eLife Assessment

Sanchez-Vasquez et al establish an innovative approach to induce aneuploidy in preimplantation embryos. This important study extends the author's previous publications evaluating the consequences of aneuploidy in the mammalian embryo. In this work, the authors investigate the developmental potential of aneuploid embryos and characterize changes in gene expression profiles under normoxic and hypoxic culture conditions. Using a solid methodology they identify sensitivity to Hif1alpha loss in aneuploid embryos, and in further convincing experiments they assess how levels of DNA damage and DNA repair are altered under hypoxic and normoxic conditions.

Reviewer #1 (Public review):

Summary:

This paper developed a model of chromosome mosaicism by using a new aneuploidy-inducing drug (AZ3146), and compared this to their previous work where they used reversine, to demonstrate the fate of aneuploid cells during murine preimplantation embryo development. They found that AZ3146 acts similarly to reversine in inducing aneuploidy in embryos, but interestingly showed that the developmental potential of embryos is higher in AZ3146-treated vs. reversine-treated embryos. This difference was associated with changes in HIF1A, p53 gene regulation, DNA damage, and fate of euploid and aneuploid cells when embryos were cultured in a hypoxic environment.

Strengths:

In the current study, the authors investigate the fate of aneuploid cells in the preimplantation murine embryo using a specific aneuploidy-inducing compound to generate embryos that were chimeras of euploid and aneuploid cells. The strength of the work is that they investigate the developmental potential and changes in gene expression profiles under normoxic and hypoxic culture conditions. Further, they also assessed how levels of DNA damage and DNA repair are altered in these culture conditions. They also assessed the allocation of aneuploid cells to the divergent cell lineages of the blastocyst stage embryo.

Weaknesses:

The authors have still not addressed the inconsistent/missing description for sample size, the appropriate number of * for each figure panel, and the statistical tests used.

The authors assign 5% oxygen as hypoxia. This is not the case as the in vivo environment is close to this value. 5% is normoxia. Clinical IVF/embryo culture occurs at 5% O2. Please adjust your narrative around this.

Reviewer #2 (Public review):

Summary:

This study by Sanchez-Vasquez is a very innovative approach to induce aneuploidy and then study the contribution of treated cells to different lineages, including post implantation. It connects well to the authors previous work to induce mosaic aneuploidies. The authors identify sensitivity to HIF1a loss in treated embryos with likely aneuploidy. This work is part of an important line of work with evaluates the consequences of aneuploidy in mammalian embryo.

Weaknesses:

Given that this is a study on the induction of aneuploidy, it would be meaningful to assess aneuploidy immediately after induction, and then again before implantation. This is also applicable to the competition experiments on page 7/8. What is shown is the competitiveness of treated cells. Because the publication centers around aneuploidy, inclusion of such data in the main figure at all relevant points would strengthen it. There is some evaluation of karyotypes only in the supplemental - why? Would be good not to rely on a single assay that the authors appear to not give much importance.

eLife Assessment

This study provides a comprehensive analysis of gene expression and bioinformatics data, offering important insights into the roles of fibroblasts in cardiac development. The large and well-analyzed single-cell RNA sequencing (scRNA-seq) dataset is compelling and a significant contribution to the field, and will be of broad interest to the scientific community.

Reviewer #2 (Public review):

This study aims to elucidate the role of fibroblasts in regulating myocardium and vascular development through signaling to cardiomyocytes and endothelial cells. This focus is significant, given that fibroblasts, cardiomyocytes, and vascular endothelial cells are the three primary cell types in the heart. The authors employed a Pdgfra-CreER-controlled diphtheria toxin A (DTA) system to ablate fibroblasts at various embryonic and postnatal stages, characterizing the resulting cardiac defects, particularly in myocardium and vasculature development. Single-cell RNA sequencing (scRNA-seq) analysis of the ablated hearts identified collagen as a crucial signaling molecule from fibroblasts that influences the development of cardiomyocytes and vascular endothelial cells.

This is an interesting manuscript; however, there are several major issues, including an over-reliance on the scRNA-seq data, which shows inconsistencies between replicates.

Some of the major issues are described below.

(1) The CD31 immunostaining data (Figure 3B-G) indicate a reduction in endothelial cell numbers following fibroblast deletion using PdgfraCreER+/-; RosaDTA+/- mice. However, the scRNA-seq data show no percentage change in the endothelial cell population (Figure 4D). Furthermore, while the percentage of Vas_ECs decreased in ablated samples at E16.5, the results at E18.5 were inconsistent, showing an increase in one replicate and a decrease in another, raising concerns about the reliability of the RNA-seq findings.

(2) Similarly, while the percentage of Ven_CMs increased at E18.5, it exhibited differing trends at E16.5 (Fig. 4E), further highlighting the inconsistency of the scRNA-seq analysis with the other data.

(3) Furthermore, the authors noted that the ablated samples had slightly higher percentages of cardiomyocytes in the G1 phase compared to controls (Fig. 4H, S11D), which aligns with the enrichment of pathways related to heart development, sarcomere organization, heart tube morphogenesis, and cell proliferation. However, it is unclear how this correlates with heart development, given that the hearts of ablated mice are significantly smaller than those of controls (Figure 3E). Additionally, the heart sections from ablated samples used for CD31/DAPI staining in Figure 3F appear much larger than those of the controls, raising further inconsistencies in the manuscript.

(4) The manuscript relies heavily on the scRNA-seq dataset, which shows inconsistencies between the two replicates. Furthermore, the morphological and histological analyses do not align with the scRNA-seq findings.

(5) There is a lack of mechanistic insight into how collagen, as a key signaling molecule from fibroblasts, affects the development of cardiomyocytes and vascular endothelial cells.

(6) In Figure 1B, Col1a1 expression is observed in the epicardial cells (Figure 1A, E11.5), but this is not represented in the accompanying cartoon.

(7) Do the PdgfraCreER+/-; RosaDTA+/- mice survive after birth when induced at E15.5, and do they exhibit any cardiac defects?

Comments on Revised Version (from BRE):

The manuscript has greatly improved following the revision, and I have no additional comments to offer.

Reviewer #3 (Public review):

Summary:

The authors investigated fibroblasts' communication with key cell types in developing and neonatal hearts, with focus on critical roles of fibroblast-cardiomyocyte and fibroblast-endothelial cells network in cardiac morphogenesis. They tried to map the spatial distribution of these cell types and reported the major pathways and signaling molecules driving the communication. They also used Cre-DTA system to ablate Pdgfra labeled cells and observed myocardial and endothelial cell defects at development. They screened the pathways and genes using sequencing data of ablated heart. Lastly they reported a compensatory collagen expression in long term ablated neonate heart. Overall, this study provides us with important insight on fibroblasts' roles in cardiac development and will be a powerful resource for collagens and ECM focused research.

Strengths:

The authors utilized good analyzing tools to investigate on multiple database of single cell sequencing and Multi-seq. They identified significant pathways, cellular and molecular interactions of fibroblasts. Additionally, they compared some of their analytic findings with human database, and identified several groups of ECM genes with varying roles in mice.

Weaknesses:

This study is majorly based on sequencing data analysis. At the bench, they used very strident technique to study fibroblast functions by ablating one of the major cell population of heart. Also, experimental validation of their analyzed downstream pathways will be required eventually.

Comments on Revised Version (from BRE):

The authors did a good job addressing the questions asked at first review. However, I have some minor concerns.

(1) The paper notes that collagen signaling is observed in FB-VasEC in humans, but not in FB-VenCM, unlike mice. Did the authors analyze predictive ligand receptor interaction as they did with control and ablated mice heart? This could add valuable new insights that how FB regulate ventricular CM in human heart.

(2) The authors provided data on Defect in CD31 expression in several models. Did they observe any other phenotypes associated with defective endothelial or vascular system? Such as, blood accumulation in pericardium, larger/smaller capillaries? Did they also examine percentage of Cdh5+ cells?

(3) Please mention the sample age of Figure 2A-C.

(4) Please follow the same style to describe X axis in graphs in Figure 3D (and all similar graphs in the manuscript) as followed in 3G.

(5) It is important to provide echocardiographic M mode images with a comparable number of cardiac cycles in control and ablated (Fig. 6H).

(6) In the long-term neonatal ablation experiments, collagen expressions return to normal. The manuscript attributes this to possible "compensatory expression," Do they have any thoughts how this is regulated? Are other cell types stepping in, or are surviving FBs proliferating?

(7) While collagen is shown to be a dominant signaling molecule, its centrality is inferred primarily from scRNA-seq and ligand-receptor predictions. Did authors try any functional rescue experiment (e.g., exogenous collagen supplementation or receptor blockade) to directly validate this pathway's role in vivo?

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Summary:

The study by Deng et al reports single cell expression analysis of developing mouse hearts and examines the requirements for cardiac fibroblasts in heart maturation. The work includes extensive gene expression profiling and bioinformatic analysis. The prenatal fibroblast ablation studies show new information on the requirement of these cells on heart maturation before birth.

The strengths of the manuscript are the new single cell datasets and comprehensive approach to ablating cardiac fibroblasts in pre and postnatal development in mice. Extensive data are presented on mouse embryo fibroblast diversity and morphology in response to fibroblast ablation. Histological data support localization of major cardiac cell types and effects of fibroblast ablation on cardiac gene expression at different times of development.

A weakness of the study is that the major conclusions regarding collagen signaling and heart maturation are based on gene expression patterns and are not functionally validated.

Reviewer #2 (Public review):

This study aims to elucidate the role of fibroblasts in regulating myocardium and vascular development through signaling to cardiomyocytes and endothelial cells. This focus is significant, given that fibroblasts, cardiomyocytes, and vascular endothelial cells are the three primary cell types in the heart. The authors employed a Pdgfra-CreER-controlled diphtheria toxin A (DTA) system to ablate fibroblasts at various embryonic and postnatal stages, characterizing the resulting cardiac defects, particularly in myocardium and vasculature development. Single-cell RNA sequencing (scRNA-seq) analysis of the ablated hearts identified collagen as a crucial signaling molecule from fibroblasts that influences the development of cardiomyocytes and vascular endothelial cells.

This is an interesting manuscript; however, there are several major issues, including an over-reliance on the scRNA-seq data, which shows inconsistencies between replicates.

We thank the reviewer for carefully reading our revised manuscript. All of the questions listed below were raised in the previous round and have been addressed in the current revision. As noted in the “Recommendations for the Authors” section, the reviewer has no additional comments at this time.

Some of the major issues are described below.

(1) The CD31 immunostaining data (Figure 3B-G) indicate a reduction in endothelial cell numbers following fibroblast deletion using PdgfraCreER+/-; RosaDTA+/- mice. However, the scRNA-seq data show no percentage change in the endothelial cell population (Figure 4D). Furthermore, while the percentage of Vas_ECs decreased in ablated samples at E16.5, the results at E18.5 were inconsistent, showing an increase in one replicate and a decrease in another, raising concerns about the reliability of the RNA-seq findings.

(2) Similarly, while the percentage of Ven_CMs increased at E18.5, it exhibited differing trends at E16.5 (Fig. 4E), further highlighting the inconsistency of the scRNA-seq analysis with the other data.

(3) Furthermore, the authors noted that the ablated samples had slightly higher percentages of cardiomyocytes in the G1 phase compared to controls (Fig. 4H, S11D), which aligns with the enrichment of pathways related to heart development, sarcomere organization, heart tube morphogenesis, and cell proliferation. However, it is unclear how this correlates with heart development, given that the hearts of ablated mice are significantly smaller than those of controls (Figure 3E). Additionally, the heart sections from ablated samples used for CD31/DAPI staining in Figure 3F appear much larger than those of the controls, raising further inconsistencies in the manuscript.

(4) The manuscript relies heavily on the scRNA-seq dataset, which shows inconsistencies between the two replicates. Furthermore, the morphological and histological analyses do not align with the scRNA-seq findings.

(5) There is a lack of mechanistic insight into how collagen, as a key signaling molecule from fibroblasts, affects the development of cardiomyocytes and vascular endothelial cells.

(6) In Figure 1B, Col1a1 expression is observed in the epicardial cells (Figure 1A, E11.5), but this is not represented in the accompanying cartoon.

(7) Do the PdgfraCreER+/-; RosaDTA+/- mice survive after birth when induced at E15.5, and do they exhibit any cardiac defects?

Reviewer #3 (Public review):

Summary:

The authors investigated fibroblasts' communication with key cell types in developing and neonatal hearts, with focus on critical roles of fibroblast-cardiomyocyte and fibroblast-endothelial cells network in cardiac morphogenesis. They tried to map the spatial distribution of these cell types and reported the major pathways and signaling molecules driving the communication. They also used Cre-DTA system to ablate Pdgfra labeled cells and observed myocardial and endothelial cell defects at development. They screened the pathways and genes using sequencing data of ablated heart. Lastly they reported a compensatory collagen expression in long term ablated neonate heart. Overall, this study provides us with important insight on fibroblasts' roles in cardiac development and will be a powerful resource for collagens and ECM focused research.

Strengths:

The authors utilized good analyzing tools to investigate on multiple database of single cell sequencing and Multi-seq. They identified significant pathways, cellular and molecular interactions of fibroblasts. Additionally, they compared some of their analytic findings with human database, and identified several groups of ECM genes with varying roles in mice.

Weaknesses:

This study is majorly based on sequencing data analysis. At the bench, they used very strident technique to study fibroblast functions by ablating one of the major cell population of heart. Also, experimental validation of their analyzed downstream pathways will be required eventually.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

Most of my comments have been adequately addressed. Additional comments on new data in the revised manuscript are below.

(1) In the new figure S11, it is not really possible to draw major conclusions on mitral valve morphology and maturation since the planes of sections to not seem comparable. Observations regarding attachment to the papillary muscle might be dependent on the particular section being evaluated. However, it is useful to see that the valves are not severely affected in the ablated animals.

We appreciate the reviewer’s comment and agree with the reviewer’s observation. Accordingly, we have updated the manuscript by removing the original conclusion-related statement and instead highlighting that the valves were not severely affected in the ablated animals (page 6).

(2) In the last supplemental figure S19, it is not possible to determine if results are or are not statistically significant for n=2 as shown for FS and EF for the ablated animals and controls. The text says that there is a trend of improved heart function, but evaluation of additional animals is needed to support this conclusion.

We thank the reviewer for the comment and agree that a sample size of n = 2 is too small to draw meaningful conclusions. As previously suggested by the reviewer, we have removed this result from the manuscript (page 10).

Reviewer #2 (Recommendations for the authors):

The manuscript has greatly improved following the revision, and I have no additional comments to offer.

Thanks!

Reviewer #3 (Recommendations for the authors):

Authors did a good job addressing questions asked at first review. However, I have some minor concerns.

(1) The paper notes that collagen signaling is observed in FB-VasEC in humans, but not in FB-VenCM, unlike mice. Did authors analyze predictive ligand receptor interaction as they did with control and ablated mice heart? This could add valuable new insights that how FB regulate ventricular CM in human heart.

Thank you. We have analyzed the predicted ligand-receptor interactions between Fb and Ven_CM, as well as between Fb and Vas_EC, using human scRNA-seq data. The results are provided as a supplemental figure (Fig. S8C).

(2) The authors provided data on Defect in CD31 expression in several models. Did they observed any other phenotypes associated with defective endothelial or vascular system? Such as, blood accumulation in pericardium, larger/smaller capillaries? Did they also examined percentage of Cdh5+ cells?

We thank the reviewer for the questions. We did not observe clear evidence of blood accumulation in the pericardium of the ablated hearts, as shown in figure 3B, 3E, 6B, and 6F. Additionally, we did not perform Cdh5 staining in either the control or ablated hearts.

(3) Please mention the sample age of Figure 2A-C.

These are single-cell mRNA sequencing data from CD1 mice across 18 developmental stages, ranging from E9.5 to P9. We have added this information to the manuscript (page 4).

(4) Please follow the same style to describe X axis in graphs in Figure 3D (and all similar graphs in manuscript) as followed in 3G.

Thank you. We assume the reviewer was referring to the descriptions in the relevant figure legends. We have updated the legend for Figure 3D to ensure consistency with the description provided for Figure 3G (page 15).

(5) It is important to provide echocardiographic M mode images with a comparable number of cardiac cycles in control and ablated (Fig. 6H).

We thank the reviewer for the comment. As explained in our previous response, the echocardiographic data for both control and mutant mice were collected in conscious animals. The differences in their cardiac cycles reflect variations in heart rate, which represent a disease phenotype and cannot be altered. Therefore, we are unable to provide M-mode images with a similar number of cardiac cycles for control and ablated mice.

(6) In the long-term neonatal ablation experiments, collagen expressions return to normal. The manuscript attributes this to possible "compensatory expression," Do they have any thoughts how this is regulated? Are other cell types stepping in, or are surviving FBs proliferating?

We thank the reviewer for the question. As suggested, the compensatory collagen expression could be driven by surviving fibroblasts or other cell types. Since we currently lack evidence to exclude either possibility, we believe both could be contributing factors.

(7) While collagen is shown to be a dominant signaling molecule, its centrality is inferred primarily from scRNAseq and ligand-receptor predictions. Did authors try any functional rescue experiment (e.g., exogenous collagen supplementation or receptor blockade) to directly validate this pathway's role in vivo?

We thank the reviewer for the comment. As noted in our previous revision in response to similar questions from the other two reviewers, we agree that these rescue experiments are of interest but are beyond the scope of the current study. We plan to pursue these investigations in future work and share our findings when available.

eLife Assessment

This paper identifies a crucial step in the regulation of tight junction formation by identifying Rho-ROCK activity-dependent activation of the serine protease Matriptase, making Claudins available for tight junction formation. The reviewers were satisfied with the revisions and found the work important and the approach convincing.

Reviewer #1 (Public review):

Summary:

The manuscript by Cho and colleagues investigates de novo tight junction formation during the differentiation of immortalized human HaCaT keratinocytes to granular-like cells, as well as during epithelial remodeling that occurs upon the apoptotic of individual cells in confluent monolayers of the representative epithelial cell line EpH4. The authors demonstrate the involvement of Rho-ROCK with well-conducted experiments and convincing images. Moreover, they unravel the underlying molecular mechanism, with Rho-ROCK activity activating the transmembrane serine protease Matriptase, which in turn leads to the cleavage of EpCAM and TROP2, respectively, releasing Claudins from EpCAM/TROP2/Claudin complexes at the cell membrane to become available for polymerization and de novo tight junction formation. These functional studies in two different cell culture systems are complemented by localization studies of the according proteins in the stratified mouse epidermis in vivo.

In total, these are new and very intriguing and interesting findings that add important new insights into the molecular mechanisms of tight junction formation, identifying Matriptase as the "missing link" in the cascade of formerly described regulators. The involvement of TROP2/EpCAM/Claudin has been reported recently (Szabo et al., Biol. Open 2022; Bugge lab), and Matriptase had been formerly described to be required for tight junction formation as well, again from the Bugge lab. Yet, the functional correlation / epistasis between them, and their relation to Rho signaling, had not been known thus far.

Strengths:

Convincing functional studies in two different cell culture systems, complemented by supporting protein localization studies in vivo. The manuscript is clearly written and most data are convincingly demonstrated, with beautiful images and movies.

Weaknesses:

The previously described weaknesses have been fully wiped out during the revisions.

Reviewer #2 (Public review):

Summary:

In this manuscript the authors investigate how epithelia maintain intercellular barrier function despite and during cellular rearrangements upon e.g. apoptotic extrusion in simple epithelia or regenerative turnover in stratified epithelia like this epidermis. A fundamental question in epithelial biology. Previous literature has shown that Rho mediated local regulation of actomyosin is essential not only for cellular rearrangement itself but also directly controls tight junction barrier function. The molecular mechanics however remained unclear. Here the authors use extensive fluorescence imaging of fixed and live cells together with genetic and drug mediated interference to show that Rho activation is required and sufficient to form de novo tight junctional strands at intercellular contacts in epidermal keratinocytes (HaCat) and mammary epithelial cells. After having confirmed previous literature they then show that Rho activation activates the transmembrane protease matriptase which cleaves EpCAM and TROP2, two claudin binding transmembrane proteins, to release claudins and enable claudin strand formation and therefore tight junction barrier function.

Strengths:

The presented mechanism is shown to be relevant for epithelial barriers being conserved in simple and stratifying epithelial cells and mainly differs due to tissue specific expression of EpCAM and TROP2. The authors present carefull state of the art imaging and logical experiments that convincingly support the statements and conclusion. The manuscript is well written and easy to follow.

Weaknesses:

Whereas the in vitro evidence of the presented mechanism is strongly supported by the data, the in vivo confirmation is mostly based on the predicted distribution of TROP2. Whereas the causality of Rho mediated matriptase activation has been nicely demonstrated it remains unclear how Rho activates matriptase.

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Summary:

The manuscript "Rho-ROCK liberates sequestered claudin for rapid de novo tight junction formation" by Cho and colleagues investigates de novo tight junction formation during the differentiation of immortalized human HaCaT keratinocytes to granular-like cells, as well as during epithelial remodeling that occurs upon the apoptotic of individual cells in confluent monolayers of the representative epithelial cell line EpH4. The authors demonstrate the involvement of Rho-ROCK with well-conducted experiments and convincing images. Moreover, they unravel the underlying molecular mechanism, with Rho-ROCK activity activating the transmembrane serine protease Matriptase, which in turn leads to the cleavage of EpCAM and TROP2, respectively, releasing Claudins from EpCAM/TROP2/Claudin complexes at the cell membrane to become available for polymerization and de novo tight junction formation. These functional studies in the two different cell culture systems are complemented by localization studies of the according proteins in the stratified mouse epidermis in vivo.

In total, these are new and very intriguing and interesting findings that add important new insights into the molecular mechanisms of tight junction formation, identifying Matriptase as the "missing link" in the cascade of formerly described regulators. The involvement of TROP2/EpCAM/Claudin has been reported recently (Szabo et al., Biol. Open 2022; Bugge lab), and Matriptase had been formerly described to be required for in tight junction formation as well, again from the Bugge lab. Yet, the functional correlation/epistasis between them, and their relation to Rho signaling, had not been known thus far.

However, experiments addressing the role of Matriptase require a little more work.

Strengths:

Convincing functional studies in two different cell culture systems, complemented by supporting protein localization studies in vivo. The manuscript is clearly written and most data are convincingly demonstrated, with beautiful images and movies.

Weaknesses:

The central finding that Rho signaling leads to increased Matriptase activity needs to be more rigorously demonstrated (e.g. western blot specifically detecting the activated version or distinguishing between the full-length/inactive and processed/active version).

First, we thank the reviewer for their fair evaluation of our manuscript and for providing constructive feedback. Regarding the detection of matriptase activation—which Reviewer 1 identified as a weakness—we fully agree that direct validation is crucial. Therefore, in this revision we have carried out additional experiments using the M69 antibody, which specifically recognizes the activated form of matriptase. Details of these new experiments are provided in our point-by-point responses below.

Reviewer #2 (Public review):

Summary:

In this manuscript, the authors investigate how epithelia maintain intercellular barrier function despite and during cellular rearrangements upon e.g. apoptotic extrusion in simple epithelia or regenerative turnover in stratified epithelia like this epidermis. A fundamental question in epithelial biology. Previous literature has shown that Rho-mediated local regulation of actomyosin is essential not only for cellular rearrangement itself but also for directly controlling tight junction barrier function. The molecular mechanics however remained unclear. Here the authors use extensive fluorescent imaging of fixed and live cells together with genetic and drug-mediated interference to show that Rho activation is required and sufficient to form novo tight junctional strands at intercellular contacts in epidermal keratinocytes (HaCat) and mammary epithelial cells. After having confirmed previous literature they then show that Rho activation activates the transmembrane protease Matriptase which cleaves EpCAM and TROP2, two claudin-binding transmembrane proteins, to release claudins and enable claudin strand formation and therefore tight junction barrier function.

Strengths:

The presented mechanism is shown to be relevant for epithelial barriers being conserved in simple and stratifying epithelial cells and mainly differs due to tissue-specific expression of EpCAM and TROP2. The authors present careful state-of-the-art imaging and logical experiments that convincingly support the statements and conclusion. The manuscript is well-written and easy to follow.

Weaknesses:

Whereas the in vitro evidence of the presented mechanism is strongly supported by the data, the in vivo confirmation is mostly based on the predicted distribution of TROP2. Whereas the causality of Rho-mediated Matriptase activation has been nicely demonstrated it remains unclear how Rho activates Matriptase.

Thank you for your valuable feedback on our manuscript. As Reviewer 2 points out, the precise mechanism by which the Rho/ROCK pathway activates matriptase remains unclear. We have discussed the possible molecular mechanisms in the Discussion section. Elucidating the detailed mechanism of matriptase activation will be the focus of our future work.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

Comment 1-1 - Matriptase activation by Rho: The authors show activation of Matriptase in western blots by the simple reduction of (full-length?) protein level in Figures 5 and 7. Most publications however show activated Matriptase either by antibodies detecting specifically the active form (including the publication referenced in this manuscript), or the appearance of the activated form next to the inactive form (based on different molecular weights). Therefore, it is not completely clear whether the treatment with Rho activators (Figure 5) results in an overall decrease of Matriptase, or really in an increase in the activated form. Therefore, the authors should show the actual increase of the active form. As a control, the impact of camostat treatment and overexpression of Hai1 on the active form of Matriptase could be included. It also should be indicated in the figure legend how long cells had been treated with the drugs before being subjected to lysis. Moreover, the western blots need to be quantified.

We performed a more rigorous analysis using the M69 antibody, which specifically recognizes the activated form of matriptase and has been widely used in previous studies（e.g. Benaud et al., 2001; Hung et al., 2004; Wang et al., 2009）. We likewise confirmed a significant increase in M69 signals by both western blotting and immunostaining from samples in which matriptase was activated by acid medium treatment (Figure 5A). Crucially, we also observed matriptase activation with the M69 antibody both in Rho/ROCK activator-treated cells (Figure 5A) and in differentiated granular-layer-like cells (Figures 7A and 7D). These findings strongly support the conclusion that matriptase is activated downstream of the Rho/ROCK pathway.

Comment 1-2 - Based on their results, the authors conclude that Matriptase cleaves TROP2 in the SG2 layer of the epidermis, which is a little contradictory to former studies, which have shown Matriptase to be most prominently expressed and active in the basal layer and only little in the spinous layer (e.g Chen et al., Matriptase regulates proliferation and early, but not terminal, differentiation of human keratinocytes. J Invest Dermatol.2013). In this light, one could also argue that inhibiting Matriptase "simply" reduces epidermal differentiation. Can other differentiation markers be tested to rule that the effects on tight junctions are secondary consequences of interferences with earlier / more global steps of keratinocyte differentiation?

As the reviewer noted, previous studies have demonstrated that matriptase is essential for keratinocyte differentiation, and that it cleaves substrates beyond EpCAM and TROP2—any of which could potentially influence the differentiation process. To test this possibility, we chose to monitor maturation of adherens junction (AJ) as an indicator of keratinocyte differentiation into granular-layer cells. Prior work has shown that during differentiation into granular-layer cells, AJs develop and experience increased intercellular mechanical tension, and that this rise in mechanical tension at AJs is critical for subsequent TJ formation (Rübsam et al., 2017). To assess AJ tension, we stained with the α-18 monoclonal antibody, which specifically recognizes the tension-dependent conformational change of α-catenin, a core AJ component. In control cells, differentiation into granular-layer like cells led to a marked increase in α-18 signal at cell–cell adhesion sites. Importantly, when HaCaT cells were treated with Camostat to inhibit matriptase and then induced to differentiate, we observed an equivalent increase in α-18 signal at AJs (Figure 7F). However, we did not detect claudin enrichment at cell-cell contacts under these conditions (Figures 7F and 7H). These results suggest that matriptase inhibition does not impair AJ maturation during granular-layer differentiation, but does profoundly disrupt TJ formation. While we cannot rule out the possibility that matriptase acts more broadly from these results, we judged that a comprehensive substrate survey lies outside the scope of the present manuscript.

Comment 1-3 - In addition, as in Figure 5, full-length levels of Matriptase in Figure 7A need to be complemented by the active version to demonstrate more convincingly that TROP2 processing coincides with (and is most likely caused by) increased Matriptase activation. In the quantification in 7B, levels actually go up again after 2 and 4 hours. How is that explained, and what would this mean with respect to tight junction formation seen at 24 h of differentiation? The TROP2 cleavage shown in Figure 7A should be quantified.

This comment is related to Comment 1-1. Using the M69 antibody, which specifically recognizes the activated matriptase, we directly demonstrated that matriptase activation occurs during the differentiation of granular layer-like cells (Figures 7A and 7D). Furthermore, we performed quantitative analysis of TROP2 cleavage and found that, compared with undifferentiated cells, differentiation into granular-layer like cells was accompanied by an increase in the cleaved TROP2 fragments (Figures 7A and 7B).

Minor points:

Comment 1-4 - Figure 1B and C: Including orthogonal views would be a nice add-on to appreciate the findings.

In the revised version, we have added the corresponding orthogonal views to Figure 1B and Figure 1C.

Comment 1-5 - Figure 2D: last row: indication of orthogonal view.

We stated that the bottom panels are orthogonal views in the figure legend of Figure 2D.

Comment 1-6 - Figure 3A: quantification is missing. GST-Rhotekin assay is missing in methods.

In the revised manuscript, we have added quantitative analysis for Figure 3A. We have also supplemented the Materials and Methods section with detailed information on the GST–Rhotekin assay used to quantify levels of active RhoA.

Comment 1-7 - Figure 4H: quantification of the Western blot is missing.

In the revised manuscript, we have added quantitative analysis for Figure 4H as Figure 4I.

Comment 1-8 - Figure 5 and 6: Quantifications of Western blots are missing.

In the revised manuscript, we have added quantitative analyses for Figure 5D as Figure 5F and for Figure 6A as Figure 6B.

Comment 1-9 - Figure 7C: quantification of the Western blot is missing.

Figure 7C does not present western blotting data. For the other western blotting results, we have added quantitative analyses as suggested by Reviewer 1.

Comment 1-10 - Figure 8I: Including Hai1 overexpression would be good for a complete picture.

Following Reviewer 1’s suggestion, we have added staining data for Hai1-overexpressing cells to Figure 8J.

Comment 1-11 - Line 377: The authors say they found Matriptase always present in lateral membranes. I did not find evidence for this in the manuscript.

Previous studies have demonstrated that in polarized epithelial cells, matriptase is localized to the basolateral membrane below TJs (Buzza et al., 2010; Wang et al., 2009). We also found that matriptase consistently localizes to the basolateral membrane but more crucially that it becomes activated there during differentiation into granular layer cells. We added these new data as Figures 7C-7E in the revised manuscript. These findings suggest that matriptase activation occurs without a change in its subcellular localization.

Comment 1-12 - Line 381: should most likely say: and ADAM17 but it is not known whether...

We corrected the sentence in the revised manuscript.

Reviewer #2 (Recommendations for the authors):

The authors have added a significant number of quantifications verifying their observations, which was a major comment in a previous version of the manuscript and thus I have only a few minor comments which should be addressed.

Comment 2-1 - It is not required to have scale bars in every image of a panel if the same scale is used.

Unnecessary scale bars were removed. Specifically, scale bars were removed from Figure 1B, 1C, 1F, 8F, 8G, and 8H.

Comment 2-2 - Throughout all figures: Please state for non-quantified images whether this is a representative example and for how many technical or biological repeats this is representative. Also for "N" number, state what the N stands for and if this is what the dots in the graph represent. Are these the number of junctions or technical, experimental or biological repeats?

In the revised manuscript, we have added the number of independent experiments and corresponding “N” values to the Quantification and Statistical Analysis subsection of the Materials and Methods.

Comment 2-3 - Some Zooms have a scale bar (6d), and some do not (e.g. 5b).

The scale bar was removed from the magnified image in Figure 6D.

eLife Assessment

This valuable study by Wu et al presents data on bacterial cell organization, demonstrating that the two structures that account for bacterial motility - the chemotaxis complex and the flagella - colocalize to the same pole in Pseudomonas aeruginosa cells. The work provides convincing results for the regulation underlying this spatial organization and its functioning.

Reviewer #1 (Public review):

Summary:

The study by Wu et al presents interesting data on bacterial cell organization, a field that is progressing now, mainly due to the advances in microscopy. Based mainly on fluorescence microscopy images, the authors aim to demonstrate that the two structures that account for bacterial motility, the chemotaxis complex and the flagella, colocalize to the same pole in Pseudomonas aeruginosa cells and to expose the regulation underlying their spatial organization and functioning.

Comments on revisions:

The authors have addressed all major and minor points that I raised in a satisfying way during the revision process. The work can now be regarded as complete, the assumptions were clarified, the results are convincing, the conclusions are justified, and the novelty has been made clear.

This manuscript will be of interest to cell biologists, mainly those studying bacteria, but not only

Reviewer #2 (Public review):

Summary:

Here, the authors studied the molecular mechanisms by which the chemoreceptor cluster and flagella motor of Pseudomonas aeruginosa (PA) are spatially organized in the cell. They argue that FlhF is involved in localizing the receptors-motor to the cell pole, and even without FlhF, the two are colocalized. Finally, the authors argue that the functional reason for this colocalization is to insulate chemotactic signaling from other signaling pathways, such as cyclic-di-GMP signaling.

Strength:

The experiments and data are high quality. It is clear that the motor and receptors co-localize, and that elevated CheY levels lead to elevated c-di-GMP.

Weakness:

The explanation for the functional importance of receptor-motor colocalization is plausible but is still not conclusively demonstrated. Colocalization might reduce CheY levels throughout the cell in order to reduce cross-talk with c-di-GMP. This would mean that if physiologically-relevant levels of CheYp near the pole were present throughout the cell, c-di-GMP levels would be elevated to a point that is problematic for the cell. Clearly demonstrating this seems challenging.

Reviewer #3 (Public review):

Summary:

The authors investigated the assembly and polar localization of the chemosensory cluster in P. aeruginosa. They discovered that a certain protein (FlhF) is required for the polar localization of the chemosensory cluster while a fully-assembled motor is necessary for the assembly of the cluster. They found that flagella and chemosensory clusters always co-localize in the cell; either at the cell pole in wild type cells or randomly-located in the cell in FlhF mutant cells. They hypothesize that this co-localization is required to keep the level of another protein (CheY-P), which controls motor switching, at low levels as the presence of high-levels of this protein (if the flagella and chemosensory clusters were not co-localized) is associated with high-levels of c-di-GMP and cell aggregations.

Strengths:

The manuscript is clearly written and straightforward. The authors applied multiple techniques to study the bacterial motility system including fluorescence light microscopy and gene editing. In general, the work enhances our understanding of the subtlety of interaction between the chemosensory cluster and the flagellar motor to regulate cell motility.

Weaknesses:

The major weakness for me in this paper is that the authors never discussed how the flagellar genes expression is controlled in P. aeruginosa. For example, in E. coli there is a transcriptional hierarchy for the flagellar genes (early, middle, and late genes, see Chilcott and Hughes, 2000). Similarly, Campylobacter and Helicobacter have a different regulatory cascade for their flagellar genes (See Lertsethtakarn, Ottemann, and Hendrixson, 2011). How does the expression of flagellar genes in P. aeruginosa compare to other species? how many classes are there for these genes? is there a hierarchy in their expression and how does this affect the results of the FliF and FliG mutants? In other words, if FliF and FliG are in class I (as in E. coli) then their absence might affect the expression of other later flagellar genes in subsequent classes (i.e., chemosensory genes). Also, in both FliF and FliG mutants no assembly intermediates of the flagellar motor are present in the cell as FliG is required for the assembly of FliF (see Hiroyuki Terashima et al. 2020, Kaplan et al. 2019, Kaplan et al. 2022). It could be argued that when the motor is not assembled then this will affect the expression of the other genes (e.g., those of the chemosensory cluster) which might play a role in the decreased level of chemosensory clusters the authors find in these mutants.

Comments on revisions:

I believe the authors have performed additional experiments that improved their manuscript and they have answered many of my comments and those of the other reviewers. I am supportive of publishing this manuscript, but I still find the following points that are not clear to me (probably I am misunderstanding some points; the authors can clarify).

(1) In response to reviewer 1, the authors say that they "analyzed and categorized the distribution of the chemotaxis complex in both wild-type and flhF mutant strains into three patterns: precise-polar, near-polar, and mid-cell localization." I can see what they mean by polar and mid-cell, but near-polar sounds a bit elusive? Can they provide examples of this stage and mention how accurately they can identify it? Also, do the pie charts they show in Figure S4 really show "significant alterations"? There is a difference between 98% and 85% as they mention in their response to reviewer 1, but I am not sure that this is significant? Probably they can explain/change the language in the text? Also, the number of cells they counted for FlhF mutant is more than the double of other strains (WT and FlhF FliF mutant)?

(2) One thing that also confused me is the following: One point that the authors stress is that FlhF localizes both the flagellum and the chemoreceptors to the pole. However, if I look at Figure 2B, the flagellum and the chemoreceptors still co-localize together (although not at the pole). If FlhF was responsible for co-localizing both of them to the pole, then wouldn't one expect them to be randomly localized in this mutant and by that I mean that they do not co-localize but that each of them (the flagellum and the chemoreceptors) are located in a different random location of the cell (not co-localized). The fact that they are still co-localized together in this mutant could also be interpreted by, for example, that FlhF localizes the flagellum to the pole and another mechanism localizes the chemoreceptors to the flagellum, hence, they still co-localize in this mutant because the chemoreceptors follow the flagellum by another mechanism to wherever it goes?

(3) In the response to reviewers, the authors mention "suggesting that the assembly of the receptor complex is likely influenced mainly by the C-ring and MS-ring structures rather than by the P ring" . However, in the article, they still write "The complete assembly of the motor serves as a partial prerequisite for the assembly of the chemotaxis complex, and its assembly site is also regulated by the polar anchor protein FlhF" despite their FlgI results which is not in accordance with this statement? Also, As I mentioned in my previous report, in FliG and FliF mutant the motor does not assemble (see Hiroyuki Terashima et al. 2020., and Kaplan et al., 2022).

(4) The authors have said in their response to my point "and currently, there is no evidence that FliA activity is influenced by proteins like FliG". I just want to clarify what I meant in my previous report: In E. coli, FliA binds to FlgM, and when the hook is assembled FlgM is secreted outside the cell allowing FliA to trigger the transcription of class III genes, which include the chemosensory genes (see Figure 5 in Beeby et al, 2020 in FEMS Microbiology, and Chilcott and Hughes, 2000). This implies that if the hook is not built, then late genes (including the chemoreceptors) should not be present. However, in Kaplan et al., 2019, the authors imaged a FliF mutant in Shewanella oneidensis (Figure S3) and still saw that chemoreceptors are present (I believe the authors must highlight this). This suggests that species such as Shewanella and Pseudomonas have a different assembly process than that E. coli, and although the authors say that in the text, I believe they still can refine this part more in the spirit of what I wrote here.

I do not like to ask for additional experiments in the second round of review, so for me if the authors modify the text to tackle these points and allow for probable alternative explanations/ highlight gaps/ modify language used for some claims, then that is fine with me.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The study by Wu et al presents interesting data on bacterial cell organization, a field that is progressing now, mainly due to the advances in microscopy. Based mainly on fluorescence microscopy images, the authors aim to demonstrate that the two structures that account for bacterial motility, the chemotaxis complex and the flagella, colocalize to the same pole in Pseudomonas aeruginosa cells and to expose the regulation underlying their spatial organization and functioning.

Strengths:

The subject is of importance.

Weaknesses:

The conclusions are too strong for the presented data. The lack of statistical analysis makes this paper incomplete. The novelty of the findings is not clear.

We have strengthened the data analysis by including appropriate statistical tests to support our conclusions more convincingly. Additionally, we have refined the description of the research background to better emphasize the novelty and significance of our findings. Please see the detailed responses below for further information.

Major issues:

(1) The novelty is in question since in the Abstract the authors highlight their main finding, which is that both the chemotaxis complex and the flagella localize to the same pole, as surprising. However, in the Introduction they state that "pathway-related receptors that mediate chemotaxis, as well as the flagellum are localized at the same cell pole17,18". I am not a pseudomonas researcher and from my short glance at these references, I could not tell whether they report colocalization of the two structures to the same pole. However, I trust the authors that they know the literature on the localization of the chemotaxis complex and flagella in their organism. See also major issue number 5 on the novelty regarding the involvement of c-di-GMP.

We thank the reviewer for this valuable comment and appreciate the opportunity to clarify our statements.

Kazunobu et al. (ref. 18) used scanning electron microscopy to preliminarily characterize the flagellation pattern of Pseudomonas aeruginosa during cell division, showing that existing flagella are located at the old pole. Zehra et al. (ref. 17), through fluorescence microscopy, observed that CheA and CheY proteins in dividing cells are typically also present at the old pole. Based on these observations, we inferred in the Introduction that the chemotaxis complex and flagellum may localize to the same cell pole.

However, this inference is indirect and lacks direct live-cell evidence of colocalization, leaving its validity to be confirmed. This uncertainty was indeed the starting point and motivation for our study.

In our work, we simultaneously visualized flagellar filaments and core chemoreceptor proteins at the single-cell level in P. aeruginosa. We characterized the assembly and spatial coordination of the chemotaxis network and flagellar motor throughout the cell cycle, providing direct evidence of their colocalization and coordinated assembly. This represents a significant advance beyond prior indirect observations and supports the novelty of our study.

Accordingly, we have revised the relevant statements in lines 71-75 of the manuscript to better reflect the current state of the literature and emphasize the novelty of our direct observations.

(2) Statistics for the microscopy images, on which most conclusions in this manuscript are based, are completely missing. Given that most micrographs present one or very few cells, together with the fact that almost all conclusions depend on whether certain macromolecules are at one or two poles and whether different complexes are in the same pole, proper statistics, based on hundreds of cells in several fields, are absolutely required. Without this information, the results are anecdotal and do not support the conclusions. Due to the importance of statistics for this manuscript, strict statistical tests should be used and reported. Moreover, representative large fields with many cells should be added as supportive information.

We thank the reviewer for this important comment, which significantly improves the rigor and persuasiveness of our manuscript.

For the colocalization analyses presented in Fig. 1D and Fig. 2B, we quantified 145 and 101 cells with fluorescently labeled flagella, respectively, and observed consistent colocalization of the chemoreceptor complexes and flagella in all examined cells (now added in the figure legends). Regarding the distribution patterns of chemoreceptors shown in Fig. 3A, we have now included comprehensive statistical analyses for both wild-type and mutant strains. For each strain, more than 300 cells were analyzed across at least three independent microscopic fields, providing robust statistical power (detailed data are presented in Fig. 3C).

To further strengthen the evidence, statistical tests were applied to confirm the significance and reproducibility of our findings (Fig. 3C). In addition, representative large-field fluorescence images containing numerous cells have been added to the supplementary materials (Fig. S1 and Fig. S3).

The problem is more pronounced when the authors make strong statements, as in lines 157-158: "The results revealed that the chemoreceptor arrays no longer grow robustly at the cell pole (Figure 2A)". Looking at the seven cells shown in Figure 2A, five of them show polar localization of the chemoreceptors. The question is then: what is the percentage of cells that show precise polar, near-polar, or mid cell localization (the three patterns shown here) in the mutant and in the wild type? Since I know that these three patterns can also be observed in WT cells, what counts is the difference, and whether it is statistically significant.

We thank the reviewer for raising this important point. Following the reviewer's suggestion, we have now analyzed and categorized the distribution of the chemotaxis complex in both wild-type and flhF mutant strains into three patterns: precise-polar, near-polar, and mid-cell localization. For each strain, more than 200 cells across three independent fields of view were quantified.

Our statistical analysis shows that in the wild-type strain, approximately 98% of cells exhibit precise polar localization of the chemotaxis complex. In contrast, the ΔflhF mutant displays a clear shift in distribution, with about 5% of cells showing mid-cell localization and 9.5% showing near-polar localization. These differences demonstrate a significant alteration in the spatial pattern upon flhF deletion.

We have revised the relevant text in lines 166-170 accordingly and included the detailed statistical data in the newly added Fig. S4.

Even for the graphs shown in Figures 3C and 3D, where the proportion of cells with obvious chemoreceptor arrays and absolute fluorescence brightness of the chemosensory array are shown, respectively, the questions that arise are: for how many individual cells these values hold and what is the significance of the difference between each two strains?

The number of cells analyzed for each strain is indicated in the original manuscript: 372 wild-type cells (line 123), 221 ΔflhF cells (line 172), 234 ΔfliG cells (line 197), 323 ΔfliF cells (line 200), 672 ΔflhFΔfliF cells (line 202), and 242 ΔmotAΔmotCD cells (line 207). For each strain, data were collected from three independent fields of view. We have now also provided the number of cells in Fig. 3 legend.

We have now performed statistical comparisons using t-tests between strains. Notably, the measured values in Fig. 3C exhibit a clear, monotonic decrease with successive gene knockouts, supporting the robustness of the observed trend.

Regarding the absolute fluorescence intensity shown in the original Fig. 3D, the mutants did not display consistent directional changes compared to the wild type. Reliable comparison of absolute fluorescence intensity requires consistent fluorescent protein maturation levels across strains. Given the likely variability in maturation levels between strains, we concluded that this data may not accurately reflect true differences in protein concentrations. Therefore, we have removed the fluorescence intensity graph from the revised manuscript to avoid potential misinterpretation.

(3) The authors conclude that "Motor structural integrity is a prerequisite for chemoreceptor self-assembly" based on the reduction in cells with chemoreceptor clusters in mutants deleted for flagellar genes, despite the proper polar localization of the chemotaxis protein CheY. They show that the level of CheY in the WT and the mutant strains is similar, based on Western blot, which in my opinion is over-exposed. "To ascertain whether it is motor integrity rather than functionality that influences the efficiency of chemosensory array assembly", they constructed a mutant deleted for the flagella stator and found that the motor is stalled while CheY behaves like in WT cells. The authors further "quantified the proportion of cells with receptor clusters and the absolute fluorescence intensity of individual clusters (Figures 3C-D)". While Figure 3DC suggests that, indeed, the flagella mutants show fewer cells with a chemotaxis complex, Figure 3D suggests that the differences in fluorescence intensity are not statistically significant. Since it is obvious that the regulation of both structures' production and localization is codependent, I think that it takes more than a Western blot to make such a decision.

We thank the reviewer for the suggestions. To further clarify that the assembly of flagellar motors and chemoreceptor clusters occurs in an orderly manner rather than being merely codependent, we performed additional experiments. Specifically, we constructed a ΔcheA mutant strain, in which chemoreceptor clusters fail to assemble. Using in vivo fluorescent labeling of flagellar filaments, we observed that the proportion of cells with flagellar filaments in the ΔcheA strain was comparable to that of the wild type (Fig. S5).

In contrast, mutants lacking complete motor structures, such as ΔfliF and ΔfliG, showed a significant reduction in the proportion of cells with obvious receptor clusters (Fig. 3C). Based on these results, we conclude that the structural integrity of the flagellar motor is, to a certain extent, a prerequisite for the self-assembly of chemoreceptor clusters.

Accordingly, we have revised the relevant statement in lines 213-217 of the manuscript to reflect this clarification.

(4) I wonder why the authors chose to label CheY, which is the only component of the chemotaxis complex that shuttles back and forth to the base of the flagella. In any case, I think that they should strengthen their results by repeating some key experiments with labeled CheW or CheA.

We thank the reviewer for this valuable suggestion. In our study, we initially focused on the positional relationship between chemoreceptor clusters and flagella, then investigated factors influencing cluster distribution and assembly efficiency. The physiological significance of motor and cluster co-localization was ultimately proposed with CheY as the starting point.

Previous work by Harwood's group demonstrated that both CheY-YFP and CheA-GFP localize to the old poles of dividing Pseudomonas aeruginosa cells. Since our physiological hypothesis centers on CheY, we chose to label CheY-EYFP in our experiments.

To further strengthen our conclusions, we constructed a plasmid expressing CheA-CFP and introduced it into the cheY-eyfp strain via electroporation. Fluorescence imaging revealed a high degree of spatial overlap between CheA-CFP and CheY-EYFP (Fig. S2), confirming that CheY-EYFP accurately marks the location of the chemoreceptor complex.

We have revised the manuscript accordingly (lines 119-123) and added these data as Fig. S2.

(5) The last section of the results is very problematic, regarding the rationale, the conclusions, and the novelty. As far as the rationale is concerned, I do not understand why the authors assume that "a spatial separation between the chemoreceptors and flagellar motors should not significantly impact the temporal comparison in bacterial chemotaxis". Is there any proof for that?

We apologize for the lack of clarity in our original explanation. The rationale behind the statement was initially supported by comparing the timescales of CheY-P diffusion and temporal comparison in chemotaxis. Specifically, the diffusion time for CheY-P to traverse the entire length of a bacterial cell is approximately 100 ms (refs 39&40), whereas the timescale for bacterial chemotaxis temporal comparison is on the order of seconds (ref 41).

To clarify and strengthen this argument, we have expanded the discussion as follows:

The diffusion coefficient of CheY in bacterial cells is about 10 µm2/s, which corresponds to an estimated end-to-end diffusion time on the order of 100 ms (refs 40&41). If the chemotaxis complexes were randomly distributed rather than localized, diffusion times would be even shorter. In contrast, the timescale for the chemotaxis temporal comparison is on the order of seconds (ref. 42). Additionally, a study by Fukuoka and colleagues reported that intracellular chemotaxis signal transduction requires approximately 240 ms beyond CheY or CheY-P diffusion time (ref. 41). Moreover, the intervals of counterclockwise (CCW) and clockwise (CW) rotation of the P. aeruginosa flagellar motor under normal conditions are 1-2 seconds, as determined by tethered cell or bead assays (refs. 30&43).

Taken together, these indicate that for P. aeruginosa, which moves via a run-reverse mode, the potential 100 ms reduction in response time due to co-localization of the chemotaxis complex and motor has a limited effect on overall chemotaxis timing.

We have revised the corresponding text accordingly (lines 238-245) to better explain this rationale.

More surprising for me was to read that "The signal transduction pathways in E. coli are relatively simple, and the chemotaxis response regulator CheY-P affects only the regulation of motor switching". There are degrees of complexity among signal transduction pathways in E. coli, but the chemotaxis seems to be ranked at the top. CheY is part of the adaptation. Perfect adaptation, as many other issues related to the chemotaxis pathway, which include the wide dynamic range, the robustness, the sensitivity, and the signal amplification (gain), are still largely unexplained. Hence, such assumptions are not justified.

We apologize for the confusion and imprecision in our original statements. Our intention was to convey that the chemotaxis pathway in E. coli is relatively simple compared to the more complex chemosensory systems in P. aeruginosa. We did not mean to generalize this simplicity to all signal transduction pathways in E. coli.

We acknowledge that E. coli chemotaxis is a highly sophisticated system, involving processes such as perfect adaptation, wide dynamic range, robustness, sensitivity, and signal amplification, many aspects of which remain incompletely understood. CheY indeed plays a crucial role in adaptation and motor switching regulation.

Accordingly, we have revised the original text (lines 249-255) to avoid any misunderstanding.

More perplexing is the novelty of the authors' documentation of the effect of the chemotaxis proteins on the c-di-GMP level. In 2013, Kulasekara et al. published a paper in eLife entitled "c-di-GMP heterogeneity is generated by the chemotaxis machinery to regulate flagellar motility". In the same year, Kulasekara published a paper entitled "Insight into a Mechanism Generating Cyclic di-GMP Heterogeneity in Pseudomonas aeruginosa". The authors did not cite these works and I wonder why.

We apologize for having been unaware of these important references and thank the reviewer for bringing them to our attention. We have now cited the eLife paper and the PhD thesis titled "Insight into a Mechanism Generating Cyclic di-GMP Heterogeneity in Pseudomonas aeruginosa" by Kulasekara et al.

Regarding novelty, there are key differences between our findings and those reported by Kulasekara et al. While they proposed that CheA influences c-di-GMP heterogeneity through interaction with a specific phosphodiesterase (PDE), our results demonstrate that overexpression of CheY leads to an increase in intracellular c-di-GMP levels.

We have revised the original text accordingly (lines 358-362) to clarify these distinctions.

(6) Throughout the manuscript, the authors refer to foci of fluorescent CheY as "chemoreceptor arrays". If anything, these foci signify the chemotaxis complex, not the membrane-traversing chemoreceptors.

We thank the reviewer for this clarification. We have revised the manuscript accordingly to refer to the fluorescent CheY foci as representing the chemotaxis complex rather than the chemoreceptor arrays.

Conclusions:

The manuscript addresses an interesting subject and contains interesting, but incomplete, data.

Reviewer #2 (Public Review):

Summary:

Here, the authors studied the molecular mechanisms by which the chemoreceptor cluster and flagella motor of Pseudomonas aeruginosa (PA) are spatially organized in the cell. They argue that FlhF is involved in localizing the receptors-motor to the cell pole, and even without FlhF, the two are colocalized. FlhF is known to cause the motor to localize to the pole in a different bacterial species, Vibrio cholera, but it is not involved in receptor localization in that bacterium. Finally, the authors argue that the functional reason for this colocalization is to insulate chemotactic signaling from other signaling pathways, such as cyclic-di-GMP signaling.

Strengths:

The experiments and data look to be high-quality.

Weaknesses:

However, the interpretations and conclusions drawn from the experimental observations are not fully justified in my opinion.

I see two main issues with the evidence provided for the authors' claims.

(1) Assumptions about receptor localization:

The authors rely on YFP-tagged CheY to identify the location of the receptor cluster, but CheY is a diffusible cytoplasmic protein. In E. coli, CheY has been shown to localize at the receptor cluster, but the evidence for this in PA is less strong. The authors refer to a paper by Guvener et al 2006, which showed that CheY localizes to a cell pole, and CheA (a receptor cluster protein) also localizes to a pole, but my understanding is that colocalization of CheY and CheA was not shown. My concern is that CheY could instead localize to the motor in PA, say by binding FliM. This "null model" would explain the authors' observations, without colocalization of the receptors and motor. Verifying that CheY and CheA are colocalized in PA would be a very helpful experiment to address this weakness.

We thank the reviewer for this valuable suggestion. We agree that verifying the colocalization of CheY and CheA would strengthen our conclusions. To address this, we constructed a plasmid expressing CheA-CFP and introduced it into the CheY-EYFP strain by electroporation. Fluorescence imaging revealed a high degree of spatial overlap between CheA-CFP and CheY-EYFP signals, indicating that CheY-EYFP indeed marks the location of the chemoreceptor complex rather than the flagellar motor.

We have revised the manuscript accordingly (lines 118-123) and included these results in the new Fig. S2.

(2) Argument for the functional importance of receptor-motor colocalization at the pole:

The authors argue that colocalization of the receptors and motors at the pole is important because it could keep phosphorylated CheY, CheY-p, restricted to a small region of the cell, preventing crosstalk with other signaling pathways. Their evidence for this is that overexpressing CheY leads to higher intracellular cdG levels and cell aggregation. Say that the receptors and motors are colocalized at the pole. In E. coli, CheY-p rapidly diffuses through the cell. What would prevent this from occurring in PA, even with colocalization?

We appreciate the reviewer's insightful question. The colocalization of both the signaling source (the kinase) and sink (the phosphatase) at the chemoreceptor complex at the cell pole results in a rapid decay of CheY-P concentration within approximately 0.2 µm from the cell pole, leading to a nearly uniform distribution elsewhere in the cell, as demonstrated by Vaknin and Berg (ref. 46). This spatial arrangement effectively confines high CheY-P levels to the pole region. When the motor is also localized at the cell pole, this reduces the need for elevated CheY-P concentrations throughout the cytoplasm, thereby minimizing potential crosstalk with other signaling pathways.

We have revised the manuscript accordingly (lines 280-286) to clarify this point.

Elevating CheY concentration may increase the concentration of CheY-p in the cell, but might also stress the cells in other unexpected ways. It is not so clear from this experiment that elevated CheY-p throughout the cell is the reason that they aggregate, or that this outcome is avoided by colocalizing the receptors and motor at the same pole. If localization of the receptor array and motor at one pole were important for keeping CheY-p levels low at the opposite pole, then we should expect cells in which the receptors and motor are not at the pole to have higher CheY-p at the opposite pole. According to the authors' argument, it seems like this should cause elevated cdG levels and aggregation in the delta flhF mutants with wild-type levels of CheY. But it does not look like this happened. Instead of varying CheY expression, the authors could test their hypothesis that receptor-motor colocalization at the pole is important for preventing crosstalk by measuring cdG levels in the flhF mutant, in which the motor (and maybe the receptor cluster) are no longer localized in the cell pole.

We thank the reviewer for raising the important point regarding potential cellular stress caused by elevated CheY concentrations, as well as for the suggestion to test the hypothesis using ΔflhF mutants.

First, as noted above, CheY-P concentration rapidly decreases away from the receptor complex. While deletion of flhF alters the position of the receptor complex, thereby shifting the region of high CheY-P concentration, it does not increase CheY-P levels elsewhere in the cell. Importantly, in the ΔflhF strain, the receptor complex and the motor still colocalize, so this mutant may not effectively test the role of receptor-motor colocalization in preventing crosstalk as suggested.

Regarding the possibility that elevated CheY levels stress the cells independently of CheY-P signaling, prior work in <i.E. coli by Cluzel et al. (ref. 11) showed that overexpressing CheY several-fold did not cause phenotypic changes, indicating that simple CheY overexpression alone may not be generally stressful. Furthermore, our data indicate that the increase in c-di-GMP levels and subsequent cell aggregation upon CheY overexpression is not an all-or-none switch but occurs progressively as CheY concentration rises.

To further confirm that CheY overexpression promotes aggregation through increased c-di-GMP levels, we performed additional experiments co-overexpressing CheY and a phosphodiesterase (PDE) from E. coli to reduce intracellular c-di-GMP. These experiments showed that PDE expression mitigates cell aggregation caused by CheY overexpression (Fig. S8).

We have revised the manuscript accordingly (lines 290-294) and added these new results in Fig. S8.

Reviewer #3 (Public Review):

Summary:

The authors investigated the assembly and polar localization of the chemosensory cluster in P. aeruginosa. They discovered that a certain protein (FlhF) is required for the polar localization of the chemosensory cluster while a fully-assembled motor is necessary for the assembly of the cluster. They found that flagella and chemosensory clusters always co-localize in the cell; either at the cell pole in wild-type cells or randomly-located in the cell in FlhF mutant cells. They hypothesize that this co-localization is required to keep the level of another protein (CheY-P), which controls motor switching, at low levels as the presence of high levels of this protein (if the flagella and chemosensory clusters were not co-localized) is associated with high-levels of c-di-GMP and cell aggregations.

Strengths:

The manuscript is clearly written and straightforward. The authors applied multiple techniques to study the bacterial motility system including fluorescence light microscopy and gene editing. In general, the work enhances our understanding of the subtlety of interaction between the chemosensory cluster and the flagellar motor to regulate cell motility.

Weaknesses:

The major weakness in this paper is that the authors never discussed how the flagellar gene expression is controlled in P. aeruginosa. For example, in E. coli there is a transcriptional hierarchy for the flagellar genes (early, middle, and late genes, see Chilcott and Hughes, 2000). Similarly, Campylobacter and Helicobacter have a different regulatory cascade for their flagellar genes (See Lertsethtakarn, Ottemann, and Hendrixson, 2011). How does the expression of flagellar genes in P. aeruginosa compare to other species? How many classes are there for these genes? Is there a hierarchy in their expression and how does this affect the results of the FliF and FliG mutants? In other words, if FliF and FliG are in class I (as in E. coli) then their absence might affect the expression of other later flagellar genes in subsequent classes (i.e., chemosensory genes). Also, in both FliF and FliG mutants no assembly intermediates of the flagellar motor are present in the cell as FliG is required for the assembly of FliF (see Hiroyuki Terashima et al. 2020, Kaplan et al. 2019, Kaplan et al. 2022). It could be argued that when the motor is not assembled then this will affect the expression of the other genes (e.g., those of the chemosensory cluster) which might play a role in the decreased level of chemosensory clusters the authors find in these mutants.

We thank the reviewer for the insightful comments. P. aeruginosa possesses a four-tiered transcriptional regulatory hierarchy controlling flagellar biogenesis. Within this system, fliF and fliG belong to class II genes and are regulated by the master regulator FleQ. In contrast, chemotaxis-related genes such as cheA and cheW are regulated by intracellular free FliA, and currently, there is no evidence that FliA activity is influenced by proteins like FliG.

To verify that the expression of core chemotaxis proteins was not affected by deletion of fliG, we performed Western blot analyses to compare CheY levels in wild-type, ΔfliF, and ΔfliG strains. We observed no significant differences, indicating that the reduced presence of receptor clusters in these mutants is not due to altered expression of chemotaxis proteins.

Accordingly, we have revised the manuscript (lines 341-348) and updated Fig. 3B to reflect these findings.

Recommendations for the authors:

Reviewing Editor (Recommendations For The Authors):

The reviewers comment on several important aspects that should be addressed, namely: the lack of statistical analysis; the need for clarifications regarding assumptions made regarding receptor localization; the functional importance of receptor-motor colocalization; and the need for an elaborate discussion of flagellar gene expression. Also, two reviewers pointed out the need to prove the co-localization of CheY and CheA; This is important since CheY is dynamic, shuttling back and forth from the chemotaxis complex to the base of the flagella, whereas CheA (or cheW or, even better, the receptors) is considered less dynamic and an integral part of the chemotaxis complex.

Reviewer #1 (Recommendations For The Authors):

Minor points:

Line 43: "ubiquitous" - I would choose another word.

We changed "ubiquitous" to "widespread".

Line 49: "order" - change to organize.

We changed "order" to "organize".

Line 52: "To grow and colonize within the host, bacteria have evolved a mechanism for migrating...". Motility "towards more favorable environments" is an important survival strategy of bacteria in various ecological niches, not only within the host.

We revised it to "grow and colonize in various ecological niches".

Line 72: Define F6 in "F6 pathway-related receptors".

The proteins encoded by chemotaxis-related genes collectively constitute the F6 pathway, which we have now explained in the manuscript text.

Line 72-73: Do references 17 &18 really report colocalization of the chemotaxis receptor and flagella to the same pole? If these or other reports document such colocalization, then the sentence in the Abstract "Surprisingly, we found that both are located at the same cell pole..." is not correct.

Kazunobu et al. (ref. 18) used scanning electron microscopy to preliminarily characterize the flagellation pattern of Pseudomonas aeruginosa during cell division, showing that existing flagella are located at the old pole. Zehra et al. (ref. 17), through fluorescence microscopy, observed that CheA and CheY proteins in dividing cells are typically also present at the old pole. Based on these observations, we inferred in the Introduction that the chemotaxis complex and flagellum may localize to the same cell pole.

However, this inference is indirect and lacks direct live-cell evidence of colocalization, leaving its validity to be confirmed. This uncertainty was indeed the starting point and motivation for our study.

In our work, we simultaneously visualized flagellar filaments and core chemoreceptor proteins at the single-cell level in P. aeruginosa. We characterized the assembly and spatial coordination of the chemotaxis network and flagellar motor throughout the cell cycle, providing direct evidence of their colocalization and coordinated assembly. This represents a significant advance beyond prior indirect observations and supports the novelty of our study.

Accordingly, we have revised the relevant statements in lines 71-75 of the manuscript to better reflect the current state of the literature and emphasize the novelty of our direct observations.

Line 108: "CheY has been shown to colocalize with chemoreceptors". The authors rely here (reference 29) and in other places on findings in E. coli. However, in the Introduction, they describe the many differences between the motility systems of P. aeruginosa and E. coli, e.g., the number of chemosensory systems and their spatial distribution (E. coli is a peritrichous bacterium, as opposed to the monotrichous bacterium P. aeruginosa). There seem to be proofs for colocalization of the Che and MCP proteins in P. aeruginosa, which should be cited here.

Thank you for pointing this out. Harwood's group reported that a cheY-YFP fusion strain exhibited bright fluorescent spots at the cell pole, which disappeared upon knockout of cheA or cheW-genes encoding structural proteins of the chemotaxis complex. This strongly suggests colocalization of CheY with MCP proteins in P. aeruginosa. We have now cited this study as reference 17 in the manuscript.

Figure 1B: Please replace the order of the schematic presentations, so that the cheY-egfp fusion, which is described first in the text, is at the top.

We have modified the order of related images in Fig. 1B.

Line 127: "by introducing cysteine mutations". Replace either by "by introducing cysteines" or by "by substituting several residues with cysteines".

We changed the relevant statement to "by introducing cysteines".

Line 144-145: "Given that the physiological and physical environments of both cell poles are nearly identical.". I think that also the physical, but certainly the physiological environment of the two poles is not identical. First, one is an old pole, and the other a new pole. Second, many proteins and RNAs were detected mainly or only in one of the poles of rod-shaped Gram-negative bacteria that are regarded as symmetrically dividing. Although my intuition is that the authors are correct in assuming that "it is unlikely that the unipolar distribution of the chemoreceptor array can be attributed to passive regulatory factors", relating it to the (false) identity between the poles is incorrect.

We thank the reviewer for this important correction. We agree that the physiological environments of the two poles are not identical, given that one is the old pole and the other the new pole, and that many proteins and RNAs show polar localization in rod-shaped Gram-negative bacteria. Accordingly, we have revised the original text (lines 150-152) to read:

“Despite potential differences in the physical and especially physiological environments at the two cell poles, it is unlikely that the unipolar distribution of the chemotaxis complex can be attributed to passive regulatory factors.”

Lines 151-154: "Considering the consistent colocalization pattern between chemosensory arrays and flagellar motors in P. aeruginosa". Does the word consistent relate to different reports on such colocalization or to the results in Figure 1D? In case it is the latter, then what is the word consistent based on? All together only 7 cells are presented in the 5 micrographs that compose Figure 1D (back to statistics...).

We thank the reviewer for raising this point. To clarify, the word "consistent" refers to the observation of colocalization shown in Figure 1D & Figure S3. As noted in the revised figure legend for Figure 1D, a total of 145 cells with labeled flagella were analyzed, all exhibiting consistent colocalization between flagella and chemosensory arrays. Additionally, we have included a new image showing a large field of co-localization in the wild-type strain as Figure S3 to better illustrate this consistency.

Figure 2A: Omit "Subcellular localization of" from the beginning of the caption.

We removed the relevant expression from the caption.

Reviewer #2 (Recommendations For The Authors):

I strongly recommend checking that CheY localizes to the receptor cluster in PA. This could be done by tagging cheA with a different fluorophore and demonstrating their colocalization. It would also be helpful to check that they are colocalized in the delta flhF mutant.

We thank the reviewer for this valuable suggestion. We constructed a plasmid expressing CheA-CFP and introduced it into the CheY-EYFP strain by electroporation. Fluorescence imaging revealed a high degree of spatial overlap between CheA-CFP and CheY-EYFP signals, indicating that CheY-EYFP indeed marks the location of the chemoreceptor complex.

We have revised the manuscript accordingly (lines 118-123) and included these results in the new Fig. S2.

The experiments under- and over-expressing CheY part seemed too unrelated to receptor-motor colocalization. I think the authors should think about a more direct way of testing whether colocalization of the motor and receptors is important for preventing signaling crosstalk. One way would be to measure cdG levels in WT and in delta flhF mutants and see if there is a significant difference.

We thank the reviewer for raising the important point regarding potential cellular stress caused by elevated CheY concentrations, as well as for the suggestion to test the hypothesis using flhF mutants.

First, as noted in the response to your 2nd comment in Public Review, CheY-P concentration rapidly decreases away from the receptor complex. While deletion of flhF alters the position of the receptor complex, thereby shifting the region of high CheY-P concentration, it does not increase CheY-P levels elsewhere in the cell. Importantly, in the ΔflhF strain, the receptor complex and the motor still colocalize, so this mutant may not effectively test the role of receptor-motor colocalization in preventing crosstalk as suggested.

Regarding the possibility that elevated CheY levels stress the cells independently of CheY-P signaling, prior work in E. coli by Cluzel et al. (ref. 11) showed that overexpressing CheY several-fold did not cause phenotypic changes, indicating that simple CheY overexpression alone may not be generally stressful. Furthermore, our data indicate that the increase in c-di-GMP levels and subsequent cell aggregation upon CheY overexpression is not an all-or-none switch but occurs progressively as CheY concentration rises.

To further confirm that CheY overexpression promotes aggregation through increased c-di-GMP levels, we performed additional experiments co-overexpressing CheY and a phosphodiesterase (PDE) from E. coli to reduce intracellular c-di-GMP. These experiments showed that PDE expression mitigates cell aggregation caused by CheY overexpression (Fig. S8).

We have revised the manuscript accordingly (lines 290-294) and added these new results in Fig. S8.

Reviewer #3 (Recommendations For The Authors):

(1) Can the authors elaborate more on the hierarchy of flagellar gene expression in P. aeruginosa and how this relates to their work?

We thank the reviewer for the suggestion. We have now described the hierarchy of flagellar gene expression in P. aeruginosa in lines 341-348.

(2) I would suggest that the authors check other flagellar mutants (than FliF and FliG) where the motor is partially assembled (e.g., any of the rod proteins or the P-ring protein), together with FlhF mutant, to see how a partially assembled motor affects the assembly of the chemosensory cluster.

We thank the reviewer for this valuable suggestion. The P ring, primarily composed of FlgI, acts as a bushing for the peptidoglycan layer, and its absence leads to partial motor assembly. We constructed a ΔflgI mutant and observed that the proportion of cells exhibiting distinct chemotactic complexes was similar to that of the wild-type strain, suggesting that the assembly of the receptor complex is likely influenced mainly by the C-ring and MS-ring structures rather than by the P ring. We have revised the original text accordingly (lines 217-220) and added the corresponding data as Figure S6.

(3) I would suggest that the authors check the levels of CheY in cells induced with different concentrations of arabinose (i.e., using western blotting just like they did in Figure 3B).

We have assessed the levels of CheY in cells induced with different concentrations of arabinose using western blotting, as suggested. The results have been incorporated into the manuscript (lines 274-275) and are presented in Figure S7.

(4) To my eyes, most of the foci in FliF-FlhF mutant in Figure 3A are located at the pole (which is unlike the FlhF mutant in Figure 2). Is this correct? I would suggest that the authors also investigate this to see where the chemosensory cluster is located.

We thank the reviewer for pointing this out. The distribution of the chemotaxis complex in the ΔflhFΔfliF strain was investigated and showed in Fig. S4. Indeed, most of the chemoreceptor foci in this mutant are located at the pole. This probably suggests that, in the absence of both FlhF and an assembled motor, the position of the receptor complex may be largely influenced by passive factors such as membrane curvature. This interesting possibility warrants further investigation in future studies.

eLife Assessment

This important study demonstrates that slow fluctuations in serotonin release during wakefulness and non-REM sleep correspond to periods of heightened arousal or enhanced offline information processing. The evidence supporting this claim is convincing, and the methodology is robust and broadly applicable, likely to benefit many researchers in the field. This work will be of significant interest to neuroscientists studying sleep, memory, and neuromodulation.

Reviewer #1 (Public review):

Summary:

In this work, authors recorded the dynamics of the 5-HT with fiber photometry from CA1 in one hemisphere and LFP from CA1 in the other hemisphere. They have observed an ultra-slow oscillation in the 5-HT signal both during wakefulness and NREM sleep. The authors have studied different phases of the ultra-slow oscillation to examine the potential difference in the occurrence of some behavioral state-related physiological phenomena (hippocampal ripples, EMG, and inter-area coherence).

Strengths:

The relation between the falling/rising phase of the ultra-slow oscillation and the ripples is sufficiently shown. There are some minor concerns about the observed relations that should be addressed with some further analysis.

Systematic observations have started to establish a strong relation between the dynamics of neural activity across the brain and measures of behavioral arousal. Such relations span a wide range of temporal scales that are heavily inter-related. Ultra-slow time scales are specifically understudied due to technical limitations and neuromodulatory systems are the strongest mechanistic candidates for controlling/modulating the neural dynamics at these time scales. The hypothesis of the relation between a specific time scale and one certain neuromodulator (5-HT in this manuscript) could have a significant impact on the understanding of the hierarchy in the temporal scales of neural activity.

Weaknesses:

weaknesses appropriately addressed by reviewers in the current version

Reviewer #2 (Public review):

Summary:

In their study, Cooper et al. investigated the spontaneous fluctuations in extracellular 5-HT release in the CA1 region of the hippocampus using GRAB5-HT3.0. Their findings revealed the presence of ultra-low frequency (less than 0.05 Hz) oscillations in 5-HT levels during both NREM sleep and wakefulness. The phase of these 5-HT oscillations was found to be related to the timing of hippocampal ripples, microarousals, electromyogram (EMG) activity, and hippocampal-cortical coherence. In particular, ripples were observed to occur with greater frequency during the descending phase of 5-HT oscillations, and stronger ripples were noted to occur in proximity to the 5-HT peak during NREM. Microarousal and EMG peaks occurred with greater frequency during the ascending phase of 5-HT oscillations. Additionally, the strongest coherence between the hippocampus and cortex was observed during the ascending phase of 5-HT oscillations. These patterns were observed in both NREM sleep and the awake state, with a greater prevalence in NREM. The authors posit that 5-HT oscillations may temporally segregate internal processing (e.g., memory consolidation) and responsiveness to external stimuli in the brain.

Strengths:

The findings of this research are novel and intriguing. Slow brain oscillations lasting tens of seconds have been suggested to exist, but to my knowledge they have never been analyzed in such a clear way. Furthermore, although it is likely that ultra-slow neuromodulator oscillations exist, this is the first report of such oscillations, and the greatest strength of this study is that it has clarified this phenomenon both statistically and phenomenologically.

Weaknesses:

As with any paper, this one has some limitations. While there is no particular need to pursue them, I will describe ten of them below, including future directions:

Contralateral recordings: 5-HT levels and electrophysiological recordings were obtained from opposite hemispheres due to technical limitations. Ipsilateral simultaneous recordings may show more direct relationships.

Sample size: The number of mice used in the experiments is relatively small (n=6). Validation with a larger sample size would be desirable.

Lack of causality: The observed associations show correlations, not direct causal relationships, between 5-HT oscillations and neural activity patterns.

