eLife Assessment

Aging reduces tissue regeneration capacity, posing challenges for an aging population. In this fundamental study, Reeves et al. show that by combining Wnt-mediated osteoprogenitor expansion (using a special bandage) with intermittent fasting, calvarial bone healing can be restored in aged animals. Intermitted fasting improves osteoprogenitor function by rescuing aging-related mitochondrial dysfunction, which can also be achieved by nicotinamide mononucleotide (NMN) supplementation or by modulating the gut microbiome. By employing rigorous histological, transcriptomic, and imaging analyses in a clinically relevant model, the authors provide compelling evidence supporting the conclusions. The therapeutic approach presented in this study shows promise for rejuvenating tissue repair, not only in bones but potentially across other tissues.

Reviewer #1 (Public review):

Aging reduces tissue regeneration capacity, posing challenges for an aging population. In this study, the authors investigate impaired bone healing in aging, focusing on calvarial bones, and introduce a two-part rejuvenation strategy. Aging depletes osteoprogenitor cells and reduces their function, which hinders bone repair. Simply increasing the number of these cells does not restore their regenerative capacity in aged mice, highlighting intrinsic cellular deficits. The authors' strategy combines Wnt-mediated osteoprogenitor expansion with intermittent fasting, which remarkably restores bone healing. Intermittent fasting enhances osteoprogenitor function by targeting NAD+ pathways and gut microbiota, addressing mitochondrial dysfunction - an essential factor in aging. This approach shows promise for rejuvenating tissue repair, not only in bones but potentially across other tissues.

This study is exciting, impressive, and novel. The data presented is robust and supports the findings well.

Reviewer #2 (Public review):

Reeves et al explore a model of bone healing in the context of aging. They show that intermittent fasting can improve bone healing, even in aged animals. Their study combines a 'bone bandage' which delivers a canonical Wnt signal with intermittent fasting and shows impacts on the CD90 progenitor cell population and the healing of a critical-sized defect in the calvarium. They also explore potential regulators of this process and identify mitochondrial dysfunction in the age-related decline of stem cells. In this context, by modulating NAD+ pathways or the gut microbiota, they can also enhance healing, hinting at an effect mediated by complex impacts on multiple pathways associated with cellular metabolism.

The study shows a remarkable finding: that age-related decreases in bone healing can be restored by intermittent fasting. There is ample evidence that intermittent fasting can delay aging, but here the authors provide evidence that in an already-aged animal, intermittent fasting can restore healing to levels seen in younger animals. This is an important finding as it may hint at the potential benefits of intermittent fasting in tissue repair.

Reviewer #3 (Public review):

Summary:

This study aims to address the significant challenge of age-related decline in bone healing by developing a dual therapeutic strategy that rejuvenates osteogenic function in aged calvarial bone tissue. Specifically, the authors investigate the efficacy of combining local Wnt3a-mediated osteoprogenitor stimulation with systemic intermittent fasting (IF) to restore bone repair capacity in aged mice. The highlights are:

(1) Novel Approach with Aged Models:<br /> This pioneering study is among the first to demonstrate the rejuvenation of osteoblasts in significantly aged animals through intermitted fasting, showcasing a new avenue for regenerative therapies.

(2) Rejuvenation Potential in Aged Tissues:<br /> The findings reveal that even aged tissues retain the capacity for rejuvenation, highlighting the potential for targeted interventions to restore youthful cellular function.

(3) Enhanced Vascular Health:<br /> The study also shows that vascular structure and function can be significantly improved in aged tissues, further supporting tissue regeneration and overall health.<br /> Through this innovative approach, the authors seek to overcome intrinsic cellular deficits and environmental changes within aged osteogenic compartments, ultimately achieving bone healing levels comparable to those seen in young mice.

Strengths:

The study is a strong example of translational research, employing robust methodologies across molecular, cellular, and tissue-level analyses. The authors leverage a clinically relevant, immunocompetent mouse model and apply advanced histological, transcriptomic, and functional assays to characterise age-related changes in bone structure and function. Major strengths include the use of single-cell RNA sequencing (scRNA-seq) to profile osteoprogenitor populations within the calvarial periosteum and suture mesenchyme, as well as quantitative assessments of mitochondrial health, vascular density, and osteogenic function. Another important point is the use of very old animals (up to 88 weeks, almost 2 years) modelling the human bone aging that usually starts >65 yo. This comprehensive approach enables the authors to identify critical age-related deficits in osteoprogenitor number, function, and microenvironment, thereby justifying the combined Wnt3a and IF intervention.

[Editors' note: The manuscript was evaluated positively by all three reviewers originally. In the revised manuscript, the authors included some new data following the reviewers' suggestions, while other comments were clarified in the response to the reviewers, and by revising the manuscript text. The new data further support the major conclusions of the paper.]

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Summary:

Aging reduces tissue regeneration capacity, posing challenges for an aging population. In this study, the authors investigate impaired bone healing in aging, focusing on calvarial bones, and introduce a two-part rejuvenation strategy. Aging depletes osteoprogenitor cells and reduces their function, which hinders bone repair. Simply increasing the number of these cells does not restore their regenerative capacity in aged mice, highlighting intrinsic cellular deficits. The authors' strategy combines Wnt-mediated osteoprogenitor expansion with intermittent fasting, which remarkably restores bone healing. Intermittent fasting enhances osteoprogenitor function by targeting NAD+ pathways and gut microbiota, addressing mitochondrial dysfunction - an essential factor in aging. This approach shows promise for rejuvenating tissue repair, not only in bones but potentially across other tissues.

Strengths:

This study is exciting, impressive, and novel. The data presented is robust and supports the findings well.

Weaknesses:

As mentioned above the data is robust and supports the findings well. I have minor comments only.

We thank the reviewer for their enthusiastic and positive assessment of our study. We appreciate the recognition of the novelty and robustness of our data and findings. We have carefully considered the reviewer's comments and have revised the manuscript accordingly. We believe these revisions further strengthen the clarity and impact of our work.

Reviewer #2 (Public review):

Summary:

Reeves et al explore a model of bone healing in the context of aging. They show that intermittent fasting can improve bone healing, even in aged animals. Their study combines a 'bone bandage' which delivers a canonical Wnt signal with intermittent fasting and shows impacts on the CD90 progenitor cell population and the healing of a critical-sized defect in the calvarium. They also explore potential regulators of this process and identify mitochondrial dysfunction in the age-related decline of stem cells. In this context, by modulating NAD+ pathways or the gut microbiota, they can also enhance healing, hinting at an effect mediated by complex impacts on multiple pathways associated with cellular metabolism.

Strengths:

The study shows a remarkable finding: that age-related decreases in bone healing can be restored by intermittent fasting. There is ample evidence that intermittent fasting can delay aging, but here the authors provide evidence that in an already-aged animal, intermittent fasting can restore healing to levels seen in younger animals. This is an important finding as it may hint at the potential benefits of intermittent fasting in tissue repair.

Weaknesses:

The authors explore potential mechanisms by which the intermittent fasting protocol might impact bone healing. However, they do not identify a magic bullet here that controls this effect. Indeed, the fact that their results with intermittent fasting can be replicated by changing the gut microbiota or modulating fundamental pathways associated with NAD, suggests that there is no single mechanism that drives this effect, but rather an overall complex impact on metabolic processes, which may be very difficult to untangle.

We thank the reviewer for their positive assessment of our study and for highlighting the significant finding that intermittent fasting can restore age-related declines in bone healing. We appreciate the observation that our results suggest a complex interplay of metabolic processes rather than a single "magic bullet" mechanism. Indeed, the ability of gut microbiota modulation or NAD+ pathway targeting to replicate intermittent fasting's benefits underscores this complexity. While we recognize the challenges of disentangling these interconnected pathways, we believe our findings offer valuable insights into the multifaceted nature of intermittent fasting's impact on aged tissue repair. We hope this study serves as a foundation for future research aimed at identifying the individual contributions of these pathways and developing targeted therapeutic strategies.

Reviewer #3 (Public review):

Summary:

This study aims to address the significant challenge of age-related decline in bone healing by developing a dual therapeutic strategy that rejuvenates osteogenic function in aged calvarial bone tissue. Specifically, the authors investigate the efficacy of combining local Wnt3a-mediated osteoprogenitor stimulation with systemic intermittent fasting (IF) to restore bone repair capacity in aged mice. The highlights are:

(1) Novel Approach with Aged Models:

This pioneering study is among the first to demonstrate the rejuvenation of osteoblasts in significantly aged animals through intermitted fasting, showcasing a new avenue for regenerative therapies.

(2) Rejuvenation Potential in Aged Tissues:

The findings reveal that even aged tissues retain the capacity for rejuvenation, highlighting the potential for targeted interventions to restore youthful cellular function.

(3) Enhanced Vascular Health:

The study also shows that vascular structure and function can be significantly improved in aged tissues, further supporting tissue regeneration and overall health.<br /> Through this innovative approach, the authors seek to overcome intrinsic cellular deficits and environmental changes within aged osteogenic compartments, ultimately achieving bone healing levels comparable to those seen in young mice.

Strengths:

The study is a strong example of translational research, employing robust methodologies across molecular, cellular, and tissue-level analyses. The authors leverage a clinically relevant, immunocompetent mouse model and apply advanced histological, transcriptomic, and functional assays to characterise age-related changes in bone structure and function. Major strengths include the use of single-cell RNA sequencing (scRNA-seq) to profile osteoprogenitor populations within the calvarial periosteum and suture mesenchyme, as well as quantitative assessments of mitochondrial health, vascular density, and osteogenic function. Another important point is the use of very old animals (up to 88 weeks, almost 2 years) modelling the human bone aging that usually starts >65 yo. This comprehensive approach enables the authors to identify critical age-related deficits in osteoprogenitor number, function, and microenvironment, thereby justifying the combined Wnt3a and IF intervention.

Weaknesses:

One limitation is the use of female subjects only and the limited exploration of immune cell involvement in bone healing. Given the known role of the immune system in tissue repair, future studies including a deeper examination of immune cell dynamics within aged osteogenic compartments could provide further insights into the mechanisms of action of IF.

We thank the reviewer for their thorough summary and positive assessment of our study, particularly highlighting its translational nature, the robust methodologies employed, and the relevance of our aged animal model. We appreciate the insightful suggestion to include male subjects and to explore immune cell dynamics in future investigations.

We acknowledge the limitation of using only female mice in the current study and agree that future studies incorporating both sexes and investigating immune cell contributions within aged osteogenic compartments would offer valuable insights into the mechanisms underlying intermittent fasting and its impact on bone healing.

Our focus on female mice was informed by their distinct characteristics, including delayed healing and higher fracture risk (PMID: 37508423, PMID: 34434120). Importantly, female mice present a more challenging case for bone repair, making them a stringent test for evaluating the effectiveness of our rejuvenation approaches. Moreover, our research protocol, approved under animal license, adhered to ethical principles and the 3Rs, allowing us to reduce the number of animals required by focusing on a single sex.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

(1) The authors should provide a justification for the use of female mice in this study. Additionally, the section on animal methods should be expanded to align with ARRIVE guidelines.

We thank the reviewer for their valuable feedback. In response to the comment regarding the use of female mice, we have included a justification in the updated manuscript. As noted, female mice were selected for this study due to their distinct characteristics, such as delayed healing and higher fracture risk (PMID: 37508423, PMID: 34434120), which provide a more challenging model for evaluating bone repair strategies. We believe this made our study a stringent test of the efficacy of the rejuvenation approaches being investigated.

Additionally, we have revised the animal methods section to ensure it aligns with the ARRIVE guidelines.

(2) Intermittent fasting can influence circadian rhythms in various ways. In the RNA-seq data, do the authors observe any changes related to circadian rhythm pathways?

The reviewer raises an important point regarding the influence of intermittent fasting (IF) on circadian rhythms. Our RNA-seq data revealed significant alterations in circadian rhythm pathways, particularly within the aged periosteal CD90+ cell population during IF. Specifically, the PAR bZip family transcription factors Dbp, Hlf, and Tef (q < 0.05) were significantly upregulated, consistent with their established roles as circadian rhythm regulators (PMID: 16814730, PMID: 31428688).

In suture CD90+ cells from the Aged + IF group, Dbp expression was significantly elevated compared to the Aged AL control group. Moreover, several other circadian-controlled genes, including Sirt1, Kat2b, Csnk1e, Ezh2, Fbxw11, and Ucp2 (p < 0.05), were also upregulated (Fig. 4b), suggesting enrichment of Clock/Per2/Arntl transcriptional targets, essential components of the circadian clock.

The observed upregulation of circadian rhythm effectors like Dbp, Hlf, and Tef further suggests a potential role for circadian transcription in CD90+ cell rejuvenation and bone repair in aged mice. While previous studies have primarily focused on the role of circadian rhythms in osteoblasts in vitro (PMID: 34579752, PMID: 30290183), our findings provide compelling evidence for their involvement in bone regeneration in vivo, providing compelling evidence for future investigation into this mechanism.

Chip-SEQ studies have shown D-box sites near promoters in Wnt/β-catenin components (e.g. Lrp6, Lrp5, Wnt8a, Fzd4) in pro-osteogenic transcription factor Zbtb16 (and see Fig 5), and in 11 of the 44 mouse collagen genes (PMID: 31428688). These components are known to regulate osteogenesis, and their proximity to circadian-controlled transcription factors suggests a possible overlap between circadian regulation and Wnt signaling in promoting bone repair.  Additionally, circadian rhythmicity, stem cell function, and Wnt signaling are interlinked (PMID: 29277155, PMID: 25414671). Food intake is a powerful regulator of the circadian rhythm in several organs (PMID: 11114885, PMID: 32363197), but little is known about the diet-circadian interaction in bone repair. The possibility that circadian transcription can be harnessed to target Aged stem cell function towards bone repair is a promising prospect.

We have incorporated this information in Figure 2 - figure supplement 3G-H, the results section as well as in the discussion.

Reviewer #2 (Recommendations for the authors):

(1) The authors refer to 'altered cellular mechanobiology', 'age-related changes in mechanobiology', etc. Here, they are using this terminology to refer to changes in F-actin intensity and nuclear shape. While I agree that these measures are indicators of a cellular response to mechanical cues, calling this 'changes in mechanobiology' doesn't sound quite correct to me. 'Mechanobiology' to me, is a field of study. Perhaps the authors should consider changing their terminology.

We appreciate the reviewer’s insightful comment on the terminology used in our manuscript. We agree that the term "mechanobiology" is a broad field of study and using it in the context of changes in F-actin intensity and nuclear shape may be misleading. We have revised the text to better reflect the specific cellular responses to mechanical cues, such as changes in the cytoskeleton and nuclear morphology, rather than referring to them as "altered mechanobiology." The updated terminology more accurately conveys the observed cellular alterations in response to mechanical forces. We have made these adjustments throughout the manuscript for clarity and precision.

(2) Three of the measures the authors use to highlight age-related changes (and rejuvenation) in their animal model are F-actin intensity, nuclear shape, and vascularisation. However, they never really explain what they believe these readouts mean practically/functionally. Indeed, it makes sense that less vascularisation would be associated with an aged phenotype and preclude healing, but this is only mentioned somewhat cursorily in the discussion. While vascularisation is discussed in the context of aging in the discussion, it is not discussed in the context of healing (which would seem relevant in the context of vascularisation being used as a readout in the healing models in response to Akk and IF treatment). Similarly, the changes in F-actin intensity and nuclear shape might suggest changes in the stiffness of the periosteum (as mentioned in the discussion), which could indeed be an indicator of an aged phenotype; however, their role in healing (in response to Akk and IF) are not clearly articulated.

We appreciate the reviewer’s insightful comments and have made revisions to clarify the implications of age-related changes in vascularization, F-actin intensity, and nuclear shape, as well as the functional significance of these observations in the context of healing and rejuvenation.

Vascularization:

Vascularization and modulation of blood flow are critical for calvarial bone repair, as supported by multiple studies (e.g., PMID: 38032405, PMID: 21156316, PMID: 25640220). Early in the calvarial repair process, blood vessels grow independently of osteoprogenitor cells, establishing a supportive environment that promotes osteoprogenitor migration and subsequent ossification (PMID: 38834586). Furthermore, angiogenic vessels from the periosteum at defect edges contribute to creating a specialized microenvironment essential for bone healing (PMID: 38834586, PMID: 38032405). Compromised vascularization significantly impairs the healing of critical-sized calvarial defects (PMID: 29702250).

Our data reveal a decline in periosteal vascularization with age, potentially compromising this microenvironment and impairing repair in aged animals. Importantly, our findings indicate that intermittent fasting (IF) reverses this phenotype by restoring periosteal vascularization. This rejuvenation of the vascular microenvironment aligns with improved bone repair outcomes in aged mice subjected to IF. We have revised the manuscript to emphasize the importance of vascularization in healing and to highlight the role of IF in restoring this critical aspect of the bone healing microenvironment.

F-actin intensity and nuclear shape:

Age-related changes in F-actin intensity and nuclear shape are associated with increased tissue stiffness, a hallmark of aging. Tissue stiffness has been shown to impair progenitor cell function and hinder repair in various systems, including neuroprogenitors (PMID: 31413369). Softening the extra cellular matrix in aged tissues has been demonstrated to partially restore progenitor function and improve repair outcomes, as seen in the case of neuroprogenitors (PMID: 31413369). In our study, IF reversed age-associated changes in F-actin expression and nuclear shape, restoring these parameters to a phenotype resembling that of younger animals. This suggests that IF mitigates the mechanical changes associated with aging, reducing tissue stiffness and rejuvenating the periosteum to facilitate improved bone healing, similar to the outcomes observed in younger models.

Following the reviewer’s advice, we have revised the text to clearly articulate the correlations and interpretations of our data regarding tissue mechanics and bone repair. Thank you for highlighting these critical aspects.

(3) In relation to my point 2) on nuclear shape, there are reports that aging is linked to changes in Lamin B1. Have the authors considered this? It might provide a clearer link between their data and the tissue-level phenotypes they observe.

Thank you for your comment regarding the potential link between aging and changes in Lamin B1. Following your suggestion, we performed Lamin B1 immunostaining on samples from Young, Adult, Aged, and Aged + IF groups. However, no significant differences in Lamin B1 levels were observed across these groups. These findings indicate that changes in Lamin B1 in osteoprogenitors are not apparent during aging, suggesting that Lamin B1 alterations in the context of aging may be tissue- and cell-type-specific.

The new data was added in Figure 1 - figure supplement 2i-j.

(4) In the data associated with Figure 2, the authors find that in the aged mice, MMP9 expression is increased, but MMP2 expression is decreased. They associate the decrease in MMP2 expression with decreased migration, but the canonical function of MMP9 should be similar to that of MMP2. Are there tissue-specific differences in the activity of MMP2/9 that could account for this?

Thank you for the thoughtful comment. While both MMP-2 and MMP-9 are involved in ECM remodeling and share some overlapping canonical functions, their roles are context-dependent and exhibit tissue-specific differences that could explain the observed changes in aged mice. MMP-2 has been shown to play a critical role in maintaining the structural and functional integrity of flat bones, such as those in the craniofacial skeleton, by supporting bone remodeling (PMID: 17400654, PMID: 17440987, PMID: 16959767). The decreased expression of MMP-2 in aged mice may impair these local processes, leading to reduced migratory capacity of osteoprogenitors and contributing to aging-related changes in flat bone structure and function.

In contrast, MMP-9 is more prominently involved in long bone remodeling, particularly at the growth plate where it regulates hypertrophic chondrocyte turnover, vascularization, and ossification during endochondral bone formation (PMID: 21611966, PMID: 9590175, PMID: 23782745, PMID: 16169742 ). Additionally, MMP-2 and MMP-9 differ in their regulation of specific ECM substrates and their interactions with bone-resident cells, which may further drive divergent outcomes in distinct bone types. For example, MMP-9’s role in osteoclastogenesis and its regulation of ECM proteins like type I collagen could be more critical in long bones, while MMP-2’s involvement in fine-tuning ECM microarchitecture may hold greater importance in flat bones.

The increased expression of MMP-9 in aged calvarial osteoprogenitors may reflect a compensatory mechanism in response to the reduced MMP-2 activity, possibly in response to increased ECM turnover demands. Further studies examining the precise molecular pathways driving these changes in osteoprogenitors will help clarify the underlying mechanisms and their contributions to age-related alterations in flat bone structure and function.

(5) In lines 391-2, the authors conclude that the data from Figure 4 shows that "during IF, CD90 cells, despite being aged, are more capable of ECM modulation and migration". The authors certainly present evidence that this is true, but the RNAseq showed that the enriched GO terms were predominantly associated with immune responses ('response to cytokine') and the proliferation phenotype seems very strong. Therefore, I would suggest that this overarching statement regarding the findings be less focussed on this one aspect of the finding, which doesn't look to be the dominant phenotype of the cellular response. And indeed, the authors move on from here to explore a mechanism associated with metabolism, not specifically with ECM remodelling.

We greatly appreciate the reviewer insight regarding the interpretation of our findings, particularly the conclusion drawn from Figure 4.

In response, we have revised the conclusion to more accurately reflect these findings.

The revised text in the conclusion now reads: " Together, these findings suggest that IF rejuvenates aged CD90+ cells, in part, by enhancing proliferation, immune response, ECM remodeling, Wnt/β-catenin pathway, and metabolism, including increased ATP levels and decreased AMPK levels.”

We hope that this adjustment better aligns with your suggestion and provides a more accurate summary of the key findings.

(6) Fasting blood glucose levels are often cited as an indicator of metabolic health. Did the authors look at this in their animals who underwent the IF protocol? Could this have had an impact on the healing response?

We thank the reviewer for this insightful comment. Throughout our study, we have withdrawn blood from the animals for various analyses that were not included in this manuscript in order to maintain focus on the osteoprogenitors.

Our analysis included the assessment of the metabolic health of the animals using fasting blood glucose levels and the area under the curve (AUC) of the intraperitoneal glucose tolerance test (IPGTT).

Fasting blood glucose levels reflect the animals' ability to maintain stable glucose levels after fasting, while the AUC from the IPGTT measures how efficiently glucose is cleared from the bloodstream following a glucose challenge. Typically, lower fasting blood glucose levels and reduced AUC indicate improved insulin sensitivity, better glucose metabolism, and enhanced metabolic control (PMID: 18812462, PMID: 19638507).

Our findings show that intermittent fasting (IF) significantly reduced both the fasting blood glucose levels and the AUC in the IPGTT. This indicates that IF enhances metabolic flexibility, likely through improved insulin sensitivity and better glucose homeostasis. By lowering fasting blood glucose, IF reduces the reliance on excessive gluconeogenesis during fasting, while a reduced AUC indicates more efficient postprandial glucose clearance, consistent with enhanced insulin action and reduced fluctuations in blood glucose levels. The new data has been incorporated in Figure 3 - figure supplement 1d-g.

Methods:

“Blood glucose level measurement

Fasting blood glucose levels were measured (Accu-Check tests strips) from 6h fasting mice by blood sampling the tail vein. For intraperitoneal glucose tolerance test (IPGTT), glucose was injected intraperitoneally (2 g/kg), and the blood glucose levels were measured after 15, 30, 60 and 120 minutes.”

Improved metabolic health through lower fasting glucose and reduced AUC can have profound implications for tissue repair (PMID: 32809434). Stable glucose levels ensure a consistent energy supply for key cellular processes, such as cell proliferation, migration, and differentiation, which are essential for regeneration. Enhanced insulin sensitivity supports nutrient delivery to cells and reduces inflammation, creating an environment conducive to tissue healing. Additionally, intermittent fasting's ability to optimize glucose metabolism and regulate insulin secretion may enhance the function of stem and progenitor cells, further improving the tissue repair process (PMID: 28843700). Together, these findings suggest a mechanistic link between improved metabolic health and the enhanced healing observed in animals subjected to intermittent fasting.

(7) In Supplementary Figure 10, the authors look at bone remodelling by assessing TRAP staining, as an indicator of osteoclast activity. I'm not sure if these data add all that much to the study. The authors have looked at bone formation at a tissue level using microCT. Here, they look at bone resorption at a cellular level with the TRAP assay. Overall, this probably suggests more bone remodelling, but the TRAP assay on its own at the cellular level could also be interpreted as an osteoporosis-like phenotype. This is clearly not the case because the authors show robust bone healing by microCT. In short, as an isolated measure of osteoclast activity at the cellular level without cellular-level assays of osteoblast activity, the interpretation of these data is not that clear. The microCT speaks far more of the phenotype and is, in my opinion, sufficient to make this point.

We thank the reviewer for their comments regarding the interpretation of the TRAP staining data and its context within the study. We appreciate the concern that, without direct assays of osteoblast activity, the TRAP assay could lead to ambiguity.

We have shown that intermittent fasting significantly increases the number and function of osteoprogenitor cells, the precursors to osteoblasts. While we acknowledge that these data do not directly measure osteoblast numbers or activity, they strongly suggest an increased capacity for osteoblast differentiation and bone formation. This aligns with the microCT findings of robust bone structure and healing.

After careful consideration and given that the microCT and histology findings  already provide robust and comprehensive evidence for bone structure and healing, we have decided to remove the TRAP staining data from the manuscript. We believe this change simplifies the manuscript and strengthens its focus on the most impactful data.

(8) In the discussion, the authors make a number of links between aging and IF. However, one of the exciting conclusions of this manuscript is that IF aids in healing in aged animals. In this context, IF has not impacted the aging process itself because the animals have not experienced an IF protocol across their lifespan, but rather only after injury. In this context, perhaps the authors should also be focussing their discussion on evidence of the short-term response to IF rather than its effects on aging, which are longer-term.

We appreciate the reviewer's comment and agree that emphasizing the short-term effects of intermittent fasting is crucial. Our study is the first to examine this protocol in Aged animals.

To address this, we have revised the discussion and highlighted how short-term IF enhances metabolic health, promotes osteoprogenitor functionality, and supports bone remodeling, as observed in our study.

Reviewer #3 (Recommendations for the authors):

(1) The authors should clarify details on intermittent fasting protocols, especially regarding caloric intake differences between fasting and non-fasting days, to aid reproducibility.

We appreciate the reviewer's suggestions and have incorporated them by clarifying the relevant details. The new data are presented in Figure 3 - figure supplement 1a-c.

Methods:

“Caloric intake calculation

To assess the caloric intake of mice, the food was weighted when made available to the mice (Win), and when removed (Wout). The daily consumed food was calculated based on the weight difference (Win - Wout), then converted to kcal (1 g = 3.02 kcal, LabDiet, 5053), and expressed as kcal/mouse/day for each cage (n cage ³ 3 with 1 to 5 mice/cage).”

(2) Did the authors evaluate the effect of their intermittent fasting protocol on fasting blood glucose levels?

Following the reviewer comment we included two measurements: 1) Fasting blood glucose, which reflects the ability to maintain glucose homeostasis during fasting, and 2) fasting blood glucose levels and the area under the curve (AUC) of the intraperitoneal glucose tolerance test (IPGTT), which measures glucose clearance efficiency after a glucose challenge. Lower values for both typically indicate improved insulin sensitivity, glucose metabolism, and metabolic control.

Our findings demonstrate that intermittent fasting significantly reduced both fasting blood glucose and IPGTT AUC, suggesting enhanced metabolic flexibility, likely through improved insulin sensitivity and glucose homeostasis. Lower fasting blood glucose with IF indicates reduced reliance on gluconeogenesis during fasting, while a reduced AUC suggests more efficient postprandial glucose clearance, consistent with enhanced insulin action and reduced blood glucose fluctuations. This new data is included in Figure 3 - figure supplement 1.

Generally, the improved metabolic environment supports tissue repair by ensuring adequate energy for cell proliferation and migration, reducing inflammation, and promoting the function of stem cells involved in tissue regeneration. Thus, this outcome of intermittent fasting may create a more favorable environment for tissue repair, potentially accelerating the healing of damaged tissues and improving overall regenerative capacity.

(3) In Figure 1E-F, the nuclei have an interesting shape and the authors quantified F-actin. Given the role of lamin B in nuclear integrity, an analysis of lamin B expression and its structural integrity in aged osteoprogenitors could provide valuable insights into cellular aging mechanisms and their potential reversal with intermittent fasting.

In response to the reviewer's comment, we performed Lamin B1 immunostaining on samples from Young, Adult, Aged, and Aged + IF groups. We observed no significant differences in Lamin B1 levels across these groups. This suggests that age-related changes in Lamin B1 are not evident in osteoprogenitors and may be tissue- or cell-type specific. The new data was added in Figure 1 - figure supplement 2i-j.

