.link.to - how should we live = (Zen)

Philosophy continually returns to three topics; - what exists, - how do we know it, and - how should we live?

how to

• being
• meaning
• living

https://hyp.is/ooPwuFy1Ee-caEcVuPaVIA/kaizenbatter.com/the-kaizen-philosophy/

should be saying Zen Human

o be properly human is to be kaizen human

Zen means “to make good.”

Kaizen continual improvement

Zen Quality continually improve

limitless potential

continual improve the human itself

auto-poietic self-improvement

Subdivision 1- summary- Whitman is emphasizing the celebration of himself as an individual; celebrating and singing are for the self and our own abilities

Subdivision 5- We should put learned religious rules and formal education aside and be happy with what they are or have provided us but not forget them as we move forward to new exploration. Now, we can deal in experiences--good or bad and experience nature and life as it comes.

Subdivision 3- Whitman is relaxing and inviting his soul to relax with him and examine the natural world around him.

Latino founded fraternity.

Latino-based fraternity with a multi-cultural membership.

One Culture, Any Race.

Wie groß?

Author response:

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

We extend our sincere gratitude to the reviewers for their constructive feedback and valuable suggestions, which have significantly contributed to enhancing the quality of our work. In response to the comments, we have meticulously revised our manuscript with the following updates:

(1) New Data Inclusion: We have incorporated new immunofluorescent staining images, FACS analysis of monocytes, and single-cell RNA sequencing (scRNAseq) expression analysis focusing on genes related to IFNGR, as well as T cell memory subsets (Trm, Tcm, and Tem).

(2) Comparative Analysis: We have conducted a comparative analysis between the active vitiligo dFBs and the ACD pAd (r5) identified in our study, which provides further insight into the immune response mechanisms.

(3) Discussion Expansion: We have expanded the discussion to include the role of tissue-resident memory (Trm) T cells in our model and have addressed the limitations of our animal model and in vitro studies.

(4) Supplemental Material: As requested by the reviewers, we have provided four new supplemental tables (Table S2 ~ S5) and specific information for antibodies used in our study.

Please see our Point-to-Point Responses to Reviewers' comments below:

Public Reviews:

Reviewer #1 (Public Review):

Summary:

In this manuscript, Liu et al. used scRNA-seq to characterize cell type-specific responses during allergic contact dermatitis (ACD) in a mouse model, specifically the hapten-induced DNFB model. Using the scRNA-seq data, they deconvolved the cell types responsible for the expression of major inflammatory cytokines such as IFNG (from CD4 and CD8 T cells), IL4/13 (from basophils), IL17A (from gd T cells), and IL1B from neutrophils and macrophages. They found the highest upregulation of a type 1 inflammatory response, centering around IFNG produced by CD4 and CD8 T cells. They further identified a subpopulation of dermal fibroblasts that upregulate CXCL9/10 during ACD and provided functional genetic evidence in their mouse model that disrupting IFNG signaling to fibroblasts decreases CD8 T cell infiltration and overall inflammation. They identify an increase in IFNG-expressing CD8 T cells in human patient samples of ACD vs. healthy control skin and co-localization of CD8 T cells with PDGFRA+ fibroblasts, which suggests this mechanism is relevant to human ACD. This mechanism is reminiscent of recent work (Xu et al., Nature 2022) showing that IFNG signaling in dermal fibroblasts upregulates CXCL9/10 to recruit CD8 T cells in a mouse model of vitiligo. Overall, this is a very wellpresented, clear, and comprehensive manuscript. The conclusions of the study are mostly well supported by data, but some aspects of the work could be improved by additional clarification of the identity of the cell types shown to be involved, including the exact subpopulation discovered by scRNA-seq and the subtype of CD8 T cell involved. The study was limited by its use of one ACD model (DNFB), which prevents an assessment of how broadly relevant this axis is. The human sample validation is slightly circumstantial and limited by the multiplexing capacity of immunofluorescence markers.

Strengths:

Through deep characterization of the in vivo ACD model, the authors were able to determine which cell types were expressing the major cytokines involved in ACD inflammation, such as IFNG, IL4/13, IL17A, and IL1B. These analyses are well-presented and thoughtful, showing first that the response is IFNG-dominant, then focusing on deeper characterization of lymphocytes, myeloid cells, and fibroblasts, which are also validated and complemented by FACS experiments using canonical markers of these cell types as well as IF staining. Crosstalk analyses from the scRNA-seq data led the authors to focus on IFNG signaling fibroblasts, and in vitro experiments demonstrate that CXCL9 and CXCL10 are expressed by fibroblasts stimulated by IFNG. In vivo functional genetic evidence demonstrates an important role for IFNG signaling in fibroblasts, as KO of Ifngr1 using Pdgfra-Cre Ifngr1 fl/fl mice, showed a reduction in inflammation and CD8 T cell recruitment.

Weaknesses:

(1) The use of one model limits an understanding of how broad this fibroblast-T cell axis is during ACD. However, the authors chose the most commonly employed model and cited additional work in a vitiligo model (another type 1 immune response).

We thanks the reviewer for pointing out this limitation. Although the DNFB-elicited ACD model is the most commonly used animal model for ACD, our study is limited by the use of only one type 1 immune response model. We have now added new data (Figure 5-figure supplement 1A) showing that the active ACD pAd (r5) and the active IFNγ-responsive vitiligo dFBs (Xu et al., 2022) are enriched with a highly similar panel of IFNγ-inducible genes. Future studies are still needed to determine whether this fibroblast-T cell axis may be broadly applied to other ACD models or to other type-1 immune response-related inflammatory skin diseases.

(2) The identity of the involved fibroblasts and T cells in the mouse model is difficult to assess as scRNA-seq identified subpopulations of these cell types, but most work in the Pdgfra-Cre Ifngr1 fl/fl mice used broad markers for these cell types as opposed to matched subpopulation markers from their scRNA-seq data.

Thanks for the reviewer's constructive comments. To better showcase the dWAT layer where PDGFRA+ pAds are enriched, we have included new histological staining and PLIN1 (adipocyte marker) in new Figure 4 - figure supplement 1F-G. As shown in Figure 4 - figure supplement 1G, the PLIN1+ dWAT layer is located in the lower dermis right above the cartilage layer.  In Figure 4-figure supplement 1I and J, we have shown that phosphor-STAT1 (pSTAT1), a key signaling molecule activated by IFNγ, was detected primarily in PDGFRA+Ly6A+ pAds in the lower dermis where dWAT is located. In addition, we have now included new data showing that the pAd (dFB_r5) cluster preferentially expressed the highest levels of both Ifngr1 and Ifngfr2 among all dFB subclusters (new Figure 5 - figure supplement 1B). Furthermore, we have included new co-staining data showing that CXCL9 largely co-localized with ICAM1(new Figure 4 - figure supplement 1K), a marker for committed pAds (Merrick et al., 2019), in the reticular dermis and dWAT region of the ACD skin, further confirming that CXCL9 is specifically induced in the pAd subset of dFBs. Additionally, we included new staining data showing that ACD-mediated induction of CXCL9 in ICAM1+ dFBs were largely suppressed upon targeted deletion of Ifngr1 in Pdgfra+ dFBs (new Figure 6 - figure supplement 1D-E).

(3) Human patient samples of ACD were co-stained with two markers at a time, demonstrating the presence of CD8+IFNG+ T cells, PDGFRA+CXCL10+ fibroblasts, and co-localization of PDGFRA+ fibroblasts and CD8+ T cells. However, no IF staining demonstrates co-expression of all 4 markers at once; thus, the human validation of co-localization of CD8+IFNG+ T cells and PDGFRA+CXCL10+ fibroblasts is ultimately indirect, although not a huge leap of faith. Although n=3 samples of healthy control and ACD samples are used, there is no quantification of any results to demonstrate the robustness of differences.

Thanks for the reviewer’s constructive comments. We have shown that PDGFRA colocalizes with CXCL10, in the dermal micro-vascular structures, where CD8+ T cells infiltrate around PDGFRA+ dFBs. We are sorry that due to technical issues (antibody compatibility), we cannot provide the four color co-staining as suggested by the reviewers. In order to demonstrate the robustness and reproducibility of the staining presented, we have now supplemented 4 independent images for both Fig. 7A and Fig. 7E in the updated Figure 7-figure supplement 1A-B.

Reviewer #2 (Public Review):

Summary:

The investigators apply scRNA seq and bioinformatics to identify biomarkers associated with DNFB-induced contact dermatitis in mice. The bioinformatics component of the study appears reasonable and may provide new insights regarding TH1-driven immune reactions in ACD in mice. However, the IF data and images of tissue sections are not clear and should be improved to validate the model.

Strengths:

The bioinformatics analysis.

Weaknesses:

The IF data presented in 4H, 6H, 7E and 7F are not convincing and need to be correlated with routine staining on histology and different IF markers for PDGFR. Some of the IF staining data demonstrates a pattern inconsistent with its target.

We are sorry for the confusion, because 4H and 6H are staining on mouse skin sections, and 7E and 7F are staining on human skin sections, therefore the patterns of PDGFRA+ dFBs appeared inconsistent between species. As shown in Fig. 4H, in mouse skin, PDGFRA+CXCL9/10+ dFBs are located between the lower reticular dermis and dWAT region, where preadipocytes are located (Sun et al., 2023). To better showcase the dWAT layer where PDGFRA+ pAds are enriched, we have included new histological staining and PLIN1 (adipocyte marker) in new Figure 4 - figure supplement 1F-G. As shown in Figure 4 - figure supplement 1G, the PLIN1+ dWAT layer is located in the lower dermis right above the cartilage layer. Furthermore, we have included new co-staining data showing that CXCL9 largely co-localized with ICAM1(new Figure 4 - figure supplement 1K), a marker for committed pAds (Merrick et al., 2019), in the reticular dermis and dWAT region of the ACD skin, further confirming that CXCL9 is specifically induced in the pAd subset of dFBs.   

As shown in Fig. 7E, in human skin, PDGFRA+CXCL10+ dFBs are located within the microvascular structures located at the dermal-epidermal junction (DEJ) region, where mesenchymal stem cells are enriched (Russell-Goldman & Murphy, 2020). We have included the corresponding HE histological staining image for Fig. 4H in new Figure 4-supplement 1F. Histological staining for Fig. 6H is the HE staining image in Fig. 6F. The histological staining for Fig. 7E and 7F is shown by Masson’s trichrome staining shown in Fig. 7C (a three-colour histological staining).

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Major comments:

(1) While the focus on fibroblast and T cell interactions and overall biological findings regarding these interactions (IFNG - CXCL9/10 - CXCR3) is sound, it is slightly confusing about which exact subpopulations of these cells are involved in ACD pathogenesis as both scRNA-seq and IF are used but very broad markers are used for IF. Regarding fibroblasts, the scRNA-seq identifies the pAd (r5) cluster of fibroblasts as the main producer of CXCL9/10. However, the expression of IFNGR1 was not shown for this subpopulation as well as for other fibroblast subpopulations. Figure 6C shows IFNGR1 staining in the Ifngr1 fl/fl control mice which appears quite broad. With the seemingly broad expression of IFNGR1, why is it that only a subpopulation of fibroblasts upregulate CXCL9/10? Is there a specific location of these pAd fibroblasts that help drive this IFNG response? Please show the expression of Ifngr1 in the fibroblast scRNA-seq data.

Thanks for the reviewer’s constructive comments. We have now included new data showing that the pAd (dFB_r5) cluster preferentially expressed higher levels of both Ifngr1 and Ifngfr2 among all dFB subclusters (new Figure 5 - figure supplement 1B). In addition, we included new co-staining data showing that CXCL9 largely co-localized with ICAM1, a marker for committed pAds (Merrick et al., 2019), in the reticular dermis and dWAT region of the ACD skin, further confirming that CXCL9 is specifically induced in the pAd subset of dFBs.

(2) Regarding T cells, it is slightly confusing regarding what role the fibroblast-produced CXCL9/10 plays on T cell migration vs. activation. This is mainly because in vitro work focuses on T cell activation, while in vivo work seems to mainly assess T cell migration into the tissue. The in vivo studies have nicely shown that CD8 T cells are the main cell type affected by Ifngr1 iKO (i.e., a reduction of these cells), but T cell activity in vivo is not assessed (in the form of IFNG production). I have the following related questions:

a. Authors do not discuss whether T cells involved in ACD in their model are tissue-resident memory T cells (Trm) or whether these are recruited from circulation. This may be possible to assess via additional analysis of the scRNA-seq data (looking for expression of Trm markers). 

Thanks for the reviewer’s constructive comments. We have now included new data showing the expression of marker genes of various memory T cells in various T cell subclusters (new Figure 2 - figure supplement 1C-D). Antigen-specific CD8 or CD4 memory T cells can be classified into CD62hi/CCR7hi/CD28hi/CD27hi/CX3CR1lo central memory T cells (Tcm), CX3CR1hi/Cd28hi/Cd27lo/CD62lo/CCR7lo effector memory T cells (Tem), and CD49ahi/CD103hi/ CD69hi/BLIMP1hi tissue-resident memory T cells (Trm) (Benichou, Gonzalez, Marino, Ayasoufi, & Valujskikh, 2017; Cheon, Son, & Sun, 2023; Mackay et al., 2013; Martin & Badovinac, 2018; Park et al., 2023). We observed that in ACD skin, CD4+ and CD8+ T cells predominantly expressed marker genes associated with Tcm including Cd28, Cd27, Ccr7, and S1pr1/Cd62l. In contrast, marker genes associated with Tem (Cx3cr1) and Trm (Itga1/Cd49a, Itgae/Cd103, Cd69 and Prdm1/Blimp1, Cd127/Il7r) were only scarcely expressed in these αβ T cells, suggesting that ACD predominantly triggers a central memory T cell response in the skin.

Furthermore, this hypothesis is supported by new lymph node gene expression results. We showed that the expression of Ifng, but not Il4 or Il17a, was rapidly induced in skin draining lymph nodes at 24 hours after ACD elicitation (new Figure 1-figure supplement 1H). This suggests a robust and systemic activation of type 1 memory T cell response in the early stage of ACD, and the migration of these lymphatic memory T cells to the skin may contribute to the exacerbation of skin inflammation.

b. Authors have focused on CXCR3 axis involvement in IFNG production (Figures 5G-H) without assessing the presumed migratory role of this axis. Presumably, CD8 T cells are recruited to the skin via the CXCL9/10-CXCR3 axis, but this would be important to clarify given other work that has demonstrated Trm involvement in ACD. Authors should at least discuss how their model and findings support, refine, or even contradict the current paradigm of Trm involvement in ACD (Lefevre et al., 2021; PMID: 34155157).

We are grateful for the constructive feedback provided by the reviewer. CXCR3 is a chemokine receptor on T cells and not only plays a pivotal role in the trafficking of type 1 T cells, but also is required for optimal generation of IFNG-secreting type 1 T cells in vivo (Groom et al., 2012). Our in vitro study is limited by only focusing on CXCL9/10-CXCR3 axis involvement in IFNγ production without studying its role in driving T cell migration. We have now addressed this limitation in the discussion section.

In the murine model of ACD, the initial sensitization phase involves exposing mouse skin to a high dose of DNFB to prime effector T cells in lymphoid organs, and this is followed by a later challenge/elicitation phase, during which the mice are re-exposed to a lower dose of DNFB in a different area of the skin, distal from the original sensitization site (Manresa, 2021; Vocanson, Hennino, Rozieres, Poyet, & Nicolas, 2009). Our updated analysis of the expression of marker genes associated with central memory T cells (Tcm), effector memory T cells (Tem), and tissue-resident memory T cells (Trm), as presented in the revised Figure 2-figure supplement 1C-D, indicates that indicate that the type-1 inflammation observed upon ACD elicitation is predominantly driven by memory T cells recruited from lymphoid organs, rather than by skin resident memory T cells. We have read the reference provided by the reviewer along with a few other related studies indicating that Trm is involved in ACD. We found that these studies performed the elicitation phase on the same skin area where the initial sensitization is conducted, and only when it results in a rapid allergen-induced skin inflammatory response, that is primarily mediated by IL17A-producing and IFNγ-producing CD8+ skin resident memory T cells (Gadsboll et al., 2020; Murata & Hayashi, 2020; Schmidt et al., 2017; Wongchang et al., 2023). These studies suggest that Trm cells establish a long-lasting local memory during the initial sensitization, and upon re-exposure to the hapten in the same skin area, these site-specific Trm cells can rapidly contribute to a robust type-1 skin inflammatory response. Therefore, a robust involvement of Trm in ACD requires a repeated exposure of the same hapten to the same skin area. We have now added related discussion in the discussion section.

c. While it may be difficult to assess given reduced numbers of CD8 T cells in the Ifngr1 iKO, is the CXCL9/10-CXCR3 axis affecting IFNG production by T cells in vivo?

Yes, we have shown in Fig. 6G that ACD-mediated induction of Ifng was significantly suppressed in the Ifngr1-iKO mice compared to the control mice.

(3) The authors cite prior work (Xu et al. Nature 2022) that demonstrated a similar mechanism for fibroblasts in recruiting vitiligo-inducing T cells. Are the pAd (r5) cluster of fibroblasts similar to the fibroblast subpopulation that drives vitiligo?

The study on mouse model of vitiligo (Xu et al. Nature 2022) did not perform single-cell RNAseq of the vitiligo mouse skin. Instead, they conducted RNAseq analysis on the sorted PDGFRA+ dFBs. Therefore, we cannot directly compare our pAd (r5) cluster with the fibroblast subpopulation that drives vitiligo. Nevertheless, by utilizing a Venn diagram to compare the top 100 lFNγ signaling dependent genes upregulated in the active vitiligo mouse dFBs and the top 100 genes enriched in our ACD pAd (dFB_r5) cells, we identified 29 commonly upregulated genes between the two conditions (Figure 5-figure supplement 1A). Furthermore, all these 29 genes were among the top IFNγ-inducible genes in primary dFBs. These shared genes include CXCL9, CXCL10, and several other downstream targets of IFNγ signaling, such as B2M, BST2, CD274, as well as the GBP family members GBP3, GBP4, GBP5, GBP7, and additional genes like H2-K1, H2-Q4, H2-Q7, H2-T23, IFIT3, ISG15, and STAT1. This result suggests that the pAd (dFB_r5) cells possess a common IFNγ-pathway gene signature with the active vitiligo mouse dFBs, indicating a potential overlap in molecular pathways.

(4) The authors should include bulk RNA-seq data from fibroblast stimulation (Figure 5b) at a minimum in the GEO submission. They should ideally include the differentially expressed genes in a supplementary table.

Thanks for the reviewer’s constructive comments. We have now included the raw FPKM file for the bulk RNAseq data shown in Fig. 5 in Supplemental Table S3, and the list for differentially expressed genes in Supplemental Table S4.

(5) The authors state that human sample stainings were n = 3 per group for healthy control and ACD (Figure 7), but no quantification or statistical testing is provided to demonstrate significant differences in findings such as co-localization of fibroblasts and T cells, IFNG+CD8+ T cells, etc.

Thanks for the reviewer’s constructive comments. We have now supplemented 4 independent images for both Fig. 7A and Fig. 7E in the new Figure 7-figure supplement 1A-B to demonstrate the robustness and reproducibility of the staining presented.

Minor comments:

(1) Figure 1G, possible typos, Il14 and Il11b are on the violin plots when I believe authors meant Il4 and Il1b.

Thank a lot for pointing out these typos. We have now made the correction in the updated manuscript figure 1.

(2) The authors label cluster 27 as neutrophils based on the expression of Ly6g and S100a8. These markers are also expressed by Cd14+ inflammatory monocytes. I believe the authors need to additionally validate that these cells are neutrophils (via staining or additional analyses). Neutrophils are notoriously difficult to capture in scRNA-seq given low RNA content. Later, they are quantified by FACS using CD11b+Ly6G+ markers, but I do not believe this would distinguish them from CD14+ monocytes. As this is a relatively minor aspect of the manuscript, I consider this a minor concern, but a finding that should be as accurate as possible as Il1b is likely important, and identifying its accurate source likewise.

Thanks a lot for reviewer’s constructive comments. According to the reviewer’s suggestion, we have now added Cd14 expression in Figure 1C, and found that indeed cluster 27 express not only expressed Ly6G but also expressed Cd14. Based on literatures, the expression of Ly6G in circulating blood, spleen, and peripheral tissues is limited to neutrophils, whereas monocytes, macrophages, and lymphocytes are negative of Ly6G (Ikeda et al., 2023; Lee, Wang, Parisini, Dascher, & Nigrovic, 2013). Therefore, Ly6G can be used as a marker to distinguish neutrophils and monocytes. Although CD14 is highly expressed in monocytes, neutrophils can also express CD14 at lower level (Antal-Szalmas, Strijp, Weersink, Verhoef, & Van Kessel, 1997). Therefore, the cluster 27 is likely a mixed population of neutrophils and monocytes. So we have changed the definition of this cluster as NEU/Mon in the updated manuscript.

To confirm the presence of neutrophils and monocytes in ACD, we have included new FACS analysis of inflammatory monocytes, which are gated as CD11B+Ly6G-F4/80-CD11C-Ly6Chi, according to published FACS protocol(Rose, Misharin, & Perlman, 2012). We found that elicitation of ACD led to a transient influx of monocytes at 24 hrs post treatment, whereas the percentage of neutrophils continued to increase by 60 hours post-treatment (Figure 3L, and Figure 3-figure supplement 1G). In addition, at 60 hrs, the percentage of neutrophils (~5%) was > 10 times greater than the percentage of monocytes (~0.4%), indicating that neutrophils are the dominant granulocytes at 60 hours post ACD elicitation.

(3) The authors should include a cluster marker table as a supplementary file to accompany Figure 1C. Only top cluster markers are shown in 1C.

Thanks a lot for reviewer’s constructive comments. We have now included the top 5 enriched genes in each cell clusters shown in Fig. 1C in supplementary Table S2.

(4) Figures 2A/B have mismatched labels. There is a gdT/ILC2 label in the 2B, but not in 2A. Please match these. Along these lines, which gdT cluster is the IL17A expressing cluster as shown in 1D? Matching these labels will clarify which population is doing what.

Thanks a lot for reviewer to point out this mistake. To avoid confusion about the T cell clusters, we have added a specific recluster# for the T cell clusters as r0~r7 (Figure 2A-B). The r4 cluster is a mixed population of δγT and ILC2, therefore termed as δγT/ILC2. As shown in Figure 2-figure supplement 1E, IL17A is primarily expressed in the δγT cell (r5). We have now corrected δγT2 to δγT/ILC2 throughout the manuscript. To avoid confusion, we have now added cluster # in updated Figure 2D.

(5) In Figure 3E, the authors used CD11B as a distinguishing marker for basophils (CD11B+) vs. mast cells (CD11B-). Mcpt8 is a better distinguishing marker, so I am wondering why the authors chose CD11B.

Thanks a lot for reviewer’s comments. In scRNAseq, we did use Mcpt8 as a basophil specific marker to distinguish basophils and mast cells (see Figure 1C). However, Mcpt8 is not a surface receptor that can be used in FACS analysis. Therefore, to distinguish basophils from mast cells by FACS, we have to choose surface markers expressed on these cells. FcεR1a is a highly specific markers expressed exclusively on basophils and mast cells, and CD11B is expressed on basophils but not in mature mast cells (Hamey et al., 2021). As a result, FACS analysis of the surface expression of CD11B and FceR1a can distinguish basophils (CD11B+ FcεR1a+) from mast cells (CD11B- FcεR1a+). The use of CD11B and FcεR1a to distinguish basophils and mast cells can also been see in a published reference study (Arinobu et al., 2005).

(6) Antibody information is missing for IF studies. No clones, catalog numbers, vendors, RRIDs, or dilutions are included in the Methods section for any of the IF data.

Thanks a lot for reviewer’s constructive comments. We have now added related information for all the antibodies we used for FACS or IF data in the method section.

(7) Figure 3 supplement E and F appear to be reversed based on legend descriptions.

Thank a lot for pointing this out. We have now made the correction in the updated Supplementary file.

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Xu, Z., Chen, D., Hu, Y., Jiang, K., Huang, H., Du, Y., . . . Chen, T. (2022). Anatomically distinct fibroblast subsets determine skin autoimmune patterns. Nature, 601(7891), 118-124. doi:10.1038/s41586-021-04221-8

eLife assessment

This important study uses single-cell RNA-seq to obtain a more granular understanding of cell subsets within allergic contact dermatitis in a model system with DNFB. The convincing data revela unique subpopulations of dermal fibroblasts as key responders to interferon gamma and likely as mediators of dermatitis. This study has many novel aspects and provides a unique resource as well.

Reviewer #1 (Public Review):

In this manuscript, Liu et al. used scRNA-seq to characterize cell type-specific responses during allergic contact dermatitis (ACD) in a mouse model, specifically the hapten-induced DNFB model. Using the scRNA-seq data, they deconvolved the cell types responsible for the expression of major inflammatory cytokines such as IFNG (from CD4 and CD8 T cells), IL4/13 (from basophils), IL17A (from gd T cells), and IL1B from neutrophils and macrophages. They found the highest upregulation of a type 1 inflammatory response, centering around IFNG produced by CD4 and CD8 T cells. They further identified a subpopulation of dermal fibroblasts (pre-adipocytes found in the dermal white adipose tissue layer) that upregulate CXCL9/10 during ACD and provide functional genetic evidence in their mouse model that disrupting IFNG signaling in fibroblasts decreases CD8 T cell infiltration and overall inflammation. They identify an increase in IFNG-expressing CD8 T cells in human patient samples of ACD vs. healthy control skin and co-localization of CD8 T cells with PDGFRA+ fibroblasts, which suggests this mechanism is relevant to human ACD. This mechanism is reminiscent of recent work showing that IFNG signaling in dermal fibroblasts upregulates CXCL9/10 to recruit CD8 T cells in a mouse model of vitiligo. Overall, this is a well-presented, clear, and comprehensive manuscript. The conclusions of the study are well supported by the data, with thoughtful discussion on study limitations by the authors. One such limitation was the use of one ACD model (DNFB), which prevents an assessment of how broadly relevant this axis is. The human sample validation is limited by the multiplexing capacity of immunofluorescence markers but shows a predominance of CD8+/IFNG+ cells and PDGFRA+/CXCL10+ cells in ACD (which are virtually absent in healthy control), along with co-localization of CD8+ cells with PDGFRA+ cells. Thus, this mechanism is likely active in human ACD.

Strengths:<br /> Through deep characterization of the in vivo ACD model using scRNA-seq, the authors were able to determine which cell types were expressing the major cytokines involved in ACD inflammation, such as IFNG, IL4/13, IL17A, and IL1B. These analyses are well-presented and thoughtful, showing first that the response is IFNG-dominant, then focusing on deeper characterization of lymphocytes, myeloid cells, and fibroblasts, which are also validated and complemented by FACS experiments using canonical markers of these cell types as well as IF staining. Crosstalk analyses from the scRNA-seq data led the authors to focus on IFNG signaling fibroblasts, and in vitro experiments demonstrate that CXCL9 and CXCL10 are expressed by fibroblasts stimulated by IFNG. In vivo functional genetic evidence demonstrates an important role for IFNG signaling in fibroblasts, as KO of Ifngr1 using Pdgfra-Cre Ifngr1 fl/fl mice, showed a reduction in inflammation and CD8 T cell recruitment. Human ACD sample staining demonstrates the likely activity of the CD8 T cell IFNG-driven fibroblast response in human disease.

Weaknesses:<br /> The use of one model limits an understanding of how broad this fibroblast-T cell axis is during ACD. However, the authors chose the most commonly employed model and compared their data to work in a vitiligo model (another type 1 immune response) to demonstrate similar mechanisms at play. Human patient samples of ACD were co-stained with two markers at a time, demonstrating the presence of CD8+IFNG+ T cells, PDGFRA+CXCL10+ fibroblasts, and co-localization of PDGFRA+ fibroblasts and CD8+ T cells. However, no IF staining demonstrates co-expression of all 4 markers at once; thus, the human validation of co-localization of CD8+IFNG+ T cells and PDGFRA+CXCL10+ fibroblasts is ultimately indirect, although more likely than not to be true.

Reviewer #2 (Public Review):

Summary: The investigators apply scRNA seq and bioinformatics to identify biomarkers associated with the DNFB-induced contact dermatitis in mice. The bioinformatics component of the study appears reasonable and may provide new insights regarding TH1 driven immune reactions in ACD in mice. However, the IF data and images of tissue sections are not clear and should be improved to validate the model.

Strengths:<br /> The bioinformatics analysis.

Weaknesses:<br /> The IF data presented in 4H, 6H, 7E and 7F are not convincing and need to be correlated with routine staining on histology and different IF markers for PDGFR. Some of the IF staining data demonstrates a pattern inconsistent with its target.

Author response:

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

Main points:

(1) We have added data for fructose in Fig. 1

(2) We have added sta1s1cs (red stars and NS) comparing Tp between fed and refed flies. 

(3) We have modified the figure for each point to the opened small circles.

(4) We have moved the data from Fig. S3 to Fig. 2 and 3.

(5) We have added the schema1c diagrams depic1ng behavioral assay in Fig. S1.

(6) We have added heatmaps for WT and Gr64f-Gal4>UAS-CsChrimson flies in Fig. S2.

(7) We have added Orco1 mutant data in Fig. S4.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

This paper presents valuable findings that gustation and feeding state influence the preferred environmental temperature preference in flies. Interestingly, the authors showed that by refeeding starved animals with the non-nutritive sugar sucralose, they are able to tune their preference towards a higher temperature in addition to nutrient-dependent warm preference. The authors show that temperature-sensing and sweet-sensing gustatory neurons (SGNs) are involved in the former but not the latter. In addition, their data indicate that pep3dergic signals involved in internal state and clock genes are required for taste-dependent warm preference behavior.

The authors made an analogy of their results to the cephalic phase response (CPR) in mammals where the thought, sight, and taste of food prepare the animal for the consumption of food and nutrients. They further linked this behavior to core regulatory genes and peptides controlling hunger and sleep in flies having homologues in mammals. These valuable behavioral results can be further inves3gated in flies with the advantage of being able to dissect the neural circuitry underlying CPR and nutrient homeostasis.

Strengths: 

(1) The authors convincingly showed that tasting is sufficient to drive warm temperature preference behavior in starved flies and that it is independent of nutrient-driven warm preference. 

(2) By using the genetic manipulation of key internal sensors and genes controlling internal feeding and sleep states such as DH44 neurons and the per genes for example, the authors linked gustation and temperature preference behavior control to the internal state of the animal. 

Weaknesses: 

(1) The title is somewhat misleading, as the term homeostatic temperature control linked to gustation only applies to starved flies. 

We agree with the reviewer's suggestion and have changed the title to "Taste triggers a homeostatic temperature control in hungry flies".

(2) The authors used a temperature preference assay and refeeding for 5 minutes, 10 minutes, and 1 hour.

Experimentally, it makes a difference if the flies are tested immediately after 10 minutes or at the same 3me point as flies allowed to feed for 1 hour. Is 10 minutes enough to change the internal state in a nutrition-dependent manner? Some of the authors' data hint at it (e.g. refeeding with fly food for 10 minutes), but it might be relevant to feed for 5/10 minutes and wait for 55/50min to do the assays at comparable time points. 

Thank you for your suggestions. The temperature preference behavioral test itself takes 30 minutes from the time the flies are placed in the apparatus until the final choice is made. This means that after the hungry flies have been refed for 5 minutes, they will determine their preferred temperature within 35 minutes. It has been shown that insulin levels peak at 10 minutes and gradually decline (Tsao, et al., PLoS Genetics 2023). However, it is unclear how subtle insulin levels affect behavior and how quickly the flies are able to consume food. These factors may contribute to temperature preference in flies. Therefore, to minimize "extraneous" effects, we decided to test the behavioral assay immediately after they had eaten the food. We have noted in the material and method section that why we chose the condition based on behavior duration and insulin effect. 

(3) A figure depicting the temperature preference assay in Figure 1 would help illustrate the experimental approach. It is also not clear why Figure 1E is shown instead of full statistics on the individual panels shown above (the data is the same). 

We have revised Figure 1A and added statistics in Figure 1BCD. We also added a figure depicting the temperature preference assay (Fig. S1).

(4) The authors state that feeding rate and amount were not changed with sucralose and glucose. However, the FLIC assay they employed does not measure consumption, so this statement is not correct, and it is unclear if the intake of sucralose and glucose is indeed comparable. This limits some of the conclusions. 

We agree and removed “amount” and have revised the MS. 

(5) The authors make a distinction between taste-induced and nutrient-induced warm preference. Yet the statistics in most figures only show the significance between the starved and refed flies, not the fed controls. As the recovery is in many cases incomplete and used as a distinction of nutritive vs nonnutritive signals (see Figure 1E) it will be important to also show these additional statistics to allow conclusions about how complete the recovery is. 

We agree with the comments and have revised the MS and figures. 

(6) The starvation period used is ranging from 1 to 3 days, as in some cases no effect was seen upon 1 day of starvation (e.g. with clock genes or temperature sensing neurons). While the authors do provide a comparison between 18-21 and 26-29 hours old flies in Figure S1, a comparison for 42-49 and 66-69 hours of starvation is missing. This also limits the conclusion as the "state" of the animal is likely quite different after 1 day vs. 3 days of starvation and, as stated by the authors, many flies die under these conditions.  

We mainly used 2 overnights of starvation.  Some flies (e.g. Ilp6 mutants) were completely healthy even after 2 overnights of starvation, we had to starve them for 3 overnights. For example, Ilp6 mutants needed 3 overnights of starvation to show a significant difference Tp between fed and starved flies. On the other hand, some flies (e.g. w1118 control flies) were very sick after 2 overnights of starvation, we had to starve them for one overnight. Therefore, the starvation conditions which we used for this manuscript are from 1- 3-overnights.

First, we confirmed the starvation time by focusing on Tp which resulted in a sta1s1cally significant Tp difference between fed and starved flies; as men1oned above, flies prefer lower temperatures when starvation is prolonged (Umezaki et al., Current Biology 2018). Therefore, if Tp was not statistically different between fed and starved flies, we extended the starva1on 1me from 1 to 3 overnights. Importantly, we show in Fig. S3 that the dura1on of starvation did not affect the recovery effect. Furthermore, since control flies do not survive 42-49 or 66-69 hours of starvation, we can not test the reviewer's suggestion. We have carefully documented the conditions in the Material and method and figure legends.

(7) In Figure 2, glucose-induced refeeding was not tested in Gr mutants or silenced animals, which would hint at post-ingestive recovery mechanisms related to nutritional intake. This is only shown later (in Figure S3) but I think it would be more fitting to address this point here. The data presented in Figure S3 regarding the taste-evoked vs nutrient-dependent warm preference is quite important while in some parts preliminary. It would nonetheless be justified to put this data in the main figures. However, some of the conclusions here are not fully supported, in part due to different and low n numbers, which due to the inherent variability of the behavior do not allow statistically sound conclusions. The authors claim that sweet GRNs are only involved in taste-induced warm preference, however, glucose is also nutritive but, in several cases, does not rescue warm preference at all upon removal of GRN function (see Figures S3A-C). This indicates that the Gal4 lines and also the involved GRs are potentially expressed in tissues/neurons required for internal nutrient sensing. 

Thank you for your suggestion. We have added Figure S3ABC (glucose refeeding using Gr mutants and silenced animals) to Figure 2. There is no low N number since we tested > 5 times, i.e. >100 flies were tested. Tp may have a variation probably due to the effect of starvation on their temperature preference. 

We did not mention that "The authors claim that sweet GRNs are only involved in taste-induced warm preference...". However, our wri1ng may not be clear enough. We agree that "...GRs may be expressed in tissues/neurons required for internal nutrient sensing. ..."  We have rewritten and revised the section.  

(8) In Figure 4, fly food and glucose refeeding do not fully recover temperature preference after refeeding. With the statistical comparison to the fed control missing, this result is not consistent with the statement made in line 252. I feel this is an important point to distinguish between state-dependent and taste/nutrition-dependent changes.  

We inserted the statistics and compared between Fed and other conditions. 

(9) The conclusion that clock genes are required for taste-evoked warm preference is limited by the observation that they ingest less sucralose. In addition, the FLIC assay does not allow conclusions about the feeding amount, only the number of food interactions. Therefore, I think these results do not allow clear-cut conclusions about the impact of clock genes in this assay.  

We agree and remove “amount” and have revised the MS. The per01 mutants ate (touched) sucralose more often than glucose. On the other hand, 1m01 mutants ate glucose more often than sucralose (Figure S6BC). However, these mutants s1ll showed a similar TP pattern for sucralose and glucose refeeding (Fig. 5CD). The results suggest that the 1m01 flies eat enough amount of sucralose over glucose that their food intake does not affect the TP behavioral phenotype. We have rewritten and revised the section.

(10) CPR is known to be influenced by taste, thought, smell, and sight of food. As the discussion focused extensively on the CPR link to flies it would be interesting to find out whether the smell and sight of food also influence temperature preference behavior in animals with different feeding states.  

We have added the data using Olfactory receptor co-receptor (Orco1) mutant, which lack olfaction, in Fig. S4. They failed to show the taste-evoked warm preference, but exhibited the nutrient-induced warm preference. Therefore, the data suggest that olfactory detection is also involved in taste-evoked warm preference. On the other hand, "seeing food" is probably more complicated, since light dramatically affects temperature preference behavior and the circadian clock that regulates temperature preference rhythms. Therefore, it will not be unlikely to draw a solid conclusion from the short set of experiments. We will address this issue in the next study.

(11) In the discussion in line 410ff the authors claim that "internal state is more likely to be associated with taste-evoked warm preference than nutrient-induced warm preference." This statement is not clear to me, as neuropeptides are involved in mediating internal state signals, both in the brain itself as well as from gut to brain. Thus, neuropeptidergic signals are also involved in nutrient-dependent state changes, the authors might just not have identified the peptides involved here. The global and developmental removal of these signals also limits the conclusions that can be drawn from the experiments, as many of these signals affect different states, circuits, and developmental progression.  

We agree with the comments. We have removed the sentences and revised the MS.  

Reviewer #2 (Public Review): 

Animals constantly adjust their behavior and physiology based on internal states. Hungry animals, desperate for food, exhibit physiological changes immediately upon sensing, smelling, or chewing food, known as the cephalic phase response (CPR), involving processes like increased saliva and gastrointestinal secretions. While starvation lowers body temperature, the mechanisms underlying how the sensation of food without nutrients induces behavioral responses remain unclear. Hunger stress induces changes in both behavior and physiological responses, which in flies (or at least in Drosophila melanogaster) leads to a preference for lower temperatures, analogous to the hunger-driven lower body temperature observed in mammals. In this manuscript, the authors have used Drosophila melanogaster to investigate the issue of whether taste cues can robustly trigger behavioral recovery of temperature preference in starving animals. The authors find that food detection triggers a warm preference in flies. Starved flies recover their temperature preference after food intake, with a distinction between partial and full recovery based on the duration of refeeding. Sucralose, an artificial sweetener, induces a warm preference, suggesting the importance of food-sensing cues. The paper compares the effects of sucralose and glucose refeeding, indicating that both taste cues and nutrients contribute to temperature preference recovery. The authors show that sweet gustatory receptors (Grs) and sweet GRNs (Gustatory Receptor Neurons) play a crucial role in taste-evoked warm preference. Optogenetic experiments with CsChrimson support the idea that the excitation of sweet GRNs leads to a warm preference. The authors then examine the internal state's influence on taste-evoked warm preference, focusing on neuropeptide F (NPF) and small neuropeptide F (sNPF), analogous to mammalian neuropeptide Y. Mutations in NPF and sNPF result in a failure to exhibit taste-evoked warm preference, emphasizing their role in this process. However, these neuropeptides appear not to be critical for nutrient-induced warm preference, as indicated by increased temperature preference during glucose and fly food refeeding in mutant flies. The authors also explore the role of hunger-related factors in regula3ng taste-evoked warm preference. Hunger signals, including diuretic hormone (DH44) and adipokinetic hormone (AKH) neurons, are found to be essential for taste-evoked warm preference but not for nutrient-induced warm preference. Additionally, insulin-like peptides 6 (Ilp6) and Unpaired3 (Upd3), related to nutritional stress, are identified as crucial for taste-evoked warm preference. The investigation then extends into circadian rhythms, revealing that taste-evoked warm preference does not align with the feeding rhythm. While flies exhibit a rhythmic feeding pattern, taste-evoked warm preference occurs consistently, suggesting a lack of parallel coordination. Clock genes, crucial for circadian rhythms, are found to be necessary for taste-evoked warm preference but not for nutrient-induced warm preference. 

Strengths: 

A well-written and interesting study, investigating an intriguing issue. The claims, none of which to the best of my knowledge controversial, are backed by a substantial number of experiments. 

Weakness: 

The experimental setup used and the procedures for assessing the temperature preferences of flies are rather sparingly described. Additional details and data presentation would enhance the clarity and replicability of the study. I kindly request the authors to consider the following points: 

i) A schematic drawing or diagram illustrating the experimental setup for the temperature preference assay would greatly aid readers in understanding the spatial arrangement of the apparatus, temperature points, and the positioning of flies during the assay. The drawing should also be accompanied by specific details about the setup (dimensions, material, etc). 

Thank you for your suggestions. We have added the schematic drawing in Fig. S1.

ii) It would be beneficial to include a visual representation of the distribution of flies within the temperature gradient on the apparatus. A graphical representation, such as a heatmaps or histograms, showing the percentage of flies within each one-degree temperature bin, would offer insights into the preferences and behaviors of the flies during the assay. In addition to the detailed description of the assay and data analysis, the inclusion of actual data plots, especially for key findings or representative trials, would provide readers with a more direct visualization of the experimental outcomes. These additions will not only enhance the clarity of the presented information but also provide the reader with a more comprehensive understanding of the experimental setup and results. I appreciate the authors' attention to these points and look forward to the potential inclusion of these elements in the revised manuscript. 

Thank you for the advice. We have added the heat map for WT and Gr64fGal4>CsChrimson data in Fig. S2. 

Reviewer #3 (Public Review): 

Summary: 

The manuscript by Yujiro Umezaki and colleagues aims to describe how taste stimuli influence temperature preference in Drosophila. Under starvation flies display a strong preference for cooler temperatures than under fed conditions that can be reversed by refeeding, demonstrating the strong impact of metabolism on temperature preference. In their present study, Umezaki and colleagues observed that such changes in temperature preference are not solely triggered by the metabolic state of the animal but that gustatory circuits and peptidergic signalling play a pivotal role in gustation-evoked alteration in temperature preference. 

The study of Umezaki is definitively interesting and the findings in this manuscript will be of interest to a broad readership. 

Strengths: 

The authors demonstrate interesting new data on how taste input can influence temperature preference during starvation. They propose how gustatory pathways may work together with thermosensitive neurons, peptidergic neurons and finally try to bridge the gap between these neurons and clock genes. The study is very interesting and the data for each experiment alone are very convincing. 

Weaknesses: 

In my opinion, the authors have opened many new questions but did not fully answer the initial question - how do taste-sensing neurons influence temperature preferences? What are the mechanisms underlying this observation? Instead of jumping from gustatory neurons to thermosensitive neurons to peptidergic neurons to clock genes, the authors should have stayed within the one question they were asking at the beginning. How does sugar sensing influence the physiology of thermos-sensation in order to change temperature preference? Before addressing all the following question of the manuscript the authors should first directly decipher the neuronal interplay between these two types of neurons. 

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors): 

Figure S3D is cited before S2, so please rearrange the numbering.

Thank you. We have changed the numbering.

I would also suggest a different color to visualize the data points in Figure S3, as some are barely visible on the dark bars (e.g. on a dark green background). 

We have revised the figures. The data points were changed to smaller opened circles. 

Reviewer #2 (Recommendations For The Authors): 

*Please, expand on the experimental procedure, and describe the assay in detail. 

We have added a scheme for the assay in Fig. S1 and also have revised the manuscript and figures.

*Show the distribution of the gradient data that the preference values are based upon. Not necessarily for all, but for select key experiments. Heatmaps for each replicate (stacked on top of each other) would be a nice way of showing this. Simple histograms would of course work as well. 

We have added heatmaps of selected key experiments that were added in Fig. S2. We have revised the manuscript and figures, correspondingly.

Reviewer #3 (Recommendations For The Authors  

The manuscript by Yujiro Umezaki and colleagues aims at describing how taste stimuli influence temperature preference in Drosophila. Under starvation, flies display a strong preference for cooler temperatures than under-fed conditions that can be reversed by refeeding, demonstrating the strong impact of metabolism on temperature preference. In their present study, Umezaki and colleagues observed that such changes in temperature preference are not solely triggered by the metabolic state of the animal but that gustatory circuits play a pivotal role in temperature preference. The study of Umezaki is definitively interesting and the findings in this manuscript will be of interest to a broad readership. However, I would like to draw the authors' attention to some points of concern: 

The title to me sounds somehow inadequate. The definition of homeostasis (Cambridge Dictionary) is as follows: "the ability or tendency of a living organism, cell, or group to keep the conditions INSIDE it the same despite any changes in the conditions around it, or this state of internal balance". What do the authors mean by homeostatic temperature control? Reading the title not knowing much about poikilotherm insects I would understand that the authors claim that Drosophila can indeed keep a temperature homeostasis as mammals do. As Drosophila is not a homoiotherm animal and thus cannot keep its body temperature stable the title should be amended.  