Limited behavioral states: The study focuses primarily on sleep and quiet wakefulness. Investigation of 5-HT oscillations during a wider range of behavioral states (e.g., exploratory behavior, learning tasks) may provide a more complete understanding.

Generalizability to other brain regions: The study focuses on the CA1 region of the hippocampus. It's unclear whether similar 5-HT oscillation patterns exist in other brain regions.

Long-term effects not assessed: Long-term effects of ultra-low 5-HT oscillations (e.g., on memory consolidation or learning) were not assessed.

Possible species differences: It's uncertain whether the findings in mice apply to other mammals, including humans.

Technical limitations: The temporal resolution and sensitivity of the GRAB5-HT3.0 sensor may not capture faster 5-HT dynamics.

Interactions with other neuromodulators: The study does not explore interactions with other neuromodulators (e.g., norepinephrine, acetylcholine) or their potential ultraslow oscillations.

Limited exploration of functional significance: While the study suggests a potential role for 5-HT oscillations in memory consolidation and arousal, direct tests of these functional implications are not included.

Reviewer #3 (Public review):

Summary:

Activity of serotonin (5-HT) releasing neurons as well as 5-HT levels in brain structures targeted by serotoninergic axons are known to fluctuate substantially across the animal's sleep/wake cycle, with high 5-HT during wakefulness (WAKE), intermediate 5-HT levels during non-REM sleep (NREM) and very low 5-HT levels during REM sleep. Recent studies have shown that during NREM, activity of 5-HT neurons in raphe nuclei oscillates at very low frequencies (0.01 - 0.05 Hz) and this ultraslow oscillation is negatively coupled to broadband EEG power. However, how exactly this 5-HT oscillation affects neural activity in downstream structures is unclear.

The present study addresses this gap by replicating the observation of the ultraslow oscillation in the 5-HT system, and further observing that hippocampal sharp wave-ripples (SWRs), biomarkers of offline memory processing, occur preferentially in barrages on the falling phase of the 5-HT oscillation during both wakefulness and NREM sleep. In contrast, the study found that the raising phase of the 5-HT oscillation is associated with microarousals during NREM and increased muscular activity during WAKE. Finally, the raising 5-HT phase was also found to be associated with increased synchrony between the hippocampus and neocortex.

In vivo findings are further supported by an ex vivo demonstration of dose-dependent serotonergic SWR modulation, lends support to the potential causal relationship between 5-HT slow oscillation and hippocampal dynamics.

Overall, the study constitutes a valuable contribution to the field by reporting a close association between, on one hand, raising 5-HT and arousal and, on the other hand, falling 5-HT and offline memory processes.

Strengths:

The study makes a compelling use of the state-of-the art methodology to address its aims: the genetically encoded 5-HT sensor used in the study is ideal for capturing the ultraslow 5-HT dynamics and the novel detection method for SWRs outperforms current state-of-the-art algorithms and will be useful to many scientists in the field. Explicit validation of both of these methods is a particular strength of this study.

The analytical methods used in the article are appropriate and are convincingly applied, the use of a general linear mixed model for statistical analysis is a particularly welcome choice as it guards against pseudoreplication while preserving statistical power.

Pharmacological demonstration of serotonergic SWR modulation in brain slices adds further weight to the possible direct role of 5-HT in hippocampal dynamics in vivo.

Overall, the manuscript makes a strong case for distinct sub-states across WAKE and NREM, associated with different phases of the 5-HT oscillation.

Weaknesses:

All in vivo evidence presented in the study is correlational, although the ex vivo results do suggest a possibility of a causal relationship between 5HT levels and hippocampal dynamics in the intact brain.

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Summary

In this work, the authors recorded the dynamics of the 5-HT with fiber photometry from CA1 in one hemisphere and LFP from CA1 in the other hemisphere. They observed an ultra-slow oscillation in the 5-HT signal during both wake fulness and NREM sleep. The authors have studied different phases of the ultra-slow oscillation to examine the potential difference in the occurrence of some behavioral state-related physiological phenomena hippocampal ripples, EMG, and inter-area coherence).

Strengths

The relation between the falling/rising phase of the ultra-slow oscillation and the ripples is sufficiently shown. There are some minor concerns about the observed relations that should be addressed with some further analysis.

Systematic observations have started to establish a strong relation between the dynamics of neural activity across the brain and measures of behavioral arousal. Such relations span a wide range of temporal scales that are heavily inter-related. Ultra-slow time-scales are specifically under-studied due to technical limitations and neuromodulatory systems are the strongest mechanistic candidates for controlling/modulating the neural dynamics at these time-scales. The hypothesis of the relation between a specific time-scale and one certain neuromodulator (5-HT in this manuscript) could have a significant impact on the understanding of the hierarchy in the temporal scales of neural activity.

Weaknesses:

One major caveat of the study is that different neuromodulators are strongly correlated across all time scales and related to this, the authors need to discuss this point further and provide more evidence from the literature (if any) that suggests similar ultra-slow oscillations are weaker or lack from similar signals recorded for other neuromodulators such as Ach and NA.

The reviewer is correct to point out that the levels of different neuromodulators are often correlated. For example, most monoaminergic neurons, including serotonergic neurons of the raphe nuclei, show similar firing rates across behavioral states, firing most during wake behavior, less during NREM, and ceasing firing during ‘paradoxical sleep’ or REM (Eban-Rothschild et al 2018). Notably, other neuromodulators, such as acetylcholine (ACh), show the opposite pattern across states, with highest levels observed during REM, an intermediate level during wake behavior, and the lowest level during NREM (Vazquez et al. 2001). Despite these differences, ultraslow oscillations of both monoaminergic and non-monoaminergic neuromodulators, have been described, albeit only during NREM sleep (Zhang et al. 2021, Zhang et al. 2024, Osorio-Ferero et al. 2021, Kjaerby et al. 2022). How ultraslow oscillations of different neuromodulators are related has been only recently explored (Zhang et al. 2024). In this study, dual recording of oxytocin (Oxt) and ACh with GRAB sensors showed that the levels of the two neuromodulators were indeed correlated at ultraslow frequencies with a 2 s temporal shift. Furthermore, this shift could be explained by a hippocampal-to-lateral septum intermediate pathway, in which the level of ACh causally impacts hippocampal activity, which then in turn controls Oxt levels. Given the known temporal relationship between ripples, ACh and Oxt, and now with our work, between ripples and 5-HT, one could infer the relative timing of ultraslow oscillations of ACh, Oxt and 5-HT. While dual recordings of norepinephrine (NE) and 5-HT have not been performed, a similar correlation with temporal shift could be hypothesized given the parallel relationships between NE and spindles (OsorioFerero et al. 2021), and 5-HT and ripples, with the known temporal delay between ripples and spindles (Staresina et al. 2023). The fact that the locus coerulus receives particularly dense projections from the dorsal raphe nucleus (Kim et al. 2004) further suggests that 5-HT ultraslow oscillations could drive NE oscillations. How exactly ultraslow oscillations of serotonin are related to ultraslow oscillations of different neuromodulators in different brain regions remains to be studied.

We have further addressed this question and how it relates to the issue of causality in the Discussion section of the manuscript (p. 13):

“In addition to the difficulties involved with typical causal interventions already mentioned, the fact that the levels of different neuromodulators are interrelated and affected by ongoing brain activity makes it very hard to pinpoint ultraslow oscillations of one specific neuromodulator as controlling specific activity patterns, such as ripple timing. While a recent paper purported to show a causative effect of norepinephrine levels on ultraslow oscillations of sigma band power, the fact that optogenetic inhibition of locus coerulus (LC) cells, but also excitation, only caused a minor reduction of the ultraslow sigma power oscillation suggests that other factors also contribute (Osorio-Forero et al., 2021). Generally, it is thought that many neuromodulators together determine brain states in a combinatorial manner, and it is probable that the 5-HT oscillations we measure, like the similar oscillations in NE, are one factor among many.

Nevertheless, given the known effects of 5-HT on neurons, it is not unlikely that the 5-HT fluctuations we describe have some impact on the timing of ripples, MAs, hippocampal-cortical coherence, or EMG signals that correlate with either the rising or descending phase. In fact, causal effects of 5-HT on ripple incidence (Wang et al. 2015, ul Haq et al. 2016 and Shiozaki et al. 2023), MA frequency (Thomas et al. 2022), sensory gating (Lee et al. 2020), which is subserved by inter-areal coherence (Fisher et al. 2020), and movement (Takahashi et al. 2000, Alvarez et al. 2022, Jacobs et al. 1991 and Luchetti et al. 2020) have all been shown. Our added findings that serotonin affects ripple incidence in hippocampal slices in a dose-dependent manner (Figure S1) further suggests that the relationship between ultraslow 5-HT oscillations and ripples we report may indeed result, at least in part, from a direct effect of serotonin on the hippocampal network.

Whether these ‘causal’ relationships between 5-HT and the different activity measures we describe can be used to support a causal link between ultraslow 5-HT oscillations and the correlated activity we report remains an open question. To that point, some studies have described changes in ultraslow oscillations due to manipulation of serotonin signaling. Specifically, reduction of 5-HT1a receptors in the dentate gyrus was recently shown to reduce the power of ultraslow oscillations of calcium activity in the same region (Turi et al. 2024). Furthermore, psilocin, which largely acts on the 5-HT2a receptor, decreased NREM episode length from around 100 s to around 60 s, and increased the frequency of brief awakenings (Thomas et al. 2022). While ultraslow oscillations were not explicitly measured in this study, the change in the rhythmic pattern of NREM sleep episodes and brief awakenings, or microarousals, suggests an effect of psilocin on ultraslow oscillations during NREM. Although these studies do not necessarily point to an exclusive role for 5-HT in controlling ultraslow oscillations of different brain activity patterns, they show that changes in 5-HT can contribute to changes in brain activity at ultraslow frequencies.”

A major question that has been left out from the study and discussion is how the same level of serotonin before and after the peak could be differentially related to the opposite observed phenomenon. What are the possible parallel mechanisms for distinguishing between the rising and falling phases? Any neurophysiological evidence for sensing the direction of change in serotonin concentration (or any other neuromodulator), and is there any physiological functionality for such mechanisms?

We have added a paragraph in the discussion to address how this differentiation of the 5-HT signal may be carried out (Discussion, paragraph #3, p. 10):

“In order for the ultraslow oscillation phase to segregate brain activity, as we have observed, the hippocampal network must somehow be able to sense the direction of change of serotonin levels. While single-cell mechanisms related to membrane potential dynamics are typically too fast to explain this calculation, a theoretical work has suggested that feedback circuits can enable such temporal differentiation, also on the slower timescales we observe (Tripp and Eliasmith, 2010). Beyond the direction of change in serotonin levels, temporal differentiation could also enable the hippocampal network to discern the steeper rising slope versus the flatter descending slope that we observe in the ultraslow 5-HT oscillations (Figure S2), which may also be functionally relevant (Cole and Voytek, 2017). The distinction between the rising and falling phase of ultraslow oscillations is furthermore clearly discernible at the level of unit responses, with many units showing preferences for either half of the ultraslow period (Figure S6). Another factor that could help distinguish the rising from the falling phase is the level of other neuromodulators, as it is likely the combination of many neuromodulators at any given time that defines a behavioral substate. Given the finding that ACh and Oxt exhibit ultraslow oscillations with a temporal shift (Zhang et al. 2024), one could posit that distinct combinations of different levels of neuromodulators could segregate the rising from the falling phase via differential effects of the combination of neuromodulators on the hippocampal network.”

Functionally, the ability to distinguish between the rising and falling phases of an oscillatory cycle is a form of phase coding. A well-known example of this can be seen in hippocampal place cells, which fire relative to the ongoing theta oscillations. The key advantage of phase coding is that it introduces an additional dimension, i.e. phase of firing, beyond the simple rate of neural firing. This allows for the multiplexing of information (Panzeri et al., 2010), enabling the brain to encode more complex patterns of activity. Moreover, phase coding is metabolically more efficient than traditional spike-rate coding (Fries et al., 2007).

Reviewer #2 (Public review):

Summary:

In their study, Cooper et al. investigated the spontaneous fluctuations in extracellular 5-HT release in the CA1 region of the hippocampus using GRAB5-HT3.0. Their findings revealed the presence of ultralow frequency (less than 0.05 Hz) oscillations in 5-HT levels during both NREM sleep and wakefulness. The phase of these 5-HT oscillations was found to be related to the timing of hippocampal ripples, microarousals, electromyogram (EMG) activity, and hippocampal-cortical coherence. In particular, ripples were observed to occur with greater frequency during the descending phase of 5-HT oscillations, and stronger ripples were noted to occur in proximity to the 5-HT peak during NREM. Microarousal and EMG peaks occurred with greater frequency during the ascending phase of 5-HT oscillations. Additionally, the strongest coherence between the hippocampus and cortex was observed during the ascending phase of 5-HT oscillations. These patterns were observed in both NREM sleep and the awake state, with a greater prevalence in NREM. The authors posit that 5-HT oscillations may temporally segregate internal processing (e.g., memory consolidation) and responsiveness to external stimuli in the brain.

Strengths:

The findings of this research are novel and intriguing. Slow brain oscillations lasting tens of seconds have been suggested to exist, but to my knowledge they have never been analyzed in such a clear way. Furthermore, although it is likely that ultra-slow neuromodulator oscillations exist, this is the first report of such oscillations, and the greatest strength of this study is that it has clarified this phenomenon both statistically and phenomenologically.

Weaknesses:

As with any paper, this one has some limitations. While there is no particular need to pursue them, I will describe ten of them below, including future directions:

(1) Contralateral recordings: 5-HT levels and electrophysiological recordings were obtained from opposite hemispheres due to technical limitations. Ipsilateral simultaneous recordings may show more direct relationships.

Although we argue that bilateral symmetry defines both the serotonin system and many hippocampal activity patterns (Methods: Dual fiber photometry and silicon probe recordings), we agree that ipsilateral recordings would be superior to describe the link between serotonin and electrophysiology in the hippocampus. In addition to noting that a recent study has adopted the same contralateral design (Zhang et al. 2024), we add a reference further supporting bilateral hippocampal synchrony, specifically of dentate spikes (Farrell et al. 2024). However, as functional lateralization has been recently proposed to underlie certain hippocampal functions in the rodent (Jordan 2020), future studies should ideally include both imaging and electrophysiology in a single hemisphere to guarantee local correlations rather than assuming inter-hemispheric synchrony. This could be accomplished using an integrated probe with attached optical fibers, as described in Markowitz et al. 2018, which is however technically more challenging and has, to our knowledge, not yet been implemented with fiber photometry recordings with GRAB sensors. Given the required separation of a few hundred micrometers between the probe shanks and the optical fiber cannula, it is important to consider whether the recordings are capturing the same neuronal populations. For example, there is a risk of recording electrical activity from dorsal hippocampal neurons while simultaneously measuring light signals from neurons in the intermediate hippocampus, which are functionally distinct populations (Fanselow and Dong 2009).

(2) Sample size: The number of mice used in the experiments is relatively small (n=6). Validation with a larger sample size would be desirable.

While larger sample sizes generally reduce the influence of random variability and minimize the impact of outliers on conclusions, our use of mixed-effects models mitigates these concerns by accounting for both inter-session and inter-mouse variability. With this approach, we explicitly model random effects, such as the variability between individual mice and sessions, alongside fixed effects (such as treatment), which ensures that our results are not driven by random fluctuations in a few individual mice or sessions. Furthermore, the inclusion of random intercepts and slopes in the models allows for the possibility that different animals and/or sessions have different baseline characteristics and respond to different degrees of magnitude to the treatment. In summary, while validating these findings with a larger sample size would certainly help detect more subtle effects, we are confident in the robustness of the conclusions presented.

(3) Lack of causality: The observed associations show correlations, not direct causal relationships, between 5-HT oscillations and neural activity patterns.

We agree that the data we present in this study is largely correlational and generally avoid claims of causality in the manuscript. In the Discussion section, we discuss barriers to interpreting typical causal interventions in vivo, such as optogenetic activation of raphe nuclei: “The two previously mentioned in vivo studies showing reduced ripple incidence…”(paragraph #10, pg. 12), as well as an added section on further causality considerations in the Discussion section of the manuscript (paragraph #12, pg. 13): “In addition to the difficulties involved with…”

Due to these barriers, as a first step, we wanted to describe how physiological changes in serotonin levels are correlated to changes in the hippocampal activity. Equipped with a deeper understanding of physiological serotonin dynamics, future studies could explore interventions that modulate serotonin in keeping with the natural range of serotonin fluctuations for a given state. On that point, another challenge which we have not mentioned in the manuscript is that modulating serotonin, or any neuromodulator’s levels, has the potential, depending on the degree of modulation, to transition the brain to an entirely different behavioral state. This then complicates interpretation, as one is not sure whether effects observed are due to the changes in the neuromodulator itself, or secondary to changes in state. At the same time, 5-HT activity drives networks which in return can change the release of other neurotransmitters, leading to indirect effects.

The results of our in vitro experiments suggest that a causal relationship between serotonin and ripples is possible (Figure S1). Though the hippocampal slice preparation is clearly an artificial model, it provides a controlled environment to isolate the effects of serotonin manipulation on the hippocampal formation, without the confounding influence of systemic 5-HT fluctuations in other brain regions. Notably, the dose-dependent effects of serotonin (5-HT) wash-in on ripple incidence observed in vitro closely mirror the inverted-U dose-response curve seen in our in vivo experiments across states, where small increases in serotonin lead to the highest ripple incidence, and both lower and higher levels correspond to reduced ripple activity. This parallel suggests that the gradual washing of serotonin in our in vitro system may mimic the tonic firing changes in serotonergic neurons that occur during state transitions in vivo. These findings underscore the importance of studying how different dynamics of serotonin modulation can differentially affect hippocampal network activity.

(4) Limited behavioral states: The study focuses primarily on sleep and quiet wakefulness. Investigation of 5-HT oscillations during a wider range of behavioral states (e.g., exploratory behavior, learning tasks) may provide a more complete understanding.

We agree that future studies should investigate a broader range of behavioral states. For this study, as we were focused on general sleep and wake patterns, our recordings were done in the home cage, and we limited ourselves to the basic behavioral states described in the paper. Future studies should be designed to investigate ultraslow 5-HT oscillations during different behaviors, such as continuous treadmill running. Specifically, a finer segregation of extended wake behaviors by level of arousal could greatly add to our understanding of the role of ultraslow serotonin oscillations.

(5) Generalizability to other brain regions: The study focuses on the CA1 region of the hippocampus. It's unclear whether similar 5-HT oscillation patterns exist in other brain regions.

Given the reported ultraslow oscillations of population activity in serotonergic neurons of the dorsal raphe nucleus (Kato et al. 2022) as well as the widespread projections of the serotonergic nuclei, we would expect a broad expression of ultraslow 5-HT oscillations throughout the brain. So far, ultraslow 5-HT oscillations have been described in the basal forebrain, as well as in the dentate gyrus, in addition to what we have shown in CA1 (Deng et al. 2024 and Turi et al. 2024). Furthermore, our results showing that hippocampal-cortical coherence changes according to the phase of hippocampal ultraslow 5-HT oscillations suggests that 5-HT can affect oscillatory activity either indirectly by modulating hippocampal cells projecting to the cortical network or directly by modulating the cortical postsynaptic targets. Given the heterogeneity in projection strength, as well as in pre- and postsynaptic serotonin receptor densities across brain regions (de Filippo & Schmitz, 2024), it would be interesting to see whether local ultraslow 5-HT oscillations are differentially modulated, e.g. in terms of oscillation power. Future studies investigating different brain regions via implantation of multiple optic fibers in different brain areas or using the mesoscopic imaging approach adopted in Deng et al. 2024, will be needed to examine the extent of spatial heterogeneity in this ultraslow oscillation.

(6) Long-term effects not assessed: Long-term effects of ultra-low 5-HT oscillations (e.g., on memory consolidation or learning) were not assessed.

While beyond the scope of our current study, we agree that an important next step would involve modulating the ultraslow serotonin oscillation after learning, and then examining potential effects on memory consolidation, presumably via changes in ripple dynamics, though many possibilities could explain potential effects. There, our results suggest it would be important to isolate effects due to the change in ultraslow oscillation features, rather than simply overall levels of 5-HT. To that end, it would be important to test different modulation dynamics, specifically modulating the oscillation strength, around a constant mean 5-HT level by carefully timed optogenetic stimulation/inhibition. Afterwards, showing a clear correlation between the strength of the 5-HT modulation and memory performance would be important to establishing the relationship, as done in Lecci et al 2017, where more prominent ultraslow oscillations of sigma power in the cortex during sleep, alongside a higher density of spindles, were correlated with better memory consolidation. Given the tight coupling of spindles and ripples during sleep, it is possible that a similar effect on memory consolidation would be observed following changes in ultraslow 5-HT oscillation power.

(7) Possible species differences: It's uncertain whether the findings in mice apply to other mammals, including humans.

We agree that the experiments should ultimately be replicated in humans. In the 2017 study by Lecci et al., the authors highlighted the shared functional requirements for sleep across species, despite apparent differences, such as variations in sleep volume. To explore these commonalities, the researchers conducted parallel experiments in both mice and humans, aiming to identify a universal organizing structure. They discovered that the ultraslow oscillation of sigma power serves this role, enabling both species to balance the competing demands of arousability and sleep imperviousness. Based on this finding, it is plausible that ultraslow oscillations of serotonin, which similarly modulate activity according to arousal levels, would serve a comparable function in humans.

(8) Technical limitations: The temporal resolution and sensitivity of the GRAB5-HT3.0 sensor may not capture faster 5-HT dynamics.

The kinetics of the GRAB5-HT3.0 sensor used in this study limit the range of serotonin dynamics we can observe. However, the ultraslow oscillations we measure reflect temporal changes on the scale of 20 s and greater, whereas the GRAB sensor we use has sub-second on kinetics and below 2 s off kinetics (Deng et al. 2024). Therefore, the sensor is capable of reporting much faster activity than the ultraslow oscillations we observe, indicating that the ultraslow 5-HT signal accurately reflects the dynamics on this time scale. Furthermore, the presence of ultraslow oscillations in spiking activity—observed in the hippocampal formation (Gonzalo Cogno et al., 2024; Aghajan et al., 2023; Penttonen et al., 1999) and in the dorsal raphe (Mlinar et al., 2016), which are not affected by the same temporal smoothing, suggests that the oscillations we record are not likely due to signal aliasing, but instead reflect genuine oscillatory activity. Of course, this does not preclude that other, faster serotonin dynamics are also present in our signal, some of which may be too fast to be observed. For instance, rapid serotonin signaling via the ionotropic 5-HT3a receptors could be missed in our recordings. Additionally, with the fiber photometry approach we adopted, we are limited to capturing spatially broad trends in serotonin levels, potentially overlooking more localized dynamics.

(9) Interactions with other neuromodulators: The study does not explore interactions with other neuromodulators (e.g., norepinephrine, acetylcholine) or their potential ultraslow oscillations.

We agree that the interaction between neuromodulators in the context of ultraslow oscillations is an important issue, which we have addressed in our response to reviewer #1 under ‘Weaknesses.’

(10) Limited exploration of functional significance: While the study suggests a potential role for 5-HT oscillations in memory consolidation and arousal, direct tests of these functional implications are not included.

We agree and reference our answer to (6) regarding memory consolidation. Regarding arousal, direct tests of arousability to different sensory stimuli during different phases of the ultraslow 5-HT oscillation during sleep would be beneficial, in addition to the indirect measures of arousal we examine in the current study, e.g. degree of movement (icEMG) and long range coherence. In line with what we have shown, Cazettes et al. (2021) has demonstrated a direct relationship between 5-HT levels and pupil size, an indicator of arousal level, which like our findings, is consistent across behavioral states.

Reviewer #3 (Public review):

Summary:

The activity of serotonin (5-HT) releasing neurons as well as 5-HT levels in brain structures targeted by serotonergic axons are known to fluctuate substantially across the animal's sleep/wake cycle, with high 5-HT levels during wakefulness (WAKE), intermediate levels during non-REM sleep (NREM) and very low levels during REM sleep. Recent studies have shown that during NREM, the activity of 5HT neurons in raphe nuclei oscillates at very low frequencies (0.01 - 0.05 Hz) and this ultraslow oscillation is negatively coupled to broadband EEG power. However, how exactly this 5-HT oscillation affects neural activity in downstream structures is unclear.

The present study addresses this gap by replicating the observation of the ultraslow oscillation in the 5-HT system, and further observing that hippocampal sharp wave-ripples (SWRs), biomarkers of offline memory processing, occur preferentially in barrages on the falling phase of the 5-HT oscillation during both wakefulness and NREM sleep. In contrast, the raising phase of the 5-HT oscillation is associated with microarousals during NREM and increased muscular activity during WAKE. Finally, the raising 5-HT phase was also found to be associated with increased synchrony between the hippocampus and neocortex. Overall, the study constitutes a valuable contribution to the field by reporting a close association between raising 5-HT and arousal, as well as between falling 5-HT and offline memory processes.

Strengths:

The study makes compelling use of the state-of-the-art methodology to address its aims: the genetically encoded 5-HT sensor used in the study is ideal for capturing the ultraslow 5-HT dynamics and the novel detection method for SWRs outperforms current state-of-the-art algorithms and will be useful to many scientists in the field. Explicit validation of both of these methods is a particular strength of this study.

The analytical methods used in the article are appropriate and are convincingly applied, the use of a general linear mixed model for statistical analysis is a particularly welcome choice as it guards against pseudoreplication while preserving statistical power.

Overall, the manuscript makes a strong case for distinct sub-states across WAKE and NREM, associated with different phases of the 5-HT oscillation.

Weaknesses:

All of the evidence presented in the study is correlational. While the study mostly avoids claims of causality, it would still benefit from establishing whether the 5-HT oscillation has a direct role in the modulation of SWR rate via e.g. optogenetic activation/inactivation of 5-HT axons. As it stands, the possibility that 5-HT levels and SWRs are modulated by the same upstream mechanism cannot be excluded.

We agree that causality claims cannot be made with our data, and acknowledge the interest in exploring causal interactions between ultraslow serotonin oscillations and the correlated activity we measure. We address this point in depth in our answer to Reviewer #2, Weaknesses #3.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

One major question in the presented data is the nature of the asymmetrical shape of the targeted slow events. How much does it reflect the 5-HT concentration and how much is this shape affected by the dynamics of the designed 5-HT sensor? This needs to be addressed in more detail referencing the original paper for the used sensor.

We have added a paragraph in the Results section of the manuscript to address the asymmetric waveform of the ultraslow 5-HT oscillations and whether it could be affected by the asymmetric kinetics of the GRAB sensor we use: “The waveform of these ultraslow 5-HT oscillations…” (Results, paragraph #4, pg. 5). We include an extended answer to the question here:

Indeed, the GRAB5-HT3.0 sensor we use in the study shows activation response kinetics which are faster than their deactivation time, with time constants at 0.25 s and 1.39 s, respectively (Deng et al. 2024). Likewise, the slope of the rising phase of the ultraslow serotonin oscillation we measure is faster than the slope of the falling phase, and the ratio of time spent in the rising phase versus the falling phase is less than 1, indicating longer falling phases (Figure S2). Although we cannot completely rule out that the asymmetric shape of the ultraslow serotonin oscillations we record is affected by this asymmetry in the 5-HT sensor kinetics, we believe this is unlikely, as the 5-HT signal clearly contains reductions in 5-HT levels that are much faster than the descending phase of the ultraslow oscillation. Although it is difficult to directly compare the different-sized signals, the reported timescales of off kinetics, on the order of a few seconds (Deng et al. 2024), are far below the tens of seconds timescale of the ultraslow oscillation. Furthermore, the finding that some dorsal raphe neurons modulate their firing rate at ultraslow frequencies, and moreover that all examples of such ultraslow oscillations shown display clear asymmetry in rising time versus decay, suggests that the asymmetry we observe in our data could be due to neural activity rather than temporal smoothing by the sensor (Mlinar et al. 2016). In this same direction, another study found similar asymmetry in extracellular 5-HT levels measured with fast scan cyclic voltammetry (FSCV), a technique with greater temporal resolution (sampling rate of 10 Hz) than GRAB sensors, after single pulse stimulation (Bunin and Wightman 1998). In this study, 5-HT was shown to be released extrasynaptically, making the longer clearing time compared to the release time intuitive. Finally, the observation that the onsets and offsets of ripple clusters, recorded with a sampling rate of 20 kHz, are precisely aligned with the peaks and troughs of ultraslow serotonin oscillations (Figure 1, H1-2, columns 2-3) suggests that the duration of the falling phase is not artificially distorted by the temporal smoothing of the sensor dynamics.

Regardless of the dynamics of the serotonin concentration, it should be noted that the elicited neuronal effect might have different dynamics compared to the 5-HT concentration that need to be more studied: to address this one can either examine the average of the broadband LFP (not high passfiltered by the amplifier) or the distribution of simultaneously recorded spiking activity around the peak of ultra-slow oscillations.

We have added Figure S6, showing unit activity relative to the phase of ultraslow serotonin oscillations.

From this analysis, we uncover three groups of units which are largely preserved across states (Figure S6, E vs. F), albeit with a slight temporal shift rightward from NREM to WAKE (Figure S6, C vs. D). Namely, some units spike preferentially during the rising phase, some during the falling phase, and a third group have no clear phase preference. Unit activity during the falling phase is unsurprising, as it is where ripples largely occur, which themselves are associated with spike bursts. During the rising phase, the unit activity we observe could correspond to firing of the hippocampal subpopulation known to be active during NREM interruption states (Jarosiewicz et al. 2002, Miyawaki et al. 2017). While the units’ phase preference was tested based on the category of rising vs. falling phase, as this division described most variation in the data, a few units in the ‘No preference’ group showed heightened activity near the oscillation peak. However, given the very small number of units with this preference, more unit data is needed to describe this group, ideally with high-density recordings. Overall, most units showed a falling vs. rising phase preference, indicating a phase coding of hippocampal activity by 5-HT ultraslow oscillations.

Related to the previous point, it would be helpful to show the average cycle shape of these oscillations (relative to the phase 0 extracted in Figure 3) and do the shape comparison across sessions and also wake/NREM

We agree, and to this end we have added Figure S2. From this waveform analysis, we show that the ultraslow serotonin oscillation is asymmetric, with the rising phase having a greater slope, but shorter length, than the falling phase. While this asymmetry is observed both in NREM and WAKE, the slope difference and length ratio difference in rising vs. falling phase is greater in NREM (Figure S2. B).

In Figure 3D, there seem to be oscillatory rhythms with faster cycles on top of the targeted oscillations. That would make the phase estimation less accurate, e.g. in the left panel, in the second cycle, it is not clear if there are two faster cycles or it is one slow cycle as targeted, and if noted in the rising phase of the second fast cycle there are no ripples. This might suggest that regardless of specific oscillation frequency whenever 5-HT is started to get released, the ripples are suppressed and once the 5-HT is not synaptically effective anymore the ripples start to get generated while the photometry signal starts to wane with the serotonin being cleared. Still, if there is any rhythmicity between bouts of no ripple, it would suggest an ultra-slow regularity in the 5-HT release.

The reviewer is correct to point out that some faster increases in serotonin, which occur on top of the ultraslow oscillations we measure, seem to be associated with decreased ripple incidence, as in the example referenced. The dominance of ultraslow frequencies in the power spectrum of the 5-HT signal suggests, however, that oscillations faster than the ultraslow oscillations we describe are far less prevalent in the data. While there may be some coupling of ripples and other measures to serotonin oscillations of different frequencies, this may be hard or impossible to detect with phase analysis based on their infrequent occurrence and nonstationary nature. In fact, we show in Figure S3 that the strongest phase modulation of ripples by ultraslow serotonin oscillations is observed in the frequencies we use (0.01-0.06 Hz). Methodologically, phase analysis indeed assumes stationary signals, which are rare if not absent in physiological data (Lo et al. 2009), however generally the narrower the frequency band, the better the phase estimation. The narrow frequency band we use provides phase estimates that are largely robust and unaffected by the presence of faster oscillations, as can be seen in the example phase traces shown in Figure 4.

The hypothesis that the rising phase burst of synaptic serotonin is what silences ripples, and that with the clearing of serotonin from the synapses, ripples recover, is a possible explanation of our findings. However, if this were the case, one could expect the ripple rate to increase over the course of the falling phase of ultraslow 5-HT oscillations, as 5-HT decreases, and peak at the trough. This is at odds with what we observe, namely a fairly uniform distribution of ripples along the falling phase (Figure 3F2,F4). Furthermore, the Mlinar et al. 2016 study describes a subpopulation of raphe neurons whose firing rates themselves oscillate at ultraslow frequencies, rather than on-off bursting at ultraslow frequencies, which would argue against this hypothesis. However, as this study looks at a small number of neurons in slices, further in vivo experiments examining firing rates of median raphe neurons are required to understand how the ultraslow oscillation of extracellular serotonin that we measure is generated as well as how it is related to ripple rates.

In Figure 3B, it is not clear why IRI is z-scored. It would be informative to have the actual value of IRI. What is the z relative to? Is it the mean value of IRI in each recording session? Is this to reduce the variability across sessions?

We have now included in Figure 3D a box plot displaying the IRI distributions across different states and sessions. To minimize inter-session variability, data were z-scored within each session for visualization purposes. However, all general linear models were based on raw data, and as a result, the raw differences in IRI are shown in Figure 3C.

Figure 3E, panel labels don't match with the caption

We are grateful to the reviewer for pointing out this mistake, which we have corrected in the updated version of the manuscript.

In the text related to Figure 3E, the related analysis can be more clearly described. "phase preference of individual ripples" does not immediately suggest that the occurring phase of each ripple relative to the targeted oscillation is extracted. I suggest performing this analysis individually for each session and summarizing the results across the sessions.

We have reworded the sentence in Results: 5-HT and ripples to better reflect the analysis performed: “Next, we calculated the ultraslow 5-HT phases at which individual ripples occurred during both NREM and WAKE (3E-F) ...”. Regarding session-level data, we have added Figure S3, which shows session level mean phase vectors, as well as the grand mean across sessions for both NREM and WAKE. Included in this figure are session level means for frequency bands outside of the ultraslow band we used in our study, intended to show that ripples are most strongly timed by the ultraslow band (0.01-0.06 Hz), reflected by the greater amplitude of the mean phase vector for this band.

Figure 3E2, based on the result of ripple-triggered 5-HT in left panels of 2H1-2, one would expect to see a preferred phase closer to 180 (toward the end of the falling phase), it would be helpful to compare and discuss the results of these two analyses.

The reviewer is correct to point out the apparent discrepancy in where the mean ripple falls with respect to the ongoing serotonin oscillation between the two figures mentioned. We have addressed this point in Results: 5-HT and ripples, paragraph #4: “This result appear to be at odds with…”.

Regarding the analysis in 3F, please also compare the power distribution of ripples between NREM and wake. This will help to better understand the potential difference behind the observed difference: how much the strong ripples are comparable between wake and NREM. It is also necessary to report the ripple detection failure rate across ripples with different strengths.

We have added a figure showing analysis done on a subset of the data in which ripples were manually curated in order to evaluate the performance of the ripple detection model (Figure S7) and explanatory text in Methods: Model performance: ‘To ensure that our model …’. In summary, while missed ripples did tend to have lower power than correctly detected ripples, including them did not change the distribution of ripples by the phase of the ultraslow serotonin oscillation (Figure S7C). We would also note that while the phase preference is noisier than what is presented in Figure 3F because this analysis was done with a small subset of all recorded ripples, the fact that ripples occur more clearly on the falling phase is visible for both detected ripples and detected + false negative ripples.

The mixed-effects model examining the influence of 5-HT ultraslow oscillation phase on ripple power revealed no significant effect of state (p = 0.088). This indicates that whether the data were collected during NREM or wake periods did not significantly impact ripple power and that the lack of a significant effect (in Figure 3G,H) in WAKE is probably not due to a difference in the distribution of ripple power between states.

4D, y label is z?

We are grateful for the reviewer to point that out, yes, the y label should be ‘z-score’, as the two traces represent z-scored 5-HT (blue) and z-scored shuffled data (orange). Figure 4D2 and Figure 2H1-2, which show similar data, have been corrected to address this oversight.

Relating to Figure 4, EMG comparison across phases of the oscillations is insightful. Two related and complementary analyses are to compare the theta and gamma power between the falling and rising phases.

We have addressed this suggestion in Figure S5 A-C. While low gamma, high gamma and theta power are modulated identically in NREM, with higher power observed during the falling phase than the rising phase, during WAKE, different patterns can be seen. Specifically, low gamma power shows no phase preference, while high gamma shows a peak near the center of the ultraslow 5-HT oscillation. Theta power, as in NREM, is higher during the falling phase of ultraslow 5-HT oscillations. Increased power across many frequency bands was shown to coincide with decreases in DRN population activity during NREM, which matches with what we report here (Kato et al. 2022). In summary, while NREM patterns are consistent in all frequency bands tested, aligning with the pattern of ripple incidence, in WAKE low and high gamma power show different relationships to ultraslow 5-HT phase.

In the manuscript, we have used the data in both Figure S5 and S6 (unit activity relative to ultraslow 5-HT oscillations), to argue against the idea that our coherence findings result from a lack of activity in the rising phase (see next question), which would have the effect of ‘artificially’ reducing coherence in the falling phase relative the rising phase. The text can be found in Results: 5-HT and hippocampal cortical coherence, paragraph #2.

The results presented in Figure 5 could be puzzling and need to be further discussed: if the ripple band activity is weak during the rising phase, in what circumstances the coherence between cortex and CA1 is specifically very strong in this band?

As mentioned in the previous answer, we have addressed this concern in Results: 5-HT and hippocampal-cortical coherence, paragraph #2. In summary, it is true that the higher coherence in rising phase than in the falling phase for the highest frequency band (termed ‘high frequency oscillation’ (HFO), 100-150 Hz) could be unexpected, given that ripples occur largely during the falling phase. A few points could help explain this finding. Firstly, it should be noted that power in the 100-150 Hz band can arise from physiological activity outside of ripples, such as filtered non-rhythmic spike bursts (Liu et al. 2022), whose coherent occurrence in the rising phase could explain the coherence findings. Secondly, coherence is a compound measure which is affected by both phase consistency and amplitude covariation (Srinath and Ray 2014), thus from only amplitude one cannot predict coherence. Furthermore, HFO power in the cortex is highest near the peak of ultraslow 5-HT oscillations (Figure S5D), as opposed to the falling phase peak in the hippocampus. This shows a lack of covariation in amplitude by phase between the hippocampus and cortex at this frequency band. An alternative explanation of our findings regarding coherence could be that in the rising phase, there is simply little to no activity, which is easier to ‘synchronize’ than bouts of high activity. Hippocampal unit activity in the rising phase (Figure S6) suggests however, that it is not likely to be the absence of activity supporting higher coherence in the rising phase across frequencies. Additional experiments using high density recordings should be conducted to examine 5-HT ultraslow oscillations and their role in gating activity across brain regions, though these results strongly suggest some role exists.

Reviewer #2 (Recommendations for the authors):

I would like to offer two comments. I believe that these are not unusual requests, and thus I would like the authors to respond.

(1) It would be prudent to investigate the possibility that the observed correlation between ultraslow and hippocampal ripples/microarousals is merely superficial and that there are unidentified confounding factors at play. For example, it would be beneficial to provide evidence that administering a serotonin receptor inhibitor result in the disappearance of the slow oscillation of ripples and microarousals, or that the correlation with ultraslow is no longer present. Please note that the former experiments do not require GRAB5-HT3.0 imaging.

We agree that causality claims cannot be made with our data and acknowledge the interest in exploring causal interactions between ultraslow serotonin oscillations and the correlated activity we measure. We address this point in depth in our answer to Reviewer #2, Weaknesses #3. We would further like to note that given the large number of serotonin receptors and the lack of selectivity of many serotonin receptor antagonists, a pharmacological approach would be difficult, though the results certainly useful. Finally, we highlight the psilocin study, which reported changes in the rhythmic occurrence of microarousals, and therefore likely ultraslow oscillations, after administering a 5-HT2a receptor agonist, suggesting a potential causal effect of 5-HT (via 5-HT2a receptor) on MA occurrence (Thomas et al. 2022).

(2) The slow frequency appears to be associated with the default mode network as observed in fMRI signals. The neural basis of the default mode network remains unclear; therefore, a more detailed examination of this possibility would be beneficial.

We agree that it would be interesting to investigate the role of 5-HT in the neural basis of the DMN.

The DMN as described in humans (Raichle et al. 2001) and rodents (Lu et al. 2012) may indeed include some parts of the hippocampus and perhaps some of our neocortical recordings could also be considered part of the DMN. The fact that the activity across the inter-connected brain structures of the DMN is correlated at ultraslow time scales (Gutierrez-Barragan et al. 2019, Mantini et al. 2007), as well as serotonin’s ability to modulate the DMN is intriguing (Helmbold et al. 2016). Further studies simultaneously recording DMN activity via fMRI and electrical activity via silicon probes, as done in Logothetis et al. 2001, could elucidate further a potential link between ultraslow oscillations and the DMN, with serotonergic modulation as a means to understand any potential contribution of serotonin.

Reviewer #3 (Recommendations for the authors):

(1) The impact of the study would benefit from an experiment causally testing the effect of hippocampal 5-HT levels on hippocampal physiology, e.g. using optogenetic manipulations.

We agree that causality claims cannot be made with our data and acknowledge the interest in exploring causal interactions between ultraslow serotonin oscillations and the correlated activity we measure. We address this point in depth in our answer to Reviewer #2, Weaknesses #3.

(2) Data presentation: the figures are of poor resolution, making some diagram details and, more importantly, some example traces (e.g. Figure 1A, right) impossible to see. This should be corrected by either increasing figure resolution or making important figure elements large enough to be readable.

We apologize for the poor resolution and have corrected it in the updated version of the manuscript.

(3) Differences in some figure panels are not statistically assessed: Figure 1H (differences in spectrum peak power), Figure 3E1 & Figure 3E3 (directional bias of the circular distributions), Figure 4C (difference from 0 mean).

We acknowledge this oversight and have added statistical tests for all three figures, as well as further information regarding the models used in Methods: Statistics.

(4) Lines 279-280: the claim that the study shows "organization of activity by ultraslow oscillations of 5-HT" implies a causal role of 5-HT in organizing hippocampal activity. I suggest that this statement be toned down to reflect the correlational nature of the presented evidence.

We have rephrased the sentence in question to the following: “In our study, including both NREM and WAKE periods allowed us to additionally show that the temporal organization of activity relative to ultraslow 5-HT oscillations operates according to the same principles in both states...”, which we believe better reflects the temporal correlation we describe.

(5) While the study claims to use the EMG (i.e. electromyograph) signal, it does not describe any electrodes placed inside the muscle in the methods section. The SleepScoreMaster toolbox used in the study estimates the EMG using high-frequency activity correlated across recording channels, so I assume this is how this signal was obtained. While such activity may well reflect muscular noise to some degree, it is an indirect measure as the electrodes are not in the muscle. Since the EMG signal is central to the message of the manuscript, the method for calculating it should be described in the methods section and it should be explicitly labelled as an indirect measure in the main text, e.g. by referring to this signal as pseudo-EMG.

We agree and have added explanatory text to the State Scoring subsection in Methods. Given that the EMG we refer to is derived from intracranial data, and not from traditional EMG probes, we now refer to the EMG as intracranial EMG, or icEMG for short, throughout the main text.

(6) Is ripple frequency or ripple duration different across the rising and falling phases of the ultraslow oscillation?

We have now investigated this suggestion in Figure S4, where we show that ripple frequency is higher in the falling phase than rising phase, while ripple duration appears to show no phase preference.

(7) Lines 315-317: I am not sure why the manuscript refers to the coupling between EMG and 5-HT levels as 'puzzling' given that, as stated, the locomotion-inducing effects of 5-HT are well documented. While the fact that even non-locomotory motor activity may be associated with 5-HT rise is certainly interesting (although not sure if 'puzzling'), the manuscript does not directly compare the association of 5-HT levels with locomotory and non-locomotory EMG spikes. Thus, I think this discussion point is not fully warranted.

We agree and have rephrased the discussion point in question to reflect that the EMG link to serotonin oscillations is not necessarily surprising, given both the literature linking 5-HT and spontaneous movement in the hippocampus, as well as the involvement of 5-HT in repetitive movements, where the role for a regularly-occurring oscillation is perhaps more intuitive.

(8) Line 441: Reference #67 does not describe the use of fiber photometry.

The reviewer is to correct to point out this typo, which has been now corrected. The reference in question should be 64, where fiber photometry experiments are described. For further clarity, we have changed our referencing scheme to include authors and years in in-text references.

(9) In Figures 3E1-3, the phase has different bounds than in the other Figures in the manuscript (0:360 vs -180:180), this should be corrected for consistency.

We agree and have made changes so that all figures have a phase range of -180 to 180°.

References

(1) Z. M Aghajan, G. Kreiman, I. Fried, Minute-scale periodicity of neuronal firing in the human entorhinal cortex. Cell Rep 42, 113271 (2023).

(2) M.A. Bunin, R.M. Wightman (1998). Quantitative Evaluation of 5-Hydroxytryptamine (Serotonin) Neuronal Release and Uptake: An Investigation of Extrasynaptic Transmission. J. Neurosci. 18 (13) 4854-4860

(3) F. Cazettes, D. Reato, J. P. Morais, A. Renart, Z. F. Mainen, Phasic Activation of Dorsal Raphe Serotonergic Neurons Increases Pupil Size. Curr Biol 31, 192-197.e4 (2021).

(4) Cole SR, Voytek B. Brain Oscillations and the Importance of Waveform Shape. Trends Cogn Sci. 21(2):137-149 (2017).

(5) F. Deng, et al., Improved green and red GRAB sensors for monitoring spatiotemporal serotonin release in vivo. Nat Methods 21, 692–702 (2024).

(6) C. Dong, et al., Psychedelic-inspired drug discovery using an engineered biosensor. Cell 184, 2779-2792.e18 (2021).

(7) A. Eban-Rothschild, L. Appelbaum, L. de Lecea, Neuronal Mechanisms for Sleep/Wake Regulation and Modulatory Drive. Neuropsychopharmacol. 43, 937–952 (2018).

(8) M. S. Fanselow, H.-W. Dong, Are the dorsal and ventral hippocampus functionally distinct structures? Neuron 65, 7–19 (2010).

(9) J. S. Farrell, E. Hwaun, B. Dudok, I. Soltesz, Neural and behavioural state switching during hippocampal dentate spikes. Nature 1–6 (2024). https://doi.org/10.1038/s41586-024-07192-8.

(10) De Filippo, R., & Schmitz, D. (2024). Transcriptomic mapping of the 5-HT receptor landscape. Patterns (New York, N.Y.), 5(10), 101048.

(11) M. J. Fisher, et al., Neural mechanisms of sensory gating: Insights from human and animal studies. NeuroImage 207, 116374 (2020).

(12) P. Fries, D. Nikolić, W. Singer, The gamma cycle. Trends in Neurosciences 30, 309–316 (2007).

(13) S. Gonzalo Cogno, et al., Minute-scale oscillatory sequences in medial entorhinal cortex. Nature 625, 338–344 (2024).

(14) D. Gutierrez-Barragan, M. A. Basson, S. Panzeri, A. Gozzi, Infraslow State Fluctuations Govern Spontaneous fMRI Network Dynamics. Current Biology 29, 2295-2306.e5 (2019).

(15) K. Helmbold, et al., Serotonergic modulation of resting state default mode network connectivity in healthy women. Amino Acids 48, 1109–1120 (2016).

(16) B. Jarosiewicz, B. L. McNaughton, W. E. Skaggs, Hippocampal Population Activity during the Small-Amplitude Irregular Activity State in the Rat. J. Neurosci. 22, 1373–1384 (2002).

(17) J. T. Jordan, The rodent hippocampus as a bilateral structure: A review of hemispheric lateralization. Hippocampus 30, 278–292 (2020).

(18) T. Kato, et al., Oscillatory Population-Level Activity of Dorsal Raphe Serotonergic Neurons Is Inscribed in Sleep Structure. J. Neurosci. 42, 7244–7255 (2022).

(19) M.A. Kim, H. S. Lee, B. Y. Lee, B. D. Waterhouse, Reciprocal connections between subdivisions of the dorsal raphe and the nuclear core of the locus coeruleus in the rat. Brain Research 1026, 56–67 (2004).

(20) C. Kjaerby, et al., Memory-enhancing properties of sleep depend on the oscillatory amplitude of norepinephrine. Nat Neurosci 25, 1059–1070 (2022).

(21) S. Lecci, et al., Coordinated infraslow neural and cardiac oscillations mark fragility and offline periods in mammalian sleep. Sci Adv 3, e1602026 (2017).

(22) A. A. Liu, et al., A consensus statement on detection of hippocampal sharp wave ripples and differentiation from other fast oscillations. Nat Commun 13, 6000 (2022).

(23) M.-T. Lo, P.-H. Tsai, P.-F. Lin, C. Lin, Y. L. Hsin, The nonlinear and nonstationary properties in eeg signals: probing the complex fluctuations by hilbert–huang transform. Adv. Adapt. Data Anal. 01, 461–482 (2009).

(24) N. K. Logothetis, J. Pauls, M. Augath, T. Trinath, A. Oeltermann, Neurophysiological investigation of the basis of the fMRI signal. Nature 412, 150–157 (2001).

(25) H. Lu, et al., Rat brains also have a default mode network. Proc Natl Acad Sci U S A 109, 3979–3984 (2012).

(26) D. Mantini, M. G. Perrucci, C. Del Gratta, G. L. Romani, M. Corbetta, Electrophysiological signatures of resting state networks in the human brain. Proc Natl Acad Sci U S A 104, 13170– 13175 (2007).

(27) J. E. Markowitz, et al., The striatum organizes 3D behavior via moment-to-moment action selection. Cell 174, 44-58.e17 (2018).

(28) H. Miyawaki, Y. N. Billeh, K. Diba, Low Activity Microstates During Sleep. Sleep 40, zsx066 (2017).

(29) B. Mlinar, A. Montalbano, L. Piszczek, C. Gross, R. Corradetti, Firing Properties of Genetically Identified Dorsal Raphe Serotonergic Neurons in Brain Slices. Front Cell Neurosci 10, 195 (2016).

(30) A. Osorio-Forero, et al., Noradrenergic circuit control of non-REM sleep substates. Current Biology 31, 5009-5023.e7 (2021).

(31) S. Panzeri, N. Brunel, N. K. Logothetis, C. Kayser, Sensory neural codes using multiplexed temporal scales. Trends in Neurosciences 33, 111–120 (2010).

(32) M. E. Raichle, et al., A default mode of brain function. Proc Natl Acad Sci U S A 98, 676–682 (2001).

(33) R. Srinath, S. Ray, Effect of amplitude correlations on coherence in the local field potential. J Neurophysiol 112, 741–751 (2014).

(34) B. P. Staresina, J. Niediek, V. Borger, R. Surges, F. Mormann, How coupled slow oscillations, spindles and ripples coordinate neuronal processing and communication during human sleep. Nat Neurosci 26, 1429–1437 (2023).

(35) C. W. Thomas, et al., Psilocin acutely alters sleep-wake architecture and cortical brain activity in laboratory mice. Transl Psychiatry 12, 77 (2022).

(36) G. F. Turi, et al., Serotonin modulates infraslow oscillation in the dentate gyrus during Non-REM sleep. eLife 13 (2025).

(37) J. Vazquez, H. A. Baghdoyan, Basal forebrain acetylcholine release during REM sleep is significantly greater than during waking. American Journal of Physiology-Regulatory, Integrative and Comparative Physiology 280, R598–R601 (2001).

(38) J. Wan, et al., A genetically encoded sensor for measuring serotonin dynamics. Nat Neurosci 24, 746–752 (2021).

(39) Y. Zhang, et al., Cholinergic suppression of hippocampal sharp-wave ripples impairs working memory. Proc. Natl. Acad. Sci. U.S.A. 118, e2016432118 (2021).

(40) Y. Zhang, et al., Interaction of acetylcholine and oxytocin neuromodulation in the hippocampus. Neuron (2024).

eLife Assessment

This important study by Lee et al. explores the heterogeneous response of non-growing bacteria to the antimicrobial peptide (AMP) tachyplesin. The authors identify a subpopulation of cells that evade lethal damage by limiting the intracellular accumulation of a fluorescently labeled tachyplesin analog. The study provides compelling evidence that reduced drug accumulation underlies the decreased susceptibility of this subpopulation to the AMP. The molecular basis of this phenotype is well supported by the data.

Reviewer #1 (Public review):

Summary:

This work contributes several important and interesting observations regarding the heterotolerance of non-growing Escherichia coli and Pseudomonas aeruginosa to the antimicrobial peptide tachyplesin. The primary mechanism of action of tachyplesin is thought to be disruption of the bacterial cell envelope, leading to leakage of cellular contents after a threshold level of accumulation. Although the MIC for tachyplesin in exponentially growing E. coli is just 1 ug/ml, the authors observe that a substantial fraction of a stationary phase population of bacteria survives much higher concentrations, up to 64 ug/ml. By using a fluorescently labelled analogue of tachyplesin, the authors show that the amount of per-cell intracellular accumulation of tachyplesin displays a bimodal distribution, and that the fraction of "low accumulators" correlates with the fraction of survivors. Using a microfluidic device, they show that low accumulators exclude propidium iodide, suggesting that their cell envelopes remain largely intact, while high accumulators of tachyplesin also stain with propidium iodide. They show that this phenomenon holds for several clinical isolates of E. coli with different genetic determinants of antibiotic resistance, and for a strain of Pseudomonas aeruginosa. However, the bimodal distribution does not occur in these organisms for several other antimicrobial peptides, or for tachyplesin in Klebsiella pneumoniae or Staphylococcus aureus, indicating some degree of specificity in the interaction between AMP and bacterial cell envelope. They next explore the dynamics of the fluorescent tachyplesin accumulation and show interestingly that a high degree of accumulation is initially seen in all cells, but that the "low accumulator" subpopulation manages to decrease the amount of intracellular fluorescence over time, while the "high accumulator"subpopulation continues to increase its intracellular fluorescence. Focusing on increased efflux as a hypothesised mechanism for the "low accumulator" phenotype, based on transcriptomic analysis of the two subpopulations, the authors screen putative efflux inhibitors to see if they can block the formation of the low accumulator subpopulation. They find that both the protonophore CCCP and the SSRI sertraline can block the formation of this subpopulation and that a combination of sertraline plus tachyplesin kills a greater fraction of the stationary phase cells than either agent alone, similar to the killing observed when growing cells are treated with tachyplesin.

Strengths:

This study provides new insight into the heterogeneous behaviours of non-growing bacteria when exposed to an antimicrobial peptide, and into the dynamics of their response. The single-cell analysis by FACS and microscopy is compelling. The results provide a much-needed single cell perspective on the phenomenon of tolerance to AMPs and a good starting point for further exploration.

Weaknesses:

The authors have substantially improved the clarity of the manuscript and have added additional experiments to probe further the location of the AMP relative to low and high accumulators, and the physiological states of these sub-populations. These experiments strengthen the assertion that low accumulators keep the AMP at the cell surface while high accumulators permit intracellular access to the AMP.

The phenomenon of the emergence of low accumulators, which are phenotypically tolerant to the antimicrobial peptide tachyplesin, is interesting and important even if there is still work to be done to understand the mechanism by which it occurs.

Reviewer #2 (Public review):

Summary:

This study reports on the existence of subpopulations of isogenic E. coli and P. aeruginosa cells that are tolerant to the antimicrobial peptide tachyplesin and are characterized by accumulation of low levels of a fluorescent tachyplesin-NBD conjugate. The authors then set out to address the molecular mechanisms, providing interesting insights even though the mechanism remains incompletely defined: The work demonstrates that increased efflux may cause this phenotype, putatively together with other changes in membrane lipid composition. The authors further demonstrate that pharmacological manipulation can prevent generation of tolerance. The authors are cautious in their interpretation and the claims made are largely justified by the data.

Strengths:

Going beyond the commonly used bulk techniques for studying susceptibility to AMPs, Lee et al. used of fluorescent antibiotic conjugates in combination with flow cytometry analysis to study variability in drug accumulation at the single cell level. This powerful approach enabled the authors to expose bimodal drug accumulation pattern that were condition dependent, but conserved across a variety of E. coli clinical isolates. Using cell sorting in combination with colony-forming unit assays as well as quantitative fluorescence microscopic analysis in a microfludics-setup the authors compellingly demonstrate that low accumulators (where fluorescence signal is mostly restricted to the membrane), can survive antibiotic treatment, whereas high accumulators (with high intracellular fluorescence) were killed.

The relevance of efflux for the ´low accumulator´ phenotype and its survival is convincingly demonstrated by the following lines of evidence: i) A time-course experiment on tachyplesin-NBD pre-loaded cells revealed that all cells initially were high accumulators, before a subpopulation of cells subsequently managed to reduce signal intensity, demonstrating that the ´low accumulator´ phenotype is an induced response and not a pre-existing property. Ii) Double-mutants deficient in the delta acrA delta tolC double-KO, which showed reduced levels of low accumulators´. Interestingly, ´low accumulator´populations were nearly abrogated in bacteria deficient in the qse quorum sensing system, suggesting its centrality for the tachyplesin response. Even though this system may control acrA, the strength of the phenotype may suggest that it may control additional as-of-yet unidenitified factors relevant in the response to tachyplesin. Iii) treatment with efflux pump inhibitor sertraline and verapamil (even though some caution needs to be taken since it is not perfectly selective, see weakness) prevents generation of low accumulators. The observation that sertraline enhances tachyplesin-based killing is an important basis for developing combination therapies.

The study convincingly illustrates how susceptibility to tachyplesin adaptively changes in a heterogeneous way dependent on the growth phases and nutrient availability. This is highly relevant also beyond the presented example of tachyplesin and similar subpopulation-based adaptive changes to the susceptibility towards antimicrobial peptides or other drugs may occur during infections in vivo and they would likely be missed by standardized in vitro susceptibility testing.

Weaknesses:

Some mechanistic questions regarding tachyplesin-accumulation and survival remain. One general shortcoming of the setup of the transcriptomics experiment is that the tachyplesin-NBD probe itself has antibiotic efficacy and induces phenotypes (and eventually cell death) in the ´high accumulator´ cells. As the authors state themselves, this makes it challenging to interpret whether any differences seen between the two groups are causative for the observed accumulation pattern of if they are a consequence of differential accumulation and downstream phenotypic effects.

Reviewer #3 (Public review):

Summary:

This important study shows that stationary phase bacteria survive antimicrobial peptide treatment by switching on efflux pumps, generating low accumulating subpopulations that evade killing-a finding with clear implications for the design of peptide based antibiotics and for researchers studying antimicrobial resistance. The evidence is solid and frequently convincing, as diverse single cell assays, genetics and chemical inhibition coherently link reduced intracellular peptide to survival, even though a few mechanistic details warrant further exploration.

Strengths:

The authors investigate how Escherichia coli (and, to a lesser extent, Pseudomonas aeruginosa) survive exposure to the antimicrobial peptide (AMP) tachyplesin. Because resistance to AMPs is thought to rely heavily on non genetic adaptations rather than on classical mutation based mechanisms, the study focuses on phenotypic heterogeneity and seeks to pinpoint the cellular processes that protect a subset of cells. Using fluorescently labelled tachyplesin, single cell imaging, flow cytometry, transcriptomics, targeted genetics, and chemical perturbations, the authors report that stationary phase cultures harbor two phenotypic states: high accumulating cells that die and low accumulating cells that survive. They further propose and show that inducible efflux activity is the primary driver of survival and show that either efflux inhibition (sertraline, verapamil) or nutrient supplementation prevents the emergence of low accumulators and boosts killing.

The experiments unambiguously reveal that the cells respond to stress heterogeneously, with two distinct subpopulations - one with better survival than the other. This primary phenotype is convincingly shown across various E. coli strains, including clinical isolates. The authors probed the underlying mechanism from several angles, with important additional experiments in the revised version that strengthens the original conclusions in several ways. Newly added efflux assays with ethidium bromide, together with proteinase treatment experiments and ΔacrAΔtolC and ΔqseB/qseC mutant data, illustrate that the low accumulating subpopulation can actively export intracellular compounds. The authors took great care to temper their language to acknowledge other potential alternatives that could explain some of the data such as altered influx, vesicle release or proteolysis, metabolic activity of the cells, indirect effects of sertraline treatment, etc. Additional metabolic dye measurements confirm that low accumulators are less metabolically active, and a new data on nutrient supplementation shows that forcing growth increases peptide uptake and lethality. The authors clarify the crucial point of where antimicrobial peptides actually bind on the cell within the broader survival mechanism and present their conclusions, along with potential caveats, with commendable clarity.

Weaknesses:

Despite these advances, the contribution of efflux may require more direct evidence to further dissect whether efflux is necessary, sufficient, or contributory. The facts that the key low-efflux mutant still retains a small fraction of survivors and that the inhibitors used may cause other physiological changes leading to higher efflux are still unaccounted for. The lipidomic and vesicle findings, while intriguing, remain descriptive, and direct tests of their functional relevance would further solidify the mechanistic models.

Conclusion:

Even with these limitations, the study provides valuable insight into non genetic resistance mechanisms to AMPs and highlights inducible heterogeneity as a critical obstacle to peptide therapeutics. In a much broader context, this study also underscores the importance of efflux physiology even for those antimicrobials that seemingly would not have intracellular targets.

Author response:

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

Reviewer 1:

We would like to thank Reviewer 1 for recognising the importance of our findings on the heterogeneity in bacterial responses to tachyplesin.

(1) A double deletion of acrA and tolC (two out of the three components of the major constitutive RND efflux pump) reduces the appearance of the low accumulator phenotype, but interestingly, the single deletions have no effect, and a well-characterised inhibitor of RND efflux pumps also has no effect. The authors identify a two-component system, qseCB, that appears necessary for the appearance of low accumulators, but this system has pleiotropic effects on many cellular systems, with only tenuous connections to efflux. The selected pharmacological agents that could prevent the appearance of low accumulators do not offer clear insight into the mechanism by which low accumulators arise, because they have diverse modes of action.

We have added that “QseBC, was previously inferred to mediate resistance to a tachyplesin analogue by upregulating efflux genes based on transcriptomic analysis and hyper susceptibility of ΔqseBΔqseC mutants[113]”. However, we have also acknowledged that “it is conceivable that the deletion of QseBC has pleiotropic effects on other cellular mechanisms involved in tachyplesin accumulation.” and that “it is also conceivable that sertraline prevented the formation of the low accumulator phenotype via efflux independent mechanisms”

These amendments are reported on lines 525-527, 532-534 and 539-541 of our revised manuscript.

(2) The transcriptomics data collected for low and high accumulator sub-populations are interesting, but in my opinion, the conclusions that can be drawn from these data remain overstated. It is not possible to make any claims about the total amount of "protein synthesis, energy production, and gene expression" on the basis of RNA-Seq data. The reads from each sample are normalised, so there is no information about the total amount of transcript. Many elements of total cellular activity are post-transcriptionally regulated, so it is impossible to assess from transcriptomics alone. Finally, the transcriptomic data are analysed in aggregated clusters of genes that are enriched for biological processes, for example: "Cluster 2 included processes involved in protein synthesis, energy production, and gene expression that were downregulated to a greater extent in low accumulators than high accumulators". However, this obscures the fact that these clusters include genes that are generally inhibitory of the process named, as well as genes that facilitate the process.

We have now acknowledged that “that our data do not take into account post-transcriptional modifications that represent a second control point to survive external stressors.”

These amendments are reported on lines 534-535 of our revised manuscript.

The raw transcript counts can be found in Figure 3 – Source Data, we had added these data in our previous manuscript as requested by this reviewer.

We would also like to clarify that we have analysed our transcriptomic data via both clustering (i.e. Figure 3) and direct comparison of genes of interest (Table S1) and transcription factors (i.e. genes that are generally inhibitory of the process named, as well as genes that facilitate the process, Figure S12).

Finally, we would like to point out that in our revised manuscript (both this and its previous version) we are stating “Cluster 2 included processes involved in protein synthesis, energy production, and gene expression that were downregulated to a greater extent in low accumulators than high accumulators”. We do not think this is an overstatement, we do not use these data to make conclusions on the total amount of "protein synthesis, energy production, and gene expression".

(3) The authors have added an experiment to attempt to assess overall metabolic activity in the low accumulator and high accumulator populations, which is a welcome addition. They apply the redox dye resazurin and observe lower resorufin (reduced form) fluorescence in the low accumulator population, which they take to indicate a lower respiration rate. This seems possible, however, an important caveat is that they have shown the low accumulator population to retain substantially lower amounts of multiple different fluorescent molecules (tachyplesin-NBD, propidium iodide, ethidium bromide) intracellularly compared to the high accumulator population. It seems possible that the low accumulator population is also capable of removing resazurin or resorufin from the intracellular space, regardless of metabolic rate. Indeed, it has previously been shown that efflux by RND efflux pumps influences resazurin reduction to resorufin in both P. aeruginosa and E. coli. By measuring only the retained redox dye using flow cytometry, the results may be confounded by the demonstrated ability of the low accumulator population to remove various fluorescent dyes. More work is needed to strongly support broad conclusions about the physiological states of the low and high accumulator populations. The phenomenon of the emergence of low accumulators, which are phenotypically tolerant to the antimicrobial peptide tachyplesin, is interesting and important even if there is still work to be done to understand the mechanism by which it occurs.

We have now clarified that these assays were performed in the presence of 50 μM CCCP and that “CCCP was included to minimise differences in efflux activity and preserve resorufin retention between low and high accumulators, though some variability in efflux may still persist.” We have now added this information on lines 401-406. This information was only present in the caption of Figure S16 of our previous version of this manuscript.

We agree with the reviewers that more work needs to be done to fully understand this new phenomenon and we had already acknowledged in our previous version of this manuscript that other mechanisms could play a role in this new phenomenon, see lines 489-517 of the current manuscript.

Reviewer 2:

We would like to thank the reviewer for recognising that all their previous comments have now been satisfactorily addressed.

(1) Some mechanistic questions regarding tachyplesin-accumulation and survival remain. One general shortcoming of the setup of the transcriptomics experiment is that the tachyplesin-NBD probe itself has antibiotic efficacy and induces phenotypes (and eventually cell death) in the ´high accumulator´ cells. As the authors state themselves, this makes it challenging to interpret whether any differences seen between the two groups are causative for the observed accumulation pattern of if they are a consequence of differential accumulation and downstream phenotypic effects.

We agree with the reviewer and we had explicitly acknowledged this possibility on lines 281-285 (of the previous and current version of this manuscript).

(2) The statement ´ Moreover, we found that the fluorescence of low accumulators decreased over time when bacteria were treated with 20 μg mL´ is, in my opinion, not supported by the data shown in Figure S4C. That figure shows that the abundance of ´low accumulator´ cells decreases over time. Following the rationale that protease K treatment may cleave surface associated/ extracellular tachyplesin-NDB, this should lead to a shift of ´low accumulator´ population to the left, indicating reduced fluorescence intensity per cell. This is not so case, but the population just disappears. However, after 120 min of treatment more cells appear in the ´high accumulator´ state. This result is somewhat puzzling.

We agree with the reviewer that our previous discussion of this data could have been misleading. We have now reworded this part of the text as following: “We found that the fluorescence of high accumulators did not decrease over time when tachyplesin-NBD was removed from the extracellular environment and bacteria were treated with 20 μg mL^-1 (0.7 μM) proteinase K, a widely-occurring serine protease that can cleave the peptide bonds of AMPs [43–45] (Figure S4B and C). These data suggest that tachyplesin-NBD primarily accumulates intracellularly in high accumulators.”

It is conceivable that extended exposure to proteinase K (i.e. we see a decrease in the abundance of low accumulators after 90 min treatment with proteinase K) increased the permeability to tachyplesin-NBD of low accumulators allowing tachyplesin-NBD to move from either the extracellular space or the membrane to the cell interior. However, we do not have data to prove this point.

Therefore, we have now removed our claim that the data obtained using proteinase K suggest that tachyplesin-NBD accumulates primarily in the membranes of low accumulators. We believe that our two separate microscopy analyses provide more direct, stronger and less ambiguous evidence that tachyplesin-NBD accumulates primarily in the membranes of low accumulators.

(3) The authors used the metabolic dye resazurin to measure the metabolic activity of low vs. high accumulators. I am not entirely convinced that the lower fluorescence resorufin fluorescence in tachyplesin-NBD accumulators really indicates lower metabolic activity, since a cell's fluorescence levels would also be affected by the cellular uptake and efflux. It appears plausible that the lower resorufin-fluorescence may result from reduced accumulation/increased efflux in the ‘low-tachyplesin NBD´ population.

We have now clarified that these assays were performed in the presence of 50 μM CCCP and that “CCCP was included to minimise differences in efflux activity and preserve resorufin retention between low and high accumulators, though some variability in efflux may still persist.” We have now added this information on lines 401-406. This information was only present in the caption of Figure S16 of our previous version of this manuscript.

(4) P8 line 343. The text should refer to Figure. 13B, instead of 14B

We have now changed the text accordingly on line 337.

Reviewer 3:

We would like to thank the reviewer for recognising that we have done a very impressive job in taking care of their comments.

(1) Despite these advances, the contribution of efflux may require more direct evidence to further dissect whether efflux is necessary, sufficient, or contributory. The facts that the key low efflux mutant still retains a small fraction of survivors and that the inhibitors used may cause other physiological changes leading to higher efflux are still unaccounted for. The lipidomic and vesicle findings, while intriguing, remain descriptive, and direct tests of their functional relevance would further solidify the mechanistic models.

We agree with the reviewers that more work needs to be done to fully understand this new phenomenon and we had already acknowledged in our previous version of this manuscript that other mechanisms could play a role in this new phenomenon, see lines 489-517 of the current manuscript.

eLife Assessment

This valuable study reports the development of a novel organoid system for studying the emergence of autorhythmic gut peristaltic contractions through the interaction between interstitial cells of Cajal and smooth muscle cells. The authors further utilized the system to provide convincing evidence for a previously unappreciated potential role for smooth muscle cells in regulating the firing rate of interstitial cells of Cajal. The work will be of interest to those studying development and physiology of the gut.

Reviewer #1 (Public review):

Summary:

In this study, the authors developed an organoid system containing smooth muscle cells (SMCs) and interstitial cells of Cajal (ICCs; pacemaker cells), but few enteric neurons. This system generates rhythmic contractions similar to those observed in the developing gut. The stereotypical arrangement of SMCs and ICCs within the organoid allowed the authors to identify these cell types without the need for antibody staining. Leveraging this feature, they used calcium imaging and pharmacological approaches to investigate how calcium transients develop through interactions between the two cell types.

The authors first show that calcium transients are synchronized among ICC-ICC, SMC-SMC, and SMC-ICC pairs. They then used gap junction inhibitors to suggest that gap junctions are specifically involved in ICC-to-SMC signaling. Finally, they applied inhibitors of myosin II and L-type Ca²⁺ channels to demonstrate that SMC contraction is crucial for the generation of rhythmic activity in ICCs, suggesting the presence of SMC-to-ICC signaling. Additionally, they show that two organoids become synchronized upon fusion, with SMCs mediating this synchronization.

Strengths:

The organoid system provides a useful model for studying the specific roles of SMCs and ICCs in live samples.

Weaknesses:

Since all functional analyses were conducted pharmacologically in vitro, the findings need to be further validated through genetic approaches in vivo in future studies.

Reviewer #2 (Public review):

Summary:

In this study, Yagasaki et al. describe an organoid system to study the interactions between smooth muscle cells (SMCs) and interstitial cells of Cajal (ICCs). While these interactions are essential for the control of rhythmic intestinal contractility (i.e., peristalsis), they are poorly understood, largely due to the complexity of and access to the in vivo environment and the inability to co-culture these cell types in vitro for long term under physiological conditions. The "gut contractile organoids" organoids described herein are reconstituted from stromal cells of the fetal chicken hindgut that rapidly reorganize into multilayered spheroids containing an outer layer of smooth muscle cells and an inner core of interstitial cells. The authors demonstrate that they contract cyclically and additionally use calcium imagining to show that these contractions occur concomitantly with calcium transients that initiate in the interstitial cell core and are synchronized within the organoid and between ICCs and SMCs. Furthermore, they use several pharmacological inhibitors to show that these contractions are dependent upon non-muscle myosin activity and, surprisingly, independent of gap junction activity. Finally, they develop a 3D hydrogel for the culturing of multiple organoids and found that they synchronize their contractile activities through interconnecting smooth muscle cells, suggesting that this model can be used to study the emergence of pacemaking activities. Overall, this study provides a relatively easy-to-establish organoid system that will be of use in studies examining the emergence of rhythmic peristaltic smooth muscle contractions and how these are regulated by interstitial cell interactions. However, further validation and quantification will be necessary to conclusively determine show the cellular composition of the organoids and how reproducible their behaviors are.

Strengths:

This work establishes a new self-organizing organoid system that can easily be generated from the muscle layers of the chick fetal hindgut to study the emergence of spontaneous smooth muscle cell contractility. A key strength of this approach is that the organoids seem to contain few cell types (though more validation is needed), namely smooth muscle cells (SMCs) and interstitial cells of Cajal (ICCs). These organoids are amenable to live imaging of calcium dynamics as well as pharmacological perturbations for functional assays, and since they are derived from developing tissues, the emergence of the interactions between cell types can be functionally studied. Thus, the gut contractile organoids represent a reductionist system to study the interactions between SMCs and ICCs in comparison to the more complex in vivo environment, which has made studying these interactions challenging.

Weaknesses:

The study lacks complementary in vivo experiments, but these will be exciting to follow up in future studies.

Reviewer #3 (Public review):

Summary:

The paper presents a novel contractile gut organoid system that allows for in vitro studying of rudimentary peristaltic motions in embryonic tissues by facilitating GCaMP-live imaging of Ca2+ dynamics, while highlighting the importance and sufficiency of ICC and SMC interactions in generating consistent contractions reminiscent of peristalsis. It also argues that ENS at later embryonic stages might not be necessary for coordination of peristalsis.