(4) The authors should explain, in the main text or the methods section, why are they only using females in this study.

We appreciate the reviewer's comment regarding the use of female mice. Female mice were chosen for this study due to their delayed healing and higher fracture risk (PMID: 37508423, PMID: 34434120), presenting a more challenging model for evaluating bone repair strategies and providing a stringent test of our rejuvenation approaches. This justification has been added to the revised manuscript. The animal methods section has also been updated to comply with ARRIVE guidelines.

(5) This story stands alone and has an incredible amount of data. However, for a follow-up study, I would like to suggest consideration of including a broader analysis of immune cell involvement within the osteogenic compartments to strengthen the mechanistic understanding of IF's impact.

We thank the reviewer for this insightful suggestion. We agree that investigating the role of immune cells within the osteogenic compartments could provide valuable mechanistic insights into how intermittent fasting influences tissue regeneration. Immune cells are key mediators of inflammation and repair, and their interactions with osteoprogenitors and other cells in the bone healing environment likely contribute to IF's effects.

While our study focuses on IF's impact on osteoprogenitor function and tissue repair, we acknowledge the importance of future research exploring immune cell involvement. Techniques like single-cell RNA sequencing or flow cytometry could characterize immune cell populations and their functional states within osteogenic niches, allowing for a deeper understanding of immune-skeletal interactions during IF-mediated bone healing. We appreciate the reviewer highlighting this promising avenue for future research.

Minor corrections to the text and figures:

(1) References formatting should be revised (eg. line 41).

The reference formatting was corrected.

(2) Line 144 - what do the authors mean by p2 in the references?

Thank you for your comment, we corrected the error and removed p2 from the reference.

Author response:

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

Reviewer #1 (Public review): 

Summary: 

Nitric oxide (NO) has been implicated as a neuromodulator in the retina. Specific types of amacrine cells (ACs) produce and release NO in a light-dependent manner. NO diffuses freely through the retina and can modulate intracellular levels of cGMP, or directly modify and modulate proteins via S-nitrosylation, leading to changes in gap-junction coupling, synaptic gain, and adaptation. Although these system-wide effects have been documented, it is not well understood how the physiological function of specific neuronal types is affected by NO. This study aims to address this gap in our knowledge. 

There are two major findings. 1) About a third of the retinal ganglion cells display cell-type specific adaptation to prolonged stimulus protocols. 2) Application of NO specifically affected Off-suppressed ganglion cells designated as G32 cells. The G32 cluster likely contains 3 ganglion cell types that are differentially affected. 

This is the first comprehensive analysis of the functional effects of NO on ganglion cells in the retina. The cell-type specificity of the effects is surprising and provides the field with valuable new information. 

Strengths: 

NO was expected to produce small effects, and considerable effort was expended in validating the system to ensure that changes in the state of the preparation would not confound any effects of NO. The authors used a sequential stimulus protocol to control for changes in the sensitivity of the retina during the extended recording periods. The approach potentially increases the sensitivity of the measurements and allows more subtle effects to be observed. 

Neural activity was measured by Ca-imaging. Responsive ganglion cells were grouped into 32 types using a clustering analysis. Initial control experiments demonstrated that the celltypes revealed by the analysis largely recapitulate those from their earlier landmark study using a similar approach. 

Application of NO to the retina modulated responses of a single cluster of cells, labeled G32, while having little effect on the remaining 31 clusters. In separate experiments, ganglion cell spiking activity was recorded on a multi-electrode array (MEA). Together the Ca-imaging and MEA recordings provide complementary approaches and demonstrate that NO modulates the temporal but not spatial properties of affected cell-types.

Weaknesses: 

The concentration of NO used in these experiments was ~0.25µM, which is 5- to 10-fold lower than the endogenous concentration previously measured in rodent retina. It is perhaps surprising that this relatively low NO concentration produced significant effects. However, the endogenous measurements were done in an eye-cup preparation, while the current experiments were performed in a bare (no choroid) preparation. Perhaps the resting NO level is lower in this preparation. It is also possible that the low concentration of NO promoted more selective effects.

Reviewer #2 (Public review): 

Neuromodulators are important for circuit function, but their roles in the retinal circuitry are poorly understood. This study by Gonschorek and colleagues aims to determine the modulatory effect of nitric oxide on the response properties of retinal ganglion cells. The authors used two photon calcium imaging and multi-electrode arrays to classify and compare cell responses before and after applying a NO donor DETA-NO. The authors found that DETA-NO selectively increases activity in a subset of contrast-suppressed RGC types. In addition, the authors found cell-type specific changes in light response in the absence of pharmacological manipulation in their calcium imaging paradigm. This study focuses on an important question and the results are interesting. The limitations of the method and data interpretation are adequately discussed in the revised manuscript. 

The authors have addressed my previous comments, included additional discussions on the limitations of the method, and provided a more careful interpretation of their data. 

Recommendations for the authors: 

Please correct the citation that reviewer #1 mentioned. In addition, a little more discussion of the NO concentration issue would be helpful. The low NO concentration is not a weakness in the data; it simply raises questions regarding the interpretation.

Thank you for these recommendations.

Regarding the citation error, we are not sure if Reviewer #1 refers to a citation   formatting error or incorrect placement. In any case, we modified the text: We  specified the extracted information regarding the NO concentrations and put the  applied concentration into that context (Lines 621-635). In addition, we made clear  that the citation of Guthrie (2014) refers to the dissertation, which can be easily  retrieved via Google Scholar. We also cited the mentioned ARVO abstract by   Guthrie and Mieler (2014). 

We hope that these modifications solve the above-mentioned issues. 

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

Reviewer #1 (Public Review):  

Summary: 

Nitric oxide (NO) has been implicated as a neuromodulator in the retina. Specific types of amacrine cells (ACs) produce and release NO in a light-dependent manner. NO diffuses freely through the retina and can modulate intracellular levels of cGMP, or directly modify and modulate proteins via S-nitrosylation, leading to changes in gap-junction coupling, synaptic gain, and adaptation. Although these system-wide effects have been documented, it is not well understood how the physiological function of specific neuronal types is affected by NO. This study aims to address this gap in our knowledge. 

Strengths: 

NO was expected to produce small effects, and considerable effort was expended in validating the system to ensure that any effects of NO would not be confounded by changes in the state of the preparation. The authors used a paired stimulus protocol to control for changes in the sensitivity of the retina during the extended recording periods. The approach potentially increases the sensitivity of the measurements and allows more subtle effects to be observed. 

Neural activity was initially measured by Ca-imaging. Responsive ganglion cells were grouped into 32 types using a clustering analysis. Initial control experiments demonstrated that the cell-types revealed here largely recapitulate those from their earlier landmark study using the same approach (Fig. 2). 

Application of NO to the retina strongly modulated responses of a single cluster of cells, labeled G32, while having little effect on the remaining 31 clusters. This result is evident in Fig. 3e. 

Separate experiments measured ganglion cell spiking activity on a multi-electrode array (MEA). Clustering analysis of the peri-stimulus spike-time histograms (PSTHs) obtained from the MEA data also revealed 32 clusters. The PSTHs for each cluster were aligned to the Ca-imaging data using a convolution approach. The higher temporal resolution of the MEA recordings indicated that NO increased the speed of sub-cluster 2 responses but had no effect on receptive field size. The physiological significance of the small change in kinetics remains unclear. 

We thank the reviewer for their detailed and constructive comments.

Weaknesses: 

The G32 cluster was further divided into three sub-types using Bayesian Information Criterion (BIC) based on the temporal properties of the Ca-responses. This sub-clustering result seems questionable due to the small difference in the BIC parameter between 2 and 3 clusters. Three sub-clusters of the G32 cluster were also revealed for the PSTH data, however, the BIC analysis was not applied to further validate this result. 

(1.1) We agree with the reviewer that this is an important point to be clarified. To this end, we repeated the analysis with n=2 clusters (see Author response image 1 below). In brief, we found that the overall interpretation did not change: Both clusters in the Ctrl1-dataset showed barely any type-specific adaptational effects, whereas under NO application, temporal contrast responses decreased (see Author response image 1 below). If requested, we would be happy to add this image to the supplementary material. 

Author response image 1.

In an additional analysis, we evaluated if n=2 or n=3 was the “better” choice for the number of clusters. In the new Supplementary Fig. S4, we compared the clusters with n=2 (top) and n=3 (bottom). For n=2, the two clusters are relatively strongly correlated for both visual stimuli, whereas for n=3, the clusters become more distinct, especially with respect to differences in the correlations for the two stimuli (Fig. S4b). For n=2, the low intra-cluster correlation (ICC) strongly suggests that cluster 2 contains multiple response types (ICC(C2) = 0.5 ± 0.48, mean ± s.d.; Fig. S4c). For n=3, the mean ICC values are high for all three clusters (ICC(C1) = 0.81 ± 0.16; ICC(C2) = 0.86 ± 0.07; ICC(C3) = 0.83 ± 0.1; mean ± s.d.). Together, this suggests that n=3 clusters captures the response diversity in G32 better than n=2 clusters. 

Finally, we performed a BIC analysis for the MEA dataset and found the optimal number of clusters to be also n=3 (see new Suppl. Fig. S5).

The alignment of sub-clusters 1, 2, and 3 identified in the Ca-imaging and the MEA recordings seemed questionable, because the temporal properties of clusters did not align well, nor did the effects of NO. 

(1.2) To address this important point, we analyzed the correlations between the control responses of the three clusters from the Ca²⁺-dataset with the ones from the MEA-dataset (see new Suppl. Fig. S7). To avoid confusion, we named the clusters in the MEA-dataset i,ii,iii (see Fig. 8). We found two of the three clusters to be highly correlated (Ca²⁺ clusters 2,3 and MEA clusters iii, ii), whereas one cluster was much less so (cluster 1 vs. cluster i), likely due to differences in response kinetics. In clusters i and ii NO application led to a release of suppression for temporal contrasts – similar to what we observed in the Ca²⁺ data (see also our new analysis of the MEA data in Suppl. Fig. S6, as discussed further below).

We agree that the cell types underlying the Ca²⁺ and MEA G32 clusters may not be the same – aligning functional types between those two methods is challenging due to several factors, mainly because while Ca²⁺ is a proxy for spiking activity, other Ca²⁺ sources as well as sub-threshold membrane potential changes affect the intracellular Ca²⁺, potentially in a cell type-specific way. We explain this now better in the text.

In any case, our main point was not to unambiguously align the cell types but to show that in both datasets, we find three subclusters of G₃₂, which are affected by NO in a differential manner, particularly their suppression to temporal contrasts.

The title of the paper indicates that nitric oxide modulates contrast suppression in a subset of mouse retinal ganglion cells, however, this result appears to be inferred from previous results showing that G32 is identified as a "suppressed-by-contrast" cell. The present study does not explicitly evaluate the amount of contrast-suppression in G32 cells. 

(1.3) The reviewer is correct in that we did not quantify contrast-suppression in G₃₂ in detail but focused on the responses to temporal contrast (chirp and moving bar) and its modulation by NO (Fig. 5). In this context, please note that G₃₂’s responses to the moving bar stimulus suggests that the cells are also suppressed by spatial contrast (i.e., an edge appearing in their RF). The functional RGC type G₃₂ (“Off suppressed 2”) was defined in an earlier study (Baden et al. 2016); it was assigned to the “Suppressed-by-Contrast” (SbC) category mainly because temporal contrast suppresses its responses. Already then, coverage analysis indicated that G₃₂ may indeed contain several RGC types – in line with our clustering analysis. It is still unclear if G₃₂ contains one (or more) of the SbC cells described by Jacoby & Schwartz (2018); in their recent study, Wienbar and Schwarz (2022) introduced the novel bursty-SbC RGC, which Goetz et al. (2022) speculated to potentially align with G₃₂.<br /> We now discuss the relationship between G₃₂ and the SbC RGCs defined in other studies in the revised manuscript.

In its current form, the work is likely to have limited impact, since the morphological and functional properties of the affected sub-cluster remain unknown. The finding that there can be cell-specific adaptation effects during experiments on in vitro retina is important new information for the field.

(1.4) Again, we thank the reviewer for the detailed and helpful feedback. We hope that the reviewer finds our revised manuscript improved.

Reviewer #1 (Recommendations For The Authors):  

Most of the calcium activity traces (dF/F) throughout the paper have neither vertical nor horizontal calibration bars. Presumably, most values are positive, but this is unclear as a zero level is not indicated anywhere. Without knowing where zero dF/F is, it is not possible to determine whether the NO increased the Ca-signal or blocked a decrease in the Ca-signal. 

Both ∆F/F and z-scoring, as we used here, are ways to normalize Ca²⁺ traces. We decided against using ∆F/F₀ because this typically assumes that F represents the cell’s Ca²⁺ resting level (F₀; without activity). However, in our measurements, the “resting” Ca²⁺ levels (i.e. before presenting a stimulus) may indeed reflect no spiking activity (e.g., in an ON RGC) but may also reflect baseline spiking activity (e.g., in an G₃₂, which has a baseline firing rate of ~10 Hz; see Fig. S6). Hence, we used z-scoring, which carries no assumption of resting Ca²⁺ level equal to no activity. In practice, we normalized all traces to the Ca²⁺ level prior to the light stimulus and defined this as zero (as described in the Methods).

We considered the reviewer’s suggestion of adding zero lines to every trace but felt that this would hamper the overall readability of the figures.

Regarding calibration bars: We made sure that horizontal bars (indicating time) are present in all figures. We decided to leave out vertical bars in Ca²⁺ responses, because as explained above, the traces are normalized (and unit-free), and within a figure all traces are scaled the same.

Points of clarification for the Methods: 

(1) The stimulus field was 800 x 600 µm. Presumably, both scan fields were contained within this region when scanning either Field 1 or Field 2 so that the adaptation level of the preparation at both locations was maintained? 

Yes, the stimulation field is always kept centered on the respective recording (scan) field and the adaptation level for each recording field was maintained.

(2) There appeared to be an indeterminate amount of time between the initial 10-minute adaptation period and Ctrl1, whereas there were no such gaps between subsequent scans. Is this likely to produce differences in adaptation state and thus represent a systematic error? 

At this time point, recording (scan) fields were selected to make sure that the cells in the field were uniformly labelled with the Ca²⁺ indicator and responsive to light stimuli. This typically happened already at the end of the light adaptation phase and/or right after. When selecting the fields, light stimuli were presented (to test responsiveness) and thereby the adaptation level was maintained independent of the duration of this procedure, minimizing systematic errors.

(3) Was the dense white noise stimulus applied during the wash-in period to maintain the adaptation state of the preparation prior to the subsequent scan? 

The dense noise was not applied throughout the wash-in period but at least 5-10min before the field was recorded with a drug (e.g., NO). 

Fig. 1d illustrates very nicely how the stimuli align with the responses. It would have been helpful to have this format continue throughout the paper but unfortunately, the vertical lines are dropped in Fig. 2a and then the stimulus waveform is omitted in Fig. 2e onwards. 

Thanks, good idea. We added the vertical lines and the stimulus waveform to the figures where they were missing to improve the readability. 

What was the rationale for selecting the concentration of the NO donor used? Is it likely to mimic natural levels? 

A DETA/NO concentration of 100 µM is commonly used in studies investigating NOinduced effects. DETA/NO has a half-life time (t_0.5) of 20 hours, which makes it more suitable for application in tissues (like our whole-mount preparation), because the donor can penetrate into the issue before releasing NO. In turn, this long t0.5 means that only a fraction of the bound NO is released per time unit.

Based on t_0.5 for DETA/NO and NO, one can roughly estimate the NO range as follows: t_0.5 of NO strongly depends on the tissue and is estimated in the second to minute range (Beckman & Koppenol, 1996). Assuming a t_0.5 for NO of 2 minutes, a freshly prepared 100 µM DETA/NO solution is expected to result within the first hour a NO concentration of approx. 0.25 µM (taking into account that 1 mole of DETA/NO releases 1.5 moles of NO molecules; see Ramamurthi & Lewis 1997).

In general, it is difficult to determine the physiological concentration of NO in the retina. Different measurements point at peaks of a few 100 nM (e.g., frog retina, ganglion cells: 0.25 µM, Kalamkarov et al. 2016; rodent inner retina, 0.1 to 0.4 µM, Micah et al. 2014). Hence, the NO concentrations we apply should be within the measured physiological range.

Fig. 3e: what are the diamond symbols? If these are the individual cells, it might be better to plot them on top of the box plots so all are visible. 

Indeed, the diamond symbols represent individual cells, yet outliers only. We decided not to plot all cells as a dot plot on top of the box plots since the readability will suffer as there are too many individual dots to show, e.g., n=251 for G₃₂ Ctrl and n=135 for G₃₂ DETA/NO.

Fig. 3: please explain more clearly the x-axis units in a-d and the y-axis units in e. 

To estimate potential response differences between the first and the second scan (i.e. either Ctrl 2 or NO), the traces were subtracted cell-pairwise (∆ Ctrl: Ctrl 2 – Ctrl 1; ∆ DETA/NO: NO – Ctrl 1). As all Ca²⁺ traces were normalized, they are unit-free. Therefore, the x-axes in Fig. 3a-d represent the mean differences of each cell per cell type, e.g., a value of zero would mean that the traces of Ctrl 1 and Ctrl 2 for a cell are identical. The y-axis in Fig. 3e is also unit-free, because technically, it is the same measure as Fig. 3a-d. But since it summarizes the control- and NO-data, we refer to this as “delta mean trace.” We tried to make this clearer in the revised manuscript and a detailed description can be found in the Methods.

Fig. 3: "...a substantial number of RGC types (34%) changed their responses to chirp and/or moving bar stimuli in the absence of any pharmacological perturbation in a highly reproducible manner...". How many of the cell types showed a significant difference? Two cell-types with p<0.001are highlighted with 3 asterisks. It would be helpful to indicate on this plot which of the other cells showed significant differences. 

Yes, this is a good idea. Thank you. We tried to add this information to the figure, but it became rather crowded. Therefore, we added a new Suppl. Fig. S3 (same style as Fig. 3) where we exclusively summarized the control-dataset. 

Fig. 7: To illustrate the transform from PSTH to Ca-imaging, why not use G32 data as an example?

Fair point. We modified the figure and added G₃₂ as an example.

It would be clearer if the cells were labeled consistently throughout the paper using their Baden cluster numbers rather than switching to the older nomenclature (JAM-B, local edge, alpha, etc), e.g. Fig. 7a,b. 

In the revised manuscript, we now changed the nomenclature to the Ca2+ Baden et al. (2016) terminology. We used the alternative cell type names here because where Fig. 7a is discussed in the manuscript, the cell type matching did not happen yet. But we agree that a consistent nomenclature is helpful.

The evidence supporting the sub-clustering of the G32 cells for the two recording methods could have been stronger. In Fig. 5, the BIC difference between 2 and 3 clusters is rather small. Is this result robust enough to justify 3 rather than 2 clusters? The BIC analysis should also be performed on the PSTH data-set to support the notion that the MEA G32 cluster also contains 3 rather than 2 sub-clusters. 

Regarding the sub-clustering of G₃₂ into n=2 or n=3 clusters for both datasets, please see our detailed reply #1.1 in our response to the public comments above.

The alignment of the three sub-clusters across the Ca-imaging and MEA data looked questionable. For example, the cluster 2 and cluster 3 traces in Fig. 5e,f look similar, with cluster 1 being more different. In Fig. 8c on the other hand, cluster 1 and 3 look similar with cluster 2 being more different. The pharmacological results also did not align well. For the Ca-imaging, NO appeared to have a large effect on cluster 1, a more modest effect on cluster 2 and less effect on cluster 3 (Fig. 5f). In comparison, the MEA results diverged, with NO producing the largest effect on cluster 2 and very modest if any effects on clusters 1 and 3 (Fig. 8c). Moreover, the temporal properties of cluster 1 and cluster 3 look very different between the Ca-imaging and MEA data. Without further comment, these differences raise concerns about the reliability of the clustering and the validity of comparisons made across the two sets of experiments. 

We agree that this is a critical point. Please see our reply #1.2 in our response to the public comments above.

Fig. 8: Transforming the PSTHs into Ca-traces is important to align the MEA recordings with the Ca-imaging data. It would also be very informative to see a more detailed overall presentation of the PSTH data since it provides a much higher temporal resolution of the responses. For example, illustrating the average PSTHs for the G32 cells under all the experimental conditions could be quite illuminating. 

To address this point, we added a new Supplementary Fig. S6, which shows the pseudo-Ca²⁺ traces for each cluster and condition next to the PSTHs. In addition, we quantified the cumulative firing rate for response features (time windows) where temporal suppression was observed in the Ca²⁺ data. This new analysis shows that during NO-application, we can see an increase in firing rate in all clusters. Nevertheless, the effect of NO on the PSTHs is admittedly small and it is better visible in the pseudo-Ca²⁺ transformed traces. One possible explanation for this difference may be that the overall firing rates are quite dynamic in G₃₂ such that a significant increase in “suppression” phases relative to the peak firing appears small.

Reviewer #2 (Public Review):  

Neuromodulators are important for circuit function, but their roles in the retinal circuitry are poorly understood. This study by Gonschorek and colleagues aims to determine the modulatory effect of nitric oxide on the response properties of retinal ganglion cells. The authors used two photon calcium imaging and multi-electrode arrays to classify and compare cell responses before and after applying a NO donor DETA-NO. The authors found that DETA-NO selectively increases activity in a subset of contrast-suppressed RGC types.

In addition, the authors found cell-type specific changes in light response in the absence of pharmacological manipulation in their calcium imaging paradigm. While this study focuses on an important question and the results are interesting, the following issues need further clarification for better interpretation of the data. 

We thank the reviewer for her/his detailed and constructive comments.

(1) Design of the calcium imaging experiments: the control-control pair has a different time course from the control-drug pair (Fig 1e). First, the control-control pair has a 10 minute interval while the control-drug pair has a 25 minute interval. Second, Control 1 Field 2 was imaged 10 min later than Control 1 Field 1 since the start of the calcium imaging paradigm. 

Given that the control dataset is used to control for time-dependent adaptational changes throughout the experiment, I wonder why the authors did not use the same absolute starting time of imaging and the same interval between the first and second round of imaging for both the control-control and the control-drug pairs. This can be readily done in one of the two ways: 1. In a set of experiment, add DETA/NO between "Control 1 Field 1 and "Control 2 Field 1" in Fig. 1e as the drug group; or 2. Omit DETA/NO in the Fig. 1e protocol as the control group to monitor the time course of adaptational changes. 

Thank you for raising this point. We hope that in the following we can clarify the reasoning behind our protocol and the analysis approach.

(2.1) Initially, we performed these experiments in different ways (also in the sequence suggested by the reviewer), before homing in on the paradigm illustrated in Fig. 1. We chose this paradigm for two reasons: First, we wanted to have for each retina both Ctrl1/Ctrl2 and Ctr1/NO data sets, to be sure that the time-dependent (adaptational) effects were not related to the general condition of an individual retina preparation. Second, we did not see obvious differences in time-dependent or NO-induced effects between paradigms. Therefore, while we cannot exclude that the absolute time between recordings can affect the observed changes, we do not think that such effects are substantial enough to change our conclusions.

In the revised manuscript, we now explicitly point at the different intervals. 

Related to the concern above, to determine NO-specific effect, the authors used the criterion that "the response changes observed for control (ΔR(Ctrl2−Ctrl1)) and NO (ΔR(NO−Ctrl1)) were significantly different". This criterion assumes that without DETA-NO, imaging data obtained at the time points of "Control 1 Field 2" and "DETA/NO Field 2" would give the same value of ΔR as ΔR(Ctrl2−Ctrl1) for all RGC types. It is not obvious to me why this should be the case, because of the unknown time-dependent trajectory of the adaptational change for each RGC type. For example, a RGC type could show stable response in the first 30 min and then change significantly in the following 30 min. DETA/NO may counteract this adaptational change, leading to the same ΔR as the control condition (false negative). Alternatively, DETA/NO may have no effect, but the nonlinear timedependent response drift can give false positive results. 

(2.2) Initially, we assumed that after adapting the retina to a certain light level, RGCs exhibit stable responses over time, such that when adding a pharmacological agent, we can identify drug-induced response changes (e.g., by calculating the response difference). To our surprise, we found that for some RGC types the responses changed between the first and the second recording (referred to as cell type-specific adaptational effects), which is why we devised the Ctrl1/Ctrl2 vs. Ctr2/NO analysis. 

The reviewer is correct in that we assume in our analysis that the adaptational- and NO-induced effects are independent and sum linearly. Further, we agree with the reviewer that there may be other possibilities, two of which are highlighted by the reviewer:

(a) Interaction: for instance, if NO compensates for the adaptational effect, we would not be able to measure this; or, if this compensation was partial, underestimate both effects. 

(b) More complex time-dependency: for example, if an RGC shows a pronounced adaptational effect with a longer delay (i.e. only after the second scan), or that a very transient NO effect has already disappeared when we perform the second scan. On the one hand, as we only can take snapshots of the RGC responses, we cannot exclude these possibilities. On the other hand, both effects (adaptational- and NO-dependent) were type-specific and reproducible between experiments (also with varying timing, see reply #2.1), which makes complex time dependencies less likely.

The revised manuscript now reflects these limitations of our recording paradigm and points out which effects can be detected, and which likely not.

I also wonder why washing-out, a standard protocol for pharmacological experiments, was not done for the calcium protocol since it was done in the MEA experiments. A reversible effect by washing in and out DETA/NO in the calcium protocol would provide a much stronger support that the observed NO modulation is due to NO and not to other adaptive changes. 

(2.3) We agree that a clear wash-out would strengthen our findings. Indeed, in the beginning of our experiments, we tried to wash-out the agent in the Ca²⁺ recordings, as we did in the MEA recordings. We soon stopped doing this in the Ca²⁺ experiments, because response quality decreased for the third scan of the same field, likely due to bleaching of fluorescent indicator and photopigment. This is why we typically restrict the total recording time of the same field of RGCs to about 30 min (~ two scans with all light stimuli). Moreover, our MEA data showed that DETA/NO can largely be washed-out, which supports that we observed NO-specific effects. Therefore, we decided against further attempts to establish the wash-out also in the Ca²⁺ experiments (e.g., shortening the recording time by presenting fewer light stimuli).

(2) Effects of Strychnine: In lines 215-219, " In the light-adapted retina, On-cone BCs boost light-Off responses in Off-cone BCs through cross-over inhibition (83, 84) and hence, strychnine affects Off-response components in RGCs - in line with our observations (Fig. S2)" However, Fig. S2 doesn't seem to show a difference in the Off-response components. Rather, the On response is enhanced with strychnine. In addition, suppressed-by-contrast cells are known to receive glycinergic inhibition from VGluT3 amacrine cells (Tien et al., 2016). However, the G32 cluster in Fig. S2 doesn't seem to show a change with strychnine. More explanation on these discrepancies will be helpful.

(2.4) We thank the reviewer for this comment. Regarding the first part, we agree that the figure does not support differences in the Off-response components. We therefore rephrased the corresponding text accordingly. Additionally, we now show all RGC types with n>3 cells per recording condition in the revised Suppl. Fig. S2 and added statistics.

Regarding the second part, there are several possible explanations for these discrepancies:

(a) The SbC (transient Off SbC) studied in Tien et al. (2016) likely corresponds to the RGC type G₂₈ (see Höfling et al. 2024). As mentioned above (see reply #1.2), it is unclear if G₃₂ corresponds to a previously described SbC, and if so, to which. Goetz et al. (2022) proposed that G₃₂ may align with the bursty-SbC (bSbC) type (their Supplemental Table 3), as described also by Wienbar and Schwartz (2022). An important feature of the bSbC type is that its contrast response function is mainly driven by intrinsic properties rather than synaptic input. If G₃₂ indeed included the bSbC, this may explain why strychnine does not interfere with the suppression of temporal contrast.

(b) In Tien et al. (2016), the authors genetically removed the VG3-ACs (see their Fig. 3) and show that this ablation reduces the inhibition of tSbC cells in a stimulus size-dependent manner. Specifically, larger light stimuli (600 µm) only show marginal effects on the IPSCs and inhibitory synaptic conductance (see their Figs. 3c,d and 3e,f, respectively). In our study, the full-field chirp had a size of 800 x 600 µm. Therefore – and assuming that G₃₂ indeed included tSbCs – our observation that strychnine did not affect temporal suppression in the full-field chirp responses would be in line with Tien et al. (2016).   

