of the authors' use of the Fyv6/Upf1 double deletion.

We thank the reviewer for the suggestion and have updated Figures 1 C-E to include additional information in the headings and labels. We have not changed the labels in Figures 2A, B but have added additional clarifying language to the legend.

In terms of rearranging the figures, we thank the reviewer for the suggestion but have decided that the figures are best left in their current ordering.

(4) The authors carefully interpret the various suppressor mutants, yet to a general reader the authors may wish to focus this section on only the most critical mutants for a better flow of the text.

We thank the reviewer for this suggestion. While this section of the manuscript does contain (to quote Reviewer #3) “extensive new information regarding functional interactions”, it was a bit long. We have reduced this section of the manuscript by ~200 words for a more focused presentation for general readers.

Reviewer #3 (Public Review):

In this manuscript the authors expand their initial identification of Fyv6 as a protein involved in the second step of pre-mRNA splicing to investigate the transcriptome-wide impact of Fyv6 on splicing and gain a deeper understanding of the mechanism of Fyv6 action.

They first use deep sequencing of transcripts in cells depleted of Fyv6 together with Upf1 (to limit loss of mis-spliced transcripts) to identify broad changes in the transcriptome due to loss of Fyv6. This includes both changes in overall gene expression, that are not deeply discussed, as well as alterations in choice of 3' splice sites - which is the focus of the rest of the manuscript

They next provide the highest resolution structure of the post-catalytic spliceosome to date; providing unparalleled insight into details of the active site and peripheral components that haven't been well characterized previously.

Using this structure they identify functionally critical interactions of Fyv6 with Syf1 but not Prp22, Prp8 and Slu7. Finally, a suppressor screen additionally provides extensive new information regarding functional interactions between these second step factors.

Overall this manuscript reports new and essential information regarding molecular interactions within the spliceosome that determine the use of the 3' splice site. It would be helpful, especially to the non-expert, to summarize these in a table, figure or schematic in the discussion.

We thank the reviewer for the positive comments and suggestions. We did include a summary figure in panel 7H. However, it was a bit buried. To highlight the summary figure more clearly, we have moved panel 7H to its own figure (Fig. 8).

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

(1) The resolution of some panels is poor, nearly illegible (e.g., Supp Fig 1A, B).

The resolution of panels in supplemental figure 1 has been increased. However, this may be an artifact of the PDF conversion process. We will pay attention to this during the publication process.

(2) Panel S6B: 6HYU is a structure of DHX8, not DDX8

We have corrected DDX8 to DHX8 in Supplemental Fig. S6D and associated figure legend.

(3) The result that Syf1 truncations can suppress the Fyv6 deletion is impressive. The subsequent discussion seems muddled. A discussion of Fyv6 binding at the first step, instead of Yju2, doesn't seem relevant here (though worthy of consideration in the discussion), given that the starting mutation is the Fyv6 deletion. Further, conjuring rebinding of Yju2 based on the data in the paper seems unnecessarily speculative (assumes that biochemical state III is on pathway), unless I am unaware of some other evidence for such rebinding. Instead, a simpler explanation would seem to be that in the absence of Fyv6, Syf1 inappropriately binds Yju2 instead at the second step and that deletion of the common Fyv6/Yju2 binding site on Syf1 suppresses this defect. In this case, the ts phenotype of the Fyv6 deletion would result from inappropriate binding of Yju2, and the splicing defect would be due to loss of Fyv6 activity. Alternatively, especially considering the work of the labs of Query and Konarska, the authors should consider the possibility that i) the Fyv6 deletion destabilizes the second step conformation, shifting an equilibrium to the first step conformation, and that ii) the Syf1 truncation destabilizes binding of Yju2, thereby restoring the equilibrium. In this case the ts phenotype of the Fyv6 deletion is due to a disturbed equilibrium and the splicing defect is due to the failure of Fyv6 to function at the second step.

We believe the reviewer is specifically referencing the final paragraph of this Results section (the paragraph that comes just before the section “Mutations in many different splicing factors…”). In retrospect, we agree that our discussion was convoluted. In particular, we emphasized rebinding of Yju2 based on its presence in the cryo-EM structure of the yeast ILS complex. However, given some uncertainties about whether or not Yju2 is a bona fide ILS component (as discussed above). We don’t think it is appropriate to over-emphasize rebinding of Yju2 and have decided to incorporate the elegant mechanisms proposed by the reviewer. This paragraph has now been edited accordingly (lines 386-395).

(4) The authors imply they have performed biochemical studies, which I think is misleading. Of course, RT-PCR and primer extension assays for example are performed in vitro, but these are an analysis of RNA events that occurred in vivo. In my view a higher threshold should be used for defining "biochemistry". To me "biochemistry" would imply that the authors have, for example, investigated 3' splice site usage in splicing extracts of the fyv6 deletion or engaged in an analysis of the Syf1-Fyv6 interaction involving the expression of the interacting domains in bacteria followed by a binding analysis in the test tube.

We disagree with the reviewer on this point. Biochemistry is defined as the “branch of sciences concerned with the chemical substances, reactions, and physico chemical processes which occur within living organisms; biological or physical chemistry.” (Oxford English Dictionary). Biochemical studies are not defined by whether or not they take place in vitro, in vivo, or even in silico. Indeed, much of the history of biochemistry (especially in studies of metabolism, for example) involved experiments occurring in vivo that reported on the molecular properties and mechanisms of biological processes. We think many of our experiments fall into this category including our structure/function analysis of splicing factors and the use of the ACT1-CUP1 reporter substrate.

(5) The monovalents are shown; inositol phosphate is shown; is the binding of Prp22 to RNA shown?

We have added a panel to Figure 3-figure supplement 4D showing density for the 3' exon within Prp22.

(6) The authors invoke undocking of the 3'SS in the P complex. Where is the 3'SS in the ILS? The author's model predicts: undocked.

In all ILS structures to date, the 3′ SS is undocked, in agreement with this prediction. We have now noted this observation in line 760.

(7) Would be helpful to show fyv6 deletion in Fig 1b.

We have included growth data for an additional fyv6 deletion strain (in a cup1Δ background) in Figure 1b. The results are quite similar to the upf1_Δ_ background except with slightly worse growth at 23°C.

Reviewer #2 (Recommendations For The Authors):

Minor comments

(1) Fig.3b is the arrow indicating the right rotation?

This typo has been fixed.

(2) Fig.4b, panel H is annotated, which should read 'F'.

This typo has been fixed.

(3) Line 178: "Finally, we analyzed the sequence features of the alternative 3ʹ SS activated by loss of Fyv6." We would suggest 'used after' instead of 'activated by'.

We have replaced ‘activated by’ with ‘with increased use after’.

(4) In Line 544, the authors speculate on a Slu7 requirement for 3'SS docking and on 3'SS docking maintenance. In the results section (Line 265) they however only mention the latter possibility. These statements should be consistent.

We thank the reviewer for pointing this out. We have added a reference to docking maintenance to the results section at line 325.

(5) Line 476: "Unexpectedly, Prp22 I1133R was actually deleterious when Fyv6 was present for this reporter." We suggest removing "actually".

We have removed ‘actually’.

(6) The authors describe the observed changes in splicing events in absolute numbers (e.g. in Fig 1c). To better assess for the reader whether these numbers reflect large or small effects of Fyv6 in defining mRNA isoforms, it would be more useful to state these as percent changes of total events or to provide a reference number for how many introns are spliced in S.c. See for example the statements in Lines 132 and 145.

We have added a percentage at line 138 that indicates ~20% of introns in yeast showed splicing changes.

Reviewer #3 (Recommendations For The Authors):

Do the authors have a proposed explanation for the observed DGE in non-intron containing genes in the Fyv6 depleted cells?

The simplest explanation is that this is an indirect effect due to splicing changes occurring in other genes (such as transcription factors, ribosomal protein genes, etc..). It is possible that this can be further dissected in the future using shorter-term knockdown of Fyv6 using Anchors Away or AID-tagging. However, that is beyond the scope of the current manuscript, and we do not wish to comment on these non-intron containing genes further at present.

Figure 2A - What is going on with the events that show no FAnS value under one condition (i.e. are up against the X or Y axis)? These are of interest as most on the Y- axis are blue.

The events along one of the axes denote alternative splice sites that are only detected under one condition (either when Fyv6 is present or when it is absent). At this stage, we do not wish to interpret these events further since most have a relatively low number of reads overall.

Author response:

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

Public Reviews: 

Reviewer #1 (Public Review): 

Tiedje et al. investigated the transient impact of indoor residual spraying (IRS) followed by seasonal malaria chemoprevention (SMC) on the plasmodium falciparum parasite population in a high transmission setting. The parasite population was characterized by sequencing the highly variable DBL$\alpha$ tag as a proxy for var genes, a method known as varcoding. Varcoding presents a unique opportunity due to the extraordinary diversity observed as well as the extremely low overlap of repertoires between parasite strains. The authors also present a new Bayesian approach to estimating individual multiplicity of infection (MOI) from the measured DBL$\alpha$ repertoire, addressing some of the potential shortcomings of the approach that have been previously discussed. The authors also present a new epidemiological endpoint, the so-called "census population size", to evaluate the impact of interventions. This study provides a nice example of how varcoding technology can be leveraged, as well as the importance of using diverse genetic markers for characterizing populations, especially in the context of high transmission. The data are robust and clearly show the transient impact of IRS in a high transmission setting, however, some aspects of the analysis are confusing.

(1) Approaching MOI estimation with a Bayesian framework is a well-received addition to the varcoding methodology that helps to address the uncertainty associated with not knowing the true repertoire size. It's unfortunate that while the authors clearly explored the ability to estimate the population MOI distribution, they opted to use only MAP estimates. Embracing the Bayesian methodology fully would have been interesting, as the posterior distribution of population MOI could have been better explored. 

We thank the reviewer for appreciating the extension of var_coding we present here. We believe the comment on maximum _a posteriori (MAP) refers to the way we obtained population-level MOI from the individual MOI estimates. We would like to note that reliance on MAP was only one of two approaches we described, although we then presented only MAP.  Having calculated both, we did not observe major differences between the two, for this data set.  Nonetheless, we revised the manuscript to include the result based on the mixture distribution which considers all the individual MOI distributions in the Figure supplement 6.

(2) The "census population size" endpoint has unclear utility. It is defined as the sum of MOI across measured samples, making it sensitive to the total number of samples collected and genotyped. This means that the values are not comparable outside of this study, and are only roughly comparable between strata in the context of prevalence where we understand that approximately the same number of samples were collected. In contrast, mean MOI would be insensitive to differences in sample size, why was this not explored? It's also unclear in what way this is a "census". While the sample size is certainly large, it is nowhere near a complete enumeration of the parasite population in question, as evidenced by the extremely low level of pairwise type sharing in the observed data. 

We consider the quantity a census in that it is a total enumeration or count of infections in a given population sample and over a given time period. In this sense, it gives us a tangible notion of the size of the parasite population, in an ecological sense, distinct from the formal effective population size used in population genetics. Given the low overlap between var repertoires of parasites (as observed in monoclonal infections), the population size we have calculated translates to a diversity of strains or repertoires.  But our focus here is in a measure of population size itself.  The distinction between population size in terms of infection counts and effective population size from population genetics has been made before for pathogens (see for example Bedford et al. for the seasonal influenza virus and for the measles virus (Bedford et al., 2011)), and it is also clear in the ecological literature for non-pathogen populations (Palstra and Fraser, 2012). 

We completely agree with the dependence of our quantity on sample size. We used it for comparisons across time of samples of the same depth, to describe the large population size characteristic of high transmission which persists across the IRS intervention. Of course, one would like to be able to use this quantity across studies that differ in sampling depth and the reviewer makes an insightful and useful suggestion.  It is true that we can use mean MOI, and indeed there is a simple map between our population size and mean MOI (as we just need to divide or multiply by sample size, respectively) (Table supplement 7).  We can go further, as with mean MOI we can presumably extrapolate to the full sample size of the host population, or to the population size of another sample in another location. What is needed for this purpose is a stable mean MOI relative to sample size.  We can show that indeed in our study mean MOI is stable in that way, by subsampling to different depths our original sample (Figure supplement 8 in the revised manuscript). We now include in the revision discussion of this point, which allows an extrapolation of the census population size to the whole population of hosts in the local area.

We have also clarified the time denominator: Given the typical duration of infection, we expect our population size to be representative of a per-generation measure_._

(3) The extraordinary diversity of DBL$\alpha$ presents challenges to analyzing the data. The authors explore the variability in repertoire richness and frequency over the course of the study, noting that richness rapidly declined following IRS and later rebounded, while the frequency of rare types increased, and then later declined back to baseline levels. The authors attribute this to fundamental changes in population structure. While there may have been some changes to the population, the observed differences in richness as well as frequency before and after IRS may also be compatible with simply sampling fewer cases, and thus fewer DBL$\alpha$ sequences. The shift back to frequency and richness that is similar to pre-IRS also coincides with a similar total number of samples collected. The authors explore this to some degree with their survival analysis, demonstrating that a substantial number of rare sequences did not persist between timepoints and that rarer sequences had a higher probability of dropping out. This might also be explained by the extreme stochasticity of the highly diverse DBL$\alpha$, especially for rare sequences that are observed only once, rather than any fundamental shifts in the population structure.

We thank the reviewer raising this question which led us to consider whether the change in the number of DBLα types over the course of the study (and intervention) follows from simply sampling fewer P. falciparum cases. We interpreted this question as basically meaning that one can predict the former from the latter in a simple way, and that therefore, tracking the changes in DBLα type diversity would be unnecessary.  A simple map would be for example a linear relationship (a given proportion of DBLα types lost given genomes lost), and even more trivially, a linear loss with a slope of one (same proportion).  Note, however, that for such expectations, one needs to rely on some knowledge of strain structure and gene composition. In particular, we would need to assume a complete lack of overlap and no gene repeats in a given genome. We have previously shown that immune selection leads to selection for minimum overlap and distinct genes in repertoires at high transmission (see for example (He et al., 2018)) for theoretical and empirical evidence of both patterns). Also, since the size of the gene pool is very large, even random repertoires would lead to limited overlap (even though the empirical overlap is even smaller than that expected at random (Day et al., 2017)). Despite these conservators, we cannot a priori assume a pattern of complete non-overlap and distinct genes, and ignore plausible complexities introduced by the gene frequency distribution.  

To examine this insightful question, we simulated the loss of a given proportion of genomes from baseline in 2012 and examined the resulting loss of DBLα types. We specifically cumulated the loss of infections in individuals until it reached a given proportion (we can do this on the basis of the estimated individual MOI values). We repeated this procedure 500 times for each proportion, as the random selection of individual infection to be removed, introduces some variation. Figure 2 below shows that the relationship is nonlinear, and that one quantity is not a simple proportion of the other.  For example, the loss of half the genomes does not result in the loss of half the DBLα types. 

Author response image 1.

Non-linear relationship between the loss of DBLα types and the loss of a given proportion of genomes. The graph shows that the removal of parasite genomes from the population through intervention does not lead to the loss of the same proportion of DBLα types, as the initial removal of genomes involves the loss of rare DBLα types mostly whereas common DBLα types persist until a high proportion of genomes are lost. The survey data (pink dots) used for this subsampling analysis was sampled at the end of wet/high transmission season in Oct 2012 from Bongo District from northern Ghana. We used the Bayesian formulation of the _var_coding method proposed in this work to calculate the multiplicity of infection of each isolate to further obtain the total number of genomes. The randomized surveys (black dots) were obtained based on “curveball algorithm” (Strona et al., 2014) which keep isolate lengths and type frequency distribution.

We also investigated whether the resulting pattern changed significantly if we randomized the composition of the isolates.  We performed such randomization with the “curveball algorithm” (Strona et al., 2014). This algorithm randomizes the presence-absence matrix with rows corresponding to the isolates and columns, to the different DBLα types; importantly, it preserves the DBLα type frequency and the length of isolates. We generated 500 randomizations and repeated the simulated loss of genomes as above. The data presented in Figure 2 above show that the pattern is similar to that obtained for the empirical data presented in this study in Ghana. We interpret this to mean that the number of genes is so large, that the reduced overlap relative to random due to immune selection (see (Day et al., 2017)) does not play a key role in this specific pattern. 

Reviewer #2 (Public Review):  

In this manuscript, Tiedje and colleagues longitudinally track changes in parasite numbers across four time points as a way of assessing the effect of malaria control interventions in Ghana. Some of the study results have been reported previously, and in this publication, the authors focus on age-stratification of the results. Malaria prevalence was lower in all age groups after IRS. Follow-up with SMC, however, maintained lower parasite prevalence in the targeted age group but not the population as a whole. Additionally, they observe that diversity measures rebounds more slowly than prevalence measures. Overall, I found these results clear, convincing, and well-presented. They add to a growing literature that demonstrates the relevance of asymptomatic reservoirs.  There is growing interest in developing an expanded toolkit for genomic epidemiology in malaria, and detecting changes in transmission intensity is one major application. As the authors summarize, there is no one-size-fits-all approach, and the Bayesian MOIvar estimate developed here has the potential to complement currently used methods. I find its extension to a calculation of absolute parasite numbers appealing as this could serve as both a conceptually straightforward and biologically meaningful metric. However, I am not fully convinced the current implementation will be applied meaningfully across additional studies. 

(1) I find the term "census population size" problematic as the groups being analyzed (hosts grouped by age at a single time point) do not delineate distinct parasite populations. Separate parasite lineages are not moving through time within these host bins. Rather, there is a single parasite population that is stochastically divided across hosts at each time point. I find this distinction important for interpreting the results and remaining mindful that the 2,000 samples at each time point comprise a subsample of the true population. Instead of "census population size", I suggest simplifying it to "census count" or "parasite lineage count".  It would be fascinating to use the obtained results to model absolute parasite numbers at the whole population level (taking into account, for instance, the age structure of the population), and I do hope this group takes that on at some point even if it remains outside the scope of this paper. Such work could enable calculations of absolute---rather than relative---fitness and help us further understand parasite distributions across hosts.

Lineages moving exclusively through a given type of host or “patch”  are not a necessary requirement for enumerating the size of the total infections in such subset.  It is true that what we have is a single parasite population, but we are enumerating for the season the respective size in host classes (children and adults). This is akin to enumerating subsets of a population in ecological settings where one has multiple habitat patches, with individuals able to move across patches.

Remaining mindful that the count is relative to sample size is an important point. Please see our response to comment (2) of reviewer 1, also for the choice of terminology. We prefer not to adopt “census count” as a census in our mind is a count, and we are not clear on the concept of lineage for these highly recombinant parasites.  Also, census population size has been adopted already in the literature for both pathogens and non-pathogens, to make a distinction with the notion of effective population size in population genetics (see our response to reviewer 1) and is consistent with our usage as outlined in the introduction. 

Thank you for the comment on an absolute number which would extrapolate to the whole host population.  Please see again our response to comment (2) of reviewer 1, on how we can use mean MOI for this purpose once the sampling is sufficient for this quantity to become constant/stable with sampling effort.

(2) I'm uncertain how to contextualize the diversity results without taking into account the total number of samples analyzed in each group. Because of this, I would like a further explanation as to why the authors consider absolute parasite count more relevant than the combined MOI distribution itself (which would have sample count as a denominator). It seems to me that the "per host" component is needed to compare across age groups and time points---let alone different studies.

Again, thank you for the insightful comment. We provide this number as a separate quantity and not a distribution, although it is clearly related to the mean MOI of such distribution. It gives a tangible sense for the actual infection count (different from prevalence) from the perspective of the parasite population in the ecological sense. The “per host” notion which enables an extrapolation to any host population size for the purpose of a complete count, or for comparison with another study site, has been discussed in the above responses for reviewer 1 and now in the revision of the discussion.

(3) Thinking about the applicability of this approach to other studies, I would be interested in a larger treatment of how overlapping DBLα repertoires would impact MOIvar estimates. Is there a definable upper bound above which the method is unreliable? Alternatively, can repertoire overlap be incorporated into the MOI estimator? 

This is a very good point and one we now discuss further in our revision. There is no predefined upper bound one can present a priori. Intuitively, the approach to estimate MOI would appear to breakdown as overlap moves away from extremely low values, and therefore for locations with low transmission intensity.  Interestingly, we have observed that this is not the case in our paper by Labbe et al. (Labbé et al., 2023) where we used model simulations in a gradient of three transmission intensities, from high to low values. The original _var_coding method performed well across the gradient. This robustness may arise from a nonlinear and fast transition from low to high overlap that is accompanied by MOI changing rapidly from primarily multiclonal (MOI > 1) to monoclonal (MOI = 1). This matter clearly needs to be investigated further, including ways to extend the estimation to explicitly include the distribution of overlap.

Smaller comments:

- Figure 1 provides confidence intervals for the prevalence estimates, but these aren't carried through on the other plots (and Figure 5 has lost CIs for both metrics). The relationship between prevalence and diversity is one of the interesting points in this paper, and it would be helpful to have CIs for both metrics when they are directly compared. 

Based on the reviewer’s advice we have revised both Figure 4 and Figure 5, to include the missing uncertainty intervals. The specific approach for each quantity is described in the corresponding caption.

Reviewer #3 (Public Review): 

Summary: 

The manuscript coins a term "the census population size" which they define from the diversity of malaria parasites observed in the human community. They use it to explore changes in parasite diversity in more than 2000 people in Ghana following different control interventions. 

Strengths: 

This is a good demonstration of how genetic information can be used to augment routinely recorded epidemiological and entomological data to understand the dynamics of malaria and how it is controlled. The genetic information does add to our understanding, though by how much is currently unclear (in this setting it says the same thing as age-stratified parasite prevalence), and its relevance moving forward will depend on the practicalities and cost of the data collection and analysis. Nevertheless, this is a great dataset with good analysis and a good attempt to understand more about what is going on in the parasite population. 

Census population size is complementary to parasite prevalence where the former gives a measure of the “parasite population size”, and the latter describes the “proportion of infected hosts”.  The reason we see similar trends for the “genetic information” (i.e., census population size) and “age-specific parasite prevalence” is because we identify all samples for var_coding based on the microscopy (i.e., all microscopy positive _P. falciparum isolates). But what is more relevant here is the relative percentage change in parasite prevalence and census population size following the IRS intervention. To make this point clearer in the revised manuscript we have updated Figure 4 and included additional panels plotting this percentage change from the 2012 baseline, for both census population size and prevalence (Figure 4EF). Overall, we see a greater percentage change in 2014 (and 2015), relative to the 2012 baseline, for census parasite population size vs. parasite prevalence (Figure 4EF) as a consequence of the significant changes in distributions of MOI following the IRS intervention (Figure 3). As discussed in the Results following the deployment of IRS in 2014 census population size decreased by 72.5% relative to the 2012 baseline survey (pre-IRS) whereas parasite prevalence only decreased by 54.5%. 

With respect to the reviewer’s comment on “practicalities and cost”, var_coding has been used to successfully amplify _P. falciparum DNA collected as DBS that have been stored for more than 5-years from both clinical and lower density asymptomatic infection, without the additional step and added cost of sWGA ($8 to $32 USD per isolates, for costing estimates see (LaVerriere et al., 2022; Tessema et al., 2020)), which is currently required by other molecular surveillance methods (Jacob et al., 2021; LaVerriere et al., 2022; Oyola et al., 2016). _Var_coding involves a single PCR per isolate using degenerate primers, where a large number of isolates can be multiplexed into a single pool for amplicon sequencing.  Thus, the overall costs for incorporating molecular surveillance with _var_coding are mainly driven by the number of PCRs/clean-ups, the number samples indexed per sequencing run, and the NGS technology used (discussed in more detail in our publication Ghansah et al. (Ghansah et al., 2023)). Previous work has shown that _var_coding can be use both locally and globally for molecular surveillance, without the need to be customized or updated, thus it can be fairly easily deployed in malaria endemic regions (Chen et al., 2011; Day et al., 2017; Rougeron et al., 2017; Ruybal-Pesántez et al., 2022, 2021; Tonkin-Hill et al., 2021).

Weaknesses: 

Overall the manuscript is well-written and generally comprehensively explained. Some terms could be clarified to help the reader and I had some issues with a section of the methods and some of the more definitive statements given the evidence supporting them. 

Thank you for the overall positive assessment. On addressing the “issues with a section of the methods” and “some of the more definitive statements given the evidence supporting them”, it is impossible to do so however, without an explicit indication of which methods and statements the reviewer is referring to. Hopefully, the answers to the detailed comments and questions of reviewers 1 and 2 address any methodological concerns (i.e., in the Materials and Methods and Results). To the issue of “definitive statements”, etc. we are unable to respond without further information.

Recommendations For The Authors:

Reviewer #1 (Recommendations For The Authors):

Line 273: there is a reference to a figure which supports the empirical distribution of repertoire given MOI = 1, but the figure does not appear to exist.

We now included the correct figure for the repertoire size distribution as Figure supplement 3 (previously published in Labbé et al (Labbé et al., 2023)). This figure was accidently forgotten when the manuscript was submitted for review, we thank the reviewer for bringing this to our attention.

Line 299: while this likely makes little difference, an insignificant result from a Kolmogorov-Smirnov test doesn't tell you if the distributions are the same, it only means there is not enough evidence to determine they are different (i.e. fail to reject the null). Also, what does the "mean MOI difference" column in supplementary table 3 mean? 

The mean MOI difference is the difference in the mean value between the pairwise comparison of the true population-level MOI distribution, that of the population-level MOI estimates from either pooling the maximum a posteriori (MAP) estimates per individual host or the mixture distribution, or that of the population-level MOI estimates from different prior choices. This is now clarified as requested in the Table supplements 3 - 6. 

Figure 4: how are the confidence intervals for the estimated number of var repertoires calculated? Also should include horizontal error bars for prevalence measures.

The confidence intervals were calculated based on a bootstrap approach. We re-sampled 10,000 replicates from the original population-level MOI distribution with replacement. Each resampled replicate is the same size as the original sample. We then derive the 95% CI based on the distribution of the mean MOI of those resampled replicates. This is now clarified as requested in the Figure 4 caption (as well as Table supplement 7 footnotes). In addition, we have also updated Figure 4AB and have included the 95% CI for all measures for clarity. 

Reviewer #2 (Recommendations For The Authors): 

-  I would like to see a plot like Supplemental Figure 8 for the upsA DBLα repertoire size. 

The upsA repertoire size for each survey and by age group has now been provided as requested in Figure supplement 5AB. 

-  Supplemental Table 2 is cut off in the pdf. 

We have now resolved this issue so that the Table supplement 2 is no longer cut off.  

Reviewer #3 (Recommendations For The Authors): 

The manuscript terms the phrase "census population size". To me, the census is all about the number of individuals, not necessarily their diversity. I appreciate that there is no simple term for this, and I imagine the authors have considered many alternatives, but could it be clearer to say the "genetic census population size"? For example, I found the short title not particularly descriptive "Impact of IRS and SMC on census population size", which certainly didn't make me think of parasite diversity.

Please see our response to comment (2) of reviewer 1. We prefer not to add “genetic” to the phrase as the distinction from effective population size from population genetics is important, and the quantity we are after is an ecological one. 

The authors do not currently say much about the potential biases in the genetic data and how this might influence results. It seems likely that because (i) patients with sub-microscopic parasitaemia were not sampled and (ii) because a moderate number of (likely low density) samples failed to generate genetic data, that the observed MOI is an overestimate. I'd be interested to hear the authors' thoughts about how this could be overcome or taken into account in the future. 

We thank the reviewer for this this comment and agree that this is an interesting area for further consideration. However, based on research from the Day Lab that is currently under review (Tan et al. 2024, under review), the estimated MOI using the Bayesian approach is likely not an “overestimate” but rather an “underestimate”. In this research by Tan et al. (2024) isolate MOI was estimated and compared using different initial whole blood volumes (e.g., 1, 10, 50, 100 uL) for the gDNA extraction. Using _var_coding and comparing these different volumes it was found that MOI was significantly “underestimated” when small blood volumes were used for the gDNA extraction, i.e., there was a ~3-fold increase in median MOI between 1μL and 100μL blood. Ultimately these findings will allow us to make computational corrections so that more accurate estimates of MOI can be obtained from the DBS in the future.

The authors do not make much of LLIN use and for me, this can explain some of the trends. The first survey was conducted soon after a mass distribution whereas the last was done at least a year after (when fewer people would have been using the nets which are older and less effective). We have also seen a rise in pyrethroid resistance in the mosquito populations of the area which could further diminish the LLIN activity. This difference in LLIN efficacy between the first and last survey could explain similar prevalence, yet lower diversity (in Figures 4B/5). However, it also might mean that statements such as Line 478 "This is indicative of a loss of immunity during IRS which may relate to the observed loss of var richness, especially the many rare types" need to be tapered as the higher prevalence observed in this age group could be caused by lower LLIN efficacy at the time of the last survey, not loss of immunity (though both could be true).  

We thank the reviewer for this question and agree that (i) LLIN usage and (ii) pyrethroid resistance are important factors to consider. 

(i) Over the course of this study self-reported LLIN usage the previous night remained high across all age groups in each of the surveys (≥ 83.5%), in fact more participants reported sleeping under an LLIN in 2017 (96.8%) following the discontinuation of IRS compared to the 2012 baseline survey (89.1%). This increase in LLIN usage in 2017 is likely a result of several factors including a rebound in the local vector population making LLINs necessary again, increased community education and/or awareness on the importance of using LLINs, among others. Information on the LLINs (i.e., PermaNet 2.0, Olyset, or DawaPlus 2.0) distributed and participant reported usage the previous night has now been included in the Materials and Methods as requested by the reviewer.

(ii) As to the reviewer’s question on increased in pyrethroid resistance in Ghana over the study period, research undertaken by our entomology collaborators (Noguchi Memorial Insftute for Medical Research: Profs. S. Dadzie and M. Appawu; and Navrongo Health Research Centre:  Dr. V. Asoala) has shown that pyrethroid resistance is a major problem across the country, including the Upper East Region. Preliminary studies from Bongo District (2013 - 2015), were undertaken to monitor for mutations in the voltage gated sodium channel gene that have been associated with knockdown resistance to pyrethroids and DDT in West Africa (kdr-w). Through this analysis the homozygote resistance kdr-w allele (RR) was found in 90% of An. gambiae s.s. samples tested from Bongo, providing evidence of high pyrethroid resistance in Bongo District dating back to 2013, i.e., prior to the IRS intervention (S. Dadzie, M. Appawu, personal communication). Although we do not have data in Bongo District on kdr-w from 2017 (i.e., post-IRS), we can hypothesize that pyrethroid resistance likely did not decline in the area, given the widespread deployment and use of LLINs.

Thus, given this information that (i) self-reported LLIN usage remained high in all surveys (≥ 83.5%), and that (ii) there was evidence of high pyrethroid resistance in 2013 (i.e., kdr-w (RR) _~_90%), the rebound in prevalence observed for the older age groups (i.e., adolescents and adults) in 2017 is therefore best explained by a loss of immunity.

I must confess I got a little lost with some of the Bayesian model section methods and the figure supplements. Line 272 reads "The measurement error is simply the repertoire size distribution, that is, the distribution of the number of non-upsA DBLα types sequenced given MOI = 1, which is empirically available (Figure supplement 3)." This does not appear correct as this figure is measuring kl divergence. If this is not a mistake in graph ordering please consider explaining the rationale for why this graph is being used to justify your point. 

We now included the correct figure for the repertoire size distribution as Figure supplement 3 (previously published in Labbé et al (Labbé et al., 2023)). This figure was accidently forgotten when the manuscript was submitted for review, we thank the reviewer for bringing our attention to this matter. We hope that the inclusion of this Figure as well as a more detailed description of the Bayesian approach helps to makes this section in the Materials and Methods clearer for the reader. 

I was somewhat surprised that the choice of prior for estimating the MOI distribution at the population level did not make much difference. To me, the negative binomial distribution makes much more sense. I was left wondering, as you are only measuring MOI in positive individuals, whether you used zero truncated Poisson and zero truncated negative binomial distributions, and if not, whether this was a cause of a lack of difference between uniform and other priors. 

Thank you for the relevant question. We have indeed considered different priors and the robustness of our  estimates to this choice and have now better described this in the text. We focused on individuals who had a confirmed microscopic asymptomatic P. falciparum infection for our MOI estimation, as median P. falciparum densities were overall low in this population during each survey (i.e., median ≤ 520 parasites/µL, see Table supplement 1). Thus, we used either a uniform prior excluding zero or a zero truncated negative binomial distribution when exploring the impact of priors on the final population-level MOI distribution.  A uniform prior and a zero-truncated negative binomial distribution with parameters within the range typical of high-transmission endemic regions (higher mean MOI with tails around higher MOI values) produce similar MOI  estimates at both the individual and population level. However, when setting the parameter range of the zero-truncated negative binomial to be of those in low transmission endemic regions where the empirical MOI distribution centers around mono-clonal infections with the majority of MOI = 1 or 2 (mean MOI » 1.5, no tail around higher MOI values), the final population-level MOI distribution does deviate more from that assuming the aforementioned prior and parameter choices. The final individual- and population-level MOI estimates are not sensitive to the specifics of the prior MOI distribution as long as this distribution captures the tail around higher MOI values with above-zero probability.   

The high MOI in children <5yrs in 2017 (immediately after SMC) is very interesting. Any thoughts on how/why? 

This result indicates that although the prevalence of asymptomatic P. falciparum infections remained significantly lower for the younger children targeted by SMC in 2017 compared 2012, they still carried multiclonal infections, as the reviewer has pointed out (Figure 3B). Importantly this upward shift in the MOI distributions (and median MOI) was observed in all age groups in 2017, not just the younger children, and provides evidence that transmission intensity in Bongo has rebounded in 2017, 32-months a er the discontinuation of IRS.  This increase in MOI for younger children at first glance may seem to be surprising, but instead likely shows the limitations of SMC to clear and/or supress the establishment of newly acquired infections, particularly at the end of the transmission season following the final cycle of SMC (i.e., end of September 2017 in Bongo District; NMEP/GHS, personal communication) when the posttreatment prophylactic effects of SMC would have waned (Chotsiri et al., 2022).  

Line 521 in the penultimate paragraph says "we have analysed only low density...." should this not be "moderate" density, as low density infections might not be detected? The density range itself is not reported in the manuscript so could be added. 

In Table supplement 1 we have provided the median, including the inter-quartile range, across each survey by age group. For the revision we have now provided the density min-max range, as requested by the reviewer. Finally, we have revised the statement in the discussion so that it now reads “….we have analysed low- to moderate-density, chronic asymptomatic infections (see Table supplement 1)……”.   

Data availability - From the text the full breakdown of the epidemiological survey does not appear to be available, just a summary of defined age bounds in the SI. Provision of these data (with associated covariates such as parasite density and host characteristics linked to genetic samples) would facilitate more in-depth secondary analyses. 

To address this question, we have updated the “Data availability statement” section with the following statement: “All data associated with this study are available in the main text, the Supporting Information, or upon reasonable request for research purposes to the corresponding author, Prof. Karen Day (karen.day@unimelb.edu.au).”  

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Chen DS, Barry AE, Leliwa-Sytek A, Smith T-AA, Peterson I, Brown SM, Migot-Nabias F, Deloron P, Kortok MM, Marsh K, Daily JP, Ndiaye D, Sarr O, Mboup S, Day KP. 2011. A molecular epidemiological study of var gene diversity to characterize the reservoir of Plasmodium falciparum in humans in Africa. PLoS One 6:e16629. doi:10.1371/journal.pone.0016629

Chotsiri P, White NJ, Tarning J. 2022. Pharmacokinetic considerations in seasonal malaria chemoprevention. Trends Parasitol. doi:10.1016/j.pt.2022.05.003

Day KP, Artzy-Randrup Y, Tiedje KE, Rougeron V, Chen DS, Rask TS, Rorick MM, Migot-Nabias F, Deloron P, Luty AJF, Pascual M. 2017. Evidence of Strain Structure in Plasmodium falciparum Var Gene Repertoires in Children from Gabon, West Africa. PNAS 114:E4103–E4111. doi:10.1073/pnas.1613018114

Ghansah A, Tiedje KE, Argyropoulos DC, Onwona CO, Deed SL, Labbé F, Oduro AR, Koram KA, Pascual M, Day KP. 2023. Comparison of molecular surveillance methods to assess changes in the population genetics of Plasmodium falciparum in high transmission. Fron9ers in Parasitology 2:1067966. doi: 10.3389/fpara.2023.1067966

He Q, Pilosof S, Tiedje KE, Ruybal-Pesántez S, Artzy-Randrup Y, Baskerville EB, Day KP, Pascual M. 2018. Networks of genetic similarity reveal non-neutral processes shape strain structure in Plasmodium falciparum. Nat Commun 9:1817. doi:10.1038/s41467-018-04219-3

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Labbé F, He Q, Zhan Q, Tiedje KE, Argyropoulos DC, Tan MH, Ghansah A, Day KP, Pascual M. 2023. Neutral vs . non-neutral genetic footprints of Plasmodium falciparum multiclonal infections. PLoS Comput Biol 19:e1010816. doi:doi.org/10.1101/2022.06.27.497801

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Charles, talk about a freaking amazing academic writer! I’m so jealous of your expansive vocabulary! Especially in your false dreams writing, your highly descriptive and emotional sentences really shone through – you’ve definitely got an ear for music! For this and the other writings, each one of your sentences contained so much meaning and clearly related to the purpose of the writing – especially with your concluding ideas!

Most of the comments I made were very minor ones - usually relating to grammar or sentence structure - because I thought you clearly stated your thoughts. One big suggestion is to try and implement shorter sentences to add a sort of “attitude to your writing.” You are fantastic at formally stating your ideas, yet sometimes I find that the most influential writings use a conversational tone here and there. I also noticed that many of your sentences end with a comma then -ing word; therefore, maybe look to change this up somehow by making shorter sentences, placing the dependent clause at the beginning of the sentence, or using different punctuation to help with structure variation. Also, I understand you didn’t have enough time to do this yet, but definitely implement some media to help your project’s visual appeal.

I like the linear structure that you went with. I think (other than possibly switching “The Duality of Vulnerability Between ‘u’ and ‘i’” and “How Kendrick Lamar Transformed Cultural Trauma Into To Pimp a Butterfly”) the order you have is great. One thing that I suggest would be to incorporate more of the tag feature at the bottom of your pages. For example, tag the bibliography page at the bottom of each page where you use cites and the lyrics page after you cite one of the song’s lyrics.

In the end, I really enjoyed your project. Of course I knew who Kendrick Lamar was, but I had no clue of all the details about his young life. I enjoyed your unique approach to this writing: analyzing two songs instead of one. The relationship that you discussed between these songs (especially in “The Duality of Vulnerability Between ‘u’ and ‘i’”) is very informative and overall intriguing – I love it when music artists tell a story with multiple songs (just as you did in false dreams)! For me, analyzing one song was hard enough but two…that’s just impressive. And the best part: you did it beautifully!

I think it would be awesome if every time in your project you talked bout the lyrics, you would hyperlink it back to here and tag this page at the bottom of your writing. It would help give your project more connections and give this lyrics page more purpose!

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

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Reply to the reviewers

Manuscript number: RC-2024-02640

Corresponding author(s): Purusharth I, Rajyaguru; Stephan Vagner

1. General Statements

This section is optional. Insert here any general statements you wish to make about the goal of the study or about the reviews.

In the manuscript titled, "RGG motif-containing Scd6/LSM14A proteins regulate the translation of specific mRNAs in response to hydroxyurea-induced genotoxic stress" we elucidate a conserved role of an RNA-binding protein with low-complexity sequences (RGG-motifs) in genotoxic stress response. This work uncovers HU-stress mediated translation regulation of SRS2, Ligase IV and RTEL1 transcripts by Scd6 (yeast)/LSM14 (human). It further identifies RNP condensates and arginine methylation as sites and means of this regulation.

We heartily thank all three reviewers for their overall encouraging comments about the significance of this manuscript. Specifically, we appreciate their view that the manuscript provides new functional insights into the role of RGG-motif-containing RNA-binding protein in genotoxic stress response. They further agree that such knowledge will impact and interest the general audience of RNA biology and stress biology.

We have carefully noted all the comments raised by three reviewers. We have addressed almost all the comments, including several by performing new experiments. The new results and their analysis have helped us improve the manuscript, allowing us to provide a stronger mechanistic and functional insight underlying the findings presented in this work. We thank the reviewers for their insightful comments. Below, we provide a point-by-point response to each of the comments.

2. Description of the planned revisions

Insert here a point-by-point reply that explains what revisions, additional experimentations and analyses are planned to address the points raised by the referees.

Reviewer 3

Major Comment 4: Page 7, top: '...indicating that Scd6 regulated the expression of SRS2 in a HU-dependent manner.' In my opinion, the results so far suggest that Scd6 and SRS2 are somehow functionally connected during HU-treatment. To substantiate the statement of the authors, they should provide a Western blot showing that the levels of SRS2 change upon Scd6 KO or OE during HU-treatment. This will also substantiate the results shown in Figs 2G-H.

Response: We thank the reviewer for this comment. Detecting Srs2 protein has been technically challenging. The SRS2 construct used in this study is untagged. Unfortunately, the commercial SRS2 antibody has been discontinued. We requested several groups who have used SRS2 antibody in their past studies but they have either closed down their labs or are unable to find an aliquot to share. We have tried tagging SRS2 with 6xHis/1XFLAG/3xFLAG tags at N and C-terminal, but unfortunately, the protein was undetectable in the Western blot analysis using either of the tag-specific antibodies. We have also tried western blot analysis using SRS2-GFP strain, but the protein does not get detected by anti-GFP antibody, probably because of very low expression.

Since we will not be able to provide western blots for Srs2 protein levels due to technical challenges, we shall provide western blots for RTEL1 (human homolog of Srs2) protein levels upon Lsm14A knockdown in the presence and absence of HU. This will validate the polysome data we have of RTEL1 regulation by LSM14A, and would, by extension, substantiate the SRS2 polysome data.

Major Comment 5: Figs 3: How are the localization of Scd6 protein and SRS2 mRNA to granules, and the levels of Srs2 protein, in cells exposed to HU after deletion of Hmt1? This would substantiate a role of Hmt1 in vivo.

Response: We will provide the data for Scd6 protein localization and SRS2 mRNA localization in granule enriched fraction upon HU treatment in Δhmt1 background. This experiment is ongoing.

3. Description of the revisions that have already been incorporated in the transferred manuscript

Reviewer 1

Major Comment 1: Fig. 1 F/G: were the delta RGG and LSM variants expressed at an equivalent level to the WT protein in these experiments?

Response: We thank the reviewer for this comment. We have quantified the total fluorescence intensity of GFP from the existing microscopy images for WT and domain deletion mutants for both Scd6 and Sbp1 (Now Figure 3A and 3D). This result (added as a new figure panel Fig 3C and 3F) indicates that the levels of Scd6∆RGG mutant is more whereas Scd6∆Lsm protein levels are comparable than WT. Similarly, Sbp1∆RGG mutant expression is comparable to WT in the given experimental conditions.

Major Comment 2: Fig. 3G: The 6 data points for the delta LSM variant are literally spread evenly up and down the graph, making these data appear highly questionable as to whether one can draw a definitive conclusion from them.

Response: We agree with the reviewer that the data points are varied. To address the scatter in data, we have performed additional experiments and added those to the existing results. Even though there is a spread in the points, except for one data point, all others show an increase in methylation of LSM domain deletion mutant compared to WT, which is statistically significant. The old blot and graph (Old Figure 3F and 3G) have now been replaced with new ones (Figure 5F and 5G) which look more convincing. The result and conclusion derived from it remain unchanged.

Minor Comments

Comment 1: Abstract: the acronym NHEJ likely will need to be defined for the general reader.

Response: The acronym has been expanded in the abstract and explained in the introduction.

Comment 2: Introduction, first paragraph: change gene expression to 'transcription' in the phrase 'Even if the contribution of gene expression to GSR..' as I assume this is what is meant here. Gene expression consists of synthesis, processing, translation and decay.

Response: The required change has been made.

Comment 3: Pg. 3 Introduction: Since they are liquid-liquid phase condensates and ribonucleoproteins (RNPs) refer to any protein-RNA interaction, I think that referring to PBs and SGs as mRNPs is a bit misleading (especially the 'major mRNPs').

Response: The statement has been rewritten.

Comment 4: Introduction: are PBs truly 'sites' of mRNA decay as stated? There are papers in the literature that would argue otherwise.

Response: The statement has been modified with more citations.

Comment 5: Pg. 3, three lines from bottom. Change LSM14 to LSM14A

Response: The addition has been done.

Comment 6: Pg. 4 top - What is an 'LCS' - containing protein? The acronym has not been defined

Response: The acronym has been defined now. We have also defined acronyms wherever they were missing.

Comment 7: Fig. S1 - there are a lot of important data in this figure that demonstrate the coordinated movement of Scd6 and Sbp1 to granules. They should be moved into the main body of the manuscript in my opinion. Likewise, a whole section of the Results is dedicated to Fig. S2 - thus I would suggest moving these data into the main body of the manuscript to assist the reader.

Response: We thank the reviewer for pointing this out. Figure S1 has now been added to the main body of the manuscript as Figure 2. Figure S2 has now been added to Figure 1 and new Figure 3. This rearrangement has improved the flow of the manuscript.

Comment 8: Fig. 1F should be flipped in the figure with panel G since G is discussed in the results section before F

Response: Figure 1F and 1G are now Figure 3A and 3D and in the same order as mentioned in the text.

Comment 9: Be sure to define all acronyms for the reader.

Response: All acronyms in the manuscript have been defined wherever applicable.

Comment 11: Fig. 3H/I: It might be optimal to calculate and compare Kd's for the methylated and unmethylated variants. Also, the labels at the top of 3H do not line up with the wells of the EMSA gel.

Response: We have calculated the Kd’s for the EMSA, and it has been added to the results section. We have also aligned the labels at the top of the EMSA gel (now Figure 5I) to match with the wells.

Reviewer 2

Major Comment 1: Fig. 2A, B. While there seems to be an effect on the lag phase, it could be revealing if the authors pls. calculate the doubling times for the strains and treatments (taking through the exponential growth phase). Furthermore, it would be good if the authors can show the rescue of phenotypes for deletion strains (ie. reintroduction of respective gene on ARS-CEN based plasmids or (if not available) with the OE plasmids.

Response: We thank the reviewer for this remark. We have calculated the doubling times for the strains in the tested conditions and added in the text. We have analyzed the effect of complementing the deletion strains with the respective genes on the CEN plasmid. We observe that Δscd6 shows tolerance to HU stress as previously seen, which gets rescued almost completely upon complementation with WT SCD6. This result has been included in the manuscript as a new figure panel (Figure S1A) . Δsbp1 also shows marginal tolerance to HU stress, but complementation with WT SBP1 only slightly rescues the phenotype, which is not statistically significant (Figure S1B). This result highlights a more important role of Scd6 as compared to Sbp1 in genotoxic stress response.

Major Comment 2 (part 1): Fig. 3H. The authors tested the 5'UTR of SRS2 for interaction with recombinant Scd6. Firstly, it is unclear why the authors have chosen the 5'UTR for investigation? Can the authors explain.

Response: We thank the reviewer for this important comment. During experimentation and analysis, we assayed Scd6 binding to two different fragments of SRS2 mRNA: 5’ and 3’UTR of same lengths (200 bases). We used the UTR fragments because there are numerous reports indicating the role of UTRs in the regulation by RNA binding proteins (https://doi.org/10.1093/bfgp/els056, https://doi.org/10.1126/science.aad9868, https://doi.org/10.1093/jxb/erae073). RNA EMSAs with purified Scd6 and in vitro transcribed UTR RNA fragments revealed a significantly better binding of Scd6 with the 5’ UTR fragment of SRS2 mRNA compared to the 3’ UTR. Therefore, we proceeded with the 5’ UTR fragment for further analysis. We have now added this as a supplementary figure panel and explanation in the manuscript text (Figure S2B).

Major Comment 2 (part 2): Secondly, the affinities are relatively low (µM), and the gel shift assay lacks a negative control. The authors should test an unrelated RNA fragment of approximately the same size to control for specificity (negative control). It is unclear whether the protein could interact with any RNA fragment through a charged RNA backbone.

Response: Our in vivo data suggests that the binding of Scd6 with SRS2 mRNA is condition and RNA-specific and is regulated by methylation (now Figure 5C, S2A and 5E). As the reviewer mentioned, Scd6, in principle, could bind to any RNA molecule given the affinity of an RNA-binding protein (with positively charged amino acids such as arginine) to RNA molecule. Nevertheless, the significant difference in the binding of Scd6 to the 5’UTR and 3’UTR fragments itself acts as a relative control for EMSA. The aim of the in vitro experiment (EMSA) was to establish the difference, if any, in the binding affinities of unmethylated vs methylated Scd6, like the in vivo data, where we observe significantly increased binding to SRS2 mRNA upon decreased Scd6 methylation.

Major Comment 2 (part 3): Thirdly, it would be good if the authors could show a Coomassie gel for the recombinant protein used in those assays.

Response: The Coomassie gel which was provided as part the supplementary data (now Figure S2C), have now been added as another gel image to the main figure (Figure 5H), next to the EMSA, for better clarity.

Major Comment 3: Methods and Materials: The Materials and Methods section lacks important information and requires further details to evaluate the study (see below 11 – 17)

Response: The comment has been duly noted.

Minor Comments

Results:

Comment 4: The numbering of Figure S1, S2 is confused in the first part of the results section. The authors should check numbering. In general, numbering should follow in the order of the text - pls. check.

Response: Based on the comment#7 by Reviewer 1, Figure S1 and S2 have now been added to the main figure, and the changes in the text have been made accordingly.

Comment 5: Pg. 5. CHX treatment leads to a decrease in Scd6-GFP and SBP-1 GFP granules. Essentially, CHX blocks translation elongation so the result indicates that puncta depend on active translation. The authors may want to add this liaising point towards the claim that mRNAs could be present in those puncta. How this results integrates with data shown in Fig. S5B*.

*

Response: We thank the reviewer for this comment. Since granules are dynamic structures that depend on active translation, CHX treatment leads to the dissociation of Scd6 and Sbp1 granules. This indicate that most of the mRNAs present in these granules could be recycled for translation in polysomes. This strategy has been used in multiple research articles for similar deductions (10.1091/mbc.E08-05-0499, https://doi.org/10.1083/jcb.151.6.1257, https://doi.org/10.1093/nar/gku582). We have now modified the text in the manuscript to accommodate this point. It has been previously reported that core components of stress granules, once formed are stable and resistant to RNase, EDTA and NaCl treatment ex vivo (https://doi.org/10.1016/j.cell.2015.12.038), even when these structures have RNA. Figure S5B (now S3C) indicates that the granule enriched fraction derived from untreated and treated cells indeed behaves like stress granule cores and not protein aggregates allowing us to proceed with downstream experiments.

Comment 6: Fig. 2H. It would be helpful to the reader, if the authors could mark the respective fraction in the polysomes taken for analysis of relative enrichments. How was this relative enrichment was calculated needs further description.

Response: The modification has been made (now Figure 4G) and added to the methods and materials.

Comment 7: Fig. S5B. 1% SDS treatment cause absence for Scd6 signal from the pellet fraction. Based on this result, I am not clear how based on this result they can claim for presence of higher order mRNA-protein complexes? Why does it exclude the possibility for Scd6 aggregates accumulating in the pellet? The authors need to explain/ modify this statement. Related to earlier findings that showed dependency of puncta upon CHX treatment, one wonders how this result matches to this earlier observation (ie.EDTA should dissassemble ribosomes)? Can the authors explain?

Response: The very stable β-zipper interactions present in prion like domains, which leads to aggregation, is resistant to 1-2% SDS treatment (https://doi.org/10.1016/j.cell.2015.12.038). Hence, we think that solubilization upon 1% SDS treatment indicates that these are not aggregates. EDTA and NaCl are capable of disrupting interactions, which are stabilized mainly by electrostatic forces. Our observations (now Figure S3C) indicate that Scd6 could be part of the more stable mRNP condensate core structure and are therefore resistant to these treatments. Such observations have been previously reported, for example, stress granules in yeast are not affected by EDTA and NaCl treatments (https://doi.org/10.1016/j.cell.2015.12.038).

Comment 8 (part 1): Fig. 5E, F. For the RNA-seq, the authors compared polysomes with free RNAs (up to 80S) and found enrichment of LIG4 and RTEL1. However, the polysomal profiling mainly shows a slight shift of those mRNAs in higher polysomes; while there is no difference compared to free fractions. How can this be explained?

Response: We observed a shift from lower polysome fractions (11-12-13) (not from free fractions) to higher polysome fractions (14-15) indicating an increased number of ribosomes translating the RTEL1 mRNA.

Comment 8 (part 2): On the line, the authors should indicate clearly what fractions were pooled for RNA seq analysis. It is also not clear how the authors quantified percentage of RNA in individual fractions (have they spiked-in an RNA?) - this needs to be stated in the M&M section.

Response: We have now added the requested information in the Materials and Methods section. Fractions 13 to 17 were pooled for RNAseq analysis. The % of RNA in each fraction was calculated as described in Panda AC et al. Bio Protoc . 2017 Feb 5;7(3):e2126. doi: 10.21769/BioProtoc.2126

Comment 9: At the end, if may be beneficial to the reader if the authors could provide a simple scheme depicting the model develop during this study.

Response: We thank the reviewer for this comment. We have included a model derived from our study as a new figure (Figure 8).

Comment 10: Supplemental Data set (.xls) The adjusted p-values are clustered and >0.05. Can the authors check and describe how those were calculated. How does it match with Volcano plots.

Response: The adjusted p-values are indeed >0.05. The p-values (and not the adjusted p-values) are plotted in the Volcano plot (now Fig. 7E)

Materials and Methods:

Comment 11: A list of primers should be given with specification of their use.

Response: The list has been added in the supplementary files (Table S3)

Comment 12: The plasmids constructed for (over)expression of proteins/ production of recombinant proteins should be added. If published, references should be added accordingly.

Response: The list has been added in the supplementary files (Table S4)

Comment 13: RIP: the media for growing yeast cells should be added. Check also other section if defined.

Response: The information has been added wherever required.

Comment 14: RT-qPCR is not sufficiently described. RT kit needs specification, PCR reaction cycles should be given.

Response: The information has been added

Comment 15: Quantification of mRNA levels in polysomes is unclear. How was the distribution of mRNA profiles determined? Have the authors added some RNA spikes to fractions?

See above.

Response: The % of RNA in each fraction was calculated as described in Panda AC et al. Bio Protoc . 2017 Feb 5;7(3):e2126. doi: 10.21769/BioProtoc.2126. Details have now been added in the Mat and Meth section.

Comment 16: The calculation for the enrichments in IPs is not described conclusively and should be added.

Response: The calculation has now been elaborated and added to the methods and materials section.

Comment 17: Polysomes fractionation (mammalian). It is indicated that the resultant supernatant was adjusted to 5M NaCl and 1 M MgCl2. This seems to be very high - is this a typo? OR why such high concentrations have been chosen?

Response: The sentence has been removed. There is no need for such adjustment.

Review 3

Major Comment 2: Fig 2A-F: The effects of Scd6 and Sbp1 deletion upon HU-treatment are very small. A more convincing effect is observed upon over-expression of both SRS2 and SCD6. What is the effect of over-expression of SCD6 and SBP1 alone (i.e. without SRS2 over-expression)?

Response: We thank the reviewer for this comment. The effects are indeed small but consistent and reproducible with two different kinds of assays (growth curve and plating assay, now Figure 4A-C). Overexpression of Scd6 or Sbp1 alone when expressed from a CEN/2u plasmid does not have any phenotype in the presence of HU (Figure S1A and S1B). Although, it has been previously reported that galactose-inducible Scd6 causes a severe growth defect (https://doi.org/10.1093/nar/gkw762), we performed spot assays with galactose inducible Scd6 and Sbp1 on control and HU plates, but did not see any difference in the extent of growth upon HU treatment. This data has now been presented as Figure S1C.

Major Comment 3: Fig 2E: Why is there an opposite effect of deletion of Scd6 and Sbp1in the SRS2 over-expression background?

Response: We thank the reviewer for this comment; however, we respectfully disagree with the idea that overexpression of SRS2 yields opposite phenotypes in SCD6 and SBP1 deletion backgrounds. Figure 2E (now Figure 4E) gives the impression that SRS2 overexpression in SBP1 deletion grows significantly more for two reasons. There was an increased spotting of Dsbp1 cells overexpressing SRS2 (row#6) as compared to Dscd6 cells overexpressing SRS2 (row#4), which is evident in the plate without HU (left panel). Additionally, there is also reduced spotting of wild-type cells overexpressing SRS2 (row#2) as compared to Dscd6 cells overexpressing SRS2 (row#4). We have now replaced these panels with another image with better loadings. Quantitation of five experiments (Figure S1F) indicates that Dsbp1 grows slightly better in both EV and SRS2 over-expression background, but the increase is not statistically significant. We interpret this data to suggest that SRS2 is not a direct target of Sbp1. Another protein perhaps performs the specific role of Sbp1 in assisting Scd6 in genotoxic stress response in Dsbp1 background.

Major Comment 6: Fig 3C: Is the increased interaction of SRS2 mRNA with Scd6 due to increased levels of SRS2 mRNA upon HU treatment? See also comment below.

Response: Based on RT-qPCR of total RNA, SRS2 mRNA levels do not seem to increase, which has now been added as a Supplementary figure (Figure S3D, left panel). Moreover, quantification of SRS2 mRNA from the FISH data also does not support an increase in mRNA levels (Figure 6D, left panel).

Major Comment 7: Fig 4A: There seems to be an enrichment of SRS2 mRNA both in the granule-enriched pellet and in the supernatant upon HU treatment in the Scd6-GFP context, suggesting increased SRS2 mRNA levels altogether. The enrichment in granules upon HU is difficult to see, as one should measure the distribution of the mRNA in the pellet relative to the supernatant. Can the authors represent the ratio pellet/supernatant normalized to a control transcript? A similar calculation can be done for the protein normalized to a control protein.

Response: As mentioned earlier, RT-qPCR data with SRS2 mRNA levels in total lysate has been added to supplementary data (Figure S3D, left panel). Based on RT-qPCR of total RNA, SRS2 mRNA levels do not seem to increase.

The quantification of SRS2 mRNA and Scd6 protein enrichment is done such that the supernatant and pellet fractions are separately normalized to their respective controls (Scd6GFP, untreated sample) and therefore do not represent the mRNA distribution but relative mRNA enrichment. However, as per the recommendation by the reviewer, the data has been replotted as a ratio of supernatant and pellet with the addition of two more data points and has been added in the main figure (Figure 6E). The data concludes increased enrichment of SRS2 mRNA in granules upon HU treatment. The previous data has been included in the supplementary data as Supplementary figure (Figure S3D, right panel).

Major Comment 8: Fig 4B: Increased juxtaposition of SRS2 mRNA and Scd6 granules upon HU treatment does not really mean increased colocalization. Granules are likely significantly apart such that increased interactions between the two partners are not explained by increased juxtaposition. Please, comment, tune-down and provide examples where increased granule juxtaposition is associated with increased interaction.

Response: We believe that the usage of term ‘juxtaposition’ is leading to misinterpretation of the data. Therefore, we have replaced it with ‘percentage area overlap’ analysis to demonstrate that the SRS2 mRNA foci indeed overlap/localize with Scd6GFP foci up to an average of 43.5% in HU stress. This analysis has been added as an additional panel (Figure 6C), indicating that the SRS2 mRNA interacts with Scd6 in the granules. Even though the granules do not overlap/localize completely, the observed area of granule overlap (43.5%) is functionally effective as it leads to the physical interaction of Scd6 and SRS2 (Figure 6E & 5C) and, consequently, repression (Figure 4H). The FISH data, granule enrichment, and RNA immunoprecipitation data demonstrate Scd6 protein and SRS2 mRNA interaction in granules.

Major Comment 9: Fig 4D: These results are in direct contradiction with those shown in Fig 1C.