Strengths:

The manuscript by Yagasaki, Takahashi, and colleagues represents an exciting new addition to the toolkit available for studying fundamental questions in the development and physiology of the hindgut. The authors carefully lay out the protocol for generating contractile gut organoids from chick embryonic hindgut and perform a series of experiments that illustrate the broader utility of these organoids for studying the gut. This reviewer is highly supportive of the manuscript following highly responsive revisions in response to prior reviewer feedback.

Author response:

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

eLife Assessment

This valuable study reports the development of a novel organoid system for studying the emergence of autorhythmic gut peristaltic contractions through the interaction between interstitial cells of Cajal and smooth muscle cells. While the utility of the organoids for studying hindgut development is well illustrated by showing, for example, a previously unappreciated potential role for smooth muscle cells in regulating the firing rate of interstitial cells of Cajal, some of the functional analyses are incomplete. There are some concerns about the specificity and penetrance of perturbations and the reproducibility of the phenotypes. With these concerns properly addressed, this paper will be of interest to those studying the development and physiology of the gut.

We greatly appreciate constructive comments raised by the Editors and all the Reviewers. We have newly conducted pharmacological experiments using Nifedipine, a L-type Ca²⁺ blocker known to operate in smooth muscles (new Fig 7). The treatment abrogated not only the oscillation of SMCs but also that in ICCs, further corroborating our model that not only ICC-to-SMC interactions but also the reverse direction, namely SMC-to-ICC feedback signals, are operating to achieve coordinated/stable rhythm of gut contractile organoids.

Concerning the issues of the specificity and penetrance in pharmacological experiments with gap junction inhibitors, we have carefully re-examined effects by multiple blockers (CBX and 18b-GA) at different concentrations (new Fig 5D and Fig. S3B).We have newly found that: (1) the effects observed by CBX (100 µM) that the latency of Ca²⁺ peaks between ICCs (preceding) and SMCs (following) was abolished are not seen by 18b-GA at any concentrations including 100 µM, implying that the latency of Ca²⁺ peaks between these cells is governed by connexin(s) that are not inhibited by18bGA. Such difference in inhibiting effects by these two drugs were previously reported in multiple model systems including guts (Daniel et al., 2007; Parsons & Huizinga, 2015; Schultz et al., 2003).

Regarding the penetrance of the drugs, we have carried out earlier administration (Day 3) of the gap junction inhibitor, either CBX (100 µM) or 18b-GA (100 µM), in the course of organoidal formation in culture when cells are still at 2D to exclude a possible penetrance problem (new Fig. S3C). There treatments render no or little effects to the patterns of organoidal contractions in a way similar to the drug administration at Day 7. As already shown in the first version, CBX (100 µM) eliminates the latency of Ca²⁺ peaks, we believe that this drug successfully penetrates into the organoid and exerts its specific effects.

Unfortunately, due to very unstable condition in climate including extreme heat and sporadically occurring bird flu epidemic since the last summer in Japan, the poultry farm must have faced problems. In the course of revision experiments, we got in a serious trouble at multiple times with unhealthy eggs/embryos lasting from last summer until present. These unfortunate incidents did not allow us to engage in the revision experiments as fully as we originally planned. Nevertheless, we did our very best within a limited time fame, and we believe that the revised version is suitable as a final version of an eLife article.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

In this study, the authors developed an organoid system that contains smooth muscle cells (SMCs) and interstitial cells of Cajal (ICCs; pacemaker) but few enteric neurons, and generates rhythmic contractions as seen in the developing gut. The stereotypical arrangements of SMCs and ICCs in the organoid allowed the authors to identify these cell types in the organoid without antibody staining. The authors took advantage of this and used calcium imaging and pharmacology to study how calcium transients develop in this system through the interaction between the two types of cells. The authors first show that calcium transients are synchronized between ICC-ICC, SMC-SMC, and SMC-ICC. They then used gap junction inhibitors to suggest that gap junctions are specifically involved in ICC-to-SMC signaling. Finally, the authors used an inhibitor of myosin II to suggest that feedback from SMC contraction is crucial for the generation of rhythmic activities in ICCs. The authors also show that two organoids become synchronized as they fuse and SMCs mediate this synchronization.

Strengths:

The organoid system offers a useful model in which one can study the specific roles of SMCs and ICCs in live samples.

Thank you very much for the constructive comments.

Weaknesses:

Since only one blocker each for gap junction and myosin II was used, the specificities of the effects were unclear.

We appreciate these comments. We have addressed those of “weaknesses” as described in “Responses to the eLife assessment” (please see above).

Reviewer #2 (Public Review):

Summary:

In this study, Yagasaki et al. describe an organoid system to study the interactions between smooth muscle cells (SMCs) and interstitial cells of Cajal (ICCs). While these interactions are essential for the control of rhythmic intestinal contractility (i.e., peristalsis), they are poorly understood, largely due to the complexity of and access to the in vivo environment and the inability to co-culture these cell types in vitro for long term under physiological conditions. The "gut contractile organoids" organoids described herein are reconstituted from stromal cells of the fetal chicken hindgut that rapidly reorganize into multilayered spheroids containing an outer layer of smooth muscle cells and an inner core of interstitial cells. The authors demonstrate that they contract cyclically and additionally use calcium imagining to show that these contractions occur concomitantly with calcium transients that initiate in the interstitial cell core and are synchronized within the organoid and between ICCs and SMCs. Furthermore, they use several pharmacological inhibitors to show that these contractions are dependent upon non-muscle myosin activity and, surprisingly, independent of gap junction activity. Finally, they develop a 3D hydrogel for the culturing of multiple organoids and found that they synchronize their contractile activities through interconnecting smooth muscle cells, suggesting that this model can be used to study the emergence of pacemaking activities. Overall, this study provides a relatively easy-to-establish organoid system that will be of use in studies examining the emergence of rhythmic peristaltic smooth muscle contractions and how these are regulated by interstitial cell interactions. However, further validation and quantification will be necessary to conclusively determine show the cellular composition of the organoids and how reproducible their behaviors are.

Strengths:

This work establishes a new self-organizing organoid system that can easily be generated from the muscle layers of the chick fetal hindgut to study the emergence of spontaneous smooth muscle cell contractility. A key strength of this approach is that the organoids seem to contain few cell types (though more validation is needed), namely smooth muscle cells (SMCs) and interstitial cells of Cajal (ICCs). These organoids are amenable to live imaging of calcium dynamics as well as pharmacological perturbations for functional assays, and since they are derived from developing tissues, the emergence of the interactions between cell types can be functionally studied. Thus, the gut contractile organoids represent a reductionist system to study the interactions between SMCs and ICCs in comparison to the more complex in vivo environment, which has made studying these interactions challenging.

Thank you very much for the constructive comments.

Weaknesses:

The study falls short in the sense that it does not provide a rigorous amount of evidence to validate that the gut organoids are made of bona fide smooth muscle cells and ICCs. For example, only two "marker" proteins are used to support the claims of cell identity of SMCs and ICCs. At the same time, certain aspects of the data are not quantified sufficiently to appreciate the variance of organoid rhythmic contractility. For example, most contractility plots show the trace for a single organoid. This leads to a concern for how reproducible certain aspects of the organoid system (e.g. wavelength between contractions/rhythm) might be, or how these evolve uniquely over time in culture. Furthermore, while this study might be able to capture the emergence of ICC-SMC interactions as they related to muscle contraction and pacemaking, it is unclear how these interactions relate to adult gastrointestinal physiology given that the organoids are derived from fetal cells that might not be fully differentiated or might have distinct functions from the adult. Finally, despite the strength of this system, discoveries made in it will need to be validated in vivo. Thank you very much for the comments, which are helpful to improve our MS. In the revised version, we have additionally used antibody against desmin, known to be a maker for mature SMCs (new Fig 3B). The signal is seen only in the peripheral cells overlapping with the αSMA staining (line 169-170).

Concerning the reproducibility, while contractility changes were shown for a representative organoid in the original version, experiments had been carried out multiple times, and consistent data were reproduced as already mentioned in the text of the first version of MS. However, we agree with this reviewer that it must be more convincing if we assess quantitatively. We have therefore conducted quantitative assessments of organoidal contractions and Ca²⁺ transients (new Fig. 2B, new Fig. 4D, new Fig 5D, E, new Fig. 6B, new Fig. 7B, new Fig. 8C, new Fig. S2, S3). Details such as repeats of experiments and size of specimens are carefully described in the revised version (Figure legends)

In particular, in place of contraction numbers/time, we have plotted “contraction intervals” between two successive peaks (Fig. 2B and others). Actually, with your suggestion, we have tried to perform a periodicity analysis of organoid contractions. Unfortunately, no clear value has been obtained, probably because the contractions/Ca²⁺ transitions are not as “regularly periodical” as seen in conventional physics. This led us to perform the peak-interval analysis. Methods to quantify the contraction intervals are carefully explained in the revised version.

As already mentioned in the “Our provisional responses” following the receipt of Reviewers’ comments, we agree that our organoids derived from embryonic hind gut (E15) might not necessarily recapitulate the full function of cells in adult. However, it has well been accepted in the field of developmental biology that studies with embryonic tissue/cells make a huge contribution to unveil complicated physiological cell functions. Nevertheless, we have carefully considered in the revised version so that the MS would not send misleading messages. We agree that in vivo validation of our gut contractile organoid must be wonderful, and this is a next step to go.

Reviewer #3 (Public Review):

Summary:

The paper presents a novel contractile gut organoid system that allows for in vitro studying of rudimentary peristaltic motions in embryonic tissues by facilitating GCaMPlive imaging of Ca²⁺ dynamics, while highlighting the importance and sufficiency of ICC and SMC interactions in generating consistent contractions reminiscent of peristalsis. It also argues that ENS at later embryonic stages might not be necessary for coordination of peristalsis.

Strengths:

The manuscript by Yagasaki, Takahashi, and colleagues represents an exciting new addition to the toolkit available for studying fundamental questions in the development and physiology of the hindgut. The authors carefully lay out the protocol for generating contractile gut organoids from chick embryonic hindgut, and perform a series of experiments that illustrate the broader utility of these organoids for studying the gut. This reviewer is highly supportive of the manuscript, with only minor requests to improve confidence in the findings and broader impact of the work. These are detailed below.

Thank you very much for the constructive comments.

Weaknesses:

(1) Given that the literature is conflicting on the role GAP junctions in potentiating communication between intestinal cells of Cajal (ICCs) and smooth muscle cells (SMCs), the experiments involving CBX and 18Beta-GA are well-justified. However, because neither treatment altered contractile frequency or synchronization of Ca++ transients, it would be important to demonstrate that the treatments did indeed inhibit GAP junction function as administered. This would strengthen the conclusion that GAP junctions are not required, and eliminate the alternative explanation that the treatments themselves failed to block GAP junction activity.

Thank you for these comments, and we agree. In the revised version, we have verified the drugs, CBX and 18b-GA, using dissociated embryonic heart cells in culture, a well-established model for the gap junction study (new Fig. S3D, line 237-239). Expectedly, both inhibitors abrogate the rhythmic beats of heart cells, and importantly, cells’ beats resume after wash-out of the drug.

(2) Given that 5uM blebbistatin increases the frequency of contractions but 10uM completely abolishes contractions, confirming that cell viability is not compromised at the higher concentration would build confidence that the phenotype results from inhibition of myosin activity. One could either assay for cell death, or perform washout experiments to test for recovery of cyclic contractions upon removal of blebbistatin. The latter may provide access to other interesting questions as well. For example, do organoids retain memory of their prior setpoint or arrive at a new firing frequency after washout?

We greatly appreciate these suggestions and also interesting ideas to explore! In the revised version, we have newly conducted washout experiments (new Fig. 6B) (10 µM drug is washed-out from culture medium), and found that contractions resume, showing that cell viability is not compromised at 10 µM concentration (line 257-259). Intriguingly, the resumed rhythm appears more regular than that before drug administration. Thus, the contraction rhythm of the organoid might be determined by cellcell interactions at any given time rather than by memory of their prior setpoint. This is an interesting issue we would like to further explore in the future. These issues, although potentially interesting, are not mentioned in the text of the revised version, since it is too early to interpret there observations.

(3) Regulation of contractile activity was attributed to ICCs, with authors reasoning that Tuj1+ enteric neurons were only present in organoids in very small numbers (~1%).

However, neuronal function is not strictly dependent on abundance, and some experimental support for the relative importance of ICCs over Tuj1+ cells would strengthen a central assumption of the work that ICCs the predominant cell type regulating organoid contraction. For example, one could envision forming organoids from embryos in which neural crest cells have been ablated via microdissection or targeted electroporation. Another approach would be ablation of Tuj1+ cells from the formed organoids via tetrodotoxin treatment. The ability of organoids to maintain rhythmic contractile activity in the total absence of Tuj1+ cells would add confidence that the ICCs are indeed the driver of contractility in these organoids.

We agree. In the revised version, we have conducted TTX administration (new Fig. S2C). Changes in contractility by this treatment is not detected, supporting the argument that neural cells/activities are not essential for rhythmic contractions of the organoid (line 178-181).

(4) Given the implications of a time lag between Ca++ peaks in ICCs and SMCs, it would be important to quantify this, including standard deviations, rather than showing representative plots from a single sample.

In the revised version, we have elaborated a series of quantitative assessments as mentioned above (please see our responses to the “eLife assessments” at the beginning of these correspondences). The latency between Ca²⁺ peaks in ICCs and SMCs is shown in new Fig. 4D, in which measured value is 700 msec-terraced since the time-lapse imaging was performed with 700 msec intervals (as already described in the first version).

117 peaks for 14 organoids have been assessed (line 218).

(5) To validate the organoid as a faithful recreation of in vivo conditions, it would be helpful for authors to test some of the more exciting findings on explanted hindgut tissue. One could explant hindguts and test whether blebbistatin treatment silences peristaltic contractions as it does in organoids, or following RCAS-GCAMP infection at earlier stages, one could test the effects of GAP junction inhibitors on Ca++ transients in explanted hindguts. These would potentially serve as useful validation for the gut contractile organoid, and further emphasize the utility of studying these simplified systems for understanding more complex phenomena in vivo.

Thank you very much for insightful comments. We would love to explore these issues in near future. Just a note is that it was previously reported that Nifedipine silences peristaltic contractions in ex-vivo cultured gut (Chevalier et al., 2024; Der et al., 2000).

(6) Organoid fusion experiments are very interesting. It appears that immediately after fusion, the contraction frequency is markedly reduced. Authors should comment on this, and how it changes over time following fusion. Further, is there a relationship between aggregate size and contractile frequency? There are many interesting points that could be discussed here, even if experimental investigation of these points is left to future work.

It would indeed be interesting to explore how cell communications affect/determine the contraction rhythm, and our novel organoids must serve as an excellent model to address these fundamental questions. We have observed multiple times that when two organoids fuse, they undergo “pause”, and resume coordinated contractions as a whole, and we have mentioned such notice briefly in the revised version (line 282). To know what is going on during this pause time should be tempting. In addition, we have an impression that the larger in size organoids grow, the slower rhythm they count. We would love to explore this in near future.

(7) Minor: As seen in Movie 6 and Figure 6A, 5uM blebbistatin causes a remarkable increase in the frequency of contractions. Given the regular periodicity of these contractions, it is a surprising and potentially interesting finding, but authors do not comment on it. It would be helpful to note this disparity between 5 and 10 uM treatments, if not to speculate on what it means, even if it is beyond the scope of the present study to understand this further.

We assume that the increase in the frequency of contractions at 5 µM might be due to a shorter refractory period caused by a decreasing magnitude (amplitude) of contraction. We have made a short description in the revised text (line 256-257).

(8) Minor: While ENS cells are limited in the organoid, it would be helpful to quantify the number of SMCs for comparison in Supplemental Figure S2. In several images, the number of SMCs appears quite limited as well, and the comparison would lend context and a point of reference for the data presented in Figure S2B.

In the revised version, the number of SMCs has been counted and added in Fig. S2B. Contrary to that SMCs are more abundant than ICCs in an intact gut, the proportion is reversed in our organoid (line 181-183). It might due to treatments during cell dissociation/plating.

(9) Minor: additional details in the Figure 8 legend would improve interpretation of these results. For example, what is indicated in orange signal present in panels C, G and H? Is this GCAMP?

We apologize for this confusion. In the revised version, we have added labeling directly in the photos of new Fig. 9 (old Fig. 8). For C, G and H, the left photo is mRuby3+GCaMP6s, and the right one is GCaMP6s only.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

I have a few comments for the authors to consider:

(1) Figure 4C: The authors propose that calcium signals propagate from ICC to SMC based on the results presented in this figure. While it is observed that the peak of the calcium signal in ICC precedes that in SMC, it's worth noting that the onset of the rise in calcium signals occurs simultaneously in ICC and SMC. Doesn't this suggest that they are activated simultaneously? The latency observed for the peaks of calcium signals could reflect different kinetics of the rise in calcium concentration in the two types of cells rather than the order of calcium signal propagation.

We greatly appreciate these comments. We have re-examined kinetics of GCaMP signals in ICC and SMC, but we did not succeed in validating rise points precisely. We agree that the possibility that the rise in calcium signals could be occurring simultaneously. To clarify these issues, analyses with higher resolution is required, such as using GCaMP6f or GCaMP7/8. Nevertheless, the disappearance of the latency of Ca²⁺ peak by CBX implies a role of gap junction in ICC to SMC signaling. In the revised version, we replaced the wording “rise” by “peak” when the latency is discussed.

(2) Figure 5C: The specific elimination of the latency in the calcium signal peaks between ICC and SMC is interesting. However, I am curious about how gap junction inhibitors specifically eliminate the latency between ICC and SMC without affecting other aspects of calcium transients in these cells, such as amplitude and synchronization among ICCs and/or SMCs. Readers of the manuscript would expect some discussion on possible mechanisms underlying this specificity. Additionally, I wonder if the elimination of the latency was observed consistently across all samples examined. The authors should provide information on the frequency and number of samples examined, and whether the elimination occurs when 18-beta-GA is used.

In the revised version, we have elaborated quantitative demonstration. For the effects by CBX on latency or Ca²⁺ peaks, a new graph has been added to new Fig 5, in which 100 µM eliminated the latency. Intriguingly, the latency appears to be attributed to a gap junction that is not inhibited by18-beta-GA (please see new Fig. S3E). As already mentioned above, inhibiting activity of both CBX and 18-beta-GA has been verified using dissociated cells of embryonic heart, a popular model for gap junction studies.

At present, we do not know how gap junction(s) contribute to the latency of Ca²⁺ peaks without affecting synchronization among ICCs and/or SMCs (we have not addressed amplitude of the oscillation in this study). Actually, it was surprising to us to find that GJ’s contribution is very limited. We do not exclude the importance of GJs, and currently speculate that GJs might be important for the initiation of contraction/oscillation signals, whereas the requirement of GJs diminishes once the ICC-SMC interacting rhythm is established. What we observed in this study might be the synchronization signals AFTER these interactions are established (Day 7 of organoidal culture). Upon the establishment, it is possible that mechanical signaling elicited by smooth muscles’ contraction might become prominent as a mediator for the (stable) synchronization, as implicated by experiments with blebbistatin and Nifedipin, the latter being newly added to the revised version (new Fig. 7). We have added such speculation, although briefly in Discussion (line 374-377)

(3) Figure 6: The significant effects of blebbistatin on calcium dynamics in both ICC and SMC are intriguing. However, since only one blocker is utilized, the specificity of the effects is unclear. If other blockers for muscle contraction are available, they should be employed. Considering that a rise in calcium concentration precedes contraction, calcium transients should persist even if muscle contraction is inhibited. One concern is whether blebbistatin inadvertently rendered the cells unhealthy. The authors should demonstrate at least that contraction and calcium transients recover after removal of the drug. The frequency and number of samples examined should be shown, as requested for Figure 5C above.

Thank you for these critical comments. A possible harmfulness of the drugs was also raised by other reviewers, and we have therefore conducted wash-out experiments in the revised version (new Fig. 6B). Contractions resume after wash-out showing that cell viability is not compromised at 10 µM concentration. The number of samples examined has been described more explicitly in the revised version. Regarding the blocker of SMC, we have newly carried out pharmacological assays using nifedipine, a blocker of a L-type Ca²⁺ channel known to operate in smooth muscle cells (new Fig 7) (Chevalier et al., 2024; Der et al., 2000). As already explained in the “Responses to eLife assessment”, the treatment abrogated ICCs’ rhythm and synchronous Ca²⁺ transients between ICCs and SMCs, further corroborating our model that not only ICC-to-SMC interactions but also SMC-to-ICC feedback signals are operating to achieve coordinated/stable rhythm of gut contractile organoids of Day 7 culture (please also see our responses shown above for Comment (2)).

Reviewer #2 (Recommendations For The Authors):

Major:

(1) The claim that organoids contain functional SMCs and ICCs is insufficient as it currently relies on only c-Kit and aSMA antibodies. This conclusion could be additionally supported by staining with other markers of contractile smooth muscle (e.g. TAGLN and MYH14) and an additional accepted marker of ICCs (e.g. ANO1/TMEM16). Moreover, it should be demonstrated whether these cells are PDGFRA+, as PDGFRA is a known marker of other mesenchymal fibroblast cell types. These experiments would additionally rule out whether these cells were simply less differentiated myofibroblasts. Given that there might not be available antibodies that react with chicken protein versions, the authors could support their conclusions using alternative approaches, such as fluorescent in situ hybridization. A more thorough approach, such as single-cell RNA sequencing to compare the cell composition of the in vitro organoids to the in vivo colon, would fully justify the use of these organoids as a system for studying in vivo cell physiology.

With these suggestions provided, we have newly stained contractile organoids with anti-desmin antibody, known to be a marker for differentiated SMCs. As shown in new Fig. 3B, desmin-positive cells perfectly overlapped with aSMA-staining, indicating that the peripherally enclosing cells are SMCs. Regarding the interior cells, as this Reviewer concerned, there are no antibodies against ANO1/TMEM16 which are available for avian specimens. The anti- c-Kit antibody used in this study is what we raised in our hands by spending years (Yagasaki et al., 2021)), in which the antibody was carefully validated in intact guts of chicken embryos by multiple methods including Western Blot analyses, immunostaining, and in situ hybridization. We have attempted several times to perform organoidal whole-mount in situ hybridization for expression of PDGFRα, but we have not succeeded so far. In addition, as explained to the Editor, the very unhealthy condition of purchased eggs these past 7 months did not allow us to continue any further. We are planning to interrogate cell types residing in the central area of the organoid, results of which will be reported in a separate paper in near future.

(2) The key ICC-SMC relationship and physiological interaction seems to arise developmentally, but the mechanisms of this transition are not well defined (Chevalier 2020). To further support the claim that ICC-SMC interactions can be interrogated in this system, this study would benefit from establishing organoids at distinct developmental stages to (a) show that they have unique contractile profiles, and (b) demonstrate that they evolve over time in vitro toward an ICC-driven mechanism.

We agree with these comments. We tried to prepare gut contractile organoids derived from different stages of development, and we had an impression that slightly younger hindguts are available for the organoid preparations. In addition, not only the hindgut, but also midgut and caecum also yield organoids. However, since formed organoids derived from these “non-E15 hindgut” vary substantially in shapes, contraction frequencies/amplitudes etc., we are currently not ready to report these preliminary observations. Instead, we decided to optimize and elaborate in vitro culture conditions by focusing on the E15 hindgut, which turned out to be most stable in our hands. Nevertheless, it is tempting to see how organoid evolves over time during gut development.

(3) This manuscript would be greatly enhanced by a functional examination of the prospective organoid ICCs. For example, the authors could test whether the c-Kit inhibitor Imatinib, which has previously been used to impair ICC differentiation and function in the developing chick gut (Chevalier 2020), has an effect on contractility at different stages.

Following the paper of (Chevalier 2020), we had already conducted similar experiments with Imatinib in the culture with our organoids, but we did not see detectable effects. In that paper, the midgut of younger embryos was used, whereas we used E15 hindgut to prepare organoids. It would be interesting to see if we add Imanitib earlier during organoidal formation, and this is a next step to go.

(4) It is claimed that there is a 690s msec delay in SMC spike relative to ICC spike, however, it is unclear where this average is derived from and whether the organoid calcium trace shown in Figure 4C is representative of the data. The latency quantification should be shown across multiple organoids, and again in the case of carbenoxolone treatment, to better understand the variations in treatment.

We apologize that the first version failed to clearly demonstrate quantitative assessments. In the revised version, we have elaborated quantitative assessments (117 peaks for 14 organoids) (line 216-218). In new Fig. 4D, measured value is 700 msecterraced since as already mentioned in the first version, the time-lapse imaging was performed with 700 msec intervals.

(5) As above, a larger issue is that only single traces are shown for each organoid. This makes it challenging to understand the variance in contractile properties across multiple organoids. While contraction frequencies are shown several times, the manuscript would benefit from additional quantifications, such as rhythm (average wavelength between events) in control and perturbed conditions.

We have substantially elaborated quantitative assessments (please also see our responses to the “Public Review”). In particular, in place of contraction numbers/time, we have plotted “contraction intervals” between two successive peaks (Fig. 2B and others). Actually, we have tried to perform a periodicity analysis of organoid contractions. Unfortunately, no clear value has been obtained, probably because the contractions/Ca²⁺ transitions are not as “regularly periodical” as seen in conventional physics. This led us to perform the peak-interval analysis. Methods to quantify the contraction intervals are carefully explained in the revised version.

(6) The synchronicity observed between ICCs and SMCs within the organoid is interesting, and should be emphasized by making analyses more quantitative so as to understand how consistent and reproducible this phenomenon is across organoids. Moreover, one of the most exciting parts of the study is the synchronicity established between organoids in the hydrogel system, but it is insufficiently quantified. For example, how rapidly is pacemaking synchronization achieved?

As we replied above to (5), and described in the responses to the “Public Review”, we have substantially elaborated quantitative assessments in the revised version. Concerning the synchronicity between ICCs and SMCs, our data explicitly show that as long as the organoid undergoes healthy contraction, they perfectly match their rhythm (Fig. 4) making it difficult to display quantitatively. Instead, to demonstrate such synchronicity more convincingly, we have carefully described the number of peaks and the number of independent organoids we analyzed in each of Figure legends. In the experiments with hydrogels, the time required for two organoids to start/resume synchronous contraction varies greatly. For example, for the experiment shown in new Fig 9F, it takes 1 day to 2 days for cells crawling out of organoids and cover the surface of the hydrogel. In the experiments shown in new Fig. 8, two organoids undergo “pause” before resuming contractions. In the revised version, we have briefly mentioned our notice and speculation that active cell communications take place during this pausing time, (line 282-283 in Result and line 437-439 in Discussion). We agree with this reviewer saying that the pausing time is potentially very interesting. However, it is currently difficult to quantify these phenomena. More elaborate experimental design might be needed.

(7) Smooth muscle layers in vivo are well organized into circular and longitudinal layers. To establish physiological relevance, the authors should demonstrate if these organoids have multiple layers (though it looks like just a single outer layer) and if they show supracellular organization across the organoid.

The immunostaining data suggest that peripherally lining cells are of a single layer, and we assume that they might be aligned in register with contracting direction. However, to clarify these issues, observation with higher resolution would be required.

(8) To further examine whether the organoids contain true functional ICCs, the authors should test whether their calcium transients are impacted by inhibitors of L-type calcium channels, such as nifedipine and nicardipine. These channels have been demonstrated to be important for SMCs but not ICCs, so one might expect to see continued transients in the core ICCs but a loss of them in SMCs (Lee et al., 1999; PMID: 10444456)

We appreciate these comments. We have accordingly conducted new experiments with Nifedipine. Contrary to the expectation, Nifedipine ceases not only organoidal contractions, but also ICC activities (and its resulting synchronization) (new Fig. 7). These findings actually corroborate our model already mentioned in the first version that ICCs receive mechanical feedback from SMC’s contraction to stably maintain their oscillatory rhythm. We believe that the additional findings with Nifedipine have improved the quality of our paper. Concerning the central cells in the organoid, we have additionally used anti-desmin antibody known to mark differentiated SMCs. Desmin signals perfectly overlap with those of aSMA in the peripheral single layer, supporting that the peripheral cells are SMCs and central cells are ICCs. The anti c-Kit antibody used in this study is what we raised in our hands by spending years (Yagasaki et al., 2021)), in which the antibody was carefully validated in intact guts of chicken embryos by multiple methods including Western Blot analyses, immunostaining, and in situ hybridization.

ANO1/TMEM16 are known to stain ICCs in mice. Antibodies against ANO1/TMEM16 available for avian specimens are awaited.

(9) Despite Tuj1+ enteric neurons only making up a small fraction of the organoids, the authors should still functionally test whether they regulate any aspect of contractility by treating organoids with an inhibitor such as tetrodotoxin to rule out a role for them.

Thank you for these advices, which are also raised by other reviewers. We have conducted TTX administration (new Fig. S2C). Changes in contractility by this treatment is not detected, supporting the argument that neural cells/activities are not essential for rhythmic contractions of the organoid (line 178-181).

(10) Finally, the manuscript is written to suggest that the focus of the study is to establish a system to interrogate ICC-SMC interactions in gut physiology and peristalsis. However, the organoids designed in this study are derived from the fetal precursors to the adult cell types. Thus, they might not accurately portray the adult cell physiology. I don't believe that this is a downfall, but rather a strength of the study that should be emphasized. That is, the focus could be shifted toward stressing the power of this new system as a reductionist, self-organizing model to examine the developmental emergence of contractile synchronization in the intestine - in particular that arising through ICC-SMC interactions.

We appreciate these advices. In the revised MS, we are careful so that our findings do not necessarily portray the physiological functions in adult gut.

Minor:

More technical information could be used in the methods:

(1) What concentration of Matrigel is used for coating, and what size were the wells that cells were deposited into?

We have added, “14-mm diameter glass-bottom dishes (Matsunami, D11130H)” and “undiluted Matrigel (Corning, 354248) at 38.5°C for 20 min” (line 471473).

(2) How were organoids transferred to the hydrogels? And were the hydrogels coated?

We have added “Organoids were transferred to the hydrogel using a glass capillary” (line 560-561).

(3) Tests for significance and p values should be added where appropriate (e.g. Figure S3B).

We have added these in Figure legend of new Fig. S3.

Reviewer #3 (Recommendations For The Authors):

This is an exciting study, and while the majority of our comments are minor suggestions to improve the clarity and impact of findings, it would be important to verify the effective disruption of GAP junction function with CBX or 18Beta-GA treatments before concluding they are not required for coordination of contractility and initiation by ICCs. It is possible that sufficient contextual support exists in the literature for the nature of treatments used, but this may need to be conveyed within the manuscript to allay concerns that the results could be explained by ineffective inhibition of GAP junctions.

Thank you very much for these advices. In the revised version, we have newly carried out experiments with dissociated embryonic heart cells cultured in vitro, a model widely used for gap junction studies (Fig. S3D). Both CBX or 18b-GA exert efficient inhibiting activity on contractions of heart cells. We have added the following sentence, “The inhibiting activity of the drugs used here was verified using embryonic heart culture (line 237-239)”.

eLife Assessment

The study presents a comprehensive multi-approach and functional investigation of RBMX2 as a host factor involved in Mycobacterium bovis pathogenesis and its potential role in promoting epithelial-mesenchymal transition and lung cancer progression. The findings are valuable since the possible connection between M. bovis and lung cancer and the underlying mechanisms provides a promising direction for future research. The evidence is solid with methods, data, and analyses broadly supporting the claims, albeit with minor weaknesses that, if addressed, will make the evidence stronger. The study remains of great interest to microbiology, oncology, and drug discovery scientists.

Reviewer #1 (Public review):

Summary:

This manuscript presents a compelling study identifying RBMX2 as a novel host factor upregulated during Mycobacterium bovis infection.

The study demonstrates that RBMX2 plays a role in:

(1) Facilitating M. bovis adhesion, invasion, and survival in epithelial cells.

(2) Disrupting tight junctions and promoting EMT.

(3) Contributing to inflammatory responses and possibly predisposing infected tissue to lung cancer development.

By using a combination of CRISPR-Cas9 library screening, multi-omics, coculture models, and bioinformatics, the authors establish a detailed mechanistic link between M. bovis infection and cancer-related EMT through the p65/MMP-9 signaling axis. Identification of RBMX2 as a bridge between TB infection and EMT is novel.

Strengths:

This topic and data are both novel and significant, expanding the understanding of transcriptomic diversity beyond RBM2 in M. bovis responsive functions.

Weaknesses:

(1) The abstract and introduction sometimes suggest RBMX2 has protective anti-TB functions, yet results show it facilitates pathogen adhesion and survival. The authors need to rephrase claims to avoid contradiction.

(2) While p65/MMP-9 is convincingly implicated, the role of MAPK/p38 and JNK is less clearly resolved.

(3) Metabolomics results are interesting but not integrated deeply into the main EMT narrative.

(4) A key finding and starting point of this study is the upregulation of RBMX2 upon M. bovis infection. However, the authors have only assessed RBMX2 expression at the mRNA level following infection with M. bovis and BCG. To strengthen this conclusion, it is essential to validate RBMX2 expression at the protein level through techniques such as Western blotting or immunofluorescence. This would significantly enhance the credibility and impact of the study's foundational observation.

(5) The manuscript would benefit from a more in-depth discussion of the relationship between tuberculosis (TB) and lung cancer. While the study provides experimental evidence suggesting a link via EMT induction, integrating current literature on the epidemiological and mechanistic connections between chronic TB infection and lung tumorigenesis would provide important context and reinforce the translational relevance of the findings.

Author response:

Public Reviews:

Reviewer #1 (Public review):

Summary:

This manuscript presents a compelling study identifying RBMX2 as a novel host factor upregulated during Mycobacterium bovis infection.

The study demonstrates that RBMX2 plays a role in:

(1) Facilitating M. bovis adhesion, invasion, and survival in epithelial cells.

(2) Disrupting tight junctions and promoting EMT.

(3) Contributing to inflammatory responses and possibly predisposing infected tissue to lung cancer development.

By using a combination of CRISPR-Cas9 library screening, multi-omics, coculture models, and bioinformatics, the authors establish a detailed mechanistic link between M. bovis infection and cancer-related EMT through the p65/MMP-9 signaling axis. Identification of RBMX2 as a bridge between TB infection and EMT is novel.

Strengths:

This topic and data are both novel and significant, expanding the understanding of transcriptomic diversity beyond RBM2 in M. bovis responsive functions.

Weaknesses:

(1) The abstract and introduction sometimes suggest RBMX2 has protective anti-TB functions, yet results show it facilitates pathogen adhesion and survival. The authors need to rephrase claims to avoid contradiction.

We sincerely appreciate the reviewer's valuable feedback regarding the need to clarify RBMX2's role throughout the manuscript. We have carefully revised the text to ensure consistent messaging about RBMX2's function in promoting M. bovis infection. Below we detail the specific modifications made:

(1) Introduction Revisions:

Changed "The objective of this study was to elucidate the correlation between host genes and the susceptibility of M.bovis infection" to "The objective of this study was to identify host factors that promote susceptibility to M.bovis infection"

Revised "RBMX2 polyclonal and monoclonal cell lines exhibited favorable phenotypes" to "RBMX2 knockout cell lines showed reduced bacterial survival"

Replaced "The immune regulatory mechanism of RBMX2" with "The role of RBMX2 in facilitating M.bovis immune evasion"

(2) Results Revisions:

Modified "RBMX2 fails to affect cell morphology and the ability to proliferate and promotes M.bovis infection" to "RBMX2 does not alter cell viability but significantly enhances M.bovis infection"

Strengthened conclusion in Figure 4: "RBMX2 actively disrupts tight junctions to facilitate bacterial invasion"

(3) Discussion Revisions:

Revised screening description: "We screened host factors affecting M.bovis susceptibility and identified RBMX2 as a key promoter of infection"

Strengthened concluding statement: "In summary, RBMX2 drives TB pathogenesis by compromising epithelial barriers and inducing EMT"

These targeted revisions ensure that:

All sections consistently present RBMX2 as promoting infection; the language aligns with our experimental finding; potential protective interpretations have been eliminated. We believe these modifications have successfully addressed the reviewer's concern while maintaining the manuscript's original structure and scientific content. We appreciate the opportunity to improve our manuscript and thank the reviewer for this constructive suggestion.

(2) >While p65/MMP-9 is convincingly implicated, the role of MAPK/p38 and JNK is less clearly resolved.

We sincerely appreciate the reviewer's insightful comment regarding the roles of MAPK/p38 and JNK in our study. Our experimental data clearly demonstrated that RBMX2 knockout significantly reduced phosphorylation levels of p65, p38, and JNK (Fig. 5A), indicating potential involvement of all three pathways in RBMX2-mediated regulation.

Through systematic functional validation, we obtained several important findings:

In pathway inhibition experiments, p65 activation (PMA treatment) showed the most dramatic effects on both tight junction disruption (ZO-1, OCLN reduction) and EMT marker regulation (E-cadherin downregulation, N-cadherin upregulation);

p38 activation (ML141 treatment) exhibited moderate effects on these processes;

JNK activation (Anisomycin treatment) displayed minimal impact.

Most conclusively, siRNA-mediated silencing of p65 alone was sufficient to:

Restore epithelial barrier function

Reverse EMT marker expression

Reduce bacterial adhesion and invasion

These results establish a clear hierarchy in pathway importance: p65 serves as the primary mediator of RBMX2's effects, while p38 plays a secondary role and JNK appears non-essential under our experimental conditions. We have now clarified this relationship in the revised Discussion section to strengthen this conclusion.

This refined understanding of pathway hierarchy provides important mechanistic insights while maintaining consistency with all our experimental data. We thank the reviewer for this valuable suggestion that helped improve our manuscript.

(3) Metabolomics results are interesting but not integrated deeply into the main EMT narrative.

Thank you for this constructive suggestion. In this article, we detected the metabolome of RBMX2 knockout and wild-type cells after Mycobacterium bovis infection, which mainly served as supporting evidence for our EMT model. However, we did not conduct an in-depth discussion of these findings. We have now added a detailed discussion of this section to further support our EMT model.

ADD:Meanwhile, metabolic pathways enriched after RBMX2 deletion, such as nucleotide metabolism, nucleotide sugar synthesis, and pentose interconversion, primarily support cell proliferation and migration during EMT by providing energy precursors, regulating glycosylation modifications, and maintaining redox balance; cofactor synthesis and amino sugar metabolism participate in EMT regulation through influencing metabolic remodeling and extracellular matrix interactions; chemokine and cGMP-PKG signaling pathways may further mediate inflammatory responses and cytoskeletal rearrangements, collectively promoting the EMT process.

(4) A key finding and starting point of this study is the upregulation of RBMX2 upon M. bovis infection. However, the authors have only assessed RBMX2 expression at the mRNA level following infection with M. bovis and BCG. To strengthen this conclusion, it is essential to validate RBMX2 expression at the protein level through techniques such as Western blotting or immunofluorescence. This would significantly enhance the credibility and impact of the study's foundational observation.

Thank you for your comment. We have supplemented the experiments in this part and found that Mycobacterium bovis infection can significantly enhance the expression level of RBMX2 protein.

(5) The manuscript would benefit from a more in-depth discussion of the relationship between tuberculosis (TB) and lung cancer. While the study provides experimental evidence suggesting a link via EMT induction, integrating current literature on the epidemiological and mechanistic connections between chronic TB infection and lung tumorigenesis would provide important context and reinforce the translational relevance of the findings.

We sincerely appreciate the valuable comments from the reviewer. We fully agree with your suggestion to further explore the relationship between tuberculosis (TB) and lung cancer. In the revised manuscript, we will add a new paragraph in the Discussion section to systematically integrate the current literature on the epidemiological and mechanistic links between chronic tuberculosis infection and lung cancer development, including the potential bridging roles of chronic inflammation, tissue damage repair, immune microenvironment remodeling, and the epithelial-mesenchymal transition (EMT) pathway. This addition will help more comprehensively interpret the clinical implications of the observed EMT activation in the context of our study, thereby enhancing the biological plausibility and clinical translational value of our findings.

ADD:There is growing epidemiological evidence suggesting that chronic TB infection represents a potential risk factor for the development of lung cancer. Studies have shown that individuals with a history of TB exhibit a significantly increased risk of lung cancer, particularly in areas of the lung with pre-existing fibrotic scars, indicating that chronic inflammation, tissue repair, and immune microenvironment remodeling may collectively contribute to malignant transformation 74. Moreover, EMT not only endows epithelial cells with mesenchymal features that enhance migratory and invasive capacity but is also associated with the acquisition of cancer stem cell-like properties and therapeutic resistance 75. Therefore, EMT may serve as a crucial molecular link connecting chronic TB infection with the malignant transformation of lung epithelial cells, warranting further investigation in the intersection of infection and tumorigenesis.

Reviewer #2 (Public review):

Summary:

I am not familiar with cancer biology, so my review mainly focuses on the infection part of the manuscript. Wang et al identified an RNA-binding protein RBMX2 that links the Mycobacterium bovis infection to the epithelial-Mesenchymal transition and lung cancer progression. Upon mycobacterium infection, the expression of RBMX2 was moderately increased in multiple bovine and human cell lines, as well as bovine lung and liver tissues. Using global approaches, including RNA-seq and proteomics, the authors identified differential gene expression caused by the RBMX2 knockout during M. bovis infection. Knockout of RBMX2 led to significant upregulations of tight-junction related genes such as CLDN-5, OCLN, ZO-1, whereas M. bovis infection affects the integrity of epithelial cell tight junctions and inflammatory responses. This study establishes that RBMX2 is an important host factor that modulates the infection process of M. bovis.

Strengths:

(1) This study tested multiple types of bovine and human cells, including macrophages, epithelial cells, and clinical tissues at multiple timepoints, and firmly confirmed the induced expression of RBMX2 upon M. bovis infection.

(2) The authors have generated the monoclonal RBMX2 knockout cell lines and comprehensively characterized the RBMX2-dependent gene expression changes using a combination of global omics approaches. The study has validated the impact of RBMX2 knockout on the tight-junction pathway and on the M. bovis infection, establishing RBMX2 as a crucial host factor.

Weaknesses:

(1) The RBMX2 was only moderately induced (less than 2-fold) upon M. bovis infection, arguing its contribution may be small. Its value as a therapeutic target is not justified. How RBMX2 was activated by M. bovis infection was unclear.

Thank you for your valuable and constructive comments. In this study, we primarily utilized the CRISPR whole-genome screening approach to identify key factors involved in bovine tuberculosis infection. Through four rounds of screening using a whole-genome knockout cell line of bovine lung epithelial cells infected with Mycobacterium bovis, we identified RBMX2 as a critical factor.

Although the transcriptional level change of RBMX2 was less than two-fold, following the suggestion of Reviewer 1, we examined its expression at the protein level, where the change was more pronounced, and we have added these results to the manuscript.

Regarding the mechanism by which RBMX2 is activated upon M. bovis infection, we previously screened for interacting proteins using a Mycobacterium tuberculosis secreted and membrane protein library, but unfortunately, we did not identify any direct interacting proteins from M. tuberculosis (https://doi.org/10.1093/nar/gkx1173).

(2) Although multiple time points have been included in the study, most analyses lack temporal resolution. It is difficult to appreciate the impact/consequence of M. bovis infection on the analyzed pathways and processes.

We appreciate the valuable comments from the reviewers. Although our study included multiple time points post-infection, in our experimental design we focused on different biological processes and phenotypes at distinct time points:

During the early phase (e.g., 2 hours post-infection), we focused on barrier phenotypes; during the intermediate phase (e.g., 24 hours post-infection), we concentrated more on pathway activation and EMT phenotypes;

And during the later phase (e.g., 48–72 hours post-infection), we focused more on cell death phenotypes, which were validated in another FII article (https://doi.org/10.3389/fimmu.2024.1431207).

We also examined the impact of varying infection durations on RBMX2 knockout EBL cellular lines via GO analysis. At 0 hpi, genes were primarily related to the pathways of cell junctions, extracellular regions, and cell junction organization. At 24 hpi, genes were mainly associated with pathways of the basement membrane, cell adhesion, integrin binding and cell migration By 48 hpi, genes were annotated into epithelial cell differentiation and were negatively regulated during epithelial cell proliferation. This indicated that RBMX2 can regulate cellular connectivity throughout the stages of M. bovis infection.

For KEGG analysis, genes linked to the MAPK signaling pathway, chemical carcinogen-DNA adducts, and chemical carcinogen-receptor activation were observed at 0 hpi. At 24 hpi, significant enrichment was found in the ECM-receptor interaction, PI3K-Akt signaling pathway, and focal adhesion. Upon enrichment analysis at 48 hpi, significant enrichment was noted in the TGF-beta signaling pathway, transcriptional misregulation in cancer, microRNAs in cancer, small cell lung cancer, and p53 signaling pathway.

Reviewer #3 (Public review):

Summary:

This study investigates the role of the host protein RBMX2 in regulating the response to Mycobacterium bovis infection and its connection to epithelial-mesenchymal transition (EMT), a key pathway in cancer progression. Using bovine and human cell models, the authors have wisely shown that RBMX2 expression is upregulated following M. bovis infection and promotes bacterial adhesion, invasion, and survival by disrupting epithelial tight junctions via the p65/MMP-9 signaling pathway. They also demonstrate that RBMX2 facilitates EMT and is overexpressed in human lung cancers, suggesting a potential link between chronic infection and tumor progression. The study highlights RBMX2 as a novel host factor that could serve as a therapeutic target for both TB pathogenesis and infection-related cancer risk.

Strengths:

The major strengths lie in its multi-omics integration (transcriptomics, proteomics, metabolomics) to map RBMX2's impact on host pathways, combined with rigorous functional assays (knockout/knockdown, adhesion/invasion, barrier tests) that establish causality through the p65/MMP-9 axis. Validation across bovine and human cell models and in clinical tissue samples enhances translational relevance. Finally, identifying RBMX2 as a novel regulator linking mycobacterial infection to EMT and cancer progression opens exciting therapeutic avenues.

Weaknesses:

Although it's a solid study, there are a few weaknesses noted below.

(1) In the transcriptomics analysis, the authors performed (GO/KEGG) to explore biological functions. Did they perform the search locally or globally? If the search was performed with a global reference, then I would recommend doing a local search. That would give more relevant results. What is the logic behind highlighting some of the enriched pathways (in red), and how are they relevant to the current study?

We appreciate the reviewer's thoughtful questions regarding our transcriptomic analysis. In this study, we employed a localized enrichment approach focusing specifically on gene expression profiles from our bovine lung epithelial cell system. This cell-type-specific analysis provides more biologically relevant results than global database searches alone.

Regarding the highlighted pathways, these represent:

(1) Temporally significant pathways showing strongest enrichment at each stage:

• 0h: Cell junction organization (immediate barrier response)

• 24h: ECM-receptor interaction (early EMT initiation)

• 48h: TGF-β signaling (chronic remodeling)

(2) Mechanistically linked to our core findings about RBMX2's role in:

• Epithelial barrier disruption

• Mesenchymal transition

• Chronic infection outcomes

We selected these particular pathways because they:

(1) Showed the most statistically significant changes (FDR <0.001)

(2) Formed a coherent biological narrative across infection stages

(3) Were independently validated in our functional assays

This targeted approach allows us to focus on the most infection-relevant pathways while maintaining statistical rigor.

(2) While the authors show that RBMX2 expression correlates with EMT-related gene expression and barrier dysfunction, the evidence for direct association remains limited in this study. How does RBMX2 activate p65? Does it bind directly to p65 or modulate any upstream kinases? Could ChIP-seq or CLIP-seq provide further evidence for direct RNA or DNA targets of RBMX2 that drive EMT or NF-κB signaling?

We sincerely appreciate the reviewer's in-depth questions regarding the mechanisms by which RBMX2 activates p65 and its association with EMT. Although the molecular mechanism remains to be fully elucidated, our study has provided experimental evidence supporting a direct regulatory relationship between RBMX2 and the p65 subunit of the NF-κB pathway. Specifically, we investigated whether the transcription factor p65 could directly bind to the promoter region of RBMX2 using CHIP experiments. The results demonstrated that the transcription factor p65 can physically bind to the RBMX2 region.

Furthermore, dual-luciferase reporter assays were conducted, showing that p65 significantly enhances the transcriptional activity of the RBMX2 promoter, indicating a direct regulatory effect of RBMX2 on p65 expression.

These findings support our hypothesis that RBMX2 activates the NF-κB signaling pathway through direct interaction with the p65 protein, thereby participating in the regulation of EMT progression and barrier function.

In our subsequent work papers, we will also employ experiments such as CLIP to further investigate the specific mechanisms through which RBMX2 exerts its regulatory functions.

(3) The manuscript suggests that RBMX2 enhances adhesion/invasion of several bacterial species (e.g., E. coli, Salmonella), not just M. bovis. This raises questions about the specificity of RBMX2's role in Mycobacterium-specific pathogenesis. Is RBMX2 a general epithelial barrier regulator or does it exhibit preferential effects in mycobacterial infection contexts? How does this generality affect its potential as a TB-specific therapeutic target?

Thank you for your valuable comments. When we initially designed this experiment, we were interested in whether the RBMX2 knockout cell line could confer effective resistance not only against Mycobacterium bovis but also against Gram-negative and Gram-positive bacteria. Surprisingly, we indeed observed resistance to the invasion of these pathogens, albeit weaker compared to that against Mycobacterium bovis.

Nevertheless, we believe these findings merit publication in eLife. Moreover, RBMX2 knockout does not affect the phenotype of epithelial barrier disruption under normal conditions; its significant regulatory effect on barrier function is only evident upon infection with Mycobacterium bovis.

Importantly, during our genome-wide knockout library screening, RBMX2 was not identified in the screening models for Salmonella or Escherichia coli, but was consistently detected across multiple rounds of screening in the Mycobacterium bovis model.

(4) The quality of the figures is very poor. High-resolution images should be provided.

Thank you for your feedback; we provided higher-resolution images.

(5) The methods are not very descriptive, particularly the omics section.

Thank you for your comments; we have revised the description of the sequencing section.

(6) The manuscript is too dense, with extensive multi-omics data (transcriptomics, proteomics, metabolomics) but relatively little mechanistic integration. The authors should have focused on the key mechanistic pathways in the figures. Improving the narratives in the Results and Discussion section could help readers follow the logic of the experimental design and conclusions.

Thank you for your valuable comments. We have streamlined the figures and revised the description of the results section accordingly.

Reviewer #2 (Public review):

Summary:

I am not familiar with cancer biology, so my review mainly focuses on the infection part of the manuscript. Wang et al identified an RNA-binding protein RBMX2 that links the Mycobacterium bovis infection to the epithelial-Mesenchymal transition and lung cancer progression. Upon mycobacterium infection, the expression of RBMX2 was moderately increased in multiple bovine and human cell lines, as well as bovine lung and liver tissues. Using global approaches, including RNA-seq and proteomics, the authors identified differential gene expression caused by the RBMX2 knockout during M. bovis infection. Knockout of RBMX2 led to significant upregulations of tight-junction related genes such as CLDN-5, OCLN, ZO-1, whereas M. bovis infection affects the integrity of epithelial cell tight junctions and inflammatory responses. This study establishes that RBMX2 is an important host factor that modulates the infection process of M. bovis.

Strengths:

(1) This study tested multiple types of bovine and human cells, including macrophages, epithelial cells, and clinical tissues at multiple timepoints, and firmly confirmed the induced expression of RBMX2 upon M. bovis infection.

(2) The authors have generated the monoclonal RBMX2 knockout cell lines and comprehensively characterized the RBMX2-dependent gene expression changes using a combination of global omics approaches. The study has validated the impact of RBMX2 knockout on the tight-junction pathway and on the M. bovis infection, establishing RBMX2 as a crucial host factor.

Weaknesses:

(1) The RBMX2 was only moderately induced (less than 2-fold) upon M. bovis infection, arguing its contribution may be small. Its value as a therapeutic target is not justified. How RBMX2 was activated by M. bovis infection was unclear.

(2) Although multiple time points have been included in the study, most analyses lack temporal resolution. It is difficult to appreciate the impact/consequence of M. bovis infection on the analyzed pathways and processes.

Reviewer #3 (Public review):

Summary:

This study investigates the role of the host protein RBMX2 in regulating the response to Mycobacterium bovis infection and its connection to epithelial-mesenchymal transition (EMT), a key pathway in cancer progression. Using bovine and human cell models, the authors have wisely shown that RBMX2 expression is upregulated following M. bovis infection and promotes bacterial adhesion, invasion, and survival by disrupting epithelial tight junctions via the p65/MMP-9 signaling pathway. They also demonstrate that RBMX2 facilitates EMT and is overexpressed in human lung cancers, suggesting a potential link between chronic infection and tumor progression. The study highlights RBMX2 as a novel host factor that could serve as a therapeutic target for both TB pathogenesis and infection-related cancer risk.

Strengths:

The major strengths lie in its multi-omics integration (transcriptomics, proteomics, metabolomics) to map RBMX2's impact on host pathways, combined with rigorous functional assays (knockout/knockdown, adhesion/invasion, barrier tests) that establish causality through the p65/MMP-9 axis. Validation across bovine and human cell models and in clinical tissue samples enhances translational relevance. Finally, identifying RBMX2 as a novel regulator linking mycobacterial infection to EMT and cancer progression opens exciting therapeutic avenues.

Weaknesses:

Although it's a solid study, there are a few weaknesses noted below.

(1) In the transcriptomics analysis, the authors performed (GO/KEGG) to explore biological functions. Did they perform the search locally or globally? If the search was performed with a global reference, then I would recommend doing a local search. That would give more relevant results. What is the logic behind highlighting some of the enriched pathways (in red), and how are they relevant to the current study?

(2) While the authors show that RBMX2 expression correlates with EMT-related gene expression and barrier dysfunction, the evidence for direct association remains limited in this study. How does RBMX2 activate p65? Does it bind directly to p65 or modulate any upstream kinases? Could ChIP-seq or CLIP-seq provide further evidence for direct RNA or DNA targets of RBMX2 that drive EMT or NF-κB signaling?

(3) The manuscript suggests that RBMX2 enhances adhesion/invasion of several bacterial species (e.g., E. coli, Salmonella), not just M. bovis. This raises questions about the specificity of RBMX2's role in Mycobacterium-specific pathogenesis. Is RBMX2 a general epithelial barrier regulator or does it exhibit preferential effects in mycobacterial infection contexts? How does this generality affect its potential as a TB-specific therapeutic target?

(4) The quality of the figures is very poor. High-resolution images should be provided.

(5) The methods are not very descriptive, particularly the omics section.

(6) The manuscript is too dense, with extensive multi-omics data (transcriptomics, proteomics, metabolomics) but relatively little mechanistic integration. The authors should have focused on the key mechanistic pathways in the figures. Improving the narratives in the Results and Discussion section could help readers follow the logic of the experimental design and conclusions.

eLife Assessment

This work describes an inference technique for extracting information about relative contributions of excitatory and inhibitory synaptic drive onto single neurons in neural networks. The electrophysiological techniques and results are of high quality, and the analytical work is novel and potentially powerful, yet with several untested assumptions underlying the approach. This is nevertheless solid work that will be valuable to neuroscience labs interested in exploring alternative approaches to studies of integrated synaptic connectivity.

Reviewer #2 (Public review):

Summary:

By measuring intracellular changes in membrane voltage from a single neuron of the medulla the authors attempted to develop a method for determining the balance of excitatory and inhibitory synaptic drive onto a single neuron.

Strengths:

This data-driven approach to explore neural circuits is described well in this study and could be valuable in identifying microcircuits that generate rhythms. Importantly, perhaps, this inference method could enable microcircuits to be studied without the need for time-consuming anatomical tracing or other more involved electrophysiological techniques. Therefore, I can see the value in developing an approach of this type.

Weaknesses:

The implications of several assumptions associated with this inference technique have been considered by the authors.

Most importantly, it is my understanding that this approach assumes a linear I-V when extracting information about the excitatory and inhibitory synaptic conductances (see equations 6 and 7). In Figure 6, the authors explore the impact of varying the reversal potential for the extraction of information about synaptic drive, but this still assumes that the underlying conductance is linear. However, open rectification will be a feature of any conductance generated by asymmetric distributions of ions (see the GHK current equation) and will therefore be a particular issue for the inhibition resulting from asymmetrical Cl- ion gradients across GABA-A receptors as well as the K+ conductance indirectly activated by GABA-B receptor activation. The mixed cation conductance that underlies most synaptic excitation will also generate a non-linear I-V relationship due to the inward rectification associated with polyamine block of AMPA receptors. The authors present evidence that the I-V relationship is linear over most of the voltage range examined, and this is a helpful addition. The authors have discussed the absence of active conductances contributing to the I-V, but I still wonder how the extraction of information concerning the excitatory and inhibitory conductances relies on the assumption of a linear I-V for these conductances.

This approach has similarities to earlier studies undertaken in the visual cortex that estimated the excitatory and inhibitory synaptic conductance changes that contributed to membrane voltage changes during receptive field stimulation. However, these approaches also involved the recording of transmembrane current changes during visual stimulation that were undertaken in voltage-clamp at various command voltages to estimate the underlying conductance changes. Molkov et al have attempted to essentially deconvolve the underlying conductance changes without this information and I am concerned that this simply may not be possible. However, I appreciate the efforts taken by the authors to address this issue.

The current balance equation (1) cited in this study is based upon the parallel conductance model developed by Hodgkin & Huxley. One key element of the HH equations is the inclusion of an estimate of the capacitive current generated due to the change in voltage across the membrane capacitance. While the present study considers the impact of membrane capacitance, a deeper discussion on how variations in capacitance across different neuron types might affect inference accuracy would be useful. Differences in capacitance could introduce variability in inferred conductances, potentially influencing model predictions.

Studies using acute slicing preparations to examine circuit effects have often been limited to the study of small microcircuits, especially feedforward and feedback interneuron circuits. It is widely accepted that any information gained from this approach will always be compromised by the absence of patterned afferent input from outside the brain region being studied. In this study, descending control from the Pons and the neocortex will not be contributing much to the synaptic drive and ascending information from respiratory muscles will also be absent completely. This may not have been such a major concern if this study had been limited to demonstrating the feasibility of a methodological approach. However, this limitation does need to be considered when using an approach of this type to speculate on the prevalence of specific circuit motifs within the medulla (Figure 4). Therefore, I would argue that some discussion of this limitation should be included in this manuscript.

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public review):

Summary:

The study aims to create a comprehensive repository about the changes in protein abundance and their modification during oocyte maturation in Xenopus laevis.

Strengths:

The results contribute meaningfully to the field.

Weaknesses:

The manuscript could have benefitted from more comprehensive analyses and clearer writing. Nonetheless, the key findings are robust and offer a valuable resource for the scientific community.

We would like to thank the reviewer for his/her positive feedback on our article. The public review points out that "The manuscript could have benefitted from more comprehensive analyses and clearer writing." We have rewritten several sections and provided more detailed explanations of the analysis and interpretation of some data (see below for details). We have also followed all of the reviewer's recommendations, some of which specifically highlighted areas lacking clarity. We would also like to thank the reviewer for pointing out some errors, for which we apologize, and which have now been corrected. We sincerely appreciate the reviewer's thorough work, as it has greatly enhanced the clarity and precision of the manuscript.

Reviewer #2 (Public review):

Summary:

The authors analyzed Xenopus oocytes at different stages of meiosis using quantitative phosphoproteomics. Their advanced methods and analyses revealed changes in protein abundances and phosphorylation states to an unprecedented depth and quantitative detail. In the manuscript they provide an excellent interpretation of these findings putting them in the context of past literature in Xenopus as well as in other model systems.

Strengths:

High quality data, careful and detailed analysis, outstanding interpretation in the context of the large body of the literature.

Weaknesses:

Merely a resource, none of the findings are tested in functional experiments.

I am very impressed by the quality of the data and the careful and detailed interpretation of the findings. In this form the manuscript will be an excellent resource to the cell division community in general, and it presents a very large number of hypotheses that can be tested in future experiments. Xenopus has been and still is a popular and powerful model system that led to critical discoveries around countless cellular processes, including the spindle, nuclear envelope, translational regulation, just to name a few. This also includes a huge body of literature on the cell cycle describing its phosphoregulation. It is indeed somewhat frustrating to see that these earlier studies using phosphomutants and phospho-antibodies were just scratching the surface. The phosphoproteomics analysis presented here reveals much more extensive and much more dynamic changes in phosphorylation states. Thereby, in my opinion, this manuscript opens a completely new chapter in this line of research, setting the stage for more systematic future studies.

We thank the reviewer for his/her extremely positive comments. The public review points out that "none of the findings are tested in functional experiments." This is entirely accurate. We focused our work on obtaining the highest quality proteomic and phosphoproteomic data possible, and then sought to highlight these data by connecting them with existing functional data from the literature. This approach has opened up research avenues with enormous, previously unforeseen potential, in a wide range of biological fields (cell cycle, meiosis, oogenesis, embryonic development, cell biology, cellular physiology, signaling, evolution, etc.). We chose not to delay publication by experimentally investigating the narrow area in which we are specialists (meiotic maturation), while our data offer a vast array of research opportunities across various fields. Our goal was, therefore, to present this extensive dataset as a resource for different scientific communities, who can explore their specific biological questions using our data. This is why we submitted our article to the "Repository" section of eLife. Nevertheless, in the context of the comparative analysis of the mouse and Xenopus phosphoproteomes performed at the reviewer’s request, we felt it was important to complement this new section with functional experiments that not only validate the proteomic data but also provide new insights into certain proteins and their regulation by Cdk1 (new paragraph lines 824-860 and new Figure 9).

We are also grateful to the reviewer for the recommendation to improve the manuscript by including more comparisons between our Xenopus data and those from other systems. We have followed this suggestion (see below), which has significantly enriched the article (new paragraph lines 824-860 and new Figure 9).

Reviewer #3 (Public review):

Summary:

The authors performed time-resolved proteomics and phospho-proteomics in Xenopus oocytes from prophase I through the MII arrest of the unfertilized egg. The data contains protein abundance and phosphorylation sites of a large number set of proteins at different stages of oocyte maturation. The large sets of the data are of high quality. In addition, the authors discussed several key pathways critical for the maturation. The data is very useful for the researchers not only researchers in Xenopus oocytes but also those in oocyte biology in other organisms.

Strengths:

The data of proteomics and phospho-proteomics in Xenopus oocyte maturation is very useful for future studies to understand molecular networks in oocyte maturation.

Weaknesses:

Although the authors offered molecular pathways of the phosphorylation in the translation, protein degradation, cell cycle regulation, and chromosome segregation. The author did not check the validity of the molecular pathways based on their proteomic data by the experimentation.

We thank the reviewer for his/her positive comments. The public review points out that "The author did not check the validity of the molecular pathways based on their proteomic data by the experimentation." This is entirely accurate. We focused our work on obtaining the highest quality proteomic and phosphoproteomic data possible, and then sought to highlight these data by connecting them with existing functional data from the literature. This approach has opened up research avenues with enormous, previously unforeseen potential, in a wide range of biological fields (cell cycle, meiosis, oogenesis, embryonic development, cell biology, cellular physiology, signaling, evolution, etc.). We chose not to delay publication by experimentally investigating the very narrow area in which we are specialists (meiotic maturation), while our data offer a vast array of research opportunities across various fields. Our goal was, therefore, to present this extensive dataset as a resource for different scientific communities, who can explore their specific biological questions using our data. This is why we submitted our article to the "Repository" section of eLife. Nevertheless, in the context of the comparative analysis of the mouse and Xenopus phosphoproteomes performed at the reviewer’s request, we felt it was important to complement this new section with functional experiments that not only validate the proteomic data but also provide new insights into certain proteins and their regulation by Cdk1 (new paragraph lines 824-860 and new Figure 9).

We have also followed all of the reviewer's recommendations and thank him/her, as the suggestions have significantly enhanced the manuscript.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

(1) Fig. 1 -> In the Figure legend "mPRβ" is called "mPRb". In the Figure, it is indicated that PKA substrates are always activated by the phosphorylation. As the relevant substrates and the mode-of-action of the Arpp19 phosphorylation are not clear at the moment, this seems to be preliminary. It could for example also be conceivable that PKA phosphorylation inhibits a translation activator. In addition, the PG-dependent translation of RINGO/Speedy should be included in the model.

We fully agree with the reviewer. PKA substrates can either be activators of the Cdk1 activation pathway, which are inhibited by phosphorylation by PKA, or repressors of the same pathway, which are activated by phosphorylation by PKA. This is now illustrated in the new Fig. 1. In addition, we have also included RINGO/Speedy in the model and in the text (lines 78-79) and corrected "mPRb" in the legend.

(2) Lane 51-52 -> it is questionable if the meiotic divisions can be called "embryonic processes"

We agree with the reviewer comment, and we have removed the word “embryonic”.

(3) Lane 53 and lane 106-107 -> recent data have indicated that transcription already starts during cell cycle 12 and 13 in most cells (e.g. Blitz and Cho: Control of zygotic genome activation in Xenopus (2021))

We apologize for this mistake. The text has been corrected and the reference added (lines 53 and 107).

(4) Lane 61-62 -> "MI" and "MII" are given as abbreviation for "first and second meiotic spindle"

The text has been clarified to explain that MI is referred to metaphase I and MII stands for metaphase II (lines 61-64).

(%) Lane 131-132 -> "single-cell" is mentioned redundantly in this sentence.

The sentence has been corrected (lines 131-132).

(6) Fig. 2B -> it is not explained what is plotted as "Average levels" on the x-Axis. Is it the average of expression over all samples or at a given time point? Are the values given as a concentration or are the values normalized? If so, how were they normalized?

We agree with the reviewer comment that “Average levels” may have been unclear. In the new Fig. 2B, we have re-plotted the graph using the average protein concentration during meiosis, measured as described in the Methods section.

(7) In Fig. 2-supplement 3E -> from the descriptions it is not entirely clear to me what the difference to the data in Fig. 2B is?

We thank the reviewer for his/her question regarding the relationship between the data in Fig. 2B and Fig. 2-supplement 3E. We confirm that the raw data visualized in Fig. 2-supplement 3E are the same as those in Fig. 2B. However, in Fig. 2-supplement 3E, the data are color-coded differently to highlight the number of proteins whose concentrations change during meiotic divisions, based on the threshold adopted. The legend of Fig. 2-supplement 3E has been modified to clarify this point.

(8) Lane 225-226 -> Kifc1 is a minus-end directed motor

This mistake has been corrected (lines 232-233).

(9) Lane 271 -> Serbp1, here mentioned to be involved in stabilization of mRNAs, has also been implicated in the regulation of ribosomes (e.g. Leesch et al. 2023). Regarding the overall topic of this manuscript, this could be mentioned as well.

We agree with the referee that the important role of Serbp1 in the control of ribosome hibernation needs to be mentioned. We have included this point in the revised manuscript together with the reference (lines 277-279).

(10) Lane 360-363 -> it is mentioned that APPL1 and Akt2 act "to induce meiosis". Furthermore, in the Nader et al. 2020 paper, Akt2 phosphorylation is reported to happen within 30min after PG treatment. In the present work, they only seem to get phosphorylated when Cdk1 is activated. Is there an explanation for this discrepancy?

Indeed, Nader et al. (2020) indicate that Akt2 is phosphorylated on Ser473 (actually, they should have mentioned Ser474, which is the phosphorylated residue on Akt2; Ser473 corresponds to the numbering of Akt1) between 5 and 30 minutes post-Pg, which supports their hypothesis of an early role for this kinase. However, these conclusions should be taken with caution, considering that their functional experiment using antisense against Akt2 depletes only 25% of the protein, the antibody used to visualize Akt2 phosphorylation also recognizes phosphorylated Akt1 and Akt3, and they did not analyze phosphorylation of the protein after 30 minutes. Therefore, we cannot determine whether the level observed at 30 minutes represents a maximum or if it is just the onset of the phosphorylation that peaks later, possibly after activation of Cdk1, for example.

Regarding our measurements: we clearly observe phosphorylation of Akt2 following Cdk1 activation on Ser131. We did not detect Akt2 phosphorylation on Ser474, but since our measurements started 1 hour post-Pg, this protein may have returned to a dephosphorylated state on Ser474.

Therefore, the observations of Nader et al. and ours involve different residues and different phosphorylation kinetics, Nader et al. limiting their analysis to the first 30 minutes, whereas we started at 1 hour.

We have revised the manuscript text to make these aspects clearer (lines 387-392).

(11) Fig. 3B -> it could be made clearer in the Figure that all these sites belong to class I

A title “Class I proteins” has been added in Fig. 3B to clarify it.

(12) Lane 433-434 -> the authors write that the proteomic data of this study confirm that PATL1 is accumulating during meiotic maturation. However, in Fig. 2B PATL1 is not among the significantly enriched proteins.

We apologize for this error. Indeed, PATL1 protein is not significantly enriched. The text has been corrected (lines 461-465).

(13) Fig. 4B -> Zar2 is color-coded to increase in abundance. This is clearly different to published results and what is shown in Fig. 2B of this manuscript.

Indeed, our dataset shows that the quantity of Zar2 decreases. This does not appear anymore in Figure 2B since Zar2 average concentration cannot be estimated. We made an error in the color coding, which has now been corrected in Figure 4B.

(14) Lane 442-444 -> it might be worth mentioning that the interaction between CPEB1 and Maskin, and thus probably its role in regulation of translation, could not be reproduced in other studies (Minshall et al.: CPEB interacts with an ovary-specific eIF4E and 4E-T in early Xenopus oocytes (2007) or Duran-Arque et al.: Comparative analyses of vertebrate CPEB proteins define two subfamilies with coordinated yet distinct functions in post-transcriptional gene regulation (2022)).

This clarification is now mentioned in the text, supported by the two references that have been added (lines 471-477).

(15) Lane 483-485 -> The meaning of these sentences is not entirely clear to me. What exactly is the similarity with the function of Emi1? What does "...binding of Cyclin B1..." mean (binding to which other protein?). What is the similarity between Emi1 and CPEB1/BTG4, both of which are regulators of mRNA stability/polyadenylation?

We apologize if these sentences were unclear. Our intention was to emphasize the central role of ubiquitin ligases in regulating multiple events during meiotic divisions. We used SCF^βTrCP, a wellstudied ubiquitin ligase in Xenopus and mouse oocytes during meiosis, as an example. SCF^βTrCP regulates the degradation of several substrates, including Emi1, Emi2, CPEB1, and Btg4, whose degradation or stabilization is essential for the proper progression of meiosis. Lastly, we highlighted that these regulatory processes, mediated by protein degradation, may be conserved in mitosis, as for example the destruction of Emi1. We have rewritten this paragraph for clarity (lines 513-518).

(16) Lane 521-522 and 572-573 -> the authors write that Myt1 was not detected in their proteome. However, in Fig. 6A they list "pkmyt1" as a class II protein. On Xenbase, "pkmyt1" is the Cdk1 kinase, "Myt1" is a transcription factor, so the authors might have been looking for the wrong protein.

We thank the reviewer for this accurate observation. We have modified the text to correct this error (lines 554 and 607).

(17) Lane 564-565 -> The authors state that Cdk1 activity can be measured by analyzing Cdc27 S428 phosphorylation. However, in vivo the net phosphorylation of a site is always depending on the relevant kinase and phosphatase activities. As S428 is a Cdk1 site, it is not unlikely that it is dephosphorylated by PP2A-B55, which by itself is under the control of Cdk1. Do the authors have direct evidence that the change in phosphorylation of S428 can only be attributed to the changes in Cdk1 activity?

There is evidence in the literature that Cdc27 is dephosphorylated by PP2A (Torres et al., 2010). In Xenopus oocytes, PP2A activity is high during prophase (Lemonnier et al., 2021) and decreases at the time of Cdk1 activation, mediated by the Greatwall-ENSA/Arpp19 system, remaining low until MII (Labbé et al., 2021). Therefore, the period where fluctuations in Cdk1 activity are difficult to assess, from NEBD to MII, corresponds to a phase of inhibited PP2A activity. As a result, the phosphorylation level of Cdc27 reflects primarily the activity of Cdk1. We have added this clarification in the text (lines 597-600).

(18) Fig. 7C and 7D -> in 7C, for Nup35/Nup53 there is a phospho-peptide GIMEVRS(60)PPLHSGG. In Fig. 7D phosphorylation of GVMEMRS(59)PLFSGG is analyzed. Is this the same phosphosite/region of Nup35/Nup53? How can there be a slightly different version of the same peptide in one protein? Are these the L- and S-version of Nup35/Nup53? It is also very surprising that the two phosphosites belong to different classes, class III and class II, respectively.

We thank the reviewer for this observation. The peptides GIMEVRS(60)PPLHSGG and GVMEMRS(59)PLFSGG correspond to the same phosphorylation site in the L and S versions of Xenopus laevis Nup35, respectively. The L version peptide was classified as Class III, while the S version was not assigned to any class due to its high phosphorylation level in prophase, which prevented it from meeting the log₂ fold-change threshold of 1 required by our analysis to detect significant differences.

(19) Table 1 -> second last column is headed "Whur, 2014"

The typo has been corrected.

(20) Fig. 8 -> Why are all the traces starting at t=1h after PG?

The labeling of the graphs in Fig. 8 has been corrected, and the traces now begin at t0.

(21) Lane 754 -> Although a minority, there are also some minus-end directed kinesins, e.g. Kifc1

We agree with the reviewer. We should have mentioned that, in addition to dyneins, some kinesins are minus-end directed motors, especially since one of them, Kifc1, is regulated at the level of its accumulation. We have rephrased the relevant sentences to incorporate this observation (lines 790-793).

(22) Section "Assembly of microtubule spindles and microtubule dynamics" -> Although this section clearly has a strong focus on phosphorylation, it might be worth mentioning again that many regulators of the microtubule spindle, e.g. TXP2, are among the upregulated proteins in Fig. 2B/C

We have already discussed that the protein levels of certain key regulators of the mitotic spindle (Tpx2, PRC1, SSX2IP, Kif11/Eg5 among others) are subject to control during meiotic maturation in a previous chapter “Protein accumulation: the machinery of cell division and DNA replication” (lines 230-239). We agree with the reviewer that this important observation can be mentioned again at the beginning of this chapter on phosphorylation control. We have added a sentence regarding this at the start of the paragraph (lines 774-775).