(3) This study uses DETA-NO as an NO donor for enhancing NO release. However, a previous study by Thompson et al., Br J Pharmacol. 2009 reported that DETA-NO can rapidly and reversible induce a cation current independent of NO release at the 100 uM used in the current study, which could potentially cause the observed effect in G32 cluster such as reduced contrast suppression and increased activity. This potential caveat should at least be discussed, and ideally excluded by showing the absence of DETA-NO effects in nNOS knockout mice, and/or by using another pharmacological reagent such as the NO donor SNAP or the nNOS inhibitor l-NAME. 

Thank you for pointing out this potential caveat. We certainly cannot exclude such side effects. However, we think that this explanation of our observations is unlikely, because Thompson et al. barely see effects at 100 µM DETA/NO; in fact, their data suggests that clear NO-independent effects on the cation-selective channel occur at much higher DETA/NO concentrations, such as 3 mM. 

In any case, in the revised manuscript, we refer to this paper in the Discussion. 

(4) Clarification of methods: In the Methods, lines 1119-1127, the authors describe the detrending, baseline subtraction, and averaging. Then, line 1129, " the mean activity r(t) was computed and then traces were normalized such that: max t(|r(t)|) = 1. How is the normalization done? Is it over the entire recording (control and wash in) for each ROI? Or is it normalized based on the mean trace under each imaging session (i.e. twice for each imaging field)? 

The normalization (z-scoring) was done for each ROI individually per stimulus and condition (Ctrl 1, Ctrl 2, DETA/NO). We normalized the traces, because the absolute Ca²⁺ signal depends on factors, such as “resting” state of the cell (e.g., silent vs. baseline spiking activity in the absence of a light stimulus) and its fluorescent dye concentration. This also means that absolute response amplitudes are difficult to interpret. Hence, we focused on analyzing relative changes per ROI and condition, which still allowed us to investigate adaptational and drug-induced effects. In the revised manuscript, we changed the corresponding paragraph for clarification.

As for the clustering of RGC types, I assume that each ROI's cluster identity remains unchanged through the comparison. If so, it may be helpful to emphasize this in the text.

Yes, this is correct. We identified G₃₂ RGCs based on their Ctrl1 responses and then compares these responses with those for Ctrl2 or NO. We now clarified this in the revised manuscript.

Reviewer #2 (Recommendations For The Authors):  

The manuscript would benefit from a discussion of how the findings in this study relate to known mechanisms of NO modulation and previously reported effects of NO manipulations on RGC activity. 

Thank you for the recommendation. We already refer to known mechanisms of NO within the retina in the Introduction. In the revised manuscript, we now added information to the Discussion.

In the abstract, "a paired-recording paradigm" could be misleading because paired recording generally refers to the simultaneous recording of two neurons. However, the paradigm in this study is essentially imaging experiments done at two time points. 

We agree with the reviewer. To avoid any confusion with paired electrophysiological recordings, we changed the term “paired-recording paradigm” to “sequential recording paradigm” and replaced the term “pair-/ed” with “sequentially recorded”.

eLife Assessment

This important study is the first comprehensive analysis of the modulatory effects of nitric oxide (NO) on the response properties of retinal ganglion cells (RGCs) in the mouse retina using two-photon calcium imaging and multi-electrode arrays (MEA). The results provide compelling evidence that a subset of suppressed-by-contrast RGCs are affected. These unexpected findings are likely of broad interest to visual neuroscientists.

Reviewer #1 (Public review):

Summary:

Nitric oxide (NO) has been implicated as a neuromodulator in the retina. Specific types of amacrine cells (ACs) produce and release NO in a light-dependent manner. NO diffuses freely through the retina and can modulate intracellular levels of cGMP, or directly modify and modulate proteins via S-nitrosylation, leading to changes in gap-junction coupling, synaptic gain, and adaptation. Although these system-wide effects have been documented, it is not well understood how the physiological function of specific neuronal types is affected by NO. This study aims to address this gap in our knowledge.

There are two major findings. 1) About a third of the retinal ganglion cells display cell-type specific adaptation to prolonged stimulus protocols. 2) Application of NO specifically affected Off-suppressed ganglion cells designated as G32 cells. The G32 cluster likely contains 3 ganglion cell types that are differentially affected.

This is the first comprehensive analysis of the functional effects of NO on ganglion cells in the retina. The cell-type specificity of the effects is surprising and provides the field with valuable new information.

Strengths:

NO was expected to produce small effects, and considerable effort was expended in validating the system to ensure that changes in the state of the preparation would not confound any effects of NO. The authors used a sequential stimulus protocol to control for changes in the sensitivity of the retina during the extended recording periods. The approach potentially increases the sensitivity of the measurements and allows more subtle effects to be observed.

Neural activity was measured by Ca-imaging. Responsive ganglion cells were grouped into 32 types using a clustering analysis. Initial control experiments demonstrated that the cell-types revealed by the analysis largely recapitulate those from their earlier landmark study using a similar approach.

Application of NO to the retina modulated responses of a single cluster of cells, labeled G32, while having little effect on the remaining 31 clusters. In separate experiments, ganglion cell spiking activity was recorded on a multi-electrode array (MEA). Together the Ca-imaging and MEA recordings provide complementary approaches and demonstrate that NO modulates the temporal but not spatial properties of affected cell-types.

Weaknesses:

The concentration of NO used in these experiments was ~0.25µM, which is 5- to 10-fold lower than the endogenous concentration previously measured in rodent retina. It is perhaps surprising that this relatively low NO concentration produced significant effects. However, the endogenous measurements were done in an eye-cup preparation, while the current experiments were performed in a bare (no choroid) preparation. Perhaps the resting NO level is lower in this preparation. It is also possible that the low concentration of NO promoted more selective effects.

Reviewer #2 (Public review):

Neuromodulators are important for circuit function, but their roles in the retinal circuitry are poorly understood. This study by Gonschorek and colleagues aims to determine the modulatory effect of nitric oxide on the response properties of retinal ganglion cells. The authors used two photon calcium imaging and multi-electrode arrays to classify and compare cell responses before and after applying a NO donor DETA-NO. The authors found that DETA-NO selectively increases activity in a subset of contrast-suppressed RGC types. In addition, the authors found cell-type specific changes in light response in the absence of pharmacological manipulation in their calcium imaging paradigm. This study focuses on an important question and the results are interesting. The limitations of the method and data interpretation are adequately discussed in the revised manuscript.

The authors have addressed my previous comments, included additional discussions on the limitations of the method, and provided a more careful interpretation of their data.

Author response:

Reviewer 1:

Summary:

This paper describes molecular dynamics simulations (MDS) of the dynamics of two T-cell receptors (TCRs) bound to the same major histocompatibility complex molecule loaded with the same peptide (pMHC). The two TCRs (A6 and B7) bind to the pMHC with similar affinity and kinetics, but employ different residue contacts. The main purpose of the study is to quantify via MDS the differences in the inter- and intra-molecular motions of these complexes, with a specific focus on what the authors describe as catch-bond behavior between the TCRs and pMHC, which could explain how T-cells can discriminate between different peptides in the presence of weak separating force.

Strengths:

The authors present extensive simulation data that indicates that, in both complexes, the number of high-occupancy interdomain contacts initially increases with applied load, which is generally consistent with the authors’ conclusion that both complexes exhibit catch-bond behavior, although to different extents. In this way, the paper somewhat expands our understanding of peptide discrimination by T-cells.

The reviewer makes thoughtful assessments of our manuscript. While our manuscript is meant to be a “short” contribution, our significant new finding is that even for TCRs targeting the same pMHC, having similar structures, and leading to similar functional outcomes in conventional assays, their response to applied load can be different. This supports out recent experimental work where TCRs targeting the same pMHC differed in their catch bond characteristics, and importantly, in their response to limiting copy numbers of pMHCs on the antigen-presenting cell (Akitsu et al., Sci. Adv., 2024; cited in our manuscript). Our present manuscript provides the physical basis where two similar TCRs respond to applied load differently. In the revised manuscript, we will make this point clearer.

Weaknesses:

While generally well supported by data, the conclusions would nevertheless benefit from a more concise presentation of information in the figures, as well as from suggesting experimentally testable predictions.

Following the reviewers’ suggestions, we will update figures and use Figure Supplements to make the main figures more concise and to simplify the overall presentation.

Regarding testable predictions, one prediction would be that B7 TCR will exhibit weaker catch bond behavior than A6. This is an important prediction because the two TCRs targeting the same pMHC have similar structures and are functionally similar in conventional assays. This prediction can be tested by single-molecule optical tweezers experiments. We also predict the A6 TCR may perform better when the number of pMHC molecules presented are limited, analogous to our recent experiments on different TCRs, Akitsu et al., Sci. Adv. (2024).

Another testable prediction for the conservation of the basic allostery mechanism is to test the Cβ FG-loop deletion mutant located at the hinge region of the β chain, yet its deletion severely impairs the catch bond formation. These predictions will be mentioned and discussed in the updated manuscript.

Reviewer 2:

In this work, Chang-Gonzalez and coworkers follow up on an earlier study on the force-dependence of peptide recognition by a T-cell receptor using all-atom molecular dynamics simulations. In this study, they compare the results of pulling on a TCR-pMHC complex between two different TCRs with the same peptide. A goal of the paper is to determine whether the newly studied B7 TCR has the same load-dependent behavior mechanism shown in the earlier study for A6 TCR. The primary result is that while the unloaded interaction strength is similar, A6 exhibits more force stabilization.

This is a detailed study, and establishing the difference between these two systems with and without applied force may establish them as a good reference setup for others who want to study mechanobiological processes if the data were made available, and could give additional molecular details for T-Cell-specialists. As written, the paper contains an overwhelming amount of details and it is difficult (for me) to ascertain which parts to focus on and which results point to the overall take-away messages they wish to convey.

As mentioned above and as the reviewer correctly pointed out, the condensed appearance of this manuscript arose largely because we intended it to be a Research Advances article as a short follow up study of our previous paper on A6 TCR published in eLife. Most of the analysis scripts for the A6 TCR study are already available on Github. We will additionally deposit sample structures and simulation scripts for the B7 TCR. Trajectory will be provided upon request given their large size.

Regarding the focus issue, it is in part due to the complex nature of the problem, which required simulations under different conditions and multi-faceted analyses. Concisely presenting the complex analyses also has been a challenge in our previous papers on TCR simulations (Hwang et al., PNAS 2020; Chang-Gonzalez et al., eLife, 2024 – both are cited in our manuscript). With updated figures and texts, we expect that the presentation will be a lot clearer. But even in the present form, the reviewer points out the main take-away message well: “The primary result is that while the unloaded interaction strength is similar, A6 exhibits more force stabilization.

Detailed comments:

(1) In Table 1 - are the values of the extension column the deviation from the average length at zero force (that is what I would term extension) or is it the distance between anchor points (which is what I would assume based on the large values. If the latter, I suggest changing the heading, and then also reporting the average extension with an asterisk indicating no extensional restraints were applied for B7-0, or just listing 0 load in the load column. Standard deviation in this value can also be reported. If it is an extension as I would define it, then I think B7-0 should indicate extension = 0+/- something.

The distance between anchor points could also be labeled in Figure 1A.

“Extension” is the distance between anchor points (blue spheres at the ends of the added strands in Fig. 1A). While its meaning should be clear in the section “Laddered extensions” in MD simulation protocol, at first glance it may lead to confusion. In a strict sense, use of “extension” for the distance is a misnomer, but we have used it in our previous two papers (Hwang et al., PNAS 2020; Chang-Gonzalez et al., eLife, 2024), so we prefer to keep it for consistency. Instead, in the caption of Table 1, we will explain its meaning, and also explicitly label it in Fig. 1A, as the reviewer suggested.

Please also note that the no-load case B7⁰ does not have a particular extension that yields zero load on average. It would in fact be very difficult to find such an extension (distance between two anchor points). To simulate the system without load, we separately built a TCR-pMHC complex without added linkers, and held the distal part of pMHC with weak harmonic restraints (explained in sections “Structure preparation” and “Systems without load”). In this way, no external force is applied to TCR as it moves relative to pMHC. We will clarify this when introducing B7⁰ in the Results section.

(2) As in the previous paper, the authors apply ”constant force” by scanning to find a particular bond distance at which a desired force is selected, rather than simply applying a constant force. I find this approach less desirable unless there is experimental evidence suggesting the pMHC and TCR were forced to be a particular distance apart when forces are applied. It is relatively trivial to apply constant forces, so in general, I would suggest this would have been a reasonable comparison. Line 243-245 speculates that there is a difference in catch bonding behavior that could be inferred because lower force occurs at larger extensions, but I do not believe this hypothesis can be fully justified and could be due to other differences in the complex.

There is indeed experimental evidence that the TCR-pMHC complex operates under constant separation. The spacing between a T-cell and an antigen-presenting cell is maintained by adhesion molecules such as the CD2CD58 pair, as explained in our paper on the A6 TCR, (Chang-Gonzalez et al., eLife, 2024; please see the bottom paragraph on page 4 of the paper). In in vitro single-molecule experiments, pulling to a fixed separation and holding is also commonly done. Detailed comparison between constant extension vs. constant force simulations is definitely a subject of our future study. We will clarify these points when explaining about the constant extension (or separation).

Regarding line 243–245, we agree with the reviewer that without further tests, lower forces at larger extensions per se cannot be an indicator that B7 forms a weaker catch bond. But with additional insight, it does have an indirect relevance. In addition to fewer TCR-pMHC contacts (Fig. 1C of our manuscript), the intra-TCR contacts are also reduced compared to those of A6 (Fig. 1D vs. Chang-Gonzalez et al., eLife, 2024, Fig. 8A,B, first column; reproduced in the figure in our response to reviewer 3 below). This shows that the B7 TCR forms a looser complex with pMHC compared to A6. With its higher compliance, the B7 TCR-pMHC complex needs to be under a greater extension than A6 to apply comparable levels of force, and it would be more difficult to achieve load-induced stabilization of the TCR-pMHC interface, hence a weaker catch bond. We will add this point when explaining the weaker catch bond behavior of B7.

(3) On a related note, the authors do not refer to or consider other works using MD to study force-stabilized interactions (e.g. for catch bonding systems), e.g. these cases where constant force is applied and enhanced sampling techniques are used to assess the impact of that applied force: https://www.cell.com/biophysj/fulltext/S0006-3495(23)00341-7, https://www.biorxiv.org/content/10.1101/2024.10.10.617580v1. I was also surprised not to see this paper on catch bonding in pMHC-TCR referred to, which also includes some MD simulations: https://www.nature.com/articles/s41467-023-38267-1

We thank the reviewer for bringing the three papers to our attention, which are:

(1) Languin-Cattoën, Sterpone, and Stirnemann, Biophys. J. 122:2744 (2023): About bacterial adhesion protein FimH.

(2) Peña Ccoa, et al., bioRxiv (2024): About actin binding protein vinculin.

(3) Choi et al., Nat. Comm. 14:2616 (2023): About a mathematical model of the TCR catch bond.

Catch bond mechanisms of FimH and vinculin are different from that of TCR in that FimH and vinculin have relatively well-defined weak- and strong-binding states where there are corresponding crystal structures. Availability of the end-state structures enable using simulation approaches such as enhanced sampling of individual states and studying the transition between the two states. In contrast, TCR does not have any structurally well-defined weakor strong-binding states, which requires a different approach. As demonstrated in our current manuscript as well as in our previous two papers (Hwang et al., PNAS 2020; Chang-Gonzalez et al., eLife, 2024), our microsecond-long simulations of the complex under realistic pN-level loads and a combination of analysis methods are effective for elucidating the catch bond mechanism of TCR. In the revised manuscript, we will cite the two papers, to compare the TCR catch bond mechanism with those of FimH and vinculin, which will offer a broader perspective.

The third paper (Choi, 2023) proposes a mathematical model to analyze extensive sets of data, and also perform new experiments and additional simulations. Of note, their model assumptions are based mainly on the steered MD (SMD) simulation in their previous paper (Wu, et al., Mol. Cell. 73:1015, 2019). In their model, formation of a catch bond (called catch-slip bond in Choi’s paper) requires partial unfolding of MHC and tilting of the TCR-pMHC interface. While further studies are needed to find whether those changes are indeed required, even so, the question remains regarding how the complex in the fully folded state can bear load and enter such a state in the first place. Our current and previous simulation studies suggest a mechanism by which ligand- and load-dependent responses occur as the first obligatory step of catch bond formation, after which partial unfolding and/or extensive conformational transitions may occur, as described in our recent paper (Akitsu et al., Sci. Adv., 2024). In the revised manuscript, we will cite Wu’s paper and briefly explain the above.

(4) The authors should make at least the input files for their system available in a public place (github, zenodo) so that the systems are a more useful reference system as mentioned above. The authors do not have a data availability statement, which I believe is required.

As mentioned above, we will make sample input files and coordinates available on Github. Data availability statement will be added.

Reviewer 3:

Summary:

The paper by Chang-Gonzalez et al. is a molecular dynamics (MD) simulation study of the dynamic recognition (load-induced catch bond) by the T cell receptor (TCR) of the complex of peptide antigen (p) and the major histocompatibility complex (pMHC) protein. The methods and simulation protocols are essentially identical to those employed in a previous study by the same group (Chang-Gonzalez et al., eLife 2024). In the current manuscript, the authors compare the binding of the same pMHC to two different TCRs, B7 and A6 which was investigated in the previous paper. While the binding is more stable for both TCRs under load (of about 10-15 pN) than in the absence of load, the main difference is that, with the current MD sampling, B7 shows a smaller amount of stable contacts with the pMHC than A6.

Strengths:

The topic is interesting because of the (potential) relevance of mechanosensing in biological processes including cellular immunology.

Weaknesses:

The study is incomplete because the claims are based on a single 1000-ns simulation at each value of the load and thus some of the results might be marred by insufficient sampling, i.e., statistical error. After the first 600 ns, the higher load of B7high than B7low is due mainly to the simulation segment from about 900 ns to 1000 ns (Figure 1D). Thus, the difference in the average value of the load is within their standard deviation (9 +/- 4 pN for B7low and 14.5 +/- 7.2 for B7high, Table 1). Even more strikingly, Figure 3E shows a lack of convergence in the time series of the distance between the V-module and pMHC, particularly for B70 (left panel, yellow) and B7low (right panel, orange). More and longer simulations are required to obtain a statistically relevant sampling of the relative position and orientation of the V-module and pMHC.

The reviewer uses data points during the last 100 ns to raise an issue with sampling. But since we are using realistic pN range forces, force fluctuates more slowly. In fact, in our simulation of B7^high, while the force peaks near 35 pN at 500 ns (Fig. 1D of our manuscript; reproduced as panels C and D below), the contact heat map shows no noticeable changes around 500 ns (Fig. 2C of our manuscript). Thus, a wider time window must be considered rather than focusing on instantaneous force.

We believe the reviewer’s concern about sampling arose also due to a lack of clear explanation. Author response image 1 below contains panels from our earlier eLife paper on the A6 TCR. Panels A and B are from Fig. 8 of the A6 paper, and panels C and D are from Fig. 1D of our present manuscript. The high-load simulations in both cases (outlined circles) fluctuate widely in force so that one might argue that sampling was insufficient. However, unless one is interested in finding the precise value of force for a given extension, sampling in our simulations was reasonable enough to distinguish between high- and low-force behaviors. To support this, we show panel E below, which is from Appendix 3–Fig. 1 of our A6 paper. Added to this panel are the average forces and standard deviations of B7^low and B7^high from Table 1 of our manuscript (red squares). Please note that all of the data were measured after 500 ns. Except for Y8A^low and dFG^low of A6 (explained below), all of the data points lie on nearly a straight line.

Author response image 1.

Thermodynamically, the force and position of the restraint (blue spheres in Fig. 1A of our manuscript) form a pair of generalized force and the corresponding spatial variable in equilibrium at temperature 300 K, which is akin to the pressure P and volume V of an ideal gas. If V is fixed, P fluctuates. Denoting the average and std of pressure as ⟨P⟩ and ∆P, respectively, Burgess showed that ∆P/⟨P⟩ is a constant (Eq. 5 of Burgess, Phys. Lett. A, 44:37; 1973). In the case of the TCRαβ-pMHC system, although individual atoms are not ideal gases, since their motion leads to the fluctuation in force on the restraints, the situation is analogous to the case where pressure arises from individual ideal gas molecules hitting the confining wall as the restraint. Thus, the near-linear behavior in panel E above is a consequence of the system being many-bodied and at constant temperature. The linearity is also an indirect indicator that sampling of force was reasonable. The fact that A6 and B7 data show a common linear profile further demonstrates the consistency in our force measurement. That said, the B7 data points (red in panel E) are elevated slightly above nearby A6 data points. This is consistent with B7 forming an overall weaker complex, both at the TCR-pMHC interface (panels A vs. C) and within intra-TCR interfaces (panels B vs. D), which can be seen by the wider ranges of color bars in panels A and B for A6 compared to panels C and D for B7.

About the two outliers of A6, Y8A^low is for an antagonist peptide and dFG^low is the Cβ FG-loop deletion mutant. Interestingly, both cases had reduced numbers of contacts with pMHC, which likely caused a wider conformational motion, hence greater fluctuation in force.

A similar argument applies to Fig. 3E of our manuscript. If precise values of the V-module to pMHC distance were needed, longer or duplicate simulations would be necessary, however, Fig. 3E as it currently stands clearly shows that B7^high maintains more stable interface compared to B7^low, which is consistent with all other measures we used, such as Fig. 3B (Hamming distance), Fig. 3C (buried surface area), and Fig. 4A–E (Vα-Vβ motion and CDR3 distance). They are also consistent with our simulations of A6.

Thus, rather than relying on peculiarities of individual trajectories, we analyze data in multiple ways and draw conclusions based on features that are consistent across different simulations. Please also note that reviewer 1 mentioned that our conclusions are “generally well supported by data.”

We will update our manuscript to concisely explain the above and also will add Panel E above as a supplement of Fig. 1.

It is not clear why ”a 10 A distance restraint between alphaT218 and betaA259 was applied” (section MD simulation protocol, page 9).

αT218 and βA259 are the residues attached to a leucine-zipper handle in in vitro optical trap experiments (Das, et al., PNAS 2015). In T cells, those residues also connect to transmembrane helices. Author response image 2 is a model of N15 TCR used in experiments in Das’ paper, constructed based on PDB 1NFD. Blue spheres represent Cα atoms corresponding to αT218 and βA259 of B7 TCR. Their distance is 6.7 ˚A. The 10-˚A distance restraint in simulation was applied to mimic the presence of the leucine zipper that prevents excessive separation of the added strands. The distance restraint is a flat-bottom harmonic potential which is activated only when the distance between the two atoms exceeds 10 ˚A, which we did not clarify in our original manuscript. The same restraint was used in our previous studies on JM22 and A6 TCRs.

We will add the figure as a supplement of Fig. 1, cite Das’ paper, and also update description of the distance restraint in the MD simulation protocol section.

Author response image 2.

eLife Assessment

This useful study reports detailed molecular dynamics simulations of T-cell receptors in complex with a peptide/MHC complex, for a better understanding of the mechanism of T-cell activation. The key observation was that tensile force applied in the direction of separation between TCR/pMHC appears to strengthen the interface, which is consistent with the catch bond scenario, although the effect is less apparent than that studied in the earlier work despite many similarities. The analyses are systematic and thus generally solid, although the level of evidence could be considered incomplete due to limited sampling based on a single trajectory for each load.

Reviewer #1 (Public review):

Summary:

This paper describes molecular dynamics simulations (MDS) of the dynamics of two T-cell receptors (TCRs) bound to the same major histocompatibility complex molecule loaded with the same peptide (pMHC). The two TCRs (A6 and B7) bind to the pMHC with similar affinity and kinetics, but employ different residue contacts. The main purpose of the study is to quantify via MDS the differences in the inter- and intra-molecular motions of these complexes, with a specific focus on what the authors describe as catch-bond behavior between the TCRs and pMHC, which could explain how T-cells can discriminate between different peptides in the presence of weak separating force.

Strengths:

The authors present extensive simulation data that indicates that, in both complexes, the number of high-occupancy inter-domain contacts initially increases with applied load, which is generally consistent with the authors' conclusion that both complexes exhibit catch-bond behavior, although to different extents. In this way, the paper somewhat expands our understanding of peptide discrimination by T-cells.

Weaknesses:

While generally well supported by data, the conclusions would nevertheless benefit from a more concise presentation of information in the figures, as well as from suggesting experimentally testable predictions.

Reviewer #2 (Public review):

In this work, Chang-Gonzalez and coworkers follow up on an earlier study on the force-dependence of peptide recognition by a T-cell receptor using all-atom molecular dynamics simulations. In this study, they compare the results of pulling on a TCR-pMHC complex between two different TCRs with the same peptide. A goal of the paper is to determine whether the newly studied B7 TCR has the same load-dependent behavior mechanism shown in the earlier study for A6 TCR. The primary result is that while the unloaded interaction strength is similar, A6 exhibits more force stabilization.

This is a detailed study, and establishing the difference between these two systems with and without applied force may establish them as a good reference setup for others who want to study mechanobiological processes if the data were made available, and could give additional molecular details for T-Cell-specialists. As written, the paper contains an overwhelming amount of details and it is difficult (for me) to ascertain which parts to focus on and which results point to the overall take-away messages they wish to convey.

Detailed comments:

(1) In Table 1 - are the values of the extension column the deviation from the average length at zero force (that is what I would term extension) or is it the distance between anchor points (which is what I would assume based on the large values. If the latter, I suggest changing the heading, and then also reporting the average extension with an asterisk indicating no extensional restraints were applied for B7-0, or just listing 0 load in the load column. Standard deviation in this value can also be reported. If it is an extension as I would define it, then I think B7-0 should indicate extension = 0+/- something. The distance between anchor points could also be labeled in Figure 1A.

(2) As in the previous paper, the authors apply "constant force" by scanning to find a particular bond distance at which a desired force is selected, rather than simply applying a constant force. I find this approach less desirable unless there is experimental evidence suggesting the pMHC and TCR were forced to be a particular distance apart when forces are applied. It is relatively trivial to apply constant forces, so in general, I would suggest this would have been a reasonable comparison. Line 243-245 speculates that there is a difference in catch bonding behavior that could be inferred because lower force occurs at larger extensions, but I do not believe this hypothesis can be fully justified and could be due to other differences in the complex.

(3) On a related note, the authors do not refer to or consider other works using MD to study force-stabilized interactions (e.g. for catch bonding systems), e.g. these cases where constant force is applied and enhanced sampling techniques are used to assess the impact of that applied force: https://www.cell.com/biophysj/fulltext/S0006-3495(23)00341-7, https://www.biorxiv.org/content/10.1101/2024.10.10.617580v1. I was also surprised not to see this paper on catch bonding in pMHC-TCR referred to, which also includes some MD simulations: https://www.nature.com/articles/s41467-023-38267-1

(4) The authors should make at least the input files for their system available in a public place (github, zenodo) so that the systems are a more useful reference system as mentioned above. The authors do not have a data availability statement, which I believe is required.

Reviewer #3 (Public review):

Summary:

The paper by Chang-Gonzalez et al. is a molecular dynamics (MD) simulation study of the dynamic recognition (load-induced catch bond) by the T cell receptor (TCR) of the complex of peptide antigen (p) and the major histocompatibility complex (pMHC) protein. The methods and simulation protocols are essentially identical to those employed in a previous study by the same group (Chang-Gonzalez et al., eLife 2024). In the current manuscript, the authors compare the binding of the same pMHC to two different TCRs, B7 and A6 which was investigated in the previous paper. While the binding is more stable for both TCRs under load (of about 10-15 pN) than in the absence of load, the main difference is that, with the current MD sampling, B7 shows a smaller amount of stable contacts with the pMHC than A6.

Strengths:

The topic is interesting because of the (potential) relevance of mechanosensing in biological processes including cellular immunology.

Weaknesses:

The study is incomplete because the claims are based on a single 1000-ns simulation at each value of the load and thus some of the results might be marred by insufficient sampling, i.e., statistical error. After the first 600 ns, the higher load of B7high than B7low is due mainly to the simulation segment from about 900 ns to 1000 ns (Figure 1D). Thus, the difference in the average value of the load is within their standard deviation (9 +/- 4 pN for B7low and 14.5 +/- 7.2 for B7high, Table 1). Even more strikingly, Figure 3E shows a lack of convergence in the time series of the distance between the V-module and pMHC, particularly for B70 (left panel, yellow) and B7low (right panel, orange). More and longer simulations are required to obtain a statistically relevant sampling of the relative position and orientation of the V-module and pMHC.

It is not clear why "a 10 A distance restraint between alphaT218 and betaA259 was applied" (section MD simulation protocol, page 9).