Response: We thank the reviewer for this comment. Figure 1C (now Figure 1B and 1C) demonstrates that Scd6 localization to puncta, when expressed from a CEN plasmid, significantly increases upon HU stress. The same trend is visible in Figure 4D (now Figure 6D) where Scd6 is expressed from a 2μ plasmid; however, it is not significant. The data in 1C and 4D (now 1C and 6D respectively) are rather inconsistent with each other than being contradictory. Nevertheless, we understand this reviewer’s concern and address it below.

The initial localization experiments were performed using Scd6 expressed from CEN plasmid or genomically tagged Scd6. Since both these versions of Scd6 are not detectable using western blotting, we used Scd6 expressed from 2μ plasmid. Localization to condensates by liquid-liquid phase separation is a concentration-driven phenomenon. Therefore, when Scd6 is expressed from a 2μ plasmid amounting to increased protein levels, its localization to puncta increases even in the absence of stress, which is visible in the quantitation provided in the figure (Figure 6D) as compared to Figure 1C. We have now analyzed the percentage granular localization (granule intensity) of Scd6 (2µ), which significantly increases upon HU stress (Figure S3A). Thus although number of Scd6 granules does not increase upon HU stress when expressed from a 2µ plasmid, there is significant increase in localization of Scd6 to granule upon HU stress (Figure S3A).

Major comment 10: Fig 5E: Can the authors provide a GO analysis of the up- and down- regulated transcripts?

Response: We have now provided a GO analysis (Table S2). However, due to the low number of regulated genes, only a few GO terms with weak scores appeared in the analysis.

Minor comments:

Comment 11: Figures S1 and S2 seem to be swapped. Please make sure that Figures and panels are arranged in the order they are mentioned in the main text.

Response: We thank the reviewer for pointing it out. Based on the comment#7 by Reviewer 1, Figure S1 and S2 have now been added to the main figure, and the changes in the text have been made accordingly. We have ensured that the order of figures matches the text.

Comment 12: Page 5, sentence: 'our results argue for the role of Scd6 and Sbp1 in HU-mediated stress response'. I do not agree, as no functional assays showing that these proteins affect HU-mediated stress response have been provided at this point of the story. Please, delete.

Response: We have removed the sentence from the existing paragraph.

Comment 13: Page 6: The authors state 'Since Dscd6 and Dsbp1 showed tolerance to chronic HU exposure...'. Where is this shown?

Response: The growth curve in Figure 2A and 2B (now Figure 4A and 4B) and the plating assay in Figure 2C (now Figure 4C) was done with hydroxyurea in the media/plate. Hence, we state that deletion of either SCD6 or SBP1 shows tolerance to chronic (or continuous) HU stress.

Comment 14: Fig 2F: The rescue by SCD6 OE is not complete, as mentioned in the main text.

Response: We have now included the quantification of the spot assay in 2F (now Figure 4F) to show that the rescue by SCD6 overexpression is complete (Fig S1G).

Comment 15: Figure 2G-H: Please, indicate in the figure what the authors consider 'translated' and 'untranslated’ fractions.

Response: The fractions have now been labelled to indicate the missing information in Figure 2G (now Figure 4G).

4. Description of analyses that authors prefer not to carry out

Review 1

Minor Comment 10: Pg. 8/Fig. S3D/4A: It would be interesting to complete the story and determine the functional relationship of Scd6 to the DNL4 mRNA

Response: It is indeed an interesting observation and is currently being pursued as part of another story. We believe it is beyond the scope of the current manuscript.

Review 3

Major Comment 1: Page 5 and Fig S2E-F: The CLHX experiment to conclude that mRNA is present in Scd6 and Sbp1 puncta is rather indirect. The fact that RNase treatment of a granule-enriched pellet has no effect (Fig S5B) does not help. The authors should perform RNase treatment of intact cells and see that the puncta disappear.

Response: We thank the reviewer for this comment. Cycloheximide treatment is a well-accepted assay to detect the presence of mRNA in granules. Since granules are dynamic structures, and these depend on active translation, CHX treatment leads to the dissociation of Scd6 and Sbp1 granules. This indicates that granule assembly depends on the availability of mRNA derived from translating ribosomes. The observation that Scd6 puncta are sensitive to cycloheximide but not to RNase A treatment is not surprising. It indeed is consistent with the properties of some of the condensates reported in the literature. For example, stress granule cores that are sensitive to cycloheximide, like Scd6 puncta, are resistant to RNase treatment in lysate, indicating that once formed, these structures are quite stable (https://doi.org/10.1016/j.cell.2015.12.038). It is interpreted to suggest that the RNAs in these condensates are protected by the RNA-binding proteins. Also, subsequently, in the study, we do RNA immunoprecipitation and granule enrichment experiments and show specific RNA enrichment with Scd6 (Figure 5C, 6A).

I noticed that you have a works cited page on each of your writings and that is very important. But I wonder if it would help to also have your entire bibliography page connected to the notes as an indirect connections (you can do this by editing the page and adding a tag)

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Reviewer #2 (Public review):

Summary:

The authors tried to determine how PA28g functions in oral squamous cell carcinoma (OSCC) cells. They hypothesized it may act through metabolic reprogramming in the mitochondria.

Strengths:

They found that the genes of PA28g and C1QBP are in an overlapping interaction network after an analysis of a genome database. They also found that the two proteins interact in coimmunoprecipitation and pull-down assays using the lysate from OSCC cells with or without expression of the exogenous genes. They used truncated C1QBP proteins to map the interaction site to the N-terminal 167 residues of C1QBP protein. They observed the levels of the two proteins are positively correlated in the cells. They provided evidence for the colocalization of the two proteins in the mitochondria and the effect on mitochondrial form and function in vitro and in vivo OSCC models, and the correlation of the protein expression with the prognosis of cancer patients.

Weaknesses:

Many data sets are shown in figures that cannot be understood without more descriptions either in the text or the legend, e.g., Fig. 1A. Similarly, many abbreviations are not defined.

The revision addressed these issues.

Some of the pull-down and coimmunoprecipitation data do not support the conclusion about the PA28g-C1QBP interaction. For example, in Appendix Fig. 1B the Flag-C1QBP was detected in the Myc beads pull-down when the protein was expressed in the 293T cells without the Myc-PA28g, suggesting that the pull-down was not due to the interaction of the C1QBP and PA28g proteins. In Appendix Fig. 1C, assume the SFB stands for a biotin tag, then the SFB-PA28g should be detected in the cells expressing this protein after pull-down by streptavidin; however, it was not. The Western blot data in Fig. 1E and many other figures must be quantified before any conclusions about the levels of proteins can be drawn.

The revision addressed these problems.

The immunoprecipitation method is flawed as it is described. The antigen (PA28g or C1QBP) should bind to the respective antibody that in turn should binds to Protein G beads. The resulting immunocomplex should end up in the pellet fraction after centrifugation, and analyzed further by Western blot for coprecipitates. However, the method in the Appendix states that the supernatant was used for the Western blot.

The revision corrected this method.

To conclude that PA28g stabilizes C1QBP through their physical interaction in the cells, one must show whether a protease inhibitor can substitute PA28q and prevent C1QBP degradation, and also show whether a mutation that disrupt the PA28g-C1QBP interaction can reduce the stability of C1QBP. In Fig. 1F, all cells expressed Myc-PA28g. Therefore, the conclusion that PA28g prevented C1QBP degradation cannot be reached. Instead, since more Myc-PA28g was detected in the cells expressing Flag-C1QBP compared to the cells not expressing this protein, a conclusion would be that the C1QBP stabilized the PA28g. Fig. 1G is a quantification of a Western blot data that should be shown.

The binding site for PA28g in C1QBP was mapped to the N-terminal 167 residues using truncated proteins. One caveat would be that some truncated proteins did not fold correctly in the absence of the sequence that was removed. Thus, the C-terminal region of the C1QBP with residues 168-283 may still bind to the PA29g in the context of full-length protein. In Fig. 1I, more Flag-C1QBP 1-167 was pull-down by Myc-PA28g than the full-length protein or the Flag-C1QBP 1-213. Why?

The interaction site in PA28g for C1QBP was not mapped, which prevents further analysis of the interaction. Also, if the interaction domain can be determined, structural modeling of the complex would be feasible using AlphaFold2 or other programs. Then, it is possible to test point mutations that may disrupt the interaction and if so, the functional effect.

The revision added AlphaFold models for the protein interaction. However, the models were not analyzed and potential mutations that would disrupt the interact were not predicted, made and tested. The revision did not addressed the request for the protease inhibitor.

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Summary:

In this manuscript, the authors have tried to dissect the functions of Proteasome activator 28γ (PA28γ) which is known to activate proteasomal function in an ATP-independent manner. Although there are multiple works that have highlighted the role of this protein in tumours, this study specifically tried to develop a correlation with Complement C1q binding protein (C1QBp) that is associated with immune response and energy homeostasis.

Strengths:

The observations of the authors hint that beyond PA28y's association with the proteasome, it might also stabilize certain proteins such as C1QBP which influences energy metabolism.

Weaknesses:

The strength of the work also becomes its main drawback. That is, how PA28y stabilizes C1QBP or how C1QBP elicits its pro-tumourigenic role under PA28y OE.<br /> In most of the experiments, the authors have been dependent on the parallel changes in the expression of both the proteins to justify their stabilizing interaction. However, this approach is indirect at best and does not confirm the direct stabilizing effect of this interaction. IP experiments do not indicate direct interaction and have some quality issues. The upregulation of C1QBP might be indirect at best. It is quite possible that PA28y might be degrading some secondary protein/complex that is responsible for C1QBP expression. Since the core idea of the work is PA28y direct interaction with C1QBP stabilizing it, the same should be demonstrated in a more convincing manner.

Thank you very much for the important comments. Using AlphaFold 3, we found that interaction between PA28γ and C1QBP may depend on amino acids 1-167 and 1-213 (Revised Appendix Figure 1D-H), which was confirmed by our immunoprecipitation (Revised Figure 1I). In the future, we will use nuclear magnetic resonance spectroscopy to analyze protein-protein interaction between PA28γ and C1QBP and demonstrate it by GST pull down in vitro experiments.

In all of the assays, C1QBP has been detected as doublet. However, the expression pattern of the two bands varies depending on the experiment. In some cases, the upper band is intensely stained and in some the lower bands. Do C1QBP isoforms exist and are they differentially regulated depending on experiment conditions/tissue types?

Thank you very much for the important comments. We have rechecked the experimental results with two bands, which may have been caused by using polyclonal antibody of C1QBP (Abcam: ab101267). Therefore, we conducted the experiment with monoclonal antibody of C1QBP (Cell Signaling Technology: #6502) and replaced the corresponding images in revised figure (Revised Figure 1E and Revised Appendix Figure 3D).

Problems with the background of the work: Line 76. This statement is far-fetched. There are presently a number of works of literature that have dealt with the metabolic programming of OSCC including identification of specific metabolites. Moreover, beyond the estimation of OCR, the authors have not conducted any experiments related to metabolism. In the Introduction, the significance of this study and how it will extend our understanding of OSCC needs to be elaborated.

Thank you very much for the important comments. Based on your suggestion, we have revised the content and updated the references (“Introduction”, Paragraph 2, Line 13-17 and Paragraph 4, Line 5-8). In addition, we plan to conduct experiments to investigate the regulation of metabolism by PA28γ and C1QBP and update our data in the future.

The modified content is as follows:

“Current research on metabolic reprogramming in OSCC primarily focused on mechanism of glycolytic metabolism and metabolic shift from glycolysis to oxidative phosphorylation (OXPHOS) of oral squamous cell carcinoma, which lays the groundwork for novel therapeutic interventions to counteract OSCC (Chen et al., 2024; Zhang et al., 2020).”

“It is the first study to describe the undiscovered role of PA28γ in promoting the malignant progression of OSCC by elevating mitochondrial function, providing new clinical insights for the treatment of OSCC.”

Reviewer #2 (Public review):

Summary:

The authors tried to determine how PA28g functions in oral squamous cell carcinoma (OSCC) cells. They hypothesized it may act through metabolic reprogramming in the mitochondria.

Strengths:

They found that the genes of PA28g and C1QBP are in an overlapping interaction network after an analysis of a genome database. They also found that the two proteins interact in coimmunoprecipitation and pull-down assays using the lysate from OSCC cells with or without expression of the exogenous genes. They used truncated C1QBP proteins to map the interaction site to the N-terminal 167 residues of C1QBP protein. They observed the levels of the two proteins are positively correlated in the cells. They provided evidence for the colocalization of the two proteins in the mitochondria, the effect on mitochondrial form and function in vitro and in vivo OSCC models, and the correlation of the protein expression with the prognosis of cancer patients.

Weaknesses:

Many data sets are shown in figures that cannot be understood without more descriptions, either in the text or the legend, e.g., Figure 1A. Similarly, many abbreviations are not defined.

Thank you very much for the important comments. We have revised the descriptions in the legend to make it easier to understand.

Some of the pull-down and coimmunoprecipitation data do not support the conclusion about the PA28g-C1QBP interaction. For example, in Appendix Figure 1B the Flag-C1QBP was detected in the Myc beads pull-down when the protein was expressed in the 293T cells without the Myc-PA28g, suggesting that the pull-down was not due to the interaction of the C1QBP and PA28g proteins. In Appendix Figure 1C, assume the SFB stands for a biotin tag, then the SFB-PA28g should be detected in the cells expressing this protein after pull-down by streptavidin; however, it was not. The Western blot data in Figure 1E and many other figures must be quantified before any conclusions about the levels of proteins can be drawn.

Thank you very much for the meticulous review. We have rechecked the experimental results, and we made a mistake in the labeling of the image. Therefore, we have corrected it in the revised figure (Revised Appendix Figure 1B, C). In addition, we have conducted a quantitative analysis of gray values to confirm the results of western blot data are accurate by Image J software.

The immunoprecipitation method is flawed as it is described. The antigen (PA28g or C1QBP) should bind to the respective antibody that in turn should binds to Protein G beads. The resulting immunocomplex should end up in the pellet fraction after centrifugation and be analyzed further by Western blot for coprecipitates. However, the method in the Appendix states that the supernatant was used for the Western blot.

Thank you very much for the careful review. We have corrected it in the revised appendix file (“Supplemental Materials and Methods”, Part“Immunoprecipitation assay”, Line 4-6).

The modified content is as follows:

The sample was shaken on a horizontal shaker for 4 h, after which the deposit was collected for western blotting.

To conclude that PA28g stabilizes C1QBP through their physical interaction in the cells, one must show whether a protease inhibitor can substitute PA28q and prevent C1QBP degradation, and show whether a mutation that disrupts the PA28g-C1QBP interaction can reduce the stability of C1QBP. In Figure 1F, all cells expressed Myc-PA28g. Therefore, the conclusion that PA28g prevented C1QBP degradation cannot be reached. Instead, since more Myc-PA28g was detected in the cells expressing Flag-C1QBP compared to the cells not expressing this protein, a conclusion would be that the C1QBP stabilized the PA28g. Figure 1G is a quantification of Western blot data that should be shown.

Thank you very much for the meticulous review. We have rechecked the experimental results, and we made a mistake in the labeling of the image. Therefore, we have corrected it in the revised figure. Compared with the control group, the presence of Myc-PA28γ significantly increased the expression level of Flag-C1QBP (Revised Figure 1F). Gray value analysis showed that in cells transfected with Myc-PA28γ, the decay rate of Flag-C1QBP was significantly slower than that of the control group (Revised Figure 1G), suggesting that PA28γ can delay the protein degradation of C1QBP and stabilize its protein level. This indicates that an increase in the level of PA28γ protein can significantly enhance the expression level of C1QBP protein, while PA28γ can slow down the degradation rate of C1QBP and improve its stability. In addition, we plan to conduct experiments to investigate the effects of protease inhibitors and PA28γ mutants on the stability of C1QBP and update our data in the future.

The binding site for PA28g in C1QBP was mapped to the N-terminal 167 residues using truncated proteins. One caveat would be that some truncated proteins did not fold correctly in the absence of the sequence that was removed. Thus, the C-terminal region of the C1QBP with residues 168-283 may still bind to the PA29g in the context of full-length protein. In Figure 1I, more Flag-C1QBP 1-167 was pulled down by Myc-PA28g than the full-length protein or the Flag-C1QBP 1-213. Why?

Thank you very much for the important comments. Immunoprecipitation is a qualitative experiment. Using AlphaFold 3, we found that interaction between PA28γ and C1QBP may depend on amino acids 1-167 and 1-213 (Revised Appendix Figure 1D-H), which was confirmed by our immunoprecipitation (Revised Figure 1I).

The interaction site in PA28g for C1QBP was not mapped, which prevents further analysis of the interaction. Also, if the interaction domain can be determined, structural modeling of the complex would be feasible using AlphaFold2 or other programs. Then, it is possible to test point mutations that may disrupt the interaction and if so, the functional effect.

Thank you very much for the important comments. Based on your suggestion, we have added relevant content to the revised appendix figure. (Revised Appendix Figure 1D-H).

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

(1) There are a lot of typos in the figure and manuscript that need to be addressed.

Thank you very much for the important comments. We have corrected the typos in the revised figure and manuscript.

(2) Figure 1A: The amount of protein that has been immunoprecipitated is more than the actual amount present in the lysate. The authors should calculate the efficiency of the precipitation to support their results.

Thank you very much for the important comments. Immunoprecipitation is a qualitative experiment. Moreover, it can enrich specific proteins and their binding partners, increase their concentration in the sample, and thus improve the sensitivity of detection.

(3) Figure 1D: The relative expression levels of C1QBP look similar in almost all cell lines except for HN12. It seems that the relation of PA28y with C1QBP is more of a cell type-specific effect. It would be better if the blots were quantified, and the differences were statistically determined.

Thank you very much for the important comments. We have conducted a quantitative analysis of gray values to confirm the results of western blot data are accurate by Image J software.

(4) Figure 1E: How do the authors quantify the expression of the protein in absolute terms? From the methods, it is understood that the flag-tagged construct is stably expressed. Under such conditions, how the authors observed the variable expression of the protein should be elaborated.

Thank you very much for the important comments. We transfected Flag-PA28γ plasmids at 0ug, 0.5ug, 1ug, and 2ug in 293T cells. After collecting the protein for Western Blot, we found that the protein expression of Flag-PA28γ gradually increased. Moreover, the increased protein expression of C1QBP is consistent with the expression of Flag-PA28γ, which indicated a dose-dependent relationship between the two proteins.

(5) Figures 1F, G: The data does not correlate with the arguments presented in the text. The authors propose that interaction with PA28y increases the stability of C1QBP. However, the experiment lacks appropriate controls. Ideally, the expression of C1QBP should be tested in the presence and absence of PA28y. Moreover, the observed difference in expression between lanes 1-4 and 5-8 for myc-PA28y needs to be explained. Are the samples from different sources with variable PA28y expression? Figure 1G quantification for C1QBP does not correlate with the figure presented in F since the expression of the protein in the first four lanes is undetectable.

Thank you very much for the meticulous review. We have rechecked the experimental results, and we made a mistake in the labeling of the image. Therefore, we have corrected it in the revised figure. Compared with the control group, the presence of Myc-PA28γ significantly increased the expression level of Flag-C1QBP (Revised Figure 1F). Gray value analysis showed that in cells transfected with Myc-PA28γ, the decay rate of Flag-C1QBP was significantly slower than that of the control group (Revised Figure 1G), suggesting that PA28γ can delay the protein degradation of C1QBP and stabilize its protein level. This indicates that an increase in the level of PA28γ protein can significantly enhance the expression level of C1QBP protein, while PA28γ can slow down the degradation rate of C1QBP and improve its stability. In addition, we plan to conduct experiments to investigate the effects of protease inhibitors and PA28γ mutants on the stability of C1QBP and update our data in the future.

(6) Appendix Figure 1B: Lane 1 does not express Myc-tagged protein but pull-down has been performed using Myc beads. Then how come flag-C1qbp is getting pulled down in lane 1 if there is no PA28y? This indicates a non-specific interaction of C1qbp with the substrata under the experimental conditions used. Similarly, in Figure 1C SFB-PA28y is expressed in both lanes but is reflected only in lane 2 and not in lane 1 even when pull-down is being performed using SFB beads, again reflecting the non-specificity of the interactions shown through immunoprecipitated.

Thank you very much for the meticulous review. We have rechecked the experimental results, and we made a mistake in the labeling of the image. Therefore, we have corrected it in the revised figure (Revised Appendix Figure 1B, C).

(7) Figure 2A: Figure 2A the co-localization of P28y with C1QBP in mitochondria is not very convincing. The authors are urged to provide high-resolution images for the same along with quantification of co-localization coefficients.

Thank you very much for the important comments. We plan to obtain high-resolution images of co-localization of PA28γ with C1QBP in mitochondria and add the quantification analysis. We will update our data in the future.

(8) Figure 2C: Mitochondria dynamics is an interplay of multiple factors. From the images, it seems that PA28y OE elevates mitochondria biogenesis in general which is having an umbrella effect on mitochondria fusion/fission and OCR. Images also do not convincingly indicate changes in mitochondrial length. The role of PA28y on mitochondria dynamics requires further justification. However, the presented data does not underline whether the changes in mitochondria behaviour are a consequence of PA28y and C1QBP interaction. Correlating higher mitochondria respiration with ROS generation is a far-fetched conclusion since, at present, there are multiple reports that suggest otherwise.

Thank you very much for the important comments. We plan to knock out the interaction regions between PA28γ and C1QBP (like amino acids 1-167 and 1-213) to confirm whether PA28γ affects mitochondrial function through C1QBP and update our data in the future.

(9) Line 157: The presented data does not substantiate the claims made that Pa28y regulates mitochondrial function through C1QBP.

Thank you very much for the important comments. Based on your suggestion, we have made some modifications to make it more accurate (“Results”, Part “PA28γ and C1QBP colocalize in mitochondria and affect mitochondrial functions”, Paragraph 3, Line 1-2).

The modified content is as follows:

“Collectively, these data suggest that PA28γ, which co-localizes with C1QBP in mitochondria, may involve in regulating mitochondrial morphology and function.”

(10) Line 159: From the past data it is not very clear how PA28y upregulates C1QBP, hence the statement is not well supported. The presented data indicates the presence of a functional association between the two proteins.

Thank you very much for the important comments. We detected the expression of C1QBP in two PA28γ-overexpressing OSCC cells (UM1 and 4MOSC2) and found an increase in C1QBP expression (Revised Figure 4B). Based on the results of the protein levels of the mitochondrial respiratory chain complex and other mitochondrial functional proteins, we believe that PA28γ regulates mitochondrial function by upregulating C1QBP.

(11) Figure 4A, B: Given the mitochondrial role of C1QBP, the lesser levels of mitochondrial proteins upon C1QBP silencing are expected. Does it get phenocopied upon PA28y silencing? Similarly, all the subsequent mitochondrial phenotypes in D should be seen in a PA28y-depleted background.

Thank you very much for the important comments. We plan to detect the mitochondrial protein expressions and OCRs of PA28γ-silenced OSCC cells. We will update our data in the future.

(12) Line 198: The presented data do indicate a functional association between these two proteins but it does not provide a solid evidence for the same.

Thank you very much for the important comments. Based on your suggestion, we have made some modifications to make it more accurate (“Discussion”, Paragraph 1, Line 9-10).

The modified content is as follows:

“Excitingly, we found the evidence that PA28γ interacts with and stabilizes C1QBP.”

(13) Line 218-220: In this work, the authors highlight the non-degradome role of PA28y and hence, this fact should be treated appropriately in discussion in line with the presented data.

Thank you very much for the important comments. Based on your suggestion, we have added relevant content to the revised manuscript (“Discussion”, Paragraph 2, Line 16-19).

The modified content is as follows:

“In addition, PA28γ can also play as a non-degradome role on tumor angiogenesis. For example, PA28γ can regulate the activation of NF-κB to promote the secretion of IL-6 and CCL2 in OSCC cells, thus promoting the angiogenesis of endothelial cells ( S. Liu et al., 2018).”

(14) Line 236-240: Although the authors' statement on organ heterogeneity being the cause for getting the contrasting result is justifiable but here there is no direct evidence of PA28y involvement in regulation of OXPHOS and its impact on cellular metabolism (glycolysis, metabolic signalling, etc).

Thank you very much for the important comments. Based on your suggestion, we have made some modifications to make it more accurate (“Discussion”, Paragraph 3, Line 7-9).

The modified content is as follows:

“Therefore, PA28γ's regulation of OXPHOS may impact cellular energy metabolism.”

(15) Line 249: No conclusive data supporting this statement.

Thank you very much for the important comments. Based on your suggestion, we have made some modifications to make it more accurate (“Discussion”, Paragraph 5, Line 1-3).

The modified content is as follows:

“Furthermore, our study reveals that PA28γ can regulate C1QBP and influence mitochondrial morphology and function by enhancing the expression of OPA1, MFN1, MFN2 and the mitochondrial respiratory complex.”

Reviewer #2 (Recommendations for the authors):

(1) The images shown in Figure 2A need to be quantified before the conclusion about the mitochondrial colocalization of the two proteins can be drawn. In Figure 2B and Appendix Figure 2A, the mitochondrial vacuoles and ridge should be indicated for general readers, and quantification should be performed before the conclusion is drawn.

Thank you very much for the important comments. We will update our data in the future.

(2) The OCR data from two cell lines are shown in Figure 2E and F. Which is which? The sentence, "The results indicated ... compared to control cells" in lines 130-132, was confusing; perhaps, it would be clear if "were significantly greater" could be deleted.

Thank you very much for the important comments. We have re-labeled the Figure 2E and F to make it clearly (Revised Figure 2E, F). Based on your suggestion, we have deleted the words in revised manuscript. (“Results”, Part “PA28γ and C1QBP colocalize in mitochondria and affect mitochondrial functions”, Paragraph 1, Line 9-11).

The modified content is as follows:

“The results indicated significantly higher basal respiration, maximal OCRs and ATP production in PA28γ-overexpressing cells compared to control cells (Fig. 2G-I and Appendix Fig. 2B-D).”

(3) Figures 4E-H show the migration, invasive, and proliferation capabilities of the cells. Which for which?

Thank you very much for the important comments. We have re-labeled the Figure 4F-H to make it clearly (Revised Figure 4F-H).

(4) In the Discussion, lines 198-201, it states that "C1QBP enhances ... function of OPA1, MNF1, MFN2..." What is the evidence? In lines 222-224, it says that "the binding sites ... may mask the specific ... modification sites". Please justify. In lines 253-254, "fuse" and fuses" are misleading, Did the authors mean "localize" and "localizes"?

Thank you very much for the important comments. Based on your suggestion, we have made some modifications to make it more accurate (“Discussion”, Paragraph 1, Line 9-13, Paragraph 2, Line 20-23, and Paragraph 5, Line 3-6).

The modified content is as follows:

“Excitingly, we found the evidence that PA28γ interacts with and stabilizes C1QBP. We speculate that aberrantly accumulated C1QBP enhances the function of mitochondrial OXPHOS and leads to the production of additional ATP and ROS by activating the expression and function of OPA1, MNF1, MFN2 and mitochondrial respiratory chain complex proteins.”

“Our study reveals that PA28γ interacts with C1QBP and stabilizes C1QBP at the protein level. Therefore, we speculate that the binding sites of PA28γ and C1QBP may mask the specific post-translational modification sites of C1QBP and inhibit its degradation.”

“Mitochondrial fusion, crucial for oxidative metabolism and cell proliferation, is regulated by MFN1, MFN2, and OPA1. The first two fuse with the outer mitochondrial membrane, while the last fuses with the inner mitochondrial membrane (Westermann, 2010).”

(5) Figure 6 was not referred to in the text. In this figure, PA28g and C1QBP are located in the inner membrane and matrix. Has this been determined? What is the blue ovals that are intermediaries of PA28g/C1QBP and OPA1/MFN1/MFN2?

Thank you very much for the important comments. According to our immunofluorescence assay (Figure 2A), PA28γ is in both the nucleus and cytoplasm. A recent study has demonstrated that PA28γ can shuttle between the nucleus and cytoplasm, participating in various cellular processes. Furthermore, GeneCard information indicates that the subcellular localization of PA28γ includes the nucleus, cytoplasm and mitochondria (Author response image 1). In this article, we mainly focus on the functions of PA28γ and C1QBP located in the cytoplasm. Therefore, figure 6 mainly displays PA28γ and C1QBP in the cytoplasm. Based on your suggestion, we have made some modifications to make it more accurate in revised figure (Revised Figure 6).

Author response image 1.

Author response:

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

eLife Assessment

This useful manuscript shows a set of interesting data including the first cryo-EM structures of human PIEZO1 as well as structures of disease-related mutants in complex with the regulatory subunit MDFIC, which generate different inactivation phenotypes. The molecular basis of PIEZO channel inactivation is of great interest due to its association with several pathologies. This manuscript provides some structural insights that may help to ultimately build a molecular picture of PIEZO channel inactivation. While the structures are of use and clear conformational differences can be seen in the presence of the auxiliary subunit MDFIC, the strength of the evidence supporting the conclusions of the paper, especially the proposed role for pore lipids in inactivation, is incomplete and there is a lack of data to support them.

We thank the editors and reviewers for taking the time and effort to review our manuscript.  The evidence supporting the key role of pore lipids in hPIEZO1 activation is as follows. i. Compared with wild-type hPIEZO1, the hydrophobic acyl chain tails of the pore lipids retracted from the hydrophobic pore region in slower inactivating mutant hPIEZO1-A1988V (Fig. 7a-b). ii. Previous electrophysiological functional studies revealed that substituting this hydrophobic pore formed by I2447, V2450, and F2454 with a hydrophilic pore prolongs the inactivation time for both PIEZO1 and PIEZO2 channels (PMID: 30628892). iii. In the structure of the HX channelopathy mutant R2456H, the interaction between the hydrophilic phosphate group head of pore lipids and R2456 is disrupted, remodeling the blade and pore module and resulting in a significantly slow-inactivating rate. iv. The interaction between pore lipids and lipidated-MDFIC stabilizes the pore lipids to reseal the pore upon activation of the hPIEZO1-MDFIC complex.

According to previously proposed models for the role of pore lipids in mechanosensitive ion channels, such as MscS (PMID: 33568813), MS K2P (PMID: 25500157) and OSCA channels (PMID: 37402734), the pore lipids seal the channel pores in closed state and could be removed in open state by mechanical force induced membrane deformation, which obeys the force-from-lipids principle. Therefore, in our putative model, the pore lipids seal the hydrophobic pore of hPIEZO1 in the closed state. Upon activation of hPIEZO1, the pore lipids retract from the hydrophobic pore and interact with multi-lipidated MDFIC, stabilizing in the inactivation state. The mild channelopathy mutants make the pore lipids retract from the hydrophobic pore and harder to close upon activation. For the severe channelopathy mutant, the interaction between the pore lipids and R2456 is disrupted, resulting in the missing of pore lipids and significantly slow-inactivating. We fully understand the concern of the role of pore lipids in our proposed model. Therefore, we have toned down our putative model.

Public Reviews:  

Reviewer #1 (Public review):  

Summary:  

This manuscript by Shan, Guo, Zhang, Chen et al., shows a raft of interesting data including the first cryo-EM structures of human PIEZO1. Clearly, the molecular basis of PIEZO channel inactivation is of great interest and as such this manuscript provides some valuable extra information that may help to ultimately build a molecular picture of PIEZO channel inactivation. However, the current manuscript though does not provide any compelling evidence for a detailed mechanism of PIEZO inactivation.

Strengths:

This manuscript documents the first cryo-EM structures of human PIEZO1 and the gain of function mutants associated with hereditary anaemia. It is also the first evidence showing that PIEZO1 gain of function mutants are also regulated by the auxiliary subunit MDFIC.

We thank reviewer #1 for the encouragement.

Weaknesses:

While the structures are interesting and clear differences can be seen in the presence of the auxiliary subunit MDFIC the major conclusions and central tenets of the paper, especially a role for pore lipids in inactivation, lack data to support them. The post-translational modification of PIEZOser# auxiliary subunit MDFIC is not modelled as a covalent interaction.

We fully understand the concern of the role of pore lipids in our proposed model. Therefore, we have toned down our putative model.

The lipids densities of the post-transcriptional modification of PIEZO1 auxiliary subunit MDFIC are shown below. As the lipids densities are not confident, we only use the single-chain lipids to represent them. And the lipidated MDFIC is proven by the MDFIC identification paper.

Author response image 1.

Reviewer #2 (Public review):

Summary:

Mechanically activated ion channels PIEZOs have been widely studied for their role in mechanosensory processes like touch sensation and red blood cell volume regulation. PIEZO in vivo roles are further exemplified by the presence of gain-of-function (GOF) or loss-of-function (LOF) mutations in humans that lead to disease pathologies. Hereditary xerocytosis (HX) is one such disease caused due to GOF mutation in Human PIEZO1, which are characterized by their slow inactivation kinetics, the ability of a channel to close in the presence of stimulus. But how these mutations alter PIEZO1 inactivation or even the underlying mechanisms of channel inactivation remains unknown. Recently, MDFIC (myoblast determination family inhibitor proteins) was shown to directly interact with mouse PIEZO1 as an auxiliary subunit to prolong inactivation and alter gating kinetics. Furthermore, while lipids are known to play a role in the inactivation and gating of other mechanosensitive channels, whether this mechanism is conserved in PIEZO1 is unknown. Thus, the structural basis for PIEZO1 inactivation mechanism, and whether lipids play a role in these mechanisms represent important outstanding questions in the field and have strong implications for human health and disease.

To get at these questions, Shan et al. use cryogenic electron microscopy (Cryo-EM) to investigate the molecular basis underlying differences in inactivation and gating kinetics of PIEZO1 and human disease-causing PIEZO1 mutations. Notably, the authors provide the first structure of human PIEZO1 (hPIEZO1), which will facilitate future studies in the field. They reveal that hPIEZO1 has a more flattened shape than mouse PIEZO1 (mPIEZO1) and has lipids that insert into the hydrophobic pore region. To understand how PIEZO1 GOF mutations might affect this structure and the underlying mechanistic changes, they solve structures of hPIEZO1 as well as two HXcausing mild GOF mutations (A1988V and E756del) and a severe GOF mutation (R2456H). Unable to glean too much information due to poor resolution of the mutant channels, the authors also attempt to resolve MCFIC-bound structures of the mutants. These structures show that MDFIC inserts into the pore region of hPIEZO1, similar to its interaction with mPIEZO1, and results in a more curved and contracted state than hPIEZO1 on its own. The authors use these structures to hypothesize that differences in curvature and pore lipid position underlie the differences in inactivation kinetics between wild-type hPIEZO1, hPIEZO1 GOF mutations, and hPIEZO1 in complex with MDFIC.

Strengths:

This is the first human PIEZO1 structure. Thus, these studies become the stepping stone for future investigations to better understand how disease-causing mutations affect channel gating kinetics.

We thank reviewer #2 for the positive comments.

Weaknesses:

Many of the hypotheses made in this manuscript are not substantiated with data and are extrapolated from mid-resolution structures.

We fully understand the concern of the role of pore lipids in our proposed model. Therefore, we have toned down our putative model.

Reviewer #3 (Public review):

Summary:

In this manuscript, the authors used structural biology approaches to determine the molecular mechanism underlying the inactivation of the PIEZO1 ion channel. To this end, the authors presented structures of human PIEZO1 and its slow-inactivating mutants. The authors also determined the structures of these PIEZO1 constructs in complexes with the auxiliary subunit MDFIC, which substantially slows down PIEZO1 inactivation. From these structures, the authors suggested an anti-correlation between the inactivation kinetics and the resting curvature of PIEZO1 in detergent. The authors also observed a unique feature of human PIEZO1 in which the lipid molecules plugged the channel pore. The authors proposed that these lipid molecules could stabilize human PIEZO1 in a prolonged inactivated state.

We thank reviewer #3 for the summary.

Strengths:

Notedly, this manuscript reported the first structures of a human PIEZO1 channel, its channelopathy mutants, and their complexes with MDFIC. The evidence that lipid molecules could occupy the channel pore of human PIEZO1 is solid. The authors' proposals to correlate PIEZO1 resting curvature and pore-resident lipid molecules with the inactivation kinetics are novel and interesting.

Thanks for the positive comments.

Weaknesses:

However, in my opinion, additional evidence is needed to support the authors' proposals.

(1) The authors determined the apo structure of human PIEZO1, which showed a more flattened architecture than that of the mouse PIEZO1. Functionally, the inactivation kinetics of human PIEZO1 is faster than its mouse counterpart. From this observation (and some subsequent observations such as the complex with MDFIC), the authors proposed the anti-correlation between curvature and inactivation kinetics. However, the comparison between human and mouse PIEZO1 structure might not be justified. For example, the human and mouse structures were determined in different detergent environments, and the choice of detergent could influence the resting curvature of the PIEZO structures.

We apologize for the misleading statement about the anti-correlation between curvature and inactivation kinetics of PIEZOs. We cannot conclude that the observation of curvature variation of mPIEZO1 and hPIEZO1 is related to their inactivation kinetics based on structural studies and electrophysiological assay. The difference in structural basis between mPIEZO1 and hPIEZO1 is what we want to state. To avoid this misleading, we have revised the manuscript. 

For the concern about detergent, we cannot fully exclude its influence on the curvature of PIEZOs. However, previously reported structures of mPiezo1 (PDB: 7WLT, 5Z10, 6B3R) were in the different detergent environments or in lipid bilayer, but the curvature of mPiezo1 is similar as shown below. Considering the high sequence similarity between mPiezo1 and hPiezo1, we hypothesize that the curvature of both hPiezo1 and mPiezo1 may be unaffected by the detergent.

Author response image 2.

Overall structural comparison of curved mPIEZO1 in the lipid bilayer (PDB: 7WLT), mPiezo1 in CHAPS (PDB: 6B3R) and mPiezo1 in Digitonin (PDB: 5Z10).

(2) Related to point 1), the 3.7 Å structure of the A1988V mutant presented by the authors showed a similar curvature as the WT but has a slower inactivating kinetics.

Based on the structural comparison between hPIEZO1 and its A1998V mutant, the retraction of pore lipids from the hydrophobic center pore in hPIEZO1-A1998V is mainly responsible for its slower inactivating kinetics.

(3) Related to point 1), the authors stated that human PIEZO1 might not share the same mechanism as mouse PIEZO1 due to its unique properties. For example, MDFIC only modifies the curvature of human PIEZO1, and lipid molecules were only observed in the pore of the human PIEZO1. Therefore, it may not be justified to draw any conclusions by comparing the structures of PIEZO1 from humans and mice.

Thanks for the constructive suggestion. To avoid this misleading, we have revised the manuscript.

(4) Related to point 1), it is well established that PIEZO1 opening is associated with a flattened structure. If the authors' proposal were true, in which a more flattened structure led to faster inactivation, we would have the following prediction: more opening is associated with faster inactivation. In this case, we would expect a pressure-dependent increase in the inactivation kinetics.

Could the authors provide such evidence, or provide other evidence along this direction?

We appreciate the reviewer’s comment. We are not claiming a relationship between the flattened structure and activation/inactivation. We only present the results of the structure of wild-type/mutant PIEZO1.

(5) In Figure S2, the authors showed representative experiments of the inactivation kinetics of PIEZO1 using whole-cell poking. However, poking experiments have high cell-to-cell variability.

The authors should also show statics of experiments obtained from multiple cells.

We have shown the statics of representative electrophysiology experiments obtained from multiple cells in Figure S2.

(6) In Figure 2 and Figure 5, when the authors show the pore diameter, it could be helpful to also show the side chain densities of the pore lining residues.

We appreciate the reviewer’s suggestion. The side chain of the pore lining restricted residues have been shown in Figure 2 and Figure 5 and the densities of pore domain have been shown in Figure S4 and S14. Interestingly, the pore lining restricted residues in mPIEZO1 and hPIEZO1 is highly conserved.

(7) The authors observed pore-plugging lipids in slow inactivating conditions such as channelopathy mutations or in complex with MDFIC. The authors propose that these lipid molecules stabilize a "deep resting state" of PIEZO1, making it harder to open and harder to inactivate once opened. This will lead to the prediction that the slow-inactivating conditions will lead to a higher activation threshold, such as the mid-point pressure in the activation curve. Is this true?

Yes, it is true. In Figure S2, the MDFIC-induced slow-inactivation conditions in hPIEZO1-MDFIC, hPIEZO1-A1988V-MDFIC, hPIEZO1-E756del-MDFIC and hPIEZO1-R2456H-MDFIC result in larger half-activation thresholds than hPIEZO1, hPIEZO1-A1988V, hPIEZO1-E756del and hPIEZO1-R2456H, respectively.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

I document the major issues below:

(1) Mouse vs Human inactivation

Line 21- "than the slower inactivating curved mouse PIEZO1 (mPIEZO1)."

Where is the data in this paper or any other paper that human PIEZO1 inactivates faster than mouse PIEZO1? This is central to the way the authors present the paper. In fact, the tau quoted for the hPIEZO1 of ~10 ms is similar to that often measured for mPIEZO1. The reference in the discussion for mouse vs human inactivation times is a review of mechanotransduction. Either the authors need to directly compare the tau of mP1 vs hP1 or quote the relevant primary literature if it exists.

As measured in HEK-PIKO cells transfected with mPiezo1, the inactivation time of mPiezo1 is 13 ± 1 ms (PMID: 29261642) at -80 mV. 

The tau is also voltage-dependent. The tau is beyond 20 ms at -60 mV for mPIEZO1 (PMID:

20813920) and for hPIEZO1 is still around 10 ms.

(2) MDFIC-lipidation

Without seeing the PDB or EMDB I can't guarantee this but from Figure 6d it seems like the Sacylation in the distal C-terminus of MDFIC is not modelled as a covalent interaction, these lipids are covalently added to the Cys residues in S-acylation via zDHHC enzymes. This should be modelled correctly.

Thanks for this suggestion. As the lipid densities of the post-transcriptional modification of PIEZOs auxiliary subunit MDFIC are not confident, we only use the single-chain lipids to represent them.

And the lipidated MDFIC is proven by the MDFIC identification paper (PMID: 37590348).

(3) Pore lipids and inactivation

The lipids close to the pore are interesting and the density for a lipid is also seen in the mouse MDFIC-PIEZO1 complex from Zhou, Ma et al, 2023. However, there is no data provided by the authors that the lipid is functionally relevant to anything. There is not even a correlation with inactivation in Figure 7. P1+MDFIC inactivates slowest yet the lipids are present within the pore. Second, there is no evidence for what these structures are: closed, or inactivated? In fact, the Xiao lab is now interpreting the 7WLU structure as inactivated.

The evidence supporting the key role of pore lipids in hPIEZO1 activation is as follows. i. Compared with wild-type hPIEZO1, the hydrophobic acyl chain tails of the pore lipids retracted from the hydrophobic pore region in slower inactivating mutant hPIEZO1-A1988V (Fig. 7a-b). ii. Previous electrophysiological functional studies revealed that substituting this hydrophobic pore formed by I2447, V2450, and F2454 with a hydrophilic pore prolongs the inactivation time for both PIEZO1 and PIEZO2 channels (PMID: 30628892). iii. In the structure of the HX channelopathy mutant R2456H, the interaction between the hydrophilic phosphate group head of pore lipids and R2456 is disrupted, remodeling the blade and pore module and resulting in a significantly slow-inactivating rate. iv. The interaction between pore lipids and lipidated-MDFIC stabilizes the pore lipids to reseal the pore upon activation of the hPIEZO1-MDFIC complex. Overall, the pore lipid is involved in inactivation, and we have toned down the statement.

(4) Cytosolic plug

There is additional cytosolic density for the human PIEZO1 that the authors intimate could be from a different binding partner. IS it possible to refine this density? Is it from the PIEZO1-tag? At the very least a little more information about this density should be given if it is going to be mentioned like this.

Our purification result shows that the protein is tag-free. We are also curious about the extra cytosolic density, but we do not know what it is.

(5) Reduced sensitivity of PIEZO1 in the presence of MDFIC and its regulatory mechanism

This was reported in the first article however no data is presented by the authors to support MDFIC increasing the mechanical energy required to open PIEZO1. The sentence in the discussion; "MDFIC enables hPIEZO1 to respond to different forces by modifying the pore module through lipid interactions." is not supported by any functional data and seems to be an over-interpretation of the structures.

We appreciate this suggestion. The half-activation threshold of hPEIZO1 and hPEIZO1-MDFIC is measured to be 7 μm and 9 μm, respectively (Fig.S2). In addition, the mechanical currents amplitude of hPIEZO1-MDFIC is extremely small compared to that of WT reaching the nA level (Fig.S2). Therefore, the less mechanosensitive hPIEZO1-MDFIC may require more mechanical energy to open than PIEZO1 WT.

6) Both referencing of the PIEZO1 literature and prose could be improved.

Thanks for the suggestion. We have improved the referencing and prose.

Reviewer #2 (Recommendations for the authors):

(1) The authors speculate that the difference in curvature between human and mouse PIEZO1 results in its fast inactivation but do not provide experimental evidence to support this idea. This claim would have been bolstered by showing that the GOF human mutations have a more curved structure, but these proved too structurally unstable to be solved at high resolution. However, the authors state that the 3.7 angstrom map solved for hPIEZO1-A1988V does have an overall similar architecture as wild-type hPIEZO1; thus, contradicting their hypothesis.

We apologize for the misleading statement. In our revised manuscript, we do not claim a relationship between the flattened structure and activation/inactivation. We only present the results of the structure of wild-type/mutant PIEZO1.

The structure comparison between the A1988V mutant and WT shows a similar architecture but a different occupancy pattern of pore lipids. Therefore, we suggested that the A1988V mutant has slightly slower inactivation kinetics, mainly due to the exit of pore lipids from the pore.

(2) The authors show that interaction with MDFIC alters hPIEZO1 structure to be more curved and use this to support their idea that changing the curvature of the protein underlies the prolonged inactivation kinetics. It has been previously shown that MDFIC does not change the structure of mPIEZO1 but does alter its inactivation and gating kinetics. How does this discrepancy fit into the inactivation model proposed by the authors? Similarly, their claim that MDFIC slows hPIEZO1 inactivation and weakens mechanosensitivity just by affecting the pore module and changing blade curvature is made based on observation and no experimental data to test it.

We have revised the manuscript to avoid misleading the relationship between the curvature and the inaction kinetics of hPIEZO1. The evidence reported previously that substitution of the hydrophobic pore, formed by I2447, V2450, and F2454, with a hydrophilic pore prolongs the inactivation time for both PIEZO1 and PIEZO2 channels (PMID: 30628892). In addition, the severe HX channelopathy mutant R2456H, wherein the interaction between the hydrophilic phosphate group head and R2456 is disrupted, leads to remodeling of the blade and pore module. Indeed, our observation is limited and further experiments will be performed to support our model.

(3) How does their model fit in cell types that have PIEZO1 (or GOF mutant PIEZO1) but not MDFIC?

In cell types that have PIEZO1 or GOF mutant PIEZO1 but not MDFIC, PIEZO1 or GOF mutant PIEZO1 may have a faster inactivation rate than those that bind to MDFIC. It can be proved that overexpressed PIEZOs exhibit faster inactivation kinetics than those in some native cell types with MDFIC expression (PMID: 20813920, 30132757).

(4) Figure S2 is missing quantification of the electrophysiology data. The authors should show summary data in addition to their representative traces including the Imax for all conditions, tau for data shown in b, and sample size for all conditions, and related statistics. The text claims that MDFIC decreases mechanosensitivity (line 156) but there is no data to support this.

For the electrophysiological assay in Figure S2, we referred to previously reported mPIEZO1 mutants (PMID: 23487776, 28716860). We confirmed that the slower inactivation phenotypes of these mutations of hPIEZO1 are similar to those of mPIEZO1.

The half-activation threshold of hPEIZO1 and hPEIZO1-MDFIC is measured to be 7 μm and 9 μm, respectively. This tendency of increased half-activation threshold of hPIEZO1 upon binding with MDFIC is also shown in the electrophysiological result of hPIEZO1 channelopathy mutants.

(5) In line 144, the authors mention that they were able to validate the MDFIC density with multilipidated cysteines on the C-terminal amphipathic helix, but they do not show the density with fitted lipids. While individual densities for some of the lipids are shown in extended Figure 12, it would be helpful to include a figure where they show the map for MDFIC with fitted lipids in it.

Thanks for the valuable suggestion. As the lipid densities of the post-transcriptional modification of PIEZOs auxiliary subunit MDFIC are not confident, we only use the single-chain lipids to represent them. And the lipidated MDFIC is proven by the MDFIC identification paper.

(6) The authors show that R2456 interacts with a lipid at the pore module and hypothesize that this underlies the fast inactivation of hPIEZO1. While they did not obtain a high-resolution structure of this mutant, this hypothesis could be tested by substituting R for side chains with different charges and performing electrophysiology to determine the effects on inactivation.

Thanks for the constructive suggestion. We will perform the electrophysiology assay for R2456 mutants with different side chains.

7) Figure 4 shows overall structure of hPIEZO1 GOF mutations A1988V and E756del in complex with MDFIC. Other than showing an overall similar structure to wildtype hPIEZO1, the authors do not show how the human mutations A1988V alter the structure of the protein at the site of change. Understanding how these mutations affect the local architecture of the protein has important relevance for human physiology.

As the GOF channelopathy mutant hPIEZO1-A1988V is structurally unstable, the density at the site of A1988V is too weak to figure out the related interaction in the structure of the hPIEZO1-A1988V mutant. 

Minor comment:

In general, the manuscript will benefit from heavy copy editing. For example, the word cartoon is misspelled in many of the figure legends.

We apologize for the mistake. The manuscript has been checked and revised.

Reviewer #3 (Recommendations for the authors):

Some portions of this manuscript were not well written. For example, at the end of the 3rd paragraph in the introduction, the authors talked about HX mutations and their correlation with malaria infection and plasma iron. This is irrelevant information and will only distract the readers. It would be ideal if the authors could go through the entire manuscript and improve its clarity.

Thanks for the suggestion. We have revised the sentences about HX mutations as suggested and improved the entire manuscript.

Décryptage du porno mainstream et exploration du porno alternatif : L'industrie, les normes et l'impact sur la perception de la sexualité

I. Datagueule #85 : "Datagaule et clitodonnées : le plaisir à la chaîne"

A. L'industrie du porno en ligne : Une domination par les "tubes"

Présentation des données clés de l'industrie du porno en ligne: Trafic, téléchargements, évolution depuis l'arrivée de l'internet haut débit.

Focus sur Pornhub, un des géants du secteur, illustrant l'ampleur du phénomène et la rapidité de consommation.

Ascension de la société MGeek, qui a racheté des studios historiques du X fragilisés par la crise de 2008.

Fonctionnement des "tubes" qui offrent un accès gratuit aux vidéos, impactant les revenus des studios.

B. Le porno mainstream : Des normes et des dérives

Le porno mainstream, majoritairement produit pour un public masculin hétérosexuel et blanc, impose ses normes.

Illustration de ces normes à travers la popularité du tag "lesbien" et la stigmatisation des scènes gays pour les acteurs.

L'émergence du "pro-am" (productions professionnelles d'amateurs) et ses conditions de tournage précaires et parfois dangereuses.

Problèmes liés aux contrats, au consentement et à la difficulté de faire retirer des contenus des plateformes.

Conditions de travail des acteurs masculins : Salaires faibles, recours à des médicaments pour la performance sexuelle et risques associés.

C. Addictivité et tabou : Des idées reçues à déconstruire

L'argument de l'addictivité du porno, souvent utilisé pour la censure, est démenti scientifiquement.

L'Organisation Mondiale de la Santé a rejeté l'ajout du visionnage de pornographie dans sa liste des troubles addictifs.

Le porno, érigé en tabou, échappe aux questionnements légitimes qui entourent les autres productions culturelles.

II. Interview de Camille Emmanuel, journaliste et auteur de "Sex Power"

A. Le regard masculin dominant dans le porno mainstream

L'industrie du porno traditionnellement dominée par une vision masculine, centrée sur le plaisir masculin et la pénétration.

Le porno mainstream reproduit les schémas traditionnels de la sexualité, ignorant le plaisir féminin et la diversité des pratiques.

Le discours dominant sur la sexualité féminine est déconstruit par des études scientifiques sur le clitoris et l'orgasme féminin.

B. L'émergence du porno alternatif : Un contre-pouvoir nécessaire

Le mouvement du porno alternatif initié par des femmes dans les années 80, pour proposer une vision différente de la sexualité.

Ce mouvement, encore niche, met en avant la diversité des pratiques, des corps et des sexualités.

Le porno alternatif se distingue par ses modes de production éthiques, respectueux du consentement et du droit du travail.

C. L'impact du porno sur la perception de la sexualité

Le porno mainstream véhicule une vision normée et limitée de la sexualité, pouvant influencer négativement la perception du public.

Le porno alternatif, en proposant une vision plus diverse et inclusive, permet de questionner les normes et de s'ouvrir à d'autres possibilités.

L'importance de se questionner sur sa propre consommation de porno et de réfléchir à l'imaginaire pornographique proposé aux générations futures.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

It is evident that studying leukocyte extravasation in vitro is a challenge. One needs to include physiological flow, culture cells and isolate primary immune cells. Timing is of utmost Importance and a reproducible setup essential. Extra challenges are met when extravasation kinetics in different vascular beds is required, e.g., across the blood-brain barrier. In this study, the authors describe a reliable and reproducible method to analyze leukocyte TEM under physiological flow conditions, including this analysis. That the software can also detect reverse TEM is a plus.

Strengths:

It is quite a challenge to get this assay reproducible and stable, in particular as there is flow included. Also for the analysis, there is currently no clear software analysis program, and many labs have their own methods. This paper gives the opportunity to unify the data and results obtained with this assay under label-free conditions. This should eventually lead to more solid and reproducible results.

Also, the comparison between manual and software analysis is appreciated.

We thank the Reviewer for their positive evaluation of our manuscript and highlighting the value of obtaining more reproducible and unbiases results, as well as detection of forward and reverse transmigration with UFMTrack.

Weaknesses:

The authors stress that it can be done in BBB models, but I would argue that it is much more broadly applicable. This is not necessarily a weakness of the study but more an opportunity to strengthen the method. So I would encourage the authors to rewrite some parts and make it more broadly applicable.

We thank the Reviewer for this suggestion. In the revised version of our manuscript, we have now emphasized the broader applicability of UFMTrack to analyze the interaction of immune cells with 2dimensional endothelial monolayers in various contexts in the abstract, introduction, and discussion sections.

Reviewer #2 (Public Review):

Summary:

This paper develops an under-flow migration tracker to evaluate all the steps of the extravasation cascade of immune cells across the BBB. The algorithm is useful and has important applications.

Strengths:

Algorithm is almost as accurate as manual tracking and importantly saves time for researchers.

We thank the Reviewer for this positive evaluation of our work.

Weaknesses:

Applicability can be questioned because the device used is 2D and physiological biology is in 3D. Comparisons to other automated tools was not performed by the authors.

We thank the Reviewer for pointing our attention to these weaknesses in our manuscript.

We have clarified in the revised manuscript that using 2D endothelial monolayer models in parallel laminar flow chambers is still a state-of-the-art methodology for studying the multi-step extravasation process of immune cells across endothelial monolayers under physiological flow by in vitro live cell imaging. These models provide excellent optical quality that is not yet achieved in 3D models. We have extended the introduction to emphasize the limitations of existing tools that motivated us to establish UFMTrack. We have furthermore extended the discussion section to highlight the features unique to our UFMTrack framework.

Reviewer #3 (Public Review):

Summary:

The authors aimed to establish a faster and more efficient method of tracking steps of T-cell extravasation across the blood brain barrier. The authors developed a framework to visualize, recognize and track the movement of different immune cells across primary human and mouse brain microvascular endothelial cells without the need for fluorescence-based imaging. The authors succinctly describe the basic requirements for tracking in the introduction followed by an in-depth account of the execution.

We thank the Reviewer for their positive evaluation of our manuscript and highlighting the value of label-free analysis of the multistep immune cell extravasation cascade with UFMTrack.

Weaknesses and Strengths:

Materials & methods and results:

(1) The methods section also lacks details of the microfluidic device that the authors talk about in the paper. Under physiological sheer stress, the T-cells detach from the pMBMEC monolayer, and are hence unable to be detected; however, this observation requires an explanation pertaining to the reason of occurrence and potential solutions to circumvent it to ensure physiologically relevant experimental parameters.

We thank the Reviewer for pointing out this oversight. We have used a custom-made microfluidic device that has been published and described in detail before. This information has now been included in the Methods Section under Point 7, and the two references describing the flow chamber in depth are mentioned below and have been included in the manuscript.  

Coisne Caroline, Ruth Lyck and Britta Engelhardt. 2013. Live cell imaging techniques to study T cell trafficking across the blood-brain barrier in vitro and in vivo. Fluids and Barriers of the CNS 10:7 doi:10.1186/20458118-10-7; 21 January 2013

Lyck R, Hideaki Nishihara, Sidar Aydin, Sasha Soldati and Britta Engelhardt. 2022. Modeling brain vasculature immune interactions in vitro. Angogenesis, 2nd edition. Editors PatriciaD’Amore and Diane Bielenberg Cold Spring Harb Perspect Med doi: 10.1101/cshperspect.a041185

T cell detachment is a physiologically relevant parameter besides T cell arrest, polarization, crawling, probing, and transmigration during the interaction with an endothelial monolayer. T cell detachment means that post-arrest, the T cell cannot engage adhesion molecules required for subsequent polarization and, eventually, transmigration. 

(2) The author describes a method for debris exclusion using UFMTrack that eliminates objects of <30 pixels in size from analysis based on a mean pixel size of 400 for T lymphocytes. However, this mean pixel size appears to stem from in-vitro activated CD8 T cells, which rapidly grow and proliferate upon stimulation. In line with this, activated lymphocytes exhibit increased cytoplasmic area, making them appear less dense or “brighter” by phase microscopy compared to naïve lymphocytes, which are relatively compact and subsequently appear dimmer. Given this, it is not clear whether UFMTrack is sufficiently trained to identify naïve human lymphocytes in circulating blood, nor smaller, murine lymphocytes. Analysis of each lymphocyte subtype in terms of pixel size and intensity would be beneficial to strengthen the claim that UFMTrack can identify each of these populations. Additionally, demonstrating that UFMTrack can correctly characterize the behavior of naïve versus activated lymphocytes isolated from murine and human sources would strengthen the claim that UFMTrack can be broadly applied to study lymphocyte dynamics in diverse models without additional training

We thank the Reviewer for the suggestion to more precisely evaluate the range of cell sizes that can be analyzed by our framework. We have included a visualization of crawling cell sizes successfully analyzed by the UFMTrack in Supplementary Figure 7. It demonstrates that the human peripheral blood mononuclear cells, that are almost twice as small as the activated mouse CD4 T cells used in these assays, can be successfully segmented, tracked, and analyzed with the UFMTrack framework. Thus, our UFMTrack framework is suitable for a broad application to differentially sized immune cells during their interaction with the endothelial cell monolayer under flow. 

(3) Average precision was compared to the analysis of UFMTrack but it is unclear how average precision was calculated. This information should have been included in the methods section

We thank the Reviewer for pointing our attention to the missing information. We have added a subsection, “Performance Analysis”, to the Materials and Methods section, where we describe the statistical methods and the performance metrics used to evaluate the UFMTrack framework.

(4) CD4 and CD8 T cells exhibit distinct biology and interaction kinetics driven in part by their MHC molecule affinity and distinct receptor expression profiles. Thus, it is unclear why two distinct mechanisms of endothelial cell activation are needed to see differences between the populations.

We thank the Reviewer for pointing out that different cytokine stimulations of endothelial cells were used in the assays used here to test our UFMTrack to analyze CD4 and CD8 T cell interactions with the endothelial monolayer. While the Reviewer is correct that CD4 and CD8 T cells use different mechanism to cross the pMBMEC monolayer as show by us (doi: 10.1002/eji.201546251.) and others and that recognition of cognate antigen on MHC class I on pMBMECs will arrest CD8 T cells and lead to CD8 T-cell mediated apoptosis ( doi: 10.1038/s41467-023-38703-2.) the focus of the present study was not on comparing CD4 and CD8 T cell interactions with the pMBMEC monolayer but rather to test suitability of UFMTrack to study the different multi-step transmigration of these T cell subsets across the endothelial monolayer. 