Reviewer #2 (Recommendations for the authors):

While I find the manuscript excellent and detailed already in its current form, I would appreciate including even more comparisons to other systems. In particular, a similar phosphoproteomics experiment has been performed in starfish oocytes undergoing meiosis (Swartz et al, eLife, 2021), and there are several studies on mitosis of diverse mammalian cells. It would be very exciting to see to what extent changes are conserved.

We thank the reviewer for this recommendation, which we have attempted to follow. We have matched our dataset of mass spectrometry using the the phosphor-occupancy_matlab package, available as part of our code repository (https://github.com/elizabeth-van-itallie) previously described in (Van Itallie et al, 2025). Unfortunately, we were unable to match our dataset with the data from Swartz et al. (2021) on starfish oocyte due to the low sequence conservation. However, we have compared our dataset with the dataset from Sun et al. (2024) on mouse oocyte maturation. We identified a total of 408 conserved phosphorylation sites, which mapped to 320 proteins in Xenopus and 277 in mice (refer to a new paragraph: lines 824-860, new Figure 9, Methods: lines 1011-1032 and 1060-1065, and Appendix 7). The phosphorylation patterns during meiosis showed a significant crossspecies correlation (Pearson r = 0.39, p < 0.0001; see new Figure 9A), demonstrating the evolutionary conservation of phosphoproteomic regulation. Important phosphorylation events, including Plk1 at T201, Gwl at S467, and Erk2 at T188, were upregulated in both species, in line with the activation of the Cdk1 and MAPK signaling cascades (Figure 6B, new Figure 9A-B). We validated several of these phosphorylation sites by western blotting and demonstrated their dependency on Cdk1 activation (new Figure 9C). Together, these findings reinforce the notion that fundamental phospho-regulatory pathways are conserved during oocyte maturation in vertebrates.

Reviewer #3 (Recommendations for the authors):

(1) Page 6, the first paragraph of Results section: Please describe the method on how the authors measured and quantified the proteomes in different stages of Xenopus oocyte maturation briefly. Without the experimental design, it is very hard to evaluate the results in the following paragraphs.

As requested by the reviewer, we added a few sentences describing the method of proteomics and phosphoproteomics measurements in oocytes resuming meiosis (lines 151-158).

(2) In the phospho-proteome, it is better to classify the amino acids for the phosphorylation such as Ser, Thr, and Tyr. Particularly how many tyrosine phosphorylations are in the list.

Our phosphosites dataset contains 80% Ser, 19.9% Thr, and 0.01% Tyr. Phospho-Tyr are slightly less abundant than what has been described in the literature (in most cells “roughly 85-90% of protein phosphorylation happens on Ser, ~10% on Thr, and less than 0.05% on Tyr" after Sharma et al., 2014. The same observation was made regarding the distribution of phosphorylated amino acids in mouse oocytes, where phospho-Tyr abundance is relatively diminished in oocytes compared to mouse organs (Sun et al., 2024). These observations are now reported in the manuscript (lines 309-313).

(3) In class II (Figure 3), when Cdk1 (line 326) is a major kinase, how many phosphorylation sites are a target of Cdk1 (with the Cdk1-motif)? Moreover, do the authors find any other consensus sequences for the phosphorylation? Those are either known or unknown. This information would be useful for the readers.

We thank the reviewer for this valuable comment. To address it, we used the kinase prediction server (https://kinase-library.phosphosite.org/kinase-library/score-site) to analyze Class II phosphosites. These new results are mentioned in lines 340-349 and illustrated in a new Figure (Figure 3—figure supplement 1A). We identified 303 sites predicted to be phosphorylated by Cdk1. Of these, 166 were also predicted as Erk1/2 targets, reflecting the similarity between Cdk1 and Erk1/2 consensus motifs.

Cdk1 substrate phosphorylation is governed by more than just the presence of a consensus sequence. In addition to its preference for the (S/T)P×(K/R) motif, Cdk1/cyclin complexes achieve specificity through docking interactions with short linear motifs (SLiMs) recognized by the cyclin subunit (as LxF motifs)(Loog & Morgan, 2005), and via the Cdk-binding subunits Cks1 or Cks2, which interact with phosphorylated threonine residues in primed substrates (Örd et al, 2019). These mechanisms promote processive multisite phosphorylation and allow Cdk1 to target substrates even at non-canonical sites. Our motif-based analysis captures only part of this complexity and may underestimate the number of true Cdk1 targets.

To further explore kinase involvement across phosphosite classes, we extended the analysis to all clusters and identified the most enriched kinase predictions for each (lines 360-365, new Figure 3— figure supplement 1B). In Class II, the most enriched kinases included Cdk1, Erk2, and Plk1, supporting the conclusions derived from the identification of the phosphosites of this Class. But others such as Cdk2, Cdk3, Cdk5, Cdk16, KIS, JNK1, and JNK3 were also identified.

(4) Figure 3B: Why do the authors show this kind of Table only for Class I, not Classes II-V? It would be informative to show candidate proteins in other classes.

We chose to present the candidate proteins from Class I in a table format because the number of phosphosites (136) was too small to allow a meaningful Gene Ontology (GO) enrichment analysis. Therefore, we manually curated the data and highlighted proteins whose Class I phosphosites are associated with specific biological processes. For Classes II–V, the higher number of phosphosites allowed us to perform GO enrichment analyses. Since several of the enriched processes were shared across different classes, and some proteins have phosphosites in multiple classes, we opted to organize the results by biological processes rather than by class. We agree with the reviewer that it is indeed valuable to highlight interesting proteins with Class II–V phosphosites. We have done so in Figures 4 through 8, using graphical representations instead of tables, in order to make the data more accessible and avoid long tables. Additionally, the Supplementary Figures provide detailed phosphorylation trends for many of the proteins discussed in the main figures.

(5) It would be nice if the authors compare this phospho-proteome in Xenopus oocyte maturation with that in mouse oocyte maturation (Sun et al. 2024) in terms of evolutional conservation of the phospho-proteomes.

We thank the reviewer for this suggestion. As now detailed in the manuscript, we compared our Xenopus phosphoproteome with the dataset from Sun et al. (2024) on mouse oocyte maturation using the the phospho_occupancy_matlab package, available as part of our code repository (https://github.com/elizabeth-van-itallie) previously described in (Van Itallie et al, 2025). We identified 408 conserved phosphorylation sites corresponding to 320 Xenopus and 277 mouse proteins (see new paragraph: lines 824-860, new Figure 9, Methods: lines 1011-1032 and 1060-1065, and Appendix 7). Phosphorylation dynamics across meiosis were significantly correlated between the species (Pearson r = 0.39, p < 0.0001; new Figure 9A), highlighting evolutionary conservation of the phosphoproteomes. Key phosphorylation events such as Plk1 at T201, Gwl at S467, and Erk2 at T188 increased in both species, consistent with activation of the Cdk1 and MAPK pathways (Figure 6B, new Figure 9A–B). We validated experimentally several of these phosphorylation sites by western blot (Erk2, Plk1, Fak1 and Akts1) and demonstrated their dependency on Cdk1 activation (new Figure 9C). Together, these new findings support the conservation of key phospho-regulatory mechanisms across vertebrate oocyte maturation.

Minor points:

(1) Reference lists: Please add Sun et al (2024) shown in line 115.

This important reference has been added (lines 115, 134, 313 and 826).

(2) Figure 1, red arrows for the inhibition: This should be "T" shape for a better understanding of these complicated pathways.

We agree with the reviewer’s remark, and we have modified Figure 1.

(3) Line 236-238: The authors referred to the absence of Cdc6 in oocyte maturation in Xenopus. However, Figure 2C shows that Cdc6 belongs to a list of accumulating proteins with Orc1 and Ocr2 etc. and the authors did not discuss this discrepancy in the text. Please clarity the claim.

We apologize for the unclear wording in our text. The section of the manuscript regarding the pre-RC components may have been misleading. The text has been revised to clarify that Cdc6 was not detected in prophase-arrested oocytes by western blot and that it accumulates during meiotic maturation after MI, enabling oocytes to replicate DNA (lines 243-250).

(4) Line 306: Please add the link to phosphosite.org.

The link has been added (line 319).

eLife Assessment

This important paper describes a comprehensive quantitative phospho-proteomic analysis of the meiotic progression of Xenopus oocytes. Using time-resolved proteomic analyses, the authors provide insights into changes in protein levels and phosphorylation states to an unprecedented depth, quality, and quantitative detail. The key findings are compelling and offer a helpful resource for the scientific community.

Reviewer #1 (Public review):

In the revised version of the manuscript, the authors have adequately addressed all our concerns. The authors should spell check their manuscript, e.g., correct phosphor-site to phospho-site etc.

Summary:

The study aims to create a comprehensive repository about the changes in protein abundance and their modification during oocyte maturation in Xenopus laevis.

Reviewer #2 (Public review):

Summary:

The authors analyzed Xenopus oocytes at different stages of meiosis using quantitative phosphoproteomics. Their advanced methods and analyses revealed changes in protein abundances and phosphorylation states to an unprecedented depth and quantitative detail. In the manuscript they provide an excellent interpretation of these findings putting them in the context of past literature in Xenopus as well as in other model systems. The clarity of these explanations improved significantly in the revised version of the manuscript, and several minor imprecisions have been corrected as well.

Strengths:

High-quality data, careful and detailed analysis, and outstanding interpretation in the context of the large body of literature.

Weaknesses:

Merely a resource, none of the findings are tested in functional experiments.

I am very impressed by the quality of the data and the careful and detailed interpretation of the findings. In this form, the manuscript will be an excellent resource to the cell division community in general, and it presents a very large number of hypotheses that can be tested in future experiments. Xenopus has been and still is a popular and powerful model system that led to critical discoveries around countless cellular processes, including the spindle, nuclear envelope, and translational regulation, just to name a few. This also includes a huge body of literature on the cell cycle describing its phosphoregulation. It is indeed somewhat frustrating to see that these earlier studies using phospho-mutants and phospho-antibodies were just scratching the surface. The phosphoproteomics analysis presented here reveals much more extensive and much more dynamic changes in phosphorylation states. Thereby, in my opinion, this manuscript opens a completely new chapter in this line of research, setting the stage for more systematic future studies.

Reviewer #3 (Public review):

Summary:

The authors performed time-resolved proteomics and phospho-proteomics in Xenopus oocytes from prophase I through the MII arrest of the unfertilized egg. The data contains protein abundance and phosphorylation sites of a large number set of proteins at different stages of oocyte maturation. The large sets of data are of high quality. In addition, the authors discussed several key pathways critical for the maturation. The data is very useful for researchers, not only researchers in Xenopus oocytes but also those in oocyte biology in other organisms.

Strengths:

The data of proteomics and phospho-proteomics in Xenopus oocyte maturation is very useful for future studies to understand molecular networks in oocyte maturation.

Weaknesses:

Although the authors offered molecular pathways of the phosphorylation in translation, protein degradation, cell cycle regulation, and chromosome segregation. The authors did not check the validity of the molecular pathways based on their proteomic data by experimentation. But this is not essential since this is a resource paper.

eLife Assessment

The authors quantified intentions and knowledge gaps in scientists' use of sex as a biological variable in their work, and used a workshop intervention to show that while willingness was high, pressure points centered on statistical knowledge and perceived additional monetary costs to research. These important findings demonstrate the difficulty in changing understanding - while interventions can improve knowledge and decrease perceived barriers, the impact was small. The evidence was solid, although the sample size was small for the intervention.

Reviewer #1 (Public review):

Summary:

The authors use the theory of planned behavior to understand whether or not intentions to use sex as a biological variable (SABV), as well as attitude (value), subjective norm (social pressure), and behavioral control (ability to conduct behavior), across scientists at a pharmacological conference. They also used an intervention (workshop) to determine the value of this workshop in changing perceptions and misconceptions. Attempts to understand the knowledge gaps were made.

Strengths:

The use of SABV is limited in terms of researchers using sex in the analysis as a variable of interest in the models (and not a variable to control). To understand how we can improve on the number of researchers examining the data with sex in the analyses, it is vital we understand the pressure points that researchers consider in their work. The authors identify likely culprits in their analyses. The authors also test an intervention (workshop) to address the main bias or impediments for researchers' use of sex in their analyses.

Weaknesses:

There are a number of assumptions the authors make that could be revisited:

(1) that all studies should contain across sex analyses or investigations. It is important to acknowledge that part of the impetus for SABV is to gain more scientific knowledge on females. This will require within sex analyses and dedicated research to uncover how unique characteristics for females can influence physiology and health outcomes. This will only be achieved with the use of female-only studies. The overemphasis on investigations of sex influences limits the work done for women's health, for example, as within-sex analyses are equally important.

(2) It should be acknowledged that although the variability within each sex is not different on a number of characteristics (as indicated by meta-analyses in rats and mice), this was not done on all variables, and behavioral variables were not included. In addition, across-sex variability may very well be different, which, in turn, would result in statistical sex significance. In addition, on some measures, there are sex differences in variability, as human males have more variability in grey matter volume than females. PMID: 33044802.

(3) The authors need to acknowledge that it can be important that the sample size is increased when examining more than one sex. If the sample size is too low for biological research, it will not be possible to determine whether or not a difference exists. Using statistical modelling, researchers have found that depending on the effect size, the sample size does need to increase. It is important to bare this in mind as exploratory analyses with small sample size will be extremely limiting and may also discourage further study in this area (or indeed as seen the literature - an exploratory first study with the use of males and females with limited sample size, only to show there is no "significance" and to justify this as an reason to only use males for the further studies in the work.

Reviewer #2 (Public review):

Summary:

The investigators tested a workshop intervention to improve knowledge and decrease misconceptions about sex inclusive research. There were important findings that demonstrate the difficulty in changing opinions and knowledge about the importance of studying both males and females. While interventions can improve knowledge and decrease perceived barriers, the impact was small.

Strengths:

The investigators included control groups and replicated the study in a second population of scientists. The results appear to be well substantiated. These are valuable findings that have practical implications for fields where sex is included as a biological variable to improve rigor and reproducibility.

Weaknesses:

I found the figures difficult to understand and would have appreciated more explanation of what is depicted, as well as greater space between the bars representing different categories.

Reviewer #3 (Public review):

Summary:

This manuscript aims to determine cultural biases and misconceptions in inclusive sex research and evaluate the efficacy of interventions to improve knowledge and shift perceptions to decrease perceived barriers for including both sexes in basic research.

Overall, this study demonstrates that despite the intention to include both sexes and a general belief in the importance of doing so, relatively few people routinely include both sexes. Further, the perceptions of barriers to doing so are high, including misconceptions surrounding sample size, disaggregation, and variability of females. There was also a substantial number of individuals without the statistical knowledge to appropriately analyze data in studies inclusive of sex. Interventions increased knowledge and decreased perception of barriers.

Strengths:

(1) This manuscript provides evidence for the efficacy of interventions for changing attitudes and perceptions of research.

(2) This manuscript also provides a training manual for expanding this intervention to broader groups of researchers.

Weaknesses:

The major weakness here is that the post-workshop assessment is a single time point, soon after the intervention. As this paper shows, intention for these individuals is already high, so does decreasing perception of barriers and increasing knowledge change behavior, and increase the number of studies that include both sexes?

Similarly, does the intervention start to shift cultural factors? Do these contribute to a change in behavior?

Author response:

Reviewer #1 (Public review):

Summary:

The authors use the theory of planned behavior to understand whether or not intentions to use sex as a biological variable (SABV), as well as attitude (value), subjective norm (social pressure), and behavioral control (ability to conduct behavior), across scientists at a pharmacological conference. They also used an intervention (workshop) to determine the value of this workshop in changing perceptions and misconceptions. Attempts to understand the knowledge gaps were made.

Strengths:

The use of SABV is limited in terms of researchers using sex in the analysis as a variable of interest in the models (and not a variable to control). To understand how we can improve on the number of researchers examining the data with sex in the analyses, it is vital we understand the pressure points that researchers consider in their work. The authors identify likely culprits in their analyses. The authors also test an intervention (workshop) to address the main bias or impediments for researchers' use of sex in their analyses.

Weaknesses:

There are a number of assumptions the authors make that could be revisited:

(1) that all studies should contain across sex analyses or investigations. It is important to acknowledge that part of the impetus for SABV is to gain more scientific knowledge on females. This will require within sex analyses and dedicated research to uncover how unique characteristics for females can influence physiology and health outcomes. This will only be achieved with the use of female-only studies. The overemphasis on investigations of sex influences limits the work done for women's health, for example, as within-sex analyses are equally important.

The Sex and Gender Equity in Research (SAGER) guidelines (1) provide guidance that “Where the subjects of research comprise organisms capable of differentiation by sex, the research should be designed and conducted in a way that can reveal sex-related differences in the results, even if these were not initially expected.”. This is a default position of inclusion where the sex can be determined and analysis assessing for sex related variability in response. This position underpins many of the funding bodies new policies on inclusion.

However, we need to place this in the context of the driver of inclusion. The most common reason for including male and female samples is for those studies that are exploring the effect of a treatment and then the goal of inclusion is to assess the generalisability of the treatment effect (exploratory sex inclusion)(2). The second scenario is where sex is included because sex is one of the variables of interest and this situation will arise because there is a hypothesized sex difference of interest (confirmatory sex inclusion).

We would argue that the SABV concept was introduced to address the systematic bias of only studying one sex when assessing treatment effect to improve the generalisability of the research. Therefore, it isn’t directly to gain more scientific knowledge on females. However, this strategy will highlight when the effect is very different between male and female subjects which will potentially generate sex specific hypotheses.

Where research has a hypothesis that is specific to a sex (e.g. it is related to oestrogen levels) it would be appropriate to study only the sex of interest, in this case females. The recently published Sex Inclusive Research Framework gives some guidance here and allows an exemption for such a scenario classifying such proposals “Single sex study justified” (3).

We plan to add an additional paragraph to the introduction to clarify the objectives behind inclusion and how this assists the research process.

(2) It should be acknowledged that although the variability within each sex is not different on a number of characteristics (as indicated by meta-analyses in rats and mice), this was not done on all variables, and behavioral variables were not included. In addition, across-sex variability may very well be different, which, in turn, would result in statistical sex significance. In addition, on some measures, there are sex differences in variability, as human males have more variability in grey matter volume than females. PMID: 33044802.

The manuscript was highlighting the common argument used to exclude the use of females, which is that females are inherently more variable as an absolute truth. We agree there might be situations, where the variance is higher in one sex or another depending on the biology. We will extend the discussion here to reflect this, and we will also link to the Sex Inclusive Research Framework (3) which highlights that in these situations researchers can utlise this argument provided it is supported with data for the biology of interest.

(3) The authors need to acknowledge that it can be important that the sample size is increased when examining more than one sex. If the sample size is too low for biological research, it will not be possible to determine whether or not a difference exists. Using statistical modelling, researchers have found that depending on the effect size, the sample size does need to increase. It is important to bare this in mind as exploratory analyses with small sample size will be extremely limiting and may also discourage further study in this area (or indeed as seen the literature - an exploratory first study with the use of males and females with limited sample size, only to show there is no "significance" and to justify this as an reason to only use males for the further studies in the work.

The reviewer raises a common problem: where researchers have frequently argued that if they find no sex differences in a pilot then they can proceed to study only one sex. The SAGER guidelines (1), and now funder guidelines (4, 5), challenge that position. Instead, the expectation is for inclusion as the default in all experiments (exploratory inclusion strategy) to allow generalisable results to be obtained. When the results are very different between the male and female samples, then this can be determined. This perspective shift (2) requires a change in mindset and understanding that the driver behind inclusion is of generalisability not exploration of sex differences. This will be added to the introduction as an additional paragraph exploring the drivers behind inclusion.

We agree with the reviewer that if the researcher is interested in sex differences in an effect (confirmatory inclusion strategy, aka sex as a primary variable) then the N will need to be higher. However, in this situation, one, of course, must have male and female samples in the same experiment to allow the simultaneous exploration to assess the dependency on sex.

Reviewer #2 (Public review):

Summary:

The investigators tested a workshop intervention to improve knowledge and decrease misconceptions about sex inclusive research. There were important findings that demonstrate the difficulty in changing opinions and knowledge about the importance of studying both males and females. While interventions can improve knowledge and decrease perceived barriers, the impact was small.

Strengths:

The investigators included control groups and replicated the study in a second population of scientists. The results appear to be well substantiated. These are valuable findings that have practical implications for fields where sex is included as a biological variable to improve rigor and reproducibility.

Thank you for assessment and highlighting these strengths. We appreciate your recognition of the value and practical implications of this work.

Weaknesses:

I found the figures difficult to understand and would have appreciated more explanation of what is depicted, as well as greater space between the bars representing different categories.

We plan to review the figures and figure legends to improve clarity of the data.

Reviewer #3 (Public review):

Summary:

This manuscript aims to determine cultural biases and misconceptions in inclusive sex research and evaluate the efficacy of interventions to improve knowledge and shift perceptions to decrease perceived barriers for including both sexes in basic research.

Overall, this study demonstrates that despite the intention to include both sexes and a general belief in the importance of doing so, relatively few people routinely include both sexes. Further, the perceptions of barriers to doing so are high, including misconceptions surrounding sample size, disaggregation, and variability of females. There was also a substantial number of individuals without the statistical knowledge to appropriately analyze data in studies inclusive of sex. Interventions increased knowledge and decreased perception of barriers. Strengths:

(1) This manuscript provides evidence for the efficacy of interventions for changing attitudes and perceptions of research.

(2) This manuscript also provides a training manual for expanding this intervention to broader groups of researchers.

Thank you for highlighting these strengths. We appreciate your recognition that the intervention was effect in changing attitudes and perception. We deliberately chose to share the material to provide the resources to allow a wider engagement.

Weaknesses:

The major weakness here is that the post-workshop assessment is a single time point, soon after the intervention. As this paper shows, intention for these individuals is already high, so does decreasing perception of barriers and increasing knowledge change behavior, and increase the number of studies that include both sexes? Similarly, does the intervention start to shift cultural factors? Do these contribute to a change in behavior?

Measuring change in behaviour following an intervention is challenging and hence we had implemented an intention score as a proxy for behaviour. We appreciate the benefit of a long-term analysis, but it was beyond the scope of this study and would need a larger dataset size to allow for attrition. We agree that the strategy implemented has weaknesses. We plan to extend the limitation section in the discussion to include these.

References

(1) Heidari S, Babor TF, De Castro P, Tort S, Curno M. Sex and Gender Equity in Research: rationale for the SAGER guidelines and recommended use. Res Integr Peer Rev. 2016;1:2.

(2) Karp NA. Navigating the paradigm shift of sex inclusive preclinical research and lessons learnt. Commun Biol. 2025;8(1):681.

(3) Karp NA, Berdoy M, Gray K, Hunt L, Jennings M, Kerton A, et al. The Sex Inclusive Research Framework to address sex bias in preclinical research proposals. Nat Commun. 2025;16(1):3763.

(4) MRC. Sex in experimental design - Guidance on new requirements https://www.ukri.org/councils/mrc/guidance-for-applicants/policies-and-guidance-for-researchers/sex-in-experimental-design/: UK Research and Innovation; 2022

(5) Clayton JA, Collins FS. Policy: NIH to balance sex in cell and animal studies. Nature. 2014;509(7500):282-3.

Author response:

Public Reviews:

Reviewer #1 (Public review):

Summary:

In this interesting and original paper, the authors examine the effect that heat stress can have on the ability of bacterial cells to evade infection by lytic bacteriophages. Briefly, the authors show that heat stress increases the tolerance of Klebsiella pneumoniae to infection by the lytic phage Kp11. They also argue that this increased tolerance facilitates the evolution of genetically encoded resistance to the phage. In addition, they show that heat can reduce the efficacy of phage therapy. Moreover, they define a likely mechanistic reason for both tolerance and genetically encoded resistance. Both lead to a reorganization of the bacterial cell envelope, which reduces the likelihood that phage can successfully inject their DNA.

Strengths:

I found large parts of this paper well-written and clearly presented. I also found many of the experiments simple yet compelling. For example, the experiments described in Figure 3 clearly show that prior heat exposure can affect the efficacy of phage therapy. In addition, the experiments shown in Figures 4 and 6 clearly demonstrate the likely mechanistic cause of this effect. The conceptual Figure 7 is clear and illustrates the main ideas well. I think this paper would work even without its central claim, namely that tolerance facilitates the evolution of resistance. The reason is that the effect of environmental stressors on stress tolerance has to my knowledge so far only been shown for drug tolerance, not for tolerance to an antagonistic species.

Weaknesses:

I did not detect any weaknesses that would require a major reorganization of the paper, or that may require crucial new experiments. However, the paper needs some work in clarifying specific and central conclusions that the authors draw. More specifically, it needs to improve the connection between what is shown in some figures, how these figures are described in the caption, and how they are discussed in the main text. This is especially glaring with respect to the central claim of the paper from the title, namely that tolerance facilitates the evolution of resistance. I am sympathetic to that claim, especially because this has been shown elsewhere, not for phage resistance but for antibiotic resistance. However, in the description of the results, this is perhaps the weakest aspect of the paper, so I'm a bit mystified as to why the authors focus on this claim. As I mentioned above, the paper could stand on its own even without this claim.

Thank you for your feedback. We understand your concern regarding the central claim that tolerance facilitates the evolution of resistance, while the paper can stand on its own without this claim, we think it provides an important layer to the interpretation of our findings. Considering your comments, we plan to revise the title and adjust to “Heat Stress Induces Phage Tolerance in Bacteria”.

More specific examples where clarification is needed:

(1) A key figure of the paper seems to be Figure 2D, yet it was one of the most confusing figures. This results from a mismatch between the accompanying text starting on line 92 and the figure itself. The first thing that the reader notices in the figure itself is the huge discrepancy between the number of viable colonies in the absence of phage infection at the two-hour time point. Yet this observation is not even mentioned in the main text. The exclusive focus of the main text seems to be on the right-hand side of the figure, labeled "+Phage". It is from this right-hand panel that the authors seem to conclude that heat stress facilitates the evolution of resistance. I find this confusing, because there is no difference between the heat-treated and non-treated cells in survivorship, and it is not clear from this data that survivorship is caused by resistance, not by tolerance/persistence. (The difference between tolerance and resistance has only been shown in the independent experiments of Figure 1B.)

Thank you for your helpful comment. Figure 2d presents colony counts from a plating assay following the phage killing experiment in Figure 2c. Bacteria collected after 0 and 2 hours of phage exposure were plated on both phage-free (−phage) and phage-containing (+phage) plates. The “−phage” condition reflects total survivors, while the “+phage” condition indicates the resistant subset.

As seen in Figure 2d (left part), heat-treated bacteria showed markedly higher survival on phage-free plates than untreated cells, which were largely eliminated by phage. However, resistant colony counts on phage-containing plates were similar between two groups (as shown in figure 2d right part), suggesting that heat stress increased survival but did not promote resistance.

To clarify, we have revised the labels in Figure 2d as follows: “Total” will replace “-phage” to indicate the total survivors from the phage killing assay, and “Resisters” will replace “+phage” to indicate the resistant survivors, which are detected on phage-containing plates. This adjustment should eliminate any confusion and better reflect the experimental design.

Figure 2F supports the resistance claim, but it is not one of the strongest experiments of the paper, because the author simply only used "turbidity" as an indicator of resistance. In addition, the authors performed the experiments described therein at small population sizes to avoid the presence of resistance mutations. But how do we know that the turbidity they describe does not result from persisters?

I see three possibilities to address these issues. First, perhaps this is all a matter of explaining and motivating this particular experiment better. Second, the central claim of the paper may require additional experiments. For example, is it possible to block heat induced tolerance through specific mutations, and show that phage resistance does not evolve as rapidly if tolerance is blocked? A third possibility is to tone down the claim of the paper and make it about heat tolerance rather than the evolution of heat resistance.

Thank you for your thoughtful comment. We appreciate the opportunity to clarify the interpretation of Figure 2f and the rationale behind the experimental design. We agree that turbidity alone cannot fully distinguish resistance from persistence. However, our earlier experiments (Figures 2d and 2e) demonstrated that heat-treated survivors remained largely susceptible to phage, indicating that heat stress does not directly induce resistance. This led us to hypothesize that heat enhances phage tolerance, which in turn increases the likelihood of resistance emergence during subsequent infection.

To test this, we used a low initial bacterial population (~10³ CFU per well) to minimize the chance of pre-existing resistance. Bacteria were exposed to phages at MOIs of 1, 10, and 100 and incubated for 24 hours in 100 µL volumes. This setup ensured:

(1) The low initial population minimizes the presence of pre-existing resistant mutants, ensuring that any phage-resistant bacteria observed arise during the infection process.

(2) The high MOI (≥ 1) ensures that each bacterial cell has a high probability of infection by at least one phage.

(3) The small volume (100 µL per well) maximizes the interaction between bacteria and phages, ensuring rapid infection of susceptible bacteria, which leads to clear wells. If resistant mutants arise, they will grow and cause turbidity.

Thus, the turbidity observed in heat-treated samples reflects de novo emergence and outgrowth of resistant mutants from a tolerant population. This assay supports the idea that heat-induced tolerance increases the probability of resistance evolution, rather than directly causing resistance.

We have revised the text to better explain this experimental logic and adjust the framing of our conclusions accordingly.

A minor but general point here is that in Figure 2D and in other figures, the labels "-phage" and "+phage" do not facilitate understanding, because they suggest that cells in the "-phage" treatment have not been exposed to phage at all, but that is not the case. They have survived previous phage treatment and are then replated on media lacking phage.

Thank you for your valuable comment. To clarify, we have revised the labels in Figure 2d as follows: “Total” will replace “-phage” to indicate the total survivors from the phage killing assay, and “Resisters” will replace “+phage” to indicate the resistant survivors, which are detected on phage-containing plates.

(2) Another figure with a mismatch between text and visual materials is Figure 5, specifically Figures 5B-F. The figure is about two different mutants, and it is not even mentioned in the text how these mutants were identified, for example in different or the same replicate populations. What is more, the two mutants are not discussed at all in the main text. That is, the text, starting on line 221 discusses these experiments as if there was only one mutant. This is especially striking as the two mutants behave very differently, as, for example, in Figure 5C. Implicitly, the text talks about the mutant ending in "...C2", and not the one ending in "...C1". To add to the confusion, the text states that the (C2) mutant shows a change in the pspA gene, but in Figure 5f, it is the other (undiscussed) mutant that has a mutation in this gene. Only pspA is discussed further, so what about the other mutants? More generally, it is hard to believe that these were the only mutants that occurred in the genome during experimental evolution. It would be useful to give the reader a 2-3 sentence summary of the genetic diversity that experimental evolution generated.

Thank you for your thoughtful comment. In our heat treatment evolutionary experiment, we isolated six distinct bacterial clones, of which two are highlighted in the manuscript as representative examples. One clone, BC2G11C1, acquired both heat tolerance and phage resistance, while another clone, BC3G11C2, became heat-tolerant but did not develop resistance to phage infection. This variation highlights the inherent diversity in evolutionary responses when exposed to selective pressures. It demonstrates that not all evolutionary pathways lead to the same outcome, even under similar stress conditions. This variability is a key observation in our study, illustrating that different genetic adaptations may arise depending on the specific mutations or genetic context, and not every strain will evolve phage resistance in parallel with heat tolerance. We have updated the manuscript to better reflect this diversity in the evolutionary trajectories observed.

Reviewer #2 (Public review):

Summary:

An initial screening of pretreatment with different stress treatments of K. pneumoniae allowed the identification of heat stress as a protection factor against the infection of the lytic phage Kp11. Then experiments prove that this is mediated not by an increase of phage-resistant bacteria but due to an increase in phage transient tolerant population, which the authors identified as bacteriophage persistence in analogy to antibiotic persistence. Then they proved that phage persistence mediated by heat shock enhanced the evolution of bacterial resistance against the phage. The same trait was observed using other lytic phages, their combinations, and two clinical strains, as well as E. coli and two T phages, hence the phenomenon may be widespread in enterobacteria.

Next, the elucidation of heat-induced phage persistence was done, determining that phage adsorption was not affected but phage DNA internalization was impaired by the heat pretreatment, likely due to alterations in the bacterial envelope, including the downregulation of envelope proteins and of LPS; furthermore, heat treated bacteria were less sensitive to polymyxins due to the decrease in LPS.

Finally, cyclic exposure to heat stress allowed the isolation of a mutant that was both resistant to heat treatment, polymyxins, and lytic phage, that mutant had alterations in PspA protein that allowed a gain of function and that promoted the reduction of capsule production and loss of its structure; nevertheless this mutant was severely impaired in immune evasion as it was easily cleared from mice blood, evidencing the tradeoffs between phage/heat and antibiotic resistance and the ability to counteract the immune response.

Strengths:

The experimental design and the sequence in which they are presented are ideal for the understanding of their study and the conclusions are supported by the findings, also the discussion points out the relevance of their work particularly in the effectiveness of phage therapy and allows the design of strategies to improve their effectiveness.

Weaknesses:

In its present form, it lacks the incorporation of some relevant previous work that explored the role of heat stress in phage susceptibility, antibiotic susceptibility, tradeoffs between phage resistance and resistance against other kinds of stress, virulence, etc., and the fact that exposure to lytic phages induces antibiotic persistence.

Thank you for your insightful comments. I appreciate your suggestion regarding the inclusion of relevant previous works. I have now incorporated additional citations to discuss these points, including studies on the relationship between heat stress and antibiotic resistance, as well as the tradeoffs between phage resistance and other stress factors.

Reviewer #3 (Public review):

PspA, a key regulator in the phage shock protein system, functions as part of the envelope stress response system in bacteria, preventing membrane depolarization and ensuring the envelope stability. This protein has been associated in the Quorum Sensing network and biofilm formation. (Moscoso M., Garcia E., Lopez R. 2006. Biofilm formation by Streptococcus pneumoniae: role of choline, extracellular DNA, and capsular polysaccharide in microbial accretion. J. Bacteriol. 188:7785-7795; Vidal JE, Ludewick HP, Kunkel RM, Zähner D, Klugman KP. The LuxS-dependent quorum-sensing system regulates early biofilm formation by Streptococcus pneumoniae strain D39. Infect Immun. 2011 Oct;79(10):4050-60.)

It is interesting and very well-developed.

(1) Could the authors develop experiments about the relationship between Quorum Sensing and this protein?

(2) It would be interesting to analyze the link to phage infection and heat stress in relation to Quorum. The authors could study QS regulators or AI2 molecules.

Thank you for your insightful comments and for bringing up the role of PspA in quorum sensing and biofilm formation. However, we would like to clarify a potential misunderstanding: the PspA discussed in our manuscript refers to phage-shock protein A, a key regulator in the bacterial envelope stress response system. This is distinct from the pneumococcal surface protein A, which has been associated with quorum sensing and biofilm formation in Streptococcus pneumoniae (as referenced in your comment).

To avoid any confusion for readers, we will ensure that our manuscript explicitly states “phage-shock protein A (PspA)” at its first mention. We appreciate your feedback and hope this clarification addresses your concern.

(3) Include the proteins or genes in a table or figure from lytic phage Kp11 (GenBank: ON148528.1).

Thank you for your helpful suggestion. We have now included a figure, as appropriate summarizing the proteins of the lytic phage Kp11 (GenBank: ON148528.1) in supplementary Figure S1.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

Issues unrelated to those discussed in the public review

(1) Figure 4a and its caption describe an evolution experiment, but they do not mention how many cycles of high-temperature treatment and growth this experiment lasted. I assume it lasted for more than one cycle, because the methods section mentions "cycles", but the number is not provided.

Thank you for pointing this out. The evolutionary experiment shown in Figure 5a involved 11 cycles of high-temperature treatment and growth. We have now explicitly stated this in the figure legend to ensure clarity: BC: Batch culture, G: Evolution cycle number, C: Colony. BC2G11C1 refers to the first colony from batvh culture 2 after 11 rounds of heat treatment.

(2) It is not clear what Figure 5F is supposed to show. What are the gray boxes? The caption claims that the figure shows non-synonymous mutations, but the only information it contains is about genes that seem to be affected by mutation. Judging from the mismatch between the main text and the figure, the mutants with these mutations may actually be mislabeled.

Thank you for your careful review. Figure 5f highlights the non-synonymous mutations identified in the evolved strains. The gray boxes represent the ancestral strain’s whole genome without mutations, serving as a control. The corresponding labels indicate the specific mutations found in each evolved strain. We have clarified this in the figure caption to improve clarity. Additionally, we have carefully reviewed the labeling to ensure accuracy and consistency between the figure, main text, and sequencing data.

(3) I think that the acronym NC, which is used in just about every figure, is explained nowhere in the paper. Spell out all acronyms at first use.

Thank you for pointing this out. We have rivewed ensure that NC is clearly defined at its first mention in the text and figure legends to improve clarity. Additionally, we have reviewed the manuscript to ensure that all acronyms are properly introduced when first used.

(4) The same holds for the acronym N.D. This is an especially important oversight because N.D. could mean "not determined" or "not detectable", which would lead to very different interpretations of the same figure.

Thank you for your careful review. We have clarified the meaning of N.D., which stands for non-detectable, at its first use to avoid ambiguity and ensure accurate interpretation in the figure legend. Additionally, we have reviewed the manuscript to ensure that all acronyms are clearly defined.

(5) The panel labels (a,b, etc.) in all figure captions are very difficult to distinguish from the rest of the text, and should be better highlighted, for example by using a bold font. However, this is a matter of journal style and will probably be fixed during typesetting.

Thank you for your suggestion. We have adjusted the figure captions to better distinguish panel labels, such as using bold font, to improve readability and final formatting will follow the journal’s style during typesetting.

(6) Line 224: enhanced insusceptibility -> reduced susceptibility.

Thank you for your suggestion. We have revised “enhanced insusceptibility” to “reduced susceptibility” for clarity and precision.

(7) Line 259: mice -> mouse.

Thank you for catching this. We have corrected “mice” to “mouse”.

Reviewer #2 (Recommendations for the authors):

I have no concerns about the experimental design and conclusions of your work; however, I strongly recommend incorporating several relevant pieces of the literature related to your work, in the discussion of your manuscript, specifically:

(1) Previous studies about the role of heat stress in phage infections, see:

Greenrod STE, Cazares D, Johnson S, Hector TE, Stevens EJ, MacLean RC, King KC. Warming alters life-history traits and competition in a phage community. Appl Environ Microbiol. 2024 May 21;90(5):e0028624. doi: 10.1128/aem.00286-24. Epub 2024 Apr 16. PMID: 38624196; PMCID: PMC11107170.

Thank you for your thoughtful comment. We have ensured to incorporate the study by Greenrod et al. (2024) into the discussion to enrich the context of our findings. As this article pointed out, a temperature of 42°C can indeed limit phage infection in bacteria, acting as a barrier from the phage’s perspective. Our study builds on this by demonstrating that bacteria pre-treated with high temperatures exhibit tolerance to phage infection. These findings, together with the work you referenced, underscore the importance of heat stress or elevated temperature in host-phage interactions, with 42°C being particularly relevant in the context of fever. We will make sure to clarify this connection in our revised manuscript.

(2) The effect of heat stress and the tolerance/resistance against other antibiotics besides polymyxins, see:

Lv B, Huang X, Lijia C, Ma Y, Bian M, Li Z, Duan J, Zhou F, Yang B, Qie X, Song Y, Wood TK, Fu X. Heat shock potentiates aminoglycosides against gram-negative bacteria by enhancing antibiotic uptake, protein aggregation, and ROS. Proc Natl Acad Sci U S A. 2023 Mar 21;120(12):e2217254120. doi: 10.1073/pnas.2217254120. Epub 2023 Mar 14. PMID: 36917671; PMCID: PMC10041086.

Thank you for bringing this study to our attention. We have incorporated the findings from Lv et al. (2023) into the discussion of our manuscript, highlighting how sublethal temperatures may facilitate the killing of bacteria by antibiotics like kanamycin. This is consistent with our data showing enhanced susceptibility of heat-shocked bacteria to kanamycin. The study also provides insights into the potential role of PMF, which is relevant to our work on PspA, and strengthens the broader context of heat stress influencing both antibiotic resistance and tolerance.

(3) Perhaps the most relevant overlooked fact was that recently it was demonstrated for E. coli, Klebsiella and Pseudomonas that pretreatment with lytic phages induced antibiotic persistence! Please discuss this finding and its implications for your work, see:

Fernández-García L, Kirigo J, Huelgas-Méndez D, Benedik MJ, Tomás M, García-Contreras R, Wood TK. Phages produce persisters. Microb Biotechnol. 2024 Aug;17(8):e14543. doi: 10.1111/1751-7915.14543. PMID: 39096350; PMCID: PMC11297538.

Sanchez-Torres V, Kirigo J, Wood TK. Implications of lytic phage infections inducing persistence. Curr Opin Microbiol. 2024 Jun;79:102482. doi: 10.1016/j.mib.2024.102482. Epub 2024 May 6. PMID: 38714140.

Thank you for suggesting this important reference. We agree that the phenomenon of phage-induced bacterial persistence is highly relevant to our study. While our manuscript focuses on the role of heat stress in bacterial tolerance and resistance, we acknowledge that bacterial persistence against phages is an established concept. We have incorporated this finding into our discussion, emphasizing how persistence and tolerance can overlap in their effects on bacterial survival, especially under stress conditions like heat treatment. This will provide a more comprehensive understanding of how phage interactions with bacteria can lead to both persistence and resistance.

(4) Finally, you observed a tradeoff pf the pspA* mutant increased phage/heat/polymyxin resistance and decreased immune evasion (perhaps by being unable to counteract phagocytosis), those tradeoffs between gaining phage resistance but losing resistance to the immune system, virulence impairment and resistance against some antibiotics had been extensively documented, see:

Majkowska-Skrobek G, Markwitz P, Sosnowska E, Lood C, Lavigne R, Drulis-Kawa Z. The evolutionary trade-offs in phage-resistant Klebsiella pneumoniae entail cross-phage sensitization and loss of multidrug resistance. Environ Microbiol. 2021 Dec;23(12):7723-7740. doi: 10.1111/1462-2920.15476. Epub 2021 Mar 27. PMID: 33754440.

Gordillo Altamirano F, Forsyth JH, Patwa R, Kostoulias X, Trim M, Subedi D, Archer SK, Morris FC, Oliveira C, Kielty L, Korneev D, O'Bryan MK, Lithgow TJ, Peleg AY, Barr JJ. Bacteriophage-resistant Acinetobacter baumannii are resensitized to antimicrobials. Nat Microbiol. 2021 Feb;6(2):157-161. doi: 10.1038/s41564-020-00830-7. Epub 2021 Jan 11. PMID: 33432151.

García-Cruz JC, Rebollar-Juarez X, Limones-Martinez A, Santos-Lopez CS, Toya S, Maeda T, Ceapă CD, Blasco L, Tomás M, Díaz-Velásquez CE, Vaca-Paniagua F, Díaz-Guerrero M, Cazares D, Cazares A, Hernández-Durán M, López-Jácome LE, Franco-Cendejas R, Husain FM, Khan A, Arshad M, Morales-Espinosa R, Fernández-Presas AM, Cadet F, Wood TK, García-Contreras R. Resistance against two lytic phage variants attenuates virulence and antibiotic resistance in Pseudomonas aeruginosa. Front Cell Infect Microbiol. 2024 Jan 17;13:1280265. doi: 10.3389/fcimb.2023.1280265. Erratum in: Front Cell Infect Microbiol. 2024 Mar 06;14:1391783. doi: 10.3389/fcimb.2024.1391783. PMID: 38298921; PMCID: PMC10828002.

Thank you for highlighting these important studies. We have incorporated the work by Majkowska-Skrobek et al. (2021), Gordillo Altamirano et al. (2021), and García-Cruz et al. (2024) into the discussion to provide further context to the evolutionary trade-offs observed in our study. The findings in these studies, which describe the cross-sensitization to antimicrobials and the loss of multidrug resistance in phage-resistant bacteria, align with our observations of trade-offs in the pspA mutant. Specifically, our results show that while the pspA mutant exhibits increased resistance to phage, heat, and polymyxins, it also experiences a decrease in immune evasion and potential virulence. These trade-offs are significant in understanding the broader consequences of developing resistance to phages and other stressors.

eLife Assessment

This important study analyzes the effect of heat treatment on phage-bacterial interactions and convincingly shows that prior heat exposure alters the bacterial cell envelope, enhancing persistence and bacterial survival when exposed to lytic phages. The study will interest researchers working on antibiotic resistance, tolerance, and phage therapy.

Reviewer #1 (Public review):

Summary:

In this interesting and original paper, the authors examine the effect that heat stress can have on the ability of bacterial cells to evade infection by lytic bacteriophages. Briefly, the authors show that heat stress increases tolerance of Klebsiella pneumoniae to infection by the lytic phage Kp11. They also argue that this increased tolerance facilitates the evolution of genetically encoded resistance to the phage. In addition, they show that heat can reduce the efficacy of phage therapy. Moreover, they define a likely mechanistic reason for both tolerance and genetically encoded resistance. Both lead to a reorganization of the bacterial cell envelope, which reduces the likelihood that phage can successfully inject their DNA.

Strengths:

I found large parts of this paper well written and clearly presented. I also found many of the experiments simple yet compelling. For example, the experiments described in figure 3 clearly show that prior heat exposure can affect the efficacy of phage therapy. In addition the experiments shown in figure 4 and 6 clearly demonstrate the likely mechanistic cause of this effect. The conceptual figure 7 is clear and illustrates the main ideas well. I think this paper would be publishable even without its central claim, namely that tolerance facilitates the evolution of resistance. The reason is that the effect of environmental stressors on stress tolerance has to my knowledge so far only been shown for drug tolerance, not for tolerance to an antagonistic species.

Weaknesses:

I did not detect any weaknesses that would require a major reorganization of the paper, or that may require crucial new experiments without which the paper should not be published. The originally submitted paper needed some work in clarifying specific and central conclusions that the authors draw, which the authors have done during revision.

Reviewer #2 (Public review):

Summary:

An initial screening of pretreatment with different stress treatments of K. pneumonia allowed the identification of heat stress as a protection factor against the infection of the lytic phage Kp11. Then experiments prove that this is mediated not by an increase of phage resistant bacteria but due to an increase in phage transient tolerant population, that the authors identified as bacteriophage persistence in analogy to antibiotic persistence. Then they proved that phage persistence mediated by heath shock enhanced the evolution of bacterial resistance against the phage. The same trait was observed using other lytic phages, their combinations and two clinical strains, as well as E. coli and two T phages, hence the phenomenon may be widespread in enterobacteria.

Next, the elucidation of heat induced phage persistence was done, determining that phage adsorption was not affected but phage DNA internalization was impaired by the heat pretreatment, likely to alterations in the bacterial envelope, including the downregulation of envelope proteins and of LPS; furthermore, heat treated bacteria were less sensitive to polymyxins due to the decrease in LPS.

Finally, cyclic exposure to heat stress allowed the isolation of a mutant that was both resistant to heat treatment, polymyxins and lytic phage, that mutant had alterations in PspA protein that allowed a gain of function and that promoted the reduction of capsule production and loss of its structure; nevertheless this mutant was severely impaired in immune evasion as it was easily cleared from mice blood, evidencing the trade-off's between phage/heat and antibiotic resistance and the ability to counteract the immune response.

Strengths:

The experimental design and the sequence in which they are presented is ideal for the understanding of their study and the conclusions are supported by the findings, also the discussion points out the relevance of their work particularly in the effectiveness of phage therapy and allow the design of strategies to improve their effectiveness.

Weaknesses:

In its present form it lacks the incorporation of some relevant previous work that explored the role of heat stress in phage susceptibility, antibiotic susceptibility, trade offs between phage resistance and resistance against other kinds of stress, virulence, etc. and the fact that exposure to lytic phages induces antibiotic persistence.

Comments on revised version:

Thanks for addressing most of my comments; however, although you replied this in the rebuttal:

"Thank you for highlighting these important studies. We have incorporated the work by Majkowska-Skrobek et al. (2021), Gordillo Altamirano et al. (2021), and García-Cruz et al. (2024) into the discussion "

I was not able to find the new section in the discussion of the manuscript.

eLife Assessment

This manuscript presents important findings on how structural color can be manipulated through a specific single-gene mutation in a motile bacterium. Compelling data provide a promising model to identify genes and molecular mechanisms supporting this widespread optical phenomenon. This work will be of interest to biophysicists and microbiologists working on structural colors and Flavobacterium.

Reviewer #1 (Public review):

Structural colors (SC) are based on nanostructures reflecting and scattering light and producing optical wave interference. All kinds of living organisms exhibit SC. However, understanding the molecular mechanisms and genes involved may be complicated due to the complexity of these organisms. Hence, bacteria that exhibit SC in colonies, such as Flavobacterium IR1, can be good models.

Based on previous genomic mining and co-occurrence with SC in flavobacterial strains, this article focuses on the role of a specific gene, moeA, in SC of Flavobacterium IR1 strain colonies on an agar plate. moeA is involved in the synthesis of the molybdenum cofactor, which is necessary for the activity of key metabolic enzymes in diverse pathways.

The authors clearly showed that the absence of moeA shifts SC properties in a way that depends on the nutritional conditions. They further bring evidence that this effect was related to several properties of the colony, all impacted by the moeA mutant: cell-cell organization, cell motility and colony spreading, and metabolism of complex carbohydrates. Hence, by linking SC to a single gene in appearance, this work points to cellular organization (as a result of cell-cell arrangement and motility) and metabolism of polysaccharides as key factors for SC in a gliding bacterium. This may prove useful for designing molecular strategies to control SC in bacterial-based biomaterials.

Reviewer #2 (Public review):

The authors constructed an in-frame deletion of moeA gene, which is involved in molybdopterin cofactor (MoCo) biosynthesis, and investigated its role in structural colors in Flavobacterium IR1. The deletion of moeA shifted colony color from green to blue, reduced colony spreading, and increased starch degradation, which was attributed to the upregulation of various proteins in polysaccharide utilization loci. This study lays the ground for developing new colorants by modifying genes involved in structural colors.

Overall, this is a well-written paper in which the authors effectively address their research questions through proper experimentation. This work will help us understand the genetic basis of structural colors in Flavobacterium and open new avenues to study the roles of additional genes and proteins in structural colors.

eLife Assessment

This manuscript offers important insights into how polyphosphate (polyP) influences protein phase separation differently from DNA. The authors present compelling evidence that polyP distinguishes among protein conformational ensembles, leading to divergent condensate maturation behaviors that include unfolding and polyproline II formation. In response to reviewer feedback, the authors addressed key concerns by incorporating charge-equivalent DNA controls and extending structural analysis to FruR variants, further reinforcing the polymer-specific effects of polyP. While some discrepancies between protein systems remain unresolved, the study enhances our understanding of how biopolymers influence protein assembly and conformational transitions.

Reviewer #1 (Public review):

In the article Goyal and colleagues investigate the role of negatively charged biopolymers, i.e., polyphosphate (polyP) and DNA, play in phase separation of cytidine repressor (CytR) and fructose repressor (FruR). The authors find that both negative polymers drive the formation of metastable protein/polymer condensates. However, polyP-driven condensates form more gel- or solid-like structures over time while DNA-driven condensates tend to dissipate over time. The authors link this disparate condensate behavior to polyP-induced structures within the enzymes. Specifically, they observe the formation of polyproline II-like structures within two tested enzyme variants in the presence of polyP. Together, their results provide a unique insight into the physical and structural mechanism by which two unique negatively charged polymers can induce distinct phase transitions with the same protein. This study will be a welcomed addition to the condensate field and provide new molecular insights into how binding partner-induced structural changes within a given protein can affect the mesoscale behavior of condensates.

Reviewer #2 (Public review):

Summary:

In the article Goyal et al. investigate how protein/polymer phase transition behavior is modulated by different binding partners-specifically, DNA and polyphosphate (PolyP). The authors show that while both DNA and PolyP can induce metastable condensates, only PolyP drives unique phase transition behaviors by effectively discriminating among initial protein ensembles with varying degrees of conformational heterogeneity, compactness, and plasticity. This selectivity is attributed to PolyP's ability to unfold the enzyme during condensate formation, supported by the observation of polyproline II-rich structures in two tested variants (CytR WT and DM). Overall, this work offers valuable insights into the mechanistic factors underlying condensation assembly and advances our understanding of how molecular interactions influence phase behavior.

Strengths:

The authors employed a well-designed and technically sound experimental approach to investigate how the initial protein conformational ensemble influences phase transition behavior in the presence of two charged polymers. Specifically, they examined phase transitions of CytR and FruR variants in the context of either polyphosphate (PolyP) or DNA, enabling a direct comparison that effectively highlights key differences. This study provides mechanistic insights into the role of PolyP in driving condensation and may contribute to a broader understanding of assembly processes involving PolyP, particularly in the context of bacterial stress responses.

Weaknesses:

The primary weakness of this manuscript lies in the lack of a consistent trend linking the unique phase transitions observed in protein/PolyP systems to the initial protein conformational ensemble. The observed differences in assembly and maturation behavior do not consistently correlate with conformational heterogeneity, plasticity, or compactness of the starting ensemble. This is particularly evident in the divergent outcomes between the CytR/PolyP and FruR/PolyP systems. Consequently, the phase behavior of protein/PolyP condensates does not reliably reflect the composition of the initial conformational ensemble, limiting its effectiveness as a probe for conformational state characterization.

eLife Assessment

This important study offers a powerful empirical test of a highly influential hypothesis in population genetics. It incorporates a large number of animal genomes spanning a broad phylogenetic spectrum and treats them in a rigorous unified pipeline, providing the convincing negative result that effective population size scales neither with the content of transposable elements nor with overall genome size. These observations demonstrate that there is still no simple, global hypothesis that can explain the observed variation in transposable element content and genome size in animals.

Reviewer #1 (Public review):

Summary:

One enduring mystery involving the evolution of genomes is the remarkable variation they exhibit with respect to size. Much of that variation is due to differences in the number of transposable elements, which often (but not always) correlates with the overall quantity of DNA. Amplification of TEs is nearly always either selectively neutral or negative with respect to host fitness. Given that larger effective population sizes are more efficient at removing these mutations, it has been hypothesized that TE content, and thus overall genome size, may be a function of effective population size. The authors of this manuscript test this hypothesis by using a uniform approach to analysis of several hundred animal genomes, using the ration of synonymous to nonsynonymous mutations in coding sequence as a measure of overall strength of purifying selection, which serves as a proxy for effective population size over time. The data convincingly demonstrates that it is unlikely that effective population size has a strong effect on TE content and, by extension, overall genome size (except for birds, which are weird).

Strengths:

Although this ground has been covered before in many other papers, the strength of this analysis is that it is comprehensive and treats all the genomes with the same pipeline, making comparisons more convincing. Although this is a negative result, it is important because it is relatively comprehensive and indicates that there will be no simple, global hypothesis that can explain the observed variation.

Weaknesses:

In the first draft, the authors slipped between assertions of correlation and assertions of cause-effect relationships not established in the results. However, they have corrected the language so that it more carefully makes this distinction.

Reviewer #3 (Public review):

The Mutational Hazard Hypothesis (MHH) suggests that lineages with smaller effective population sizes should accumulate slightly deleterious transposable elements leading to larger genome size. Marino and colleagues tested the MHH using a set of 807 vertebrate, mollusc and insect species. The authors mined repeats de novo and estimated dN/dS for each genome. Then, they used dN/dS and life history traits as reliable proxies for effective population size and tested for correlations between these proxies and repeat content while accounting for phylogenetic nonindependence. The results suggest that overall, lineages with lower effective population sizes do not exhibit increases in repeat content or genome size. This contrasts with expectations from the MHH. The authors speculate that changes in genome size may be driven by lineage-specific host-TE conflicts rather than effective population size.

Strengths:

The general conclusions of this paper are supported by a powerful dataset of phylogenetically diverse species. Furthermore, the hypothesis tested is important and has proved challenging to test in the past due to technical challenges and confounding factors. The use of C-values rather than assembly size for many species (when available) helps to mitigate the challenges associated with underrepresentation of repetitive regions in short-read based genome assemblies. Overall, both the phylogenetic breadth of species considered and the approaches employed make the results highly convincing.

Weaknesses:

My primary concerns were related to possible biases in the author's data due to their approach to TE annotation. The authors have sufficiently acknowledged and addressed these concerns in their revised manuscript. I note no further weaknesses.

eLife Assessment

This manuscript reports valuable findings on the role of the Srs2 protein in turning off the DNA damage signaling response initiated by Mec1 (human ATR) kinase. The data provide convincing evidence that Srs2 interaction with PCNA and ensuing SUMO modification is required for checkpoint downregulation. However, while the model that Srs2 acts at gaps after camptothecin-induced DNA damage is reasonable, direct experimental evidence for this is currently lacking. The work will be of interest to cell biologists studying genome integrity.

Reviewer #1 (Public review):

Overall, the data presented in this manuscript is of good quality. Understanding how cells control RPA loading on ssDNA is crucial to understanding DNA damage responses and genome maintenance mechanisms. The authors used genetic approaches to show that disrupting PCNA binding and SUMOylation of Srs2 can rescue the CPT sensitivity of rfa1 mutants with reduced affinity for ssDNA. In addition, the authors find that SUMOylation of Srs2 depends on binding to PCNA and the presence of Mec1.

Comments on previous revisions:

I am satisfied with the revisions made by the authors, which helped clarify some points that were confusing in the initial submission.

Reviewer #2 (Public review):

This is an interesting paper that delves into the post-translational modifications of the yeast Srs2 helicase and proteins with which it interacts in coping with DNA damage. The authors use mutants in some interaction domains with RPA and Srs2 to argue for a model in which there is a balance between RPA binding to ssDNA and Srs2's removal of RPA.

The manuscript mostly addresses previous concerns by doubling down on the model without providing additional direct evidence of interactions between Srs2 and PCNA, and that "precise sites of Srs2 actions in the genome remain to be determined." One additional Srs2 allele has been examined, showing some effect in combination with rfa1-zm2.

Reviewer #3 (Public review):

The superfamily I 3'-5' DNA helicase Srs2 is well known for its role as an anti-recombinase, stripping Rad51 from ssDNA, as well as an anti-crossover factor, dissociating extended D-loops and favoring non-crossover outcome during recombination. In addition, Srs2 plays a key role in in ribonucleotide excision repair. Besides DNA repair defects, srs2 mutants also show a reduced recovery after DNA damage that is related to its role in downregulating the DNA damage signaling or checkpoint response. Recent work from the Zhao laboratory (PMID: 33602817) identified a role of Srs2 in downregulating the DNA damage signaling response by removing RPA from ssDNA. This manuscript reports further mechanistic insights into the signaling downregulation function of Srs2.

Using the genetic interaction with mutations in RPA1, mainly rfa1-zm2, the authors test a panel of mutations in Srs2 that affect CDK sites (srs2-7AV), potential Mec1 sites (srs2-2SA), known sumoylation sites (srs2-3KR), Rad51 binding (delta 875-902), PCNA interaction (delta 1159-1163), and SUMO interaction (srs2-SIMmut). All mutants were generated by genomic replacement and the expression level of the mutant proteins was found to be unchanged. This alleviates some concern about the use of deletion mutants compared to point mutations. Double mutant analysis identified that PCNA interaction and SUMO sites were required for the Srs2 checkpoint dampening function, at least in the context of the rfa1-zm2 mutant. There was no effect of this mutants in a RFA1 wild type background. This latter result is likely explained by the activity of the parallel pathway of checkpoint dampening mediated by Slx4, and genetic data with an Slx4 point mutation affecting Rtt107 interaction and checkpoint downregulation support this notion. Further analysis of Srs2 sumoylation showed that Srs2 sumoylation depended on PCNA interaction, suggesting sequential events of Srs2 recruitment by PCNA and subsequent sumoylation. Kinetic analysis showed that sumoylation peaks after maximal Mec1 induction by DNA damage (using the Top1 poison camptothecin (CPT)) and depended on Mec1. This data are consistent with a model that Mec1 hyperactivation is ultimately leading to signaling downregulation by Srs2 through Srs2 sumoylation. Mec1-S1964 phosphorylation, a marker for Mec1 hyperactivation and a site found to be needed for checkpoint downregulation after DSB induction, did not appear to be involved in checkpoint downregulation after CPT damage. The data are in support of the model that Mec1 hyperactivation when targeted to RPA-covered ssDNA by its Ddc2 (human ATRIP) targeting factor, favors Srs2 sumoylation after Srs2 recruitment to PCNA to disrupt the RPA-Ddc2-Mec1 signaling complex. Presumably, this allows gap filling and disappearance of long-lived ssDNA as the initiator of checkpoint signaling, although the study does not extend to this step.

Strengths:

(1) The manuscript focuses on the novel function of Srs2 to downregulate the DNA damage signaling response and provide new mechanistic insights.

(2) The conclusions that PCNA interaction and ensuing Srs2-sumoylation are involved in checkpoint downregulation are well supported by the data.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Overall, the data presented in this manuscript is of good quality. Understanding how cells control RPA loading on ssDNA is crucial to understanding DNA damage responses and genome maintenance mechanisms. The authors used genetic approaches to show that disrupting PCNA binding and SUMOylation of Srs2 can rescue the CPT sensitivity of rfa1 mutants with reduced affinity for ssDNA. In addition, the authors find that SUMOylation of Srs2 depends on binding to PCNA and the presence of Mec1.

Comments on revisions:

I am satisfied with the revisions made by the authors, which helped clarify some points that were confusing in the initial submission.

Thank you.

Reviewer #2 (Public Review):

This revised manuscript mostly addresses previous concerns by doubling down on the model without providing additional direct evidence of interactions between Srs2 and PCNA, and that "precise sites of Srs2 actions in the genome remain to be determined." One additional Srs2 allele has been examined, showing some effect in combination with rfa1-zm2. Many of the conclusions are based on reasonable assumptions about the consequences of various mutations, but direct evidence of changes in Srs2 association with PNCA or other interactors is still missing. There is an assumption that a deletion of a Rad51-interacting domain or a PCNA-interacting domain have no pleiotropic effects, which may not be the case. How SLX4 might interact with Srs2 is unclear to me, again assuming that the SLX4 defect is "surgical" - removing only one of its many interactions.

Previous studies have already provided direct evidence for the interaction between Srs2 and PCNA through the Srs2’s PIM region (Armstrong et al, 2012; Papouli et al, 2005); we have added these citations in the text. Similarly. Srs2 associations with SUMO and Rad51 have also been demonstrated (Colavito et al, 2009; Kolesar et al, 2016; Kolesar et al., 2012), and these studies were cited in the text.

We did not state that a deletion of a Rad51-interacting domain or a PCNA-interacting domain have no pleiotropic effects. We only assessed whether these previously characterized mutant alleles could mimic srs2∆ in rescuing rfa1-zm2 defects.

We assessed the genetic interaction between slx4-RIM and srs2-∆PIM mutants, and not the physical interaction between the two proteins. As we described in the text, our rationale for this genetic test is based on that the reports that both slx4 and srs2 mutants impair recovery from the Mec1 induced checkpoint, thus they may affect parallel pathways of checkpoint dampening.

One point of concern is the use of t-tests without some sort of correction for multiple comparisons - in several figures. I'm quite sceptical about some of the p < 0.05 calls surviving a Bonferroni correction. Also in 4B, which comparison is **? Also, admittedly by eye, the changes in "active" Rad53 seem much greater than 5x. (also in Fig. 3, normalizing to a non-WT sample seems odd).

Claims made in this work were based only on pairwise comparison not multi-comparison. We have now made this point clearer in the graphs and in Method. As the values were compared between a wild-type strain and a specific mutant strain, or between two mutants, we believe that t-test is suitable for statistical analysis.

Figure 4B, ** indicates that the WT value is significantly different from that of the slx4-RIM srs2-∆PIM double mutant and from that of srs2-∆PIM single mutant. We have modified the graph to indicate the pair-wide comparison. The 5-fold change of active Rad53 levels was derived by comparing the values between the srs2∆ PIM slx4^RIM-TAP double mutant and wild-type Slx4-TAP. In Figure 3, normalization to the lowest value affords better visualization. This is rather a stylish issue; we would like to maintain it as the other reviewers had no issues.

What is the WT doubling time for this strain? From the FACS it seems as if in 2 h the cells have completed more than 1 complete cell cycle. Also in 5D. Seems fast...

Wild-type W303 strain has less than 90 min doubling time as shown by many labs, and our data are consistent with this. The FACS profiles for wild-type cells shown in Figures 3C, 4C, and 5C are consistent with each other, showing that after G1 cells entered the cell cycle, they were in G2 phase at the 1-hour time points, and then a percentage of the cells exited the first cell cycle by two hours.

I have one over-arching confusion. Srs2 was shown initially to remove Rad51 from ssDNA and the suppression of some of srs2's defects by deleting rad51 made a nice, compact story, though exactly how srs2's "suppression of rad6" fit in isn't so clear (since Rad6 ties into Rad18 and into PCNA ubiquitylation and into PCNA SUMOylation). Now Srs2 is invoked to remove RPA. It seems to me that any model needs to explain how Srs2 can be doing both. I assume that if RPA and Rad51 are both removed from the same ssDNA, the ssDNA will be "trashed" as suggested by Symington's RPA depletion experiments. So building a model that accounts for selective Srs2 action at only some ssDNA regions might be enhanced by also explaining how Rad51 fits into this scheme.

While the anti-recombinase function of Srs2 was better studied, its “anti-RPA” role in checkpoint dampening was recently described by us (Dhingra et al, 2021) following the initial report by the Haber group some time ago (Vaze et al, 2002). A better understanding of this new role is required before we can generate a comprehensive picture of how Srs2 integrates the two functions (and possibly other functions). Our current work addresses this issue by providing a more detailed understanding of this new role of Srs2.

Single molecular data showed that Srs2 strips both RPA and Rad51 from ssDNA, but this effect is highly dynamic (i.e. RPA and Rad51 can rebind ssDNA after being displaced) (De Tullio et al, 2017). As such, generation of “deserted” ssDNA regions lacking RPA and Rad51 in cells can be an unlikely event. Rather, Srs2 can foster RPA and Rad51 dynamics on ssDNA. Additional studies will be needed to generate a model that integrates the anti-recombinase and the anti-RPA roles of Srs2.

As a previous reviewer has pointed out, CPT creates multiple forms of damage. Foiani showed that 4NQO would activate the Mec1/Rad53 checkpoint in G1- arrested cells, presumably because there would be singlestrand gaps but no DSBs. Whether this would be a way to look specifically at one type of damage is worth considering; but UV might be a simpler way to look. As also noted, the effects on the checkpoint and on viability are quite modest. Because it isn't clear (at least to me) why rfa1 mutants are so sensitive to CPT, it's hard for me to understand how srs2-zm2 has a modest suppressive effect: is it by changing the checkpoint response or facilitating repair or both? Or how srs2-3KR or srs2-dPIM differ from rfa1-zm2 in this respect. The authors seem to lump all these small suppressions under the rubric of "proper levels of RPA-ssDNA" but there are no assays that directly get at this. This is the biggest limitation.

CPT treatment is an ideal condition to examine how cells dampen the DNA damage checkpoint, because while most genotoxic conditions (e.g. 4NQO, MMS) induce both the DNA replication checkpoint and the DNA damage checkpoint, CPT was shown to only induced the latter (Menin et al, 2018; Minca & Kowalski, 2011; Redon et al, 2003; Tercero et al, 2003). Future studies examining 4NQO and UV conditions can further expand our understanding of checkpoint dampening in different conditions.

We have previously provided evidence to support the conclusion that srs2 suppression of rfa1-zm is partly mediated by changing checkpoint levels (Dhingra et al., 2021). We cannot exclude the possibility that the suppression may also be related to changes of DNA repair; we have now added this note in the text.

Regarding direct testing RPA levels on DNA, we have previously shown that srs2∆ increased the levels of chromatin associated Rfa1 and this is suppressed by rfa1-zm2 (Dhingra et al., 2021). We have now included chromatin fractionation data to show that srs2-∆PIM also led to an increase of Rfa1 on chromatin, and this was suppressed by rfa1-zm2 (new Fig. S2).

Srs2 has also been implicated as a helicase in dissolving "toxic joint molecules" (Elango et al. 2017). Whether this activity is changed by any of the mutants (or by mutations in Rfa1) is unclear. In their paper, Elango writes: "Rare survivors in the absence of Srs2 rely on structure-specific endonucleases, Mus81 and Yen1, that resolve toxic joint-molecules" Given the involvement of SLX4, perhaps the authors should examine the roles of structure-specific nucleases in CPT survival?

Srs2 has several roles, and its role in RPA antagonism can be genetically separated from its role in Rad51 regulation as we have shown in our previous work (Dhingra et al., 2021) and this notion is further supported by evidence presented in the current work. Srs2’s role in dissolving "toxic joint molecules” was mainly observed during BIR (Elango et al, 2017). Whether it is related to checkpoint dampening will be interesting to address in the future but is beyond of the scope of the current work that seeks to answer the question how Srs2 regulates RPA during checkpoint dampening. Similarly, determining the roles of Mus81 and Yen1 and other structural nucleases in CPT survival is a worthwhile task but it is a research topic well separated from the focus of this work.

Experiments that might clarify some of these ambiguities are proposed to be done in the future. For now, we have a number of very interesting interactions that may be understood in terms of a model that supposes discriminating among gaps and ssDNA extensions by the presence of PCNA, perhaps modified by SUMO. As noted above, it would be useful to think about the relation to Rad6.

Several studies have shown that Srs2’s functional interaction with Rad6 is based on Srs2-mediated recombination regulation (reviewed by (Niu & Klein, 2017). Given that recombinational regulation by Srs2 is genetically separable from the Srs2 and RPA antagonism (Dhingra et al., 2021), we do not see a strong rationale to examine Rad6 in this work, which addresses how Srs2 regulates RPA. With this said, this study has provided basis for future studies of possible cross-talks among different Srs2-mediated pathways.

Reviewer #3 (Public Review):

The superfamily I 3'-5' DNA helicase Srs2 is well known for its role as an anti-recombinase, stripping Rad51 from ssDNA, as well as an anti-crossover factor, dissociating extended D-loops and favoring non-crossover outcome during recombination. In addition, Srs2 plays a key role in in ribonucleotide excision repair. Besides DNA repair defects, srs2 mutants also show a reduced recovery after DNA damage that is related to its role in downregulating the DNA damage signaling or checkpoint response. Recent work from the Zhao laboratory (PMID: 33602817) identified a role of Srs2 in downregulating the DNA damage signaling response by removing RPA from ssDNA. This manuscript reports further mechanistic insights into the signaling downregulation function of Srs2.

Using the genetic interaction with mutations in RPA1, mainly rfa1-zm2, the authors test a panel of mutations in Srs2 that affect CDK sites (srs2-7AV), potential Mec1 sites (srs2-2SA), known sumoylation sites (srs2-3KR), Rad51 binding (delta 875-902), PCNA interaction (delta 1159-1163), and SUMO interaction (srs2SIMmut). All mutants were generated by genomic replacement and the expression level of the mutant proteins was found to be unchanged. This alleviates some concern about the use of deletion mutants compared to point mutations. Double mutant analysis identified that PCNA interaction and SUMO sites were required for the Srs2 checkpoint dampening function, at least in the context of the rfa1-zm2 mutant. There was no effect of this mutants in a RFA1 wild type background. This latter result is likely explained by the activity of the parallel pathway of checkpoint dampening mediated by Slx4, and genetic data with an Slx4 point mutation affecting Rtt107 interaction and checkpoint downregulation support this notion. Further analysis of Srs2 sumoylation showed that Srs2 sumoylation depended on PCNA interaction, suggesting sequential events of Srs2 recruitment by PCNA and subsequent sumoylation. Kinetic analysis showed that sumoylation peaks after maximal Mec1 induction by DNA damage (using the Top1 poison camptothecin (CPT)) and depended on Mec1. This data are consistent with a model that Mec1 hyperactivation is ultimately leading to signaling downregulation by Srs2 through Srs2 sumoylation. Mec1-S1964 phosphorylation, a marker for Mec1 hyperactivation and a site found to be needed for checkpoint downregulation after DSB induction, did not appear to be involved in checkpoint downregulation after CPT damage. The data are in support of the model that Mec1 hyperactivation when targeted to RPA-covered ssDNA by its Ddc2 (human ATRIP) targeting factor, favors Srs2 sumoylation after Srs2 recruitment to PCNA to disrupt the RPA-Ddc2-Mec1 signaling complex. Presumably, this allows gap filling and disappearance of long-lived ssDNA as the initiator of checkpoint signaling, although the study does not extend to this step.

Strengths:

(1) The manuscript focuses on the novel function of Srs2 to downregulate the DNA damage signaling response and provide new mechanistic insights.

(2) The conclusions that PCNA interaction and ensuing Srs2-sumoylation are involved in checkpoint downregulation are well supported by the data.

Weaknesses:

(1) Additional mutants of interest could have been tested, such as the recently reported Pin mutant, srs2-Y775A (PMID: 38065943), and the Rad51 interaction point mutant, srs2-F891A (PMID: 31142613).

(2) The use of deletion mutants for PCNA and RAD51 interaction is inferior to using specific point mutants, as done for the SUMO interaction and the sites for post-translational modifications.

(3) Figure 4D and Figure 5A report data with standard deviations, which is unusual for n=2. Maybe the individual data points could be plotted with a color for each independent experiment to allow the reader to evaluate the reproducibility of the results.

Comments on revisions:

In this revision, the authors adequately addressed my concerns. The only issue I see remaining is the site of Srs2 action. The authors argue in favor of gaps and against R-loops and ssDNA resulting from excessive supercoiling. The authors do not discuss ssDNA resulting from processing of onesided DSBs, which are expected to result from replication run-off after CPT damage but are not expected to provide the 3'-junction for preferred PCNA loading. Can the authors exclude PCNA at the 5'-junction at a resected DSB?

We have now added a sentence stating that we cannot exclude the possibility that PCNA may be positioned at a 5’-junction, as this can be observed in vitro, albert that PCNA loading was seen exclusively at a 3’-junction in the presence of RPA (Ellison & Stillman, 2003; Majka et al, 2006).

Recommendations For the authors:

Reviewer #2 (Recommendations For the authors):

A Bonferroni correction should be made for the multiple comparisons in several figures.

Specific comments:

l. 41. This is a too long and confusing sentence.