Homeostasis means a state of balance between all the body systems necessary for the body to survive and function properly. Drosophila are ectotherms, so the source of temperature comes from the environment, and their body temperature is very similar to that of their environment. However, the flies' temperature regulation is not simply a passive response to temperature. Instead, they actively seek a temperature based on their internal state. We have shown that the preferred temperature increases during the day and decreases during the night, showing a circadian rhythm of temperature preference (TPR). Because their environmental temperature is very close to their body temperature, TPR gives rise to body temperature rhythms (BTR). We have shown that TPR is similar to BTR in mammals. (Kaneko et al., Current Biology 2012 and Goda et al., JBR 2023). Similarly, we showed that the hungry flies choose a lower temperature so that the body temperature is also lower. Therefore, our data suggest that the fly maintains its homeostasis by using the environmental temperature to adjust its body temperature to an appropriate temperature depending on its internal state. Therefore, I would like to keep the title as "Taste triggers a homeostatic temperature control in hungry flies" We have added more explana1on in the Introduc1on and Discussion.

Accordingly, the authors compare the preference of flies to cooler temperatures to the reduced body temperature of mammals (Lines 64 - 65). However, according to the cited literature the reduced body temperature in starved rats is discussed to reduce metabolic heat production (Sakurada et al., 2000). The authors should more rigorously give a short summary of the findings in the cited papers and the original interpretation to help the reader not get confused.

In flies, it has been shown that a lower temperature means a lower metabolic rate, and a higher temperature means a higher metabolic rate. Therefore, hungry flies choose a lower temperature where their metabolic rate is lower and they do not need as much heat.

Similarly, in mammals, starvation causes a lower body temperature, hypothermia. Body temperature is controlled by the balance between heat loss and heat production. The starved mammals showed lower heat production. We have added this information to the introduction. 

The authors show that 5 min fly food refeeding causes a par3al recovery of the naïve temperature preference of the flies (Figure 1B) and that feeding of sucralose par3ally rescues the preference whereas glucose rescues the preference similar to refeeding with fly food would do. As glucose is both sweet and metabolically valuable it would be clearer for the reader if the authors start with the fly food experiment and then show the glucose experiment to show that the altered temperature preference depends on the food component glucose. From there they can further argue that glucose is both sweet (hedonic value) and metabolically valuable. And to disentangle sweetness from metabolism one needs a sugar that is sweet but cannot be metabolized - sucralose. 

Thank you for your advice. Since the data with sucralose is the one we want to highlight the most, we decided to present it in the order of sucralose, glucose, and fly food.

In the sucralose experiment the authors omit the 5 min data point and only show the 10 min time point. As Figure 1F indicates that both Glucose and Sucralose elicit the same attractiveness in the flies and that sweetness influences the temperature preference, it is important that the authors show the 5 min temperature preference too to underline the effect of the sweet taste stimulus on the fly behavior independent from the caloric value. Further, the authors should demonstrate not only the cumulative touches but how much sucralose or glucose may already be consumed by the fly in the depicted time frames. 

It is interesting to see how much sucralose or glucose the flies consume over the time frames shown. Although the cumula1ve exposure to sugar is ideally equivalent to the amount of sugar, we need a different way to actually measure the amount of sugar. We will now emphasize "cumulative touches" rather than "amount of sugar" in the text. In the next study, we will look at how much sucralose or glucose the fly has already consumed.

Sucralose and Glucose have a similar molecular structure - it would be interesting to see how the sweet taste of a sugar with a different molecular structure like fructose and its receptor Gr43b (Myamato & Amrein 2014) may contribute to temperature preferences.  

Sucralose and Glucose are not structurally similar. That said, we tested fructose refeeding anyway. The hungry flies showed a taste-evoked warm preference after fructose refeeding. We have added data in Figure 1E and F. The data suggest that sweet taste is more important than sugar structure. We also tested Gr43b>CsChrimson. However, the flies do not show the taste-evoked warm preference (data not shown). The data suggest that Gr43b is not the major receptor controlling taste-evoked warm preference. We have revised the manuscript.

Both sugars appear similarly attractive to the flies (Figure 1F) - are water, sucralose, and glucose presented in a choice assay or are these individually in separate experiments? 

Water, sucralose, and glucose were individually presented in separate experiments. We clarified it in the figure legend.

Subsequently, the authors address the question of how sweet taste may influence temperature preferences in flies. To this end, the authors first employ gustatory receptor mutants for Gr5a, Gr64a, and Gr61a and demonstrate that sucralose feeding does not rescue temperature preference in the absence of sweet taste receptors. In an alternative approach, the authors do not use mutants but an expression of UAS:Kir in Gr64F neurons. Taking a closer look at the graph it appears that the Kir expressing flies have an increased (nearly 1{degree sign}C) temperature preference than the starved mutant flies. Is this preference change related to the mutation directly and what would be the result if Kir would be conditionally only expressed after development is completed, or is the observed temperature preference related to the Gr64f-Gal4 line? If the latter would be the case perhaps the authors may want to bring the flies to the same genetic background to allow for a more direct comparison of the temperature preferences. 

The Gr64fGal4>Kir flies show a ~one degree higher preferred temperature under starvation compared to the mutants. However, the phenotype is similar to the controls, Gr64fGal4/+ flies, under starvation. Therefore, this phenotype is not due to either the mutation or the Kir effect. Most importantly, the Gr64fGal4>Kir flies failed to show a taste-evoked warm preference. Together with other mutant data, we concluded that sweet GRNs are required for taste-evoked warm preference.

Overall, the figure legend for Figure 2 is very cryptic and should be more detailed.

We have revised the figure legend for Figure 2. 

To shed light on the mechanisms underlying the changes in temperature preferences through gustatory stimuli the authors next blocked heat and cold sensing neurons in fed and starved flies and found out that TrpA1 expressing anterior cells and R11F02-Gal4 expressing neurons both participate in sweetness-induced alteration of temperature preference in starved animals. At this point, it should be explicitly indicated in the figure that the flies need more than one overnight starva3on to display the behavior (Figure 3A). 

We have revised the manuscript.

The data provided by the authors indicate a kind of push-and-pull mechanism between heat and cold-sensing neurons under starvation that is somehow influenced by sweet taste sensing. Further, the authors demonstrate that TrpA1-as well as R11F02-Gal4 driven Chrimson activation is sufficient to partially rescue temperature preference under starvation. At this point is unclear why the authors use a tubGal80ts expression system but not for the TrpA1SH-Gal4 driven Chrimson. As the development itself and the conditions under which the animals were raised may have influence on the temperature preference it is important that both groups are equally raised if the authors want to directly compare with each other. 

As we wrote in the Material and Method, the R11F02-Gal4>uas-CsChrimson flies died during the development. Therefore, we had to use tubGal80ts. On the other hand, the TrpA1-Gal4>CsChrimson flies can survive to adults. As we mentioned in MS, all flies were treated with ATR after they had fully developed into adults. This means that both TrpA1-Gal4 and R11F02-Gal4 expressing cells are ac1vated by red light via CsChrimson only in adult stages. We carefully revised the MS.

It is a pity that the authors at this point have decided to not deepen the understanding of the circuitry between thermo-sensation and metabolic homeostasis but subsequently change the focus of their study to investigate how internal state influences taste-evoked warm preference in hungry flies. Using mutants for NPF and sNPF the authors demonstrate that both peptides play a pivotal role in taste-evoked warm preference after sucrose feeding but not for nutrient-induced warm preference. Similarly, they found that DH44, AKH and dILP6, Upd2 and Upd3 neurons are also required for taste-evoked warm preference but not for nutrient-induced warm preference. Here again, the authors do not keep the systems stable and change between inhibition of neurons through Kir and mutants for peptides. For a better comparison, it would be preferable to use always exactly the same technique to inhibit neuron signalling.

It would be interesting to find the neural circuity of thermo-sensation and metabolic homeostasis, but we do not have any luck so far. We will continue to look into the neural circuits which control taste-evoked warm preference and nutrient-induced warm preference. Since UAS-Kir is such a strong reporter, it may kill the flies sometime. So we couldn't use UAS-Kir for all Gal4 flies. 

DH44 is expressed in the brain and in the abdominal ganglion where they share the expression pattern with 4 Lk neurons per hemisphere. Seeing the impact of Lk signalling in metabolism (AlAnzi et al., 2010) the authors should provide evidence that the observed effect is indeed because of DH44 and not Lk.

It would be interesting to see if Lk may play a role in taste-evoked warm preference and/or nutrient-induced warm preference. We would like to systematically screen which neuropeptides and receptors are involved in the behavior in the next study. 

Seeing the results on dILP6 it is interesting that Li and Gong (2015) could show in larvae that cold-sensing neurons directly interact with dILP neurons in the brain. It would be interesting to see whether similar circuitry may exist in adult flies to regulate temperature preferences and these peptidergic neurons. Further, it appears interesting that again these animals need much longer time to display the observed shift in temperature (which again should be clearly indicated in the figure legend too). These observations should be more carefully considered in the discussion part too.

We have revised the manuscript.

In the last part of the study, the authors investigate how sensory input from temperature-sensitive cells may transmit information to central clock neurons and how these in turn may influence temperature preference under starvation. The experiments assume that DH44-expressing neurons play a role in the output pathway of the central clock. Using the clock gene null mutants per and tim the authors show that even though the animals display a significant starvation response neither per nor tim mutants exhibited taste-evoked warm preference, indicating a taste but not nutrient-evoked temperature preference regulation. 

The authors demonstrate interesting new data on how taste input can influence temperature preference during starvation. They propose how gustatory pathways may work together with thermosensitive neurons, peptidergic neurons and finally try to bridge the gap between these neurons and clock genes. The study is very interesting and the data for each experiment alone are very convincing. However, in my opinion, the authors have opened many new questions but did not fully answer the initial question - how do taste-sensing neurons influence temperature preferences? What are the mechanisms underlying this observation? Instead of jumping from gustatory neurons to thermosensitive neurons to peptidergic neurons to clock genes, the authors should have stayed within the one question they were asking at the beginning. How does sugar sensing influence the physiology of thermos-sensation? Before addressing all the following questions of the manuscript the authors should first directly decipher the neuronal interplay between these two types of neurons. 

Thank you for your suggestion. It would be interesting to find the neural circuity of thermo-sensation and metabolic homeostasis. We have tried but there is no luck so far. 

The authors could e.g., employ Ca or cAMP-imaging in anterior or cold-sensitive cells and see how the responsiveness of these cells may be altered after sugar feeding. Or at least follow the idea of Li and Gong about the thermos-regulation of dILP-expressing neurons. 

Thank you for your suggestion. Since we do not know how dlLP-expression neurons are involved in temperature response in the adult flies. We will focus on the cells using Calcium imaging for the next study.

Anatomical analysis using the GRASP technique may further help to understand the interplay of these neurons and give new insights into the circuitry underlying food preference alteration under starvation. 

Thank you for your suggestion. It would be interesting to find the neural circuity of thermo-sensation and metabolic homeostasis. We have tried but there is no luck so far.  

Minor comments: 

Line 51: Hungry animals are desperate for food - I think the authors should not anthropomorphize at this point too\ much but rather strictly describe how the animals change their behavior without any interpretation of the mental state of the animal. 

We have modified the manuscript.

Line 80: Hunger and satiety dramatically affect animal behavior and physiology and control feeding - please not only cite the papers but also give a short overview of the cited papers on which behaviors are altered and how. 

We have revised the manuscript. 

Overall statistic: The authors do comparative statistics always against starved animals throughout but often state in the text a comparison against fed (Line 111: "but did not reach that of the fed flies") I think the authors should describe the date according to their statistics and keep this constant throughout the paper. 

Sorry for the confusion. We originally had it, but we removed it. We have added the additional statistical analyses.  

Figure legends: Overall the figure legends could be more developed and more detailed.

We have revised the manuscript.

eLife assessment

This paper presents valuable findings that gustation and nutrition might independently influence the preferred environmental temperature in flies. The evidence supporting the main claims is solid and well presented. The finding that flies might thus exhibit a cephalic phase response similar to mammals will be of value for future investigations.

Reviewer #1 (Public Review):

Summary:

This paper presents valuable findings that gustation and feeding state influence the preferred environmental temperature preference in flies. Interestingly, the authors showed that by refeeding starved animals with non-nutritive sugar sucralose, they are able to tune their preference towards a higher temperature in addition to nutrient-dependent warm preference. The authors show that temperature sensing and sweet sensing gustatory neurons (SGNs) are involved in the former but not the latter. In addition, their data indicate that peptidergic signals involved in internal state and clock genes are required for taste-dependent warm preference behavior.

The authors made an analogy of their results to the cephalic phase response (CPR) in mammals where the thought, sight and taste of food prepares the animal for the consumption of food and nutrients. The authors showed that taste triggers CPR-induced temperature preference behaviors in flies. The authors also briefly covered that the combined modalities of smell and taste induced CPR responses, showing that starved orco mutant flies failed to recover temperature preference after refeeding with sucralose.

The findings of this work hold promising future research prospects, for example, whether the sight of food influences temperature preference behavior in hungry flies, or whether taste, smell and sight work together or independently in promoting CPR responses.

Futhermore, these valuable behavioral results can be further investigated in flies with the advantage of being able to dissect the neural circuitry underlying CPR and nutrient homeostasis.

Strengths:

(1) The authors convincingly showed that tasting is sufficient to drive warm temperature preference behavior in starved flies and show that it is independent of nutrient-driven warm preference.<br /> (2) By using the genetic manipulation of key internal sensors and genes controlling internal feeding and sleep state such as DH44 neurons and the per genes for eg the authors linked gustation and temperature preference behavior control to the internal state of the animal.

Weaknesses:

Most of the weaknesses of the paper have been addressed in the revision. The points mentioned below are meant to improve readability of the paper and to promote understanding of the significance of the work.<br /> (1) Supplementary fig 1 could replace Figure 1A. The purpose of Figure 1F is not clear to me as the comparison between the different food substances is not separately addressed anywhere in the text.<br /> (2) The data for the orco receptor mutant could be placed in the main figures to justify the discussion emphasising CPR-like responses.

Reviewer #2 (Public Review):

Animals constantly adjust behavior and physiology based on internal states. Hungry animals, desperate for food, exhibit physiological changes immediately upon sensing, smelling, or chewing food, known as the cephalic phase response (CPR), involving processes like increased saliva and gastrointestinal secretions. While starvation lowers body temperature, the mechanisms underlying how the sensation of food without nutrients induces behavioral responses remain unclear. Hunger stress induces changes in both behavior and physiological responses, which in flies (or at least in Drosophila melanogaster) leads to a preference for lower temperatures, analogous to the hunger-driven lower body temperature observed in mammals. In this manuscript, the authors have used Drosophila melanogaster to investigate the issue of whether taste cues can robustly trigger behavioral recovery of temperature preference in starving animals. The authors find that food detection triggers a warm preference in flies. Starved flies recover their temperature preference after food intake, with a distinction between partial and full recovery based on the duration of refeeding. Sucralose, an artificial sweetener, induces a warm preference, suggesting the importance of food-sensing cues. The paper compares the effects of sucralose and glucose refeeding, indicating that both taste cues and nutrients contribute to temperature preference recovery. The authors show that that sweet gustatory receptors (Grs) and sweet GRNs (Gustatory Receptor Neurons) play a crucial role in taste-evoked warm preference. Optogenetic experiments with CsChrimson support the idea that the excitation of sweet GRNs leads to a warm preference. The authors then examine the internal state's influence on taste-evoked warm preference, focusing on neuropeptide F (NPF) and small neuropeptide F (sNPF), analogous to mammalian neuropeptide Y. Mutations in NPF and sNPF result in a failure to exhibit taste-evoked warm preference, emphasizing their role in this process. However, these neuropeptides appear not to be critical for nutrient-induced warm preference, as indicated by increased temperature preference during glucose and fly food refeeding in mutant flies. The authors also explore the role of hunger-related factors in regulating taste-evoked warm preference. Hunger signals, including diuretic hormone (DH44) and adipokinetic hormone (AKH) neurons, are found to be essential for taste-evoked warm preference but not for nutrient-induced warm preference. Additionally, insulin-like peptide 6 (Ilp6) and Unpaired3 (Upd3), related to nutritional stress, are identified as crucial for taste-evoked warm preference. The investigation then extends into circadian rhythms, revealing that taste-evoked warm preference does not align with the feeding rhythm. While flies exhibit a rhythmic feeding pattern, taste-evoked warm preference occurs consistently, suggesting a lack of parallel coordination. Clock genes, crucial for circadian rhythms, are found to be necessary for taste-evoked warm preference but not for nutrient-induced warm preference.

Strengths:

A well-written and interesting study, investigating an intriguing issue. The claims, none of which to the best of my knowledge controversial, are backed by a substantial number of experiments.

Weakness:

The experimental setup used and the procedures for assessing the temperature preferences of flies is rather sparingly described. Additional details and data presentation would enhance the clarity and replicability of the study. I kindly request the authors to consider the following points: i) A schematic drawing or diagram illustrating the experimental setup for the temperature preference assay would greatly aid readers in understanding the spatial arrangement of the apparatus, temperature points, and the positioning of flies during the assay. The drawing should also be accompanied by specific details about the setup (dimensions, material, etc). ii) It would be beneficial to include a visual representation of the distribution of flies within the temperature gradient on the apparatus. A graphical representation, such as a heatmaps or histograms, showing the percentage of flies within each one-degree temperature bin, would offer insights into the preferences and behaviors of the flies during the assay. In addition to the detailed description of the assay and data analysis, the inclusion of actual data plots, especially for key findings or representative trials, would provide readers with a more direct visualization of the experimental outcomes. These additions will not only enhance the clarity of the presented information but also provide the reader with a more comprehensive understanding of the experimental setup and results. I appreciate the authors' attention to these points and look forward to the potential inclusion of these elements in the revised manuscript.

Update: The revised manuscript now includes heatmaps showing the distribution of the flies across the temperature bins. As well as a schematic drawing of the behavioral setup.

eLife assessment

In this manuscript, Jain and colleagues explore whether increasing adult-born neurons is protective against status epilepticus and the development of spontaneous recurrent seizures (chronic epilepsy) in a mouse pilocarpine model of temporal lobe epilepsy. This is an important work that provides solid data, contradicting previous studies on suppressing chronic seizures by reduction in adult-born neurons.

Reviewer #1 (Public Review):

Summary:

As adult-born granule neurons have been shown to play diverse roles, both positive and negative, to modulate hippocampal circuitry and function in epilepsy, understanding the mechanisms by which altered neurogenesis contribute to seizures is important for future therapeutic strategies. The work by Jain et al., demonstrates that increasing adult-born neurons (not increasing adult neurogenesis because BrdU birthdating was not performed in this study) before status epilepticus (SE) leads to a suppression in chronic seizures in the pilocarpine model of temporal lobe epilepsy. This work is potentially interesting because previous studies showed suppressing adult-born neurons led to reduced chronic seizures.

To increase adult-born neurons, the authors conditionally delete the pro-apoptotic gene Bax using a tamoxifen inducible Nestin-CreERT2 which has been previously published to increase proliferation and survival of adult-born neurons by Sahay et al. (although this was not shown in this study). After 6 weeks of tamoxifen injection, the authors subject male and female mice to pilocarpine induced SE. In the first study, at 2 hours after pilocarpine, the authors examine latency to the first seizure, severity and total number of acute seizures, and power during SE. In the second study in a separate group of mice, the authors examine chronic seizure number and frequency, seizure duration, postictal depression, and seizure distribution/cluster seizures for 3 weeks after pilocarpine. Overall, the study concludes that increasing adult-born neurons in the normal adult brain can reduce epilepsy in females specifically.

Strengths:

(1) The study is sex matched and reveals differences in response to increasing adult-born neurons in chronic seizures between male and females.

(2) The EEG recording parameters are stringent, and analysis of chronic seizures is comprehensive. In two separate experiments, the electrodes were implanted to record EEG from cortex as well as hippocampus. The recording is done for 10 hours post pilocarpine to analyze acute seizures, and for 3 weeks continuous video EEG recording was done to analyze chronic seizures.

Weaknesses:

(1) Increased DCX alone (without birthdating with BrdU) could indicate increased survival of adult-born neurons, not proliferation or birth of newborn neurons per se. While prior work has demonstrated that tamoxifen injection in adult mice showed an increase in dentate gyrus neurogenesis based on studies of BrdU, Ki67, and DCX (Sahay et al., 2011), the dynamics of adult-born neurons (proliferation, differentiation, and/or survival) could be different in epileptic (pilocarpine-treated) animals. Other stages, e.g., proliferation of neural precursors or maturation of adult-born dentate granule cells, was not examined. Analysis of additional stages of adult neurogenesis may reveal additional cellular understanding and add impact of the work on the field.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

As adult-born granule neurons have been shown to play diverse roles, both positive and negative, to modulate hippocampal circuitry and function in epilepsy, understanding the mechanisms by which altered neurogenesis contributes to seizures is important for future therapeutic strategies. The work by Jain et al. demonstrates that increasing adult neurogenesis before status epilepticus (SE) leads to a suppression of chronic seizures in the pilocarpine model of temporal lobe epilepsy. This work is potentially interesting because previous studies showed suppressing neurogenesis led to reduced chronic seizures.

To increase neurogenesis, the authors conditionally delete the pro-apoptotic gene Bax using a tamoxifen-inducible Nestin-CreERT2 which has been previously published to increase proliferation and survival of adult-born neurons by Sahay et al. After 6 weeks of tamoxifen injection, the authors subjected male and female mice to pilocarpine-induced SE. In the first study, at 2 hours after pilocarpine, the authors examine latency to the first seizure, severity and total number of acute seizures, and power during SE. In the second study in a separate group of mice, at 3 weeks after pilocarpine, the authors examine chronic seizure number and frequency, seizure duration, postictal depression, and seizure distribution/cluster seizures. Overall, the study concludes that increasing adult neurogenesis in the normal adult brain can reduce epilepsy in females specifically. However, important BrdU birthdating experiments in both male and female mice need to be included to support the conclusions made by the authors. Furthermore, speculative mechanisms lacking direct evidence reduce enthusiasm for the findings.

There are two suggestions. First, BrdU birthdating of newborn neurons is important to add to the paper so that there is support for the conclusions. Second, speculative text reduced enthusiasm. In response, we clarified the conclusions. We do not think that the clarified conclusions require BrdU birthdating (discussed further below). We also removed two schematics (and associated text) that we think the reviewer was referring to when speculation was mentioned.

We also want to point out something minor -that the times of injections listed above are not correct.

a. Seizures were not measured 2 hrs after pilocarpine; that is when the anticonvulsant diazepam was administered to males. 

b. Seizures were not measured 3 weeks after pilocarpine; the duration of recording was 3 weeks.  

(1) BrdU birthdating is required for conclusions.

We think that the Reviewer was suggesting birthdating because we were not clear about our conclusions, and we apologize for the confusion. The Reviewer stated that we concluded: “conditionally deleting Bax in Nestin-Cre+ cells leads to increased neurogenesis and hilar ectopic granule cells, thereby reducing chronic seizures.”  (Note this is a quote from the review).

However, we did not intend to conclude that. We intended to conclude that conditionally deleting Bax in Nestin-Cre+ mice reduced chronic seizures in the mouse model of epilepsy that we used. Also, that conclusion only pertained to females. Please note we did not conclude that hilar ectopic granule cells led to reduced seizures. We also concluded that Bax deletion increased neurogenesis in female mice. We have revised the text to make the conclusions clear.

Abstract, starting on line 67:

The results suggest that selective Bax deletion to increase adult neurogenesis can reduce experimental epilepsy, and the effect shows a striking sex difference.

Results, starting on line 448:

Because Cre+ epileptic females had increased numbers of immature neurons relative to Cre- females at the time of SE, and prior studies show that Cre+ females had less neuronal damage after SE (Jain et al., 2019), female Cre+ mice might have had reduced chronic seizures because of high numbers of immature neurons. However, the data do not prove a causal role.

Starting on line 477:

...we hypothesized that female Cre+ mice would have fewer hilar ectopic GCs than female Cre- mice. However, that female Cre+ mice did not have fewer hilar ectopic GCs.

Discussion, starting on line 563:

The chronic seizures, measured 4-7 weeks after pilocarpine, were reduced in frequency by about 50% in females. Therefore, increasing young adult-born neurons before the epileptogenic insult can protect against epilepsy. However, we do not know if the protective effect was due to the greater number of new neurons before SE or other effects. Past data would suggest that increased numbers of newborn neurons before SE leads to a reduced SE duration and less neuronal damage in the days after SE. That would be likely to lessen the epilepsy after SE. However, there may have been additional effects of larger numbers of newborn neurons prior to SE.

Conclusions, starting on line 745:

In the past, suppressing adult neurogenesis before SE was followed by fewer hilar ectopic GCs and reduced chronic seizures. Here, we show that the opposite - enhancing adult neurogenesis before SE and increased hilar ectopic GCs - do not necessarily reduce seizures. We suggest instead that protection of the hilar neurons from SE-induced excitotoxicity was critical to reducing seizures. The reason for the suggestion is that the survival of hilar neurons would lead to persistence of the normal inhibitory functions of hilar neurons, protecting against seizures. However, this is only a suggestion at the present time because we do not have data to prove it. Additionally, because protection was in females, sex differences are likely to have played an important role. Regardless, the results show that enhancing neurogenesis of young adult-born neurons in Nestin-Cre+ mice had a striking effect in the pilocarpine model, reducing chronic seizures in female mice.

The Reviewer is correct that it would be interesting to know when the increase in adult neurogenesis occurred that was critical to the effect. For example, was it the initial increase following Bax deletion but before pilocarpine-induced SE, or the increase in neurogenesis following SE, or increased adult neurogenesis in the chronic stage of epilepsy. It also might be that related aspects of neurogenesis played a role such as the degree that maturation was normal in adult-born neurons. We have not pursued the experiments to identify these aspects of neurogenesis because of how much work it would entail. Also, approaches to conclude cause-effect relationships are going to be difficult. 

(2) Speculation.

We removed the text and supplemental figures with schematics that we think were the overly speculative parts of the paper the Reviewer mentioned.

Strengths:

(1) The study is sex-matched and reveals differences in response to increasing adult neurogenesis in chronic seizures between males and females.

(2) The EEG recording parameters are stringent, and the analysis of chronic seizures is comprehensive. In two separate experiments, the electrodes were implanted to record EEG from the cortex as well as the hippocampus. The recording was done for 10 hours post pilocarpine to analyze acute seizures, and for 3 weeks continuous video EEG recording was done to analyze chronic seizures.

Weaknesses:

(1) Cells generated during acute seizures have different properties to cells generated in chronic seizures. In this study, the authors employ two bouts of neurogenesis stimuli (Bax deletion dependent and SE dependent), with two phases of epilepsy (acute and chronic). There are multiple confounding variables to effectively conclude that conditionally deleting Bax in Nestin-Cre+ cells leads to increased neurogenesis and hilar ectopic granule cells, thereby reducing chronic seizures.

As mentioned above, with a clarification of our conclusions we think we have addressed the concern. We believe that we conditionally deleted Bax in Nestin-expressing cells. We believe we found that female mice had reduced loss of hilar mossy cells and somatostatin-expressing neurons after SE, and fewer chronic seizures after SE. While it makes sense that increased neurogenesis caused the reduced seizures, we acknowledge it was not proved.

We do not make conclusions about the role of hilar ectopic granule cells. However, we note that they appear to have been similar in number across groups, which suggests they played no role in the results. This is very surprising and therefore adds novelty.

(2) Related to this is the degree of neurogenesis between Cre+ and Cre- mice and the nature of the sex differences. It is crucial to know the rate/fold change of increased neurogenesis before pilocarpine treatment and whether it is different between male and female mice.

We agree that if sex differences in adult neurogenesis could be shown by a sex difference in rate, fold change, maturation, and other characteristics.  However, sex differences can also be shown by a change in doublecortin (DCX), which is what we did. We respectfully submit that we do not see an exhaustive study is critical.

As a result, we have clarified DCX was studied either before SE or in the period of chronic seizures:

Results, starting on line 406:

III. Before and after epileptogenesis, Cre+ female mice exhibited more immature neurons than Cre- female mice but that was not true for male mice.

Starting on line 446:

Therefore, elevated DCX occurred after chronic seizures had developed in Cre+ mice but the effect was limited to females.

Discussion, starting on line 592:

This study showed that conditional deletion of Bax from Nestin-expressing progenitors increased young adult-born neurons in the DG when studied 6 weeks after deletion and using DCX as a marker of immature neurons.

(3) The authors observe more hilar Prox1 cells in Cre+ mice compared to Cre- mice. The authors should confirm the source of the hilar Prox1+ cells.

This is an excellent question but it is unclear that it is critical to the seizures since both sexes showed more hilar Prox1 cells in Cre+ mice but only the females had fewer seizures than Cre- mice. This is the additional text to describe the results (starting on Line 493):

In past studies, hilar ectopic GCs have been suggested to promote seizures (Scharfman et al., 2000; Jung et al., 2006; Cho et al., 2015). Therefore, we asked if the numbers of hilar ectopic GCs correlated with the numbers of chronic seizures. When Cre- and Cre+ mice were compared (both sexes pooled), there was a correlation with numbers of chronic seizures (Fig. 6D1) but it suggested that more hilar ectopic GCs improved rather than worsened seizures. However, the correlation was only in Cre- mice, and when sexes were separated there was no correlation (Fig. 6D3).

When seizure-free interval was examined with sexes pooled, there was a correlation for Cre+ mice (Fig. 6D2) but not Cre- mice. Strangely, the correlations of Cre+ mice with seizure-free interval (Fig. 6D2, D4) suggest ectopic GCs shorten the seizure-free interval and therefore worsen epilepsy, opposite of the correlative data for numbers of chronic seizures. In light of these inconsistent results it seems that hilar ectopic granule cells had no consistent effect on chronic seizures.

(4) The biggest weakness is the lack of mechanism. The authors postulate a hypothetical mechanism to reconcile how increasing and decreasing adult-born neurons in GCL and hilus and loss of hilar mossy and SOM cells would lead to opposite effects - more or fewer seizures. The authors suggest the reason could be due to rewiring or no rewiring of hilar ectopic GCs, respectively, but do not provide clear-cut evidence.

As we mention above, we removed the supplemental figures with schematics because they probably were what seemed overly speculative.

We acknowledge that mechanism is not proven by our study. However, we would like to mention that in our view, showing preservation of hilar mossy cells and SOM cells, but not PV cells, does add mechanistic data to the paper. We understand more experiments are necessary.

Reviewer #2 (Public Review):

Summary:

In this manuscript, Jain et al explore whether increasing adult neurogenesis is protective against status epilepticus (SE) and the development of spontaneous recurrent seizures (chronic epilepsy) in a mouse pilocarpine model of TLE. The authors increase adult neurogenesis via conditional deletion of Bax, a pro-apoptotic gene, in Nestin-CreERT2Baxfl/fl mice. Cre- littermates are used as controls for comparisons. In addition to characterizing seizure phenotypes, the authors also compare the abundance of hilar ectopic granule cells, mossy cells, hilar SOM interneurons, and the degree of neuronal damage between mice with increased neurogenesis (Cre+) vs Cre- controls. The authors find less severe SE and a reduction in chronic seizures in female mice with pre-insult increased adult-born neurons. Immunolabeling experiments show these females also have preservation of hilar mossy cells and somatostatin interneurons, suggesting the pre-insult increase in adult neurogenesis is protective.

Strengths:

(1) The finding that female mice with increased neurogenesis at the time of pilocarpine exposure have fewer seizures despite having increased hilar ectopic granule cells is very interesting.

(2) The work builds nicely on the group's prior studies.

(3) Apparent sex differences are a potentially important finding.

(4) The immunohistochemistry data are compelling.

(5) Good controls for EEG electrode implantation effects.

(6) Nice analysis of most of the SE EEG data.

Weaknesses:

(1) In addition to the Cre- littermate controls, a no Tamoxifen treatment group is necessary to control for both insertional effects and leaky expression of the Nestin-CreERT2 transgene.

About “leaky” expression, we have not found expression to be leaky. We checked by injecting a Cre-dependent virus so that mCherry would be expressed in those cells that had Cre.  The results were published as Supplemental Figure 9 in Jain et al. (2019).

In the revised manuscript we also mention a study that examined three Nestin-CreERT2 mouse lines (Sun et al., 2014). One of the mouse lines was ours. The leaky expression was not in the mouse line we use. We have added these points to the revised manuscript:

Methods, section II starting on line 791:

Although Nestin-Cre-ERT2 mouse lines have been criticized because  they can have leaky expression, the mouse line used in the present study did not (Sun et al., 2014), which we confirmed (Jain et al., 2019).

(2) The authors suggest sex differences; however, experimental procedures differed between male and female mice (as the authors note). Female mice received diazepam 40 minutes after the first pilocarpine-induced seizure onset, whereas male mice did not receive diazepam until 2 hours post-onset. The former would likely lessen the effects of SE on the female mice. Therefore, sex differences cannot be accurately assessed by comparing these two groups, and instead, should be compared between mice with matching diazepam time courses.

We agree that a shorter delay between pilocarpine and diazepam would be likely to lead to less damage. However, the latency from pilocarpine to SE varied, making the time from the onset of SE to diazepam variable. Most of the variability was in females. By timing the diazepam injection differently in males and females, we could make the time from the onset of SE to diazepam similar between females and males. We had added a supplemental figure to show that our approach led to no significant differences between females and males in the latency to SE, time between SE and diazepam injection, and time between pilocarpine and diazepam injection. We also show that Cre+ females and Cre- females were not different in these times, so it could not be related to the neuroprotection of Cre+ females.

Additionally, the authors state that female mice that received diazepam 2 hours post-onset had severe brain damage. This is concerning as it would suggest that SE is more severe in the female than in the male mice.

We regret that our language was misleading. We intended to say females had more morbidity and mortality than males (lack of appetite and grooming, death in the days after SE) when we gave DZP 2 hrs after Pilo. We actually don’t know why because there were no differences in severity of SE. We think the females had worse outcome when they had a short latency to SE.  These females had a longer period of SE before DZP than males, probably leading to worse outcome. To correct this we gave DZP to females sooner. Then morbidity and mortality was improved in females. 

Interestingly, after we did this we saw females did not always have a short latency to SE. We maintained the same regimen however, to be consistent. As the new supplemental figure (above) shows, there were significant sex differences in the latency to SE, time between SE and DZP, and time between pilocarpine and DZP.

(3) Some sample sizes are low, particularly when sex and genotypes are split (n=3-5), which could cause a type II statistical error.

We agree and have noted this limitation in the Discussion:

Additional considerations, starting on line 739:

This study is limited by the possibilities of type II statistical errors in those instances where we divided groups by genotype and sex, leading to comparisons of 3-5 mice/group.

(4) Several figures show a datapoint in the sex and genotype-separated graphs that is missing from the corresponding male and female pooled graphs (Figs. 2C, 2D, 4B).

We are very grateful to the Reviewer for pointing out the errors. They are corrected.

(5) In Suppl Figs. 1B & 1C, subsections 1c and 2c, the EEG trace recording is described as the end of SE; however, SE appears to still be ongoing in these traces in the form of periodic discharges in the EEG.

The Reviewer is correct.  It is a misconception that SE actually ends completely. The most intense seizure activity may, but what remains is abnormal activity that can last for days. Other investigators observe the same and have suggested that it argues against the concept of a silent period between SE and chronic epilepsy. We had discussed this in our prior papers and had referenced how we define SE.  In the revised manuscript we add the information to the Methods section instead of referencing a prior study:

Methods, starting on line 899:

SE duration was defined in light of the fact that the EEG did not return to normal after the initial period of intense activity. Instead, intermittent spiking occurred for at least 24 hrs, as we previously described (Jain et al., 2019) and has been described by others (Mazzuferi et al., 2012; Bumanglag and Sloviter, 2018; Smith et al., 2018). We therefore chose a definition that captured the initial, intense activity. We defined the end of this time as the point when the amplitude of the EEG deflections were reduced to 50% or less of the peak deflections during the initial hour of SE. Specifically, we selected the time after the onset of SE when the EEG amplitude in at least 3 channels had dropped to approximately 2 times the amplitude of the EEG during the first hour of SE, and remained depressed for at least 10 min (Fig. S2 in (Jain et al., 2019). Thus, the duration of SE was defined as the time between the onset and this definition of the "end" of SE.

(6) In Results section II.D and associated Fig.3, what the authors refer to as "postictal EEG depression" is more appropriately termed "postictal EEG suppression". Also, postictal EEG suppression has established criteria to define it that should be used.

We find suppression is typical in studies of ECT or humans (Esmaeili et al., 2023; Gascoigne et al., 2023; Hahn et al., 2023; Kavakbasi et al., 2023; Langroudi et al., 2023; Karl et al., 2024; Vilan et al., 2024; Zhao et al., 2024) and animal research uses the term postictal depression(Kanner et al., 2010; Krishnan and Bazhenov, 2011; Riljak et al., 2012; Singh et al., 2012; Carballosa-Gonzalez et al., 2013; Kommajosyula et al., 2016; Smith et al., 2018; Uva and de Curtis, 2020; Medvedeva et al., 2023). Therefore we think depression is a more suitable term.

The example traces in Fig. 3A and B should also be expanded to better show this potential phenomenon.

We expanded traces in Fig. 3 as suggested. They are in Fig 3A.

(7) In Fig.5D, the area fraction of DCX in Cre+ female mice is comparable to that of Cre- and Cre+ male mice. Is it possible that there is a ceiling effect in DCX expression that may explain why male Cre+ mice do not have a significant increase compared to male Cre- mice?

We thank the Reviewer for the intriguing possibility. We now mention it in the manuscript:

Results, starting on line 456:

It is notable that the Cre+ male mice did not show increased numbers of immature neurons at the time of chronic seizures but Cre+ females did. It is possible that there was a “ceiling” effect in DCX expression that would explain why male Cre+ mice did not have a significant increase in immature neurons relative to male Cre- mice.

(8) In Suppl. Fig 6, the authors should include DCX immunolabeling quantification from conditional Cre+ male mice used in this study, rather than showing data from a previous publication.

We have made this revision.

(9) In Fig 8, please also include Fluorojade-C staining and quantification for male mice.

The additional data for males have been added to part D.

(10) Page 13: Please specify in the first paragraph of the discussion that findings were specific to female mice with pre-insult increases in adult-born neurogenesis.

This has been done.

Minor:

(11) In Fig. 1 and suppl. figure 1, please clarify whether traces are from male or female mice.

We have clarified.

(12) Please be consistent with indicating whether immunolabeling images are from female or male mice.

a. Fig 5B images labeled as from "Cre- Females" and "Cre+ Females".

b. Suppl. Fig 8: Images labeled as "Cre- F" and "Cre+ F".

c. Fig 6: sex not specified.

d. Fig. 7: sex only specified in the figure legend.

e. Fig 8: only female mice were included in these experiments, but this is not clear from the figure title or legend.

We revised all figures according to the comments.

(13) Page 4: the last paragraph of the introduction belongs within the discussion section.

We recognize there is a classic view that any discussion of Results should not be in the Introduction. However, we find that view has faded and more authors make a brief summary statement about the Results at the end of the Introduction. We would like to do so because it allow Readers to understand the direction of the study at the outset, which we find is helpful.

(14) Page 6: The sentence "The data are consistent with prior studies..." is unnecessary.

We have removed the text.

(15) Suppl. Fig 6A: Please include representative images of normal condition DCX immunolabeling.

We have added these data. There is an image of a Cre- female, Cre+ female, Cre- male and Cre+ male in the new figure, Supplemental Figure 6. All mice had tamoxifen at 6 weeks of age and were perfused 6 weeks later. None of the mice had pilocarpine.

(16) In Suppl. Fig 7C, I believe the authors mean "no loss of hilar mossy and SOM cells" instead of "loss of hilar mossy and SOM cells".

This Figure was removed because of the input from Reviewer 1 suggesting it was too speculative.

Reviewer #1 (Recommendations For The Authors):

(1) The main claim of the study is that increasing adult neurogenesis decreases chronic seizures. However, to quantify adult-born neurons, DCX immunoreactivity is used as the sole metric to determine neurogenesis. This is insufficient as changes in DCX-expressing cells could also be an indicator of altered maturation, survival, and/or migration, not proliferation per se. To claim that increasing adult neurogenesis is associated with a reduction of chronic seizures, the authors should perform a pulse/chase (birth dating) experiment with BrdU and co-labeling with DCX.

We think that increased DCX does reflect increased adult neurogenesis. However, we agree that one does not know if it was due to increased proliferation, survival, etc. We also note that this mouse line has been studied thoroughly to show there was increased neurogenesis with BrdU, Ki67 and DCX. We mention that paper in the revised text:

Methods, starting on line 786:

It was shown that after tamoxifen injection in adult mice there is an increase in dentate gyrus neurogenesis based on studies of bromo-deoxyuridine, Ki67, and doublecortin (Sahay et al., 2011).

(2) As mentioned above, analysis of DCX staining alone months after TAM injections is limited. Instead, the cells could be labelled by BrdU prior to TAM injection, following which quantification of BrdU+/Prox1+ cells at 6 weeks post TAM injection should be performed in Cre+ and Cre- mice (males and females) to yield the rate of neurogenesis increase.

We respectfully disagree that birthdating cells is critical. Using DCX staining just before SE, we know the size of the population of cells that are immature at the time of SE. This is what we think is most important because these immature neurons are those that appear to affect SE, as we have already shown.

(3) To confirm the source of the hilar Prox1+ cells, a dual BrdU/EdU labeling approach would be beneficial. BrdU injection could be given before TAM injection and EdU injection before pilocarpine to label different cohorts of neural stem cells. Co-staining with Prox1 at different time points will help in identifying the origin of hilar ectopic cells.

We are grateful for the ideas of the Reviewer. We hesitate to do these experiments now because it seems like a new study to find out where hilar granule cells come from.

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Author response:

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

Reviewer #1 (Public Review):

Comment 1: One of the only demonstrations of the expression and physiological significance of TRPCs in VTA DA neurons was published by (Rasmus et al., 2011; Klipec et al., 2016) which are not cited in this paper. In their study, TRPC4 expression was detected in a uniformly distributed subset of VTA DA neurons, and TRPC4 KO rats showed decreased VTA DA neuron tonic firing and deficits in cocaine reward and social behaviors. Update: The authors say they have added a discussion of these papers, but I do not see it in the updated manuscript.

We thank the reviewer for the suggestion. The discussion for this has been added (line 557-565).

Comment 2: The authors should report the results (exact data values) of female mice in the results text, or pool the male and female data if the sex differences are not significant.

We agree with reviewer. Some experiments were further redone with female and the data of male and female mice have been reported in the results of text.

Comment 3: The selectivity of drugs should be referred as "selective" rather than "specific". 

Thanks, “specific” has been changed to “selective”.  

Comment 4: Line 62: typo, "substantia nigra". 

Thanks, “substantial nigra” has been changed to “substantia nigra” in line 65.  

Comment 5: Line 77: some new studies suggest that NALCN might have voltage dependency

(rectification).

Thanks, description of NALCN voltage dependence has been corrected in line 81-83.

Comment 6: Line 175: change "less" to "fewer". 

Thanks, “less” has been changed to “fewer”.

Comment 7: Line 299: choose one - "was not ... or" or "was neither ... nor". 

Thanks, this error has been corrected. 

Comment 8: In Figure 1Aii and Figure 3Bi, it was not specified in the results text or figure legend that C1-C5 represent individual cell until the legend for Figure 4.

Thanks, these description about gel have been added in the figure legends. 

Reviewer #2 (Public Review): 

Comment 1: From the previous review, we mentioned that " 'The HCN' as written in line 69 is a bit misleading, as HCN channels in the heart and brain are different members of a family of channels, although as written in the text, it seems that they are identical." This is still the case (now line 73).

We agreed with the reviewer’s comments. The introduction about HCN has been corrected (line 74-78). 

Comment 2: The authors state in line 112 that "most of the experiments were also repeated in female mice" - this is true in the case of most electrophysiological experiments, although not behavioral experiments. Authors should amend the statement in line 112 and clarify in the Discussion section which findings are generalizable between sexes; e.g.:

a.  Discussion of HCN contribution to VTA DA activity (beginning line 453) should clarify male mice. 

b.  Similarly, any discussion of behavioral findings should clarify male mice. 

We agreed with the reviewer’s comments. The sexes of mice used have been noted in the results and discussion. 

Comment 3: The authors' statement in lines 179-183 ("In contrast, fewer GABAergic neuronal markers (Glutamic acid decarboxylase, GAD1/2 and vesicular GABA transporter, VGAT) co-expressed with the DA neurons, which is consistent with previous studies that VTA DA neurons co-expressing GABAergic neuronal markers mainly project to the lateral habenula") is a little confusing - as stated, it seems that the authors are confirming DA/GABA coexpression in VTA-LHb neurons, which is not the case.

We agreed with the reviewer’s comments. We corrected this statement (line 182-186).