(5) The BMECs are barrier tissues but were cultured on µdishes in this study. To study the transmigration of T-cells across the endothelium, the model would have been more relevant on a semi-permeable membrane instead of a closed surface.

We understand the critique of the Reviewer, but laminar flow chambers with endothelial monolayers still provide a state-of-the-art and established methodology to study immune cell migration across endothelial monolayers by in vitro live cell imaging including endothelial cells forming the blood-brain barrier.  

(6) Methods are provided for the isolation and expansion of human effector and memory CD4+ T cells. However, there is no mention of specific CD4+ T cell populations used for analysis with UFMTrack, nor a clear breakdown of tracking efficiency for each subpopulation. Further, there is no similar method for the isolation of CD8+ T cell compartments. A clear breakdown of the performance efficiency of UFMTrack with each cell population investigated in this study would provide greater insight into the software’s performance with regard to tracking the behavior and movement of distinct immune populations.

We thank the Reviewer for this comment. Since a fair performance evaluation requires collecting reliable and consistent manual annotations, in this work we have performed such analysis only for the mouse CD8 T-cell population migrating on the pMBMEC monolayer. We have chosen this as a reference since it is a different cell population than the one the segmentation model was trained on. This provides an insight into how high performance is expected when other immune cell types are studied than the ones used for model development.

(7) The results section is quite extensive and discusses details of establishment of the framework while highlighting both the pros and cons of the different aspects of the process, for example the limitation of the two models, 2D and 2D+T were highlighted well. However, the results section includes details which may be more fitting in the methods section.

We thank the Reviewer for highlighting the extensive work carried out in the development of our UFMTrack framework. We decided to include in the results section only the description of key elements and design decisions taken when developing the framework, such as the need to include a time series of images for successful segmentation of the transmigrated cells. At the same time, the majority of implementational details can be found in the Supplementary Material.

(8) A few statements in the results section lacked literary support, which was not provided in the discussion either, such as support for increased variance of T-cell instantaneous speed on stimulated vs non-stimulated pMBMECs. Another example is the enhancement of cytokine stimulation directed T-cell movement on the pMBMECs that the authors observed but failed to relay the physiological relevance of it. The authors don’t provide enough references for developments in the field prior to their work which form the basis and need for this technology.

We thank the Reviewer for this comment and for asking for literature references. However, we cannot provide such references as these are original observations we made by employing the UFMTrack framework.  This shows that UFMTrack observes T-cell behaviors that have previously been overlooked. Their physiological relevance will have to be explored in separate studies. We have extended the introduction section to include the details on the existing methods developed in the field, as well as their weaknesses that motivated the development of the UFMTrack framework.

(9) The rationale for use of OT-1 and 2D2-derived murine lymphocytes is unclear here. The OT-1 model has been generated to study antigen-specific CD8+ T cell responses, while the 2D2 model has been generated to recapitulate CD4 T cell-specific myelin oligodendrocyte glycoprotein (MOG) responses.

To establish and test the UFMTrack framework, we have made use of the specific T-cell subsets and endothelial cell models we generally use within our research context. Especially for animal work, this is according to the 3R rules requesting to reduce animal experimentation.  

Figures and text:

(1) There are certain discrepancies and misarrangement of figures and text. For example, discussion of the effect of sheer flow on T cell attachment as part of the introduction in figure 1 and then mentioning it in the text again in the results section as part of figure 4 is repetitive.

We thank the Reviewer for pointing our attention to this misarrangement. We have adjusted the label of Figure 4 to emphasize that this effect is correctly captured by the UFMTrack.

(2) Section IV, subsection 1 of the results section, refers to ‘data acquisition section above’ in line 279, however the said section is part of materials and methods which is provided towards the end of the manuscript.

We thank the Reviewer for pointing our attention to this misarrangement. We have adjusted the text to reflect the correct chapter order.

(3) There are figures in the manuscript that have not been referenced in the results section, for example, figure 3A and B. Figure 1 hasn’t been addressed until subsection 7 of materials and methods

We thank the Reviewer for pointing our attention to this misarrangement. We have adjusted the text to refer to all figure panels and the clarification of the cell multiplicity estimation in the supplementary information section. References to Figure 1 were added in the introduction section to illustrate the in vitro under flow imaging setup as well as the typical T cell behaviors in such experiments.

(4) A lack of significance but an observed trend of increased variance of T cell instantaneous speed is reported in line 296-298; however, the graph (figure 4G) shows a significant change in instantaneous speed between non-stimulated and TNFα-stimulated systems. This is misleading to the readers.

We thank the Reviewer for pointing our attention to this discrepancy. We have expanded the text to indicate a low statistical significance for the TNF and no significance but just a trend for the IL1-beta conditions.

(5) The authors talk about three beginner experimentors testing the manual T cell tracking process but figure 5 only showcases data from two experimentors without stating the reason for excluding experimentor 1.

We thank the Reviewer for pointing our attention to this ambiguity. While both the migration analysis and the manual cell tracking were performed by all three beginner experimenters, the cell tracking data for the first one was unfortunately lost due to a hardware failure.

Discussion:

(1) While the discussion captures the major takeaways from the paper, it lacks relevant supporting references to relate the observation to physiological conditions and applicability.

This study is not about the physiological relevance of the microfluidic devices and immune cells used but rather about advancing methodology to analyze dynamic immune cell behavior on endothelial monolayers under physiological flow. Therefore, the discussion does not extend to comparing the physiological relevance of the specific in vitro models employed in this study.   

(2) The discussion lacks connection to the results since the figures were not referenced while discussing an observed trend

We thank the Reviewer for pointing our attention to this misarrangement. We have included the references to the relevant figures as well as supporting references.

(3) The authors briefly looked into mouse and human BMECs and their individual interaction with Tcells, but don’t discuss the differences between the two, if any, that challenged their framework.

We thank the Reviewer for pointing our attention to this weakness. We have added to the discussion section clarifications on the challenges of analyzing the T cell interactions with the HBMEC and the BMDM interactions with the pMBMEC monolayer.

(4) Even though though the imaging tool relies on difference in appearance for detection, the authors talk about lack of feasibility in detecting transmigration of BMDMs due to their significantly different appearance. The statement lacks a problem solving approach to discuss how and why this was the case.

We thank the Reviewer for pointing our attention to this weakness and apologize for the misleading explanation of the problem of analyzing the BMDM sample. Since the transmigrated part of the macrophages differs in appearance from a transmigrated part of a T cell, its detection by a Deep Neural Network trained on the T cell data is worse than that for the T cells. At the same time, the detection performance before the transmigration is sufficient for the BMDM migration analysis. The potential approaches to alleviate this are added to the discussion section.

Relevance to the field:

Utilizing the framework provided by the authors, the application can be adapted and/or utilized for visualizing a range of different cell types, provided they are different in appearance. However, this would require extensive changes to the script and won’t be adaptable in its current form.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

The authors should announce in the abstract that the software analysis Track is downloadable and free to use for all researchers. They may consider providing some sort of helpdesk, although I realize that that may run into too much time.

As said above, they stress that it can be done in BBB models, but I would argue that it is much more broadly applicable.

We thank the Reviewer for these suggestions. We have emphasized the broader applicability of UFMTrack in the abstract and pointed out the public availability of the code and data.

Can they add an experiment that shows that it also works for neutrophils for example? I understand that on paper yes it should work, but the neutrophils are of course faster etc.

This is an excellent suggestion, but we tested UFMTrack within the current framework of ongoing research, which does not include the investigation of neutrophil transmigration across endothelial monolayers.  

Also, the combination of different leukocytes in one TEM assay would really be a step forward. If the software can detect different-sized leukocytes, then this should be possible.

We thank the Reviewer for this suggestion. We have added Supplementary Figure 7, demonstrating the range of cell sizes that were successfully analyzed by the UFMTrack framework throughout our manuscript. We also added a statement to the discussion that according to this data, “simply by discriminating cells by size, it is possible to extend UFMTrack to study the interaction of several types of immune cells migrating on top of a cellular monolayer under flow.”

Extra challenges: can the method also discriminate between paracellular and transcellular migration modes? In particular for T-cells this is known to happen.

We thank the Reviewer for this suggestion. We have added this to the potential applications of UFMTrack in the discussion section. While this differentiation is not feasible relying solely on the phasecontrast imaging data, UFMTrack can simplify this analysis by providing automatically the predictions of the transmigration locations, for analysis of the fluorescent data of the junctional labels.

Reviewer #2 (Recommendations For The Authors):

This paper develops an under-flow migration tracker to evaluate all the steps of the extravasation cascade of immune cells across the BBB. The algorithm is useful and has important applications. There are several points that need to be addressed, particularly about the claims made by the authors.

Please see the comments below for more details:

• Lines 88-92: Add a citation for the characteristics of the BBB as a barrier

We have added two references accordingly.  

• Lines 94-95: Can the authors indicate what models were used for these studies and how those compare to their in vitro model? In addition, can the authors say whether T cells were manually tracked in this study to translate results to the clinic and whether the results were successful when translated to the clinic? This may enhance the argument that automatic trackers are needed if the translation was not 100% successful

This introductory paragraph summarizes in vivo and in vitro observations from several laboratories. Although these studies include manual tracking of T cells, they do not necessarily distinguish all sequential steps of the multi-step T cell transmigration cascade. Thus, automated tracking may provide additional insights, allowing for increased translation of findings to the clinic.  

• Lines 96-98: Citing the work of Roger Kamm and Noo Li Jeon would be helpful here as they pioneered these BBB microfluidic models and have protocol papers on how to build them and how to use them for cancer cell extravasation studies. Roger Kamm has also worked on several extravasation studies with neutrophils, monocytes, and PBMCs from 3D vasculatures in microfluidic devices, under flow using pressurized fluid or recirculating pumps. Mentioning those would be helpful as they are directly related to what the authors are presenting in their paper.

We thank the Reviewer for this comment, and we consider the work of Roger Kamm and Noo Li Jeon as very valuable for the field. However, these authors have focused on developing functional 3D microfluidic devices, including, e.g., all cells of the neurovascular unit which is not the focus of this present study that solely employed parallel flow chamber devices and endothelial monolayers.  

• Lines 110-116: Can the authors comment on the use of ImageJ or similar automatic tracking tools and how these compare to the under-flow migration tracker developed in this paper? Several groups use ImageJ to track cellular migration successfully and in an automatic manner with short intervals between each frame. One paper that comes to mind is Chen et al: DOI: 10.1073/pnas.1715932115 where neutrophil migration in 3D was assessed with ImageJ in microfluidic devices of the vasculature. If the authors can highlight differences between their tool and what is currently available and used for automatic tracking (e.g. ImageJ), this would help in understanding the advantages of the migration tracker developed in this paper.

• Lines 118-121: Add citations for the current state of the art for T cell extravasation tracking

We thank the Reviewer for these suggestions. We have extended the introduction to add more details on the available tools for tracking migrating immune cells and their limitations, as well as the discussion section to emphasize the features unique to the developed UFMTrack framework.

• Figure 1: The device used by the authors is considered to be a 2D microfluidic device with a monolayer of mouse brain endothelial cells. I would recommend the authors to carefully revise the claims made in the paper to mention that this is a 2D device as opposed to a 3D device, in order to not mislead readers who may be expecting these analyses to be performed in 3D vasculatures.

We thank the Reviewer for this suggestion. We have included in the summary the mention of the 2dimensional nature of the employed BBB model.

• Figure 1: The T cells used in this study are not fluorescently-labeled but the authors mention that this is an issue from current state-of-the-art tools. I would recommend that the authors remove this point as being an issue because it is not addressed in their paper. The T cells are also not labeled in this study so this limitation of other systems is not addressed in this paper.

We apologize to the Reviewer as we do not understand this question. There will be many experimental conditions not allowing to study fluorescently tagged T cells. Therefore, UFMTrack is tailored to follow and analyze T cells and other immune cells during their interaction with endothelial monolayers independent of a fluorescence tag.  

• Figure 1: Was the shear stress controlled manually with a syringe? Or with the use of a pressure controller? I would clarify this aspect and discuss human errors that can be introduced from manually controlling the pressure applied to the monolayer.

We thank the Reviewer for pointing our attention to this ambiguity. We have added a mention of the automated syringe pump used to control the shear stress in the text where the values of shear stress applied to the sample are first mentioned.

• Figure 1: Does T cell attachment occur within the first 5 minutes? Can the authors comment on how they chose this timeline and the percentage of T cells that are washed off at the second step at 1.5 dynes/cm^2? Is 30 seconds enough to ensure all the non-adhered T cells are washed off with 1.5 dyns/cm^2?

Superfusion of the T cells over the endothelial monolayer is performed under 0.5 dynes/cm2 to allow the T cells to settle on the endothelial cell monolayer under flow. After increasing to physiological, flow non adherent T cells detach within 30 seconds, as described by the Reviewer. We have included in the Methods Section Point 7 the references describing in depth the design of the flow chamber device and methods used here.  

• Line 154: How many images were used in the training vs. testing dataset for T cell migrations?

We thank the Reviewer for pointing our attention to this missing information. We have added the sizes of the training and validation datasets. Specifically, the 226MPix of available imaging data was split into 154Mpix training and 37 MPix validation sets. The gap in between was introduced to avoid a correlation between validation and training set that would compromise the performance evaluation.

• Are the supplementary videos at real speed or accelerated?

We thank the Reviewer for pointing our attention to this missing information. The videos are sped up by a factor of 96. We have added this information to the Supplementary video descriptions.  

• Lines 208 216: Can the authors comment on how their initial adhesion timeframe of 30sec before starting the recording at 5.5min affects the number of T cells with rapid displacement? 30 seconds may not be enough to ensure T cells have adhered to the endothelium

Please see our comment above. The methodology used in the present assays has been set up and validated in numerous publications. We have included in the Methods Section under Point 7 the references describing in depth the design of the flow chamber device and the methods used here.  

• Lines 275-277: Was the number of testing images 18? Can the authors comment on how this compares to training dataset size and whether these numbers are enough to achieve robust results?

We apologize for this ambiguity in our manuscript. The framework was evaluated on 18 imaging datasets, each corresponding to 32 minutes of recording, not 18 images. We have added this clarification to the “CD4+ T cell analysis” subsection. The total size of these datasets is 18 datasets * 191 timeframe/dataset * 9.9MPix/frame = 34MPix

• Figure 4B: Can the authors add statistics here? Individual datapoints on the error bars would be helpful too. 

We thank the Reviewer for pointing our attention to this weakness. The data corresponds to the statistical errors as evaluated based on all cells in the 18 datasets. We have added the total number of cells in each of the endothelium stimulation conditions to the text.

• Figure 4C-J: Can the authors put individual datapoints here as well and explain whether they considered each T cell to be one datapoint or each endothelium (averaging all T cells) to be one datapoint? 

We thank the Reviewer for this suggestion. However, adding about one thousand points corresponding to each cell would be impractical. We thus present the distributions of the evaluated from the data metrics as a histogram on the violin plot instead of the swarm plot.

• Figure 4: Did the authors wash the monolayers before introducing T cells? Soluble unbound cytokines may still be present and there are two different questions that would be studied here: “Is the inflamed endothelium affecting T cell migration?” (if washing was performed) or “Is T cell and microenvironmental inflammation affecting T cell migration?” (if no washing was performed)

The endothelial monolayers are “washed” by starting the flow in the flow chamber device and this is before superfusing the T cells over the endothelial monolayer. We agree that our flow chamber device combined with UFMTrack will allow to address all these questions.

• Figure 4I: Are all the T cells decelerating? (negative AM speed)

We thank the Reviewer for this question. The cells are moving along the flow, which, in our experiments, is from left to right. The vector of speed is thus pointing against the x-axis, and thus the AM speed is negative.

• Lines 302 306: Please explain how this compares to ImageJ or similar trackers that can achieve similar outputs. 

We thank the Reviewer for this question. We have added a statement in the “T-cell tracking” section emphasizing that standard trackers are incapable of correctly capturing large displacements.

• Lines 306-309: It is not lower for TNF stimulation though. How do the authors address this? TNF is also a pro-inflammatory cytokine.

We have previously shown that stimulation of pMBMECs with IL-1 and TNF-a induces different cell surface levels of ICAM-1 and VCAM-1, which will influence T cell behavior on the pMBMEC monolayer.  

• Lines 313-315: Could this be because the monolayer was not washed and soluble cytokines affected T cell response directly?

Please see our answer to lines 306-309.  

• Lines 319: Please cite Roger Kamm and Noo Li Jeon’s papers on BBB models with human BMECs, pericytes and astrocytes in 3D microfluidic devices.

We thank the Reviewer again for pointing out these studies. As mentioned above, as our present study does not explore 3D models of the BBB, we think it does not fit into the framework of our study to elaborate on 3D models of the BBB. In addition, this would require the inclusion of a discussion of the work of others like, e.g., Peter Searson and others.  

• Figure 5: Several statistics are missing from parts of the figure. Please add those.

We apologize – but we do not understand which statistical analysis the Reviewer is missing from this Figure.  

• Can the authors comment on the number of T cells perfused over the monolayer and if this ratio of T cells to endothelial cells makes physiological sense? Too many T cells may result in endothelium inflammation and increased diapedesis.

The number of T cells used to suprerfuse over the endothelial monolayer is tested to avoid aggregation of T cells in suspension and thus artificial interactions with the endothelial monolayer. T cell behavior on the pMBMEC monolayer remains the same over the dilution of factor 10.  

• Lines 381 383: How does this compare to analyses that look at the cross-section of the endothelium? It is difficult to assess transmigration looking at the top view of the endothelium. Perhaps, cross-section assessments will identify differences in manual vs. automatic tracking.

There is, to the best of our knowledge, no microscopic device that would allow for in vitro live cell imaging of a live endothelial monolayer – this is in the presence of tissue culture medium – from the side at a resolution that would allow to define transmigration. Our current study rather shows the UFMTrack can distinguish cells moving above or below the endothelial monolayer.  

• Figure 5J: This is probably the most important argument of the paper. If the authors can show statistical differences in their graph, this would greatly help convince readers that this tool is necessary and actually computationally efficient compared to manual work by researchers.

We thank the Reviewer for this suggestion. However, comparing a single data point for automated measurement with four manual experimenter analysts is not a statistically sound comparison. We believe that Figure 5K is clearly showing the factor 5 difference in analysis speed as compared to manual analysis. More importantly, though, the automated analysis is taking the machine time, lifting the need for the experimenter to invest even 1/5th of the original analysis time.

• Figure 6: Did the authors use autologous immune cells and endothelial cells? This is particularly relevant with the use of human-derived T cells (line 436) on the BMEC monolayer. Can the authors comment on non-self reactivity by the T cells encountering BMEC from another human subject?

Autologous T cell interaction with BMECs would only be possible when using hiPSC-derived EECM-BMECs and the T cells from the same individual. All other experimental frameworks will not include autologous interactions. This is the experimental framework used by most authors studying immune cell interactions with commercially available donors. We have not studied alloreactive interactions in our assays and thus cannot further comment.  

• Figure 6M,N,O: How does this compare to ImageJ for tracking of fluorescent cells? I recommend the authors to try that, at least for this section, as this may enhance their argument for their tool vs. standard tools like ImageJ if success rates are higher for their tool.

We thank the Reviewer for this suggestion. We included a note on the analysis of the fluorescent datasets using the  TrackMate plugin for imageJ performed previously in our lab in the “Human T cells on immobilized recombinant BBB adhesion molecules” subsection.

• Figure 6: Please put individual datapoints on the bar or violin plots where they are missing.

We thank the Reviewer for this suggestion. However, adding about one thousand points corresponding to each cell would be impractical. We thus present the distributions of the evaluated from the data metrics as a histogram on the violin plot instead of the swarm plot.

• Lines 467-471: This argument is important and should be mentioned earlier in the introduction.

Another point that can be mentioned is the application of this platform to imaging modalities in vivo (mouse or human) given that there is no fluorescent staining in these cases. This review may be relevant: https://doi.org/10.1002/jcb.10454

We thank the Reviewer for this suggestion. We have clarified in the introduction that UFMTrack does not require fluorescent labels of the imaged migrating cells and relies solely on the phase contrast imaging data.

• Discussion: Please address a few more potential applications to this study. One can be cancer and immune infiltration.

We thank the Reviewer for this suggestion. We have elaborated on additional potential applications to the discussion section.

Reviewer #3 (Recommendations For The Authors):

(1) Line 327-328: The authors talk about ‘As we have previously shown…pMBMEC monolayers differs between CD4+ and CD8+ cells…’. Where was this shown? If it was in a previously published article, please provide a reference.

We have added these missing references.  

(2) Line 353: Please provide clear location on where to find the associated information instead of stating ‘see below’.

We thank the Reviewer for pointing our attention to this ambiguity. We have corrected the phrase to “see next paragraph”

(3) Line 439: Please correct the acronym to BMECs

We thank the Reviewer for pointing our attention to this typo. We have corrected it.

Author response:

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

Summary of revisions

Title

We have changed the title of the manuscript to “Chromatin endogenous cleavage provides a global view of yeast RNA polymerase II transcription kinetics”.

Text

Additional discussion of the patterns for elongation factors added (detailed below).

Small text changes throughout, as mentioned in the detailed response below.

Figures

Updated legend-image in Figure 2F to reflect correct colors

Added Figure 2 – supplement 1F – RNAPII enrichment with shorter promoter dwell times

Added Figure 2 - supplement 2 with ChIP-seq outcomes (and text legend)

Removed gene numbers in Figure 5C and put them in the legend.

Substituted Med1 and Med8 ChEC over Rap1 sites in Figure 5F.

Moved kin28-is growth inhibition to Figure 5 – Supplement 1.

Substituted a new panel overlaying the RNAPII enrichment over UASs or promoters for all three strains in Figure 7D.

Improved the labeling and legend of Figure 7E

Methods

Added ChIP-seq performed to confirm that the MNase fusion proteins are able to produce the expected pattern for ChIP.

Point-by-point response to reviewers’ comments

Reviewer 1:

(1) Extending this work to elongation factors Ctk1 and Spt5 unexpectedly give strong signals near the PIC location and little signals over the coding region. This, and mapping CTD S2 and S5 phosphorylation by ChEC suggests to me that, for some reason, ChEC isn't optimal for detecting components of the elongation complex over coding regions. 

(3) mapping the elongation factors Spt5 and Ctk1 by ChEC gives unexpected results as the signals over the coding sequences appear weak but unexpectedly strong at promoters and terminators. It would be helpful if the authors could comment on reasons why ChEC may not work well with elongation factors. For example, could this be something to do with the speed of Pol elongation and/or the chromatin structure of coding sequences such that coding sequence DNA is less accessible to MNase cleavage? 

(7) The mintbodys are an interesting attempt to measure Pol II CTD modifications during elongation but give unexpected results as the signals in the coding region are lower than at promoters and terminators. It seems like ChIP is still a much better option for elongation factors unless I'm missing something. 

We agree with the reviewer that this is a point that could confuse the reader.  Therefore, we have devoted two additional paragraphs to possible interpretations of our data in the Discussion:

ChEC with factors involved in elongation (Ctk1, Spt5, Ser2p-RNAPII), when normalized to total RNAPII, showed greater enrichment over the CDS (Figure 3G), as expected. However, it is surprising that we also observed clear enrichment of these factors at promoters (e.g. Figure 3A, E & F). The association of elongation factors with the promoter seems to be biologically relevant. Changes in transcription correlate with changes in ChEC enrichment for these factors and modifications (Figure 4C). Blocking initiation by inhibiting TFIIH kinase led to a reduction of Ser5p RNAPII and Ser2p RNAPII over both the promoter and the transcribed region (Figure 5G). This suggests either that the true signal of these factors over transcribed regions is less evident by ChEC than by ChIP or that ChEC can reveal interactions of elongation factors at early stages of transcription that are missed by ChIP. The expectations for enrichment of elongation factors and phosphorylated CTD are largely based on ChIP data. Because ChIP fails to capture RNAPII enrichment at UASs and promoters, it is possible that ChIP also fails to capture promoter interaction of factors involved in elongation as well.

Factors important for elongation can also function at the promoter. For example, Ctk1 is required for the dissociation of basal transcription factors from RNAPII at the promoter (Ahn et al., 2009). Transcriptional induction leads to increases in Ctk1 ChEC enrichment both over the promoter and over the 3’ end of the transcribed region (Figure 4C). Dynamics of Spt4/5 association with RNAPII from in vitro imaging (Rosen et al., 2020) indicate that the majority of Spt4/5 binding to RNAPII does not lead to elongation; Spt4/5 frequently dissociates from DNA-bound RNAPII. Association of Spt4/5 with RNAPII may represent a slow, inefficient step in the transition to productive elongation. If so, then ChEC-seq2 may capture transient Spt4/5 interactions that occur prior to productive elongation, producing enrichment of Spt5 at the promoter.

(2) Finally, the role of nuclear pore binding by Gcn4 is explored, although the results do not seem convincing (10) In Figure 7, it's not convincing to me that ChEC is revealing the reason for the transcriptional defect in the Gcn4 PD mutant. The plots in panel D look nearly the same and I don't follow the authors' description of the differences stated in the text. In panel A, replotting the data in some other way might make the transcriptional differences between WT and Gcn4 PD mutants more obvious. 

The phenotype of the gcn4-pd mutant is a quantitative decrease in transcription and this leads to a quantitative decrease, rather than qualitative loss, of RNA polymerase II over the promoter, without impacting the association of RNA polymerase II over the UAS region. This effect is small but statistically significant (p = 4e5). We have changed the title of this section of the manuscript to “ChEC-seq2 suggests a role for the NPC in stabilizing promoter association of RNAPII”. Also, to make comparison clearer, we have plotted the data together in the revised figure (Figure 7D).

The magnitude of the decrease is not large, but we would highlight that is almost as large as that produced by inhibiting the Kin28 kinase (Figure 5H). Because the promoter-bound RNAPII is poorly captured by ChIP, this effect might be difficult to observe by techniques other than ChEC. Obviously, more mechanistic studies will need to be performed to fully understand this phenotype, but this result supports a role for the interaction with the nuclear pore complex in either enhancing the transfer of RNA polymerase II from the enhancer to the promoter or in preventing its dissociation from the promoter.

I think that the related methods cut&run/cut&tag have been used to map elongating pol II. The authors should summarize what is known from this approach in the introduction and/or discussion. 

CUT&RUN has been used to map RNAPII in mammals, but we are not aware of reports in S. cerevisiae.  Work from the Henikoff Lab in yeast mapped transcription factors and histone modifications (PMIDs 28079019 and 31232687).  A report using CUT&RUN in a human cell line reported a promoter-5’ bias of RNAPII that appeared to be dependent on fragment length (PMID 33070289). Regardless, the report highlights a key distinction between yeast and other eukaryotes: paused RNAPII. Indeed, paused RNAPII dominates ChIP-seq tracks in metazoans, and so we are hesitant to speculate between CUT&RUN in other species vs. ChEC-seq2 in S. cerevisiae. 

Are the Rpb1, Rpb3, TFIIA, and TFIIE cleavage patterns expected based on the known structure of the PIC (Figures 2C, E)? 

Rpb1 and 3 show peaks at approximately -17 and +34 with respect to TATA. TFIIA (Toa2) shows peaks at -12 and + 12.  And TFIIE (Tfa1) shows a peak around +34 (Figure 2C & E):

As shown in the supplementary movie (based on the cMed-PIC structure; PDB #5OQM; Schilbach et al., 2017), upon binding to TBP/TFIID, TFIIA would be expected to cleave slightly upstream and downstream of the protected TATA (-12 and +12), while TFIIE binds downstream after the +12 site is protected and would be closest to the +34 unprotected site (to the right in the image below). RNAPII, which binds the fully assembled PIC, should be able to access either the upstream site (-12) or the downstream site (+34). Rpb1’s unstructured carboxy terminal domain, to which MNase is fused, would give it maximum flexibility, which likely explains why Rpb1 cleaves both at -12 and +34, with a preference for -12. Rpb3 also cleaves both sites, but without an obvious preference. 

Author response image 1.

Author response image 2.

cleavage at -12, +12 and +34

Author response image 3.

Highlighted sites corresponding to the peaks in TFIIA assembled with TBP:

Author response image 4.

The complete PIC, protecting the +12 site, but leaving the +34 site exposed: 

(6) Figure 2 S1: Pol II ChIP in the coding region gives a better correlation with transcription vs ChEC in promoters. Also, Pol II ChIP at terminators is almost as good as ChEC at promoters for estimating transcription. This latter point seems at odds with the text. The authors should comment on this and modify the text as needed. 

Thank you for this comment.  We have clarified the text.

In Figures 4 and 5, it's hard to tell how well changes in transcription correlate with changes in Pol II ChEC signals. It might be helpful to have a scatterplot or some other type of plot so that this relationship can be better evaluated. 

While we find corresponding increase/decrease in ChEC-seq2 signal in genes identified as up/downregulated by SLAM-seq, the magnitude in change is not well correlated between the two techniques.  This was not surprising, because neither ChIP nor ChEC correlate especially well with SLAM-seq (Figure 2 – supplement 1E).

In Figure 5, it's unclear why Pol association with Rap1 is being measured. Buratowski/Gelles showed that Pol associates with strong acidic activators - presumably through Mediator. Rap1 supposedly does not bind Mediator - so how is Pol associating here? Perhaps it would be better to measure Pol binding at STM genes that show Mediator-UAS binding. 

Thank you; this is a good point.  We chose Rap1 because we had generated high-confidence binding sites in our strains under these conditions by ChEC-seq2. The results suggest that RNAPII is recruited well to these sites and that this recruitment does not require TFIIB. However, in disagreement with the notion that Mediator does not interact with Rap1, ChEC with Mediator subunits Med1 and Med8 also show peaks at these sites (new Figure 5F; the old Figure 5F is now Figure 5 – Supplement 1).  Therefore, either these sites are co-occupied by other transcription factors that mind Mediator, or Mediator is recruited by Rap1.  In either case, this correlates with binding of RNAPII. 

Reviewer 2:

(1) The term "nascent transcription" is all too often used interchangeably for NET-seq, PRO-seq, 4sUseq, and other assays that often provide different types of information. The authors should make it clear their use of the term refers to SLAM-seq data. 

We have clarified throughout the manuscript that nascent transcription measured by SLAM-seq.

The authors should explicitly state that experiments were performed in S. cerevisiae in the Results section. 

We have made it clear in the title and the text that these experiments were performed in S. cerevisiae.

Lines 216-218 state that "None of the 24 predicted the strong signal over the transcribed region with promoter depletion characteristic of ChIP-seq". I understand the authors' point, but there are parameter combinations that produce a flat profile with slightly less signal over the promoter (e.g., 5 sec dwell times and 3000 bp/ min elongation rate). If flanking windows were included, this profile would look something like ChIP-seq. I'd encourage the authors to be more precise with their language. 

Thank you for highlighting this over-statement.

We have now clarified the text and added another supplementary panel as follows:

“While some combinations predicted a relatively flat distribution across the gene with lower levels in the promoter, none of the 24 predicted the strong signal over the transcribed region with promoter depletion characteristic of ChIP-seq. Only very short promoter dwell times (i.e., < 1s), produced the low promoter occupancy seen in ChIP-seq (Figure 2 – supplement 1F).”

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The manuscript by Choi and co-authors presents "P3 editing", which leverages dual-component guide RNAs (gRNA) to induce protein-protein proximity. They explore three strategies for leveraging prime-editing gRNA (pegRNA) as a dimerization module to create a molecular proximity sensor that drives genome editing, splitting a pegRNA into two parts (sgRNA and petRNA), inserting self-splicing ribozymes within pegRNA, and dividing pegRNA at the crRNA junction. Among these, splitting at the crRNA junction proved the most promising, achieving significant editing efficiency. They further demonstrated the ability to control genome editing via protein-protein interactions and small molecule inducers by designing RNA-based systems that form active gRNA complexes. This approach was also adaptable to other genome editing methods like base editing and ADAR-based RNA editing.

Strengths:

The study demonstrates significant advancements in leveraging guide RNA (gRNA) as a dimerization module for genome editing, showcasing its high specificity and versatility. By investigating three distinct strategies-splitting pegRNA into sgRNA and petRNA, inserting self-splicing ribozymes within the pegRNA, and dividing the pegRNA at the repeat junction-the researchers present a comprehensive approach to achieving molecular proximity and reconstituting function. Among these methods, splitting the pegRNA at the repeat junction emerged as the most promising, achieving editing efficiencies up to 76% of the control, highlighting its potential for further development in CRISPR-Cas9 systems. Additionally, the study extends genome editing control by linking protein-protein interactions to RNA-mediated editing, using specific protein-RNA interaction pairs to regulate editing through engineered protein proximity. This innovative approach expands the toolkit for precision genome editing, demonstrating the feasibility of controlling genome editing with enhanced specificity and efficiency.

Weaknesses:

The initial experiments with splitting the pegRNA into sgRNA and petRNA showed low editing efficiency, less than 2%. Similarly, inserting self-splicing ribozymes within pegRNA was inefficient, achieving under 2% editing efficiency in all constructs tested, possibly hindered by the prime editing enzyme. The editing efficiency of the crRNA and petracrRNA split at the repeat junction varied, with the most promising configurations only reaching 76% of the control efficiency. The RNA-RNA duplex formation's inefficiency might be due to the lack of additional protein binding, leading to potential degradation outside the Cas9-gRNA complex. Extending the approach to control genome editing via protein-protein interactions introduced complexity, with a significant trade-off between efficiency and specificity, necessitating further optimization. The strategy combining RADARS and P3 editing to control genome editing with specific RNA expression events exhibited high background levels of non-specific editing, indicating the need for improved specificity and reduced leaky expression. Moreover, P3 editing efficiencies are exclusively quantified after transfecting DNA into HEK cells, a strategy that has resulted in past reproducibility concerns for other technologies. Overall, the various methods and combinations require further optimization to enhance efficiency and specificity, especially when integrating multiple synthetic modules.

Thank you for this accurate summary and assessment of the strengths and weaknesses of the P3 editing as it stands. Looking ahead, we agree that further optimizations will be important, as will characterizing the performance of P3 editing in additional cellular contexts. The revised Discussion (see below) now makes these points more clearly.

Reviewer #2 (Public Review):

Choi et al. describe a new approach for enabling input-specific CRISPR-based genome editing in cultured cells. While CRISPR-Cas9 is a broadly applied system across all of biology, one limitation is the difficulty in inducing genome editing based on cellular events. A prior study, from the same group, developed ENGRAM - which relies on activity-dependent transcription of a prime editing guide RNA, which records a specific cellular event as a given edit in a target DNA "tape". However, this approach is limited to the detection of induced transcription and does not enable the detection of broader molecular events including protein-protein interactions or exposure to small molecules. As an alternative, this study envisioned engineering the reconstitution of a split prime editing guide RNA (pegRNA) in a protein-protein interaction (PPI)-dependent manner. This would enable location- and content-specific genome editing in a controlled setting.

The authors explored three different design possibilities for engineering a PPI-dependent split pegRNA. First, they tried splitting pegRNA into a functional sgRNA and corresponding prime editing transRNA, incorporating reverse-complementary dimerization sequences on each guide half. This approach, however, resulted in low editing efficiency across 7 different designs with various complementary annealing template lengths (<2% efficiency). They also tried inserting a self-splicing ribozyme within the pegRNA, which produces a functional pegRNA post-transcriptionally. The incorporation of a split-ribozyme, dependent on a PPI, could have been used to reconstitute the split pegRNA in an event-controlled manner. However again, only modest levels of editing were observed with the self-splicing ribozyme design (<2%). Finally, they tried splitting the pegRNA at the repeat:anti-repeat junction that was used to join the original dual-guide system comprised of a crRNA and tracrRNA, into a single-guide RNA. They incorporated the prime editing features into the tracrRNA half, to create petracrRNA. Dimerization was initially induced by different complementary RNA annealing sequences. Using this design, they were able to induce an editing efficiency of ~28% (compared to 37% efficiency using a positive control epegRNA guide).

Having identified a suitable split pegRNA system, they next sought to induce the reconstitution of the two halves in a PPI-dependent manner. They replaced the complementary RNA annealing sequences with two different RNA aptamers (MS2 and BoxB). MS2 detects the MCP protein, while BoxB detects the LambdaN protein. Close proximity between MCP and LambdaN would thus bring together the two split pegRNA halves, creating a functional pegRNA that would enable prime editing at a specific target site. They demonstrated that they could induce MCP-BoxB proximity by fusing them to different dimerizing protein partners: 1) constitutive epitope-nanobody/antibody pairs such as scFv/GCN4 or NbALFA/ALFA-Tag; 2) split-GFP; or 3) chemically-induced protein pairs such as FKBP/FRB or ABI/PYL. For all of these approaches, they could achieve between ~20-60% normalized editing efficiency (relative to positive control editing levels with epegRNA). Additional mutation of the linkers between the RNA and aptamers could increase editing efficiency but also increase non-specific background editing even in the absence of an induced PPI.

Additional applications of this overall strategy included incorporating the design with different DNA base editors, with the most promising examples shown with the base editors CBE4max and ABE8. It should be noted that these specific examples used a non-physiological LambdaN-MCP direct fusion protein as the "bait" that induced reconstitution of the two halves of the guideRNA, rather than relying on a true induced PPI. They also demonstrated that the recently reported RADARS strategy could be incorporated into their system. In this example, they used an ADAR-guide-RNA to drive the expression of a LambdaN-PCP fusion protein in the presence of a specific target RNA molecule, IL6. This induced LambdaN-PCP protein could then reconstitute the split peg-RNAs to drive prime editing. To enable this last application, they replaced the MS2 aptamer in their pegRNA with the PP7 aptamer that binds the PCP protein (this was to avoid crosstalk with RADARS, which also uses MS2/MCP interaction). Using this strategy, they observed a normalized editing efficiency of around 12% (but observed non-specific editing of around 8% in the absence of the target RNA).

Strengths:

The strengths of this paper include an interesting concept for engineering guide RNAs to enable activity-dependent genome editing in living cells in the future, based on discreet protein-protein interactions (either constitutively, spatially, or chemically induced). Important groundwork is laid down to engineer and improve these guide RNAs in the future (especially the work describing altering the linkers in Supplementary Figure 3 - which provides a path forward).

Weaknesses:

In its current state, the editing efficiency appears too low to be applied in physiological settings. Much of the latter work in the paper relies on a LambdaN-MCP direction fusion protein, rather than two interacting protein pairs. Further characterizations in the future, especially varying the transfection amounts/durations/etc of the various components of the system, would be beneficial to improve the system. It will also be important to demonstrate editing at additional sites; to characterize how long the PPI must be active to enable efficient prime editing; and how reversible the reconstitution of the split pegRNA is.

Thank you for this assessment of the strengths and weaknesses of the P3 editing as it stands. Looking ahead, we agree that further optimizations will be important, including along the lines suggested by the reviewer, as will further characterization of the system with respect to dependencies, reversibility, etc. The revised Discussion (see below) now makes these points more clearly.

Recommendations for the authors:

Reviewing Editor comments:

It would be helpful to better describe the nature of improvements (on-targeting and/or off-targeting) that would be needed to effectively use this approach in vitro and in vivo applications.

We agree, and have accordingly revised the last paragraph of our discussion to better describe what improvements are needed for in vitro and in vivo applications:

“In our view, there are four outstanding challenges for P3 editing to be broadly useful: evaluating additional cellular contexts, the method’s efficiency and specificity, understanding the limit of detectable protein-protein interactions, and the development of sensors compatible with multiplex P3 editing within the same cell. First, we have thus far only conducted P3 editing in HEK293T cells, and obviously needs to be tested in additional cell types. Second, both the efficiency and specificity of the P3 editing need to be improved before it can be used as a selective editing tool in model systems. We have explored how modifying the crRNA and petracrRNA pair sequences can tune the efficiency-vs-specificity tradeoff, but alternative avenues to improvement (e.g., better docking of RNA-aptamers such as MS2, BoxB, or PP7 by testing more linker sequences that place crRNA and petracrRNA for duplex formation) may be more fruitful in terms of achieving high efficiency and specificity at once (e.g., >50% editing in the setting of a specific protein-protein interaction, and <1% editing without it). Second, it is not clear whether weak and transient interactions among proteins can be used to trigger P3 editing. Assuming the genome editing complex formation is reversible, improving P3 editing efficiency may be able to capture different strengths of protein-protein interactions, although some interactions may be too transient to promote functional guide RNA formation. Finally, the current P3 editing design uses a pair of RNA aptamers and their corresponding protein binders, limiting the multiplex detection of protein-protein pairs. More orthogonal protein-RNA pairs need to be identified (e.g., using a massively parallel platform (Buenrostro et al., 2014) and/or computational prediction (Baek et al., 2023)) to allow for large numbers of P3 sensors for different protein-protein interactions to be deployed within the same cell. Overcoming these four challenges is necessary for P3 editing to be broadly useful for gating genome editing on physiological levels of specific protein-protein interactions in a multiplex fashion.”

Reviewer #2 (Recommendations For The Authors):

It does not appear that all plasmids necessary to reproduce the results of this paper have been deposited to addgene, but only a small subset. The authors might include that these plasmids are available upon request, if not uploaded to a public repository.

We have added a statement that additional plasmids are available upon request. Our Data Availability Statement reads (with the added sentence underlined):

“Raw sequencing data have been uploaded to Sequencing Read Archive (SRA) with the associated BioProject ID PRJNA1004865. The following plasmids have been deposited to Addgene: pU6-crRNA-MS2, pU6-BoxB-petracrRNA, pCMV-LambdaN-MCP, pCMV-LambdaN-NbALFA,  and pCMV-ALFA-MCP (Addgene ID 207624 - 207628). The rest of the plasmids used in this study are available upon request.”

It could be useful to include somewhere why, specifically, editing the guide RNAs as opposed to the Cas9 itself is advantageous. Light-inducible split Cas9s have been engineered, and I imagine other PPI-inducible split Cas9s have also been engineered. A specific mention of the advantages of using engineered split pegRNAs could put the significance of this work in a better context.

Thanks for raising this, and we agree. We have revised the first paragraph of the Results section to highlight why we think splitting the guide RNAs as opposed to Cas9 might be advantageous:

“In the split architecture, the “dimerization module” is a key sensor component. Although strategies that split the protein component of the genome editing complex have been described (e.g., split-Cas9 (Yu et al., 2020)), we reasoned that having the guide RNA serve as the dimerization module rather than the protein, i.e. by splitting it into two parts, and making the restoration of its function dependent on a molecular proximity event, would afford even more control. For example, if multiple split gRNAs were present within the same cell, they could be independently controlled, whereas a split Cas9 would only allow a single control point.  In our initial experiments, we focused on splitting the pegRNA used in prime editing.”

Author response:

Public Reviews:

Reviewer #1 (Public review):

Summary:

In this study from Zhu and colleagues, a clear role for MED26 in mouse and human erythropoiesis is demonstrated that is also mapped to amino acids 88-480 of the human protein. The authors also show the unique expression of MED26 in later-stage erythropoiesis and propose transcriptional pausing and condensate formation mechanisms for MED26's role in promoting erythropoiesis. Despite the author's introductory claim that many questions regarding Pol II pausing in mammalian development remain unanswered, the importance of transcriptional pausing in erythropoiesis has actually already been demonstrated (Martell-Smart, et al. 2023, PMID: 37586368, which the authors notably did not cite in this manuscript). Here, the novelty and strength of this study is MED26 and its unique expression kinetics during erythroid development.

Strengths:

The widespread characterization of kinetics of mediator complex component expression throughout the erythropoietic timeline is excellent and shows the interesting divergence of MED26 expression pattern from many other mediator complex components. The genetic evidence in conditional knockout mice for erythropoiesis requiring MED26 is outstanding. These are completely new models from the investigators and are an impressive amount of work to have both EpoR-driven deletion and inducible deletion. The effect on red cell number is strong in both. The genetic over-expression experiments are also quite impressive, especially the investigators' structure-function mapping in primary cells. Overall the data is quite convincing regarding the genetic requirement for MED26. The authors should be commended for demonstrating this in multiple rigorous ways.

Thank you for your positive feedback.

Weaknesses:

(1) The authors state that MED26 was nominated for study based on RNA-seq analysis of a prior published dataset. They do not however display any of that RNA-seq analysis with regards to Mediator complex subunits. While they do a good job showing protein-level analysis during erythropoiesis for several subunits, the RNA-seq analysis would allow them to show the developmental expression dynamics of all subunit members.

Thank you for this helpful suggestion. While we did not originally nominate MED26 based on RNA-seq analysis, we have analyzed the transcript levels of Mediator complex subunits in our RNA-seq data across different stages of erythroid differentiation (Author response image 1). The results indicate that most Mediator subunits, including MED26, display decreased RNA expression over the course of differentiation, with the exception of MED25, as reported previously (Pope et al., Mol Cell Biol 2013. PMID: 23459945).

Notably, our study is based on initial observations at the protein level, where we found that, unlike most other Mediator subunits that are downregulated during erythropoiesis, MED26 remains relatively abundant. Protein expression levels more directly reflect the combined influences of transcription, translation and degradation processes within cells, and are likely more closely related to biological functions in this context. It is possible that post-transcriptional regulation (such as m6A-mediated improvement of translational efficiency) or post-translational modifications (like escape from ubiquitination) could contribute to the sustained levels of MED26 protein, and this will be an interesting direction for future investigation.

Author response image 1,

Relative RNA expression of Mediator complex subunits during erythropoiesis in human CD34+ erythroid cultures. Different differentiation stages from HSPCs to late erythroblasts were identified using CD71 and CD235a markers, progressing sequentially as CD71-CD235a-, CD71+CD235a-, CD71+CD235a+, and CD71-CD235a+. Expression levels were presented as TPM (transcripts per million).

(2) The authors use an EpoR Cre for red cell-specific MED26 deletion. However, other studies have now shown that the EpoR Cre can also lead to recombination in the macrophage lineage, which clouds some of the in vivo conclusions for erythroid specificity. That being said, the in vitro erythropoiesis experiments here are convincing that there is a major erythroid-intrinsic effect.

Thank you for this insightful comment. We recognize that EpoR-Cre can drive recombination in both erythroid and macrophage lineages (Zhang et al., Blood 2021, PMID: 34098576). However, EpoR-Cre remains the most widely used Cre for studying erythroid lineage effects in the hematopoietic community. Numerous studies have employed EpoR-Cre for erythroid-specific gene knockout models (Pang et al, Mol Cell Biol 2021, PMID: 22566683; Santana-Codina et al., Haematologica 2019, PMID: 30630985; Xu et al., Science 2013, PMID: 21998251.).

While a GYPA (CD235a)-Cre model with erythroid specificity has recently been developed (https://www.sciencedirect.com/science/article/pii/S0006497121029074), it has not yet been officially published. We look forward to utilizing the GYPA-Cre model for future studies. As you noted, our in vivo mouse model and primary human CD34+ erythroid differentiation system both demonstrate that MED26 is essential for erythropoiesis, suggesting that the regulatory effects of MED26 in our study are predominantly erythroid-intrinsic.

(3) The donor chimerism assessment of mice transplanted with MED26 knockout cells is a bit troubling. First, there are no staining controls shown and the full gating strategy is not shown. Furthermore, the authors use the CD45.1/CD45.2 system to differentiate between donor and recipient cells in erythroblasts. However, CD45 is not expressed from the CD235a+ stage of erythropoiesis onwards, so it is unclear how the authors are detecting essentially zero CD45-negative cells in the erythroblast compartment. This is quite odd and raises questions about the results. That being said, the red cell indices in the mice are the much more convincing data.

Thank you for your careful and thorough feedback. We have now included negative staining controls (Author response image 2A, top). We agree that CD45 is typically not expressed in erythroid precursors in normal development. Prior studies have characterized BFU-E and CFU-E stages as c-Kit+CD45+Ter119−CD71low and c-Kit+CD45−Ter119−CD71high cells in fetal liver (Katiyar et al, Cells 2023, PMID: 37174702).

However, our observations indicate that erythroid surface markers differ during hematopoiesis reconstitution following bone marrow transplantation.  We found that nearly all nucleated erythroid progenitors/precursors (Ter119+Hoechst+) express CD45 after hematopoiesis reconstitution (Author response image 2A, bottom).

To validate our assay, we performed next-generation sequencing by first mixing mouse CD45.1 and CD45.2 total bone marrow cells at a 1:2 ratio. We then isolated nucleated erythroid progenitors/precursors (Ter119+Hoechst+) by FACS and sequenced the CD45 gene locus by targeted sequencing. The resulting CD45 allele distribution matched our initial mixing ratio, confirming the accuracy of our approach (Author response image 2B).

Moreover, a recent study supports that reconstituted erythroid progenitors can indeed be distinguished by CD45 expression following bone marrow transplantation (He et al., Nature Aging 2024, PMID: 38632351. Extended Data Fig. 8). 

In conclusion, our data indicate that newly formed erythroid progenitors/precursors post-transplant express CD45, enabling us to identify nucleated erythroid progenitors/precursors by Ter119+Hoechst+ and determine their origin using CD45.1 and CD45.2 markers.

Author response image 2.

Representative flow cytometry gating strategy of erythroid chimerism following mouse bone marrow transplantation. A. Gating strategy used in the erythroid chimerism assay. B. Targeted sequencing result of Ter119+Hoechst+ cells isolated by FACS. The cell sample was pre-mixed with 1/3 CD45.2 and 2/3 CD45.1 bone marrow cells. Ptprc is the gene locus for CD45.

(4) The authors make heavy use of defining "erythroid gene" sets and "non-erythroid gene" sets, but it is unclear what those lists of genes actually are. This makes it hard to assess any claims made about erythroid and non-erythroid genes.

Thank you for this helpful suggestion. We defined "erythroid genes" and "non-erythroid genes" based on RNA-seq data from Ludwig et al. (Cell Reports 2019. PMID: 31189107. Figure 2 and Table S1). Genes downregulated from stages k1 to k5 are classified as “non-erythroid genes,” while genes upregulated from stages k6 to k7 are classified as “erythroid genes.” We will add this description in the revised manuscript.

(5) Overall the data regarding condensate formation is difficult to interpret and is the weakest part of this paper. It is also unclear how studies of in vitro condensate formation or studies in 293T or K562 cells can truly relate to highly specialized erythroid biology. This does not detract from the major findings regarding genetic requirements of MED26 in erythropoiesis.

Thank you for the rigorous feedback. Assessing the condensate properties of MED26 protein in primary CD34+ erythroid cells or mouse models is indeed challenging. As is common in many condensate studies, we used in vitro assays and cellular assays in HEK293T and K562 cells to examine the biophysical properties (Figure S7), condensation formation capacity (Figure 5C and Figure S7C), key phase-separation regions of MED26 protein (Figure S6), and recruitment of pausing factors (Figure 6A-B) in live cells. We then conducted functional assays to demonstrate that the phase-separation region of MED26 can promote erythroid differentiation similarly to the full-length protein in the CD34+ system and K562 cells (Figure 5A). Specifically, overexpressing the MED26 phase-separation domain accelerates erythropoiesis in primary human erythroid culture, while deleting the Intrinsically Disordered Region (IDR) impairs MED26’s ability to form condensates and recruit PAF1 in K562 cells.

In summary, we used HEK293T cells to study the biochemical and biophysical properties of MED26, and the primary CD34+ differentiation system to examine its developmental roles. Our findings support the conclusion that MED26-associated condensate formation promotes erythropoiesis.

(6) For many figures, there are some panels where conclusions are drawn, but no statistical quantification of whether a difference is significant or not.

Thank you for your thorough feedback. We have checked all figures for statistical quantification and added the relevant statistical analysis methods to the corresponding figure legends (Figure 2L and Figure S4C) to clarify the significance of the observed differences. The updated information will be incorporated into the revised manuscript.

Reviewer #2 (Public review):

Summary:

The manuscript by Zhu et al describes a novel role for MED26, a subunit of the Mediator complex, in erythroid development. The authors have discovered that MED26 promotes transcriptional pausing of RNA Pol II, by recruiting pausing-related factors.

Strengths:

This is a well-executed study. The authors have employed a range of cutting-edge and appropriate techniques to generate their data, including: CUT&Tag to profile chromatin changes and mediator complex distribution; nuclear run-on sequencing (PRO-seq) to study Pol II dynamics; knockout mice to determine the phenotype of MED26 perturbation in vivo; an ex vivo erythroid differentiation system to perform additional, important, biochemical and perturbation experiments; immunoprecipitation mass spectrometry (IP-MS); and the "optoDroplet" assay to study phase-separation and molecular condensates.

This is a real highlight of the study. The authors have managed to generate a comprehensive picture by employing these multiple techniques. In doing so, they have also managed to provide greater molecular insight into the workings of the MEDIATOR complex, an important multi-protein complex that plays an important role in a range of biological contexts. The insights the authors have uncovered for different subunits in erythropoiesis will very likely have ramifications in many other settings, in both healthy biology and disease contexts.

Thank you for your thoughtful summary and encouraging feedback.

Weaknesses:

There are almost no discernible weaknesses in the techniques used, nor the interpretation of the data. The IP-MS data was generated in HEK293 cells when it could have been performed in the human CD34+ HSPC system that they employed to generate a number of the other data. This would have been a more natural setting and would have enabled a more like-for-like comparison with the other data.

Thank you for your positive feedback and insightful suggestions. We will perform validation of the immunoprecipitation results in CD34+ derived erythroid cells to further confirm our findings.

Reviewer #3 (Public review):

Summary:

The authors aim to explore whether other subunits besides MED1 exert specific functions during the process of terminal erythropoiesis with global gene repression, and finally they demonstrated that MED26-enriched condensates drive erythropoiesis through modulating transcription pausing.

Strengths:

Through both in vitro and in vivo models, the authors showed that while MED1 and MED26 co-occupy a plethora of genes important for cell survival and proliferation at the HSPC stage, MED26 preferentially marks erythroid genes and recruits pausing-related factors for cell fate specification. Gradually, MED26 becomes the dominant factor in shaping the composition of transcription condensates and transforms the chromatin towards a repressive yet permissive state, achieving global transcription repression in erythropoiesis.

Thank you for your positive summary and feedback.

Weaknesses:

In the in vitro model, the author only used CD34+ cell-derived erythropoiesis as the validation, which is relatively simple, and more in vitro erythropoiesis models need to be used to strengthen the conclusion.

Thank you for your thoughtful suggestions. We have shown that MED26 promotes erythropoiesis using the primary human CD34+ differentiation system (Figure 2 K-M and Figure S4) and have demonstrated its essential role in erythropoiesis through multiple mouse models (Figure 2A-G and Figure S1-3). Together, these in vitro and in vivo results support our conclusion that MED26 regulates erythropoiesis. However, we are open to further validating our findings with additional in vitro erythropoiesis models, such as iPSC or HUDEP erythroid differentiation systems.

Reviewer #2 (Public review):

Summary:

The manuscript by Zhu et al describes a novel role for MED26, a subunit of the Mediator complex, in erythroid development. The authors have discovered that MED26 promotes transcriptional pausing of RNA Pol II, by recruiting pausing-related factors.

Strengths:

This is a well-executed study. The authors have employed a range of cutting-edge and appropriate techniques to generate their data, including: CUT&Tag to profile chromatin changes and mediator complex distribution; nuclear run-on sequencing (PRO-seq) to study Pol II dynamics; knockout mice to determine the phenotype of MED26 perturbation in vivo; an ex vivo erythroid differentiation system to perform additional, important, biochemical and perturbation experiments; immunoprecipitation mass spectrometry (IP-MS); and the "optoDroplet" assay to study phase-separation and molecular condensates.

This is a real highlight of the study. The authors have managed to generate a comprehensive picture by employing these multiple techniques. In doing so, they have also managed to provide greater molecular insight into the workings of the MEDIATOR complex, an important multi-protein complex that plays an important role in a range of biological contexts. The insights the authors have uncovered for different subunits in erythropoiesis will very likely have ramifications in many other settings, in both healthy biology and disease contexts.

Weaknesses:

There are almost no discernible weaknesses in the techniques used, nor the interpretation of the data. The IP-MS data was generated in HEK293 cells when it could have been performed in the human CD34+ HSPC system that they employed to generate a number of the other data. This would have been a more natural setting and would have enabled a more like-for-like comparison with the other data.

Welcome back.

This is part two of this lesson.

We're going to continue immediately from the end of part one.

So let's get started.

Okay, so the instance is now in an available state.

Let's just close down this informational dialogue at the top.

And let's just minimize this menu on the left.

Let's maximize the amount of screen space that we have for this specific purpose.

So I just want us to go inside this database instance and explore together the information that we have available.

So I talked in the theory lesson how every RDS instance is given an endpoint name and an endpoint port.

So this is the information that we'll use to connect to this RDS instance.

Networking wise, this instance has been provisioned in US-EAST-1A.

It's in the Animals for Life VPC and it's used our A4L subnet group that we created at the start of this demo.

And that means that it's currently utilizing all three database subnets in the Animals for Life VPC.

But it's chosen because we only deployed one instance to use US-EAST-1A.

Now this is the VPC security group that we're going to need to configure.

So right click on this and open it in a new tab and move to that tab.

This is the security group which controls access to this RDS instance.

So let's expand this at the bottom.

So currently it has my IP address being the only source allowed to connect into this RDS instance.

So the only inbound rule on the security group protecting this RDS instance is allowing my IP address.

So we're going to click on Edit and then click on Add Rule.

And we're going to add a rule which allows our other instances to connect to this RDS instance.

So first in the type drop down click and then type mySQL to get the same option as the line above and then click to select.

Next go ahead and type instance into the source box and then select the migrate to RDS-instance security group.

Now this is the security group that's used by any instances deployed by our one click deployment.

And this allows those instances to connect to our RDS instance and that's what we want.

So go ahead and select that and then click on Save Rules.

And this means now that our WordPress instance can communicate with RDS.

So now let's move back to the RDS tab and then make sure we're inside the A4L WordPress database instance.

So that's the connectivity and the security tab.

We also have the monitoring tab and it's here where you can see various CloudWatch provided metrics about the database instance.

You also have logs and events related to this instance.

So if we go and have a look at recent events we can see all of the events such as when the database instance was created, when its first backup was created.

And you can explore those because they might be different in your environment.

You can click on the Configuration tab and see the current configuration of the RDS instance.

The Maintenance and Backups tab is where you can configure the maintenance and backup processes and then of course you can tag the RDS instance.

Now in other lessons in this section of the course and depending on what course you're taking I will be talking about many of these options, what you can modify and which actions you can perform on RDS instances.

But for now we're just going to move on with this demo.

So the next step is that we need to migrate our existing data into this RDS instance.

So what we're going to do is to click on the Connectivity and Security tab and we're going to leave this open.

We're going to need this endpoint name and port very shortly.

You should still have a tab open to the EC2 console.

If you don't you can reach that by going on Services and then opening EC2 in a new tab.

But I want you to select the A4L-WordPress instance and then right click and connect to it using Instance Connect.

So go ahead and do that.

Once you've done that we're going to start referring to the lesson commands document.

So make sure you've got that open.

We're going to use this command to take a backup of the existing MariaDB database.

So we need to replace a placeholder.

What we need to do is delete this and replace it with the private IP address of the MariaDB EC2 instance.

So go back to the EC2 console, select the DB-WordPress instance and copy the private IP version 4 address into your clipboard.

And then let's move back to the WordPress instance and paste that in.

Go ahead and press Enter and it will prompt you for the password.

And the password is the same Animals for Life strong password that we've been using everywhere.

Copy that into your clipboard.

So this is the password for the A4L WordPress user on the MariaDB EC2 instance.

So paste that in and press Enter and then LS-LA to confirm that we now have this A4L WordPress.SQL database backup file.

And we do, so that's good.

So as we did in the previous demo lesson, we're going to take this backup file and we're going to import it into the new destination database, which is going to be the RDS instance.

To do that, we'll use this command, but we're going to need to replace the placeholder hostname with the CNAME of the RDS instance.