Author response:

Reviewer #1 (Public review):

The significance of the target molecule and mechanisms may help in understanding the molecular mechanisms of metformin.

We greatly appreciate the reviewer’s insightful comment regarding the significance of the target molecule and its mechanisms in understanding the molecular actions of metformin. ATP5I is responsible for the dimerization of the F₁F₀-ATPase(1-3). Hence, we propose conducting BN-PAGE followed by a western blot using the β-subunit of the F1 domain of F1F0-ATP synthase to investigate whether metformin affects its dimerization. This will provide a more direct evidence of the on target action of metformin on ATP5I. Due to the high abundance of F₁F₀-ATP synthase in cells and the slow ability of metformin to enter mitochondria, we plan to perform long-term treatments (3 and 6 days) with high concentrations of metformin (10 mM) to enhance the likelihood of detecting subtle yet biologically relevant shifts in the monomer and dimer populations. Prolonged exposure is expected to reveal the cumulative effects of metformin on F₁F₀-ATP synthase dimers/monomers ratio. We do not expect that metformin will totally mimic the cumulative effect of the dimerization as in ATP5I KO cells but we think it will be important to report to what extent this ratio is affected.

Reviewer #2 (Public review):

(1) The interpretation of the cellular co-localization of the biotin-biguanide conjugate with TOMM20 (Figure 1-D) as mitochondrial "accumulation" of the conjugate is overstated because it cannot exclude binding of the conjugate to the mitochondrial membrane. It would have been more convincing if additional incubations with the biotin-biguanide conjugate in combination with metformin had shown that metformin is competitive with the biotin-conjugate.

We appreciate the reviewer’s insightful comment and agree that the resolution provided by fluorescence microscopy makes it challenging to pinpoint the specific mitochondrial compartment where the biotin-biguanide conjugate localizes, even with additional markers such as TOMM20 antibodies for the inner mitochondrial membrane. While it remains a possibility that the conjugate binds to the mitochondrial surface, another plausible explanation is that the biotin moiety may facilitate entry into mitochondria through a biotin-specific transporter, adding further mechanistic intricacies. Furthermore, while a competition assay with metformin might help investigate interactions with mitochondrial targets and transporters (OCT family), it would not compete for biotin-mediated transport. Thus, while we acknowledge the reviewer’s suggestion, we believe such an experiment may not provide conclusive evidence regarding the conjugate’s mitochondrial localization or mechanism of entry. Instead, we will revise the manuscript to more accurately describe the findings as "mitochondrial association" rather than "mitochondrial accumulation," ensuring that our interpretation remains consistent with the resolution and limitations of the data presented.

(2) The manuscript reports the identification of 69 proteins by mass spectrometry of the pull-down assay of which 31 proteins were eluted by metformin. However, no Mass Spectrometry data is presented of the peptides identified. The methodology does not state the minimum number of peptides (1, 2?) that were used for the identification of the 31/69 proteins.

Concerning the mass spectrometry results, our intention was to provide a comprehensive table summarizing these findings in a separate data sheet, as part of the data availability section. To address the reviewer’s comment and ensure full transparency, we will include this table as supplementary material in the revised manuscript. Additionally, we will update the methodology section to explicitly state these criteria and ensure clarity regarding the identification process.

(3) The validation of ATP5I was based on the use of recombinant protein (which was 90% pure) for the SPR and the use of a single antibody to ATP5I. The validity of the immunoblotting rests on the assumption that there is no "non-specific" immunoactivity in the relevant mol wt range. Information on the validation of the antibody would be helpful.

Regarding the recombinant protein used for SPR, its purity was evaluated using a Coomassie-stained gel. For the antibody used in immunoblotting, its specificity was validated through knockout cell lines, ensuring minimal concerns about non-specific immunoactivity within the relevant molecular weight range. Unfortunately, the KO data comes in the paper after the first immunoblots are presented. In the revised manuscript, we will clearly outline these validation steps in the methods section and additional manufacturer documentation for the antibody we used.

(4) Knock-out of ATP5I markedly compromised the NAD/NADH ratio (Fig.3A) and cell proliferation (Figure 3D). These effects may be associated with decreased mitochondrial membrane potential which could explain the low efficacy of metformin (and most of the data in Figures 3-5). This possibility should be discussed. Effects of [metformin] on the NAD/NADH ratio in control cells and ATP5I-KO would have been helpful because the metformin data on cell growth is normalized as fold change relative to control, whereas the NAD/NADH ratio would represent a direct absolute measurement enabling comparison of the absolute effect in control cells with ATP5I KO.

The mitochondrial membrane potential depends on a functional electron transport chain which drives proton pumping from the matrix to the intermembrane space. Metformin can decrease the mitochondrial membrane potential and this usually explained as a consequence of complex I inhibition(4). It has been published the metformin requires this membrane potential to accumulate in mitochondria so the actions of metformin are self-limiting due to this requirement. The reviewer is right that ATP5I KO cells could be resistant to metformin because they may have a lower membrane potential. We do not believe this to be the case because the response to phenformin, another biguanide that can enter mitochondria through the membrane without the need of the OCT transporters(5), is also affected in ATP5IKO cells. Of note, compensatory mechanisms such as enhanced glycolysis, as observed in ATP5I-KO cells (elevated ECAR and increased sensitivity to 2-D-deoxyglucose), and the ATPase activity of F₁F₀-ATP synthase could potentially help maintain membrane potential suggesting that this might not be an issue in the ATP5I KO cells. We will discuss these possibilities in the revised manuscript.

Nevertheless, to experimentally address this point, we propose measuring mitochondrial membrane potential using tetramethylrhodamine methyl ester (TMRE) and ATP levels using luciferase-based assays (CellTiter-Glo) in ATP5I-KO cells.

Regarding the NAD+/NADH in both control and KO cells may not be very helpful because this ratio can be corrected by LDH which is induced as part of the glycolytic adaptation that occurs after inhibition of respiration. Since our KO cells have been propagated already for several passages, the extent of this adaptation is likely different from metformin-treated cells. As we mentioned in answering Reviewer 1, we will provide a more direct measurement of metformin acting on ATP5I: the levels of F1F0-ATPase dimers and monomers.

(5) Figure-6 CRISPR/Cas9 KO at 16mM metformin in comparison with 70nM rotenone and 2 micromolar oligomycin (in serum-containing medium). The rationale for the use of such a high concentration of metformin has not been explained. In liver cells metformin concentrations above 1mM cause severe ATP depletion, whereas therapeutic (micromolar) concentrations have minimal effects on cellular ATP status. The 16mM concentration is ~2 orders of magnitude higher than therapeutic concentrations and likely linked to compromised energy status. The stronger inhibition of cell proliferation by 16mM metformin compared with rotenone or oligomycin raises the issue of whether the changes in gene expression may be linked to the greater inhibition of mitochondrial metabolism. Validation of the cellular ATP status and NAD/NADH with metformin as compared with the two inhibitors could help the interpretation of this data.

To address the reviewer’s final comment, we would like to clarify the rationale behind our experimental approach. NALM-6 cells are very glycolytic, have low respiration rates, and weak dependence on ATP5I (DepMap score: -0.47)(6). The concentration of 16 mM metformin was chosen based on the IC50 for this cell line. This approach aligns with our focus on the anticancer mechanism of action rather than the antidiabetic effects of metformin. Both ATP status and NAD+/NADH ratios will depend on the extent of the compensatory glycolysis. On the other hand, our genetic screening evaluates cell proliferation as an integration of all metabolic activities required for the process. This unbiased screening revealed a common pathway affected by metformin and oligomycin different that the pathway affected by rotenone, which is consistent with the finding that metformin acts of the F₁F₀ATPase.

Reviewer #3 (Public review):

(1) Most of the data are based on measurements of the oxygen consumption rate (OCR) and extracellular acidification rate (ECAR) measured by the Seahorse analyser in control and ATP5l KO cells. However, these measurements are conducted by a single injection of a biguanide, followed over time and presented as fold change. By doing so, the individual information on the effect of metformin and derivate on control and KO cells are lost. In addition, the usual measurement of OCR is coupled with certain inhibitors and uncouplers, such as oligomycin, FCCP, and Antimycin A/rotenone, to understand the contribution of individual complexes to respiration. Since biguanides and ATP5l KO affect protein levels of components of complex I and IV, it would be informative to measure their individual contributions/effects in the Seahorse. To further strengthen the data, it would be helpful to obtain measurements of actual ATP levels in these cells, as this would explain the activation of AMPK.

We appreciate the reviewer’s observations regarding the Seahorse measurements and acknowledge the potential limitations of presenting the data as fold change. Due to experimental challenges in maintaining KP-4 and ATP5I-KO cells with sufficient nutrients, caused by their rapid glucose uptake and subsequent lactate production, it was more practical to present the Seahorse results in this format. Using inhibitors at each time point during the Seahorse experiment was not feasible, as the delay between inhibitor injections and the corresponding changes in oxygen consumption rate (OCR) and extracellular acidification rate (ECAR) would introduce variability and complicate the interpretation of dynamic responses. Nevertheless, we recognize the importance of understanding the contributions of specific respiratory complexes to OCR and ECAR. To address this, we will include a representative figure showcasing a typical Seahorse analysis, highlighting ATP turnover and proton leak after oligomycin addition, maximal respiration with FCCP, and disruption with rotenone and antimycin A. While these experiments are inherently complex due to the metabolic demands of ATP5I-KO cells, this approach will provide a clearer breakdown of mitochondrial activity. Furthermore, as mentioned in our response to Reviewer 2, we will measure ATP levels using a luciferase-based assay (CellTiter-Glo) in both control and ATP5I-KO cells to better explain AMPK activation. This will provide additional context to strengthen the interpretation of mitochondrial function and metabolic compensation mechanisms in these cells.

(2) The authors report on alterations in mitochondrial morphology upon ATP5l KO, which is measured by subjective quantifications of filamentous versus puncta structures. Fiji offers great tools to quantify the mitochondrial network unbiasedly and with more accuracy using deconvolution and skeletonization of the mitochondria, providing the opportunity to measure length, shape, and number quantitatively. This will help to understand better, whether mitochondria are really fragmented upon ATP5l KO and rescued by its re-introduction.

Concerning the analysis of mitochondrial morphology, we acknowledge the potential benefits of using Fiji and additional plugins such as MiNA for more accurate and unbiased quantification. Indeed, this approach could provide stronger evidence for mitochondrial fragmentation upon ATP5I-KO and its potential rescue by ATP5I reintroduction. We will consider integrating this methodology into our analysis to enhance the precision and robustness of our findings.

(3) Finally, the authors report in the last part of the paper a genetic CRISPR/Cas9 KO screen in NALM-6 cells cultured with high amounts of metformin to identify potential new mediators of metformin action. It is difficult to connect that to the rest of the paper because a) different concentrations of metformin are used and b) the metabolic effects on energy consumption are not defined. They argue about the molecular function of the obtained hits based on literature and on a comparison of the pattern of genetic alterations based on treatments with known inhibitors such as oligomycin and rotenone. However, a direct connection is not provided, thus the interpretation at the end of the results that "the OMA1-DEL1-HRI pathway mediates the antiproliferative activity of both biguanides and the F1ATPase inhibitor oligomycin" while increasing glycolysis, needs to be toned down. This is an interesting observation, but no causality is provided. In general, this part stands alone and needs to be better connected to the rest of the paper.

NALM-6 are very glycolytic, have low respiration rates, and weak dependence on ATP5I(6), forcing us to use higher concentrations of metformin to inhibit their growth. Recent results show that metformin targets PEN2 in the cytosol to increase AMPK activity, controlling both the glucose lowering and the life span extension abilities of metformin 7. This work raises the question whether the antiproliferative and anticancer effects of metformin are due to a mitochondrial activity or are controlled by this new pathway of AMPK activation. Hence, the genetic screening was performed to unbiasedly find how metformin works. The results provide compelling evidence for mitochondria and in particular the ATP synthase as potential targets of metformin and a foundation for future studies. We will revise the text and abstract to better reflect the exploratory nature of this finding and ensure clarity.

(1) Paumard, P. et al. Two ATP synthases can be linked through subunits i in the inner mitochondrial membrane of Saccharomyces cerevisiae. Biochemistry 41, 10390-10396 (2002). https://doi.org/10.1021/bi025923g

(2) Paumard, P. et al. The ATP synthase is involved in generating mitochondrial cristae morphology. EMBO J 21, 221-230 (2002). https://doi.org/10.1093/emboj/21.3.221

(3) Habersetzer, J. et al. ATP synthase oligomerization: from the enzyme models to the mitochondrial morphology. Int J Biochem Cell Biol 45, 99-105 (2013). https://doi.org/10.1016/j.biocel.2012.05.017

(4) Xian, H. et al. Metformin inhibition of mitochondrial ATP and DNA synthesis abrogates NLRP3 inflammasome activation and pulmonary inflammation. Immunity 54, 1463-1477 e1411 (2021). https://doi.org/10.1016/j.immuni.2021.05.004

(5) Hawley, S. A. et al. Use of cells expressing gamma subunit variants to identify diverse mechanisms of AMPK activation. Cell metabolism 11, 554-565 (2010). https://doi.org/10.1016/j.cmet.2010.04.001

(6) Hlozkova, K. et al. Metabolic profile of leukemia cells influences treatment efficacy of L-asparaginase. BMC Cancer 20, 526 (2020). https://doi.org/10.1186/s12885-020-07020-y

(7) Ma, T. et al. Low-dose metformin targets the lysosomal AMPK pathway through PEN2. Nature 603, 159-165 (2022). https://doi.org/10.1038/s41586-022-04431-8

eLife Assessment

This valuable manuscript describes ATP5I, a subunit of F1Fo-ATP synthase, as a key target of medicinal biguanides, however, it provides incomplete evidence of a direct interaction between ATP5I and metformin. The knockout of ATP5I in pancreatic cancer cells mimics biguanide treatment, inducing a metabolic switch from OXPHOS to glycolysis due to a compromised expression of the Complex I protein NDUFB8. This results in a markedly decreased NAD/NADH ratio and decreased cell proliferation. These findings point out ATP5I as a promising mitochondrial target for cancer therapies and contribute to our understanding of metformin's mechanism of action since many of its molecular mechanisms remain poorly understood.

Reviewer #1 (Public review):

Summary:

In the manuscript entitled 'The Role of ATP Synthase Subunit e (ATP5I) in 1 Mediating the Metabolic and Antiproliferative 2 Effects of Biguanides', Lefrancois G et al. identifies ATP5I, a subunit of F1Fo-ATP synthase, as a key target of medicinal biguanides. ATP5I stabilizes F1Fo-ATP synthase dimers, essential for cristae morphology, but its role in cancer metabolism is understudied. The research shows ATP5I interacts with a biguanide analogue, and its knockout in pancreatic cancer cells mimics biguanide treatment effects, including altered mitochondria, reduced OXPHOS, and increased glycolysis. ATP5I knockout cells resist biguanide-induced antiproliferative effects, but reintroducing ATP5I restores the effects of metformin and phenformin. These findings highlight ATP5I as a promising mitochondrial target for cancer therapies. The manuscript is well written.

Strengths:

Demonstrated the experiments in systematic and well-accepted methods.

Weaknesses:

The significance of the target molecule and mechanisms may help in understanding the molecular mechanisms of metformin.

Reviewer #2 (Public review):

Summary:

The mechanism(s) by which the therapeutic drug metformin lowers blood glucose in type 2 diabetes and inhibits cell proliferation at higher concentrations remain contentious. Inhibition of complex 1 of the mitochondrial respiratory chain with consequent changes in cellular metabolites which favour allosteric activation of phosphofructokinase-1, allosteric inhibition of fructose bisphosphatase-1 and cAMP signalling and activation of AMPK which phosphorylates transcription factors are candidate mechanisms. The current manuscript proposes the e-subunit of ATP-synthase as a putative binding protein of biguanides and demonstrates that it regulates the expressivity of the Complex 1 protein NDUFB8.

Strengths:

(1) The metformin conjugate and metformin show comparable efficacy on inhibition of cell proliferation in the millimolar range.

(2) Demonstration of compromised expression of the Complex I protein NDUFB8 by the ATP5I knockout and its reversal by ATP5I expression is an important strength of the study. This shows that the decreased "sensitivity" to metformin in the ATP5I knock-out cells could be due to various proteins.

(3) Demonstration of converse effects of ATP5I KO and re-expression ATP5I on the NAD/NADH ratio.

Weaknesses:

(1) The interpretation of the cellular co-localization of the biotin-biguanide conjugate with TOMM20 (Figure 1-D) as mitochondrial "accumulation" of the conjugate is overstated because it cannot exclude binding of the conjugate to the mitochondrial membrane. It would have been more convincing if additional incubations with the biotin-biguanide conjugate in combination with metformin had shown that metformin is competitive with the biotin-conjugate.

(2) The manuscript reports the identification of 69 proteins by mass spectrometry of the pull-down assay of which 31 proteins were eluted by metformin. However, no Mass Spectrometry data is presented of the peptides identified. The methodology does not state the minimum number of peptides (1, 2?) that were used for the identification of the 31/69 proteins.

(3) The validation of ATP5I was based on the use of recombinant protein (which was 90% pure) for the SPR and the use of a single antibody to ATP5I. The validity of the immunoblotting rests on the assumption that there is no "non-specific" immunoactivity in the relevant mol wt range. Information on the validation of the antibody would be helpful.

(4) Knock-out of ATP5I markedly compromised the NAD/NADH ratio (Fig.3A) and cell proliferation (Figure 3D). These effects may be associated with decreased mitochondrial membrane potential which could explain the low efficacy of metformin (and most of the data in Figures 3-5). This possibility should be discussed. Effects of [metformin] on the NAD/NADH ratio in control cells and ATP5I-KO would have been helpful because the metformin data on cell growth is normalized as fold change relative to control, whereas the NAD/NADH ratio would represent a direct absolute measurement enabling comparison of the absolute effect in control cells with ATP5I KO.

(5) Figure-6 CRISPR/Cas9 KO at 16mM metformin in comparison with 70nM rotenone and 2 micromolar oligomycin (in serum-containing medium). The rationale for the use of such a high concentration of metformin has not been explained. In liver cells metformin concentrations above 1mM cause severe ATP depletion, whereas therapeutic (micromolar) concentrations have minimal effects on cellular ATP status. The 16mM concentration is ~2 orders of magnitude higher than therapeutic concentrations and likely linked to compromised energy status. The stronger inhibition of cell proliferation by 16mM metformin compared with rotenone or oligomycin raises the issue of whether the changes in gene expression may be linked to the greater inhibition of mitochondrial metabolism. Validation of the cellular ATP status and NAD/NADH with metformin as compared with the two inhibitors could help the interpretation of this data.

Reviewer #3 (Public review):

Most of the data are based on measurements of the oxygen consumption rate (OCR) and extracellular acidification rate (ECAR) measured by the Seahorse analyser in control and ATP5l KO cells. However, these measurements are conducted by a single injection of a biguanide, followed over time and presented as fold change. By doing so, the individual information on the effect of metformin and derivate on control and KO cells are lost. In addition, the usual measurement of OCR is coupled with certain inhibitors and uncouplers, such as oligomycin, FCCP, and Antimycin A/rotenone, to understand the contribution of individual complexes to respiration. Since biguanides and ATP5l KO affect protein levels of components of complex I and IV, it would be informative to measure their individual contributions/effects in the Seahorse. To further strengthen the data, it would be helpful to obtain measurements of actual ATP levels in these cells, as this would explain the activation of AMPK.

The authors report on alterations in mitochondrial morphology upon ATP5l KO, which is measured by subjective quantifications of filamentous versus puncta structures. Fiji offers great tools to quantify the mitochondrial network unbiasedly and with more accuracy using deconvolution and skeletonization of the mitochondria, providing the opportunity to measure length, shape, and number quantitatively. This will help to understand better, whether mitochondria are really fragmented upon ATP5l KO and rescued by its re-introduction.

Finally, the authors report in the last part of the paper a genetic CRISPR/Cas9 KO screen in NALM-6 cells cultured with high amounts of metformin to identify potential new mediators of metformin action. It is difficult to connect that to the rest of the paper because a) different concentrations of metformin are used and b) the metabolic effects on energy consumption are not defined. They argue about the molecular function of the obtained hits based on literature and on a comparison of the pattern of genetic alterations based on treatments with known inhibitors such as oligomycin and rotenone. However, a direct connection is not provided, thus the interpretation at the end of the results that "the OMA1-DEL1-HRI pathway mediates the antiproliferative activity of both biguanides and the F1ATPase inhibitor oligomycin" while increasing glycolysis, needs to be toned down. This is an interesting observation, but no causality is provided. In general, this part stands alone and needs to be better connected to the rest of the paper.

eLife Assessment

This valuable study utilizes a newly developed approach to culture T gondii bradyzoites in myotubes, and then takes advantage of the antiparasitic compound collection known as the Pathogen Box, to find compounds that target both tachyzoite and bradyzoite forms of the parasite. A set of compounds yielding patterns consistent with targeting the mitochondrial bc1 complex was explored further, with solid evidence for changes in ATP production in bradyzoites to support the conclusions about the importance of this complex. The paper will be interesting for parasitologists studying drug discovery of apicomplexan parasites.

Reviewer #1 (Public review):

Summary:

The authors' goal was to advance the understanding of metabolic flux in the bradyzoite cyst form of the parasite T. gondii, since this is a major form of transmission of this ubiquitous parasite, but very little is understood about cyst metabolism and growth.

Nonetheless, this is an important advance in understanding and targeting bradyzoite growth.

Strengths:

The study used a newly developed technique for growing T. gondii cystic parasites in a human muscle-cell myotube format, which enables culturing and analysis of cysts. This enabled the screening of a set of anti-parasitic compounds to identify those that inhibit growth in both vegetative (tachyzoite) forms and bradyzoites (cysts). Three of these compounds were used for comparative Metabolomic profiling to demonstrate differences in metabolism between the two cellular forms.

One of the compounds yielded a pattern consistent with targeting the mitochondrial bc1 complex and suggests a role for this complex in metabolism in the bradyzoite form, an important advance in understanding this life stage.

Weaknesses:

Studies such as these provide important insights into the overall metabolic differences between different life stages, and they also underscore the challenge of interpreting individual patterns caused by metabolic inhibitors due to the systemic level of some of the targets, so that some observed effects are indirect consequences of the inhibitor action. While the authors make a compelling argument for focusing on the role of the bc1 complex, there are some inconsistencies in the patterns that underscore the complexity of metabolic systems.

Reviewer #2 (Public review):

Summary:

A particular challenge in treating infections caused by the parasite Toxoplasma gondii is to target (and ultimately clear) the tissue cysts that persist for the lifetime of an infected individual. The study by Maus and colleagues leverages the development of a powerful in vitro culture system for the cyst-forming bradyzoite stage of Toxoplasma parasites to screen a compound library for candidate inhibitors of parasite proliferation and survival. They identify numerous inhibitors capable of inhibiting both the disease-causing tachyzoite and the cyst-forming bradyzoite stages of the parasite. To characterize the potential targets of some of these inhibitors, they undertake metabolomic analyses. The metabolic signatures from these analyses lead them to identify one compound (MMV1028806) that interferes with aspects of parasite mitochondrial metabolism. The authors claim that MV1028806 targets the bc1 complex of the mitochondrial electron transport chain of the parasite, although the evidence for this is indirect and speculative. Nevertheless, the study presents an exciting approach for identifying and characterizing much-needed inhibitors for targeting tissue cysts in these parasites.

Strengths:

The study presents convincing proof-of-principle evidence that the myotube-based in vitro culture system for T. gondii bradyzoites can be used to screen compound libraries, enabling the identification of compounds that target the proliferation and/or survival of this stage of the parasite. The study also utilizes metabolomic approaches to characterize metabolic 'signatures' that provide clues to the potential targets of candidate inhibitors, although falls short of identifying the actual targets.

Weaknesses:

(1) The authors claim to have identified a compound in their screen (MMV1028806) that targets the bc1 complex of the mitochondrial electron transport chain (ETC). The evidence they present for this claim is indirect (metabolomic signatures and changes in mitochondrial membrane potential) and could be explained by the compound targeting other components of the ETC or affecting mitochondrial biology or metabolism in other ways. In order to make the conclusion that MMV1028806 targets the bc1 complex, the authors should test specifically whether MMV1028806 inhibits bc1-complex activity (i.e. in a direct enzymatic assay for bc1 complex activity). Testing the activity of MMV1028806 against other mitochondrial dehydrogenases (e.g. dihydroorotate dehydrogenase) that feed electrons into the ETC might also provide valuable insights. The experiments the authors perform also do not directly measure whether MMV1028806 impairs ETC activity, and the authors could also test whether this compound inhibits mitochondrial O2 consumption (as would be expected for a bc1 inhibitor).

(2) The authors claim that compounds targeting bradyzoites have greater lipophilicity than other compounds in the library (and imply that these compounds also have greater gastrointestinal absorbability and permeability across the blood-brain barrier). While it is an attractive idea that lipophilicity influences drug targeting against bradyzoites, the effect seems pretty small and is complicated by the fact that the comparison is being made to compounds that are not active against parasites. If the authors are correct in their assertion that lipophilicity is a major determinant of bradyzoicidal compounds compared to compounds that target tachyzoites alone, you would expect that compounds that target tachyzoites alone would have lower lipophilicity than those that target bradyzoites. It would therefore make more sense to (statistically) compare the bradyzoicidal and dual-acting compounds to those that are only active in tachyzoites (visually the differences seem small in Figure S2B). This hypothesis would be better tested through a structure-activity relationship study of select compounds (which is beyond the scope of the study). Overall, the evidence the authors present that high lipophilicity is a determinant of bradyzoite targeting is not very convincing, and the authors should present their conclusions in a more cautious manner.

(3) Page 11 and Figure 7. The authors claim that their data indicate that ATP is produced by the mitochondria of bradyzoites "independently of exogenous glucose and HDQ-target enzymes." The authors cite their previous study (Christiansen et al, 2022) as evidence that HDQ can enter bradyzoites, since HDQ causes a decrease in mitochondrial membrane potential. Membrane potential is linked to the synthesis of ATP via oxidative phosphorylation. If HDQ is really causing a depletion of membrane potential, is it surprising that the authors observe no decrease in ATP levels in these parasites? Testing the importance of HDQ-target enzymes using genetic approaches (e.g. gene knockout approaches) would provide better insights than the ATP measurements presented in the manuscript, although would require considerable extra work that may be beyond the scope of the study. Given that the authors' assay can't distinguish between ATP synthesized in the mitochondrion vs glycolysis, they may wish to interpret their data with greater caution.

Reviewer #3 (Public review):

Summary:

The authors describe an exciting 400-drug screening using a MMV pathogen box to select compounds that effectively affect the medically important Toxoplasma parasite bradyzoite stage. This work utilises a bradyzoites culture technique that was published recently by the same group. They focused on compounds that affected directly the mitochondria electron transport chain (mETC) bc1-complex and compared them with other bc1 inhibitors described in the literature such as atovaquone and HDQs. They further provide metabolomics analysis of inhibited parasites which serves to provide support for the target and to characterise the outcome of the different inhibitors.

Strengths:

This work is important as, until now, there are no effective drugs that clear cysts during T. gondii infection. So, the discovery of new inhibitors that are effective against this parasite stage in culture and thus have the potential to battle chronic infection is needed. The further metabolic characterization provides indirect target validation and highlights different metabolic outcomes for different inhibitors. The latter forms the basis for new studies in the field to understand the mode of inhibition and mechanism of bc1-complex function in detail.

The authors focused on the function of one compound, MMV1028806, that is demonstrated to have a similar metabolic outcome to burvaquone. Furthermore, the authors evaluated the importance of ATP production in tachyzoite and bradyzoites stages and under atovaquone/HDQs drugs.

Weaknesses:

Although the authors did experiments to identify the metabolomic profile of the compounds and suggested bc-1 complex as the main target of MMV1028806, they did not provide experimental validation for that.

Author response:

We thank the reviewers for taking the time to read and critically assess our manuscript.

We agree with the main points and they will be addressed in both writing and in additional experiments in a revised version of the paper.

The shared and major point of criticism are non-conclusive metabolomic data that indicate the bc1-complex in T. gondii as a MMV1028806 target tachyzoites and bradyzoites. Regarding the former, our conclusion was mainly based on both metabolite abundance changes that are observed after treatment with one bona-fide bc1-complex inhibitor atovaquone and also steady-state stable isotope incorporation patterns. While it is true that secondary effects of metabolic inhibition occur and are often dominant, isotope labelling equilibria take more time to establish and may reflect more accurately blocked metabolic reactions i.e. the primary target.