Comment 4: Additional information could be included in the Methods section description of Western Blotting procedures - e.g., what thickness of tissue and what size gauge were used to dissect VTA for these experiments?

Thanks. The description of tissue in Western Blotting procedures has been added.

Comment 5:

a. Grammatical errors in line 23 of Abstract (also lines 31-32)

b. "drove" should read "strove" in line 92 

c. Grammatical errors in lines 401, 444, and 448 

We thank the reviewer for pointing out grammatical errors and we corrected them.

Reviewer #3 (Public Review): 

Comment 1: The main strength of this study lies on a comprehensive bottom-up approach ranging from patch-clamp recordings to behavioral tasks. These tasks mainly address anxiety-like behaviors and so-called depression-like behaviors (sucrose choice, forced swim test, tail suspension test). The results gathered by means of these procedures are clearcut. However, the reviewer believes that the authors should be more cautious when interpreting immobility responses to stress (forced swim, tail suspension) as "depression-like" responses. These stress models have been routinely used (and validated) in the past to detect the antidepressant properties of compounds under investigation, which by no means indicates that these are depression models. For readers interested by this debate, I suggest to read e.g. De Kloet and Molendijk (Biol. Pscyhiatry 2021).

We thank the reviewer for the suggestion. We will be more careful and rigorous in the selection of stress models in our subsequent research work.

Editor's note:

Should you choose to revise your manuscript, please include full statistical reporting including exact p-values wherever possible alongside the summary statistics (test statistic and df) and 95% confidence intervals. These should be reported for all key questions and not only when the p-value is less than 0.05.

We have added the full statistical reporting including exact p-values wherever possible alongside the summary statistics (test statistic and df) and 95% confidence intervals into the results and the figure legends of the revised manuscript.

eLife assessment

This important study examined the mechanisms underlying reduced excitability of ventral tegmental area dopamine neurons in mice that underwent a chronic mild unpredictable stress treatment. The authors identify NALCN and TRPC6 channels as key mechanisms that regulate spontaneous firing of ventral tegmental area dopamine neurons and examined their roles in reduced firing in mice that underwent a chronic mild unpredictable stress treatment. The authors' conclusions on neurophysiological data are supported by multiple approaches and are convincing, although the relevance of the behavioral results to human depression remains unclear.

Reviewer #1 (Public Review):

Wang et al., present a paper aiming to identify NALCN and TRPC6 channels as key mechanisms regulating VTA dopaminergic neuron spontaneous firing and investigating whether these mechanisms are disrupted in a chronic unpredictable stress model mouse.

Major strengths:

This paper uses multiple approaches to investigate the role of NALCN and TRPC6 channels in VTA dopaminergic neurons.

Reviewer #2 (Public Review):

This paper describes the results of a set of complementary and convergent experiments aimed at describing roles for the non-selective cation channels NALCN and TRPC6 in mediating subthreshold inward depolarizing currents and action potential generation in VTA DA neurons under normal physiological conditions. In general, the authors have responded satisfactorily to reviewer comments, and the revised manuscript is improved.

Reviewer #3 (Public Review):

The authors of this study have examined which cation channels specifically confer to ventral tegmental area dopaminergic neurones their autonomic (spontaneous) firing properties. Having brought evidence for the key role played by NALCN and TRPC6 channels therein, the authors aimed at measuring whether these channels play some role in so-called depression-like (but see below) behaviors triggered by chronic exposure to different stressors. Following evidence for a down-regulation of TRPC6 protein expression in ventral tegmental area dopaminergic cells of stressed animals, the authors provide evidence through viral expression protocols for a causal link between such a down-regulation and so-called depression-like behaviors. The main strength of this study lies on a comprehensive bottom-up approach ranging from patch-clamp recordings to behavioral tasks. These tasks mainly address anxiety-like behaviors and so-called depression-like behaviors (sucrose choice, forced swim test, tail suspension test). The results gathered by means of these procedures are clearcut.

As genes get further away from the telomere, their % under-replication decreases.

The GO terms were likely shown to also contribute to the finding that these are unessential genes?

Knock outs of genes being replicated after metaphase have no affect on the fitness.

The big fat yellow spot is genes not replicating late in mitosis. As you move to the right along the x-axis, these genes do replicate late in mitosis and have the same affect on fitness in WT as the other ones do.

Correlation: * Replication timing and mutations rates in yeast (& humans) * Distance to telomere and mutation rates in yeast

Finally, when they inhibit Cdk, chromatin bridges are resolved in MEN-defic. cells.

Cells arrested in metaphase had stalled replication relative to cells in metaphase that had Cdk inhibited.

Stable G-rich regions near telomeres are replicated later than the rest of the genome.

Regions with many stable G-rich regions, transposable elements, and fragile sites are not replicated as much in metaphase.

After release from G1, subtelomeric regions were more under-replicated than regions further away from telomeric regions.

As you move further from the telomere, the underreplicated DNA decreases.

??

The authors used DNA copy number to distinguish the reasoning behind DNA synthesis late in mitosis. They found that subtelomeric regions were more underreplicated in metaphase than in telophase.

The signs of DNA synthesis occurring in anaphase could be due to a number of possibilities: * Repair of replicated DNA * Replication of diverse genomic regions * Replication of specific genomic regions

I feel like we know it's not replicated DNA, because we see single stranded DNA being carried through anaphase.

Why do we care if it is diverse of specific regions? - we want to know what regions of DNA are being replicated.

Identification of single stranded DNA throughout anaphase in cells without MENS

Another protein they looked at was DNA polymerase delta and epsilon.

When knocked out, they saw that MEN reactivation caused bridge disappearance after actomyosin ring (green) contraction.

mCherry = bridges

MEN reactivation allowed the cells to resolve their anaphase bridges. However, topoisomerase did not play a role in this.

They wanted to identify what proteins were involved in the resolution of anaphase bridges.

They looked at topoisomerase, because this is the protein that helps fix intertwined sister chromatids - which may have been contributing to the bridges.

However, inhibition of this protein did not not affect the resolution of anaphase bridges.

They also saw that when the ATP analog was washed out from the reaction, the bridges resolved themselves. This was in cells that had cohesin present.

Men inhibition did affect the amount and longevity of chromatin bridges in anaphase. So, the authors next sought to identify if persistent cohesion (holds sister chromatids together) was the reason that these bridges were forming.

They did this by inhibiting both MEN and cohesion in one cell population, and then inhibiting only cohesion in another, and observing their affect on each other.

They saw that MEN bridges were stable in both cell populations throughout anaphase.

The authors next looked into the cohesion of sister chromatin, seeking to identify if the cohesion of sister chromatids led to the anaphase bridges.

They did this by mutating MEN using an ATP analog (NA-PP1), and by heat inactivating cohesin. They also reintroduced MEN to see if chromatin bridges were affected.

Reviewer #1 (Public Review):

Summary:

This paper reports an intracranial SEEG study of speech coordination, where participants synchronize their speech output with a virtual partner that is designed to vary its synchronization behavior. This allows the authors to identify electrodes throughout the left hemisphere of the brain that have activity (both power and phase) that correlates with the degree of synchronization behavior. They find that high-frequency activity in the secondary auditory cortex (superior temporal gyrus) is correlated to synchronization, in contrast to primary auditory regions. Furthermore, activity in the inferior frontal gyrus shows a significant phase-amplitude coupling relationship that is interpreted as compensation for deviation from synchronized behavior with the virtual partner.

Strengths:

(1) The development of a virtual partner model trained for each individual participant, which can dynamically vary its synchronization to the participant's behavior in real-time, is novel and exciting.

(2) Understanding real-time temporal coordination for behaviors like speech is a critical and understudied area.

(3) The use of SEEG provides the spatial and temporal resolution necessary to address the complex dynamics associated with the behavior.

(4) The paper provides some results that suggest a role for regions like IFG and STG in the dynamic temporal coordination of behavior both within an individual speaker and across speakers performing a coordination task.

Weaknesses:

(1) The main weakness of the paper is that the results are presented in a largely descriptive and vague manner. For instance, while the interpretation of predictive coding and error correction is interesting, it is not clear how the experimental design or analyses specifically support such a model, or how they differentiate that model from the alternatives. It's possible that some greater specificity could be achieved by a more detailed examination of this rich dataset, for example by characterizing the specific phase relationships (e.g., positive vs negative lags) in areas that show correlations with synchronization behavior. However, as written, it is difficult to understand what these results tell us about how coordination behavior arises.

(2) In the results section, there's a general lack of quantification. While some of the statistics reported in the figures are helpful, there are also claims that are stated without any statistical test. For example, in the paragraph starting on line 342, it is claimed that there is an inverse relationship between rho-value and frequency band, "possibly due to the reversed desynchronization/synchronization process in low and high frequency bands". Based on Figure 3, the first part of this statement appears to be true qualitatively, but is not quantified, and is therefore impossible to assess in relation to the second part of the claim. Similarly, the next paragraph on line 348 describes optimal clustering, but statistics of the clustering algorithm and silhouette metric are not provided. More importantly, it's not entirely clear what is being clustered - is the point to identify activity patterns that are similar within/across brain regions? Or to interpret the meaning of the specific patterns? If the latter, this is not explained or explored in the paper.

(3) Given the design of the stimuli, it would be useful to know more about how coordination relates to specific speech units. The authors focus on the syllabic level, which is understandable. But as far as the results relate to speech planning (an explicit point in the paper), the claims could be strengthened by determining whether the coordination signal (whether error correction or otherwise) is specifically timed to e.g., the consonant vs the vowel. If the mechanism is a phase reset, does it tend to occur on one part of the syllable?

(4) In the discussion the results are related to a previously-described speech-induced suppression effect. However, it's not clear what the current results have to do with SIS, since the speaker's own voice is present and predictable from the forward model on every trial. Statements such as "Moreover, when the two speech signals come close enough in time, the patient possibly perceives them as its own voice" are highly speculative and apparently not supported by the data.

(5) There are some seemingly arbitrary decisions made in the design and analysis that, while likely justified, need to be explained. For example, how were the cutoffs for moderate coupling vs phase-shifted coupling (k ~0.09) determined? This is noted as "rather weak" (line 212), but it's not clear where this comes from. Similarly, the ROI-based analyses are only done on regions "recorded in at least 7 patients" - how was this number chosen? How many electrodes total does this correspond to? Is there heterogeneity within each ROI?

Reviewer #2 (Public Review):

Summary:

This paper investigates the neural underpinnings of an interactive speech task requiring verbal coordination with another speaker. To achieve this, the authors recorded intracranial brain activity from the left hemisphere in a group of drug-resistant epilepsy patients while they synchronised their speech with a 'virtual partner'. Crucially, the authors were able to manipulate the degree of success of this synchronisation by programming the virtual partner to either actively synchronise or desynchronise their speech with the participant, or else to not vary its speech in response to the participant (making the synchronisation task purely one-way). Using such a paradigm, the authors identified different brain regions that were either more sensitive to the speech of the virtual partner (primary auditory cortex), or more sensitive to the degree of verbal coordination (i.e. synchronisation success) with the virtual partner (secondary auditory cortex and IFG). Such sensitivity was measured by (1) calculating the correlation between the index of verbal coordination and mean power within a range of frequency bands across trials, and (2) calculating the phase-amplitude coupling between the behavioural and brain signals within single trials (using the power of high-frequency neural activity only). Overall, the findings help to elucidate some of the left hemisphere brain areas involved in interactive speaking behaviours, particularly highlighting the high-frequency activity of the IFG as a potential candidate supporting verbal coordination.

Strengths:

This study provides the field with a convincing demonstration of how to investigate speaking behaviours in more complex situations that share many features with real-world speaking contexts e.g. simultaneous engagement of speech perception and production processes, the presence of an interlocutor, and the need for inter-speaker coordination. The findings thus go beyond previous work that has typically studied solo speech production in isolation, and represent a significant advance in our understanding of speech as a social and communicative behaviour. It is further an impressive feat to develop a paradigm in which the degree of cooperativity of the synchronisation partner can be so tightly controlled; in this way, this study combines the benefits of using pre-recorded stimuli (namely, the high degree of experimental control) with the benefits of using a live synchronisation partner (allowing the task to be truly two-way interactive, an important criticism of other work using pre-recorded stimuli). A further key strength of the study lies in its employment of stereotactic EEG to measure brain responses with both high temporal and spatial resolution, an ideal method for studying the unfolding relationship between neural processing and this dynamic coordination behaviour.

Weaknesses:

One major limitation of the current study is the lack of coverage of the right hemisphere by the implanted electrodes. Of course, electrode location is solely clinically motivated, and so the authors did not have control over this. However, this means that the current study neglects the potentially important role of the right hemisphere in this task. The right hemisphere has previously been proposed to support feedback control for speech (likely a core process engaged by synchronous speech), as opposed to the left hemisphere which has been argued to underlie feedforward control (Tourville & Guenther, 2011). Indeed, a previous fMRI study of synchronous speech reported the engagement of a network of right hemisphere regions, including STG, IPL, IFG, and the temporal pole (Jasmin et al., 2016). Further, the release from speech-induced suppression during a synchronous speech reported by Jasmin et al. was found in the right temporal pole, which may explain the discrepancy with the current finding of reduced leftward high-frequency activity with increasing verbal coordination (suggesting instead increased speech-induced suppression for successful synchronisation). The findings should therefore be interpreted with the caveat that they are limited to the left hemisphere, and are thus likely missing an important aspect of the neural processing underpinning verbal coordination behaviour.

A further limitation of this study is that its findings are purely correlational in nature; that is, the results tell us how neural activity correlates with behaviour, but not whether it is instrumental in that behaviour. Elucidating the latter would require some form of intervention such as electrode stimulation, to disrupt activity in a brain area and measure the resulting effect on behaviour. Any claims therefore as to the specific role of brain areas in verbal coordination (e.g. the role of the IFG in supporting online coordinative adjustments to achieve synchronisation) are therefore speculative.

Reviewer #1 (Public Review):

Summary:

Johnston and Smith used linear electrode arrays to record from small populations of neurons in the superior colliculus (SC) of monkeys performing a memory-guided saccade (MGS) task. Dimensionality reduction (PCA) was used to reveal low-dimensional subspaces of population activity reflecting the slow drift of neuronal signals during the delay period across a recording session (similar to what they reported for parts of the cortex: Cowley et al., 2020). This SC drift was correlated with a similar slow-drift subspace recorded from the prefrontal cortex, and both slow-drift subspaces tended to be associated with changes in arousal (pupil size). These relationships were driven primarily by neurons in superficial layers of the SC, where saccade sensitivity/selectivity is typically reduced. Accordingly, delay-period modulations of both spiking activity and pupil size were independent of saccade-related activity, which was most prevalent in deeper layers of the SC. The authors suggest that these findings provide evidence of a separation of arousal- and motor-related signals. The analysis techniques expand upon the group's previous work and provide useful insight into the power of large-scale neural recordings paired with dimensionality reduction. This is particularly important with the advent of recording technologies which allow for the measurement of spiking activity across hundreds of neurons simultaneously. Together, these results provide a useful framework for comparing how different populations encode signals related to cognition, arousal, and motor output in potentially different subspaces.

The conclusions drawn by this paper, however, are only partially supported by the data. Additional statistical comparisons and clarifications are needed.

Comments:

(1) The authors make fairly strong claims that "arousal-related fluctuations are isolated from neurons in the deep layers of the SC" (emphasis added). This conclusion is based on comparisons between a "slow drift axis", a low-dimensional representation of neuronal drift, and other measures of arousal (Figures 2C, 3) and motor output sensitivity (Figures 2B, 3B). However, the metrics used to compare the slow-drift axis and motor activity were computed during separate task epochs: the delay period (600-1100 ms) and a peri-saccade epoch (25 ms before and after saccade initiation), respectively. As the authors reference, deep-layer SC neurons are typically active only around the time of a saccade. Therefore, it is not clear if the lack of arousal-related modulations reported for deep-layer SC neurons is because those neurons are truly insensitive to those modulations, or if the modulations were not apparent because they were assessed in an epoch in which the neurons were not active. A potentially more valuable comparison would be to calculate a slow-drift axis aligned to saccade onset.

(2) More generally, arousal-related signals may persist throughout multiple different epochs of the task. It would be worthwhile to determine whether similar "slow-drift" dynamics are observed for baseline, sensory-evoked, and saccade-related activity. Although it may not be possible to examine pupil responses during a saccade, there may be systematic relationships between baseline and evoked responses.

(3) The relationships between changes in SC activity and pupil size are quite small (Figures 2C & 5C). Although the distribution across sessions (Figure 2C) is greater than chance, they are nearly 1/4 of the size compared to the PFC-SC axis comparisons. Likewise, the distribution of r2 values relating pupil size and spiking activity directly (Figure 5) is quite low. We remain skeptical that these drifts are truly due to arousal and cannot be accounted for by other factors. For example, does the relationship persist if accounting for a very simple, monotonic (e.g., linear) drift in pupil size and overall firing rate over the course of an individual session?

(4) It is not clear how the final analysis (Figure 6) contributes to the authors' conclusions. The authors perform PCA on: (i) residual spiking responses during the delay period binned according to pupil size, and (ii) spiking responses in the saccade epoch binned according to target location (i.e., the saccade tuning curve). The corresponding PCs are the spike-pupil axis and the saccade tuning axis, respectively. Unsurprisingly, the spike-pupil axis that captures variance associated with arousal (and removes variance associated with saccade direction) was not correlated with a saccade-tuning axis that captures variance associated with saccade direction and omits arousal. Had these measures been related it would imply a unique association between a neuron's preferred saccade direction and pupil control- which seems unlikely. The separation of these axes thus seems trivial and does not provide evidence of a "mechanism...in the SC to prevent arousal-related signals interfering with the motor output." It remains unknown whether, for example, arousal-related signals may impact trial-by-trial changes in neuronal gain near the time of a saccade, or alter saccade dynamics such as acceleration, precision, and reaction time.

Reviewer #2 (Public Review):

Summary:

Neurons in motor-related areas have increasingly been shown to carry also other, non-motoric signals. This creates a problem of avoidance of interference between the motor and non-motor-related signals. This is a significant problem that likely affects many brain areas. The specific example studied here is interference between saccade-related activity and slow-changing arousal signals in the superior colliculus. The authors identify neuronal activity related to saccades and arousal. Identifying saccade-related activity is straightforward, but arousal-related activity is harder to identify. The authors first identify a potential neuronal correlate of arousal using PCA to identify a component in the population activity corresponding to slow drift over the recording session. Next, they link this component to arousal by showing that the component is present across different brain areas (SC and PFC), and that it is correlated with pupil size, an external marker of arousal. Having identified an arousal-related component in SC, the authors show next that SC neurons with strong motor-related activity are less strongly affected by this arousal component (both SC and PFC). Lastly, they show that SC population activity patterns related to saccades and pupil size form orthogonal subspaces in the SC population.

Strengths:

A great strength of this research is the clear description of the problem, its relationship with the performed analysis, and the interpretation of the results. the paper is very well written and easy to follow.

An additional strength is the use of fairly sophisticated analysis using population activity.

Weaknesses:

(1) The greatest weakness in the present research is the fact that arousal is a functionally less important non-motoric variable. The authors themselves introduce the problem with a discussion of attention, which is without any doubt the most important cognitive process that needs to be functionally isolated from oculomotor processes. Given this introduction, one cannot help but wonder, why the authors did not design an experiment, in which spatial attention and oculomotor control are differentiated. Absent such an experiment, the authors should spend more time explaining the importance of arousal and how it could interfere with oculomotor behavior.

(2) In this context, it is particularly puzzling that one actually would expect effects of arousal on oculomotor behavior. Specifically, saccade reaction time, accuracy, and speed could be influenced by arousal. The authors should include an analysis of such effects. They should also discuss the absence or presence of such effects and how they affect their other results.

(3) The authors use the analysis shown in Figure 6D to argue that across recording sessions the activity components capturing variance in pupil size and saccade tuning are uncorrelated. however, the distribution (green) seems to be non-uniform with a peak at very low and very high correlation specifically. The authors should test if such an interpretation is correct. If yes, where are the low and high correlations respectively? Are there potentially two functional areas in SC?

Reviewer #3 (Public Review):

Summary:

This study looked at slow changes in neuronal activity (on the order of minutes to hours) in the superior colliculus (SC) and prefrontal cortex (PFC) of two monkeys. They found that SC activity shows slow drift in neuronal activity like in the cortex. They then computed a motor index in SC neurons. By definition, this index is low if the neuron has stronger visual responses than motor responses, and it is low if the neuron has weaker visual responses and stronger motor responses. The authors found that the slow drift in neuronal activity was more prevalent in the low motor index SC neurons and less prevalent in the high motor index neurons. In addition, the authors measured pupil diameter and found it to correlate with slow drifts in neuronal activity, but only in the neurons with lower motor index of the SC. They concluded that arousal signals affecting slow drifts in neuronal modulations are brain-wide. They also concluded that these signals are not present in the deepest SC layers, and they interpreted this to mean that this minimizes the impact of arousal on unwanted eye movements.

Strengths:

The paper is clear and well-written.

Showing slow drifts in the SC activity is important to demonstrate that cortical slow drifts could be brain-wide.

Weaknesses:

However, I am concerned about two main points:

First, the authors repeatedly say that the "output" layers of the SC are the ones with the highest motor indices. This might not necessarily be accurate. For example, current thresholds for evoking saccades are lowest in the intermediate layers, and Mohler & Wurtz 1972 suggested that the output of the SC might be in the intermediate layers. Also, even if it were true that the high motor index neurons are the output, they are very few in the authors' data (this is also true in a lot of other labs, where it is less likely to see purely motor neurons in the SC). So, this makes one wonder if the electrode channels were simply too deep and already out of the SC? In other words, it seems important to show distributions of encountered neurons (regardless of the motor index) across depth, in order to better know how to interpret the tails of the distributions in the motor index histogram and in the other panels of Figure Supplement 1. I elaborate more on these points in the detailed comments below.

Second, the authors find that the SC cells with a low motor index are modulated by pupil diameter. However, this could be completely independent of an "arousal signal". These cells have substantial visual responses. If the pupil diameter changes, then their activity should be influenced since the monkey is watching a luminous display. So, in this regard, the fact that they do not see "an arousal signal" in most motor neurons (through the pupil diameter analyses) is not evidence that the arousal signal is filtered out from the motor neurons. It could simply be that these neurons simply do not get affected by the pupil diameter because they do not have visual sensitivity. So, even with the pupil data, it is still a bit tricky for me to interpret that arousal signals are excluded from the "output layers" of the SC.

I think that a remedy to the first point above is to change the text to make it a bit more descriptive and less interpretive. For example, just say that the slow drifts were less evident among the neurons with high motor index.

For the second point, I think that it is important to consider the alternative caveat of different amounts of light entering the system. Changes in light level caused by pupil diameter variations can be quite large.

eLife assessment

This study shows that retinal bipolar cell subtype-specific differences in the size of synaptic ribbon-associated vesicle pools contribute to the transient versus sustained kinetics of the responses of retinal ganglion cells. The findings are important and the data is extensive and solid, however, there is also the possibility that glutamate release could be modulated by the kinetics of presynaptic inhibition at bipolar cell terminals and this may contribute to mediating the transient and/or sustained kinetics of glutamate release. This work will be of broad interest to researchers working on synaptic transmission, retinal signal processing, and sensory neurobiology.

Reviewer #1 (Public Review):

Summary:

In the retina, parallel processing of cone photoreceptor output under bright light conditions dissects critical features of our visual environment and is fundamental to visual function. Cone photoreceptor signals are sampled by several types of bipolar cells and passed onto the ganglion cells. At the output of retinal processing, retinal ganglion cells send about 40 different codes of the visual scene to the brain for further processing. In this study, the authors focus on whether subtype-specific differences in the size of synaptic ribbon-associated vesicle pools of bipolar cells contribute to different retinal ganglion cell (RGC) responses. Specifically, inputs to ON alpha RGCs producing transient versus sustained kinetics (ON-S vs. ON-T, respectively) are compared. The authors first demonstrate that ON-S vs. ON-T RGCs are readily identifiable in a whole mount preparation and respond differently to both static and to a spatially uniform, randomly fluctuating (Gaussian noise) light stimulus. Liner-nonlinear (LN) models were used to estimate the transformation between visual input and excitatory synaptic input for each RGCs; these models suggested the presence of transient versus sustained kinetics already in the excitatory inputs to ON-T and ON-S RGCs. Indeed, the authors show that (glutamatergic) excitatory inputs to ON-S vs. ON-T RGCs are of distinct kinetics. The subtypes of bipolar cells providing input to ON-S are known (i.e., type 6 and 7), but the source of excitatory bipolar inputs to ON-T RGCs needed to be determined. In a tedious process, it is elegantly shown here that ON-T RGCs receive most of their excitatory inputs from type 5 and 6 bipolars. Interestingly, the temporal properties of light-evoked responses of type 5, 6, and 7 bipolars recorded from the somas were indistinguishable and rather sustained, suggesting that the origin of transient kinetics of excitatory inputs to ON-T RGCs suggested by the LN model might be found in the processing of visual signals at the bipolar cell axon terminal. Blocking GABA- or glycinergic inhibitory inputs did not alter the light-evoked excitatory input kinetics to ON-T and ON-S RGCs. Two-photon glutamate sensor imaging revealed significantly faster kinetics of light-evoked glutamate signals at ON-T versus ON-S RGCs. Detailed EM analysis of bipolar cell ribbon synapses onto ON-T and ON-S RGCs revealed fewer ribbon-associated vesicles at ON-T synapses, which is consistent with stronger paired-flash depression of light-evoked excitatory currents in ON-T RGCS versus ON-S RGCs. This study suggests that bipolar subtype-specific differences in the size of synaptic ribbon-associated vesicle pools contribute to transient versus sustained kinetics in RGCs.

Strengths:

The use of multiple, state-of-the-art tools and approaches to address the kinetics of bipolar to ganglion cell synapse in an identified circuit.

Weaknesses:

For the most part, the data in the paper support the conclusions, and the authors were careful to try to address questions in multiple ways. Two-photon glutamate sensor imaging experiment showing that blocking GABA- and glycinergic inhibition does not change the kinetics of light-evoked glutamate signals at ON-T RGCs would strengthen the conclusion that bipolar subtype-specific differences in the size of synaptic ribbon-associated vesicle pools contribute to transient versus sustained kinetics in RGCs.

Reviewer #2 (Public Review):

Summary:

Goal of the study. The authors tried to pinpoint the origins of transient and sustained responses measured at retinal ganglion cells (rgcs), which is the output layer of the retina. Response characteristics of rgcs are used to group them into different types. The diversity of rgc types represents the ability of the retina to transform visual inputs into distinct output channels. They find that the physical dimensions of bipolar cell's synaptic ribbons (specialized release sites/active zones) vary across the different types of cone on-bpcs, in ways that they argue could facilitate transient or sustained release. This diversity of release output is what they argue underlies the differences in on-rgcs response characteristics, and ultimately represents a mechanism for creating parallel cone-driven channels.

Strengths:

The major strengths of the study are the anatomical approaches employed and the use of the "glutamate sniffer" to assay synaptic glutamate levels. The outline of the study is elegant and reflects the strengths of the authors.

Weaknesses:

The major weakness is that the ambitious outline is not matched with a complete set of results, and the set of physiological protocols is disjointed, not sufficient to bridge the systems-level question with the presynaptic release question.

Major comments on the results and suggestions.

The ribbon model of release has been explored for decades and needs to be further adapted to systems-level work. The study under consideration by Kuo et al. takes on this task. Unfortunately, the experimental design does not permit a level of control over presynaptic/bpc behavior that is comparable to earlier studies, nor do they manipulate release in ways that test the ribbon model (i.e., paired recordings or Ribeye-ko). Furthermore, the data needs additional evaluation, and the presentation and interpretations should draw on published biophysical and molecular studies.

To build a ribbon-centric context, consider recent literature that supports the assertion that ribbons play a role in forming AZ release sites and facilitating exocytosis. Reference Ribeye-ko studies. For example, ribbonless bpcs show an 80% reduction in release (Maxeiner et al EMBO J 2016), the ribbonless retina exhibits signaling deficits at the output layer (Okawa et al ...Rieke, ..Wong Nat Comm 2019), and ribbonless rods show an 80% reduction the readily releasable pool (RRP) of SVs (Grabner Moser, elife 2021). In addition, the authors could refer to whole-cell membrane capacitance studies on mammalian rods, cones, and bpcs, because the size of the RRP of SVs scales with the dimensions and numbers of ribbons (total ribbon footprint). For comparison, bipolars see the review by Wan and Heidelberger 2011. For a comparison of mammalian rods and cones, see, rods: Grabner and Moser (2021 eLife), Mueller.. Regus Leidig et al. (2019; J Neurosci) and cones Grabner ...DeVries (Nat Comm 2023). A comparison of cell types shows that the extent of release is (1) proportional to the total size of the ribbon footprint, and (2) less release is witnessed when ribbons are deleted (also see photo ablation studies by Snellman.... And Mehta..Zenisek, Nat Neurosci and Neuron).

Ribbon morphology may change in an activity-dependent manner. The rod ribbon AZ has been reported to lengthen in the dark (Dembla et al 2020), and deletion of the ribbon shortens the length of the AZ (defined by Cav1,4 or RIM2); in addition, the Ribeye-ko AZs fail to change in size with light and dark conditioning. Furthermore, EM studies on rod and cone AZs in light and dark argue that the number of SVs at the base of the ribbon increases in the dark, when PRs are depolarized (see Figure 10, Babai et al 2016 JNeurosci). Lastly, using goldfish Mb1 on-bipolars, Hull et al (2006, J Neurophysio) correlated an increase in release efficiency with an increase in ribbon numbers, which accompanied daylight. >> When release activity is high, ribbon AZ length increases (Dembla, rods), the number of docked SVs increases (Babai, rods cones), and the number of ribbons increases (Hull, diurnal Mb1s).

The results under review, Kuo et al., were attained with SBF-SEM, which has the benefit of addressing large-volume questions as required here, yet it achieves lower spatial resolution than what is attained with TEM tomography and FIB-EM. Ideally, the EM description would include SV size, and the density of ribbon-tethered SVs that are docked at the plasma membrane, because this is where the SVs fuse (additional non-ribbon release sites may also exist? Mehta ... Singer 2014 J Neurosci). Studies by Graydon et al 2011 and 2014 (both in J Neurosci), and Jean ... Moser et al 2018 (eLife) are good examples of quantitative estimates of SVs docking sites at ribbons. SBF-SEM does not allow for an assessment of SVs within 5 nm of the PM, but if the authors can identify the number of SVs that appear within the limit of resolution (10 to 15 nm) from the PM, then this data would be useful. Also, what dimension(s) of the large ribbons make them larger? Typically, ribbons are fixed in height (at least in the outer retina, 200 to 250 nm), but their length varies and the number ribbons per terminal varies. Is the larger ribbon size observed in type 6 bpcs do to longer ribbons, or taller ribbons? A longer ribbon likely has more docked SVs. An additional possibility is that more SVs are about the ribbon-PM footprint, either more densely packed and/or expanding laterally (see definitions in Jean....Moser, elife 2018).

The ribbon literature given above makes the argument that ribbons increase exocytotic output, and morphological studies suggest that release activity enhances 1) ribbon length (Dembla) and 2) the density of SVs near the PM (Babai). These findings could lead one to propose that type 6 bpcs (inputs to On-sustained) are more active than type 5i (feed into On-transient). Here Kuo et al. show that the bpcs have similar Vm (measured from the soma) in response to light stimulation. Does Vm predict release? Not entirely as the authors acknowledge, because: Cav channel properties, SV availability, and negative feedback are all downstream of bpc Vm. The only experiment performed to test downstream factors focused on negative feedback from amacrines. The data presented in Figures 5C-F led me to conclude the opposite of what the authors concluded. My impression is that the T-ON rgc exhibits strong disinhibition when GABA-blockers are applied (the initial phase is greatly increased in amplitude and broadened with the drug), which contrasts with the S-On rgc responses that show a change in the amplitude of the initial phase but not its width (taus would be nice). Here and in many places the authors refer to changes in release kinetics, without implementing a useful description of kinetics. For instance, take the cumulative current (charge) in Figure 5C and fit the control and drug traces to arrive at taus, and their respective amplitudes, and use these values to describe kinetic phases. One final point, the summary in Figure 5D has a p: 0.06, very close to the cutoff for significance, which begs for more than an n = 5. Given that previous studies have shown that bpc output is shaped by immediate msec GABA feedback, in ways that influence kinetic phases of release (..Mb1 bipolars, see Vigh et al 2005 Neuron), more attention to this matter is needed before the authors rule out feedback inhibition in favor of ribbon size. If by chance, type 5i bpcs are under uniquely strong feedback inhibition, then ribbon size may result from less activity, not less output resulting from smaller ribbons.

As mentioned above, the behavior of Cav channels is important here. This is difficult to address with voltage clamps from the soma, especially in the Vm range relevant to this study. Given that it has previously been modeled that the rod bpc to AII pathway adapts to prolonged depolarization of rbcs through downregulating Cav channel-mediated Ca2+ influx (Grimes ....Rieke 2014 Neuron), it seems important for Kou et al to test if there is a difference in Cav regulation between type 6 and 5i bpcs. Ca2+ imaging with a GCaMP strategy (Baden....Lagnado Current Biology, 2011) or filling the presynapse with Ca dyes (see inner hair cells: Ozcete and Moser, EMBO J 2020) would allow for the correlation of [Ca]intra with GluSnf signals (both local readouts).

Stimulation protocol and presentation of Glutamate Sniffer data in Figure 6. In all of your figures where you state steady st as a % of pk amplitude, please indicate in the figure where you estimate steady state. Alternatively, if you take the cumulative dF/F signal, then you can fit the different kinetic phases. From the appearance of the data, the Sustained Glu signals look like square waves (Figure 6B ROI1-4), without a transient at onset, which is not predicted in your ribbon model that assumes different kinetic phases (1. depletion of docked SVs, and 2. refilling and repriming). The Transient responses (Figure 6B ROI5-8) are transient and more compatible with a depressing ribbon scheme. If you take the cumulative, for all of the On-S and compare it to all of the On-T responses, my guess is the cumulative dF/F will be 10 to 20 larger for the S-On. Would you conclude that bpc inputs to On-S (type 6) release 20-fold more SVs per 4 seconds on a per ribbon basis, and does the surface area of the type 6 bpcs account for this difference? From Figures 8B and D, the volume of the ribbon is ~2 fold greater for type 6 vs 5i, but the Surface Area (both faces of ribbon) is more relevant to your model that claims ribbon size is the pivotal factor. If making cumulative traces, and comparisons on an absolute scale is unfounded, then we need to know how to compare different observations. The classic ribbon models always have a conversion factor such as the capacitance of an SV or q size that is used to derive SV numbers from total dCm or Qcontent. See Kim ....et al von Gersdorff, 2023, Cell Reports. Why not use the Gaussian noise stimulus in Fig 6 as in Figure 1 and 2?

Figure 7. What is the recovery time for mammalian cones derived from ribbon-based models? There are estimates from membrane capacitance studies. Ground squirrel cones take 0.7 to 1 sec to recover the ultrafast, primed pool of SVs when probed with a paired-pulse protocol (Grabner ...DeVries 2016, Neuron). Their off-bpcs take anywhere from under 0.2 sec to a second to recover, which is a combination of many synaptic factors (Grabner ...DeVries Nat Comm 2023). Rod On bpcs take over a second (Singer Diamond 2006, reviewed Wan and Heidelberger 2011). In Figure 7B, the recovery time is ~150 ms for the responses measured at rgcs. This brief recovery time is incompatible with existing ribbon models of release. Whole-cell membrane capacitance measurements would be helpful here.

Experimental Suggestion: Add GABA blockers and see if type 5i bpc responds with more release (GluSniff) and prolonged [Ca2+] intra (GCaMP). Compare this to type 6 bpc behavior with GABA/gly blockers. This will rule in or out whether feedback inhibition is involved.

Reviewer #3 (Public Review):

Summary:

Different types of retinal ganglion cell (RGC) have different temporal properties - most prominently a distinction between sustained vs. transient responses to contrast. This has been well established in multiple species, including mice. In general, RGCs with dendrites that stratify close to the ganglion cell layer (GCL) are sustained; whereas those that stratify near the middle of the inner plexiform layer (IPL) are transient. This difference in RGC spiking responses aligns with similar differences in excitatory synaptic currents as well as with differences in glutamate release in the respective layers - shown previously and here, with a glutamate sensor (iGluSnFR) expressed in the RGCs of interest. Differences in glutamate release were not explained by differences in the distinct presynaptic bipolar cells' voltage responses, which were quite similar to one another. Rather, the difference in transient vs. sustained responses seems to emerge at the bipolar cell axon terminals in the form of glutamate release. This difference in the temporal pattern of glutamate release was correlated with differences in the size of synaptic ribbons (larger in the bipolar cells with more sustained responses), which also correlated with a greater number of vesicles in the vicinity of the larger ribbons.

The main conclusion of the study relates to a correlation (because it is difficult to manipulate ribbon size or vesicle density experimentally): the bipolar cells with increased ribbon size/vesicle number would have a greater possibility of sustained release, which would be reflected in the postsynaptic RGC synaptic currents and RGC firing rates. This model proposes a mechanism for temporal channels that is independent of synaptic inhibition. Indeed, some experiments in the paper suggest that inhibition cannot explain the transient nature of glutamate release onto one of the RGC types. Still, it is surprising that such a diverse set of inhibitory interneurons in the retina would not play some role in diversifying the temporal properties of RGC responses.

Strengths:

(1) The study uses a systematic approach to evaluating temporal properties of retinal ganglion cell (RGC) spiking outputs, excitatory synaptic inputs, presynaptic voltage responses, and presynaptic glutamate release. The combination of these experiments demonstrates an important step in the conversion from voltage to glutamate release in shaping response dynamics in RGCs.

(2) The study uses a combination of electrophysiology, two-photon imaging, and scanning block-face EM to build a quantitative and coherent story about specific retinal circuits and their functional properties.

Weaknesses:

(1) There were some interesting aspects of the study that were not completely resolved, and resolving some of these issues may go beyond the current study. For example, it was interesting that different extracellular media (Ames medium vs. ACSF) generated different degrees of transient vs. sustained responses in RGCs, but it was unclear how these media might have impacted ion channels at different levels of the circuit that could explain the effects on temporal tuning.

(2) It was surprising that inhibition played such a small role in generating temporal tuning. At the same time, there were some gaps in the investigation of inhibition (e.g., IPSCs were not measured in either of the RGC types; pharmacology was used to investigate responses only in the transient RGCs).

(3) There could be additional discussion and references to the literature describing several topics, including: temporal dynamics of glutamate release at different levels of the IPL; previous evidence that release sites from a single presynaptic neuron can differ in their temporal properties depending on the postsynaptic target; previous investigations of the role of inhibition in temporal tuning within retinal circuitry.

eLife assessment

This important study examined the dynamics of attentional reorientation in working memory by assessing alpha-band lateralization in EEG recordings and saccade bias and provides convincing evidence for a second stage of internal attentional deployment during WM. This work provides novel insights into the dynamic mechanism in WM and will be of broad interest and impact to cognitive neuroscience, including attention and working memory. Performing additional analysis to disentangle the roles of saccade and micro-saccade and to show behavioral relevance would further strengthen the conclusion.

Reviewer #1 (Public Review):

In the study "Re-focusing visual working memory during expected and unexpected memory tests" by Sisi Wang and Freek van Ede, the authors investigate the dynamics of attentional re-orienting within visual working memory (VWM). Utilizing a robust combination of behavioral measures, electroencephalography (EEG), and eye tracking, the research presents a compelling exploration of how attention is redirected within VWM under varying conditions. The research question addresses a significant gap in our understanding of cognitive processes, particularly how expected and unexpected memory tests influence the focus and re-focus of attention. The experimental design is meticulously crafted, enabling a thorough investigation of these dynamics. The figures presented are clear and effectively illustrate the findings, while the writing is concise and accessible, making the complex concepts understandable. Overall, this study provides valuable insights into the mechanisms of visual working memory and attentional re-orienting, contributing meaningfully to the field of cognitive neuroscience. Despite the strengths of the manuscript, there are several areas where improvements could be made.

Microsaccades or Saccades?

In the manuscript, the terms "microsaccades" and "saccades" are used interchangeably. For instance, "microsaccades" are mentioned in the keywords, whereas "saccades" appear in the results section. It is crucial to differentiate between these two concepts. Saccades are large, often deliberate eye movements used for scanning and shifting attention, while microsaccades are small, involuntary movements that maintain visual perception during fixation. The authors note the connection between microsaccades and attention, but it is not well-recognized that saccades are directly linked to attention. Despite the paradigm involving a fixation point, it remains unclear whether large eye movements (saccades) were removed from the analysis. The authors mention the relationship between microsaccades and attention but do not clarify whether large eye movements (saccades) were excluded from the analysis. If large eye movements were removed during data processing, this should be documented in the manuscript, including clear definitions of "microsaccades" and "saccades." If such trials were not removed, the contribution of large eye movements to the results should be shown, and an explanation provided as to why they should be considered.

Alpha Lateralization in Attentional Re-orienting

In the attentional orienting section of the results (Figure 2), the authors effectively present EEG alpha lateralization results with time-frequency plots and topographic maps. However, in the attentional re-orienting section (Figure 3), these visualizations are absent. It is important to note that the time period in attentional orienting differs from attentional re-orienting, and consequently, the time-frequency plots and topographic maps may also differ. Therefore, it may be invalid to compute alpha lateralization without a clear alpha activity difference. The authors should consider including time-frequency plots and topographic maps for the attentional re-orienting period to validate their findings.

Onset and Offset Latency of Saccade Bias

The use of the 50% peak to determine the onset and offset latency of the saccade bias is problematic. For example, if one condition has a higher peak amplitude than another, the standard for saccade bias onset would be higher, making the observed differences between the onset/offset latencies potentially driven by amplitude rather than the latencies themselves. The authors should consider a more robust method for determining saccade bias onset and offset that accounts for these amplitude differences.

Control Analysis for Trials Not Using the Initial Cue

The control analysis for trials where participants did not use the initial cue raises several questions:

(1) The authors claim that "unlike continuous alpha activity, saccades are events that can be classified on a single-trial level." However, alpha activity can also be analyzed at the single-trial level, as demonstrated by studies like "Alpha Oscillations in the Human Brain Implement Distractor Suppression Independent of Target Selection" by Wöstmann et al. (2019). If single-trial alpha activity can be used, it should be included in additional control analyses.

(2) The authors aimed to test whether the re-orienting signal observed after the test is not driven exclusively by trials where participants did not use the initial cue. They hypothesized that "in such a scenario, we should only observe attention deployment after the test stimulus in trials in which participants did not use the preceding retro cue." However, if the saccade bias is the index for attentional deployment, the authors should conduct a statistical test for significant saccade bias rather than only comparing toward-saccade after-cue trials with no-toward-saccade after-cue trials. The null results between the two conditions do not immediately suggest that there is attention deployment in both conditions.

(3) Even if attention deployment occurs in both conditions, the prolonged re-orienting effect could also be caused by trials where participants did not use the initial cue. Unexpected trials usually involve larger and longer brain activity. The authors should perform the same analysis on the time after the removal of trials without toward-saccade after the cue to address this potential confound.

Reviewer #2 (Public Review):

Summary:

This study utilized EEG-alpha activity and saccade bias to quantify the spatial allocation of attention during a working memory task. The findings indicate a second stage of internal attentional deployment following the appearance of a memory test, revealing distinct patterns between expected and unexpected test trials. The spatial bias observed during the expected test suggests a memory verification process, whereas the prolonged spatial bias during the unexpected test suggests a re-orienting response to the memory test. This work offers novel insights into the dynamics of attentional deployment, particularly in terms of orienting and re-orienting following both the cue and memory test.

Strengths:

The inclusion of both EEG-alpha activity and saccade bias yields consistent results in quantifying the attentional orienting and re-orienting processes. The data clearly delineate the dynamics of spatial attentional shifts in working memory. The findings of a second stage of attentional re-orienting may enhance our understanding of how memorized information is retrieved.

Weaknesses:

Although analyses of neural signatures and saccade bias provided clear evidence regarding the dynamics of spatial attention, the link between these signatures and behavioral performance remains unclear. Given the novelty of this study in proposing a second stage of 'verification' of memory contents, it would be more informative to present evidence demonstrating how this verification process enhances memory performance.

Reviewer #3 (Public Review):

Summary:

Wang and van Ede investigate whether and how attention re-orients within visual working memory following expected and unexpected centrally presented memory tests. Using a combination of spatial modulations in neural activity (EEG-alpha lateralization) and gaze bias quantified as time courses of microsaccade rate, the authors examined how retro cues with varying levels of reliability influence attentional deployment and subsequent memory performance. The conclusion is that attentional re-orienting occurs within visual working memory, even when tested centrally, with distinct patterns following expected and unexpected tests. The findings provide new value for the field and are likely of broad interest and impact, by highlighting working memory as an action-bound process (in)dependent on (an ambiguous) past.