So go ahead and delete this placeholder, then go back to the RDS console and I'll want you to copy the endpoint name into your clipboard.

So select it, right click and then copy.

We won't need the port number because this is the standard MySQL port and if you don't specify it, most applications will assume this default.

So just make sure that you have the endpoint DNS name or endpoint CNAME in your clipboard.

And then back on the WordPress EC2 instance, go ahead and paste this database name into this command and press Enter.

And again, you'll be asked for the password and that's the same Animals for Life strong password.

So copy that into your clipboard, paste that in and press Enter.

And that's imported this A4LWordPress.SQL file into the RDS instance.

So now we need to follow the same process and change WordPress so that it points at the RDS instance.

And we do that by moving to where the WordPress configuration file is.

So cd space forward slash var forward slash ww w forward slash html and press Enter.

And then shudu.

So we have admin privileges, nano, which is a text editor and then wp-config.php and press Enter.

Then we need to scroll down and we're looking for where it says DB host and currently it has a host name here.

Now if you go back to the EC2 console and you look at the A4L-DB-WordPress instance, you'll see that its private IP version for DNS name is what's listed inside this configuration item.

So it's currently pointing at this dedicated database instance.

What we need to do is replace that and we're going to replace it with the RDS database DNS name or the CNAME of this RDS instance.

So copy that into your clipboard and then go ahead and delete this private DNS name for the MariaDB EC2 instance and then paste in the RDS endpoint name, also known as the RDS CNAME.

Once you've done that, control O and Enter to save and control X to exit.

And now our WordPress instance is pointing at the RDS instance for its database.

Now we can verify that by checking WordPress, move back to instances, select the WordPress instance, copy the public IP version for addressing to your clipboard.

Don't use this open address link.

Open that in a new tab.

Go ahead and just click on the best cats ever to verify the functionality and it does look as though it's working.

And to verify that, if we go back to the EC2 console, select the A4L-DB-WordPress instance and right click and then stop that instance.

Now the original database that was providing database services to WordPress is going to move into a stopped state.

And if our WordPress blog continues functioning, we know that it's using the RDS instance.

So let's keep refreshing and wait for this to change into a stopped state.

There we go.

It's stopped.

And if we go back to our WordPress page and refresh, it still loads.

And so we know that it's now using RDS for its database services.

So at this point, that's everything that I wanted you to do in this demo lesson.

You've stepped through the process of provisioning an RDS instance.

So you've created a subnet group, provisioned the instance itself, explored the functionality of the instance, including how to provide access to it by selecting a security group.

And then editing that security group to allow access.

You've performed a database migration and you've explored how the RDS instance is presented in the console.

So that's everything that you need to do within this demo lesson.

And don't worry, we're going to be exploring much more of the advanced functionality of RDS as we move through this section of the course.

For now, though, I want us to clear up the infrastructure that we've created as part of this demo lesson.

Now, because we've provisioned RDS manually outside of CloudFormation, unfortunately, there is a little bit more manual work involved in the cleanup.

So I want you to go to the RDS console, move to databases, select this database, click on actions, and then select delete.

Now it will prompt you to create a final snapshot and we're not going to do that.

We're not going to retain automated backups and so you'll need to acknowledge that upon instance deletion, automated backups including any system snapshots and pointing time recoveries will no longer be available.

And don't worry, I'll be talking about backups and recovery in another lesson in this section of the course.

For now, just acknowledge that and then type delete me into this box and confirm the deletion.

Now this deletion is going to take a few minutes.

It's not an immediate process.

It will start in a deleting state and we need to wait for this process to be completed before we continue the cleanup.

So go ahead and pause this video and wait for this instance to fully delete before continuing.

Now that the instance has been deleted, it vanishes from this list.

Next, we need to delete the subnet group that we created earlier.

So click on subnet groups, select the subnet group and then delete it.

You'll need to confirm that deletion.

Once done, it too should vanish from that list.

Next, go to the tab you've got open to the VPC console, scroll down and select security groups.

Now look through this list and locate the security group that you created as part of provisioning the RDS instance.

It should be called a4LVPC-RDS-SG.

Select that, click on actions and then delete security group and you'll need to confirm that process as well.

Once that's deleted, the final step is to go to the cloud formation console and then you'll need to delete the cloud formation stack that was created using the one-click deployment at the start of the demo.

It should be called migrate to RDS.

Select it, click on delete and confirm that deletion.

And once deleted, the account will be returned into the same state as it was at the start of the demo lesson.

So all of the infrastructure that we've used will be removed from the account and the account will be in the same state as at the start of the demo.

Now I hope you've enjoyed this demo and that we're repeating the same WordPress installation and then the creation of the blog post over and over again.

But I want you to get used to different parts of this process over and over again.

You need to know why not to use a database on EC2.

You need to know why not to perform a lot of these processes manually.

From this point onward in the course, we're going to be using RDS to evolve our WordPress design into something that is truly elastic.

And so all of these processes, the things I'm having you repeat are really useful to aid in your understanding of all of these different components.

So from this point onward, we're going to be automating the creation of RDS and focusing on the specific pieces of functionality that you need to understand.

But at this point, that's everything that you need to do in this demo.

So go ahead, complete the video and when you're ready, I look forward to you joining me in the next.

Welcome back and in this video I want to talk about geolocation routing which is another routing policy available within Route 53.

Now this is going to be a pretty brief video so let's jump in and get started.

In many ways geolocation routing is similar to latency.

Only instead of latency the location of customers and the location of resources are used to influence resolution decisions.

With geolocation routing when you create records you tag the records with the location.

Now this location is generally a country so using ISO standard country codes it can be continents again using ISO continent codes such as SA for South America in this case or records can be tagged with default.

Now there's a fourth type which is known as a subdivision.

In America you can tag records with the state that the record belongs to.

Now when a user is making a resolution request an IP check verifies the location of the user.

Depending on the DNS system this can be the user directly or the resolver server but in most cases these are one and the same in terms of the user's location.

So we have the location of the user and we have the location of the records.

What happens next is important because geolocation doesn't return the closest record it only returns relevant records.

When a resolution request happens Route 53 takes the location of the user and it starts checking for any matching records.

First if the user doing the resolution request is based in the US then it checks the state of the user and it tries to match any records which have a state allocated to them.

If any records match they're returned and the process stops.

If no state records match then it checks the country of the user.

If any records are tagged with that country then they're returned and the process stops.

Then it checks the continent.

If any records match the continent that the user is based in then they're returned and the process stops.

Now you can also define a default record which is returned if no record is relevant for that user.

If nothing matches though so there are no records that match the user's location and there's no default record then a no answer is returned.

So to stress again this type of routing policy does not return the closest record it only returns any which are applicable or the default or it returns no answer.

So geolocation is ideal if you want to restrict content.

For example providing content for the US market only.

If you want to do that then you can create a US record and only people located in the US will receive that record as a response for any queries.

You can also use this policy type to provide language specific content or to load balance across regional endpoints based on customer location.

Now one last time because this is really important for the exam and for real world usage.

This routing policy type is not about the closest record geolocation returns relevant locations only.

You will not get a Canadian record returned if you're based in the UK and no closer records exist.

The smallest type of record is a subdivision which is a US state then you have country then you have continent and finally optionally a default record.

Use the geolocation routing policy if you want to route traffic based on the location of your customers.

Now it's important that you understand which is why I've stressed this so much that geolocation isn't about proximity.

It's about location.

You only have records returned if the location is relevant.

So if you're based in the US but are based in a different state than a record you won't get that record.

If you're based in the US and there is a record which is tagged as the US as a country then you will get that record returned.

If there isn't a country specific record but there is one for the continent that you're in you'll get that record returned and then the default is a catchall.

It's optional if you choose to add it then it's returned if your user is in a location where you don't have a specific record tagged to that location.

Now that's everything that I wanted to cover in this video.

Thanks for watching.

Go ahead and complete the video and when you're ready I look forward to you joining me in the next.

I am also concerned about the lack of free resources and locally generated content.

When using Leonardo.ai and others, the interface was such that most of your art became public (free version) and you had not copyright over it. - that was the best case. Switching over to ComfyUI, I've been able to generate art locally, do unlimited iterations and develop skill that I could not with web models.

Adobe comes with a huge price tag, privacy concerns, and being locked out of your work if you stop paying.

/---------------------------------------------------------- For comparison: Look up -> Krita + acly vs adobe or midjourney... https://www.perplexity.ai/search/how-does-krita-acly-compare-to-14E5y_ZSTEScfRwNhqElBA

Krita with the Acly AI diffusion plugin offers some compelling AI image generation capabilities that can be compared to Adobe's Generative Fill and Midjourney, though there are some key differences:

Strengths of Krita + Acly

Automatic Mode Switching Krita's implementation automatically switches between simple generation and inpainting based on whether you've made a selection. This streamlines the workflow compared to manually toggling between modes[1].

Layer Support The plugin adds layer functionality similar to Photoshop, allowing you to stack generations for backgrounds, subjects, etc. This provides more flexibility in composing complex images[1].

Model Compatibility It's compatible with popular AI models like ControlNet and Lora, giving users access to a wide range of generation options[1][2].

Open Source and Free Unlike Adobe's offerings, Krita and the Acly plugin are completely free and open source[4][5].

Limitations

GPU Requirements For optimal performance, especially with live generation, a powerful GPU (RTX 4070 or better) is recommended[2].

Model Size The default models used are typically smaller (around 6GB) compared to Adobe's large proprietary models, which may impact generation quality in some cases[4].

Work in Progress Some features, like inpainting with transparent backgrounds, are still being refined[1].

Comparison to Adobe and Midjourney

• Ease of Use: Adobe's Generative Fill is known for its user-friendly interface within Photoshop. Krita's implementation is getting close, but may require more tweaking.

• Output Quality: Adobe and Midjourney likely have an edge in overall output quality due to their larger, more extensively trained models.

• Integration: Krita offers better integration with traditional digital painting workflows compared to Midjourney, which is primarily focused on text-to-image generation.

• Cost: Krita + Acly is free, while Adobe and Midjourney require subscriptions.

• Customization: Krita allows for more customization and use of open-source models, which can be an advantage for advanced users.

While Krita + Acly may not yet match the polish of Adobe's AI tools or the raw generation power of Midjourney, it offers a compelling free alternative that's rapidly improving. For many users, especially those already familiar with Krita, it could be an excellent option for integrating AI generation into their workflow.

Citations: [1] https://civitai.com/articles/4008/is-krita-the-better-tool-for-you [2] https://www.reddit.com/r/StableDiffusion/comments/17ytw1g/krita_is_by_far_the_best_software_for_ai_image/ [3] https://krita-artists.org/t/is-there-a-object-selection-tool-like-in-photoshop/56825 [4] https://www.youtube.com/watch?v=Qv9G5QsvlxQ [5] https://www.youtube.com/watch?v=suwwW9eE5qI [6] https://news.ycombinator.com/item?id=40672281 [7] https://forums.macrumors.com/threads/humans-have-nothing-to-fear-from-adobe.2419287/ [8] https://news.ycombinator.com/item?id=38342670

Author response:

eLife Assessment 

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

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

Public Reviews:

We thank the Reviewers for their comments and suggestions, prompting new analyses and additions that strengthened our report.

Reviewer #1 (Public review): 

Summary: 

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

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

We have previously showed that neural replay of MEG activity representing the practiced skill correlated with micro-offline gains during rest intervals of early learning, 1 consistent with the recent report that hippocampal ripples during these offline periods predict human motor sequence learning2.  However, decoding accuracy in our earlier work1 needed improvement.  Here, we reported a strategy to improve decoding accuracy that could benefit future studies of neural replay or BCI using MEG.

Weaknesses: 

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

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

Head movement artefacts are highly unlikely to contribute meaningfully to our results for the following reasons. First, in addition to ICA denoising, all “recordings were visually inspected and marked to denoise segments containing other large amplitude artifacts due to movements” (see Methods). Second, the response pad was positioned in a manner that minimized wrist, arm or more proximal body movements during the task. Third, while head position was not monitored online for this study, the head was restrained using an inflatable air bladder, and head position was assessed at the beginning and at the end of each recording. Head movement did not exceed 5mm between the beginning and end of each scan for all participants included in the study. Fourth, we agree that despite the steps taken above, it is possible that minor head movements could still contribute to some remaining variance in the MEG data in our study. The Reviewer states a concern that “it is conceivable that small head movements would correlate highly with the vigor of individual finger movements”. However, in order for any such correlations to meaningfully impact decoding performance, such head movements would need to: (A) be consistent and pervasive throughout the recording (which might not be the case if the head movements were related to movement vigor and vigor changed over time); and (B) systematically vary between different finger movements, and also between the same finger movement performed at different sequence locations (see 5-class decoding performance in Figure 4B). The possibility of any head movement artefacts meeting all these conditions is extremely unlikely.

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

Author response image 1.

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

In fact, inspection of the eye position data revealed that a majority of participants on most trials displayed random walk gaze patterns around a center fixation point, indicating that participants did not attend to the asterisk position on the display. This is consistent with intrinsic generation of the action sequence, and congruent with the fact that the display does not provide explicit feedback related to performance. A similar real-world example would be manually inputting a long password into a secure online application. In this case, one intrinsically generates the sequence from memory and receives similar feedback about the password sequence position (also provided as asterisks), which is typically ignored by the user. The minimal participant engagement with the visual task display observed in this study highlights another important point – that the behavior in explicit sequence learning motor tasks is highly generative in nature rather than reactive to stimulus cues as in the serial reaction time task (SRTT).  This is a crucial difference that must be carefully considered when designing investigations and comparing findings across studies.

We observed that initial keypress decoding accuracy was predominantly driven by contralateral primary sensorimotor cortex in the initial practice trials before transitioning to bilateral frontoparietal regions by trials 11 or 12 as performance gains plateaued.  The contribution of contralateral primary sensorimotor areas to early skill learning has been extensively reported in humans and non-human animals. 1,3-5  Similarly, the increased involvement of bilateral frontal and parietal regions to decoding during early skill learning in the non-dominant hand is well known.  Enhanced bilateral activation in both frontal and parietal cortex during skill learning has been extensively reported6-11, and appears to be even more prominent during early fine motor skill learning in the non-dominant hand12,13.  The frontal regions identified in these studies are known to play crucial roles in executive control14, motor planning15, and working memory6,8,16-18 processes, while the same parietal regions are known to integrate multimodal sensory feedback and support visuomotor transformations6,8,16-18, in addition to working memory19. Thus, it is not surprising that these regions increasingly contribute to decoding as subjects internalize the sequential task.  We now include a statement reflecting these considerations in the revised Discussion.

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

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

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

Author response image 2.

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

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

We definitely agree with the Reviewer that some inter-parcel features representing neighboring (or spatially contiguous) voxels are likely to be correlated. This has been well documented in the MEG literature20,21 and is a particularly important confound to address in functional or effective connectivity analyses (not performed in the present study). In the present analysis, any correlation between adjacent voxels presents a multi-collinearity problem, which effectively reduces the dimensionality of the input feature space. However, as long as there are multiple groups of correlated voxels within each parcel (i.e. - the effective dimensionality is still greater than 1), the intra-parcel spatial patterns could still meaningfully contribute to the decoder performance. Two specific results support this assertion.

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

Author response image 3.

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

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

We have already addressed the issue of movement related artefacts in the first response above. With respect to a focus on frequency ranges above movement frequencies, the Reviewer states the “accuracy for those still seems impressive and may provide a slightly more biologically plausible assessment”. First, it is important to note that cortical delta-band oscillations measured with local field potentials (LFPs) in macaques is known to contain important information related to end-effector kinematics22,23 muscle activation patterns24 and temporal sequencing25 during skilled reaching and grasping actions. Thus, there is a substantial body of evidence that low-frequency neural oscillatory activity in this range contains important information about the skill learning behavior investigated in the present study. Second, our own data shows (which the Reviewer also points out) that significant information related to the skill learning behavior is also present in higher frequency bands (see Figure 2A and Figure 3—figure supplement 1). As we pointed out in our earlier response to questions about the hybrid space decoder architecture (see above), it is likely that different, yet complimentary, information is encoded across different temporal frequencies (just as it is encoded across different spatial frequencies). Again, this interpretation is supported by our data as the highest performing classifiers in all cases (when holding all parameters constant) were always constructed from broadband input MEG data (Figure 2A and Figure 3—figure supplement 1).  

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

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

First, previous fMRI work in humans performing a similar sequence learning task showed that flexibility in brain network composition (i.e. – changes in brain region members displaying coordinated activity) is up-regulated in novel learning environments and explains differences in learning rates across individuals26.  This work supports our interpretation of the present study data, that brain networks engaged in sequential motor skills rapidly reconfigure during early learning.

Second, frontoparietal network activity is known to support motor memory encoding during early learning27,28. For example, reactivation events in the posterior parietal29 and medial prefrontal30,31 cortex (MPFC) have been temporally linked to hippocampal replay, and are posited to support memory consolidation across several memory domains32, including motor sequence learning1,33,34.  Further, synchronized interactions between MPFC and hippocampus are more prominent during early learning as opposed to later stages27,35,36, perhaps reflecting “redistribution of hippocampal memories to MPFC” 27.  MPFC contributes to very early memory formation by learning association between contexts, locations, events and adaptive responses during rapid learning37. Consistently, coupling between hippocampus and MPFC has been shown during, and importantly immediately following (rest) initial memory encoding38,39.  Importantly, MPFC activity during initial memory encoding predicts subsequent recall40. Thus, the spatial map required to encode a motor sequence memory may be “built under the supervision of the prefrontal cortex” 28, also engaged in the development of an abstract representation of the sequence41.  In more abstract terms, the prefrontal, premotor and parietal cortices support novice performance “by deploying attentional and control processes” 42-44 required during early learning42-44. The dorsolateral prefrontal cortex DLPFC specifically is thought to engage in goal selection and sequence monitoring during early skill practice45, all consistent with the schema model of declarative memory in which prefrontal cortices play an important role in encoding46,47.  Thus, several prefrontal and frontoparietal regions contributing to long term learning 48 are also engaged in early stages of encoding. Altogether, there is strong biological support for the involvement of bilateral prefrontal and frontoparietal regions to decoding during early skill learning.  We now address this issue in the revised manuscript.

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

We agree with the Reviewer that our previous “offline neural representation” nomenclature could be misinterpreted. In the revised manuscript we refer to this difference as the “offline neural representational change”. Please, note that our previous work did link offline neural activity (i.e. – 16-22 Hz beta power and neural replay density during inter-practice rest periods) to observed micro-offline gains49.

Reviewer #2 (Public review): 

Summary 

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

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

Weaknesses 

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

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

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

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

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

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

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

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

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

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

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

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

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

The Reviewer suggests that the current data is not convincing enough to show that contextualization occurs during rest and raises two important concerns: 1) the relationship between online contextualization and micro-online gains is not shown, and 2) the online distance was calculated differently from its offline counterpart (i.e. - instead of calculating the distance between last IndexOP5 and first IndexOP1 from a single trial, the distance was calculated for each sequence within a trial and then averaged).

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

Author response image 4.

Distribution of individual subject correlation coefficients between contextualization changes occurring during practice or rest with  micro-online and micro-offline performance gains. Note that, the correlation distributions were significantly higher for the relationship between contextualization changes during rest and micro-offline gains than for contextualization changes during practice and either micro-online or offline gain.

With respect to the second concern highlighted above, we agree with the Reviewer that one limitation of the analysis comparing online versus offline changes in contextualization as presented in the reviewed manuscript, is that it does not eliminate the possibility that any differences could simply be explained by the passage of time (which is smaller for the online analysis compared to the offline analysis). The Reviewer suggests an approach that addresses this issue, which we have now carried out.   When quantifying online changes in contextualization from the first IndexOP1 the last IndexOP5 keypress in the same trial we observed no learning-related trend (Author response image 5, right panel). Importantly, offline distances were significantly larger than online distances regardless of the measurement approach and neither predicted online learning (Author response image 6).

Author response image 5.

Trial by trial trend of offline (left panel) and online (middle and right panels) changes in contextualization. Offline changes in contextualization were assessed by calculating the distance between neural representations for the last IndexOP5 keypress in the previous trial and the first IndexOP1 keypress in the present trial. Two different approaches were used to characterize online contextualization changes. The analysis included in the reviewed manuscript (middle panel) calculated the distance between IndexOP1 and IndexOP5 for each correct sequence, which was then averaged across the trial. This approach is limited by the lack of control for the passage of time when making online versus offline comparisons. Thus, the second approach controlled for the passage of time by calculating distance between the representations associated with the first IndexOP1 keypress and the last IndexOP5 keypress within the same trial. Note that while the first approach showed an increase online contextualization trend with practice, the second approach did not.

Author response image 6.

Relationship between online contextualization and online learning is shown for both within-sequence (left; note that this is the online contextualization measure used in the reviewd manuscript) and across-sequence (right) distance calculation. There was no significant relationship between online learning and online contextualization regardless of the measurement approach.

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

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

Reviewer #3 (Public review): 

Summary: 

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

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

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

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

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

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

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

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

Weaknesses: 

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

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

The authors interpret the ordinal position information captured by their decoding approach as a reflection of neural coding dedicated to the local context of a movement (Figure 4). One way to dissociate ordinal position information from information about the moving effectors is to train a classifier on one sequence and test the classifier on other sequences that require the same movements, but in different positions50. In the present study, however, participants trained to repeat a single sequence (4-1-3-2-4). As a result, ordinal position information is potentially confounded by the fixed finger transitions around each of the two critical positions (first and fifth press). Across consecutive correct sequences, the first keypress in a given sequence was always preceded by a movement of the index finger (=last movement of the preceding sequence), and followed by a little finger movement. The last keypress, on the other hand, was always preceded by a ring finger movement, and followed by an index finger movement (=first movement of the next sequence). Figure 4 - Supplement 2 shows that finger identity can be decoded with high accuracy (>70%) across a large time window around the time of the key press, up to at least +/-100 ms (and likely beyond, given that decoding accuracy is still high at the boundaries of the window depicted in that figure). This time window approaches the keypress transition times in this study. Given that distinct finger transitions characterized the first and fifth keypress, the classifier could thus rely on persistent (or "lingering") information from the preceding finger movement, and/or "preparatory" information about the subsequent finger movement, in order to dissociate the first and fifth keypress. Currently, the manuscript provides no evidence that the context information captured by the decoding approach is more than a by-product of temporally extended, and therefore overlapping, but independent neural representations of consecutive keypresses that are executed in close temporal proximity - rather than a neural representation dedicated to context. 

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

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

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

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

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

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

Finally, the Reviewer hints that one way to address this issue would be to compare MEG responses before and after learning for sequences typed at a fixed speed. However, given that the speed-accuracy trade-off should improve with learning, a comparison between unlearned and learned skill states would dictate that the skill be evaluated at a very low fixed speed. Essentially, such a design presents the problem that the post-training test is evaluating the representation in the unlearned behavioral state that is not representative of the acquired skill. Thus, this approach would not address our experimental question: “do neural representations of the same action performed at different locations within a skill sequence contextually differentiate or remain stable as learning evolves”.

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

The Reviewer argues that the comparison of last finger movement of a trial and the first in the next trial are performed in different circumstances and contexts. This is an important point and one we tend to agree with. For this task, the first sequence in a practice trial (which is pre-planned offline) is performed in a somewhat different context from the sequence iterations that follow, which involve temporally overlapping planning, execution and evaluation processes.  The Reviewer is particularly concerned about a difference in the temporal mixing effect issue raised above between the first and last keypresses performed in a trial. However, in contrast to the Reviewers stated argument above, findings from Korneysheva et. al (2019) showed that neural representations of individual actions are competitively queued during the pre-planning period in a manner that reflects the ordinal structure of the learned sequence.  Thus, mixing effects are likely still present for the first keypress in a trial. Also note that we now present new control analyses in multiple responses above confirming that hypothetical mixing effects between adjacent keypresses do not explain our reported contextualization finding. A statement addressing these possibilities raised by the Reviewer has been added to the Discussion in the revised manuscript.

In relation to pre-planning, ongoing MEG work in our lab is investigating contextualization within different time windows tailored specifically for assessing how sequence skill action planning evolves with learning.

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

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

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

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

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

Given this high correlation, we firmly agree with the Reviewer that the issue of eye movement-related artefacts is still an important one to address. Fortunately, we did collect eye movement data during the MEG recordings so were able to investigate this. As detailed in the response to Reviewer #1 above, we found that gaze positions and eye-movement velocity time-locked to visual display updates (i.e. – a change in asterisk position above the displayed sequence) did not reflect the asterisk location above chance levels (Overall cross-validated accuracy = 0.21817; see Author response image 1). Furthermore, an inspection of the eye position data revealed that a majority of participants on most trials displayed random walk gaze patterns around a center fixation point, indicating that participants did not attend to the asterisk position on the display. This is consistent with intrinsic generation of the action sequence, and congruent with the fact that the display does not provide explicit feedback related to performance. As pointed out above, a similar real-world example would be manually inputting a long password into a secure online application. In this case, one intrinsically generates the sequence from memory and receives similar feedback about the password sequence position (also provided as asterisks), which is typically ignored by the user. Notably, the minimal participant engagement with the visual task display observed in this study highlights an important difference between behavior observed during explicit sequence learning motor tasks (which is highly generative in nature) with reactive responses to stimulus cues in a serial reaction time task (SRTT).  This is a crucial difference that must be carefully considered when comparing findings across studies. All elements pertaining to this new control analysis are now included in the revised manuscript.

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

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

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

This statement is incorrect. The original Bonstrup et al (2019) 49 paper clearly states that micro-offline gains must be carefully interpreted based upon the behavioral context within which they are observed, and lays out the conditions under which one can have confidence that micro-offline gains reflect offline learning.  In fact, the excellent meta-analysis of Pan & Rickard (2015) 51, which re-interprets the benefits of sleep in overnight skill consolidation from a “reactive inhibition” perspective, was a crucial resource in the experimental design of our initial study49, as well as in all our subsequent work. Pan & Rickard stated:

“Empirically, reactive inhibition refers to performance worsening that can accumulate during a period of continuous training (Hull, 1943). It tends to dissipate, at least in part, when brief breaks are inserted between blocks of training. If there are multiple performance-break cycles over a training session, as in the motor sequence literature, performance can exhibit a scalloped effect, worsening during each uninterrupted performance block but improving across blocks52,53. Rickard, Cai, Rieth, Jones, and Ard (2008) and Brawn, Fenn, Nusbaum, and Margoliash (2010) 52,53 demonstrated highly robust scalloped reactive inhibition effects using the commonly employed 30 s–30 s performance break cycle, as shown for Rickard et al.’s (2008) massed practice sleep group in Figure 2. The scalloped effect is evident for that group after the first few 30 s blocks of each session. The absence of the scalloped effect during the first few blocks of training in the massed group suggests that rapid learning during that period masks any reactive inhibition effect.”

Crucially, Pan & Rickard51 made several concrete recommendations for reducing the impact of the reactive inhibition confound on offline learning studies. One of these recommendations was to reduce practice times to 10s (most prior sequence learning studies up until that point had employed 30s long practice trials). They stated:

“The traditional design involving 30 s-30 s performance break cycles should be abandoned given the evidence that it results in a reactive inhibition confound, and alternative designs with reduced performance duration per block used instead 51. One promising possibility is to switch to 10 s performance durations for each performance-break cycle Instead 51. That design appears sufficient to eliminate at least the majority of the reactive inhibition effect 52,53.”

We mindfully incorporated recommendations from Pan and Rickard51  into our own study designs including 1) utilizing 10s practice trials and 2) constraining our analysis of micro-offline gains to early learning trials (where performance monotonically increases and 95% of overall performance gains occur), which are prior to the emergence of the “scalloped” performance dynamics that are strongly linked to reactive inhibition effects. 

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

We strongly disagree with the Reviewer’s assertion that “there is no direct evidence in the literature that micro-offline gains really result from offline learning, i.e., an improvement in skill level.”  The initial Bönstrup et al. (2019) 49 report was followed up by a large online crowd-sourcing study (Bönstrup et al., 2020) 54. This second (and much larger) study provided several additional important findings supporting our interpretation of micro-offline gains in cases where the important behavioral conditions clarified above were met (see Author response image 7 below for further details on these conditions).

Author response image 7.

Micro-offline gains observed in learning and non-learning contexts are attributed to different underlying causes. (A) Micro-offline and online changes relative to overall trial-by-trial learning. This figure is based on data from Bönstrup et al. (2019) 49. During early learning, micro-offline gains (red bars) closely track trial-by-trial performance gains (green line with open circle markers), with minimal contribution from micro-online gains (blue bars). The stated conclusion in Bönstrup et al. (2019) is that micro-offline gains only during this Early Learning stage reflect rapid memory consolidation (see also 54). After early learning, about practice trial 11, skill plateaus. This plateau skill period is characterized by a striking emergence of coupled (and relatively stable) micro-online drops and micro-offline increases. Bönstrup et al. (2019) as well as others in the literature 55-57, argue that micro-offline gains during the plateau period likely reflect recovery from inhibitory performance factors such as reactive inhibition or fatigue, and thus must be excluded from analyses relating micro-offline gains to skill learning.  The Non-repeating groups in Experiments 3 and 4 from Das et al. (2024) suffer from a lack of consideration of these known confounds.

Evidence documented in that paper54 showed that micro-offline gains during early skill learning were: 1) replicable and generalized to subjects learning the task in their daily living environment (n=389); 2) equivalent when significantly shortening practice period duration, thus confirming that they are not a result of recovery from performance fatigue (n=118);  3) reduced (along with learning rates) by retroactive interference applied immediately after each practice period relative to interference applied after passage of time (n=373), indicating stabilization of the motor memory at a microscale of several seconds consistent with rapid consolidation; and 4) not modified by random termination of the practice periods, ruling out a contribution of predictive motor slowing (N = 71) 54.  Altogether, our findings were strongly consistent with the interpretation that micro-offline gains reflect memory consolidation supporting early skill learning. This is precisely the portion of the learning curve Pan and Rickard51 refer to when they state “…rapid learning during that period masks any reactive inhibition effect”.

This interpretation is further supported by brain imaging evidence linking known memory-related networks and consolidation mechanisms to micro-offline gains. First, we reported that the density of fast hippocampo-neocortical skill memory replay events increases approximately three-fold during early learning inter-practice rest periods with the density explaining differences in the magnitude of micro-offline gains across subjects1. Second, Jacobacci et al. (2020) independently reproduced our original behavioral findings and reported BOLD fMRI changes in the hippocampus and precuneus (regions also identified in our MEG study1) linked to micro-offline gains during early skill learning. 33 These functional changes were coupled with rapid alterations in brain microstructure in the order of minutes, suggesting that the same network that operates during rest periods of early learning undergoes structural plasticity over several minutes following practice58. Third, even more recently, Chen et al. (2024) provided direct evidence from intracranial EEG in humans linking sharp-wave ripple events (which are known markers for neural replay59) in the hippocampus (80-120 Hz in humans) with micro-offline gains during early skill learning. The authors report that the strong increase in ripple rates tracked learning behavior, both across blocks and across participants. The authors conclude that hippocampal ripples during resting offline periods contribute to motor sequence learning. 2

Thus, there is actually now substantial evidence in the literature directly supporting the assertion “that micro-offline gains really result from offline learning”.  On the contrary, according to Gupta & Rickard (2024) “…the mechanism underlying RI [reactive inhibition] is not well established” after over 80 years of investigation60, possibly due to the fact that “reactive inhibition” is a categorical description of behavioral effects that likely result from several heterogenous processes with very different underlying mechanisms.

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

It is important to point out that the recent work of Gupta & Rickard (2022,2024) 55 does not present any data that directly opposes our finding that early skill learning49 is expressed as micro-offline gains during rest breaks. These studies are essentially an extension of the Rickard et al (2008) paper that employed a massed (30s practice followed by 30s breaks) vs spaced (10s practice followed by 10s breaks) to assess if recovery from reactive inhibition effects could account for performance gains measured after several minutes or hours. Gupta & Rickard (2022) added two additional groups (30s practice/10s break and 10s practice/10s break as used in the work from our group). The primary aim of the study was to assess whether it was more likely that changes in performance when retested 5 minutes after skill training (consisting of 12 practice trials for the massed groups and 36 practice trials for the spaced groups) had ended reflected memory consolidation effects or recovery from reactive inhibition effects. The Gupta & Rickard (2024) follow-up paper employed a similar design with the primary difference being that participants performed a fixed number of sequences on each trial as opposed to trials lasting a fixed duration. This was done to facilitate the fitting of a quantitative statistical model to the data.  To reiterate, neither study included any analysis of micro-online or micro-offline gains and did not include any comparison focused on skill gains during early learning. Instead, Gupta & Rickard (2022), reported evidence for reactive inhibition effects for all groups over much longer training periods. Again, we reported the same finding for trials following the early learning period in our original Bönstrup et al. (2019) paper49 (Author response image 7). Also, please note that we reported in this paper that cumulative micro-offline gains over early learning did not correlate with overnight offline consolidation measured 24 hours later49 (see the Results section and further elaboration in the Discussion). Thus, while the composition of our data is supportive of a short-term memory consolidation process operating over several seconds during early learning, it likely differs from those involved over longer training times and offline periods, as assessed by Gupta & Rickard (2022).

In the recent preprint from Das et al (2024) 61,  the authors make the strong claim that “micro-offline gains during early learning do not reflect offline learning” which is not supported by their own data.   The authors hypothesize that if “micro-offline gains represent offline learning, participants should reach higher skill levels when training with breaks, compared to training without breaks”.  The study utilizes a spaced vs. massed practice group between-subjects design inspired by the reactive inhibition work from Rickard and others to test this hypothesis. Crucially, the design incorporates only a small fraction of the training used in other investigations to evaluate early skill learning1,33,49,54,57,58,62.  A direct comparison between the practice schedule designs for the spaced and massed groups in Das et al., and the training schedule all participants experienced in the original Bönstrup et al. (2019) paper highlights this issue as well as several others (Author response image 8):

Author response image 8.

(A) Comparison of Das et al. Spaced & Massed group training session designs, and the training session design from the original Bönstrup et al. (2019) 49 paper. Similar to the approach taken by Das et al., all practice is visualized as 10-second practice trials with a variable number (either 0, 1 or 30) of 10-second-long inter-practice rest intervals to allow for direct comparisons between designs. The two key takeaways from this comparison are that (1) the intervention differences (i.e. – practice schedules) between the Massed and Spaced groups from the Das et al. report are extremely small (less than 12% of the overall session schedule) and (2) the overall amount of practice is much less than compared to the design from the original Bönstrup report 49  (which has been utilized in several subsequent studies). (B) Group-level learning curve data from Bönstrup et al. (2019) 49 is used to estimate the performance range accounted for by the equivalent periods covering Test 1, Training 1 and Test 2 from Das et al (2024). Note that the intervention in the Das et al. study is limited to a period covering less than 50% of the overall learning range.

First, participants in the original Bönstrup et al. study 49 experienced 157.14% more practice time and 46.97% less inter-practice rest time than the Spaced group in the Das et al. study (Author response image 8).  Thus, the overall amount of practice and rest differ substantially between studies, with much more limited training occurring for participants in Das et al.  

Second, and perhaps most importantly, the actual intervention (i.e. – the difference in practice schedule between the Spaced and Massed groups) employed by Das et al. covers a very small fraction of the overall training session. Identical practice schedule segments for both the Spaced & Massed groups are indicated by the red shaded area in Author response image 8. Please note that these identical segments cover 94.84% of the Massed group training schedule and 88.01% of the Spaced group training schedule (since it has 60 seconds of additional rest). This means that the actual interventions cover less than 5% (for Massed) and 12% (for Spaced) of the total training session, which minimizes any chance of observing a difference between groups.

Also note that the very beginning of the practice schedule (during which Figure R9 shows substantial learning is known to occur) is labeled in the Das et al. study as Test 1.  Test 1 encompasses the first 20 seconds of practice (alternatively viewed as the first two 10-second-long practice trials with no inter-practice rest). This is immediately followed by the Training 1 intervention, which is composed of only three 10-second-long practice trials (with 10-second inter-practice rest for the Spaced group and no inter-practice rest for the Massed group). Author response image 8 also shows that since there is no inter-practice rest after the third Training practice trial for the Spaced group, this third trial (for both Training 1 and 2) is actually a part of an identical practice schedule segment shared by both groups (Massed and Spaced), reducing the magnitude of the intervention even further.

Moreover, we know from the original Bönstrup et al. (2019) paper49 that 46.57% of all overall group-level performance gains occurred between trials 2 and 5 for that study. Thus, Das et al. are limiting their designed intervention to a period covering less than half of the early learning range discussed in the literature, which again, minimizes any chance of observing an effect.

This issue is amplified even further at Training 2 since skill learning prior to the long 5-minute break is retained, further constraining the performance range over these three trials. A related issue pertains to the trials labeled as Test 1 (trials 1-2) and Test 2 (trials 6-7) by Das et al. Again, we know from the original Bönstrup et al. paper 49 that 18.06% and 14.43% (32.49% total) of all overall group-level performance gains occurred during trials corresponding to Das et al Test 1 and Test 2, respectively. In other words, Das et al averaged skill performance over 20 seconds of practice at two time-points where dramatic skill improvements occur. Pan & Rickard (1995) previously showed that such averaging is known to inject artefacts into analyses of performance gains.

Furthermore, the structure of the Test in Das et. al study appears to have an interference effect on the Spaced group performance after the training intervention.  This makes sense if you consider that the Spaced group is required to now perform the task in a Massed practice environment (i.e., two 10-second-long practice trials merged into one long trial), further blurring the true intervention effects. This effect is observable in Figure 1C,E of their pre-print. Specifically, while the Massed group continues to show an increase in performance during test relative to the last 10 seconds of practice during training, the Spaced group displays a marked decrease. This decrease is in stark contrast to the monotonic increases observed for both groups at all other time-points.

Interestingly, when statistical comparisons between the groups are made at the time-points when the intervention is present (as opposed to after it has been removed) then the stated hypothesis, “If micro-offline gains represent offline learning, participants should reach higher skill levels when training with breaks, compared to training without breaks”, is confirmed.

The data presented by Gupta and Rickard (2022, 2024) and Das et al. (2024) is in many ways more confirmatory of the constraints employed by our group and others with respect to experimental design, analysis and interpretation of study findings, rather than contradictory. Still, it does highlight a limitation of the current micro-online/offline framework, which was originally only intended to be applied to early skill learning over spaced practice schedules when reactive inhibition effects are minimized49. Extrapolation of this current framework to post-plateau performance periods, longer timespans, or non-learning situations (e.g. – the Non-repeating groups from Experiments 3 & 4 in Das et al. (2024)), when reactive inhibition plays a more substantive role, is not warranted. Ultimately, it will be important to develop new paradigms allowing one to independently estimate the different coincident or antagonistic features (e.g. - memory consolidation, planning, working memory and reactive inhibition) contributing to micro-online and micro-offline gains during and after early skill learning within a unifying framework.

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Author response:

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

Reviewer #4

We sincerely appreciate the time and effort you have taken to review our manuscript. We followed your recommendations to polish the text and make it easier to understand.

Regarding terms and terminology, we changed “non-breeding” everywhere in the text to “over- wintering.”

Regarding the title, as it was suggested by reviewer #1 as his recommendation, we tried to find a compromise and make the changes you suggested but left part of the suggestion from reviewer #1. So, now it’s “Foxtrot migration and dynamic over-wintering range of an arctic raptor”

Thank you for highlighting the importance of snow cover and changes in snow cover as a possible factor of over-wintering movements. We appreciate your feedback and have explored several approaches to address this issue. Specifically, we examined how both snow cover extent and changes in snow cover influenced movement distance. However, we found no effect of either factor on movement distance.

Our data show that birds leave their sites in October and move southwest, even though snow cover is minimal at that time. They also leave their sites in November and in subsequent months, regardless of the snow cover levels. Thus, we observed no pattern of birds leaving sites when snow cover reaches a specific threshold (e.g., 75-80%). Similarly, we found no evidence of birds staying in areas with a certain snow cover extent (e.g., 30%), nor did they leave sites when snow cover increased by a specific amount (e.g., by 10 or 20%).

It is possible that more experienced birds anticipate that October plots will become inaccessible later in the winter and, therefore, leave early without waiting for significant snow accumulation. Alternatively, other factors, such as brief heavy snowfalls, may trigger movement, even if these do not lead to sustained increases in snow cover. Multiple factors, possibly acting asynchronously, could also play a role. This complexity adds an interesting dimension to the study of ecological patterns. However, in this study, we chose to focus on describing the migration pattern itself and its impact on aspects like over-winter range determination and population dynamics. While we have prioritized this approach, we remain committed to further analyzing the data to uncover additional details about this behavior.

In response to your suggestion, we have expanded the Methods sections to clarify that we tested the effects of snow cover and changes in snow cover on distance (Lines 241-246); the Results section (Lines 348-349). We have also included the relevant plots in the Supplementary Materials. In the Discussion, we noted that this approach did not reveal any significant dependence and acknowledged that this issue requires further investigation (Lines 422-459).

---------

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

Reviewer #2:

We sincerely appreciate the time and effort you have taken to review our manuscript. 

First of all, we apologize for publishing the preprint without incorporating certain adjustments outlined in our earlier response, particularly in the Methods section. This was due to an oversight regarding the different versions of the manuscript. We have corrected this mistake. Our response to the feedback on this section (Methods), with line numbers of the changes made, is immediately below this response. In addition, we have included the units of measurement (mean and standard deviation) in both the results and figure captions for clarity.

To focus on the main point regarding wintering strategies, we acknowledge that in the previous versions, this aspect was inadequately addressed and caused some confusion. In the revised edition, both the Introduction and the Discussion have been thoroughly reworked.

As you suggested, we have removed the long introductory paragraph and all references to foxtrot migrations from the Introduction. As a result, the Introduction is now short and to the point. In the second paragraph, we explain why we propose the wintering strategies outlined (L74-81).

In the Discussion, we've added a substantial new section at the beginning that discusses different wintering strategies. We have also updated Figure 4 accordingly. Previously, we erroneously suggested that Montagu's harrier and other African-Palaearctic migrants might adopt wintering strategies similar to those we describe. Upon further investigation, however, we found that almost all African-Palaearctic migrants exhibit an itinerant wintering strategy. Conversely, the strategy we describe is primarily observed in mid-latitude wintering species.

We have shown that, unlike itinerancy, the birds in our study don't pause for 1-2 months at multiple non-breeding sites, but instead migrate significant distances, up to 1000 km, throughout the winter. Furthermore, unlike itinerancy, the sites they reach are consistently snow-free throughout the year. Following the logic of publications on Montagu's harriers (Schlaich et al. 2023), our birds do not wait for favorable conditions at the next site, as is typical of itinerancy. Moreover, this behavior is influenced by external factors such as snow cover dynamics and occurs primarily in mid-latitudes. Researchers studying a species similar to our subject, the Common buzzard, observed a similar pattern and termed it "prolonged autumn migration" rather than itinerancy. Although their transmitters stopped working in mid-winter, precluding a full observation of the annual cycle, they captured the essence of continued migration at a slower pace, distinct from itinerancy. We've detailed all of these findings in a new section.

In addition, we acknowledge the mischaracterization of the implications of our research as ‘Conservation implications’ and have corrected this to ‘Mapping ranges and assessing population trends’, as you suggested.

Finally, we've rewritten the Conclusion, removing overly grandiose statements and simply summarizing the main findings.

We appreciate your time and effort in reviewing our manuscript. With your invaluable input, it has become clearer, more concise, and easier to understand.

Dataset: unclear what is the frequency of GPS transmissions. Furthermore, information on relative tag mass for the tracked individuals should be reported.

We have included this information in our manuscript (L 115-122). We also refer to the study in which this dataset was first used and described in detail (L 123).

Data pre-processing: more details are needed here. What data have been removed if the bird died? The entire track of the individual? Only the data classified in the last section of the track? The section also reports on an 'iterative procedure' for annotating tracks, which is only vaguely described. A piecewise regression is mentioned, but no details are provided, not even on what is the dependent variable (I assume it should be latitude?).

Regarding the deaths, we only removed the data when the bird was already dead. We estimated the date of death and excluded tracking data corresponding to the period after the bird's death. We have corrected the text to make this clear (L 130-131).

Regarding the piecewise regression. We have added a detailed description on lines 136-148.

Data analysis: several potential issues here:

(1) Unclear why sex was not included in all mixed models. I think it should be included.

Our dataset contains 35 females and eight males (L116). This ratio does not allow us to include sex in all models and adequately assess the influence of this factor. At the same time, because adult females disperse farther than males in some raptor species, we conducted a separate analysis of the dependence of migration distance on sex (Table S8) and found no evidence for this in our species. We have written about that in the Methods (L177-181) and after in the Results (L277-278).

(2) Unclear what is the rationale of describing habitat use during migration; is it only to show that it is a largely unsuitable habitat for the species? But is a formal analysis required then? Wouldn't be enough to simply describe this?

Habitat use and snow cover determine the two main phases (quick and slow) of the pattern we describe. We believe that habitat analysis is appropriate in this case, and a simple description would be uninformative and not support our conclusions.

(3) Analysis of snow cover: such a 'what if' analysis is fine but it seems to be a rather indirect assessment of the effect of snow cover on movement patterns. Can a more direct test be envisaged relating e.g. daily movement patterns to concomitant snow cover? This should be rather straightforward. The effectiveness of this method rests on among-year differences in snow cover and timing of snowfall. A further possibility would be to demonstrate habitat selection within the entire non-breeding home range of an individual in relation snow cover. Such an analysis would imply associating presenceabsence of snow to every location within the non-breeding range and testing whether the proportion of locations with snow is lower than the proportion of snow of random locations within the entire nonbreeding home range (95% KDE) for every individual (e.g. by setting a 1/10 ratio presence to random locations).

The proposed analysis will provide an opportunity to assess whether the Rough-legged buzzard selects areas with the lowest snow cover, but will not provide an opportunity to follow the dynamics and will therefore give a misleading overall picture. This is especially true in the spring months. In March-April, Rough-legged buzzards move northeast and are in an area that is not the most open to snow. At this time, areas to the southwest are more open to snow (this can be seen in Figure 3b). If we perform the proposed analysis, the control points for this period would be both to the north (where there is more snow) and to the south (where there is less snow) from the real locations, and the result would be that there is no difference in snow cover. 

A step-selection analysis could be used, as we did in our previous work (Curk et al 2020 Sci Rep) with the same Rough-legged buzzards (but during migration, not winter). But this would only give us a qualitative idea, not a quantitative one - that Rough-legged Buzzards move from snow (in the fall) and follow snowmelt progression (in the spring). 

At the same time, our analysis gives a complete picture of snow cover dynamics in different parts of the non-breeding range. This allows us to see that if Rough-legged buzzards remained at their fall migration endpoint without moving southwest, they would encounter 14.4% more snow cover (99.5% vs. 85.1%). Although this difference may seem small (14.4%), it holds significance for rodent-hunting birds, distinguishing between complete and patchy snow cover.

Simultaneously, if Rough-legged buzzards immediately flew to the southwest and stayed there throughout winter, they would experience 25.7% less snow cover (57.3% vs. 31.6%). Despite a greater difference than in the first case, it doesn't compel them to adopt this strategy, as it represents the difference between various degrees of landscape openness from snow cover.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

In this work by Wang et al., the authors use single-molecule super-resolution microscopy together with biochemical assays to quantify the organization of Nipah virus fusion protein F (NiV-F) on cell and viral membranes. They find that these proteins form nanoscale clusters which favors membrane fusion activation, and that the physical parameters of these clusters are unaffected by protein expression level and endosomal cleavage. Furthermore, they find that the cluster organization is affected by mutations in the trimer interface on the NiV-F ectodomain and the putative oligomerization motif on the transmembrane domain, and that the clusters are stabilized by interactions among NiV-F, the AP2-complex, and the clathrin coat assembly. This work improves our understanding of the NiV fusion machinery, which may have implications also for our understanding of the function of other viruses.

Strengths:

The conclusions of this paper are well-supported by the presented data. This study sheds light on the activation mechanisms underlying the NiV fusion machinery.

Weaknesses:

The authors provide limited details of the convolutional neural network they developed in this work. Even though custom-codes are made available, a description of the network and specifications of how it was used in this work would aid the readers in assessing its performance and applicability. The same holds for the custom-written OPTICS algorithm. Furthermore, limited details are provided for the imaging setup, oxygen scavenging buffer, and analysis for the single-molecule data, which limits reproducibility in other laboratories. The claim of 10 nm resolution is not backed up by data and seems low given the imaging conditions and fluorophores used. Fourier Ring Correlation analysis would have validated this claim. If the authors refer to localization precision rather than resolution, then this should be specified and appropriate data provided to support this claim.

We thank reviewer 1 for these suggestions. We described key steps in imaging setup, singlemolecule data reconstruction, the OPTICS algorithm in cluster identification, and 1D CNN in

classification of the OPTICS data in the Materials and Methods section. We also provided a recipe for the imaging buffer. We refer to 10 nm localization precision rather than resolution. The localization precision achieved by our SMLM system is shown in the Author response image 1.

Author response image 1.

The localization precision of the custom-built SMLM. Shows the distribution of localization error at the x (dX), y (dY), and z (dZ) direction in nanometer of blinks generated from Alexa Flour 647 labeled to NiV-F expressed on the plasma membrane of PK13 cells. The lateral precision is <10 nm and the axial precision is < 20 nm. 

Reviewer #2 (Public Review): 

Summary:

In this manuscript, Wang and co-workers employ single molecule light microscopy (SMLM) to detect NiV fusion protein (NiV-F) in the surface of cells. They corroborate that these glycoproteins form microclusters (previously seen and characterized together with the NiVG and Nipah Matrix protein by Liu and co-workers (2018) also with super-resolution light microscopy). Also seen by Liu and coworkers the authors show that the level of expression of NiV-F does not alter the identity of these microclusters nor endosomal cleavage. Moreover, mutations and the transmembrane domain or the hexamer-of-trimer interface seem to have a mild effect on the size of the clusters that the authors quantified.

Importantly, it has also been shown that these particles tend to cluster in Nipah VLPs.

We thank reviewer #2 for the comments and suggestions. This paper is built on Liu et al 1 to further characterize the nanoclusters formed by NiV-F and their role in membrane fusion activation. While Liu et al. studied the NiV glycoprotein distribution at the NiV assembly sites to inform mechanisms in NiV assembly and release, Wang et al. analyzed the nanoorganization and distribution of NiV-F at the prefusion conformation, providing insights into the membrane fusion activation mechanisms.  

Strengths:

The authors have tried to perform SMLM in single VLPs and have shown partially the importance of NiV-F clustering.

Weaknesses:

The labelling strategy for the NiV-F is not sufficiently explained. The use of a FLAG tag in the extracellular domain should be validated and compared with the unlabelled WT NiV-F when expressed in functional pseudoviruses (for example HIV-1 based particles decorated with NiV-F). This experiment should also be carried out for both infection and fusion (including BlaM-Vpr as a readout for fusion). I would also suggest to run a time-of-addition BlaM experiment to understand how this particular labelling strategy affects single virion fusion as compared to the the WT.  

We thank reviewer #2 for this suggestion. We have made various efforts to validate the expression and function of FLAG-tagged NiV-F. The NiV-F-FLAG shows comparable cell surface expression levels and induces similar cell-cell fusion levels in 293T cells as that of untagged NiV-F 1. The NiV-F-FLAG also showed similar levels of virus entry as untagged NiV-F when both were pseudotyped on a recombinant Vesicular Stomatitis Virus (VSV) with the VSV glycoprotein replaced by a Renilla luciferase reporter gene (VSV-ΔG-rLuc; Fig. S1D). We also performed a virus entry kinetics assay using NiV VLPs expressing NiV-M-βlactamase (NiV-M-Bla), NiV-G-HA, and NiV-F-FLAG, NiV-F-AU1 or untagged NiV-F. The intracellular AU1 tag is located at the C-terminus of NiV-F (Genbank accession no. AY816748.1). However, we detected different levels of NiV-M-Bla in equal volume of VLPs, suggesting that the tags in NiV-F affect the budding of the VLPs (Author response image 2A). Therefore, we performed fusion kinetics assay by using VLPs expressing the same levels of NiV-M-Bla. Among them, the NiV-F-FLAG on VLPs shows the most efficient fusion between VLP and HEK293T cell membranes (Author response image 2B), significantly more efficient than that of untagged NiV-F and NiV-FAU1. However, we cannot attribute the enhanced fusion activity to the FLAG tag, because the readout of this assay relies on both the levels of β-lactamase (introduced by NiV-M-Bla in VLPs) and the NiV-F constructs. The tags in NiV-F could affect both the budding of VLPs and the stoichiometry of F and M in individual VLPs. We did not use the HIV-based pseudovirus system because the incorporation of NiV-F into HIV pseudoviruses requires a C-terminal deletion 2,3.

In summary, the FLAG tag does not affect cell-cell fusion 1 and virus entry when pseudotyped to the recombinant VSV-ΔG-rLuc viruses (Fig. S1D). Given that we do not observe any difference in clustering between an HA- and FLAG-tagged NiV-F constructs on PK13 cell surface (Fig. S1A-C), we conclude that the FLAG tag has minimal effect on both the fusion activity and the nanoscale distribution of NiV-F. 

Author response image 2.

Viral entry is not affected by labeling of NiV-F. A) Western blot analysis of NiV-M-Bla in NiV-VLPs generated by HEK293T cells expressing NiV-M-Bla, NiV-G-HA and NiV-F-FLAG, untagged NiV-F, or NiV-F-AU1. Equal volume of VLPs were separated by a denaturing 10% SDS–PAGE and probed against β-lactamase (SANTA CRUZ, sc-66062). B) NiV-VLPs expressing NiV-M-BLa, NiV-G-HA, and NiV-F-FLAG, untagged NiV-F or NiV-F-AU1 expression plasmids were bond to the target HEK293T cells loaded with CCF2-AM dye at 4°C. The Blue/Green (B/G) ratio was measured at 37°C for 4 hrs at a 3-min interval. Results were normalized to the maximal B/G ratio of NiV-F-FLAG-NiV VLPs. Results from one representative experiment out of three independent experiments are shown. 

It would also be very important to compare the FLAG labelling approach with recent advances in the field (for instance incorporating noncanonical amino acids (ncAAs) into NiVF by amber stop-codon suppression, followed by click chemistry). 

We are greatly thankful for this comment from reviewer #2. Labeling noncanonical amino acids (ncAAs) with biorthogonal click chemistry is indeed a more precise labeling strategy compared to the traditional epitope labeling approach used in this paper. We will explore the applications of ncAAs labeling in single-molecule localization imaging and virus-host interactions in future projects. 

In this paper, the FLAG tag inserted in NiV-F protein seems to have minimal effect on the NiV-F-induced virus entry and cell-cell fusion 1 (Fig. S1). Although the FLAG tag labeling approach may increase the detectable size of NiV-F nanoclusters due to the use of the antibody complex, it should not affect our conclusions drawn from the relative comparisons between wt and mutant NiV-F or control and drug-treated cells. 

The correlation between the existence of microclusters of a particular size and their functionality is missing. Only cell-cell fusion assays are shown in supplementary figures and clearly, single virus entry and fusion cannot be compared with the biophysics of cell-cell fusion. Not only the environment is completely different, membrane curvature and the number of NiV-F drastically varies also. Therefore, specific fusion assays (either single virus tracking and/or time-of-addition BlaM kinetics with functional pseudoviruses) are needed to substantiate this claim.  

We thank Reviewer 2 for the suggestion. To support the link between F clustering and viruscell membrane fusion, we conducted pseudotyped virus entry and VLP fusion kinetics assays, as shown in revised Figure S4. The viral entry results (Fig. S4 E and F) corroborate that of the cell-cell fusion assay (Fig. S4A and B) and previously published data 4. The fusion kinetics confirmed that the real-time fusion kinetics was affected by mutations at the hexameric interface, with the hypo-fusogenic mutants L53D and V108D exhibited reduced entry efficiency while the hyper-fusogenic mutant Q393L showed increased efficiency (Fig. S4G and H). The results were described in detail in the revised manuscript. 

Additionally, we performed a pseudotyped virus entry assay on the LI4A (Fig. S6F and G) and YA (Fig. S7F and G) mutants to verify the function of these mutants on viruses in revised Supplemental Figures. Neither LI4A nor YA incorporated into the VSV/NiV pseudotyped viruses as shown by the Western blot analyses of the pseudovirions (Fig. S6F and S7F), and thus did not induce virus entry, consisting with the cell-cell fusion results (Fig. S6C, D and Fig. S7C, D). We did not perform the entry kinetic assay of these two mutants as they do not incorporate into VLPs or pseudovirions. 

The authors also claim they could not characterize the number of NiV-F particles per cluster. Another technique such as number and brightness (Digman et al., 2008) could support current SMLM data and identify the number of single molecules per cluster. Also, this technology does not require complex microscopy apparatus. I suggest they perform either confocal fluorescence fluctuation spectroscopy or TIRF-based nandb to validate the clusters and identify how many molecule are present in these clusters.  

We thank reviewer 2 for this suggestion. Determining the true copy number of NiV-F in individual clusters could verify whether the F clusters on the plasma membrane are hexamer-of-trimer assemblies. Regardless, it does not affect our conclusion that the organization of NiV-F into nanoclusters affects the membrane fusion triggering ability. The confocal fluorescence fluctuation spectroscopy (FFS) and TIRF-based analyses are accessible tools for quantifying fluorophore copy numbers and/or stoichiometry based on fluorescence fluctuation or photobleaching. However, these methods are unable to quantify the number of proteins in individual clusters because they analyze fluorophores either in the entire cell (as in wide-field epifluorescence microscopy coupled with FFS and TIRF-coupled photobleaching) 5–7 or within a large excitation volume (confocal laser scanning microscopycoupled FFS) 8. Both of these volumes are significantly larger than a single NiV-F cluster, which has an average diameter of 24-26 nm (Fig. 1F). 

The current SMLM setup is useful for characterizing the protein distribution and organization. However, quantifying the true protein copy number within a nanocluster is challenging because of the stochasticity of fluorophore blinking and the unknown labeling stoichiometry 9–11. To address the challenge in fluorophore blinking, quantitative DNA-PAINT (qDNA-PAINT) may be used because the on-off frequency of the fluorophores is tied to the well-defined kinetic constants of DNA binding and the influx rate of the imager strands, rather than the stochasticity of fluorophore blinking. Thus, the frequency of blinks can be translated to protein counting 12. To address the challenge in unknown labeling stoichiometry, DNA origami can be used as a calibration standard 11. DNA origami supports handles at a regular space with several to tens of nanometers apart, and the handles can be conjugated with a certain number of proteins of interest. The copy number of protein interest in the experimental group can be determined by comparing the SMLM localization distribution of the sample to that of the DNA origami calibration standard. Given the requirement of a more sophisticated SMLM setup and a high-precision calibration tool, we will explore the quantification of NiV-F copy numbers in nanoclusters in a future project. 

Also, it is not clear how many cells the authors employ for their statistics (at least 30-50 cells should be employed and not consider the number of events blinking events. I hope the authors are not considering only a single cell to run their stats... The differences between the mutants and the NiV-F is minor even if their statistical analyses give a difference (they should average the number and size of the clusters per cell for a total of 30-50 cells with experiments performed at least in three different cells following the same protocol). Overall, it seems that the authors have only evaluated a very low number of cells.

We disagree with this comment from Reviewer #2. The sample size for cluster analysis in SMLM images was chosen by considering the target of the study (cells and VLPs) and the data acquisition and analysis standards in the SMLM imaging field. We also noted the sample size (# of ROI and cells) in the figure legend. 

Below, we compared the sample sizes in our study to those in similar studies that used comparable imaging and cluster analysis methods from 2015 to 2024. The classical clustering analysis methods are categorized into global clustering (e.g. nearest neighbor analysis, Ripley’s K function, and pair correlation function) and complete clustering, such as density-based analysis (e.g. DBSCAN, Superstructure, FOCAL, ToMATo) and Tessellationbased analysis (e.g. Delaunay triangulation, Voronoii Tessellation). The global clustering analysis method provides spatial statistics for global protein clustering or organization (e.g. clustering extent), while the complete clustering approach extracts information from a single-cluster level, such as the morphology and localization density of individual clusters. We used the density-based analyses, DBSCAN and OPTICS, for cluster analysis on cell plasma membranes and VLP membranes. 