Regardless, we will follow the excellent suggestions to functionally assay particular mitochondrial electron transfer reactions to corroborate or revise our conclusions regarding the primary MMV1028806 target.

For more details please refer the full author responses that will accompany the revised manuscript.

eLife Assessment

The study provides valuable insights into the mechanisms underlying neurovascular coupling; the authors present solid evidence demonstrating that layer II/III pyramidal neurons can induce vasoconstriction under conditions of intense optogenetic stimulation. They identify three distinct signalling pathways responsible for this effect, involving direct action on smooth muscle cells, as well as indirect modulation via interneurons or astrocytes. This work will be of interest to the broader neuroscience community and has potential implications for understanding pathological microcirculation in the brain, particularly in conditions characterized by strong excitatory neuronal activation. There are however questions that should be clarified, especially the conflict between three identified parallel pathways and the observed complete inhibition of the constriction by blockage of the NPY Y1 receptors.

Reviewer #1 (Public review):

Neuronal activity spatiotemporal fine-tuning of cerebral blood flow balances metabolic demands of changing neuronal activity with blood supply. Several 'feed-forward' mechanisms have been described that contribute to activity-dependent vasodilation as well as vasoconstriction leading to a reduction in perfusion. Involved messengers are ionic (K+), gaseous (NO), peptides (e.g., NPY, VIP), and other messengers (PGE2, GABA, glutamate, norepinephrine) that target endothelial cells, smooth muscle cells, or pericytes. Contributions of the respective signaling pathways likely vary across brain regions or even within specific brain regions (e.g., across the cortex) and are likely influenced by the brain's physiological state (resting, active, sleeping) or pathological departures from normal physiology.

The manuscript "Elevated pyramidal cell firing orchestrates arteriolar vasoconstriction through COX-2-derived prostaglandin E2 signaling" by B. Le Gac, et al. investigates mechanisms leading to activity-dependent arteriole constriction. Here, mainly working in brain slices from mice expressing channelrhodopsin 2 (ChR2) in all excitatory neurons (Emx1-Cre; Ai32 mice), the authors show that strong optogenetic stimulation of cortical pyramidal neurons leads to constriction that is mediated through the cyclooxygenase-2 / prostaglandin E2 / EP1 and EP3 receptor pathway with contribution of NPY-releasing interneurons and astrocytes releasing 20-HETE. Specifically, using a patch clamp, the authors show that 10-s optogenetic stimulation at 10 and 20 Hz leads to vasoconstriction (Figure 1), in line with a stimulation frequency-dependent increase in somatic calcium (Figure 2). The vascular effects were abolished in the presence of TTX and significantly reduced in the presence of glutamate receptor antagonists (Figure 3). The authors further show with RT-PCR on RNA isolated from patched cells that ~50% of analyzed cells express COX-1 or -2 and other enzymes required to produce PGE2 or PGF2a (Figure 4). Further, blockade of COX-1 and -2 (indomethacin), or COX-2 (NS-398) abolishes constriction. In animals with chronic cranial windows that were anesthetized with ketamine and medetomidine, 10-s long optogenetic stimulation at 10 Hz leads to considerable constriction, which is reduced in the presence of indomethacin. Blockade of EP1 and EP3 receptors leads to a significant reduction of the constriction in slices (Figure 5). Finally, the authors show that blockade of 20-HETE synthesis caused moderate and NPY Y1 receptor blockade a complete reduction of constriction.

The mechanistic analysis of neurovascular coupling mechanisms as exemplified here will guide further in-vivo studies and has important implications for human neuroimaging in health and disease. Most of the data in this manuscript uses brain slices as an experimental model which contrasts with neurovascular imaging studies performed in awake (headfixed) animals. However, the slice preparation allows for patch clamp as well as easy drug application and removal. Further, the authors discuss their results in view of differences between brain slices and in vivo observations experiments, including the absence of vascular tone as well as blood perfusion required for metabolite (e.g., PGE2) removal, and the presence of network effects in the intact brain. The manuscript and figures present the data clearly; regarding the presented mechanism, the data supports the authors' conclusions. Some of the data was generated in vivo in head-fixed animals under anesthesia; in this regard, the authors should revise the introduction and discussion to include the important distinction between studies performed in slices, or in acute or chronic in-vivo preparations under anesthesia (reduced network activity and reduced or blockade of neuromodulation, or in awake animals (virtually undisturbed network and neuromodulatory activity). Further, while discussed to some extent, the authors could improve their manuscript by more clearly stating if they expect the described mechanism to contribute to CBF regulation under 'resting state conditions' (i.e., in the absence of any stimulus), during short or sustained (e.g., visual, tactile) stimulation, or if this mechanism is mainly relevant under pathological conditions; especially in the context of the optogenetic stimulation paradigm being used (10-s long stimulation of many pyramidal neurons at moderate-high frequencies) and the fact that constriction leading to undersupply in response to strongly increased neuronal activity seems counterintuitive?

Reviewer #2 (Public review):

The present study by Le Gac et al. investigates the vasoconstriction of cerebral arteries during neurovascular coupling. It proposes that pyramidal neurons firing at high frequency lead to prostaglandin E2 (PGE2) release and activation of arteriolar EP1 and EP3 receptors, causing smooth muscle cell contraction. The authors further claim that interneurons and astrocytes also contribute to vasoconstriction via neuropeptide Y (NPY) and 20-hydroxyeicosatetraenoic acid (20-HETE) release, respectively. The study mainly uses brain slices and pharmacological tools in combination with Emx1-Cre; Ai32 transgenic mice expressing the H134R variant of channelrhodopsin-2 (ChR2) in the cortical glutamatergic neurons for precise photoactivation. Stimulation with 470 nm light using 10-second trains of 5-ms pulses at frequencies from 1-20 Hz revealed small constrictions at 10 Hz and robust constrictions at 20 Hz, which were abolished by TTX and partially inhibited by a cocktail of glutamate receptor antagonists. Inhibition of cyclooxygenase-1 (COX-1) or -2 (COX-2) by indomethacin blocked the constriction both ex vivo (slices) and in vivo (pial artery), and inhibition of EP1 and EP3 showed the same effect ex vivo. Single-cell RT-PCR from patched neurons confirmed the presence of the PGE2 synthesis pathway.

While the data are convincing, the overall experimental setting presents some limitations. How is the activation protocol comparable to physiological firing frequency? The delay (minutes) between the stimulation and the constriction appears contradictory to the proposed pathway, which would be expected to occur rapidly. The experiments are conducted in the absence of vascular "tone," which further questions the significance of the findings. Some of the targets investigated are expressed by multiple cell types, which makes the interpretation difficult; for example, cyclooxygenases are also expressed by endothelial cells. Finally, how is the complete inhibition of the constriction by the NPY Y1 receptor antagonist BIBP3226 consistent with a direct effect of PGE2 and 20-HETE in arterioles? Overall, the manuscript is well-written with clear data, but the interpretation and physiological relevance have some limitations. However, vasoconstriction is a rather understudied phenomenon in neurovascular coupling, and the present findings may be of significance in the context of pathological brain hypoperfusion.

eLife Assessment

This valuable study combines experiments and theory to investigate the putative role of spontaneous correlated activity in establishing aligned topographic maps of neural activity in higher-order sensory areas, and will be of interest to researchers studying multisensory integration and brain development. However, the evidence presented is incomplete, as there are notable disconnects between the experimental data and the modeling setup, and there are methodological details that are either unclear or missing, limiting the strength of the claims.

Reviewer #1 (Public review):

Dwulet et al. combined experimental and modeling approaches to investigate how correlated spontaneous activity in the mouse's primary visual (V1) and primary somatosensory (S1) areas drives the development of multisensory integration in area RL. Notably, they focused on early developmental stages, before sensory experience occurs. Consistent with previous experimental findings, the authors first demonstrated that spontaneous activity becomes more sparse across development in all three areas, as measured by event amplitude, event duration, and participation ratio. Using a linear mixed model analysis to compare the maturation of this spontaneous activity, they found evidence that S1 matured the fastest. The authors then presented experimental evidence suggesting that these spontaneous events were moderately correlated both spatially and temporally.

They hypothesized that activity-dependent mechanisms use these correlations to establish connectivity across these regions. To test this hypothesis, the authors modeled a feedforward network with connections from S1 to RL and from V1 to RL, where the strength of connections depended on a Hebbian term for potentiation and a heterosynaptic term for depression. By investigating different levels of V1-S1 correlations, they found that moderate levels of correlation led to the significant development of topographically organized connectivity while maintaining a mix of bimodal and unimodal cells in RL. Additionally, when simulating a network with a more mature S1, they observed that topographical maps improved not only between S1 and RL but also between V1 and RL. Finally, the authors use linear regression to suggest that the mixture of bimodal and unimodal cells in RL is optimal for encoding the maximum amount of information from both V1 and S1.

However, there are significant gaps between the experimental data and the modeling setup, which weaken the paper's conclusions. Additionally, some key details are omitted, making it difficult to fully assess their analysis and interpret some of their figures.

(1) Some of the statistical measures and techniques in Figure 1 could benefit from clearer definitions. While the thresholds for activation (peak with at least 5% dF/F0) and events (20% of recorded cells activated simultaneously) are provided, event duration and participation rate are not clearly defined. Based on this definition of event alone, it is unclear why the minimum participation rate in Figure 1F is not 20%. Additionally, the conclusion that S1 matures earlier than RL and V1 could be strengthened by including a direct comparison between S1 and RL, as the current analysis only compares these areas to V1.

(2) The wide-field experiments in Figure 2 could be expanded to support the feedforward modeling assumptions. Currently, the spatial and temporal correlations presented leave open the possibility that these spontaneous events are traveling waves propagating from V1 to RL to S1 (or vice versa). This scenario would suggest a different connectivity scheme for the model. Clarifying this point with additional data analysis, specifically including temporal correlations involving RL, could provide stronger support for the model's assumptions.

(3) The functional correlation map in Figure 2D appears contradictory to the authors' modeling assumption that inputs are correlated spatially in V1 and S1. While V1 seed points align topographically with RL, this organization breaks down when extended into S1. In contrast, and in support of the modeling assumption, Figure 2E shows clearer topography across all three regions. A discussion of this discrepancy would be helpful, as it's a key conclusion of the figure. Additionally, it is unclear when this data was collected during development. Clarifying the developmental stage and analyzing how this map changes over time could strengthen the results.

(4) The modeling of spontaneous events with fixed amplitude and duration seems inconsistent with the experimental data in Figure 1, which shows variability in these parameters. This is particularly confusing in Figure 4, where S1 maturation is modeled as a stronger topographical alignment with RL, but the experimental data defines maturation based on amplitude, duration, and event rates. Justifying these modeling choices or adapting the model to reflect experimental variability would create a better connection between the theory and data.

(5) Several important details of the mathematical model are missing or unclear, partly due to typos. The Results section mentions the general framework of the input correlation matrix (e.g., "S1 and V1 neurons were driven by a combination of events, independent and shared in each V1 and S1" and "each independent event activated a randomly chosen, contiguous set of neurons"), but the specifics are not fully explained. Additionally, the caption of Figure 5 refers to a non-linear transfer function (a sigmoid), but these details are not provided in the Methods section, which instead suggests a linear model was used. A careful review of the main text and Methods section would help ensure that all the necessary details are included and that the story is both complete and accurate.

(6) While Figure 5 supports the paper's conclusion that a mixture of unimodal and bimodal neurons in RL optimizes information encoding, the authors missed an opportunity to strengthen the connection between the model and experimental data. Specifically, they could apply this reconstruction method to the experimental data and examine how RL's ability to reconstruct V1/S1 activity changes across development. Their model predicts that this performance would improve over time, and if this trend is observed in the experimental data, it would provide strong validation that these feedforward connections are developing in line with the model's predictions.

Reviewer #2 (Public review):

The authors aim to investigate the role of spontaneous activity in shaping the development of multisensory integration in the brain, specifically focusing on the connections between primary visual and somatosensory sensory areas (V1 and S1) and a higher-order cortical area rostro-lateral to V1 (RL). They seek to understand how spontaneous activity guides the formation of aligned topographic maps and the emergence of bimodal neurons in RL.

First, the authors found that spontaneous activity in all three areas sparsifies over time, but S1 exhibits more mature patterns earlier than V1 and RL. They claimed that correlated activity among neighboring regions of these areas during development carries topographic information. These data were used to implement a computational model that employed Hebbian rules of synaptic plasticity. The model indicated that correlated spontaneous activity can generate topographic connectivity between S1/V1 and RL and bimodal neurons in RL. The model suggested that the more mature spontaneous activity in S1 can guide map alignment between V1 and RL. In addition, the model also suggested that a mixture of bimodal and unimodal neurons in RL is optimal for decoding information from V1 and S1.

While the data presented in the manuscript is promising and provides preliminary insights into the role of spontaneous activity in multisensory integration, it would be beneficial to strengthen the experimental foundation regarding the correlation between V1, S1, and RL. Incorporating more rigorous spatio-temporal analyses of spontaneous activity could enhance the robustness of these findings.

Here are some important concerns:

(1) The analysis of how spatial topography influences activity correlations in Figure 2 has several issues.<br /> 1a. While squares in V1 and S1 covered a small area of these sensory areas, the correlated territories in RL covered the entire area of RL. The topographic map in V1 continues caudally, so where is the rest of the map in RL? Something similar applies to the relationship between S1 and RL.<br /> 1b. It is essential to know how areas were drawn. High precision is required.<br /> 1c. It is not clear if correlated activity means different events in sync or large events that cover 2 or all 3 cortical areas of interest. The figure points to the second option, which contradicts the size of events at these stages, mainly in the oldest mice analyzed here.<br /> 1d. It is fundamental to know in detail and provide examples of how the detection of events was performed. For instance, could the dispersion of light from an event in V1 close to RL cause the detection of activity in RL?

(2) For the correlations among V1, S1, and RL, it is crucial to have a consistent method to delineate the borders of cortical areas. The authors mention in one sentence that areas were drawn according to a reference map. More details are needed to convince the reader that the borders are accurate, especially because their shape and position change with age.

(3) The results from the model seem to be based on the initial bias in connectivity between neighboring cells from the different areas. Then, it seems straightforward that implementing correlated activity with Hebbian and synaptic depression rules will force the strengthening of connections between spatially close cells. Despite this apparent predisposition of the model towards a defined outcome, the flaws in the experimental data used prevent a rigorous interpretation of the computational model.

(4) In the Introduction, the authors nicely and briefly explain the role of primary and higher-order sensory cortices in information processing. They also explain how spontaneous activity during development helps to build these circuits by refining connections or establishing hierarchies. They continue explaining the relevance of aligning different topographic maps to allow multisensory integration. Then they provide some examples of sites of multisensory integration. This provides a general context for the data presented in the Results section; however, and importantly, there is no specific introduction of why they are interested in RL and its interaction with V1 and S1. The authors should introduce the RL area and explain why it is an interesting site for multisensory processing.

(5) The results shown in Figure 1 corroborate published data from Golshani et al, Rochefort et al, Murakami et al. While the reproduction of data is more than welcome, the authors should specify which part of the data is completely new and acknowledge clearly the rest as corroboration of previous data. The sentence "As described in previous experiments ..." partially acknowledges this fact but is not clear enough. In addition, the transition between this part of the manuscript and the next data is not smooth. Data seems to be used to feed the model so perhaps the organization of the manuscript leaves room for improvement.

Reviewer #3 (Public review):

Summary:

The study by Dwulet et al. explores how the development of spontaneous neural activity in primary sensory cortices influences the co-alignment of multiple sensory modalities in higher-order brain areas (HOAs). To address this question, they focus on connectivity between the primary visual (V1) and somatosensory (S1) cortices and an associative cortical area (RL) in mice. The authors combine experimental (wide-field and two-photon calcium imaging) and computational approaches to show that spontaneous activity matures at a different pace across these brain regions. Their data indicate that S1 develops more rapidly than V1, which is possibly beneficial for RL's integration of visual and somatosensory inputs through correlated spontaneous activity. Using a computational model, they demonstrate that a moderate correlation between V1 and S1 activity can optimally guide the formation of bimodal neurons in RL, which are crucial for maximizing the decodability of multisensory stimuli. This finding highlights the role of correlated spontaneous activity in primary sensory cortices in establishing co-aligned topographic multimodal sensory representations in downstream circuits.

Strengths:

The manuscript is well written and it provides strong enough evidence to support the main claim of the authors. The insights on the role of correlated activity on instructing co-aligned multisensory maps in HOAs are not trivial and are an important advancement for the field.

Weaknesses:

In the opinion of this reviewer, the study has no major weaknesses. A drawback of the work is that none of the predictions of the computational modeling have been corroborated through mechanistic experimental manipulations of early brain activity.

Author response:

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

The reviewers suggest a number of experiments and re-analyses to strengthen their claims and enhance the impact of the study. While a number of these are longer term, below is a summary of experiments and analyses recommended by the reviewers that can be accomplished in the shorter term:

(1) Clarification of statistical approaches, quantification, data presentation and description of cerebellar anatomical nomenclature (e.gs. detailed statistical methods for the GEO dataset analysis, FDR correction, quantification in Figs 2-4)

The revised manuscript will provide detailed statistical methods including FDR  correction for GEO dataset analyses and quantification. Please see specific responses to GEO dataset analyses below.

(2) Improved quality of images for select immunostains and in situ hybridization

The revised manuscript will address the quality of the images as indicated by the reviewers.

(3) Include a control group of hGFAP-Cre mice with loxP sites but without Sufu deletion to assess the effects of Cre-induced double-strand breaks on phosphorylated H2AX-DSB signaling.

The breeding scheme we used to generate homozygous SUFU conditional mutants will not generate pups carrying only hGFAP-Cre. Thus, we are unable to compare expression of gH2AX expression in littermates that do not carry loxP sites. The reviewer is correct in pointing out the possibility of Cre recombinase activity inducing double-strand breaks on its own. However, it is likely that any hGFAP-Cre induced double-strand breaks does not sufficiently cause the phenotypes we observed in homozygous mutants (Sufu-cKO) mice because the cerebellum of mice carry heterozygous SUFU mutations (hGFAP-Cre;Sufu-fl/+) do not display the profound cerebellar phenotypes observed in Sufu-cKO mice. We cannot rule out, however, any undetectable abnormalities that could be present which may require further analyses.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

SUFU modulates Sonic hedgehog (SHH) signaling and is frequently mutated in the B-subtype of SHH-driven medulloblastoma. The B-subtype occurs mostly in infants, is often metastatic, and lacks specific treatment. Yabut et al. found that Fgf5 was highly expressed in the B-subtype of SHH-driven medulloblastoma by examining a published microarray expression dataset. They then investigated how Fgf5 functions in the cerebellum of mice that have embryonic Sufu loss of function. This loss was induced using the hGFAP-cre transgene, which is expressed in multiple cell types in the developing cerebellum, including granule neuron precursors (GNPs) derived from the rhombic lip. By measuring the area of Pax6+ cells in the external granule cell layer (EGL) of Sufu-cKO mice at postnatal day 0, they find Pax6+ cells occupy a larger area in the posterior lobe adjacent to the secondary fissure, which is poorly defined. They show that Fgf5 RNA and phosphoErk1/2 immunostaining are also higher in the same disrupted region. Some of the phosphoErk1/2+ cells are proliferative in the Sufu-cKO. Western blot analysis of Gli proteins that modulate SHH signaling found reduced expression and absence of Gli1 activity in the region of cerebellar dysgenesis in Sufu-cKO mice. This suggests the GNP expansion in this region is independent of SHH signaling. Amazingly, intraventricular injection of the FGFR1-2 antagonist AZD4547 from P0-4 and examined histologically at P7 found the treatment restored cytoarchitecture in the cerebella of Sufu-cKO mice. This is further supported by NeuN immunostaining in the internal granule cell layer, which labels mature, non-diving neurons, and KI67 immunostaining, indicating dividing cells, and primarily found in the EGL. The mice were treated beginning at a timepoint when cerebellar cytoarchitecture was shown to be disrupted and it is indistinguishable from control following treatment. Figure 3 presents the most convincing and exciting data in this manuscript.

Sufu-cKO do not readily develop cerebellar tumors. The authors detected phosphorylated H2AX immunostaining, which labels double-strand breaks, in some cells in the EGL in regions of cerebellar dysgenesis in the Sufu-cKO, as was cleaved Caspase 3, a marker of apoptosis. P53, downstream of the double-strand break pathway, the protein was reduced in Sufu-cKO cerebellum. Genetically removing p53 from the Sufu-cKO cerebellum resulted in cerebellar tumors in 2-month old mice. The Sufu;p53-dKO cerebella at P0 lacked clear foliation, and the secondary fissure, even more so than the Sufu-cKO. Fgf5 RNA and signaling (pERK1/2) were also expressed ectopically.

The conclusions of the paper are largely supported by the data, but some data analysis need to be clarified and extended.

(1) The rationale for examining Fgf5 in medulloblastoma is not sufficiently convincing. The authors previously reported that Fgf15 was upregulated in neocortical progenitors of mice with conditional loss of Sufu (PMID: 32737167). In Figure 1, the authors report FGF5 expression is higher in SHH-type medulloblastoma, especially the beta and gamma subtypes mostly found in infants. These data were derived from a genome-wide dataset and are shown without correction for multiple testing, including other Fgfs. Showing the expression of other Fgfs with FDR correction would better substantiate their choice or moving this figure to later in the manuscript as support for their mouse investigations would be more convincing.

To assess FGF5 (ENSG00000138675) expression in MB tissues, we used Geo2R (Barrett et al., 2013) to analyze published human MB subtype expression arrays from accession no. GSE85217 (Cavalli et al., 2017). GEO2R is an interactive web tool that compares expression levels of genes of interest (GOI) between sample groups in the GEO series using original submitter-supplied processed data tables. We entered the GOI Ensembl ID and organized data sets according to age and MB subgroup or MB^SHH subtype classifications. GEO2R results presented gene expression levels as a table ordered by FDR-adjusted (Benjamini & Hochberg) p-values, with significance level cut-off at 0.05, processed by GEO2R’s built-in limma statistical test. Resulting data were subsequently exported into Prism (GraphPad). We generated scatter plots presenting FGF5 expression levels across all MB subgroups (Figure 1A) and MB^SHH subtypes (Figure 1D). We performed additional statistical analyses to compare FGF5 expression levels between MB subgroups and MB^SHH subtypes and graphed these data as violin plots (Figure 1B, 1C, and 1E). For these analyses, we used one-way ANOVA with Holm-Sidak’s multiple comparisons test, single pooled variance. P value ≤0.05 was considered statistically significant. Graphs display the mean ± standard error of the mean (SEM).

Author response image 1.

Comparative expression of FGF ligands, FGF5, FGF10, FGF12, and FGF19, across all MB subgroups. FGF12 expression is not significantly different, while FGF5, FGF10, and FGF19, show distinct upregulation in MB<sup>SHH subgroup (MB^WNT n=70, MB^SHH n=224, MB^GR3 n=143, MB^GR4 n=326).

Expression of the 21 known FGF ligands were also analyzed. Many FGFs did not exhibit differential expression levels in MB^SHH compared to other MB subgroups, such as with FGF12 in Figure 1. FGF5, FGF10, and FGF19 (the human orthologue of mouse FGF15) all showed specific upregulation in MB^SHH compared to other MB subgroups (Author response image 1), supporting our previous observations that FGF15 is a downstream target of SHH signaling (Yabut et al., 2020), as the reviewer pointed out. However, further stratification of MB^SHH patient data revealed that only FGF5 specifically showed upregulation in infants with MBSHH (MB^SHHβ and MB^SHHγ Author response image 2) indicating a more prominent role for FGF5 in the developing cerebellum and driver of MB^SHH tumorigenesis in this dynamic environment.

Author response image 2.

Comparative expression of FGF5, FGF10, and FGF19 in different MB^SHH subtypes. FGF5 specifically show mRNA relative levels above 6 in 81% of MB^SHH infant patient tumors (n=80 MB^SHHα and MB^SHHγ tumors) unlike 35% of MB^SHHα (n=65) or 0% of MB^SHHδ  (n=75) tumors.

(2) The Sufu-cKO cerebellum lacks a clear anchor point at the secondary fissure and foliation is disrupted in the central and posterior lobes. It would be helpful for the authors to review Sudarov & Joyner (PMID: 18053187) for nomenclature specific to the developing cerebellum.

The reviewers are correct that the cerebellar foliation is severely disrupted in central and posterior lobes, as per Sudarov and Joyner (Neural Development 2007). This nomenclature may be referred to describe the regions referred in this manuscript.

(3) The metrics used to quantify cerebellar perimeter and immunostaining are not sufficiently described. It is unclear whether the individual points in the bar graph represent a single section from independent mice, or multiple sections from the same mice. For example, in Figures 2B-D. This also applies to Figure 3C-D.

All quantification were performed from 2-3 20 um cerebellar sections of 3-6 independent mice per genotype analyzed. Individual points in the bar graphs represent the average cell number (quantified from 2-3 sections) from each mice. Figure 2B show data points from n=4 mice per genotype. Figure 2C show data from n=3 mice per genotype. Figure 2D show data from n=6 mice per genotype.  Figure 3C-D show data from n=3 mice per genotype.

(4) The data on Fgf5 RNA expression presented in Figure 2E are not sufficiently convincing. The perimeter and cytoarchitecture of the cerebellum are difficult to see and the higher magnification shown in 2F should be indicated in 2E.

The lack of foliation in Sufu-cKO cerebellum is clear particularly when visualizing the perimeter via DAPI labeling (Figure 2E). The expression area of FGF5 is also visibly larger, given that all images in Figure 2E are presented in the same scale (scale bars = 500 um). 

(5) The data presented in Figure 3 are not sufficiently convincing. The number of cells double positive for pErk and KI67 (Figure 3B) are difficult to see and appear to be few, suggesting the quantification may be unreliable.

We used KI67+ expression to provide a molecular marker of regions to be quantified in both WT and Sufu-cKO sections. Quantification of labeled cells were performed in images obtained by confocal microscopy, enabling imaging of 1-2 um optical slices since Ki67 or pERK expression might not localize within the same cellular compartments. We relied on continuous DAPI nuclear staining to distinguish individual cells in each optical slice and the colocalization of of Ki67 and pERK. All quantification were performed from 2-3 20 um cerebellar sections of 3-6 independent mice per genotype analyzed. Individual points in the bar graphs represent the average cell number (quantified from 2-3 sections) from each mice.

(6) The data presented in Figure 4F-J would be more convincing with quantification. The Sufu;p53-dKO appears to have a thickened EGL across the entire vermis perimeter, and very little foliation, relative to control and single cKO cerebella. This is a more widespread effect than the more localized foliation disruption in the Sufu-cKO. 

We agree with the reviewers that quantification of these phenotypes provide a solid measure of the defects. The phenotypes of Sufu:p53-dKO cerebellum are so profound requiring  in-depth characterization that will be the focus of future studies.

(7) Figure 5 does not convincingly summarize the results. Blue and purple cells in sagittal cartoon are not defined. Which cells express Fgf5 (or other Fgfs) has not been determined. The yellow cells are not defined in relation to the initial cartoon on the left.

The revised manuscript will address this confusion by clearly labeling the cells and their roles in the schematic diagram.

Reviewer #2 (Public Review):

Summary:

Mutations in SUFU are implicated in SHH medulloblastoma (MB). SUFU modulates Shh signaling in a context-dependent manner, making its role in MB pathology complex and not fully understood. This study reports that elevated FGF5 levels are associated with a specific subtype of SHH MB, particularly in pediatric cases. The authors demonstrate that Sufu deletion in a mouse model leads to abnormal proliferation of granule cell precursors (GCPs) at the secondary fissure (region B), correlating with increased Fgf5 expression. Notably, pharmacological inhibition of FGFR restores normal cerebellar development in Sufu mutant mice.

Strengths:

The identification of increased FGF5 in subsets of MB is novel and a key strength of the paper.

Weaknesses:

The study appears incomplete despite the potential significance of these findings. The current paper does not fully establish the causal relationship between Fgf5 and abnormal cerebellar development, nor does it clarify its connection to SUFU-related MB. Some conclusions seem overstated, and the central question of whether FGFR inhibition can prevent tumor formation remains untested.