Strengths:

The study uniquely integrates behavioral data (accuracy and reaction time), EEG-alpha activity, and gaze tracking to provide a comprehensive analysis of attentional re-orienting within visual working memory. As typical for this research group, the validity of the findings follows from the task design that effectively manipulates the reliability of retro cues and isolates attentional processes related to memory tests. The use of well-established markers for spatial attention (i.e. alpha lateralization) and more recently entangled dependent variable (gaze bias) is commendable. Utilizing these dependent metrics, the concise report presents a thorough analysis of the scaling effects of cue reliability on attentional deployment, both at the behavioral and neural levels. The clear demonstration of prolonged attentional deployment following unexpected memory tests is particularly noteworthy, although there are no significant time clusters per definition as time isn't a factor in a statistical sense, the jackknife approach is convincing. Overall, the evidence is compelling allowing the conclusion of a second stage of internal attentional deployment following both expected and unexpected memory tests, highlighting the importance of memory verification and re-orienting processes.

Weaknesses:

I want to stress upfront that these weaknesses are not specific to the presented work and do not affect my recommendation of the paper in its present form.

The sample size is consistent with previous studies, a larger sample could enhance the generalizability and robustness of the findings. The authors acknowledge high noise levels in EEG-alpha activity, which may affect the reliability of this marker. This is a general issue in non-invasive electrophysiology that cannot be handled by the authors but an interested reader should be aware of it. Effectively, the sensitivity of the gaze analysis appears "better" in part due to the better SNR. The latter also sets the boundaries for single-tiral analyses as the authors correctly mention. In terms of generalizability, I am convinced that the main outcome will likely generalize to different samples and stimulus types. Yet, as typical for the field future research could explore different contexts and task demands to validate and extend the findings. The authors provide here how and why (including sharing of data and code).

eLife assessment

This valuable study investigates the contribution of far-red light photo-acclimated cyanobacteria to primary production in intertidal beachrock habitats. Though the study presents solid evidence, the text would benefit from an improved discussion section and some additional methodological details.

Reviewer #1 (Public Review):

Summary:

Mosshammer et al. studied the oxygenic photosynthetic productivity of beachrock samples containing cyanobacteria with different pigment compositions. The use of longer wavelength absorbing chlorophylls in some cyanobacteria (chlorophylls d and f) allows their photosystems to use light further in the red than canonical chlorophyll a photosystems. As such, their distribution in visible light-shaded environments, such as the beachrock studied by Mosshammer et al., allows them to perform oxygenic photosynthesis using wavelengths not capable of driving photosynthesis in most cyanobacteria, algae, or plants.

By adapting measuring systems they have previously used to study these types of beachrock samples, the authors attempt to mimic a more natural light penetration through the beachrock in order to measure oxygen production. By doing so with different wavelengths and intensities, the authors are able to show that far-red light-driven oxygen production is potentially capable of driving high levels of gross primary production.

Strengths:

The manuscript builds on previous measurement techniques used by the authors while focussing on illumination from the top of a sample rather than the specific microbial layers themselves. This provides a more environmentally realistic understanding of the beachrock community, as well as far-red light-driven photosynthesis.

The manuscript benefits from using previously defined methods to further characterize complex environmental samples.

Weaknesses:

The manuscript suffers from a lack of discussion and interpretation of the findings, and as such is more of a report.

Using the envionmental beachrock samples has inherent complications, from the variation in rock morphology, to the microbial community composition of different samples as well as within a single sample. It would benefit the authors to discuss these technical difficulties in more detail, as the light penetration through the beachrock is likely greatly limiting measurements of chlorophyll f and/or chlorophyll d-driven photosynthesis in the beachrock.

This can be seen in the different luminescence measurements (Figure 2 and supplements), that the different samples have clear differences in far-red light-driven oxygen production. While the BLACK sample produces oxygen with 740nm LED filtered with a NIR-75N filter, neither of the other two samples produce measureable oxygen under this condition. Conversely, this sample results in the lowest level of gross photosynthesis when measuring dissolved oxygen. A more detailed discussion of the variation between and within samples and measurements would benefit the overall results of the manuscript.

The PINK beachrock sample has the highest level of chlorophyll d per chlorophyll a. As FaRLiP cyanobacteria only incorporate 1 chlorophyll d per photosystem II, and none in photosytem I, is there a (relatively) high composition of Acaryochloris species in the PINK sample? If normalized to the reflectance minima can more distinct populations be identified?

For Figure 1, multiple points should be clarified. The first is that the HPLC methods are estimates of concentrations, as the extinction coefficients are not correct for the solvent solution for which the pigments elute, and are likely to be differently incorrect for each pigment. This results in quantitatively incorrect data, but qualitative comparisons between samples likely remain valid. Secondly, the pigment concentrations can also be misleading. Within the cyanobacterial cells, photosystem I harbors approximately 3 times as many chlorophylls as photosystem II. While the community numbers and photosystem stoichiometry are not necessarily relevant to the current study, the red shift in absorbance between photosystem II and photosystem I is of importance for the measurements performed. How cyanobacterial cells with differing concentrations of photosystems will absorb the red tail of the far-red LEDs, as well as impact the light penetration would be a useful discussion point.

The different samples used are from varying beachrock zonations but have the same chlorophyll f per chlorophyll a concentrations. A discussion of why this might be would be useful.

For the luminescence measurements (Figure 2 and supplements), no oxygen production is seen in the BROWN or PINK beachrock samples when the 740nm LED is filtered with a NIR-75N filter. This is likely due to multiple factors (low initial intensity compounded by penetration depth, community composition, etc.) but should be discussed. While the authors say that Chrooccidiopsis species dominate the samples, variation of absorbance between different chlorophyll f containing cyanobacteria has also been measured (see Tros et al. 2021, Chem), and the extent to which even chlorophyll f species extend into the far-red varies. Discussions about these implications would help with their characterization of the luminescence data. While the authors discuss that based on their respiration measurements the oxygen may be being consumed, resulting in an inability to measure it (lines 147-150), other explanations are clearly viable.

For the luminescence measurements, no oxygen production is discernable in the endolithic region when excited with visible light, which is at a much stronger intensity than the near-infrared light used. However, both Acaryochloris and chlorophyll f cyanobacteria are capable of driving photosynthesis with visible light. As the intensities used are much brighter than for the NIR measurements, presumably generated oxygen would be higher than what could be immediately consumed by respiration. It is important that the authors address this.

A highlighted point by the authors is the >20% of photosynthesis driven by NIR in the beachrock at comparable irradiation. However, this statement is deceiving for multiple reasons.<br /> (1) The irradiation is likely not comparable for what is reaching the cells. This is not a problem per se as illumination from above is the point, but does skew the interpretation.<br /> (2) The >20% value comes from the maximum amount of gross photosynthesis driven by NIR at ~1400 umol photons m-2s-1, whereas at other comparable illuminations the value is much, much lower (<1%). A likely interpretation of such data is that while the chlorophyll f endolithic layer is capable of producing a relatively large amount of oxygen, it is likely far less productive under most illuminations, though not zero.

The authors have the difficult task of weaving in results from laboratory, uniculture or isolated photosystem measurements with their environmental-based results. This is especially clear in lines 172-183. While the authors are correct that measurements of trapping times in chlorophyll f containing photosystems have been measured and are slower in chlorophyll f photosystem II and photosystem I relative to all chlorophyll a photosystems, the quantum yield for trapping remains high in chlorophyll f photosystem I (Tros et al. 2021, Chem). The quantum yield of trapping for chlorophyll f photosystem II is much lower for chlorophyll f than chlorophyll a complex, though improved by the attachment of phycobilisomes. However, these are intrinsic physical properties of the complexes that are not modulated in response to the environments. This could be interpreted that at low photon flux densities as measured in these experiments, the endolithic near infrared-driven oxygen production could be limited by an overall lower quantum efficiency of trapping the captured light and thus minimizing photosynthetic productivity relative to a theoretical level based on the efficiency of the chlorophyll a photosystem II. How the variations in intensity and spectral composition impact the cyanobacterial community likely involves many other factors and has not been addressed (though see Nurnberg et al. 2018, Science and Viola et al. 2022 eLife for further discussions).

Reviewer #2 (Public Review):

The authors investigate the role of near-infrared photosynthesis in primary production across three beachrock communities. This work is particularly pertinent as more cyanobacteria with far-red light acclimation capacities are discovered, underscoring the need to assess their contributions to primary production. However, the manuscript is currently very difficult to follow due to unclear correlations between the text and figures and the samples analyzed in the different experiments.. Additional explanations would also enhance clarity. For example, it would be beneficial for the authors to better define the three communities, as distinctions are not apparent. Another example is the pigment analysis, where the extinction coefficients for pigments vary in different solvents. Quantification by chromatography should use calibration curves for all pigments, not just Chl a, as is currently done. Pigments can be easily purified from cyanobacteria for this purpose.

Reviewer #3 (Public Review):

Summary:

On islands in the pacific, beachrock occurs near high tide level, composed of calcareous material. The surface of the beach rock is colonised by cyanobacteria and some eukaryotic algae. On Heron Island on the Southern Great Barrier Reef, beach rock occurs on the north and south side of the island in continuous slabs, which slope gently upwards toward the island. Thus the upper beach rock is only inundated at extreme high tides. On the south side, the major photosynthetic organism is a cyanobacterium Chroococcidiopsis, which forms tough smooth mats over all the beach rock. This cyanobacterium belongs to a newly discovered class called FaRLiP photosynthesisers, which carry out conventional photosynthesis under visible radiation using chlorophyll a (Chl a) but which deactivate most of the Chl a under near infra -red radiation (NIR) and produce chlorophyll f and chlorophyll d which can absorb NIR (700 - 760 nm). These NIR Chl molecules are repositioned in the reaction centres. In addition, an NIR-activated allophycocyanin (a phycobiliprotein) is synthesised and placed in the reaction centres. These FaRLiP cyanobacteria can carry out photosynthesis and primary production when placed under NIR. Here it is shown that in the mats of Chroococcidiopsis on the beach rock the upper layers carry out conventional photosynthesis while the lower layers carry out FaRLiP photosynthesis. It is shown that the FaRLiP-activated lower layers can produce up to 20% of the total photosynthetic primary production.

Strengths:

The authors have researched sections of beachrock obtained from the beach rock on Heron Island. The Beach Rock on Heron Island occurs on both sides of the Island lying in a semi-horizontal position slightly sloping upwards toward the Island. At normal high tide, only the upper parts are not submerged. Black crusts occur in the uppermost parts of the beachrock. Brown crusts occur in the intermediate sites and pink crusts occur at the lowest part of the beachrock.

The crusts are made up largely of cyanobacteria and the major component is a cyanobacterium of one species, tentatively identified by shape, pigmentation, and partial DNA analysis as Chroococcidiopsis.

In this investigation sections of the beach rock from different levels have been analysed using three techniques:

(1) Hyperspectral analysis to determine the layout of pigmented cells and their spectra.

(2) Bioluminescence to determine the spectra of the cells in the sections.

(3) Oxygen analysis, using luminescence lifetime imaging on special films closely applied to vertical sections of the beachrock.

(4) Oxygen production from the surface of three-dimensional blocks of beach rock, illuminated with white light or Near Infra Red (NIR) radiation, from above.

In addition, pigmentation has been analysed by High Performance Liquid Chromatography (HPLC).

These techniques allow the following conclusions:

(1) Scytonemin is a main screening compound for UV irradiation.

(2) Carotenoids also play a part in screening from UV and probably visible radiation.

(3) The cyanobacteria occur near the rock surface and contain Chl a plus some Chl f and a small amount of Chl d.

(4) HPLC pigment analysis confirms the presence of Chl a plus Chl f and a small amount of Chl d.

(5) The deeper layer with FaRLiP cyanobacteria produces oxygen under both visible light and NIR irradiation, with different P vs I curves.

(6) Using the oxygen chamber to measure oxygen exchange above the beach rock surface, it was shown that high respiration meant that only with the brown samples was significant oxygen released to the water column at lower light levels, i.e. respiration accounted for most of the primary production of oxygen except at the highest visible light intensities. And with NIR much lower levels of oxygen production only breaking compensation significantly in the brown samples.

(7) FaRLiP primary production was significant in the deeper layer.

The major new conclusion from these studies is that FaRLiP photosynthesis is a significant factor in this biofilm, and possibly other biofilms. Visible light is mostly absorbed in the upper layers and NIR reaching the lower layers induces FaRLiP photosynthesis and primary production, which can be up to 20% of the total primary production of the film.

Weaknesses:

The techniques are sufficient to justify the conclusions, especially the new result that the FaRLiP photosynthesis deeper in the films is surprisingly active with relatively high primary productivity. This is an important conclusion but it must be realised that there is some way to go to polish up the results and gain more quantitative results.

Firstly the beachrock is a heterogeneous material. So cutting a section leaves a non-homogeneous surface where various sand grains are removed, cut, or not removed. This means that when applying a luminescence film, the results are dependent on the uniformity of the surface or rather the lack of conformity. This needs to be taken into consideration in future studies.

Furthermore, previous papers have revealed that pits in the beach rock are important sites for FaRLiP cyanobacteria and the paper needs to make clear that these pits were avoided here.

Secondly, while Chroococcidiopsis is the major alga/cyanobacterium present, other algae/cyanobacteria are present and their presence needs to be factored into the results. In this regard we need more microscopic images of the surface and cross-sections of the beachrock, to reveal the nature of the bacterial and algal organisms.

Thirdly, it is not clear from this paper how far the identification of Chroococcidiopsis is firm. Presumably preliminary DNA analyses have been carried out on tell-tale genes (rRNA?). At some stage, a complete genome will be needed. Mention should be made about what has been done and what is contemplated.

Fourthly, the acclimation to FaRLiP is time-dependent. How long does it take in these beach rock sections? And has sufficient notice been taken of this time-dependent process?

Fifthly, FaRLiP is a sophisticated system as shown by Mascoli et al, 2022. It is activated in NIR by red-shifted allophycocyanin. It is also dependent on the allocation of Chl f and Chl d to special positions in the reaction centre. All this may take some time and be light-dependent. This may explain the curious increase in the slopes of light vs productivity of Fig 4 (Pink and Black) for NIR light.

The fifth point needs to be taken into account in any rewrite of the paper. The authors assume that the upwardly sloping P vs I curve is explained as follows:<br /> "This can be explained by the light attenuation due to scattering and absorption in the compacted beachrock biofilm, which prevented saturation of NIR-driven photosynthesis in the endolithic layer even at levels of incident light similar to solar irradiation on mid-day exposed beachrock."

Activation of the FaRLiP system also needs to be considered.

"In Kenya, many households report a lack of cash as an impediment to investing in preventive health products, such as insecticide-treated mosquito nets. However, by providing people with a lockable metal box, a padlock, and a passbook that a household simply labels with the name of a preventive health product, researchers increased savings, and investment in these products rose by 66–75 percent (Dupas and Robinson 2013)."

This study reshaped my understanding of the fact that easy behavioral interventions, such as providing a lockable box and a passbook, can remarkably improve investment in important health products. In my opinion, this binds to what makes a country rich versus poor because it shows that economic success does not depend on the availability of resources only, but how well policies impact human behavior to improve management of the resources and foster prosperity.

"First, people make most judgments and most choices automatically, not deliberatively: we call this “thinking automatically.” Second, how people act and think often depends on what others around them do and think: we call this “thinking socially.” Third, individuals in a given society share a common perspective on making sense of the world around them and understanding themselves: we call this “thinking with mental models.”"

This quote has prompted a couple questions for me. Firstly, what is the difference between automatic vs deliberate thinking, and how do they affect decision making? Secondly, In what ways do social norms and behaviors shape economic outcomes and development policies? This relates to our guiding question because it suggests that economic disparities might be informed by the way individuals, and societies at large, make decisions.

"The title of this Report, Mind, Society, and Behavior, captures the idea that paying attention to how humans think (the processes of mind) and how history and context shape thinking (the infl uence of society) can improve the design and implementation of development policies and interventions that target human choice and action (behavior). To put it differently, development policy is due for its own redesign based on careful consideration of human factors."

This quote highlights a main idea of the article by underlining the fact that development policies have to take human psychology, social influence, and behavior into account. Failure to do so will leave out the missing link in understanding why some countries are economically successful and others fail (rational vs irrational). That is, if one incorporates the insights of the way human beings think, the societal context in which choices are framed, and behavior that ultimately drives economic outcomes, then he/she is better placed to set policies attuned more closely toward growth and inequalities for the sake of a country's wealth.

"The mind, society, and behavior framework points to new tools for achieving development objectives, as well as new means of increasing the effectiveness of existing interventions. It provides more entry points for policy and new tools that practitioners can draw on in their efforts to reduce poverty and increase shared prosperity. The mind, society, and behavior framework points to new tools for achieving development objectives, as well as new means of increasing the effectiveness of existing interventions. It provides more entry points for policy and new tools that practitioners can draw on in their efforts to reduce poverty and increase shared prosperity." Pg. 4

This quote really sums up this reports message and really shows what the author is trying to say. Their thoughts are clearly stated in the quote above and also relates to the inquiry question as it later explains how all of these factors relate to decision making and poverty,

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5167530/

"In contrast, a recent effort in South Africa to teach financial literacy through an engaging television soap opera improved the financial choices that individuals made. Financial messages were embedded in a soap opera about a financially reckless character. Households that watched the soap opera for two months were less likely to gamble and less likely to purchase goods through an expensive installment plan."

This passage shows the powerful influence of something as simple as a soap opera on financial behavior. Simply putting the narrative of someone with poor financial management can help people improve theirs (as it is factored in their automatic thinking). Things like these don't have a very tangible output, so it may be hard to track, but it is a very interesting insight which has good potential for change in the future. This connects to the text as it shows how poor peoples decisions are easily influenced and that putting messages into their heads can influence their economic choices.

"However, in mixed-caste groups, revealing the boys’ castes before puzzle-solving sessions created a signifi - cant “caste gap” in achievement in which low-caste boys underperformed the high-caste boys by 23 per- cent, controlling for other individual variables (Hoff and Pandey 2006, 2014). Making caste salient to the test takers invoked identities, which in turn affected perfor- mance."

A question I have about this quote and all mental models in general is how wasy are they really to change? As unless they are convinced or persuaded otherwise will their mental models shift signifcantly from something like a soap opera?

"Human sociality (the tendency of people to be concerned with and associate with each other) adds a layer of complexity and realism to the analysis of human decision making and behavior. Because many economic policies assume individuals are self-regarding, autonomous decision makers, these policies often focus on external material incentives, like prices."

Why does assuming people are self-regarding and autonomous overlook the social and psychological influences of poverty? How can policy makers even take into account social aspects given they are fairly intangible (compared to prices at least)?

This relates to our inquiry question as it talks about oversights of policy makers which influence the decisions people in poverty make.

"However, in mixed-caste groups, revealing the boys’ castes before puzzle-solving sessions created a signifi - cant “caste gap” in achievement in which low-caste boys underperformed the high-caste boys by 23 per- cent, controlling for other individual variables (Hoff and Pandey 2006, 2014). Making caste salient to the test takers invoked identities, which in turn affected perfor- mance." Pg. 12

This quote really suprised me as I did not think that sometime like a caste system and people's cast being revealsed to have that big of an impact on their performace.

"In Kenya, many households report a lack of cash as an impediment to investing in preventive health products, such as insecticide-treated mosquito nets. However, by providing people with a lockable metal box, a padlock, and a passbook that a household simply labels with the name of a preventive health product, researchers increased savings, and investment in these products rose by 66–75 percent"

Through this passage, the author shows how poverty shapes individual economic choices by highlighting barriers that prevent low-income individuals in Kenya to make good financial decisions. A lack of savings can make people not buy preventive products, despite the need. By just providing a box it can mentally help people allocate funds. This shows that even a minor non-physical adjustment in the environment can significantly influence behaviors. Poverty doesn't just reduce resources, it reduces the ability to manage resources, which shape different choices,

Author response:

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

We thank the Reviewers and Editors for the constructive comments, which we believe have significantly improved the quality of our manuscript.

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

Reviewer #1 (Public Review):

(1) With respect to the predictions, the authors propose that the subjects, depending on their linguistic background and the length of the tone in a trial, can put forward one or two predictions. The first is a short-term prediction based on the statistics of the previous stimuli and identical for both groups (i.e. short tones are expected after long tones and vice versa). The second is a long-term prediction based on their linguistic background. According to the authors, after a short tone, Basque speakers will predict the beginning of a new phrasal chunk, and Spanish speakers will predict it after a long tone.

In this way, when a short tone is omitted, Basque speakers would experience the violation of only one prediction (i.e. the short-term prediction), but Spanish speakers will experience the violation of two predictions (i.e. the short-term and long-term predictions), resulting in a higher amplitude MMN. The opposite would occur when a long tone is omitted. So, to recap, the authors propose that subjects will predict the alternation of tone durations (short-term predictions) and the beginning of new phrasal chunks (long-term predictions).

The problem with this is that subjects are also likely to predict the completion of the current phrasal chunk. In speech, phrases are seldom left incomplete. In Spanish is very unlikely to hear a function-word that is not followed by a content-word (and the opposite happens in Basque). On the contrary, after the completion of a phrasal chunk, a speaker might stop talking and a silence might follow, instead of the beginning of a new phrasal chunk.

Considering that the completion of a phrasal chunk is more likely than the beginning of a new one, the prior endowed to the participants by their linguistic background should make us expect a pattern of results actually opposite to the one reported here.

We thank the Reviewer #1 for this pertinent comment and the opportunity to address this issue. A very similar concern was also raised by Reviewer #2. Below we try to clarify the motivations that led us to predict that the hypothesized long-term predictions should manifest at the onset (and not within or the end) of a perceptual chunk. 

Reviewers #1 and #2 contest a critical assumption of our study i.e., the fact that longterm predictions should occur at the beginning of a rhythmic chunk as opposed to its completion. They also contest the prediction deriving from this view i.e., omitting the first sound in a perceptual chunk (short for Spanish, long for Basque) would lead to larger error responses than omitting a later element. They suggest an alternative view: the omission of tones at the end of a perceptual rhythmic chunk would evoke larger error responses than omissions at its onset, as subjects are more likely to predict the completion of the chunk than its beginning. This view predicts an interaction effect in the opposite direction of our findings. 

While we acknowledge this as a plausible hypothesis, we believe that the current literature provides strong support for our view. Indeed, many studies in the rhythm and music perception literature have investigated the ERP responses to deviant sounds and omissions placed at different positions within rhythmic patterns (e.g., Ladinig et al., 2009; Bouwer et al., 2016; Brochard et al., 2003; Potter et al., 2009; Yabe et al., 2001). For instance, Lading et al., 2009 presented participants with metrical rhythmical sound sequences composed of eight tones. In some deviant sequences, the first or a later tone was omitted. They found that earlier omissions elicited earlier and higher-amplitude MMN responses than later omissions (irrespective of attention). Overall, this and other studies showed that the amplitude of ERP responses are larger when deviants occur at positions that are expected to be the “start” of a perceptual group - “on the beat” in musical terms - and decline toward the end of the chunk. According to some of these studies, the first element of a chunk is particularly important to track the boundaries of temporal sequences, which is why more predictive resources are invested at that position. We believe that this body of evidence provides robust bases for our hypotheses and the directionality of our predictions.

An additional point that should be considered concerns the amplitude of the prediction error response elicited by the omission. From a predictive coding perspective, the omission of the onset of a chunk should elicit larger error responses because the system is expecting the whole chunk (i.e., two tones/more acoustic information). On the other hand, the omission of the second tone - in the transition between two tones within the chunk - should elicit a smaller error response because the system is expecting only the missing tone (i.e. less acoustic information). 

Given the importance of these points, we have now included them in the updated version of the paper, in which we try to better clarify the rationale behind our hypothesis (see Introduction section, around the 10th paragraph).

(2) The authors report an interaction effect that modulates the amplitude of the omission response, but caveats make the interpretation of this effect somewhat uncertain. The authors report a widespread omission response, which resembles the classical mismatch response (in MEG) with strong activations in sensors over temporal regions. Instead, the interaction found is circumscribed to four sensors that do not overlap with the peaks of activation of the omission response.

We thank the Reviewer for this comment. As mentioned in the provisional response, the approach employed to identify the presence of an interaction effect was conservative: We utilized a non-parametric test on combined gradiometers data, without making a priori assumptions about the location of the effect, and employed small cluster thresholds (cfg.clusteralpha = 0.05) to increase the chances of detecting highly localized clusters with large effect sizes. The fact that the interaction effect arises in a relatively small cluster of sensors does not alter its statistical robustness. It should be also considered that in the present analyses we focused on planar gradiometer data that, compared to magnetometers and axial gradiometers, present more fine-grained spatial resolution and are more suited for picking up relatively small effects. 

The partial overlap of the cluster with the activation peaks may simply reflect the fact that different sources contribute to the generation of the omission-MMN, which has been reported in several studies (e.g., Zhang et al., 2018; Ross & Hamm, 2020).  We value the Reviewer’s input and are grateful for the opportunity to address these considerations.

Furthermore, the boxplot in Figure 2E suggests that part of the interaction effect might be due to the presence of two outliers (if removed, the effect is no longer significant). Overall, it is possible that the reported interaction is driven by a main effect of omission type which the authors report, and find consistently only in the Basque group (showing a higher amplitude omission response for long tones than for short tones). Because of these points, it is difficult to interpret this interaction as a modulation of the omission response.

We thank the Reviewer for the comment and appreciate the opportunity to address these concerns. We have re-evaluated the boxplot in Figure 2E and want to clarify that the two participants mentioned by Reviewer #1, despite being somewhat distant from the rest of the group, are not outliers according to the standard Tukey’s rule. As shown in the figure below, no participant fell outside the upper (Q3+1.5xIQR) and lower whiskers (Q1-1.5xIQR) of the boxplot. 

Moreover, we believe that the presence of a main effect of omission type does not impact the interpretation of the interaction, especially considering that these effects emerge over distinct clusters of channels (see Fig. 1 C; Supplementary Fig. 2 A). 

Based on these considerations - and along with the evidence collected in the control study and the source reconstruction data reported in the new version of the manuscript - we find it unlikely that the interaction effect is driven by outliers or by a main effect of omission type. We appreciate the opportunity provided by the Reviewer to address these concerns, as we believe they strengthen the claim that the observed effect is driven by the hypothesized long-term linguistic priors rather than uncontrolled group differences.

Author response image 1.

It should also be noted that in the source analysis, the interaction only showed a trend in the left auditory cortex, but in its current version the manuscript does not report the statistics of such a trend.

We  appreciate  the  Reviewer’s  suggestion  to  incorporate  more comprehensive source analyses. In the new version of the paper, we perform new analyses on the source data using a new Atlas with more fine-grained parcellations of the regions of interests (ROIs) (Brainnetome atlas; Fan et al., 2016) and focusing on peak activity to increase response’s sensitivity in space and time. We therefore invite the Reviewer to read the updated part on source reconstruction included in the Results and Methods sections of the paper.  

Reviewer #1 (Recommendations For The Authors):

While I have described my biggest concerns with respect to this work in the public review, here I list more specific points that I hope will help to improve the manuscript. Some of these are very minor, but I hope you will still find them constructive. 

(1) I understand the difficulties implied in recruiting subjects from two different linguistic groups, but with 20 subjects per group and a between-groups design, the current study is somewhat underpowered. A post-hoc power analysis shows an achieved power of 46% for medium effect sizes (d = 0.5, and alpha = 0.05, one-sided test). A sensitivity analysis shows that the experiment only has 80% power for effect sizes of d = 0.8 and above. It would be important to acknowledge this limitation in the manuscript. 

We thank the Reviewer for reporting these analyses. It must be noted that our effect of interest was based on Molnar et al.’s (2016) behavioral experiment, in which a sample size of 16 subjects per group was sufficient to detect the perceptual grouping effect. In Yoshida et al., (2010), the perceptual grouping effect emerged with two groups of 20 7–8-month-old Japanese and English-learning infants. Based on these previous findings, we believe that a sample size of 20 participants per group can be considered appropriate for the current MEG study. We clarified these aspects in the Participants section of the manuscript, in which we specified that previous behavioral studies detected the perceptual grouping with similar sample sizes. Moreover, to acknowledge the limitation highlighted by the Reviewer, we also include the power and sensitivity analysis in a note in the same section (see note 2 in the Participants section).

(2) All the line plots in the manuscript could be made much more informative by adding 95% CI bars. For example, in Figure 4A, the omission response for the long tone departs from the one for the short tone very early. Adding CIs would help to assess the magnitude of that early difference. Error bars are present in Figure 3, but it is not specified what these bars represent. 

Thanks for the comments. We added the explanation of the error bars in the new version of Figure 3. For the remaining figures, we prefer maintaining the current version of the ERF, as the box-plots accompanying them provide information about the distribution of the effect across participants.

(3) In the source analysis, there is only mention of an interaction trend in the left auditory cortex, but no statistics are presented. If the authors prefer to mention such a trend, I think it would be important to provide its stats to allow the reader to assess its relevance. 

We performed new analysis on the source data, all reported in the updated version of the manuscript.

(4) In the discussion section, the authors refer to the source analysis and state that "the interaction is evident in the left". But if only a statistical trend was observed, this statement would be misleading. 

We agree with this comment. We invite the Reviewer to check the new part on source reconstruction, in which contrasts going in the same direction of the sensor level data are performed.

(5) In the discussion the authors argue that "This result highlights the presence of two distinct systems for the generation of auditory" that operate at different temporal scales, but the current work doesn't offer evidence for the existence of two different systems. The effects of long-term priors and short-term priors presented here are not dissociated and instead sum up. It remains possible that a single system is in place, collecting statistics of stimuli over a lifetime, including the statistics experienced during the experiment. 

Thanks for pointing that out. We changed the sentence above as follows: “This result highlights the presence of an active predictive system that relies on natural sound statistics learned over a lifetime to process incoming auditory input”.

(6) In the discussion, the authors acknowledge that the omission response has been interpreted both as pure prediction and as pure prediction error. Then they declare that "Overall, these findings are consistent with the idea that omission responses reflect, at least in part, prediction error signals.". However an argument for this statement is not provided. 

Thanks for pointing out this lack of argument. In the new version of the manuscript, we explained our rationale as follows: “Since sensory predictive signals primarily arise in the same regions as the actual input, the activation of a broader network of regions in omission responses compared to tones suggests that omission responses reflect, at least in part, prediction error signals”.

(7) In the discussion the authors present an alternative explanation in which both groups might devote more resources to the processing of long events, because these are relevant content words. Following this, they argue that "Independently on the interpretation, the lack of a main effect of omission type in the control condition suggests that the long omission effect is driven by experience with the native language." However as there was no manipulation of duration in the control experiment, a lack of the main effect of omission type there does not rule out the alternative explanation that the authors put forward. 

This is correct; thanks for noticing it. We removed the sentence above to avoid ambiguities.

Minor points: 

(8) The scale of the y-axis in Figure 2C might be wrong, as it goes from 9 to 11 and then to 12. If the scale is linear, the top value should be 13, or the bottom value should be 10. 

Figure 2C has been modified accordingly, thanks for noticing the error.

(9) There is a very long paragraph starting on page 7 and ending on page 8. Toward the end of the paragraph, the analysis of the control condition is presented. That could start a new paragraph.

Thanks for the suggestion. We modified the manuscript as suggested.

Reviewer #2 (Public Review):

(1) Despite the evidence provided on neural responses, the main conclusion of the study reflects a known behavioral effect on rhythmic sequence perceptual organization driven by linguistic background (Molnar et al. 2016, particularly). Also, the authors themselves provide a good review of the literature that evidences the influence of longterm priors in neural responses related to predictive activity. Thus, in my opinion, the strength of the statements the authors make on the novelty of the findings may be a bit far-fetched in some instances.

Thanks for the suggestion. A similar point was also advanced by Reviewer 1. In general, we believe our work speaks about the predictive nature of such experiencedependent  effects, and show that these linguistic priors shape sensory processes at very early stages. This is discussed in the sixth and seventh paragraphs of the Discussion section. In the new version of the article, we modified some statements and tried to make them more coherent with the scope of the present work. For instance, we changed "This result highlights the presence of two distinct systems for the generation of auditory predictive models, one relying on the transition probabilities governing the recent past, and another relying on natural sound statistics learned over a lifetime“ with “This result highlights the presence of an active predictive system that relies on natural sound statistics learned over a lifetime to process incoming auditory input”.

(2) Albeit the paradigm is well designed, I fail to see the grounding of the hypotheses laid by the authors as framed under the predictive coding perspective. The study assumes that responses to an omission at the beginning of a perceptual rhythmic pattern will be stronger than at the end. I feel this is unjustified. If anything, omission responses should be larger when the gap occurs at the end of the pattern, as that would be where stronger expectations are placed: if in my language a short sound occurs after a long one, and I perceptually group tone sequences of alternating tone duration accordingly, when I hear a short sound I will expect a long one following; but after a long one, I don't necessarily need to expect a short one, as something else might occur.

A similar point was advanced by Reviewer #1. We tried to clarify the rationale behind our hypothesis. Please refer to the response provided to the first comment of Reviewer #1 above.

(3) In this regard, it is my opinion that what is reflected in the data may be better accounted for (or at least, additionally) by a different neural response to an omission depending on the phase of an underlying attentional rhythm (in terms of Large and Jones rhythmic attention theory, for instance) and putative underlying entrained oscillatory neural activity (in terms of Lakatos' studies, for instance). Certainly, the fact that the aligned phase may differ depending on linguistic background is very interesting and would reflect the known behavioral effect.

We thank the Reviewer for this comment. We explored in more detail the possibility that the aligned phase may differ depending on linguistic background, which is indeed a very interesting hypothesis. In the phase analyses reported below we focused on the instantaneous phase angle time locked to the onset of short and long tones presented in the experiment.

In short, we extracted time intervals of two seconds centered on the onset of the tones for each participant (~200 trials per condition) and using a wavelet transform (implemented in Fieldtrip ft_freqanalysis) we targeted the 0.92 Hz frequency that corresponds to the rhythm of presentation of our pairs of tones. We extracted the phase angle for each time point and using the circular statistics toolbox implemented in Matlab we computed the Raleigh z scores across all the sensor space for each tone (long and short tone) and group (Spanish (Spa) dominants and Basque (Eus) dominants). This method evaluates the instantaneous phase clustering at a specific time point, thus evaluating the presence of a specific oscillatory pattern at the onset of the specific tone. 

Author response image 2.

Here we observe that the phase clustering was stronger in the right sensors for both groups. The critical point is to evaluate the phase angle (estimated in phase radians) for the two groups and the two tones and see if there are statistical differences. We focused first on the sensor with higher clustering (right temporal MEG1323) and observed very similar phase angles for the two groups both for long and short tones (see image below). We then focused on the four left fronto-temporal sensor pairs who showed the significant interaction: here we observed one sensor (MEG0412) with different effects for the two groups (interaction group by tone was significant, p=0.02): for short tones the “Watson (1961) approximation U2 test” showed a p-value of 0.11, while for long tones the p-value was 0.03 (after correction for multiple comparisons). 

Overall, the present findings suggest the tendency to phase aligning differently in the two groups to long and short tones in the left fronto-temporal hemisphere. However, the effect could be detected only in one gradiometer sensor and it was not statistically robust. The effect in the right hemisphere was statistically more robust, but it was not sensitive to group language dominance. 

Due to the inconclusive nature of these analyses regarding the role of language experience in shaping the phase alignment to rhythmic sound sequences, we prefer to keep these results in the public review rather than incorporating them in the article.  Nonetheless, we believe that this decision does not undermine the main finding that the group differences in the MMN amplitude are driven by long-term predictions – especially in light of the many studies indicating the MMN as a putative index of prediction error (e.g., Bendixen et al., 2012; Heilbron and Chait, 2018). Moreover, as suggested in the preliminary reply, despite evoked responses and oscillations are often considered distinct electrophysiological phenomena, current evidence suggests that these phenomena are interconnected (e.g., Studenova et al., 2023). In our view, the hypotheses that the MMN reflects differences in phase alignment and long-term prediction errors are not mutually exclusive.

Author response image 3.

(4) Source localization is performed on sensor-level significant data. The lack of  sourcelevel statistics weakens the conclusions that can be extracted. Furthermore, only the source reflecting the interaction pattern is taken into account in detail as supporting their hypotheses, overlooking other sources. Also, the right IFG source activity is not depicted, but looking at whole brain maps seems even stronger than the left. To sum up, source localization data, as informative as it could be, does not strongly support the author's claims in its current state. 

A similar comment was also advanced by Reviewer #1 (comment 2). We appreciate the suggestion to incorporate more comprehensive source analyses. In the new version of the paper, we perform new analyses on the source data using a new Atlas with more fine-grained parcellations of the ROIs, and focusing on peak activity to increase response’s sensitivity in space and time. We therefore invite the Reviewer to read the updated part on source reconstruction included in the Results and Methods sections of the paper. 

In the article, we report only the source reconstruction data from ROIs in the left hemisphere, because it is there that the interaction effect arises at the sensor level. However, we also explored the homologous regions in the right hemisphere, as requested by the Reviewer. A cluster-based permutation test focusing on the interaction between language group and omission type was performed on both the right STG and IFG data. No significant interaction emerged in any of these regions. Below a plot of the source activity time series over ROIs in the right STG and IFG. 

Author response image 4.

Reviewer #2 (Recommendations For The Authors):

In this set of private recommendations for the authors, I will outline a couple of minor comments and try to encourage additional data analyses that, in my opinion, would strengthen the evidence provided by the study. 

(1) As I noted in the public review, I believe an oscillatory analysis of the data would, on one hand, provide stronger support for the behavioral effect of rhythmic perceptual organization given the lack of behavioral direct evidence; and, on the other hand, provide evidence (to be discussed if so) for a role of entrained oscillation phase in explaining the different pattern of omission responses. One analysis the authors could try is to measure the phase angle of an oscillation, the frequency of which relates to the length of the binary pattern, at the onset of short and long tones, separately, and compare it across groups. Also, single trials of omission responses could be sorted according to that phase. 

Thanks for the suggestion. Please see phase analyses reported above.

(2) I wonder why source activity for the right IFG was not shown. I urge the authors to provide and discuss a more complete picture of the source activity found. Given the lack of source statistics (which could be performed), I find it a must to give an overall view. I find it so because I believe the distinction between perceptual grouping effects due to inherent acoustic differences across languages or semantic differences is so interesting. 

Thanks again for the invitation to provide a more complete picture of the source activity data. As mentioned in the response above, we invite the Reviewer to read the new related part included in the Results and Methods sections of the paper. In our updated source reconstruction analysis, we find that some regions around the left STG show a pattern that resembles the one found at the sensor-level, providing further support for the “acoustic” (rather than syntactic/semantic) nature of the effect. 

We did not report ROI analysis on the right hemisphere because the interaction effect at sensor level emerged on the left hemisphere. Yet, we included a summary of this analysis in the public response above. 

(3) Related to this, I have to acknowledge I had to read the whole Molnar et al. (2016) study to find the only evidence so far that, acoustically, in terms of sound duration, Basque and Spanish differ. This was hypothesized before but only at Molnar, an acoustic analysis is performed. I think this is key, and the authors should give it a deeper account in their manuscript. I spend my review of this study thinking, well, but when we speak we actually bind together different words and the syllabic structure does not need to reflect the written one, so maybe the effect is due to a high-level statistical prior related to the content of the words... but Molnar showed me that actually, acoustically, there's a difference in accent and duration: "Taken together, Experiments 1a and 1b show that Basque and Spanish exhibit the predicted differences in terms of the position of prosodic prominence in their phonological phrases (Basque: trochaic, Spanish: iambic), even though the acoustic realization of this prominence involves not only intensity in Basque but duration, as well. Spanish, as predicted, only uses duration as a cue to mark phrasal prosody." 

Thanks for the suggestion, the distinction in terms of sound duration in Spanish and Basque reported by Molnar is indeed very relevant for the current study. 

We add a few sentences to highlight the acoustic analysis by Molnar and the consequent acoustic nature of the reported effect.

In the introduction: “Specifically, the effect has been proposed to depend on the quasiperiodic alternation of short and long auditory events in the speech signal – reported in previous acoustic analyses (Molnar et al., 2016) – which reflect the linearization of function words (e.g., articles, prepositions) and content words (e.g., nouns, adjectives, verbs).”

In the discussion, paragraph 3, we changed “We hypothesized that this effect is linked to a long-term “duration prior” originating from the syntactic function-content word order of language, and specifically, from its acoustic consequences on the prosodic structure” with “We hypothesized that this effect is linked to a long-term “duration prior” originating from the acoustic properties of the two languages, specifically from the alternation of short and long auditory events in their prosody”.

In the discussion, end of paragraph eight: “The reconstruction of cortical sources associated with the omission of short and long tones in the two groups showed that an interaction effect mirroring the one at the sensor level was present in the left STG, but not in the left IFG (fig. 3, B, C, D). Pairwise comparisons within different ROIs of the left STG indicated that the interaction effect was stronger over primary (BA 41/42) rather than associative (BAs 22) portions of the auditory cortex. Overall, these results suggest that the “duration prior” is linked to the acoustic properties of a given language rather than its syntactic configurations”.

Now, some minor comments: 

(1) Where did the experiments take place? Were they in accordance with the Declaration of Helsinki? Did participants give informed consent? 

All the requested information has been added to the updated version of the manuscript. Thanks for pointing out this.

(2) The fixed interval should be called inter-stimulus interval. 

Thanks for pointing this out. We changed the wording as suggested.

(3) The authors state that "Omission responses allow to examine the presence of putative error signals decoupled from bottom-up sensory input, offering a critical test for predictive coding (Walsh et al 2020, Heilbron and Chait, 2018).". However the way omission responses are computed in their study is by subtracting the activity from the previous tone. This necessarily means that in the omission activity analyzed, there's bottom-up sensory input activity. As performing another experiment with a control condition in which a sequence of randomly presented tones with different durations to compare directly the omission activity in both sequences (experimental and control) is possibly too demanding, I at least urge the authors to incorporate the fact that their omission responses do reflect also tone activity. And consider, for future experiments, the inclusion of further control conditions. 

Thanks for the opportunity to clarify this aspect. Actually, the way we computed the omission MMN is not by subtracting the activity of the previous tone from the omission, but by subtracting the activity of randomly selected tones across the whole experiment. That is, we randomly selected around 120 long and short tones (i.e., about the same number as the omissions); we computed the ERF for the long and short tones; we subtracted these ERF from the ERF of the corresponding short and long omissions. We clarified these aspects in both the Materials and Methods (ERF analysis paragraph) and Results section.

Moreover, the subtraction strategy - which is the standard approach to calculate the MMN - allows to handle possible neural carryover effects arising from the perception of the tone preceding the omission.

The sentence "Omission responses allow to examine the presence of putative error signals decoupled from bottom-up sensory input, offering a critical test for predictive coding (Walsh et al 2020, Heilbron and Chait, 2018)." simply refer to the fact that the error responses resulting from an omission are purely endogenous, as omissions are just absence of an expected input (i.e., silence). On the other hand, when a predicted sequence of tones is disrupted by an auditory deviants (e.g., a tone with a different pitch or duration than the expected one), the resulting error response is not purely endogenous, but it partially includes the response to the acoustic properties of the deviant.

(4) When multiple clusters emerged from a comparison, only the most significant cluster was reported. Why? 

We found more than one significant cluster only in the comparison between pure omissions vs tones (figure 2 A, B). The additional significant cluster from this comparison is associated with a P-value of 0.04, emerges slightly earlier in time, and goes in the same direction as the cluster reported in the paper i.e., larger ERF responses for omission vs tones. We added a note specifying the presence of this second cluster, along with a figure on the supplementary material (Supplementary Fig. 1 A, B).

(5) Fig 2, if ERFs are baseline corrected -50 to 0ms, why do the plots show pre-stimulus amplitudes not centered at 0? 

This is because we combined the latitudinal and longitudinal gradiometers on the ERF obtained after baseline correction, by computing the root mean square of the signals at each sensor position (see also  https://www.fieldtriptoolbox.org/example/combineplanar_pipelineorder/). This information is reported in the methods part of the article.

(6) Fig 2, add units to color bars. 

Sure.

(7) Fig 2 F and G, put colorbar scale the same for all topographies. 

Sure, thanks for pointing this out.

(8) The interaction effect language (Spanish; Basque) X omission type (short; long) appears only in a small cluster of 4 sensors not located at the locations with larger amplitudes to omissions. Authors report it as left frontotemporal, but it seems to me frontocentral with a slight left lateralization.

(1) the fact that the cluster reflecting the interaction effect does not overlap with the peaks of activity is not surprising in our view. Many sources contribute to the generation of the MMN. The goal of our work was to establish whether there is also evidence for a long-term system (among the many) contributing to this. That is why we perform a first analysis on the whole omission response network (likely including many sources and predictive/attentional systems), and then we zoom in and focus on our hypothesized interaction. We never claim that the main source underlying the omissionMMM is the long-term predictive system. 

(2) The exact location of those sensors is at the periphery of the left-hemisphere omission response, which mainly reflects activity from the left temporal regions. The sensor location of this cluster could be influenced by multiple factors, including (i) the direction of the source dipoles determining an effect; (ii) the combination of multiple sources contributing to the activity measured at a specific sensor location, whose unmixing could be solved only with a beamforming source approach. Based on the whole evidence we collected also in the source analyzes we concluded that the major contributors to the sensor-level interaction are emerging from both frontal and temporal regions.