Author response table 1.

The comparison of imaging methods, analysis methods, and sample size in the current study to other studies conducted from 2015 to 2024.

They should also compare the level of expression (with the number of molecules per cell provided by number and brightness) with the total number of clusters. 

We thank reviewer 2 for this suggestion. We compared the level of expression with the total number of clusters for F-WT in Figure 1I in the main text.  

The same applies to the VLP assay. I assume the authors have only taken VLPs expressing both NiV-M and NiV-F (and NiV-G). But even if this is not clearly stated I would urge the authors to show how many viruses were compared per condition (normally I would expect 300 particles per condition coming from three independent experiments. As a negative control to evaluate the cluster effect I would mix the different conditions. Clearly you have clusters with all conditions and the differences in clustering depending on each condition are minimal. Therefore you need to increase the n for all experiments.

We thank reviewer 2 for this comment. We acquired and analyzed more images of NiV VLPs bearing F-WT, Q393L, L53D, and V108D. Results are shown in the revised Figure 4 and the number of VLPs (>300) used for analysis is specified in the figure legend. An increased number of VLP images does not affect the classification result in Figure 4C. 

As for the suggestion on “evaluating the cluster effect at different mixed conditions”, I assume that reviewer 2 would like to see how the presence of different viral structural proteins (F, M, and G) on VLPs could affect F clustering.  We showed that the organization of NiV envelope proteins on the VLP membrane is similar in the presence or absence of NiV-M by direct visualization 27, suggesting that the effect of NiV-M on F-WT clustering on VLPs is minimal. We also show comparable incorporation of NiV-F among the NiV-F hexamer-oftrimer mutants (Fig. 4A). Therefore, we did not test the F clustering at different F, M, and G combinations in this paper. However, this could be an interesting question to pursue in a paper focusing on NiV VLP production. 

Reviewer #3 (Public Review):

Summary:

The manuscript by Wang and colleagues describes single molecule localization microscopy to quantify the distribution and organization of Nipah virus F expressed on cells and on virus-like particles. Notably the crystal structure of F indicated hexameric assemblies of F trimers. The authors propose that F clustering favors membrane fusion.

Strengths:

The manuscript provides solid data on imaging of F clustering with the main findings of:

-  F clusters are independent of expression levels

-  Proteolytic cleavage does not affect F clustering

-  Mutations that have been reported to affect the hexamer interface reduce clustering on cells and its distribution on VLPs - - F nanoclusters are stabilized by AP

Weaknesses:

The relationship between F clustering and fusion is per se interesting, but looking at F clusters on the plasma membrane does not exclude that F clustering occurs for budding. Many viral glycoproteins cluster at the plasma membrane to generate micro domains for budding. 

This does not exclude that these clusters include hexamer assemblies or clustering requires hexamer assemblies. 

We thank reviewer #3 for this question. We did not focus on the role of NiV-F clusters for budding in the current manuscript, although this is an interesting topic to pursue. In this manuscript, we observed that NiV VLP budding is decreased for some cluster-disrupting mutants, such as F-YA, and F-LI4A. however, F-V108D showed increased budding compared to F-WT (Fig. 4A). We also observed that VLPs and VSV/NiV pseudoviruses expressing L53D have little NiV-G (Fig. 4A, Fig. S4F and S4H), although the incorporation level of L53D is comparable to that of wt F in both VLPs and pseudovirions (Fig. 4A and Fig. S4F). L53D is a hypofusogenic mutant with decreased clustering ability. Therefore, our current data do not show a clear link between F clustering and NiV VLP budding or glycoprotein incorporation. 

We reported that both NiV-F and -M form clusters at the plasma membrane although NiV-F clusters are not enriched at the NiV-M positive membrane domains 1. This result indicates that NiV-M is the major driving force for assembly and budding, while NiV-F is passively incorporated into the assembly sites. The central role of NiV-M in budding is also supported by a recent study showing that NiV-M induces membrane curvature by binding to PI(4,5)P2 in the inner leaflet of the plasma membrane 28. However, the expression of NiV-F alone induces the production of vesicles bearing NiV-F 29 and NiV-F recruits vesicular trafficking and actin cytoskeleton factors to VLPs either alone or in combination with NiV-G and -M, indicating a potential autonomous role in budding 30. Additionally, several electron microscopy studies show that the paramyxovirus F forms 2D lattice interspersed above the M lattice, suggesting the participation of F in virus assembly and budding. Nonetheless, the evidence above suggests that NiV-F may play a role in budding, but our data cannot correlate NiV-F clustering to budding. 

Assuming that the clusters are important for entry, hexameric clusters are not unique to Nipah virus F. Similar hexameric clusters have been described for the HEF on influenza virus C particles (Halldorsson et al 2021) and env organization on Foamy virus particles (Effantin et al 2016), both with specific interactions between trimers. What is the organization of F on Nipah virus particles? If F requires to be hexameric for entry, this should be easily imaged by EM on infectious or inactivated virus particles. 

We thank reviewer #3 for this suggestion. The hexamer-of-trimer NiV-F is observed on the VLP surface by electron tomography 4. The NiV-F hexamer-of-trimers are arranged into a soccer ball-like structure, with one trimer being part of multiple hexamer-of-trimers. The implication of NiV-F clusters in virus entry and the potential mechanism for NiV-F higherorder structure formation are discussed in the revised manuscripts. 

AP stabilization of the F clusters is curious if the clusters are solely required for entry? Virus entry does not recruit the clathrin machinery. Is it possible that F clusters are endocytosed in the absence of budding? 

We thank reviewer #3 for this question. The evidence from the current study does not exclude the role of NiV-F clustering in virus budding. NiV-F is known to be endocytosed in the virus-producing cells for cleavage by Cathepsin B or L at endocytic compartments at a pH-dependent manner31–33 in the absence of budding. However, given that all cleaved and uncleaved NiV-F have an endocytosis signal sequence at the cytoplasmic tail and are able to interact with AP-2 for endosome assembly and the cleaved and uncleaved F may have similar clustering patterns (Fig. 2), we do not think NiV-F clustering is specifically regulated for the cleavage of NiV-F. A plausible hypothesis is that NiV-F clusters are stabilized by multiple intrinsic factors (e.g. trimer interface) and host factors (e.g. AP-2) on cell membrane for cell-cell fusion and virus budding. We linked the clustering to the fusion ability of NiV-F in this study, but the NiV-F clustering may also be important in facilitating virus budding. Once in the viruses, the higher-order assembly of the clusters (e.g. lattice) may form due to protein enrichment, and the cell factors may not be the major maintenance force. 

Clusters are required for budding. 

Other points:

Fig. 3: Some of the V108D and L53D clusters look similar in size than wt clusters. It seems that the interaction is important but not absolutely essential. Would a double mutant abrogate clustering completely?

We thank Reviewer #3 for the suggestion. We generated a double mutant of NIV-F with L53D and V108D (NiV-F-LV) and assessed its expression and processing. Although the mutant retained processing capability, it exhibited minimal surface expression, making it unfeasible to analyze its nano-organization on the cell or viral membrane.

Author response image 4.

The expression and fusion activity of Flag-tagged NiV-F and NiV-F L53D-V108D (LV). (A) Representative western blot analysis of NiV-F-WT, LV in the cell lysate of 293T cells. 293T cells were transfected by NiV-F-WT or the LV mutant. The empty vector was used as a negative control. The cell lysates were analyzed on SDS-PAGE followed by western blotting after 28hrs post-transfection. F0 and F2 were probed by the M2 monoclonal mouse antiFLAG antibody. GAPDH was probed by monoclonal mouse anti-GAPDH. (B) Representative images of 293T cell-cell fusion induced by NiV-G and NiV-F-WT or NiV-F-LV. 293T cells were co-transfected with plasmids coding for NiV-G and empty vector (NC) or NiV-F constructs. Cells were fixed at 18 hrs post-transfection. Arrows point to syncytia. Scale bar: 10um. (C) Relative cell-cell fusion levels in 293T cells in (B). Five fields per experiment were counted from three independent experiments. Data are presented as mean ± SEM. (D) The cell surface expression levels of NiV-F-WT, NiV-F-LV in 293T cells measured by flow cytometry. Mean fluorescence Intensity (MFI) values were calculated by FlowJo and normalized to that of F-WT. Data are presented as mean ± SEM of three independent experiments. Statistical significance was determined by the unpaired t-test with Welch’s correction (*P<0.05, **P<0.01, ***P<0.001, ****P<0.0001). Values were compared to that of the NiV-F-WT.

Fig. 4: The distribution of F on VLPs should be confirmed by cryoEM analyses. This would also confirm the symmetry of the clusters. The manuscript by Chernomordik et al. JBC 2004 showed that influenza HA outside the direct contact zone affects fusion, which could be further elaborated in the context of F clusters and the fusion mechanism.

We thank reviewer 3 for this suggestion. The distribution of F on VLPs was resolved by electron tomogram which showed that the NiV-F hexamer-of-trimers are arranged into a soccer ball-like structure 4. The role of influenza HA outside of the contact zone in fusion activation is an interesting phenomenon. It may address the energy transmission within and among clusters. We will pursue this topic in a future project.  

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

•  Please define all used abbreviations throughout the manuscript and in the SI.

We defined the abbreviations at their first usage. 

•  The sentence starting with "Additionally, ..." on line 155 appears to be incomplete.

We corrected this sentence.  

•  The statement starting with "As reported, ..." on line 181 should be supported by a reference.

We added a reference. 

•  In Fig. 4C, it is unclear what the x and y axes represent.  

Fig. 4C is a t-SNE plot for visualizing high-dimensional data in a low-dimensional space. It maintains the local data structure but does not represent exact quantitative relationships. In other words, points that are close together in Fig. 4C are also close in the high-dimensional space, meaning the OPTICS plots, which reflect the clustering patterns, are similar for two points that are positioned near each other in Fig. 4C. Therefore, the x and y axes do not represent the original, quantitative data, and thus the axis titles are meaningless.  

•  The reference on line 306 appears to be unformatted.

We reformatted the reference.  

Reviewer #2 (Recommendations For The Authors):

The authors need to include the overall statistics for each experiment (at least 30 to 50 cells with three independent experiments are needed). 

We highlighted the sample size (number of ROI and number of cells) used for analysis in the figure legend. The determination of the sample size is justified in Table 1 in the response letter. 

The authors need to generate a functional pseudovirus system (for example HIVpp/NiV F) to run both infectivity and fusion experiments (including Apr-BlaM assay). 

We tested viral entry using a VSV/NiV pseudovirus system and the viral entry kinetics using VLPs expressing NiV-M-β-lactamase. The results are presented in Fig. S1, S4, S6, and S7.  

Reviewer #3 (Recommendations For The Authors):

Even low resolution EM data on VLPs or viruses would strengthen the conclusions.

We thank this reviewer for the suggestion. We cited the NiV VLP images acquired by electron tomography 4, but we currently have limited resources to perform cryoEM on NiV VLPs.  

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kalo di notal "pointer to node"

ok intinya ini pake tag node biar bisa memberi instruksi ke preprocessor membuat struct baru node dengan Address yang beda-beda.

pada umumnya itu typedef Node (cara aksesnya)

cuman karna ini pake tag, instead of mengubah, kita membuat baru dengan address yang bisa beda beda

ini typedef struct node* Address (perhatikan bahwa struct di tulis juga)

ini tag

tag dan type nya dibedakan (node dan Node) untuk mengatasi strictnya bahasa C dimana tipe tidak bisa di declare setelah operasi lain. dibuatlah jadi tag, im not to sure so ask again ke GPT

Author response:

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

We are grateful to the reviewers for their positive assessment of the revised version of the article.

Please find below our answers to the last, minor comments of the reviewers.

We thank the reviewer for this important comment. In our live imaging experiments, we actually tracked the dorsal and ventral borders of the omp:yfp positive clusters in control and sly mutant embryos. These measurements showed that the omp:yfp positive clusters are more elongated along the DV axis in mutants as compared with control siblings, as seen on fixed samples (data not shown), suggesting that this difference in tissue shape is not due to fixation.

Reviewer #4 (Public review):

Summary:

In this elegant study XX and colleagues use a combination of fixed tissue analyses and live imaging to characterise the role of Laminin in olfactory placode development and neuronal pathfinding in the zebrafish embryo. They describe Laminin dynamics in the developing olfactory placode and adjacent brain structures and identify potential roles for Laminin in facilitating neuronal pathfinding from the olfactory placode to the brain. To test whether Laminin is required for olfactory placode neuronal pathfinding they analyse olfactory system development in a well-established laminin-gamma-1 mutant, in which the laminin-rich basement membrane is disrupted. They show that while the OP still coalesces in the absence of Laminin, Laminin is required to contain OP cells during forebrain flexure during development and maintain separation of the OP and adjacent brain region. They further demonstrate that Laminin is required for growth of OP neurons from the OP-brain interface towards the olfactory bulb. The authors also present data describing that while the Laminin mutant has partial defects in neural crest cell migration towards the developing OP, these NCC defects are unlikely to be the cause of the neuronal pathfinding defects upon loss of Laminin. Altogether the study is extremely well carried out, with careful analysis of high-quality data. Their findings are likely to be of interest to those working on olfactory system development, or with an interest in extracellular matrix in organ morphogenesis, cell migration, and axonal pathfinding.

Strengths:

The authors describe for the first time Laminin dynamics during the early development of the olfactory placode and olfactory axon extension. They use an appropriate model to perturb the system (lamc1 zebrafish mutant), and demonstrate novel requirements for Laminin in pathfinding of OP neurons towards the olfactory bulb.

The study utilises careful and impressive live imaging to draw most of its conclusions, really drawing upon the strengths of the zebrafish model to investigate the role of laminin in OP pathfinding. This imaging is combined with deep learning methodology to characterise and describe phenotypes in their Laminin-perturbed models, along with detailed quantifications of cell behaviours, together providing a relatively complete picture of the impact of loss of Laminin on OP development.

Weaknesses:

Some of the statistical tests are performed on experiments where n=2 for each condition (for example the measurements in Figure S2) - in places the data is non-significant, but clear trends are observed, and one wonders whether some experiments are under-powered.

We initially planned the electron microscopy experiments in order to analyse 3 embryos per genotype per stage. However, because of technical issues we could not perform the measurements in all the cases, explaining why we have n = 2 in some of the graphs. The trends were quite clear, so we chose to keep these data in the article. We believe they nicely complement the immunostaining data assessing basement membrane integrity in control and mutant embryos.

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

Public Reviews: 

Reviewer #1 (Public Review): 

Summary: 

The authors describe the dynamic distribution of laminin in the olfactory system and forebrain. Using immunohistochemistry and transgenic lines, they found that the olfactory system and adjacent brain tissues are enveloped by BMs from the earliest stages of olfactory system assembly. They also found that laminin deposits follow the axonal trajectory of axons. They performed a functional analysis of the sly mutant to analyse the function of laminin γ1 in the development of the zebrafish olfactory system. Their study revealed that laminin enables the shape and position of placodes to be maintained late in the face of major morphogenetic movements in the brain, and its absence promotes the local entry of sensory axons into the brain and their navigation towards the olfactory bulb. 

Strengths: 

- They showed that in the sly mutants, no BM staining of laminin and Nidogen could be detected around the OP and the brain. The authors then elegantly used electron microscopy to analyse the ultrastructure of the border between the OP and the brain in control and sly mutant conditions. 

- To analyse the role of laminin γ1-dependent BMs in OP coalescence, the authors used the cluster size of Tg(neurog1:GFP)+ OP cells at 22 hpf as a marker. They found that the mediolateral dimension increased specifically in the mutants. However, proliferation did not seem to be affected, although apoptosis appeared to increase slightly at a later stage. This increase could therefore be due to a dispersal of cells in the OP. To test this hypothesis, the authors then analysed the cell trajectories and extracted 3D mean square displacements (MSD), a measure of the volume explored by a cell in a given period of time. Their conclusion indicates that although brain cell movements are increased in the absence of BM during coalescence phases, overall OP cell movements occur within normal parameters and allow OPs to condense into compact neuronal clusters in sly mutants. The authors also analysed the dimensions of the clusters composed of OMP+ neurons. Their results show an increase in cluster size along the dorso-ventral axis. These results were to be expected since, compared with BM, early neurog1+ neurons should compact along the medio-lateral axis, and those that are OMP+ essentially along the dorso-ventral axis. In addition to the DV elongation of OP tissue, the authors show the existence of isolated and ectopic (misplaced) YFP+ cells in sly mutants. 

- To understand the origin of these phenotypes, the authors analysed the dynamic behaviour of brain cells and OPs during forebrain flexion. The authors then quantitatively measured brain versus OPs in the sly mutant and found that the OP-brain boundary was poorly defined in the sly mutant compared with the control. Once again, the methods (cell tracks, brain size, and proliferation/apoptosis, and the shape of the brain/OP boundary) are elegant but the results were expected. 

- They then analysed the dynamic behaviour of the axon using live imaging. Thus, olfactory axon migration is drastically impaired in sly mutants, demonstrating that Laminin γ1dependent BMs are essential for the growth and navigation of axons from the OP to the olfactory bulb. 

- The authors therefore performed a quantitative analysis of the loss of function of Laminin γ1. They propose that the BM of the OP prevents its deformation in response to mechanical forces generated by morphogenetic movements of the neighbouring brain. 

Weaknesses: 

- The authors did not analyse neurog1 + axonal migration at the level of the single cell and instead made a global analysis. An analysis at the cell level would strengthen their hypotheses.  

- Rescue experiments by locally inducing Laminin expression would have strengthened the paper. 

- The paper lacks clarity between the two neuronal populations described (early EONs and late OSNs).  

- The authors quantitatively measured brain versus OPs in the sly mutant and found that the OP-brain boundary was poorly defined in the sly mutant compared with the control. Once again, the methods (cell tracks, brain size, proliferation/apoptosis, and the shape of the brain/OP boundary) are elegant but the results were expected. 

- A missing point in the paper is the effect of Laminin γ1 on the migration of cranial NCCs that interact with OP cells. The authors could have analysed the dynamic distribution of neural crest cells in the sly mutant. 

We thank the reviewer for the overall positive assessment of our work, and we carefully responded to all her/his insightful comments below. Live imaging experiments to (1) visualise exit and entry point formation with only a few axons labelled, (2) characterise the behaviour of single neurog1:GFP-positive neurons/axons during OP coalescence and to (3) analyse the migration of cranial NCC are now included in the revised manuscript to address the reviewer’s questions, and reinforce our initial conclusions.

Reviewer #2 (Public Review): 

Summary: 

This manuscript addresses the role of the extracellular matrix in olfactory development. Despite the importance of these extracellular structures, the specific roles and activities of matrix molecules are still poorly understood. Here, the authors combine live imaging and genetics to examine the role of laminin gamma 1 in multiple steps of olfactory development. The work comprises a descriptive but carefully executed, quantitative assessment of the olfactory phenotypes resulting from loss of laminin gamma. Overall, this is a constructive advance in our understanding of extracellular matrix contributions to olfactory development, with a well-written Discussion with relevance to many other systems. 

Strengths: 

The strengths of the manuscript are in the approaches: the authors have combined live imaging, careful quantitative analyses, and molecular genetics. The work presented takes advantage of many zebrafish tools including mutants and transgenics to directly visualize the laminin extracellular matrix in living embryos during the developmental process. 

Weaknesses: 

The weaknesses are primarily in the presentation of some of the imaging data. In certain cases, it was not straightforward to evaluate the authors' interpretations and conclusions based on the single confocal sections included in the manuscript. For example, it was difficult to assess the authors' interpretation of when and how laminin openings arise around the olfactory placode and brain during olfactory axon guidance. 

We thank the reviewer for the overall positive assessment of our work, and we carefully responded to all her/his insightful comments below. To address these comments, live imaging data to visualise exit and entry point formation with a sparse labelling of axons, and z-stacks showing how exit and entry points are organised in 3D, have been added to the revised manuscript.

Reviewer #3 (Public Review): 

This is a beautifully presented paper combining live imaging and analysis of mutant phenotypes to elucidate the role of laminin γ1-dependent basement membranes in the development of the zebrafish olfactory placode. The work is clearly illustrated and carefully quantified throughout. There are some very interesting observations based on the analysis of wild-type, laminin γ1, and foxd3 mutant embryos. The authors demonstrate the importance of a Laminin γ1-dependent basement membrane in olfactory placode morphogenesis, and in establishing and maintaining both boundaries and neuronal connections between the brain and the olfactory system. There are some very interesting observations, including the identification of different mechanisms for axons to cross basement membranes, either by taking advantage of incompletely formed membranes at early stages, or by actively perforating the membrane at later ones. 

This is a valuable and important study but remains quite descriptive. In some cases, hypotheses for mechanisms are stated but are not tested further. For example, the authors propose that olfactory axons must actively disrupt a basement membrane to enter the brain and suggest alternative putative mechanisms for this, but these are not tested experimentally. In addition, the authors propose that the basement membrane of the olfactory placode acts to resist mechanical forces generated by the morphogenetic movement of the developing brain, and thus to prevent passive deformation of the placode, but this is not tested anywhere, for example by preventing or altering the brain movements in the laminin γ1 mutant. 

We thank the reviewer for the overall positive assessment of our work and for suggesting interesting experiments to attempt in the future, and we carefully responded to all her/his constructive comments below.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

In general, it would be easier to draw conclusions and compare data if the authors used similar stages throughout the article. 

Throughout the article we tried to focus on a series of stages that cover both the coalescence of the OP (up to 24 hpf) and later stages of olfactory system development spanning the brain flexure process (28, 32, 36 hpf). However, for technical reasons it was not always possible to stick to these precise stages in some of our experiments. Also, in Fig. 1E-J, we picked in the movies some images illustrating specific cell or axonal behaviours, and thus the corresponding stages could not match exactly the stage series used in Fig. 1A-D and elsewhere in the article. Nevertheless, this stage heterogeneity does not affect our main conclusions.

It would be useful to schematise the olfactory placode and the brain in an insert to clearly visualise the system in each figure. 

We hope that the schematic which was initially presented in Fig. 1K already helps the reader to understand how the system is organised. Although we have not added more schematic views to represent the system in each figure (we think this would make the figures overcrowded), we have added additional legends to point to the OP and the brain in the pictures in order to clarify the localisation of each tissue.

In the Summary, the authors refer to the integrity of the basement membrane. I don't think there is any attempt to affect basement membrane integrity in the article. It would be important to do so to look at the effect on CNS-PNS separation and axonal elongation. 

In the Summary, we use the term « integrity of the basement membrane » to mention that we have analysed this integrity in the sly mutant. Given the results of our immunostainings against three main components of the basement membrane (Laminin, Collagen IV and Nidogen), as well as our EM observations, we see the sly mutant as a condition in which the integrity of the basement membrane is strongly affected.

Rescue experiments by locally inducing Laminin expression would have strengthened the paper. 

We have attempted to rescue the sly mutant phenotypes by introducing the mutation in the transgenic TgBAC(lamC1:lamC1-sfGFP) background, in which Laminin γ1 tagged with sfGFP is expressed under the control of its own regulatory sequences (Yamaguchi et al., 2022). To do so, we crossed sly+/-;Tg(omp:yfp) fish with sly+/-; Tg(lamC1:LamC1-sfGFP) fish. Surprisingly, while a rescue of the global embryo morphology was observed, no clear rescue of the olfactory system defects could be detected at 36 hpf. This could be due to the fact that the expression level of LamC1-sfGFP obtained with one copy of the transgene is not sufficient to rescue the olfactory system phenotypes, or that the sfGFP tag specifically affects the function of the Laminin 𝛾1 chain during the development of the olfactory system, making it unable to rescue the defects. Given the results of our first attemps, we decided not to continue in this direction.

(1) Developing OP & brain are surrounded by laminin-containing BM (already described by Torrez-Pas & Whitlock in 2014). 

"we first noticed the appearance of a continuous Laminin-rich BM surrounding the brain from 14-18 hpf, while around the OP, only discrete Laminin spots were detected at this stage (Fig. 1A, A'). " 

Around 8ss for Torrez-Pas & Whitlock (before 14 hpf). Can you modify the text, or show an 8ss stage embryo? As far as I know, the authors do not show images at 14hpf. Please correct this sentence or show a 14 hpf picture. 

The reviewer is right, we do not show any 14 hpf stage in the images and thus have removed this stage in the text and replaced it by 17 hpf.

In Figure 1A, the labelling of laminin 111 does not appear to be homogeneous along the brain.

Is this true? 

At this stage the brain’s BM revealed by the Laminin immunostaining appears fairly continuous (while the OP’s one is clearly dotty and less defined), but indeed very tiny/local interruptions of the signal can been seen along the structure as detected by the reviewer. We thus modified the text to mention these tiny interruptions.

How is the Laminin antibody used by the authors specific to laminin 111?  

We thank the reviewer for raising this important point. The immunogen used to produce this rabbit polyclonal antibody is the Laminin protein isolated from the basement membrane of a mouse Engelbreth Holm-Swarm sarcoma (EHS). It is thus likely to recognise several Laminin isoforms and not only Laminin 111. We thus replaced Laminin 111 by Laminin when mentioning this antibody in the text and Figures.

Please schematise in Figure 1K the stages you have tested and shown here in the article i.e. stages 18 - 22 - 28 -36 hpf using immunohistochemistry and 17-26-27-29-33 and 38 hpf using transgenics for laminin 111 and LamC1 respectively.  

As suggested by the reviewer, we changed the stages in the schematics for stages we have presented in Figure 1 (analysed either with immunostaining or in live imaging experiments). We chose to represent 17 - 22 - 26 - 33 hpf (and thus adapted some of the schematics for them to match these stages).  

Please specify in the Figure 1 legend for panels A to D whether this is a 3D projection or a zsection.

We indicated in the Figure 1 legend that all these images are single z-sections (as well as for panels E-J).

Furthermore, the schematisation in Fig. 1K does not reflect what the authors show: at 22 hpf laminin 111 labelling appears to be present only near the brain, and no labelling lateral to the olfactory placode and anteriorly and posteriorly. Thus, the schematisation in Figure 1K needs to be modified to reflect what the authors show.

We agree with the reviewer that the Laminin staining at this stage is observed around the medial region of the OP, but not more laterally. We modified the schematic view accordingly in Figure 1K. Anterior and posterior sides of the OP are not represented in this schematic because we chose to represent a frontal view rather than a dorsal view.

The authors suggest that" the laminin-rich BM of OP assembles between 18 and 22 hpf, during the late phase of OP coalescence". However, their data indicate that this BM assembles around 28hpf (Figure 1C). Can they clarify this point?

What we meant with this sentence is that we cleary see two distinct BMs from 22 hpf. However, as noticed by the reviewer, the OP’s BM is only present around the medial/basal regions of the OP and does not surround the whole OP tissue at this stage. We modified the text to clarify this point (in particular by mentioning that the OP’s BM starts to assemble between 18 and 22 hpf), and replaced the image shown in Figure 1B, B’ with a more representative picture (the previous z-section was taken in very dorsal regions of the OP).

It would be useful to disrupt these cells that have a cytoplasmic expression of Laminin-sfGFP, to analyse their contribution to BM and OP coalescence.

Indeed it will be interesting in the future to test specifically the role of the cells expressing cytoplasmic Laminin-sfGFP around and within the OP, as proposed by the reviewer. Laser ablation of these cells could be attempted, but due to their very superficial localisation, close to the skin, we believe these ablations (with the protocol/set-up we currently use in the lab) would impair the skin integrity, preventing us to conclude. We consider that the optimisation of this experiment is out of the scope of the present work.

Tg(-2.0ompb:gapYFP)rw032 marks ciliated olfactory sensory neurons (OSNs) (Sato et al., 2005). The authors should mention this. 

Please see our detailed response to the next point below.

Points to be clarified: 

-Tg(-2.0ompb:gapYFP)rw032 marks ciliated olfactory sensory neurons (OSNs) (Sato et al., 2005). The authors should mention this here. Moreover, the authors refer to "OP neurons" throughout the article. In the development of the olfactory organ, two types of neurons have been described in the literature: early EONs (12hpf-26hpf) and later OSNs. Each could have a specific role in the establishment and maintenance of the BM described by the authors. The authors need to clarify this point as, in Figure 1 for example, they use a marker for Tg(neurog1:GFP) EONs and a marker for ciliated OSNs without distinction. The distinction between EONs and OSNs comes a little late in the text and should be placed higher up. 

As mentioned by the reviewer, according to the initial view of neurogenesis in the OP, OP neurons are born in two waves. A transient population of unipolar, dendrite-less pioneer neurons would differentiate first, in the ventro-medial region of the OP and elongate their axons dorsally out of the placode, along the brain wall. These pioneer axons would then be used as a scaffold by later born OSNs located in the dorso-lateral rosette to outgrow their axons towards the olfactory bulb (Whitlock and Westerfield, 1998). 

Another study further characterised OP neurogenesis and showed that the first neurons to differentiate in the OP (the early olfactory neurons or EONs) express the Tg(neurog1:GFP) transgene (Madelaine et al., 2011). As mentioned by the authors in the discussion of this article, neurog1:GFP+ neurons appear much more numerous than the previously described pioneer neurons, and may thus include pioneers but also other neuronal subtypes.

We would like here to share additional, unpublished observations from our lab that further suggest that the situation is more complex than the pioneer/OSN and EON/OSN nomenclatures. First, in many of our live imaging experiments, we can clearly visualise some neurog1:GFP+ unipolar neurons, initially located in a medial position in the OP, which intercalate and contribute to the dorsolateral rosette (where OSNs are proposed to be located) at the end of OP coalescence, from 22-24 hpf. Second, in fixed tissues, we observed that most neurog1:GFP+ neurons located in the rosette at 32 hpf co-express the Tg(omp:meRFP) transgene (Sato et al., 2005). These observations suggest that at least a subpopulation of neurog1:GFP+ neurons could incorporate in the dorsolateral rosette and become ciliated OSNs during development. We can share these results with the reviewer upon request. Further studies are thus needed to clarify and describe the neuronal subpopulations and lineage relationships in the OP, but this detailed investigation is out of the scope and focus of the present study. 

An additional complication comes from the fact that, as shown and acknowledged by the authors in Miyasaka et al., 2005, the Tg(omp:meYFP) line (6kb promoter) labels ciliated OSNs in the rosette but also some unipolar, ventral neurons (around 10 neurons at 1 dpf, Miyasaka et al. 2005, Figure 3A, white arrowheads). This was also observed using the 2 kb promoter Tg(omp:meYFP) line (see for instance Miyasaka et al., 2007) and in our study, we can indeed detect these ventro-medial neurons labelled in the Tg(omp:meYFP) line (2 kb promoter), see for instance Figure 1C’, D’ or Movie 6. It is unclear whether these unipolar omp:meYFPpositive cells are pioneer neurons or EONs expressing the omp:meYFP transgene, or OSN progenitors that would be located basally/ventrally in the OP at these stages.

For all these reasons, we decided to present in the text the current view of neurogenesis in the OP but instead of attributing a definitive identity to the neurons we visualise with the transgenic lines, we prefer to mention them in the manuscript (and in the rest of the response to the reviewers) as neurons expressing neurog1:GFP or omp:meYFP transgenes (or cells/axons/neurons expressing RFP in the Tg(cldnb:Gal4; UAS:RFP) background).

What we also changed in the text to be more clear on this point:

- we moved higher up in the text, as suggested by reviewer 1, the description of the current model of neurogenesis in the OP,

- we mentioned that neurog1:GFP+ neurons are more numerous than the initially described pioneer neurons, as discussed in Madelaine et al., 2011,

- we wrote more clearly that the Tg(omp:meYFP) line labels ciliated OSNs but also a subset of unipolar, ventral neurons (Miyasaka et al., 2005), and pointed to these ventral neurons in Figure 1C’, D’,

- in the initial presentation of the current view of OP neurogenesis we renamed neurog1:GFP+ into EONs to be coherent with Madelaine et al., 2011.

- To visualise pioneer axons, the authors should use an EONS marker such as neurog1 because, to my knowledge, OMP only marks OSN axons and not pioneer axons.  

To visualise neurog1:GFP+ axons during OP coalescence, we performed live imaging upon injection of the neurog1:GFP plasmid (Blader et al., 2003) in the Tg(cldnb:Gal4; UAS:RFP) background (n = 4 mutants and n = 4 controls from 2 independent experiments). We observed some GFP+ placodal neurons exhibiting retrograde axon extension in both controls and sly mutants. In such experiments it is very difficult to quantify and compare the number of neurons/axons showing specific behaviours between different experimental conditions/genetic background. Indeed, due to the cytoplasmic localisation of GFP, the axons can only be seen in neurons expressing high levels of GFP, and due to the injection the number of such neurons varies a lot in between embryos, even in a given condition. Nevertheless, our qualitative observations reinforce the idea that the basement membrane is not absolutely required for mediolateral movements and retrograde axon extension of neurog1:GFP+ neurons in the OP. We added examples of images extracted from these new live imaging experiments in the revised Fig. S5A, B.

- The authors should analyse the presence of laminin in the OP and forebrain in conjunction with neural crest cell dynamics (using a Sox10 transgenic line for example) to refine their entry and exit point hypotheses. 

As described in the answer to the next point, we performed new experiments in which we visualised NCC migration in the Tg(neurog1:GFP) background, which allowed us to analyse the localisation of NCC at the forebrain/OP boundary, in ventral and dorsal positions, both in sly mutant embryos and control siblings.

- A dynamic analysis of the distribution of neural crest cells in the sly mutant over time and during OP coalescence would be important. 

The dynamics of zebrafish cranial NCC migration in the vicinity of the OP has been previously analysed using sox10 reporter lines (Harden et al., 2012, Torres-Paz and Whitlock, 2014, Bryan et al., 2020). To address the point raised by the reviewer, we performed live imaging from 16 to 32 hpf on sly mutants and control siblings carrying the Tg(neurog1:GFP) and Tg(UAS:RFP) transgenes and injected with a sox10(7.2):KalTA4 plasmid (Almeida et al., 2015). This allows the mosaic labelling of cells that express or have expressed sox10 during their development which, in the head region at these stages, represents mostly NCC and their derivatives. 3 independent experiments were carried out (n = 4 mutant embryos in which 8 placodes could be analysed; n = 6 control siblings in which 10 placodes could be analysed). A new movie (Movie 9) has been added to the revised article to show representative examples of control and mutant embryos.

From these new data, we could make the following observations:

- As expected from previous studies (Harden et al., 2012, Torres-Paz and Whitlock, 2014, Bryan et al., 2020), in control embryos a lot of NCC had already migrated to reach the vicinity of the OP when the movies begin at 16 hpf, and were then seen invading mainly the interface between the eye and the OP (10/10 placodes). Surprisingly, in sly mutants, a lot of motile NCC had also reached the OP region at 16 hpf in all the analysed placodes (8/8), and populated the eye/OP interface in 7/8 placodes (10/10 in controls). Counting NCC or tracking individual NCC during the whole duration of the movies was unfortunately too difficult to achieve in these movies, because of the low level of mosaicism (a high number of cells were labelled) and of the high speed of NCC movements (as compared with the 10 min delta t we chose for the movies). 

- in some of the control placodes we could detect a few NCC that populated the forebrain/OP interface, either ventrally, close to the exit point of the axons (4/10 placodes), or more dorsally (8/10 placodes). By contrast, in sly mutants, NCC were observed in the dorsal region of the brain/OP boundary in only 2/8 placodes, and in the ventral brain/OP frontier in only 2/8 placodes as well. Interestingly, in these 2 last samples, NCC that had initially populated the ventral region of the brain/OP interface were then expelled from the boundary at later stages.

We reported these observations in a new Table that is presented in revised Fig. S6B. In addition, instances of NCC migrating at the eye/OP or forebain/OP interfaces are indicated with arrowheads on Movie 9. Previous Figure S6 was splitted into two parts presenting NCC defects in sly mutants (revised Figure S6) and in foxd3 mutants (revised Figure S7).

Altogether, these new data suggest that the first postero-anterior phase of NCC migration towards the OP, as well as their migration in between the eye and OP tissues, is not fully perturbed in sly mutants. The subset of NCC that populate the OP/forebrain seem to be more specifically affected, as these NCC show defects in their migration to the interface or the maintenance of their position at the interface. Since the crestin marker labels mostly NCC at the OP/forebrain interface at 32 hpf (revised Fig. S6A), this could explain why the crestin ISH signal is almost lost in sly mutants at this stage.

(2) Laminin distribution suggests a role in olfactory axon development 

"Laminin 111 immunostaining revealed local disruptions in the membrane enveloping the OP and brain, precisely where YFP+ axons exit the OP (exit point) and enter the brain (entry point) (Fig. 1C-D')." Can the authors quantify this situation? It would be important to analyse this behaviour on the scale of a neuron and thus axonal migration to strengthen the hypotheses. 

As suggested by the reviewer, to better visualise individual axons at the exit and entry point, we used mosaic red labelling of OP axons. To achieve this sparse labelling, we took advantage of the mosaic expression of a red fluorescent membrane protein observed in the Tg(cldnb:Gal4; UAS:lyn-TagRFP) background. The unpublished Tg(UAS:lyn-TagRFP) line was kindly provided by Marion Rosello and Shahad Albadri from the lab of Filippo Del Bene. We crossed the Tg(cldnb:Gal4; UAS:lyn-TagRFP) line with the TgBAC(lamC1:lamC1-sfGFP) reporter and performed live imaging on 2 embryos/4 placodes, in a frontal view. A new movie (Movie 3 in the revised article) shows examples of exit and entry point formation in this context.This allowed us to visualise the formation of the exit and entry points in more samples (6 embryos and 12 placodes in total when we pool the two strategies for labelling OP axons) and through the visualisation of a small number of axons, and reinforce our initial conclusions. 

(3) The integrity of BMs around the brain and the OP is affected in the sly mutant 

Why do the authors analyse the distribution of collagen IV and Nidogen and not proteoglycans and heparan sulphate? 

We attempted to label more ECM components such as proteoglycans and heparan sulfate, but whole-mount immunostainings did not work in our hands.

A dynamic analysis of the distribution of neural crest cells in the sly mutant over time and during OP coalescence would be important. 

See our detailed response to this point above.  

(4) Role of Laminin γ1-dependent BMs in OP coalescence 

The authors use the size of the Tg(neurog1:GFP)+ OP cell cluster at 22 hpf as a marker.  The authors should count the number of cells in the OP at the indicated time using a nuclear dye to check that in the sly mutant the number of cells is the same over time. Two time points as analysed in Figure S2 may not be sufficient to quantify proliferation which at these stages should be almost zero according to Whitlock & Westerfield and Madelaine et al.

Counting the neurog1:GFP+ cell numbers in our existing data was unfortunately impossible, due to the poor quality of the DAPI staining. We are nevertheless confident that the number of cells within neurog1:GFP+ clusters is fairly similar between controls and sly mutants at 22 hpf, since the OP dimensions are the same for AP and DV dimensions, and only slightly different for the ML dimension. In addition, we analysed proliferation and apoptosis within the neurog1:GFP+ cluster at 16 and 21 hpf and observed no difference between controls and mutants.

(5) Role of Laminin γ1-dependent BMs during the forebrain flexure 

In Figure 4F at 32hpf, the presence of 77% ectopic OMP+ cells medially should result in an increase in dimensions along the M-L? This is not the case in the article. The authors should clarify this point. 

As we explained in the Material and Methods, ectopic fluorescent cells (cells that are physically separated from the main cluster) were not taken into account for the measurement of the OP dimensions. This is now also also mentioned in the legends of the Figures (4 and S3) showing the quantifications of OP dimensions.

Cell distribution also seems to be affected within the OMP+ cluster at 36hpf, with fewer cells laterally and more medially. The authors should analyse the distribution of OMP+ cells in the clusters. in sly mutants and controls to understand whether the modification corresponds to the absence of BM function. 

On the pictures shown in Figure 4F,G, we agree that omp:meYFP+ cells appear to be more medially distributed in the mutant, however this is not the case in other sections or samples, and is rather specific to the z-section chosen for the Figure. We found that the ML dimension is unchanged in mutants as compared with controls, except for the 28 hpf stage where it is smaller, but this appears to be a transient phenomenon, since no change is detected at earlier or later stages (Figure 4A-D and Figure S3A-L). The difference we observe at 28 hpf is now mentioned in the revised manuscript.

The conclusions of Figures 4 and S3 would rather be that laminin allows OMP+ cells to be oriented along the medio-lateral axis whereas it would control their position along the dorsoventral axis. The authors should modify the text. It would be useful to map the distribution of OMP+ cells along the dorsoventral and mediolateral axes. The same applies to Neurog1+ cells. An analysis of skin cell movements, for example, would be useful to determine whether the effects are specific.  

We are confident that the measurements of OP dimensions in AP, DV and ML are sufficient to describe the OP shape defects observed in the sly mutants. Analysing cell distribution along the 3 axes as well as skin cell movements will be interesting to perform in the future but we consider these quantifications as being out of the scope of the present work.

(6) Laminin γ1-dependent BMs are required to define a robust boundary between the OP and the brain 

The authors must weigh this conclusion "Laminin γ1-dependent BMs serve to establish a straight boundary between the brain and OP, preventing local mixing and late convergence of the two OPs towards each other during flexion movement." Indeed, they don't really show any local mixing between the brain and OP cells. They would need to quantify in their images (Figure 5A-A' and Figure S4 A-A') the percentage of cells co-labelled by HuC and Tg(cldnb:GFP). 

We agree with the reviewer and thus replaced « reveal » by « suggest » in the conclusion of this section. 

(7) Role of Laminin γ1-dependent BMs in olfactory axon development 

An analysis of the retrograde extension movement in the axons of OMP+ ectopic neurons in the sly1 mutant condition would be useful to validate that the loss of laminin function does not play a role in this event. 

Indeed, even though we can visualise instances of retrograde extension occurring normally in sly mutants, we can not rule out that this process is affected in a subset of OP neurons, for instance in ectopic cells, which often show no axon or a misoriented axon. We added a sentence to mention this in the revised manuscript.

Minor comments and typos: 

Please check and mention the D-V/L-M or A-P/L-M orientation of the images in all figures. 

This has been checked.

Legend Figure 1: "distalmost" is missing a space "distal most". 

We checked and this word can be written without a space.

Figure 1 panel C: check the orientation (I am not sure that Dorsal is up). 

We double-checked and confirm that dorsal is up in this panel.

Movie 1 Legend: "aroung "the OP should be around the OP. 

Thanks to the reviewer for noticing the typo, we corrected it.

Reviewer #2 (Recommendations For The Authors):

The comments below are relatively minor and mostly raise questions regarding images and their presentation in the manuscript. 

• Figure 1, visualization of exit and entry points: It is a bit difficult to visualize the axon exit and entry points in these images, and in particular, to understand how the exit and entry points in C and D correspond to what is seen in F, F', H, and H'. There appears to be one resolvable break in the staining in C and D, whereas there are two distinct breaks in F-H'. Are these single optical sections? Is it possible to visualize these via 3-dimensional rendering? 

All the images presented in Figure 1 are single z-sections, which is now indicated in the Figure legend. As noticed by the reviewer, Laminin immunostainings on fixed embryos at 28 and 36 hpf suggested that the exit and entry points are facing each other, as shown in Figure 1C-D’. However, in our live imaging experiments we always observed that the exit point is slightly more ventral than the entry point (of about 10 to 20 µm). This discrepancy could be due to the fixation that precedes the immunostaining procedure, which could modify slightly the size and shape of cells/tissues. We added a sentence on this point in the text. In addition, we added new movies of the LamC1-sfGFP reporter with sparse red axonal labelling (Movie 3, see response to reviewer 1), as well as z-stacks presenting the organisation of exit and entry points in 3D (Movie 4), which should help to better illustrate the mechanisms of exit and entry point formation.

• Movie 2, p. 6, "small interruptions of the BM were already present near the axon tips, along the ventro-medial wall of the OP." This is a bit difficult to assess since the movie seems to show at least one other small interruption in the BM in addition to the exit point, in particular, one slightly dorsal to the exit point. Was this seen in other samples, or in different optical sections? 

Indeed the exit and entry points often appear as regions with several, small BM interruptions, rather than single holes in the BM. We now show in revised Movie 4 the two z-stacks (the merge and the single channel for green fluorescence) corresponding to the last time points of the movies showing exit and entry point formation in Movie 2, where several BM interruptions can be seen for both the exit and entry points. We had already mentioned this observation in the legend of Movie 2, and we added a sentence on this point in the main text of the revised manuscript. This is also represented for both exit and entry points in the new schematics in revised Fig. 1K and its legend. 

• Movie 2, p. 6, "The opening of the entry point through the brain BM was concomitant with the arrival of the RFP+ axons, suggesting that the axons degrade or displace BM components to enter the brain." Similar to the questions regarding the exit point, it was a bit difficult to evaluate this statement. There appears to be a broader region of BM discontinuity more dorsal to the arrowhead in Movie 2. A single-channel movie of just the laminin fluorescence might help to convey the extent of the discontinuity. As with above, was this seen in other samples, or in different optical sections?  

See our response to the previous comment.

• Figure 1H, I, "the distal tip of the RFP+ axons migrated in close proximity with the brain's BM." This is again a bit difficult to see, and quite different than what is seen in Figure 4A, in which the axons do not seem close to the BM in this section. Is it possible to visualize this via 3-dimensional rendering? 

In fixed embryos or in live imaging experiments, we observed that, once entered in the brain, the distal tips (the growth cones) of the axons are located close to the BM of the brain. However, this is not the case of the axon shafts which, as development proceeds, are located further away from the BM. This can clearly be seen at 36 hpf in Figure 1D’ and Figure 4A, as spotted by the reviewer. We modified the text to clarify this point.

• Figure 2J, J', p. 7, the gap between the OP and brain cells of sly mutants "was most often devoid of electron-dense material." It is difficult to see this loss of electron-dense material in 2J'. The thickness of the space is quantified well and is clearly smaller, but the change in electron-dense material is more difficult to see.  

We looked at Figure 2 again and it seems clear to us that there is electron-dense material between the plasma membranes in controls, which is practically not seen (rare spots) in the mutants. We added a sentence mentioning that we rarely see electron-dense spots in sly mutants.

• Figure 5E-F': There are concerns about evaluating the shape of a tissue based on nuclear position. Is there a way to co-stain for cell boundaries (maybe actin?), and then quantify distortion of the dlx+ cell population using the cell boundaries, rather than nuclear staining? 

We agree with the reviewer that it is not ideal to evaluate the shape of the OP/brain boundary based on a nuclear staining. As explained in the text, we could not use the Tg(eltC:GFP) or Tg(cldnb:Gal4; UAS:RFP) reporter lines for this analysis, due to ectopic or mosaic expression. However we are confident that the segmentation of the Dlx3b immunostaining reflects the organisation of the cells at the OP/brain tissue boundary: in other data sets in which we performed Dlx3b staining with membrane labelling independently of the present study and in the wild type context, we clearly see that cell membranes are juxtaposed to the Dlx3b nuclear staining (in other words, the cytoplasm volume of OP cells is very small). 

• Figure S5E: It would be helpful to see representative images for each of the categories (Proper axon bundle; Ventral projections; Medial projections) or a schematic to understand how the phenotypes were assessed. 

To address this point we added a schematic view to illustrate the phenotypes assessed in each column of the table in revised Figure S5G.

• Figure 6, p. 12, "Laminin gamma 1-dependent BMs are essential for growth and navigation of the axons...": What fraction of the tracked axons managed to exit the OP? Given the quantitative analyses in Figure 6, one might interpret this to mean that laminin gamma 1 is not essential for axon growth (speed and persistence are largely unchanged), but rather, primarily for navigation. 

As noticed by the reviewer, the speed and persistence of axonal growth cones are largely unchanged in the sly mutants (except for the reduced persistence in the 200-400 min window, and an increased speed in the 800-1000 min window), showing that the growth cones are still motile. However, as shown by the tracks, they tend to wander around within the OP, close to the cell bodies, which results in the end in a perturbed growth of the axons. The navigation issues are rather revealed by the analysis of fixed Tg(omp:meYFP) embryos presented in the table of Figure S5G. We modified the text to separate more clearly the conclusions of the two types of experiments (fixed, transgenic embryos versus live, mosaically labelled embryos).

Reviewer #3 (Recommendations For The Authors):

Testing the hypotheses mentioned in the public review will be interesting experiments for a follow-up study, but are not essential revisions for this manuscript. 

I have only a few minor suggestions for revisions: 

P8 subheading 'Role of Laminin γ1-dependent BMs in OP coalescence' - since no major role was demonstrated here, this heading should be reworded.  

We agree with the reviewer and replaced the previous title by « OP coalescence still occurs in the sly mutant ».

P11, line 3 - the authors conclude that the forebrain is smaller 'due to' the inward convergence of the OPs. I do not think it is possible to assign causation to this when the mutant disrupts Laminin γ1 systemically - it is equally possible that the OPs move inward due to a failure of the brain to form in the normal shape. Thus, the wording should be changed here. (In the Discussion on p15, the authors mention the 'apparent distortion' of the brain, and say that it is 'possibly due' to the inward migration of the placodes', but again this could be toned down.) 

We agree with the reviewer’s comment and changed the wording of our conclusions in the Results section.

P11 and Fig. S5 - The table and text seem to be saying opposite things here. The text on p11 (3rd paragraph) indicates that the normal exit point is ventral and that this is disrupted in the mutant, with axons exiting dorsally. However, in the table, at each time point there is a higher % of axons exiting ventrally in the mutant. Please clarify. The table does not provide a % value for axons exiting dorsally - it might help to add a column to show this value. 

We are grateful to the reviewer for pointing this out, and we apologize for the lack of clarity in the first version of the manuscript. We have modified the text and Figure S5 in order to clarify the different points raised by the reviewer in this comment. The Table in Fig. S5G does not represent the % of axons showing defects, but the % of embryos showing the phenotypes. In addition, an embryo is counted in the ventral or medial projection category if it shows at least one ventral or medial projection (even if its shows a proper bundle). This is now clearly indicated in the title of the columns in the table itself and in the legend. The embryos in which the axons exit dorsally in sly mutants are actually those counted in the left column of the Table (they exit dorsally and form a bundle), as shown by the new schematics added below the table. We also added this information in the title of the left column, and mention in the legend the pictures in which this dorsal exit can be observed in the article (Figures 4B and S3E’). Having more sly mutant embryos with axons exiting dorsally is thus compatible with more embryos showing at least one ventral projection.

Fig. S6, shows the lack of neural crest cells between the olfactory placode and the brain in both laminin γ1 mutants (without a basement membrane) and foxd3 mutants (which retain the membrane). Comparison of the two mutants here is a neat experiment and the result is striking, demonstrating that it is the basement membrane, and not the neural crest, that is required for correct morphology of the olfactory placode. I think this figure should be presented as a main figure, rather than supplementary.  

Our new live imaging characterisation of NCC migration in sly mutants and control siblings (Movie 9) revealed that at 32 hpf, in the vicinity of the OP, NCC (or their derivatives) are much more numerous than the subset of NCC showing crestin expression by in situ hybridisation (compare the end of our control movie – 32 hfp, with crestin ISH shown in Figure S6A for instance). 

Thus, the extent of the NCC migration defects should be analysed in more detail in the foxd3 mutant in the future (using live imaging or other NCC markers), and for this reason we chose to keep this dataset in the supplementary Figures.

One of the first topics covered in the Discussion section is the potential role of Collagen. I was surprised to see the description on P15 'the dramatic disorganization of the Collagen IV pattern observed by immunofluorescence in the sly mutant', as I hadn't picked this up from the Results section of the paper. I went back to the relevant figure (Fig. 2) and description on p7, which does not give the same impression: 'in sly mutants, Collagen IV immunoreactivity was not totally abolished'. This suggested to me that there was only minor (not dramatic) disorganisation of the Collagen IV. This needs clarification.  

The linear, BM-like Collagen IV staining was lost in sly mutants, but not the fibrous staining which remained in the form of discrete patches surrounding the OP. We modified the text in the Results section as well as in the Figure 2 legend to clarify our observations made on embryos immunostained for Collagen IV.

Typos etc 

P5 - '(ii) above of the neuronal rosette' - delete the word 'of'. 

P5 two lines below this - ensheathed. 

P10 - '3 distinct AP levels' (delete s from distincts). 

P10 - distortion (not distorsion) . 

P12 - 'From 14 hpf, they' should read 'From 14 hpf, neural crest cells'. 

P15, line 1 - 'is a consequence of' rather than 'is consecutive of'? 

P22 'When the data were not normal,' should read 'When the data were not normally distributed,'. 

We thank the reviewer for noticing these typos and have corrected them.

General 

Please number lines in future manuscripts for ease of reference. 

This has been done.

Ralph Waldo Emerson, Stephen E. Whicher (1959). “The Early Lectures of Ralph Waldo Emerson”, p.327, Harvard University Press

via https://www.azquotes.com/author/4490-Ralph_Waldo_Emerson/tag/hate

Most of the second half of John Smith account talks about trade relations and exploratory missions between various indigenous tribes in the area. (Smith, John “A True Relation of Such Occurrences) Because of this, there wasn't too much to compare to the movie. For one, the movie is short and I can understand why a lot of the mundane time that is spent setting up a colony wasn't included. ("Goldberg, Eric, and Mike Gabriel. 1995. Pocahontas") The movie also deviates so far from this, and other scholarly sources, that there isn't much to compare. Overall, this source is heavily biased with a fair bit of exaggeration, that modern day historians know not to be true. At the same time, half of what John Smith talks about is a gateway into what life was like in the early days of Jamestown. It talks about disease, conditions of the settlement, various indigenous tribes (which the rest of the world knew relatively little about), the land and new sources of food which was an important source at the time it was published. (Smith, John “A True Relation of Such Occurrences) While Disney's Pocahontas isn't that close to this primary source, nor is it to other sources detailing this part of history, it does a good job in respecting Native American culture. There weren't a lot of films depicting Native Americans in a positive or appropriate light before this movie was made. While there is still shockingly low number of films that tell Native American stories, Pocahontas I think was a small catalyst for future indigenous movies due to its commercial success.

Bibliography: “A True Relation of Such Occurrences and Accidents of Note as Hath Hapned in Virginia Since the First Planting of That Colony, Which Is Now Resident in the South Part Thereof, till the Last Returne from Thence. Written by Captaine Smith One of the Said Collony, to a Worshipfull Friend of His in England.,” April 13, 2005. http://web.archive.org/web/20050413081941/http://etext.lib.virginia.edu/etcbin/toccer-new2?id=J1007.xml&images=images/modeng&data=/texts/english/modeng/parsed&tag=public&part=all.

Goldberg, Eric, and Mike Gabriel. 1995. Pocahontas. United States: Buena Vista Pictures.

reply to @ayjay @annie at https://micro.blog/ayjay/48571448

@ayjay I've long presumed you were digitally commonplacing by means of your websites, so thanks for laying out some of your specific current thoughts on the process.

​@Annie I've been collecting examples of bloggers who are using their personal websites as commonplaces, zettelkasten, and "thought spaces" at https://indieweb.org/commonplace_book. If you're interested, you'll also find examples and details to explore in my own digital commonplace. "Thought spaces" is an interesting entry point.

Doctorow goes through more of his process in which he's saving both his "box of delights" and the refashioning of them into longer pieces in "20 years a blogger". In his case it's (now) all done on Pluralistic and syndicated out from there, in much the same way @dave has done for years.

No tag

Was the cytoplasmic background also brighter here (not just the localized signal)? I'm just naively wondering if the achilles tag is causing some stress that is increasing background autofluorescence in the cell (given the onset of uniform cytoplasmic fluorescence in Fig 1).

Reviewer #1 (Public review):

Summary:

The authors describe a method to probe both the proteins associated with genomic elements in cells, as well as 3D contacts between sites in chromatin. The approach is interesting and promising, and it is great to see a proximity labeling method like this that can make both proteins and 3D contacts. It utilizes DNA oligomers, which will likely make it a widely adopted method. However, the manuscript over-interprets its successes, which are likely due to the limited appropriate controls, and of any validation experiments. I think the study requires better proteomic controls, and some validation experiments of the "new" proteins and 3D contacts described. In addition, toning down the claims made in the paper would assist those looking to implement one of the various available proximity labeling methods and would make this manuscript more reliable to non-experts.

Strengths:

(1) The mapping of 3D contacts for 20 kb regions using proximity labeling is beautiful.

(2) The use of in situ hybridization will probably improve background and specificity.

(3) The use of fixed cells should prove enabling and is a strong alternative to similar, living cell methods.

Weaknesses:

(1) A major drawback to the experimental approach of this study is the "multiplexed comparisons". Using the mtDNA as a comparator is not a great comparison - there is no reason to think the telomeres/centrosomes would look like mtDNA as a whole. The mito proteome is much less complex. It is going to provide a large number of false positives. The centromere/telomere comparison is ok, if one is interested in what's different between those two repetitive elements. But the more realistic use case of this method would be "what is at a specific genomic element"? A purely nuclear-localized control would be needed for that. Or a genomic element that has nothing interesting at it (I do not know of one). You can see this in the label-free work: non-specific, nuclear GO terms are enriched likely due to the random plus non-random labeling in the nucleus. What would a Telo vs general nucleus GSEA look like? (GSEA should be used for quantitative data, no GO). That would provide some specificity. Figures 2G and S4A are encouraging, but a) these proteins are largely sequestered in their respective locations, and b) no validation by an orthogonal method like ChIP or Cut and Run/Tag is used.

You can also see this in the enormous number of "enriched" proteins in the supplemental volcano plots. The hypothesis-supporting ones are labeled, but do the authors really believe all of those proteins are specific to the loci being looked at? Maybe compared to mitochondria, but it's hard to believe there are not a lot of false positives in those blue clouds. I believe the authors are more seeing mito vs nucleus + Telo than the stated comparison. For example, if you have no labeling in the nucleus in the control (Figures 1C and 2C) you cannot separate background labeling from specific labeling. Same with mito vs. nuc+Telo. It is not the proper control to say what is specifically at the Telo.

I would like to see a Telo vs nuclear control and a Centromere vs nuc control. One could then subtract the background from both experiments, then contrast Telo vs Cent for a proper, rigorous comparison. However, I realize that is a lot of work, so rewriting the manuscript to better and more accurately reflect what was accomplished here, and its limitations, would suffice.

(2) A second major drawback is the lack of validation experiments. References to literature are helpful but do not make up for the lack of validation of a new method claiming new protein-DNA or DNA-DNA interactions. At least a handful of newly described proximal proteins need to be validated by an orthogonal method, like ChIP qPCR, other genomic methods, or gel shifts if they are likely to directly bind DNA. It is ok to have false positives in a challenging assay like this. But it needs to be well and clearly estimated and communicated.

(3) The mapping of 3D contacts for 20 kb regions is beautiful. Some added discussion on this method's benefits over HiC-variants would be welcomed.

(4) The study claims this method circumvents the need for transfectable cells. However, the authors go on to describe how they needed tons of cells, now in solution, to get it to work. The intro should be more in line with what was actually accomplished.

(5) Comments like "Compared to other repetitive elements in the human genome...." appear to circumvent the fact that this method is still (apparently) largely limited to repetitive elements. Other than Glopro, which did analyze non-repetitive promoter elements, most comparable methods looked at telomeres. So, this isn't quite the advancement you are implying. Plus, the overlap with telomeric proteins and other studies should be addressed. However, that will be challenging due to the controls used here, discussed above.

Author response:

Public Reviews:

Reviewer #1 (Public review):

Summary:

The authors describe a method to probe both the proteins associated with genomic elements in cells, as well as 3D contacts between sites in chromatin. The approach is interesting and promising, and it is great to see a proximity labeling method like this that can make both proteins and 3D contacts. It utilizes DNA oligomers, which will likely make it a widely adopted method. However, the manuscript over-interprets its successes, which are likely due to the limited appropriate controls, and of any validation experiments. I think the study requires better proteomic controls, and some validation experiments of the "new" proteins and 3D contacts described. In addition, toning down the claims made in the paper would assist those looking to implement one of the various available proximity labeling methods and would make this manuscript more reliable to non-experts.