Reviewer #3 (Public Review):

Summary:

The interaction between FGF signaling and SHH-mediated GNP expansion in MB, particularly in the context of Sufu LoF, has just begun to be understood. The manuscript by Yabut et al. establishes a connection between ectopic FGF5 expression and GNP over-expansion in a late-stage embryonic Sufu LoF model. The data provided links region-specific interaction between aberrant FGF5 signaling with the SHH subtype of medulloblastoma. New data from Yabut et al. suggest that ectopic FGF5 expression correlates with GNP expansion near the secondary fissure in Sufu LoF cerebella. Furthermore, pharmacological blockade of FGF signaling inhibits GNP proliferation. Interestingly, the data indicate that the timing of conditional Sufu deletion (E13.5 using the hGFAP-Cre line) results in different outcomes compared to later deletion (using Math1-cre line, Jiwani et al., 2020). This study provides significant insights into the molecular mechanisms driving GNP expansion in SHH subgroup MB, particularly in the context of Sufu LoF. It highlights the potential of targeting FGF5 signaling as a therapeutic strategy. Additionally, the research offers a model for better understanding MB subtypes and developing targeted treatments.

Strengths:

One notable strength of this study is the extraction and analysis of ectopic FGF5 expression from a subset of MB patient tumor samples. This translational aspect of the study enhances its relevance to human disease. By correlating findings from mouse models with patient data, the authors strengthen the validity of their conclusions and highlight the potential clinical implications of targeting FGF5 in MB therapy.

The data convincingly show that FGFR signaling activation drives GNP proliferation in Sufu, conditional knockout models. This finding is supported by robust experimental evidence, including pharmacological blockade of FGF signaling, which effectively inhibits GNP proliferation. The clear demonstration of a functional link between FGFR signaling and GNP expansion underscores the potential of FGFR as a therapeutic target in SHH subgroup medulloblastoma.

Previous studies have demonstrated the inhibitory effect of FGF2 on tumor cell proliferation in certain MB types, such as the ptc mutant (Fogarty et al., 2006)(Yaguchi et al., 2009). Findings in this manuscript provide additional support suggesting multiple roles for FGF signaling in cerebellar patterning and development.

Weaknesses:

In the GEO dataset analysis, where FGF5 expression is extracted, the reporting of the P-value lacks detail on the statistical methods used, such as whether an ANOVA or t-test was employed. Providing comprehensive statistical methodologies is crucial for assessing the rigor and reproducibility of the results. The absence of this information raises concerns about the robustness of the statistical analysis.

The revised manuscript will include the following detailed explanation of the statistical analyses of the GEO dataset:

For the analysis of expression values of FGF5 (ENSG00000138675), we obtained these values using Geo2R (Barrett et al., 2013), which directly analyze published human MB subtype expression arrays from accession no. GSE85217 (Cavalli et al., 2017). GEO2R is an interactive web tool that compares expression levels of genes of interest (GOI) between sample groups in the GEO series using original submitter-supplied processed data tables. We simply entered the GOI Ensembl ID and organized data sets according to age and MB subgroup or MBSHH subtype classifications. GEO2R results presented gene expression levels as a table ordered by FDR-adjusted (Benjamini & Hochberg) p-values, with significance level cut-off at 0.05, processed by GEO2R’s built-in limma statistical test. Resulting data were subsequently exported into Prism (GraphPad). We generated scatter plots presenting FGF5 expression levels across all MB subgroups (Figure 1A) and MBSHH subtypes (Figure 1D). We performed additional statistical analyses to compare FGF5 expression levels between MB subgroups and MBSHH subtypes and graphed these data as violin plots (Figure 1B, 1C, and 1E). For these analyses, we used one-way ANOVA with Holm-Sidak’s multiple comparisons test, single pooled variance. P value ≤0.05 was considered statistically significant. Graphs display the mean ± standard error of the mean (SEM). Sample sizes were:

Author response table 1.

Another concern is related to the controls used in the study. Cre recombinase induces double-strand DNA breaks within the loxP sites, and the control mice did not carry the Cre transgene (as stated in the Method section), while Sufu-cKO mice did. This discrepancy necessitates an additional control group to evaluate the effects of Cre-induced double-strand breaks on phosphorylated H2AX-DSB signaling. Including this control would strengthen the validity of the findings by ensuring that observed effects are not artifacts of Cre recombinase activity.

The breeding scheme we used to generate homozygous SUFU conditional mutants will not generate pups carrying only hGFAP-Cre. Thus, we are unable to compare expression of gH2AX expression in littermates that do not carry loxP sites. The reviewer is correct in pointing out the possibility of Cre recombinase activity inducing double-strand breaks on its own. However, it is likely that any hGFAP-Cre induced double-strand breaks does not sufficiently cause the phenotypes we observed in homozygous mutants (Sufu-cKO) mice because the cerebellum of mice carry heterozygous SUFU mutations (hGFAP-Cre;Sufu-fl/+) do not display the profound cerebellar phenotypes observed in Sufu-cKO mice. We cannot rule out, however, any undetectable abnormalities that could be present which may require further analyses.

Although the use of the hGFAP-Cre line allows genetic access to the late embryonic stage, this also targets multiple celltypes, including both GNPs and cerebellar glial cells. However, the authors focus primarily on GNPs without fully addressing the potential contributions of neuron-glial interaction. This oversight could limit the understanding of the broader cellular context in which FGF signaling influences tumor development. 

The reviewer is correct in that hGFAP-Cre also targets other cell types, such as cerebellar glial cells, which are generated when Cre-expression has begun. It is possible that cerebellar glial cell development is also compromised in Sufu-cKO mice and may disrupt neuron-glial interaction, due to or independently of FGF signaling. In-depth studies are required to interrogate how loss of SUFU specifically affect development of cerebellar glial cells and influence their cellular interactions in the developing cerebellum.

Recommendations for the authors:

Editorial Comments:

The reviewers suggest a number of steps to improve the manuscript that include additional experiments and a deeper analyses and re-evaluation of existing data. Short of significant new experiments, there appears to be number of straightforward analyses that can improve the study:

(1) Reanalyses of statistical and quantitative approaches used (e.gs FDR correction, cerebellar deficits, GEO analyses.

The revised manuscript will include detailed information on the statistical and quantitative approaches as addressed in our response to the reviewer’s comments.

(2) More clear presentation of qualitative labeling approaches (immunohistochemistry and in situ hybridization).

A detailed description of the protocols used will be included in  the methods section for labeling methods in the revised manuscript.

Reviewer #1 (Recommendations For The Authors):

AZD4547 treatment of the dKO mice would provide more convincing evidence that FGF-targeted treatments could curtail tumor growth in these mice or refute the suggestion that FGF-targeted treatment could prevent tumor growth.

We agree that performing AZD4547 treatment on Sufu-dKO mice will strengthen these studies. However, we are unable to address since these mice are now unavailable. We hope that future studies will address these.

Atoh1 is referred to as Math1 (older nomenclature) and should be corrected.

The revised manuscript will include this change in nomenclature.

Check verb tense throughout the manuscript.

We will edit the manuscript further to check verb tenses prior to submission of the revised manuscript.

Reviewer #2 (Recommendations For The Authors):

Specific Comments:

(1) The identification of increased FGF5 in subsets of MB is novel and a key strength of the paper. However, the causal relationship between FGF5 and MB remains unestablished. Based on the current data, FGF5 can only be considered a biomarker for stratifying MB.

We agree with the reviewer that our studies do not provide direct evidence that FGF5 cause MB. Future investigation focusing on determining if FGF5 inhibition leads to phenotypic rescue will strongly establish the relationship between FGF5 and MB. The reviewer is also correct that our studies reveal that FGF5 acts as a potential biomarker, as we mentioned in the Discussion section.

(2) The upregulation of Fgf5 in Sufu-deficient cerebella is crucial to this study, yet the presented data are unconvincing to support this conclusion. In comparing Fgf5 expression between WT and Sufu mutants (Figures 2E, F and 4I), the cerebellar sections differ significantly, with mutant sections seemingly from a more lateral position. The authors should provide images of mutant sections from more comparable positions to accurately assess the effect of Sufu deficiency on Fgf5 expression. Additionally, the signals in Figure 2F resemble non-specific backgrounds rather than specific RNAscope signals.

The WT and mutant sections analyzed were carefully selected from comparable levels. The abnormal foliation in Sufu-cKO make the mutant sections look like they are from the lateral cerebellum.

Figure 2F (enlarged regions) point to punctate RNAScope signals which is characteristic of this labeling method (see RBFOX3 or GFAP labeling in DAPI-labeled cells in the mouse brain at https://acdbio.com/science/applications/research-areas/neuroscience). The higher number of punctate signals in some, but not all, DAPI-labeled cells in Figure 2F indicate that the FGF5 RNAScope signal is specific.

(3) Jiwani et al. (2020) reported that Fgf8 also expressed in region B of the EGL, is upregulated in Sufu-deficient cerebella and is necessary and sufficient for Sufu mutant GCP proliferation. The current study does not distinguish whether the FGFR inhibitor AZD4547 blocks Fgf5 and Fgf8 function in restoring cerebellar histology in Sufu mutants.

AZD4547 potently inhibits FGFR1, FGFR2, and FGFR3 autophosphorylation (Gavine et al., Cancer Research, 2012). FGF8 is reported to bind to these receptors (Ornitz and Itoh, 2015). Thus, the reviewer is correct that the studies will not distinguish between FGF5 or FGF8 activity. Further investigation on FGF8 expression and the effects of its inhibition in the Sufu-cKO neonatal cerebellum will determine whether tumorigenic processes are driven by either FGF5 or FGF8. Nevertheless, we postulate that FGF5 is exerting a greater effect in activating FGF signaling in the developing cerebellum given that it is highly expressed along the external granule layers of the developing cerebellum (Author response image 3).

Author response image 3.

Expression of FGF5 and FGF8 in the P4 mouse cerebellum (Allen Brain Atlas, https://developingmouse.brain-map.org )

(4) The authors should show whether AZD4547 treatment restores normal Fgf5 expression. Importantly, they need to test whether AZD4547 rescues the proliferation defect observed in Sufu;p53 double mutants.

We agree that performing AZD4547 treatment on Sufu-dKO mice will strengthen these studies. However, we are unable to address since these mice are now unavailable. We hope that future studies will address these.

(5) Jiwani et al. (2020) showed that deleting Sufu with Atoh1-Cre promotes Gli3R and suppresses Gli2 levels, leading to increased cell proliferation and delayed cell cycle exit in the central lobe. The findings of the current study (Supplementary Figure 1) seem to differ from this previous report, yet both studies conclude that Sufu-KO disrupts differentiation. The authors should provide an explanation for this discrepancy.

Our results align completely with the findings by Jiwani et al. (2020). Both studies showed reduced levels of Gli3R, showing nearly 50% reduction, when Sufu is deleted (see Figure 4A-4D in Jiwani et al., 2020).

(6) The hGFAP-Cre mouse line is used to delete Sufu from the cerebellum, but it is not commonly used for GCP-specific deletion. The authors need to provide a reference or more details on the temporal and spatial activity of the Cre line, as the cited paper describes its generation but offers little information on its activity in the developing cerebellum.

We appreciate the reviewer’s reminder to include the reference for the Schuller et al. 2008 paper. This study characterized the hGFAP-Cre temporal and spatial expression in the developing cerebellum, including granule cell precursors. We will include this reference in the revised manuscript.

(7) Based on the provided data, it is difficult to determine which cell types express Fgf5. Given that hGFAP-cre may delete Sufu in other cerebellar cell types, the authors should demonstrate that Fgf5 is expressed in granule cells or granule cell precursors.

Future studies will focus on further characterization of the role of FGF5 in cerebellar development, including the identity cells expressing FGF5. The reviewer is correct in that hGFAP-Cre also targets other cell types and that Sufu deletion in these cells induced ectopic FGF5 expression.

(8) The provided data show an increase in pERK+ cells in GCPs at the secondary fissure. This increase may simply reflect an accumulation of GCPs. It is unconvincing that there is an increase in pERK due to the loss of Sufu.

The reviewer is correct that the increase in GCPs will also result increase the number of pERK+ cells. To control for this, our quantification reflects the number of cells per unit area where Ki67+ cells. With these parameters, we found that there is an increased density of pERK+ cells in a given Ki67+ region. All quantification were performed from 2-3 20 um cerebellar sections of 3-6 independent mice per genotype analyzed. Individual points in the bar graphs represent the average cell number (quantified from 2-3 sections) from each mice.

(9) No data are provided on MB formation in Sufu-cKO; p53- mutants, and it is unknown whether FGFR inhibitors block tumor formation.

We agree that performing AZD4547 treatment on Sufu-dKO mice will strengthen these studies. However, we are unable to address since these mice are now unavailable. We hope that future studies will address these.

(10) The authors frequently mention "preneoplastic lesions" of GCPs in Sufu mutant mice. What evidence supports this claim?

Preneoplastic lesions are defined as cells carrying genetic and phenotypic alterations that show higher risk of malignancy (such as MB) but lack the capacity to grow autonomously in the absence of a secondary factor (Feo et al., 2011). In Sufu-cKO mice, we see abnormally proliferating and behaving granule precursor cells that do not grow autonomously, in the absence of a p53 LOF. The combined deletion of Sufu and p53 transforms these cells to become neoplastic.

(11) Fgf5 is normally expressed in region B. What is its potential function? Does AZD4547 affect normal development? 

Future studies will focus on further characterization of the role of FGF5 in cerebellar development, including the identity cells expressing FGF5. Regarding AZD4547, we did not observe any obvious difference between AZD4547-treated and vehicle-treated cerebelli. These indicate that AZD4547 inhibition of FGFRs under physiologic conditions does not significantly disrupt normal cerebellar development.

(12) Figure 3G: It is unclear which specimens were treated with AZD4547. The authors mention treatment in line 281 but contradict themselves in the figure legend.

We thank the reviewer for pointing out this typo. Cerebellar tissues shown in Figure 3G were all treated with AZD4547. The figure legend will be corrected in the revised manuscript.

(13) Figure 4J: The higher magnification images of the pERK/Ki67 staining appear identical in the control and Sufu;p53-dKO. The authors need to correct the mistake.

We thank the reviewer for pointing this out. We will correct this figure in the revised manuscript.

Minor Comments:

(1) Whenever possible, images comparing WT and mutants should be presented at the same scale within a figure. For example, readers might easily conclude that mutant brains are smaller than controls in Figure 4G.

Unfortunately, because the cerebellum of Sufu;p53-dKO mice are significantly bigger, we are unable to show the whole cerebellum in the same scale in Figure 4G. We wanted to emphasize the significant and abnormal cerebellar growth in this figure.

(2) The figure legend for Supplementary Figure 2 is missing.

Thank you for pointing this out. We will add a figure legend in this Supplementary data in the revised manuscript.

(3) The authors state that the expansion of Pax6+ GNPs in the newborn Sufu-cKO cerebellum (Figure 2) occurs in similar anatomical subregions where infantile MB tumors typically arise (Tan et al., 2018). The cited paper describes more abundant SHH MB in the cerebellar hemisphere. The authors need to elaborate on their statement to clarify this point.

The reviewer is correct in that Tan et al., 2018 observed tumors arising from the cerebellar hemisphere. More specifically, these tumors arise in the posterior/ventral regions of the cerebellar hemispheres (Figure 2 in Tan et al., 2018). Similarly, Sufu-cKO mice have more severe defects in the posterior/ventral regions of the cerebellar hemisphere (Figures 2A and 3F) and therefore corroborate the findings by Tan et al., that abnormal SHH signaling in these regions results in increased sensitivity to MB formation.

Reviewer #3 (Recommendations For The Authors):

Figure1 [Upregulated FGF5 expression in MBS-HH tumors]

- Statistical analysis from the Geo expression dataset does not provide enough detail. At least, the authors should mention whether they have made any adjustments from the default settings and how they extracted/plotted the FGF5 expression (Figure 1BCE).

For the analysis of expression values of FGF5 (ENSG00000138675), we obtained these values using Geo2R (Barrett et al., 2013), which directly analyze published human MB subtype expression arrays from accession no. GSE85217 (Cavalli et al., 2017). GEO2R is an interactive web tool that compares expression levels of genes of interest (GOI) between sample groups in the GEO series using original submitter-supplied processed data tables. We simply entered the GOI Ensembl ID and organized data sets according to age and MB subgroup or MBSHH subtype classifications. GEO2R results presented gene expression levels as a table ordered by FDR-adjusted (Benjamini & Hochberg) p-values, with significance level cut-off at 0.05, processed by GEO2R’s built-in limma statistical test. Resulting data were subsequently exported into Prism (GraphPad). We generated scatter plots presenting FGF5 expression levels across all MB subgroups (Figure 1A) and MB^SHH subtypes (Figure 1D). We performed additional statistical analyses to compare FGF5 expression levels between MB subgroups and MB^SHH subtypes and graphed these data as violin plots (Figure 1B, 1C, and 1E). For these analyses, we used one-way ANOVA with Holm-Sidak’s multiple comparisons test, single pooled variance. P value ≤0.05 was considered statistically significant. Graphs display the mean ± standard error of the mean (SEM). See Author response table 1 for sample sizes.

Figure 3 [Ectopic activation of FGF signaling in the EGL of P0 Sufu-cKO cerebellum]

- Gil1-lz mice reference wrong. Correct Bai CB, et al. 2002

- Generation of Sufu-cKO;Gli1-LacZ triple transgenic mice not described 

- Veh vs. treated not labelled (Figure 3F)

We will address these minor text changes in the revised manuscript. A more detailed description of the generation of Sufu-cKO;Gli1-LacZ triple transgenic will also be included in the Methods section.

Figure 5 [Proposed model]

- In the text, Figure 5 is mistaken for Figure 8. 

We will address these minor text changes in the revised manuscript.

eLife Assessment

This study provides valuable new insight into the role of Fgf signalling in SUFU mutation-linked cerebellar tumors and indicates novel therapeutic interventions via inhibition of Fgf signalling. The potential impact of this work is therefore very high and it is supported by solid evidence. However, due to current limitations in the full identification of the cell types secreting FGF5, and issues with robustness of evaluation of genetically engineered animals, the validation of some interpretations awaits future experiments.

Reviewer #1 (Public review):

Summary:

SUFU modulates Sonic hedgehog (SHH) signaling and is frequently mutated in the B-subtype of SHH driven medulloblastoma. The B-subtype occurs mostly in infants, is often metastatic, and lacks specific treatment. Yabut et al. found Fgf5 was highly expressed in the B-subtype of SHH driven medulloblastoma by examining a published microarray expression dataset. They then investigated how Fgf5 functions in the cerebellum of mice that have embryonic Sufu loss of function. This loss was induced using the hGFAP-cre transgene, which is expressed multiple cell types in the developing cerebellum, including granule neuron precursors (GNPs) derived from the rhombic lip. By measuring the area of Pax6+ cells in the external granule cell layer (EGL) of Sufu-cKO mice at postnatal day 0, they find Pax6+ cells occupy a larger area in the posterior lobe adjacent to the secondary fissure, which is poorly defined. They show that Fgf5 RNA and phosphoErk1/2 immunostaining are also higher in the same disrupted region. Some of the phosphoErk1/2+ cells are proliferative in the Sufu-cKO. Western blot analysis of Gli proteins that modulate SHH signaling found reduced expression and absence of Gli1 activity in the region of cerebellar dysgenesis in Sufu-cKO mice. This suggests the GNP expansion in this region is independent of SHH signaling. Amazingly, intraventricular injection of the FGFR1-2 antagonist AZD4547 from P0-4 and examined histologoically at P7 found the treatment restored cytoarchitecture in the cerebella of Sufu-cKO mice. This is further supported by NeuN immunostaining in the internal granule cell layer, which labels mature, non-diving neurons, and KI67 immunostaining, indicating dividing cells, and primarily found in the EGL. The mice were treated beginning at a timepoint when cerebellar cytoarchitecture was shown to be disrupted and it is indistinguishable from control following treatment. Fig.3 presents the most convincing and exciting data in this manuscript.

Sufu-cKO do not readily develop cerebellar tumors. The authors detected phosphorylated H2AX immunostaining, which labels double strand breaks, was in some cells in the EGL in regions of cerebellar dysgenesis in the Sufu-cKO, as was cleaved Caspase 3, a marker of apoptosis. P53, downstream of the double strand break pathway, protein was reduced in Sufu-cKO cerebellum. Genetically removing p53 from the Sufu-cKO cerebellum resulted in cerebellar tumors in 2 mo mice. The Sufu;p53-dKO cerebella at P0 lacked clear foliation, and the secondary fissure, even more so than the Sufu-cKO. Fgf5 RNA and signaling (pERK1/2) were also expressed ectopically.

In the revised manuscript, additional details have been added to clarify statistical analyses and to state limitations of the reported results in the absence of further experimental analyses.

Reviewer #2 (Public review):

Summary:

Mutations in SUFU are implicated in SHH medulloblastoma (MB). SUFU modulates Shh signaling in a context-dependent manner, making its role in MB pathology complex and not fully understood. This study reports that elevated FGF5 levels are associated with a specific subtype of SHH MB, particularly in pediatric cases. The authors demonstrate that Sufu deletion in a mouse model leads to abnormal proliferation of granule cell precursors (GCPs) at the secondary fissure (region B), correlating with increased Fgf5 expression. Notably, pharmacological inhibition of FGFR restores normal cerebellar development in Sufu mutant mice.

Strengths:

The identification of increased FGF5 in subsets of MB is novel and a key strength of the paper.

Weaknesses:

The study appears incomplete despite the potential significance of these findings. The current paper does not fully establish the causal relationship between Fgf5 and abnormal cerebellar development, nor does it clarify its connection to SUFU-related MB. Some conclusions seem overstated, and the central question of whether FGFR inhibition can prevent tumor formation remains untested.

Comments on revisions:

In this revised version, many of the concerns and comments raised by this and other reviewers remain unaddressed and require attention in future studies. The manuscript does not demonstrate significant improvement.

Specific Comments:

(1) In the figure provided by the authors, FGF5 appears to be highly expressed beneath the GCPs. Regarding our comment and Reviewer 1's Comment 7, it is essential to identify the cell types secreting FGF5 and clarify whether it functions in a paracrine or autocrine manner. This should be incorporated into the working model illustrated in Figure 5.<br /> (2) Contrary to the authors' claim that their results align completely with Jiwani et al. (2020), there is a discrepancy in the data. Jiwani et al. reported an increase in Gli2 levels in the Sufu mutant, whereas the current study shows the opposite result. This inconsistency needs to be addressed.

Reviewer #3 (Public review):

Summary:

The interaction between FGF signaling and SHH-mediated GNP expansion in MB, particularly in the context of Sufu LoF, has just begun to be understood. The manuscript by Yabut et al. establishes a connection between ectopic FGF5 expression and GNP over-expansion in a late stage embryonic Sufu LoF model. The data provided links region-specific interaction between aberrant FGF5 signaling with SHH subtype of medulloblastoma. New data from Yabut et al. suggest that ectopic FGF5 expression correlates with GNP expansion near the secondary fissure in Sufu LoF cerebella. Furthermore, pharmacological blockade of FGF signaling inhibits GNP proliferation. Interestingly, the data indicate that the timing of conditional Sufu deletion (E13.5 using the hGFAP-Cre line) results in different outcomes compared to later deletion (using Math1-cre line, Jiwani et al., 2020). This study provides significant insights into the molecular mechanisms driving GNP expansion in SHH subgroup MB, particularly in the context of Sufu LoF. It highlights the potential of targeting FGF5 signaling as a therapeutic strategy. Additionally, the research offers a model for better understanding MB subtypes and developing targeted treatments.

Strengths:

One notable strength of this study is the extraction and analysis of ectopic FGF5 expression from a subset of MB patient tumor samples. This translational aspect of the study enhances its relevance to human disease. By correlating findings from mouse models with patient data, the authors strengthen the validity of their conclusions and highlight the potential clinical implications of targeting FGF5 in MB therapy.

The data convincingly show that FGFR signaling activation drives GNP proliferation in Sufu conditional knockout models. This finding is supported by robust experimental evidence, including pharmacological blockade of FGF signaling, which effectively inhibits GNP proliferation. The clear demonstration of a functional link between FGFR signaling and GNP expansion underscores the potential of FGFR as a therapeutic target in SHH subgroup medulloblastoma.

Previous studies have demonstrated the inhibitory effect of FGF2 on tumor cell proliferation in certain MB types, such as the ptc mutant (Fogarty et al., 2006)(Yaguchi et al., 2009). Findings in this manuscript provide additional support suggesting multiple roles for FGF signaling in cerebellar patterning and development.

Weaknesses:

In the GEO dataset analysis, where FGF5 expression is extracted, the reporting of the P-value lacks detail on the statistical methods used, such as whether an ANOVA or t-test was employed. Providing comprehensive statistical methodologies is crucial for assessing the rigor and reproducibility of the results. The absence of this information raises concerns about the robustness of the statistical analysis.

Another concern is related to the controls used in the study. Cre recombinase induces double-strand DNA breaks within the loxP sites, and the control mice did not carry the Cre transgene (as stated in the Method section), while Sufu-cKO mice did. This discrepancy necessitates an additional control group to evaluate the effects of Cre-induced double-strand breaks on phosphorylated H2AX-DSB signaling. Including this control would strengthen the validity of the findings by ensuring that observed effects are not artifacts of Cre recombinase activity.

Although the use of the hGFAP-Cre line allows genetic access to late embryonic stage, this also targets multiple cell types, including both GNPs and cerebellar glial cells. However, the authors focus primarily on GNPs without fully addressing the potential contributions of neuron-glial interaction. This oversight could limit the understanding of the broader cellular context in which FGF signaling influences tumor development.

- Statistical analysis from the Geo expression dataset:<br /> The reviewer is satisfied with the revisions provided by the author and considers Figure 1 substantially improved.

- Generation of Sufu-cKO;Gli1-LacZ triple transgenic mice not described:<br /> >The reviewer finds that the supplementary Figure 1 revisions provided by the author do not fully address the concerns raised, and the issue remains unresolved.

- Request control group to evaluate the effects of Cre induced double-strand breaks on phosphorylated H2AX-DSB signaling:<br /> >Despite the revisions, control group (hGFAPCre;Sufu-fl/+) highlighted in the author response does not present in the revision, leaving this issue unresolved.

- hGFAP-Cre line also targets multiple celltypes, including both GNPs and cerebellar glial cells:<br /> >The author acknowledges the limitations of the study, and the reviewer concurs, noting that it enhances the contextual understanding of the findings.

eLife Assessment

Briola and co-authors determined the structure of the human CTF18 clamp loader bound to PCNA to high resolution, analyzed the structure, and tested a new mechanism involving a human-specific Ctf18 beta-hairpin docking onto Rfc5, which represents a valuable contribution. The data are solid and complement data recently published by others.

Reviewer #1 (Public review):

Summary:

The authors report the structure of the human CTF18-RFC complex bound to PCNA. Similar structures (and more) have been reported by the O'Donnell and Li labs. This study should add to our understanding of CTF18-RFC in DNA replication and clamp loaders in general. However, there are numerous major issues that I recommend the authors fix.

Strengths:

The structures reported are strong and useful for comparison with other clamp loader structures that have been reported lately.

Weaknesses:

The structures don't show how CTF18-RFC opens or loads PCNA. There are recent structures from other groups that do examine these steps in more detail, although this does not really dampen this reviewer's enthusiasm. It does mean that the authors should spend their time investigating aspects of CTF18-RFC function that were overlooked or not explored in detail in the competing papers. The paper poorly describes the interactions of CTF18-RFC with PCNA and the ATPase active sites, which are the main interest points. The nomenclature choices made by the authors make the manuscript very difficult to read.

Reviewer #2 (Public review):

Summary

Briola and co-authors have performed a structural analysis of the human CTF18 clamp loader bound to PCNA. The authors purified the complexes and formed a complex in solution. They used cryo-EM to determine the structure to high resolution. The complex assumed an auto-inhibited conformation, where DNA binding is blocked, which is of regulatory importance and suggests that additional factors could be required to support PCNA loading on DNA. The authors carefully analysed the structure and compared it to RFC and related structures.

Strength & Weakness

Their overall analysis is of high quality, and they identified, among other things, a human-specific beta-hairpin in Ctf18 that flexibly tethers Ctf18 to Rfc2-5. Indeed, deletion of the beta-hairpin resulted in reduced complex stability and a reduction in a primer extension assay with Pol ε. This is potentially very interesting, although some more work is needed on the quantification. Moreover, the authors argue that the Ctf18 ATP-binding domain assumes a more flexible organisation, but their visual representation could be improved.

The data are discussed accurately and relevantly, which provides an important framework for rationalising the results.

All in all, this is a high-quality manuscript that identifies a key intermediate in CTF18-dependent clamp loading.