Sentence shortened: “These data suggest that Srs2 recruitment to PCNA proximal ssDNA-RPA filaments followed by its sumoylation can promote checkpoint recovery, whereas Srs2 action is minimized at regions with no proximal PCNA to permit RPA-mediated ssDNA protection”.

l. 60. Identify Ddc2 and Mec1 as ATRIP and ATR.

Done.

l. 125 "fails to downregulate RPA levels on chromatin and Mec1-mediated DDC..." fails to downregulate RPA and fails to reduce Mec1-mediated DDC?

Sentence modified: “fails to downregulate both the RPA levels on chromatin and the Mec1-mediated DDC”

l. 204 "consistent with the notion that Srs2 has roles beyond RPA regulation"... What other roles? It's stripping of Rad51? Removing toxic joint molecules? Something else?

Sentence modified: “consistent with the notion that Srs2 has roles beyond RPA regulation, such as in Rad51 regulation and removing DNA joint molecules”.

l. 249 "Significantly, srs2-ΔPIM and -3KR increased the percentage of rfa1-zm2 cells transitioning into the G1 phase" No. Just back to normal. As stated in l. 258: "258 We found that srs2-ΔPIM and srs2-3KR mutants on their own behaved normally in the two DDC assays described above." All of these effects are quite small.

Sentence modified: “Compared with rfa1-zm2 cells, srs2-∆PIM rfa1-zm2 and srs2-3KR rfa1-zm2 cells showed increased percentages of cells transitioning into the G1 phase”.

l. 468 "Our previous work has provided several lines of evidence to support that Rad51 removal by Srs2 is separable from the Srs2-RPA antagonism (Dhingra et al., 2021). What evidence? See my comment above about not having both proteins removed at the same time.

We have addressed this point in our initial rebuttal and some key points are summarized below. In our previous report (Dhingra et al., 2021), we provided several lines of evidence to support the conclusion that Rad51 is not relevant to the Srs2-RPA antagonism. For example, while rad51∆ rescues the hyper-recombination phenotype of srs2∆ cells, rad51∆ did not affect the hyper-checkpoint phenotype of srs2∆. In contrast, rfa1-zm1/zm2 have the opposite effects, that is, rfa1zm1/zm2 suppressed the hyper-checkpoint, but not the hyper-recombination, phenotype of srs2∆ cells. The differential effects of rad51∆ and rfa1-zm1/zm2 were also seen for the ATPase dead allele of Srs2 (srs2K41A). For example, rfa1-zm2 rescued hyper-checkpoint and CPT sensitivity of srs2-K41A cells, while rad51∆ had neither effect. These and other data described by Dhingra et al (2021) suggest that Srs2’s effects on checkpoint vs. recombination can be separated genetically. Consistent with our conclusion summarized above, deleting the Rad51 binding domain in Srs2 (srs2-∆Rad51BD) has no effect on rfa1-zm2 phenotype in CPT (Fig. 2D). This data provides yet another evidence that Srs2 regulation of Rad51 is separable from the Srs2RPA antagonism.

l. 525 "possibility, we tested the separation pin of Srs2 (Y775), which was shown to enables its in vitro helicase activity during the revision of our work..." ?? there was helicase activity during the revision of your work? Please fix the sentence.

Sentence modified: “we tested the separation pin of Srs2 (Y775). This residue was shown to be key for the Srs2’s helicase activity in vitro in a report that was published during the revision of our work (Meir et al, 2023).”

Fig. 3. "srs2-ΔPIM and -3KR allow better G1 entry of rfa1-zm2 cells." is it better entry or less arrest at G2/M? One implies better turning off of a checkpoint, the other suggests less activation of the checkpoint.

This is a correct statement. For all strains examined in Figure 3, cells were seen in G2/M phase after 1-hour CPT treatment, suggesting proper arrest.

References:

Armstrong AA, Mohideen F, Lima CD (2012) Recognition of SUMO-modified PCNA requires tandem receptor motifs in Srs2. Nature 483: 59-63

Colavito S, Macris-Kiss M, Seong C, Gleeson O, Greene EC, Klein HL, Krejci L, Sung P (2009) Functional significance of the Rad51-Srs2 complex in Rad51 presynaptic filament disruption. Nucleic Acids Res 37: 6754-6764.

De Tullio L, Kaniecki K, Kwon Y, Crickard JB, Sung P, Greene EC (2017) Yeast Srs2 helicase promotes redistribution of single-stranded DNA-bound RPA and Rad52 in homologous recombination regulation. Cell Rep 21: 570-577

Dhingra N, Kuppa S, Wei L, Pokhrel N, Baburyan S, Meng X, Antony E, Zhao X (2021) The Srs2 helicase dampens DNA damage checkpoint by recycling RPA from chromatin. Proc Natl Acad Sci U S A 118: e2020185118

Elango R, Sheng Z, Jackson J, DeCata J, Ibrahim Y, Pham NT, Liang DH, Sakofsky CJ, Vindigni A, Lobachev KS et al (2017) Break-induced replication promotes formation of lethal joint molecules dissolved by Srs2. Nat Commun 8: 1790

Ellison V, Stillman B (2003) Biochemical characterization of DNA damage checkpoint complexes: clamp loader and clamp complexes with specificity for 5' recessed DNA. PLoS Biol 1: E33

Kolesar P, Altmannova V, Silva S, Lisby M, Krejci L (2016) Pro-recombination Role of Srs2 Protein Requires SUMO (Small Ubiquitin-like Modifier) but Is Independent of PCNA (Proliferating Cell Nuclear Antigen) Interaction. J Biol Chem 291: 7594-7607.

Kolesar P, Sarangi P, Altmannova V, Zhao X, Krejci L (2012) Dual roles of the SUMO-interacting motif in the regulation of Srs2 sumoylation. Nucleic Acids Res 40: 7831-7843.

Majka J, Binz SK, Wold MS, Burgers PM (2006) Replication protein A directs loading of the DNA damage checkpoint clamp to 5'-DNA junctions. J Biol Chem 281: 27855-27861

Meir A, Raina VB, Rivera CE, Marie L, Symington LS, Greene EC (2023) The separation pin distinguishes the pro- and anti-recombinogenic functions of Saccharomyces cerevisiae Srs2. Nat Commun 14: 8144

Menin L, Ursich S, Trovesi C, Zellweger R, Lopes M, Longhese MP, Clerici M (2018) Tel1/ATM prevents degradation of replication forks that reverse after Topoisomerase poisoning. EMBO Rep 19: e45535

Minca EC, Kowalski D (2011) Replication fork stalling by bulky DNA damage: localization at active origins and checkpoint modulation. Nucleic Acids Res 39: 2610-2623

Niu H, Klein HL (2017) Multifunctional roles of Saccharomyces cerevisiae Srs2 protein in replication, recombination and repair. FEMS Yeast Res 17: fow111

Papouli E, Chen S, Davies AA, Huttner D, Krejci L, Sung P, Ulrich HD (2005) Crosstalk between SUMO and ubiquitin on PCNA is mediated by recruitment of the helicase Srs2p. Mol Cell 19: 123-133

Redon C, Pilch DR, Rogakou EP, Orr AH, Lowndes NF, Bonner WM (2003) Yeast histone 2A serine 129 is essential for the efficient repair of checkpoint-blind DNA damage. EMBO Rep 4: 678-684

Tercero JA, Longhese MP, Diffley JFX (2003) A central role for DNA replication forks in checkpoint activation and response. Mol Cell 11: 1323-1336

Vaze MB, Pellicioli A, Lee SE, Ira G, Liberi G, Arbel-Eden A, Foiani M, Haber JE (2002) Recovery from checkpointmediated arrest after repair of a double-strand break requires Srs2 helicase. Mol Cell 10: 373-385

eLife Assessment

This study reports a fundamental observation concerning cell death regulation by the anti-apoptotic BCL2 family NOXA. The authors convincingly demonstrate that NOXA is destabilized through the interaction with WSB2, a substrate receptor in CRL5 ubiquitin ligase complex, sensitizing the cells to treatments. These are key findings for cell biologists and cancer researchers as they identified a new target impacting drug responsiveness in cancer therapies.

Reviewer #1 (Public Review):

Summary:

In this manuscript, Jiao D et al reported the induction of synthetic lethality by combined inhibition of anti-apoptotic BCL-2 family proteins and WSB2, a substrate receptor in CRL5 ubiquitin ligase complex. Mechanistically, WSB2 interacts with NOXA to promote its ubiquitylation and degradation. Cancer cells deficient in WSB2, as well as heart and liver tissues from Wsb2-/- mice exhibit high susceptibility to apoptosis induced by inhibitors of BCL-2 family proteins. The anti-apoptotic activity of WSB2 is partially dependent on NOXA.

Overall, the finding that WSB2 disruption triggers synthetic lethality to BCL-2 family protein inhibitors by destabilizing NOXA is rather novel. The manuscript is largely hypothesis-driven, with experiments that are adequately designed and executed. However, there are quite a few issues for the authors to address, including those listed below.

Specific comments from the previous round of review:

(1) At the beginning of the Results section, a clear statement is needed as to why the authors are interested in WSB2 and what brought them to analyze "the genetic co-dependency between WSB2 and other proteins".

(2) In general, the biochemical evidence supporting the role of WSB2 as a SOCS box-containing substrate-binding receptor of CRL5 E3 in promoting NOXA ubiquitylation and degradation is relatively weak. First, since NOXA2 binds to WSB2 on its SOCS box, which consists of a BC box for Elongin B/C binding and a CUL5 box for CUL5 binding, it is crucial to determine whether the binding of NOXA on the SOCS box affects the formation of CRL5WSB2 complex. The authors should demonstrate the endogenous binding between NOXA and the CRL5WSB2 complex. Additionally, the authors may also consider manipulating CUL5, SAG, or ElonginB/C to assess if it would affect NOXA protein turnover in two independent cell lines. Second, in all the experiments designed to detect NOXA ubiquitylation in cells, the authors utilized immunoprecipitation (IP) with FLAG-NOXA/NOXA, followed by immunoblotting (IB) with HA-Ub. However, it is possible that the observed poly-Ub bands could be partly attributed to the ubiquitylation of other NOXA binding proteins. Therefore, the authors need to consider performing IP with HA-Ub and subsequently IB with NOXA. Alternatively, they could use Ni-beads to pull down all His-Ub-tagged proteins under denaturing conditions, followed by the detection of FLAG-tagged NOXA using anti-FLAG Ab. The authors are encouraged to perform one of these suggested experiments to exclude the possibility of this concern. Furthermore, an in vitro ubiquitylation assay is crucial to conclusively demonstrate that the polyubiquitylation of NOXA is indeed mediated by the CRL5WSB2 complex.

(3) In their attempt to map the binding regions between NOXA and WSB2, the authors utilized exogenous proteins of both WSB2 and NOXA. To strengthen their findings, it would be more convincing to perform IP with exogenous wt/mutant WSB2 or NOXA and subsequently perform IB to detect endogenous NOXA or WSB2, respectively. Additionally, an in vitro binding assay using purified proteins would provide further evidence of a direct binding between NOXA and WSB2.

Comments on latest version:

The authors have adequately addressed my previous comments.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

I In this manuscript, Jiao D et al reported the induction of synthetic lethal by combined inhibition of anti-apoptotic BCL-2 family proteins and WSB2, a substrate receptor in CRL5 ubiquitin ligase complex. Mechanistically, WSB2 interacts with NOXA to promote its ubiquitylation and degradation. Cancer cells deficient in WSB2, as well as heart and liver tissues from Wsb2-/- mice exhibit high susceptibility to apoptosis induced by inhibitors of BCL-2 family proteins. The anti-apoptotic activity of WSB2 is partially dependent on NOXA.

Overall, the finding, that WSB2 disruption triggers synthetic lethality to BCL-2 family protein inhibitors by destabilizing NOXA, is rather novel. The manuscript is largely hypothesis-driven, with experiments that are adequately designed and executed. However, there are quite a few issues for the authors to address, including those listed below.

Specific comments:

(1) At the beginning of the Results section, a clear statement is needed as to why the authors are interested in WSB2 and what brought them to analyze "the genetic co-dependency between WSB2 and other proteins".

We thank the reviewer for raising this important point. We agree that a clear rationale should be provided at the beginning of the Results section. As reported in previous studies [Ref: 1, 2, 3], strong synthetic interactions have been observed between WSB2 and several mitochondrial apoptosis-related factors, including MCL-1, BCL-xL, and MARCH5. We have referenced these findings in the Discussion section. Motivated by these studies, we became interested in the role of WSB2 and aimed to investigate the specific mechanisms underlying its synthetic lethality with anti-apoptotic BCL-2 family members. We will revise the beginning of the Results section to clearly state this rationale.

(1) McDonald, E.R., 3rd et al. Project DRIVE: A Compendium of Cancer Dependencies and Synthetic Lethal Relationships Uncovered by Large-Scale, Deep RNAi Screening. Cell 170, 577-592 e510 (2017).

(2) DeWeirdt, P.C. et al. Genetic screens in isogenic mammalian cell lines without single cell cloning. Nat Commun 11, 752 (2020).

(3) DeWeirdt, P.C. et al. Optimization of AsCas12a for combinatorial genetic screens in human cells. Nat Biotechnol 39, 94-104 (2021).

(2) In general, the biochemical evidence supporting the role of WSB2 as a SOCS box-containing substrate-binding receptor of CRL5 E3 in promoting NOXA ubiquitylation and degradation is relatively weak. First, since NOXA binds to WSB2 on its SOCS box, which consists of a BC box for Elongin B/C binding and a CUL5 box for CUL5 binding, it is crucial to determine whether the binding of NOXA on the SOCS box affects the formation of CRL5WSB2 complex. The authors should demonstrate the endogenous binding between NOXA and the CRL5WSB2 complex. Additionally, the authors may also consider manipulating CUL5, SAG, or ElonginB/C to assess if it would affect NOXA protein turnover in two independent cell lines.

We thank the reviewer for raising this important point. To determine whether endogenous NOXA binds to the intact CRL5^WSB2 complex, we performed co-immunoprecipitation assays using an antibody against NOXA. Indeed, NOXA co-immunoprecipitated with all subunits of the CRL5^WSB2 complex (Figure 2—figure supplement 1D), suggesting that NOXA binding to WSB2 does not disrupt interactions between WSB2 and the other CRL5 subunits. Moreover, depletion of CRL5 complex components (RBX2/SAG, CUL5, ELOB, or ELOC) through siRNAs in C4-2B or Huh-7 cells also resulted in a marked increase in NOXA protein levels.

Second, in all the experiments designed to detect NOXA ubiquitylation in cells, the authors utilized immunoprecipitation (IP) with FLAG-NOXA/NOXA, followed by immunoblotting (IB) with HA-Ub. However, it is possible that the observed poly-Ub bands could be partly attributed to the ubiquitylation of other NOXA binding proteins. Therefore, the authors need to consider performing IP with HA-Ub and subsequently IB with NOXA. Alternatively, they could use Ni-beads to pull down all His-Ub-tagged proteins under denaturing conditions, followed by the detection of FLAG-tagged NOXA using anti-FLAG Ab. The authors are encouraged to perform one of these suggested experiments to exclude the possibility of this concern. Furthermore, an in vitro ubiquitylation assay is crucial to conclusively demonstrate that the polyubiquitylation of NOXA is indeed mediated by the CRL5WSB2 complex.

We appreciate the reviewer for raising these important considerations regarding our ubiquitylation assays. We fully acknowledge the reviewer's concern that classical ubiquitination assays could potentially detect ubiquitination of proteins interacting with NOXA. However, we would like to clarify that our experimental conditions effectively mitigate this issue. Specifically, cells were lysed using buffer containing 1% SDS followed by boiling at 105°C for 5 minutes. These rigorous denaturing conditions ensure disruption of non-covalent protein interactions, thereby effectively eliminating the possibility of detecting ubiquitination signals from NOXA-associated proteins.

Regarding the suggestion to perform an in vitro ubiquitination assay, we agree this experiment would indeed provide additional evidence. However, due to significant technical complexities associated with reconstituting CRL5-based E3 ubiquitin ligase activity in vitro—which would require the expression and purification of at least six recombinant proteins—such experiments are rarely performed in this context. Furthermore, NOXA is uniquely localized as a membrane protein on the mitochondrial outer membrane, posing additional significant challenges for protein expression and purification. Given the robustness of our current in vivo ubiquitylation assay under stringent denaturing conditions, we believe our existing data sufficiently and conclusively demonstrate NOXA ubiquitination mediated by the CRL5^WSB2 complex.

(3) In their attempt to map the binding regions between NOXA and WSB2, the authors utilized exogenous proteins of both WSB2 and NOXA. To strengthen their findings, it would be more convincing to perform IP with exogenous wt/mutant WSB2 or NOXA and subsequently perform IB to detect endogenous NOXA or WSB2, respectively. Additionally, an in vitro binding assay using purified proteins would provide further evidence of a direct binding between NOXA and WSB2.

We thank the reviewer for raising these important issues. In response to the reviewer’s suggestion to map the binding regions between NOXA and WSB2 more convincingly, we have indeed performed semi-endogenous Co-IP assays, which yielded results consistent with our exogenous protein experiments (Figure 3—figure supplement 1A, B). Concerning the recommendation to further validate direct interaction using purified recombinant proteins, we encountered substantial technical difficulties in obtaining pure and soluble recombinant WSB2 protein. Additionally, given that NOXA is an outer mitochondrial membrane protein and the interaction occurs on mitochondria, we believe that an in vitro binding assay may have limited physiological relevance. We hope the reviewer can appreciate these practical challenges and our current evidence supporting the strong interaction between NOXA and WSB2.

Reviewer #2 (Public Review):

Summary:

Exploring the DEP-MAP database and two drug-screen databases, the authors identify WSB2 as an interactor of several BCL2 proteins. In follow-up experiments, they show that CRL5/WSB2 controls NOXA protein levels via K48 ubiquitination following direct protein-protein interaction, and cell death sensitivity in the context of BH3 mimetic treatment, where WSB2 depletion synergizes with drug treatment.

Strengths:

The authors use a set of orthogonal methods across different model cell lines and a new WSB2 KO mouse model to confirm their findings. They also manage to correlate WSB2 expression with poor prognosis in prostate and liver cancer, supporting the idea that targeting WSB2 may sensitize cancers for treatment with BH3 mimetics.

Weaknesses:

The conclusions drawn based on the findings in cancer patients are very speculative, as regulation of NOXA cannot be the sole function of CRL5/WSB2 and it is hence unclear what causes correlation with patient survival. Moreover, the authors do not provide a clear mechanistic explanation of how exactly higher levels of NOXA promote apoptosis in the absence of WSB2. This would be important knowledge, as usually high NOXA levels correlate with high MCL1, as they are turned over together, but in situations like this, or loss of other E3 ligases, such as MARCH, the buffering capacity of MCL1 is outrun, allowing excess NOXA to kill (likely by neutralizing other BCL2 proteins it usually does not bind to, such as BCLX). Moreover, a necroptosis-inducing role of NOXA has been postulated. Neither of these options is interrogated here.

Recommendations For The Authors:

Reviewer #1 (Recommendations For The Authors):

(1) Figure 2J. The authors showed that "the mRNA levels of NOXA were even reduced in WSB2-KO cells compared to parental cells". What is the possible mechanism? This point should at least be discussed.

We thank the reviewer for raising these important issues. The underlying mechanisms for the significantly lower mRNA levels of NOXA following the KO of WSB2 are not fully understood at present. However, we propose that this could represent a form of negative feedback regulation at the level of gene expression. Specifically, when the protein levels of BNIP3/3L rise sharply, it may activate mechanisms that suppress their own mRNA synthesis or stability, serving as a buffering system to prevent further protein accumulation. Such negative feedback loops may be critical for maintaining cellular homeostasis and avoiding excessive protein production. Moreover, this phenomenon is frequently observed in other studies investigating substrates targeted by E3 ubiquitin ligases for degradation. We have elaborated on this point in the Discussion section.

(2) Figure 2M. A previous study has clearly demonstrated that NOXA is subjected to ubiquitylation and degradation by CRL5 E3 ligase (PMID: 27591266). This paper should be cited. Also, in that publication, NOXA ubiquitylation is via the K11 linkage, not the K48 linkage. The authors should include K11R mutant in their assay.

We thank the reviewer for raising this important issue. We thank the reviewer for suggesting the relevant reference (PMID: 27591266), which we have now cited accordingly. Additionally, we would like to clarify that our new in vivo ubiquitination assays included the K11R and K11-only ubiquitin mutants, and our data demonstrate that WSB2-mediated NOXA ubiquitination indeed involves the K11 linkage ubiquitination（Figure 2—figure supplement 1E).

(3) Figure 3H, J. The authors stated, "By mutating these lysine residues to arginine, we found that WSB2-mediated NOXA ubiquitination was completely abolished". Which one of the three lysine residues is playing the dominant role?

We thank the reviewer for raising this important issue. To address this, we generated FLAG-NOXA mutants individually substituting lysine residues K35, K41, and K48 with arginine. In vivo ubiquitination assays demonstrated that lysine 48 (K48) is the predominant residue responsible for WSB2-mediated NOXA ubiquitination (Figure 3—figure supplement 1C).

(4) Figure 3N. The authors need to show that the fusion peptide containing C-terminal NOXA peptide competitively inhibits the interaction between endogenous WSB2 and NOXA and extends the protein half-life of NOXA, leading to NOXA accumulation.

We sincerely thank the reviewer for raising these important issues. As suggested, we investigated whether the fusion peptide containing the C-terminal NOXA sequence competitively disrupts the interaction between endogenous WSB2 and NOXA, subsequently influencing NOXA stability. Our results demonstrated that treatment with this fusion peptide indeed significantly reduced the endogenous interaction between WSB2 and NOXA (Figure 3—figure supplement 1D). Furthermore, we observed that the peptide dose-dependently increased endogenous NOXA protein levels and prolonged its protein half-life, thereby resulting in the accumulation of NOXA (Figure 3N; Figure 3—figure supplement 1E, F). These findings collectively indicate that the fusion peptide competitively inhibits the WSB2-NOXA interaction, stabilizes NOXA protein, and enhances its accumulation.

(5) Figure 4. a) It would be better to investigate whether WSB2 knockdown can sensitize cancer cells to the treatment with ABT-737 or AZD5991, evidenced by a decrease in both IC50 values and clonogenic survival rates and whether such sensitization is dependent on NOXA. b) The authors need to show the levels of cleaved caspase-3/7/9 and the percentages of apoptotic cells in shNC cells upon silencing of WSB2 in Figure 4A-F. c) It will be more convincing to repeat the experiment to show synthetic lethality by WSB2 disruption and MCL-1 inhibitor AZD5991 treatment using another cell line, such as WSB2-deficient Huh-7 cells in Figure 4 I&J.

We sincerely thank the reviewer for these valuable and constructive suggestions. Regarding point (a): We believe that our current Western blot and flow cytometry data (Figure 4G–L) have already provided strong evidence that WSB2 depletion enhances apoptosis in response to ABT-737 and AZD5991. Therefore, we consider that additional IC50 and clonogenic survival assays, while informative, may not be essential for supporting our conclusion. Furthermore, as shown in Figure 5A–F, we found that silencing NOXA largely, though not completely, reversed the enhanced apoptosis triggered by these inhibitors in WSB2-deficient cells, suggesting that the sensitization effect is at least partially dependent on NOXA.

Regarding point (b): We have shown that WSB2 knockout alone had no impact on the levels of cleaved caspase-3/7/9 or the percentages of apoptotic cells in Huh-7 and C4-2B cells (Figure 4G-L and Figure 4—figure supplement 1A-D), indicating that WSB2 loss does not induce apoptosis on its own under basal conditions.

Regarding point (c): We appreciate the reviewer’s suggestion and have now repeated the experiment in WSB2 knockout Huh-7 cells. The new results further support the synthetic lethality between WSB2 loss and AZD5991 treatment (Figure 4—figure supplement 1C, D).

(6) Figure 5A/C/E. The effect of siNOXA is minor, if any, for cleavage of caspases. The same thing for Figure 6F/H.

We appreciate the reviewer’s insightful observation regarding the relatively modest effect of shNOXA on caspase cleavage in Figures 5A/C/E and Figures 6F/H. Indeed, we acknowledge that the reduction in caspase cleavage following NOXA knockdown is moderate. However, consistent with our discussions in the manuscript, NOXA knockdown significantly—but not completely—rescued the increased apoptosis observed in WSB2-deficient cells treated with BCL-2 family inhibitors. This suggests that while NOXA plays a notable role, additional mechanisms or unidentified targets may also be involved in WSB2-mediated regulation of apoptosis.

(7) Figure 5 I&J. The authors may consider performing IHC staining, immunofluorescence, or WB analysis to show the levels of NOXA and cleaved caspases or PARP in xenograft tumors. This would provide in vivo evidence of significant apoptosis induction resulting from the co-administration of ABT-737 and R8-C-terminal NOXA peptide.

We appreciate the reviewer's thoughtful suggestion regarding additional immunohistochemical or immunofluorescence analyses in xenograft tumors. However, due to current limitations in available antibodies suitable for reliable detection of NOXA by IHC and IF, we are unable to perform these experiments. We greatly appreciate the reviewer's understanding of this technical constraint. Nevertheless, our existing data collectively supports the conclusion that the combination of ABT-737 and R8-C-terminal NOXA peptide significantly enhances apoptosis in vivo.

(8) Figure 7. Does an inverse correlation exist between the protein levels of WSB2 and NOXA in RPAD or LIHC tissue microarrays? On page 12, in the first paragraph, Figure 7M-P was cited incorrectly.

We sincerely thank the reviewer for raising this important issue. As mentioned above, due to current limitations regarding the availability of suitable antibodies that can reliably detect NOXA by IHC, we regret that it is not feasible to experimentally address this question at this time.

Additionally, we have carefully corrected the citation error involving Figure 7M-P on page 12, as pointed out by the reviewer.

(9) Figure S1D. BCL-W levels were reduced upon WSB2 overexpression, which should be acknowledged.

We sincerely thank the reviewer for raising this important issue. We acknowledge that BCL-W protein levels were slightly reduced upon WSB2 overexpression in Figure S1D. However, this effect is distinct from the pronounced reduction observed in NOXA protein levels. We have revised the manuscript to clarify this point. Additionally, we recognize that transient overexpression systems may occasionally lead to non-specific or artifactual changes. Our exogenous expression and co-immunoprecipitation experiments did not support an interaction between BCL-W and WSB2. Therefore, the observed reduction of BCL-W under these conditions may not reflect a physiologically relevant regulation.

(10) Figure S4. Given WSB2 KO mice are viable; the authors may consider determining whether these mice are more sensitive to radiation-induced tissue damage or but more resistant to radiation-induced tumorigenesis?

We sincerely thank the reviewer for this insightful and biologically meaningful suggestion. We agree that investigating the potential role of WSB2 in radiation-induced tissue damage and tumorigenesis would be of great interest. However, conducting such experiments requires access to specialized irradiation facilities, which are currently unavailable to us. Nevertheless, we recognize the value of this line of investigation and plan to explore it in our future studies.

(11) All data were displayed as mean{plus minus}SD. However, for data from three independent experiments, it is more appropriate to present the results as mean{plus minus}SEM, not mean{plus minus}SD.

We sincerely thank the reviewer for highlighting this important issue. In line with the reviewer's suggestion, we have revised the manuscript accordingly and now present data from three independent experiments as mean ± SEM.

(12) The figure legends require careful review: i) The low dose of ABT-199 (Figure 6H) and the dose of ABT-199 used in Figure 6I are missing. ii) The legends for Figure S1D-E are incorrect. iii) The name of the antibody in the legend of Figure S3C is incorrect.

We sincerely thank the reviewer for raising these important issues. We have carefully corrected all the errors mentioned. In addition, we have thoroughly reviewed the manuscript to prevent similar errors.

Reviewer #2 (Recommendations For The Authors):

The authors focus on NOXA, after initially identifying WSB2 to interact with several BCL2 proteins. The rationale behind this is that WSB2 depletion or overexpression affects NOXA levels, but none of the other BCL2 proteins tested, as stated in the text. Yet, BCLW is also depleted upon overexpression of WSB2 (Supplementary Figure 1). How does this phenomenon relate to the sensitization noted, is BCL-W higher in WSB2 KO cells? It does not seem so though. This warrants discussion.

We appreciate the reviewer for raising this important issue. Our results showed that overexpression of WSB2 markedly reduced NOXA levels, while the levels of other BCL-2 family proteins remained unaffected or minimally affected, such as BCL-W (Figure 2—figure supplement 1A). Furthermore, depletion of WSB2 through shRNA-mediated KD or CRISPR/Cas9-mediated KO in C4-2B cells or Huh-7 cells led to a marked increase in the steady-state levels of endogenous NOXA, without affecting other BCL-2 family proteins examined, included BCL-W (Figure 2A-C, Figure 2—figure supplement 2A, B).

If WSB2 depletion does not affect MCL1 levels, how does excess NOXA actually kill? Does it bind to any (other) prosurvival proteins under conditions of WSB2 depletion? Is the MCL1 half-life changed?

We appreciate the reviewer for raising this important point. NOXA is a BH3-only protein known to promote apoptosis primarily by binding to and neutralizing anti-apoptotic BCL-2 family members, especially MCL-1, via its BH3 domain. It can inhibit MCL-1 either through competitive binding or by facilitating its ubiquitination and subsequent proteasomal degradation. In our system, the total protein levels of MCL-1 remained unchanged in WSB2 knockout cells, suggesting that NOXA may not be promoting apoptosis through enhanced MCL-1 degradation. Instead, we speculate that the accumulation of NOXA in WSB2-deficient cells enhances apoptosis by sequestering MCL-1 through direct binding, thereby freeing pro-apoptotic effectors such as BAK and BAX. In line with our observations, Nakao et al. reported that deletion of the mitochondrial E3 ligase MARCH5 led to a pronounced increase in NOXA expression, while leaving MCL-1 protein levels unchanged in leukemia cell lines (Leukemia. 2023 ;37:1028-1038., PMID: 36973350).

Additionally, NOXA has been reported to interact with other anti-apoptotic proteins, including BCL-XL. It is therefore possible that under conditions of WSB2 depletion, excess NOXA may also bind to BCL-XL and relieve its inhibition of BAX/BAK, further contributing to apoptosis. Future experiments assessing NOXA binding partners in WSB2-deficient cells would help clarify this mechanism.

I think some initial insights into the mechanism underlying the sensitization would add a lot to this study. Is there a role of BFL1/A1 in any of these cell lines, as it can also rather selectively bind to NOXA and is sometimes deregulated in cancer?

We appreciate the reviewer for raising this important issue. While BFL1/A1 is indeed another anti-apoptotic BCL-2 family member that can selectively bind to NOXA and has been implicated in cancer, our study primarily focuses on the WSB2-NOXA axis. However, given its potential involvement in apoptosis regulation, it would be an interesting direction for future studies to explore whether BFL1/A1 contributes to NOXA-mediated sensitization in specific cellular contexts.

Otherwise, this is a very nice and convincing study.

eLife Assessment

This paper reports on a correlation between diminished cardiolipin content and the severity of steatohepatitis in human subjects. This is supported further by experimental evidence from mice in which the gene encoding a key enzyme in cardiolipin synthesis has been compromised in the liver. The correlations established between lipidology, mitochondrial function, and the induction of respiration and oxidative stress are notable and will be useful to researchers in the field. However, given that the causal relationship between lipid perturbation and the progression of steatohepatitis implied in the title has not been tested experimentally, the evidence supporting the paper's key conclusion is incomplete.

Reviewer #1 (Public review):

Summary:

The manuscript by Brothwell and colleagues describes a central role for hepatic cardiolipin deficiency in MASH. The authors identify cardiolipin as a mediator of two long-standing problems in the field: how dysregulated lipid metabolism relates to altered mitochondrial metabolism during MASLD, and what the innate changes are in the steatotic liver that cause the increased respiration. The authors identified reduced liver cardiolipin in humans with MASH and in a variety of mouse models with MASH. When they knocked out hepatic cardiolipin synthesis, mice developed steatosis and inflammation. These mice also recapitulated the elevated hepatic oxidative metabolism and oxidative stress found in obese humans with MASLD. Some of the in vivo functional data related to glucose homeostasis and substrate metabolism could be stronger, and interpretation of the in vitro flux data needs some clarification, but in both cases, the data are not essential to the main conclusions of the manuscript. Overall, the study offers compelling evidence that cardiolipin is reduced in MASLD and that impaired cardiolipin synthesis is sufficient to recapitulate many features of MASLD.

Strengths:

The main strengths of the study are:

(1) The identification of reduced cardiolipin levels in the liver of humans with MASLD and in a variety of mouse models of MASLD.

(2) The finding that loss of cardiolipin synthesis recapitulates steatosis and inflammation in MASH.

(3) The finding that loss of cardiolipin increases mitochondrial respiration, ROS production, and fat oxidation (in a separate hepatocyte cell line), again recapitulates several previous studies in obese humans with MASLD.

(4) Evidence, though less definitive, that cardiolipin deficiency promotes electron leak by disrupting respiratory supercomplexes and preventing CoQ reduction.

Weaknesses:

(1) Figure 3A-D tries to make the point that liver CLS KO causes defects in substrate handling in vivo, based on glucose and pyruvate tolerance tests. The KO mice have a blunted response to a glucose tolerance test, but the pyruvate tolerance test showed very little (almost no) effect on glucose levels in either WT or LKO mice. The small blunting of the response in the LKO is impossible to interpret (if it's real), since the ability to clear glucose is also increased, and no tracers were used. It might be useful to monitor pyruvate and lactate levels during the experiment. However, this reviewer doesn't think the data is essential to prove the authors' main points.

(2) After presenting convincing evidence that respiration is elevated in isolated mitochondria from CLS KO liver, the authors follow up the findings by investigating whether 13C-palmitate and 13C-glucose oxidation are altered by CLS knockdown in murine Hepa1-6 cells (Figure 4). A few comments are worth mentioning about Figure 4:

a. It is not clear why the authors chose to use a hepatoma cell line rather than primary hepatocytes from LKO mice. The latter would be more convincing, since there could be important differences in metabolism between hepatoma cells and hepatocytes (e.g., preference for fatty acids vs glucose). Nevertheless, I think the approach is sufficient to test the general effect of loss of CLS on substrate metabolism.

b. The authors use the M+2 enrichments of TCA cycle intermediates to infer rates of oxidation of [U-13C]palmitate or [U-13C]glucose. It is important to note that this kind of data reports fractional carbon sources (i.e., substrate preference) rather than rates of oxidation. For example, data from the 13C-palmitate experiment indicates that the CLS KD cells increase the fractional contribution from 13C palmitate (compared to glucose, for example) to the TCA cycle, but the actual rate of palmitate oxidation is not implicit in the data. However, it is reasonable to suggest that, in combination with the increased rates of O2 consumption observed in isolated mitochondria, this data supports increased fat oxidation.

c. I have some concern that the [U-13C]glucose experiment is more complicated to interpret than the description implies. I'm not sure what happens in this cell line, but in the liver, most labeling from pyruvate (i.e., originating from glucose in this case) enters the TCA cycle via pyruvate carboxylase, with smaller amounts entering via PDH (depending on the nutritional state). Since one could expect pyruvate carboxylase to contribute M+3 labeled TCA cycle intermediates initially, and M+2 on the first turn of the cycle, it's hard to conclude what the data indicates about glucose oxidation. The authors could generalize the conclusion by framing the TCA cycle enrichment data as the contribution of glucose carbons and noting in Figure 4A that pyruvate carbons can enter the TCA cycle via PDH or pyruvate carboxylase, without attempting to assign their relative contributions. There are better ways to do it, but it's a small nuance here since the authors aren't making a critical point about the pathways.

Reviewer #2 (Public review):

In this study, the authors show that alterations in the lipid composition of the inner mitochondrial membrane, particularly changes in cardiolipin (CL) content, lead to defects in electron transport, supercomplex formation, and oxidative stress. Using liver-specific CLS knockout mice, which are characterized by dysfunctional capacity for cardiolipin synthesis, the authors highlight an underappreciated role for CL in MASH pathology. Overall, this is an interesting study highlighting the importance of functional/physiological electron transport (and in this context, electron leakage) in MASH pathophysiology. Despite that, this manuscript has several weaknesses that require attention.

(1) For all LKO studies, it is stated that the decrease in hepatic CL is causal for the observed phenotype. However, it is evident that many other lipids are impacted by CLS KO, including a marked increase in hepatic PG. In this respect, the authors show no evidence that the observed metabolic phenotype is indeed due to the reduction in CL and not to other accompanying changes.

(2) In the results, the authors highlight that 'MASLD has been shown to alter the total cellular lipidome in liver.' Given that this study focused on CL, it would be useful to include specific studies that pointed to changes in hepatic CL content in MASLD/MASH/fibrosis.

(3) The initial human mitochondrial lipidomics studies show a reduction in mitochondrial CL and PG content. What was the content/expression of CL synthase and PGP synthase in these samples? If this cannot be assessed, is there any association of CLS or PGPS expression and MASLD/fibrosis (etc) in publicly available databases (e.g, GEP liver) that may explain the reduction in mitochondrial PG and CL content?

(4) The validation of MASH in patients (Figure 1B) is not convincing (ie., no quantification/scoring provided). NAS /fibrosis scoring (according to Kleiner) would help to define if all patients have indeed MASH, and what subset has fibrosis. Could the reduction in CL/PG content be (also) associated with fibrosis? In addition, Masson's Trichrome should be added to Figure 1B.

(5) In human lipidomics, the authors suggest that reductions are observed in tetralinoleoyl CL (Figure 1C). However, Figure 1C only shows the combined FA acyl chain length + unsaturation, therefore not allowing for FA-specific ID (unless such data are available from the LC/MS analysis).

(6) Figures 1 J/K/I. It is obvious that the background in all murine immunoblotting analysis has been altered. The authors should provide unaltered images for these immunoblots.

(7) For Figure 1, it is unclear what is meant by 'we performed all mitochondrial lipidomic analyses by quantifying lipids per mg of mitochondrial proteins'. Was the murine lipidomics carried out on fractionated mitochondria or whole liver? If whole liver, then how were the data corrected, particularly given that PG is not a mitochondria-specific lipid?

(8) While total CL content seems indeed decreased across the different mouse models, this is mostly due to 1-2 CL species showing a pronounced reduction, with the remainder being unaltered. This should at least be acknowledged in the results. This is similarly the case in the LKO livers.

(9) Figure 2. A secondary biochemical analysis of changes in lipid content should be provided, e.g., total triglyceride content, particularly given that the histology analysis does not show any major changes in hepatic lipid droplets/steatosis. In addition, the Masson's Trichrome staining shows almost no collagen deposition.

(10) Figure 3. 'CLS deletion modestly reduced glucose handling' should be reworded. The LKO mice show improved glucose tolerance (despite the MASH phenotype), which is not evident from the above wording.

(11) Looking at the mechanism behind the increase in hepatic steatosis, the authors state that lipid accumulation can occur due to increased lipogenesis, or dysfunctional VLDL secretion or beta oxidation, and subsequently assessed the relevant proteins/pathways. What about fatty acid uptake, which is also one of the four major pathways impacted in MASLD? This should be included in this assessment in Figure 3.

(12) For Figure 5A, it is simply stated 'CLS deletion promotes liver fibrosis in standard chow-fed condition', and it is unclear what is highlighted within the selected EM images and what the arrows refer to. The authors should clarify this within the text.

Reviewer #3 (Public review):

Summary:

Mitochondrial oxphos causes lipid accumulation, leading to MASH, although the mechanism has been poorly understood. In this study, Funai and colleagues identify that reductions in cardiolipin in the mitochondria cause disruptions in the electron transport chain. Knockout of cardiolipin synthase was sufficient to drive MASH phenotypes, increase respiratory capacity, and cause electron leak at complexes II and III. It is well established that loss of cardiolipin increases ROS. Studies to date have been performed on whole tissue lysates, but to rule out which changes in mitochondrial lipids are driven by changes in mitochondrial number versus lipid synthesis/turnover, the authors uniquely purified mitochondria from human and mouse livers in MASH and NASH models for this study. This study provides critical information to the field that will inevitably help us better understand the mechanisms underlying MASH and NASH onset. The evidence provided is both convincing and compelling. With further suggested revision experiments, this study has the potential to change our understanding of MASH and NASH pathogenesis.

Strengths:

The authors use a unique approach of lipidomics on purified mitochondria. They also analyze many distinct MASH models and provide a unique resource for the field of comprehensive lipidomics analysis of the different ways in which MASH can be induced. The use of human tissue elevates the impact/significance of the findings.

Weaknesses:

The data on the super complexes was the least compelling, and frankly, I do not think the authors needed those data to make a compelling argument! The authors should shift their focus more to the compelling electron leak data they have collected. If possible, it would also strengthen the work to include cardiolipin rescues on more of the experiments. Finally, expanding their explanations of the model systems would be very helpful for the readership.

Reviewer #4 (Public review):

Summary:

Here, the authors wish to shed light on factors that contribute to the development of liver disease in what used to be called 'the metabolic syndrome'. This is a human-health problem of considerable significance, and the insights they provide, namely the implication of a defect in mitochondrial cardiolipin (CL) content to the progression from metabolic dysfunction-associated steatotic liver disease to steatohepatitis, are plausible.

Strengths:

The experimental evidence proffered is derived from the observation of lower levels of (CL) in mitochondria from the liver of patients undergoing liver transplant or resection due to end-stage steatohepatitis compared with mitochondria derived from livers of patients with other conditions. This correlation is buttressed by observations made in mice with liver-selective compromise in CL synthesis and which suggest a pathological environment associated with mitochondrial dysfunction and enhanced oxidative stress, features deemed to play a role in the progression from steatotic liver disease to steatohepatitis.

The paper is well written, and the findings are well explained and superficially convincing.

Weaknesses:

It is unclear how much can be learned from compromising a key enzyme that produces a key mitochondrial lipid in a busy metabolic organ like the liver - isn't the discovery of a mitochondrial defect in such a context rather trivial? And how reliably can these findings be related to the human observations? Most importantly, the chain of causality implied by the title is unproven: the key question of whether or not (somehow) preventing the drop in cardiolipin content affects the course of steatohepatitis remains unanswered.

eLife Assessment

This important manuscript investigates the role of olfactory cues in Pieris brassicae larvae, focusing on their interactions with the host plant Brassica oleracea and the parasitoid wasp Cotesia glomerata. The authors' demonstration that impaired olfactory perception reduces caterpillar performance and increases susceptibility to parasitism is convincing. These findings highlight the ecological significance of olfaction in mediating feeding behavior and predator avoidance in herbivorous insects.

Reviewer #1 (Public review):

Summary:

The manuscript focuses on the olfactory system of Pieris brassicae larvae and the importance of olfactory information in their interactions with the host plant Brassica oleracea and the major parasitic wasp Cotesia glomerata. The authors used CRISPR/Cas9 to knockout odorant receptor co-receptors (Orco), and conducted a comparative study on the behavior and olfactory system of the mutant and wild-type larvae. The study found that Orco-expressing olfactory sensory neurons in antennae and maxillary palps of Orco knockout (KO) larvae disappeared, and the number of glomeruli in the brain decreased, which impairs the olfactory detection and primary processing in the brain. Orco KO caterpillars show weight loss and loss of preference for optimal food plants; KO larvae also lost weight when attacked by parasitoids with the ovipositor removed, and mortality increased when attacked by untreated parasitoids. On this basis, the authors further studied the responses of caterpillars to volatiles from plants attacked by the larvae of the same species and volatiles from plants on which the caterpillars were themselves attacked by parasitic wasps. Lack of OR mediated olfactory inputs prevents caterpillars from finding suitable food sources and from choosing spaces free of enemies.

Strengths:

The findings help to understand the important role of olfaction in caterpillar feeding and predator avoidance, highlighting the importance of odorant receptor genes in shaping ecological interactions.

Weaknesses:

There are the following major concerns:

(1) Possible non-targeted effects of Orco knockout using CRISPR/Cas9 should be analyzed and evaluated in Materials and Methods and Results

(2) Figure 1E: Only one olfactory receptor neuron was marked in WT. There are at least three olfactory sensilla at the top of the maxillary palp. Therefore, to explain the loss of Orco expressing neurons in the mutant (Figure 1F), a more rigorous explanation of the photo is required.

(3) In Figure 1G, H, the four glomeruli circled by dotted lines: their corresponding relationship between the two figures needs to be further clarified.

(4) Line 130: Since the main topic in this study is the olfactory system of larvae, the experimental results of this part are all about antennal electrophysiological responses, mating frequency and egg production of female and male adults of wild type and Orco KO mutant, it may be considered to include this part in the supplementary files. It is better to include some data about the olfactory responses of larvae.

(5) Line 166: The sentences in the text is about the choice test between " healthy plant vs. infested plant", while in Fig 3C, it is "infested plant vs. no plant". The content in the text does not match the figure.

(6) Lines 174-178: Fig 3A showed that the body weight of Orco KO larvae in the absence of parasitic wasps also decreased compared with that of WT. Therefore, in the experiments of Fig 3A and E, the difference in the body weight of Orco KO larvae in the presence or absence of parasitic wasps without ovipositors should also be compared. The current data cannot determine the reduced weight of KO mutant is due to the Orco knockout or the presence of parasitic wasps.

(7) Lines 179-181: Fig 3F show that the survival rate of larvae of Orco KO mutant decreased in the presence of parasitic wasps, and the difference in survival rate of larvae of WT and Orco KO mutant in the absence of parasitic wasps should also be compared. The current data cannot determine whether the reduced survival of the KO mutant is due to the Orco knockout or the presence of parasitic wasps.

(8) In Figure 4B, why do the compounds tested had no volatiles derived from plants? Cruciferous plants have the well-known mustard bomb. In the behavioral experiments the larvae responses to ITC compounds were not included, which is suggested to be explained in the discussion section.

(9) The custom-made setup and the relevant behavioral experiments in Fig 4C needs to be described in detail (Line 545).

(10) Materials and Methods Line 448: 10 μL paraffin oil should be used for negative control.

Comments on revised version:

The authors have replied my concerns and made revisions accordingly.

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Summary:

The manuscript focuses on the olfactory system of Pieris brassicae larvae and the importance of olfactory information in their interactions with the host plant Brassica oleracea and the major parasitic wasp Cotesia glomerata. The authors used CRISPR/Cas9 to knockout odorant receptor coreceptors (Orco), and conducted a comparative study on the behavior and olfactory system of the mutant and wild-type larvae. The study found that Orco-expressing olfactory sensory neurons in antennae and maxillary palps of Orco knockout (KO) larvae disappeared, and the number of glomeruli in the brain decreased, which impairs the olfactory detection and primary processing in the brain. Orco KO caterpillars show weight loss and loss of preference for optimal food plants; KO larvae also lost weight when attacked by parasitoids with the ovipositor removed, and mortality increased when attacked by untreated parasitoids. On this basis, the authors further studied the responses of caterpillars to volatiles from plants attacked by the larvae of the same species and volatiles from plants on which the caterpillars were themselves attacked by parasitic wasps. Lack of OR-mediated olfactory inputs prevents caterpillars from finding suitable food sources and from choosing spaces free of enemies.

Strengths:

The findings help to understand the important role of olfaction in caterpillar feeding and predator avoidance, highlighting the importance of odorant receptor genes in shaping ecological interactions.

Weaknesses:

There are the following major concerns:

(1) Possible non-targeted effects of Orco knockout using CRISPR/Cas9 should be analyzed and evaluated in Materials and Methods and Results.

Thank you for your suggestion. In the Materials and Methods, we mention how we selected the target region and evaluated potential off-target sites by Exonerate and CHOPCHOP. Neither of these methods found potential off-target sites with a more-than-17-nt alignment identity. Therefore, we assumed no off-target effect in our Orco knockout. Furthermore, we did not find any developmental differences between wildtype and knockout caterpillars when these were reared on leaf discs in Petri dishes (Fig S4). We will further highlight this information on the off-target evaluation in the Results section.

(2) Figure 1E: Only one olfactory receptor neuron was marked in WT. There are at least three olfactory sensilla at the top of the maxillary palp. Therefore, to explain the loss of Orcoexpressing neurons in the mutant (Figure 1F), a more rigorous explanation of the photo is required.

Thank you for pointing this out. The figure shows only a qualitative comparison between WT and KO and we did not aim to determine the total number of Orco positive neurons in the maxillary palps or antennae of WT and KO caterpillars, but please see our previous work for the neuron numbers in the caterpillar antennae (Wang et al., 2024). We did indeed find more than one neuron in the maxillary palps, but as these were in very different image planes it was not possible to visualize them together. However, we will add a few sentences in the Results and Discussion section to explain the results of the maxillary palp Orco staining.

(3) In Figure 1G, H, the four glomeruli are circled by dotted lines: their corresponding relationship between the two figures needs to be further clarified.

Thank you for pointing this out. The four glomeruli in Figure 1G and 1H are not strictly corresponding. We circled these glomeruli to highlight them, as they are the best visualized and clearly shown in this view. In this study, we only counted the number of glomeruli in both WT and KO, however, we did not clarify which glomeruli are missing in the KO caterpillar brain. We will further clarify this in the figure legend.

(4) Line 130: Since the main topic in this study is the olfactory system of larvae, the experimental results of this part are all about antennal electrophysiological responses, mating frequency, and egg production of female and male adults of wild type and Orco KO mutant, it may be considered to include this part in the supplementary files. It is better to include some data about the olfactory responses of larvae.

Thank you for your suggestion. We do agree with your suggestion, and we will consider moving this part to the supplementary information. Regarding larval olfactory response, we unfortunately failed to record any spikes using single sensillum recordings due to the difficult nature of the preparation; however we do believe that this would be an interesting avenue for further research.

(5)Line 166: The sentences in the text are about the choice test between " healthy plant vs. infested plant", while in Fig 3C, it is "infested plant vs. no plant". The content in the text does not match the figure.

Thank you for pointing this out. The sentence is “We compared the behaviors of both WT and Orco KO caterpillars in response to clean air, a healthy plant and a caterpillar-infested plant”. We tested these three stimuli in two comparisons: healthy plant vs no plant, infested plant vs no plant. The two comparisons are shown in Figure 3C separately. We will aim to describe this more clearly in the revised version of this manuscript.

(6) Lines 174-178: Figure 3A showed that the body weight of Orco KO larvae in the absence of parasitic wasps also decreased compared with that of WT. Therefore, in the experiments of Figure 3A and E, the difference in the body weight of Orco KO larvae in the presence or absence of parasitic wasps without ovipositors should also be compared. The current data cannot determine the reduced weight of KO mutant is due to the Orco knockout or the presence of parasitic wasps.

Thank you for pointing this out. We did not make a comparison between the data of Figures 3A and 3E since the two experiments were not conducted at the same time due to the limited space in our BioSafety III greenhouse. We do agree that the weight decrease in Figure 3E is partly due to the reduced caterpillar growth shown in Figure 3A. However, we are confident that the additional decrease in caterpillar weight shown in Figure 3E is mainly driven by the presence of disarmed parasitoids. To be specific, the average weight in Figure 3A is 0.4544 g for WT and 0.4230 g for KO, KO weight is 93.1% of WT caterpillars. While in Figure 3E, the average weight is 0.4273 g for WT and 0.3637 g for KO, KO weight is 85.1% of WT caterpillars. We will discuss this interaction between caterpillar growth and the effect of the parasitoid attacks more extensively in the revised version of the manuscript.

(7) Lines 179-181: Figure 3F shows that the survival rate of larvae of Orco KO mutant decreased in the presence of parasitic wasps, and the difference in survival rate of larvae of WT and Orco KO mutant in the absence of parasitic wasps should also be compared. The current data cannot determine whether the reduced survival of the KO mutant is due to the Orco knockout or the presence of parasitic wasps.

We are happy that you highlight this point. When conducting these experiments, we selected groups of caterpillars and carefully placed them on a leaf with minimal disturbance of the caterpillars, which minimized hurting and mortality. We did test the survival of caterpillars in the absence of parasitoid wasps from the experiment presented in Figure 3A, although this was missing from the manuscript. There is no significant difference in the survival rate of caterpillars between the two genotypes in the absence of wasps (average mortality WT = 8.8 %, average mortality KO = 2.9 %; P = 0.088, Wilcoxon test), so the decreased survival rate is most likely due to the attack of the wasps. We will add this information to the revised version of the manuscript.

(8) In Figure 4B, why do the compounds tested have no volatiles derived from plants? Cruciferous plants have the well-known mustard bomb. In the behavioral experiments, the larvae responses to ITC compounds were not included, which is suggested to be explained in the discussion section.

Thank you for the suggestion. We assume you mean Figure 4D/4E instead of Figure 4B. In Figure 4B, many of the identified chemical compounds are essentially plant volatiles, especially those from caterpillar frass and caterpillar spit. In Figure 4D/4E, most of the tested chemicals are derived from plants. But indeed, we did not include ITCs, based on information from the EAG results in Figures 2A & 2B. Butterfly antennae did not respond strongly to ITCs, so we did not include ITCs in the larval behavioural tests. Instead, the tested chemicals in Figure 4D/4E either elicit high EAG responses of butterflies or have been identified as “important” by VIP scores in the chemical analyses. In the EAG results of Plutella xylostella (Liu et al., 2020), moths responded well to a few ITCs, the tested ITCs in our study are actually adopted from this study except for those that were not available to us. However, butterflies did not show a strong response to the tested ITCs; therefore, we did not include ITCs because we expected that Pieris brassicae caterpillars are not likely to show good responses to ITCs. We will add this explanation to the revised version of our manuscript.

(9) The custom-made setup and the relevant behavioral experiments in Figure 4C need to be described in detail (Line 545).

We will add more detailed descriptions for the setup and method in the Materials and Methods.

(10) Materials and Methods Line 448: 10 μL paraffin oil should be used for negative control.

Thank you for pointing this out. We used both clean filter paper and clean filter paper with 10 μL paraffin oil as negative controls, but we did not find a significant difference between the two controls. Therefore, in the EAG results of Figure 2A/2B, we presented paraffin oil as one of the tested chemicals. We will re-run our statistical tests with paraffin oil as negative control, although we do not expect any major differences to the previous tests.

Reviewer #2 (Public review):

Summary:

This manuscript investigated the effect of olfactory cues on caterpillar performance and parasitoid avoidance in Pieris brassicae. The authors knocked out Orco to produce caterpillars with significantly reduced olfactory perception. These caterpillars showed reduced performance and increased susceptibility to a parasitoid wasp.

Strengths:

This is an impressive piece of work and a well-written manuscript. The authors have used multiple techniques to investigate not only the effect of the loss of olfactory cues on host-parasitoid interactions, but also the mechanisms underlying this.

Weaknesses:

(1) I do have one major query regarding this manuscript - I agree that the results of the caterpillar choice tests in a y-maze give weight to the idea that olfactory cues may help them avoid areas with higher numbers of parasitoids. However, the experiments with parasitoids were carried out on a single plant. Given that caterpillars in these experiments were very limited in their potential movement and source of food - how likely is it that avoidance played a role in the results seen from these experiments, as opposed to simply the slower growth of the KO caterpillars extending their period of susceptibility? While the two mechanisms may well both take place in nature - only one suggests a direct role of olfaction in enemy avoidance at this life stage, while the other is an indirect effect, hence the distinction is important.

We do agree with your comment that both mechanisms may be at work in nature and we do address this in the Discussion section. In our study, we did find that wildtype caterpillars were more efficient in locating their food source and did grow faster on full plants than knockout caterpillars. This faster growth will enable wildtype caterpillars to more quickly outgrow the life-stages most vulnerable to the parasitoids (L1 and L2). The olfactory system therefore supports the escape from parasitoids indirectly by enhancing feeding efficiency directly.

Figure 3D shows that WT caterpillars prefer infested plants without parastioids to infested plants with parasitoids. In addition, we observed that caterpillars move frequently between different leaves. Therefore, we speculate that WT caterpillars make use of volatiles from the plant or from (parasitoid-exposed) conspecifics via their spit or faeces to avoid parts of the plant potentially attracting natural enemies. Knockout caterpillars are unable to use these volatile danger cues and therefore do not avoid plant parts that are most attractive to their natural enemies, making KO caterpillars more susceptible and leading to more natural enemy harassment. Through this, olfaction also directly impacts the ability of a caterpillar to find an enemy-free feeding site.

We think that olfaction supports the enemy avoidance of caterpillars via both these mechanisms, although at different time scales. Unfortunately, our analysis was not detailed enough to discern the relative importance of the two mechanisms we found. However, we feel that this would be an interesting avenue for further research. Moreover, we will sharpen our discussion on the potential importance of the two different mechanisms in the revised version of this manuscript.

(2) My other issue was determining sample sizes used from the text was sometimes a bit confusing. (This was much clearer from the figures).

We will revise the sample size in the text to make it more clear.

(3) I also couldn't find the test statistics for any of the statistical methods in the main text, or in the supplementary materials.

Thank you for pointing this out. We will provide more detailed test statistics in the main text and in the supplementary materials of the revised version of the manuscript.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

(1) Abstract

Line 24: "optimal food plant" should be changed to "optimal food plants"

Thank you for the suggestion, we will revise it.

(2) Introduction

Lines 44-46: The sentence should be rephrased.

Thank you for the suggestion, we will revise it.

Line 50: "are" should be changed to "is".

Thank you for the suggestion, we will revise it.

Lines 57 and 58: Please provide the Latin names of "brown planthoppers" and "striped stem borer".

Thank you for the suggestion, we will revise it.

Line 85: "investigate the influence of odor-guided behavior by this primary herbivore on the next trophic levels"; similarly, Line 160: "investigate if caterpillars could locate the optimal host-plant when supplied with differently treated plants". These sentences are not very accurate in describing the relevant experiments. A: Thank you for the suggestion, we will revise them.

Reviewer #2 (Recommendations for the authors):

(1) L53 Remove the "the" from "Under the strong selection pressure"

Thank you for the suggestion, we will revise it.

(2) L80 I suggest adding a reference for the spitting behaviour, e.g. Muller et al 2003.

Thank you for the suggestion, we will add it.

(3) L89 establishing a homozygous KO insect colony.

Thank you for the suggestion, we will revise it.

(4) L107 perhaps this goes against the journal style but I always like to see acronyms explained the first time they are used.

Thank you for the suggestion, we will try to make it more understandable.

(5) L146-148 sentence difficult to read - consider rephrasing.

Thank you for the suggestion, we will revise it.

(6) L230 do you mean still produce? Rather than still reproduce?

Thank you for the suggestion, we will revise it.

(7) L233 missing an and before "a greater vulnerability to the parasitoid wasp".

Thank you for pointing this out, we will revise it.

(8) L238 malfunctional is a strange word choice.

Thank you for pointing this out, we will revise it.

(9) L181 - can the authors confirm that this lower survival was due to parasitism by the wasps?

This question is similar to Q(7) of Reviewer 1, so we quote our answer for Q(7) here:

When conducting these experiments, we selected groups of caterpillars and carefully placed them on a leaf with minimal disturbance of the caterpillars, which minimized hurting and mortality. We did test the survival of caterpillars in the absence of parasitoid wasps from the experiment presented in Figure 3A, although this was missing from the manuscript. There is no significant difference in the survival rate of caterpillars between the two genotypes in the absence of wasp (average mortality WT = 8.8 %, average mortality KO = 2.9 %; P = 0.088, Wilcoxon test), so the decreased survival rate is most likely due to the attack of the wasps. We will add this information to the revised version of the manuscript.

(10) L474 - has it been tested if wasps still behave similarly after their ovipositor has been removed?

Thank you for pointing out this issue. We did not strictly compare if disarmed and untreated wasps have similar behaviors. However, we did observe if disarmed wasps can actively move or fly after recovering from anesthesia before releasing into a cage, otherwise we would replace with another active one.

eLife Assessment

This is an important study that characterized proteins associated with electrical synapses in zebrafish and mouse retinal neurons using proximity labeling approaches, complemented by biochemical and histological validations. The resulting protein interactome datasets are convincing and reveal novel scaffold proteins at the electrical synapse. Additional quantification and validation would strengthen the work further.

Reviewer #1 (Public review):

This study aims to identify the proteins that compose the electrical synapse, which are much less understood than those of the chemical synapse. Identifying these proteins is important to understand how synaptogenesis and conductance are regulated in these synapses.

Using a proteomics approach, the authors identified more than 50 new proteins and used immunoprecipitation and immunostaining to validate their interaction of localization. One new protein, a scaffolding protein (Sipa1l3), shows particularly strong evidence of being an integral component of the electrical synapse. The function of Sipa1l3 remains to be determined.

Another strength is the use of two different model organisms (zebrafish and mice) to determine which components are conserved across species. This approach also expands the utility of this work to benefit researchers working with both species.

The methodology is robust and there is compelling evidence supporting the findings.

Reviewer #2 (Public review):

Summary:

This study aimed to uncover the protein composition and evolutionary conservation of electrical synapses in retinal neurons. The authors employed two complementary BioID approaches: expressing a Cx35b-TurboID fusion protein in zebrafish photoreceptors and using GFP-directed TurboID in Cx36-EGFP-labeled mouse AII amacrine cells. They identified conserved ZO proteins and endocytosis components in both species, along with over 50 novel proteins related to adhesion, cytoskeleton remodeling, membrane trafficking, and chemical synapses. Through a series of validation studies¬-including immunohistochemistry, in vitro interaction assays, and immunoprecipitation-they demonstrate that novel scaffold protein SIPA1L3 interacts with both Cx36 and ZO proteins at electrical synapse. Furthermore, they identify and localize proteins ZO-1, ZO-2, CGN, SIPA1L3, Syt4, SJ2BP, and BAI1 at AII/cone bipolar cell gap junctions.

Strengths:

The study demonstrates several significant strengths in both experimental design and validation approaches. First, the dual-species approach provides valuable insights into the evolutionary conservation of electrical synapse components across vertebrates. Second, the authors compare two different TurboID strategies in mice and demonstrate that the HKamac promoter and GFP-directed approach can successfully target the electrical synapse proteome of mouse AII amacrine cells. Third, they employed multiple complementary validation approaches-including retinal section immunohistochemistry, in vitro interaction assays, and immunoprecipitation-providing evidence supporting the presence and interaction of these proteins at electrical synapses.

Weaknesses:

The major weakness of this paper is the insufficient number of replicates in the proteomics datasets. The zebrafish datasets include only two biological replicates, while the mouse dataset has only one high-quality replicate. Due to the limited number of replicates, it is not possible to determine which enriched proteins are statistically significant.

Additionally, the Neutravidin staining in the TurboID condition is not restricted to where Cx35 is expressed but is broadly distributed throughout the INL and IPL in the zebrafish retina (Figure 1B, bottom). Therefore, it is necessary to include NeutrAvidin staining in non-labeled retinas to verify whether the biotinylated proteins are specifically associated with Cx35 expression. Although the western blot results showed increased protein enrichment in the TurboID condition compared to non-labeled retinas, this does not confirm that the streptavidin pull-down proteins are associated with Cx35.

Similarly, it is important to include NeutrAvidin staining in both TurboID and non-labeled conditions in the mouse retina to verify that the biotinylated proteins are specifically associated with gap junctions.

Reviewer #3 (Public review):

Summary:

This study by Tetenborg S et al. identifies proteins that are physically closely associated with gap junctions in retinal neurons of mice and zebrafish using BioID, a technique that labels and isolates proteins in proximal to a protein of interest. These proteins include scaffold proteins, adhesion molecules, chemical synapse proteins, components of the endocytic machinery, and cytoskeleton-associated proteins. Using a combination of genetic tools and meticulously executed immunostaining, the authors further verified the colocalizations of some of the identified proteins with connexin-positive gap junctions. The findings in this study highlight the complexity of gap junctions. Electrical synapses are abundant in the nervous system, yet their regulatory mechanisms are far less understood than those of chemical synapses. This work will provide valuable information for future studies aiming to elucidate the regulatory mechanisms essential for the function of neural circuits.

Strengths:

A key strength of this work is the identification of novel gap junction-associated proteins in AII amacrine cells and photoreceptors using BioID in combination with various genetic tools. The well-studied functions of gap junctions in these neurons will facilitate future research into the functions of the identified proteins in regulating electrical synapses.

The authors have addressed my concerns in the revised manuscript.

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public review):

Summary:

This study aims to identify the proteins that compose the electrical synapse, which are much less understood than those of the chemical synapse. Identifying these proteins is important to understand how synaptogenesis and conductance are regulated in these synapses. The authors identified more than 50 new proteins and used immunoprecipitation and immunostaining to validate their interaction of localization. One new protein, a scaffolding protein, shows particularly strong evidence of being an integral component of the electrical synapse. However, many key experimental details are missing (e.g. mass spectrometry), making it difficult to assess the strength of the evidence.

Strengths:

One newly identified protein, SIPA1L3, has been validated both by immunoprecipitation and immunohistochemistry. The localization at the electrical synapse is very striking.<br /> A large number of candidate interacting proteins were validated with immunostaining in vivo or in vitro.

Weaknesses:

There is no systematic comparison between the zebrafish and mouse proteome. The claim that there is "a high degree of evolutionary conservation" was not substantiated.

We have added a table as supplementary figure 3 that shows a comparison of all candidates. While there are differences in both proteomes, components such as ZO proteins and the endocytosis machinery are clearly conserved.

No description of how mass spectrometry was done and what type of validation was done.

We have contacted the mass spec facility we worked with and added a paragraph explaining the mass spec. procedure in the material and methods section.

The threshold for enrichment seems arbitrary.

Yes, the thresholds are somewhat arbitrary. This is due to the fact that experiments that captured larger total amounts of protein (mouse retina samples) had higher signal-to-noise ratio than those that captured smaller total amounts of protein (zebrafish retina). This allowed us to use a more stringent threshold in the mouse dataset to focus on high probability captured proteins.

Inconsistent nomenclature and punctuation usage.

We have scanned through the manuscript and updated terms that were used inconsistently in the interim revision of the manuscript.

The description of figures is very sparse and error-prone (e.g. Figure 6).

In Figure 1B, there is very broad non-specific labeling by avidin in zebrafish (In contrast to the more specific avidin binding in mice, Figure 2B). How are the authors certain that the enrichment is specific at the electrical synapse?

The enrichment of the proteins we identified is specific for electrical synapses because we compared the abundance of all candidates between Cx35b-V5-TurboID and wildtype retinas. Proteins that are components of electrical synapses, will only show up in the Cx35b-V5-TurboID condition. The western blot (Strep-HRP) in figure 1C shows the differences in the streptavidin labeling and hence the enrichment of proteins that are part of electrical synapses. Moreover, while the background appears to be quite abundant in sections, biotinylation is a rare posttranslational modification and mainly occurs in carboxylases: The two intense bands that show up above 50 and 75 kDa. The background mainly originates from these two proteins. Therefore, it is easy to distinguish specific hits from non-specific background.

In Figure 1E, there is very little colocalization between Cx35 and Cx34.7. More quantification is needed to show that it is indeed "frequently associated."

We agree that “frequently associated” is too strong as a statement. We corrected this and instead wrote “that Cx34.7 was only expressed in the outer plexiform layer (OPL) where it was associated with Cx35b at some gap junctions” in line 151. There are many gap junctions at which Cx35b is not colocalized with Cx34.7.

Expression of GFP in HCs would potentially be an issue, since GFP is fused to Cx36 (regardless of whether HC expresses Cx36 endogenously) and V5-TurboID-dGBP can bind to GFP and biotinylate any adjacent protein.

Thank you for this suggestion! There should be no Cx36-GFP expression in horizontal cells, which means that the nanobody cannot bind to anything in these cells. Moreover, to recognize specific signals from non-specific background, we included wild type retinas throughout the entire experiments. This condition controls for non-specific biotinylation.

Figure 7: the description does not match up with the figure regarding ZO-1 and ZO-2.

It appears that a portion of the figure legend was left out of the submitted version of the manuscript. We have put the legend for panels A through C back into the manuscript in the interim revision.

Reviewer #2 (Public review):

Summary:

This study aimed to uncover the protein composition and evolutionary conservation of electrical synapses in retinal neurons. The authors employed two complementary BioID approaches: expressing a Cx35b-TurboID fusion protein in zebrafish photoreceptors and using GFP-directed TurboID in Cx36-EGFP-labeled mouse AII amacrine cells. They identified conserved ZO proteins and endocytosis components in both species, along with over 50 novel proteins related to adhesion, cytoskeleton remodeling, membrane trafficking, and chemical synapses. Through a series of validation studies¬-including immunohistochemistry, in vitro interaction assays, and immunoprecipitation - they demonstrate that novel scaffold protein SIPA1L3 interacts with both Cx36 and ZO proteins at electrical synapse. Furthermore, they identify and localize proteins ZO-1, ZO-2, CGN, SIPA1L3, Syt4, SJ2BP, and BAI1 at AII/cone bipolar cell gap junctions.

Strengths:

The study demonstrates several significant strengths in both experimental design and validation approaches. First, the dual-species approach provides valuable insights into the evolutionary conservation of electrical synapse components across vertebrates. Second, the authors compare two different TurboID strategies in mice and demonstrate that the HKamac promoter and GFP-directed approach can successfully target the electrical synapse proteome of mouse AII amacrine cells. Third, they employed multiple complementary validation approaches - including retinal section immunohistochemistry, in vitro interaction assays, and immunoprecipitation-providing evidence supporting the presence and interaction of these proteins at electrical synapses.

Weaknesses:

The conclusions of this paper are supported by data; however, some aspects of the quantitative proteomics analysis require clarification and more detailed documented. The differential threshold criteria (>3 log2 fold for mouse vs >1 log2 fold for zebrafish) will benefit from biological justification, particularly given the cross-species comparison. Additionally, providing details on the number of biological or technical replicates used in this study, along with analyses of how these replicates compare to each other, would strengthen the confidence in the identification of candidate proteins. Furthermore, including negative controls for the histological validation of proteins interacting with Cx36 could increase the reliability of the staining results.

While the study successfully characterized the presence of candidate proteins at the electrical synapses between AII amacrine cells and cone bipolar cells, it did not compare protein compositions between the different types of electrical synapses within the circuit. Given that AII amacrine cells form both homologous (AII-AII) and heterologous (AII-cone bipolar cell) electrical synapses-connections that serve distinct functional roles in retinal signaling processing-a comparative analysis of their molecular compositions could have provided important insights into synapse specificity.

Reviewer #3 (Public review):

Summary:

This study by Tetenborg S et al. identifies proteins that are physically closely associated with gap junctions in retinal neurons of mice and zebrafish using BioID, a technique that labels and isolates proteins proximal to a protein of interest. These proteins include scaffold proteins, adhesion molecules, chemical synapse proteins, components of the endocytic machinery, and cytoskeleton-associated proteins. Using a combination of genetic tools and meticulously executed immunostaining, the authors further verified the colocalizations of some of the identified proteins with connexin-positive gap junctions. The findings in this study highlight the complexity of gap junctions. Electrical synapses are abundant in the nervous system, yet their regulatory mechanisms are far less understood than those of chemical synapses. This work will provide valuable information for future studies aiming to elucidate the regulatory mechanisms essential for the function of neural circuits.

Strengths:

A key strength of this work is the identification of novel gap junction-associated proteins in AII amacrine cells and photoreceptors using BioID in combination with various genetic tools. The well-studied functions of gap junctions in these neurons will facilitate future research into the functions of the identified proteins in regulating electrical synapses.

Thank you for these comments.

Weaknesses:

I do not see major weaknesses in this paper. A minor point is that, although the immunostaining in this study is beautifully executed, the quantification to verify the colocalization of the identified proteins with gap junctions is missing. In particular, endocytosis component proteins are abundant in the IPL, making it unclear whether their colocalization with gap junction is above chance level (e.g. EPS15l1, HIP1R, SNAP91, ITSN in Figure 3B).

Recommendations for the authors:

Reviewer #2 (Recommendations for the authors):

(1) It would be helpful to include a comprehensive summary of the results from the quantitative proteomics analyses, such as the number of proteins detected in each species and the number of proteins associated with each GO term. Additionally, a clear figure or table highlighting the specific proteins conserved between zebrafish and mice would strengthen the evidence for evolutionary conservation of proteins at electrical synapses.

We have added the raw data we received from our mass spec facility including a comparison of all the candidates for different species. Supplementary figure 3.

(2) A more detailed description of the number of experimental and/or technical replicates would improve the technical rigor of the study. For example, what was the rationale for using different log2 fold-change cutoffs in mice versus zebrafish? Are the replicates consistent in terms of protein enrichment?

We have added raw data from individual experiments as a supplement (Excel spreadsheet). We have two replicates from zebrafish and two from mice. The first experiment in mice was conducted with fewer retinas and a different promoter (human synapsin promoter) and didn’t yield nearly as many candidates. We are currently running a third experiment with 35 mouse retinas which will most likely detect more candidates as we have identified currently. We can update the proteome in this paper once the analysis is complete. It is not feasible to conduct these experiments with multiple replicates at the same time, since the number of animals that have to be used is simply too high, especially since very specific genotypes are required that are difficult obtain.

(3) It would be interesting to determine whether there are differences in the presence of candidate proteins between AII-AII gap junctions and AII-cone bipolar cell gap junctions. Given that the subcellular localization of AII-AII gap junctions differs from that of AII-cone bipolar cell gap junctions (with most AII-AII gap junctions located below AII-cone ones), histological validations of the proteins shown in Figure 6 can be repeated for AII-AII gap junctions. This would help reveal similarities or differences in the protein compositions of these two types of gap junctions.

Thank you for this suggestion. We had similar plans. However, we realized that homologous gap junctions are difficult to recognize with GFP. The dense GFP labeling in the proximal IPL, where AII-AII gap junctions are formed, does not allow us to clearly trace the location of individual dendrites from different cells. Detecting AII-AII gap junctions would require intracellular dye Injections of neighboring AII cells. Unfortunately, we don’t have a set up that would allow this. Bipolar cell terminals, on the contrary, are a lot easier to detect with markers such as SCGN, which is why we decided to focus on AII/ONCB gap junctions.

(4) In Figures 1 and 2, it would be helpful to clarify in the figure legends whether the proteins in the interaction networks represent all detected proteins or only those selected based on log2 fold-change or other criteria.

Thank you for this suggestion! We have added a description in lines 643 and 662.

(5) In Figure 1A (bottom panel), please include a negative control for the Neutravidin staining result from the non-labeling group.