Strengths:

(1) The mapping of 3D contacts for 20 kb regions using proximity labeling is beautiful.

(2) The use of in situ hybridization will probably improve background and specificity.

(3) The use of fixed cells should prove enabling and is a strong alternative to similar, living cell methods.

Weaknesses:

(1) A major drawback to the experimental approach of this study is the "multiplexed comparisons". Using the mtDNA as a comparator is not a great comparison - there is no reason to think the telomeres/centrosomes would look like mtDNA as a whole. The mito proteome is much less complex. It is going to provide a large number of false positives. The centromere/telomere comparison is ok, if one is interested in what's different between those two repetitive elements. But the more realistic use case of this method would be "what is at a specific genomic element"? A purely nuclear-localized control would be needed for that. Or a genomic element that has nothing interesting at it (I do not know of one). You can see this in the label-free work: non-specific, nuclear GO terms are enriched likely due to the random plus non-random labeling in the nucleus. What would a Telo vs general nucleus GSEA look like? (GSEA should be used for quantitative data, no GO). That would provide some specificity. Figures 2G and S4A are encouraging, but a) these proteins are largely sequestered in their respective locations, and b) no validation by an orthogonal method like ChIP or Cut and Run/Tag is used.

You can also see this in the enormous number of "enriched" proteins in the supplemental volcano plots. The hypothesis-supporting ones are labeled, but do the authors really believe all of those proteins are specific to the loci being looked at? Maybe compared to mitochondria, but it's hard to believe there are not a lot of false positives in those blue clouds. I believe the authors are more seeing mito vs nucleus + Telo than the stated comparison. For example, if you have no labeling in the nucleus in the control (Figures 1C and 2C) you cannot separate background labeling from specific labeling. Same with mito vs. nuc+Telo. It is not the proper control to say what is specifically at the Telo.

I would like to see a Telo vs nuclear control and a Centromere vs nuc control. One could then subtract the background from both experiments, then contrast Telo vs Cent for a proper, rigorous comparison. However, I realize that is a lot of work, so rewriting the manuscript to better and more accurately reflect what was accomplished here, and its limitations, would suffice.

(2) A second major drawback is the lack of validation experiments. References to literature are helpful but do not make up for the lack of validation of a new method claiming new protein-DNA or DNA-DNA interactions. At least a handful of newly described proximal proteins need to be validated by an orthogonal method, like ChIP qPCR, other genomic methods, or gel shifts if they are likely to directly bind DNA. It is ok to have false positives in a challenging assay like this. But it needs to be well and clearly estimated and communicated.

(3) The mapping of 3D contacts for 20 kb regions is beautiful. Some added discussion on this method's benefits over HiC-variants would be welcomed.

(4) The study claims this method circumvents the need for transfectable cells. However, the authors go on to describe how they needed tons of cells, now in solution, to get it to work. The intro should be more in line with what was actually accomplished.

(5) Comments like "Compared to other repetitive elements in the human genome...." appear to circumvent the fact that this method is still (apparently) largely limited to repetitive elements. Other than Glopro, which did analyze non-repetitive promoter elements, most comparable methods looked at telomeres. So, this isn't quite the advancement you are implying. Plus, the overlap with telomeric proteins and other studies should be addressed. However, that will be challenging due to the controls used here, discussed above.

We thank the Reviewer for their careful reading of manuscript and constructive suggestions. We plan to substantially revise the framing and presentation of manuscript to address the concerns raised by all three reviewers.

Reviewer #2 (Public review):

Summary

Liu and MacGann et al. introduce the method DNA O-MAP that uses oligo-based ISH probes to recruit horseradish peroxidase for targeted proximity biotinylation at specific DNA loci. The method's specificity was tested by profiling the proteomic composition at repetitive DNA loci such as telomeres and pericentromeric alpha satellite repeats. In addition, the authors provide proof-of-principle for the capture and mapping of contact frequencies between individual DNA loop anchors.

Strengths

Identifying locus-specific proteomes still represents a major technical challenge and remains an outstanding issue (1). Theoretically, this method could benefit from the specificity of ISH probes and be applied to identify proteomes at non-repetitive DNA loci. This method also requires significantly fewer cells than other ISH- or dCas9-based locus-enrichment methods. Another potential advantage to be tested is the lack of cell line engineering that allows its application to primary cell lines or tissue.

Weaknesses

The authors indicate that DNA O-MAP is superior to other methods for identifying locus-specific proteomes. Still, no proof exists that this method could uncover proteomes at non-repetitive DNA loci. Also, there is very little validation of novel factors to confirm the superiority of the technique regarding specificity.

The authors first tested their method's specificity at repetitive telomeric regions, and like other approaches, expected low-abundant telomere-specific proteins were absent (for example, all subunits of the telomerase holoenzyme complex). Detecting known proteins while identifying noncanonical and unexpected protein factors with high confidence could indicate that DNA O-MAP does not fully capture biologically crucial proteins due to insufficient enrichment of locus-specific factors. The newly identified proteins in Figure 1E might still be relevant, but independent validation is missing entirely. In my opinion, the current data cannot be interpreted as successfully describing local protein composition.

Finally, the authors could have discussed the limitations of DNA O-MAP and made a fair comparison to other existing methods (2-5). Unlike targeted proximity biotinylation methods, DNA O-MAP requires paraformaldehyde crosslinking, which has several disadvantages. For instance, transient protein-protein interactions may not be efficiently retained on crosslinked chromatin. Similarly, some proteins may not be crosslinked by formaldehyde and thus will be lost during preparation (6).

(1) Gauchier M, van Mierlo G, Vermeulen M, Dejardin J. Purification and enrichment of specific chromatin loci. Nat Methods. 2020;17(4):380-9.

(2) Dejardin J, Kingston RE. Purification of proteins associated with specific genomic Loci. Cell. 2009;136(1):175-86.

(3) Liu X, Zhang Y, Chen Y, Li M, Zhou F, Li K, et al. In Situ Capture of Chromatin Interactions by Biotinylated dCas9. Cell. 2017;170(5):1028-43 e19.

(4) Villasenor R, Pfaendler R, Ambrosi C, Butz S, Giuliani S, Bryan E, et al. ChromID identifies the protein interactome at chromatin marks. Nat Biotechnol. 2020;38(6):728-36.

(5) Santos-Barriopedro I, van Mierlo G, Vermeulen M. Off-the-shelf proximity biotinylation for interaction proteomics. Nat Commun. 2021;12(1):5015.

(6) Schmiedeberg L, Skene P, Deaton A, Bird A. A temporal threshold for formaldehyde crosslinking and fixation. PLoS One. 2009;4(2):e4636.

We thank the Reviewer for their constructive feedback on our work. As noted above, we plan to substantially revise the framing and presentation of manuscript to address the concerns raised by all three reviewers.

Reviewer #3 (Public review):

Significance of the Findings:

The study by Liu et al. presents a novel method, DNA-O-MAP, which combines locus-specific hybridisation with proximity biotinylation to isolate specific genomic regions and their associated proteins. The potential significance of this approach lies in its purported ability to target genomic loci with heightened specificity by enabling extensive washing prior to the biotinylation reaction, theoretically improving the signal-to-noise ratio when compared with other methods such as dCas9-based techniques. Should the method prove successful, it could represent a notable advancement in the field of chromatin biology, particularly in establishing the proteomes of individual chromatin regions - an extremely challenging objective that has not yet been comprehensively addressed by existing methodologies.

Strength of the Evidence:

The evidence presented by the authors is somewhat mixed, and the robustness of the findings appears to be preliminary at this stage. While certain data indicate that DNA-O-MAP may function effectively for repetitive DNA regions, a number of the claims made in the manuscript are either unsupported or require further substantiation. There are significant concerns about the resolution of the method, with substantial biotinylation signals extending well beyond the intended target regions (megabases around the target), suggesting a lack of specificity and poor resolution, particularly for smaller loci. Furthermore, comparisons with previous techniques are unfounded since the authors have not provided direct comparisons with the same mass spectrometry (MS) equipment and protocols. Additionally, although the authors assert an advantage in multiplexing, this claim appears overstated, as previous methods could achieve similar outcomes through TMT multiplexing. Therefore, while the method has potential, the evidence requires more rigorous support, comprehensive benchmarking, and further experimental validation to demonstrate the claimed improvements in specificity and practical applicability.

We thank the Reviewer for providing detailed critiques of our manuscript. As noted above, we plan to substantially revise the framing and presentation of manuscript to address the concerns raised by all three reviewers.

Reviewer #3 (Public review):

Summary:

The study explores the cellular and circuit features that distinguish dentate gyrus semilunar granule cells and granule cells activated during contextual memory formation. The authors tag memory and enriched environment-activated dentate granule cells and semilunar granule cells and show their reactivation in an appropriate context a week later. They perform patch clamp recordings from activated and surrounding neurons to understand cellular driving the selective activation of semilunar granule cells and granule cells. Authors perform dual patch clamp recordings from various pairs of labeled semilunar granule cells, labeled granule cells, unlabeled granule cells, and unlabeled semilunar granule cells. The sustained firing of semilunar granule cells explained their preferential activation. In addition, activated neurons received correlated inputs.

Strengths:

The authors confirmed engram cell properties of activated semilunar granule cells and granule cells in two different paradigms, validated using an enriched environment paradigm.

The authors carefully separate semilunar granule cells from granule cells, using electrophysiology and morphology. Cell filling to confirm morphology further strengthens confidence.

The dual patch recordings, which are technically challenging, are carefully performed, and the presence of synaptic activity is confirmed.

Finally, the correlation analysis of EPSCs on labeled neurons is rigorous.

Weaknesses:

(1) Engram cells are (i) activated by a learning experience, (ii) physically or chemically modified by the learning experience, and (iii) reactivated by subsequent presentation of the stimuli present at the learning experience (or some portion thereof), resulting in memory retrieval. The authors show that exposure to Barnes Maze and the enriched environment-activated semilunar granule cells and granule cells preferentially in the superior blade of the dentate gyrus, and a significant fraction were reactivated on re-exposure. However, physical or chemical modification by experience was not tested. Experience modifies engram cells, and a common modification is the Hebbian, i.e., potentiation of excitatory synapses. The authors recorded EPSCs from labeled and unlabeled GCs and SGCs. Was there a difference in the amplitude or frequency of EPSCs recorded from labeled and unlabeled cells?

(2) The authors studied five sequential sections, each 250 μm apart across the septotemporal axis, which were immunostained for c-Fos and analyzed for quantification. Is this an adequate sample? Also, it would help to report the dorso-ventral gradient since more engram cells are in the dorsal hippocampus. Slices shown in the figures appear to be from the dorsal hippocampus.

(3) The authors investigated the role of surround inhibition in establishing memory engram SGCS and GCs. Surprisingly, they found no evidence of lateral inhibition in the slice preparation. Interneurons, e.g., PV interneurons, have large axonal arbors that may be cut during slicing. Similarly, the authors point out that some excitatory connections may be lost in slices. This is a limitation of slice electrophysiology.

Reviewer #1 (Public review):

In this manuscript, Sun et al report the development of a POST-IT (Pup-On-target for Small molecule Target Identification Technology) approach for drug target identification. Generally, this new technology applies a non-diffusive proximity tagging system by utilizing an engineered fusion of proteasomal accessory factor A (PafA) and HaloTag to transfer prokaryotic ubiquitin-like protein (Pup) to proximal proteins upon directly binding to the small molecule. After the pupylated targets are captured, they are able to be detected by mass spectrometry. Significant optimization (Lys-Arg and other mutations) was conducted to eliminate the interference of self-pupylation, polypupylation, and depupylation, POST-IT was successfully applied for the target identification of 2 well-known drugs: dasatinib and hydroxychloroquine, which yielded SEPHS2 and VPS37C as their new potential targets, respectively. Furthermore, POST-IT was also applied in live zebrafish embryos, highlighting its potential for broad biological research and drug development.

This work was well designed and the experiments were logically conducted. The solid results support POST-IT as a promising technology for new drug target identification.

Weakness and limitations:

(1) The technology requires a halo-tagged derivation of the active compound, and the linked position will have a huge impact on the potential "target hits" of the molecules. Given the fact that most of the active molecules lack of structure-activity relationship information, it is very challenging to identify the optimal position of the halo tag linkage.

(2) Although POST-IT works in zebrafish embryos, there is still a long way to go for the broad application of the technology in other animal models.

(3) The authors identified SEPHS2 as a new potential target of dasatinib and further validated the direct binding of dasatinib with this protein. However, considering the super strong activity of dasatinib against c-Src (sub nanomolar IC50 value), it is hard to conclude the contribution of SEPHS2 binding (micromolar potency) to its antitumor activity.

Technical Feedback (8/20)

HTML Structure (3/5)

• ✓ Good heading variety (h1-h4)
• ✗ Structural issues:
• <h3> nested inside <h3>
• Invalid custom tags (<p1>, <p2>)
• ✓ Links properly embedded
• ⚠ Image implementation:
• Alt attributes present
• Some invalid URLs (\ddd)
• Inconsistent sources

CSS Implementation (4/10)

• ✗ Missing IDs completely
• ✗ No class usage
• ✗ Style issues:
• Invalid selectors (p1, p2)
• Redundant background declarations
• Overuse of inline styles
• ⚠ Limited styling scope:
• Basic font sizing
• Simple background images
• Needs more detailed styling

Code Quality (1/5)

• ✗ Organization problems:
• Excessive <br> tags
• Missing closing tags
• Poor indentation
• ✗ Invalid elements:
• Custom paragraph tags
• Broken image sources
• Improper tag nesting

Priority Fixes:

1. Fix HTML structure:
2. Replace <p1>, <p2> with <p>
3. Correct heading hierarchy
4. Add proper closing tags

5. Fix image URLs

6. Improve CSS:

7. Add 5+ unique IDs
8. Create reusable classes
9. Remove redundant styles

10. Replace inline styling

11. Clean up code:

12. Replace <br> with margins/padding
13. Improve indentation
14. Fix tag nesting

Your content structure shows promise, but needs technical cleanup. Focus first on using valid HTML elements and building a proper CSS structure with IDs and classes.

Technical Feedback (18/20)

HTML Structure (5/5)

• ✓ Excellent heading hierarchy (h1-h3)
• ✓ Well-organized paragraphs
• ✓ Links properly implemented:
• Correct <a> tag usage
• Good use of target="_blank"
• ✓ Images:
• Properly embedded
• Well-styled

CSS Implementation (9/10)

• ✓ Strong ID usage:
• #intro
• #ind
• #iow
• #pel
• ✓ Effective class reuse:
• .div1 across sections
• .div2 across sections
• ✓ Comprehensive tag styling:
• Headers
• Paragraphs
• Images
• ⚠ Minor optimization needed:
• Redundant properties in .div1 and .div2
• Could combine common styles

Code Quality (4/5)

• ✓ Clean, readable structure
• ✓ Proper indentation
• ✓ Good element nesting
• ✗ Missing </head> closing tag

Suggestions for Perfect Score:

1. Optimize CSS:
2. Create shared class for common div styles
3. Combine repeated properties
4. Add missing </head> tag

Excellent work overall! Your code is clean and well-structured. Making these minor improvements would make it perfect. Your use of classes and IDs is particularly strong, showing good understanding of CSS organization.

Technical Feedback (8/20)

HTML Structure (2/5)

• ✗ Critical structure issues:
• <div> inside <h1> (invalid)
• Multiple <body> tags
• Missing closing tags for <a> and <ol>
• ✗ Invalid <frame> usage (should be <iframe>)
• ⚠ Heading issues:
• Uses <h1> and <h3>
• Improper nesting
• ⚠ Custom paragraph ID used instead of <p> tag
• ✓ Images have alt attributes

CSS Implementation (5/10)

• ⚠ Problematic ID usage:
• #heading
• #heading2
• #p (should be tag selector)
• Split styles that should be unified
• ✗ Class issues:
• Invalid .container: syntax
• Missing second reusable class
• ✗ Style problems:
• Redundant h1/h3 styles
• Inline styles in <body>
• Misplaced colons
• Missing semicolons

Code Quality (1/5)

• ✗ Major structural issues:
• Multiple body tags
• Broken closing tags
• Improper element nesting
• ✗ Redundant styling
• ✗ Invalid HTML/CSS syntax
• ✗ Disorganized code structure

Critical Fixes:

1. Fix HTML structure:
2. Remove extra <body> tag
3. Fix tag nesting
4. Close all tags properly
5. Correct element usage:
6. Replace <frame> with <iframe>
7. Use proper <p> tags
8. Fix CSS:
9. Remove .container: syntax error
10. Consolidate heading styles
11. Move inline styles to CSS file
12. Clean up redundant code
13. Fix all closing tags

Hi Collin, I really enjoyed your projects concept; however, your project needs significant technical improvements to function properly. Focus first on fixing the HTML structure and closing tags, then work on organizing your CSS more efficiently.

Technical Feedback (19/20)

HTML Structure (5/5)

• ✓ Excellent heading hierarchy (h1-h5)
• ✓ Proper paragraph structure
• ✓ Media well-implemented (YouTube, Giphy iframes)
• ✓ Images correctly formatted
• ⚠ Minor note: Redundant <a> tag after Giphy embed

CSS Implementation (10/10)

• ✓ Strong ID usage:
• #containerOne
• #leftnote
• #rightnote
• #color
• #define
• #bold
• #big
• ✓ Effective class reuse:
• .notes
• .small
• ✓ Comprehensive tag styling:
• Tables (table, th, td)
• Headings
• Paragraphs

Code Quality (4/5)

• ✓ Clean, readable structure
• ✓ Good separation of concerns
• ✓ Proper indentation
• ⚠ Some inline styles could move to CSS file

Suggestions for Perfect Score:

1. Remove redundant <a> tag after Giphy
2. Move inline styles to CSS file
3. Review table styling for consistency

Outstanding work on HTML structure and CSS implementation. Your code is clean, semantic, and well-organized. Minor tweaks to styling organization, but overall nice job.

Technical Feedback (8/20)

HTML Structure (3/5)

• ✓ Main heading (<h1>) used correctly
• ✗ Custom tags used instead of standard <p> tags (<p1>, <p2>, <p4>)
• ⚠ Links present but have syntax errors (missing >)
• ✓ Image (elio2.jpeg) implemented correctly
• ✗ Needs more section headings

CSS Implementation (4/10)

• ⚠ IDs implemented:
• #containerone
• #containertwo
• #floatingpup
• Need 2 more to meet requirements
• ⚠ Classes:
• .container used well
• Need another reusable class (3+ instances)
• ✗ Style issues:
• Invalid syntax (opacity: .7.5)
• Missing semicolons
• Broken custom tag selectors

Code Quality (1/5)

• ✗ Invalid HTML elements (<div1>, <p1>, <p2>, <p4>)
• ✗ Improper CSS formatting
• ✗ Missing closing brackets
• ✗ Invalid font-family declarations
• ✗ Misused quotes in div1 styling

Priority Fixes:

1. Replace custom tags (<p1>, <p2>, <p4>) with standard <p> tags
2. Add 2 more unique IDs
3. Create another reusable class
4. Fix link syntax
5. Clean up CSS syntax (semicolons, opacity values)

Enjoyed this project overall! Address the technical issues listed here and this will be a solid submission.

Technical Feedback (14/20)

HTML Structure (4/5)

• ✓ Good use of heading hierarchy (h1-h6)
• ✓ Paragraphs properly tagged
• ✗ Two empty <p> tags need content
• ✗ Missing navigation links
• ⚠ Images work but need better organization

CSS Implementation (7/10)

• ✓ Multiple IDs used effectively
• ✓ Good reuse of .container class
• ✗ #rain1 ID exists in CSS but not in HTML
• ✗ .container class missing style definitions
• ✗ Layout issues due to undefined container styles

Code Quality (3/5)

• ✗ HTML syntax errors:
• Missing > in <link> tag
• Unclosed final <div>
• ✗ Unused CSS selectors (p2, h2, #rain1)
• ✗ Inconsistent margin spacing
• ✗ Div structure needs optimization

Key Improvements:

1. Add navigation links
2. Define .container styles
3. Fix HTML syntax errors
4. Clean up unused CSS
5. Improve div organization

Overall I really enjoyed your concept and visuals that reinforced the theme of the page. Focus on cleaning up the technical details and completing the missing style definitions to take this to the next level.

Existing facilities that can filter carbon dioxide out of the air only have the capacity to capture 0.01 million metric tons of CO2 globally today, costing companies like Microsoft as much as $600 per ton of CO2. That’s very little capacity with a very high price tag.

Calma, Justine. “Trying to Reverse Climate Change Won’t Save Us, Scientists Warn.” Msn.Com, 1729, https://www.msn.com/en-us/news/technology/trying-to-reverse-climate-change-won-t-save-us-scientists-warn/ar-AA1sN6OC?ocid=msedgntp&cvid=20987699b6484dd5c9aad7c390f9e4cd&ei=4.

Reviewer #3 (Public review):

Summary:

This work investigated the immune response in the murine retina after focal laser lesions. These lesions are made with close to 2 orders of magnitude lower laser power than the more prevalent choroidal neovascularization model of laser ablation. Histology and OCT together show that the laser insult is localized to the photoreceptors and spares the inner retina, the vasculature, and the pigment epithelium. As early as 1-day after injury, a loss of cell bodies in the outer nuclear layer is observed. This is accompanied by strong microglial proliferation at the site of injury in the outer retina where microglia do not typically reside. The injury did not seem to result in the extravasation of neutrophils from the capillary network constituting one of the main findings of the paper. The demonstrated paradigm of studying the immune response and potentially retinal remodeling in the future in vivo is valuable and would appeal to a broad audience in visual neuroscience. However, there are some issues with the conclusions drawn from the data and analysis that can be addressed to further bolster the manuscript.

Strengths:

Adaptive optics imaging of the murine retina is cutting edge and enables non-destructive visualization of fluorescently labeled cells in the milieu of retinal injury. As may be obvious, this in vivo approach is beneficial for studying fast and dynamic immune processes on a local time scale - minutes and hours, and also for the longer days-to-months follow-up of retinal remodeling as demonstrated in the article. In certain cases, the in vivo findings are corroborated with histology.

The analysis is sound and accompanied by stunning video and static imagery. A few different sets of mouse models are used, (a) two different mouse lines, each with a fluorescent tag for neutrophils and microglia, (b) two different models of inflammation - endotoxin-induced uveitis (EAU) and laser ablation are used to study differences in the immune interaction.

One of the major advances in this article is the development of the laser ablation model for 'mild' retinal damage as an alternative to the more severe neovascularization models. While not directly shown in the article, this model would potentially allow for controlling the size, depth, and severity of the laser injury opening interesting avenues for future study.

Weaknesses:

(1) It is unclear based on the current data/study to what extent the mild laser damage phenotype is generalizable to disease phenotypes. The outer nuclear cell loss of 28% and a complete recovery in 2 months would seem quite mild, thus the generalizability in terms of immune-mediated response in the face of retinal remodeling is not certain, specifically whether the key finding regarding the lack of neutrophil recruitment will be maintained with a stronger laser ablation.

(2) Mice numbers and associated statistics are insufficient to draw strong conclusions in the paper on the activity of neutrophils, some examples are below :

a) 2 catchup mice and 2 positive control EAU mice are used to draw inferences about immune-mediated activity in response to injury. If the goal was to show 'feasibility' of imaging these mouse models for the purposes of tracking specific cell type behavior, the case is sufficiently made and already published by the authors earlier. It is possible that a larger sample size would alter the conclusion.

b) There are only 2 examples of extravasated neutrophils in the entire article, shown in the positive control EAU model. With the rare extravasation events of these cells and their high-speed motility, the chance of observing their exit from the vasculature is likely low overall, therefore the general conclusions made about their recruitment or lack thereof are not justified by these limited examples shown.

c) In Figure 3, the 3-day time point post laser injury shows an 18% reduction in the density of ONL nuclei (p-value of 0.17 compared to baseline). In the case of neutrophils, it is noted that "Control locations (n = 2 mice, 4 z-stacks) had 15 {plus minus} 8 neutrophils per sq.mm of retina whereas lesioned locations (n = 2 mice, 4 z-stacks) had 23 {plus minus} 5 neutrophils per sq.mm of retina (Figure 10b). The difference between control and lesioned groups was not statistically significant (p = 0.19)." These data both come from histology. While the p-values - 0.17 and 0.19 - are similar, in the first case a reduction in ONL cell density is concluded while in the latter, no difference in neutrophil density is inferred in the lesioned case compared to control. Why is there a difference in the interpretation where the same statistical test and methodology are used in both cases? Besides this statistical nuance, is there an alternate possibility that there is an increased, albeit statistically insignificant, concentration of circulating neutrophils in the lesioned model? The increase is nearly 50% (15 {plus minus} 8 vs. 23 {plus minus} 5 neutrophils per sq.mm) and the reader may wonder if a larger animal number might skew the statistic towards significance.

(2) The conclusions on the relative activity of neutrophils and microglia come from separate animals. The reader may wonder why simultaneous imaging of microglia and neutrophils is not shown in either the EAU mice or the fluorescently labeled catchup mice where the non-labeled cell type could possibly be imaged with phase-contrast as has been shown by the authors previously. One might suspect that the microglia dynamics are not substantially altered in these mice compared to the CX3CR1-GFP mice subjected to laser lesions, but for future applicability of this paradigm of in vivo imaging assessment of the laser damage model, including documenting the repeatability of the laser damage model and the immune cell behavior, acquiring these data in the same animals would be critical.

(3) Along the same lines as above, the phase contrast ONL images at time points from 3-day to 2-month post laser injury are not shown and the absence of this data is not addressed. This missing data pertains only to the in vivo imaging mice model but are conducted in histology that adequately conveys the time-course of cell loss in the ONL. It is suggested that the reason be elaborated for the exclusion of this data and the simultaneous imaging of microglia and neutrophils mentioned above. Also, it would be valuable to further qualify and check the claims in the Discussion that "ex vivo analysis confirms in vivo findings" and "Microglial/neutrophil discrimination using label-free phase contrast"

Diaspora: the jews living outside Israel (https://www.merriam-webster.com/dictionary/diaspora)

Interesting musical roots for Reggae... Wonder if this is still present?

Mystical roots.

(Note, I give this the fiction tag because I might want to look into this mystical religion for fiction writing as inspiration)

Logical that marijuana (a drug) is correlated with the mystical concept of communicating with diseased spirits for marijuana makes you hallucinate (or perhaps it's demonic in nature?)

It is important to create a shit tag (literally), to keep track of songs to delete.

Chris M. recommends to use a layered system for music categorization:

• Layer 1) Genres / Subgenres
• Layer 2) Energy
• Layer 3) Vibe

Genre itself is the main overall (and broad) genre. Subgenres are tag-like and related to when you want to play it more granularly.

Energy is a measurement of the average energy of the song.

Vibes refer to the emotions and memories it brings up to you and potentially others you play it for. Some questions he asks: - 1) How does it make me feel? - 2) What does it remind me of? - 3) Where would I play it? - 4) When would I play it? - 5) Why would I play it? - 6) Who would I play it for?

Author response:

Reviewer #1 (Public Review):

Summary

The authors asked if parabrachial CGRP neurons were only necessary for a threat alarm to promote freezing or were necessary for a threat alarm to promote a wider range of defensive behaviors, most prominently flight.

Major Strengths of Methods and Results

The authors performed careful single-unit recording and applied rigorous methodologies to optogenetically tag CGRP neurons within the PBN. Careful analyses show that single-units and the wider CGRP neuron population increases firing to a range of unconditioned stimuli. The optogenetic stimulation of experiment 2 was comparatively simpler but achieved its aim of determining the consequence of activating CGRP neurons in the absence of other stimuli. Experiment 3 used a very clever behavioral approach to reveal a setting in which both cue-evoked freezing and flight could be observed. This was done by having the unconditioned stimulus be a "robot" traveling along a circular path at a given speed. Subsequent cue presentation elicited mild flight in controls and optogenetic activation of CGRP neurons significantly boosted this flight response. This demonstrated for the first time that CGRP neuron activation does more than promote freezing. The authors conclude by demonstrating that bidirectional modulation of CGRP neuron activity bidirectionally aTects freezing in a traditional fear conditioning setting and aTects both freezing and flight in a setting in which the robot served as the unconditioned stimulus. Altogether, this is a very strong set of experiments that greatly expand the role of parabrachial CGRP neurons in threat alarm.

We would like to sincerely thank the reviewer for the positive and insightful comments on our work. We greatly appreciate the acknowledgment of our new behavioral approach, which allowed us to observe a dynamic spectrum of defensive behaviors in animals. Our use of the robot-based paradigm, which enables the observation of both freezing and flight, has been instrumental in expanding our understanding of how parabrachial CGRP neurons modulate diverse threat responses. We are pleased that the reviewer found this methodological innovation to be a valuable contribution to the field.

Weaknesses

In all of their conditioning studies the authors did not include a control cue. For example, a sound presented the same number of times but unrelated to US (shock or robot) presentation. This does not detract from their behavioral findings. However, it means the authors do not know if the observed behavior is a consequence of pairing. Or is a behavior that would be observed to any cue played in the setting? This is particularly important for the experiments using the robot US.

We appreciate the reviewer’s insightful comment regarding the absence of a control cue in our conditioning studies. First, we would like to mention that, in response to the Reviewer 3, we have updated how we present our flight data by following methods from previously published papers (Fadok et al., 2017; Borkar et al., 2024). Instead of counting flight responses, we calculated flight scores as the ratio of the velocity during the CS to the average velocity in the 7 s before the CS on the conditioning day (or 10 s for the retention test). This method better captures both the speed and duration of fleeing during CS. With this updated approach, we observed a significant difference in flight scores between the ChR2 and control groups, even during conditioning, which may partly address the reviewer’s concern about whether the observed behavior is a consequence of CS-US pairing.

However, we agree with the reviewer that including an unpaired group would provide stronger evidence, and in response, we conducted an additional experiment with an unpaired group. In this unpaired group, the CS was presented the same number of times, but the robot US was delivered randomly within the inter-trial interval. The unpaired group did not exhibit any notable conditioned freezing or flight responses. We believe that this additional experiment, now reflected in Figure 3, further strengthens our conclusion that the fleeing behavior is driven by associative learning between the CS and US, rather than a reaction to the cue itself.

The authors make claims about the contribution of CGRP neurons to freezing and fleeing behavior, however, all of the optogenetic manipulations are centered on the US presentation period. Presently, the experiments show a role for these neurons in processing aversive outcomes but show little role for these neurons in cue responding or behavior organizing. Claims of contributions to behavior should be substantiated by manipulations targeting the cue period.

We appreciate the reviewer’s constructive comments. We would like to emphasize that our primary objective in this study was to investigate whether activating parabrachial CGRP neurons—thereby increasing the general alarm signal—would elicit different defensive behaviors beyond passive freezing. To this end, we focused on manipulating CGRP neurons during the US period rather than the cue period.

Previous studies have shown that CGRP neurons relay US signals, and direct activation of CGRP neurons has been used as the US to successfully induce conditioned freezing responses to the CS during retention tests (Han et al., 2015; Bowen et al., 2020). In our experiments, we also observed that CGRP neurons responded exclusively to the US during conditioning with the robot (Figure 1F), and stimulating these neurons in the absence of any external stimuli elicited strong freezing responses (Figure 2B). These findings, collectively, suggest that activation of CGRP neurons during the CS period would predominantly result in freezing behavior.

Therefore, we manipulated the activity of CGRP neurons during the US period to examine whether adjusting the perceived threat level through these neurons would result in diverse dfensive behaivors when paired with chasing robot. We observed that enhancing CGRP neuron activity while animals were chased by the robot at 70 cm/s made them react as if chased at a higher speed (90 cm/s), leading to increased fleeing behaviors. While this may not fully address the role of these neurons in cue responding or behavior organizing, we found that silencing CGRP neurons with tetanus toxin (TetTox) abolished fleeing behavior even when animals were chased at high speeds (90 cm/s), which usually elicits fleeing without CGRP manipulation (Figure 5). This supports the conclusion that CGRP neurons are necessary for processing fleeing responses.

In summary, manipulating CGRP neurons during the US period was essential for effectively investigating their role in adjusting defensive responses, thereby expanding our understanding of their function within the general alarm system. We hope this clarifies our experimental design and addresses the concern the reviewer has raised.

Appraisal

The authors achieved their aims and have revealed a much greater role for parabrachial CGRP neurons in threat alarm.

Discussion

Understanding neural circuits for threat requires us (as a field) to examine diverse threat settings and behavioral outcomes. A commendable and rigorous aspect of this manuscript was the authors decision to use a new behavioral paradigm and measure multiple behavioral outcomes. Indeed, this manuscript would not have been nearly as impactful had they not done that. This novel behavior was combined with excellent recording and optogenetic manipulations - a standard the field should aspire to. Studies like this are the only way that we as a field will map complete neural circuits for threat.

We sincerely thank the reviewer for their positive and encouraging comments. We are grateful for the acknowledgment of our efforts in employing a novel behavioral paradigm to study diverse defensive behaviors. We are pleased that our work contributes to advancing the understanding of neural circuits involved in threat responses.

Reviewer #3 (Public Review):

Strengths:

The study used optogenetics together with in vivo electrophysiology to monitor CGRP neuron activity in response to various aversive stimuli including robot chasing to determine whether they encode noxious stimuli diTerentially. The study used an interesting conditioning paradigm to investigate the role of CGRP neurons in the PBN in both freezing and flight behaviors.

Weakness:

The major weakness of this study is that the chasing robot threat conditioning model elicits weak unconditioned and conditioned flight responses, making it diTicult to interpret the robustness of the findings. Furthermore, the conclusion that the CGRP neurons are capable of inducing flight is not substantiated by the data. No manipulations are made to influence the flight behavior of the mouse. Instead, the manipulations are designed to alter the intensity of the unconditioned stimulus.

We sincerely thank the reviewer for the thoughtful and constructive comments on our manuscript. In response to this feedback, we revisited our analysis of the flight responses and compared our methods with those used in previous literatures examining similar behaviors.

We reviewed a study investigating sex differences in defensive behavior using rats (Gruene et al., 2015). In that study, the CS was presented for 30 s, and active defensive behvaior – referred to as ‘darting’ – was quantified as ‘Dart rate (dart/min)’. This was calculated by doubling the number of darts counted during the 30-s CS presentation to extrapolate to a per-min rate. The highest average dart rate observed was approximatley 1.5. Another relevant studies using mice quantified active defensive behavior by calculating a flight score—the ratio of the average speed during each CS to the average speed during the 10 s pre-CS period (Fadok et al., 2017; Borkar et al., 2024). This method captures multiple aspects of flight behavior during CS presentation, including overall velocity, number of bouts, and duration of fleeing. Moreover, it accounts for each animal’s individual velocity prior to the CS, reflecting how fast the animals were fleeing relative to their baseline activity.

In our original analysis, we quantified flight responses by counting rapid fleeing movements, defined as movements exceeding 8 cm/s. This approach was consistent with our previous study using the same robot paradigm to observe unique patterns of defensive behavior related to sex differences (Pyeon et al., 2023). Based on our earlier findings, where this approach effectively identified significant differences in defensive behaviors, we believed that this method was appropriate for capturing conditioned flight behavior within our specific experimental context. However, prompted by the reviewer's insightful comments, we recognized that our initial method might not fully capture the robustness of the flight responses. Therefore, we re-analyzed our data using the flight score method described by Fadok and colleagues, which provides a more sensitive measure of fleeing during the CS.

Re-analyzing our data revealed a more robust flight response than previously reported, demonstrating that additional CGRP neuron stimulation promoted flight behavior in animals during conditioning, addressing the concern that the data did not substantiate the role of CGRP neurons in inducing flight. In addition, we would like to emphasize the findings from our final experiment, where silencing CGRP neurons, even under high-threat conditions (90 cm/s), prevented animals from exhibiting flight responses. This demonstrates that CGRP neurons are necessary in influencing flight responses.

We have updated all flight data in the manuscript and revised the relevant figures and text accordingly. We appreciate the opportunity to enhance our analysis. The reviewer's insightful observation led us to adopt a better method for quantifying flight behavior, which substantiates our conclusion about the role of CGRP neurons in modulating defensive responses.

Borkar, C.D., Stelly, C.E., Fu, X., Dorofeikova, M., Le, Q.-S.E., Vutukuri, R., et al. (2024). Top- down control of flight by a non-canonical cortico-amygdala pathway. Nature 625(7996), 743-749.

Bowen, A.J., Chen, J.Y., Huang, Y.W., Baertsch, N.A., Park, S., and Palmiter, R.D. (2020). Dissociable control of unconditioned responses and associative fear learning by parabrachial CGRP neurons. Elife 9, e59799.

Fadok, J.P., Krabbe, S., Markovic, M., Courtin, J., Xu, C., Massi, L., et al. (2017). A competitive inhibitory circuit for selection of active and passive fear responses. Nature 542(7639), 96-100.

Gruene, T.M., Flick, K., Stefano, A., Shea, S.D., and Shansky, R.M. (2015). Sexually divergent expression of active and passive conditioned fear responses in rats. Elife 4, e11352.

Han, S., Soleiman, M.T., Soden, M.E., Zweifel, L.S., and Palmiter, R.D. (2015). Elucidating an a_ective pain circuit that creates a threat memory. Cell 162(2), 363-374.

Pyeon, G.H., Lee, J., Jo, Y.S., and Choi, J.-S. (2023). Conditioned flight response in female rats to naturalistic threat is estrous-cycle dependent. Scientific Reports 13(1), 20988.

Reviewer #1 (Public Review):

Summary

The authors asked if parabrachial CGRP neurons were only necessary for a threat alarm to promote freezing or were necessary for a threat alarm to promote a wider range of defensive behaviors, most prominently flight.

Major Strengths of Methods and Results

The authors performed careful single-unit recording and applied rigorous methodologies to optogenetically tag CGRP neurons within the PBN. Careful analyses show that single-units and the wider CGRP neuron population increases firing to a range of unconditioned stimuli. The optogenetic stimulation of experiment 2 was comparatively simpler but achieved its aim of determining the consequence of activating CGRP neurons in the absence of other stimuli. Experiment 3 used a very clever behavioral approach to reveal a setting in which both cue-evoked freezing and flight could be observed. This was done by having the unconditioned stimulus be a "robot" traveling along a circular path at a given speed. Subsequent cue presentation elicited mild flight in controls and optogenetic activation of CGRP neurons significantly boosted this flight response. This demonstrated for the first time that CGRP neuron activation does more than promote freezing. The authors conclude by demonstrating that bidirectional modulation of CGRP neuron activity bidirectionally affects freezing in a traditional fear conditioning setting and affects both freezing and flight in a setting in which the robot served as the unconditioned stimulus. Altogether, this is a very strong set of experiments that greatly expand the role of parabrachial CGRP neurons in threat alarm.

Weaknesses

In all of their conditioning studies the authors did not include a control cue. For example, a sound presented the same number of times but unrelated to US (shock or robot) presentation. This does not detract from their behavioral findings. However, it means the authors do not know if the observed behavior is a consequence of pairing. Or is a behavior that would be observed to any cue played in the setting? This is particularly important for the experiments using the robot US.

The authors make claims about the contribution of CGRP neurons to freezing and fleeing behavior, however, all of the optogenetic manipulations are centered on the US presentation period. Presently, the experiments show a role for these neurons in processing aversive outcomes but show little role for these neurons in cue responding or behavior organizing. Claims of contributions to behavior should be substantiated by manipulations targeting the cue period.

Appraisal

The authors achieved their aims and have revealed a much greater role for parabrachial CGRP neurons in threat alarm.

Discussion

Understanding neural circuits for threat requires us (as a field) to examine diverse threat settings and behavioral outcomes. A commendable and rigorous aspect of this manuscript was the authors decision to use a new behavioral paradigm and measure multiple behavioral outcomes. Indeed, this manuscript would not have been nearly as impactful had they not done that. This novel behavior was combined with excellent recording and optogenetic manipulations - a standard the field should aspire to. Studies like this are the only way that we as a field will map complete neural circuits for threat.

Résumé de la vidéo [00:00:00][^1^][1] - [00:10:23][^2^][2]:

Cette vidéo explore comment les adolescentes YouTubeuses mettent en scène leur féminité en ligne. Elle présente les recherches de Claire Balle, sociologue, sur les pratiques numériques des jeunes filles sur YouTube.

Points forts : + [00:00:00][^3^][3] Développement de l'identité féminine * Affirmation identitaire en ligne * Étude des vidéos de filles et garçons * Importance des vidéos "je suis bizarre" et "anti-boyfriend tag" + [00:02:47][^4^][4] Proximité et sociabilité * Partage d'expériences personnelles * Attente de soutien des abonnés * Mention fréquente d'autres YouTubeuses + [00:04:46][^5^][5] Utilisation de l'intimité * Validation de l'identité par les pairs * Différences de genre dans l'expression de l'intimité * Sexualité et honte corporelle chez les filles + [00:06:30][^6^][6] Caractéristiques féminines involontaires * Manies et habitudes perçues comme féminines * Exigences dans le domaine amoureux * Perfectionnisme et propreté + [00:07:52][^7^][7] Dramatisation et standardisation * Effets de dramatisation pour représenter la féminité * Standardisation des modes de présentation * Influence des médias et réseaux sociaux

Welcome back and in this demo lesson you're going to learn how to install the Docker engine inside an EC2 instance and then use that to create a Docker image.

Now this Docker image is going to be running a simple application and we'll be using this Docker image later in this section of the course to demonstrate the Elastic Container service.

So this is going to be a really useful demo where you're going to gain the experience of how to create a Docker image.

Now there are a few things that you need to do before we get started.

First as always make sure that you're logged in to the I am admin user of the general AWS account and you'll also need the Northern Virginia region selected.

Now attached to this lesson is a one-click deployment link so go ahead and click that now.

This is going to deploy an EC2 instance with some files pre downloaded that you'll use during the demo lesson.

Now everything's pre-configured you just need to check this box at the bottom and click on create stack.

Now that's going to take a few minutes to create and we need this to be in a create complete state.

So go ahead and pause the video wait for your stack to move into create complete and then we're good to continue.

So now this stack is in a create complete state and we're good to continue.

Now if you're following along with this demo within your own environment there's another link attached to this lesson called the lesson commands document and that will include all of the commands that you'll need to type as you move through the demo.

Now I'm a fan of typing all commands in manually because I personally think that it helps you learn but if you are the type of person who has a habit of making mistakes when typing along commands out then you can copy and paste from this document to avoid any typos.

Now one final thing before we finish at the end of this demo lesson you'll have the opportunity to upload the Docker image that you create to Docker Hub.

If you're going to do that then you should pre sign up for a Docker Hub account if you don't already have one and the link for this is included attached to this lesson.

If you already have a Docker Hub account then you're good to continue.

Now at this point what we need to do is to click on the resources tab of this stack and locate the public EC2 resource.

Now this is a normal EC2 instance that's been provisioned on your behalf and it has some files which have been pre downloaded to it.

So just go ahead and click on the physical ID next to public EC2 and that will move you to the EC2 console.

Now this machine is set up and ready to connect to and I've configured it so that we can connect to it using Session Manager and this avoids the need to use SSH keys.

So to do that just right-click and then select connect.

You need to pick Session Manager from the tabs across the top here and then just click on connect.

Now that will take a few minutes but once connected you should see this prompt.

So it should say SH- and then a version number and then dollar.

Now the first thing that we need to do as part of this demo lesson is to install the Docker engine.

The Docker engine is the thing that allows Docker containers to run on this EC2 instance.

So we need to install the Docker engine package and we'll do that using this command.

So we're using shudu to get admin permissions then the package manager DNF then install then Docker.

So go ahead and run that and that will begin the installation of Docker.

It might take a few moments to complete it might have to download some prerequisites and you might have to answer that you're okay with the install.

So press Y for yes and then press enter.

Now we need to wait a few moments for this install process to complete and once it has completed then we need to start the Docker service and we do that using this command.

So shudu again to get admin permissions and then service and then the Docker service and then start.

So type that and press enter and that starts the Docker service.

Now I'm going to type clear and then press enter to make this easier to see and now we need to test that we can interact with the Docker engine.

So the most simple way to do that is to type Docker space and then PS and press enter.

Now you're going to get an error.

This error is because not every user of this EC2 instance has the permissions to interact with the Docker engine.

We need to grant permissions for this user or any other users of this EC2 instance to be able to interact with the Docker engine and we're going to do that by adding these users to a group and we do that using this command.

So shudu for admin permissions and then user mod -a -g for group and then the Docker group and then EC2 -user.

Now that will allow a local user of this system, specifically EC2 -user, to be able to interact with the Docker engine.

Okay so I've cleared the screen to make it slightly easier to see now that we've added EC2 -user the ability to interact with Docker.

So the next thing is we need to log out and log back in of this instance.

So I'm going to go ahead and type exit just to disconnect from session manager and then click on close and then I'm going to reconnect to this instance and you need to do the same.

So connect back in to this EC2 instance.

Now once you're connected back into this EC2 instance we need to run another command which moves us into EC2 user so it basically logs us in as EC2 -user.

So that's this command and the result of this would be the same as if you directly logged in to EC2 -user.

Now the reason we're doing it this way is because we're using session manager so that we don't need a local SSH client or to worry about SSH keys.

We can directly log in via the console UI we just then need to switch to EC2 -user.

So run this command and press enter and we're now logged into the instance using EC2 -user and to test everything's okay we need to use a command with the Docker engine and that command is Docker space ps and if everything's okay you shouldn't see any output beyond this list of headers.

What we've essentially done is told the Docker engine to give us a list of any running containers and even though we don't have any it's not erred it's simply displayed this empty list and that means everything's okay.

So good job.

Now what I've done to speed things up if you just run an LS and press enter the instance has been configured to download the sample application that we're going to be using and that's what the file container.zip is within this folder.

I've configured the instance to automatically extract that zip file which has created the folder container.

So at this point I want you to go ahead and type cd space container and press enter and that's going to move you inside this container folder.

Then I want you to clear the screen by typing clear and press enter and then type ls space -l and press enter.

Now this is the web application which I've configured to be automatically downloaded to the EC2 instance.

It's a simple web page we've got index.html which is the index we have a number of images which this index.html contains and then we have a docker file.

Now this docker file is the thing that the docker engine will use to create our docker image.

I want to spend a couple of moments just stepping you through exactly what's within this docker file.

So I'm going to move across to my text editor and this is the docker file that's been automatically downloaded to your EC2 instance.

Each of these lines is a directive to the docker engine to perform a specific task and remember we're using this to create a docker image.

This first line tells the docker engine that we want to use version 8 of the Red Hat Universal base image as the base component for our docker image.

This next line sets the maintainer label it's essentially a brief description of what the image is and who's maintaining it in this case it's just a placeholder of animals for life.

This next line runs a command specifically the yum command to install some software specifically the Apache web server.

This next command copy copies files from the local directory when you use the docker command to create an image so it's copying that index.html file from this local folder that I've just been talking about and it's going to put it inside the docker image in this path so it's going to copy index.html to /var/www/html and this is where an Apache web server expects this index.html to be located.

This next command is going to do the same process for all of the jpegs in this folder so we've got a total of six jpegs and they're going to be copied into this folder inside the docker image.

This line sets the entry point and this essentially determines what is first run when this docker image is used to create a docker container.

In this example it's going to run the Apache web server and finally this expose command can be used for a docker image to tell the docker engine which services should be exposed.

Now this doesn't actually perform any configuration it simply tells the docker engine what port is exposed in this case port 80 which is HTTP.

Now this docker file is going to be used when we run the next command which is to create a docker image.

So essentially this file is the same docker file that's been downloaded to your EC2 instance and that's what we're going to run next.

So this is the next command within the lesson commands document and this command builds a container image.

What we're essentially doing is giving it the location of the docker file.

This dot at the end contains the working directory so it's here where we're going to find the docker file and any associated files that that docker file uses.

So we're going to run this command and this is going to create our docker image.

So let's go ahead and run this command.

It's going to download version 8 of UBI which it will use as a starting point and then it's going to run through every line in the docker file performing each of the directives and each of those directives is going to create another layer within the docker image.

Remember from the theory lesson each line within the docker file generally creates a new file system layer so a new layer of a docker image and that's how docker images are efficient because you can reuse those layers.

Now in this case this has been successful.

We've successfully built a docker image with this ID so it's giving it a unique ID and it's tagged this docker image with this tag colon latest.

So this means that we have a docker image that's now stored on this EC2 instance.

Now I'll go ahead and clear the screen to make it easier to see and let's go ahead and run the next command which is within the lesson commands document and this is going to show us a list of images that are on this EC2 instance but we're going to filter based on the name container of cats and this will show us the docker image which we've just created.

So the next thing that we need to do is to use the docker run command which is going to take the image that we've just created and use it to create a running container and it's that container that we're going to be able to interact with.

So this is the command that we're going to use it's the next one within the lesson commands document.

It's docker run and then it's telling it to map port 80 on the container with port 80 on the EC2 instance and it's telling it to use the container of cats image and if we run that command docker is going to take the docker image that we've got on this EC2 instance run it to create a running container and we should be able to interact with that container.

So if you go back to the AWS console if we click on instances so look for a4l-public EC2 that's in the running state.

I'm just going to go ahead and select this instance so that we can see the information and we need the public IP address of this instance.

Go ahead and click on this icon to copy the public IP address into your clipboard and then open that in a new tab.

Now be sure not to use this link to the right because that's got a tendency to open the HTTPS version.

We just need to use the IP address directly.

So copy that into your clipboard open a new tab and then open that IP address and now we can see the amazing application if it fits i sits in a container in a container and this amazing looking enterprise application is what's contained in the docker image that you just created and it's now running inside a container based off that image.

So that's great everything's working as expected and that's running locally on the EC2 instance.

Now in the demo lesson for the elastic container service that's coming up later in this section of the course you have two options.

You can either use my docker image which is this image that I've just created or you can use your own docker image.

If you're going to use my docker image then you can skip this next step.

You don't need a docker hub account and you don't need to upload your image.

If you want to use your own image then you do need to follow these next few steps and I need to follow them anyway because I need to upload this image to docker hub so that you can potentially use it rather than your own image.

So I'm going to move back to the session manager tab and I'm going to control C to exit out of this running container and I'm going to type clear to clear the screen and make it easier to see.

Now to upload this to docker hub first you need to log in to docker hub using your credentials and you can do that using this command.

So it's docker space login space double hyphen username equals and then your username.

So if you're doing this in your own environment you need to delete this placeholder and type your username.

I'm going to type my username because I'll be uploading this image to my docker hub.

So this is my docker hub username and then press enter and it's going to ask for the corresponding password to this username.

So I'm going to paste in my password if you're logging into your docker hub you should use your password.

Once you've pasted in the password go ahead and press enter and that will log you in to docker hub.

Now you don't have to worry about the security message because whilst your docker hub password is going to be stored on the EC2 instance shortly we're going to terminate this instance which will remove all traces of this password from this machine.

Okay so again we're going to upload our docker image to docker hub so let's run this command again and you'll see because we're just using the docker images command we can see the base image as well as our image.

So we can see red hat UBI 8.

We want the container of cats latest though so what you need to do is copy down the image ID of the container of cats image.

So this is the top line in my case container of cats latest and then the image ID.

So then we need to run this command so docker space tag and then the image ID that you've just copied into your clipboard and then a space and then your docker hub username.

In my case it's actrl with 1L if you're following along you need to use your own username and then forward slash and then the name of the image that you want this to be stored as on docker hub so I'm going to use container of cats.

So that's the command you need to use so docker tag and then your image ID for container of cats and then your username forward slash container of cats and press enter and that's everything we need to do to prepare to upload this image to docker hub.

So the last command that we need to run is the command to actually upload the image to docker hub and that command is docker space push so we're going to push the image to docker hub then we need to specify the docker hub username so again this is my username but if you're doing this in your environment it needs to be your username and then forward slash and then the image name in my case container of cats and then colon latest and once you've got all that go ahead and press enter and that's going to push the docker image that you've just created up to your docker hub account and once it's up there it means that we can deploy from that docker image to other EC2 instances and even ECS and we're going to do that in a later demo in this section of the course.

Now that's everything that you need to do in this demo lesson you've essentially installed and configured the docker engine you've used a docker file to create a docker image from some local assets you've tested that docker image by running a container using that image and then you've uploaded that image to docker hub and as I mentioned before we're going to use that in a future demo lesson in this section of the course.

Now the only thing that remains to do is to clear up the infrastructure that we've used in this demo lesson so go ahead and close down all of these extra tabs and go back to the cloud formation console this is the stack that's been created by the one click deployment link so all you need to do is select this stack it should be called EC2 docker and then click on delete and confirm that deletion and that will return the account into the same state as it was at the start of this demo lesson.

Now that is everything you need to do in this demo lesson I hope it's been useful and I hope you've enjoyed it so go ahead and complete the video and when you're ready I look forward to you joining me in the next.

Welcome back.

This is part two of this lesson.

We're going to continue immediately from the end of part one.

So let's get started.

We just need to give this a brief moment to perform that reboot.

So just wait a couple of moments and once you have right click again, select Connect.

We're going to use EC2 instance connect again.

Make sure the user's correct and then click on Connect.

Now, if it doesn't immediately connect you to the instance, if it appears to have frozen for a couple of seconds, that's fine.

It just means that the instance hasn't completed its restart.

Wait for a brief while longer and then attempt another connect.

This time you should be connected back to the instance and now we need to verify whether we can still see our volume attached to this instance.

So do a DF space -k and press Enter and you'll note that you can't see the file system.

That's because before we rebooted this instance, we used the mount command to manually mount the file system on our EBS volume into the EBS test folder.

Now that's a manual process.

It means that while we could interact with that before the reboot, it doesn't automatically mount that file system when the instance restarts.

To do that, we need to configure it to auto-mount when the instance starts up.

So to do that, we need to get the unique ID of the EBS volume, which is attached to this instance.

And to get that, we run a shudu space blkid.

Now press Enter and that's going to list the unique identifier of all of the volumes attached to this instance.

You'll see the boot volume listed as devxvda1 and the EBS volume that we've just attached listed as devxvdf.

So we need the unique ID of the volume that we just added.

So that's the one next to xvdf.

So go ahead and select this unique identifier.

You'll need to make sure that you select everything between the speech marks and then copy that into your clipboard.

Next, we need to edit the FSTAB file, which controls which file systems are mounted by default.

So we're going to run a shudu and then space nano, which is our editor, and then a space, and then forward slash ETC, which is the configuration directory on Linux, another forward slash and then FSTAB and press Enter.

And this is the configuration file for which file systems are mounted by our instance.

And we're going to add a similar line.

So first we need to use uuid, which is the unique identifier, and then the equal symbol.

And then we need to paste in that unique ID that we just copied to our clipboard.

Once that's pasted in, press Space.

This is the ID of the EBS volume, so the unique ID.

Next, we need to provide the place where we want that volume to be mounted.

And that's the folder we previously created, which is forward slash EBS test.

Then a space, we need to tell the OS which file system is used, which is xfs, and then a space.

And then we need to give it some options.

You don't need to understand what these do in detail.

We're going to use defaults, all one word, and then a comma, and then no fail.

So once you've entered all of that, press Ctrl+O to save that file, and Enter, and then Ctrl+X to exit.

Now this will be mounted automatically when the instance starts up, but we can force that process by typing shudu space mount space-a.

And this will perform a mount of all of the volumes listed in the FS tab file.

So go ahead and press Enter.

Now if we do a df space-k and press Enter, you'll see that our EBS volume once again is mounted within the EBS test folder.

So I'm going to clear the screen, then I'm going to move into that folder, press Enter, and then do an ls space-la, and you'll see that our amazing test file still exists within this folder.

And that shows that the data on this file system is persistent, and it's available even after we reboot this EC2 instance, and that's different than instance store volumes, which I'll be demonstrating later on.

At this point, we're going to shut down this instance because we won't be needing it anymore.

So close down this tab, click on Instances, right-click on instance one-AZA, and then select Stop Instance.

You'll need to confirm it, refresh that and wait for it to move into a stopped state.

Once it has stopped, go down and click on Volumes, select the EBS test volume, right-click and detach it.

We're going to detach this volume from the instance that we've just stopped.

You'll need to confirm that, and that will begin the process and it will detach that volume from the instance, and this demonstrates how EBS volumes are completely separate from EC2 instances.

You can detach them and then attach them to other instances, keeping the data that's on that volume.

Just keep refreshing.

We need to wait for that to move into an available state, and once it has, we're going to right-click, select Attach Volume, click inside the instance box, and this time, we're going to select instance two-AZA.

It should be the only one listed now in a running state.

So select that and click on Attach.

Just refresh that and wait for that to move into an in-use state, which it is, then move back to instances, and we're going to connect to the instance that we just attached that volume to.

So select instance two-AZA, right-click, select Connect, and then connect to that instance.

Once we connected to that instance, remember this is an instance that we haven't interacted with this EBS volume with.

So this instance has no initial configuration of this EBS volume, and if we do a DF-K, you'll see that this volume is not mounted on this instance.

What we need to do is do an LS, BLK, and this will list all of the block devices on this instance.

You'll see that it's still using XVDF because this is the device ID that we configured when attaching the volume.

Now, if we run this command, so shudu, file, S, and then the device ID of this EBS volume, notice how now it shows a file system on this EBS volume because we created it on the previous instance.

We don't need to go through all of the process of creating the file system because EBS volumes persist past the lifecycle of an EC2 instance.

You can interact with an EBS volume on one instance and then move it to another and the configuration is maintained.

We're going to follow the same process.

We're going to create a folder called EBSTEST.

Then we're going to mount the EBS volume using the device ID into this folder.

We're going to move into this folder and then if we do an LS, space-LA, and press Enter, you'll see the test file that you created in the previous step.

It still exists and all of the contents of that file are maintained because the EBS volume is persistent storage.

So that's all I wanted to verify with this instance that you can mount this EBS volume on another instance inside the same availability zone.

At this point, close down this tab and then click on Instances and we're going to shut down this second EC2 instance.

So right-click and then select Stop Instance and you'll need to confirm that process.

Wait for that instance to change into a stop state and then we're going to detach the EBS volume.

So that's moved into the stopped state.

We can select Volumes, right-click on this EBSTEST volume, detach the volume and confirm that.

Now next, we want to mount this volume onto the instance that's in Availability Zone B and we can't do that because EBS volumes are located in one specific availability zone.

Now to allow that process, we need to create a snapshot.

Snapshots are stored on S3 and replicated between multiple availability zones in that region and snapshots allow us to take a volume in one availability zone and move it into another.

So right-click on this EBS volume and create a snapshot.

Under Description, just use EBSTESTSNAP and then go ahead and click on Create Snapshot.

Just close down any dialogues, click on Snapshots and you'll see that a snapshot is being created.

Now depending on how much data is stored on the EBS volume, snapshots can either take a few seconds or anywhere up to several hours to complete.

This snapshot is a full copy of all of the data that's stored on our original EBS volume.

But because the snapshot is stored in S3, it means that we can take this snapshot, right-click, create volume and then create a volume in a different availability zone.

Now you can change the volume type, the size and the encryption settings at this point, but we're going to leave everything the same and just change the availability zone from US-EAST-1A to US-EAST-1B.

So select 1B in availability zone, click on Add Tag.

We're going to give this a name to make it easier to identify.

For the value, we're going to use EBS Test Volume-AZB.

So enter that and then create the volume.

Close down any dialogues and at this point, what we're doing is using this snapshot which is stored inside S3 to create a brand new volume inside availability zone US-EAST-1B.

At this point, once the volume is in an available state, make sure you select the right one, then we can right-click, we can attach this volume and this time when we click in the instance box, you'll see the instance that's in availability zone 1B.

So go ahead and select that and click on Attach.

Once that volume is in use, go back to Instances, select the third instance, right-click, select Connect, choose Instance Connect, verify the username and then connect to the instance.