Reviewer #3 (Public review):

Summary:

CTF18-RFC is an alternative eukaryotic PCNA sliding clamp loader that is thought to specialize in loading PCNA on the leading strand. Eukaryotic clamp loaders (RFC complexes) have an interchangeable large subunit that is responsible for their specialized functions. The authors show that the CTF18 large subunit has several features responsible for its weaker PCNA loading activity and that the resulting weakened stability of the complex is compensated by a novel beta hairpin backside hook. The authors show this hook is required for the optimal stability and activity of the complex.

Relevance:

The structural findings are important for understanding RFC enzymology and novel ways that the widespread class of AAA ATPases can be adapted to specialized functions. A better understanding of CTF18-RFC function will also provide clarity into aspects of DNA replication, cohesion establishment, and the DNA damage response.

Strengths:

The cryo-EM structures are of high quality enabling accurate modelling of the complex and providing a strong basis for analyzing differences and similarities with other RFC complexes.

Weaknesses:

The manuscript would have benefitted from more detailed biochemical analysis to tease apart the differences with the canonical RFC complex.

I'm not aware of using Mg depletion to trap active states of AAA ATPases. Perhaps the authors could provide a reference to successful examples of this and explain why they chose not to use the more standard practice in the field of using ATP analogues to increase the lifespan of reaction intermediates.

Overall appraisal:

Overall the work presented here is solid and important. The data is sufficient to support the stated conclusions and so I do not suggest any additional experiments.

eLife Assessment

This manuscript describes an important study of the giant virus Jyvaskylavirus. The characterisation presented is solid, although, in the current form, it is not clear to what extent these findings change our perception of how giant viruses, especially those isolated from a cold environment, function. The work will be of interest to virologists working on giant viruses as well as those working with other members of the PRD1/Adenoviridae lineage.

Reviewer #1 (Public review):

This study presents Jyvaskylavirus, a new member of the Marseilleviridae family, infecting Acanthamoeba castellanii. The study provides a detailed and comprehensive genomic and structural analysis of Jyvaskylavirus. The authors identified ORF142 as the capsid penton protein and additional structural proteins that comprise the virion. Using a combination of imaging techniques the authors provide new insights into the giant virus architecture and lifecycle. The study could be improved by providing atomic coordinates and refinement statistics, comparisons with available giant virus structures could be expanded, and the novelty in terms of the first isolated example of a giant virus from Finland could be expounded upon.

The study contributes new structural and genomic diversity to the Marseilleviridae family, hinting at a broader distribution and ecological significance of giant viruses than previously thought.

Reviewer #2 (Public review):

Summary:

This paper describes the molecular characterisation of a new isolate of the giant virus Jyvaskylavirus, a member of the Marseilleviridae family infecting Acanthamoeba castellanii. The isolate comes from a boreal environment in Finland, showcasing that giant viruses can thrive in this ecological niche. The authors came up with a non-trivial isolation procedure that can be applied to characterise other members of the family and will be beneficial for the virology field. The genome shows typical Marseilleviridae features and phylogenetically belongs to their clade B. The structural characterisation was performed on the level of isolated virion morphology by negative stain EM, virions associated with cells either during the attachment or release by helium microscopy, the visualisation of the virus assembly inside cells using stained thin sections, and lastly on the protein secondary structure level by reconstructing ~6 A icosahedral map of the massive virion using cryoEM. The cryoEM density combined with gene product structure prediction enabled the identification and functional assessment of various virion proteins.

Strengths:

The detailed description of the virus isolation protocol is the largest strength of the paper and this reviewer believes it can be modified for isolating various viruses infecting small eukaryotes. The cryoEM map allows us to understand how exceptionally large virions of these viruses are stabilised by minor capsid proteins and nicely demonstrates the integration of medium-resolution cryoEM with protein structure prediction in deciphering virion protein function. The visualisation of ongoing virus assembly inside virus factories brings interesting hypotheses about the process that; however, needs to be verified in the next studies.

Weaknesses:

The conclusions from helium microscopy images are overinterpreted, as the native membrane structure cannot be preserved in a fixed and dehydrated sample. In the image, there are many other parts of the curved membrane and a lot of virions, to me it seems the specific position of the highlighted virion could arise by a random chance. The claim that the cells were imaged in the near-original state by this method should be therefore omitted. Also, no mass spectrometry data are presented that would supplement and confirm the identity of virion proteins which predicted models were fitted into the cryoEM density. For a general virology reader outside of the giant virus field, the results presented in the current state might not have enough influence and the section should be rewritten to better showcase the novelty of findings.

eLife Assessment

This important study describes a novel flow-responsive gene and its role in regulating the inflammation-associated transcription factor IRF5. While the in vivo experiments are solid, the in vitro data is inadequate since embryonic fibroblasts are used throughout despite the work aiming to investigate mechanisms of endothelial cell activation in atherosclerosis.

Reviewer #1 (Public review):

Summary:

The authors report the role of a novel gene Aff3ir-ORF2 in flow-induced atherosclerosis. They show that the gene is anti-inflammatory in nature. It inhibits the IRF5-mediated athero-progression by inhibiting the causal factor (IRF5). Furthermore, the authors show a significant connection between shear stress and Aff3ir-ORF2 and its connection to IRF5 mediated athero-progression in different established mice models which further validates the ex vivo findings.

Strengths:

(1) An adequate number of replicates were used for this study.<br /> (2) Both in vitro and in vivo validation was done.<br /> (3) The figures are well presented.<br /> (4) In vivo causality is checked with cleverly designed experiments.

Weaknesses:

(1) Inflammatory proteins must be measured with standard methods e.g ELISA as mRNA level and protein level does not always correlate.

(2) RNA seq analysis has to be done very carefully. How does the euclidean distance correlate with the differential expression of genes. Do they represent the neighborhood? If they do how does this correlation affect the conclusion of the paper?

(3) The volcano plot does not indicate the q value of the shown genes. It is advisable to calculate the q value for each of the genes which represents the FDR probability of the identified genes.

(4) GO enrichment was done against the Global gene set or a local geneset? The authors should provide more detailed information about the analysis.

(5) If the analysis was performed against a global gene set. How does that connect with this specific atherosclerotic microenvironment?

(6) What was the basal expression of genes and how did the DGE (differential gene expression) values differ?

(7) How was IRF5 picked from GO analysis? was it within the 20 most significant genes?

(8) Microscopic studies should be done more carefully? There seems to be a global expression present on the vascular wall for Aff3ir-ORF2 and the expression seems to be similar to AFF3 in Figure 1.

Reviewer #2 (Public review):

Summary:

The authors recently uncovered a novel nested gene, Aff3ir, and this work sets out to study its function in endothelial cells further. Based on differences in expression correlating with areas of altered shear stress, they investigate a role for the isoform Aff3ir-ORF2 in endothelial activation and development of atherosclerosis downstream of disturbed shear stress. Using a knockout mouse model and in vivo overexpression experiments, they demonstrate a strong potential for Aff3ir-ORF2 to alleviate atherosclerosis. They find that Aff3ir-ORF2 interacts with the pro-inflammatory transcription factor IRF5 and retains it in the cytoplasm, hence preventing upregulation of inflammation-associated genes. The data expands our knowledge of IRF5 regulation which could be relevant to researchers studying various inflammatory diseases as well as adding to our understanding of atherosclerosis development.

Strengths:

The in vivo data is solid using immunofluorescence staining to assess AFF3ir-ORF2 expression, a knockout mouse model, overexpression and knockdown studies, and rescue experiments in combination with two atherosclerotic models to demonstrate that Aff3ir-ORF2 can lessen atherosclerotic plaque formation in ApoE-/- mice.

Weaknesses:

While the in vivo data is generally convincing, a few data panels have issues and will need addressing. Also, the knockout mouse model will need to be described, since the paper referred to in the manuscript does not actually report any knockout mouse model. Hence it is unclear how Aff3ir-ORF2 is targeted, but Figure S2B shows that targeting is partial, since about 30% expression remains at the RNA level in MEFs isolated from the knockout mice.

While the effect on atherosclerosis is clear, the conclusion that this is the result of reduced endothelial cell activation is not supported by the data. The mouse model is described as a global knockout and the shRNA knockdowns (Figure 5) and overexpression data in Figure 2 are not cell type-specific. Only the overexpression construct in Figure 6 uses an ICAM-2 promoter construct, which drives expression in endothelial cells, though leaky expression of this promoter has been reported in the literature. Therefore, other cell types such as smooth muscle cells or macrophages could be responsible for the effects observed.

The weakest part of the manuscript is the in vitro experiments. While they are solidly executed, all experiments are performed in MEFs, and results are interpreted as being equivalent to endothelial cell responses. There is also an RNA-seq experiment performed on MEFs from the Aff3ir-ORF2 knockout and control mice, but the data is not disclosed other than showing some non-identifiable expression differences. The data is used to hypothesise on a role for IRF5 in the effects observed with Aff3ir-ORF2 knockout.

Overall, the paper succeeds in demonstrating a link between Aff3ir-ORF2 and atherosclerosis, but the cell types involved and mechanisms remain unclear. The study also shows a functional interaction between Aff3ir-ORF2 and IRF5 in embryonic fibroblasts, but any relevance of this mechanism for atherosclerosis or any cell types involved in the development of this disease remains largely speculative.

Reviewer #3 (Public review):

This study is to demonstrate the role of Aff3ir-ORF2 in the atheroprone flow-induced EC dysfunction and ensuing atherosclerosis in mouse models. Overall, the data quality and comprehensiveness are convincing. In silico, in vitro, and in vivo experiments and several atherosclerosis were well executed. To strengthen further, the authors can address human EC relevance.

Major comments:

(1) The tissue source in Figures 1A and 1B should be clarified, the whole aortic segments or intima? If aortic segment was used, the authors should repeat the experiments using intima, due to the focus of the current study on the endothelium.

(2) Why were MEFs used exclusively in the in vitro experiments? Can the authors repeat some of the critical experiments in mouse or human ECs?

(3) The authors should explain why AFF3ir-ORF2 overexpression did not affect the basal level expression of ICAM-1, VCAM-1, IL-1b, and IL-6 under ST conditions (Figure 2A-C).

(4) Please include data from sham controls, i.e., right carotid artery in Figure 2E.

(5) Given that the merit of the study lies in the effect of different flow patterns, the legion areas in AA and TA (Figure 3B, 3C) should be separately compared.

(6) For confirmatory purposes for the variations of IRF5 and IRF8, can the authors mine available RNA-seq or even scRNA-seq data on human or mouse atherosclerosis? This approach is important and could complement the current results that are lacking EC data.

(7) With the efficacy of using AAV-ICAM2-AFF3ir-ORF2 in atherosclerosis reduction (Figure 6), the authors are encouraged to use lung ECs isolated from the AFF3ir-ORF2-/-mice to recapitulate its regulation of IRF5.

eLife Assessment

This important study demonstrates that screening by artificial intelligence can identify relevant novel compounds for interacting with KATP channels. The experimental work is compelling. The broader significance of this work relates to the possibility that KATP channel mutations linked to congenital hyperinsulinism may be effectively rescued to the cell surface with a drug, which could normalize insulin secretion or enhance the effectiveness of existing KATP channel activators such as diazoxide.

Reviewer #1 (Public review):

Summary:

Multiple compounds that inhibit ATP-sensitive potassium (KATP) channels also chaperone channels to the surface membrane. The authors used an artificial intelligence (AI)-based virtual screening (AtomNet) to identify novel compounds that exhibit chaperoning effects on trafficking-deficient disease-causing mutant channels. One compound, which they named Aekatperone, acts as a low affinity, reversible inhibitor and effective chaperone. A cryoEM structure of KATP bound to Aekatperone showed that the molecule binds at the canonical inhibitory site.

Strengths and weaknesses:

The details of the AI screening itself are inevitably opaque but appear to differ from classical virtual screening in not involving any physical docking of test compounds into the target site. The authors mention criteria that were used to limit the number of compounds so that those with high similarity to known binders and 'sequence identity' (does this mean structural identity) were excluded. The identified molecules contain sulfonylurea-like moieties. How different are they from other sulfonylure4as?

The experimental work confirming that Aekatperone acts to traffic mutant KATP channels to the surface and acts as a low affinity, reversible, inhibitor is comprehensive and clear, with very convincing cell biological and patch-clamp data, as is the cryoEM structural analysis, for which the group are leading experts. In addition to the three positive chaperone-effective molecules, the authors identified a large number of compounds that are predicted binders but apparently have no chaperoning effect. Did any of them have an inhibitory action on channels? If so, does this give clues to separating chaperoning from inhibitory effects?

The authors suggest that the novel compound may be a promising therapeutic for the treatment of congenital hyperinsulinism due to trafficking defective KATP mutations. Because they are low-affinity, reversible, inhibitors. This is a very interesting concept, and perhaps a pulsed dosing regimen would allow trafficking without constant channel inhibition (which otherwise defeats the therapeutic purpose), although it is unclear whether the new compound will offer advantages over earlier low-affinity sulfonylurea inhibitor chaperones. These include tolbutamide which has very similar affinity and effect to Aekatperone. As the authors point out this (as well as other sulfonylureas) is currently out of favor because of potential adverse cardiovascular effects, but again, it is unclear why Aekatperone should not have the same concerns.

Reviewer #2 (Public review):

Summary:

In their study 'AI-Based Discovery and CryoEM Structural Elucidation of a KATP Channel Pharmacochaperone', ElSheikh and colleagues undertake a computational screening approach to identify candidate drugs that may bind to an identified binding pocket in the SUR1 subunit of KATP channels. Other KATP channel inhibitors such as glibenclamide have been previously shown to bind in this pocket, and in addition to inhibition of KATP channel function, these inhibitors can very effectively rescue cell surface expression of trafficking deficient KATP mutations that cause excessive insulin secretion (Congenital Hyperinsulinism). However, a challenge for their utility for the treatment of hyperinsulinism has been that they are powerful inhibitors of the channels that are rescued to the channel surface. In contrast, successful therapeutic pharmacochaperones (eg. CFTR chaperones) permit the function of the channels rescued to the cell membrane. Thus, a key criterion for the authors' approach, in this case, was to identify relatively low-affinity compounds that target the glibenclamide binding site (and be washed off) - these could potentially rescue KATP surface expression but also permit KATP function.

Strengths:

The main findings of the manuscript include:

(1) Computational screening of a large virtual compound library, followed by functional screening of cell surface expression, which identified several potential candidate pharmacochaperones that target the glibenclamide binding site.

(2) Prioritization and functional characterization of Aekatperone as a low-affinity KATP inhibitor which can be readily 'washed off' in patch clamp and cell-based efflux assays. Thus the drug clearly rescues cell surface expression but can be manipulated experimentally to permit the function of rescued channels.

(3) Determination of the binding site and dynamics of this candidate drug by cryo-EM, and functional validation of several residues involved in drug sensitivity using mutagenesis and patch clamp.

The experiments are well-conceived and executed, and the study is clearly described. The results of the experiments are very straightforward and clearly support the conclusions drawn by the authors. I found the study to provide important new information about the KATP chaperone effects of certain drugs, with interesting considerations in terms of ion channel biology and human disease.

Weaknesses:

I don't have any major criticisms of the study as described, but I had some remaining questions that could be addressed in a revision.

(1) The chaperones can effectively rescue KATP trafficking mutants, but clearly not as strongly as the higher affinity inhibitor glibenclamide. Is this relationship between inhibitory potency, and efficacy of trafficking an intrinsic challenge of the approach? I suspect that it may be an intractable problem in the sense that the inhibitor-bound conformation that underlies the chaperone effect cannot be uncoupled from the inhibited gating state. But this might not be true (many partial agonist drugs with low efficacy can be strongly potent, for example). In this case, the approach is really to find a 'happy medium' of a drug that is a weak enough inhibitor to be washed away, but still strong enough to exert some satisfactory chaperone effect. Could some additional clarity be added in the discussion on whether the chaperone and gating effects can be 'uncoupled'?

(2) Based on the western blots in Figure 2B, the rescue of cell surface expression appears to require a higher concentration of AKP compared to the concentration-response of channel inhibition (~9 microM in Figure 3, perhaps even more potent in patch clamp in Figure 2C). Could the authors clarify/quantify the concentration response for trafficking rescue?

(3) A future challenge in the application of pharmacochaperones of this type in hyperinsulinism may be the manipulation of chaperone concentration in order to permit function. In experiments, it is straightforward to wash off the chaperone, but this would not be the case in an organism. I wondered if the authors had attempted to rescue channel function with diazoxide in the presence of AKP, rather than after washing off (ie. is AKP inhibition insurmountable, or can it be overcome by sufficient diazoxide).

(4) Do the authors have any information about the turnover time of KATP after the wash-off of the chaperone (how stable are the rescued channels at the cell surface)? This is a difficult question to probe when glibenclamide is used as a chaperone, but may be much simpler to address with a lower affinity chaperone like AKP.

eLife Assessment

In this important manuscript, the authors investigate the phospho-regulation of the C. elegans kinesin-2 motor protein OSM-3, revealing that the kinase, NEKL-3, phosphorylates a serine/threonine patch at the hinge region of the motor to mediate autoinhibition until it reaches the ciliary middle segment. The findings are supported by robust genetic data, in vivo imaging, and motility assays with wild-type and mutant motors, although the methods section lacks detailed protocols for NEKL-3 assays and in silico analyses. Overall, the study provides a solid contribution to understanding the regulation of OSM-3 kinesin activity.

Reviewer #1 (Public review):

Summary:

This manuscript is a focused investigation of the phosphor-regulation of a C. elegans kinesin-2 motor protein, OSM-3. In C-elegans sensory ciliary, kinesin-2 motor proteins Kinesin-II complex and OSM-3 homodimer transport IFT trains anterogradely to the ciliary tip. Kinesin-II carries OSM-3 as an inactive passenger from the ciliary base to the middle segment, where kinesin-II dissociates from IFT trains and OSM-3 gets activated and transports IFT trains to the distal segment. Therefore, activation/inactivation of OSM-3 plays an essential role in its ciliary function.

Strengths:

In this study, using mass spectrometry, the authors have shown that the NEKL-3 kinase phosphorylates a serine/threonine patch at the hinge region between coiled coils 1 and 2 of an OSM-3 dimer, referred to as the elbow region in ubiquitous kinesin-1. Phosphomimic mutants of these sites inhibit OSM-3 motility both in vitro and in vivo, suggesting that this phosphorylation is critical for the autoinhibition of the motor. Conversely, phospho-dead mutants of these sites hyperactivate OSM-3 motility in vitro and affect the localization of OSM3 in C. elegans. The authors also showed that Alanine to Tyrosine mutation of one of the phosphorylation rescues OS-3 function in live worms.

Weaknesses:

Collectively, this study presents evidence for the physiological role of OSM-3 elbow phosphorylation in its autoregulation, which affects ciliary localization and function of this motor. Overall, the work is well performed, and the results mostly support the conclusions of this manuscript. However, the work will benefit from additional experiments to further support conclusions and rule out alternative explanations, filling some logical gaps with new experimental evidence and in-text clarifications, and improving writing.

Reviewer #2 (Public review):

Summary:

The regulation of kinesin is fundamental to cellular morphogenesis. Previously, it has been shown that OSM-3, a kinesin required for intraflagellar transport (IFT), is regulated by autoinhibition. However, it remains totally elusive how the autoinhibition of OSM-3 is released. In this study, the authors have shown that NEKL-3 phosphorylates OSM-3 and releases its autoinhibition.

The authors found NEKL-3 directly phosphorylates OSM-3 (although the method is not described clearly) (Figure 1). The phophorylated residue is the "elbow" of OSM-3. The authors introduced phospho-dead (PD) and phospho-mimic (PM) mutations by genome editing and found that the OSM-3(PD) protein does not form cilia, and instead, accumulates to the axonal tips. The phenotype is similar to another constitutive active mutant of OSM-3, OSM-3(G444A) (Imanishi et al., 2006; Xie et al., 2024). osm-3(PM) has shorter cilia, which resembles with loss of function mutants of osm-3 (Figure 3). The authors did structural prediction and showed that G444E and PD mutations change the conformation of OSM-3 protein (Figure 3). In the single-molecule assays G444E and PD mutations exhibited increased landing rate (Figure 4). By unbiased genetic screening, the authors identified a suppressor mutant of osm-3(PD), in which A489T occurs. The result confirms the importance of this residue. Based on these results, the authors suggest that NEKL-3 induces phosphorylation of the elbow domain and inactivates OSM-3 motor when the motor is synthesized in the cell body. This regulation is essential for proper cilia formation.

Strengths:

The finding is interesting and gives new insight into how the IFT motor is regulated.

Weaknesses:

The methods section has not presented sufficient information to reproduce this study.

eLife Assessment

This important study provides a framework for applying single-cell transcriptome data and network analysis from genetically diverse mouse cells to identify novel driver genes underlying the role of genetic loci associated with bone mineral density. The evidence supporting the identification of the driver genes and the conclusion of the paper is convincing. Overall, this approach may be broadly applicable and of interest to researchers investigating the genetics of complex diseases.

Reviewer #1 (Public review):

In this manuscript, Dillard and colleagues integrate cross-species genomic data with a systems approach to identify potential driver genes underlying human GWAS loci and establish the cell type(s) within which these genes act and potentially drive disease. Specifically, they utilize a large single-cell RNA-seq (scRNA-seq) dataset from an osteogenic cell culture model - bone marrow-derived stromal cells cultured under osteogenic conditions (BMSC-OBs) - from a genetically diverse outbred mouse population called the Diversity Outbred (DO) stock to discover network driver genes that likely underlie human bone mineral density (BMD) GWAS loci. The DO mice segregate over 40M single nucleotide variants, many of which affect gene expression levels, therefore making this an ideal population for systems genetic and co-expression analyses. The current study builds on previously published work from the same group that used co-expression analysis to identify co-expressed "modules" of genes that were enriched for BMD GWAS associations. In this study, the authors utilize a much larger scRNA-seq dataset from 80 DO BMSC-OBs, infer co-expression-based and Bayesian networks for each identified mesenchymal cell type, focused on networks with dynamic expression trajectories that are most likely driving differentiation of BMSC-OBs, and then prioritized genes ("differentiation driver genes" or DDGs) in these osteogenic differentiation networks that had known expression or splicing QTLs (eQTL/sQTLs) in any GTEx tissue that colocalized with human BMD GWAS loci. The systems analysis is impressive, the experimental methods are described in detail, and the experiments appear to be carefully done. The computational analysis of the single-cell data is comprehensive and thorough, and the evidence presented in support of the identified DDGs, including Tpx2 and Fgfrl1, is for the most part convincing. Some limitations in the data resources and methods hamper enthusiasm somewhat and are discussed below. Overall, while this study will no doubt be valuable to the BMD community, the cross-species data integration and analytical framework may be more valuable and generally applicable to the study of other diseases, especially for diseases with robust human GWAS data but for which robust human genomic data in relevant cell types is lacking.

Specific strengths of the study include the large scRNA-seq dataset on BMSC-OBs from 80 DO mice, the clustering analysis to identify specific cell types and sub-types, the comparison of cell type frequencies across the DO mice, and the CELLECT analysis to prioritize cell clusters that are enriched for BMD heritability (Figure 1). The network analysis pipeline outlined in Figure 2 is also a strength, as is the pseudotime trajectory analysis (results in Figure 3). One weakness involves the focus on genes that were previously identified as having an eQTL or sQTL in any GTEx tissue. The authors rightly point out that the GTEx database does not contain data for bone tissue, but the reason that eQTLs can be shared across many tissues - this assumption is valid for many cis-eQTLs, but it could also exclude many genes as potential DDGs with effects that are specific to bone/osteoblasts. Indeed, the authors show that important BMD driver genes have cell-type-specific eQTLs. Furthermore, the mesenchymal cell type-specific co-expression analysis by iterative WGCNA identified an average of 76 co-expression modules per cell cluster (range 26-153). Based on the limited number of genes that are detected as expressed in a given cell due to sparse per-cell read depth (400-6200 reads/cell) and dropouts, it's hard to believe that as many as 153 co-expression modules could be distinguished within any cell cluster. I would suspect some degree of model overfitting here and would expect that many/most of these identified modules have very few gene members, but the methods list a minimum module size of 20 genes. How do the numbers of modules identified in this study compare to other published scRNA-seq studies that use iterative WGCNA?

In the section "Identification of differentiation driver genes (DDGs)", the authors identified 408 significant DDGs and found that 49 (12%) were reported by the International Mouse Knockout [sic] Consortium (IMPC) as having a significant effect on whole-body BMD when knocked out in mice. Is this enrichment significant? E.g., what is the background percentage of IMPC gene knockouts that show an effect on whole-body BMD? Similarly, they found that 21 of the 408 DDGs were genes that have BMD GWAS associations that colocalize with GTEx eQTLs/sQTLs. Given that there are > 1,000 BMD GWAS associations, is this enrichment (21/408) significant? Recommend performing a hypergeometric test to provide statistical context to the reported overlaps here.

Reviewer #2 (Public review):

Summary:

In this manuscript, Farber and colleagues have performed single-cell RNAseq analysis on bone marrow-derived stem cells from DO Mice. By performing network analysis, they look for driver genes that are associated with bone mineral density GWAS associations. They identify two genes as potential candidates to showcase the utility of this approach.

Strengths:

The study is very thorough and the approach is innovative and exciting. The manuscript contains some interesting data relating to how cell differentiation is occurring and the effects of genetics on this process. The section looking for genes with eQTLs that differ across the differentiation trajectory (Figure 4) was particularly exciting.

Weaknesses:

The manuscript is in parts hard to read due to the use of acronyms and there are some questions about data analysis that need to be addressed.

eLife Assessment

The present study described GEARBOCS, an adeno-associated virus tool for in vivo gene editing in astrocytes, which is both timely and of importance for glial biologists, who often are troubled by efficient gene targeting in astrocytes. Overall, the finding is valuable, and the strength of the evidence is solid. Presumably, there will be great potential associated with GEARBOCS applications in the future.

Reviewer #1 (Public review):

Summary:

The manuscript by Bindu et al. created an AAV-based tool (GEARAOCS) to perform in vivo genome editing of mouse astrocytes. The authors engineered a versatile AAV vector that allows for gene deletion through NHNJ, site-specific knock-in by HDR, and gene trap. By utilizing this tool, the authors deleted Sparcl1 virally in subsets of astrocytes and showed that thalamocortical synapses in cortical layer IV are indeed reduced during a critical period of ocular dominance plasticity and in adulthood, whereas there is no change in excitatory synapse number in cortical layer II/III. Furthermore, the authors made a VAMP2 gene-trap AAV vector and showed that astrocyte-derived VAMP2 is required for the maintenance of both excitatory and inhibitory synapses.

Strengths:

This AAV-based tool is versatile for astrocytic gene manipulation in vivo. The work is innovative and exciting, given the paucity of tools available to probe astrocytes in vivo.

Weaknesses:

Several important considerations need to be made for the validation and usage of this tool, including:

Major points:

(1) Efficiency and specificity of spCas9-sgRNA mediated gene knockout in astrocytes. In Figure 3, the authors utilized Sparcl1 gene deletion as the proof-of-principle experiment. The readout for Sparcl1 KO efficiency is solely the immunoreactivity using an antibody raised against Sparcl1. As the method is based on NHEJ, the indels can be diverse and can occur in one allele or two. For the tool and proof-of-principle experiment, it will be important to know the percentage of editing near the PAM site, as well as the actual sequences of indels. This can be done by single-cell PCR of edited astrocytes, similar to the published work (Ye... Chen, Nature Biotechnology 2019).

(2) Along the same line, the authors showed that GEARBOCS TagIn of Sparcl1 resulted in 12.49% efficiency based on the immunohistochemistry of mCherry tag. It is understandable that the knock-in efficiency is much reduced as compared to gene knockout. However, it remains unclear if those 12.49% knock-in cells represent sequence-correct ones, as spCas9-mediated HDR is also an error-prone process, and it may accidentally alter nucleotides near the PAM site without causing the frameshift. The author will need to consider the related evidence or make comments in the discussion.

(3) What are the efficiencies of Sparcl1 GEARBOCS GeneTrap (Figure 3V) and Vamp2 GeneTrap and HA TagIn (Figure 5)?

Minor points:

(1) Figure 3H-J. The authors only showed the representative images of Sparcl1 KO. Please consider including the control (without gRNA), given that there are still many Sparcl1+ signals in Figure 3I (likely because of its expression in other cell types?).