Reviewer #3 (Public Review):

(1) The main weaknesses are the strength of the effects and generalisability. The sample size is also relatively small by today's standards, with N=20 in each group. Furthermore, the crucial effects are all mostly in the .01>P<.05 range, such as the crucial interaction P=.03. It would be nice to see it replicated in the future, with more participants and other languages. It would also have been nice to see behavioural data that could be correlated with neural data to better understand the real-world consequences of the effect.

We appreciate the positive feedback from Reviewer #3. We agree that it would be nice to see this study replicated in the future with larger sample sizes and a behavioral counterpart. Below are a few comments concerning the weakness highlighted: 

(i) Concerning the sample size: a similar point was raised by Reviewer #1. We report our reply as presented above: “Despite a sample size of 20 participants per group can be considered relatively small for detecting an effect in a between-group design, it must be noted that our effect of interest was based on Molnar et al.’s (2016) experiment, where a sample size of 16 subjects per group was sufficient to detect the perceptual grouping effect. In Yoshida et al., 2010, the perceptual grouping effect arose with two groups of 20 7–8-month-old Japanese and English-learning infants. Based on these findings, we believe that a sample size of 20 participants per group can be considered appropriate for the current study”. We clarified these aspects in the new version of the manuscript.

(ii) We believe that the lack of behavioral data does not undermine the main findings of this study, given the careful selection of the participants and the well-known robustness of the perceptual grouping effect (e.g., Iversen 2008; Yoshida et al., 2010; Molnar et al. 2014; Molnar et al. 2016). As highlighted by Reviewer #2, having Spanish and Basque dominant “speakers as a sample equates that in Molnar et al. (2016), and thus overcomes the lack of direct behavioral evidence for a difference in rhythmic grouping across linguistic groups. Molnar et al. (2016)'s evidence on the behavioral effect is compelling, and the evidence on neural signatures provided by the present study aligns with it”. (iii) Regarding the fact that the “crucial effects are all mostly in the .01>P<.05 range”: we want to stress that the approach we used to detect the interaction effect was conservative, using a cluster-based permutation approach with no a priori assumptions about the location of the effect. The robustness of our approach has also been highlighted by Reviewer 2: “Data analyses. Sound, state-of-the-art methodology in the event-related field analyses at the sensor level.” In sum, despite some crucial effects being in the .01>P<.05 range, we believe that the statistical soundness of our analysis, combined with the lack of effect in the control condition, provides compelling evidence for our H1.

Reviewer #3 (Recommendations For The Authors):

Figures - Recommend converting all diagrams and plots to vector images to ensure they remain clear when zoomed in the PDF format. 

Sure, thanks. 

Figure 1: To improve clarity, the representation of sound durations in panels C and D should be revisited. The use of quavers/eighth notes can be confusing for those familiar with musical notation, as they imply isochrony. If printed in black and white, colour distinctions may be lost, making it difficult to discern the different durations. A more universal representation, such as spectrograms, might be more effective. 

Thanks for the suggestion. It’s true that the quavers/eighth notes might be confusing in that respect. However, we find this notation as a relatively standard approach to define paradigms in auditory neuroscience, see for instance the two papers below. In the new version of the manuscript, we specified in the captions under the figure that the notes refer to individual tones, in order to avoid ambiguities.

- Wacongne, C., Labyt, E., Van Wassenhove, V., Bekinschtein, T., Naccache, L., & Dehaene, S. (2011). Evidence for a hierarchy of predictions and prediction errors in human cortex. Proceedings of the National Academy of Sciences, 108(51), 20754-20759.

- Dehaene, S., Meyniel, F., Wacongne, C., Wang, L., & Pallier, C. (2015). The neural representation of sequences: from transition probabilities to algebraic patterns and linguistic trees. Neuron, 88(1), 2-19.

Figure 2 : In panel C of Figure 2, please include the exact p-value for the interaction observed. Refrain from using asterisks or "n.s." and opt for exact p-values throughout for the sake of clarity. 

Thank you for your suggestion. We have included the exact p-value for the interaction in panel C of Figure 2. However, for the remaining figures, we have chosen to maintain the use of asterisks and "n.s.". We would like our pictures to convey the key findings concisely, while the numerical details can be found in the article text. The caption below the image also provides guidance on the interpretation of the p-values: (statistical significance: **p < 0.01, *p < 0.05, and ns p > 0.05).  

Figure 3 Note typo "Omission reponse"

Fixed. Thanks for noticing the typo. 

A note: we moved the figure reflecting the main effect of long tone omission and the lack of main effect of language background (Figure 4 in the previous manuscript) in the supplementary material (Supplementary Figure 2).

References

Bendixen, A., SanMiguel, I., & Schröger, E. (2012). Early electrophysiological indicators for predictive processing in audition: a review. International Journal of Psychophysiology, 83(2), 120-131.

Heilbron, M., & Chait, M. (2018). Great expectations: is there evidence for predictive coding in auditory cortex?. Neuroscience, 389, 54-73.

Iversen, J. R., Patel, A. D., & Ohgushi, K. (2008). Perception of rhythmic grouping depends on auditory experience. The Journal of the Acoustical Society of America, 124(4), 22632271.

Molnar, M., Lallier, M., & Carreiras, M. (2014). The amount of language exposure determines nonlinguistic tone grouping biases in infants from a bilingual environment. Language Learning, 64(s2), 45-64.

Molnar, M., Carreiras, M., & Gervain, J. (2016). Language dominance shapes non-linguistic rhythmic grouping in bilinguals. Cognition, 152, 150-159.

Ross, J. M., & Hamm, J. P. (2020). Cortical microcircuit mechanisms of mismatch negativity and its underlying subcomponents. Frontiers in Neural Circuits, 14, 13.

Simon, J., Balla, V., & Winkler, I. (2019). Temporal boundary of auditory event formation: An electrophysiological marker. International Journal of Psychophysiology, 140, 53-61.

Studenova, A. A., Forster, C., Engemann, D. A., Hensch, T., Sander, C., Mauche, N., ... & Nikulin, V. V. (2023). Event-related modulation of alpha rhythm explains the auditory P300 evoked response in EEG. bioRxiv, 2023-02.

Yoshida, K. A., Iversen, J. R., Patel, A. D., Mazuka, R., Nito, H., Gervain, J., & Werker, J. F. (2010). The development of perceptual grouping biases in infancy: A Japanese-English cross-linguistic study. Cognition, 115(2), 356-361.

Zhang, Y., Yan, F., Wang, L., Wang, Y., Wang, C., Wang, Q., & Huang, L. (2018). Cortical areas associated with mismatch negativity: A connectivity study using propofol anesthesia. Frontiers in Human Neuroscience, 12, 392.

Ladinig, O., Honing, H., Háden, G., & Winkler, I. (2009). Probing attentive and preattentive emergent meter in adult listeners without extensive music training. Music Perception, 26(4), 377-386. 

Brochard, R., Abecasis, D., Potter, D., Ragot, R., & Drake, C. (2003). The “ticktock” of our internal clock: Direct brain evidence of subjective accents in isochronous sequences. Psychological Science, 14(4), 362-366.

Potter, D. D., Fenwick, M., Abecasis, D., & Brochard, R. (2009). Perceiving rhythm where none exists: Event-related potential (ERP) correlates of subjective accenting. Cortex, 45(1), 103-109.

Bouwer, F. L., Werner, C. M., Knetemann, M., & Honing, H. (2016). Disentangling beat perception from sequential learning and examining the influence of attention and musical abilities on ERP responses to rhythm. Neuropsychologia, 85, 80-90.

eLife assessment

This study presents important observations about how the human brain uses long-term priors (acquired during our lifetime of listening) to make predictions about expected sounds - an open question in the field of predictive processing. The evidence presented is solid and based on state-of-the-art statistical analysis, but limited by a relatively low N and low magnitude for the interaction effect.

Reviewer #1 (Public Review):

Summary:

In this work, the authors study whether the human brain uses long term priors (acquired during our lifetime) regarding the statistics of auditory stimuli to make predictions respecting auditory stimuli. This is an important open question in the field of predictive processing.

To address this question, the authors cleverly profit from the naturally existing differences in two linguistic groups. While speakers of Spanish use phrases in which function-words (short words like, articles and prepositions) are followed by content-words (longer words like nouns, adjectives and verbs), speakers of Basque use phrases with the opposite order. Because of this, speakers of Spanish usually hear phrases in which short words are followed by longer words, and speakers of Basque experience the opposite. This difference in the order of short and longer words is hypothesized to result in a long term duration prior that is used to make predictions regarding the likely durations of incoming sounds, even if they are not linguistic in nature.

To test this, the authors used MEG to measure the mismatch responses (MMN) elicited by the omission of short and long tones that were presented in alternation. The authors report an interaction between the language background of the participants (Spanish, Basque) and the type of omission MMN (short, long), which goes in line with their predictions. They supplement these results with a source level analysis.

Strengths:

This work has many strengths. To test the main question, the authors profit from naturally occurring differences in the everyday auditory experiences of two linguistic groups, which allows to test the effect of putative auditory priors consolidated over the years. This is a direct way of testing the effect of long term priors.

The fact that the priors in question are linguistic and that the experiment was conducted using non-linguistic stimuli (i.e. simple tones), allows to test if these long term priors generalize across auditory domains.

The experimental design is elegant and the analysis pipeline appropriate. This work is very well written. In particular the introduction and discussion sections are clear and engaging. The literature review is complete.

Weaknesses:

The authors report a widespread omission response, which resembles the classical mismatch response (in MEG planar gradiometers) with strong activations in sensors over temporal regions. However the interaction reported is circumscribed to four sensors that do not overlap with the peaks of activation of the omission response.

Reviewer #2 (Public Review):

Summary:

Morucci et al. tested the influence of linguistic prosody long-term priors in forming predictions about simple acoustic rhythmic tone sequences composed of alternating tone duration, by violating context-dependent short-term priors formed during sequence listening. Spanish and Basque participants were selected due to the different rhythmic prosody of the two languages (functor-initial vs. Functor final, respectively), despite a common cultural background. The authors found that neuromagnetic responses to casual tone omissions reflected the linguistic prosody pattern of the participant's dominant language: in Spanish speakers, omission responses were larger to short tones, whereas in Basque speakers, omission responses were larger to long tones. Source localization of these responses revealed this interaction pattern in the left auditory cortex, which the authors interpret as reflecting a perceptual bias due to acoustic cues (inherent linguistic rhythms, rather than linguistic content). Importantly, this pattern was not found when the rhythmic sequence entailed pitch, rather than duration, cues. To my knowledge, this is the first study providing neural signatures of a known behavioral effect linking ambiguous rhythmic tone sequence perceptual organization to linguistic experience.

The conclusions of the study are well supported by the data. The hypotheses, albeit allowing alternative perspectives, are well justified according to the existing literature. Albeit with inconclusive results, additional analyses to test entrained oscillatory activity to the perceived rhythms have been performed, which adds explanatory power to the study.

Strengths:

(1) The choice of participants. The bilingual population of the Basque country is perfect for performing studies which need to control for cultural and socio-economic background while having profound linguistic differences. In this sense, having dominant Basque speakers as a sample equates that in Molnar et al. (2016), and thus overcomes the lack of direct behavioral evidence for a difference in rhythmic grouping across linguistic groups. Molnar et al. (2016)'s evidence on the behavioral effect is compelling, and the evidence on neural signatures provided by the present study aligns with it.

(2) The experimental paradigm. It is a well designed acoustic sequence, which considers aspects such as gap length insertion, to be able to analyze omission responses free from subsequent stimulus-driven responses, and which includes a control sequence which uses pitch instead of duration as a cue to rhythmic grouping, which provides a stronger case for the differences found between groups to be due to prosodic duration cues.

(3) Data analyses. Sound, state-of-the-art methodology in the event-related field analyses at the sensor and source levels.

Weaknesses:

(1) The main conclusion of the study reflects a known behavioral effect on rhythmic sequence perceptual organization driven by linguistic background (Molnar et al. 2016, particularly) and, thus, the novelty of the findings is restricted to neural activity evidence.

(2) Although the paradigm is well designed, there are alternative views in formulating the hypotheses. For instance, one could argue that, according to predictive coding views, omission responses should be larger when the gap occurs at the end of the pattern, as that would be where stronger expectations are placed. However, the authors provide good justification based on previous literature for the expectation of larger omission responses at the downbeat of a rhythmic pattern.

Reviewer #3 (Public Review):

Summary:

The paper investigates the effects of long-term linguistic experience on early auditory processing, a subject that has been relatively less studied compared to short-term influences. Using MEG, the study examines brain responses to auditory stimuli in speakers of Spanish and Basque, whose syntactic rules provide different degrees of exposure to durational patterns (long-short vs short-long). The findings suggest that both long-term language experience as well as short-term transitional probabilities can shape auditory predictive coding for non-linguistic sound sequences, evidenced by differences in mismatch negativity amplitudes localised to left auditory cortex.

Strengths:

The study integrates linguistics and auditory neuroscience in an interesting interdisciplinary way that may interest linguists as well as neuroscientists. The fact that long-term language experience affects early auditory predictive coding is important for understanding group and individual differences in domain-general auditory perception. It has importance for neurocognitive models of auditory perception (e.g. inclusion of long-term priors), and will be of interest to researchers in linguistics, auditory neuroscience, and the relationship between language and perception. The inclusion of a control condition based on pitch is also a strength.

Weaknesses:

The main weaknesses are the strength of the effects and generalisability. Only two languages were examined, Spanish and Basque. The sample size is also relatively small by today's standards, with N=20 in each group. Furthermore, the crucial effects are all mostly in the .01>P<.05 range, such as the crucial interaction P=.03, although I note the methods used to derive the results are sound and state-of-the-art. It would be nice to see it replicated in the future, with more participants and other languages. It would also have been nice to see behavioural data that could be correlated with neural data to better understand the real-world consequences of the effect.

accelerate! je früher desto besser. overpopulation is real. depopulation must happen.

Pg 10 - Sex is about Intimacy

We have sex because we are intimacy and touch deprived. We rarely engage in abandon, timelessness, or transcendence nowadays (bc we're all material atheists)...but good sex has all of these; it breaks through the mundane and alientation.

Pg 7 - Shallow Consent.

Consent = "You can have sex with the person that wants to have sex with you in the same way that you can share a sandwich with the person who wants to share a sandwich with you." But the issue with this is that sex is not a sandwhich. It is not an isolated instance, rather meaningless object.

https://www.repubblica.it/green-and-blue/2024/08/19/news/clima_ghiacciaio_sul_monte_bianco_ha_perso_300_metri_di_spessore_in_174_anni-423452844/

narrow-minded wie immer. wir müssen die todesrate steigern gegen übervölkerung.<br /> wenn du ne bessere lösung hast, immer her damit... alles andere ist nur bla bla bla.

https://www.liberation.fr/environnement/climat/a-blendecques-avec-les-premiers-deplaces-climatiques-du-pas-de-calais-forces-de-quitter-leurs-maisons-et-leurs-vies-20240817_42FU3OHIMJGE3IVHO5RLO5XP6Q/

https://www.theguardian.com/commentisfree/article/2024/aug/18/inciting-rioters-in-britain-was-a-test-run-for-elon-musk-just-see-what-he-plans-for-america

What about money borrowed without the minister's approval?

OK

OK

OK

OK

Reviewer #3 (Public Review):

Summary:

This study aims to understand gene regulation of the plant bacterial pathogen Pseudomonas syringae. Although the function of some TFs has been characterized in this strain, a global picture of the gene regulatory network remains elusive. The authors conducted a large-scale ChIP-seq analysis, covering 170 out of 301 TFs of this strain, and revealed gene regulatory hierarchy with functional validation of some previously uncharacterized TFs.

Strength:

- This study provides one of the largest ChIP-seq datasets for a single bacterial strain, covering more than half of its TFs. This impressive resource enabled comprehensive systems-level analysis of the TF hierarchy.<br /> - This study identified novel gene regulation and function with validations through biochemical and genetic experiments.<br /> - The authors conducted broad analyses including comparisons between different bacterial strains, providing further insights into the diversity and conservation of gene regulatory mechanisms.

Reviewer #2 (Public Review):

Summary:

The phytopathogenic bacterium Pseudomonas syringae is comprised of many pathovars with different host plant species and has been used as a model organism to study bacterial pathogenesis in plants. Transcriptional regulation is key to plant infection and adaptation to host environments by this bacterium. However, researches have focused on limited number of transcription factors (TFs) that regulate virulence-related pathways. Thus, a comprehensive, systems-level understanding of regulatory interactions between transcription factors in P. syringae has not been achieved.

This study by Sun et al performed ChIP-seq analysis of 170 out of 301 TFs in P. syringae pv. syringae 1448A and used this unique dataset to infer transcriptional regulatory networks in this bacterium. The network analyses revealed hierarchical interactions between TFs, various network motifs, and co-regulation of target genes by TF pairs, which collectively mediate information flow. As discussed, the structure and properties of the P. syringae transcriptional regulatory networks are somewhat different from those identified in humans, yeast, and E. coli, highlighting the significance of this study. Further, the authors made use of the P. syringae transcriptional regulatory networks to find TFs of unknown functions to be involved in virulence-related pathways. For some of these TFs, their target specificity and biological functions, such as motility and biofilm formation, were experimentally validated. Of particular interest is the finding that despite conservation of TFs between P. syringae pv. syringae 1448A, P. syringae pv. tomato DC3000, P. syringae pv. syringae B728a, and P. syringae pv. actinidiae C48, some of the conserved TFs show different repertoires of target genes in these four P. syringae strains.

Strengths:

This study presents a systems-level analysis of transcriptional regulatory networks in relation to P. syringae virulence and metabolism, highlights differences in transcriptional regulatory landscapes of conserved TFs between different P. syringae strains, and develops a user-friendly database for mining the ChIP-seq data generated in this study. These findings and resources will be valuable to researchers in the fields of systems biology, bacteriology, and plant-microbe interactions.

Weaknesses:

No major weaknesses were found, but some of the results may need to be interpreted with caution. ChIP-seq was performed with bacterial strains overexpressing TFs. This may cause artificial binding of TFs to promoters which may not occur when TFs are expressed at physiological levels. Another caution is applied to the interpretation of the biological functions of TFs during plant infection, as biological roles of the tested TFs are mostly based on in vitro experiments.

This work advances our understanding of transcriptional regulation of virulence and metabolic pathways in plant pathogenic bacteria. Solid evidence for the claims is provided by computational analysis of newly generated data on the genome-wide binding of 170 transcription factors to their target genes, together with experimental validation of the biological functions of some of these transcription factors. The findings and resources from this study will be valuable to researchers in the fields of systems biology, bacteriology, and plant-microbe interactions.

eLife assessment

This work advances our understanding of transcriptional regulation of virulence and metabolic pathways in plant pathogenic bacteria. Solid evidence for the claims is provided by computational analysis of newly generated data on the genome-wide binding of 170 transcription factors to their target genes, together with experimental validation of the biological functions of some of these transcription factors. The findings and resources from this study will be valuable to researchers in the fields of systems biology, bacteriology, and plant-microbe interactions.

Est-ce que quelqu'un sait comment faire la technique dans la vidéo pour ouvrir une balise et intercaler directement le contenu que l'on veut à l'intérieur ?

нормы общения

See videos in Video Library:

"Cover of the CPU dismounting" Watch Video/Animation

"Display dismounting" Watch Video/Animation

"Display mounting" Watch Video/Animation

Humidity control is achieved through monitoring the negative pressure differential behind the top fan wheel , between the centre and periphery of the fan. The pressure differential behind the fan varies as the atmosphere in the cook chamber varies.

Generally:

• The less humid, the higher the voltage of P1

• The higher the fan rpm, the higher the voltage of P1

During the calibration process the CPU is educated with specific voltages from the P1 pressure switch for known fan speeds, temperature and humidity.

• The Fan motor inverter sends fan speed data to the CPU through the Bus system.

• B4 (Humidity) Thermocouple connected to terminal X5 on the A10 I/O board and situated behind the top fan (secured on the outside of the oven liner) measures the air temp behind the fan and sends this data to the CPU from A10 via the Bus system. *

• P1 pressure sensor connected to terminal X1 on the A10 I/O board measures the differential pressure across the back of the fan wheel, converts the pressure reading to a voltage and delivers the results to the CPU from A10 via the Bus system, where they are stored both in the CPU and on the SD card (iCombi Pro) and External Eprom (iCombi Classic). These clearly defined reference voltages, created during the calibration process and stored in the CPU, enable the iCombi to create any specific climate requested by the chef.

Example: 160 degrees C – 60% humidity – Fan speed 1000 rpm

• The processor will ensure a fan speed of 1000 rpm via the communication with the fan motor inverter, via the bus system.

• Y5 humidity valve is closed, and heat is applied via the hot air heating elements or Gas heat exchanger, to 160 deg C. Communicated to the CPU by B4 thermocouple via A10 and the Bus system.

• Humidity is delivered to the chamber via the steam generator and pressure sensor P1 monitors the changing negative differential pressure behind the fan until a predetermined value calculated from data stored during the calibration process is reached.

If the signal from P1 pressure sensor overruns the predetermined value, the steam generator will shut down, Y5 Humidity valve will open and negative pressure behind the fan wheel will draw ambient air into the oven pushing humidity down into the control box and out through the vent stack..

As this occurs the P1 sensor voltage signal will indicate the reduction in humidity, Y5 will close again ad the steam generator will start again.

To be clear!

"The difference between Steam Control and Humidity control is that with Humidity control, the chef can select a value for cabinet humidity below the steam saturation point, for example 70 %".

cos(pi-m<ABD) is used here because when you arrange the vectors tail touching tail on the origin, the resulting angle between them is supplementary to m<ABD.

Length of BD is unknown? So what will let us know, trig?

Because I have my fingertip with the position and also angle, the vector is determined. So it is actually the next joint up that angle can vary.

magnitudes

my target coordinates are the end of the finger (finger-tip) at (x,y) with constraints with a 105 degree angle.

How can we make it 3D vectors? What are the differences?

Already shown that simple geometry will not cut it -- study 2 things. Linear algebra, and trigonometry.

noch übergreifender

übergreifend

new one

test @gledonne

This is important. Building a solid "B2B2C" partnership model is the unique prerogative of a marketplace.

Farmdrop pioneered this model in London (and later Bristol and other areas) before going into administration. They made a significant effort to 'tell the story' of the producers and the products, with photography, interviews, biographies and much more available for each producer.

"Farmdrop was an online grocer with a focus on farm-to-table food sourced from local farmers, fishermen, and other producers; as well as ethically sourced household products." - Wikipedia

Important to understand the Forces of Progress here.

eLife assessment

This useful study reports that the Drosophila transcription factor sisterless A (sisA) regulates the expression of Sex-lethal (Sxl) in female germ cells. The data supporting claims regarding the genetic requirement of sisA are convincing, but the characterization of the cis-regulatory elements controlling Sxl expression in the female germline is viewed as incomplete. The work will be of significant interest to colleagues studying reproductive biology and sex determination.

Reviewer #1 (Public Review):

Summary:

In Drosophila melanogaster, expression of Sex-lethal (Sxl) protein determines sexual identity and drives female development. Functional Sxl protein is absent from males where splicing includes a termination codon-containing "poison" exon. Early during development, in the soma of female individuals, Sxl expression is initiated by an X chromosome counting mechanism that activates the Sxl establishment promoter (SxlPE) to produce an initial amount of Sxl protein. This then suppresses the inclusion of the "poison" exon, directing the constructive splicing of Sxl transcripts emerging from the Sxl maintenance promotor (SxlPM) which is activated at a later stage during development irrespective of sex. This autoregulatory loop maintains Sxl expression and commits to female development.

Sxl also determines the sexual identity of the germline. Here Sxl expression generally follows the same principles as in somatic tissues, but the way expression is initiated differs from the soma. This regulation has so far remained elusive.

In the presented manuscript, Goyal et al. show that activation of Sxl expression in the germline depends on additional regulatory DNA sequences, or sequences different from the ones driving initial Sxl expression in the soma. They further demonstrate that sisterless A (sisA), a transcription factor that is required for activation of Sxl expression in the soma, is also necessary, but not sufficient, to initiate the expression of functional Sxl protein in female germ cells. sisA expression precedes Sxl induction in the germline and its ablation by RNAi results in impaired expression of Sxl, formation of ovarian tumors, and germline loss, phenocopying the loss of Sxl. Intriguingly, this phenotype can be rescued by the forced expression of Sxl, demonstrating that the primary function of sisA in the germline is the induction of Sxl expression.

Strengths:

The clever design of probes (for RNA FISH) and reporters allowed the authors to dissect Sxl expression from different promoters to get novel insight into sex-specific gene regulation in the germline. All experiments are carefully controlled. Since Sxl regulation differs between the soma and the germline, somatic tissues provide elegant internal controls in many experiments, ensuring e.g. functionality of the reporters. Similarly, animals carrying newly generated alleles (e.g. genomic tagging of the Sxl locus) are fertile and viable, demonstrating that the genetic manipulation does not interfere with protein function. The conclusions drawn from the experimental data are sound and advance our understanding of how Sxl expression is induced in the female germline.

Weaknesses:

The assays employed by the authors provide valuable information on when Sxl promoters become active. However, since no information on the stability of the gene products (i.e. RNA and protein) is available, it remains unclear when the SxlPE promoter is switched off in the germline (conceptually it only needs to be active for a short time period to initiate production of functional Sxl protein). As correctly stated by the authors, the persisting signals observed in the germline might therefore not reflect the continuous activity of the SxlPE promoter.

Mapping of regulatory elements and their function: SxlPE with 1.5 kb of flanking upstream sequence is sufficient to recapitulate early Sxl expression in the soma. The authors now provide evidence that beyond that, additional DNA sequences flanking the SxlPE promoter are required for germline expression. However, a more precise mapping was not performed. Also, due to technical limitations, the authors could not precisely map the sisA binding sites. Since this protein is also involved in the somatic induction of Sxl, its binding sites likely reside in the region 1.5kb upstream of the SxlPE promoter, which has been reported to be sufficient for somatic regulation. The regulatory role of the sequences beyond SxlPE-1.5kb therefore remains unaddressed and it remains to be investigated which trans-acting factor(s) exert(s) its/their function(s) via this region.

The central question of how Sxl expression is initiated and controlled in the germline still remains unanswered. Since sisA is zygotically expressed in both the male and the female germline (Figure 4D), it is unlikely the factor that restricts Sxl expression to the female germline.

How does weak expression of Sxl in male tissues or expression above background after knockdown of sisA reconcile with the model that an autoregulatory feedback loop enforces constant and clonally inheritable Sxl expression once Sxl is induced? Is the current model for Sxl expression too simple or are we missing additional factors that modulate Sxl expression (such as e.g. Sister of Sex-lethal)? While I do not expect the authors to answer these questions, I would expect them to appropriately address these intriguing aspects in the discussion.

Reviewer #2 (Public Review):

Summary:

The authors wanted to determine whether cis-acting factors of Sxl - two different Sxl promoters in somatic cells - regulate Sxl in a similar way in germ cells. They also wanted to determine whether trans-acting factors known to regulate Sxl in the soma also regulate Sxl in the germline.

Regarding the cis-acting factors, they examine the Sxl "establishment promoter" (SxlPE) that is activated in female somatic cells by the presence of two X chromosomes. Slightly later in development, dosage compensation equalizes X chromosome expression in males and females and so X chromosomes can no longer be counted. The second Sxl promoter is the "maintenance promoter," (SxlPM), which is activated in both sexes. The mRNA produced from the maintenance promoter has to be alternatively splicing from early Sxl protein generated earlier in development by the PE. This leads to an autoregulatory loop that maintains Sxl expression in female somatic cells. The authors used fluorescent in situ hybridization (FISH) with oligopaints to determine the temporal activation of the PE or PM promoters. They find that - unlike the soma - the PE does not precede the PM and instead is activated contemporaneously or later than the PM - this is confusing with the later results (see below). Next, they generated transcriptional reporter constructs containing large segments of the Sxl locus, the 1.5 kb used in somatic studies, a 5.2 kb reporter, and a 10.2 kb. Interestingly the 1.5 kb reporter that was reported to recapitulate Sxl expression in soma and germline was not observed by the authors. The 5.2 kb reporter was observed in female somatic cells but not in germ cells. Only when they include an additional 5 kb downstream of the 5.2 kb reporter (here the 10.2 kb reporter) they did see expression in germ cells but this occurred at the L1 stages. Their data indicate that Sxl activity in the germ requires different cis-regulation than the soma and that the PE is activated later in germ cells than in somatic cells. The authors next use gene editing to insert epitope tags in two distinct strains in the hopes of creating an early Sxl and a later Sxl protein derived from the PE and PM, respectively. The HA-tagged protein from the PE was seen in somatic cells but never in the germline, possibly due to very low expression. The FLAG-tagged late Sxl protein is observed in L2 germ cells. Because the early HA-Sxl protein is not perceptible in germ cells, it is not possible to conclude its role in the germline. However, because late FLAG-Sxl was only observed in L2 germ cells and the PE was detected in L1, this leaves open the possibility that PE produces early HA-Sxl (which currently cannot be detected), which then alternatively splices the transcript from the PM. In other words, the soma and germline could have a similar temporal relationship between the two Sxl promoters. While I agree with the authors about this conclusion, the earlier work with the oligopaints leads to the conclusion that SE is active after PM. This is confusing.

Next, the authors wanted to turn their attention to the trans-acting factors that regulate Sxl in the soma, including Sisterless A (SisA), SisB, Runt, and the JAK/STAT ligand Unpaired. Using germline RNAi, the authors found that only knockdown of SisA causes ovarian tumors, similar to the loss of Sxl, suggesting that SisA regulates Sxl (ie the PE) in both the soma and the germline. They generated a SisA null allele using CRISPR/Cas9 and these animals had ovarian tumors and germ cell-less ovaries. FISH revealed that sisA is activated in primordial germ cells in stages 3-6 before the activation of Sxl. They used CRISPR-Cas9 to generate an endogenously-tagged SisA and found that tagged SisA was expressed in stage 3-6 PCGs, which is consistent with activating PE in the germline. They showed that sisA is upstream of Sxl as germline depletion of sisA led to a significant decrease in expression from the 10.2 kb PE reporter and in SXL protein. The authors could rescue the ovarian tumors and loss of Sxl protein upon germline depletion of sisA by supplying Sxl from another protein (the otu promoter). These data indicate that sisA is necessary for Sxl activation in the germline. However, ectopic sisA in germ cells in the testis did not lead to ectopic Sxl, suggesting that sisA is not sufficient to activate Sxl in the germline.

Strengths:

(1) The genetic and genomic approaches in this study are top-notch and they have generated reagents that will be very useful for the field.

(2) Excellent use of powerful approaches (oligo paint, reporter constructs, CRISPR-Cas9 alleles).

(3) The combination of state of art approaches and quantification of phenotypes allows the authors to make important conclusions.

Weaknesses:

(1) Confusion in line 127 (this indicates that SxlPE is not activated before SxlPM in the germline) about PE not being activated before the PM in the germline when later figures show that PE is activated in L1 and late Sxl protein is seen in L2. It would be helpful to the readers if the authors edited the text to avoid this confusion. Perhaps more explanation of the results at specific points would be helpful.

Reviewer #3 (Public Review):

Summary:

The mechanisms governing the initial female-specific activation of Sex-lethal (Sxl) in the soma, the subsequent maintenance of female-specific expression and the various functions of Sxl in somatic sex determination and dosage compensation are well documented. While Sxl is also expressed in the female germline where it plays a critical role during oogenesis, the pathway that is responsible for turning Sxl on in germ cells has been a long-standing mystery. This manuscript from Goyal et al describes studies aimed at elucidating the mechanism(s) for the sex-specific activation of the Sex-lethal (Sxl) gene in the female germline of Drosophila.

In the soma, the Sxl establishment promoter, Sxl-Pe, is regulated in pre-cellular blastoderm embryos in somatic cells by several X-linked transcription factors (sis-a, sis-b, sis-c and runt). At this stage of development, the expression of these transcription factors is proportional to gene dose, 2x females and 1x in males. The cumulative two-fold difference in the expression of these transcription factors is sufficient to turn Sxl-Pe on in female embryos. Transcripts from the Sxl-Pe promoter encode an "early" version of the female Sxl protein, and they function to activate a splicing positive autoregulatory loop by promoting the female-specific splicing of the initial pre-mRNAs derived from the Sxl maintenance promoter, Sxl-Pm (which is located upstream of Sxl-Pm). These female Sxl-Pm mRNAs encode a Sxl protein with a different N-terminus from the Sxl-Pe mRNAs, and they function to maintain female-specific splicing in the soma during the remainder of development.

In this manuscript, the authors are trying to understand how the Sxl-Pm positive autoregulatory loop is established in germ cells. If Sxl-Pe is used and its activation precedes Sxl-Pm as is true in the soma, they should be able to detect Sxl-Pe transcripts in germ cells before Sxl-Pm transcripts appear. To test this possibility, they generated RNA FISH probes complementary to the Sxl-Pe first exon (which is part of an intron sequence in the Sxl-Pm transcript) and to a "common sequence" that labels both Sxl-Pe and Sxl-Pm transcripts. Transcripts labeled by both probes were detected in germ cells beginning at stage 5 (and reaching a peak at stage 10), so either the Sxl-Pm and Sxl-Pe promoters turn on simultaneously, or Sxl-Pe is not active.

They next switched to Sxl-Pe reporters. The first Sxl-Pe:gfp reporter they used has a 1.5 kb upstream region which in other studies was found to be sufficient to drive sex-specific expression in the soma of blastoderm embryos. Also like the endogenous Sxl gene it is not expressed in germ cells at this early stage. In 2011, Hashiyama et al reported that this 1.5 kb promoter fragment was able to drive gfp expression in Vasa-positive germ cells later in development in stage 9/10 embryos. However, because of the high background of gfp in the nearby soma, their result wasn't especially convincing. Though they don't show the data, Goyal et al indicated that unlike Hashiyama et al they were unable to detect gfp expressed from this reporter in germ cells. Goyal et al extended the upstream sequences in the reporter to 5 kb, but they were still unable to detect germline expression of gfp.

Goyal et al then generated a more complicated reporter which extends 5 kb upstream of the Sxl-Pe start site and 5 kb downstream-ending at or near 4th exon of the Sxl-Pm transcript (the Sxl-Pe10 kb reporter). (The authors were not explicit as to whether the 5 kb downstream sequence extended beyond the 4th exon splice junction-in which case splicing could potentially occur with an upstream exon(s)-or terminated prior to the splice junction as seems to be indicated in their diagram.) With this reporter, they were able to detect sex-specific gfp expression in the germline beginning in L1 (first instar larva). With the caveat that gfp detection might be delayed compared to the onset of reporter activation, these findings indicated that the sequences in the reporter are able to drive sex-specific transcription in the germline at least as early as L1.

The authors next tagged the N-terminal end of the Sxl-Pe protein with HA (using Crispr/Cas9) and the N-terminal end of Sxl-Pm protein with Flag. They report that the HA-Sxl-Pe protein is first detected in the soma at stage 9 of embryogenesis. Somatic HA-Sxl-Pe protein persists into L1, but is no longer detected in L2. However, while somatic HA-Sxl-Pe protein is detected, they were unable to detect HA-Sxl-Pe protein in germ cells. In the case of FLAG-Sxl-Pm, it could first be detected in L2 germ cells indicating that at this juncture the Sxl-positive autoregulatory loop has been activated. This contrasts with Sxl-Pm transcripts which are observed in a few germ cells at stage 5 of embryogenesis, and in most germ cells by stage 10. The authors propose (based on the expression pattern of the Sxl-Pe10kb reporter and the appearance of Flag-Sxl-Pm protein) that Sxl-Pe comes on in germ cells in L1, and that the Sxl-Pe protein activates the female splicing of Sxl-Pm transcripts, giving detectable Flag-Sxl-Pm proteins beginning in L2.

To investigate the signals that activate Sxl-Pe in germ cells, the authors tested four of the X-linked genes (sis-a, sis-b, sis-c, and runt) that function to activate Sxl-Pe in the soma in early embryos. RNAi knockdown of sis-b, sis-c, and runt had no apparent effect on oogenesis. In contrast, knockdown of sis-a resulted in tumorous ovaries, a phenotype associated with Sxl mutations. (Three different RNAi transgenes were tested-two gave this phenotype, the third did not.) Sxl-Pe10kb reporter activity in L1 female germ cells is also dependent on sis-A.

Several approaches were used to confirm a role for sis-a in a) oogenesis and b) the activation of the Sxl-Pm autoregulatory loop. They showed that sis-a germline clones (using tissue-specific Crispr/Cas9 editing) resulted in the tumorous ovary phenotype and reduced the expression of Sxl protein in these ovaries. They found that sis-a transcripts and GFP-tagged Sis-A protein are present in germ cells. Finally, they showed tumorous ovary phenotype induced by germline RNAi knockdown of sis-a can be partially rescued by expressing Sxl in the germ cells.

Critique:

While this manuscript addresses a longstanding puzzle - the mechanism activating the Sxl autoregulatory loop in female germ cells-and likely identified an important germline transcriptional activator of Sxl, sis-a, the data that they've generated doesn't make a compelling story. At every step, there are puzzle pieces that don't fit the narrative. In addition, some of their findings are inconsistent with many previous studies.

(1) The authors used RNA FISH to time the expression of Sxl-Pe and Sxl-Pm transcripts in germ cells. Transcripts complementary to Sxl-Pe and Sxl-Pm were detected at the same time in embryos beginning at stage 5. This is not a definitive experiment as it could mean a) that Sxl-Pe and Sxl-Pm turn on at the same time, b) that Sxl-Pe comes on after Sxl-Pm (as suggested by the Sxl-Pe10kb reporter) or c) Sxl-Pe never comes on.

(2) Hashiyama et al reported that they detected gfp expression in stage 9/10 germ cells from a 1.5 kb Sxl-Pe-gfp. As noted above, this result wasn't entirely convincing and thus it isn't surprising that Goyal et al were unable to reproduce it. Extending the upstream sequences to just before the 1st exon of Sxl-Pm transcripts also didn't give gfp expression in germ cells. Only when they added 5 kb downstream did they detect gfp expression. However, from this result, it isn't possible to conclude that the Sxl-Pe promoter is actually driving gfp expression in L1 germ cells. Instead, the Sxl promoter active in the germ line could be anywhere in their 10 kb reporter.

(3) At least one experiment suggests that Sxl-Pe never comes on in germ cells. The authors tagged the N-terminus of the Sxl-Pe protein with HA and the N-terminus of the Sxl-Pm protein with Flag. Though they could detect HA-Sxl-Pe protein in the soma, they didn't detect it in germ cells. On the other hand, the Flag-Sxl-Pm protein was detected in L2 germ cells (but not earlier). These results would more or less fit with those obtained for the 10 kb reporter and would support the following model: Prior to L1, Sxl-Pm transcripts are expressed and spliced in the male pattern in both male and female germ cells. During L1, Sxl protein expressed via a mechanism that depends upon a 10 kb region spanning Sxl-Pe (but not on Sxl-Pe) is produced and by L2 there are sufficient amounts of this protein to switch the splicing of Sxl-Pm transcripts from a male to a female pattern-generating Flag-tagged Sxl-Pm protein.

(4) The 10kb reporter is sex-specific, but not germline-specific. The levels of gfp in female L1 somatic cells are equal to if not greater than those in L1 female germ cells. That the Sxl-Pe10kb reporter is active in the soma complicates the conclusion that it represents a germ line-specific promoter. Germline activity is, however, sensitive to sis-A knockdowns which is plus. Presumably, somatic expression of the reporter wouldn't be sensitive to a (late) sis-A knockdown- but this wasn't shown.

(5) Their results with the HA-Sxl-Pe protein don't fit with many previous studies-assuming that the authors have explained their results properly. They report that HA-Sxl-Pe protein is first detected in the soma at stage 9 of embryogenesis and that it then persists till L2. However, previous studies have shown that Sxl-Pe transcripts and then Sxl-Pe proteins are first detected in ~NC11-NC12 embryos. In RNase protection experiments, the Sxl-Pe exon is observed in 2-4 hr embryos, but not detected in 5-8 hr, 14-12 hr, L1, L2, L3, or pupae. Northerns give pretty much the same picture. Western blots also show that Sxl-Pe proteins are first detectable around the blastoderm stage. So it is not at all clear why HA-Sxl-Pe proteins are first observed at stage 9 which, of course, is well after the time that the Sxl-Pm autoregulatory loop is established.

Given the obvious problems with the initial timing of somatic expression described here, it is hard to know what to make of the fact that HA-tagged Sxl-Pe proteins aren't observed in germ cells.

As for the presence of HA-Sxl-Pe proteins later than expected: While RNase protection/Northern experiments showed that Sxl-Pe mRNAs are expressed in 2-4 hr embryos and disappear thereafter, one could argue from the published Western experiments that the Sxl-PE proteins expressed at the blastoderm stage persist at least until the end embryogenesis, though perhaps at somewhat lower levels than at earlier points in development. So the fact that Goyal et al were able to detect HA-Sxl-Pe proteins in stage 9 embryos and later on in L1 larva probably isn't completely unexpected. What is unexpected is that the HA-Sxl-Pe proteins weren't present earlier.

(6) The authors use RNAi and germline clones to demonstrate that sis-A is required for proper oogenesis: when sis-A activity is compromised in germ cells, i) tumorous ovary phenotypes are observed and ii) there is a reduction in the expression of Sxl-Pm protein. They are also able to rescue the phenotypic effects of sis-a knockdown by expressing a Sxl-Pm protein. While the experiments indicating sis-a is important for normal oogenesis and that at least one of its functions is to ensure that sufficient Sxl is present in the germline stem cells seem convincing, other findings would make the reader wonder whether Sis-A is actually functioning (directly) to activate Sxl transcription from promoter X.

The authors show that sis-a mRNAs and proteins are expressed in stage 3-5 germ cells (PGCs). This is not unexpected as the X-linked transcription factors that turn Sxl-Pe on are expressed prior to nuclear migration, so their protein products should be present in early PGCs. The available evidence suggests that their transcription is shut down in PGCs by the factors responsible for transcriptional quiescence (e.g., nos and pgc) in which case transcripts might be detected in only one or two PGC-which fits with their images. However, it is hard to believe that expression of Sis-A protein in pre-blastoderm embryos is relevant to the observed activation of the Sxl-Pm autoregulatory loop hours later in L2 larva.

It is also not clear how the very low level of gfp-Sis-A seen in only a small subset of migrating germ cells in stage 10 embryos (Figure S6) would be responsible for activating the Sxl-Pe10kb reporter in L1. It seems likely that the small amount of protein seen in stage 10 embryos is left over from the pre-cellular blastoderm stage. In this case, it would not be surprising to discover that the residual protein is present in both female and male stage 10 germ cells. This would raise further doubts about the relevance of the gfp-Sis-A at these early stages.

In fact, given the evidence presented implicating sis-a in activating Sxl, (the germline activation of the Sxl-Pe10kb reporter, the RNAi knockdowns, and the germ cell-specific sis-a clones) it is clear that the sis-A RNAs and proteins seen in pre-cellular blastoderm PGCs aren't relevant. The germline clone experiment (and also the RNAi knockdowns) indicates that sis-A must be transcribed in germ cells after Cas9 editing has taken place. Presumably, this would be after transcription is reactivated in the germline (~stage 10) and after the formation of the embryonic gonad (stage 14) so that the somatic gonadal cells can signal to the germ cells. With respect to the reporter, the relevant time frame for showing that sis-A is present in germ cells would be even later in L1.

(7) As noted above, the data in this manuscript do not support the idea that Sxl-Pe proteins activate the Sxl-Pm female splicing in the germline. Flybase indicates that there is at least one other Sxl promoter that could potentially generate a transcript that includes the male exon but still could encode a Sxl protein. This promoter "Sxl-Px" is located downstream of Sxl-Pm and from its position it could have been included in the authors' 10 kb reporter. The reported splicing pattern of the endogenous transcript skips exon2, and instead links an exon just downstream of Sxl-Px to the male exon. The male exon is then spliced to exon4. If the translation doesn't start and end at one of the small upstream orfs in the exons close to Sxl-Px and the male exon, a translation could begin with an AUG codon in exon4 that is in frame with the Sxl protein coding sequence. This would produce a Sxl protein that lacks aa sequences from N-terminus, but still retains some function.

Another possible explanation for how gfp is expressed from the 10 kb reporter is that the transcript includes the "z" exon described by Cline et al., 2010.

Author response:

Public Reviews: 

Reviewer #1 (Public Review): 

Summary: 

In Drosophila melanogaster, expression of Sex-lethal (Sxl) protein determines sexual identity and drives female development. Functional Sxl protein is absent from males where splicing includes a termination codon-containing "poison" exon. Early during development, in the soma of female individuals, Sxl expression is initiated by an X chromosome counting mechanism that activates the Sxl establishment promoter (SxlPE) to produce an initial amount of Sxl protein. This then suppresses the inclusion of the "poison" exon, directing the constructive splicing of Sxl transcripts emerging from the Sxl maintenance promotor (SxlPM) which is activated at a later stage during development irrespective of sex. This autoregulatory loop maintains Sxl expression and commits to female development. 