Now we're going to follow the same process with this instance.

So first, we need to list all of the attached block devices using LSBLK.

You'll see the volume we've just created from that snapshot, it's using device ID XVDF.

We can verify that it's got a file system using the command that we've used previously and it's showing an XFS file system.

Next, we create our folder which will be our mount point.

Then we mount the device into this mount point using the same command as we've used previously, move into that folder and then do a listing using LS-LA and you should see the same test file you created earlier and if you cap this file, it should have the same contents.

This volume has the same contents because it's created from a snapshot that we created of the original volume and so its contents will be identical.

Go ahead and close down this tab to this instance, select instances, right click, stop this instance and then confirm that process.

Just wait for that instance to move into the stopped state.

We're going to move back to volumes, select the EBS test volume in availability zone 1B, detach that volume and confirm it and then just move to snapshots and I want to demonstrate how you have the option of right clicking on a snapshot.

You can copy the snapshot and choose a different regions.

So as well as snapshots giving you the option of moving EBS volume data between availability zones, you can also use snapshots to copy data between regions.

Now I'm not going to do this process but I could select a different region, for example, Asia Pacific Sydney and copy that snapshot to the Sydney region.

But there's no point doing that because we just have to remember to clean it up afterwards.

That process is fairly simple and will allow us to copy snapshots between regions.

It might take some time again depending on the amount of data within that snapshot but it is a process that you can perform either as part of data migration or disaster recovery processes.

So go ahead and click on cancel and at this point we're just going to clear things up because this is the end of this first phase of this demo lesson.

So right click on this snapshot and just delete the snapshot and confirm that.

Then go to volumes, select the volume in US East 1A, right click, delete that volume and confirm.

Select the volume in US East 1B, right click, delete volume and confirm.

And that just means we've tidied up both of those EBS volumes within this account.

Now that's the end of this first stage of this set of demo lessons.

All the steps until this point have been part of the free tier within AWS.

What follows won't be part of the free tier.

We're going to be creating a larger instant size and this will have a cost attached but I want to use it to demonstrate instant store volumes and how you can interact with them and some of their unique characteristics.

So I'm going to move into a new video and this new video will have an associated charge.

So you can either watch me perform the steps or you can do it within your own environment.

Now go ahead and complete this video and when you're ready, you can move on to the next video where we're going to investigate instant store volumes.

Welcome back and we're going to use this demo lesson to get some experience of working with EBS and Instant Store volumes.

Now before we get started, this series of demo videos will be split into two main components.

The first component will be based around EBS and EBS snapshots and all of this will come under the free tier.

The second component will be based on Instant Store volumes and will be using larger instances which are not included within the free tier.

So I'm going to make you aware of when we move on to a part which could incur some costs and you can either do that within your own environment or watch me do it in the video.

If you do decide to do it in your own environment, just be aware that there will be a 13 cents per hour cost for the second component of this demo series and I'll make it very clear when we move into that component.

The second component is entirely optional but I just wanted to warn you of the potential cost in advance.

Now to get started with this demo, you're going to need to deploy some infrastructure.

To do that, make sure that you're logged in to the general account, so the management account of the organization and you've got the Northern Virginia region selected.

Now attached to this demo is a one click deployment link to deploy the infrastructure.

So go ahead and click on that link.

That's going to open this quick create stack screen and all you need to do is scroll down to the bottom, check this capabilities box and click on create stack.

Now you're going to need this to be in a create complete state before you continue with this demo.

So go ahead and pause the video, wait for that stack to move into the create complete status and then you can continue.

Okay, now that's finished and the stack is in a create complete state.

You're also going to be running some commands within EC2 instances as part of this demo.

Also attached to this lesson is a lesson commands document which contains all of those commands and you can use this to copy and paste which will avoid errors.

So go ahead and open that link in a separate browser window or separate browser tab.

It should look something like this and we're going to be using this throughout the lesson.

Now this cloud formation template has created a number of resources, but the three that we're concerned about are the three EC2 instances.

So instance one, instance two and instance three.

So the next thing to do is to move across to the EC2 console.

So click on the services drop down and then either locate EC2 under all services, find it in recently visited services or you can use the search box at the top type EC2 and then open that in a new tab.

Now the EC2 console is going through a number of changes so don't be alarmed if it looks slightly different or if you see any banners welcoming you to this new version.

Now if you click on instances running, you'll see a list of the three instances that we're going to be using within this demo lesson.

We have instance one - az a.

We have instance two - az a and then instance one - az b.

So these are three instances, two of which are in availability zone A and one which is in availability zone B.

Next I want you to scroll down and locate volumes under elastic block store and just click on volumes.

And what you'll see is three EBS volumes, each of which is eight GIB in size.

Now these are all currently in use.

You can see that in the state column and that's because all of these volumes are in use as the boot volumes for those three EC2 instances.

So on each of these volumes is the operating system running on those EC2 instances.

Now to give you some experience of working with EBS volumes, we're going to go ahead and create a volume.

So click on the create volume button.

The first thing you'll need to do when creating a volume is pick the type and there are a number of different types available.

We've got GP2 and GP3 which are the general purpose storage types.

We're going to use GP3 for this demo lesson.

You could also select one of the provisioned IOPS volumes.

So this is currently IO1 or IO2.

And with both of these volume types, you're able to define IOPS separately from the size of the volume.

So these are volume types that you can use for demanding storage scenarios where you need high-end performance or when you need especially high performance for smaller volume sizes.

Now IO1 was the first type of provisioned IOPS SSD introduced by AWS and more recently they've introduced IO2 and enhanced it which provides even higher levels of performance.

In addition to that we do have the non-SSD volume types.

So SC1 which is cold HDD, ST1 which is throughput optimized HDD and then of course the original magnetic type which is now legacy and AWS don't recommend this for any production usage.

For this demo lesson we're going to go ahead and select GP3.

So select that.

Next you're able to pick a size in GIB for the volume.

We're going to select a volume size of 10 GIB.

Now EBS volumes are created within a specific availability zone so you have to select the availability zone when you're creating the volume.

At this point I want you to go ahead and select US-EAST-1A.

When creating volume you're also able to specify a snapshot as the basis for that volume.

So if you want to restore a snapshot into this volume you can select that here.

At this stage in the demo we're going to be creating a blank EBS volume so we're not going to select anything in this box.

We're going to be talking about encryption later in this section of the course.

You are able to specify encryption settings for the volume when you create it but at this point we're not going to encrypt this volume.

We do want to add a tag so that we can easily identify the volume from all of the others so click on add tag.

For the key we're going to use name.

For the value we're going to put EBS test volume.

So once you've entered both of those go ahead and click on create volume and that will begin the process of creating the volume.

Just close down any dialogues and then just pay attention to the different states that this volume goes through.

It begins in a creating state.

This is where the storage is being provisioned and then made available by the EBS product.

If we click on refresh you'll see that it changes from creating to available and once it's in an available state this means that we can attach it to EC2 instances.

And that's what we're going to do so we're going to right click and select attach volume.

Now you're able to attach this volume to EC2 instances but crucially only those in the same availability zone.

EBS is an availability zone scoped service and so you can only attach EBS volumes to EC2 instances within that same availability zone.

So if we select the instance box you'll only see instances in that same availability zone.

Now at this point go ahead and select instance 1 in availability zone A.

Once you've selected it you'll see that the device field is populated and this is the device ID that the instance will see for this volume.

So this is how the volume is going to be exposed to the EC2 instance.

So if we want to interact with this instance inside the operating system this is the device that we'll use.

Now different operating systems might see this in slightly different ways.

So as this warning suggests certain Linux kernels might rename SDF to XVDF.

So we've got to be aware that when you do attach a volume to an EC2 instance you need to get used to how that's seen inside the operating system.

How we can identify it and how we can configure it within the operating system for use.

And I'm going to demonstrate that in the next part of this demo lesson.

So at this point just go ahead and click on attach and this will attach this volume to the EC2 instance.

Once that's attached to the instance and you see the state change to in use then just scroll up on the left hand side and select instances.

We're going to go ahead and connect to instance 1 in availability zone A.

This is the instance that we just attached that EBS volume to so we want to interact with this instance and see how we can see the EBS volume.

So right click on this instance and select connect and then you could either connect with an SSH client or use instance connect and to keep things simple we're going to connect from our browser so select the EC2 instance connect option make sure the user's name is set to EC2-user and then click on connect.

So now we connected to this EC2 instance and it's at this point that we'll start needing the commands that are listed inside the lesson commands document and again this is attached to this lesson.

So first we need to list all the block devices which are connected to this instance and we're going to use the LSBLK command.

Now if you're not comfortable with Linux don't worry just take this nice and slowly and understand at a high level all the commands that we're going to run.

So the first one is LSBLK and this is list block devices.

So if we run this we'll be able to see a list of all of the block devices connected to this EC2 instance.

You'll see the root device this is the device that's used to boot the instance it contains the instance operating system you'll see that it's 8 gig in size and then this is the EBS volume that we just attached to this instance.

You'll see that device ID so XVDF and you'll see that it's 10 gig in size.

Now what we need to do next is check whether there is a file system on this block device.

So this block device we've created it with EBS and then we've attached it to this instance.

Now we know that it's blank but it's always safe to check if there's any file system on a block device.

So to do that we run this command.

So we're going to check are there any file systems on this block device.

So press enter and if you see just data that indicates that there isn't any file system on this device and so we need to create one.

You can only mount file systems under Linux and so we need to create a file system on this raw block device this EBS volume.

So to do that we run this command.

So shoo-doo again is just giving us admin permissions on this instance.

MKFS is going to make a file system.

We specify the file system type with hyphen t and then XFS which is a type of file system and then we're telling it to create this file system on this raw block device which is the EBS volume that we just attached.

So press enter and that will create the file system on this EBS volume.

We can confirm that by rerunning this previous command and this time instead of data it will tell us that there is now an XFS file system on this block device.

So now we can see the difference.

Initially it just told us that there was data, so raw data on this volume and now it's indicating that there is a file system, the file system that we just created.

Now the way that Linux works is we mount a file system to a mount point which is a directory.

So we're going to create a directory using this command.

MKDIR makes a directory and we're going to call the directory forward slash EBS test.

So this creates it at the top level of the file system.

This signifies root which is the top level of the file system tree and we're going to make a folder inside here called EBS test.

So go ahead and enter that command and press enter and that creates that folder and then what we can do is to mount the file system that we just created on this EBS volume into that folder.

And to do that we use this command, mount.

So mount takes a device ID, so forward slash dev forward slash xvdf.

So this is the raw block device containing the file system we just created and it's going to mount it into this folder.

So type that command and press enter and now we have our EBS volume with our file system mounted into this folder.

And we can verify that by running a df space hyphen k.

And this will show us all of the file systems on this instance and the bottom line here is the one that we've just created and mounted.

At this point I'm just going to clear the screen to make it easier to see and what we're going to do is to move into this folder.

So cd which is change directory space forward slash EBS test and then press enter and that will move you into that folder.

Once we're in that folder we're going to create a test file.

So we're going to use this command so shudu nano which is a text editor and we're going to call the file amazing test file dot txt.

So type that command in and press enter and then go ahead and type a message.

It can be anything you just need to recognize it as your own message.

So I'm going to use cats are amazing and then some exclamation marks.

Then I'm going to press control o and enter to save that file and then control x to exit again clear the screen to make it easier to see.

And then I'm going to do an LS space hyphen LA and press enter just to list the contents of this folder.

So as you can see we've now got this amazing test file dot txt.

And if we cat the contents of this so cat amazing test file dot txt you'll see the unique message that you just typed in.

So at this point we've created this file within the folder and remember the folder is now the mount point for the file system that we created on this EBS volume.

So the next step that I want you to do is to reboot this EC2 instance.

To do that type sudo space and then reboot and press enter.

Now this will disconnect you from this session.

So you can go ahead and close down this tab and go back to the EC2 console.

Just go ahead and click on instances.

Okay, so this is the end of part one of this lesson.

It was getting a little bit on the long side and so I wanted to add a break.

It's an opportunity just to take a rest or grab a coffee.

Part two will be continuing immediately from the end of part one.

So go ahead complete the video and when you're ready join me in part two.

Author response:

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

New Experiments

(1) Activation-dependent dynamics of PKA with the RIα regulatory subunit, adding to the answer to Reviewers 1 and 2. To determine the dynamics of all PKA isoforms, we have added experiments that used PKA-RIα as the regulatory subunit. We found differential translocation between PKA-C (co-expressed with PKA-RIα) and PKA-RIα (Figure 1–figure supplement 3), similar to the results when PKA-RIIα or PKA-RIβ was used.

(2) PKA-C dynamics elicited by a low concentration of norepinephrine, addressing Reviewer 3’s comment. We have found that PKA-C (co-expressed with RIIα) exhibited similar translocation into dendritic spines in the presence of a 5x lowered concentration (2 μM) of norepinephrine, suggesting that the translocation occurs over a wide range of stimulus strengths (Figure 1-figure supplement 2).

Reviewer #1 (Public Review):

Summary:

This is a short self-contained study with a straightforward and interesting message. The paper focuses on settling whether PKA activation requires dissociation of the catalytic and regulatory subunits. This debate has been ongoing for ~ 30 years, with renewed interest in the question following a publication in Science, 2017 (Smith et al.). Here, Xiong et al demonstrate that fusing the R and C subunits together (in the same way as Smith et al) prevents the proper function of PKA in neurons. This provides further support for the dissociative activation model - it is imperative that researchers have clarity on this topic since it is so fundamental to building accurate models of localised cAMP signalling in all cell types. Furthermore, their experiments highlight that C subunit dissociation into spines is essential for structural LTP, which is an interesting finding in itself. They also show that preventing C subunit dissociation reduces basal AMPA receptor currents to the same extent as knocking down the C subunit. Overall, the paper will interest both cAMP researchers and scientists interested in fundamental mechanisms of synaptic regulation.

Strengths:

The experiments are technically challenging and well executed. Good use of control conditions e.g untransfected controls in Figure 4.

We thank the reviewer for their accurate summarization of the position of the study in the field and for the positive evaluation of our study.

Weaknesses:

The novelty is lessened given the same team has shown dissociation of the C subunit into dendritic spines from RIIbeta subunits localised to dendritic shafts before (Tillo et al., 2017). Nevertheless, the experiments with RII-C fusion proteins are novel and an important addition.

We thank the reviewer for noticing our earlier work. The first part of the current work is indeed an extension of previous work, as we have articulated in the manuscript. However, this extension is important because recent studies suggested that the majority of PKA-RIIβ are axonal localized. The primary PKA subtypes in the soma and dendrite are likely PKA-RIβ or PKA-RIIα. Although it is conceivable that the results from PKA-RIIβ can be extended to the other subunits, given the current debate in the field regarding PKA dissociation (or not), it remains important to conclusively demonstrate that these other regulatory subunit types also support PKA dissociation within intact cells in response to a physiological stimulant. To complete the survey for all PKA-R isoforms, we have now added data for PKA-RIα (New Experiment #1), as they are also expressed in the brain (e.g., https://www.ncbi.nlm.nih.gov/gene/5573). Additionally, as the reviewer points out, our second part is a novel addition to the literature.

Reviewer #2 (Public Review):

Summary:

PKA is a major signaling protein that has been long studied and is vital for synaptic plasticity. Here, the authors examine the mechanism of PKA activity and specifically focus on addressing the question of PKA dissociation as a major mode of its activation in dendritic spines. This would potentially allow us to determine the precise mechanisms of PKA activation and address how it maintains spatial and temporal signaling specificity.

Strengths:

The results convincingly show that PKA activity is governed by the subcellular localization in dendrites and spines and is mediated via subunit dissociation. The authors make use of organotypic hippocampal slice cultures, where they use pharmacology, glutamate uncaging, and electrophysiological recordings.

Overall, the experiments and data presented are well executed. The experiments all show that at least in the case of synaptic activity, the distribution of PKA-C to dendritic spines is necessary and sufficient for PKA-mediated functional and structural plasticity.

The authors were able to persuasively support their claim that PKA subunit dissociation is necessary for its function and localization in dendritic spines. This conclusion is important to better understand the mechanisms of PKA activity and its role in synaptic plasticity.

We thank the reviewer for their positive evaluation of our study.

Weaknesses:

While the experiments are indeed convincing and well executed, the data presented is similar to previously published work from the Zhong lab (Tillo et al., 2017, Zhong et al 2009). This reduces the novelty of the findings in terms of re-distribution of PKA subunits, which was already established. A few alternative approaches for addressing this question: targeting localization of endogenous PKA, addressing its synaptic distribution, or even impairing within intact neuronal circuits, would highly strengthen their findings. This would allow us to further substantiate the synaptic localization and re-distribution mechanism of PKA as a critical regulator of synaptic structure, function, and plasticity.

We thank the reviewer for noticing our earlier work. The first part of the current work is indeed an extension of previous work, as we have articulated in the manuscript. However, this extension is important because recent studies suggested that the majority of PKA-RIIβ are axonal localized. The primary PKA subtypes in the soma and dendrite are likely PKA-RIβ or PKA-RIIα. Although it is conceivable that the results from PKA-RIIβ can be extended to the other subunits, given the current debate in the field regarding PKA dissociation (or not), it remains important to conclusively demonstrate that these other regulatory subunit types also support PKA dissociation within intact cells in response to a physiological stimulant. To complete the survey for all PKA-R isoforms, we have now added data for PKA-RIα (New Experiment #1), as they are also expressed in the brain (e.g., https://www.ncbi.nlm.nih.gov/gene/5573). Additionally, as Reviewer 1 points out, our second part is a novel addition to the literature.

We also thank the reviewer for suggesting the experiments to examine PKA’s synaptic localization and dynamics as a key mechanism underlying synaptic structure and function. We agree that this is a very interesting topic. At the same time, we feel that this mechanistic direction is open ended at this time and beyond what we try to conclude within this manuscript: prevention of PKA dissociation in neurons affects synaptic function. Therefore, we will save the suggested direction for future studies. We hope the reviewer understand.

Reviewer #3 (Public Review):

Summary:

Xiong et al. investigated the debated mechanism of PKA activation using hippocampal CA1 neurons under pharmacological and synaptic stimulations. Examining the two PKA major isoforms in these neurons, they found that a portion of PKA-C dissociates from PKA-R and translocates into dendritic spines following norepinephrine bath application. Additionally, their use of a non-dissociable form of PKC demonstrates its essential role in structural long-term potentiation (LTP) induced by two-photon glutamate uncaging, as well as in maintaining normal synaptic transmission, as verified by electrophysiology. This study presents a valuable finding on the activation-dependent re-distribution of PKA catalytic subunits in CA1 neurons, a process vital for synaptic functionality. The robust evidence provided by the authors makes this work particularly relevant for biologists seeking to understand PKA activation and its downstream effects essential for synaptic plasticity.

Strengths:

The study is methodologically robust, particularly in the application of two-photon imaging and electrophysiology. The experiments are well-designed with effective controls and a comprehensive analysis. The credibility of the data is further enhanced by the research team's previous works in related experiments. The conclusions of this paper are mostly well supported by data. The research fills a significant gap in our understanding of PKA activation mechanisms in synaptic functioning, presenting valuable insights backed by empirical evidence.

We thank the reviewer for their positive evaluation of our study.

Weaknesses:

The physiological relevance of the findings regarding PKA dissociation is somewhat weakened by the use of norepinephrine (10 µM) in bath applications, which might not accurately reflect physiological conditions. Furthermore, the study does not address the impact of glutamate uncaging, a well-characterized physiologically relevant stimulation, on the redistribution of PKA catalytic subunits, leaving some questions unanswered.

We agreed with the Reviewer that testing under physiological conditions is critical especially given the current debate in the literature. That is why we tested PKA dynamics induced by the physiological stimulant, norepinephrine. It has been suggested that, near the release site, local norepinephrine concentrations can be as high as tens of micromolar (Courtney and Ford, 2014). Based on this study, we have chosen a mid-range concentration (10 μM). At the same time, in light of the Reviewer’s suggestion, we have now also tested PKA-RIIα dissociation at a 5x lower concentration of norepinephrine (2 μM; New Experiment #2). The activation and translocation of PKA-C is also readily detectible under this condition to a degree comparable to when 10 μM norepinephrine was used.

Regarding the suggested glutamate uncaging experiment, it is extremely challenging because of finite signal-to-noise ratios in our experiments. From our past studies, we know that activated PKA-C can diffuse three dimensionally, with a fraction as membrane-associated proteins and the other as cytosolic proteins. Although we have evidence that its membrane affinity allows it to become enriched in dendritic spines, it is not known (and is unlikely) that activated PKA-C is selectively targeted to a particular spine. Glutamate uncaging of a single spine presumably would locally activate a small number of PKA-C. It will be very difficult to trace the 3D diffusion of these small number of molecules in the presence of surrounding resting-state PKA-C molecules. Finally, we hope the reviewer agrees that, regardless of the result of the glutamate uncaging experiment, the above new experiment (New Experiment #2) already indicate that certain physiologically relevant stimuli can drive PKA-C dissociation from PKA-R and translocation to spines, supporting our conclusion.

Reviewer #2 (Recommendations For The Authors):

It was a pleasure reading your paper, and the results are well-executed and well-presented.

My main and only recommendations are two ways to further expand the scope of the findings.

First, I believe addressing the endogenous localization of PKA-C subunit before and after PKA activation would be highly important to validate these claims. Overexpression of tagged proteins often shows vastly different subcellular distribution than their endogenous counterparts. Recent technological advances with CRISPR/Cas9 gene editing (Suzuki et al Nature 2016 and Gao et al Neuron 2019 for example) which the Zhong lab recently contributed to (Zhong et al 2021 eLife) allow us to tag endogenous proteins and image them in fixed or live neurons. Any experiments targeting endogenous PKA subunits that support dissociation and synaptic localization following activation would be very informative and greatly increase the novelty and impact of their findings.

We agreed that addressing the endogenous PKA dynamics is important. However, despite recent progress, endogenous labeling using CRISPR-based methods remains challenging and requires extensive optimization. This is especially true for signaling proteins whose endogenous abundance is often low. We have tried to label PKA catalytic subunits and regulatory subunits using both the homologous recombination-based method SLENDR and our own non-homologous end joining-based method CRISPIE. We did not succeed, in part because it is very difficult to see any signal under wide-field fluorescence conditions, which makes it difficult to screen different constructs for optimizing parameters. It is also possible that, at the endogenous abundance, the label is just not bright enough to be seen. Nevertheless, for both PKA type Iβ and type IIα that we studied in this manuscript, we have correlated the measured parameters (specifically, Spine Enrichment Index or SEI) with the overexpression level (Figure 1-figure supplement 1). We found that they are not strongly correlated with the expression level under our conditions. By extrapolating to non-overexpression conditions, our conclusion remains valid.

To overcome the inability to label endogenous PKA subunits using CRISPR-based methods, we have also attempted a conditional knock-in method call ENABLED that we previously developed to label PKA-Cα. In preliminary results, we found that endogenously label PKA were very dim. However, in a subset of cells that are bright enough to be quantified, the PKA catalytic subunit indeed translocated to dendritic spines upon stimulation (see Additional Fig. 1 in the next page), corroborating our results using overexpression. These results, however, are not ready to be published because characterization of the mouse line takes time and, at this moment, the signal-to-noise ratio remains low. We hope that the reviewer can understand.

Author response image 1.

Endogeneous PKA-Cα translocate to dendritic spines upon activation.

Second, experiments which would advance and validate these findings in vivo would be highly valuable. This could be achieved in a number of ways - one would be overexpression of tagged PKA versions and examining sub-cellular distribution before and after physiological activation in vivo. Another possibility is in vivo perturbation - one would speculate that disruption or tethering of PKA subunits to the dendrite would lead to cell-specific functional and structural impairments. This could be achieved in a similar manner to the in vitro experiments, with a PKA KO and replacement strategy of the tethered C-R plasmid, followed by structural or functional examination of neurons.

I would like to state that these experiments are not essential in my opinion, but any improvements in one of these directions would greatly improve and extend the impact and findings of this paper.

We thank the reviewer for the suggestion and the understanding. The suggested in vivo experiments are fascinating. However, in vivo imaging of dendritic spine morphology is already in itself challenging. The difficulty greatly increases when trying to detect partial, likely transient translocation of a signaling protein. It is also very difficult to knock down endogenous PKA while simultaneously expressing the R-C construct in a large number of cells to achieve detectable circuit or behavioral effect (and hope that compensation does not happen over weeks). We hope the reviewer agrees that these experiments would be their own project and go beyond the time and scope of the current study.

Reviewer #3 (Recommendations For The Authors):

Please elaborate on the methods used to visualize PKA-RIIα and PKA-RIβ subunits.

As suggested, we have now included additional details for visualizing PKA-Rs in the text. Specifically, we write (pg. 5): “…, as visualized using expressed PKA-R-mEGFP in separate experiments (Figs. 1A-1C).”.

It seems that you're able to make notes on commits. Since a commit can be referenced by a tag, or branch, you can make notes on those, too -- kind of.

Reviewer #1 (Public review):

Summary:

In their manuscript, Zhou et al. analyze the factors controlling the activation and maintenance of a sustained cell cycle block in response to persistent DNA DSBs. By conditionally depleting components of the DDC using auxin-inducible degrons, the authors verified that some of them are only required for the activation (e.g., Dun1) or the maintenance (e.g., Chk1) of the DSB-dependent cell cycle arrest, while others such as Ddc2, Rad24, Rad9 or Rad53 are required for both processes. Notably, they further show that after a prolonged arrest (>24 h) in a strain carrying two DSBs, the DDC becomes dispensable and the mitotic block is then maintained by SAC proteins such as Mad1, Mad2 or the mitotic exit network (MEN) component Bub2.

Strengths:

The manuscript dissects the specific role of different components of the DDC and the SAC during the induction of a cell cycle arrest induced by DNA damage, as well as their contribution for the short-term and long-term maintenance of a DNA DSB-induced mitotic block. Overall, the experiments are well described and properly executed, and the data in the manuscript are clearly presented. The conclusions drawn are generally well supported by the experimental data. Their observations contribute to drawing a clearer picture of the relative contribution of these factors to the maintenance of genome stability in cells exposed to permanent DNA damage.

Weaknesses:

The main weakness of the study is that it is fundamentally based on the use of the auxin-inducible degron (AID) strategy to deplete proteins. This widely used method allows an efficient depletion of proteins in the cell. However, the drawback is that a tag is added to the protein, which can affect the functionality of the targeted protein or modify its capacity to interact with others. In fact, three of the proteins that are depleted using the AID systems are shown to be clearly hypomorphic, and hence their capacity to induce a strong checkpoint response might be compromised. A corroboration of at least some of the results using an alternative manner to eliminate the proteins would help to strengthen the conclusions of the manuscript.

Author response:

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

Reviewer #1 (Recommendations For The Authors):

To hopefully contribute to more strongly support the conclusions drawn by the authors, I am including a series of concerns regarding the manuscript, as well as some suggestions that could be useful to address these issues:

(1) The main results of this study derive from the use of auxin-inducible degron (AID)-tagged proteins. Despite the great advantages of the AID strategy to conditionally deplete proteins, the AID tag can affect the normal function of a protein. In fact, some of the AID-labeled DDC components generated in this work are shown to be hypomorphic. Hence, the manuscript would have benefited from the additional confirmation of some of the observations using a different way to eliminate the proteins (e.g., temperature-sensitive mutants).

Most ts mutants are also hypomorphic; hence we don’t see there is much advantage to their use. The addition of the AID to these proteins alone does not interfere with the ability to sustain checkpoint arrest as demonstrated in Figure S1. Instead we found that by overexpressing Rad9-AID we could demonstrate that inactivating Rad9 after 15 h behaved the same way as the inactivation of Ddc2, significantly strengthening our finding that the DDC checkpoint becomes dispensable while the SAC takes over. 

(2) In cells depleted of Rad53-AID, the deletion of CHK1 stimulates an earlier release from a mitotic arrest induced by two DSBs (Figures 2D and 3C). Likewise, the authors claim that a faster escape from the cell cycle block can also be observed when upstream factors such as Ddc2, Rad9, or Rad24 are depleted in the absence of CHK1 (Figures 2A-C and Figures 3D-F). However, this earlier release from the cell cycle arrest, if at all, is only slightly noticeable in a Rad9-AID background (Figures 2B and 3E). In this sense, it is also worth pointing out that Rad9-AID chk1Δ (Figure 3E) and Rad24-AID chk1Δ (Figure 3F) cells were only evaluated up to 7 h, while in all other instances, cells were followed for 9 h, which hinders a fair assessment of the differences in the release from the cell cycle arrest.

As noted above, we have now been able to examine Rad9 over the long-time frame.

(3) Although only 25% of the cells depleted for Dun1 remained in G2/M arrest 7 h following the induction of two DSBs, it is shocking that Rad53 was nonetheless still phosphorylated after the cells had escaped the cell cycle blockage (Figure 4A).

This persistence of Rad53 phosphorylation is also seen with the inactivation of Mad2, allowing escape in spite of continued Rad53 phosphorylation.

(4) Generation of Rad9-AID2 and Rad24-AID2 strains did not fully restore the function of these proteins, since most cells had adapted 24 h after induction of two DSBs (Figure S1C). Nonetheless, Rad9-AID2 and Rad24-AID2 are still likely more stable than their AID counterparts, and hence the authors could have instead used the AID2 proteins for the experiments in Figure 2 to better evaluate the role of Rad9 and Rad24 in the maintenance of the DDC-dependent arrest.

We note again that we have found a way to study Rad9 up to 24 h. 

(5) Deletion of BFA1 has been shown to promote the escape from a cell cycle arrest triggered by telomere uncapping (Wang et al. 2000, Hu et al. 2001, Valerio-Santiago et al. 2013). Likewise, while cells carrying the cdc5-T238A allele cannot adapt to a checkpoint arrest induced by one irreparable DSB, BFA1 deletion rescues the adaptation defect of this mutant CDC5 allele (Rawal et al., 2016). The authors show how, using AID-degrons of Bfa1 and Bub2, that only Bub2, but not Bfa1, is required to maintain a prolonged cell cycle arrest after the induction of two DSBs. To reinforce this point, and as shown for mad2Δ cells (Figure S6A), the authors could perform a complete time course using both the Bfa1-AID and a bfa1Δ mutant to demonstrate that they do indeed show the same behavior in terms of the adaptation to a two DSB-induced cell cycle arrest.

We thank the reviewer for noting these other instances where bfa1D promoted an escape from arrest. We tested a 2-DSB bfa1 deletion, data has been added to Figure S9E-F. We did not observe a difference in the percentage of cells escaping arrest between the 2-DSB bfa1 deletion and the 2-DSB BFA1-AID strains.

(6) Bypass or adaptation of a checkpoint-induced cell cycle arrest in S. cerevisiae often leads to cells entering a new cell cycle without doing cytokinesis and, hence, to the accumulation of rebudded cells. However, the experiments shown in the manuscript only account for G1 or budded cells with either one or two nuclei. Do any of the mutants show cytokinesis problems and subsequent rebudding of the cells? If so, this should have been also noted and quantified in the corresponding assays.

In the cases we have studied we have not seen instances where the cells re-bud without completing mitosis (at least as assessed by the formation of budded cells with two distinct DAPI staining masses). In the morphological assays we have done, we score the continuation of the cell cycle by the appearance of multiple buds, G1, and small budded cells. In our adaptation assays when cells escaped G2/M arrest they formed microcolonies indicating no short-term deficiency in cell division.

(7) The location of the DSB relative to the centromere of a chromosome seems to be a factor that determines the capacity of the SAC to sustain a prolonged cell cycle arrest. The authors discuss the possibility that the DSB could somehow affect the structure of the kinetochore. Did they evaluate whether Mad1 or Mad2 were more actively recruited to kinetochores in those strains that more strongly trigger the SAC after induction of the DSBs?

We have not attempted to follow Mad1/2 recruitment. ChIP-seq could be used to monitor Mad1/2 localization at the 16 centromeres in response to DSBs and the spread of g-H2AX across the centromere. Our previous data showed that g-H2AX could spread across the centromere region and could create a change that would be detected by Mad1/2.  This change does not, however, affect the mitotic behavior of a strain in which the H2A genes have been modified to the possibly phosphomimetic H2A-S129E allele.

(8) The authors could speculate in the discussion about the reasons that could explain why the DDC is required for the maintenance of checkpoint arrest at early stages but then becomes dispensable for the preservation of a prolonged cell DNA DSB-induced cycle arrest, which is instead sustained at later stages by the SAC.

Our suggestion is that cells would have adapted, but modification of the centromere region engages SAC.

Finally, some minor issues are:

(1) The lines in the graphs that display the results from adaptation assays (e.g., Figures 1B and 1E) or cell and nuclear morphology (e.g., Figures 1D and 1G) are too thick. This makes it sometimes difficult to distinguish the actual percentages of cells in each category, particularly in the experiments monitoring nuclear division.

Fixed

(2) While both the adaptation assay and the analysis of nuclear division in Figures 1E and 1G, respectively, show a complete DDC-dependent arrest at 4h, the Western blot in Figure 1F suggests that Rad53 is not phosphorylated at that time point. Do these figures represent independent experiments? Ideally, the analysis of cell budding and nuclear division, which is performed in liquid cultures, and the Western blot displaying Rad53 phosphorylation should correspond to the same experiment.

Cell budding in liquid cultures and adaptation assays were performed in triplicate with 3 biological replicates and the collective results are shown in each graph showing the percentage of large-budded cells. Western blot samples were collected in each liquid culture experiment. The western blot in 1G is a representative western blot.

(3) It is somewhat confusing that the blots for the proteins are not displayed in the same order in Figures 2A (Rad53 at the top) and 2B or 2C (Rad53 in the middle).

Fixed.  We place Rad53 – the relevant protein - at the top.

Reviewer #2 (Recommendations For The Authors):

(1) Yeast with the two breaks responds to DNA damage checkpoint (DDC) until sometimes 4-15 h post DNA damage. Since the auxin-induced degradation does not completely deplete all the tagged proteins in cells, the results should be more carefully considered and not to interpret if the checkpoint entry or maintenance depends on each target protein's ability to induce Rad53 phosphorylation. It should be theoretically possible if checkpoint maintenance requires only a modest amount of checkpoint factors especially because the experiments involve the induction of one or two DSBs. The low levels of DDC factors may be insufficient for Rad53 activation but could still be effective for cell cycle arrest. Indeed, the Haber group showed that the mating type switch did not induce Rad53 phosphorylation but still invoked detectable DNA damage response. To test such possibilities, the authors might consider employing yet another marker for DDC such as H2A or Chk1 phosphorylation besides Rad53 autophosphorylation. Alternatively, the authors might check if auxin-induced depletion also disrupts break-induced foci formation for checkpoint maintenance or their enrichment at DNA breaks using ChIP assays at various points post-damage.

DAPI staining of Ddc2-AID cells show that when IAA is added 4 h after DSB induction (Figure S3A), cells escape G2/M arrest as evidenced by the increase in large-budded cells with 2 DAPI signals, small budded cells, and G1 cells. Overexpression of Ddc2 can sustain the checkpoint past 24 h, but without SAC proteins like Mad2 they will eventually adapt (Figure S6B).

That Rad9-AID or Rad24-AID in the absence of added auxin (but in the presence of TIR1) is unable to sustain arrest suggests to us that low levels of Rad9 or Rad24 are not sufficient to maintain arrest.  As the reviewer notes, normal MAT switching doesn’t cause Rad53 phosphorylation or arrest, though early damage-induced events such as H2A phosphorylation do occur.  But our point is that Rad9 or Ddc2 is needed to maintain arrest only up to a certain point, after which they become superfluous and a different checkpoint arrest is imposed. At that point apparently a low level of these proteins plays no obvious role.

(2) It is interesting that DDC no longer responds to the damage signaling after 15 h of DSB-induced prolonged checkpoint arrest after two DNA double-strand breaks. Is this also applicable to other adaptation mutants? The results might improve the broad impact of the current conclusions. It is also possible that the transition from DDC to SPC depends on simply the changes in signaling or in part due to the molecular changes in the status of DNA breaks or its flanking regions. Indeed, the proposed model suggests that the spreading of H2A phosphorylation to centromeric regions induces SAC and thus mitotic arrest. The authors could measure H2A phosphorylation near the centromere using ChIP assays at various intervals post-DNA damage. It is particularly interesting if depletion of Ddc2 at 15 h post DNA damage does not alter the level of H2A phosphorylation at or near centromere.

Our previous data have suggested that the involvement of the SAC in prolonging DSB-induced arrest involved post-translational modification of centromeric chromatin such as the Mec1- and Tel1-dependent phosphorylation of the histone H2A (Dotiwala). In budding yeast there is also a similar DSB-induced modification of histone H2B (Lee et al.). To ask if there is an intrinsic activation of the SAC if the regions around centromeres were modified by checkpoint kinase phosphorylation, we examined cell cycle progression in strains in which histone H2A or histone H2B was mutated to their putative phosphomimetic forms (H2A-S129E and H2B-T129E).  As shown in Figure S11, there was no effect on the growth rate of these strains, or of the double mutant, suggesting that cells did not experience a delay in entering mitosis because of these modifications. We note that although histone H2A-S129E is recognized by an antibody specific for the phosphorylation of histone H2A-S129, the mutation to S129E may not be fully phosphomimetic. 

(3) It is puzzling why Rad9-AID or Rad24-AID are proficient for DDC establishment but cannot sustain permanent arrest in the two break cells. It appears Rad53 phosphorylation for DDC is weaker in cells expressing Rad9-AID or Rad24-AID according to Fig.2B and C even though their protein level before IAA treatment is still robust. This might also explain why the results of depleting Rad53 and Rad9 are very different. It also raises concern if the effect of Rad24 depletion on checkpoint maintenance is in part due to the weaker checkpoint establishment. It might be necessary to use the AID2 system to redo Rad24 depletion to exclude such a possibility.

We believe that the AID mutants are very sensitive to the low level of IAA present in yeast.  The instability of the protein is entirely dependent on the TIR1 SCF factor, so the proteins themselves are not intrinsically defective; they are just subject to degradation.  Overexpressing Rad9 allowed us to evaluate its role at late time points. 

(4) It is intriguing that the switch from DDC to SAC might take place at around 12 h when yeasts with a single unrepairable break ignore DDC and resume cell cycling (so-called "adaptation"). Since 4h and 15h are far apart and the transition point from DDC to SAC likely takes place between these two points, it will be very helpful to analyze and compare cell cycle exit after 24 h by treating IAA at multiple points between 4-15h.

When we add IAA to Mad2-AID and Mad1-AID 4 h after DSB induction, cells remain arrested for up to 12 h after DSB induction. At 15 h cells begin to exit checkpoint arrest indicating that the handoff of checkpoint arrest must occur between 12 to 15 h after DSB induction. If we degraded DNA damage checkpoint proteins at any point before Mad2, Mad1, and Bub2 begin to contribute to checkpoint arrest, then arrested cells will likely adapt in a similar manner to when IAA was added 4 h after DSB induction.

(5) Some of the Western blot quality is poor. For instance, in Figure 6C, Mad1-AID level after IAA addition is not compelling especially because the TIR level (the loading control) is also very low.

In Figure 6C, while the relative levels of TIR1 are similar in the IAA treated and untreated samples, there is no detectable amount of Mad1-AID in the IAA treated samples indicating that Mad1-AID was successful degraded with the AID system.

(6) Fig. 8 is complex. It might be helpful to define the different types of arrows in the figure. The legend also has a spelling error, Rad23 should be Rad24.

We’ve defined what each arrow means in the legend and corrected the spelling error in the figure legend.

Reviewer #3 (Recommendations For The Authors):

Major concerns:

Much of the manuscript states that two unrepairable DSBs lead to a long and severe G2/M arrest. Two main cytological approaches are used to make this statement: bud size and number on plates after micromanipulation (microcolony assay), and cell and nuclear morphology in liquid cultures. While the latter gives a clear pattern that can be assigned to a G2/M block as expected by DDC, i.e. metaphase-like mononucleated cells with large buds, the former can only tell whether cells eventually reach a second S phase (large budded cells on the plate can be in a proper G2/M arrest, but can also be in an anaphase block or even in the ensuing G1). The authors always performed the microcolony assay, but there are several cases where the much more informative budding/DAPI assay is missing. These include Dun1-aid and others, but more importantly chk1D and its combinations with DDC proteins. Incidentally, for the microcolony assay, it is more accurate to label the y-axis of the corresponding graphs (and in the figure legends and main text) with something like "large budded cells"; "G2/M arrested cells" is misleading.

Figures have been updated to more accurately reflect what we are measuring.

The results obtained with the Bfa1/Bub2 partner are intriguing. These two proteins form a complex whose canonical function is to prevent exit from mitosis until the spindle is properly aligned, acting in a distinct subpathway within the SAC that blocks MEN rather than anaphase onset. The data presented by the authors suggest that, on the one hand, both SAC subpathways work together to block the cell cycle. However, why does canonical SAC (Mad1/Mad2) inactivation not lead to a transition from G2/M (metaphase-like) arrested cells to anaphase-like arrest maintained by Bfa1-Bub2? Since Bfa1-Bub2 is a target of DDC, is it possible that DDC knockdown also inactivates this checkpoint, allowing adaptation? On the other hand, can the authors provide more data to confirm and strengthen their claim of a Bfa1-independent Bub2 role in prolonged arrest? Perhaps long-term protein localization and PTM changes. Bub2-independent roles for Bfa1 have been reported, but not vice versa, to the best of my knowledge.

In the mitotic exit network Bfa1/Bub2 prime activation of the pathway by bringing Tem1 to spindle pole bodies. Phosphorylation of Bfa1 causes Tem1 to be released and phosphorylate Cdc5 to trigger exit by MEN. It has been shown that DNA damage, in a cdc13-1 ts mutant, phosphorylates Bfa1 in a Rad53 and Dun1 dependent manner. This phosphorylation of Bfa1 could release Tem1 and prime cells to exit checkpoint arrest when cells pass through anaphase. Looking at Tem1 localization to spindle pole bodies and interactions with Bfa1/Bub2 in response to DNA damage might give insight into why cells don’t experience an anaphase-like arrest when they are released by either deactivation of the DNA damage checkpoint or SAC.

We have previously shown that a deletion of bub2 in a 1-DSB background shortens DSB-induced checkpoint arrest. Deletion of bfa1 in a 2-DSB background showed ~80-70% of cells stuck in a large-budded state as measured through an adaptation assay tracking the morphology of G1 cells on a YP-Gal plate and DAPI staining. Deletion or degradation of bfa1 might not release cells from arrest because the Mad2/Mad1 prevent cells from transitioning into anaphase. Our DAPI data for Bub2-AID shows an increase in cells with 2 DAPI signals (transition into anaphase) and small budded cells indicating that degradation of Bub2 is releasing cells into anaphase and allowing cells to complete mitosis.

Further suggestions:

It would be richer if authors could provide more than one experimental replicate in some panels (e.g., S1A,B; S4A; and S6B).

S1C confirms that Rad9-AID and Rad24-AID will adapt by 24 h even with the point mutant TIR1(F74G) which has lower basal degradation than TIR1. S4A has been updated with additional experimental replicates. The 48 h timepoint after DSB induction was to show the importance of Mad2 even when Ddc2 is overexpressed.

Figure 1: Rearrange figure panels when they are first mentioned in the text. For example, it makes more sense to have the plate adaptation assay as panel B for both 1-DSB and 2-DSB strains, budding plus DAPI as panel C, and Rad53 as panel D.

These figures have been rearranged in the order that they are mentioned in the paper.

Figure 5: Correct Ph-5-IAA in the Rad53 WBs (it should be 5-Ph-IAA).

This has been corrected.

Figure S2: The straight line under the "+IAA" text box is misleading. I think it should also cover the "-2" time point, right? Also, check the figure legend. Information is missing and does not correspond to the figure layout.

This has been corrected.

Figure S3: Perhaps "Cell cycle profile as determined by budding and DAPI staining" is a better and more accurate legend title.

The legend title has been updated to “Cell cycle profile as determined by budding and DAPI staining in Ddc2-AID and Rad53-AID mutants ± IAA 4 h after galactose.”

Figure S5: Detection of both Rad53 and Ddc2 in the same blot could lead to misinterpretation as hyperphosphorylated Rad53 appears to coincide with Ddc2 migration.

Figure S5A-B are representative western blots where Rad53 was probed to show activation of the DNA damage checkpoint by Rad53 phosphorylation. When measuring the relative abundance of Ddc2 we did not probe all blots for Rad53.

Table S1: Include the post-hoc test used for comparisons after ANOVA.

A Sidak post-hoc test was used in PRISM for the one-way ANOVA test. PRISM listed the Sidak post-hoc test as the recommended test to correct for multiple comparisons. A column has been added to S. Table 1 to show which post-hoc test was used.

Page 10, line 4: The putative additive effect of chk1 knockout with Dun1 depletion should also be compared to chk1 alone (in Figure 3A).

We address the additive effect of chk1 knockout with Dun1-AID depletion in a later section on Page 11, line 6. Since we had not explored possible effects from downstream targets of Rad53 for prolonging checkpoint arrest when Rad53 was depleted, we did not mention the effect of the chk1 knockout on Dun1 depletion.

Page 14, second paragraph, line 4: "Figure 6A-D", is it not?

Figure S6A is measuring checkpoint arrest in a deletion of mad2 in a 2-DSB strain. Figure 6A-D shows how degradation of Mad2-AID and Mad1-AID after the handoff of arrest causes cells to exit the checkpoint in a Rad53 independent manner.

Author response:

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

Public Review:

Summary:

Bursicon is a key hormone regulating cuticle tanning in insects. While the molecular mechanisms of its function are rather well studied--especially in the model insect Drosophila melanogaster, its effects and functions in different tissues are less well understood. Here, the authors show that bursicon and its receptor play a role in regulating aspects of the seasonal polyphenism of Cacopsylla chinensis. They found that low temperature treatment activated the bursicon signaling pathway during the transition from summer form to winter form and affect cuticle pigment and chitin content, and cuticle thickness. In addition, the authors show that miR-6012 targets the bursicon receptor, CcBurs-R, thereby modulating the function of bursicon signaling pathway in the seasonal polyphenism of C. chinensis. This discovery expands our knowledge of the roles of neuropeptide bursicon action in arthropod biology.

However, the study falls short of its claim that it reveals the molecular mechanisms of a seasonal polyphenism. While cuticle tanning is an important part of the pear psyllid polyphenism, it is not the equivalent of it. First, there are other traits that distinguish between the two morphs, such as ovarian diapause (Oldfield, 1970), and the role of bursicon signaling in regulating these aspects of polyphenism were not measured. Thus, the phenotype in pear psyllids, whereby knockdown bursicon reduces cuticle tanning seems to simply demonstrate the phenotypes of Drosophila mutants for bursicon receptor (Loveall and Deitcher, 2010, BMC Dev Biol) in another species (Fig. 2I, 4H). Second, the study fails to address the threshold nature of cuticular tanning in this species, although it is the threshold response (specifically, to temperature and photoperiod) that distinguishes this trait as a part of a polyphenism. Whereas miR-6012 was found to regulate bursicon expression, there no evidence is provided that this microRNA either responds to or initiates a threshold response to temperature. In principle, miR-6012 could regulate bursicon whether or not it is part of a polyphenism. Thus, the impact of this work would be significantly increased if it could distinguish between seasonal changes of the cuticle and a bona fide reflection of polyphenism.

Thanks for your valuable suggestion. We concur with the review’s comment that cuticle tanning does not equate to the C. chinensis polyphenism. To better reflect the core focus of our research, we have revised the title to "Neuropeptide Bursicon and its receptor mediated the transition from summer-form to winter-form of Cacopsylla chinensis".

In response to the reviewer's inquiry regarding the threshold nature of cuticular tanning in C. chinensis, we have included a detailed analysis of the phenotypic changes (including nymph phenotypes, cuticle pigment absorbance, and cuticle thickness) during the transition from summer-form to winter-form in C. chinensis at distinct time intervals (3, 6, 9, 12, 15 days) under different temperature conditions (10°C and 25°C). As shown in Figure S1, nymphs exhibit a light yellow and transparent coloration at 3, 6, and 9 days, while nymphs at 12 and 15 days display shades of yellow-green or blue-yellow under 25°C conditions. At 10°C conditions, the abdomen end turns black at 3, 6, and 9 days. By the 12 days, numerous light black stripes appear on the chest and abdomen of nymphs at 10°C. At 15 days, nymphs exhibit an overall black-brown appearance, featuring dark brown stripes on the left and right sides of each chest and abdominal section. Furthermore, the end of the abdomen and back display a large black-brown coloration at 10°C (Figure S1A). The UV absorbance of the total pigment extraction at a 300 nm wavelength markedly increases following 10°C exposure for 6, 9, 12, and 15 days compared to the 25°C treatment group (Figure S1B). Cuticle thicknesses also increased following 10°C exposure for 6, 9, 12, and 15 days compared to the 25°C treatment group (Figure S1C). The detailed results (L122-143), materials and methods (L647-652), and discussion (L319-322) have been added in our revised manuscript.

Regarding the response of miR-6012 to temperature, we have already determined its expression at 3, 6, 10 days under different temperatures in the previous Figure 5E. We now included additional time intervals (9, 12, 15 days) in the updated Figure 5E. Our results indicate a significant decrease in the expression levels of miR-6012 after 10°C treatment for 3, 6, 9, 12, 15 days compared to the 25°C treatment group. Detailed information regarding this has been integrated into the Materials and Methods (Line 608-610) of our revised manuscript.

Strengths:

This study convincingly identifies homologs of the genes encoding the bursicon subunits and its receptor, showing an alignment with those of another psyllid as well as more distant species. It also demonstrates that the stage- and tissue-specific levels of bursicon follow the expected patterns, as informed by other insect models, thus validating the identity of these genes in this species. They provide strong evidence that the expression of bursicon and its receptor depend on temperature, thereby showing that this trait is regulated through both parts of the signaling mechanism.

Several parallel measurements of the phenotype were performed to show the effects of this hormone, its receptor, and an upstream regulator (miR-6012), on cuticle deposition and pigmentation (if not polyphenism per se, as claimed). Specifically, chitin staining and TEM of the cuticle qualitatively show difference between controls and knockdowns, and this is supported by some statistical tests of quantitative measurements (although see comments below). Thus, this study provides strong evidence that bursicon and its receptor play an important role in cuticle deposition and pigmentation in this psyllid.

The study identified four miRNAs which might affect bursicon due to sequence motifs. By manipulating levels of synthetic miRNA agonists, the study successfully identified one of them (miR-6012) to cause a cuticle phenotype. Moreover, this miRNA was localized (by FISH) to the cuticle, body-wide. To our knowledge, this is the first demonstrated function for this miRNA, and this study provides a good example of using a gene of known function as an entry point to discovering others influencing a trait. Thus, this finding reveals another level of regulation of cuticle formation in insects.

Weaknesses:

(1) The introduction to this manuscript does not accurately reflect progress in the field of mechanisms underlying polyphenism (e.g., line 60). There are several models for polyphenism that have been used to uncover molecular mechanisms in at least some detail, and this includes seasonal polyphenisms in Hemiptera. Therefore, the justification for this study cannot be predicated on a lack of knowledge, nor is the present study original or unique in this line of research (e.g., as reviewed by Zhang et al. 2019; DOI: 10.1146/annurev-ento-011118-112448). The authors are apparently aware of this, because they even provide other examples (lines 104-108); thus the introduction seems misleading as framed.

Thanks for your excellent suggestion. We have added the paper of Zhang et al. 2019 which recommended by reviewer (DOI: 10.1146/annurev-ento-011118-112448) in Line 57 of our revised manuscript. The statement has been revised to “However, the specific molecular mechanism underling temperature-dependent polyphenism still require further clarification” in Line 60-61 of our revised manuscript.

(2) The data in Figure 2H show "percent of transition." However, the images in 2I show insects with tanned cuticle (control) vs. those without (knockdown). Yet, based on the description of the Methods provided, there appears to be no distinction between "percent of transition" and "percent with tanning defects". This an important distinction to make if the authors are going to interpret cuticle defects as a defect in the polyphenism. Furthermore, there is no mention of intermediate phenotypes. The data in 2H are binned as either present or absent, and these are the phenotypes shown in 2I. Was the phenotype really an all-or-nothing response? Instead of binning, which masks any quantitative differences in the tanning phenotypes, the authors should objectively quantify the degree of tanning and plot that. This would show if and to what degree intermediate tanning phenotypes occurred, which would test how bursicon affects the threshold response. This comment also applies to the data in Figures 4G and 6G. Since cuticle tanning is present in more insect than just those with seasonal polyphenism, showing how this responds as a threshold is needed to make claims about polyphenism.

We appreciate your insightful comments. As shown in Figure 1 of our published paper (Zhang et al., 2013; doi.org/10.7554/eLife.88744.3) and Figure 2C-2I of the current manuscript, the transition from summer-form to winter-form entails not only external cuticular tanning but also alterations in internal cuticular chitin levels and cuticle thickness. While external cuticular tanning serves as a prominent and easily observable indicator of this transition, it is crucial to acknowledge that internal changes also play a significant role and should be taken into consideration. Therefore, we propose that the term "percent of transition" may be more suitable than "percent with tanning defects" to describe this process accurately.

In order to provide a more visually comprehensive understanding of the phenotypic changes during the transition from summer-form to winter-form, we have included images at different time points (3, 6, 9, 12, 15 days) under different temperature conditions in Figure S1A of our revised manuscript. Specifically, under the 10°C condition, nymphs exhibit abdomen tanning after 6 and 9 days of treatment, while the thorax remains untanned. By days 12 to 15, both the abdomen and thorax of the nymphs show tanning, resulting in the majority of summer-form nymphs transitioning into winter-form, as depicted in Figure 2I for comparison. This observation indicates the presence of a critical threshold for cuticle tanning of C. chinensis following exposure to 10°C. Nymphs that did not undergo the transition to winter-form succumbed to the cold, highlighting the absence of intermediate phenotypes at 12-15 days under the 10°C condition. The UV absorbance of the total pigment extraction at a 300 nm wavelength markedly increases following 10°C exposure for 6, 9, 12, and 15 days compared to the 25°C treatment group (Figure S1B). Additionally, cuticle thickness shows an increase following 10°C exposure for 6, 9, 12, and 15 days compared to the 25°C treatment group (Figure S1C). These results highlight the relationship between the threshold of cuticular tanning and the transition process. The detailed description and information have been added in Results (L122-143), Materials and Methods (L647-652), and Discussion (L319-322) of our manuscript.

(3) This study also does not test the threshold response of cuticle phenotypes to levels of bursicon, its receptor, or miR-6012. Hormone thresholds are the most widespread and, in most systems where polyphenism has been studied, the defining characteristic of a polyphenism (e.g., Nijhout, 2003, Evol Dev). Quantitative (not binned) measurements of a polyphenism marker (e.g., chitin) should be demonstrated to result as a threshold titer (or in the case of the receptor, expression level) to distinguish defects in polyphenism from those of its component trait.

Thanks for your valuable feedback. We have supplemented additional data on the phenotypes (Figure S1A), cuticle pigment absorbance (Figure S1B), cuticle thickness (Figure S1C), expression levels of bursicon (Figure 1E and 1F), its receptors (Figure 3G), and miR-6012 (Figure 5E) corresponding to nymphs treated over different time periods (3, 6, 9, 12, 15 days) under both 10°C and 25°C conditions in our revised manuscript.

While all these identified markers exhibit a strong correlation with the transition from summer-form to winter-form, it is important to note that they are not suitable as definitive thresholds due to the nature of relative gene expression quantification and chitin content assessment, rather than absolute quantitation. Further, given that tanning hormones are neuropeptides present in trace amounts in insects, unlike steroid hormones, determining their titers poses a considerable challenge.

(4) Cuticle issue:

(a) Unlike Fig. 6D and F, Figs. 2D and F do not correspond to each other. Especially the lack and reduction of chitin in ds-a+b! By fluorescence microscopy there is hardly any signal, whereas by TEM there is a decent cuticle. Additionally, the dsGFP control cuticle in 2D is cut obliquely with a thick and a thin chitin layer. This is misleading.

Thanks for your insightful feedback. We have replaced the previous WGA chitin staining images in the dsCcbursα+β treatment of Figure 2D with new representative images aligning with Figure 2F. Furthermore, the presence of both thin and thick chitin layers observed in the dsEGFP treatment of Figure 2D could potentially be ascribed to the chitin content in the insect midgut or fat body as previously discussed (Zhu et al., 2016). It is notable that during the process of cuticle staining, the chitin located in the midgut and fat body of C. chinensis may exhibit green fluorescence, leading to the appearance of a thin chitin layer. A detailed analysis and elucidation of these observations have been added in the discussion section (Lines 347-352) of our revised manuscript.

Zhu KY, Merzendorfer H, Zhang W, Zhang J, Muthukrishnan S. Biosynthesis, Turnover, and Functions of Chitin in Insects. Annu Rev Entomol. 2016;61:177-196. doi:10.1146/annurev-ento-010715-023933.

(b) In Figs. 2F and 4F, the endocuticle appears to be missing, a portion of the procuticle that is produced post-molting. As tanning is also occurring post-molting, there seems to be a general problem with cuticle differentiation at this time point. This may be a timing issue. Please clarify.

Thank you for your suggestion. The insect cuticle typically comprises three distinct layers (endocuticle, exocuticle, and epicuticle), with the thickness of each layer varying among different insect species. Cuticle differentiation is closely linked to the molting cycle of insects (Mrak et al., 2017). In our study, nymphal cuticles exhibited normal differentiation patterns, characterized by a thin epicuticle and comparable widths of the endocuticle and exocuticle following dsEGFP treatment, as illustrated in Figure 2F and 4F. Conversely, nymphs treated with dsCcBurs-α, dsCcBurs-β, and dsCcburs-R displayed impaired development, manifesting only the exocuticle without a discernible endocuticle layer. These findings suggest that bursicon genes and their receptor play a pivotal role in regulating insect cuticle development (Costa et al., 2016). We have added some discussion about these results in Lines 356-367 of our revised manuscript.

Mrak, P., Bogataj, U., Štrus, J., & Žnidaršič, N. (2017). Cuticle morphogenesis in crustacean embryonic and postembryonic stages. Arthropod structure & development, 46(1), 77–95. https://doi.org/10.1016/j.asd.2016.11.001

Costa, C. P., Elias-Neto, M., Falcon, T., Dallacqua, R. P., Martins, J. R., & Bitondi, M. (2016). RNAi-mediated functional analysis of Bursicon genes related to adult cuticle formation and tanning in the Honeybee, Apis mellifera. PloS one, 11(12), e0167421. https://doi.org/10.1371/journal.pone.0167421

(c) To provide background information, it would be useful analyze cuticle formation in the summer and winter morphs of controls separately by light and electron microscopy. More baseline data on these two morphs is needed.

Thanks for your valuable feedback. To provide more background information about cuticle formation, we supplied the results of nymph phenotypes, cuticle pigment absorbance, and cuticle thickness at distinct time intervals (3, 6, 9, 12, 15 days) under different temperatures of 10°C and 25°C in Figure S1 of our revised manuscript. Hope these results can help better understand the baseline data on these two morphs.

(d) For the TEM study, it is not clear whether the same part of the insect's thorax is being sectioned each time, or if that matters. There is not an obvious difference in the number of cuticular layers, but only the relative widths of those layers, so it is difficult to know how comparable those images are. This raises two questions that the authors should clarify. First, is it possible that certain parts of the thoracic cuticle, such as those closer to the intersegmental membrane, are naturally thinner than other parts of the body? Second, is the tanning phenotype based on the thickness or on the number of chitin layers, or both? The data shown later in Figure 4I, J convincingly shows that the biosynthesis pathway for chitin is repressed, but any clarification of what this might mean for deposition of chitin would help to understand the phenotypes reported. Also, more details on how the data in Fig. 2G were collected would be helpful. This also goes for the data in Fig. 4 (bursicon receptor knockdowns).