We only tested the biotinylation for wild type retinas in cell lysates and western blots as shown in figure 1C, which shows an entirely different biotinylation pattern.

(6) In Figure 2B, please include the results of Neutravidin staining for both the labeling and non-labeling groups.

Same comment: We see the differences in the biotinylation pattern on western blots, which is distinct for Cx36-EGFP and wild type retinas, although both genotypes were injected with the same AAV construct and the same dose of biotin. We hope that this provides sufficient evidence for the specificity of our approach.

(7) In Figure 5B, the sizes of multiple proteins detected by Western blotting are inconsistent and confusing. For example, the size of Cx36 in the "FLAG-SJ2BP" panel differs from that in the other three panels. Additionally, in the "Myc-SIPA1L3+" panel, the size of SIPA1l3 appears different between the input and IP conditions.

Thank you for pointing this out! The differences in the molecular weight can be explained by dimerization. We have indicated the position of the dimer and the monomer bands with arrows. Especially, when larger amounts of Cx36 are coprecipitated Cx36 preferentially occurs as a dimer. This can also be seen in our previous publication:

S. Tetenborg et al., Regulation of Cx36 trafficking through the early secretory pathway by COPII cargo receptors and Grasp55. Cellular and Molecular Life Sciences 81, 1-17 (2024). Figure 1D

The band that occurs above 150kDa in the SIPA1L3 input is most likely a non-specific product. The specific band for SIPA1L3 can be seen in the IP sample, which has the appropriate molecular weight. We often see much better immuno reactivity for the protein of interest in IP samples, because the protein is concentrated in these experiments which facilitates its detection.

(8) How specific are the antibodies used for validating the proteins in this study? Given that many proteins, such as EPS15l1, HIP1R, SNAP91, GPrin1, SJ2BP, Syt4, show broad distribution in the IPL (Figure 3B, 4A, 6D), it is important to validate the specificity of these antibodies. Additionally, including negative controls in the histological validation would strengthen the reliability of the results.

We carefully selected the antibodies based on western blot data, that confirmed that each antibody detected an antigen of appropriate size. Moreover, the distribution of the proteins mentioned is consistent with function of each protein described in the literature. EPS15L1 and GPrin1 for instance are both membrane-associated, which is evident in Hek cells. Figure 5C.

A true negative control would require KO tissue and we don’t think that this is feasible at this point.

(9) In Figure 7F, the model could be improved by highlighting which components may be conserved between zebrafish and mice, as well as which components are conserved between the AII-AII junction and AII-cone bipolar cell junction?

Thank you for this suggestion. However, we don’t think that this is necessary as our study primarily focuses on the AII amacrine cell.

Currently we are unable to distinguish differences in the composition of AII-AII and AII-ONCB junctions as described above.

(10) Are there any functional measurements that could support the conclusion that "loss of Cx36 resulted in a quantitative defect in the formation of electrical synapse density complex"?

The loss of electrical synapse density proteins is shown by these immunostaining comparisons. Functional measurements necessarily depend on the function of the electrical synapse itself, which is gone in the case of the Cx36 KO. It is not clear that a different functional measurement can be devised.

Reviewer #3 (Recommendations for the authors):

(1) It would be very helpful if there were page and line numbers on the manuscript.

Line and page numbers have been added.

(2) Typos in the 3rd paragraph, the sentence 'which is triggered by the influx of Calcium though non-synaptic NMDA...'

Should it read '... Calcium THROUGH non-synaptic NMDA'?

We have corrected this typo.

(3) Figure 1B: please add a description of the top panels, 'Cx36 S293'.

A description of the top panels has been added to the figure legend in line. Line 639.

(4) Figure 1C: what do the arrows indicate?

We apologize for the confusion. The arrows in the western blot indicate the position of the Cx35-V5-TurboID construct, which can be detected with streptavidin-HRP and the V5 antibody. We have added a description for these arrows to the figure legend. See line 641.

(5) Related to the point in the 'Weakness', there are some descriptions of how well some of the gap junction-associated proteins colocalize with Cx36 in immunostaining. For example, 'In comparison to the scaffold proteins, however, the colocalization of Cx36 with each of these endocytic components, was clearly less frequent and more heterogenous, which appears to reflect different stages in the life cycle of Cx36' and 'All of these proteins showed considerable colocalization with Cx36 in AII amacrine cell dendrites'. It would be nice to see quantification data to support these claims.

Thank you for this suggestion. We have added a colocalization analysis to figure 3 (C & D). We quantified the colocalization for the endocytosis proteins Eps15l1 and Hip1r. This quantification included a flipped control to rule out random overlap. For both proteins we confirmed true colocalization (Figure 3D).

(6) In Figure 5B, it would be helpful if there were arrows or some kind in western blottings to indicate which bands are supposed to be the targeted proteins.

We have added arrows in IP samples to indicate bands representing the corresponding protein.

(7) In the sentence including 'for the PBM of Cx36, as it is the case for ZO-1', what is PBM?

The PBM means PDZ binding motif. We have added an explanation for this abbreviation in line 244.

(8) Please add a description of the Cx35b promoter construct in the Method section.

The Cx35b Promoter is a 6.5kb fragment. We will make the clone available via Addgene to ensure that all details of the clone can be accessed via snapgene or alternative software.

eLife Assessment

This valuable study explores changes in remote memory impairment in an amyloid pathology mouse model, demonstrating that progressive deficits coincide with inhibitory interneuron alterations. While the findings shed light on circuit remodeling in this model, the mechanistic links between heightened inhibition and memory loss are currently incomplete. Additional data and deeper analysis may be needed to fully substantiate the authors' interpretations.

Reviewer #1 (Public review):

This study presents evidence that remote memory in the APP/PS1 mouse model of Alzheimer's disease (AD) is associated with PV interneuron hyperexcitability and increased inhibition of cortical engram cells. Its strength lies in the fact that it explores a neglected aspect of memory research - remote memory impairments related to AD (for which the primary research focus is usually on recent memory impairments) -which has received minimal attention to date. While the findings are intriguing, the weakness of the paper hovers around purely correlational types of evidence and superficial data analyses, which require substantial revisions as outlined below.

Major concerns:

(1) In light of previous work, including that by the authors themselves, the data in Figure 1 should be complemented by measurements of recent memory recall in order to assess whether remote memories are exclusively impaired or whether remote memory recall merely represents a continuation of recent memory impairments.

(2) Figure 2 shows electrophysiological properties of PV cells in the mPFC that correlate with the behavior shown in Figure 1. However, the mice used in Figure 2 are different than the mice used in Figure 1. Thus, the data are correlative at best, and the authors need to confirm that behavioral impairments in the APP/PS1 mice crossed to PV-Cre (and SST-Cre mice) used in Figure 2 are similar to those of the APP/PS1 mice used in Figure 1. Without that, no conclusions between behavioral impairments and electrophysiological as well as engram reactivation properties can be made, and the central claims of the paper cannot be upheld.

(3) The reactivation data starting in Figure 3 should be analysed in much more depth: a) The authors restrict their analysis to intra-animal comparisons, but additional ones should be performed, such as inter-animal (WT vs APP/PS1) as well as inter-age (12-16w vs 16-20w). In doing so, reactivation data should be normalized to chance levels per animal, to account for differences in labelling efficiency - this is standard in the field (see original Tonegawa papers and for a reference). This could highlight differences in total reactivation that are already apparent, such as for instance in WT vs APP/PS1 at 20w (Figure 3o), and highlight a decrease in reactivation in AD mice at this age, contrary to what is stated in lines 213-214. b) Comparing the proportion of mcherry+ cells in PV- and PV+ is problematic, considering that the PV- population is not "pure" like the PV+, but rather likely to represent a mix of different pyramidal neurons (probably from several layers), other inhibitory neurons like SST and maybe even glial cells. Considering this, the statement on line 218 is misleading in saying that PVs are overrepresented. If anything, the same populations should be compared across ages or groups. c) A similar concern applies to the mcherry- population in Figure 4, which could represent different types of neurons that were never active, compared to the relatively homogeneous engram mcherry+ population. This could be elegantly fixed by restricting the comparison to mCherry+Fos+ vs mCherry+Fos- ensembles, and could indicate engram reactivation-specific differences in perisomatic inhibition by PV cells.

(4) At several instances, there are some doubts about the statistical measures having been employed: a) In Figure 4f, it is unclear why a repeated measurement ANOVA was used as opposed to a regular ANOVA. b) In Supplementary Figure 2b, a Mann-Whitney test was used, supposedly because the data were not normally distributed. However, when looking at the individual data points, the data does seem to be normally distributed. Thus, the authors need to provide the test details as to how they measured the normalcy of distribution.

Minor concerns:

(1) Line 117: The authors cite a recent memory impairment here, as shown by another paper. However, given the notorious difficulty in replicating behavioral findings, in particular in APP/PS1 mice (number of backcrossings, housing conditions, etc., might differ between laboratories), such a statement cannot be made. The authors should either show in their own hands that recent memory is indeed affected at 12 weeks of age, or they should omit this statement.

(2) Pertaining to Figure 3, low-resolution images of the mPFC should be provided to assess the spread of injection and the overall degree of double-positive cells.

Reviewer #2 (Public review):

This study presents a comprehensive investigation of remote memory deficits in the APP/PS1 mouse model of Alzheimer's disease. The authors convincingly show that these deficits emerge progressively and are paralleled by selective hyperexcitability of PV interneurons in the mPFC. Using viral-TRAP labeling and patch-clamp electrophysiology, they demonstrate that inhibitory input onto labeled engram cells is selectively increased in APP/PS1 mice, despite unaltered engram size or reactivation. These findings support the idea that alterations in inhibitory microcircuits may contribute to cognitive decline in AD.

However, several aspects of the study merit further clarification. Most critically, the central paradox, i.e., increased inhibitory input without an apparent change in engram reactivation, remains unresolved. The authors propose possible mechanisms involving altered synchrony or impaired output of engram cells, but these hypotheses require further empirical support. Additionally, the study employs multiple crossed transgenic lines without reporting the progression of amyloid pathology in the mPFC, which is important for interpreting the relationship between circuit dysfunction and disease stage. Finally, the potential contribution of broader network dysfunction, such as spontaneous epileptiform activity reported in APP/PS1 mice, is also not addressed.

eLife Assessment

This valuable study presents a novel approach to enhance the therapeutic potential of mesenchymal stromal cells (MSCs) by genetically modifying their glycogen synthesis pathway, resulting in increased glycogen accumulation and improved cell survival under starvation conditions, particularly in the context of experimental pulmonary fibrosis. The methods and findings are generally solid and could be strengthened by investigating the kinetics of persistence, the immunomodulatory effects, and the underlying improved mechanism of action of MSCs in this pulmonary fibrosis model. If confirmed, this approach could suggest potential methods to improve the therapeutic functionality of MSCs in cell therapy strategies.

Reviewer #1 (Public review):

Summary:

This study provides the first evidence that glucose availability, previously shown to support cell survival in other models, is also a key determinant for post-implantation MSC survival in the specific context of pulmonary fibrosis. To address glucose depletion in this context, the authors propose an original, elegant, and rational strategy: enhancing intracellular glycogen stores to provide transplanted MSCs with an internal energy reserve. This approach aims to prolong their viability and therapeutic functionality after implantation.

Strengths:

The efficacy of this metabolic engineering strategy is robustly demonstrated both in vitro and in an orthotopic mouse model of pulmonary fibrosis.

Comments and questions for clarification:

(1) Glycogen biosynthesis typically involves several enzymes. In this context, could the authors comment on the effect of overexpressing a single enzyme - especially a mutant version - on the structure or quality of the glycogen synthesized?

(2) Regarding the in vitro starvation experiments (Figure 2C), what oxygen conditions (pO₂) were used? Are these conditions physiologically relevant and representative of the in vivo lung microenvironment?

(3) In the in vitro model, how many hours does it take for the intracellular glycogen reserve to be completely depleted under starvation conditions?

(4) For the in vivo model, is there a quantitative analysis of the survival kinetics of the transplanted cells over time for each group? This would help to better assess the role and duration of glycogen stores as an energy buffer after implantation.

(5) Finally, the study was performed in male mice only. Could sex differences exist in the efficacy or metabolism of the engineered MSCs? It would be helpful to discuss whether the approach could be expected to be similarly effective in female subjects.

(6) The number of mice for each group and time point should be specified.

Reviewer #2 (Public review):

Summary:

In this article, the authors investigate enhancing the therapeutic and regenerative properties of mesenchymal stem cells (MSCs) through genetic modification, specifically by overexpressing genes involved in the glycogen synthesis pathway. By creating a non-phosphorylatable mutant form of glycogen synthase (GYSmut), the authors successfully increased glycogen accumulation in MSCs, leading to significantly improved cell survival under starvation conditions. The study highlights the potential of glycogen engineering to improve MSC function, especially in inflammatory or energy-deficient environments. However, critical gaps in the study's design, including the lack of validation of key findings, limited differentiation assessments, and missing data on MSC-GYSmut resistance to reactive oxygen species (ROS), necessitate further exploration.

Strengths:

(1) Novel Approach: The study introduces an innovative method of enhancing MSC function by manipulating glycogen metabolism.

(2) Increased Glycogen Storage: The genetic modification of GYS1, resulting in GYSmut, significantly increased glycogen accumulation, leading to improved MSC survival under starvation, which has strong implications for enhancing MSC therapeutic properties in energy-deficient environments.

(3) Potential Therapeutic Impact: The findings suggest significant therapeutic potential for MSCs in conditions that require improved survival, persistence, and immunomodulation, especially in inflammatory or energy-limited settings.

(4) In Vivo Validation: The in vivo murine model of pulmonary fibrosis demonstrated the improved survival and persistence of MSC-GYSmut, supporting the translational potential of the approach.

Weaknesses:

(1) Lack of Differentiation Assessments: The study did not evaluate key MSC differentiation pathways, including chondrogenic and osteogenic differentiation. The absence of analysis of classical MSC surface markers and multipotency limits the understanding of the full potential of MSC-GYSmut.

(2) Missing Validation of RNA Sequencing Data: Although RNA sequencing data revealed promising transcriptomic changes in chondrogenesis and metabolic pathways, these findings were not experimentally validated, limiting confidence.

(3) Lack of ROS Resistance Analysis: Resistance to reactive oxygen species (ROS), an important feature for MSCs under regenerative conditions, was not assessed, leaving out a critical aspect of MSC function.

(4) Inconsistencies in In Vivo Data: There is a discrepancy between the number of animals shown in the figures and the graph (three individuals vs. five animals), as well as missing details on how luciferase signal intensity was quantified, requiring further clarification.

(5) Limited Exploration of Immunosuppressive Properties: The study did not address the immunosuppressive functions of MSC-GYSmut, which are critical for MSC-based therapies in clinical settings.

Conclusion:

The study presents an exciting new direction for enhancing MSC function through glycogen metabolism engineering. While the results show promise, key experiments and validations are missing, and several areas, such as differentiation capacity, ROS resistance, and immunosuppressive properties, require further investigation. Addressing these gaps would solidify the conclusions and strengthen the potential clinical applications of MSC-GYSmut in regenerative medicine.

eLife Assessment

Valencia et al. combine elegant in vitro biochemical experiments with functional assays in cardiomyocytes to determine which properties of the FHOD3 formin are essential for sarcomere assembly. Using separation-of-function mutants, they show that FHOD3's elongation activity, rather than its nucleation, capping, or bundling activities, is key to its sarcomeric function. This is an important finding and the data presented in the manuscript are convincing; however, the presence of FHOD3 at filament barbed ends in the TIRF elongation assays should probably be verified directly in a future study.

Reviewer #1 (Public review):

Summary:

Formins are complex proteins with multiple effects on actin filament assembly, including nucleation, capping with processive elongation, and bundling. Determining which of these activities are important for a given biological process and normal cellular function is a major challenge.

Here, the authors study the formin FHOD3L, which is essential for normal sarcomere assembly in muscle cells. They identify point mutants of FHOD3L in which formin nucleation and elongation/bundling activities are functionally separated. Expression of these mutants in neonatal rat ventricular myocytes shows that the control of actin filament elongation by formin is the major activity required for normal assembly of functional sarcomeres.

Strengths:

The strength of this work is to combine sensitive biochemical assays with excellent work in neonatal rat ventricular myocytes. This combination of approaches is highly effective for analyzing the function of proteins with multiple activities in vitro. The authors have pushed the experiments and data analysis as far as possible with the technologies available to them.

Weaknesses:

FHOD3L is not the easiest formin to study because of its relatively weak nucleation activity and the short duration of capping events. This difficulty imposes rigorous biochemical analysis and careful interpretation of the data. As the authors acknowledge, it will be important in future to perform complementary multi-color TIRF experiments to confirm that the brief accelerations in the elongation of actin filaments are indeed due to FHOD3 binding.

Reviewer #3 (Public review):

Valencia et al. aim to elucidate the biochemical and cellular mechanisms through which the human formin FHOD3 drives sarcomere assembly in cardiomyocytes. To do so, they combined rigorous in vitro biochemical assays with comprehensive in vivo characterizations, evaluating two wild type FHOD3 isoforms and two function-separating mutants. Surprisingly, they found that both wild type FHOD3 isoforms can nucleate new actin filaments, as well as elongate existing actin filaments in conjunction with profilin following barbed-end capping. This is in addition to FHOD3's proposed role as an actin bundler. Next, the authors focused on the longer isoform FHOD3L due to its essential role in sarcomere assembly in cardiomyocytes. They asked whether FHOD3L promote sarcomere assembly through its activity in actin nucleation or rather elongation. To do so, the authors designed two function-separating mutants: the K1193L mutation in the FH2 domain, known for its importance in actin nucleation, and the glycine-serine linker substitution in the FH1 domain ("GS-FH1",) known for its requirement in actin elongation. They demonstrated that while K1193L maintains its elongation activity and greatly diminishes nucleation and bundling, in GS-FH1 keeps its nucleation activity while lose its capacity to drive elongation. Armed with these tools, the authors attempted to rescue FHOD3L siRNA-treated neonatal rat ventricular myocytes (NRVM) with transgenes carrying wild type, K1193L, or GS-FH1 mutant forms of human FHOD3. In each condition, they evaluated the numbers and morphology of sarcomeres, as well as their ability to beat and generate cardiac rhythm. The authors found that while the wild type FHOD3L and the K1193L mutant can rescue sarcomere morphology and physiology, the GS-FH1 mutant fails to do so. Given that in GS-FH1 mainly elongation activity is compromised, the authors concluded that the elongation activity of FHOD3 is essential for its role in sarcomere assembly in cardiomyocytes, while its nucleator activity is dispensable. Overall, this important study provided a broadened view on the biochemical activities of FHOD3, and a pioneering view on a possible cellular mechanism of how FHOD3L drives sarcomere assembly. If further validated, this can lead to new mechanistic models of sarcomere assembly and potentially new therapeutic targets of cardiomyopathy.

The conclusions of this paper are mostly well supported by the comprehensive biochemical analyses performed by the authors. In my original assessment, I raised the point that the extreme low level of GS-FH1 signal in transfected cells in Figure 6A may reflect a failure of actin-binding by this construct in vivo, rather than its inability of driving elongation. The authors have thoroughly addressed this concern by: 1) providing new images of the GS-FH1 rescue condition with HA-FHOD3L signal intensities matching that of the K1193L rescue condition, and 2) quantitatively demonstrating that the expression levels in the GS-FH1 rescue condition are comparable with that of wild type FHOD3L rescue condition. This is nicely complemented by the new phalloidin staining of the GS-FH1 rescue condition, which showcased additional details of actin puncta reminiscent of that present in muscle stress fibers or premyofibrils. Overall, I am now convinced that the GS-FH1 cannot rescue sarcomere formation even when expressed at comparable levels. Given that GS-FH1 demonstrates actin elongation defects in vitro, it is reasonable to conclude that the actin elongation function of FHOD3L is essential for sarcomere formation in vivo.

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Summary:

Formins are complex proteins with multiple effects on actin filament assembly, including nucleation, capping with processive elongation, and bundling. Determining which of these activities is important for a given biological process and normal cellular function is a major challenge.

Here, the authors study the formin FHOD3L, which is essential for normal sarcomere assembly in muscle cells. They identify point mutants of FHOD3L in which formin nucleation and elongation/bundling activities are functionally separated. Expression of these mutants in neonatal rat ventricular myocytes shows that the control of actin filament elongation by formin is the major activity required for the normal assembly of functional sarcomeres.

Strengths:

The strength of this work is to combine sensitive biochemical assays with excellent work in neonatal rat ventricular myocytes. This combination of approaches is highly effective for analyzing the function of proteins with multiple activities in vitro.

Weaknesses:

FHOD3L does not seem to be the easiest formin to study because of its relatively weak nucleation activity and the short duration of capping events. This difficulty imposes rigorous biochemical analysis and careful interpretation of the data, which should be improved in this work.

We thank the reviewer for their praise and appreciation of our work. Indeed, FHOD3L is a challenging formin to work with.

Important points are raised here and below regarding the brief elongation events we reported. As suggested, we performed more rigorous analysis of the data and present it in the revised manuscript. We now report that from 45 dim regions analyzed, in three independent experiments with wild type FHOD3L, we detected 40 bursts. (The remaining five could be formin falling off too quickly to detect or the dim spots could be regions of inhomogeneity in intensity, not due to formin.) For comparison to the presented data with FHOD3L-CT, we analyzed the filaments in TIRF assays with no formin present. As the reviewers point out, inhomogeneities in filament intensity are normal. Thus, we examined any dim spots for pauses and/or bursts. As is now reported in Figure 2G,H, the velocity of growth of these dim spots is indistinguishable from the velocity of the rest of the filament. We acknowledge that our numbers may not be perfectly accurate, due to the noise in our system, we believe that the difference of 3-4 fold increase versus no change in rate is substantial and convincing.

We also determined the number of dim spots per length of filament. We found a higher frequency when FHOD3L-CT or FHOD3S-CT was present vs no formin, as now shown in Figure 2 – supplements 1G and 2E.

We were asked about the pauses we observe before bursts of elongation and how we know they are functionally relevant. The short answer is that we do not know. We reported them because they were so common: Of the 40 bursts, pauses preceded the burst in 38 cases. We cannot rule out that this pause reflects an interaction with the surface but might expect the frequency to be lower if it were. We revise the text to make our conclusions about pauses more circumspect.

We are convinced that the brief dim events we observed in the presence of FHOD3L-CT, in fact, reflect formin-mediated elongation and worked hard to improve their presentation, in addition to the added analysis. We include new kymographs, including examples from FHOD3L, FHOD3S, K1193L, and actin alone. We hope that the reviewers are also convinced.

This does not preclude our interest in the microfluidics and two-color assays, which will be pursued in the future. We have reached out to a colleague who is set up to repeat these measurements with microfluidics-assisted TIRF. The noise should be greatly reduced and the system is also optimal for directly visualizing labeled FHOD3, as suggested. We expect these experimental approaches will provide additional insights.

Reviewer #2 (Public review):

This article elucidates the biochemical and cellular mechanisms by which the FHOD-family of formins, particularly FHOD3, contributes to sarcomere formation and contractility in cardiomyocytes. Formins are mainly known to nucleate and elongate actin filaments, with certain family members also exhibiting capping, severing, and bundling activities. Although FHOD3 has been well-established as essential for sarcomere assembly in cardiomyocytes, its precise biochemical functions and contributions to actin dynamics remain poorly understood.

In this study, the authors combine in vitro biochemical assays with cellular experiments to dissect FHOD3's roles in actin assembly and sarcomere formation. They demonstrate that FHOD3 nucleates actin filaments and acts as a transient elongator, pausing elongation after an initial burst of filament growth. Using separation-of-function mutants, they show thatFHOD3's elongation activity - rather than its nucleation, capping, or bundling capabilities - is key for its sarcomeric function.

The experiments have been conducted rigorously and well-analyzed, and the paper is clearly written. The data presented support the authors' conclusions. I appreciate the detailed description and rationale behind the FHOD3 constructs used in this study.

We are happy to hear others find paper to be clearly written and well described.

However, I was somewhat surprised and a bit disappointed that while the authors conducted single-color TIRF experiments to observe the effects of FHOD3 on single filaments, they did not use fluorescently labeled FHOD3 to directly visualize its behavior. Incorporating such experiments would significantly strengthen their conclusions regarding FHOD3's bursts of elongation interspersed with capping activity. While I understand this might require a few additional weeks of experiments, these data would add considerable value by directly testing the proposed mechanism.

We appreciate the suggestion and hope to incorporate a two-color approach soon. As noted, FHOD3L is not always easy to work with and we do not have a functional labeled copy of the protein at this time.

There is a typo in the word "required" in line number 30. The authors also use fit data to extract parameters in several panels (e.g., Figures 2b, 2d, 3a, and 3b). While these fit functions may be intuitive to actin experts, explicitly describing the fit functions in the figure legends or methods would greatly benefit the broader readership.

Thank you for these comments. We updated the indicated figures and described the analysis in greater detail.

Reviewer #3 (Public review):

Valencia et al. aim to elucidate the biochemical and cellular mechanisms through which the human formin FHOD3 drives sarcomere assembly in cardiomyocytes. To do so, they combined rigorous in vitro biochemical assays with comprehensive in vivo characterizations, evaluating two wild-type FHOD3 isoforms and two function-separating mutants. Surprisingly, they found that both wild-type FHOD3 isoforms can nucleate new actin filaments, as well as elongate existing actin filaments in conjunction with profilin following barbed-end capping. This is in addition to FHOD3's proposed role as an actin bundler. Next, the authors asked whether FHOD3L promotes sarcomere assembly in cardiomyocytes through its activity in actin nucleation or rather elongation. With two function-separating mutants, the authors evaluated the numbers and morphology of sarcomeres, as well as their ability to beat and generate cardiac rhythm. The authors found that while the wild-type FHOD3L and the K1193L mutant can rescue sarcomere morphology and physiology, the GS-FH1 mutant fails to do so. Given that in GS-FH1 mainly elongation activity is compromised, the authors concluded that the elongation activity of FHOD3 is essential for its role in sarcomere assembly in cardiomyocytes, while its nucleator activity is dispensable. Overall, this important study provided a broadened view on the biochemical activities of FHOD3, and a pioneering view on a possible cellular mechanism of how FHOD3L drives sarcomere assembly. If further validated, this can lead to new mechanistic models of sarcomere assembly and potentially new therapeutic targets of cardiomyopathy.

The conclusions of this paper are mostly well supported by the comprehensive biochemical analyses performed by the authors. However, the sarcomere assembly defect phenotype in the GS-FH1 rescue condition requires further investigation, as the extremely low level of GS-FH1 signal in transfected cells in Figure 6A may reflect a failure of actin-binding by this construct in vivo, rather than its inability to drive elongation. Though the authors do show in Figure 6 that GS-FH1 can bind to normal-looking sarcomeres when they are present, this may be due to a lack of siRNA activity in these cells, such that endogenous FHOD3L is still present. In this possible scenario, GS-FH1 may dimerize with endogenous FHOD3L. The authors should demonstrate that GS-FH1 alone can indeed interact with existing actin filaments in vivo. While this has been clearly demonstrated in vitro, given the more complex biochemical environment in vivo where additional unknown binding partners may present, cautions should be made when extrapolating findings from the former to the latter.

The reviewer is concerned about the low protein levels in the GS-FH1 rescue experiments as reflected in the HA fluorescence intensity distributions shown in Fig. 5 Supplement 2A. While the scenario proposed could explain our observations with the GSFH1 rescues it is quite complex. Nor does the scenario preclude the conclusion that the FH1 domain is critical. We agree that the observed sarcomeres are likely to be residual in cells with incomplete RNAi. We now include the image of a cell that is still full of sarcomeres and note that the GH-FH1 is expressed at a relatively high level and striated throughout the cell. We interpret this as evidence that GS-FH1 is stable when suitable binding sites are available. We cannot exclude that there is more GS-FH1 because there was more endogenous FHOD3L with which to heterodimerize. If the GS-FH1 heterodimer were simply poisoning the wild type protein, we do not expect that it would be bound correctly to sarcomeres. If, instead, heterodimers have some activity, it seems far from sufficient to rescue sarcomere formation, suggesting that two functional FH1 domains are critical.

Furthermore, we do not see evidence of correlation between protein levels and rescue at the level present in these cells (addressed below). Unfortunately, the proposed IP to test whether FHOD3L binds actin in vivo would only potentially report on filament side binding (both direct and indirect). It would not address whether the GS-FH1 mutant functions as a nucleator, elongator, bundler and/or capping protein in vivo.

The critical question that we can address is whether the phenotype is due to low protein levels, assuming the protein present is functional, or due to loss of elongation activity by FHOD3L. To address this question, we returned to our data.

First, we plotted the distributions of the intensities of the cells we analyzed further, in addition to the automated readout of all of the cells in the dish (Fig. 4 supplement 1). These cells were selected randomly and, as should be the case, the distributions of their intensities agree well with the original distributions for the three different rescue constructs: FHOD3L, K1193L, and GS-FH1 (Fig. 6 supplement 1). We then asked whether there was any correlation in HA intensities with the sarcomere metrics. As seen in our pilot data, no correlation is evident in any of the three cases across the range of intensities we collected (400 – 2700 a.u.) (old Fig. 6 supplement C,D,E). We now replace the data from pilot experiments with analysis of HA intensities and sarcomere metrics from the data sets included in the paper (new Fig 6. Supplement 1). Again, little to no correlation was observed (the single highest r-squared value is 0.2 and the remaining eight values are less than or equal to 0.08).

To more specifically address the question of whether low HA fluorescence intensity is likely to reflect sufficient protein levels to build sarcomeres we re-examined two data sets from the FHOD3L WT rescue data. We found that, by chance, the first replicate of data from the wild type rescue has a comparable intensity distribution to that of the GSFH1 rescues (580 +/- 261 / cell vs. 548 +/- 105 / cell). In addition, we collected all of the data from cells with intensity levels <720, designed to mimic the distribution of the GS-FH1 cells (Fig. 6 supplement 3). We then compared the sarcomere metrics (sarcomere number, sarcomere length, sarcomere width) between the full data set and the two low intensity subsets:

• Sarcomere number is the only non-normal metric. We therefore used the Mann Whitney U test, which shows no difference between all 3 WT distributions.

• We compared Z-line lengths by one-way ANOVA and Tukey's post hoc tests, again finding no significant difference for all distributions.

• Sarcomere length shows a weakly significant difference (p=0.038) between the whole WT data set and bio rep 1, but no difference between the whole WT data set and the HA<720 group.

Thus, cells expressing wild type FHOD3L at levels comparable to levels detected in GS-FH1 mutant rescues, are fully rescued. Based on these findings we conclude that the expression levels in the GS-FH1 are high enough to rescue the FHOD3 knock down, supporting our conclusion that the defect is due to loss of elongation activity. We have added this analysis and discussion to the revised manuscript.

Recommendations for the authors:

Reviewing Editor Comments:

You will see that the 3 reviewers are very positive about your work and appreciate the elegant combination of biochemical assays and functional tests in cardiomyocytes. We've had a long discussion with them and we all agree that two experiments deserve further effort to make the conclusions of your paper more convincing.

Thank you.

The first experiment is the TIRF elongation assay, where the two biochemist Reviewers remain doubtful that these short events are really due to the presence of a formin at the end of the filament. One of them suggests that two-color imaging with a labeled formin should clearly prove this point.

We agree that the elongation assays can be improved. Given the similarity of processivity of Fhod3L, Fhod3S and Drosophila FhodA (measured by a distinct method), we are inclined to believe them. However, the reviewer raises an excellent point about the accuracy of the measurements given the resolution (and noise) of the data. We are interested in the two-color imaging assay but do not believe it will necessarily simplify the analysis. We suspect that Fhod spends more time at/near the barbed end than is apparent based on elongation rates. The fact that we see repeated events on individual filaments at such low concentrations of FHOD3L (0.1 nM) supports this idea. Otherwise, the likelihood of FHOD3L finding barbed ends so often is really quite low.

We will return to these experiments, using alternate methods, curious to see what else we learn. In the meantime, we conducted more thorough analysis, including controls, and improved visualization of example traces. Data for elongation analysis and kymographs were acquired with Jfilament. We stretched the x-axis (time) in kymographs for FHOD3L-CT (Fig. 2F), FHOD3S-CT (Fig. 2, supplement 2C), FHOD3L-CT K1193L (Fig. 3, supplement 1A), and actin alone (Fig 2G), and highlighted regions of analysis. The slopes for these regions, separated based on intensity, were fit to the data in KaleidaGraph. The fits are offset from the data such that they do not obscure the filaments and corresponding rates are given. The fact that we never see fast dim regions when FHOD3 is not present, as shown in Fig. 2H and that the frequency of dim events is markedly increased (Fig. 2-supplements 1G and 2E) give us confidence that the events are real. We acknowledge in the text that the precise values of the short events may be inaccurate due to the resolution of our experiments. We hope the reviewers are convinced by the improved analysis.

The second experiment is the sarcomere assembly defect phenotype in the GS-FH1 rescue condition. This requires further investigation, as the extremely low level of GS-FH1 signal in transfected cells in Figure 6A may reflect a failure of actin-binding/nucleation in vivo, rather than its inability to elongate F-actin. Although you show that GS-FH1 can bind to sarcomeres when they are present, this may be due to a lack of siRNA activity in these cells, such that endogenous FHOD3L is still present. In this possible scenario, GS-FH1 could dimerize with endogenous FHOD3L.

We agree that the sarcomeres we see are likely to be residual and could reflect some remaining endogenous FHOD3. The reviewers are concerned about the low protein levels in the GSFH1 rescues. First, we do not agree that the levels are “extremely” low. Through careful analysis, we established that 3xHA-FHOD3L intensities between 300 and 3000 a.u./um² were sufficient for full rescue. The mean for the GSFH1 experiments is 533 +/- 93, which is well within this range. Furthermore, we did not observe correlation between sarcomere number, length, or width and HA intensity over the full range collected for wild type FHOD3L or within the GS-FH1 data. We previously showed pilot data but now show correlation analysis for every analyzed cell (Fig. 4 – figure supplement 1 D-F). We conducted this analysis on all of the mutant rescue experiments (Fig. 6-supplement 1). Finally, we identified two subpopulations of the wildtype rescue data. One is all of the cells with HA intensity < 720, which gives a distribution of mean 545 +/- 85. The second set is the first biological replicate of wild type rescue, which has a distribution of mean 560 +/- 160. Again correlation shows little relationship between HA levels and sarcomere metrics. Nevertheless, we show intensity level matched images in Fig 6, as opposed to images reflecting average intensities.

The critical question remains whether the phenotype is due to low protein levels or due to loss of elongation by FHOD3L. Notably, we now show a cell that is full of sarcomeres and has relatively high FHOD3L levels as well, consistent with available binding sites stabilizing mutant protein but not ruling out heterodimerization (Fig. 6 – figure supplement 2C). Others have expressed mutant FHOD3L in a wild type background in mice. They observed poisoning, consistent with heterodimerization. Thus, it is possible that, as suggested, the FHOD3L-GSFH1 detected in sarcomeres is in fact heterodimerized with residual endogenous FHOD3L. In this case, we would still conclude that the protein is not functional enough to rescue, supporting a role for the FH1 domain.

In the future, we plan to perform experiments with compromised, but not inactive, FH1 domains, as we discuss in the paper.

We hope that you will find these comments useful.

Yes, the comments were thoughtful and helped us write a better paper. Thank you.

Reviewer #1 (Recommendations for the authors):

Some experiments should be described and analyzed more carefully. This lack of clarity calls into question the interpretation of some experiments. Overall, this study is not yet as convincing as it should be.

Main recommendations:

(1) Formin elongation phases in the TIRF experiment are not convincing. They are rare and it is difficult to see any significant difference between the control movie without FHOD3L-CT and the movie with FHOD3L-CT. Filaments assembled in the absence of FHOD3L-CT also show some fluorescence inhomogeneity (which is normal), and measurements of formin elongation rates and capping times are not convincing (for example, the kymograph of the control profilin-actin situation in Figure 2F also shows a fast elongation phase on the right).

Please see response above. We conducted more thorough analysis and created improved visualizations. We hope the data are more convincing now.

It is also difficult to understand how an accurate measurement can be made from these noisy kymographs, and the method section should explain that precisely.

This is a valid point. We added details of analysis to the methods section and we discuss the fact that the measurements are at the limit of our resolution in the paper. We rely on the large (~3-fold) difference in elongation, more than specific elongation rates for our interpretation.

One of the problems is that these events are too transient to quantify well with noisy data. I noticed that the formin concentration used in these movies is quite low (0.1 nM FHOD3L-CT). Is there a reason for this? Is it possible to increase the formin concentration to increase the number of formin capping/elongation events and provide more convincing movies?

We acknowledge that the data are noisy. We felt that it was necessary to perform experiments with filaments only tethered at one end, leaving the growing end free. We did so, in part, because when we did experiments with biotinylated actin to anchor the filaments down, we observed pauses in the absence of formin. Ultimately, we compromised, using anchored seeds and a relatively low concentration of NEM-myosin to decrease motion of the actin filaments.

The experiments were performed with such low FHOD3L-CT because it was a potent nucleator in TIRF assays, making data analysis nearly impossible with more formin present. FHOD3S-CT and FHOD3L-CT K1193L behaved somewhat differently between these experiments and we were able to perform them with 1 nM formin.

Not seeing formin at the tip of the filaments is an additional difficulty because we do not know if these pauses occur because formin is stuck to the coverslips (which could very well happen with these sticky proteins) or freely bound at the end of a filament as the text suggests. Is there any argument in favor of one scenario over the other?

This will be an important experiment. As described above, we suspect that Fhod spends more time at/near the barbed end than is apparent based on elongation data. The fact that we see repeated events on individual filaments at such low concentrations of FHOD3L (0.1 nM) supports this idea. Otherwise, the likelihood of FHOD3L finding barbed ends so often is really quite low. In order to address the question about the cause of pauses, we reviewed our data, finding that 38 of 40 bursts were preceded by pauses. We do, however, discuss that we cannot rule out non-specific interactions with the surface.

(2) Pyrene elongation assays in the presence of profilin are actually more convincing to test the elongation ability of formins. However, such an assay is not presented for all mutants. It should be.

While we agree to some extent with this comment, we did not include the pyrene data for all of the mutants because the shapes of the curves were even more complicated than those seen with wild type FHOD3L-CT rendering them uninterpretable.

(3) Some experiments (e.g. in Figure 2E) are performed with yeast profilin, while others (e.g. in Figure 2F) are performed with human profilin. Obviously, both profilins could modulate formin activity differently and the side-by-side interpretation of both experiments is difficult. Could the authors stick to human profilin for all experiments?

We used to always perform pyrene assays with yeast profilin because it was known to be insensitive to pyrene. These data were collected before we realized that the affinity of human profilin for actin is so high that we could probably do everything with this profilin. We have compared the two profilins for other formins, e.g. Delphilin, Capu, and did not observe detectable differences.

Minor recommendations:

(1) The pyrene assays with the light blue colored curve choice are not ideal. I have difficulties seeing some of the curves.

Thank you. We added symbols to a subset of the traces to make them more visible.

(2) In the same curves, I can't understand what the +3.75 and 0.078 numbers mean. Could these results be plotted in a clearer way?

These values are the lowest concentrations in the range tests. They were matching light blue with black outline for visibility. We added symbols and changed the color of the numbering for improved visibility/understanding.

(3) In Figure 2D, is the Kd of I1163A really determined only from 2 experimental data points?

Of course not. We now show the figure with extended axes in Fig. 2 - figure supplement 1C.

(4) In Figure 2C, the shape of the curves suggests that this is not a pure capping assay, but a mix of capping and nucleation. It's not dramatic but could lead to an under-estimation of the capping efficiency.

We agree with the reviewer that the complicated shapes confound interpretation. Our analysis is based on the earliest slopes, in part, for this reason. We added discussion of this complication to the text.

Reviewer #3 (Recommendations for the authors):

Suggestions for additional experiments:

(1) To evaluate whether GS-FH1 alone can indeed interact with existing actin filaments in vivo, the authors may consider performing immunoprecipitation assays with GS-FH1 extracted from rescued NRVMs.

An IP of GS-FH1 from cells could show actin filament side binding but, unfortunately, will not provide any information about filament end binding, which is of much greater interest.

It will be helpful to show phalloidin staining in GS-FH1 rescues in a similar manner as in Figure 6-supplement 1, panel B, and compare that with mock rescue in Figure 4 panel D. It will be essential to prove this prior to concluding that actin elongation activity is essential for sarcomere assembly.

This is an excellent suggestion. We now include images of phalloidin stained cells from both K1193L and GS-FH1 rescues (Fig. 6A’ – supplement 2A,B). We were intrigued to see small actin punctae that were sometimes aligned. We speculate that these could be pre-premyofibrils and suggest that this is further evidence that the GS-FH1 protein is not completely unstable.

(2) Prior to sarcomere assembly, a-actinin is known to form short bundles with actin filaments (I-Z-I complex) without clearly defined periodicity. This semi-ordered state then transforms into the more ordered sarcomeres with periodic spacing. It will be valuable to show the phalloidin staining in addition to the a-actinin IF consistently across all conditions. This may lead to further insights into the defects of sarcomere assembly. Along the same vein, higher magnification images showcasing several sarcomeres will help the readers evaluate these defects.

We agree that there are additional valuable measurements to be made. In order to favor synchronized contraction, we plated the cells at too high a density to reliably identify IZI complexes. We have included some zoomed in images of the phalloidin staining.

Recommendations for improving the writing:

The authors mentioned the interaction between cardiac MyBP-C and FHOD3L as essential for the localization of FHOD3L to the C-line of the sarcomere. Can they discuss whether this interaction is important for the role of FHOD3L in sarcomere assembly? If so, how?

This is a very interesting question that we cannot answer at this time.

Minor corrections to the text and figures:

In the legend of Figure 2-Figure Supplement 1, the labels of (F) and (E) are swapped.

Thank you for catching this.

Author response:

eLife Assessment

This useful study presents Altair-LSFM, a solid and well-documented implementation of a light-sheet fluorescence microscope (LSFM) designed for accessibility and cost reduction. While the approach offers strengths such as the use of custom-machined baseplates and detailed assembly instructions, its overall impact is limited by the lack of live-cell imaging capabilities and the absence of a clear, quantitative comparison to existing LSFM platforms. As such, although technically competent, the broader utility and uptake of this system by the community may be limited.

We thank the reviewers and editors for their thoughtful evaluation of our work and for recognizing the technical strengths of the Altair-LSFM platform, including the custom-machined baseplates and detailed documentation provided to support accessibility and reproducibility. We respectfully disagree, however, with the assessment that the system lacks live-cell imaging capabilities. We are fully confident in the system’s suitability for live-cell applications and will demonstrate this by including representative live-cell imaging data in the revised manuscript, along with detailed instructions for implementing environment control. Moreover, we will expand our discussion to include a broader, more quantitative comparison to existing LSFM platforms—highlighting trade-offs in cost, performance, and accessibility—to better contextualize Altair’s utility and adaptability across diverse research settings.

Public Reviews:

Reviewer #1 (Public review):

Summary:

The article presents the details of the high-resolution light-sheet microscopy system developed by the group. In addition to presenting the technical details of the system, its resolution has been characterized and its functionality demonstrated by visualizing subcellular structures in a biological sample.

Strengths:

(1) The article includes extensive supplementary material that complements the information in the main article.

(2) However, in some sections, the information provided is somewhat superficial.

Our goal was to make the supplemental content as comprehensive and useful as possible. In addition to the materials provided with the manuscript, our intention is for the online documentation (available at thedeanlab.github.io/altair) to serve as a living resource that evolves in response to user feedback. For this reason, we are especially interested in identifying and expanding any sections that are perceived as superficial, and we would greatly appreciate the reviewer’s guidance on which areas would benefit from further elaboration.

Weaknesses:

(1) Although a comparison is made with other light-sheet microscopy systems, the presented system does not represent a significant advance over existing systems. It uses high numerical aperture objectives and Gaussian beams, achieving resolution close to theoretical after deconvolution. The main advantage of the presented system is its ease of construction, thanks to the design of a perforated base plate.

We appreciate the reviewer’s assessment and the opportunity to clarify our intent. Our primary goal was not to introduce new optical functionality beyond that of existing high-performance light-sheet systems, but rather to reduce the barrier to entry for non-specialist labs.

(2) Using similar objectives (Nikon 25x and Thorlabs 20x), the results obtained are similar to those of the LLSM system (using a Gaussian beam without laser modulation). However, the article does not mention the difficulties of mounting the sample in the implemented configuration.

We agree that there are practical challenges associated with handling 5 mm diameter coverslips. However, the Nikon 25x can readily be replaced by a Zeiss W Plan-Apochromat 20x/1.0 objective, which eliminates the need for the 5 mm coverslip[1]. In the revised manuscript, we will more explicitly detail the practical challenges in handling a 5 mm coverslip and mention the alternative detection objective.

(3) The authors present a low-cost, open-source system. Although they provide open source code for the software (navigate), the use of proprietary electronics (ASI, NI, etc.) makes the system relatively expensive. Its low cost is not justified.

We understand the reviewer’s concern regarding the use of proprietary control hardware such as the ASI Tiger Controller and NI data acquisition cards. While lower-cost alternatives for analog and digital control (e.g., microcontroller-based systems) do exist, our choice was intentional. By relying on a unified and professionally supported platform, we minimize the complexity of sourcing, configuring, and integrating components from disparate vendors—each of which would otherwise demand specialized technical expertise. Moreover, in future releases, we aim to further streamline the system by eliminating the need for the NI card, consolidating all optoelectronic control through the ASI Tiger Controller. This approach allows users to purchase a fully assembled and pre-configured system that can be operational with minimal effort.

It is worth noting that the ASI components are not the primary cost driver. The full set—including XYZ and focusing stages, a filter wheel, a tube lens, the Tiger Controller, and basic optomechanical adapters—costs approximately $27,000, or ~18% of the total system cost. Additional cost reductions are possible. For example, replacing the motorized sample positioning and focusing stages with manual alternatives could reduce the cost by ~$12,000. However, this would eliminate key functionality such as autofocusing, 3D tiling, and multi-position acquisition. Open-source mechanical platforms such as OpenFlexure could in principle be adapted, but they would require custom assembly and would need to be integrated into our control software. Similarly, the filter wheel could be omitted in favor of a multi-band emission filter, reducing the cost by ~$5,000. However, this comes at the expense of increased spectral crosstalk, often necessitating spectral unmixing. An industrial CMOS camera—such as the Ximea MU196CR-ON, recently demonstrated in a Direct View Oblique Plane Microscopy configuration[2]—could substitute for the sCMOS cameras typically used in high-end imaging. However, these industrial sensors often exhibit higher noise floors and lower dynamic range, limiting sensitivity for low-signal imaging applications.

While a $150,000 system represents a significant investment, we consider it relatively cost-effective in the context of advanced light-sheet microscopy. For comparison, commercially available systems with similar optical performance—such as LLSM systems from 3i or Zeiss—are several-fold more expensive.

(4) The fibroblast images provided are of exceptional quality. However, these are fixed samples. The system lacks the necessary elements for monitoring cells in vivo, such as temperature or pH control.

We thank the reviewer for their positive comment regarding the quality of our fibroblast images. As noted, the current manuscript focuses on the optical design and performance characterization of the system, using fixed specimens to validate resolution and imaging stability. We acknowledge the importance of environmental control for live-cell imaging. Temperature regulation is routinely implemented in our lab using flexible adhesive heating elements paired with a power supply and PID controller. For pH stabilization in systems that lack a 5% CO₂ atmosphere, we typically supplement the imaging medium with 10–25 mM HEPES buffer. In the revised manuscript, we will introduce a modified sample chamber capable of maintaining user-specified temperatures, along with detailed assembly instructions. We will also include representative live-cell imaging data to demonstrate the feasibility of in vitro imaging using this system.

Reviewer #2 (Public review):

Summary:

The authors present Altair-LSFM (Light Sheet Fluorescence Microscope), a high-resolution, open-source microscope, that is relatively easy to align and construct and achieves sub-cellular resolution. The authors developed this microscope to fill a perceived need that current open-source systems are primarily designed for large specimens and lack sub-cellular resolution or are difficult to construct and align, and are not stable. While commercial alternatives exist that offer sub-cellular resolution, they are expensive. The authors' manuscript centers around comparisons to the highly successful lattice light-sheet microscope, including the choice of detection and excitation objectives. The authors thus claim that there remains a critical need for high-resolution, economical, and easy-to-implement LSFM systems.

Strengths:

The authors succeed in their goals of implementing a relatively low-cost (~ USD 150K) open-source microscope that is easy to align. The ease of alignment rests on using custom-designed baseplates with dowel pins for precise positioning of optics based on computer analysis of opto-mechanical tolerances, as well as the optical path design. They simplify the excitation optics over Lattice light-sheet microscopes by using a Gaussian beam for illumination while maintaining lateral and axial resolutions of 235 and 350 nm across a 260-um field of view after deconvolution. In doing so they rest on foundational principles of optical microscopy that what matters for lateral resolution is the numerical aperture of the detection objective and proper sampling of the image field on to the detection, and the axial resolution depends on the thickness of the light-sheet when it is thinner than the depth of field of the detection objective. This concept has unfortunately not been completely clear to users of high-resolution light-sheet microscopes and is thus a valuable demonstration. The microscope is controlled by an open-source software, Navigate, developed by the authors, and it is thus foreseeable that different versions of this system could be implemented depending on experimental needs while maintaining easy alignment and low cost. They demonstrate system performance successfully by characterizing their sheet, point-spread function, and visualization of sub-cellular structures in mammalian cells, including microtubules, actin filaments, nuclei, and the Golgi apparatus.

We thank the reviewer for their thoughtful summary of our work. We are pleased that the foundational optical principles, design rationale, and emphasis on accessibility came through clearly. We agree that the approach used to construct the microscope is highly modular, and we anticipate that these design principles will serve as the basis for additional system variants tailored to specific biological samples and experimental contexts. To support this, we provide all Zemax simulations and CAD files openly on our GitHub repository, enabling advanced users to build upon our design and create new functional variants of the Altair system.

Weaknesses:

There is a fixation on comparison to the first-generation lattice light-sheet microscope, which has evolved significantly since then:

(1) The authors claim that commercial lattice light-sheet microscopes (LLSM) are "complex, expensive, and alignment intensive", I believe this sentence applies to the open-source version of LLSM, which was made available for wide dissemination. Since then, a commercial solution has been provided by 3i, which is now being used in multiple cores and labs but does require routine alignments. However, Zeiss has also released a commercial turn-key system, which, while expensive, is stable, and the complexity does not interfere with the experience of the user. Though in general, statements on ease of use and stability might be considered anecdotal and may not belong in a scientific article, unreferenced or without data.

The referee is correct that our comparisons reference the original LLSM design, which was simultaneously disseminated as an open-source platform and commercialized by 3i. While we acknowledge that newer variants of LLSM have been developed—including systems incorporating adaptive optics[3] and the MOSAIC platform (which remains unpublished)—the original implementation remains the most widely described and cited in the literature. It is therefore the most appropriate point of comparison for contextualizing Altair’s performance, complexity, and accessibility. Importantly, this version of LLSM is far from obsolete; it continues to be one of the most commonly used imaging systems at Janelia Research Campus’s Advanced Imaging Center.

We acknowledge that more recent commercial implementation by Zeiss has addressed several of the practical limitations associated with the original design. In particular, we agree that the Zeiss Lattice Lightsheet 7 system, which integrates a meniscus lens to facilitate oblique imaging through a coverslip, offers a user-friendly experience—albeit with a modest tradeoff in resolution (reported deskewed resolution: 330 nm × 330 nm × 500–1000 nm).

While we recognize that statements on usability and stability can be subjective, one objective proxy for system complexity is the number of optical elements that require precise alignment during assembly. The original LLSM setup includes approximately 29 optical components that must each be carefully positioned laterally, angularly, and coaxially along the optical path. In contrast, the first-generation Altair system contains only 9 such elements. By this metric, Altair is considerably simpler to assemble and align, supporting our overarching goal of making high-resolution light-sheet imaging more accessible to non-specialist laboratories. In the revised manuscript, we will clarify the scope of our comparison and provide more precise language about what we mean by complexity (e.g., number of optical elements needed to align).

(2) One of the major limitations of the first generation LLSM was the use of a 5 mm coverslip, which was a hinderance for many users. However, the Zeiss system elegantly solves this problem, and so does Oblique Plane Microscopy (OPM), while the Altair-LSFM retains this feature, which may dissuade widespread adoption. This limitation and how it may be overcome in future iterations is not discussed.

We agree that the use of 5 mm diameter coverslips, while enabling high-NA imaging in the current Altair-LSFM configuration, may serve as an inconvenience for many users. We will discuss this more explicitly in the revised manuscript. Specifically, we note that changing the detection objective is sufficient to eliminate the need for a 5 mm coverslip. For example, as demonstrated in Moore et al., Lab Chip 2021, pairing the Zeiss W Plan-Apochromat 20x/1.0 objective with the Thorlabs TL20X-MPL allows imaging beyond the physical surfaces of both objectives, removing the constraint imposed by small-format coverslips[1]. In the revised manuscript, we will propose this modification as a straightforward path for increasing compatibility with more conventional sample mounting formats.

(3) Further, on the point of sample flexibility, all generations of the LLSM, and by the nature of its design, the OPM, can accommodate live-cell imaging with temperature, gas, and humidity control. It is unclear how this would be implemented with the current sample chamber. This limitation would severely limit use cases for cell biologists, for which this microscope is designed. There is no discussion on this limitation or how it may be overcome in future iterations.

We appreciate the reviewer’s emphasis on the importance of environmental control for live-cell imaging applications. It is worth noting that the original LLSM design, including the system commercialized by 3i, provided temperature control only, without integrated gas or humidity regulation. Despite this, it has been successfully used by a wide range of scientists to generate important biological insights.

We agree that both OPM and the Zeiss implementation of LLSM offer clear advantages in terms of environmental control, as we previously discussed in detail in Sapoznik et al., eLife, 2020[4]. However, assembly of high numerical aperture OPM systems is highly technical, and no open-source variant of OPM delivers sub-cellular scale resolution yet.

(4) The authors' comparison to LLSM is constrained to the "square" lattice, which, as they point out, is the most used optical lattice (though this also might be considered anecdotal). The LLSM original design, however, goes far beyond the square lattice, including hexagonal lattices, the ability to do structured illumination, and greater flexibility in general in terms of light-sheet tuning for different experimental needs, as well as not being limited to just sample scanning. Thus, the Alstair-LSFM cannot compare to the original LLSM in terms of versatility, even if comparisons to the resolution provided by the square lattice are fair.

We thank the reviewer for this comment. It is true that our discussion focused primarily on the square lattice implementation of LLSM. While this could be viewed as a subset of the system’s broader capabilities, we chose this focus intentionally, as the square lattice remains by far the most commonly used variant in practice. Even in the original LLSM publication, 16 out of 20 figure subpanels utilized the square lattice, with only one panel each representing the hexagonal lattice in SIM mode, a standard Bessel beam in incoherent SIM mode, a hex lattice in dithered mode, and a single Bessel in dithered mode. This usage pattern largely reflects the operational simplicity of the square lattice: it minimizes sidelobe growth and enables more straightforward alignment and data processing compared to hexagonal or structured illumination modes.

In 2019, we performed an exhaustive accounting of published illumination modes in LLSM and found that the SIM mode had only been used in two additional peer-reviewed publications at that time. We will consider updating this table in the revised manuscript and will expand our discussion to acknowledge the broader flexibility of the LLSM platform—including its capacity for structured illumination and alternative light-sheet geometries. However, we will also emphasize that, despite these advanced capabilities, the square lattice remains the dominant mode used by the community and therefore serves as a fair and practical benchmark for comparison.

(5) There is no demonstration of the system's live-imaging capabilities or temporal resolution, which is the main advantage of existing light-sheet systems.

In the revised manuscript, we will include a demonstration of live-cell imaging to directly validate the system’s suitability for dynamic biological applications. We will also characterize the temporal resolution of the system. As a sample-scanning microscope, the imaging speed is primarily limited by the performance of the Z-piezo stage. For simplicity and reduced optoelectronic complexity, we currently power the piezo through the ASI Tiger Controller. We will expand the supplementary material to describe the design criteria behind this choice, including potential trade-offs, and provide data quantifying the achievable volume rates under typical operating conditions.

While the microscope is well designed and completely open source, it will require experience with optics, electronics, and microscopy to implement and align properly. Experience with custom machining or soliciting a machine shop is also necessary. Thus, in my opinion, it is unlikely to be implemented by a lab that has zero prior experience with custom optics or can hire someone who does. Altair-LSFM may not be as easily adaptable or implementable as the authors describe or perceive in any lab that is interested, even if they can afford it. The authors indicate they will offer "workshops," but this does not necessarily remove the barrier to entry or lower it, perhaps as significantly as the authors describe.

We appreciate the reviewer’s perspective and agree that building any high-performance custom microscope—Altair-LSFM included—requires a baseline familiarity with optics and instrumentation. Our goal is not to eliminate this requirement entirely, but to significantly reduce the technical and logistical barriers that typically accompany custom light-sheet microscope construction.

Importantly, no machining experience or in-house fabrication capabilities are required—users can simply submit provided design files and specifications directly to the vendor. We will make this process as straightforward as possible by supplying detailed instructions, recommended materials, and vendor-ready files. Additionally, we draw encouragement from the success of related efforts such as mesoSPIM, which has seen over 30 successful implementations worldwide using a similar model of exhaustive online documentation, open-source control software, and community support through user meetings and workshops.

We recognize that documentation alone is not always sufficient, and we are committed to further lowering barriers to adoption. To this end, we are actively working with commercial vendors to streamline procurement and reduce the logistical burden on end users. Additionally, Altair-LSFM is supported by a Biomedical Technology Development and Dissemination (BTDD) grant, which provides dedicated resources for hosting workshops, offering real-time community support, and generating supplementary materials such as narrated video tutorials. We will expand our discussion in the revised manuscript to better acknowledge these implementation challenges and outline our ongoing strategies for supporting a broad and diverse user base.

There is a claim that this design is easily adaptable. However, the requirement of custom-machined baseplates and in silico optimization of the optical path basically means that each new instrument is a new design, even if the Navigate software can be used. It is unclear how Altair-LSFM demonstrates a modular design that reduces times from conception to optimization compared to previous implementations.

We appreciate the reviewer’s comment and agree that our language regarding adaptability may have been too strong. It was not our intention to suggest that the system can be easily modified without prior experience. Meaningful adaptations of the optical or mechanical design would require users to have expertise in optical layout, optomechanical design, and alignment.

That said, for labs with sufficient expertise, we aim to facilitate such modifications by providing comprehensive resources—including detailed Zemax simulations, CAD models, and alignment documentation. These materials are intended to reduce the development burden for those seeking to customize the platform for specific experimental needs.

In the revised manuscript, we will clarify this point and explicitly state in the discussion what technical expertise is required to modify the system. We will also revise our language around adaptability to better reflect the intended audience and realistic scope of customization.

Reviewer #3 (Public review):

Summary:

This manuscript introduces a high-resolution, open-source light-sheet fluorescence microscope optimized for sub-cellular imaging.

The system is designed for ease of assembly and use, incorporating a custom-machined baseplate and in silico optimized optical paths to ensure robust alignment and performance. The authors demonstrate lateral and axial resolutions of ~235 nm and ~350 nm after deconvolution, enabling imaging of sub-diffraction structures in mammalian cells.

The important feature of the microscope is the clever and elegant adaptation of simple gaussian beams, smart beam shaping, galvo pivoting and high NA objectives to ensure a uniform thin light-sheet of around 400 nm in thickness, over a 266 micron wide Field of view, pushing the axial resolution of the system beyond the regular diffraction limited-based tradeoffs of light-sheet fluorescence microscopy.

Compelling validation using fluorescent beads and multicolor cellular imaging highlights the system's performance and accessibility. Moreover, a very extensive and comprehensive manual of operation is provided in the form of supplementary materials. This provides a DIY blueprint for researchers who want to implement such a system.

Strengths:

(1) Strong and accessible technical innovation: With an elegant combination of beam shaping and optical modelling, the authors provide a high-resolution light-sheet system that overcomes the classical light-sheet tradeoff limit of a thin light-sheet and a small field of view. In addition, the integration of in silico modelling with a custom-machined baseplate is very practical and allows for ease of alignment procedures. Combining these features with the solid and super-extensive guide provided in the supplementary information, this provides a protocol for replicating the microscope in any other lab.

(2) Impeccable optical performance and ease of mounting of samples: The system takes advantage of the same sample-holding method seen already in other implementations, but reduces the optical complexity. At the same time, the authors claim to achieve similar lateral and axial resolution to Lattice-light-sheet microscopy (although without a direct comparison (see below in the "weaknesses" section). The optical characterization of the system is comprehensive and well-detailed. Additionally, the authors validate the system imaging sub-cellular structures in mammalian cells.

(3) Transparency and comprehensiveness of documentation and resources: A very detailed protocol provides detailed documentation about the setup, the optical modeling, and the total cost.

Weaknesses:

(1) Limited quantitative comparisons: Although some qualitative comparison with previously published systems (diSPIM, lattice light-sheet) is provided throughout the manuscript, some side-by-side comparison would be of great benefit for the manuscript, even in the form of a theoretical simulation. While having a direct imaging comparison would be ideal, it's understandable that this goes beyond the interest of the paper; however, a table referencing image quality parameters (taken from the literature), such as signal-to-noise ratio, light-sheet thickness, and resolutions, would really enhance the features of the setup presented. Moreover, based also on the necessity for optical simplification, an additional comment on the importance/difference of dual objective/single objective light-sheet systems could really benefit the discussion.

In the revised manuscript, we will expand our discussion to include a broader range of light-sheet microscope designs and imaging modes, including both single- and dual-objective configurations. We agree that highlighting the trade-offs between these approaches—such as working distance, sample geometry constraints, and alignment complexity—will enhance the overall context and utility of the manuscript.

To further aid comparison, we will include a summary table referencing key image quality parameters such as lateral and axial resolution, and illumination beam NA for Altair-LSFM. Where available, we will reference values from published work—such as the axial resolution reported in Valm et al. (Nature, 2017)—to provide a clearer benchmark. Because such comparisons can be technically nuanced, especially when comparing across systems with different geometries and sample mounting constraints, we will also include a supplementary note outlining the assumptions and limitations of these comparisons.

(2) Limitation to a fixed sample: In the manuscript, there is no mention of incubation temperature, CO₂ regulation, Humidity control, or possible integration of commercial environmental control systems. This is a major limitation for an imaging technique that owes its popularity to fast, volumetric, live-cell imaging of biological samples.

We thank the reviewer for highlighting this important consideration. In the revised manuscript, we will provide a detailed description of how temperature control can be implemented using flexible adhesive heating elements, a power supply, and a PID controller. Step-by-step assembly instructions and recommended components will be included to facilitate adoption by users interested in live-cell imaging. We also note that most light-sheet microscopy systems capable of sub-cellular resolution—including the original LLSM design, diSPIM, and ASLM—typically do not incorporate integrated CO₂ or humidity control. These systems often rely on HEPES-buffered media to maintain pH stability, which is generally sufficient for short- to intermediate-term imaging. While full environmental control may be necessary for extended time-lapse studies, it is not a prerequisite for high-resolution volumetric imaging in many applications. Nonetheless, we will include a discussion of the challenges associated with adding CO₂ and humidity control to open or semi-enclosed architectures like Altair-LSFM, and outline potential future paths for integration with commercial incubation systems.

(3) System cost and data storage cost: While the system presented has the advantage of being open-source, it remains relatively expensive (considering the 150k without laser source and optical table, for example). The manuscript could benefit from a more direct comparison of the performance/cost ratio of existing systems, considering academic settings with budgets that most of the time would not allow for expensive architectures. Moreover, it would also be beneficial to discuss the adaptability of the system, in case a 30k objective could not be feasible. Will this system work with different optics (with the obvious limitations coming with the lower NA objective)? This could be an interesting point of discussion. Adaptability of the system in case of lower budgets or more cost-effective choices, depending on the needs.

We thank the reviewer for raising this important point. First, we would like to clarify that the quoted $150k cost estimate includes the optical table and laser source. We apologize for any confusion and will communicate this more effectively in the revised manuscript.

We agree that adaptability is a key concern, especially in academic settings with limited budgets. The detection path can be readily altered depending on experimental needs and cost constraints. For example, in our discussion of alternatives to the 5 mm coverslip geometry, we will describe how switching to a Zeiss W Plan-Apochromat 20x/1.0 in combination with a compatible excitation objective allows high-resolution imaging while accommodating more conventional sample formats. We will expand this to include cost-effective alternatives as well.

We will also expand our discussion on cost-reduction strategies and the associated trade-offs. These include replacing motorized stages with manual ones, omitting the filter wheel in favor of a multi-band emission filter, or using industrial-grade cameras in place of scientific CMOS detectors. While each change entails some loss in functionality or sensitivity, such modifications allow users to tailor the system to their specific budget and application.

Finally, we recognize the challenge in communicating exact costs of commercial systems due to variability in configuration and pricing. Nonetheless, we will include approximate figures where possible and note that comparable commercial systems—such as LLSM platforms from 3i and Zeiss—are several-fold more expensive than the system presented here.

Last, not much is said about the need for data storage. Light-sheet microscopy's bottleneck is the creation of increasingly large datasets, and it could be beneficial to discuss more about the storage needs and the quantity of data generated.

Data storage is indeed a critical consideration in light-sheet microscopy. In the revised manuscript, we will provide a note outlining typical volume dimensions for live-cell imaging experiments along with the associated data overhead. This will include estimates for voxel counts, bit depth, time-lapse acquisitions, and multi-channel datasets to help users anticipate storage needs. We will also briefly discuss strategies for managing large datasets, file types and compression formats.

Conclusion:

Altair-LSFM represents a well-engineered and accessible light-sheet system that addresses a longstanding need for high-resolution, reproducible, and affordable sub-cellular light-sheet imaging. While some aspects-comparative benchmarking and validation, limitation for fixed samples-would benefit from further development, the manuscript makes a compelling case for Altair-LSFM as a valuable contribution to the open microscopy scientific community.

References

(1) Moore, R. P. et al. A multi-functional microfluidic device compatible with widefield and light sheet microscopy. Lab Chip 22, 136-147 (2021). https://doi.org/10.1039/d1lc00600b

(2) Lamb, J. R., Mestre, M. C., Lancaster, M. & Manton, J. D. Direct-view oblique plane microscopy. Optica 12, 469-472 (2025). https://doi.org/10.1364/OPTICA.558420

(3) Liu, T. L. et al. Observing the cell in its native state: Imaging subcellular dynamics in multicellular organisms. Science 360 (2018). https://doi.org/10.1126/science.aaq1392

(4) Sapoznik, E. et al. A versatile oblique plane microscope for large-scale and high-resolution imaging of subcellular dynamics. eLife 9 (2020). https://doi.org/10.7554/eLife.57681

(5) Huisken, J. & Stainier, D. Y. Even fluorescence excitation by multidirectional selective plane illumination microscopy (mSPIM). Opt Lett 32, 2608-2610 (2007). https://doi.org/10.1364/ol.32.002608

(6) Ricci, P. et al. Removing striping artifacts in light-sheet fluorescence microscopy: a review. Prog Biophys Mol Biol 168, 52-65 (2022). https://doi.org/10.1016/j.pbiomolbio.2021.07.003

eLife Assessment

This useful study presents Altair-LSFM, a solid and well-documented implementation of a light-sheet fluorescence microscope (LSFM) designed for accessibility and cost reduction. While the approach offers strengths such as the use of custom-machined baseplates and detailed assembly instructions, its overall impact is limited by the lack of live-cell imaging capabilities and the absence of a clear, quantitative comparison to existing LSFM platforms. As such, although technically competent, the broader utility and uptake of this system by the community may be limited.

Reviewer #1 (Public review):

Summary:

The article presents the details of the high-resolution light-sheet microscopy system developed by the group. In addition to presenting the technical details of the system, its resolution has been characterized and its functionality demonstrated by visualizing subcellular structures in a biological sample.

Strengths:

(1) The article includes extensive supplementary material that complements the information in the main article.

(2) However, in some sections, the information provided is somewhat superficial.

Weaknesses:

(1) Although a comparison is made with other light-sheet microscopy systems, the presented system does not represent a significant advance over existing systems. It uses high numerical aperture objectives and Gaussian beams, achieving resolution close to theoretical after deconvolution. The main advantage of the presented system is its ease of construction, thanks to the design of a perforated base plate.

(2) Using similar objectives (Nikon 25x and Thorlabs 20x), the results obtained are similar to those of the LLSM system (using a Gaussian beam without laser modulation). However, the article does not mention the difficulties of mounting the sample in the implemented configuration.

(3) The authors present a low-cost, open-source system. Although they provide open source code for the software (navigate), the use of proprietary electronics (ASI, NI, etc.) makes the system relatively expensive. Its low cost is not justified.

(4) The fibroblast images provided are of exceptional quality. However, these are fixed samples. The system lacks the necessary elements for monitoring cells in vivo, such as temperature or pH control.

Reviewer #2 (Public review):

Summary:

The authors present Altair-LSFM (Light Sheet Fluorescence Microscope), a high-resolution, open-source microscope, that is relatively easy to align and construct and achieves sub-cellular resolution. The authors developed this microscope to fill a perceived need that current open-source systems are primarily designed for large specimens and lack sub-cellular resolution or are difficult to construct and align, and are not stable. While commercial alternatives exist that offer sub-cellular resolution, they are expensive. The authors' manuscript centers around comparisons to the highly successful lattice light-sheet microscope, including the choice of detection and excitation objectives. The authors thus claim that there remains a critical need for high-resolution, economical, and easy-to-implement LSFM systems.

Strengths:

The authors succeed in their goals of implementing a relatively low-cost (~ USD 150K) open-source microscope that is easy to align. The ease of alignment rests on using custom-designed baseplates with dowel pins for precise positioning of optics based on computer analysis of opto-mechanical tolerances, as well as the optical path design. They simplify the excitation optics over Lattice light-sheet microscopes by using a Gaussian beam for illumination while maintaining lateral and axial resolutions of 235 and 350 nm across a 260-um field of view after deconvolution. In doing so they rest on foundational principles of optical microscopy that what matters for lateral resolution is the numerical aperture of the detection objective and proper sampling of the image field on to the detection, and the axial resolution depends on the thickness of the light-sheet when it is thinner than the depth of field of the detection objective. This concept has unfortunately not been completely clear to users of high-resolution light-sheet microscopes and is thus a valuable demonstration. The microscope is controlled by an open-source software, Navigate, developed by the authors, and it is thus foreseeable that different versions of this system could be implemented depending on experimental needs while maintaining easy alignment and low cost. They demonstrate system performance successfully by characterizing their sheet, point-spread function, and visualization of sub-cellular structures in mammalian cells, including microtubules, actin filaments, nuclei, and the Golgi apparatus.

Weaknesses:

There is a fixation on comparison to the first-generation lattice light-sheet microscope, which has evolved significantly since then:

(1) The authors claim that commercial lattice light-sheet microscopes (LLSM) are "complex, expensive, and alignment intensive", I believe this sentence applies to the open-source version of LLSM, which was made available for wide dissemination. Since then, a commercial solution has been provided by 3i, which is now being used in multiple cores and labs but does require routine alignments. However, Zeiss has also released a commercial turn-key system, which, while expensive, is stable, and the complexity does not interfere with the experience of the user. Though in general, statements on ease of use and stability might be considered anecdotal and may not belong in a scientific article, unreferenced or without data.

(2) One of the major limitations of the first generation LLSM was the use of a 5 mm coverslip, which was a hinderance for many users. However, the Zeiss system elegantly solves this problem, and so does Oblique Plane Microscopy (OPM), while the Altair-LSFM retains this feature, which may dissuade widespread adoption. This limitation and how it may be overcome in future iterations is not discussed.

(3) Further, on the point of sample flexibility, all generations of the LLSM, and by the nature of its design, the OPM, can accommodate live-cell imaging with temperature, gas, and humidity control. It is unclear how this would be implemented with the current sample chamber. This limitation would severely limit use cases for cell biologists, for which this microscope is designed. There is no discussion on this limitation or how it may be overcome in future iterations.

(4) The authors' comparison to LLSM is constrained to the "square" lattice, which, as they point out, is the most used optical lattice (though this also might be considered anecdotal). The LLSM original design, however, goes far beyond the square lattice, including hexagonal lattices, the ability to do structured illumination, and greater flexibility in general in terms of light-sheet tuning for different experimental needs, as well as not being limited to just sample scanning. Thus, the Alstair-LSFM cannot compare to the original LLSM in terms of versatility, even if comparisons to the resolution provided by the square lattice are fair.

(5) There is no demonstration of the system's live-imaging capabilities or temporal resolution, which is the main advantage of existing light-sheet systems.

While the microscope is well designed and completely open source, it will require experience with optics, electronics, and microscopy to implement and align properly. Experience with custom machining or soliciting a machine shop is also necessary. Thus, in my opinion, it is unlikely to be implemented by a lab that has zero prior experience with custom optics or can hire someone who does. Altair-LSFM may not be as easily adaptable or implementable as the authors describe or perceive in any lab that is interested, even if they can afford it. The authors indicate they will offer "workshops," but this does not necessarily remove the barrier to entry or lower it, perhaps as significantly as the authors describe.

There is a claim that this design is easily adaptable. However, the requirement of custom-machined baseplates and in silico optimization of the optical path basically means that each new instrument is a new design, even if the Navigate software can be used. It is unclear how Altair-LSFM demonstrates a modular design that reduces times from conception to optimization compared to previous implementations.

Reviewer #3 (Public review):

Summary:

This manuscript introduces a high-resolution, open-source light-sheet fluorescence microscope optimized for sub-cellular imaging.

The system is designed for ease of assembly and use, incorporating a custom-machined baseplate and in silico optimized optical paths to ensure robust alignment and performance. The authors demonstrate lateral and axial resolutions of ~235 nm and ~350 nm after deconvolution, enabling imaging of sub-diffraction structures in mammalian cells.