Sxl also determines the sexual identity of the germline. Here Sxl expression generally follows the same principles as in somatic tissues, but the way expression is initiated differs from the soma. This regulation has so far remained elusive. 

In the presented manuscript, Goyal et al. show that activation of Sxl expression in the germline depends on additional regulatory DNA sequences, or sequences different from the ones driving initial Sxl expression in the soma. They further demonstrate that sisterless A (sisA), a transcription factor that is required for activation of Sxl expression in the soma, is also necessary, but not sufficient, to initiate the expression of functional Sxl protein in female germ cells. sisA expression precedes Sxl induction in the germline and its ablation by RNAi results in impaired expression of Sxl, formation of ovarian tumors, and germline loss, phenocopying the loss of Sxl. Intriguingly, this phenotype can be rescued by the forced expression of Sxl, demonstrating that the primary function of sisA in the germline is the induction of Sxl expression. 

Strengths: 

The clever design of probes (for RNA FISH) and reporters allowed the authors to dissect Sxl expression from different promoters to get novel insight into sex-specific gene regulation in the germline. All experiments are carefully controlled. Since Sxl regulation differs between the soma and the germline, somatic tissues provide elegant internal controls in many experiments, ensuring e.g. functionality of the reporters. Similarly, animals carrying newly generated alleles (e.g. genomic tagging of the Sxl locus) are fertile and viable, demonstrating that the genetic manipulation does not interfere with protein function. The conclusions drawn from the experimental data are sound and advance our understanding of how Sxl expression is induced in the female germline. 

Weaknesses: 

The assays employed by the authors provide valuable information on when Sxl promoters become active. However, since no information on the stability of the gene products (i.e. RNA and protein) is available, it remains unclear when the SxlPE promoter is switched off in the germline (conceptually it only needs to be active for a short time period to initiate production of functional Sxl protein). As correctly stated by the authors, the persisting signals observed in the germline might therefore not reflect the continuous activity of the SxlPE promoter. 

Mapping of regulatory elements and their function: SxlPE with 1.5 kb of flanking upstream sequence is sufficient to recapitulate early Sxl expression in the soma. The authors now provide evidence that beyond that, additional DNA sequences flanking the SxlPE promoter are required for germline expression. However, a more precise mapping was not performed. Also, due to technical limitations, the authors could not precisely map the sisA binding sites. Since this protein is also involved in the somatic induction of Sxl, its binding sites likely reside in the region 1.5kb upstream of the SxlPE promoter, which has been reported to be sufficient for somatic regulation. The regulatory role of the sequences beyond SxlPE-1.5kb therefore remains unaddressed and it remains to be investigated which trans-acting factor(s) exert(s) its/their function(s) via this region. 

We agree that a more precise mapping of the essential elements within the 10.2 kb reporter is an important direction in which to proceed. Unfortunately, this is out of the scope of the current manuscript given current lab personnel. In regard to the 1.5 kb promoter that activates SxlPE in the soma, we do not feel that the Sisa binding sites are necessarily in this region. It is important to note that, while the 1.5 kb promoter is sufficient for female-specific expression in the soma, it may not contain all of the regulatory elements that normally regulate PE from the endogenous locus. Activation of PE in the soma is thought to be regulated by a combination of positive-acting factors (SisA, SisB, etc.) and repressive factors (e.g. Dpn) that set a threshold for PE activation. Much more work would need to be done to determine whether all of these factors bind to the 1.5 kb promoter, or whether additional sequences are also involved to control the proper timing and robustness of normal Sxl PE activation in the soma.

The central question of how Sxl expression is initiated and controlled in the germline still remains unanswered. Since sisA is zygotically expressed in both the male and the female germline (Figure 4D), it is unlikely the factor that restricts Sxl expression to the female germline. 

X chromosome “counting” elements like SisA are always expressed in both males and females, but it is thought that the 2X does of them in females activates PE, while the 1X does in males does not. Thus, we do expect SisA to be expressed in both males and females as we observed.

How does weak expression of Sxl in male tissues or expression above background after knockdown of sisA reconcile with the model that an autoregulatory feedback loop enforces constant and clonally inheritable Sxl expression once Sxl is induced? Is the current model for Sxl expression too simple or are we missing additional factors that modulate Sxl expression (such as e.g. Sister of Sex-lethal)? While I do not expect the authors to answer these questions, I would expect them to appropriately address these intriguing aspects in the discussion. 

It is difficult to know what is “background” and what is actual weak Sxl expression in males. We agree that, if it is real, then why it doesn’t activate autoregulation of the Sxl PM transcript is mysterious. And yes, the current model for female-specific expression of Sxl in the soma may well be incomplete. Sxl PM transcript is present in the testis based on community RNA-seq data and our own analysis of male vs. female bam-mutant gonads (PMID 31329582), but it is at lower levels. Whether the lower level in the testis is due to tissue differences or sex-specific regulation of RNA levels is unknown. Our observations that the HA-tagged Sxl Early protein remains present in somatic cells in L1 larvae, and that GFP expression from the 10.2 kb Sxl PE-GFP can be detected in the soma until L2 could either be due to perdurance of the protein products, or continued sex-specific expression of PE long after the time that it was thought to shut off. This is also long after dosage compensation should have equalized the expression of X chromosome gene expression, meaning that X chromosomes can no longer be “counted” by factors like SisA and SisB. Thus, sex-specific expression of PE at this time would require another mechanism besides the current model (such as feedback regulation of Sxl PE transcription from downstream factors).

Reviewer #2 (Public Review): 

Summary: 

The authors wanted to determine whether cis-acting factors of Sxl - two different Sxl promoters in somatic cells - regulate Sxl in a similar way in germ cells. They also wanted to determine whether trans-acting factors known to regulate Sxl in the soma also regulate Sxl in the germline. 

Regarding the cis-acting factors, they examine the Sxl "establishment promoter" (SxlPE) that is activated in female somatic cells by the presence of two X chromosomes. Slightly later in development, dosage compensation equalizes X chromosome expression in males and females and so X chromosomes can no longer be counted. The second Sxl promoter is the "maintenance promoter," (SxlPM), which is activated in both sexes. The mRNA produced from the maintenance promoter has to be alternatively splicing from early Sxl protein generated earlier in development by the PE. This leads to an autoregulatory loop that maintains Sxl expression in female somatic cells. The authors used fluorescent in situ hybridization (FISH) with oligopaints to determine the temporal activation of the PE or PM promoters. They find that - unlike the soma - the PE does not precede the PM and instead is activated contemporaneously or later than the PM - this is confusing with the later results (see below). Next, they generated transcriptional reporter constructs containing large segments of the Sxl locus, the 1.5 kb used in somatic studies, a 5.2 kb reporter, and a 10.2 kb. Interestingly the 1.5 kb reporter that was reported to recapitulate Sxl expression in soma and germline was not observed by the authors. The 5.2 kb reporter was observed in female somatic cells but not in germ cells. Only when they include an additional 5 kb downstream of the 5.2 kb reporter (here the 10.2 kb reporter) they did see expression in germ cells but this occurred at the L1 stages. Their data indicate that Sxl activity in the germ requires different cis-regulation than the soma and that the PE is activated later in germ cells than in somatic cells. The authors next use gene editing to insert epitope tags in two distinct strains in the hopes of creating an early Sxl and a later Sxl protein derived from the PE and PM, respectively. The HA-tagged protein from the PE was seen in somatic cells but never in the germline, possibly due to very low expression. The FLAG-tagged late Sxl protein is observed in L2 germ cells. Because the early HA-Sxl protein is not perceptible in germ cells, it is not possible to conclude its role in the germline. However, because late FLAG-Sxl was only observed in L2 germ cells and the PE was detected in L1, this leaves open the possibility that PE produces early HA-Sxl (which currently cannot be detected), which then alternatively splices the transcript from the PM. In other words, the soma and germline could have a similar temporal relationship between the two Sxl promoters. While I agree with the authors about this conclusion, the earlier work with the oligopaints leads to the conclusion that SE is active after PM. This is confusing. 

The temporal relationship between Sxl PE and Sxl PM in the germline is indeed confusing. One source of confusion comes from whether one is discussing Sxl protein production or promoter activity. As the reviewer nicely summarizes, our transcription analysis with oligopaints indicates that, unlike in the soma, Sxl PE is NOT on in the germline prior to PM. Our other data indicate that PE is instead likely only active well after transcription from PM has begun. However, this still means that the temporal order of the EARLY and LATE Sxl proteins can be the same as the soma. Even if PM is active well before PE in the germline, the PE transcript cannot produce any functional protein in the absence of being alternatively spliced by the Sxl protein (Sxl autoregulation). Thus, even if PM is active before PE in the germline, we would not expect to observe any LATE Sxl protein until the PE promoter comes on, and produces a pulse of EARLY Sxl protein. The fact that we observe LATE Sxl protein at L2 is consistent with our observation that the 10.2 kb Sxl PE reporter is active at L1. We will attempt to explain all of this better in a revised manuscript.

Next, the authors wanted to turn their attention to the trans-acting factors that regulate Sxl in the soma, including Sisterless A (SisA), SisB, Runt, and the JAK/STAT ligand Unpaired. Using germline RNAi, the authors found that only knockdown of SisA causes ovarian tumors, similar to the loss of Sxl, suggesting that SisA regulates Sxl (ie the PE) in both the soma and the germline. They generated a SisA null allele using CRISPR/Cas9 and these animals had ovarian tumors and germ cell-less ovaries. FISH revealed that sisA is activated in primordial germ cells in stages 3-6 before the activation of Sxl. They used CRISPR-Cas9 to generate an endogenously-tagged SisA and found that tagged SisA was expressed in stage 3-6 PCGs, which is consistent with activating PE in the germline. They showed that sisA is upstream of Sxl as germline depletion of sisA led to a significant decrease in expression from the 10.2 kb PE reporter and in SXL protein. The authors could rescue the ovarian tumors and loss of Sxl protein upon germline depletion of sisA by supplying Sxl from another protein (the otu promoter). These data indicate that sisA is necessary for Sxl activation in the germline. However, ectopic sisA in germ cells in the testis did not lead to ectopic Sxl, suggesting that sisA is not sufficient to activate Sxl in the germline. 

Strengths: 

(1) The genetic and genomic approaches in this study are top-notch and they have generated reagents that will be very useful for the field. 

(2) Excellent use of powerful approaches (oligo paint, reporter constructs, CRISPR-Cas9 alleles). 

(3) The combination of state of art approaches and quantification of phenotypes allows the authors to make important conclusions. 

Weaknesses: 

(1) Confusion in line 127 (this indicates that SxlPE is not activated before SxlPM in the germline) about PE not being activated before the PM in the germline when later figures show that PE is activated in L1 and late Sxl protein is seen in L2. It would be helpful to the readers if the authors edited the text to avoid this confusion. Perhaps more explanation of the results at specific points would be helpful. 

We agree--see response above.

Reviewer #3 (Public Review): 

Summary: 

The mechanisms governing the initial female-specific activation of Sex-lethal (Sxl) in the soma, the subsequent maintenance of female-specific expression and the various functions of Sxl in somatic sex determination and dosage compensation are well documented. While Sxl is also expressed in the female germline where it plays a critical role during oogenesis, the pathway that is responsible for turning Sxl on in germ cells has been a long-standing mystery. This manuscript from Goyal et al describes studies aimed at elucidating the mechanism(s) for the sex-specific activation of the Sex-lethal (Sxl) gene in the female germline of Drosophila. 

In the soma, the Sxl establishment promoter, Sxl-Pe, is regulated in pre-cellular blastoderm embryos in somatic cells by several X-linked transcription factors (sis-a, sis-b, sis-c and runt). At this stage of development, the expression of these transcription factors is proportional to gene dose, 2x females and 1x in males. The cumulative two-fold difference in the expression of these transcription factors is sufficient to turn Sxl-Pe on in female embryos. Transcripts from the Sxl-Pe promoter encode an "early" version of the female Sxl protein, and they function to activate a splicing positive autoregulatory loop by promoting the female-specific splicing of the initial pre-mRNAs derived from the Sxl maintenance promoter, Sxl-Pm (which is located upstream of Sxl-Pm). These female Sxl-Pm mRNAs encode a Sxl protein with a different N-terminus from the Sxl-Pe mRNAs, and they function to maintain female-specific splicing in the soma during the remainder of development. 

In this manuscript, the authors are trying to understand how the Sxl-Pm positive autoregulatory loop is established in germ cells. If Sxl-Pe is used and its activation precedes Sxl-Pm as is true in the soma, they should be able to detect Sxl-Pe transcripts in germ cells before Sxl-Pm transcripts appear. To test this possibility, they generated RNA FISH probes complementary to the Sxl-Pe first exon (which is part of an intron sequence in the Sxl-Pm transcript) and to a "common sequence" that labels both Sxl-Pe and Sxl-Pm transcripts. Transcripts labeled by both probes were detected in germ cells beginning at stage 5 (and reaching a peak at stage 10), so either the Sxl-Pm and Sxl-Pe promoters turn on simultaneously, or Sxl-Pe is not active. 

They next switched to Sxl-Pe reporters. The first Sxl-Pe:gfp reporter they used has a 1.5 kb upstream region which in other studies was found to be sufficient to drive sex-specific expression in the soma of blastoderm embryos. Also like the endogenous Sxl gene it is not expressed in germ cells at this early stage. In 2011, Hashiyama et al reported that this 1.5 kb promoter fragment was able to drive gfp expression in Vasa-positive germ cells later in development in stage 9/10 embryos. However, because of the high background of gfp in the nearby soma, their result wasn't especially convincing. Though they don't show the data, Goyal et al indicated that unlike Hashiyama et al they were unable to detect gfp expressed from this reporter in germ cells. Goyal et al extended the upstream sequences in the reporter to 5 kb, but they were still unable to detect germline expression of gfp. 

Goyal et al then generated a more complicated reporter which extends 5 kb upstream of the Sxl-Pe start site and 5 kb downstream-ending at or near 4th exon of the Sxl-Pm transcript (the Sxl-Pe10 kb reporter). (The authors were not explicit as to whether the 5 kb downstream sequence extended beyond the 4th exon splice junction-in which case splicing could potentially occur with an upstream exon(s)-or terminated prior to the splice junction as seems to be indicated in their diagram.) With this reporter, they were able to detect sex-specific gfp expression in the germline beginning in L1 (first instar larva). With the caveat that gfp detection might be delayed compared to the onset of reporter activation, these findings indicated that the sequences in the reporter are able to drive sex-specific transcription in the germline at least as early as L1. 

The authors next tagged the N-terminal end of the Sxl-Pe protein with HA (using Crispr/Cas9) and the N-terminal end of Sxl-Pm protein with Flag. They report that the HA-Sxl-Pe protein is first detected in the soma at stage 9 of embryogenesis. Somatic HA-Sxl-Pe protein persists into L1, but is no longer detected in L2. However, while somatic HA-Sxl-Pe protein is detected, they were unable to detect HA-Sxl-Pe protein in germ cells. In the case of FLAG-Sxl-Pm, it could first be detected in L2 germ cells indicating that at this juncture the Sxl-positive autoregulatory loop has been activated. This contrasts with Sxl-Pm transcripts which are observed in a few germ cells at stage 5 of embryogenesis, and in most germ cells by stage 10. The authors propose (based on the expression pattern of the Sxl-Pe10kb reporter and the appearance of Flag-Sxl-Pm protein) that Sxl-Pe comes on in germ cells in L1, and that the Sxl-Pe protein activates the female splicing of Sxl-Pm transcripts, giving detectable Flag-Sxl-Pm proteins beginning in L2. 

To investigate the signals that activate Sxl-Pe in germ cells, the authors tested four of the X-linked genes (sis-a, sis-b, sis-c, and runt) that function to activate Sxl-Pe in the soma in early embryos. RNAi knockdown of sis-b, sis-c, and runt had no apparent effect on oogenesis. In contrast, knockdown of sis-a resulted in tumorous ovaries, a phenotype associated with Sxl mutations. (Three different RNAi transgenes were tested-two gave this phenotype, the third did not.) Sxl-Pe10kb reporter activity in L1 female germ cells is also dependent on sis-A. 

Several approaches were used to confirm a role for sis-a in a) oogenesis and b) the activation of the Sxl-Pm autoregulatory loop. They showed that sis-a germline clones (using tissue-specific Crispr/Cas9 editing) resulted in the tumorous ovary phenotype and reduced the expression of Sxl protein in these ovaries. They found that sis-a transcripts and GFP-tagged Sis-A protein are present in germ cells. Finally, they showed tumorous ovary phenotype induced by germline RNAi knockdown of sis-a can be partially rescued by expressing Sxl in the germ cells. 

Critique: 

While this manuscript addresses a longstanding puzzle - the mechanism activating the Sxl autoregulatory loop in female germ cells-and likely identified an important germline transcriptional activator of Sxl, sis-a, the data that they've generated doesn't make a compelling story. At every step, there are puzzle pieces that don't fit the narrative. In addition, some of their findings are inconsistent with many previous studies. 

We respect and appreciate this reviewer for the detailed comments. However, we feel that the claim that our work doesn’t “make a compelling story” and that many “pieces…don’t fit the narrative” is incorrect. The main issue that this reviewer raises is that we do not know if Sxl “early” transcription in the germline initiates from the Pe promoter. This is true, which we fully acknowledge, but the detail of whether “germline early” transcription of Sxl initiates from Pe or from other, as yet undefined, germline promoter does not affect the main conclusions of the paper. These conclusions are that a) regulation of Sxl in the germline is fundamentally different from in the soma and 2) despite point (1), sisA acts as an activator of Sxl in both the soma and the germline. Neither of these main points is disputed by this reviewer.

(1) The authors used RNA FISH to time the expression of Sxl-Pe and Sxl-Pm transcripts in germ cells. Transcripts complementary to Sxl-Pe and Sxl-Pm were detected at the same time in embryos beginning at stage 5. This is not a definitive experiment as it could mean a) that Sxl-Pe and Sxl-Pm turn on at the same time, b) that Sxl-Pe comes on after Sxl-Pm (as suggested by the Sxl-Pe10kb reporter) or c) Sxl-Pe never comes on. 

When designing this experiment, we wanted to test whether the “soma model” of Pe activation before Pm was also true in the germ cells. Our data clearly demonstrate that transcripts beginning downstream of Pe are not expressed prior to transcripts beginning downstream of Pm. Thus, we can state that the “soma model” of Pe first and then Pm does not occur in the germline, which is very interesting. However, we cannot make any other conclusions about Pe in the germline from these data, as the reviewer indicates.

(2) Hashiyama et al reported that they detected gfp expression in stage 9/10 germ cells from a 1.5 kb Sxl-Pe-gfp. As noted above, this result wasn't entirely convincing and thus it isn't surprising that Goyal et al were unable to reproduce it. Extending the upstream sequences to just before the 1st exon of Sxl-Pm transcripts also didn't give gfp expression in germ cells. Only when they added 5 kb downstream did they detect gfp expression. However, from this result, it isn't possible to conclude that the Sxl-Pe promoter is actually driving gfp expression in L1 germ cells. Instead, the Sxl promoter active in the germ line could be anywhere in their 10 kb reporter. 

We agree that we have not determined the transcriptional start sites for Sxl in the germline and it is possible that the 10.2 kb reporter uses a different promoter than Pe, as long as that transcript can also be spliced into exon 4 where the GFP tag has been placed. The three types of experiments conducted—FISH to regions of the nascent transcripts, tagged versions of the different predicted ORFs, and promoter-GFP constructs—are extensive, but all have different limitations. Indeed, it would be challenging to determine the transcription start sites in the germline, as it would require obtaining enough L1 larvae to be able to dissociate the animals, or isolated gonads, into single cells in order to FACS purify the germ cells for RACE or long-read sequencing (I’m not sure that L1 larval single-nucleus seq would be enough for calling start sites). Otherwise, there would be no way to determine if expected or unexpected transcripts came from the soma or the germline. We can consider these experiments in the future.

Fortunately, the main conclusions from this paper do not require knowing whether the germline uses Pe or some other “germline early” promoter that can produce Sxl protein in the absence of autoregulation by existing Sxl protein. The observations that a nascent transcript including the region downstream of Pm is observed in embryonic germ cells, but that the tagged LATE protein is not observed until L2, suggest that the transcript produced in early germ cells cannot produce a functional protein. This is consistent with the need for Sxl autoregulation of the Pm transcript in the germline as in the soma, as was previously thought. This is further supported by the observations that activity of the 10.2 kb reporter is only observed in L1 germ cells, and that the LATE Sxl protein is only observed in germ cells after this point. Thus, we can conclude that either Pe, or another “germline early” promoter, acts to produce female-specific Sxl protein to initiate autoregulation of Sxl splicing and protein production in the germline. We feel that this is a significant advance for the field, and we will make it more clear in the text that the initial expression of Sxl in the germline may not be from the Pe promoter.

Other conclusions of the manuscript are unaffected by the start site for “germline early” Sxl transcription, including that the germline activates Sxl protein expression much later than the soma, which calls into question previous work indicating an early role for Sxl in the germline. Also unaffected is our conclusion that different enhancer sequences are required for activation of Sxl expression in the germline than in the soma, consistent with previous work demonstrating that the genetics of Sxl activation in the germline are different than in the soma. Lastly, our conclusions that sisA acts upstream of Sxl, and is required for Sxl germline expression, either directly or indirectly, are also unaffected by the nature of the Sxl “germline early” start site.

(3) At least one experiment suggests that Sxl-Pe never comes on in germ cells. The authors tagged the N-terminus of the Sxl-Pe protein with HA and the N-terminus of the Sxl-Pm protein with Flag. Though they could detect HA-Sxl-Pe protein in the soma, they didn't detect it in germ cells. On the other hand, the Flag-Sxl-Pm protein was detected in L2 germ cells (but not earlier). These results would more or less fit with those obtained for the 10 kb reporter and would support the following model: Prior to L1, Sxl-Pm transcripts are expressed and spliced in the male pattern in both male and female germ cells. During L1, Sxl protein expressed via a mechanism that depends upon a 10 kb region spanning Sxl-Pe (but not on Sxl-Pe) is produced and by L2 there are sufficient amounts of this protein to switch the splicing of Sxl-Pm transcripts from a male to a female pattern-generating Flag-tagged Sxl-Pm protein. 

As described above, it is indeed possible that another promoter besides Pe is active as the “germline early” promoter. We will make this more clear in a revised version, but the major conclusions of the manuscript are unaffected.

(4) The 10kb reporter is sex-specific, but not germline-specific. The levels of gfp in female L1 somatic cells are equal to if not greater than those in L1 female germ cells. That the Sxl-Pe10kb reporter is active in the soma complicates the conclusion that it represents a germ line-specific promoter. Germline activity is, however, sensitive to sis-A knockdowns which is plus. Presumably, somatic expression of the reporter wouldn't be sensitive to a (late) sis-A knockdown- but this wasn't shown. 

We are confused by this comment because we do not conclude that the Pe is a germline-specific promoter. Pe is known to be expressed in the soma, from considerable previous work cited by this reviewer, and the simplest model is that Pe is used in both the soma and the germline, as reflected by our 10.2 kb reporter. It is actually quite interesting how late this promoter seems active in the soma, contrary to current dogma, but we did not study somatic activation of Sxl in this work.

(5) Their results with the HA-Sxl-Pe protein don't fit with many previous studies-assuming that the authors have explained their results properly. They report that HA-Sxl-Pe protein is first detected in the soma at stage 9 of embryogenesis and that it then persists till L2. However, previous studies have shown that Sxl-Pe transcripts and then Sxl-Pe proteins are first detected in ~NC11-NC12 embryos. In RNase protection experiments, the Sxl-Pe exon is observed in 2-4 hr embryos, but not detected in 5-8 hr, 14-12 hr, L1, L2, L3, or pupae. Northerns give pretty much the same picture. Western blots also show that Sxl-Pe proteins are first detectable around the blastoderm stage. So it is not at all clear why HA-Sxl-Pe proteins are first observed at stage 9 which, of course, is well after the time that the Sxl-Pm autoregulatory loop is established. 

Given the obvious problems with the initial timing of somatic expression described here, it is hard to know what to make of the fact that HA-tagged Sxl-Pe proteins aren't observed in germ cells. 

As for the presence of HA-Sxl-Pe proteins later than expected: While RNase protection/Northern experiments showed that Sxl-Pe mRNAs are expressed in 2-4 hr embryos and disappear thereafter, one could argue from the published Western experiments that the Sxl-PE proteins expressed at the blastoderm stage persist at least until the end embryogenesis, though perhaps at somewhat lower levels than at earlier points in development. So the fact that Goyal et al were able to detect HA-Sxl-Pe proteins in stage 9 embryos and later on in L1 larva probably isn't completely unexpected. What is unexpected is that the HA-Sxl-Pe proteins weren't present earlier. 

We thank the reviewer for this detailed analysis. Since we were not focused on somatic expression of Sxl in this work, it is possible that stage 9 was the earliest stage we observed in our experiments, rather than the earliest stage in which it is ever observed. We will repeat these experiments to verify when the HA-tagged early Sxl protein is first observed. However, these comments have no bearing on our conclusions about Sxl expression in the germline, which is the focus of this manuscript.

(6) The authors use RNAi and germline clones to demonstrate that sis-A is required for proper oogenesis: when sis-A activity is compromised in germ cells, i) tumorous ovary phenotypes are observed and ii) there is a reduction in the expression of Sxl-Pm protein. They are also able to rescue the phenotypic effects of sis-a knockdown by expressing a Sxl-Pm protein. While the experiments indicating sis-a is important for normal oogenesis and that at least one of its functions is to ensure that sufficient Sxl is present in the germline stem cells seem convincing, other findings would make the reader wonder whether Sis-A is actually functioning (directly) to activate Sxl transcription from promoter X. 

It is true that we do not know the binding specificity for SisA, which is why we have made no claims about the directness of SisA regulation of Sxl. This does not change our conclusions that sisA is upstream of Sxl activation, since loss of sisA function has a similar phenotype to loss of Sxl, loss of sisA blocks Sxl protein expression, and expression of Sxl rescues the sisA mutant phenotype.

The authors show that sis-a mRNAs and proteins are expressed in stage 3-5 germ cells (PGCs). This is not unexpected as the X-linked transcription factors that turn Sxl-Pe on are expressed prior to nuclear migration, so their protein products should be present in early PGCs. The available evidence suggests that their transcription is shut down in PGCs by the factors responsible for transcriptional quiescence (e.g., nos and pgc) in which case transcripts might be detected in only one or two PGC-which fits with their images. However, it is hard to believe that expression of Sis-A protein in pre-blastoderm embryos is relevant to the observed activation of the Sxl-Pm autoregulatory loop hours later in L2 larva. 

It is also not clear how the very low level of gfp-Sis-A seen in only a small subset of migrating germ cells in stage 10 embryos (Figure S6) would be responsible for activating the Sxl-Pe10kb reporter in L1. It seems likely that the small amount of protein seen in stage 10 embryos is left over from the pre-cellular blastoderm stage. In this case, it would not be surprising to discover that the residual protein is present in both female and male stage 10 germ cells. This would raise further doubts about the relevance of the gfp-Sis-A at these early stages. 

In fact, given the evidence presented implicating sis-a in activating Sxl, (the germline activation of the Sxl-Pe10kb reporter, the RNAi knockdowns, and the germ cell-specific sis-a clones) it is clear that the sis-A RNAs and proteins seen in pre-cellular blastoderm PGCs aren't relevant. The germline clone experiment (and also the RNAi knockdowns) indicates that sis-A must be transcribed in germ cells after Cas9 editing has taken place. Presumably, this would be after transcription is reactivated in the germline (~stage 10) and after the formation of the embryonic gonad (stage 14) so that the somatic gonadal cells can signal to the germ cells. With respect to the reporter, the relevant time frame for showing that sis-A is present in germ cells would be even later in L1. 

The reviewer is correct in wondering how early sisA transcription can affect late Sxl activation, and we are clear about this conundrum in our manuscript. However, they are incorrect about the early sisA expression. Our experiments examining nascent sisA transcripts indicate that sisA is zygotically expressed in the formed germ cells rather than being leftover from expression in early nuclei. The fact that only a portion of germ cells express sisA at any time may well be due to a timing issue, where not all germ cells express sisA at the same time. They are also incorrect about the timing of Cas9 editing in the germline—the guide RNAs are expressed from a general promoter that is active both maternally and in the early embryo, and the Cas9 RNA from the nos promoter is deposited in the germ plasm where it is translated long before cellularization, meaning that sisA CRISPR knockout can begin at the earliest stages of germ cell formation or before.

(7) As noted above, the data in this manuscript do not support the idea that Sxl-Pe proteins activate the Sxl-Pm female splicing in the germline. Flybase indicates that there is at least one other Sxl promoter that could potentially generate a transcript that includes the male exon but still could encode a Sxl protein. This promoter "Sxl-Px" is located downstream of Sxl-Pm and from its position it could have been included in the authors' 10 kb reporter. The reported splicing pattern of the endogenous transcript skips exon2, and instead links an exon just downstream of Sxl-Px to the male exon. The male exon is then spliced to exon4. If the translation doesn't start and end at one of the small upstream orfs in the exons close to Sxl-Px and the male exon, a translation could begin with an AUG codon in exon4 that is in frame with the Sxl protein coding sequence. This would produce a Sxl protein that lacks aa sequences from N-terminus, but still retains some function. 

Another possible explanation for how gfp is expressed from the 10 kb reporter is that the transcript includes the "z" exon described by Cline et al., 2010.

As discussed above, the exact location of the start site for the Sxl transcript in the germline remains to be determined, but does not affect the main conclusions of the paper.

Reviewer #1 (Public Review):

Review after revision

Of note the main results of this article are very similar to the results present in the previous manuscript (same Figures 1 to 9, addition of Figure 10 with no quantification).<br /> Unfortunately, the main weaknesses of the article have not been addressed:

(1) The main findings have been obtained in clones of Jurkat cells. They have not been confirmed in primary T cells. The only experiment performed in primary cells is shown in Figure S7 (primary human T lymphoblasts) for which only the distribution of FMNL1 is shown without quantification. No results presenting the effect of FMNL1 KO and expression of mutants in primary T cells are shown.

(2) Analysis in- depth of the defect in actin remodeling (quantification of the images, analysis of some key actors of actin remodeling) is still lacking. Only F-actin is shown, no attempt to look more precisely at actors of actin remodeling has been done.

(3) The defect in the secretion of extracellular vesicles is still very preliminary. Examples of STED images given by the authors are nice, yet no quantification is performed.

(4) Results shown in Figure S12 on the colocalization of proteins phosphorylated on Ser/Thr are still not convincing. It seems indeed that "phospho-PKC" is labeling more preferentially the CMAC positive cells (Raji) than the Jurkat T cells. It is thus particularly difficult to conclude on the co-localization and even more on the recruitment of phosphorylated-FMNL1 at the IS. Thus, these experiments are not conclusive and cannot be the basis even for their cautious conclusion: "Although all these data did not allow us to infer that FMNL1b is phosphorylated at the IS due to the resolution limit of confocal and STED microscopes, the results are compatible with the idea that both endogenous FMNL1 and YFP-FMNL1bWT are specifically phosphorylated at the cIS".

The study would benefit from a more careful statistical analysis. The dot plots showing polarity are presented for one experiment. Yet, the distribution of the polarity is broad. Results of the 3 independent experiments should be shown and a statistical analysis performed on the independent experiments.

Reviewer #2 (Public Review):

Summary

Based on i) the documented role of FMNL1 proteins in IS formation; ii) their ability to regulate F-actin dynamics; iii) the implication of PKCdelta in MVB polarization to the IS and FMNL1beta phosphorylation; and iv) the homology of the C-terminal DAD domain of FMNL1beta with FMNL2, where a phosphorylatable serine residue regulating its auto-inhibitory function had been previously identified, the authors have addressed the role of S1086 in the FMNL1beta DAD domain in F-actin dynamics, MVB polarization and exosome secretion, and investigated the potential implication of PKCdelta, which they had previously shown to regulate these processes, in FMNL1beta S1086 phosphorylation. They demonstrate that FMNL1beta is indeed phosphorylated on S1086 in a PKCdelta-dependent manner and that S1086-phosphorylated FMNL1beta acts downstream of PKCdelta to regulate centrosome and MVB polarization to the IS and exosome release. They provide evidence that FMNL1beta accumulates at the IS where it promotes F-actin clearance from the IS center, thus allowing for MVB secretion.

Strengths

The work is based on a solid rationale, which includes previous findings by the authors establishing a link between PKCdelta, FMNL1beta phosphorylation, synaptic F-actin clearance and MVB polarization to the IS. The authors have thoroughly addressed the working hypotheses using robust tools. Among these, of particular value is an expression vector that allows for simultaneous RNAi-based knockdown of the endogenous protein of interest (here all FMNL1 isoforms) and expression of wild-type or mutated versions of the protein as YFP-tagged proteins to facilitate imaging studies. The imaging analyses, which are the core of the manuscript, have been complemented by immunoblot and immunoprecipitation studies, as well as by the measurement of exosome release (using a transfected MVB/exosome reporter to discriminate exosomes secreted by T cells).

Weaknesses

The authors have satisfactorily addressed the weaknesses pointed out in my previous review.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

First, all the experiments are performed in Jurkat T cells that may not recapitulate the regulation of polarization in primary T cells.

To extend our results in Jurkat cells forming IS to primary cells, we have now performed experiments using synapses established by Raji cells and either primary T cells  (TCRmediated) or primary CAR T cells (CAR-mediated) (new Suppl. Fig. S7). These experiments clearly show the presence of FMNL1 at these two different IS classes (new Suppl. Fig. S7), similar to what was found in Jurkat-Raji synapses. In addition, since most of the experiments were performed in Jurkat cells, we have changed the title of our manuscript, to be faithful to the main body of our results. New sentences dealing with this important issue have been included in the Results and Discussion sections.

Moreover, all the experiments analyzing the role of PKCdelta are performed in one clone of wt or PKCdelta KO Jurkat cells. This is problematic since clonal variation has been reported in Jurkat T cells.

Referee is right, this is the reason why we have studied three different control clones (C3, C9, C7) and three PKCdelta-interfered clones (P5, P6 and S4) all derived from JE6.1 clone and the results have been previously published (Herranz et al 2019)(Bello-Gamboa et al 2020). All these clones expressed similar levels of the relevant cell surface molecules and formed synaptic conjugates with similar efficiency (Herranz et al 2019). The P5, P6 and S4 clones exhibited a similar defect in MVB/MTOC polarization when compared with the control clones (Herranz et al 2019)(Bello-Gamboa et al 2020). Experiments developed by other researchers using a different clone of Jurkat (JE6.1) and primary CD4+ and CD8+ lymphocytes interfered in FMNL1 (Gomez et al. 2007), showed a comparable defect in MTOC polarization to that found in our control clones when were transiently interfered in FMNL1 (Bello-Gamboa et al 2020, this manuscript). In this manuscript we have studied, instead of canonical JE6.1 clone, C3 and C9 control clones derived from JE6.1, since the puromycin-resistant control clones (containing a scramble shRNA) were isolated by limiting dilution together with the PKCdelta-interfered clones (Herranz et al. 2019), thus C3 and C9 clones are the best possible controls to compare with P5 and P6 clones. Please realize that microsatellite analyses, available upon request, supports the identity of our C3 clone with JE6.1. Moreover, when GFP-PKCdelta was transiently expressed in the three PKCdelta-interfered clones, MTOC/MVB polarization was recovered to control levels (Herranz et al. 2019). Therefore, the deficient MTOC/MVB polarization in all these clones is exclusively due to the reduction in PKCdelta expression (Herranz et al 2019), and thus clonal variation cannot underlie our results in stable clones. We have now included new sentences to address this important point and to mention the inability of FMNL1betaS1086D to revert the deficient MTOC polarization occurring in P6 PKCdelta-interfered clone, as occurred in P5 clone. Due to the fact we have now included more figures and panels to satisfy editor and referees’s comments, we have not included the dot plot data corresponding to C9 and P6 clones to avoid a too long and repetitive manuscript. Since all the FMNL1 interference and FMNL1 variants reexpression experiments were performed in transient assays (2-4 days after transfection), there was no chance for any clonal variation in these short-time experiments. Moreover, internal controls using untransfected cells or Raji cells unpulsed with SEE were carried out in all these transient experiments.

Finally, although convincing, the defect in the secretion of vesicles by T cells lacking phosphorylation of FMNL1beta on S1086 is preliminary. It would be interesting to analyze more precisely this defect. The expression of the CD63‑GFP in mutants by WB is not completely convincing. Are other markers of extracellular vesicles affected, e.g. CD3 positive?

We acknowledge this comment. It is true that the mentioned results do not directly demonstrate the presence of exosomes at the synaptic cleft of the synapses, since the nanovesicles were harvested from the cell culture supernatants from synaptic conjugates and these nanovesicles could be produced by multi‑directional degranulation of MVBs. To address this important issue, we have performed STED super‑resolution imaging of the immune synapses made by control and FMNL1-interfered cells. Nanosized (100-150 nm) CD63+ vesicles can be found in the synaptic cleft between APC and control cells with polarized MVBs, whereas we could not detect these vesicles in the synaptic cleft from FMNL1-interfered cells that maintain unpolarized MVBs (New Fig. 10). New sentences have been included in the Results and Discussion dealing with this important point. Regarding the use of CD3 as a marker of extracellular vesicles, please realize that CD3 is neither an enriched nor a specific marker of exosomes, since it is also present in plasma membrane shedding vesicles, molting vesicles from microvilli, apoptotic bodies and small cell fragments, apart from exosomes, thus we have preferred to use the canonic exosome marker CD63 as a general exosome reporter readout, for WB and immunofluorescence (MVBs, exosomes), time-lapse of MVBs (suppl. Video 8) and super resolution experiments (Fig. 10).   

Reviewer #2 (Public Review):

Summary:

The authors have addressed the role of S1086 in the FMNL1beta DAD domain in 4 F-actin dynamics, MVB polarization, and exosome secretion, and investigated the potential implication of PKCdelta, which they had previously shown to regulate these processes, in FMNL1beta S1086 phosphorylation. This is based on:

(1) the documented role of FMNL1 proteins in IS formation

(2) their ability to regulate F-actin dynamics

(3) the implication of PKCdelta in MVB polarization to the IS and FMNL1beta phosphorylation

(4) the homology of the C-terminal DAD domain of FMNL1beta with FMNL2, where a phosphorylatable serine residue regulating its auto-inhibitory function had been previously identified. They demonstrate that FMNL1beta is indeed phosphorylated on S1086 in a PKCdelta-dependent manner and that S1086-phosphorylated FMNL1beta acts downstream of PKCdelta to regulate centrosome and MVB polarization to the IS and exosome release. They provide evidence that FMNL1beta accumulates at the IS where it promotes F-actin clearance from the IS center, thus allowing for MVB secretion.  

Strengths

The work is based on a solid rationale, which includes previous findings by the authors establishing a link between PKCdelta, FMNL1beta phosphorylation, synaptic F-actin clearance, and MVB polarization to the IS. The authors have thoroughly addressed the working hypotheses using robust tools. Among these, of particular value is an expression vector that allows for simultaneous RNAi-based knockdown of the endogenous protein of interest (here all FMNL1 isoforms) and expression of wild-‐‑type or mutated versions of the protein as YFP‐tagged proteins to facilitate imaging studies. The imaging analyses, which are the core of the manuscript, have been complemented by immunoblot and immunoprecipitation studies, as well as by the measurement of exosome release (using a transfected MVB/exosome reporter to discriminate exosomes secreted by T cells).

Weaknesses

The data on F-‐‑actin clearance in Jurkat T cells knocked down for FMNL1 and expressing wild-type FMNL1 or the non‑phosphorylatable or phosphomimetic mutants thereof would need to be further strengthened, as this is a key message of the manuscript. Also, the entire work has been carried out on Jurkat cells. Although this is an excellent model easily amenable to genetic manipulation and biochemical studies, the key finding should be validated on primary T cells

Referee’s global assessment is right. To extend our results in Jurkat cells forming IS, we have now performed experiments using synapses established by Raji cells and either primary T cells (TCR-mediated) or primary CAR T cells (CAR-mediated) (new Suppl. Fig. S7). These experiments clearly show the presence of FMNL1 at these two different IS classes (new Suppl. Fig. S7), similar to what was found in Jurkat-Raji synapses. In addition, since most of the experiments were performed in Jurkat cells, we have changed the title of our manuscript, to be faithful to the main body of our results. New sentences have been included in Results and Discussion to address these important points.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

This study shows the role of the phosphorylation of FMNL1b on S1086 on the polarity of T lymphocytes in T lymphocytes, which is a new and interesting finding. It would be important to confirm some of the key results in primary T cells and to analyze in-depth the defect in actin remodeling (quantification of the images, analysis of some key actors of actin remodeling). The description of the defect in the secretion of extracellular vesicles would also benefit from a more accurate analysis of the content of vesicles. 

Referee is right.  We have now performed experiments using synapses containing Raji cells and either primary T cells (TCR-mediated) or primary CAR T cells (CAR-mediated) (new Suppl. Fig. S7). These experiments clearly show the presence of FMNL1 at these two different IS classes, similar to what was found in Jurkat-‐‑Raji synapses. Moreover, since most of the experiments were performed in Jurkat cells, we have changed the title of our manuscript, to be faithful to the main body of our results. Regarding the use of CD63 instead of other markers such as for instance,  CD3 (as stated by the other referee), please realize that CD3 is neither an enriched nor a specific marker of exosomes, since it is also present in plasma membrane shedding vesicles, molting vesicles from microvilli, apoptotic bodies and small cell fragments, apart from exosomes, thus we have preferred to use the accepted consensus, canonic extracellular vesicle marker CD63 (International Society of Extracellular Vesicles positioning, Thery et al 2018, doi: 10.1080/20013078.2018.1535750. eCollection 2018., Alonso et al. 2011) as a general exosome reporter readout, for both WB, immunofluorescence (MVBs, exosomes) and super-resolution experiments. Accordingly, GFP-‐‑CD63 reporter plasmid was used for exosome secretion in transient expression studies and living cell time-lapse experiments (Suppl. Video 8). Any other exosome marker will also be present in Raji cells and will not allow to analyse exclusively the secretion of exosomes by the effector Jurkat cells, since B lymphocytes produce a large quantity of exosomes upon MHC‑II stimulation by Th lymphocytes (Calvo et al, 2020, doi:10.3390/ijms21072631). To reinforce the exosome data in the context of the immune synapse, STED super-resolution imaging of the immune synapses made by control and FMNL1‑interfered cells was performed. Nanosized (100-150 nm) CD63+ vesicles can be found in the synaptic cleft of control cells with polarized MVBs, whereas we could no detect these vesicles in the synaptic cleft from FMNL1-interfered cells that maintain unpolarized MVBs (new Fig. 10).

Moreover, all the videos are not completely illustrative. For example, in video 2 it would be more appropriate show only the z plane corresponding to the IS to see more precisely the F-actin remodeling relative to CD63 labeling.

Referee is right. It is true that the upper rows in some videos may distract the reader of the main message contained in the lower row, that includes the 90º turn-generated, zx plane corresponding to the IS interface. Accordingly, we have maintained the still images of the whole synaptic conjugates in the first row from video 2; this will allow the reader to perceive a general view of the fluorochromes on the whole cell conjugates, as a reference, and to compare precisely the F-actin remodeling relative to CD63 labeling only at the zx interface (lower row). We have now processed the videos 1 and 5 following similar criteria

The quality of videos 3 and 4 are not good enough. For video 7, it seems that the labeling of phospho-‐‑Ser is very broad at the IS, which is expected since it should label all the proteins that are phosphorylated by PKCs. The resolution of microscopy (at the best 200 to 300 nm) does not allow us to conclude on the co-‐localization of FMNL1b with phospho-‐‑Ser and is thus not conclusive. Finally, the study would benefit from a more careful statistical analysis. The dot plots showing polarity are presented for one experiment. Yet, the distribution of the polarity is broad. Results of the 3 independent experiments should be shown and a statistical analysis performed on the independent experiments

Referee is right, we have amended video settings (brightness/contrast) in videos 3 and 4 to improve this issue. In addition, we would like to remark that the translocation of proteins to cellular substructures in living cells is not a trivial issue, since certain protein localizations are too dynamic to be properly imaged with enough spatial resolution. The equilibrium resulting from the association/dissociation of a certain protein to the membrane, in addition to the protein diffusion naturally occurring in living cells, as well as signal intensity fluctuations inherent to the stochastic nature of fluorescence emission often provide barriers for image quality (Shroff et al, 2024). Thus, additional image blurring is expected when compared with that observed in fixed samples. However, we think it is important to provide the potential readers with a dynamic view of FMNL1 localization, which can only be achieved through real-time videos, in addition to the still frames from the same videos provided in Fig. 6A (the referee did not argue against the inclusion of these frames), together with images from fixed cells in Fig 6B, for comparison. This is the reason why we have preferred to maintain the improved videos to complement the results of some spare frames from the videos, together with images from fixed cells in the same figure (Fig. 6).