Thanks for your great comment. The TEM investigation adhered to a standardized protocol was used as previous description (Zhang et al., 2023), Initially, insect heads were uniformly excised and then fixed in 4% paraformaldehyde. Subsequently, a consistent cutting and staining procedure was executed at a uniform distance above the insect's thorax. The dorsal region of the thorax was specifically chosen for subsequent fluorescence imaging or transmission electron microscopy assessments with the specific objective of quantifying cuticle thickness. Regarding the measurement of cuticle thickness, use the built-in measuring ruler on the software to select the top and bottom of the same horizontal line on the cuticle. Measure the cuticle of each nymph at two close locations. Six nymphs were used for each sample. Randomly select 9 values and plot them. The related description has been added in the Materials and Methods (Line 660-668) of our revised manuscript.

Zhang, S.D., Li, J.Y., Zhang, D.Y., Zhang, Z.X., Meng, S.L., Li, Z., & Liu, X.X. (2023). MiR-252 targeting temperature receptor CcTRPM to mediate the transition from summer-form to winter-form of Cacopsylla chinensis. eLife, 12. https://doi.org/10.7554/eLife.88744

(5) Tissue issue:

The timed experiments shown in all figures were done in whole animals. However, we know from Drosophila that Bursicon activity is complex in different tissues. There is, thus, the possibility, that the effects detected on different days in whole animals are misleading because different tissues--especially the brain and the epidermis, may respond differentially to the challenge and mask each other's responses. The animal is small, so the extraction from single tissue may be difficult. However, this important issue needs to be addressed.

Thanks for your excellent suggestion. We express our heartfelt appreciation to the reviewer for their valuable input regarding the challenges involved in dissecting various tissue sections from the diminutive early instar nymphs of C. chinensis. In light of the metamorphic transition of C. chinensis across developmental stages, this study concentrated on examining the extensive phenotypic alterations. Consequently, intact samples of C. chinensis were specifically chosen for for qPCR analysis. The related descriptions have been added in the Materials and Methods (Line 513, 517, 553, 555, and 613) and Discussion (Line 327-329) of our revised manuscript.

(6) No specific information is provided regarding the procedure followed for the rescue experiments with burs-α and burs-β (How were they done? Which concentrations were applied? What were the effects?). These important details should appear in the Materials and Methods and the Results sections.

Thanks for your excellent suggestion. For the rescue experiments, the dsRNA of CcBurs-R and proteins of burs α-α, burs β-β homodimers, or burs α-β heterodimer (200 ng/μL) were fed together. The concentration of heterodimer protein of CcBurs-α+β was 200 ng/μL. The heterodimer protein of CcBurs-α+β fully rescued the effect of RNAi-mediated knockdown on CcBurs-R expression, while α+α or β+β homodimers did not (Figure 3F). Feeding the α+β heterodimer protein fully rescued the defect in the transition percent and morphological phenotype after CcBurs-R knockdown (Figure 4G-4H). We have added the detailed methods of rescued experiments and specific concentrations in the Materials and Methods (Line 561-563), and Results (Line 263) of our revised manuscript.

(7) Pigmentation

(a) The protocol used to assess pigmentation needs to be validated. In particular, the following details are needed: Were all pigments extracted? Were pigments modified during extraction? Were the values measured consistent with values obtained, for instance, by light microscopy (which should be done)?

Thanks for your excellent comment. Our protocol for pigment extracted as detailed in Bombyx mori, the cuticles were pulverized in liquid nitrogen and then dissolved in 30 milliliters of acidified methanol (Futahashi et al., 2012; Osanai-Futahashi et al., 2012). Thus, all cuticle pigments were dissected and treated with acidified methanol. Pigments were not modified during extraction.. The details description have been integrated into the Materials and Methods (Line 630-633) of our revised manuscript.

Futahashi, R., Kurita, R., Mano, H., & Fukatsu, T. (2012). Redox alters yellow dragonflies into red. Proceedings of the National Academy of Sciences of the United States of America, 109(31), 12626–12631. https://doi.org/10.1073/pnas.1207114109

Osanai-Futahashi, M., Tatematsu, K. I., Yamamoto, K., Narukawa, J., Uchino, K., Kayukawa, T., Shinoda, T., Banno, Y., Tamura, T., & Sezutsu, H. (2012). Identification of the Bombyx red egg gene reveals involvement of a novel transporter family gene in late steps of the insect ommochrome biosynthesis pathway. The Journal of biological chemistry, 287(21), 17706–17714. https://doi.org/10.1074/jbc.M111.321331

(b) In addition, pigmentation occurs post-molting; thus, the results could reflect indirect actions of bursicon signaling on pigmentation. The levels of expression of downstream pigmentation genes (ebony, lactase, etc) should be measured and compared in molting summer vs. winter morphs.

Thanks for your valuable suggestion. Actually, we already studied the function of some downstream pigmentation genes, including ebony, Lactase, Tyrosine hydroxylase, Dopa decarboxylase, and Acetyltransferase. The variations in the expression patterns of these genes are closely tied to the molting dynamics of nymphs undergoing transitions between summer-form and winter-form. These findings will put in another manuscript currently being prepared for submission, thus detailed outcomes are not suitable for inclusion in the current manuscript.

(8) L236: "while the heterodimer protein of CcBurs α+β could fully rescue the effect of CcBurs-R knockdown on the transition percent (Figure 4G 4H)". This result seems contradictory. If CcBurs-R is the receptor of bursicon, the heterodimer protein of CcBurs α+β should not be able to rescue the effect of CcBurs-R knockdown insects. How can a neuropeptide protein rescue the effect when its receptor is not there! If these results are valid, then the CcBurs-R would not be the (sole) receptor for CcBurs α+β heterodimer. This is a critical issue for this manuscript and needs to be addressed (also in L337 in Discussion).

Thanks for your insightful suggestion. Following the administration of dsCcBur-R to C. chinensis, the expression of CcBurs-R exhibited a reduction of approximately 66-82% as depicted in Figure 4A, rather than complete suppression. Activation of endogenous CcBurs-R through feeding of the α+β heterodimer protein results in an increase in CcBurs-R expression, with the effectiveness of the rescue effect contingent upon the dosage of the α+β heterodimer protein. Consequently, the capacity of the α+β heterodimer protein to effectively mitigate the impacts of CcBurs-R knockdown on the conversion rate is clearly demonstrated. We have added additional discussion in Line 396-403 of our revised manuscript.

(9) Fig. 5D needs improvement (the magnification is poor) and further explanation and discussion. mi6012 and CcBurs-R seem to be expressed in complementary tissues--do we see internal tissues also (see problem under point 2)? Again, the magnification is not high enough to understand and appreciate the relationships discussed.

Thanks for your valuable suggestion. In order to enhance the resolution of the magnified images, we conducted FISH co-localization of miR-6012 and CcBurs-R in 3rd instar nymphs and obtained detailed zoomed-in images. As shown in the magnified view of Figure 5D, miR-6012 and CcBurs-R appear to exhibit complementary expression patterns in tissues. During the FISH assays, epidermis transparency of C. chinensis was achieved via decolorization treatment. Noteworthy observations from Figure 3G and Figure 5E reveal an inverse correlation in the expression profiles of CcBurs-R and miR-6012. Consequently, the FISH results distinctly highlight a significant disparity in the expression levels of CcBurs-R and miR-6012 within the same tissue. We have added related explanation and discussion in Line 291-293 of our revised manuscript.

(10) The schematic in Fig. 7 is a useful summary, but there is a part of the logic that is unsupported by the data, specifically in terms of environmental influence on cuticle formation (i.e., plasticity). What is the evidence that lower temperatures influence expression of miR-6012? The study measures its expression over life stages, whether with an agonist or not, over a single temperature. Measuring levels of expression under summer form-inducing temperature is necessary to test the dependence of miR-6012 expression on temperature. Otherwise, this result cannot be interpreted as polyphenism control, but rather the control of a specific trait.

Thanks for your great suggestion. We actually conducted the assessment of miR-6012 expression at specific time intervals (3, 6, 9, 12, 15 days) under different temperatures of 10°C and 25°C. As depicted in Figure 5E, the expression levels of miR-6012 were notably reduced at 10°C compared to 25°C. Additionally, the evaluation of agomir-6012 expression level of C. chinensis under 25°C conditions at various time points (3, 6, 9, 12, 15 days) revealed no significant changes. Hence, we suggest that the impact of miR-6012 on the seasonal morphological transition is influenced upon temperature.

Recommendations for the authors:

The authors report a novel role of Bursicon and its receptor in regulating the seasonal polyphenism of Cacopsylla chinensis. They found that low temperature treatment (10°C) activated the Bursicon signaling pathway during the transition from summer-form to winter-form, which influences cuticle pigment content, cuticle chitin content, and cuticle thickness. Moreover, the authors identified miR-6012 and show that it targets CcBurs-R, thereby modulating the function of Bursicon signaling pathway in the seasonal polyphenism of C. chinensis. This discovery expands our knowledge of multiple roles of neuropeptide bursicon action in arthropod biology. However, the m

anuscript does have several major weaknesses, described under "Public review", which the authors need to address.

Major issues:

(1) L152-154 Fig S2E and S2F: Bursicon has been shown to be expressed in the CNS in a specific set of neurons. For example, In the larval CNS of Manduca sexta, bursicon expression is restricted to the subesophageal ganglion (SG), thoracic ganglia, and first abdominal ganglion. Pharate pupae and pharate adults show expression of this heterodimer in all ganglia. In Drosophila larvae, expression of a bursicon heterodimer is confined to abdominal ganglia. The additional neurons in the ventral nerve cord express only burs. In pharate adults, bursicon is produced by neurons in the SG and abdominal ganglia. I am wondering where bursicon subunits are expressed in the C. chinensis CNS? Since the authors have the antibodies, it would be useful to include immunocytochemical staining of bursicon alpha and beta in the CNS. The qPCR results from head or other tissues (Fig S2E and S2F) is not the most informative way to document localization of gene expression. Regarding the qPCR results, they show that the cuticle and the fat body express CcBurs-α and CcBurs-β. Can the authors confirm this unexpected results independently?

Thanks for your insightful comment. In this study, we did not directly used antibodies targeting bursicon subunits, instead, the bursicon subunits along with a histidine tag were integrated into the expression vector pcDNA3.1 using homologous recombination. The experimental procedures were executed as follows: initially, the histidine tag was fused to the pcDNA3.1-mCherry vector through homologous recombination to generate the recombinant plasmid pcDNA3.1-his-mCherry. Subsequently, the amino acid sequences of the two bursicon subunits were introduced into the pcDNA3.1-his-mCherry vector via homologous recombination to produce the recombinant plasmids pcDNA3.1-CcBurs-α-his-mCherry and pcDNA3.1-CcBurs-β-his-mCherry. Finally, the P2A sequence was incorporated into the vector using reverse PCR to yield the recombinant plasmids pcDNA3.1-CcBurs-α-his-P2A-mCherry and pcDNA3.1-CcBurs-β-his-P2A-mCherry. Consequently, the bursicon subunits, along with the histidine tag, were capable of generating fusion proteins with the histidine tag. Western blot analysis was conducted using antibodies targeting the histidine tag, enabling the detection of histidine expression, which corresponds to the expression of the bursicon subunits. However, they are not suitable to conduct the in vivo immunocytochemical staining of bursicon alpha and beta in the CNS.

Due to the diminutive size of the C. chinensis nymphs, dissection of the central nervous system (CNS) was unfeasible, precluding specific assessment of bursicon expression in the CNS. Prior literature has documented the expression of bursicon subunits in the epidermis and fat body of C. chinensis. Studies suggest that bursicon subunits not only play a role in the melanization and sclerotization processes of insect epidermis but also have significant roles in insect immunity (An et al., 2012). The presence of bursicon subunits in the epidermis, gut, and fat body of C. chinensis may indicate their crucial roles in the immune functions of these tissues. Further investigation is required to elucidate the specific immune functions they perform, hinting at the potential expression of these bursicon subunits in these two tissues.

An, S., Dong, S., Wang, Q., Li, S., Gilbert, L. I., Stanley, D., & Song, Q. (2012). Insect neuropeptide bursicon homodimers induce innate immune and stress genes during molting by activating the NF-κB transcription factor Relish. PloS one, 7(3), e34510. https://doi.org/10.1371/journal.pone.0034510

(2) L222: "CcBurs-R is the Bursicon receptor of C. chinensis". Is this statement supported by affinity binding assay results?

Thanks for your excellent suggestion. We employed a fluorescence-based assay to quantify calcium ion concentrations and investigate the binding affinities of bursicon heterodimers and homodimers to the bursicon receptor across varying concentrations. Our findings suggest that activation of the receptor by the burs α-β heterodimer leads to significant alterations in intracellular calcium ion levels, whereas stimulation with burs α-α and burs β-β homodimers, in conjunction with Adipokinetic hormone (AKH), maintains consistent intracellular calcium ion levels. Consequently, this research definitively identifies CcBurs-R as the bursicon receptor. For further details, please refer to the Materials and Methods (Lines 493-504), Results (Lines 231-239), and Discussion (Lines 377-384) of our revised manuscript.

(3) L245 Figure 4I-4J: Since knockdown of bursicon and its receptor cause a decrease pigment accumulation in the cuticle, it would be useful to examine 1-2 rate limiting enzyme-encoding genes in the bursicon regulated cuticle darkening process if possible (as was done for genes involved in cuticle thickening).

Thanks for your excellent comment. Following the further study, a thorough analysis was conducted to evaluate the impact of bursicon and its receptor on the expression levels of Lactase, Tyrosine hydroxylase, Dopa decarboxylase, Acetyltransferase, and the effects of RNA interference targeting these genes on the seasonal morphological transition. The findings underscored their role in the bursicon-mediated cuticle darkening process. However, as this section is slated for inclusion in an upcoming manuscript intended for submission, it is deemed unsuitable for incorporation into the current manuscript.

Minor issues:

(1) L75 "stronger resistance (Ge et al., 2019; Tougeron et al., 2021)". Stronger resistance to what? Stronger resistance to environmental stress or weather condition? Please clarify.

Thanks for your excellent suggestion. We have changed the statement to “stronger resistance to weather condition” in Line 75 of our revised manuscript.

(2) L132 Figure 1A and 1B: Bursicon sequence was first identified and functionally characterized in Drosophila melanogaster: is there any reason why Drosophila bursicon sequences were not included in the comparison?

Thanks for your excellent comment. We have added the sequence of Burs-α and Burs-β of D. melanogaster in the sequence alignment results of Figure 1A and 1B of our revised manuscript.

(3) Although the authors clearly identify and validate the function for the bursicon genes and its receptor's, there is no mention of whether duplicates of this gene are also present in the pear psyllid. This has been known to happen in otherwise conserved hormone pathways (e.g., insulin receptor in some insects), so a formal check of this should be done.

Thanks for your excellent comment. As shown in Figure S2A-S2B and 3B, there are two bursicon subunit genes and only one bursicon receptor gene in our selected insect species, for examples Drosophila melanogaster, Diaphorina citri, Bemisia tabaci, Nilaparvata lugens, and Sogatella furcifera. In our transcriptome database of C. chinensis, we also only identified two bursicon subunit genes and only one bursicon receptor gene.

(4) Line 41: Here, as in the title, "fascinating" is a subjective judgement that does not improve a study's presentation.

Thanks for your great comment. We have changed "fascinating" to "transformation" in Line 41 and also revised the title of our revised manuscript.

(5) Line 44: What makes some fields "cutting-edge" and others not?

Thanks for your excellent suggestion. The expression of "in cutting-edge fields" has been deleted in Line 44 of our revised manuscript.

(6) Line 97: This is a peculiar choice of reference for the concept of slower development in cold temperatures. The concept of degree-days and growth rates is old and widespread in entomology.

Thanks for your insightful comment. The reference of Nyamaukondiwa et al., 2011 in Line 95 has been deleted in our revised manuscript.

(7) Lines 149-150: What justifies the assumption that higher levels of expression mean a more important role? This gene might be just as necessary for development of the summer form, even if expressed at lower levels.

Thanks for your excellent suggestion. This sentence has been revised to “Increased gene expression levels may potentially contribute to the transition from summer-form to winter-form in C. chinensis.” in Line 168-169 of our revised manuscript.

(8) The blue arrow in Fig. 7 is confusing.

Thanks for your excellent suggestion. In Figure 7, the blue arrow represents the down-regulated expression of miR-6012. We have added a description about the blue arrow in Figure 7 of our revised manuscript.

Miles touches on the interplay of knowledge written down on index cards and the knowledge which is kept only in one's mind. Some practitioners in the space from 2013-2024 seem to imply that they're writing almost everything out in far deeper detail than Miles would indicate. In his incarnation, much of the knowledge might be more quickly indicated by a short sentence or heading which the brain can associate to longer explanations.

This sort of indexing is akin to some of the method potentially seen in Marshall Mathers' zettelkasten.

I'm creating a tag here for "card index for productivity" to track the idea of productivity in writing which I'm specifically using separately from the tag "card index as productivity system" which is used to describe their use for project tracking systems in systems like GTD, Memindex, etc.

Author response:

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

Many thanks to the editors for the reviewing of the revised manuscript.

We are very grateful to the Reviewers for their time and for the appreciation of the revision.

We thank the Reviewer 3 for acknowledging the use of sulforhodamine B (SRB) fluorescence as a real-time readout of astrocyte volume dynamics. Experimental data in brain slices were provided to validate this approach.<br /> The incomplete matching of our observation with early reported data in cultured astrocytes (e.g., Solenov et al., AJP-Cell, 2004), might reflect certain of their properties differing from the slice/in vivo counterparts as discussed in the manuscript.<br /> The study (T.R. Murphy et al., Front Cell Neurosci., 2017) showed that AQP4 knockout increased astrocyte swelling extent in response to hypoosmotic solution in brain slices (Fig 9), and discussed '... AQP4 can provide an efficient efflux pathway for water to leave astrocytes.’ Correspondingly, our data suggest that AQP4 mediate astrocyte water efflux in basal conditions.<br /> We have discussed the study (Igarashi et al., NeuroReport 2013); our current data would help to understand the cellular mechanisms underlying the finding of Igarashi et al.

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

Pham and colleagues provide an illuminating investigation of aquaporin-4 water flux in the brain utilizing ex vivo and in vivo techniques. The authors first show in acute brain slices, and in vivo with fiber photometry, SRB-loaded astrocytes swell after inhibition of AQP4 with TGN-020, indicative of tonic water efflux from astrocytes in physiological conditions. Excitingly, they find that TGN-020 increases the ADC in DW-MRI in a region-specific manner, potentially due to AQP4 density. The resolution of the DW-MRI cannot distinguish between intracellular or extracellular compartments, but the data point to an overall accumulation of water in the brain with AQP4 inhibition. These results provide further clarity on water movement through AQP4 in health and disease.

Overall, the data support the main conclusions of the article, with some room for more detailed treatment of the data to extend the findings.

Strengths:

The authors have a thorough investigation of AQP4 inhibition in acute brain slices. The demonstration of tonic water efflux through AQP4 at baseline is novel and important in and of itself. Their further testing of TGN-020 in hyper- and hypo-osmotic solutions shows the expected reduction of swelling/shrinking with AQP4 blockade.

Their experiment with cortical spreading depression further highlights the importance of water efflux from astrocytes via AQP4 and transient water fluxes as a result of osmotic gradients. Inhibition of AQP4 increases the speed of tissue swelling, pointing to a role in the efflux of water from the brain.

The use of DW-MRI provides a non-invasive measure of water flux after TGN-020 treatment.

We thank the reviewer for the insightful comments.

Weaknesses:

The authors specifically use GCaMP6 and light sheet microscopy to image their brain sections in order to identify astrocytic microdomains. However, their presentation of the data neglects a more detailed treatment of the calcium signaling. It would be quite interesting to see whether these calcium events are differentially affected by AQP4 inhibition based on their cellular localization (ie. processes vs. soma vs. vascular end feet which all have different AQP4 expressions).

Following the suggestion, we provide new data on the effect of AQP4 inhibition on spontaneous calcium signals in perivascular astrocyte end-feet. As shown now in Fig.S2, acute application of TGN020 induced Ca2+ oscillations in astrocyte end-feet regions where the GCaMP6 labeling lines the profile of the blood vessel. It is noted that on average, the strength of basal Ca2+ signals in the end-feet is higher than that observed across global astrocyte territories (4.65 ± 0.55 vs. 1.45 ± 0.79, p < 0.01), as does the effect of TGN (8.4 ± 0.62 vs. 6.35 ± 0.97, p < 0.05; Fig S2 vs. Fig 2B). This likely reflects the enrichment of AQP4 in astrocyte end-feet. We describe the data in Fig.S2, and on page 8, line 20 – 23.  

We now use the transgenic line GLAST-GCaMP6 for cytosolic GCaMP6 expression in astrocytes. Spontaneous calcium signals, reflected by transient fluorescence rises, occur in discrete micro-domains whereas the basal GCaMP6 fluorescence in the soma is weak. In the present condition, it is difficult to unambiguously discriminate astrocyte soma from the highly intermingled processes. 

The authors show the inhibition of AQP4 with TGN-020 shortens the onset time of the swelling associated with cortical spreading depression in brain slices. However, they do not show quantification for many of the other features of CSD swelling, (ie. the duration of swelling, speed of swelling, recovery from swelling).

Regarding the features of the CSD swelling, we have performed new analysis to quantify the duration of swelling, speed of swelling and the recovery time from swelling in control condition and in the presence of TGN-020. The new analysis is now summarized in Fig. S5. Blocking AQP4 with TGN-020 increases the swelling speed, prolongs the duration of swelling and slows down the recovery from swelling, confirming our observation that acute inhibition of AQP4 water efflux facilitates astrocyte swelling while restrains shrinking. We describe the result on page 11, line 19-21. 

Significance:

AQP4 is a bidirectional water channel that is constitutively open, thus water flux through it is always regulated by local osmotic gradients. Still, characterizing this water flux has been challenging, as the AQP4 channel is incredibly water-selective. The authors here present important data showing that the application of TGN-020 alone causes astrocytic swelling, indicating that there is constant efflux of water from astrocytes via AQP4 in basal conditions. This has been suggested before, as the authors rightfully highlight in their discussion, but the evidence had previously come from electron microscopy data from genetic knockout mice.

AQP4 expression has been linked with the glymphatic circulation of cerebrospinal fluid through perivascular spaces since its rediscovery in 2012 [1]. Further studies of aging[2], genetic models[3], and physiological circadian variation[4] have revealed it is not simply AQP4 expression but AQP4 polarization to astrocytic vascular endfeet that is imperative for facilitating glymphatic flow. Still, a lingering question in the field is how AQP4 facilitates fluid circulation. This study represents an important step in our understanding of AQP4's function, as the basal efflux of water via AQP4 might promote clearance of interstitial fluid to allow an influx of cerebrospinal fluid into the brain. Beyond glymphatic fluid circulation, clearly, AQP4-dependent volume changes will differentially alter astrocytic calcium signaling and, in turn, neuronal activity.

(1) Iliff, J.J., et al., A Paravascular Pathway Facilitates CSF Flow Through the Brain Parenchyma and the Clearance of Interstitial Solutes, Including Amyloid β. Sci Transl Med, 2012. 4(147): p. 147ra111.

(2) Kress, B.T., et al., Impairment of paravascular clearance pathways in the aging brain. Ann Neurol, 2014. 76(6): p. 845-61.

(3) Mestre, H., et al., Aquaporin-4-dependent Glymphatic Solute Transport in the Rodent Brain. eLife, 2018. 7.

(4) Hablitz, L., et al., Circadian control of brain glymphatic and lymphatic fluid flow. Nature Communications, 2020. 11(1).

We thank the reviewer in acknowledging the significance of our study and the functional implication in brain glymphatic system. We have now highlighted the mentioned studies as well as the potential implication glymphatic fluid circulation (page 4, line 9-10; page 5, line 1-3; and page 19, line 3-10). 

Reviewer #2 (Public Review):

Summary:

The paper investigates the role of astrocyte-specific aquaporin-4 (AQP4) water channel in mediating water transport within the mouse brain and the impact of the channel on astrocyte and neuron signaling. Throughout various experiments including epifluorescence and light sheet microscopy in mouse brain slices, and fiber photometry or diffusion-weighted MRI in vivo, the researchers observe that acute inhibition of AQP4 leads to intracellular water accumulation and swelling in astrocytes. This swelling alters astrocyte calcium signaling and affects neighboring neuron populations. Furthermore, the study demonstrates that AQP4 regulates astrocyte volume, influencing mainly the dynamics of water efflux in response to osmotic challenges or associated with cortical spreading depolarization. The findings suggest that AQP4-mediated water efflux plays a crucial role in maintaining brain homeostasis, and indicates the main role of AQP4 in this mechanism. However authors highlight that the report sheds light on the mechanisms by which astrocyte aquaporin contributes to the water environment in the brain parenchyma, the mechanism underlying these effects remains unclear and not investigated. The manuscript requires revision.

Strengths:

The paper elucidates the role of the astrocytic aquaporin-4 (AQP4) channel in brain water transport, its impact on water homeostasis, and signaling in the brain parenchyma. In its idea, the paper follows a set of complimentary experiments combining various ex vivo and in vivo techniques from microscopy to magnetic resonance imaging. The research is valuable, confirms previous findings, and provides novel insights into the effect of acute blockage of the AQP4 channel using TGN-020.

We thank the reviewer for the constructive comments.

Weaknesses:

Despite the employed interdisciplinary approach, the quality of the manuscript provides doubts regarding the significance of the findings and hinders the novelty claimed by the authors. The paper lacks a comprehensive exploration or mention of the underlying molecular mechanisms driving the observed effects of astrocytic aquaporin-4 (AQP4) channel inhibition on brain water transport and brain signaling dynamics. The scientific background is not very well prepared in the introduction and discussion sections. The important or latest reports from the field are missing or incompletely cited and missconcluded. There are several citations to original works missing, which would clarify certain conclusions. This especially refers to the basis of the glymphatic system concept and recently published reports of similar content. The usage of TGN-020, instead of i.e. available AER-270(271) AQP4 blocker, is not explained. While employing various experimental techniques adds depth to the findings, some reasoning behind the employed techniques - especially regarding MRI - is not clear or seemingly inaccurate. Most of the time the number of subjects examined is lacking or mentioned only roughly within the figure captions, and there are lacking or wrongly applied statistical tests, that limit assessment and reproducibility of the results. In some cases, it seems that two different statistical tests were used for the same or linked type of data, so the results are contradictory even though appear as not likely - based on the figures. Addressing these limitations could strengthen the paper's impact and utility within the field of neuroscience, however, it also seems that supplementary experiments are required to improve the report.

The current data hint at a tonic water efflux from astrocyte AQP4 in physiological condition, which helps to understand brain water homeostasis and the functional implication for the glymphatic system. The underlying molecular and cellular mechanisms appear multifaceted and functionally interconnected, as discussed (page 14 line 8 –page 15, line 3). We agree that a comprehensive exploration will further advance our understanding.

The introduction and discussion are now strengthened by incorporating the important advances in glymphatic system while highlighting the relevant studies. 

The use of TGN-020 was based on its validation by wide range of ex vivo and in vivo studies including the use of heterologous expression system and the AQP4 KO mice. The validation of AER-270(271, the water soluble prodrug) using AQP4 KO mice is reported recently (Giannetto et al., 2024). AER-271 was noted to impact brain water ADC (apparent diffusion coefficient evaluated by diffusion-weighted MRI) in AQP4 KO mice ~75 min after the drug application (Giannetto et al., 2024). This likely reflects that AER270(271) is also an inhibitor for κΒ nuclear factor (NF-κΒ) whose inhibition could reduce CNS water content independent of AQP4 targeting (Salman et al., 2022). In addition, the inhibition efficiency of AER-270(271) seems lower than TGN-020 (Farr et al., 2019; Giannetto et al., 2024; Huber et al., 2009; Salman et al., 2022). We have now supplemented this information in the manuscript (page 7, line 1-6 and page15, line 7-17).

The description on the DW-MRI is now updated (page 4, line 10-14). 

We also performed new experiments and data analysis as described in a point-to-point manner below in the section ‘Recommendations For The Authors’.

Reviewer #3 (Public Review):

Summary:

In this manuscript, the authors propose that astrocytic water channel AQP4 represents the dominant pathway for tonic water efflux without which astrocytes undergo cell swelling. The authors measure changes in astrocytic sulforhodamine fluorescence as the proxy for cell volume dynamics. Using this approach, they perform a technically elegant series of ex vivo and in vivo experiments exploring changes in astrocytic volume in response to AQP4 inhibitor TGN-020 and/or neuronal stimulation. The key finding is that TGN-020 produces an apparent swelling of astrocytes and modifies astrocytic cell volume regulation after spreading depolarizations. Additionally, systemic application of TGN-020 produced changes in diffusion-weighted MRI signal, which the authors interpret as cellular swelling. This study is perceived as potentially significant. However, several technical caveats should be strongly considered and perhaps addressed through additional experiments.

Strengths:

(1) This is a technically elegant study, in which the authors employed a number of complementary ex vivo and in vivo techniques to explore functional outcomes of aquaporin inhibition. The presented data are potentially highly significant (but see below for caveats and questions related to data interpretation).

(2) The authors go beyond measuring cell volume homeostasis and probe for the functional significance of AQP4 inhibition by monitoring Ca2+ signaling in neurons and astrocytes (GCaMP6 assay).

(3) Spreading depolarizations represent a physiologically relevant model of cellular swelling. The authors use ChR2 optogenetics to trigger spreading depolarizations. This is a highly appropriate and much-appreciated approach.

We thank the reviewer for the effort in evaluating our work.

Weaknesses:

(1) The main weakness of this study is that all major conclusions are based on the use of one pharmacological compound. In the opinion of this reviewer, the effects of TGN-020 are not consistent with the current knowledge on water permeability in astrocytes and the relative contribution of AQP4 to this process.

Specifically: Genetic deletion of AQP4 in astrocytes reduces plasmalemmal water permeability by ~two-three-fold (when measured a 37oC, Solenov et al., AJP-Cell, 2004). This is a significant difference, but it is thought to have limited/no impact on water distribution. Astrocytic volume and the degree of anisosmotic swelling/shrinkage are unchanged because the water permeability of the AQP4null astrocytes remains high. This has been discussed at length in many publications (e.g., MacAulay et al., Neuroscience, 2004; MacAulay, Nat Rev Neurosci, 2021) and is acknowledged by Solenov and Verkman (2004).

Keeping this limitation in mind, it is important to validate astrocytic cell volume changes using an independent method of cell volume reconstruction (diameter of sulforhodamine-labeled cell bodies? 3D reconstruction of EGFP-tagged cells? Else?)

Solenov and coll. used the calcein quenching assay and KO mice demonstrating AQP4 as a functional water channel in cultured astrocytes (Solenov et al., 2004). AQP4 deletion reduced both astrocyte water permeability and the absolute amplitude of swelling over comparable time, and also slowed down cell shrinking, which overall parallels our results from acute AQP4 blocking. Yet in Solenovr’s study, the time to swelling plateau was prolonged in AQP4 KO astrocytes, differing from our data from the pharmacological acute blocking. This discrepancy may be due to compensatory mechanisms in chronic AQP4 KO, or reflect the different volume responses in cultured astrocytes from brain slices or in vivo results as suggested previously (Risher et al., 2009). 

Soma diameter might be an indicator of cell volume change, yet it is challenging with our current fluorescence imaging method that is diffraction-limited and insufficient to clearly resolve the border of the soma in situ. In addition, the lateral diameter of cell bodies may not faithfully reflect the volume changes that can occur in all three dimensions. Rapid 3D imaging of astrocyte volume dynamics with sufficient high Z-axis resolution appears difficult with our present tools. 

We have now accordingly updated the discussion with relevant literatures being cited (page 17 line 14 – page 18, line 3).

(2) TGN-020 produces many effects on the brain, with some but not all of the observed phenomena sensitive to the genetic deletion of AQP4. In the context of this work, it is important to note that TGN020 does not completely inhibit AQP4 (70% maximal inhibition in the original oocyte study by Huber et al., Bioorg Med Chem, 2009). Thus, besides not knowing TGN-020 levels inside the brain, even

"maximal" AQP4 inhibition would not be expected to dramatically affect water permeability in astrocytes.

This caveat may be addressed through experiments using local delivery of structurally unrelated AQP4 blockers, or, preferably, AQP4 KO mice.

It is an important point that TGN-020 partially blocks AQP4, implying the actual functional impact of AQP4 per se might be stronger than what we observed. TGN provides a means to acutely probe AQP4 function in situ, still we agree, its limitation needs be acknowledged. We mention this now on page 15, line 7-9 and 14-17.

We agree that local delivery of an alternative blocker will provide additional information. Meanwhile, local delivery requires the stereotaxic implantation of cannula, which would cause inflammations to surrounding astrocytes (and neurons). The recently introduced AQP4 blocker AER-270(271) has received attention that it influences brain water dynamics (ADC in DW-MRI) in AQP4 KO mice (Giannetto et al., 2024), recalling that AER-270(271) is also an inhibitor for κΒ nuclear factor (NF-κΒ). This pathway can potentially perturb CNS water content and influence brain fluid circulation, in an AQP4independent manner (Salman et al., 2022). The inhibition efficiency on mouse AQP4 of AER-270 (~20%, Farr et al., 2019; Salman et al., 2022) appears lower than TGN-020 (~70%, Huber et al., 2009).

We chose to use the pharmacological compound to achieve acute blocking of AQP4 thereby avoiding the chronic genetics-caused alterations in brain structural, functional and water homeostasis. Multiple lines of evidence including the recent study (Gomolka et al., 2023), have shown that AQP4 KO mice alters brain water content, extracellular space and cellular structures, which raises concerns to use the transgenic mouse to pinpoint the physiological functions of the AQP4 water channel. 

We have now mentioned the concerns on AQP4 pharmacology by supplementing additional literatures in the field (page 15, line 8-18). 

(3) This reviewer thinks that the ADC signal changes in Figure 5 may be unrelated to cellular swelling. Instead, they may be a result of the previously reported TGN-020-induced hyphemia (e.g., H. Igarashi et al., NeuroReport, 2013) and/or changes in water fluxes across pia matter which is highly enriched in AQP4. To amplify this concern, AQP4 KO brains have increased water mobility due to enlarged interstitial spaces, rather than swollen astrocytes (RS Gomolka, eLife, 2023). Overall, the caveats of interpreting DW-MRI signal deserve strong consideration.

The previous observation show that TGN-020 increases regional cerebral blood flow in wild-type mice but not in AQP4 KO mice (Igarashi et al., 2013). Our current data provide a possible mechanism explanation that TGN-020 blocking of astrocyte AQP4 causes calcium rises that may lead to vasodilation as suggested previously (Cauli and Hamel, 2018). We now add updates to the discussion on page 15, line 3-7.

We are in line with the reviewer regarding the structural deviations observed with the AQP4 KO mice

(Gomolka et al., 2023), now mentioned on page 19, line 3-5. Following the Reviewer’s suggestion, we have also updated the interpretation of the DW-MRI signal and point that in addition to being related to the astrocyte swelling, the ADC signal changes may also be caused by indirect mechanisms, such as the transient upregulation of other water-permeable pathways in compensating AQP4 blocking. We now describe this alternative interpretation and the caveats of the DW-MRI signals (page 20, line 1-8). 

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Private recommendations

My more broad experimental suggestions are in the "weaknesses" section. Some minor points that would improve the manuscript are included below:

(1) A more detailed explanation for why SRB fluorescence reflects the astrocyte volume changes, whereas typical intracellular GFP does not.

As an engineered fluorescence protein, the GFP has been used to tag specific type of cells. Meanwhile, as a relatively big protein (MW, 26.9 kDa), the diffusion rate of EGFP is expected to be much less than SRB, a small chemical dye (MW, 558.7 Da). Also, the IP injection of SRB enables geneticsless labeling of brain astrocytes, so to avoid the influence of protein overexpression on astrocyte volume and water transport responses. We have now stated this point in the manuscript (page 13, line 21 – page 14, line 4).

(2) Figure 1 panel B should have clear labels on the figure and a description in the legend to delineate which part of the panel refers to hyper- or hypo-osmotic treatment.

We have now updated the figure and the legend.  

(3) For Figure 2, what is the rationale for analyzing the calcium signaling data between the cell types differently?

We analyzed calcium micro-domains for astrocytes as their spontaneous signals occur mainly in discrete micro-domains (Shigetomi et al., 2013). While for neurons, we performed global analysis by calculating the mean fluorescence of imaging field of view, because calcium signal changes were only observed at global level rather than in micro-domains. This information is now included (page 24, line1820).

(4) For Figure 3, the authors mention that TGN-020 likely caused swelling prior to the hypotonic solution administration. Do they have any measurements from these experiments prior to the TGN-020 application to use as a "true baseline" volume?

The current method detects the relative changes in astrocyte volume (i.e., transmembrane water transport), which nevertheless is blind to the absolute volume value. We have no readout on baseline volumes.  

(5) For Figures 3 and 4, did the authors see any evidence for regulatory volume decrease? And is this impaired by TGN-020? It is a well-characterized phenomenon that astrocytes will open mechanosensitive channels to extrude ions during hypo-osmotic induced swelling. This process is dependent on AQP4 and calcium signaling [5]

Mola and coll. provided important results demonstrating the role of AQP4 in astrocyte volume regulation (Mola et al., 2016). In the present study in acute brain slices, when we applied hypotonic solution to induce astrocyte swelling, our protocol did not reveal rapid regulatory volume decrease (e.g., Fig. 3D). When we followed the volume changes of SRB-labeled astrocytes during optogenetically induced CSD, we observed the phase of volume decrease following the transient swelling (Fig. 4F), where the peak amplitude and the degree of recovery were both reduced by inhibiting AQP4 with TGN020. These data imply that regulatory astrocyte volume decrease may occur in specific conditions, which intriguingly has been suggested to be absent in brain slices and in vivo (e.g., Risher et al., 2009). We have not specifically investigated this phenomenon, and now briefly discuss this point on page18 line 6-14.

(6) Figure 5 box plots do not show all data points, could the authors modify to make these plots show all the animals, or edit the legend to clarify what is plotted?

We have now updated the plot and the legend. This plot is from all animals (n = 7 per condition).

(7) pg. 9 line 6, there is a sentence that seems incomplete or otherwise unfinished. "We first followed the evoked water efflux and shrinking induced by hypertonic solution while."

Fixed (now, page 9 line 17-18). 

(8)  During the discussion on pg 13 line 11, it may be more clear to describe this as the cotransport of water into the cells with ions/metabolites as reviewed by Macaulay 2021 [6].

We agree; the text is modified following this suggestion (now page14, line 12-13).  

(1) Iliff, J.J., et al., A Paravascular Pathway Facilitates CSF Flow Through the Brain Parenchyma and the Clearance of Interstitial Solutes, Including Amyloid β. Sci Transl Med, 2012. 4(147): p. 147ra111.

(2) Kress, B.T., et al., Impairment of paravascular clearance pathways in the aging brain. Ann Neurol, 2014. 76(6): p. 845-61.

(3) Mestre, H., et al., Aquaporin-4-dependent Glymphatic Solute Transport in the Rodent Brain. eLife, 2018. 7.

(4) Hablitz, L., et al., Circadian control of brain glymphatic and lymphatic fluid flow. Nature Communications, 2020. 11(1).

(5) Mola, M., et al., The speed of swelling kinetics modulates cell volume regulation and calcium signaling in astrocytes: A different point of view on the role of aquaporins. Glia, 2016. 64(1).

(6) MacAulay, N., Molecular mechanisms of brain water transport. Nat Rev Neurosci, 2021. 22(6): p. 326-344.

We thank the reviewer. These important literatures are now supplemented to the manuscript together with the corresponding revisions.

Reviewer #2 (Recommendations For The Authors):

In its concept, the paper is interesting and provides additional value - however, it requires revision.

Below, I provide the following remarks for the following sections/ pages/lines:

ABSTRACT/page 2 (remarks here refer to the rest of the manuscript, where these sentences are repeated):

- It seems that the 'homeostasis' provides not only physical protection, but also determines the diffusion of chemical molecules...' Please correct the sentence as it is grammatically incorrect.

It is now corrected (page 2, line 1).

- The term 'tonic water' is not clear. I understand, after reading the paper, that it is about tonicity of the solutes injected into the mouse.

We use the term ‘tonic’ to indicate that in basal conditions, a constant water efflux occurs through the APQ4 channel.

- 'tonic aquaporin water efflux maintains volume equilibrium' - I believe it is about maintaining volume and osmotic equilibrium?

This description is now refined (now page 2, line 10).

- It is not clear whether the tonic water outflow refers to the cellular level or outflow from the brain parenchyma (i.e., glymphatic efflux)

It refers to the cellular level. 

INTRODUCTION/page 3:

- 'clearance of waste molecules from the brain as described in the glymphatic system' - The original papers describing the phenomena are not cited: Iliff et al. 2012, 2013, Mestre et al. 2018, as well as reviews by Nedergaard et al.

Indeed. We have now cited these key literatures (now page 4, line 10).

- 'brain water diffusion is the basis for diffusion-weighted magnetic resonance imaging (DW-MRI)' - The statement is wrong. it is the mobility of the water protons that DWI is based on, but not the diffusion of molecules in the brain. This should be clarified and based on the DW-MRI principle and the original works by Le Bihan from 1986, 1988, or 2015.

This sentence is now updated (page 4, line10-14).

- Similarly, I suggest correcting or removing the citations and the sentence part regarding the clinical use of DWI, as it has no value here. Instead, it would be worth mentioning what actually ADC reflects as a computational score, and what were the results from previous studies assessing glymphatic systems using DWI. This is especially important when considering the mislocalization of the AQP4 channel.

We now states recent studies using DW-MRI to evaluate glymphatic systems (page 4, line16-17).  

- 'In the brain, AQP4 is predominantly expressed in astrocytes'-please review the citations. I suggest reading the work by Nielsen 1997, Nagelhus 2013, Wolburg 2011, and Li and Wang from 2017. To my best knowledge, in the brain AQP4 is exclusively expressed in astrocytes.

Thanks for the reviewer. It is described that while enriched in astrocytes, AQP4 is also expressed in ependymal cells lining the ventricles (e.g., (Mayo et al., 2023; Verkman et al., 2006)). ‘predominantly’ is now removed (page 4, line 21).

- The conclusion: ' Our finding suggests that aquaporin acts as a water export route in astrocytes in physiological conditions, so as to counterbalance the constitutive intracellular water accumulation caused by constant transmitter and ion uptake, as well as the cytoplasmic metabolism processes. This mechanism hence plays a necessary role in maintaining water equilibrium in astrocytes, thereby brain water homeostasis' seems to be slightly beyond the actual findings in the paper. I suggest clarifying according to the described phenomena.

We have now refined the conclusion sticking to the experimental observations (page 5, line16-18).

- The introduction lacks important information on existing AQP4 blockers and their effects, pros and cons on why to use TGN-020. Among others, I would refer to recent work by Giannetto et al 2024, as well as previous work of Mestre et al. 2018 and Gomolka et al. 2023.

We initiated the study by using TGN-020 as an AQP4 blocker because it has been validated by wide range of ex vivo and in vivo studies as documented in the text (page 7, line 1-6). We also update discussions on the recent advances in validating the AQP4 blocker AER-270(271) while citing the relevant studies (page 15, line 7-17).  

RESULTS:

- Page 5, lines 19-20: '...transport, we performed fluorescence intensity translated (FIT) imaging.' - this term was never introduced in the methods so it is difficult for the reader to understand it at first sight. -'To this end,' - it is not clear which action refers to 'this'. (is it about previous works or the moment that the brain samples were ready for imaging? Please clarify, as it is only starting to be clear after fully reading the methods.

We now refine the description give the principle of our imaging method first, then explain the technical steps. To avoid ambiguity, the term ‘To this end’ is removed. The updated text is now on page 6, line 1-3.  

- From page 6 onwards - all references to Figures lack information to which part of the figure subpanel the information refers (top/middle bottom or left/middle/right).

We apologize. The complementary indication is now added for figure citations when applicable.  

- 'whereas water export and astrocyte shrinking upon hyperosmotic manipulation increased astrocyte fluorescence (Figure 1B). Hence, FIT imaging enables real-time recording of astrocyte transmembrane water transport and volume dynamics.' - this part seems to be undescribed or not clear in the methods.

We have now refined this description (page 6, line 19-20).

- Page 6, lines 17-22: TGN-020. In addition to the above, I suggest familiarizing also with the following works by Igarashi 2011. doi: 10.1007/s10072-010-0431-1, and by Sun 2022. doi: 10.3389/fimmu.2022.870029.

These studies are now cited (page 7, line 3-4).

- Page 7: ' AQP4 is a bidirectional channel facilitating... ' - AQP4 water channel is known as the path of least resistance for water transfer, please see Manley, Nature Medicine, 2000 and Papadopoulos, Faseb J, 2004.

This sentence is now updated (page 7, line 12-13).

- ' astrocyte AQP4 by TGN-020 caused a gradual decrease in SRB fluorescence intensity, indicating an intracellular water accumulation' - tissue slice experiment is a very valuable method. However it seems right, the experiment does not comment on the cell swelling that may occur just due to or as a superposition of tissue deterioration and the effect of TGN-020. The AQP4 channel is blocked, and the influx of water into astrocytes should be also blocked. Thus, can swelling be also a part of another mechanism, as it was also observed in the control group? I suggest this should be addressed thoroughly.

We performed this experiment in acute brain slices to well control the pharmacological environment and gain spatial-temporal information. Post slicing, the brain slices recovered > 1hr prior to recording, so that the slices were in a stable state before TGN-020 application as evidenced by the stable baseline. The constant decrease in the control trace is due to photobleaching which did not change its curve tendency in response to vehicle. TGN-020, in contrast, caused a down-ward change suggesting intracellular water accumulation and swelling. 

The experiment was performed at basal condition without active water influx; a decrease in SRB fluorescence hints astrocyteintracellular water buildup. This result shows that in basal condition, astrocyte aquaporin mediates a constant (i.e., tonic) water efflux; its blocking causes intracellular water accumulation and swelling. 

We have accordingly updated the description of this part (page 7, line 15-20).

- From the Figure 1 legend: Only 4 mice were subjected to the experiment, and only 1 mouse as a control. I suggest expanding the experiment and performing statistics including two-way ANOVA for data in panels B, C, and D, as no results of statistical tests confirm the significance of the findings provided.

The panel B confirms that cytosolic SRB fluorescence displays increasing tendency upon water efflux and volume shrinking, and vice versa. As for the panel C, the number of mice is now indicated. Also, the downward change in the SRB fluorescence was now respectively calculated for the phases prior and post to TGN (and vehicle) application, and this panel is accordingly updated. TGN-020 induced a declining in astrocyte SRB fluorescence, which is validated by t-test performed in MATLAB. To clarify, we now add cross-link lines to indicate statistical significance between the corresponding groups (Fig 1C, middle). As for panel D, we calculated the SRB fluorescence change (decrease) relative to the photobleaching tendency illustrated by the dotted line. The significance was also validated by t-test performed in MATLAB.  

- Figure 1: Please correct the figure - pictures in panel A are low quality and do not support the specificity of SRB for astrocytes. Panels B-D are easier to understand if plotted as normal X/Y charts with associated statistical findings. Some drawings are cut or not aligned.

In GFAP-EGFP transgenic, astrocytes are labeled by EGFP. SRB labeling (red fluorescence) shows colocalization with EGFP-positive astrocytes, meanwhile not all EGFP-positive astrocytes are labeled by SRB. The PDF conversion procedure during the submission may also somehow have compromised image quality. We have tried to update and align the figure panels.  

- Page 12: ' TGN-020 increased basal water diffusion within multiple regions including the cortex,

hippocampus and the striatum in a heterogeneous manner (Figure 5C).'

This sentence is updated now (page 12, line 12 – page13, line 2). It reads ‘The representative images reveal the enough image quality to calculate the ADC, which allow us to examine the effect of TGN-020 on water diffusion rate in multiple regions (Fig. 5C).’

- The expression of AQP4 within the brain parenchyma is known to be heterogenous. Please familiarize yourself with works by Hubbard 2015, Mestre 2018, and Gomolka 2023. A correlation between ADC score and AQP4 expression ROI-wise would be useful, but it is not substantial to conduct this experiment.

We thank the reviewer. This point is stressed on page 19, line 12-14.

DISCUSSION:

- Most of the issues are commented on above, so I suggest following the changes applied earlier. -Page 16: 'We show by DW-MRI that water transport by astrocyte aquaporin is critical for brain water homeostasis.' This statement is not clear and does not refer to the actual impact of the findings. DWI is allowed only to verify the changes of ADC fter the application of TGN-020. I suggest commenting on the recent report by Giannetto 2024 here.

This sentence is now refined (page 19, line 1-2), followed by the updates commenting on the recent studies employing DW-MRI to evaluate brain fluid transport, including the work of (Giannetto et al., 2024) (page 19, line 3-10). 

METHODS:

- Page 18: no total number of mice included in all experiments is provided, as well as no clearly stated number of mice used in each experiment. Please correct.

We have now double checked the number of the mice for the data presented and updated the figure legends accordingly (e.g., updates in legends fig1, fig5, etc).

-  Page 18, line 7: 'Axscience' is not a producer of Isoflurane, but a company offering help with scientific manuscript writing. If this company's help was used, it should be stated in the acknowledgments section. Reference to ISOVET should be moved from line 15 to line 7.

We apologize. We did not use external writing help, and now have removed the ‘Axcience’. The Isoflurane was under the mark ‘ISOVET’ from ‘Piramal’. This info is now moved up (page 21, line 11). 

- Page 18, line 9: ' modified artificial cerebrospinal fluid (aCSF)'. Additional information on the reason for the modified aCSF would be useful for the reader.

In this modified solution, the concentration of depolarizing ions (Na+, Ca2+) was reduced to lower the potential excitotoxicity during the tissue dissection (i.e., injury to the brain) for preparing the brain slices. Extra sucrose was added to balance the solution osmolarity. This solution has been used previously for the dissection and the slicing steps in adult mice (Jiang et al., 2016). We now add this justification in the text and quote the relevant reference (page 21, line14-16). 

- Page 19, line 6: a reasoning for using Tamoxifen would be helpful for the reader.

The Glast-CreERT2 is an inducible conditional mouse line that expresses Cre recombinase selectively in astrocytes upon tamoxifen injection. We now add this information in the text (page 22, line 10-11). 

- Line 8 - 'Sigma'

Fixed.

- Line 7/8: It is not clear if ethanol is of 10% solution or if proportions of ethanol+tamoxifen to oil were of 1:9. The reasoning for each performed step is missing.

We have now clarified the procedure (page 22, line 11-15).

- Line 10: '/' means 'or'?

Here, we mean the bigenic mice resulting from the crossing of the heterozygous Cre-dependent GCaMP6f and Glast-CreERT2 mouse lines. We now modify it to ‘Glast-CreERT2::Ai95GCaMP6f//WT’, in consistence with the presentation of other mouse lines in our manuscript (page 22, line 16).

- Lines 22-23: being in-line with legislation was already stated at the beginning of the Methods so I suggest combining for clearance.

Done. 

- Page 21, line 4: it is good to mention which printer was used, but it would be worth mentioning the material the chamber was printed from - was it ABS?

Yes. We add this info in the text now (page 24, line 5).

- Line 9 -'PI' requires spelling out.

It is ‘Physik Instrumente’, now added (page 24, line 10).

- Line 11-12: What is the reason for background subtraction - clearer delineation of astrocytes/ increasing SNR in post-processing, or because SRB signal was also visible and changing in the background over time? Was the background removed in each frame independently (how many frames)? How long was the time-lapse and was the F0 frame considered as the first frame acquired? The background signal should be also measured and plotted alongside the astrocytic signal, as a reference (Figure 1). This should be clarified so that steps are to be followed easily.

We sought to follow the temporal changes in SRB fluorescence signal. The acquired fluorescent images contain not only the SRB signals, but also the background signals consisting of for instance the biological tissue autofluorescence, digital camera background noise and the leak light sources from the environments. The value of the background signal was estimated by the mean fluorescence of peripheral cell-free subregions (15 × 15 µm²) and removed from all frames of time-lapse image stack. The traces shown in the figures reflect the full lengths of the time-lapse recordings. F0 was identified as the mean value of the 10 data points immediately preceding the detected fluorescence changes. The text is now updated (page 24 line 21 - page 25 line 5).

- Line 15: Was astrocyte image delineation performed manually or automatically? Where was the center of the region considered in the reference to the astrocyte image? It would be good to see the regions delineated for reference.

Astrocytes labeled by SRB were delineated manually with the soma taken as the center of the region of interest. We now exemplify the delineated region in Fig 1A, bottom.

- Page 22, line 2: 'x4 objective'.

Added (now, page 25, line 16). 

- Line 3: 'barrels' - reference to publication or the explanation missing.

The relevant reference is now added on barrel cortex (Erzurumlu and Gaspar, 2020) (page 25, line 19-20). 

- Line 19: were the coordinates referred to = bregma?

Yes. This info is now added (page 26, line 12). 

- Line 20: was the habituation performed directly at the acquisition date? It is rather difficult to say that it was a habituation, but rather acute imaging. I suggest correcting, that mice were allowed to familiarize themselves with the setup for 30 minutes prior to the imaging start.

In this context, although it is a very nice idea and experiment, the influence of acute stress in animals familiar with the setup only from the day of acquisition is difficult to avoid. It is a major concern, especially when considering norepinephrine as a master driver of neuronal and vascular activity through the brain, and strong activation of the hypothalamic-adrenal axis in response to acute stress. It is well known, that the response of monoamines is reduced in animals subjected to chronic v.s acute stress, but still larger than that if the stressor is absent.

Major remark: The animals should, preferably, be imaged at least after 3 days of habituation based on existing knowledge. I suggest exploring the topic of the importance of habituation. It is difficult though, to objectively review these findings without considering stress and associated changes in vascular dynamics.

Many thanks for the reviewer to help to precise this information. The text is accordingly updated to describe the experiment (now page 26, line 14). 

- Page 23, line 17: number of animals included in experiments missing.

The number of animals is added in Methods (page 27, line 12) and indicated in the legend of Figure 5. 

- Line 18/19: were the respiratory effects observed after injection of saline or TGN-020? Since DWI was performed, the exclusion of perfusive flow on ADC is impossible.

I suggest an additional experiment in n=3 animals per group, verifying the HR (and if possible BP) response after injection of TGN-020 and saline in mice.

The respiratory rate has been recorded. We added the averaged respiratory rate before and after injection of TGN-020 or saline (now, Fig. S6; page 13, line 5-6).

- Line 22: Please, provide the model of the scanner, the model of the cryoprobe, as well as the model of the gradient coil used, otherwise it is difficult to assess or repeat these experiments.

We have now added the information of MRI system in Methods section (page 27, line17-21).

- Page 24: line 3/4: although the achieved spatial resolution of DWI was good and slightly lower than desired and achievable due to limitations of the method itself as well as cryoprobe, it is acceptable for EPI in mice.

Still, there is no direct explanation provided on the reasoning for using surface instead of volumetric coil, as well as on assuming an anisotropic environment (6 diffusion directions) for DWI measurements. This is especially doubtful if such a long echo-time was used alongside lower-thanpossible spatial resolution. Longer echo time would lower the SNR of the depicted signal but also would favor the depiction of signal from slow-moving protons and larger water pools. On the other hand, only 3 b-values were used, which is the minimum for ADC measurements, while a good research protocol could encompass at least 5 to increase the accuracy of ADC estimation and avoid undersampling between 250 and 1800 b-values. What was the reason for choosing this particular set of b-values and not 50, 600, and 2000? Besides, gradient duration time was optimally chosen, however, I have concerns about the decision for such a long gradient separation times.

If the protocol could have been better optimized, the assessment could have been also performed in respiratory-gated mode, allowing minimization of the effects of one of the glymphatic system driving forces.

Thus, I suggest commenting on these issues.

We chose the cryoprobe to increase the signal-to-noise ratio (SNR) in DW-MRI with long echo-time and high b-value. The volume coil has a more homogeneous SNR in the whole brain rather than the cryoprobe, but SNR should be reduced compared with cryoprobe. We confirmed that, even at the ventral part of the brain, the image quality of DW-MRI images was enough to investigate the ADC with cryoprobe (Fig. 5B-C). This is mentioned now in Methods (page 27, line 17-21).

We performed DW-MRI scanning for 5 min at each time-point using the condition of anisotropic resolution and 3 b-values, to investigate the time-course of ADC change following the injection of TGN020. Because the effect of TGN-020 appears about dozen of minutes post the injection (Igarashi et al., 2011), fast DW-MRI scanning is required. If isotropic DW-MRI with lower echo-time and more direction is used, longer scan time at each time point is required, maybe more than 1h. We agree that three bvalues is minimum to calculate the ADC and more b-values help to increase the accuracy. However, to achieve the temporal resolution so as to better catch the change of water diffusion, we have decided to use the minimum b-values. The previous study also validates the enough accuracy of DW-MRI with three b-values (Ashoor et al., 2019). Furthermore, previous study that used long diffusion time (> 20 ms) and long echo time (40 ms) shows the good mean diffusivity (Aggarwal et al., 2020), supporting that our protocol is enough to investigate the ADC. We have now updated the description (page 28 line 5-9).  The reason why we choose the b = 250 and 1800 s/mm² is that 2000 s/mm² seems too high to get the good quality of image. In the previous study, we have optimized that ADC is measurable with b = 0, 250, and 1800 s/mm² (Debacker et al., 2020). 

- Page 24, line 7: What was the post-processing applied for images acquired over 70 minutes? Did it consider motion-correction, co-registration, or drift-correction crucial to avoid pitfalls and mismatches in concluding data?

The motion correction and co-registration were explained in Methods (page 28, line 12-14).

Also, were these trace-weighted images or magnitude images acquired since DTI software was used for processing - while ADC fitting could be reliably done in Matlab, Python, or other software. Thus, was DSI software considering all 3 b-values or just used 0 and 1800 for the calculation of mean diffusivity for tractography (as ADC). The details should be explained.

DSIstudio was used with all three b values (b = 0, 250, and 1800 s/mm²) to calculate the ADC. We added the description in Methods (page 28, line 16-18).

To make sure that the results are not affected by the MR hardware, I suggest performing 3 control measurements in a standard water phantom, and presenting the results alongside the main findings.

Thanks for this suggestion. We have performed new experiments and now added the control measurement with three phantoms, that is water, undecane, and dodecane. These new data are summarized now in Fig. S7, showing the stability of ADC throughout the 70 min scanning. We have updated the description on Method part (page 28, line 9-11) and on the Results (page 13, line 6-8).  

- Line 13: were the ROI defined manually or just depicted from previously co-registered Allen Brain atlas?

The ROIs of the cortex, the hippocampus, and the striatum were depicted with reference to Allen mouse brain atlas (https://scalablebrainatlas.incf.org/mouse/ABA12). This is explained in Methods (page 28, line 14-16).

- Line 10: why the average from 1st and 2nd ADC was not considered, since it would reduce the influence of noise on the estimation of baseline ADC?

We are sorry that it was a typo. The baseline was the average between 1st and 2nd ADC. We corrected the description (page 28, line 20).

STATISTIC:

Which type of t-test - paired/unpaired/two samples was used and why? Mann-Whitney U-tets are used as a substitution for parametric t-tests when the data are either non-parametric or assuming normal distribution is not possible. In which case Bonferroni's-Holm correction was used? - I couldn't find any mention of any multiple-group analysis followed by multiple comparisons. Each section of the manuscript should have a description of how the quantitative data were treated and in which aim. I suggest carefully correcting all figures accordingly, and following the remarks given to the Figure 1.

We used unpaired t-test for data obtained from samples of different conditions. Indeed, MannWhitney U-test is used when the data are non-parametric deviating from normal distributions.  Bonferroni-Holm correction was used for multiple comparisons (e.g., Fig. 4D-E).

Reviewer #3 (Recommendations For The Authors):

I think that the following statement is insufficient: "The authors commit to share data, documentation, and code used in analysis". My understanding is eLife expects that all key data to be provided in a supplement.

We thank the reviewer; we follow the publication guidelines of eLife. 

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