The important feature of the microscope is the clever and elegant adaptation of simple gaussian beams, smart beam shaping, galvo pivoting and high NA objectives to ensure a uniform thin light-sheet of around 400 nm in thickness, over a 266 micron wide Field of view, pushing the axial resolution of the system beyond the regular diffraction limited-based tradeoffs of light-sheet fluorescence microscopy.

Compelling validation using fluorescent beads and multicolor cellular imaging highlights the system's performance and accessibility. Moreover, a very extensive and comprehensive manual of operation is provided in the form of supplementary materials. This provides a DIY blueprint for researchers who want to implement such a system.

Strengths:

(1) Strong and accessible technical innovation:

With an elegant combination of beam shaping and optical modelling, the authors provide a high-resolution light-sheet system that overcomes the classical light-sheet tradeoff limit of a thin light-sheet and a small field of view. In addition, the integration of in silico modelling with a custom-machined baseplate is very practical and allows for ease of alignment procedures. Combining these features with the solid and super-extensive guide provided in the supplementary information, this provides a protocol for replicating the microscope in any other lab.

(2) Impeccable optical performance and ease of mounting of samples:

The system takes advantage of the same sample-holding method seen already in other implementations, but reduces the optical complexity. At the same time, the authors claim to achieve similar lateral and axial resolution to Lattice-light-sheet microscopy (although without a direct comparison (see below in the "weaknesses" section). The optical characterization of the system is comprehensive and well-detailed. Additionally, the authors validate the system imaging sub-cellular structures in mammalian cells.

(3) Transparency and comprehensiveness of documentation and resources:

A very detailed protocol provides detailed documentation about the setup, the optical modeling, and the total cost.

Weaknesses:

(1) Limited quantitative comparisons:

Although some qualitative comparison with previously published systems (diSPIM, lattice light-sheet) is provided throughout the manuscript, some side-by-side comparison would be of great benefit for the manuscript, even in the form of a theoretical simulation. While having a direct imaging comparison would be ideal, it's understandable that this goes beyond the interest of the paper; however, a table referencing image quality parameters (taken from the literature), such as signal-to-noise ratio, light-sheet thickness, and resolutions, would really enhance the features of the setup presented. Moreover, based also on the necessity for optical simplification, an additional comment on the importance/difference of dual objective/single objective light-sheet systems could really benefit the discussion.

(2) Limitation to a fixed sample:

In the manuscript, there is no mention of incubation temperature, CO₂ regulation, Humidity control, or possible integration of commercial environmental control systems. This is a major limitation for an imaging technique that owes its popularity to fast, volumetric, live-cell imaging of biological samples.

(3) System cost and data storage cost:

While the system presented has the advantage of being open-source, it remains relatively expensive (considering the 150k without laser source and optical table, for example). The manuscript could benefit from a more direct comparison of the performance/cost ratio of existing systems, considering academic settings with budgets that most of the time would not allow for expensive architectures. Moreover, it would also be beneficial to discuss the adaptability of the system, in case a 30k objective could not be feasible. Will this system work with different optics (with the obvious limitations coming with the lower NA objective)? This could be an interesting point of discussion. Adaptability of the system in case of lower budgets or more cost-effective choices, depending on the needs.

Last, not much is said about the need for data storage. Light-sheet microscopy's bottleneck is the creation of increasingly large datasets, and it could be beneficial to discuss more about the storage needs and the quantity of data generated.

Conclusion:

Altair-LSFM represents a well-engineered and accessible light-sheet system that addresses a longstanding need for high-resolution, reproducible, and affordable sub-cellular light-sheet imaging. While some aspects-comparative benchmarking and validation, limitation for fixed samples-would benefit from further development, the manuscript makes a compelling case for Altair-LSFM as a valuable contribution to the open microscopy scientific community.

eLife Assessment

This important study identifies a novel Legionella effector, Lfat1, which binds F-actin via a coiled-coil domain and structurally resembles the RID toxin, with cryo-EM revealing key interactions mediated by a hydrophobic helical hairpin. While the study is mostly complete and has compelling data, a few minor changes are recommended.

Reviewer #1 (Public review):

The manuscript by Zeng et al. describes the discovery of an F-actin-binding Legionella pneumophila effector, which they term Lfat1. Lfat1 contains a putative fatty acyltransferase domain that structurally resembles the Rho-GTPase Inactivation (RID) domain toxin from Vibrio vulnificus, which targets small G-proteins. Additionally, Lfat1 contains a coiled-coil (CC) domain.

The authors identified Lfat1 as an actin-associated protein by screening more than 300 Legionella effectors, expressed as GFP-fusion proteins, for their co-localization with actin in HeLa cells. Actin binding is mediated by the CC domain, which specifically binds to F-actin in a 1:1 stoichiometry. Using cryo-EM, the authors determined a high-quality structure of F-actin filaments bound to the actin-binding domain (ABD) of Lfat1. The structure reveals that actin binding is mediated through a hydrophobic helical hairpin within the ABD (residues 213-279). A Y240A mutation within this region increases the apparent dissociation constant by two orders of magnitude, indicating a critical role for this residue in actin interaction.

The ABD alone was also shown to strongly associate with F-actin upon overexpression in cells. The authors used a truncated version of the Lfat1 ABD to engineer an F-actin-binding probe, which can be used in a split form. Finally, they demonstrate that full-length Lfat1, when overexpressed in cells, fatty acylates host small G-proteins, likely on lysine residues.

While this is a solid study, the authors should consider the following points when preparing a revised manuscript:

Major points:

(1) Legionella effectors are often activated by binding to eukaryote-specific host factors, including actin. The authors should test the following: a) whether Lfat1 can fatty acylate small G-proteins in vitro; b) whether this activity is dependent on actin binding; and c) whether expression of the Y240A mutant in mammalian cells affects the fatty acylation of Rac3 (Figure 6B), or other small G-proteins.

(2) It should be demonstrated that lysine residues on small G-proteins are indeed targeted by Lfat1. Ideally, the functional consequences of these modifications should also be investigated. For example, does fatty acylation of G-proteins affect GTPase activity or binding to downstream effectors?

(3) Line 138: Can the authors clarify whether the Lfat1 ABD induces bundling of F-actin filaments or promotes actin oligomerization? Does the Lfat1 ABD form multimers that bring multiple filaments together? If Lfat1 induces actin oligomerization, this effect should be experimentally tested and reported. Additionally, the impact of Lfat1 binding on actin filament stability should be assessed. This is particularly important given the proposed use of the ABD as an actin probe.

(4) Line 180: I think it's too premature to refer to the interaction as having "high specificity and affinity." We really don't know what else it's binding to.

(5) The authors should reconsider the color scheme used in the structural figures, particularly in Figures 2D and S4.

(6) In Figure 3E, the WT curve fits the data poorly, possibly because the actin concentration exceeds the Kd of the interaction. It might fit better to a quadratic.

(7) The authors propose that the individual helices of the Lfat1 ABD could be expressed on separate proteins and used to target multi-component biological complexes to F-actin by genetically fusing each component to a split alpha-helix. This is an intriguing idea, but it should be tested as a proof of concept to support its feasibility and potential utility.

Reviewer #2 (Public review):

Summary:

The manuscript by Zheng et al reports the structural and biochemical study of novel effectors from the bacterial pathogen Legionella pneumophila. The authors continued from results from their earlier screening for L. pneumophila proteins that affect host F-actin dynamics to show that Llfat1 (Lpg1387) interacts with actin via a novel actin-binding domain (ABD). The authors also determined the structure of the Lfat1 ABD-F-actin complex, which allowed them to develop this ABD as a probe for F-actin. Finally, the authors demonstrated that Llfat1 is a lysine fatty acyltransferase that targets several small GTPases in host cells.

Strengths:

This is a very complete work that shows the structure of a novel bacterial actin-binding protein in complex with F-actin, and the biochemical activity of the protein was also revealed. Overall, this is a very exciting study and should be of great interest to scientists in both bacterial pathogenesis and the actin cytoskeleton of eukaryotic cells.

Weaknesses:

(1) The authors should use biochemical reactions to analyze the KFAT of Llfat1 on one or two small GTPases shown to be modified by this effector in cellulo. Such reactions may allow them to determine the role of actin binding in its biochemical activity. This notion is particularly relevant in light of recent studies that actin is a co-factor for the activity of LnaB and Ceg14 (PMID: 39009586; PMID: 38776962; PMID: 40394005). In addition, the study should be discussed in the context of these recent findings on the role of actin in the activity of L. pneumophila effectors.

(2) The development of the ABD domain of Llfat1 as an F-actin domain is a nice extension of the biochemical and structural experiments. The authors need to compare the new probe to those currently commonly used ones, such as Lifeact, in labeling of the actin cytoskeleton structure.

eLife Assessment

The work provides important insights into how this lncRNA regulates gene expression via complex mechanisms, however, the biological relevance awaits validation in other models. This paper provides extensive and carefully analysed data that is of value in efforts to understand the role of the lncRNA EPB41L4A-AS1 in a human cell line. The data is generally convincing and supported by clever integrative analysis; however, the known extensive artefacts from individual Gapmer oligonucleotides cast some doubt over the interpretation of those experiments where only one targeting and one control Gapmer are used.

Reviewer #1 (Public review):

Monziani and Ulitsky present a large and exhaustive study on the lncRNA EPB41L4A-AS1 using a variety of genomic methods. They uncover a rather complex picture of an RNA transcript that appears to act via diverse pathways to regulate the expression of large numbers of genes, including many snoRNAs. The activity of EPB41L4A-AS1 seems to be intimately linked with the protein SUB1, via both direct physical interactions and direct/indirect of SUB1 mRNA expression.

The study is characterised by thoughtful, innovative, integrative genomic analysis. It is shown that EPB41L4A-AS1 interacts with SUB1 protein and that this may lead to extensive changes in SUB1's other RNA partners. Disruption of EPB41L4A-AS1 leads to widespread changes in non-polyA RNA expression, as well as local cis changes. At the clinical level, it is possible that EPB41L4A-AS1 plays disease-relevant roles, although these seem to be somewhat contradictory with evidence supporting both oncogenic and tumour suppressive activities.

A couple of issues could be better addressed here. Firstly, the copy number of EPB41L4A-AS1 is an important missing piece of the puzzle. It is apparently highly expressed in the FISH experiments. To get an understanding of how EPB41L4A-AS1 regulates SUB1, an abundant protein, we need to know the relative stoichiometry of these two factors. Secondly, while many of the experiments use two independent Gapmers for EPB41L4A-AS1 knockdown, the RNA-sequencing experiments apparently use just one, with one negative control (?). Evidence is emerging that Gapmers produce extensive off-target gene expression effects in cells, potentially exceeding the amount of on-target changes arising through the intended target gene. Therefore, it is important to estimate this through the use of multiple targeting and non-targeting ASOs, if one is to get a true picture of EPB41L4A-AS1 target genes. In this Reviewer's opinion, this casts some doubt over the interpretation of RNA-seq experiments until that work is done. Nonetheless, the Authors have designed thorough experiments, including overexpression rescue constructs, to quite confidently assess the role of EPB41L4A-AS1 in snoRNA expression.

It is possible that EPB41L4A-AS1 plays roles in cancer, either as an oncogene or a tumour suppressor. However, it will in the future be important to extend these observations to a greater variety of cell contexts.

This work is valuable in providing an extensive and thorough analysis of the global mechanisms of an important regulatory lncRNA and highlights the complexity of such mechanisms via cis and trans regulation and extensive protein interactions.

Reviewer #2 (Public review):

Summary:

In this manuscript, Monziani et al. identified long noncoding RNAs (lncRNAs) that act in cis and are coregulated with their target genes located in close genomic proximity. The authors mined the GeneHancer database, and this analysis led to the identification of four lncRNA-target pairs. The authors decided to focus on lncRNA EPB41L4A-AS1.

They thoroughly characterised this lncRNA, demonstrating that it is located in the cytoplasm and the nuclei, and that its expression is altered in response to different stimuli. Furthermore, the authors showed that EPB41L4A-AS1 regulates EPB41L4A transcription, leading to a mild reduction in EPB41L4A protein levels. This was not recapitulated with siRNA-mediated depletion of EPB41L4AAS1. RNA-seq in EPB41L4A-AS1-depleted cells with single LNA revealed 2364 DEGs linked to pathways including the cell cycle, cell adhesion, and inflammatory response. To understand the mechanism of action of EPB41L4A-AS1, the authors mined the ENCODE eCLIP data and identified SUB1 as an lncRNA interactor. The authors also found that the loss of EPB41L4A-AS1 and SUB1 leads to the accumulation of snoRNAs, and that SUB1 localisation changes upon the loss of EPB41L4A-AS1. Finally, the authors showed that EPB41L4A-AS1 deficiency did not change the steady-state levels of SNORA13 nor RNA modification driven by this RNA. The phenotype associated with the loss of EPB41L4A-AS1 is linked to increased invasion and EMT gene signature.

Overall, this is an interesting and nicely done study on the versatile role of EPB41L4A-AS1 and the multifaceted interplay between SUB1 and this lncRNA, but some conclusions and claims need to be supported with additional experiments. My primary concerns are using a single LNA gapmer for critical experiments, increased invasion, and nucleolar distribution of SUB1- in EPB41L4A-AS1-depleted cells. These experiments need to be validated with orthogonal methods.

Strengths:

The authors used complementary tools to dissect the complex role of lncRNA EPB41L4A-AS1 in regulating EPB41L4A, which is highly commendable. There are few papers in the literature on lncRNAs at this standard. They employed LNA gapmers, siRNAs, CRISPRi/a, and exogenous overexpression of EPB41L4A-AS1 to demonstrate that the transcription of EPB41L4A-AS1 acts in cis to promote the expression of EPB41L4A by ensuring spatial proximity between the TAD boundary and the EPB41L4A promoter. At the same time, this lncRNA binds to SUB1 and regulates snoRNA expression and nucleolar biology. Overall, the manuscript is easy to read, and the figures are well presented. The methods are sound, and the expected standards are met.

Weaknesses:

The authors should clarify how many lncRNA-target pairs were included in the initial computational screen for cis-acting lncRNAs and why MCF7 was chosen as the cell line of choice. Most of the data uses a single LNA gapmer targeting EPB41L4A-AS1 lncRNA (eg, Fig. 2c, 3B, and RNA-seq), and the critical experiments should be using at least 2 LNA gapmers. The specificity of SUB1 CUT&RUN is lacking, as well as direct binding of SUB1 to lncRNA EPB41L4A-AS1, which should be confirmed by CLIP qPCR in MCF7 cells. Finally, the role of EPB41L4A-AS1 in SUB1 distribution (Figure 5) and cell invasion (Figure 8) needs to be complemented with additional experiments, which should finally demonstrate the role of this lncRNA in nucleolus and cancer-associated pathways. The use of MCF7 as a single cancer cell line is not ideal.

Reviewer #3 (Public review):

Summary:

In this paper, the authors made some interesting observations that EPB41L4A-AS1 lncRNA can regulate the transcription of both the nearby coding gene and genes on other chromosomes. They started by computationally examining lncRNA-gene pairs by analyzing co-expression, chromatin features of enhancers, TF binding, HiC connectome, and eQTLs. They then zoomed in on four pairs of lncRNA-gene pairs and used LNA antisense oligonucleotides to knock down these lncRNAs. This revealed EPB41L4A-AS1 as the only one that can regulate the expression of its cis-gene target EPB41L4A. By RNA-FISH, the authors found this lncRNA to be located in all three parts of a cell: chromatin, nucleoplasm, and cytoplasm. RNA-seq after LNA knockdown of EPB41L4A-AS1 showed that this increased >1100 genes and decreased >1250 genes, including both nearby genes and genes on other chromosomes. They later found that EPB41L4A-AS1 may interact with SUB1 protein (an RNA-binding protein) to impact the target genes of SUB1. EPB41L4A-AS1 knockdown reduced the mRNA level of SUB1 and altered the nuclear location of SUB1. Later, the authors observed that EPB41L4A-AS1 knockdown caused an increase of snRNAs and snoRNAs, likely via disrupted SUB1 function. In the last part of the paper, the authors conducted rescue experiments that suggested that the full-length, intron- and SNORA13-containing EPB41L4A-AS1 is required to partially rescue snoRNA expression. They also conducted SLAM-Seq and showed that the increased abundance of snoRNAs is primarily due to their hosts' increased transcription and stability. They end with data showing that EPB41L4A-AS1 knockdown reduced MCF7 cell proliferation but increased its migration, suggesting a link to breast cancer progression and/or metastasis.

Strengths:

Overall, the paper is well-written, and the results are presented with good technical rigor and appropriate interpretation. The observation that a complex lncRNA EPB41L4A-AS1 regulates both cis and trans target genes, if fully proven, is interesting and important.

Weaknesses:

The paper is a bit disjointed as it started from cis and trans gene regulation, but later it switched to a partially relevant topic of snoRNA metabolism via SUB1. The paper did not follow up on the interesting observation that there are many potential trans target genes affected by EPB41L4A-AS1 knockdown and there was limited study of the mechanisms as to how these trans genes (including SUB1 or NPM1 genes themselves) are affected by EPB41L4A-AS1 knockdown. There are discrepancies in the results upon EPB41L4A-AS1 knockdown by LNA versus by CRISPR activation, or by plasmid overexpression of this lncRNA.

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Summary:

Mollá-Albaladejo et al. investigate the neurons downstream of GR64f and Gr66a, called G2Ns. They identify downstream neurons using trans-Tango labeling with RFP and then perform bulk RNA-seq on the RFP-sorted cells. Gene expression is up- or downregulated between the cell populations and between fed and starved states. They specifically identify Leukocinin as a neuropeptide that is upregulated in starved Gr66a cells. Leucokinin cells, identified by a GAL4 line indeed show higher expression when starved, especially in the SEZ. Furthermore, Leucokinin cells colocalize with the transTango signal from downstream neurons of both GRs. This connection is confirmed with GRASP. According to EM data, Leucokinin cells in the SEZ receive a lot of input and connect to many downstream neurons. In behavior experiments performed with flies lacking Leucokinin neurons, flies show reduced responsiveness to sugar and bitter mixtures when starved. The authors suggest that Leucokinin neurons integrate bitter and sugar tastes and that their output is modified by a hunger state.

Strengths:

The authors use a multitude of tools to identify SELK neurons downstream of taste sensory neurons and as starvation-sensitive cells. This study provides an example of how combining genetic labeling, RNA-seq, and EM analysis can be combined to investigate neural circuits.

Weaknesses:

The authors do not show a functional connection between sensory neurons and SELK neurons. Additionally, data from RNA seq, anatomical studies, and EM analysis are sometimes contradictory in terms of connectivity. GRASP signal is not foolproof that cells are synaptically connected.

We appreciate the reviewer’s comments. Unfortunately, we have not successfully demonstrated a functional response of SELK neurons using in vivo calcium imaging with UAS-GCaMP7 (we tried f, m, and s versions), primarily due to challenges in obtaining stable signals. We stimulated GRNs using sucrose, caffeine, or a mixture of both, and maybe even if the concentrations were high, they were not enough to induce a response.

Regarding GRASP, we acknowledge its limitations as a standalone technique for establishing genuine synaptic connections between neurons, as some signals may reflect false positives resulting from the mere proximity of the candidate neurons. To strengthen our findings, we complemented these results by demonstrating the positive colocalization of the Leucokinin antibody signal over the Gr66aGal4>trans-TANGO and Gr64f-Gal4>trans-TANGO (Figure 4), confirming that Leucokinin neurons are indeed postsynaptic to both sweet and bitter GRNs. Moreover, we incorporated BacTrace data to highlight the direct connectivity between sweet and bitter GRNs (now Figure 5E).

In the revised manuscript, we have introduced the active-GRASP technique (Macpherson et al., 2015). In this version of GRASP, the presynaptic half of GFP (GFP 1-10) is fused to synaptobrevin, which becomes accessible in the membrane of the presynaptic neuron within the synaptic cleft upon presynaptic stimulation (in our case, by stimulating with sucrose sweet Gr64f^GRNs and with caffeine the bitter Gr66a^GRNs). Utilizing this technique, we successfully demonstrated (see new Figure 5B and 5D) that when presented with water, no signal was detected in the Gr66a-LexA, Lk-Gal4 > active-GRASP, or Gr64f-LexA, Lk-Gal4 > active-GRASP transgene flies. However, in the presence of caffeine, Gr66aLexA, Lk-Gal4 > active-GRASP transgene flies exhibited a clear signal in the SEZ, and similarly, sucrose presentation to Gr64f-LexA, Lk-Gal4 > active-GRASP transgene flies yielded a detectable signal. The results obtained from active-GRASP provide additional evidence supporting the connectivity between SELK neurons and both Gr64f^GRNs and Gr66a^GRNs, further indicating the functional connectivity of the GRNs and SELK neurons.

The authors describe a behavioral phenotype when flies are starved, however, they do not use a specific driver for the described cell type, thus they should also tone down their claims.

We agree with the reviewer that the Lk-Gal4 driver line used labels SELK, LHLK, and ABLK neurons. The behavior examined in this paper, the Proboscis Extension Response (PER), measures the initiation of feeding. Although the neural circuit involved in this behavior is primarily confined to the SEZ where SELK neurons are located, we cannot rule out the possibility that other Lk neurons may also play a role in the process. To restrict expression of the Tetanus Toxin, we have utilized the tsh-Gal80 (Clyne et al., 2008) transgene in combination with the Lk-Gal4>UAS-TNT and Lk-Gal4>UAS-TNT^imp constructs to prevent the expression of the Tetanus Toxin in ABLK neurons, thereby restricting its expression to the SELK and LHLK neurons in the central brain. The new results (Sup Figure 7A) indicate that ABLK neurons do not play a role in integrating sweet and bitter information. However, we acknowledge the reviewer's point that we are still silencing LHLK neurons, so we have adjusted our claims to align more closely with our data

Generally, the authors do not provide a big advancement to the field and some of the results are contradictory with previous publications.

We believe our work does not contradict previous findings, nor does it invalidate the role of ABLK neurons in water homeostasis or the role of LHLK neurons in regulating sleep via starvation. We provide additional information on the possible role of SELK neurons in integrating gustatory information. The location of SELK neurons in the SEZ suggests that they may play a role in feeding behavior, and we have demonstrated that these neurons are indeed involved in integrating gustatory information to influence feeding decisions. We consider we have contributed by highlighting a new role for the Leucokinin neuropeptide in feeding behavior.

Reviewer #2 (Public review):

Summary:

A core task of the brain is processing sensory cues from the environment. The neural mechanisms of how sensory information is transmitted from peripheral sense organs to subsequent being processing in defined brain centers remain an important topic in neuroscience. The taste system hereby assesses the palatability of food by evaluating the chemical composition and nutrient content while integrating the current need for energy by assessing the satiation level of the organism. The current manuscript provides insights into the early circuits of gustatory coding using the fruit fly as a model. By combining trans-tango and FACS- based bulk RNAseq to assess the target neurons of sweet sensing (using Gr64fGal4) and bitter sensing (using Gr66a-Gal4) in a first set of experiments the authors investigate genes that are differentially expressed or co-expressed in normal and starved conditions. With a focus on neuropeptides and neurotransmitters, different expressions in the different conditions were assessed resulting in the identification of Leucokinin as a potentially interesting gene. The notion is further supported by RNAseq of Lk- Gal4>mCD8:GFP sorted cells and immunostainings. GRASP and BacTrace experiments further support that the two Lk- expressing cells in the SEZ should indeed be postsynaptic to both types of sensories. Using EM-based connectomics data (based on a previous publication by Engert et al.), the authors also look for downstream targets of the bitter versus sweet gustatory neurons to identify the Lk-neurons. Based on the morphology they identify candidates and further depict the potential downstream neurons in the connectome, which appears largely in agreement with GRASP experiments. Finally silencing the Lk- neurons shows an increased PER response in starved flies (when combined with bitter compounds) as well as increased feeding neurons shows an increased PER response in starved flies (when combined with bitter compounds) as well as increased feeding in a FlyPad assay. Strengths:

Overall this is an intriguing manuscript, which provides insight into the organization of 2nd order gustatory neurons. It specifically provides strong evidence for the Lk-neurons as a target of sweet and bitter GRNs and provides evidence for their role in regulating sweet vs bitter-based behavioral responses. Particularly the integration of different techniques and datasets in an elegant fashion is a strong side of the manuscript. Moreover to put the known LK-neurons into the context of 2nd order gustatory signalling is strengthening the knowledge about this pathway.

Weaknesses:

I do not see any major weakness in the current manuscript. Novelty is to some degree lessened by the fact, that the RNAseq approach did not identify new neurons but rather put the known LK-neurons as major findings. Similarly, the final behavioral section is not very deep and to some degree corroborates the previous publication by the Keene and Nässel labs - that said, the model they propose is indeed novel (but lacks depth in analyses; e.g. there is no physiology that would support the modulation of Lk neurons by either type of GRN). The connectomic section appears a bit out of place and after reading it it's not really clear what one should make of the potential downstream neurons (particularly since the Lk-receptor expression has been previously analyzed); here it might have been interesting to address if/how Lk-neurons may signal directly via a classical neurotransmitter (an information that might be found easily in the adult brain single-cell data).

We thank the reviewer for the comment. Indeed, we attempted in vivo Ca imaging but were unsuccessful. We have rewritten the connectomic section to better integrate it with the rest of the text and have reanalyzed the data obtained. We considered gathering data from the single-cell adult dataset, but this dataset includes the entire adult fly brain, encompassing SELK and LHLK neurons, making it impossible to differentiate between the two types of Lk neurons. Any further analysis will require transcriptomic analysis of SELK via scRNAseq under the different metabolic conditions tested in this study work.

Reviewer #3 (Public review):

Summary:

To make feeding decisions, animals need to process three types of information: positive cues like sweetness, negative cues like bitterness, and internal states such as hunger or satiety. This study aims to identify where the information is integrated into the fruit fly brain. The authors applied RNA sequencing on second-order gustatory neurons responsible for sweet and bitter processing, under fed and starved conditions. The sequencing data reveal significant changes in gene expression across sweet vs. bitter pathways and fed vs. starved states. The authors focus on the neuropeptide Leucokinin (Lk), whose expression is dependent on the starvation state. They identify a pair of neurons, named SELK neurons, which express Lk and receive direct input from both sweet and bitter gustatory neurons. These SELK neurons are ideal candidates to integrate gustatory and internal state information. Behavioral experiments show that blocking these neurons in starved flies alters their tolerance to bitter substances during feeding.

Strengths:

(1) The study employs a well-designed approach, targeting specific neuronal populations, which is more efficient and precise compared to traditional large-scale genetic screening methods.

(2) The RNAseq results provide valuable data that can be utilized in future studies to explore other molecules beyond Lk.

(3) The identification of SELK neurons offers a promising avenue for future research into how these neurons integrate conflicting gustatory signals and internal state information.

Weaknesses:

(1) Unfortunately, due to technical challenges, the authors were unable to directly image the functional activity of SELK neurons.

(2) In the behavioral experiments, tetanus toxin was used to block SELK neurons. Since these neurons may release multiple neurotransmitters or neuropeptides, the results do not specifically demonstrate that Leucokinin (Lk) is the critical factor, as suggested in Figure 8. To address this, I recommend using RNAi to inhibit Lk expression in SELK neurons and comparing the outcomes to wild-type controls via the PER assay.

We appreciate the author's comments and suggestions. As noted, Tetanus Toxin silences the neuron’s activity, affecting the functioning of various neurotransmitters and neuropeptides released by the targeted neuron. In response to the reviewer's recommendation, we employed an RNAi line specifically designed to silence Leucokinin production in Lk-expressing neurons.

The results presented in Supplementary Figure 7B demonstrate that knocking down Leucokinin in Lk neurons significantly reduces the flies' tolerance to caffeine in sweet food.

It is crucial to highlight that the sucrose concentration used in Figure 7C was 50mM, whereas in Supplementary Figure 7B, it was increased to 100mM. This adjustment was necessary because the Lk-Gal4, UAS-RNAi, and Lk-Gal4>UAS-RNAi transgenic lines exhibited reduced sensitivity to sucrose compared to the Lk-Gal4>UAS-TNT or Lk-Gal4>UAS-TNT^imp lines. We aimed to establish a sucrose concentration that would elicit a 50% Proboscis Extension Response (PER) without adding any other compound, thereby allowing us to evaluate the additional effect of caffeine in the food.

However, according to the data derived from the connectome, SELK neurons might be cholinergic, and this neurotransmitter might be involved in controlling also the behavior of the flies.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

To get more evidence for connections between sensory cells and SELK neurons, could the authors also analyze a second available EM data set? Would setting a different threshold (>5 synapses) reveal connections to both sensories? Comparisons between SELK in- and outputs from EM data and Tango labeling also seem to differ quite a lot based on provided images - can the authors count cell bodies in the stainings? Further proof would be to provide functional imaging data that shows that SELK neurons respond to sugar and bitter compounds.

In this study, we utilized the recently published EM dataset for the Drosophila central brain connectome (Dorkenwald et al., 2024; Flywire.ai). Changing the number of synapses affects the counts of pre- and postsynaptic neurons. We set a threshold of more than five synapses, as recommended by Flywire, to avoid false positives (Dorkenwald et al., 2024). This threshold has been widely used in recent papers (Engert et al., 2022; Shiu et al., 2022; Walker et al., 2025).

The neuron counts in the connectomic data differ from those in the trans- and retro-TANGO experiments. In our initial trans-TANGO experiment, which labeled postsynaptic neurons in the Gr64fGal4 and Gr66a-Gal4 transgenic lines, we counted the labeled neurons (see Supplementary Figure 1C) and observed considerable variability between different brains. Due to anticipated variability, we did not count the labeled neurons from trans-TANGO and retro-TANGO techniques in the Leucokinin neurons. Furthermore, neither technique labels all postsynaptic or presynaptic neurons, respectively. A recent study on the retro-TANGO technique (Sorkac et al., 2023) found a minimum threshold: the presynaptic neuron must form a certain number of synapses with the neuron of interest to be adequately labeled. According to this paper, the established threshold is 17 synapses. It is likely that the trans-TANGO technique also has a threshold relating to the number of labeled neurons, contingent on the synapse count. This would explain the discrepancy between the two results.

Unfortunately, we have not been able to provide functional data pointing to the activation of SELK neurons by sucrose or caffeine. However, our active-GRASP data indicates that the connectivity between Gr64f^GRNs and Gr66a^GRNs with SELK neurons is present and functional.

How many Leucokinin-positive cells are in the SEZ? Does the RNA-seq data provide further information about the SELK neurons? Potential receptor candidates for how they integrate hunger signals? AMPKa was described to be required in LHLK neurons.

There are two SELK neurons in the SEZ. Due to the nature of our bulk RNA sequencing (RNAseq), we cannot link any additional gene expressions detected in our transcriptomic analysis specifically to the SELK neurons regarding the integration of various signaling processes. Furthermore, the single-cell RNA sequencing (scRNAseq) data available from the Drosophila brain, as reported by Li et al. (2022), does not allow accurate differentiation between SELK and LHLK neurons. To understand how these neurons integrate both metabolic and sensory information, it is crucial to conduct a focused RNAseq study specifically on the SELK neurons to understand how these neurons integrate both metabolic and sensory information. This targeted analysis would provide the necessary insights to elucidate their functional roles better. However, according to the data derived from the connectome, SELK neurons might be cholinergic, and this neurotransmitter might be involved in controlling also the behavior of the flies.

According to previous studies (Yurgel et al., 2019), the Lk-GAL4 line is also expressed in the VNC, thus the authors could make use of the tsh-GAL80 tool to clean up the line. This study also performed GCaMP imaging in fed and 24h starved animals in SELK and couldn't find a difference, can the authors explain this discrepancy?

We thank the reviewer for this suggestion. We have now added a new piece of data using the tsh-Gal80 transgene in our PER experiments (Supplementary Figure 7A). Blocking the expression of TNT in the ABLK neurons does not affect the main conclusion of the behavioral results. As stated previously, we were unable to obtain in vivo Ca imaging responses in SELK neurons upon exposure to sucrose, caffeine, or mixtures of sucrose and caffeine. We do not believe this is a discrepancy with previous works like Yurgel et al., 2019. It is likely that we faced technical issues regarding expression stability and that the stimulation was possibly too weak to detect changes in GFP levels

Reviewer #2 (Recommendations for the authors):

As mentioned above I do not have any major comments on the manuscript, but there are a few points that I feel should be considered:

(1) The identification of the Lk-candidate neurons in the connectome remains a bit mysterious. In the method sections, this reads as follows "manual and visual criteria were applied to identify the neurons of interest ". a) What precisely was done to get to the candidates?b) Are there alternative candidates that may be Lk-neurons? c) How would another neuron affect the conclusion of the downstream analysis?

We thank the reviewer for this comment. We have now modified and added new information in the connectomic section, reinforcing our conclusions and correcting the results obtained.

Our GRASP, BacTRace, and immunohistochemistry experiments pointed to SELK neurons as postsynaptic to both Gr64f^GRNs (sweet) and Gr66a^GRNs (bitter). To identify which neurons in the connectome could be the SELK neurons, we utilized a previously described set of GRNs already identified in the connectome (Shiu et al., 2022). We extracted all postsynaptic neurons to the sweet and bitter GRNs identified and intersected both datasets, retaining only those candidate hits receiving simultaneous input from sweet and bitter GRNs. This process yielded a total of 333 hits. Through visual inspection, we discarded all hits that were merely neuronal fragments or neurons that clearly were not our candidates. We narrowed the list down to a final set of 17 candidate neurons whose arborization was located in the SEZ. We reduced the candidates to two final entries from this list: ID 720575940623529610 (GNG.276) and ID 720575940630808827 (GNG.685). The GNG.276 neuron had a counterpart in the SEZ identified as GNG.246. Both of these neurons were annotated as DNg70 in the Flywire database. GNG.685 had a counterpart identified as GNG.595, and these two neurons were classified as DNg68. In both cases, the neuronal candidates, DNg70 and DNg68, were classified as descending neurons, a characteristic of previously described SELK neurons (Nässel et al., 2021). In our initial analysis published in bioRxiv and sent for revision, we identified DNg70 as potentially the SELK neurons based solely on the morphology of the neurons via visual inspection. However, we employed a better method to determine which candidate is more likely to be the SELK neurons, concluding that DNg68, rather than DNg70, represents the SELK neurons. Briefly, we performed an immunohistochemistry for GFP in the Lk-Gal4>UAS-CD8:GFP flies. We aligned the resulting image in a Drosophila reference brain (JRC2018 U) using the CMTK Registration plugin in ImageJ. The resulting image was skeletonized using the Single Neurite Tracer plugin in ImageJ and later uploaded to the Flywire Gateway platform to compare the structure of the aligned and skeletonized SELK neurons to our candidates. This comparison clearly indicated that the DNg68 neurons are the best candidates for representing the SELK neurons, rather than DNg70. We have updated the text and Figures 6 and Supplementary Figure 6 to reflect the new results. These new results do not alter the conclusions of the paper.

(2) In the transcriptomic experiments It seems that the raw transcripts are reporters, rather than normalised data. Why?

All transcriptomic data is normalized. In Figure 1 the differential expression was calculated using Deseq2 normalized counts. In Figure 2, Transcripts Per Million (TPM) were calculated using the Salmon package and normalized for the gene length.

(3) The expression of nAChRbeta1 in the transcriptomic data is rather striking. However, this remains currently not addressed: is this expression real?

We have not confirmed the upregulation or downregulation in gene expression for other but for Leucokinin, which is our main interest. We found the presence of nAChRbeta1 interesting, as GRNs are cholinergic (Jaeger et al., 2018), suggesting that it would make sense to find cholinergic receptors in G2Ns. However, it is possible that these receptors are expressed in all G2Ns and serve as a common means of communication.

(4) The description of the behavioural experiments in the results section is rather brief. I had a hard time following it since the genotypes are not repeated nor is it stated what is different in the experimental group vs control (but instead simply what changes in the experimental group, in a rather discussion-like fashion).

We thank the reviewer for the comment, we have rewritten this section to improve its clarity.

(5) If I understand the genetics for the behavioural experiments correctly it addresses the entire Lk-Gal4 expressing population, thus it is not possible to describe the role of the two SEZ neurons, but rather LkGal4 neurons. This should be clarified.

We thank the reviewer for this comment. Indeed, the Lk-Gal4 driver we used drives expression in all Leucokinin neurons, making it impossible to distinguish between the SELK, LHLK, or ABLK neurons. We have added a new piece of behavioral data by using the tsh-Gal80 transgene to prevent the expression of TNT in the ABLK neurons (Supplementary Figure 7A), but still we cannot distinguish between SELK and LHLK. We have rewritten the text to clarify this fact.

Reviewer #3 (Recommendations for the authors):

Overall, the manuscript is well-written, I only have one minor suggestion for improvement. In Figure 8C, please clarify the use of TNT to block Lk release.

We thank the reviewer for the comment, we have clarified the use of TNT in the text.

References Clyne, J. D. & Miesenböck, G. Sex-Specific Control and Tuning of the Pattern Generator for Courtship Song in Drosophila. Cell 133, 354–363 (2008).

Dorkenwald, S. et al. Neuronal wiring diagram of an adult brain. Nature 634, 124–138 (2024).

Engert, S., Sterne, G. R., Bock, D. D. & Scott, K. Drosophila gustatory projections are segregated by taste modality and connectivity. Elife 11, e78110 (2022).

Jaeger, A. H. et al. A complex peripheral code for salt taste in Drosophila. Elife 7, e37167 (2018).

Macpherson, L. J. et al. Dynamic labelling of neural connections in multiple colours by trans-synaptic fluorescence complementation. Nat Commun 6, 10024 (2015).

Nässel, D. R. Leucokinin and Associated Neuropeptides Regulate Multiple Aspects of Physiology and Behavior in Drosophila. Int J Mol Sci 22, 1940 (2021).

Shiu, P. K., Sterne, G. R., Engert, S., Dickson, B. J. & Scott, K. Taste quality and hunger interactions in a feeding sensorimotor circuit. eLife 11, e79887 (2022).

Walker, S. R., Peña-Garcia, M. & Devineni, A. V. Connectomic analysis of taste circuits in Drosophila. Sci. Rep. 15, 5278 (2025).

eLife Assessment

This study provides valuable insights into the organization of second-order circuits of gustatory neurons, particularly in how these circuits integrate opposing taste inputs and are modulated by metabolic state to regulate feeding behavior. Through an elegant combination of complementary techniques, the authors identify the target neurons involved in gustatory integration. The evidence supporting their conclusions is convincing.

Reviewer #1 (Public review):

Summary:

Mollá-Albaladejo et al. investigate the neurons downstream of GR64f and Gr66a, called G2Ns. They identify downstream neurons using trans-Tango labeling with RFP and then perform bulk RNA-seq on the RFP-sorted cells. Gene expression is up- or downregulated between the cell populations and between fed and starved states. They specifically identify Leukocinin as a neuropeptide that is upregulated in starved Gr66a cells. Leucokinin cells, identified by a GAL4 line, indeed show higher expression when starved, especially in the SEZ. Furthermore, Leucokinin cells colocalize with the trans-Tango signal from downstream neurons of both GRs. This connection is confirmed with GRASP and active GRASP. According to EM data, Leucokinin cells in the SEZ receive a lot of input and connect to many downstream neurons. In behavior experiments performed with flies lacking Leucokinin neurons, flies show reduced responsiveness to sugar and bitter mixtures when starved. The authors suggest that Leucokinin neurons integrate bitter and sugar tastes and that their output is modified by a hunger state.

Strengths:

The authors use a multitude of tools to identify SELK neurons downstream of taste sensory neurons and as starvation-sensitive cells. This study provides an example of how combining genetic labeling, RNA-seq, and EM analysis can be used to investigate the function of specific neural circuits.

Weaknesses:

The authors now provide more evidence to show a functional connection between sensory neurons and SELK neurons, for example, by using active GRASP, however, different staining methods reveal different connectivity patterns. The authors describe a behavioral phenotype when flies are starved, however, the phenotype can still not clearly be assigned to the SELK neurons.

Reviewer #2 (Public review):

Summary:

A core task of the brain is processing sensory cues from the environment. The neural mechanisms of how sensory information is transmitted from peripheral sense organs to subsequent being processing in defined brain centers remains an important topic in neuroscience. The taste system hereby assesses the palatability of food by evaluating the chemical composition and nutrient content while integrating the current need of energy by assessing the satiation level of the organism. The current manuscript provides insights into the early circuits gustatory coding using the fruit fly as model. By combining trans-tango and FACS-based bulk RNAseq to assess the target neurons of sweet sensing (using by Gr64f-Gal4) and bitter sensing (using Gr66a-Gal4) in a first set of experiments the authors investigate genes that are differentially expressed or co-expressed in normal and starved conditions. With a focus on neuropeptides and neurotransmitters differential expression in the different conditions were assessed resulting in the identification of Leucokinin as potentially interesting gene. The notion is further supported by RNAseq of Lk-Gal4>mCD8:GFP sorted cells and immunostainings. GRASP and BacTrace experiments further supports that the two Lk expressing cells in the SEZ should indeed be postsynaptic to both type of sensors. Using EM-based connectomics data (based on a previous publication by Engert et al.), the authors also look for downstream targets of the bitter versus sweet gustatory neurons to identify the Lk-neurons. Based on morphology they identify candidates and further depict the potential downstream neurons in the connectome, which appears largely in agreement with GRASP experiments. Finally silencing the Lk-neurons shows an increased PER response in starved flies (when combined with bitter compounds) as well as increased feeding in a FlyPad assay.

Strengths:

Overall this is an intriguing manuscript, which provides insight into the organization of 2nd order gustatory neurons. It specifically provides strong evidence for the Lk-neurons as target of sweet and bitter GRNs and provides evidence for their role in regulating sweet vs bitter based behavioral responses. Particularly the integration of different techniques and datasets in an elegant fashion is a strong side of the manuscript. Moreover to put the known LK-neurons into the context of 2nd order gustatory signalling is strengthening the knowledge about this pathway.

Weaknesses:

I do not see any major weakness in the current manuscript. Novelty is to some degree lessened by the fact, that the RNAseq approach did not identify new neurons but rather put the known LK-neurons as major finding. Similarly the final behavioral section is not very deep and to some degree corroborates the previous publication by the Keene and Nässel labs- that said, the model they propose is indeed novel (but lacks depth in analyses, e.g. there is no physiology that would support the modulation of Lk neurons by either type of GRN). The connectomic section appears a bit out of place and after reading it it's not really clear what one should make of the potential downstream neurons (particularly since the Lk-receptor expression has been previously analyzed); here it might have been interesting to address if/how Lk-neurons may signal directly via a classical neurotransmitter (an information that might be found easily in the adult brain single-cell data).

Comments on the latest version:

I feel all points have been included to a satisfactory degree.

Reviewer #3 (Public review):

Summary:

To make feeding decisions, animals need to process three types of information: positive cues like sweetness, negative cues like bitterness, and internal states such as hunger or satiety. This study aims to identify where the information is integrated in the fruit fly brain. The authors applied RNA sequencing on second-order gustatory neurons responsible for sweet and bitter processing, under fed and starved conditions. The sequencing data reveal significant changes in gene expression across sweet vs. bitter pathways and fed vs. starved states. The authors focus on the neuropeptide Leucokinin (Lk), whose expression is dependent on the starvation state. They identify a pair of neurons, named SELK neurons, which express Lk and receive direct input from both sweet and bitter gustatory neurons. These SELK neurons are ideal candidates to integrate gustatory and internal state information. Behavioral experiments show that blocking these neurons in starved flies alters their tolerance to bitter substances during feeding.

Strengths:

(1) The study employs a well-designed approach, targeting specific neuronal populations, which is more efficient and precise compared to traditional large-scale genetic screening methods.

(2) The RNAseq results provide valuable data that can be utilized in future studies to explore other molecules beyond Lk.

(3) The identification of SELK neurons offers a promising avenue for future research into how these neurons integrate conflicting gustatory signals and internal state information.

Weaknesses:

Unfortunately, due to technical challenges, the authors were unable to directly image the functional activity of SELK neurons.

Author response:

Reviewer #1:

As this code was developed for use with a 4096 electrode array, it is important to be aware of double-counting neurons across the many electrodes. I understand that there are ways within the code to ensure that this does not happen, but care must be taken in two key areas. Firstly, action potentials traveling down axons will exhibit a triphasic waveform that is different from the biphasic waveform that appears near the cell body, but these two signals will still be from the same neuron (for example, see Litke et al., 2004 "What does the eye tell the brain: Development of a System for the Large-Scale Recording of Retinal Output Activity"; figure 14). I did not see anything that would directly address this situation, so it might be something for you to consider in updated versions of the code.

We thank the reviewer for this insightful comment. We agree that signals from the same neuron may be collected by adjacent channels. To address this concern in our software, we plan to add a routine to SpikeMAP that allows users to discard nearby channels where spike count correlations exceed a pre-determined threshold. Because there is no ground truth to map individual cells to specific channels on the hd-MEA, a statistical approach is warranted.

Secondly, spike shapes are known to change when firing rates are high, like in bursting neurons (Harris, K.D., Hirase, H., Leinekugel, X., Henze, D.A. & Buzsáki, G. Temporal interaction between single spikes and complex spike bursts in hippocampal pyramidal cells. Neuron 32, 141-149 (2001)). I did not see this addressed in the present version of the manuscript.

This is a valid concern. To ensure that firing rates are relatively constant over the duration of a recording, we will plot average spike rates using rolling windows of a fixed duration. We expect that population firing rates will remain relatively stable across the duration of recordings.

Another area for possible improvement would be to build on the excellent validation experiments you have already conducted with parvalbumin interneurons. Although it would take more work, similar experiments could be conducted for somatostatin and vasoactive intestinal peptide neurons against a background of excitatory neurons. These may have different spike profiles, but your success in distinguishing them can only be known if you validate against ground truth, like you did for the PV interneurons.

We agree that further cycles of experiments could be performed with SOM, VIP, and other neuronal subtypes, and we hope that researchers will take advantage of SpikeMAP too. We will clarify this possibility in the Discussion section of the manuscript.

Reviewer #2:

Summary:

While I find that the paper is nicely written and easy to follow, I find that the algorithmic part of the paper is not really new and should have been more carefully compared to existing solutions. While the GT recordings to assess the possibilities of a spike sorting tool to distinguish properly between excitatory and inhibitory neurons are interesting, spikeMAP does not seem to bring anything new to state-of-the-art solutions, and/or, at least, it would deserve to be properly benchmarked. I would suggest that the authors perform a more intensive comparison with existing spike sorters.

We thank the reviewer for this comment. As detailed in Table 1, SpikeMAP is the only method that performs E/I sorting on large-scale multielectrodes, hence a comparison to competing methods is not currently possible. That being said, many of the pre-processing steps of SpikeMAP (Figure 1) involve methods that are already well-established in the literature and available under different packages. To highlight the contribution of our work and facilitate the adoption of SpikeMAP, we plan to provide a “modular” portion of SpikeMAP that is specialized in performing E/I sorting and can be added to the pipeline of other packages such as KiloSort more clearly.  This modularized version of the code will be shared freely along with the more complete version already available.

Weaknesses:

(1) The global workflow of spikeMAP, described in Figure 1, seems to be very similar to that of Hilgen et al. 2020 (10.1016/j.celrep.2017.02.038). Therefore, the first question is what is the rationale of reinventing the wheel, and not using tools that are doing something very similar (as mentioned by the authors themselves). I have a hard time, in general, believing that spikeMAP has something particularly special, given its Methods, compared to state-of-the-art spike sorters.

We agree with the reviewers that there are indeed similarities between our work and the Hilgen et al. paper. However, while the latter employs optogenetics to stimulate neurons on a large-scale array, their technique does not specifically target inhibitory (e.g., PV) neurons as described in our work. We will clarify our paper accordingly.

This is why, at the very least, the title of the paper is misleading, because it lets the reader think that the core of the paper will be about a new spike sorting pipeline. If this is the main message the authors want to convey, then I think that numerous validations/benchmarks are missing to assess first how good spikeMAP is, with reference to spike sorting in general, before deciding if this is indeed the right tool to discriminate excitatory vs inhibitory cells. The GT validation, while interesting, is not enough to entirely validate the paper. The details are a bit too scarce for me, or would deserve to be better explained (see other comments after).

The title of our work will be edited to make it clear that while elements of the pipeline are well-established and available from other packages, we are the first to extend this pipeline to E/I sorting on large-scale arrays.

(2) Regarding the putative location of the spikes, it has been shown that the center of mass, while easy to compute, is not the most accurate solution [Scopin et al, 2024, 10.1016/j.jneumeth.2024.110297]. For example, it has an intrinsic bias for finding positions within the boundaries of the electrodes, while some other methods, such as monopolar triangulation or grid-based convolution, might have better performances. Can the authors comment on the choice of the Center of Mass as a unique way to triangulate the sources?

We agree with the reviewer and will point out limits of the center-of-mass algorithm based on the article of Scopin et al (2024). Further, we will augment the existing code library to include monopolar triangulation or grid-based convolution as options available to end-users.

(3) Still in Figure 1, I am not sure I really see the point of Spline Interpolation. I see the point of such a smoothing, but the authors should demonstrate that it has a key impact on the distinction of Excitatory vs. Inhibitory cells. What is special about the value of 90kHz for a signal recorded at 18kHz? What is the gain with spline enhancement compared to without? Does such a value depend on the sampling rate, or is it a global optimum found by the authors?

We will clarify these points. Specifically, the value of 90kHz was chosen because it provided a reasonable temporal characterization of spikes; this value, however, can be adjusted within the software based on user preference.

(4) Figure 2 is not really clear, especially panel B. The choice of the time scale for the B panel might not be the most appropriate, and the legend filtered/unfiltered with a dot is not clear to me in Bii.

We will re-check Fig.2B which seems to have error in rendering, likely due to conversion from its original format.

In panel E, the authors are making two clusters with PCA projections on single waveforms. Does this mean that the PCA is only applied to the main waveforms, i.e. the ones obtained where the amplitudes are peaking the most? This is not really clear from the methods, but if this is the case, then this approach is a bit simplistic and does not really match state-of-the-art solutions. Spike waveforms are quite often, especially with such high-density arrays, covering multiple channels at once, and thus the extracellular patterns triggered by the single units on the MEA are spatio-temporal motifs occurring on several channels. This is why, in modern spike sorters, the information in a local neighbourhood is often kept to be projected, via PCA, on the lower-dimensional space before clustering. Information on a single channel only might not be informative enough to disambiguate sources. Can the authors comment on that, and what is the exact spatial resolution of the 3Brain device? The way the authors are performing the SVD should be clarified in the methods section. Is it on a single channel, and/or on multiple channels in a local neighbourhood?

Here, the reviewer is suggesting that it may be better to perform PCA on several channels at once, since spikes can occur at several channels at the same time. To address this concern, small routine will be written allowing users to choose how many nearby channels to be selected for PCA.

(5) About the isolation of the single units, here again, I think the manuscript lacks some technical details. The authors are saying that they are using a k-means cluster analysis with k=2. This means that the authors are explicitly looking for 2 clusters per electrode? If so, this is a really strong assumption that should not be held in the context of spike sorting, because, since it is a blind source separation technique, one cannot pre-determine in advance how many sources are present in the vicinity of a given electrode. While the illustration in Figure 2E is ok, there is no guarantee that one cannot find more clusters, so why this choice of k=2? Again, this is why most modern spike sorting pipelines do not rely on k-means, to avoid any hard-coded number of clusters. Can the authors comment on that?

It is true that k=2 is a pre-determined choice in our software. In practice, we found that k>2 leads to poorly defined clusters. However, we will ensure that this parameter can be adjusted in the software. Furthermore, if the user chooses not to pre-define this value, we will provide the option to use a Calinski-Harabasz criterion to select k.

(6) I'm surprised by the linear decay of the maximal amplitude as a function of the distance from the soma, as shown in Figure 2H. Is it really what should be expected? Based on the properties of the extracellular media, shouldn't we expect a power law for the decay of the amplitude? This is strange that up to 100um away from the soma, the max amplitude only dropped from 260 to 240 uV. Can the authors comment on that? It would be interesting to plot that for all neurons recorded, in a normed manner V/max(V) as function of distances, to see what the curve looks like.

We share the reviewer’s concern and will add results that include a population of neurons to assess the robustness of this phenomenon.

(7) In Figure 3A, it seems that the total number of cells is rather low for such a large number of electrodes. What are the quality criteria that are used to keep these cells? Did the authors exclude some cells from the analysis, and if yes, what are the quality criteria that are used to keep cells? If no criteria are used (because none are mentioned in the Methods), then how come so few cells are detected, and can the authors convince us that these neurons are indeed "clean" units (RPVs, SNRs, ...)?

We applied stringent criteria to exclude cells, and we will revise the main text to be clear about these criteria, which include a minimum spike rate and the use of LDA to separate out PCA clusters. For the cells that were retained, we will include SNR estimates.

(8) Still in Figure 3A, it looks like there is a bias to find inhibitory cells at the borders, since they do not appear to be uniformly distributed over the MEA. Can the authors comment on that? What would be the explanation for such a behaviour? It would be interesting to see some macroscopic quantities on Excitatory/Inhibitory cells, such as mean firing rates, averaged SNRs... Because again, in Figure 3C, it is not clear to me that the firing rates of inhibitory cells are higher than Excitatory ones, whilst they should be in theory.       

We will include a comparison of firing rates for E and I neurons. It is possible that I cells are located at the border of the MEA due to the site of injections of the viral vector, and not because of an anatomical clustering of I cells per se. We will clarify the text accordingly.

(9) For Figure 3 in general, I would have performed an exhaustive comparison of putative cells found by spikeMAP and other sorters. More precisely, I think that to prove the point that spikeMAP is indeed bringing something new to the field of spike sorting, the authors should have compared the performances of various spike sorters to discriminate Exc vs Inh cells based on their ground truth recordings. For example, either using Kilosort [Pachitariu et al, 2024, 10.1038/s41592-024-02232-7], or some other sorters that might be working with such large high-density data [Yger et al, 2018, 10.7554/eLife.34518].

As mentioned previously, Kilosort and related approaches do not address the problem of E/I identification (see Table 1). However, they do have pre-processing steps in common with SpikeMAP. We will add some specific comparison points – for instance, the use of k-means and PCA (which is more common across packages) and the use of cubic spline interpolation (which is less common). Further, we will provide a stand-alone E/I sorting module that can be added to the pipeline of other packages, so that users can use this functionality without having to migrate their entire analysis.

(10) Figure 4 has a big issue, and I guess the panels A and B should be redrawn. I don't understand what the red rectangle is displaying.

We apologize for this issue. It seems there was a rendering problem when converting the figure from its original format. We will address this issue in the revised version of the manuscript.

(11) I understand that Figure 4 is only one example, but I have a hard time understanding from the manuscript how many slices/mice were used to obtain the GT data? I guess the manuscript could be enhanced by turning the data into an open-access dataset, but then some clarification is needed. How many flashes/animals/slices are we talking about? Maybe this should be illustrated in Figure 4, if this figure is devoted to the introduction of the GT data.

We will mention how many flashes/animals/slices were employed in the GT data and provide open access to these data.

(12) While there is no doubt that GT data as the ones recorded here by the authors are the most interesting data from a validation point of view, the pretty low yield of such experiments should not discourage the use of artificially generated recordings such as the ones made in [Buccino et al, 2020, 10.1007/s12021-020-09467-7] or even recently in [Laquitaine et al, 2024, 10.1101/2024.12.04.626805v1]. In these papers, the authors have putative waveforms/firing rate patterns for excitatory and inhibitory cells, and thus, the authors could test how good they are in discriminating the two subtypes.

We thank the reviewer for the suggestion that SpikeMAP could be tested on artificially generated spike trains and will add the citation of the two papers mentioned. We hope future efforts will employ SpikeMAP on both synthetic and experimental data to explore the neural dynamics of E and I neurons in healthy and pathological circuits of the brain.

eLife Assessment

In this manuscript, the authors describe a software package for automatic differentiation of action potentials generated by excitatory and inhibitory neurons, acquired using high-density microelectrode arrays. The work is valuable as it offers a tool with the potential to automatically identify these neuron types in vitro. However, it is incomplete due to limited comparison with ground truth data from optogenetically identified interneuron subtypes and with existing spike sorting pipelines available to users.

Reviewer #1 (Public review):

Summary:

The authors note that while many software packages exist for spike sorting, these do not automatically differentiate with known accuracy between excitatory and inhibitory neurons. Moreover, most existing spike sorting packages are for in vivo use, where the majority of electrodes are separated from each other by several hundred microns or more. There is a need for spike sorting packages that can take advantage of high-density electrode arrays where all electrodes are within a few tens of microns of other electrodes. Here, the authors offer such a software package with SpikeMAP, and they validate its performance in identifying parvalbumin interneurons that were optogenetically stimulated.

Strengths:

The main strength of this work is that the authors use ground truth measures to show that SpikeMAP can take features of spike shapes to correctly identify known parvalbumin interneurons against a background of other neuron types. They use spike width and peak to peak distance as the key features for distinguishing between neuron types, a method that has been around for many years (Barthó, Peter, et al. "Characterization of neocortical principal cells and interneurons by network interactions and extracellular features." Journal of neurophysiology 92.1 (2004): 600-608.), but whose performance has not been validated in the context of high density electrode arrays.

Another strength of this approach is that it is automated - a necessity if your electrode array has 4096 electrodes. Hand-sorting or even checking such a large number of channels is something even the cruelest advisor would not wish upon a graduate student. With such large channel counts, it is essential to have automated methods that are known to work accurately. Hence, the combination of validation and automation is an important advance.

A nice feature of this work is that with high-density electrode arrays, the spike waveforms appear on multiple nearby electrodes simultaneously. And since spike amplitudes fall off with distance, this allows triangulation of neuron locations within the regular electrode array. Thus, spike correlations between neuron types, or within neuron types, can be plotted as a function of distance. While SpikeMAP is not the first to do this (Peyrache, Adrien, et al. "Spatiotemporal dynamics of neocortical excitation and inhibition during human sleep." Proceedings of the National Academy of Sciences 109.5 (2012): 1731-1736.), it is a welcome capability of this package.

It is also good that the code for this package is open-source, allowing a community of people (I expect in vitro labs will especially want to use this) to use the code and further improve it.

Weaknesses:

As this code was developed for use with a 4096 electrode array, it is important to be aware of double-counting neurons across the many electrodes. I understand that there are ways within the code to ensure that this does not happen, but care must be taken in two key areas. Firstly, action potentials traveling down axons will exhibit a triphasic waveform that is different from the biphasic waveform that appears near the cell body, but these two signals will still be from the same neuron (for example, see Litke et al., 2004 "What does the eye tell the brain: Development of a System for the Large-Scale Recording of Retinal Output Activity"; figure 14). I did not see anything that would directly address this situation, so it might be something for you to consider in updated versions of the code. Secondly, spike shapes are known to change when firing rates are high, like in bursting neurons (Harris, K.D., Hirase, H., Leinekugel, X., Henze, D.A. & Buzsáki, G. Temporal interaction between single spikes and complex spike bursts in hippocampal pyramidal cells. Neuron 32, 141-149 (2001)). I did not see this addressed in the present version of the manuscript.

Another area for possible improvement would be to build on the excellent validation experiments you have already conducted with parvalbumin interneurons. Although it would take more work, similar experiments could be conducted for somatostatin and vasoactive intestinal peptide neurons against a background of excitatory neurons. These may have different spike profiles, but your success in distinguishing them can only be known if you validate against ground truth, like you did for the PV interneurons.

Appraisal:

This work addresses the need for an automated spike sorting software package for high-density electrode arrays. Although no spike sorting software is flawless, the package presented here, SpikeMAP, has been validated on PV interneurons, inspiring a degree of confidence. This is a good start, and further validation on other neuron types could increase that confidence. Groups doing in vitro experiments, where 4096 electrode arrays are more common, could find this system particularly helpful.

Reviewer #2 (Public review):

Summary:

In this paper, entitled "SpikeMAP: An unsupervised spike sorting pipeline for cortical excitatory and inhibitory 2 neurons in high-density multielectrode arrays with ground-truth validation", the authors present spikeMAP, a pipeline for the analysis of large-scale recordings of in vitro cortical activity. According to the authors, spikeMAP not only allows for the detection of spikes produced by single neurons (spike sorting), but also allows for the reliable distinction between genetically determined cell types by utilizing viral and optogenetic strategies as ground-truth validation. While I find that the paper is nicely written and easy to follow, I find that the algorithmic part of the paper is not really new and should have been more carefully compared to existing solutions. While the GT recordings to assess the possibilities of a spike sorting tool to distinguish properly between excitatory and inhibitory neurons are interesting, spikeMAP does not seem to bring anything new to state-of-the-art solutions, and/or, at least, it would deserve to be properly benchmarked. I would suggest that the authors perform a more intensive comparison with existing spike sorters.

Strengths:

The GT recordings with optogenetic activation of the cells, based on the opsins, is interesting and might provide useful data to quantify how good spike sorting pipelines are, in vitro, to discriminate between excitatory and inhibitory neurons. Such an approach can be quite complementary to artificially generated ground truth.

Weaknesses:

(1) The global workflow of spikeMAP, described in Figure 1, seems to be very similar to that of Hilgen et al. 2020 (10.1016/j.celrep.2017.02.038). Therefore, the first question is what is the rationale of reinventing the wheel, and not using tools that are doing something very similar (as mentioned by the authors themselves). I have a hard time, in general, believing that spikeMAP has something particularly special, given its Methods, compared to state-of-the-art spike sorters. This is why, at the very least, the title of the paper is misleading, because it lets the reader think that the core of the paper will be about a new spike sorting pipeline. If this is the main message the authors want to convey, then I think that numerous validations/benchmarks are missing to assess first how good spikeMAP is, with reference to spike sorting in general, before deciding if this is indeed the right tool to discriminate excitatory vs inhibitory cells. The GT validation, while interesting, is not enough to entirely validate the paper. The details are a bit too scarce for me, or would deserve to be better explained (see other comments after).

(2) Regarding the putative location of the spikes, it has been shown that the center of mass, while easy to compute, is not the most accurate solution [Scopin et al, 2024, 10.1016/j.jneumeth.2024.110297]. For example, it has an intrinsic bias for finding positions within the boundaries of the electrodes, while some other methods, such as monopolar triangulation or grid-based convolution,n might have better performances. Can the authors comment on the choice of the Center of Mass as a unique way to triangulate the sources?

(3) Still in Figure 1, I am not sure I really see the point of Spline Interpolation. I see the point of such a smoothing, but the authors should demonstrate that it has a key impact on the distinction of Excitatory vs. Inhibitory cells. What is special about the value of 90kHz for a signal recorded at 18kHz? What is the gain with spline enhancement compared to without? Does such a value depend on the sampling rate, or is it a global optimum found by the authors?

(4) Figure 2 is not really clear, especially panel B. The choice of the time scale for the B panel might not be the most appropriate, and the legend filtered/unfiltered with a dot is not clear to me in Bii. In panel E, the authors are making two clusters with PCA projections on single waveforms. Does this mean that the PCA is only applied to the main waveforms, i.e. the ones obtained where the amplitudes are peaking the most? This is not really clear from the methods, but if this is the case, then this approach is a bit simplistic and does not really match state-of-the-art solutions. Spike waveforms are quite often, especially with such high-density arrays, covering multiple channels at once, and thus the extracellular patterns triggered by the single units on the MEA are spatio-temporal motifs occurring on several channels. This is why, in modern spike sorters, the information in a local neighbourhood is often kept to be projected, via PCA, on the lower-dimensional space before clustering. Information on a single channel only might not be informative enough to disambiguate sources. Can the authors comment on that, and what is the exact spatial resolution of the 3Brain device? The way the authors are performing the SVD should be clarified in the methods section. Is it on a single channel, and/or on multiple channels in a local neighbourhood?

(5) About the isolation of the single units, here again, I think the manuscript lacks some technical details. The authors are saying that they are using a k-means cluster analysis with k=2. This means that the authors are explicitly looking for 2 clusters per electrode? If so, this is a really strong assumption that should not be held in the context of spike sorting, because, since it is a blind source separation technique, one can not pre-determine in advance how many sources are present in the vicinity of a given electrode. While the illustration in Figure 2E is ok, there is no guarantee that one can not find more clusters, so why this choice of k=2? Again, this is why most modern spike sorting pipelines do not rely on k-means, to avoid any hard-coded number of clusters. Can the authors comment on that?

(6) I'm surprised by the linear decay of the maximal amplitude as a function of the distance from the soma, as shown in Figure 2H. Is it really what should be expected? Based on the properties of the extracellular media, shouldn't we expect a power law for the decay of the amplitude? This is strange that up to 100um away from the soma, the max amplitude only dropped from 260 to 240 uV. Can the authors comment on that? It would be interesting to plot that for all neurons recorded, in a normed manner V/max(V) as function of distances, to see what the curve looks like.

(7) In Figure 3A, it seems that the total number of cells is rather low for such a large number of electrodes. What are the quality criteria that are used to keep these cells? Did the authors exclude some cells from the analysis, and if yes, what are the quality criteria that are used to keep cells? If no criteria are used (because none are mentioned in the Methods), then how come so few cells are detected, and can the authors convince us that these neurons are indeed "clean" units (RPVs, SNRs, ...)?

(8) Still in Figure 3A, it looks like there is a bias to find inhibitory cells at the borders, since they do not appear to be uniformly distributed over the MEA. Can the authors comment on that? What would be the explanation for such a behaviour? It would be interesting to see some macroscopic quantities on Excitatory/Inhibitory cells, such as mean firing rates, averaged SNRs... Because again, in Figure 3C, it is not clear to me that the firing rates of inhibitory cells are higher than Excitatory ones, whilst they should be in theory.

(9) For Figure 3 in general, I would have performed an exhaustive comparison of putative cells found by spikeMAP and other sorters. More precisely, I think that to prove the point that spikeMAP is indeed bringing something new to the field of spike sorting, the authors should have compared the performances of various spike sorters to discriminate Exc vs Inh cells based on their ground truth recordings. For example, either using Kilosort [Pachitariu et al, 2024, 10.1038/s41592-024-02232-7], or some other sorters that might be working with such large high-density data [Yger et al, 2018, 10.7554/eLife.34518].

(10) Figure 4 has a big issue, and I guess the panels A and B should be redrawn. I don't understand what the red rectangle is displaying.

(11) I understand that Figure 4 is only one example, but I have a hard time understanding from the manuscript how many slices/mices were used to obtain the GT data? I guess the manuscript could be enhanced by turning the data into an open-access dataset, but then some clarification is needed. How many flashes/animals/slices are we talking about? Maybe this should be illustrated in Figure 4, if this figure is devoted to the introduction of the GT data.

(12) While there is no doubt that GT data as the ones recorded here by the authors are the most interesting data from a validation point of view, the pretty low yield of such experiments should not discourage the use of artificially generated recordings such as the ones made in [Buccino et al, 2020, 10.1007/s12021-020-09467-7] or even recently in [Laquitaine et al, 2024, 10.1101/2024.12.04.626805v1]. In these papers, the authors have putative waveforms/firing rate patterns for excitatory and inhibitory cells, and thus, the authors could test how good they are in discriminating the two subtypes.

eLife Assessment

This study provides important information on the ultrastructural organization of layer 1 of the human neocortex. The quantitative assessment of various synaptic parameters, astrocytic coverage and mitochondrial morphology is based on convincing experimental approaches. These data provide new information on the detailed morphology of human neocortical tissue that will be of interest to neuroscientists working on different network functions.

Reviewer #1:

Summary:

The Authors investigated the anatomical features of the excitatory synaptic boutons in layer 1 of the human temporal neocortex. They examined the size of the synapse, the macular or the perforated appearance and the size of the synaptic active zone, the number and volume of the mitochondria, the number of the synaptic and the dense core vesicles, also differentiating between the readily releasable, the recycling and the resting pool of synaptic vesicles. The coverage of the synapse by astrocytic processes was also assessed, and all the above parameters were compared to other layers of the human temporal neocortex. The Authors conclude that the subcellular morphology of the layer 1 synapses is suitable for the functions of the neocortical layer, i.e. the synaptic integration within the cortical column. The low glial coverage of the synapses might allow the glutamate spillover from the synapses enhancing synaptic crosstalk within this cortical layer.

Strengths:

The strengths of this paper are the abundant and very precious data about the fine structure of the human neocortical layer 1. Quantitative electron microscopy data (especially that derived from the human brain) are very valuable, since this is a highly time- and energy consuming work. The techniques used to obtain the data, as well as the analyses and the statistics performed by the Authors are all solid, strengthen this manuscript, and support the conclusions drawn in the discussion.

Reviewer #2:

The study of Rollenhagen et al examines the ultrastructural features of Layer 1 of human temporal cortex. The tissue was derived from drug-resistant epileptic patients undergoing surgery, and was selected as further from the epilepsy focus, and as such considered to be non-epileptic. The analyses has included 4 patients with different age, sex, medication and onset of epilepsy. The manuscript is a follow-on study with 3 previous publications from the same authors on different layers of the temporal cortex:

Layer 4 - Yakoubi et al 2019 eLife

Layer 5 - Yakoubi et al 2019 Cerebral Cortex,

Layer 6 - Schmuhl-Giesen et al 2022 Cerebral Cortex

They find, the L1 synaptic boutons mainly have single active zone a very large pool of synaptic vesicles and are mostly devoid of astrocytic coverage.

Strengths:

The MS is well written easy to read. Result section gives a detailed set of figures showing many morphological parameters of synaptic boutons and surrounding glial elements. The authors provide comparative data of all the layers examined by them so far in the Discussion. Given that anatomical data in human brain are still very limited, the current MS has substantial relevance. The work appears to be generally well done, the EM and EM tomography images are of very good quality. The analyses is clear and precise.

Weaknesses:

The authors made all the corrections required and answered all of my concerns, included additional data sets, and clarified statements where needed.

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Summary:

The Authors investigated the anatomical features of the excitatory synaptic boutons in layer 1 of the human temporal neocortex. They examined the size of the synapse, the macular or the perforated appearance and the size of the synaptic active zone, the number and volume of the mitochondria, the number of the synaptic and the dense core vesicles, also differentiating between the readily releasable, the recycling and the resting pool of synaptic vesicles. The coverage of the synapse by astrocytic processes was also assessed, and all the above parameters were compared to other layers of the human temporal neocortex. The Authors conclude that the subcellular morphology of the layer 1 synapses is suitable for the functions of the neocortical layer, i.e. the synaptic integration within the cortical column. The low glial coverage of the synapses might allow the glutamate spillover from the synapses enhancing synaptic crosstalk within this cortical layer.

Strengths:

The strengths of this paper are the abundant and very precious data about the fine structure of the human neocortical layer 1. Quantitative electron microscopy data (especially that derived from the human brain) are very valuable, since this is a highly time- and energy consuming work. The techniques used to obtain the data, as well as the analyses and the statistics performed by the Authors are all solid, strengthen this manuscript, and support the conclusions drawn in the discussion.

Comments on latest version:

The third version of this paper has been substantially improved. The English is significantly better, there are only few paragraphs and sentences which are hard to understand (see my comments and suggestions below). Almost all of my suggestions were incorporated.

We would like to thank the reviewer for the comments and incorporated the suggestions within the latest version of the manuscript.

Remaining minor concerns:

About epileptic and non-epileptic (non-affected) tissue. I am aware that temporal lobe neocortical tissue derived from epileptic patients is regarded as non-affected by many groups, and they are quite similar to the cortex of non-epileptic (tumour) patients in their electrophysiological properties and synaptic physiology. But please, note, that one paper you cited did not use samples from epileptic patients, but only tissue from non-epileptic tumor patients (Molnár et al. PLOS 2008).

When you look deeper, and make thorough comparison of tissues derived from epileptic and non-epileptic patients, there are differences in the fine structure, as well as in several electrophysiological features. See for example Tóth et al., J Physiol, 2018, where higher density of excitatory synapses were found in L2 of neocortical samples derived from epileptic patients compared to non-epileptic (tumor) patients. Furthermore, the appearance of population bursts is similar, but their occurrence is more frequent and their amplitude is higher in tissue from epileptic compared to non-epileptic patients. So, I still cannot agree, that temporal neocortex of epileptic patients with the seizure focus in the hippocampus would be non-affected. Therefore I suggested to use the term biopsy tissue.

We are thankful for this comment on using non-epileptic tissue also by others. We are also aware that Molnár et al. 2008 worked with tumor tissue.

It is still not emphasized in the first paragraph of the Discussion, that only excitatory axon terminals were investigated.

We now mentioned in the first paragraph of the discussion that only excitatory synaptic boutons were investigated.

The text in the Results and the Discussion are somewhat inconsistent.

The last two paragraphs of the Results section ends with several sentences which should be part of the discussion, such as line 328: This finding strongly supports multivesicular release... or line 344: --- pointing towards a layer-specific regulation of the putative RRP. Moreover, the results suggest that... and line 370: ... it is most likely... Please, correct this.

We disagree with the reviewer on these points because these sentences summarizes the findings.

The first paragraph of the Discussion summarizes the work of the quantitative EM work and gives one conclusion about the astrocytic coverage. This last sentence is inconsistent with the other parts of the paragraph. I would either write that "astrocytic coverage was also investigated" (or something similar), or move this sentence to the paragraph which discusses the astrocytic coverage.

Results line 180-183. "Special connections" between astrocytic processes and synaptic boutons are mentioned, but not shown. Either show these (but then prove with staining!), or leave out this paragraph.

We deleted this paragraph as suggested.

Reviewer #2 (Public review):

Summary:

The study of Rollenhagen et al examines the ultrastructural features of Layer 1 of human temporal cortex. The tissue was derived from drug-resistant epileptic patients undergoing surgery, and was selected as further from the epilepsy focus, and as such considered to be non-epileptic. The analyses has included 4 patients with different age, sex, medication and onset of epilepsy. The manuscript is a follow-on study with 3 previous publications from the same authors on different layers of the temporal cortex:

Layer 4 - Yakoubi et al 2019 eLife

Layer 5 - Yakoubi et al 2019 Cerebral Cortex,

Layer 6 - Schmuhl-Giesen et al 2022 Cerebral Cortex

They find, the L1 synaptic boutons mainly have single active zone a very large pool of synaptic vesicles and are mostly devoid of astrocytic coverage.