Regarding video 7, we agree that colocalization is limited by the spatial resolution of confocal  microscopy,  and this fact does not allow us to infer that FMNL1beta is phosphorylated at the IS. However, please realize we have never concluded this in our manuscript.  Instead, we claimed that “colocalization of endogenous FMNL1 and YFP‑FMNL1βWT with anti‑phospho‑Ser  …is compatible with the idea that both endogenous FMNL1 and YFP‑FMNL1βWT are specifically phosphorylated at the cIS”. Moreover, we have now performed colocalization in super‑resolved STED microscopy images, that reduces the XY resolution down to 30-­40 nm (Suppl. Fig. S12), and the results also support colocalization of endogenous FMNL1 with anti-phospho‑Ser PKC at the IS within a 30 nm resolution limit. We have now somewhat softened our conclusion: “Although all these data did not allow us to infer that FMNL1β is phosphorylated at the IS due to the resolution limit of confocal and STED microscopes, the results are compatible with the idea that both endogenous FMNL1 and YFP-FMNL1βWT are specifically phosphorylated at the cIS”.   

Regarding statistical analyses we agree the dot distribution in the polarity experiments is quite broad, but this is consistent with the end point strategy used by a myriad of research groups (including ourselves) to image an intrinsically stochastic, rapid and asynchronous processes such as immune synapse formation and to score MTOC/MVB  polarization (Calvo et al 2018, https://doi.org/10.3389/fimmu.2018.00684). Despite this fact,  ANOVA  analyses have underscored the statistical significance of all the experiments represented by dot plot experiments. We cannot average or perform meta statistical analyses by combining the equivalent cohort results from independent experiments, since we have observed that small variations of certain variables (SEE concentration, cell recovery, time after transfection, etc.) affect synapse formation and PI values among experiments without altering the final outcome in each case. Please, note that our manuscript includes now 10  multi‑panel figures,  12  multi‑panel supplementary figures and 8 videos, and it is already quite large.  Thus,  we feel the inclusion of redundant, triplicate dot plot figures will dilute and distract to any potential reader from the main message of our already comprehensive contribution. We have now included new sentences at the figure legends to remark ANOVA analyses were executed separately in all the 3 independent experiments.

Reviewer #2 (Recommendations For The Authors):

(1) The key findings should be validated on primary CD4+ T cells (of which Jurkat is a transformed model).

Referee is right. However, as commented by the other referee, the data from activating surfaces clearly shows that the synaptic actin architecture of the immune synapse from primary CD8+ T cells is essentially indistinguishable and thus unbiased from that of Jurkat T cells, but different to that of primary CD4+ cells (Murugesan, 2016). Thus, our data in Jurkat T cells are directly applicable to the synaptic architecture of primary CD8+ cells. In addition, to definitely extend our results in Jurkat cells forming IS, we have performed experiments using synapses established by Raji cells and either primary T cells (TCR-mediated) or primary CAR T cells (CAR-mediated) (new Suppl. Fig. S7) challenged by Raji cells. We have preferred to work with mixed CD4+ and CD8+ cells in order to maintain potential interactions in trans between these subpopulations that may affect or influence IS formation. These experiments clearly show the presence of FMNL1 at these two different IS classes (new Suppl. Fig. S7), similar to what was found in JurkatRaji synapses. Moreover, since most of the experiments were performed in Jurkat cells as stated by the referee, we have changed the title of our manuscript, to circumscribe our results to the model we have used and to be faithful to the main body of our results.

(2) The image of wt YFP-­FMNL1beta in Figure 4A displays a weak CD63 signal and shows an asymmetric polarization of both the centrosome and MVBs. It should be replaced with a more representative one.

Referee is right. Accordingly, we have modified the CD63 channel settings (brightness/contrast) in this panel to make it comparable to the other panels in the same figure. In addition, thanks to this referee´s comment, we have realized the position of the MTOC (yellow dot) in the diagram in the right side of the YFP-FMNL1betaWT panels row appeared mislocated, producing the mentioned apparent asymmetry with respect to MVBs’s center of mass (green dot) position. This mistake leads to an apparent segregation between the position of the center of mass of these organelles which certainly does not correspond with the real image. We have now amended the scheme and we apologize for this mistake.

(3) The images showing F-­actin clearance at the IS (Figure 8, S4, S5) are not very convincing, also when looking at the MFI along the T cell-­‐‑APC interface in the en-­‐face  views.  Since  the  F-­actin  signal  also  includes  some  signal  from  the  APC, transfecting T cells with an actin reporter to selectively image T cell actin could better clarify this key point.

Referee´s point is correct. However, we (83), and other researchers using the proposed actin reporter approach in the same Raji/Jurkat IS model (Fig. 4 in ref 84) have already excluded the possibility that actin cytoskeleton of Raji cells can also contribute to the measurements of synaptic F-actin. In Materials and Methods, page 37, lines 1048-1055 we included this related sentence:  ¨It is important to remark that MHC-II-antigen triggering on the B cell side of the Th synapse does not induce noticeable F-­actin changes along the synapse (i.e. F-­actin clearing at the central IS), in contrast to TCR stimulation on T cell side (84) (85) (3). In addition, we have observed that majority of F‐‑actin changes along the IS belongs to the Jurkat cell (83). Thus, the contribution to the analyses of the residual, invariant F‐actin from the B cell is negligible using our protocol (83).

Thus, we can exclude this caveat may affect our results.

(4) A similar consideration applies to the MVB distribution in the en‑face images. For example, in Figure S5 the MVB profile, with some peripheral distribution, does not appear very different in cells expressing wt YFP‑tagged FMNL1beta versus the S1086A‑expressing cells.

The referee's assessment regarding Supp. Figure S5 is valid. Using only the plot profile, the outcomes obtained with YFP-FMNL1βWT may appear comparable to those derived from YFP-FMNL1βS1086A. Nonetheless, this resemblance is attributed to the plot profile's exclusive consideration of the MVBs signal in the interface from the immune synapse region (white rectangle). The upper images (second row), where the whole cell is displayed, illustrate that in YFP-FMNL1βWT, MVB are specifically accumulated within this specific region, in contrast to the scattered distribution observed in YFP-FMNL1βS1086A, where MVB are dispersed throughout the cell without distinction. While MVBs are evident in both instances within the synapse region, the reason behind this observation is different. The YFP-FMNL1βWT transfected cell (third column) shows a pronounced MVB concentration within the synaptic area (white rectangle), which leads to MVB PI=0.52, whereas the YFP-FMNL1βS1086A transfected cell (fourth column), as it presents a scattered distribution of MVB throughout the cell, also exhibits some MVB (but only a small proportion of the total cellular MVB) in the synaptic area, which yields MVB PI=-0.09. Please realise that the position of the center of mass of the distribution of MVB (MVBC) labelled in this figure (white squares) is an unbiased parameter that mirrors MVB center of mass polarization. A new sentence has been included in the figure legend to clarify this important point.

(5) The image in the first row in Figure 6B does not show a clear accumulation of FMNL1beta at the IS, possibly because the T cell is in contact with two APCs. This image should be replaced.

Referee is right Therefore, we have replaced the quoted example with a single cell:cell synapse that shows a clearer and more localized accumulation in the cIS, thereby avoiding the mentioned caveat.

(6) In Figure 2A the last row shows what appears to be a T:T cell conjugate (with one cell expressing the YFP-­‐‑tagged protein). The image should be replaced with another showing a T cell-­APC (blue) conjugate.

Referee is right, we have accordingly replaced the mentioned image with a T cell:APC conjugate.

(7) The Discussion is very long and dispersive. It would benefit from shortening it and making it more focused.

Referee is right, we have shortened and focused it, by eliminating the whole second and third paragraphs of the discussion. Moreover, a whole paragraph in page 24 has been also deleted.

We have also focussed the discussion towards the new data in primary T lymphocytes.

The model presenting the structure is split into 3 main diagrams (Spatial system, Structural system, Enclosure system) to help the viewer identify how different parts hold up the structure.

what is considered the basic elements?

How can these majorly effect the way an architecture is built?

To what extent do they accommodate human activity?

In what ways can the basic elements of form and space be manipulated?

What are the basic elements of form and space?

Is this similar to the design process? What problems are there to solve?

how can architects expanding their understanding of design language?

Version 2 of this preprint has been peer-reviewed and recommended by Peer Community in Zoology.<br /> See the peer reviews and the recommendation.

ebenfalls: gmd:contact ist lediglich fallback

gmd:contact ist der Metadatenkontakt und wird alls Fallback hier verwendet. Evtl. so hier beschreiben.

eLife assessment

This valuable manuscript describes evidence of sex differences in specific corticostriatal projections during alcohol consumption, and this is noteworthy given the increasing rates/levels of drinking in females and their liability for Alcohol Use disorder. The authors provide solid evidence of the lateralisation of the activity of the circuit, but other evidence is incomplete, particularly with regard to how the drinking measure relates to intoxication. There are some inconsistencies that make it difficult to reconcile the photometry and behavioral data. The findings would benefit from causal assessment in the future. The findings will be of interest to researchers investigating functional circuitry underlying alcohol-driven behaviors.

Reviewer #1 (Public Review):

Summary:

This paper uses a model of binge alcohol consumption in mice to examine how the behaviour and its control by a pathway between the anterior insular cortex (AIC) to the dorsolateral striatum (DLS) may differ between males and females. Photometry is used to measure the activity of AIC terminals in the DLS when animals are drinking and this activity seems to correspond to drink bouts in males but not females. The effects appear to be lateralized with inputs to the left DLS being of particular interest.

Strengths:

Increasing alcohol intake in females is of concern and the consequences for substance use disorder and brain health are not fully understood, so this is an area that needs further study. The attempt to link fine-grained drinking behaviour with neural activity has the potential to enrich our understanding of the neural basis of behaviour, beyond what can be gleaned from coarser measures of volumes consumed etc.

Weaknesses:

The introduction to the drinking in the dark (DID) paradigm is rather narrow in scope (starting line 47). This would be improved if the authors framed this in the context of other common intermittent access paradigms and gave due credit to important studies and authors that were responsible for the innovation in this area (particularly studies by Wise, 1973 and returned to popular use by Simms et al 2010 and related papers; e.g., Wise RA (1973). Voluntary ethanol intake in rats following exposure to ethanol on various schedules. Psychopharmacologia 29: 203-210; Simms, J., Bito-Onon, J., Chatterjee, S. et al. Long-Evans Rats Acquire Operant Self-Administration of 20% Ethanol Without Sucrose Fading. Neuropsychopharmacol 35, 1453-1463 (2010).) The original drinking in the dark demonstrations should also be referenced (Rhodes et al., 2005). Line 154 Theile & Navarro 2014 is a review and not the original demonstration.

When sex differences in alcohol intake are described, more care should be taken to be clear about whether this is in terms of volume (e.g. ml) or blood alcohol levels (BAC, or at least g/kg as a proxy measure). This distinction was often lost when lick responses were being considered. If licking is similar (assuming a single lick from a male and female brings in a similar volume?), this might mean males and females consume similar volumes, but females due to their smaller size would become more intoxicated so the implications of these details need far closer consideration. What is described as identical in one measure, is not in another.

While the authors have some previous data on the AIC to DLS pathway, there are many brain regions and pathways impacted by alcohol and so the focus on this one in particular was not strongly justified. Since photometry is really an observational method, it's important to note that no causal link between activity in the pathway and drinking has been established here.

It would be helpful if the authors could further explain whether their modified lickometers actually measure individual licks. While in some systems contact with the tongue closes a circuit which is recorded, the interruption of a photobeam was used here. It's not clear to me whether the nose close to the spout would be sufficient to interrupt that beam, or whether a tongue protrusion is required. This detail is important for understanding how the photometry data is linked to behaviour. The temporal resolution of the GCaMP signal is likely not good enough to capture individual links but I think more caution or detail in the discussion of the correspondence of these events is required.

Even if the pattern of drinking differs between males and females, the use of the word "strategy" implies a cognitive process that was never described or measured.

Reviewer #2 (Public Review):

Summary:

This study looks at sex differences in alcohol drinking behaviour in a well-validated model of binge drinking. They provide a comprehensive analysis of drinking behaviour within and between sessions for males and females, as well as looking at the calcium dynamics in neurons projecting from the anterior insula cortex to the dorsolateral striatum.

Strengths:

Examining specific sex differences in drinking behaviour is important. This research question is currently a major focus for preclinical researchers looking at substance use. Although we have made a lot of progress over the last few years, there is still a lot that is not understood about sex-differences in alcohol consumption and the clinical implications of this.

Identifying the lateralisation of activity is novel, and has fundamental importance for researchers investigating functional anatomy underlying alcohol-driven behaviour (and other reward-driven behaviours).

Weaknesses:

Very small and unequal sample sizes, especially females (9 males, 5 females). This is probably ok for the calcium imaging, especially with the G-power figures provided, however, I would be cautious with the outcomes of the drinking behaviour, which can be quite variable.

For female drinking behaviour, rather than this being labelled "more efficient", could this just be that female mice (being substantially smaller than male mice) just don't need to consume as much liquid to reach the same g/kg. In which case, the interpretation might not be so much that females are more efficient, as that mice are very good at titrating their intake to achieve the desired dose of alcohol.

Reviewer #3 (Public Review):

Summary:

In this manuscript by Haggerty and Atwood, the authors use a repeated binge drinking paradigm to assess how water and ethanol intake changes in male in female mice as well as measure changes in anterior insular cortex to dorsolateral striatum terminal activity using fiber photometry. They find that overall, males and females have similar overall water and ethanol intake, but females appear to be more efficient alcohol drinkers. Using fiber photometry, they show that the anterior insular cortex (AIC) to dorsolateral striatum projections (DLS) projections have sex, fluid, and lateralization differences. The male left circuit was most robust when aligned to ethanol drinking, and water was somewhat less robust. Male right, and female and left and right, had essentially no change in photometry activity. To some degree, the changes in terminal activity appear to be related to fluid exposure over time, as well as within-session differences in trial-by-trial intake. Overall, the authors provide an exhaustive analysis of the behavioral and photometric data, thus providing the scientific community with a rich information set to continue to study this interesting circuit. However, although the analysis is impressive, there are a few inconsistencies regarding specific measures (e.g., AUC, duration of licking) that do not quite fit together across analytic domains. This does not reduce the rigor of the work, but it does somewhat limit the interpretability of the data, at least within the scope of this single manuscript.

Strengths:

- The authors use high-resolution licking data to characterize ingestive behaviors.<br /> - The authors account for a variety of important variables, such as fluid type, brain lateralization, and sex.<br /> - The authors provide a nice discussion on how this data fits with other data, both from their laboratory and others'.<br /> - The lateralization discovery is particularly novel.

Weaknesses:

- The volume of data and number of variables provided makes it difficult to find a cohesive link between data sets. This limits interpretability.<br /> - The authors describe a clear sex difference in the photometry circuit activity. However, I am curious about whether female mice that drink more similarly to males (e.g., less efficiently?) also show increased activity in the left circuit, similar to males. Oppositely, do very efficient males show weaker calcium activity in the circuit? Ultimately, I am curious about how the circuit activity maps to the behaviors described in Figures 1 and 2.<br /> - What does the change in water-drinking calcium imaging across time in males mean? Especially considering that alcohol-related signals do not seem to change much over time, I am not sure what it means to have water drinking change.

Author response:

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

Public Reviews: 

Reviewer #1 (Public Review): 

Summary: 

This paper uses a model of binge alcohol consumption in mice to examine how the behaviour and its control by a pathway between the anterior insular cortex (AIC) to the dorsolateral striatum (DLS) may differ between males and females. Photometry is used to measure the activity of AIC terminals in the DLS when animals are drinking and this activity seems to correspond to drink bouts in males but not females. The effects appear to be lateralized with inputs to the left DLS being of particular interest. 

Strengths: 

Increasing alcohol intake in females is of concern and the consequences for substance use disorder and brain health are not fully understood, so this is an area that needs further study. The attempt to link fine-grained drinking behaviour with neural activity has the potential to enrich our understanding of the neural basis of behaviour, beyond what can be gleaned from coarser measures of volumes consumed etc. 

Weaknesses: 

The introduction to the drinking in the dark (DID) paradigm is rather narrow in scope (starting line 47). This would be improved if the authors framed this in the context of other common intermittent access paradigms and gave due credit to important studies and authors that were responsible for the innovation in this area (particularly studies by Wise, 1973 and returned to popular use by Simms et al 2010 and related papers; e.g., Wise RA (1973). Voluntary ethanol intake in rats following exposure to ethanol on various schedules. Psychopharmacologia 29: 203-210; Simms, J., Bito-Onon, J., Chatterjee, S. et al. Long-Evans Rats Acquire Operant Self-Administration of 20% Ethanol Without Sucrose Fading. Neuropsychopharmacol 35, 1453-1463 (2010).)

We appreciate the reviewer’s perspective on the history of the alcohol research field. There are hundreds of papers that could be cited regarding all the numerous different permutations of alcohol drinking paradigms. This study is an eLife “Research Advances” manuscript that is a direct follow-up study to a previously published study in eLife (Haggerty et al., 2022) that focused on the Drinking in the Dark model of binge alcohol drinking. This study must be considered in the context of that previous study (they are linked), and thus we feel that a comprehensive review of the literature is not appropriate for this study.

The original drinking in the dark demonstrations should also be referenced (Rhodes et al., 2005). Line 154 Theile & Navarro 2014 is a review and not the original demonstration. 

This is a good recommendation. We have added this citation to Line 33 and changed Line 154.

When sex differences in alcohol intake are described, more care should be taken to be clear about whether this is in terms of volume (e.g. ml) or blood alcohol levels (BAC, or at least g/kg as a proxy measure). This distinction was often lost when lick responses were being considered. If licking is similar (assuming a single lick from a male and female brings in a similar volume?), this might mean males and females consume similar volumes, but females due to their smaller size would become more intoxicated so the implications of these details need far closer consideration. What is described as identical in one measure, is not in another. 

As shown in Figure 1, all measures of intake are reported as g/kg for both water and alcohol to assess intakes across fluids that are controlled by body weights. We do not reference changes in fluid volume or BACs to compare differences in measured lickometry or photometric signals, except in one instance where we suggest that the total volume of water (ml) is greater than the total amount of alcohol (ml) consumed in DID sessions, but this applies generally to all animals, regardless of sex, across all the experimental procedures.

In Figure 2 – Figure Supplement 1 we show drinking microstructures across single DID sessions, and that males and females drink similarly, but not identically, when assessing drinking measures at the smallest timescale that we have the power to detect with the hardware we used for these experiments. Admittedly, the variability seen in these measures is certainly non-zero, and while we are tempted to assume that there exist at least some singular drinks that occur identically between males and females in the dataset that support the idea that females are simply just consuming more volume of fluid per singular drink, we don’t have the sampling resolution to support that claim statistically. Further, even if females did consume more volume per singular drink that males, we do not believe that is enough information to make the claim that such behavior leads to more “intoxication” in females compared males, as we know that alcohol behaviors, metabolism, and uptake/clearance all differ significantly by sex and are contributing factors towards defining an intoxication state. We’ve amended the manuscript to remove any language of referencing these drinking behaviors as identical to clear up the language.

No conclusions regarding the photometry results can be drawn based on the histology provided. Localization and quantification of viral expression are required at a minimum to verify the efficacy of the dual virus approach (the panel in Supplementary Figure 1 is very small and doesn't allow terminals to be seen, and there is no quantification). Whether these might differ by sex is also necessary before we can be confident about any sex differences in neural activity. 

We provide hit maps of our fiber placements and viral injection centers, as we have, and many other investigators do regularly for publication based on histological verification. Figure 1A clearly shows the viral strategy taken to label AIC to DLS projections with GCaMP7s, and a representative image shows green GCaMP positive terminals below the fiber placement. Considering the experiments, animals without proper viral expression did not display or had very little GCaMP signal, which also serves as an additional expression-based control in addition to typical histology performed to confirm “hits”. These animals with poor expression or obvious misplacement of the fiber probes were removed as described in the methods. Further, we also report our calcium signals as z-scored differences in changes in observed fluorescence, thus we are comparing scaled averages of signals across sexes, and days, which helps minimize any differences between “low” or “high” viral transduction levels at the terminals, directly underneath the tips of the fibers.

While the authors have some previous data on the AIC to DLS pathway, there are many brain regions and pathways impacted by alcohol and so the focus on this one in particular was not strongly justified. Since photometry is really an observational method, it's important to note that no causal link between activity in the pathway and drinking has been established here. 

As mentioned above, this article is an eLife Research Advances article that builds on our previous AIC to DLS work published in eLife (Haggerty et al., 2022). Considering that this is a linked article, a justification for why this brain pathway was chosen is superfluous. In addition, an exhaustive review of all the different brain regions and pathways that are affected by binge alcohol consumption to justify this pathway seems more appropriate to a review article than an article such as this.  

We make no claims that photometric recordings are anything but observational, but we did observe these signals to be different when time-locked to the beginning of drinking behaviors. We describe this link between activity in the pathway and drinking throughout the manuscript. It is indeed correlational, but just because it is not causal does not mean that our findings are invalid or unimportant.

It would be helpful if the authors could further explain whether their modified lickometers actually measure individual licks. While in some systems contact with the tongue closes a circuit which is recorded, the interruption of a photobeam was used here. It's not clear to me whether the nose close to the spout would be sufficient to interrupt that beam, or whether a tongue protrusion is required. This detail is important for understanding how the photometry data is linked to behaviour. The temporal resolution of the GCaMP signal is likely not good enough to capture individual links but I think more caution or detail in the discussion of the correspondence of these events is required. 

The lickometers do not capture individual licks, but a robust quantification of the information they capture is described in Godynyuk et al. 2019 and referenced in multiple other papers (Flanigan et al. 2023, Haggerty et al. 2022, Grecco et al. 2022, Holloway et al. 2023) where these lickometers have been used. However, individual lick tracking is not a requirement for tracking drinking behaviors more generally. The lickometers used clearly track when the animals are at the bottles, drinking fluids, and we have used the start of that lickometer signal to time-lock our photometry signals to drinking behaviors. We make no claims or have any data on how photometric signals may be altered on timescales of single licks. In regard to how AIC to DLS signals change on the second time scale when animals initiate drinking behaviors, we believe we explain these signals with caution and in context of the behaviors they aim to describe.

Even if the pattern of drinking differs between males and females, the use of the word "strategy" implies a cognitive process that was never described or measured. 

We use the word strategy to describe a plan of action that is executed by some chunking of motor sequences that amounts to a behavioral event, in this case drinking a fluid. We do not mean to imply anything further than this by using this specific word.

Reviewer #2 (Public Review): 

Summary: 

This study looks at sex differences in alcohol drinking behaviour in a well-validated model of binge drinking. They provide a comprehensive analysis of drinking behaviour within and between sessions for males and females, as well as looking at the calcium dynamics in neurons projecting from the anterior insula cortex to the dorsolateral striatum. 

Strengths: 

Examining specific sex differences in drinking behaviour is important. This research question is currently a major focus for preclinical researchers looking at substance use. Although we have made a lot of progress over the last few years, there is still a lot that is not understood about sex-differences in alcohol consumption and the clinical implications of this. 

Identifying the lateralisation of activity is novel, and has fundamental importance for researchers investigating functional anatomy underlying alcohol-driven behaviour (and other reward-driven behaviours). 

Weaknesses: 

Very small and unequal sample sizes, especially females (9 males, 5 females). This is probably ok for the calcium imaging, especially with the G-power figures provided, however, I would be cautious with the outcomes of the drinking behaviour, which can be quite variable. 

For female drinking behaviour, rather than this being labelled "more efficient", could this just be that female mice (being substantially smaller than male mice) just don't need to consume as much liquid to reach the same g/kg. In which case, the interpretation might not be so much that females are more efficient, as that mice are very good at titrating their intake to achieve the desired dose of alcohol. 

We agree that the “more efficient” drinking language could be bolstered by additional discussion in the text, and thus have added this to the manuscript starting at line 440.

I may be mistaken, but is ANCOVA, with sex as the covariate, the appropriate way to test for sex differences? My understanding was that with an ANCOVA, the covariate is a continuous variable that you are controlling for, not looking for differences in. In that regard, given that sex is not continuous, can it be used as a covariate? I note that in the results, sex is defined as the "grouping variable" rather than the covariate. The analysis strategy should be clarified. 

In lines 265-267, we explicitly state that the covariate factor was sex, which is mathematically correct based on the analyses we ran. We made an in-text error where we referred to sex as a grouping variable on Line 352, when it should have been the covariate. Thank you for the catch and we have corrected the manuscript.

But, to reiterate, we are attempting to determine if the regression fits by sex are significantly different, which would be reported as a significant covariate. Sex is certainly a categorical variable, but the two measures at which we are comparing them against are continuous, so we believe we have the validity to run an ANCOVA here.

Reviewer #3 (Public Review): 

Summary: 

In this manuscript by Haggerty and Atwood, the authors use a repeated binge drinking paradigm to assess how water and ethanol intake changes in male in female mice as well as measure changes in anterior insular cortex to dorsolateral striatum terminal activity using fiber photometry. They find that overall, males and females have similar overall water and ethanol intake, but females appear to be more efficient alcohol drinkers. Using fiber photometry, they show that the anterior insular cortex (AIC) to dorsolateral striatum projections (DLS) projections have sex, fluid, and lateralization differences. The male left circuit was most robust when aligned to ethanol drinking, and water was somewhat less robust. Male right, and female and left and right, had essentially no change in photometry activity. To some degree, the changes in terminal activity appear to be related to fluid exposure over time, as well as within-session differences in trial-by-trial intake. Overall, the authors provide an exhaustive analysis of the behavioral and photometric data, thus providing the scientific community with a rich information set to continue to study this interesting circuit. However, although the analysis is impressive, there are a few inconsistencies regarding specific measures (e.g., AUC, duration of licking) that do not quite fit together across analytic domains. This does not reduce the rigor of the work, but it does somewhat limit the interpretability of the data, at least within the scope of this single manuscript. 

Strengths: 

- The authors use high-resolution licking data to characterize ingestive behaviors. 

- The authors account for a variety of important variables, such as fluid type, brain lateralization, and sex. 

- The authors provide a nice discussion on how this data fits with other data, both from their laboratory and others'. 

- The lateralization discovery is particularly novel. 

Weaknesses: 

- The volume of data and number of variables provided makes it difficult to find a cohesive link between data sets. This limits interpretability.

We agree there is a lot of data and variables within the study design, but also believe it is important to display the null and positive findings with each other to describe the changes we measured wholistically across water and alcohol drinking.

- The authors describe a clear sex difference in the photometry circuit activity. However, I am curious about whether female mice that drink more similarly to males (e.g., less efficiently?) also show increased activity in the left circuit, similar to males. Oppositely, do very efficient males show weaker calcium activity in the circuit? Ultimately, I am curious about how the circuit activity maps to the behaviors described in Figures 1 and 2. 

In Figure 3C, we show that across the time window of drinking behaviors, that female mice who drink alcohol do have a higher baseline calcium activity compared to water drinking female mice, so we believe there are certainly alcohol induced changes in AIC to DLS within females, but there remains to be a lack of engagement (as measured by changes in amplitude) compared to males. So, when comparing consummatory patterns that are similar by sex, we still see the lack of calcium signaling near the drinking bouts, but small shifts in baseline activity that we aren’t truly powered to resolve (using an AUC or similar measurements for quantification) because the shifts are so small. Ultimately, we presume that the AIC to DLS inputs in females aren’t the primary node for encoding this behavior, and some recent work out of David Werner’s group (Towner et al. 2023) suggests that for males who drink, the AIC becomes a primary node of control, whereas in females, the PFC and ACC, are more engaged. Thus, the mapping of the circuit activity onto the drinking behaviors more generally represented in Figures 1 and 2 may be sexually dimorphic and further studies will be needed to resolve how females engage differential circuitry to encode ongoing binge drinking behaviors.

- What does the change in water-drinking calcium imaging across time in males mean? Especially considering that alcohol-related signals do not seem to change much over time, I am not sure what it means to have water drinking change. 

The AIC seems to encode many physiologically relevant, interoceptive signals, and the water drinking in males was also puzzling to us as well. Currently, we think it may be both the animals becoming more efficient at drinking out of the lickometers in early weeks and may also be signaling changes due to thirst states of taste associated with the fluid. While this is speculation, we need to perform more in-depth studies to determine how thirst states or taste may modulate AIC to DLS inputs, but we believe that is beyond the scope of this current study.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors): 

Line 45 - states alcohol use rates are increasing in females across the past half-decade. I thought this trend was apparent over the past half-century? Please consider revising this. 

According to NIAAA, the rates of alcohol consumption in females compares to males has been closing for about the past 100 years now, but only recently are those trends starting to reverse, where females are drinking similar amounts or more than males.

Placing more of the null findings into supplemental data would make the long paper more accessible to the reader. 

In reference to reviewer’s three’s point as well, there is a lot of data we present, and we hope for others to use this data, both null and positive findings in their future work. As formatted on eLife’s website, we think it is important to place these findings in-line as well.

Reviewer #2 (Recommendations For The Authors): 

In addition to the points raised about analysis and interpretation in the Public Review, I have a minor concern about the written content. I find the final sentence of the introduction "together these findings represent targets for future pharmacotherapies.." a bit unjustified and meaningless. The findings are important for a basic understanding of alcohol drinking behaviour, but it's unclear how pharmacotherapies could target lateralised aic inputs into dls. 

There are on-going studies (CANON-Pilot Study, BRAVE Lab, Stanford) for targeted therapies that use technologies like TMS and focused ultrasound to activate the AIC to alleviate alcohol cravings and decrease heavy drinking days. The difficulty with these next-generation therapeutics is often targeting, and thus we think this work may be of use to those in the clinic to further develop these treatments. We agree that this data does not support the development of pharmacotherapies in a traditional sense, and thus have removed the word and added text to reference TMS and ultrasound approaches to bolster this statement in lines 101+.

eLife assessment

This paper introduces an efficient approach to identify subunits in the receptive fields of retinal ganglion cells. The general approach has been used in this application previously and this limits the conceptual advance of the paper. The improved speed is valuable, as it allows a more thorough exploration of the control parameters in this analysis and facilitates application to larger populations of cells. Validation of the approach is convincing. The paper would benefit from a more thorough exploration of the method and its limitations, or an extension of the new results about subunit populations.

Reviewer #1 (Public Review):

Summary:

This paper introduces an efficient approach to infer properties of receptive-field subunits from the ensemble of spike-triggered stimuli. This is an important general problem in sensory coding. The results introduced in the paper make a solid contribution to both how subunits can be identified and how subunits of different types are coordinated in space.

Strengths:

A primary strength of the paper is the development of approaches that substantially speed non-negative matrix factorization and by doing so create an opportunity for a more systematic exploration of how the procedure depends on various control parameters. The improved procedure is well documented and the direct comparisons with previous procedures are helpful. The improved efficiency enabled several improvements in the procedure - notably tests of good procedures for initializing NNMF and tests of the dependence of the results on the sparsity regularization parameter.

A second strength of the paper is the exploration of the spatial relationship between different subunits. This, to my knowledge, is new and is an interesting direction. There are some concerns about this analysis (see weaknesses below), but if this analysis can be strengthened it will provide new information that will be important both functionally and developmentally.

Weaknesses:

A primary concern is that choices made about parameters for several aspects of the analysis appear to be made subjectively. Much of this centers around how much of the structure in the extracted subunits is imposed by the procedure itself, and how much reflects the underlying neural circuitry. Some specific issues related to this concern are:

- Sparsity: the use of the autocorrelation function to differentiate real vs spurious subunits should be documented and validated. For example, can the authors split data in half and show that the real subunits are stable?

- Choice of regularization: the impact of the regularization parameter on subunit properties is nicely documented. However, the choice of an appropriate regularization parameter seems somewhat arbitrary. Line 253-256 is an example of this problem: this sentence sounds circular - as if the sparsity factor was turned up until the authors obtained what they expected to obtain. Could the choice of this parameter significantly impact the properties of the extracted subunits? How sensitive are the subunit properties to that parameter? Some additional control analyses are needed to validate the parameter choice (see the crossvalidation comment below).

- Crossvalidation was not used to identify the regularization constraint value because the weight matrix from NNMF does not generalize beyond the data it was fit to. Could the authors instead hold the components matrix fixed and recompute the weight matrix, and use that approach for cross-validation (especially since it is really the components matrix that needs validating)?

The paper would benefit from a more complete comparison with known anatomy. For example, can the authors estimate the number of cones within each subunit? This is well-constrained both anatomically (at least in macaque) and, especially for midget ganglion cell subunits, functionally. In macaque, most midget bipolar cells get input from single cones, so the number of extracted subunits should be close to the number of cones. This would be a useful point of comparison for the current work.

Is the analysis of the spatial relationship between different subunit mosaics robust to the incompleteness of those mosaics? The argument on lines 496-503 should be backed up by more analysis. For example, if subunits are removed from regions where the mosaic is pretty complete, do the authors change the spatial dependence? Alternatively, could they use synthetic mosaics with properties like those measured to check the sensitivity to missing cells?

NNMF relies on accounting for each spike-triggered stimulus with a linear combination of components. Would nonlinearities - e.g. those in the bipolar cell outputs - substantially change the results?

Does the approach work for cells that receive input from multiple bipolar types? Some ganglion cells, e.g. in mice, receive input from multiple bipolar types, each accounting for a sizable percentage of the total input. There is similar anatomical work indicating that parasol cells may receive input from multiple diffuse bipolar types. It is not clear whether the current approach works in cases where the subunits of a single ganglion cell overlap. Some discussion of this would be useful.

Reviewer #2 (Public Review):

Summary:

Identifying spatial subunits within the receptive field of retinal ganglion cells can help study spatial nonlinearities and upstream computations performed by the bipolar cells. The authors significantly accelerate the implementation of the previously proposed Spike Triggered semi-non-negative Matrix Factorization (STNMF) method to identify the subunits. The authors also propose a few method improvements - better initialization; new stability-based criteria for selecting the regularization strength, and hyperparameter selection across cell types.

The authors then apply this new method to RGC populations in both the salamander retina and the macaque (marmoset) retina. The authors document the subunit sizes, numbers, and overlap across cell types. The neuroscience finding describes the anti-coordination of ON and OFF parasol receptive fields, but not for the corresponding subunits.

Overall, the authors claim that a faster and more accurate method makes scale-up to large neuronal populations feasible.

Strengths:

- The paper is well-written, easy to read and the figures are clear. The limitations are also made clear.

- The scientific findings are novel and seem to be well supported.

- The claimed speed-up of the method is potentially important for practical applications to large populations. Each innovation of the method is well-supported.

- This is a serious effort to improve the method and document the subunits in primate retina.

Weaknesses:

- The description of the method is confusing. Currently, the new method is described in the context of changes from existing methods. As someone who is not familiar with previous methods, it is very confusing to follow the details.

- I think it will help a lot with clarity to have a concise flowchart/pseudocode to summarize the algorithm and separate it from a description of the main changes from previous methods.

- Separate pseudocodes can be provided for the main method, initialization, regularization parameter selection using consensus, and identifying the regularization parameter across cell types.

- While the new method clearly shows a drastic improvement compared to the previous method on a laptop, would it be possible to get the same improvement on the previous method if it was implemented with GPU (as is standard for most AI/ML algorithms)?

- For the calculation of subunits across multiple cells, can you run multiple parallel jobs on the same computer? This may make some innovations unnecessary (like setting the same regularization strength across multiple cells).

- There are two main innovations in this paper: the fast and approximate method, and analysis of subunit mosaics for primate RGCs. It would be helpful to include an analysis of the primate RGC subunits using the older, slower, but more exact method and show that the major scientific results can be reproduced. This would validate the new method in an end-to-end manner. While this may take a while to run, it may be helpful in the supplement.

- It would be important to understand the data-efficiency of the method. The approximate method may deviate more from the exact method when the amount of data is limited.

- Would it be possible to have a few steps of the exact method at the end to ensure that the solution truly optimizes the objective function?

- Does the number of estimated subunits change with the number of observed spikes? If so, the estimates of subunit number/size must be interpreted with caution.

Reviewer #3 (Public Review):

Summary:

This work addresses the problem of determining the subunit composition of receptive fields of retinal ganglion cells (RGCs). RGCs process stimuli through non-linear transforms that largely (although not entirely) reflect the individual contributions of their input bipolar cells, which themselves process visual stimuli nonlinearly. Thus, using the correct system identification methods might correctly model the RGC cells, while revealing details of the underlying circuit, including the function of the presynaptic components. It is now well established that a model of the form of an LNLN cascade can potentially capture this bipolar-RGC circuit, although the devil is in the details. The authors present an improved method of non-negative matrix factorization (NMF) - which is one approach to this system identification problem - that can speed things up by a factor of 100, and in doing so infer plausible mosaics of the bipolar cell types supporting the identified RGC types that are recorded from.

As written, the focus of this paper seems almost entirely methodological, supporting the sped-up version of NMF, called STNMF. The >100x speedup potentially makes a lot more measurements available, since it enables much more comprehensive scans across model meta-parameters, although has its own complications that must also be methodologically addressed. The results presented are largely a demonstration and validation of the potential power of this approach using example recordings in the peripheral marmoset retina. I do not think the results themselves are meant to be evaluated as definitive, since they are often based on examples and are largely confirmatory of what is already known.

Strengths:

I have very few concerns about the paper methodologically: these methods are well laid out and demonstrated (at least up to the level of my expertise and interest), including validation with established literature.

I am also enthusiastic about some of the potential results in the retina outlined (but not fully fleshed out) in the later sections of the paper.

Weaknesses:

My main critique is to question the conceptual advance in this paper: what did we learn, and what is the targeted audience of interest? Establishing this is particularly dire for this manuscript since NMF has already been established and expounded on as a useful approach in this context (including by the author most recently in 2017) so any of the scientific results is already achievable with enough computer power using existing approaches. As currently cast, the conceptual advances here are purely methodological and relate to the utility of speeding up the approach. Also, they do not appear to generalize to other problems outside of the narrow range that it is currently applied.

Thus, two paths to improving the manuscript would be either:<br /> (1) target readers interested in the retina by fully fleshing out the current results and add more to make this into a paper about the retina rather than about the STNMF method, or<br /> (2) demonstrate that the methods might be useful outside of the very narrow set of conditions specific to identifying nonlinear bipolar cell subunits in peripheral retina under white noise stimulation.

In its current state, the Discussion addressing limitations and generality seems to suggest applicability past this narrow condition, which I do not think is the case: but would be happy to be convinced otherwise.

For fleshing out scientific results, in the current manuscript, they are currently presented to validate the approach and are largely confirmatory for what we already know about the retina (which allows for this validation). Also, much of the results are measurements based on examples, and not accumulated past a single recording in some cases. Finally, it is not clear to the extent that these results depend on the specific recordings in the peripheral marmoset retina: what about more central in the retina, or in other species?

For demonstrating the utility of the methodology: here are some of the main limitations to generalizing past this specific case:<br /> (1) the necessity of linear or near-linear processing in previous layers;<br /> (2) lack of any negative components;<br /> (3) lack of ability to account for other influences on spiking than the positive contributions of LN subunits;<br /> (4) necessity of white noise stimulation that is specifically sized for a uniform subunit size.

Together, I believe this precludes potential applications to other areas in the brain: further back in the visual system will require non-linear transforms as well as the convergence of positive and negative inputs. Other sensory systems like the auditory system are even more non-linear well before getting to even mid-level pre-cortical structures and also combine positive and negative influences. Given the importance of inhibition in the retina (including what is thought to be an important role of amacrine cells in shaping RGC responses), it is not clear how general this approach is in the retina, although the specific results shown are believable. How could this approach generalize, realistically? Could applications to other types of data be demonstrated, and/or plausibly get by these fundamental limitations? How?

eLife assessment

This valuable study examines the role of the interaction between cytoplasmic N- and C-terminal domains in voltage-dependent gating of Kv10.1 channels. The authors suggest that they have identified a hidden open state in Kv10.1 mutant channels, thus providing a window for observing early conformational transitions associated with channel gating. The evidence supporting the major conclusions is solid, but additional work is required to determine the molecular mechanism underlying the observations in this study. Learning the molecular mechanisms could be significant in understanding the gating mechanisms of the KCNH family and will appeal to biophysicists interested in ion channels and physiologists interested in cancer biology.

Gating of Kv10 channels is unique because it involves coupling between non-domain swapped voltage sensing domains, a domain-swapped cytoplasmic ring assembly formed by the N- and C-termini, and the pore domain. Recent structural data suggests that activation of the voltage sensing domain relieves a steric hindrance to pore opening, but the contribution of the cytoplasmic domain to gating is still not well understood. This aspect is of particular importance because proteins like calmodulin interact with the cytoplasmic domain to regulate channel activity. The effects of calmodulin (CaM) in WT and mutant channels with disrupted cytoplasmic gating ring assemblies are contradictory, resulting in inhibition or activation, respectively. The underlying mechanism for these discrepancies is not understood. In the present manuscript, Reham Abdelaziz and collaborators use electrophysiology, biochemistry and mathematical modeling to describe how mutations and deletions that disrupt inter-subunit interactions at the cytoplasmic gating ring assembly affect Kv10.1 channel gating and modulation by CaM. In the revised manuscript, additional information is provided to allow readers to identify within the Kv10.1 channel structure the location of E600R, one of the key channel mutants analyzed in this study. However, the mechanistic role of the cytoplasmic domains that this study focuses on, as well as the location of the ΔPASCap deletion and other perturbations investigated in the study remain difficult to visualize without additional graphical information.

The authors focused mainly on two structural perturbations that disrupt interactions within the cytoplasmic domain, the E600R mutant and the ΔPASCap deletion. By expressing mutants in oocytes and recording currents using Two Electrode Voltage-Clamp (TEV), it is found that both ΔPASCap and E600R mutants have biphasic conductance-voltage (G-V) relations and exhibit activation and deactivation kinetics with multiple voltage-dependent components. Importantly, the mutant-specific component in the G-V relations is observed at negative voltages where WT channels remain closed. The authors argue that the biphasic behavior in the G-V relations is unlikely to result from two different populations of channels in the oocytes, because they found that the relative amplitude between the two components in the G-V relations was highly reproducible across individual oocytes that otherwise tend to show high variability in expression levels. Instead, the G-V relations for all mutant channels could be well described by an equation that considers two open states O1 and O2, and a transition between them; O1 appeared to be unaffected by any of the structural manipulations tested (i.e. E600R, ΔPASCap, and other deletions) whereas the parameters for O2 and the transition between the two open states were different between constructs. The O1 state is not observed in WT channels and is hypothesized to be associated with voltage sensor activation. O2 represents the open state that is normally observed in WT channels and is speculated to be associated with conformational changes within the cytoplasmic gating ring that follow voltage sensor activation, which could explain why the mutations and deletions disrupting cytoplasmic interactions affect primarily O2.

Severing the covalent link between the voltage sensor and pore reduced O1 occupancy in one of the deletion constructs. Although this observation is consistent with the hypothesis that voltage-sensor activation drives entry into O1, this result is not conclusive. Structural as well as functional data has established that the coupling of the voltage sensor and pore does not entirely rely on the S4-S5 covalent linker between the sensor and the pore, and thus the severed construct could still retain coupling through other mechanisms, which is consistent with the prominent voltage dependence that is observed. If both states O1 and O2 require voltage sensor activation, it is unclear why the severed construct would affect state O1 primarily, as suggested in the manuscript, as opposed to decreasing occupancy of both open states. In line with this argument, the presence of Mg2+ in the extracellular solution affected both O1 and O2. This finding suggests that entry into both O1 and O2 requires voltage-sensor activation because Mg2+ ions are known to stabilize the voltage sensor in its most deactivated conformations.

Activation towards and closure from O1 is slow, whereas channels close rapidly from O2. A rapid alternating pulse protocol was used to take advantage of the difference in activation and deactivation kinetics between the two open components in the mutants and thus drive an increasing number of channels towards state O1. Currents activated by the alternating protocol reached larger amplitudes than those elicited by a long depolarization to the same voltage. This finding is interpreted as an indication that O1 has a larger macroscopic conductance than O2. In the revised manuscript, the authors performed single-channel recordings to determine why O1 and O2 have different macroscopic conductance. The results show that at voltages where the state O1 predominates, channels exhibited longer open times and overall higher open probability, whereas at more depolarized voltages where occupancy of O2 increases, channels exhibited more flickery gating behavior and decreased open probability. These results are informative but not conclusive since single-channel amplitudes could not be resolved at strong depolarizations, limiting the extent to which the data could be analyzed. In the last revision, the authors have included one representative example showing inhibition of single channel activity by the Kv10-specific inhibitor astemizole. Group data analysis would be needed to conclusively establish that the currents that were recorded indeed correspond to Kv10 channels.

It is shown that conditioning pulses to very negative voltages result in mutant channel currents that are larger and activate more slowly than those elicited at the same voltage but starting from less negative conditioning pulses. In voltage-activated curves, O1 occupancy is shown to be favored by increasingly negative conditioning voltages. This is interpreted as indicating that O1 is primarily accessed from deeply closed states in which voltage sensors are in their most deactivated position. Consistently, a mutation that destabilizes these deactivated states is shown to largely suppress the first component in voltage-activation curves for both ΔPASCap and E600R channels.

The authors then address the role of the hidden O1 state in channel regulation by calcium-calmodulin (CaM). Stimulating calcium entry into oocytes with ionomycin and thapsigargin, assumed to enhance CaM-dependent modulation, resulted in preferential potentiation of the first component in ΔPASCap and E600R channels. This potentiation was attenuated by including an additional mutation that disfavors deeply closed states. Together, these results are interpreted as an indication that calcium-CaM preferentially stabilizes deeply closed states from which O1 can be readily accessed in mutant channels, thus favoring current activation. In WT channels lacking a conducting O1 state, CaM stabilizes deeply closed states and is therefore inhibitory. It is found that the potentiation of ΔPASCap and E600R by CaM is more strongly attenuated by mutations in the channel that are assumed to disrupt interaction with the C-terminal lobe of CaM than mutations assumed to affect interaction with the N-terminal lobe. These results are intriguing but difficult to interpret in mechanistic terms. The strong effect that calcium-CaM had on the occupancy of the O1 state in the mutants raises the possibility that O1 can be only observed in channels that are constitutively associated with CaM. To address this, a biochemical pull-down assay was carried out to establish that only a small fraction of channels are associated with CaM under baseline conditions. These CaM experiments are potentially very interesting and could have wide physiological relevance. However, the approach utilized to activate CaM is indirect and could result in additional non-specific effects on the oocytes that could affect the results.

Finally, a mathematical model is proposed consisting of two layers involving two activation steps for the voltage sensor, and one conformational change in the cytoplasmic gating ring - completion of both sets of conformational changes is required to access state O2, but accessing state O1 only requires completion of the first voltage-sensor activation step in the four subunits. The model qualitatively reproduces most major findings on the mutants. Although the model used is highly symmetric and appears simple, the mathematical form used for the rate constants in the model adds a layer of complexity to the model that makes mechanistic interpretations difficult. In addition, many transitions that from a mechanistic standpoint should not depend on voltage were assigned a voltage dependence in the model. These limitations diminish the mechanistic insight that can be reliably extracted from the model.

Author response:

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

We appreciate the feedback provided and refer to our previous response for detailed explanations regarding our decisions on some of the recommendations made by the referees and editors. We have introduced changes as follows:

• We added a supplementary Figure to Figure 5 to show inhibition by Astemizole at the single channel level.

• We have corrected Figure 7A, where the normalized current did not reach 1 as a maximum. We had overlooked that this is expected when the prepulse was -160 mV, and the IV is strongly biphasic, but not when coming from -100 mV. We are thankful for this observation, which served to identify that the values for one of the cells were inverted with respect to the others (the sequence of stimuli was different during recording, and this information got lost in the analysis procedure). We have corrected this and made sure that such a mistake had not happened anywhere else.

• Finally, we have corrected a typo in the discussion, as indicated in the review.

We include a version with changes marked and a clean version of the manuscript.

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

Learn more at Review Commons

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Reply to the reviewers

We would like to thank the reviewers for their overall positive assessment of our manuscript. We have used their constructive feedback to substantially improve our manuscript as described below.

Reviewer #1

Evidence, reproducibility and clarity

This study by Reyes at al is a well conducted analysis of memory B cell dynamics of Plasmodium falciparum (Pf) -specific B cell populations over the course of reducing Pf prevalence in ten Ugandan adults. The data is presented well and the authors provide compelling evidence that 1. There is an overall loss of Ag specific B cells with reduction in exposure and 2. Different antigens (MSP1/AMA-1 vs CIDRa-1) generate different flavors of long lived responses. However, additional clarity to the reader should be provided on certain topics (listed below).

Major comments: 1. While the premise of the study (reduced Pf transmission due to the use of indoor residual spraying (IRS)) is an important one, I think the authors must take into consideration that 9/10 subjects had at least one Pf positive episode between Time Points 1 and 2 (Figure 1). Also, it looks from Fig 1 that some samples were collected at a time of Pf positive test (green squares), while in Table S1 none of the subjects have a positive parasite status at TP1.

We recognize that most individuals had detectable parasitemia before and after time point (TP) 1. In our manuscript, we therefore do not report the time between TP1 and TP2, because we agree that the length of this time interval is not relevant in our study methodology. We only mention the time between the last known P. falciparum infection and collection of blood at the second time point. We use the sample collected at TP1 only as a representative sample obtained during a time with high P. falciparum exposure and do not make any claims based on the time between TP1 and TP2. The occurrence of infections after sample collection at TP1 confirms that parasite transmission was still high at this time. We have added a schematic of the relative levels of parasite transmission to Figure 1 to emphasize this.

With respect to infection status, none of the donors were blood smear positive at TP1. However, as mentioned in Table S1, parasites were detected in three individuals using the more sensitive LAMP assay. These three individuals are therefore marked as parasite positive in Figure 1. Table S1 has been modified to highlight the parasite status of these three individuals.

1. Figure S1A: What is trBC? Figure S1B: What is Strep? Are the strep positive cells also CIDR-1 positive and were they gated out? Why is APC used for MZ-1 and one of the MSP1-AMA-1 tetramers? Do these stainings come from multiple panels?

All abbreviations of B cell populations were defined in the figure legend (for example, trBC stands for transitional B cells). To facilitate the interpretation of Figure S1, we have now included the definitions of these abbreviations in the figure.

Strep stands for streptavidin, which has now also been clarified in the figure. In our gating strategy, we used the term “strep” to denote cells that bound to both CIDRa1 and MSP1/AMA1 tetramers, which we interpreted as non-specific binding to streptavidin or other components of the antigen tetramers. Only the “non-strep” cells were used to gate on antigen-specific cells. We have added this clarification to the figure legend.

In panel B, we accidentally used the term MZ (for merozoite) to describe tetramers of the merozoite antigens MSP1 / AMA1. These labels are interchangeable, but to avoid confusion, MZ-1 has been changed to MSP1 / AMA1.

1. Figure 3A: how many cells does the umap plot represent? Were there a total of 3555 Ag specific B cells that were non-naive (Figure 3E)?

It is correct that there were a total of 3,555 antigen-specific B cells used for the clustering shown in panel A. This information has been added to Figure 3A.

1. Could the authors comment on why in Figure 3, Ig isotype expression was not considered for clustering? This would allow for characterization of DN sub populations/ clusters in addition to the CD21-CD27- ABCs? It looks like IgD expression was low across the clusters (Figure 3D). Was this the case for the cells considered in this analysis, or was it excluded? If it was truly low expressed, how were the assessments in Figure 2 made?

From prior experience, we know that Ig isotype information tends to dominate in the clustering, which would result in major clusters based on IgM, IgD, IgG, and IgA expression, not on expression of other markers. This is illustrated in the example below. The UMAP on the left shows clusters in green and red that consist of IgG+ and IgA+ B cells, respectively. The UMAP on the right shows that switched memory (swM) B cells and DN B cells are found in both IgG and IgA clusters. Because we were mainly interested in identifying different subsets of B cells, irrespective of Ig isotype, we did not include Ig isotype in the clustering. We have clarified in the manuscript that Ig isotypes were excluded from the analysis to prevent these from dominating the clustering:

“Unsupervised clustering was then performed based on expression of all markers, except for Ig isotypes to prevent these from dominating the clustering.”

IgD expression among cell clusters shown in Figure 3 was low because only non-naïve B cells were included in the analyis. The majority of non-naïve cells are class-switched memory B cells and DN B cells, which by definition do not express IgD (see gating strategy in Figure S1A). Figure 2 shows all B cell populations, including naïve B cells and non-naïve B cell populations (unswitched memory, switched memory, and DN), that were gated based on IgD and CD27 expression.

5.Are there differences in these designations / phenotypes of DN populations of atBCs vs CD21-CD27- atBCs?

In the malaria field, atypical B cells are typically defined as CD21-CD27-. The definition of DN2 B cells comes from the autoimmunity field and is stricter: IgD-CD27-CD21-CD11c+ B cells. In our manuscript, we define atypical B cells in a stricter way than typically done in the malaria field, following published guidelines for the identification of B cell subsets (https://doi.org/10.3389/fimmu.2019.02458). Using these guidelines, atypical B cells and DN2 B cells are phenotypically identical. We have added a reference to these published guidelines in the Results section:

“Following published guidelines for the identification of B cell populations (21), total CD19+ B cells were divided into naïve B cells (IgD+CD27-), unswitched memory B cells (IgD+CD27+), switched memory B cells (IgD-CD27+), and double negative B cells (IgD-CD27-).”

1. Lines 258-259: In considering only switched MBCs, what clusters from Figure 3a were included? There seem to be 2588 sw MBCs (Table S3, Figure 4). Do the remaining cells (967 cells) come from clusters 2, 5 and 6 (and excludes the atBC clusters)

This analysis did not use the clusters presented in Figure 3, but instead used switched memory B cells gated as shown in Figure S1A. The reason for this is that the clusters in Figure 3 were generated using antigen-specific B cells and cannot be reproduced using non-antigen-specific B cells. Thus, it is not possible to separate all other B cells into the same six clusters. The only way to compare expression of certain markers between antigen-specific and non-antigen specific switched memory B cells is to gate on these populations manually. We have now tried to clarify this in the manuscript as follows:

“we determined the percentages of CD95+ cells and CD11c+ cells among antigen-specific switched memory B cells and the total population of switched memory B cells (gated manually as shown in Figure S1A).”

Minor comments: 1. Line 178- 179: Was there a specific measure of rate of decline made for these cells?

We did not calculate a rate of decline of antigen-specific B cells for several reasons: 1) the time between TP1 and TP2 is not the same for all people in the study, 2) the time between last exposure and TP2 is not the same for all people, and 3) the rate of decline is most likely not linear and cannot accurately be estimated with only two data points. We have changed the wording of this sentence such that we do not use the word “rate”:

“we did not observe a difference in the percentage of B cells with specificity for merozoite antigens or variant surface antigens that were lost.”

In addition, we included the percentage of reduction in size in the paragraph before this section:

“we observed that both populations decreased in size by about 50%, although these differences were not statistically significant.”

Significance

General assessment: Strengths: The authors provide evidence that the dynamics of antigen specific cells in humans can vary with exposure and with the nature of the antigen. They have nicely discussed the potential causes for these differences (Discussion), although they should include the findings of Ambegaonkar et al that ABCs in malaria may be restricted to responding specifically to membrane bound antigens (PMCID: PMC7380957)

As suggested by the reviewer, we have added a paragraph to the Discussion section to discuss the results reported by Ambegaonkar et al. and how the difference between soluble vs. membrane-bound antigens may have an effect on how these antigens are perceived by B cells:

The difference between soluble and membrane-bound antigens may also have a direct effect on how these antigens are perceived by B cells. Atypical B cells have been shown to be restricted to recognition of membrane-bound antigens (41). The interaction of a B cell with membrane-associated antigen allows the formation of an immunological synapse. Inhibitory receptors expressed by atypical B cells are excluded from this synapse, resulting in B cell receptor signaling and differentiation towards antibody-secreting cells (41). This could explain why atypical B cell subset 1 that expresses the highest levels of the inhibitory receptor FcRL5 is enriched for recognition of the CIDRα1 domain of membrane-bound protein PfEMP1. It should however be noted that soluble antigen can also be presented effectively in membrane-context by conventional dendritic cells, follicular dendritic cells, and subcapsular macrophages in secondary lymphoid organs, especially when it is part of an immune complex (reviewed in (42)). This would provide a route for atypical B cells to also respond to soluble merozoite antigens, such as MSP1 and AMA1.

Limitations: 1. Outlined above, and as the authors also mention, a small sample size and homogenous population. 2. The evidence for reduced transmission is not clear, and the negative parasite tests for donors shown in Table S1 do not match with Figure 1 data. 3. Lack of IgD expression across clusters (Figure 3D- the authors will need to clarify this point) would require re-analysis of Figure 2 data

1. We have provided clarification in response to the points raised by the reviewer.

2. We believe there is clear evidence for reduced transmission, from a median of almost 2 infections per person per year prior to the implementation of IRS to a median parasite-free period of 1.7 years prior to sample collection at TP2. To further emphasize this, we have summarized the number of P. falciparum infections among the ten individuals included in this study (now included in Table S3):

year

Pf infections

comment

2012

20

2013

19

TP1

2014

20

TP1

2015

8

Start IRS

2016

0

TP2

This reduced parasite exposure is reflected in a decrease in immune activation as presented in Figure 2. We have clarified that the data in Table S1 did indeed match those shown in Figure 1.

1. We have clarified that IgD expression is low in the clusters presented in Figure 3 because naïve B cells were excluded from this analysis.

Advances: This study highlights the importance of studying antigen specific B cells in humans in the context of natural infection and the use of high-parameter tools such as spectral flow cytometry in assessing a large quantity of data from limited clinical samples. These data are important to inform better vaccine design. Studies in inbred animals can be quite limited or different from human B cell responses.

Audience: This study will be of interest to malariologists and B cell immunologists. Atypical B cells are relevant to many infectious diseases and auto immunity, while the dynamics of memory B cells in malaria all be relevant to those interested in vaccine design against blood stage antigens.

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Reviewer #2

Evidence, reproducibility and clarity

Summary: In this study, the authors compared long-lived total and antigen (ag)-specific B-cell levels in a cohort of 10 Ugandan malaria patient samples that were collected before and after local reduction of P. falciparum transmission (pre/post-IRS). The focus is on the novel comparison of the two most common malaria antigens: merozoite antigens (MSP1/AMA1) and variant surface antigens (CIDRα1). Using high-parameter spectral flow cytometry, they also characterized the phenotype of the different populations of cells. Their main findings include 1) a decrease in activated but maintenance of resting ag-specific B-cells in the post-IRS sample and 2) CD95 and CD11c, as the only differentially expressed markers between MSP1/AMA1-specific and CIDRα1-specific long-lived memory B cells. Their further phenotypic characterization suggests functional consequences with MSP1/AMA1-specific B-cells being poised for rapid antibody-secreting cell differentiation while CIDRα1-specific B cells were enriched among a subset of atypical B cells that seem poised for antigen presentation (CD86+CD11chi/ AtBC1). Their findings consolidate and further expand our knowledge of long-lived B-cell levels during P. falciparum malaria and report/compare (for the first time to my knowledge) a differential selection of long-lived B-cell levels between these 2 antigen specificities. Overall, the manuscript is straightforward and well-written and the authors did a good job explaining their methodology, findings, and interpretations. I believe the major gap missing in this study is the reconciliation of long-lived antigen-specific B-cell levels with the serum antigen-specific antibody levels of these patients against the same 2 antigens (MSP1/AMA1 and CIDRα1) in the experiments and the discussion. The antibody data would strengthen their main argument and is the main missing piece for characterizing more completely the long-lived antigen-specific humoral responses. Below are my suggestions that would help improve the manuscript:

Major comments: 1. Serum Anti-Pf antibodies: Do the authors have access to the serum/plasma of these patients? It would be important to correlate the total and ag-specific B-cell populations with levels of serum IgG antibodies against those specific Pf antigens (MSP1/AMA1 and CIDRα1) and total IgG levels to strengthen their point about long-lived humoral responses.

To our understanding, the rationale for such an analysis would be that if IgG levels correlated with the size of a certain B cell population, it would suggest that this B cell population is implicated in the production of IgG against a particular antigen. While a correlation between the percentage of memory B cells and IgG titers has been observed for antigens from several viruses and bacteria (1-4), other studies have reported the absence of such a correlation (4-7). Similarly, for P. falciparum antigens, a moderate correlation between memory B cell abundance and IgG titers has been observed for some merozoite antigens, but not for others (8, 9). The lack of a correlation between the magnitude of the memory B cell and the antibody response fits with the prevailing model that memory B cells and plasma cells are two independently controlled arms of the humoral immune system (10, 11). Given the lack of strong evidence that the levels of IgG titers and memory B cells are interconnected, we do not think this analysis will be informative.

An alternative analysis would be to study the contribution of B cell subsets to the production of IgG after re-exposure, similar to a recent study that identified T-bet+ memory B cells as the main contributors to antibody responses following influenza virus vaccination (12). Unfortunately, we are unable to perform this analysis in this study population, because only four of the individuals included in this study (spanning calendar years 2012 – 2016) were recruited into a follow up cohort (calendar years 2017 – 2019), and none of these four people were infected during this later time frame.

We have however added this future direction to the Discussion section:

To determine the contribution of different memory B cell subsets to the recall response against P. falciparum, it would be interesting to analyze IgG responses upon re-infection. However, none of the individuals included in this study experienced a recorded P. falciparum infection post-IRS, preventing us from performing such an analysis.

References

1. Crotty et al., J Immunol (2003), https://doi.org/10.4049/jimmunol.171.10.4969
2. Quinn et al., J Infect Dis (2004), https://doi.org/10.1086/423937
3. Cohen et al., Cell Rep Med (2021), https://doi.org/10.1016/j.xcrm.2021.100354
4. Amanna et al., New England J Med (2007), https://doi.org/10.1056/nejmoa066092
5. Leyendeckers et al., Eur J Immunol (1999), https://doi.org/10.1002/(sici)1521-4141(199904)29:04%3C1406::aid-immu1406%3E3.0.co;2-p
6. Nanan et al., Vaccine (2001), https://doi.org/10.1016/s0264-410x(01)00328-0
7. Goel et al., Science Immunol (2021), https://doi.org/10.1126/sciimmunol.abi6950
8. Rivera-Correa et al., eLife (2019), https://doi.org/10.7554/elife.48309
9. Jahnmatz et al., Front Immunol (2021), https://doi.org/10.3389/fimmu.2020.619398
10. Weisel et al., Immunity (2016), https://doi.org/10.1016/j.immuni.2015.12.004
11. Shinnakasu et al., Nat Immunol (2016), https://doi.org/10.1038/ni.3460
12. Nellore et al., Immunity (2023), https://doi.org/10.1016/j.immuni.2023.03.00

    1. Correlation between populations and initial parasite load: Are the levels between any of the populations at any time point correlated significantly in any way? If the statistical power/N allows it, please perform a correlation array between all populations using all samples both total and ag-specific and initial parasite load.

We agree that this analysis could be very interesting. However, in most recorded infection cases, parasitemia was submicroscopic and parasite load was not reported. Information about parasite density in the blood prior to TP1 is available for only half of the individuals in this study. In these people, the last known parasite density was recorded between three months to two years prior to TP1. Given the small number of individuals for whom these data are available and the large variation in time between parasitemia and sampling, we do not have sufficient data to perform this analysis.

1. Figure 2: Why were total and ag-specific plasmablasts/plasma cells not included in this figure? Please include to compare levels in these two time points.

We did not include the levels of total and antigen-specific plasmablasts (PBs) in Figure 2 because the percentages of PBs are relatively low, and very few antigen-specific PBs were detected. We have now included the levels of total PBs in Figure 2A and the percentages of antigen-specific PBs in Supplementary Figure 2. The percentage of PBs among total B cells decreased by about 50% between TP1 and TP2, in line with a decrease in immune activation.

1. Healthy baseline: The study is missing "healthy" controls as a reference. I presume this is because each patient is its uninfected control in the post-IRS sample. In methods, they mentioned they used two naïve-USA B-cells as technical controls. It would be important to include and maybe expand (to match age and gender)on that specific data from those controls as supplementary figures to support their findings:
2. Show negative Tetramer staining for these samples (to understand the background).
3. Levels of all the USA controls total B cell populations and compared to the pre/post-IRS samples to understand "baseline" or "non-endemic" control levels.

1. We have included flow cytometry plots of tetramer staining for the non-P. falciparum exposed donors (pooled B cells from two US donors) to show the level of background for these probes. These plots are shown in Figure S1B.

2. We have used data from P. falciparum-naive US donors (n = 7) that we generated for a prior study to show the average level of total B cell populations in Figure 2, and the percentage of switched memory B cells that express CD95, CD11c, T-bet, and FcRL5 in Figure 4.

Minor comments: 1. In the gating strategy (S1), please include the percentage of each population of that representative example.

We have added the percentages for all gated populations to Figure S1.

1. For Figure 2, since not every panel has the same N, please include the N for each panel in the figure or a supplementary table.

All panels in Figure 2 show data for all 10 individuals. However, since some data points are overlapping, it may appear that some panels show data from fewer individuals. Specifically, no antigen-specific DN1 cells were detected pre- and post-IRS for four individuals. These data points therefore overlap and are not visible. To avoid confusion, we had mentioned this in the legend to Figure 2 (see text in orange). We have tried to further clarify this by emphasizing in the figure legend that data from all 10 individuals are shown (see text in red):

“Figure 2: Abundance of total and antigen-specific B cell subsets in the circulation during high parasite transmission and in the absence of P. falciparum exposure. The percentage of B cell subsets among circulating B cells is shown for total B cells (A), MSP1/AMA1-specific B cells (B), and CIDRα1-specific B cells (C). For MSP1/AMA1-specific B cells and CIDRα1-specific B cells, the total percentage among all circulating B cells is also shown (right most graphs in each panel). All panels show data for all 10 individuals. In panels B and C, no antigen-specific DN1 cells were detected pre- and post-IRS for four individuals. These data points therefore overlap and are not clearly visible. Differences between groups were evaluated using a Wilcoxon matched-pairs signed-rank test. P values

1. Please mention the history of past and chronic co-infections of these 10 patients. Particularly if they had any other active or recent infection when the sample was taken.

Four individuals had active or recent infections in the three months prior to sample collection, with upper respiratory tract infections being the most common. This information has been included in Table S3, with a reference to these data in the Methods section. We have also included a link to ClinEpiDB where additional information about the cohort participants, including medical history, can be found.

1. Discussion: further discussion with relevant literature on the following points is needed to consolidate cellular and antibody studies: a. Whether the presence of long-lived ag-specific B-cell responses correlates with sustained levels of IgG against Pf antigens. b. The different types of antibodies (protective/pathogenic) that these different B-cell populations have been reported to produce during malaria.

a. We have added the following paragraph to the Discussion section:

To determine how these different long-lived B cell subsets contribute to protection against P. falciparum infection, it would be important to analyze the connection between the cellular repertoire and plasma IgG. For P. falciparum antigens, a moderate correlation between memory B cell abundance and IgG titers has been observed for some merozoite antigens, but not for others (28, 44). This is in line with studies for other pathogens, that showed a correlation between the percentage of memory B cells and IgG titers for antigens from several viruses and bacteria (48-51), while other studies have reported the absence of such a correlation (51-54). The lack of a correlation between the magnitude of the memory B cell and the antibody response fits with the prevailing model that memory B cells and plasma cells are two independently controlled arms of the humoral immune system (55, 56). To determine the contribution of different memory B cell subsets to the recall response against P. falciparum, it would be interesting to analyze IgG responses upon re-infection. However, none of the individuals included in this study experienced a recorded P. falciparum infection post-IRS, preventing us from performing such an analysis.

b. We have added additional discussion about the types of antigens recognized by atypical B cells to the Discussion section:

Prior studies have shown that while atypical B cells harbor reactivity against P. falciparum antigens (9,18), they are also enriched for autoreactivity (43). Specifically, atypical B cells produce antibodies against the membrane lipid phosphatidylserine, which can induce the destruction of uninfected erythrocytes and contribute to anemia (44).

Significance

General assessment:

Strengths: - Novelty in contrasting two different types of P. falciparum antigen responses at the B-cell level. - The use of tetramers is a cutting-edge technique to assess this question. - Analyses were thorough and found contrasting differences in antigen-specific B-cell populations (atypical vs classical) between these 2 antigens for the first time (to my knowledge). - Well-written manuscript with clear data, methodology, and conclusions

Limitations: - Missing serum/plasma antibody data to support their claim about long-lived humoral responses and reconciliation of ag-specific B-cell levels and ag-specific antibody levels in experiments and discussion. - Limited N of 10 patients of the same gender (female), some population analyses had even fewer samples. - Missing baseline levels for non-endemic uninfected control for B-cell populations for comparison.

• We have included a discussion about the correlation between plasma antibody and memory B cell responses in the Discussion section.

• We have clarified that some data points overlap in Figure 2, giving the impression that data from fewer than 10 individuals were shown.

• We have included baseline levels of 1) tetramer reactivity (Figure S1), 2) the size of B cell populations (Figure 2), and 3) expression of select markers (Figure 4).

Advance: The study consolidates antigen-specific responses with the discovery of recently characterized populations (ex. atypical) and finds novel differences between two types of malaria antigen responses at the B-cell level and between specific populations responding differentially to these antigens. The findings are incremental (role of B-cell population in malaria-specific responses), conceptual (contrasting two types of B-cell antigen responses in the same infection), and clinical (finding significant differences in patients).

Audience: This study will attract basic B-cell immunology scientists, infectious disease clinicians/scientists, vaccinologists, and both basic malaria immunology and clinical audiences.

Reviewer expertise: Malaria, immunology, antibodies.

__Reviewer #3 __

Evidence, reproducibility and clarity: The authors analysed the antigen specificity and phenotypes of B cells during high P falciparum transmission and after a period of successful malaria control with IRS in Uganda. The gap between the two sampling time points is close to two years.

They use antigen probes for MSP1/AMA1 and CIDRalpha1, two antigens expressed at different stages of P. falciparum life cycle-merozoites and infected red cells, respectively. While MSP1/AMA1 are involved in the parasite's invasion of red blood cells, CIDRalpha1 is a domain of PFEMP1, a large family of antigenically variant proteins that mediates the sequestration of infected red cells in small blood vessels.

They found that the percentage of activated antigen-specific memory B cells declined with malaria control. However, detectable frequencies of antigen-specific memory B cells were retained after malaria control, which confirms earlier reports.

However, they also demonstrate that the phenotypic characteristics of memory B cells are associated with antigen specificity. The retained MSP1/AMA1-specific B cells were mostly CD95+CD11c+ memory B cells and FcRL5-Tbet- atypical B cells. In contrast, the retained CIDRalpha1-specific B cells were enriched among a subpopulation of atypical B cells.

These findings suggest differences exist in how the MSA1/AMA1 and CIDRalpha1 y are recognised and processed by the human immune system and how the immune response responds to them upon re-infection with P falciparum.

Major issues affecting the conclusion: The findings and conclusions of this study, whilst positively exciting and informative, are based on the analyses of very few cells (at times). Even the authors themselves acknowledge this. I expect the authors to address this issue by toning down their reporting and conclusions (where appropriate). Ultimately, we need to have the confidence that these results are reproducible.

We appreciate the reviewer’s concern about the numbers of antigen-specific cells included in our analyses, which is an inherent limitation of this approach. However, we would like to point out that most analyses included a substantial number of antigen-specific B cells:

Figure 3D: 158 to 2,038 cells per group

Figure 4: an average of 26 to 184 cells per donor

Figure 5B: 55 to 508 cells per group

Figure 5C: 10 to 334 cells per group*

* The group with 10 cells is an outlier here. All other groups contain at least 104 cells. Because this one condition had such a small number of cells, we specifically mentioned this number in the text.

The numbers of cells for analyses shown in Figures 3D and 5B were already included in the figures. All the other numbers were mentioned in Table S3. To further clarify the number of cells included in each analysis, we have added the number of cells to Figures 4 and 5C.

To tone down our reporting, we have rephrased some of our conclusions, and now present our section headers in past tense to make these statements reflect our observation instead of a definitive conclusion. For example:

Conclusion: “The loss of MSP1/AMA1-specific and CIDRα1-specific B cells in the circulation was similar, but the phenotype of long-lived MSP1/AMA1-specific and CIDRα1-specific B cells appeared to differ.”

Section header: “Long-lived MSP1/AMA1-specific and CIDRα1-specific B cells differed in phenotype”

Finally, in the Discussion section, we have added a statement to our paragraph describing the limitations of our study to stress the importance of reproducing our findings:

All in all, it will be important to perform additional studies of the phenotype and functionality of long-lived B cells with specificity for P. falciparum antigens to reproduce and extend our findings.

Minor comments: Figure 2D-I found this figure, and its presentation is unclear. Notably, using contour plots doesn't allow the reader to appreciate the density of the cells being presented.

To facilitate the interpretation of this figure, we have changed the plot type to a contour plot with density color gradient, and added the number of cells shown in each plot. (Please note that this panel has been renumbered to C.)

Figure 4 - label the y-axis.

The y-axis was labeled with “%”, which we have expanded to “% of B cells expressing marker of interest”.

__Significance: __The study design-as outlined-allowed for the analyses of the specificity and phenotypic characteristics of residual P falciparum-specific memory B cells after 1.7 years of little to no P falciparum exposure. The cell phenotyping methods presented are also appropriate. However, antigen-specific cells are rare in blood circulation, and as the authors themselves acknowledge in the discussion, some of the results are based on very few cells. This means we cannot be sure all the results presented are reproducible.

Previous studies demonstrated that P falciparum memory B cells are maintained long after cessation of antigen exposure. However, few (if any) detailed antigen-specific and phenotypic analyses of the characteristics of P falciparum-specific memory B cells following a long period of no exposure exist. Thus, this study presents an incremental advance in our knowledge. In addition, the association of antigen specificity with cell phenotypes is a new concept in malaria immunology. The research presented will greatly interest infectious disease immunologists and vaccinologists.

I am an infectious disease immunologist with substantial experience in malaria immunology.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

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Referee #3

Evidence, reproducibility and clarity

The authors analysed the antigen specificity and phenotypes of B cells during high P falciparum transmission and after a period of successful malaria control with IRS in Uganda. The gap between the two sampling time points is close to two years.

They use antigen probes for MSP1/AMA1 and CIDRalpha1, two antigens expressed at different stages of P. falciparum life cycle-merozoites and infected red cells, respectively. While MSP1/AMA1 are involved in the parasite's invasion of red blood cells, CIDRalpha1 is a domain of PFEMP1, a large family of antigenically variant proteins that mediates the sequestration of infected red cells in small blood vessels.

They found that the percentage of activated antigen-specific memory B cells declined with malaria control. However, detectable frequencies of antigen-specific memory B cells were retained after malaria control, which confirms earlier reports.

However, they also demonstrate that the phenotypic characteristics of memory B cells are associated with antigen specificity. The retained MSP1/AMA1-specific B cells were mostly CD95+CD11c+ memory B cells and FcRL5-Tbet- atypical B cells. In contrast, the retained CIDRalpha1-specific B cells were enriched among a subpopulation of atypical B cells.

These findings suggest differences exist in how the MSA1/AMA1 and CIDRalpha1 y are recognised and processed by the human immune system and how the immune response responds to them upon re-infection with P falciparum.

Major issues affecting the conclusion:

The findings and conclusions of this study, whilst positively exciting and informative, are based on the analyses of very few cells (at times). Even the authors themselves acknowledge this. I expect the authors to address this issue by toning down their reporting and conclusions (where appropriate). Ultimately, we need to have the confidence that these results are reproducible.

Minor comments:

Figure 2D-I found this figure, and its presentation is unclear. Notably, using contour plots doesn't allow the reader to appreciate the density of the cells being presented.

Figure 4 - label the y-axis.

Significance

The study design-as outlined-allowed for the analyses of the specificity and phenotypic characteristics of residual P falciparum-specific memory B cells after 1.7 years of little to no P falciparum exposure. The cell phenotyping methods presented are also appropriate. However, antigen-specific cells are rare in blood circulation, and as the authors themselves acknowledge in the discussion, some of the results are based on very few cells. This means we cannot be sure all the results presented are reproducible.

Previous studies demonstrated that P falciparum memory B cells are maintained long after cessation of antigen exposure. However, few (if any) detailed antigen-specific and phenotypic analyses of the characteristics of P falciparum-specific memory B cells following a long period of no exposure exist. Thus, this study presents an incremental advance in our knowledge. In addition, the association of antigen specificity with cell phenotypes is a new concept in malaria immunology. The research presented will greatly interest infectious disease immunologists and vaccinologists.

I am an infectious disease immunologist with substantial experience in malaria immunology.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

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Referee #2

Evidence, reproducibility and clarity

Summary:

In this study, the authors compared long-lived total and antigen (ag)-specific B-cell levels in a cohort of 10 Ugandan malaria patient samples that were collected before and after local reduction of P. falciparum transmission (pre/post-IRS). The focus is on the novel comparison of the two most common malaria antigens: merozoite antigens (MSP1/AMA1) and variant surface antigens (CIDRα1). Using high-parameter spectral flow cytometry, they also characterized the phenotype of the different populations of cells. Their main findings include 1) a decrease in activated but maintenance of resting ag-specific B-cells in the post-IRS sample and 2) CD95 and CD11c, as the only differentially expressed markers between MSP1/AMA1-specific and CIDRα1-specific long-lived memory B cells. Their further phenotypic characterization suggests functional consequences with MSP1/AMA1-specific B-cells being poised for rapid antibody-secreting cell differentiation while CIDRα1-specific B cells were enriched among a subset of atypical B cells that seem poised for antigen presentation (CD86+CD11chi/ AtBC1). Their findings consolidate and further expand our knowledge of long-lived B-cell levels during P. falciparum malaria and report/compare (for the first time to my knowledge) a differential selection of long-lived B-cell levels between these 2 antigen specificities. Overall, the manuscript is straightforward and well-written and the authors did a good job explaining their methodology, findings, and interpretations. I believe the major gap missing in this study is the reconciliation of long-lived antigen-specific B-cell levels with the serum antigen-specific antibody levels of these patients against the same 2 antigens (MSP1/AMA1 and CIDRα1) in the experiments and the discussion. The antibody data would strengthen their main argument and is the main missing piece for characterizing more completely the long-lived antigen-specific humoral responses. Below are my suggestions that would help improve the manuscript:

Major comments:

1. Serum Anti-Pf antibodies: Do the authors have access to the serum/plasma of these patients? It would be important to correlate the total and ag-specific B-cell populations with levels of serum IgG antibodies against those specific Pf antigens (MSP1/AMA1 and CIDRα1) and total IgG levels to strengthen their point about long-lived humoral responses.
2. Correlation between populations and initial parasite load: Are the levels between any of the populations at any time point correlated significantly in any way? If the statistical power/N allows it, please perform a correlation array between all populations using all samples both total and ag-specific and initial parasite load.
3. Figure 2: Why were total and ag-specific plasmablasts/plasma cells not included in this figure? Please include to compare levels in these two time points.
4. Healthy baseline: The study is missing "healthy" controls as a reference. I presume this is because each patient is its uninfected control in the post-IRS sample. In methods, they mentioned they used two naïve-USA B-cells as technical controls. It would be important to include and maybe expand (to match age and gender)on that specific data from those controls as supplementary figures to support their findings:
5. Show negative Tetramer staining for these samples (to understand the background).
6. Levels of all the USA controls total B cell populations and compared to the pre/post-IRS samples to understand "baseline" or "non-endemic" control levels.

Minor comments:

1. In the gating strategy (S1), please include the percentage of each population of that representative example.
2. For Figure 2, since not every panel has the same N, please include the N for each panel in the figure or a supplementary table.
3. Please mention the history of past and chronic co-infections of these 10 patients. Particularly if they had any other active or recent infection when the sample was taken.
4. Discussion: further discussion with relevant literature on the following points is needed to consolidate cellular and antibody studies: a. Whether the presence of long-lived ag-specific B-cell responses correlates with sustained levels of IgG against Pf antigens. b. The different types of antibodies (protective/pathogenic) that these different B-cell populations have been reported to produce during malaria.

Significance

General assessment:

Strengths:

• Novelty in contrasting two different types of P. falciparum antigen responses at the B-cell level.
• The use of tetramers is a cutting-edge technique to assess this question.
• Analyses were thorough and found contrasting differences in antigen-specific B-cell populations (atypical vs classical) between these 2 antigens for the first time (to my knowledge).
• Well-written manuscript with clear data, methodology, and conclusions

Limitations:

• Missing serum/plasma antibody data to support their claim about long-lived humoral responses and reconciliation of ag-specific B-cell levels and ag-specific antibody levels in experiments and discussion.
• Limited N of 10 patients of the same gender (female), some population analyses had even fewer samples.
• Missing baseline levels for non-endemic uninfected control for B-cell populations for comparison.

Advance:

The study consolidates antigen-specific responses with the discovery of recently characterized populations (ex. atypical) and finds novel differences between two types of malaria antigen responses at the B-cell level and between specific populations responding differentially to these antigens. The findings are incremental (role of B-cell population in malaria-specific responses), conceptual (contrasting two types of B-cell antigen responses in the same infection), and clinical (finding significant differences in patients).

Audience:

This study will attract basic B-cell immunology scientists, infectious disease clinicians/scientists, vaccinologists, and both basic malaria immunology and clinical audiences.

Reviewer expertise:

Malaria, immunology, antibodies.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

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Referee #1

Evidence, reproducibility and clarity

This study by Reyes at al is a well conducted analysis of memory B cell dynamics of Plasmodium falciparum (Pf) -specific B cell populations over the course of reducing Pf prevalence in ten Ugandan adults. The data is presented well and the authors provide compelling evidence that 1. There is an overall loss of Ag specific B cells with reduction in exposure and 2. Different antigens (MSP1/AMA-1 vs CIDRa-1) generate different flavors of long lived responses. However, additional clarity to the reader should be provided on certain topics (listed below).

Major comments:

1. While the premise of the study (reduced Pf transmission due to the use of indoor residual spraying (IRS)) is an important one, I think the authors must take into consideration that 9/10 subjects had at least one Pf positive episode between Time Points 1 and 2 (Figure 1). Also, it looks from Fig 1 that some samples were collected at a time of Pf positive test (green squares), while in Table S1 none of the subjects have a positive parasite status at TP1.
2. Figure S1A: What is trBC? Figure S1B: What is Strep? Are the strep positive cells also CIDR-1 positive and were they gated out? Why is APC used for MZ-1 and one of the MSP1-AMA-1 tetramers? Do these stainings come from multiple panels?
3. Figure 3A: how many cells does the umap plot represent? Were there a total of 3555 Ag specific B cells that were non-naive (Figure 3E)?
4. Could the authors comment on why in Figure 3, Ig isotype expression was not considered for clustering? This would allow for characterization of DN sub populations/ clusters in addition to the CD21-CD27- ABCs? It looks like IgD expression was low across the clusters (Figure 3D). Was this the case for the cells considered in this analysis, or was it excluded? If it was truly low expressed, how were the assessments in Figure 2 made?
5. Are there differences in these designations / phenotypes of DN populations of atBCs vs CD21-CD27- atBCs?
6. Lines 258-259: In considering only switched MBCs, what clusters from Figure 3a were included? There seem to be 2588 sw MBCs (Table S3, Figure 4). Do the remaining cells (967 cells) come from clusters 2, 5 and 6 (and excludes the atBC clusters)

Minor comments:

1. Line 178- 179: Was there a specfic measure of rate of decline made for these cells?

Significance

General assessment:

Strengths: The authors provide evidence that the dynamics of antigen specific cells in humans can vary with exposure and with the nature of the antigen. They have nicely discussed the potential causes for these differences (Discussion), although they should include the findings of Ambegaonkar et al that ABCs in malaria may be restricted to responding specifically to membrane bound antigens (PMCID: PMC7380957)

Limitations:

1. Outlined above, and as the authors also mention, a small sample size and homogenous population.
2. The evidence for reduced transmission is not clear, and the negative parasite tests for donors shown in Table S1 do not match with Figure 1 data.
3. Lack of IgD expression across clusters (Figure 3D- the authors will need to clarify this point) would require re-analysis of Figure 2 data

Advances: This study highlights the importance of studying antigen specific B cells in humans in the context of natural infection and the use of high-parameter tools such as spectral flow cytometry in assessing a large quantity of data from limited clinical samples. These data are important to inform better vaccine design. Studies in inbred animals can be quite limited or different from human B cell responses.

Audience: This study will be of interest to malariologists and B cell immunologists. Atypical B cells are relevant to many infectious diseases and auto immunity, while the dynamics of memory B cells in malaria all be relevant to those interested in vaccine design against blood stage antigens.

x

al mismo tiempo

.4 chitchatter

1938 Corona Zephyr Typewriter Service Adjustment Methods by [[Theodore Munk]]

袁少凯到此一游

稍后

详细学习一下关于 Javascript 的 import

如果使用 map 渲染列表元素的时候，回调函数返回的是我们抽出来的组件（不是原始的 li），那么 key 定义在自定义组件上。

不推荐使用 array index as keys 的原因：

稍后

https://robinpokorny.medium.com/index-as-a-key-is-an-anti-pattern-e0349aece318

数组的 map 方法，回调函数的第二个参数可以获取到 index。

使用 map 生成的元素，每一个都必须有一个 key 属性，原因在于如下文档：

稍后

https://legacy.reactjs.org/docs/reconciliation.html#recursing-on-children

稍后

关于在 VS Code 中配置代码片段快捷键。

当打印多个对象到 console.log 中时，它们都会变成可检查的字符串

当某些东西不工作时，不要只是猜测什么是错的。相反，要记录或使用一些其他的调试方法。

这是全栈开发课程中 React 入门的部分

这里强调了，如何定义事件处理程序是风格问题。两种风格：1）把事件处理函数定义成一个调用（返回定制化函数）；2）把事件处理函数定义称谓一个函数

React 中，事件处理程序不能是一个函数的调用，而是一个函数或者函数引用。但这里介绍了一个“函数工厂”的模式，就是调用返回函数的函数，来定制化函数。

React 开发者工具中展示的 Hooks 顺序是按照 App 中定义的顺序来的。

在代码中的任何地方加入 debugger 命令，然后浏览器的 Source 标签页进行断点调试。

对 Java 程序员太良心了，告诉我们 console.log 里用逗号分隔就行。

在 16.8.0 之前没有 React Hooks 的时候，需要状态的组件必须被定义为 class。以后在维护遗留代码的时候可能会碰到。

这个例子再一次说明，更新 state 状态的时候，不要更改原来的 state，而是生成一个全新的状态，然后更新进去。

Такой вариант мне тоже кажется некорректным. Только на одной оси будет сила тока, а на другой напряжение?

У меня в конце строки.

Не 8888?

The goddess Ishtar further reflects the ambivalence and complexity of femininity in relation to heroism. While maintaining divine power and contributing to Gilgamesh's trials, her portrayal as capricious and vindictive underscores a cultural sentiment that power in women could be as magnificent yet perilous as strength in men. Ishtar’s interaction with Gilgamesh highlights a manipulation of traditional gender roles, where her amorous advances and subsequent wrath explore themes of vulnerability and danger associated with femininity, offering a critical lens on how the ancients perceived godly yet humanlike emotional turmoil.

The "Shahnameh," or "Book of Kings," composed by Ferdowsi in the 10th century, stands as a cornerstone of Persian literary heritage. This epic not only recounts tales of dynastic glory and tragedy but also deepens our understanding of socio-cultural norms, including those surrounding gender. Syavash's story is a testament to this, embodying virtues and traits traditionally associated with both masculine and feminine roles, which in turn accentuate his heroism.

Democrats claim to oppose "threats to democracy" but replace their candidates after nomination to maintain control over the party faithful. They appointed Biden after Kamala's campaign was rightly and completely destroyed in a debate with Tulsi Gabbard but the facts of Kamala's incompetent and tyrannical past, abusing minorities for political gain: https://www.youtube.com/watch?v=o1-CRrMDSLs

Democrats ignore democracy at the drop of a hat when they believe it suits them.

Bill Penzey Jr is gaslighting readers again with this statement. The facts are that Hillary Clinton used a private email server for official communications as Secretary of State (2009-2013). The FBI found over 100 classified emails on the server that were not encrypted as required, this included 65 emails deemed "Secret" and 22 deemed "Top Secret".

After the investigation began, her IT team deleted ~30,000 emails and destroyed devices by smashing them with hammers, preventing a full investigation. The Obama Administration corrupted FBI found her “extremely careless” (an understatement) but did not recommend charges. This impacted her 2016 presidential campaign - but not because she was "over-prepared and used emails".

From his statement, it is apparent that Bill Penzey Jr. is not a serious person capable of grasping established facts about the world around him and has no business pontificating on political matters. At worst, he's spreading deliberate misinformation.

Though democrats in politics, the Biden family, and the media lied repeatedly before the election, Hunter Biden’s laptop was confirmed to be confirmed his.

This is relevant because it contained incriminating photos of Hunter Biden; as a result, Hunter Biden was tried and convicted on Federal gun charges.

There are still outstanding lawsuits relating to the contents of the laptop as of this writing.

Bill Penzey, Jr. knows this, and by suggesting that Americans were merely upset that Hunter had a computer is an attempt at gaslighting people, which is dishonest and manipulative.

spread

was

were

is

has

seems

explore

have

appear

They were once physical beings; this backs up the thought of some sort of afterlife by saying they became gleaming beings it makes me think of an angel.

The "children" are shaped and stay in that form forever.

They believe in a higher being in which they go to search for answers.

DOI: 10.1016/j.celrep.2021.108875

Resource: (Santa Cruz Biotechnology Cat# sc-47778, RRID:AB_626632)

Curator: @Naa003

SciCrunch record: RRID:AB_626632

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What is this?

DOI: 10.1016/j.celrep.2021.108892

Resource: (Cell Signaling Technology Cat# 7074, RRID:AB_2099233)

Curator: @Naa003

SciCrunch record: RRID:AB_2099233

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What is this?