Strengths:

The MS is well written easy to read. Result section gives a detailed set of figures showing many morphological parameters of synaptic boutons and surrounding glial elements. The authors provide comparative data of all the layers examined by them so far in the Discussion. Given that anatomical data in human brain are still very limited, the current MS has substantial relevance. The work appears to be generally well done, the EM and EM tomography images are of very good quality. The analyses is clear and precise.

Weaknesses:

The authors made all the corrections required and answered all of my concerns, included additional data sets, and clarified statements where needed.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

Minor suggestions:

Synaptic density, lines 189-193. If you say "comparatively" high, then compare to something (cite your own work for the other layers, and tell the approximative values for the other layers). Same in line 194 comparably high to what? Other option: say "relatively high".

We corrected the sentences as suggested by the reviewer.

Line 206: When present, mitochondria (comma missing)

Corrected as suggested by the reviewer.

Line 265: Dot is missing at the end of the sentence (after Shapira et al. 2003)

Corrected as suggested by the reviewer.

Lines 300-301: Check the English for this sentence: significant difference BETWEEN TWO sublaminae and not significant difference for both sublaminae.

Corrected as suggested by the reviewer.

Lines 304-305: Check the sentence, please, it is not understandable without the text in parenthesis.

Corrected as suggested by the reviewer.

Line 354 Dot missing at the end of the sentence (after Figure 6A, B)

Corrected as suggested by the reviewer.

Line 354-358: Please rephrase this sentence (too complicated, not understandable). I do not understand why results of the L4, L5, L6 are described here. What does it mean "Astrocytes and their fine processes formed a relatively dense, but a comparably loose network within the neuropil in L1"? Dense or loose?

In the experiment measuring the volume fraction of astrocytic processes (Figure 6C), all six cortical layers were analyzed, thus we compared the values obtained for L1 with the results for L4, L5 and L6. For more clarity, we rephrased the sentence: “Astrocytes and their fine processes formed a relatively dense network in L4 and L5, but a comparably loose one within the neuropil in L1…” We also rephrased other sentences in this paragraph (as also suggested below).

Lines 359-369: Please rephrase this paragraph. The sentences are too complicated, have too many parentheses, and are not understandable. I suggest to write first how many synapses were examined in L1 and L4, then how many of them were on spine and on dendrites (either n or %). Then give the values how many (n or %) of them were "tripartite synapses", out of spine synapses and of dendritic synapses in both layers. How many of them were partially covered in both layers. Please, write the data in a systematic way. The best would be to give the values in a table as well. This way it will be more understandable (now, it is chaotic, hard to follow).

We rephrased the paragraph and added a new table (3).

Line 383: Dot missing from the end of the sentence.

Corrected as suggested by the reviewer.

Line 436: Reconsider "comparably low compared to". The comparably means what in this case? The whole paragraph is hard to understand, please, check and review for improvements to the use of English or use chatGPT to check it.

We corrected the sentence according to the reviewer’s suggestion.

Line 487: Same thing again: "The comparably largest size of the RP in L1 when compared..." What would you like to say with "comparably"? Check the meaning of this word in a dictionary, please. I have the feeling that you are using this word instead of "relatively".

Corrected as suggested by the reviewer.

Line 488 "and TO that found fot L4 and L5 in rodents..."

Corrected as suggested by the reviewer.

Line 493-495: Same again, comparably when compared, correct, please.

Corrected as suggested by the reviewer.

Supplemental figures: Now I do understand why Hu-01 and Hu-02 are twice, and I think, 3 patients were examined for L1a and three for L1b. But which side is which on the subfigures? Left side (Hu-01, 02 03) was used for L1a, or L1b? Could you write this in the legend, or mark on the figure (at least at one subfigure), please?

We implemented a comment for clarity.

eLife Assessment

This theoretical study makes a useful contribution to our understanding of a subtype of type 2 diabetes - ketosis-prone diabetes mellitus (KPD) - with a potential impact on our broader understanding of diabetes and glucose regulation. The article presents an ordinary differential equation-based model for KPD that incorporates a number of distinct timescales - fast, slow, as well as intermediate, incorporating a key hypothesis of reversible beta cell deactivation. The presented evidence is solid and shows that observed clinical disease trajectories may be explained by a simple mathematical model in a particular parameter regime.

Reviewer #1 (Public review):

The goal of this work is to understand the clinical observation of a subgroup of diabetics who experience extremely high levels of blood glucose levels after a period of high carbohydrate intake. These symptoms are similar to the onset of Type 1 diabetes but, crucially, have been observed to be fully reversible in some cases.

The authors interpret these observations by analyzing a simple yet insightful mathematical model in which β-cells temporarily stop producing insulin when exposed to high levels of glucose. For a specific model realization of such dynamics (and for specific parameter values) they show that such dynamics lead to two distinct stable states. One is the relatively normal/healthy state in which β-cells respond appropriately to glucose by releasing insulin. In contrast, when enough β-cells "refuse" to produce insulin in a high-glucose environment, there is not enough insulin to reduce glucose levels, and the high-glucose state remains locked in because the high-glucose levels keep β-cells in their inactive state. The presented mathematical analysis shows that in their model the high-glucose state can be entered through an episode of high glucose levels and that subsequently the low-glucose state can be re-entered through prolonged insulin intake.

The strength of this work is twofold. First, the intellectual sharpness of translating clinical observations of ketosis-prone type 2 diabetes (KPD) into the need for β-cell responses on intermediate timescales. Second, the analysis of a specific model clearly establishes that the clinical observations can be reproduced with a model in which β-cells dynamics reversibly enter a non-insulin-producing state in a glucose-dependent fashion.

The likely impact of this work is a shift in attention in the field from a focus on the short and long-term dynamics in glucose regulation and diabetes progression to the intermediate timescales of β-cell dynamics. I expect this to lead to much interest in probing the assumptions behind the model to establish what exactly the process is by which patients enter a 'KPD state'. Furthermore, I expect this work to trigger much research on how KPD relates to "regular" type 2 diabetes and to lead to experimental efforts to find/characterize previously overlooked β-cell phenotypes.

In summary, the authors claim that observed clinical dynamics and possible remission of KPD can be explained through introducing a temporarily inactive β-cell state into a "standard model" of diabetes. The evidence for this claim comes from analyzing a mathematical model and clearly presented.

Reviewer #2 (Public review):

In this manuscript, Ridout et al. present an intriguing extension of beta cell mass-focused models for diabetes. Their model incorporates reversible glucose-dependent inactivation of beta cell mass, which can trigger sudden-onset hyperglycemia due to bistability in beta cell mass dynamics. Notably, this hyperglycemia can be reversed with insulin treatment. The model is simple, elegant, and thought-provoking.

Author response:

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

Public reviews:

Concerning the grounding in experimental phenomenology, it would be beneficial to identify specific experiments to strengthen the model. In particular, what evidence supports reversible beta cell inactivation? This could potentially be tested in mice, for instance, by using an inducible beta cell reporter, treating the animals with high glucose levels, and then measuring the phenotype of the marked cells. Such experiments, if they exist, would make the motivation for the model more compelling.

There is some direct evidence of reversible beta cell inactivation in rodent / in vitro models. We had already mentioned this in the discussion, but we have added some text emphasizing / clarifying the role of this evidence (lines 359–362).

Others have also argued that some analyses of insulin treatment in conventional T2D, which has a stronger effect in patients with higher glucose before treatment, provides indirect evidence of reversal of glucotoxicity. We have also mentioned this in the revised paper (lines 284–285).

For quantitative experiments, the authors should be more specific about the features of beta cell dysfunction in KPD. Does the dysfunction manifest in fasting glucose, glycemic responses, or both? Is there a ”pre-KPD” condition? What is known about the disease’s timescale?

The answers to some of these questions are not entirely clear—patients present with very high glucose, and thus must be treated immediately. Due to a lack of antecedent data it is not entirely clear what the pre-KPD condition is, but there is some evidence that KPD is at least not preceded by diabetes symptoms. This point is already noted in the introduction of the paper and Table 1. However, we have added a small note clarifying that this does not rule out mild hyperglycemia, as in prediabetes (and indeed, as our model might predict) (lines 76–77). Similarly, due to the necessity of immediate insulin treatment, it is not clear from existing data whether the disorder manifests more strongly in fasting glucose or glucose response, although it is likely in both. (We might infer this since continuous insulin treatment does not produce fasting hypoglycemia, and the complete lack of insulin response to glucose shortly after presentation should produce a strong effect in glycemic response.) We believe our existing description of KPD lists all of the relevant timescales, however we have also slightly clarified this description in response to the first referee’s comments (lines 66–73, 83)

The authors should also consider whether their model could apply to other conditions besides KPD. For example, the phenomenology seems similar to the ”honeymoon” phase of T1D. Making a strong case for the model in this scenario would be fascinating.

This is an excellent idea, which had not occurred to us. We have briefly discussed this possibility in the remission (lines 281–291), but plan to analyze it in more detail in a future manuscript.

Reviewer #1 (Recommendations for the author):

Whenever simulation results are presented, parameter values should be specified right there in the figure captions.

We have added the values of glucotoxicity parameters to the caption of Figure 2. In other figures, we have explicitly mentioned which panel of Figure 2 the parameters are taken from. Description of the non-glucotoxicity parameters is a bit cumbersome (there are a lot of them, but our model of fast dynamics is slightly different from Topp et al. so it does not suffice to simply say we took their parameters) so we have referred the reader to the Materials and Methods for those.

I was confused by the language in Figure 4. Could the authors clarify whether they argue that: (1) the observed KPD behaviour is the result of the system switching from one stable state to another when perturbed with high glucose intake? (2) the observed KPD behaviour is the result of one of the steady states disappearing with high glucose intake?

What we mean to say is that during a period of high sugar intake or exogeneous insulin treatment, one of the fixed points is temporarily removed—it is still a fixed point of the “normal” dynamics, but not a fixed point of the dynamics with the external condition added. Since when glucose (insulin) intake is high enough, only the low (high)-β fixed point is present, under one of these conditions the dynamics flow toward that fixed point. When the external influx of glucose/insulin is turned off, both fixed points are present again—but if the dynamics have moved sufficiently far during the external forcing, the fixed point they end up in will have switched from one fixed point to the other. We have edited the text to make this clearer (lines 153–185). Do note, however, that in response to both referee’s comments (see below), Figures 3 and 4 have been replaced with more illuminating ones. This specific point is now addressed by the new Figure 3.

The adaptation of the prefactor ’c’ was confusing to me. I think I understood it in the end, but it sounded like, ”here’s a complication, but we don’t explain it because it doesn’t really matter”. I think the authors can explain this better (or potentially leave out the complication with ’c’ altogether?).

Indeed, the existence of an adaptation mechanism is important for our overall picture of diabetes pathogenesis, but not for many of our analyses, which assume prediabetes. Nonetheless, we agree that the current explanation of it’s role is confusing because of its vagueness. We have elaborated the explanation of the type of dynamics we assume for c, adding an equation for its dynamics to the “Model” section of the Materials and methods, explained in lines 456–465. We have also amended Figure 1 to note this compensation.

I expect the main impact of this work will be to get clinical practitioners and biomedical researchers interested in the intermediate timescale dynamics of β-cells and take seriously the possibility that reversible inactive states might exist. But this impact will only be achieved when the results are clearly and easily understandable by an audience that is not familiar with mathematical modelling. I personally found it difficult to understand what I was supposed to see in the figures at first glance. Yes, the subtle points are indeed explained in the figure captions, but it might be advantageous to make the points visually so clear that a caption is barely needed. For example, when claiming that a change in parameters leads to bistability, why not plot the steady state values as a function of that parameter instead of showing curves from which one has to infer a steady state?

I would advise the authors to reconsider their visual presentation by, e.g., presenting the figures to clinical practitioners or biomedical researchers with just a caption title to test whether such an audience can decipher the point of the figure! This is of course merely a personal suggestion that the authors may decide to ignore. I am making this suggestion only because I believe in the quality of this work and that improving the clarity of the figures and the ease with which one can understand the main points would potentially lead to a much larger impact on the presented results.

This is a very good point. We have made several changes. Firstly, we have added smaller panels showing the dynamics of β to Figure 2; previously, the reader had to infer what was happening to β from G(t). Secondly, we have completely replaced the two figures showing dβ/dt, and requiring the reader to infer the fixed points of β, with bifurcation diagrams that simply show the fixed points of G and β. The new figures show through bifurcation diagrams how there are multiple fixed points in KPD, how glucose or insulin treatment force the switching of fixed points, and how the presence of bistability depends on the rate of glucotoxicity. (These new figures are Fig. 3–5 in the revised manuscript.)

Could the authors explicitly point out what could be learned from their work for the clinic? At the moment treatment consists of giving insulin to patients. If I understand correctly, nothing about the current treatment would change if the model is correct. Is there maybe something more subtle that could be relevant to devising an optimal treatment for KPD patients?

This is another very good point. We have added a new figure (Fig. 7) in our results section showing how this model, or one like it, can be analyzed to suggest an insulin treatment schedule (once parameters for an individual patient can be measured), and added some discussion of this point (lines 224–240) as well as lifestyle changes our model might suggest for KPD patients to the discussion (lines 413–425).

Similarly, could the authors explicitly point out how their model could be experimentally tested? For example, are the functions f(G) and g(G) experimentally accessible? Related to that, presumably the shape of those functions matters to reproduce the observed behaviour. Could the authors comment on that / analyze how reproducing the observed behaviour puts constraints on the shape of the used functions and chosen parameter values?

g(G) has not been carefully measured in cellular data, however it could be in more quantative versions of existing experiments. Further, our model indeed requires some general features for the forms of f(G) and g(G) to produce KPD-like phenomena. We have added some comment on this to the discussion section of the revised manuscript (lines 367–372).

Could the authors explicitly spell out which parameters they think differ between individual KPD patients, and which parameters differ between KPD patients and ’regular’ type 2 diabetics?

In general we expect all parameters should vary both among KPD patients and between KPD / “conventional” T2D. The primary parameter determining whether KPD and conventional T2D, is seen, however, is the ratio kIN/kRE. We have elaborated on both these points in the revised mansuscript. (Lines 186–192, 250–257.)

I was confused about the timescale of remission. At one point the authors write “KPD patients can often achieve partial remission: after a few weeks or months of treatment with insulin” but later the authors state that “the duration of the remission varies from 6 months to 10 years”.

The former timescale is the typical timescale achieve remission. After remission is reached, however, it may or may not last—patients may experience a relapse, where their condition worsens and they again require insulin. We have edited the text to clarify this distinction (lines 66–73).

When the authors talk about intermediate timescales in the main text could they specify an actual unit of time, such as days, weeks, or months as it would relate to the rate constants in their model for those transitions?

We have done so (lines 86–87, figure 1 caption, figure 2 caption). Getting KPD-like behavior requires (at high glucose) the deactivation process to be somewhat faster than the reactivation process, so the relevant scales are between weeks (reactivation) and days (deactivation at high G).

The authors state ”Our simple model of β-cell adaptation also neglects the known hyperglycemiainduced leftward shift in the insulin secretion curve f(G) in Eq. (2)) ”. This seems an important consideration. Could the authors comment on why they did not model this shift, and/or explicitly discuss how including it is expected to change the model dynamics?

We agree that this process seems potentially relevant, as it seems to happen on a relatively fast timescale compared to glucose-induced β-cell death. It is, however, not so well characterized quantitatively that including it is a simple matter of putting in known values—we would be making assumptions that would complicate the interpretation of our results.

It is clear that this effect will need to be considered when quanitatively modelling real patient data. However, it is also straightforward to argue that this effect by itself cannot produce KPD-like symptoms, and will only tend to reduce the rate of glucotoxocity necessary to produce bibstability. We have added a discussion of this in the revisions (lines 307–315). We have also, in general, expanded the discussion of the effects that each neglected detail we have mentioned is expected to have (lines 292–315).

The authors end with a statement that their results may “contribute to explanation of other observations that involve rapid onset or remission of diabetes-like phenomena, such as during pregnancy or for patients on very low calorie diets.” Could the authors spell out exactly how their model potentially relates to these phenomena?

Our thinking is that, even when another direct cause, such as loss of insulin resistance, is implicated in reversal of diabetes, some portion of the effect may be explained by reversal of glucotoxicity. This is indeed at this point just a hypothesis, but we have expanded on it briefly in the revision. (Lines 281–291.)

Minor typos:

In Figure 2.D the last zero of 200 on the axis was cut off.

Line 359 - there is a missing word ”in the analysis”.

We have fixed these typos, thanks.

Reviewer #2 (Recommendations for the author):

The manuscript could be significantly improved in two key areas: the presentation of the analysis, and the relation with experimental phenomenology.

Regarding the analysis presentation, the figures could be substantially enhanced with minimal effort from the authors. At present, they are sparse, lack legends, and offer only basic analysis. The authors should consider presenting, for example, a bifurcation diagram for beta cell mass and fasting glucose levels as a function of kIN, and how insulin sensitivity and average meal intake modulate this relationship. The goal should be to present clear, testable predictions in an intuitive manner. Currently, the specific testable predictions of the model are unclear.

The response to this question is copied from the reponses to related questions from the first referee.

This is a very good point. We have made several changes. Firstly, we have added smaller panels showing the dynamics of β to Figure 2; previously, the reader thad to infer what was happening to β from G(t). Secondly, we have completely replaced the two figures showing dβ/dt, and requiring the reader to infer the fixed points of β, with bifurcation diagrams that simply show the fixed points of G and β. The new figures show through bifurcation diagrams how there are multiple fixed points in KPD, how glucose or insulin treatment force the switching of fixed points, and how the presence of bistability depends on the rate of glucotoxicity. We have also supplemented our phase diagram that shows the effects of SI and the total beta cell population with bifurcation diagrams showing β as SI and βTOT are varied. (These new figures are Fig. 3–5 in the present manuscript.) Finally, we have added another figure analyzing the model’s predictions for the optimal insulin treatment and the resulting time needed to achieve remission (Fig. 7)

eLife Assessment

The findings are important and intriguing, with theoretical or practical implications beyond a single subfield. The computational methods employed are clever and sophisticated and the strength of evidence is convincing. Both the hypotheses and the exploratory nature of additional analyses are clearly stated.

Reviewer #1 (Public review):

Summary:

The authors use a sophisticated and novel task design and Bayesian computational modeling to test their hypothesis that information generalization (operationalized as a combination of self-insertion and social contagion) in social situations is disrupted in Borderline Personality Disorder. Their main finding relates to the observation that two different models best fit the two tested groups: While the model assuming both self-insertion and social contagion to be present when estimating others' social value preferences fit the control group best, a model assuming neither of these processes provided the best fit to BPD participants.

Strengths:

The two revisions have substantially strengthened the paper and the manuscript is much clearer and easier to follow now. The introduction now precisely states the author's hypotheses, and the connections to the theoretical framework are presented with much greater clarity. I appreciate that the authors now clearly label exploratory analyses where applicable.

The strengths of the presented work lie in the sophisticated task design and the thorough investigation of their theory by use of mechanistic computational models to elucidate social decision-making and learning processes in BPD. Although at present it is not clear whether the differing strategies in impression formation observed in BPD are in any way causal to negative outcomes in the condition, the study represents an important step towards better understanding cognitive processes in BPD. The paradigm and models are also potentially relevant for the investigation of other psychiatric conditions.

eLife Assessment

Argunşah et al. investigate the mechanisms underlying the differential response dynamics of barrel vs septa domains in shaping the responses to single vs multiple whiskers. Based on the observation of a higher density of SST+ interneurons in the septa, the authors investigate the hypothesis that Elfn1-dependent short-term plasticity shapes these responses. This important study is, however, supported by incomplete evidence; factors restricting the strength of evidence are the limited spatial resolution of the multi-unit activity, as well as the lack of a mechanistic explanation. This provocative and intellectually stimulating hypothesis provides a contribution to work on how different cell types shape cortical representation.

Reviewer #1 (Public review):

Summary:

Argunşah et al. describe and investigate the mechanisms underlying the differential response dynamics of barrel vs septa domains of the whisker-related primary somatosensory cortex (S1). Upon repeated stimulation, the authors report that the response ratio between multi- and single-whisker stimulation increases in layer (L) 4 neurons of the septal domain, while remaining constant in barrel L4 neurons. This difference is attributed to the short-term plasticity properties of interneurons, particularly somatostatin-expressing (SST+) neurons. This claim is supported by the increased density of SST+ neurons found in L4 of the septa compared to barrels, along with a stronger response of (L2/3) SST+ neurons to repeated multi- vs single-whisker stimulation. The role of the synaptic protein Elfn1 is then examined. Elfn1 KO mice exhibited little to no functional domain separation between barrel and septa, with no significant difference in single- versus multi-whisker response ratios across barrel and septal domains. Consistently, a decoder trained on WT data fails to generalize to Elfn1 KO responses. Finally, the authors report a relative enrichment of S2- and M1-projecting cell densities in L4 of the septal domain compared to the barrel domain.

Strengths:

This paper describes and aims to study a circuit underlying differential response between barrel columns and septal domains of the primary somatosensory cortex. This work supports the view that barrel and septal domains contribute differently to processing single versus multi-whisker inputs, suggesting that the barrel cortex multiplexes sensory information coming from the whiskers in different domains.

Weaknesses:

While the observed divergence in responses to repeated SWS vs MWS between the barrel and septal domains is intriguing, the presented evidence falls short of demonstrating that short-term plasticity in SST+ neurons critically underpins this difference. The absence of a mechanistic explanation for this observation limits the work's significance. The measurement of SST neurons' response is not specific to a particular domain, and the Elfn1 manipulation does not seem to be specific to either stimulus type or a particular domain. The study's reach is further constrained by the fact that results were obtained in anesthetized animals, which may not generalize to awake states. The statistical analysis appears inappropriate, with the use of repeated independent tests, dramatically boosting the false positive error rate. Furthermore, the manuscript suffers from imprecision; its conclusions are occasionally vague or overstated.

The authors suggest a role for SST+ neurons in the observed divergence in SWS/MWS responses between barrel and septal domains. However, this remains speculative, and some findings appear inconsistent. For instance, the increased response of SST+ neurons to MWS versus SWS is not confined to a specific domain. Why, then, would preferential recruitment of SST+ neurons lead to divergent dynamics between barrel and septal regions? The higher density of SST+ neurons in septal versus barrel L4 is not a sufficient explanation, particularly since the SWS/MWS response divergence is also observed in layers 2/3, where no difference in SST+ neuron density is found. Moreover, SST+ neuron-mediated inhibition is not necessarily restricted to the layer in which the cell body resides. It remains unclear through which differential microcircuits (barrel vs septum) the enhanced recruitment of SST+ neurons could account for the divergent responses to repeated SWS versus MWS stimulation.

The Elfn1 KO mouse model seems too unspecific to suggest the role of the short-term plasticity in SST+ neurons in the differential response to repeated SWS vs MWS stimulation across domains. Why would Elfn1-dependent short-term plasticity in SST+ neurons be specific to a pathway, or a stimulation type (SWS vs MWS)? Moreover, the authors report that Elfn1 knockout alters synapses onto VIP+ as well as SST+ neurons (Stachniak et al., 2021; previous version of this paper)-so why attribute the phenotype solely to SST+ circuitry? In fact, the functional distinctions between barrel and septal domains appear largely abolished in the Elfn1 KO.

Reviewer #2 (Public review):

Summary:

Argunsah and colleagues demonstrate that SST-expressing interneurons are concentrated in the mouse septa and differentially respond to repetitive multi-whisker inputs. Identifying how a specific neuronal phenotype impacts responses is an advance.

Strengths:

(1) Careful physiological and imaging studies.

(2) Novel result showing the role of SST+ neurons in shaping responses.

(3) Good use of a knockout animal to further the main hypothesis.

(4) Clear analytical techniques.

Weaknesses:

No major weaknesses were identified by this reviewer. Overall I appreciated the paper but feel it overlooked a few issues and had some recommendations on how additional clarifications could strengthen the paper. These include:

(1) Significant work from Jerry Chen on how S1 neurons that project to M1 versus S2 respond in a variety of behavioral tasks should be included (e.g. PMID: 26098757). Similarly, work from Barry Connor's lab on intracortical versus thalamocortical inputs to SST neurons, as well as excitatory inputs onto these neurons (e.g. PMID: 12815025) should be included.

(2) Using Layer 2/3 as a proxy to what is happening in layer 4 (~line 234). Given that layer 2/3 cells integrate information from multiple barrels, as well as receiving direct VPm thalamocortical input, and given the time window that is being looked at can receive input from other cortical locations, it is not clear that layer 2/3 is a proxy for what is happening in layer 4.

(3) Line 267, when discussing distinct temporal response, it is not well defined what this is referring to. Are the neurons no longer showing peaks to whisker stimulation, or are the responses lasting a longer time? It is unclear why PV+ interneurons which may not be impacted by the Elfn1 KO and receive strong thalamocortical inputs, are not constraining activity.

(4) Line 585 "the earliest CSD sink was identified as layer 4..." were post-hoc measurements made to determine where the different shank leads were based on the post-hoc histology?

(5) For the retrograde tracing studies, how were the M1 and S2 injections targeted (stereotaxically or physiologically)? How was it determined that the injections were in the whisker region (or not)?

(6) Were there any baseline differences in spontaneous activity in the speta versus barrel regions, and did this change in the KO animals?

Reviewer #3 (Public review):

Summary:

This study investigates the functional differences between barrel and septal columns in the mouse somatosensory cortex, focusing on how local inhibitory dynamics, particularly involving Elfn1-expressing SST⁺ interneurons, may mediate temporal integration of multi-whisker (MW) stimuli in septa. Using a combination of in vivo multi-unit recordings, calcium imaging, and anatomical tracing, the authors propose that septa integrate MW input in an Elfn1-dependent manner, enabling functional segregation from barrel columns.

Strengths:

The core hypothesis is interesting and potentially impactful. While barrels have been extensively characterized, septa remain less understood, especially in mice, and this study's focus on septal integration of MW stimuli offers valuable insights into this underexplored area. If septa indeed act as selective integrators of distributed sensory input, this would add a novel computational role to cortical microcircuits beyond what is currently attributed to barrels alone. The narrative of this paper is intellectually stimulating.

Weaknesses:

The methods used in the current study lack the spatial and cellular resolution needed to conclusively support the central claims. The main physiological findings are based on unsorted multi-unit activity (MUA) recorded via low-channel-count silicon probes. MUA inherently pools signals from multiple neurons across different distances and cell types, making it difficult to assign activity to specific columns (barrel vs. septa) or neuron classes (e.g., SST⁺ vs. excitatory). The recording radius (~50-100 µm or more) and the narrow width of septa (~50-100 µm or less) make it likely that MUA from "septal" electrodes includes spikes from adjacent barrel neurons. The authors do not provide spike sorting, unit isolation, or anatomical validation that would strengthen spatial attribution. Calcium imaging is restricted to SST⁺ and VIP⁺ interneurons in superficial layers (L2/3), while the main MUA recordings are from layer 4, creating a mismatch in laminar relevance.

Furthermore, while the role of Elfn1 in mediating short-term facilitation is supported by prior studies, no new evidence is presented in this paper to confirm that this synaptic mechanism is indeed disrupted in the knockout mice used here. Additionally, since Elfn1 is constitutively knocked out from development, the possibility of altered circuit formation-including changes in barrel structure and interneuron distribution, cannot be excluded and is not addressed.

eLife Assessment

This important study reports that the human posterior inferotemporal cortex (hPIT) functions as an attentional priority map, integrating both top-down and bottom-up attentional signals rather than serving solely as an object-processing region. The experiments and analyses are well conducted and provide convincing evidence that hPIT bridges dorsal and ventral attention networks and is robustly modulated by attention across diverse visual tasks. The study will be relevant for researchers investigating visual attention, high-level visual cortex, and the neural mechanisms that integrate endogenous and exogenous attentional control.

Reviewer #1 (Public review):

The manuscript titled "The distinct role of human PIT in attention control" by Huang et al. investigates the role of the human posterior inferotemporal cortex (hPIT) in spatial attention. Using fMRI experiments and resting-state connectivity analyses, the authors present compelling evidence that hPIT is not merely an object-processing area, but also functions as an attentional priority map, integrating both top-down and bottom-up attentional processes. This challenges the traditional view that attentional control is localized primarily in frontoparietal networks.

The manuscript is strong and of high potential interest to the cognitive neuroscience community. Below, I raise questions and suggestions to help with the reliability, methodology, and interpretation of the findings.

(1) The authors argue that hPIT satisfies the criteria for a priority map, but a clearer justification would strengthen this claim. For example, how does hPIT meet all four widely recognized criteria, such as spatial selectivity, attentional modulation, feature invariance, and input integration, when compared to classical regions such as LIP or FEF? A more systematic summary of how hPIT meets these benchmarks would be helpful. Additionally, to what extent are the observed attentional modulations in hPIT independent of general task difficulty or behavioral performance?

(2) The authors report that hPIT modulation is invariant to stimulus category, but there appear to be subtle category-related effects in the data. Were the face, scene, and scrambled images matched not only in terms of luminance and spatial frequency, but also in terms of factors such as semantic familiarity and emotional salience? This may influence attentional engagement and bias interpretation.

(3) The result that attentional load modulates hPIT is important and adds depth to the main conclusions. However, some clarifications would help with the interpretation. For example, were there observable individual differences in the strength of attentional modulation? How consistent were these effects across participants?

(4) The resting-state data reveal strong connections between hPIT and both dorsal and ventral attention networks. However, the analysis is correlational. Are there any complementary insights from task-based functional connectivity or latency analyses that support a directional flow of information involving hPIT? In addition, do the authors interpret hPIT primarily as a convergence hub receiving input from both DAN and VAN, or as a potential control node capable of influencing activity in these networks? Also, were there any notable differences between hemispheres in either the connectivity patterns or attentional modulation?

(5) A few additional questions arise regarding the anatomical characteristics of hPIT: How consistent were its location and size across participants? Were there any cases where hPIT could not be reliably defined? Given the proximity of hPIT to FFA and LOp, how was overlap avoided in ROI definition? Were the functional boundaries confirmed using independent contrasts?

Reviewer #2 (Public review):

Summary

This study investigates the role of the human posterior inferotemporal cortex (hPIT) in attentional control, proposing that hPIT serves as an attentional priority map that integrates both top-down (endogenous) and bottom-up (exogenous) attentional processes. The authors conducted three types of fMRI experiments and collected resting-state data from 15 participants. In Experiment 1, using three different spatial attention tasks, they identified the hPIT region and demonstrated that this area is modulated by attention across tasks. In Experiment 2, by manipulating the presence or absence of visual stimuli, they showed that hPIT exhibits strong attentional modulation in both conditions, suggesting its involvement in both bottom-up and top-down attention. Experiment 3 examined the sensitivity of hPIT to stimulus features and attentional load, revealing that hPIT is insensitive to stimulus category but responsive to task load - further supporting its role as an attentional priority map. Finally, resting-state functional connectivity analyses showed that hPIT is connected to both dorsal and ventral attention networks, suggesting its potential role as a bridge between the two systems. These findings extend prior work on monkey PITd and provide new insights into the integration of endogenous and exogenous attention.

Strengths

(1) The study is innovative in its use of specially designed spatial attention tasks to localize and validate hPIT, and in exploring the region's role in integrating both endogenous and exogenous attention, as prior works focus primarily on its involvement in endogenous attention.

(2) The authors provided very comprehensive experiment designs with clear figures and detailed descriptions.

(3) A broad range of analyses was conducted to support the hypothesis that hPIT functions as an attentional priority map -- including experiments of attentional modulation under both top-down and bottom-up conditions, sensitivity to stimulus features and task load, and resting-state functional connectivity. These analyses showed consistent results.

(4) Multiple appropriate statistical analyses - including t-tests, ANOVAs, and post-hoc tests - were conducted, and the results are clearly reported.

Weaknesses

(1) The sample size is relatively small (n = 15), and inter-subject variability is big in Figures 5 and 6, as seen in the spread of individual data points and error bars. The analysis of attention-modulated voxel map intersections appears to be influenced by multiple outliers.

(2) The authors acknowledge important limitations, including the lack of exploration of feature-based attention and the temporal constraints inherent to fMRI.

(3) Prior research has established that regions such as the prefrontal cortex (PFC) and posterior parietal cortex (PPC) are involved in both endogenous and exogenous attention and have been proposed as attentional priority maps. It remains unclear what is uniquely contributed by hPIT, how it functionally interacts with these classical attentional hubs, and whether its role is complementary or redundant. The study would benefit from more direct comparisons with these regions.

(4) The functional connectivity analysis is only performed on resting-state data, and this approach does not capture context-dependent interactions. Task-based data analysis can provide stronger evidence.

(5) The study does not report whether attentional modulation in hPIT is consistent across the two hemispheres. A comparison of hemispheric effects could provide important insight into lateralization and inter-individual variability, especially given the bilateral localization of hPIT.

Author response:

Reviewer #1 (Public review):

The manuscript titled "The distinct role of human PIT in attention control" by Huang et al. investigates the role of the human posterior inferotemporal cortex (hPIT) in spatial attention. Using fMRI experiments and resting-state connectivity analyses, the authors present compelling evidence that hPIT is not merely an object-processing area, but also functions as an attentional priority map, integrating both top-down and bottom-up attentional processes. This challenges the traditional view that attentional control is localized primarily in frontoparietal networks.

The manuscript is strong and of high potential interest to the cognitive neuroscience community. Below, I raise questions and suggestions to help with the reliability, methodology, and interpretation of the findings.

Thank you for a nice summary of the key points of our study. Below you will find our responses to your questions.

(1) The authors argue that hPIT satisfies the criteria for a priority map, but a clearer justification would strengthen this claim. For example, how does hPIT meet all four widely recognized criteria, such as spatial selectivity, attentional modulation, feature invariance, and input integration, when compared to classical regions such as LIP or FEF? A more systematic summary of how hPIT meets these benchmarks would be helpful. Additionally, to what extent are the observed attentional modulations in hPIT independent of general task difficulty or behavioral performance?

Great suggestions! For the first suggestion, we will include a clearer justification in the revised manuscript. For the second one, all participants received task practice prior to scanning, and task accuracy exceeded 90% (we will explicitly report the accuracy rate in revision), suggesting the tasks were not overly demanding. Although ceiling effects limit the interpretability of behavioral-performance correlations, we argue that higher task demands would likely require greater attentional effort, leading to stronger modulation in hPIT, which aligns with our findings when we manipulated the attentional load.

(2) The authors report that hPIT modulation is invariant to stimulus category, but there appear to be subtle category-related effects in the data. Were the face, scene, and scrambled images matched not only in terms of luminance and spatial frequency, but also in terms of factors such as semantic familiarity and emotional salience? This may influence attentional engagement and bias interpretation.

The response of hPIT is generally insensitive to stimulus category, however, the reviewer is correct in noticing that attentional modulation in hPIT is slightly stronger to faces than scenes and scrambled images. Although faces used in the task had neutral expressions and the scene pictures were also neutral, it is indeed possible that potential semantic familiarity or emotional salience may contribute to the subtle category-related effects in the results of experiment 3. This point will be noted in the revised manuscript.

(3) The result that attentional load modulates hPIT is important and adds depth to the main conclusions. However, some clarifications would help with the interpretation. For example, were there observable individual differences in the strength of attentional modulation? How consistent were these effects across participants?

Yes, individual differences exist. In the revised manuscript, we will include individual subject data points in the figure 6B.

(4) The resting-state data reveal strong connections between hPIT and both dorsal and ventral attention networks. However, the analysis is correlational. Are there any complementary insights from task-based functional connectivity or latency analyses that support a directional flow of information involving hPIT? In addition, do the authors interpret hPIT primarily as a convergence hub receiving input from both DAN and VAN, or as a potential control node capable of influencing activity in these networks? Also, were there any notable differences between hemispheres in either the connectivity patterns or attentional modulation?

We agree that besides resting-state connection, task-based functional connectivity analyses would have the potential to provide additional information about whether hPIT serves as a convergence node or a control hub. While fMRI data are not the best to generate directional flow of information due to the low temporal resolution, we will conduct task-based functional connectivity analyses.

We also observed modest hemispheric asymmetries in connectivity—for instance, both left and right hPIT showed stronger connectivity with right-hemisphere attention nodes. This will be described in the revised supplement.

(5) A few additional questions arise regarding the anatomical characteristics of hPIT: How consistent were its location and size across participants? Were there any cases where hPIT could not be reliably defined? Given the proximity of hPIT to FFA and LOp, how was overlap avoided in ROI definition? Were the functional boundaries confirmed using independent contrasts?

The size and location of hPIT are generally consistent across subjects, as shown in Supplementary Figure 1. The consistency is also supported by figure 4C. The hPIT is defined by conjunction maps across three tasks and then manually delineated avoiding overlapping voxels with FFA and LOp. The FFA was defined using an independent contrast (Exp3 contrast [face-scene]) and the Lop location was defined by anatomical parcellation (Glasser et al., 2016).

Reviewer #2 (Public review):

Summary

This study investigates the role of the human posterior inferotemporal cortex (hPIT) in attentional control, proposing that hPIT serves as an attentional priority map that integrates both top-down (endogenous) and bottom-up (exogenous) attentional processes. The authors conducted three types of fMRI experiments and collected resting-state data from 15 participants. In Experiment 1, using three different spatial attention tasks, they identified the hPIT region and demonstrated that this area is modulated by attention across tasks. In Experiment 2, by manipulating the presence or absence of visual stimuli, they showed that hPIT exhibits strong attentional modulation in both conditions, suggesting its involvement in both bottom-up and top-down attention. Experiment 3 examined the sensitivity of hPIT to stimulus features and attentional load, revealing that hPIT is insensitive to stimulus category but responsive to task load - further supporting its role as an attentional priority map. Finally, resting-state functional connectivity analyses showed that hPIT is connected to both dorsal and ventral attention networks, suggesting its potential role as a bridge between the two systems. These findings extend prior work on monkey PITd and provide new insights into the integration of endogenous and exogenous attention.

Strengths

(1) The study is innovative in its use of specially designed spatial attention tasks to localize and validate hPIT, and in exploring the region's role in integrating both endogenous and exogenous attention, as prior works focus primarily on its involvement in endogenous attention.

(2) The authors provided very comprehensive experiment designs with clear figures and detailed descriptions.

(3) A broad range of analyses was conducted to support the hypothesis that hPIT functions as an attentional priority map -- including experiments of attentional modulation under both top-down and bottom-up conditions, sensitivity to stimulus features and task load, and resting-state functional connectivity. These analyses showed consistent results.

(4) Multiple appropriate statistical analyses - including t-tests, ANOVAs, and post-hoc tests - were conducted, and the results are clearly reported.

Thank you for a nice summary of the key points and strengths of our study.

Weaknesses

(1) The sample size is relatively small (n = 15), and inter-subject variability is big in Figures 5 and 6, as seen in the spread of individual data points and error bars. The analysis of attention-modulated voxel map intersections appears to be influenced by multiple outliers.

We agree that the sample size (n = 15) is not ideal, and we acknowledge that some data points in Figures 5 and 6 appear to be potential outliers. However, according to conventional outlier detection criteria, all data points are within three standard deviations of the group mean and were therefore retained for analysis. Moreover, the attention-modulated voxel intersection map shown in Figure 4C is insensitive to outliers, because the intersection map plotted is based on the number of subjects.

(2) The authors acknowledge important limitations, including the lack of exploration of feature-based attention and the temporal constraints inherent to fMRI.

Yes, we hope to address these limitations in future studies.

(3) Prior research has established that regions such as the prefrontal cortex (PFC) and posterior parietal cortex (PPC) are involved in both endogenous and exogenous attention and have been proposed as attentional priority maps. It remains unclear what is uniquely contributed by hPIT, how it functionally interacts with these classical attentional hubs, and whether its role is complementary or redundant. The study would benefit from more direct comparisons with these regions.

In this study, we define the ROI base on intersection across three different types of spatial attention tasks, and the hPIT stands out in showing spatial attentional modulation across tasks. This could be due to the weak lateralized responses in PFC/PPC. To evaluate whether a region qualifies as a priority map, we applied four criteria (as mentioned in introduction). While dorsal and ventral attention network (DAN and VAN) regions can be considered important components of the priority map system, our findings suggest that among the regions tested, hPIT meets all four criteria. In Experiment 2, we included regions such as VFC (as part of PFC) and IPS (as part of PPC), and our findings suggest these areas are more involved in top-down attention. We agree with the reviewer’s suggestion and will perform additional analysis on PPC and PFC.

(4) The functional connectivity analysis is only performed on resting-state data, and this approach does not capture context-dependent interactions. Task-based data analysis can provide stronger evidence.

We acknowledge that resting-state FC is limited in assessing task-specific communication. To further investigate the role of hPIT, we plan to conduct task-based functional connectivity analyses.

(5) The study does not report whether attentional modulation in hPIT is consistent across the two hemispheres. A comparison of hemispheric effects could provide important insight into lateralization and inter-individual variability, especially given the bilateral localization of hPIT.

We thank the reviewer for this suggestion. hPIT was localized bilaterally using the same intersection-based method in Experiment 1. We have now performed additional analysis and found in Experiment 3, the difference in attentional modulation between high and low load conditions was significant in the right hPIT but not in the left. This result will be reported in the revised manuscript.

eLife Assessment

This study provides important findings on the neural circuits underlying dishabituation of the olfactory avoidance response in Drosophila. The data as presented provide solid evidence that the dishabituation involves distinct pathways from habituation. They show that reward-activated dopaminergic neurons provide input for within-modal dishabituation, while punishment-activated dopaminergic neurons provide input for cross-modal dishabituation. The work will interest neuroscientists, particularly behavioral neuroscientists working on habituation, neural circuits, and the dopaminergic system.

Reviewer #1 (Public review):

Summary:

Charonitakis and co-authors characterize dishabituation in adult flies, where they use olfactory habituation to octanol, then dishabituate the flies with disruptions of electric shock or yeast odors. They systematically investigate the neurotransmitters and neural circuits involved in dishabituation and figure out a lot about how this process works in the brain, as an independent circuit. I like the paper, and I like the very structured approach to figuring out the problem.

Strengths:

The introduction nicely sets the stage for the work presented, bringing in knowledge from other organisms and motivating the study.

The results section lays out a logical set of experiments, using a common set of behavioral assays in many flies exposed to thermogenetic or optogenetic manipulation. The paper systematically figures out the necessity and/or sufficiency of specific brain regions and neurotransmitters, culminating in a new understanding of how the important process of dishabituation works.

I like the bar graph representation for the data throughout, with the helpful icons - if a paper figures are going to be 90% bar graphs, it helps when they are super clear like this! And I like how all the parts build up to the conclusion in the last figure, nicely summarizing the thorough characterization of dishabituation.

Weaknesses:

There are no major concerns, but some material could be added for clarity and to make the work more accessible to a more general scientific audience. A figure clearly showing the habituation protocol and the use of the dishabituators would be a good addition, even if the procedure has been done before and is cited. There can always be readers who are seeing this for the first time.

It would also be nice to comment on other ways dishabituation can happen (for example, when the stimulus is removed for a short time and returns) and what their time scales are.

And more generally, the paper could perhaps improve by making a stronger case for why the results are important not just for flies but for neuroscience in general.

Reviewer #2 (Public review):

This is an interesting study in Drosophila comparing potentially differential requirements for subsets of Kenyon Cells (KCs) and Dopaminergic neurons (DANS) in olfactory dishabituation driven by either a novel odor ("homosensory") or footshock ("heterosensory). The authors measure olfactory aversion to Octanol (OCT) in a T-maze, induce olfactory habituation with a 4-minute prior exposure to OCT, and use either brief yeast odor (YO) or footshock (FS) to achieve dishabituation. The major observation that YO-mediated dishabituation is mediated by reward-activated DANs (PAM cluster), while FS-mediated dishabituation is mediated by punishment-activated PPL-DANs is generally solid and convincing. Also convincing are experiments showing the involvement of KCs in the pathway for YO and FS-induced dishabituation, and the argument that KCs drive DAN activation that causes dishabituation, though not experimentally shown, is more than reasonable. The work is significant because, as the authors take pains to point out, circuits and pathways for dishabituation have been very lightly studied, and clear identification of dopaminergic neuron subsets in dishabituation achieved by different means represents unique and interesting progress.

However, the claim that this represents a fundamental difference between homosensory and heterosensory pathways for dishabituation is overstated. The introductory section does not adequately present current broad models for habituation and dishabituation. There are many different time scales, even for Drosophila olfactory habituation. These, as well as potential underlying mechanistic differences, need to be acknowledged; any claim should be specifically qualified for the time scales being studied here. Additionally, there are several unclear, vague, and inaccurate sections and statements. A more careful, precise, and considered presentation of current views, as well as more measured claims of the impact of the findings, would substantially enhance my enthusiasm.

Reviewer #3 (Public review):

Summary:

In this manuscript, Charonitakis, Pasadaki et al. investigated the neural circuits underlying homosensory/within-modal and heterosensory/cross-modal dishabituation of the olfactory avoidance response in Drosophila. Taking advantage of the accessible and sophisticated gene expression manipulation tools in the flies, this study traced neural pathways underlying response facilitation caused by different types of sensory stimuli and revealed both distinct and convergent neural components underlying these different forms of behavioral plasticity. The study first demonstrated that olfactory habituation of the octanol avoidance response can be facilitated by either a different odor (homosensory stimulus) or a foot shock (heterosensory stimulus). Then, the flies' nervous system was manipulated with gene expression tools to identify key neural components involved in mediating the behavioral facilitation caused by different types of sensory stimuli. It was found that different sensory stimuli are input into different parts of the nervous system, and signals converge in the mushroom bodies to generate response facilitation. It was also found that these facilitatory pathways are different from the olfactory habituation pathway in the lateral horns.

Strengths:

The authors took full advantage of the advanced genetic tools in flies and performed a series of experiments to pinpoint neural components in each pathway.

Weaknesses:

The key issue is that the main concepts of this manuscript appear to be based on a misunderstanding/misinterpretation of the literature. As the authors set out to settle the debate "whether the novel dishabituating stimulus elicits sensitization of the habituated circuits, or it engages distinct neuronal routes to bypass habituation reinstating the naïve response", it seems that the authors based their investigation on the premise that "sensitization" is mediated by a facilitatory process within the S-R pathway, and "dishabituation" by a facilitatory process outside the S-R pathway. This is not the status quo in the field, particularly with the prevailing theory like the Dual-Process Theory.

The original version of Dual-Process Theory (Groves and Thompson 1970, but also see Thompson 2008, Neurobiol Learn Mem) already hypothesized that habituation happens within the specific S-R pathway, and sensitization occurs separately in an "organism-wide" state system that modulates the output of all S-R pathways. Dishabituation is recognized by the Dual-Process Theory as sensitization (organism-wide facilitation) manifested on top of existing habituation (depressed S-R pathway). This notion has been supported by a wide range of studies, including cat spinal cord reflex (e.g. Spencer et al. 1966) and work in Aplysia on heterosynaptic facilitation for both sensitization and dishabituation. Therefore, simply showing that the newly identified facilitatory pathways are outside the S-R habituation pathway is insufficient to demonstrate dishabituation.

As behavioral facilitation of a habituated response can be achieved by dishabituating (specific recovery of the S-R pathway) and/or superimposed sensitizing (organism-wide) processes, dishabituation and sensitization of this olfactory response must be first dissociated; however, the study provided no evidence for the dissociation. Without this piece of evidence, the claim of this paper that the newly identified pathways mediate dishabituation is not fully supported.

The literature review of this manuscript has some discrepancies. In the introduction, the authors wrote "initial studies in Aplysia were consistent with the "dual-process theory" (Groves and Thompson 1979), where response recovery due to dishabituation appeared to result from sensitization superimposed on habituation, thus driving reversal of the attenuated response (Carew, Castellucci et al. 1971, Hochner, Klein et al. 1986, Marcus, Nolen et al. 1988, Ghirardi, Braha et al. 1992, Cohen, Kaplan et al. 1997, Antonov, Kandel et al. 1999, Hawkins, Cohen et al. 2006)." Hochner 1986 and Marcus 1988 in fact indicated otherwise. Hochner 1986 suggests that dishabituation and sensitization involve different molecular processes, while Marcus 1988 showed that dishabituation and sensitization have different behavioral characteristics. Therefore, the authors' statement is not supported by the cited literature.

Author response:

Below, we will address point by point any and all concerns of the reviewers.

Reviewer #1:

There are no major concerns, but some material could be added for clarity and to make the work more accessible to a more general scientific audience.

We will add text for clarity and to make the work more accessible to a general audience per this comment and similar suggestions of the other reviewers.

(1.1) A figure clearly showing the habituation protocol and the use of the dishabituators would be a good addition, even if the procedure has been done before and is cited. There can always be readers who are seeing this for the first time.

We do think this is a good idea as the time scales of the experiment will be clearly marked as well and we plan to generate one in the revised manuscript.

(1.2) It would also be nice to comment on other ways dishabituation can happen (for example, when the stimulus is removed for a short time and returns) and what their time scales are.

If the stimulus is withheld, spontaneous recovery occurs, a process distinct from dishabituation and worth exploring on its own. In a previous publication (Semelidou et al. eLife 2018;7:e39569), we have shown that in this habituation paradigm with 4 min exposure either to the aversive Octanol, or the attractive Ethyl Acetate, spontaneous recovery occurs on or after 6 minutes after the habituated stimulus is withheld. This contrasts the immediate effect of the single dishabituating stimulus, delivered for a few seconds at the end of exposure to the habituator. Granted that per Thomson (Neurobiol Learn Mem. 2009), spontaneous recovery is a characteristic of habituation, we will work this point in the text.

(1.3) And more generally, the paper could perhaps improve by making a stronger case for why the results are important not just for flies but for neuroscience in general.

Thank you for the encouragement. We will try to rationally generalize our findings.

Reviewer #2:

(2.1) However, the claim that this represents a fundamental difference between homosensory and heterosensory pathways for dishabituation is overstated.

We had no intention of stating more than the fact that footshock and yeast odor dishabituators relay these stimuli to the mushroom bodies via distinct dopaminergic neurons, hence differentiating distinct dishabituating stimuli via the mechanosensory (footshock) and olfactory (yeast odor) modalities as they engage the mushroom bodies. As the reviewer suggests we will use more measured and specific language to state the above.

(2.2) The introductory section does not adequately present current broad models for habituation and dishabituation.

This was not done intentionally, but rather because we aimed at a less extended introductory section and ostensibly this resulted in brief and possibly inadequate presentation of current habituation models. We will present a much more detailed introduction and detail of habituation and dishabituation models in the revised manuscript (Also see reply to point 3.5 below).

(2.3) There are many different time scales, even for Drosophila olfactory habituation. These, as well as potential underlying mechanistic differences, need to be acknowledged; any claim should be specifically qualified for the time scales being studied here.

We understand and appreciate the point of the reviewer, as well as its significance and we will address this both in the revised text, but also by the paradigm figure we will add as stated above (point 1.1), where the time scales will be explicitly included and emphasized.

(2.4) Additionally, there are several unclear, vague, and inaccurate sections and statements. A more careful, precise, and considered presentation of current views, as well as more measured claims of the impact of the findings, would substantially enhance my enthusiasm.

We will address these concerns of course, though pointing out the specific offending parts would ascertain addressing them thoroughly. As stated above, we will incorporate current views in the introduction and when discussing our results and their impact.

Reviewer #3:

(3.1) The key issue is that the main concepts of this manuscript appear to be based on a misunderstanding/misinterpretation of the literature. As the authors set out to settle the debate "whether the novel dishabituating stimulus elicits sensitization of the habituated circuits, or it engages distinct neuronal routes to bypass habituation reinstating the naïve response", it seems that the authors based their investigation on the premise that "sensitization" is mediated by a facilitatory process within the S-R pathway, and "dishabituation" by a facilitatory process outside the S-R pathway. This is not the status quo in the field, particularly with the prevailing theory like the Dual-Process Theory.

We appreciate the reviewer’s comment and the opportunity to clarify the conceptual framework of our work. Our intention was in fact to test the Groves and Thomson hypothesis (Neurobiol Learn Mem. 2009), in our olfactory habituation system. As such, dishabituation could have been the result of a facilitatory process within the S-R pathway, or from mechanisms outside of it. Our experimental design allowed to distinguish these possibilities and our results clearly show that dishabituation involves circuitry outside the S-R pathway. We do thank the reviewer for pointing out that we have not articulated clearly this intention and we will take care to communicate this effectively in the revised manuscript.

(3.2) The original version of Dual-Process Theory (Groves and Thompson 1970, but also see Thompson 2008, Neurobiol Learn Mem) already hypothesized that habituation happens within the specific S-R pathway, and sensitization occurs separately in an "organism-wide" state system that modulates the output of all S-R pathways.

As mentioned above, we are aware of the Dual-Process hypothesis. In fact, our data demonstrate that activity outside the olfactory S-R pathway, engaging novel neuronal circuits, mediates dishabituation. Unlike habituation, these circuits mediating dishabituation include at minimum, the mushroom bodies, the dopaminergic system and the APL neurons. In our view this does not support the “organism-wide state” system, but rather particular circuits that in agreement with the Groves and Thomson hypothesis, are outside the S-R pathway and modulate its behavioral output. We will work these concepts in the discussion section of the revised manuscript.

(3.3) Dishabituation is recognized by the Dual-Process Theory as sensitization (organism-wide facilitation) manifested on top of existing habituation (depressed S-R pathway). This notion has been supported by a wide range of studies, including cat spinal cord reflex (e.g. Spencer et al. 1966) and work in Aplysia on heterosynaptic facilitation for both sensitization and dishabituation. Therefore, simply showing that the newly identified facilitatory pathways are outside the S-R habituation pathway is insufficient to demonstrate dishabituation.

We respectfully disagree with the concluding sentence here. In all of our experiments, we observe a clear recovery of olfactory avoidance after exposure to the footshock, or yeast odor dishabituators. Moreover, the dishabituators are emulated by (photo)activation of particular neuronal circuits and the recovery of olfactory avoidance is blocked when these circuits are silenced. Regardless of whether this recovery is classified as dishabituation via sensitization or another facilitatory process, the key point is that the habituated response is reliably reinstated contingent upon the dishabituating stimulus. We believe this meets the established criteria for dishabituation.

(3.4) As behavioral facilitation of a habituated response can be achieved by dishabituating (specific recovery of the S-R pathway) and/or superimposed sensitizing (organism-wide) processes, dishabituation and sensitization of this olfactory response must be first dissociated; however, the study provided no evidence for the dissociation. Without this piece of evidence, the claim of this paper that the newly identified pathways mediate dishabituation is not fully supported.

We agree with the reviewer that we have not provided specific evidence dissociating dishabituation and sensitization of the particular olfactory response beyond the evidence implicating particular circuitry in the outcome of facilitation of the olfactory response.

It should be noted that in photoactivation of the implicated circuitries in naïve flies, we do not observe enhanced octanol avoidance, suggesting that activation of these circuits alone does not induce sensitization. Moreover, our results show that neither footshock nor yeast odor drive an organism-wide sensitization, as silencing specific circuits was sufficient to block dishabituation—something that would not be expected if a global sensitization process was responsible of reinstating the olfactory response.

Nonetheless, we will also attempt to dissociate sensitization from dishabituation using mutants previously reported deficient in sensitization (Duerr and Quinn, PNAS 1982), assuming these mutants retain normal olfactory habituation. We will also try sensitization protocols in the case of within-modal dishabituation to further clarify the underlying mechanisms. In principle, this includes using diluted Octanol as the habituating stimulus and attempt dishabituation with concentrated octanol.

(3.5) The literature review of this manuscript has some discrepancies. In the introduction, the authors wrote "initial studies in Aplysia were consistent with the "dual-process theory" (Groves and Thompson 1979), where response recovery due to dishabituation appeared to result from sensitization superimposed on habituation, thus driving reversal of the attenuated response (Carew, Castellucci et al. 1971, Hochner, Klein et al. 1986, Marcus, Nolen et al. 1988, Ghirardi, Braha et al. 1992, Cohen, Kaplan et al. 1997, Antonov, Kandel et al. 1999, Hawkins, Cohen et al. 2006)." Hochner 1986 and Marcus 1988 in fact indicated otherwise. Hochner 1986 suggests that dishabituation and sensitization involve different molecular processes, while Marcus 1988 showed that dishabituation and sensitization have different behavioral characteristics. Therefore, the authors' statement is not supported by the cited literature.

We are grateful to the reviewer for pointing out these significant discrepancies, consequent of multiple rounds of edits followed by our own oversight. These important publications for this manuscript will be referenced properly in the revised version of the manuscript.

eLife Assessment

This manuscript presents a valuable computational tool for identifying 3-5 gene regulatory network topologies capable of generating oscillatory dynamics. The application of Monte Carlo Tree Search to circuit design is novel and effectively expands the scale at which non-linear behaviours can be explored in silico. The efficiency of the proposed algorithm is convincing, and the work will be of interest to the systems and synthetic biology communities. While the evolutionary implications remain unclear, the methodological contribution represents a significant advance in the field.

Joint Public Review:

This manuscript presents an algorithm for identifying network topologies that exhibit a desired qualitative behaviour, with a particular focus on oscillations. The approach is first demonstrated on 3-node networks, where results can be validated through exhaustive search, and then extended to 5-node networks, where the search space becomes intractable. Network topologies are represented as directed graphs, and their dynamical behaviour is classified using stochastic simulations based on the Gillespie algorithm. To efficiently explore the large design space, the authors employ reinforcement learning via Monte Carlo Tree Search (MCTS), framing circuit design as a sequential decision-making process.

This work meaningfully extends the range of systems that can be explored in silico to uncover non-linear dynamics and represents a valuable methodological advance for the fields of systems and synthetic biology.

Strengths

The evidence presented is strong and compelling. The authors validate their results for 3-node networks through exhaustive search, and the findings for 5-node networks are consistent with previously reported motifs, lending credibility to the approach. The use of reinforcement learning to navigate the vast space of possible topologies is both original and effective, and represents a novel contribution to the field. The algorithm demonstrates convincing efficiency, and the ability to identify robust oscillatory topologies is particularly valuable. Expanding the scale of systems that can be systematically explored in silico marks a significant advance for the study of complex gene regulatory networks.

Weaknesses

The principal weakness of the manuscript lies in the interpretation of biological robustness. The authors identify network topologies that sustain oscillatory behaviour despite perturbations to the system or parameters. However, in many cases, this persistence is due to the presence of partially redundant oscillatory motifs within the network. While this observation is interesting and of clear value for circuit design, framing it as evidence of evolutionary robustness may be misleading. The "mutant" systems frequently exhibit altered oscillatory properties, such as changes in frequency or amplitude. From a functional cellular perspective, mere oscillation is insufficient - preservation of specific oscillation characteristics is often essential. This is particularly true in systems like circadian clocks, where misalignment with environmental cycles can have deleterious effects. Robustness, from an evolutionary standpoint, should therefore be framed as the capacity to maintain the functional phenotype, not merely the qualitative behaviour.

A secondary limitation is that, despite the methodological advances, the scale of the systems explored remains modest. While moving from 3- to 5-node systems is non-trivial, five elements still represent a relatively small network. It is somewhat surprising that the algorithm does not scale further, particularly when considering the performance of MCTS in other domains - for instance, modern chess engines routinely explore far larger decision trees. A discussion on current performance bottlenecks and potential avenues for improving scalability would be valuable.

Finally, it is worth noting that the emergence of oscillations in a model often depends not only on the topology but also critically on parameter choices and the nature of the nonlinearities. The use of Hill functions and high Hill coefficients is a common strategy to induce oscillatory dynamics. Thus, the reported results should be interpreted within the context of the modelling assumptions and parameter regimes employed in the simulations.

eLife Assessment

This useful study reports findings that support the use of the Open Field Test in Drosophila as a model to study "emotion-like states", which are behavioral responses to several stressful or aversive treatments, and resilience upon their subsequent removal. Behavioral data, by employing established stress-causing treatments and genetic manipulations, are solid. While the results and conceptual framework of this work will be of interest to behaviorists regardless of animal models, the novelty of this work over previous studies could have been clearer.

Reviewer #1 (Public review):

Summary:

Animal behavior is continuously influenced by the internal state moment-by-moment, including emotion primitives, as the authors pointed out. Although emotion is a more human-related state, evolutionary conservation is undeniable, which can be inferred by the behavioral manifestation. To further elaborate on the neuronal mechanisms of emotion primitives, the simplest behavioral parameter related to emotional primitives should be well-characterized. In this study, the authors described in detail wall-following behavior (WAFO) and the total walking distance (TOWA) using flies after subjecting them to various conditions or flies being genetically manipulated according to the previous reports that could affect emotion primitives. Overall, the study is well designed and structured. In addition, the discussion on emotion primitives will be of value to the field.

Strengths:

The strength of this study is its use of a simple behavioral parameter, TOWA, and also a simple design of behavior, WAFO. The importance of the behavioral assay is reproducibility and comparability. In fact, the author demonstrated a summary of comparisons where different treatments result in scalable behavioral changes in WAFO and TOWA.

Weaknesses:

The weakness of the study is the lack of further experiments to support their assumption related to TOWA.

The authors suggested that TOWA can be interpreted as a behavioral proxy for exogenously induced arousal. However, it could be interpreted as higher activity, although the authors argued that the circadian clock increasing locomotor activity around ZT0 and ZT12 does not affect TOWA, and therefore TOWA is not related to the locomotor activity per se. As the author cited, flies lose locomotor activity in the circular arena of 6.6 cm in diameter, whereas they continuously move during a 1-h recording in the authors' arena of 1 cm in diameter.

I would agree that the arena of 1 cm in diameter, but not 6.6 cm in diameter, serves as an exogenous stimulus inducing arousal, and TOWA is manifested by arousal. However, TOWA would also be affected by other behavioral parameters, including the activity, motivation for exploration, or perception of the space. Therefore, it could be reasonable to re-examine some of the flies tested in this study in the circular arena of 6.6 cm in diameter. If arousal is biased by the components presented in Figure 6 and TOWA can assess mainly exogenously induced arousal, the treatment altering TOWA in the arena of 1 cm in diameter would not affect their behavior in the arena of 6.6 cm in diameter. My concern is that Figure 6 may demonstrate too simplistic a diagram to interpret the results. I would suggest adding the experiments using the arena of 6.6 cm diameter or softening the argument.

Reviewer #2 (Public review):

Summary:

This work seeks to establish the Open Field Test (OFT) as a paradigm to measure emotion-like states in the fruit fly Drosophila. To do this, the authors first applied various stressors and aversive stimuli to wild-type flies and tracked their locomotion. By measuring wall-following (WAFO) and total walking (TOWA), they showed that these behaviors are generally increased by stressors, but return to baseline levels after their removal. Then, they used the same approach to analyze the effects of pharmacological, genetic, and neuronal activity manipulations, showing that diazepam, serotonin, dopamine, and neuropeptide F affect locomotion in the OFT in largely expected ways that are consistent with their functions in rodents. Finally, the authors demonstrate that wild-type fly strains from the laboratory or caught in the wild differ significantly in their OFT behavior, with wild-caught flies generally behaving as if more 'stressed'. Given the numerous advantages of Drosophila, this study can form the foundation for using the OFT in conjunction with this animal model to elucidate the molecular and neuronal mechanisms that underlie emotion primitives.

Strengths:

The main strength of the paper is the rigorous use of several stressful or aversive treatments and their subsequent removal to show that WAFO is a robust proxy for stress-like emotional primitives across multiple stimuli. The pharmacological, molecular, and neuronal activity manipulations, although more limited in scope, lend further credence to the authors' central claim.

Weaknesses:

The conceptual advance of this research is unclear, as previous work (Mohammad et al., 2016, Curr Biol.) carried out similar treatments and manipulations and reached largely similar conclusions. Moreover, while WAFO is a good proxy for 'stress', I am not convinced that TOWA necessarily represents an emotional state in all cases. Indeed, as the authors themselves acknowledge, changes in total walking may be associated with other factors, such as starvation-induced hyperactivity, physical exhaustion after sleep deprivation, increased sex drive after mating, alcohol sedation, etc. Another unclear point is the interpretation of some unexpected results, such as the finding that both serotonin transporter overexpression and its knockdown give the same phenotype. Finally, there are some issues with the use of the OFT in rodent research (e.g., inconsistent effects of anxiolytic drugs; see Rosso et al., 2022, Neurosci Biobehav Rev., for a meta-analysis). These should be explained to place the Drosophila findings in their appropriate context.

Reviewer #3 (Public review):

Summary:

This is a valuable study providing solid evidence that the putative non-canonical initiation factor eIF2A has little or no role in the translation of any expressed mRNAs in cultured human (primarily HeLa) cells. Previous studies have implicated eIF2A in GTP-independent recruitment of initiator tRNA to the small (40S) ribosomal subunit, a function analogous to canonical initiation factor eIF2, and in supporting initiation on mRNAs that do not require scanning to select the AUG codon or that contain near-cognate start codons, especially upstream ORFs with non-AUG start codons, and may use the cognate elongator tRNA for initiation. Moreover, the detected functions for eIF2A were limited to, or enhanced by, stress conditions where canonical eIF2 is phosphorylated and inactivated, suggesting that eIF2A provides a back-up function for eIF2 in such stress conditions. CRISPR gene editing was used to construct two different knock-out cell lines that were compared to the parental cell line in a large battery of assays for bulk or gene-specific translation in both unstressed conditions and when cells were treated with inhibitors that induce eIF2 phosphorylation. None of these assays identified any effects of eIF2A KO on translation in unstressed or stressed cells, indicating little or no role for eIF2A as a back-up to eIF2 and in translation initiation at near-cognate start codons, in these cultured cells.

The study is very thorough and generally well executed, examining bulk translation by puromycin labeling and polysome analysis and translational efficiencies of all expressed mRNAs by ribosome profiling, with extensive utilization of reporters equipped with the 5'UTRs of many different native transcripts to follow up on the limited number of genes whose transcripts showed significant differences in translational efficiencies (TEs) in the profiling experiments. They also looked for differences in translation of uORFs in the profiling data and examined reporters of uORF-containing mRNAs known to be translationally regulated by their uORFs in response to stress, going so far as to monitor peptide production from a uORF itself. The high precision and reproducibility of the replicate measurements instil strong confidence that the myriad of negative results they obtained reflects the lack of eIF2A function in these cells rather than data that would be too noisy to detect small effects on the eIF2A mutations. They also tested and found no evidence for a recent claim that eIF2A localizes to the cytoplasm in stress and exerts a global inhibition of translation. Given the numerous papers that have been published reporting functions of eIF2A in specific and general translational control, this study is important in providing abundant, high-quality data to the contrary, at least in these cultured cells.

Strengths:

The paper employed two CRISPR knock-out cell lines and subjected them to a combination of high-quality ribosome profiling experiments, interrogating both main coding sequences and uORFs throughout the translatome, which was complemented by extensive reporter analysis, and cell imaging in cells both unstressed and subjected to conditions of eIF2 phosphorylation, all in an effort to test previous conclusions about eIF2A functioning as an alternative to eIF2.

Weaknesses:

No major issues were observed as the authors have provided additional evidence of the extent of ISR induction by tunicamycin. The discussion was also expanded to address concerns stemming from the previous version of the manuscript.

[Editors note: Reviewers and editors concluded that the authors revised the article in a satisfactory manner and no further concerns were raised]

Reviewer #2 (Public review):

Summary

Roiuk et al describe a work in which they have investigated the role of eIF2A in translation initiation in mammals without much success. Thus, the manuscript focuses on negative results. Further, the results, while original, are generally not novel, but confirmatory, since related claims have been made before independently in different systems with Haikwad et al study recently published in eLife being the most relevant.

Despite this, we find this work highly important. This is because of a massive wealth of unreliable information and speculations regarding eIF2A role in translation arising from series of artifacts that began at the moment of eIF2A discovery. This, in combination with its misfortunate naming (eIF2A is often mixed up with alpha subunit of eIF2, eIF2S1) has generated a widespread confusion among researchers who are not experts in eukaryotic translation initiation. Given this, it is not only justifiable but critical to make independent efforts to clear up this confusion and I very much appreciate the authors' efforts in this regard.

Strengths

The experimental investigation described in this manuscript is thorough, appropriate and convincing.

Weaknesses

No major weaknesses as the authors have improved their presentation.

eLife Assessment

In this valuable study, Roiuk et al combined ribosome profiling and reporter assays to provide compelling evidence that eIF2A does not have a major impact on mRNA translation in HeLa cells. These findings are consistent with several recent publications that disaffirm the previously proposed role of eIF2A in directing protein synthesis under stress. Considering that stress-dependent perturbations in translation play a major role in homeostasis and several pathological states (e.g., cancer and neurological disorders), this work should be of broad interest to researchers studying regulation of gene expression, stress-adaptation, cancer and neurobiology.

Reviewer #1 (Public review):

Summary:

Beyond what is stated in the title of this paper, not much needs to be summarized. eIF2A in HeLa cells promotes translation initiation of neither the main ORFs nor short uORFs under any of the conditions tested.

Strengths:

Very comprehensive, in fact, given the huge amount of purely negative data, an admirably comprehensive and well-executed analysis of the factor of interest.

Weaknesses:

The study is limited to the HeLa cell line, which is now addressed and clearly stated by the authors.

eLife Assessment

This important study demonstrates the significance of incorporating biological constraints in training neural networks to develop models that make accurate predictions under novel conditions. By comparing standard sigmoid recurrent neural networks (RNNs) with biologically constrained RNNs, the manuscript offers compelling evidence that biologically grounded inductive biases enhance generalization to perturbed conditions. This manuscript will appeal to a wide audience in systems and computational neuroscience.

Reviewer #1 (Public review):

I congratulate the authors on this beautiful work.

This manuscript introduces a biologically informed RNN (bioRNN) that predicts the effects of optogenetic perturbations in both synthetic and in vivo datasets. By comparing standard sigmoid RNNs (σRNNs) and bioRNNs, the authors make a compelling case that biologically grounded inductive biases improve generalization to perturbed conditions. This work is innovative, technically strong, and grounded in relevant neuroscience, particularly the pressing need for data-constrained models that generalize causally.

I have some suggestions for improvement, which I present in the order of re-reading the paper.

Major

(1) In line 76, the authors make a very powerful statement: 'σRNN simulation achieves higher similarity with unseen recorded trials before perturbation, but lower than the bioRNN on perturbed trials.' I couldn't find a figure showing this. This might be buried somewhere and, in my opinion, deserves some spotlight - maybe a figure or even inclusion in the abstract.

(2) It's mentioned in the introduction (line 84) and elsewhere (e.g., line 259) that spiking has some advantage, but I don't see any figure supporting this claim. In fact, spiking seems not to matter (Figure 2C, E). Please clarify how spiking improves performance, and if it does not, acknowledge that. Relatedly, in line 246, the authors state that 'spiking is a better metric but not significant' when discussing simulations. Either remove this statement and assume spiking is not relevant, or increase the number of simulations.

(3) The authors prefer the metric of predicting hits over MSE, especially when looking at real data (Figure 3). I would bring the supplementary results into the main figures, as both metrics are very nicely complementary. Relatedly, why not add Pearson correlation or R2, and not just focus on MSE Loss?

(4) I really like the 'forward-looking' experiment in closed loop! But I felt that the relevance of micro perturbations is very unclear in the intro and results. This could be better motivated: why should an experimentalist care about this forward-looking experiment? Why exactly do we care about micro perturbation (e.g., in contrast to non-micro perturbation)? Relatedly, I would try to explain this in the intro without resorting to technical jargon like 'gradients'.

Minor

(1) In the intro, the authors refer to 'the field' twice. Personally, I find this term odd. I would opt for something like 'in neuroscience'.

(2) Line 45: When referring to previous work using data-constrained RNN models, Valente et al. is missing (though it is well cited later when discussing regularization through low-rank constraints).

(3) Line 11: Method should be methods (missing an 's').

(4) In line 250, starting with 'So far', is a strange choice of presentation order. After interpreting the results for other biological ingredients, the authors introduce a new one. I would first introduce all ingredients and then interpret. It's telling that the authors jump back to 2B after discussing 2C.

(5) The black dots in Figure 3E are not explained, or at least I couldn't find an explanation.

Reviewer #2 (Public review):

Sourmpis et al. present a study in which the importance of including certain inductive biases in the fitting of recurrent networks is evaluated with respect to the generalization ability of the networks when exposed to untrained perturbations.

The work proceeds in three stages:<br /> (1) a simple illustration of the problem is made. Two reference (ground-truth) networks with qualitatively different connectivity, but similar observable network dynamics, are constructed, and recurrent networks with varying aspects of design similarity to the reference networks are trained to reproduce the reference dynamics. The activity of these trained networks during untrained perturbations is then compared to the activity of the perturbed reference networks. It is shown that, of the design characteristics that were varied, the enforced sign (Dale's law) and locality (spatial extent) of efference were especially important.<br /> (2) The intuition from the constructed example is then extended to networks that have been trained to reproduce certain aspects of multi-region neural activity recorded from mice during a detection task with a working-memory component. A similar pattern is demonstrated, in which enforcing the sign and locality of efference in the fitted networks has an influence on the ability of the trained networks to predict aspects of neural activity during unseen (untrained) perturbations.<br /> (3) The authors then illustrate the relationship between the gradient of the motor readout of trained networks with respect to the net inputs to the network units, and the sensitivity of the motor readout to small perturbations of the input currents to the units, which (in vivo) could be controlled optogenetically. The paper is concluded with a proposed use for trained networks, in which the models could be analyzed to determine the most sensitive directions of the network and, during online monitoring, inform a targeted optogenetic perturbation to bias behavior.

The authors do not overstate their claims, and in general, I find that I agree with their conclusions. A couple of points to be made:

(1) Some aspects of the methods are unclear. For comparisons between recurrent networks trained from randomly initialized weights, I would expect that many initializations were made for each model variant to be compared, and that the performance characteristics are constructed by aggregating over networks trained from multiple random initializations. I could not tell from the methods whether this was done or how many models were aggregated.

2) It is possible that including perturbation trials in the training sets would improve model performance across conditions, including held-out (untrained) perturbations (for instance, to units that had not been perturbed during training). It could be noted that if perturbations are available, their use may alleviate some of the design decisions that are evaluated here.