(2) In figure 3Q-T, it appears that some Cas9-EGFP+ astrocytes (Q) do not express Sparcl1 (R). Is Sparcl1 expressed in subsets of astrocytes? Does Cas9-EGFP or Sparcl1-TagIn alter Sparcl1 endogenous expression?

(3) On Page 8, for the explanation of the design of the GEARBOCS construct, the authors have made a self-citation (#43). That was a BioRxiv paper that is being reviewed currently.

(4) For Figures 4 and 6, the graphs seem to be made in R with the x-axis labeled as "Condition". The y-axis labels are too small to read properly, especially in print. It would be better to make the graphs clearer like Figure 2 and Figure 3.

(5) On Page 13, "Figures 3V-Y" were referred to. However, there are no Figures 3W, X, and Y.

(6) There are a few typos in the manuscript, including line 900 "immunofluorescence microscopy images of a Cas9-EGFP-positive astrocytes (green)".

Reviewer #2 (Public review):

Summary:

The present study described GEARBOCS, an adeno-associated virus tool for in vivo gene editing in astrocytes. This tool is timely and important for glial biologists who often are troubled by efficient gene targeting in astrocytes. Overall the significance of the finding is valuable, and the strength of the evidence is solid. Presumably, there will be great potential associated with GEARBOCS applications in the future.

Strengths:

As efficient tools for targeting non-neuronal cells in the brains are rather limited for astrocytes and microglia, GEARBOCS adds to the small pool of currently available tools and will provide new options for glial biologists studying these tools. As the study revealed, GEARBOCS are capable of knockout and knockin manipulations for genes of interest, also ascribed with reporter tracking and gene-trap strategy. The promising multi-functional tool will advance our understanding of astrocytes and help to further elucidate the mechanism of neuron-glia interaction.

Weaknesses:

Even though the tool seems promising and powerful. the authors failed to provide more evidence on the robustness and specificity of GEARBOCS. Also, the advantages of GEARBOCS over some of the traditional methods were not clearly stated. Some of these concerns are described below.

Reviewer #3 (Public review):

Summary:

Sivadasan Bindu et al. developed a CRISPR/Cas9-based gene-editing strategy using a single AAV vector, named GEARBOCS (Gene Editing in AstRocytes Based On CRISPR/Cas9 System), which enables precise genome manipulation in astrocytes. This tool was shown to effectively perform knockout, tagging, and reporter knock-in gene modifications. The utility of GEARBOCS was demonstrated in two cases: establishing astrocytes as essential for the synaptogenic factor Sparcl1 in thalamocortical synapse maintenance, and revealing that cortical astrocytes express the Vamp2 protein, which is vital for maintaining synapse numbers.

Strengths:

Astrocytes play a crucial role in brain development and function, but studying them in vivo has been challenging due to limited molecular tools for manipulation. Sivadasan Bindu et al. developed a valuable system called GEARBOCS for effective astrocyte infection via retro-orbital injection.

Weaknesses:

The manuscript provides data only from the cerebral cortex and results from P42. Additional data from other brain regions and various time points (e.g., P0-15) are needed. Results from local injection experiments would also enhance the utility of this tool for the broader glial research community.

eLife Assessment

This is an important study that describes the development of optical biosensors for various Rab GTPases and explores the contributions of Rab10 and Rab4 to structural and functional plasticity at hippocampal synapses during glutamate uncaging. Most of the evidence supporting the conclusions of the paper is solid, while the evidence supporting the finding that Rab10 activation during structural LTP is sustained is incomplete due to the characterization of the relevant sensor.

Reviewer #1 (Public review):

Summary:

Wang et al. created a series of specific FLIM-FRET sensors to measure the activity of different Rab proteins in small cellular compartments. They apply the new sensors to monitor Rab activity in dendritic spines during induction of LTP. They find sustained (30 min) inactivation of Rab10 and transient (5 min) activation of Rab4 after glutamate uncaging in zero Mg. NMDAR function and CaMKII activation are required for these effects. Knockdown of Rab4 reduced spine volume change while knockdown of Rab10 boosted it and enhanced functional LTP (in KO mice). To test Rab effects on AMPA receptor exocytosis, the authors performed FRAP of fluorescently labeled GluA1 subunits in the plasma membrane. Within 2-3 min, new AMPARs appear on the surface via exocytosis. This process is accelerated by Rab10 knock-down and slowed by Rab4 knock-down. The authors conclude that CaMKII promotes AMPAR exocytosis by i) activating Rab4, the exocytosis driver and ii) inhibiting Rab10, possibly involved in AMPAR degradation.

Strengths:

The work is a technical tour de force, adding fundamental insights to our understanding of the crucial functions of different Rab proteins in promoting/preventing synaptic plasticity. The complexity of compartmentalized Ras signaling is poorly understood and this study makes substantial inroads. The new sensors are thoroughly characterized, seem to work very well, and will be quite useful for the neuroscience community and beyond (e.g. cancer research). The use of FLIM for read-out is compelling for precise activity measurements in rapidly expanding compartments (i.e., spines during LTP).

Weaknesses:

The interpretation of the FRAP experiments (Figure 5, Ext. Data Figure 13) is not straightforward as spine volume and surface area greatly expand during uncaging. I appreciate the correction for the added spine membrane shown in Extended Data Figure 14i, but shouldn't this be a correction factor (multiplication) derived from the volume increase instead of a subtraction?

Also, experiments were not conducted or analyzed blind, risking bias in the selection/exclusion of experiments for analysis. This reduces my confidence in the results.

Reviewer #2 (Public review):

Summary:

Wang et al. developed a set of optical sensors to monitor Rab protein activity. Their investigation into Rab activity in dendritic spines during structural long-term plasticity (sLTP) revealed sustained Rab10 inactivation (>30min) and transient Rab4 activation (~5 min). Through pharmacological and genetic manipulation to constitutively activate or inhibit Rab proteins, they found that Rab10 negatively regulates sLTP and AMPA receptor insertion, while Rab4 positively influences sLTP but only in the transient phase. The optical sensors provide new tools for studying Rab activity in cells and neurobiology. However, a full understanding of the timing of Rab activity will require a detailed characterization of sensor kinetics.

Strengths:

(1) Introduction of a series of novel sensors that can address numerous questions in Rab biology.

(2) Multiple methods to manipulate Rab proteins to reveal the roles of Rab10 and rab4 in LTP.

(3) Discovery of Rab4 activation and Rab10 inhibition with different kinetics during sLTP, correlating with their functional roles in the transient (Rab4) and both transient and sustained (Rab10) phases of sLTP.

Weaknesses:

(1) Lack of characterization of sensor kinetics, making it difficult to determine if the observed Rab kinetics during sLTP were due to sensor behavior or actual Rab activity.

(2) It is crucial to assess whether the overexpression of Rab proteins as reporters, affects Rab activity and cellular structure and physiology (e.g. spine number and size).

(3) The paper does not explain the apparently different results between NMDA receptor activation and glutamate uncaging. NMDA receptor activation increased Rab10 activity, while glutamate uncaging decreased it. NMDA receptor activation resulted in sustained Rab4 activation, whereas glutamate uncaging caused only brief activation of about 5 minutes. A potential explanation, ideally supported by data, is needed.

(4) There is a discrepancy between spine phenotype and sLTP potential with Rab10 perturbation. Rab10 perturbation affected spine density but not size, suggesting a role in spinogenesis rather than sLTP. However, glutamate uncaging affected sLTP, and spinogenesis was not examined. Explaining the discrepancy between spine size and sLTP potential is necessary. Exploring spinogenesis with glutamate uncaging would strengthen these results. Additionally, Figure 4j shows no change in synaptic transmission with Rab10 knockout, despite an increase in spine density. An explanation, ideally supported by data, is needed for the unchanged fEPSP slope despite an increase in spine density.

(5) Spine volume was imaged using acceptor fluorophores (mCherry, or mCherry/Venus) at 920nm, where the two-photon cross-section of mCherry is minimal. 920nm was also used to excite the donor fluorophore, hence the spine volume measurement based on total red channel fluorescence is the sum of minimal mCherry fluorescence from direct 920nm excitation, bleed-through from the green channel, and FRET. This confounded measurement requires correction and clarification.

Reviewer #3 (Public review):

Summary:

This study examines the roles of Rab10 and Rab4 proteins in structural long-term potentiation (sLTP) and AMPA receptor (AMPAR) trafficking in hippocampal dendritic spines using various different methods and organotypic slice cultures as the biological model.

The paper shows that Rab10 inactivation enhances AMPAR insertion and dendritic spine head volume increase during sLTP, while Rab4 supports the initial stages of these processes. The key contribution of this study is identifying Rab10 inactivation as a previously unknown facilitator of AMPAR insertion and spine growth, acting as a brake on sLTP when active. Rab4 and Rab10 seem to be playing opposing roles, suggesting a somewhat coordinated mechanism that precisely controls synaptic potentiation, with Rab4 facilitating early changes and Rab10 restricting the extent and timing of synaptic strengthening.

Strengths:

The study combines multiple techniques such as FRET/FLIM imaging, pharmacology, genetic manipulations, and electrophysiology to dissect the roles of Rab10 and Rab4 in sLTP. The authors developed highly sensitive FRET/FLIM-based sensors to monitor Rab protein activity in single dendritic spines. This allowed them to study the spatiotemporal dynamics of Rab10 and Rab4 activity during glutamate uncaging-induced sLTP. They also developed various controls to ensure the specificity of their observations. For example, they used a false acceptor sensor to verify the specificity of the Rab10 sensor response.

This study reveals previously unknown roles for Rab10 and Rab4 in synaptic plasticity, showing their opposing functions in regulating AMPAR trafficking and spine structural plasticity during LTP.

Weaknesses:

In sLTP, the initial volume of stimulated spines is an important determinant of induced plasticity. To address changes in initial volume and those induced by uncaging, the authors present Extended Data Figure 2. In my view, the methods of fitting, sample selection, or both may pose significant limitations for interpreting the overall results. While the initial spine size distribution for Rab10 experiments spans ~0.1-0.4 fL (with an unusually large single spine at the upper end), Rab4 spine distribution spans a broader range of ~0.1-0.9 fL. If the authors applied initial size-matched data selection or used polynomials rather than linear fitting, panels a, b, e, f, and g might display a different pattern. In that case, clustering analysis based on initial size may be necessary to enable a fair comparison between groups not only for this figure but also for main Figures 2 and 3.

Another limitation is the absence of in vivo validation, as the experiments were performed in organotypic hippocampal slices, which may not fully replicate the complexity of synaptic plasticity in an intact brain, where excitatory and inhibitory processes occur concurrently. High concentrations of MNI-glutamate (4 mM in this study) are known to block GABAergic responses due to its antagonistic effect on GABA-A receptors, thereby precluding the study of inhibitory network activity or connectivity [1], which is already known to be altered in organotypic slice cultures.

[1] https://www.frontiersin.org/journals/neural-circuits/articles/10.3389/neuro.04.002.2009/full

eLife Assessment

This study provides a valuable set of analyses and theoretical derivations to understand the mechanisms used by recurrent neural networks (RNNs) to perform context-dependent accumulation of evidence. The novelty of some of the findings needs clarification, and additional details need to be provided for some of the analyses. However, the results regarding the dimensionality and neural dynamical signatures of RNNs are solid and provide new avenues to study the mechanisms underlying context-dependent computations.

Reviewer #1 (Public review):

Summary:

This paper investigates how recurrent neural networks (RNNs) can perform context-dependent decision-making (CDM). The authors use low-rank RNN modeling and focus on a CDM task where subjects are presented with sequences of auditory pulses that vary in location and frequency, and they must determine either the prevalent location or frequency based on an external context signal. In particular, the authors focus on the problem of differentiating between two distinct selection mechanisms: input modulation, which involves altering the stimulus input representation, and selection vector modulation, which involves altering the "selection vector" of the dynamical system.

First, the authors show that rank-one networks can only implement input modulation and that higher-rank networks are required for selection vector modulation. Then, the authors use pathway-based information flow analysis to understand how information is routed to the accumulator based on context. This analysis allows the authors to introduce a novel definition of selection vector modulation that explicitly links it to changes in the effective coupling along specific pathways within the network.

The study further generates testable predictions for differentiating selection vector modulation from input modulation based on neural dynamics. In particular, the authors find that:<br /> (1) A larger proportion of selection vector modulation is expected in networks with high-dimensional connectivity.<br /> (2) Single-neuron response kernels exhibiting specific profiles (peaking between stimulus onset and choice onset) are indicative of neural dynamics in extra dimensions, supporting the presence of selection vector modulation.<br /> (3) The percentage of explained variance (PEV) of extra dynamical modes extracted from response kernels at the population level can serve as an index to quantify the amount of selection vector modulation.

Strengths:

The paper is clear and well-written, and it draws bridges between two recent important approaches in the study of CDM: circuit-level descriptions of low-rank RNNs, and differentiation across alternative mechanisms in terms of neural dynamics. The most interesting aspect of the study involves establishing a link between selection vector modulation, network dimensionality, and dimensionality of neural dynamics. The high correlation between the networks' mechanisms and their dimensionality (Figure 7d) is surprising since differentiating between selection mechanisms is generally a difficult task, and the strength of this result is further corroborated by its consistency across multiple RNN hyperparameters (Figure 7-Figure Supplement 1 and Figure 7-figure supplement 2). Interestingly, the correlation between the selection mechanism and the dimensionality of neural dynamics is also high (Figure 7g), potentially providing a promising future avenue for the study of neural recordings in this task.

Weaknesses:

The first part of the manuscript is not particularly novel, and it would be beneficial to clearly state which aspects of the analyses and derivations are different from previous literature. For example, the derivation that rank-1 RNNs cannot implement selection vector modulation is already present in the Extended Discussion of Pagan et al., 2022 (Equations 42-43). Similarly, it would be helpful to more clearly explain how the proposed pathway-based information flow analysis differs from the circuit diagram of latent dynamics in Dubreuil et al., 2022.

With regard to the results linking selection vector modulation and dimensionality, more work is required to understand the generality of these results, and how practical it would be to apply this type of analysis to neural recordings. For example, it is possible to build a network that uses input modulation and to greatly increase the dimensionality of the network simply by adding additional dimensions that do not directly contribute to the computation. Similarly, neural responses might have additional high-dimensional activity unrelated to the task. My understanding is that the currently proposed method would classify such networks incorrectly, and it is reasonable to imagine that the dimensionality of activity in high-order brain regions will be strongly dependent on activity that does not relate to this task.

Finally, a number of aspects of the analysis are not clear. The most important element to clarify is how the authors quantify the "proportion of selection vector modulation" in vanilla RNNs (Figures 7d and 7g). I could not find information about this in the Methods, yet this is a critical element of the study results. In Mante et al., 2013 and in Pagan et al., 2022 this was done by analyzing the RNN linearized dynamics around fixed points: is this the approach used also in this study? Also, how are the authors producing the trial-averaged analyses shown in Figures 2f and 3f? The methods used to produce this type of plot differ in Mante et al., 2013 and Pagan et al., 2022, and it is necessary for the authors to explain how this was computed in this case.

I am also confused by a number of analyses done to verify mathematical derivations, which seem to suggest that the results are close to identical, but not exactly identical. For example, in the histogram in Figure 6b, or the histogram in Figure 7-figure supplement 3d: what is the source of the small variability leading to some of the indices being less than 1?

Reviewer #2 (Public review):

This manuscript examines network mechanisms that allow networks of neurons to perform context-dependent decision-making.

In a recent study, Pagan and colleagues identified two distinct mechanisms by which recurrent neural networks can perform such computations. They termed these two mechanisms input-modulation and selection-vector modulation. Pagan and colleagues demonstrated that recurrent neural networks can be trained to implement combinations of these two mechanisms, and related this range of computational strategies with inter-individual variability in rats performing the same task. What type of structure in the recurrent connectivity favors one or the other mechanism however remained an open question.

The present manuscript addresses this specific question by using a class of mechanistically interpretable recurrent neural networks, low-rank RNNs.

The manuscript starts by demonstrating that unit-rank RNNs can only implement the input-modulation mechanism, but not the selection-vector modulation. The authors then build rank three networks that implement selection-vector modulation and show how the two mechanisms can be combined. Finally, they relate the amount of selection-vector modulation with the effective rank, ie the dimensionality of activity, of a trained full-rank RNN.

Strengths:

(1) The manuscript is written in a straightforward manner.<br /> (2) The analytic approach adopted in the manuscript is impressive.<br /> (3) Very clear identification of the mechanisms leading to the two types of context-dependent modulation.<br /> (4) Altogether this manuscript reports remarkable insights into a very timely question.

Weaknesses:

- The introduction could have been written in a more accessible manner for any non-expert readers.

eLife Assessment

This valuable study introduces a novel method for controlling generalization and interference in neural networks undergoing continual learning. The authors provide solid evidence that their parsimonious method performs better than online gradient descent in several continual learning situations while providing biologically plausible links to three-factor learning rules. However, empirical validation is limited to linear networks, which raises questions about the generality of the results in non-linear networks. While the work is interesting to theoretical and experimental neuroscientists, improving the article presentation by clearly defining terminology before using it and providing more details on the setup of the simulation experiments would be vital to make the article more accessible.

Reviewer #1 (Public review):

Summary:

This paper advances a new understanding of plasticity in artificial neural networks. It shows that weight changes can be decomposed into two components: the first governs the magnitude (or gain) of responses in a particular layer; the second governs the relationship of those responses to the input to that layer. Then, it shows that separate control of these two factors via a surprise-based metaplasticity can avoid catastrophic forgetting as well as induce successful generalization in different conditions, through a series of simulation experiments in linear networks. The authors argue that separate control of the two factors may be at work in the brain and may underlie the ability of humans and other animals to perform successful sequential learning. The paper is hampered by confusing terminology and the precise setup of some of the simulations is unclear. The paper also focuses exclusively on the linear case, which limits confidence in the generality of the results. The paper would also benefit from the inclusion of specific predictions for neural data that would confirm the idea that the separate control of these two factors underlies successful continual learning in the brain.

Strengths:

(1) The theoretical framework developed by the paper is interesting, and could have wide applicability for both training networks and for understanding plasticity.

(2) The simulations convincingly show benefits to the coordinated eligibility model of plasticity advanced by the authors.

Weaknesses:

(1) The simulation results are limited to simple tasks in linear networks, it would be interesting to see how the intuitions developed in the linear case extend to nonlinear networks.

(2) The terminology is somewhat confusing and this can make the paper difficult to follow in some places.

(3) The details of some of the simulations are lacking.

Reviewer #2 (Public review):

Summary:

Scott and Frank propose a new method for controlling generalization and interference in neural networks that undergo continual learning. Their method called coordinated eligibility models (CEM), relies on the factorization of synaptic updates into input-driven and output-driving factors. They subsequently employ the fact that it is sufficient to orthogonalize any one of these two factors across different data points to nullify the interference during learning. They exemplify this on a number of toy tasks while comparing their result to vanilla gradient.

Strengths:

The specific mechanism proposed here is novel (while, as authors acknowledge, there is a large number of other mechanisms for the selective recruitment of synapses for the prevention of catastrophic forgetting). Furthermore, it is simple, elegant, and to a large extent biologically plausible, potentially pointing to specific and testable aspects of learning dynamics.

Weaknesses:

(1) Scope and toy nature of experiments: the model was only applied to very simple problems tailored specifically to demonstrate the strengths of the CEM method. Furthermore, single hyperparameter setting is presented for every scenario which leaves it questionable how general the numerical results are. The selection of input, output dimensionality and data set size also seems to be underexplored. Will a larger curriculum, smaller or larger dimension, compromise any of the CEM ingredients? Restriction to linear models seems arbitrary (it should be a no-time test to add non-linearity within a pytorch framework that authors used), and applicability for any non-synthetic problem is not obvious.

It is also unclear to what extent of domain knowledge is needed for surprise signals to be successfully generated. Can the authors make a stronger case about novel curriculum entries being easily recognizable by cosine distance, either in the brain or in machine learning? Can they alternatively demonstrate their method on a less toy benchmark (e.g. permuted MNIST from Kirkpatrick et al 2017 that they cite)?

Another limitation is that unlike smoother models of plasticity budgets (e.g. Kirkpatrick et al 17, Zenke et al 17), here eligibility seems to be lost forever, once surprise is applied. What happens to the model if more data from a previously visited task becomes available? Will the system be able to continue learning within the right context and how does CEM perform compared to other catastrophic-forgetting-prevention strategies?

(2) The clarity and organization must be improved. Specifically, the balance between verbal descriptions, equations, figures, and their captions needs to be improved. For example - two full-size equations are dedicated to the application of linear regression (around lines 183 and 236) while by far less obvious math such as settings for fig 7, including 'feature loadings', 'demands', etc., is presented in a hardly readable mixture figure and main text. Similarly, the surprise mechanism which is a key ingredient for the model is presented in a very non-straightforward fashion, scattered between the main text, figure, and methods. The figure legends are poorly informative in many cases as well (see minor comments for examples).

Reviewer #3 (Public review):

Summary:

This paper describes a modification of gradient descent learning, and shows in several simulations that this modification allows online learning of linear regression problems where naive gradient descent fails. The modification starts from the observation that the rank-1 weight update of online gradient learning can be written as the outer product Δw ∝ g xᵀ of a vector g and the input x. Modifying this update rule, by projecting g or x to some subspaces, i.e. Δw ∝ Pg (Qx)ᵀ, allows for preventing the typical catastrophic forgetting behavior of online gradient descent, as confirmed in the simulations. The projection matrices P and Q are updated with a "surprise"-modulation rule.

Strengths:

I find it interesting to explore the benefits of alternatives to naive online gradient learning for continual learning.

Weaknesses:

The novelty and advancement in our theoretical understanding of plasticity in neural systems are unclear. I appreciate gaining insights from simple mathematical arguments and simulations with toy models, but for this paper, I do not yet clearly see what I learned: on the mathematical/ML/simulation side it is unclear how it relates to the continual learning literature, on the neuroscience/surprise side I see only a number of papers cited but not any clear connection to data or novel insights.

More specifically:

(1) It is unclear what exactly the "coordinated eligibility theory" is. Is any update rule that satisfies Equation 4 included in the coordinated eligibility theory? If yes, what is the point: any update rule can be written in this way, including standard online gradient descent. If no, what is it? It is not Equation 5 it seems, because this is called "one of the simplest coordinated eligibility models".

(2) There is a lot of work on continual learning which is not discussed, e.g. "Orthogonal Gradient Descent for Continual Learning" (Farajtabar et al. 2019), "Continual learning in low-rank orthogonal subspaces" (Chaudhry et al. 2020), or "Keep Moving: identifying task-relevant subspaces to maximise plasticity for newly learned tasks" (Anthes et al. 2024), to name just a few. What is the novelty of this work relative to these existing works? Is the novelty in the specific projection operator? If yes, what are the benefits of this projection operator in theory and simulations? How would, for example, the approach of Farajtabar et al. 2019 perform on the tasks in Figures 3-7?

(3) There is also work on using surprise signals for multitask learning in models of biological neural networks, e.g. "Fast adaptation to rule switching using neuronal surprise" (Barry et al. 2023).

(4) What is the motivation for the projection to the unit sphere in Equation 5?

(5) What is the motivation for the surprise definition? For example, why cos(x⋅μ) = cos(|x||μ|cos(θ)) = cos(cos(θ))? (Assuming x and μ have unit length and θ is the angle between x and μ).

eLife Assessment

This study presents an important contribution to the understanding of neural speech tracking, demonstrating how minimal background noise can enhance the neural tracking of the amplitude-onset envelope. The evidence supporting the claims of the author is solid, through a well-designed series of EEG experiments. This work will be of interest to auditory scientists, particularly those investigating biological markers of speech processing.

Reviewer #1 (Public review):

This paper presents a comprehensive study of how neural tracking of speech is affected by background noise. Using five EEG experiments and Temporal response function (TRF), it investigates how minimal background noise can enhance speech tracking even when speech intelligibility remains very high. The results suggest that this enhancement is not attention-driven but could be explained by stochastic resonance. These findings generalize across different background noise types and listening conditions, offering insights into speech processing in real-world environments.

I find this paper well-written, the experiments and results are clearly described. However, I have a few comments that may be useful to address.

(1) The behavioral accuracy and EEG results for clear speech in Experiment 4 differ from those of Experiments 1-3. Could the author provide insights into the potential reasons for this discrepancy? Might it be due to linguistic/ acoustic differences between the passages used in experiments? If so, what was the rationale behind using different passages across different experiments?

(2) Regarding peak amplitude extraction, why were the exact peak amplitudes and latencies of the TRFs for each subject not extracted, and instead, an amplitude average within a 20 ms time window based on the group-averaged TRFs used? Did the latencies significantly differ across different SNR conditions?

(3) How is neural tracking quantified in the current study? Does improved neural tracking correlate with EEG prediction accuracy or individual peak amplitudes? Given the differing trends between N1 and P2 peaks in babble and speech-matched noise in experiment 3, how is it that babble results in greater envelope tracking compared to speech-matched noise?

(4) The paper discusses how speech envelope-onset tracking varies with different background noises. Does the author expect similar trends for speech envelope tracking as well? Additionally, could you explain why envelope onsets were prioritized over envelope tracking in this analysis?

Reviewer #2 (Public review):

The author investigates the role of background noise on EEG-assessed speech tracking in a series of five experiments. In the first experiment, the influence of different degrees of background noise is investigated and enhanced speech tracking for minimal noise levels is found. The following four experiments explore different potential influences on this effect, such as attentional allocation, different noise types, and presentation mode.

The step-wise exploration of potential contributors to the effect of enhanced speech tracking for minimal background noise is compelling. The motivation and reasoning for the different studies are clear and logical and therefore easy to follow. The results are discussed in a concise and clear way. While I specifically like the conciseness, one inevitable consequence is that not all results are equally discussed in depth.

Based on the results of the five experiments, the author concludes that the enhancement of speech tracking for minimal background noise is likely due to stochastic resonance. Given broad conceptualizations of stochastic resonance as a noise benefit this is a reasonable conclusion.

This study will likely impact the field as it provides compelling support questioning the relationship between speech tracking and speech processing.

eLife Assessment

This important study identifies neurotrophin signaling as a molecular mechanism underlying previous findings of structural plasticity in central dopaminergic neurons of the adult fly brain. The authors present solid evidence for neurotrophin signaling in shaping the structure and synapses of certain dopaminergic circuits. The work suggests an intriguing potential link between neurotrophin signaling and experience-induced structural plasticity but further research will be necessary to establish this connection definitively.

Reviewer #1 (Public review):

Summary:

Sun et al. are interested in how experience can shape the brain and specifically investigate the plasticity of the Toll-6 receptor-expressing dopaminergic neurons (DANs). To learn more about the role of Toll-6 in the DANs, the authors examine the expression of the Toll-6 receptor ligand, DNT-2. They show that DNT-2 expressing cells connect with DANs and that loss of function of DNT-2 in these cells reduces the number of PAM DANs, while overexpression causes alterations in dendrite complexity. Finally, the authors show that alterations in the levels of DNT-2 and Toll-6 can impact DAN-driven behaviors such as climbing, arena locomotion, and learning and long-term memory.

Strengths:

The authors methodically test which neurotransmitters are expressed by the 4 prominent DNT-2 expressing neurons and show that they are glutamatergic. They also use Trans-Tango and Bac-TRACE to examine the connectivity of the DNT-2 neurons to the dopaminergic circuit and show that DNT-2 neurons receive dopaminergic inputs and output to a variety of neurons including MB Kenyon cells, DAL neurons, and possibly DANS.

Weaknesses:

(1) To identify the DNT-2 neurons, the authors use CRISPR to generate a new DN2-GAL4. They note that they identified at least 12 DNT-2 plus neurons. In Supplementary Figure 1A, the DNT-2-GAL4 driver was used to express a UAS-histoneYFP nuclear marker. From these figures, it looks like DNT-2-GAL4 is labeling more than 12 neurons. Is there glial expression? This question is relevant as it is not clear how many other cell types are being manipulated with the DNT-2-GAL4 driver is used in subsequent experiments. For example, is DNT-2-GAL4--> DNT-2-RNAi is reducing DNT2 in many neurons or glia effects could be indirect.

(2) In Figure 2C the authors show that DNT-2 upregulation leads to an increase in TH levels using q-RT-PCR from whole heads. However, in Figure 3G they also show that DNT-2 overexpression also causes an increase in the number of TH neurons. It is unclear whether TH RNA increases due to expression/cell or number of TH neurons in the head.

(3)DNT-2 is also known as Spz5 and has been shown to activate Toll-6 receptors in glia (McLaughlin et al., 2019), resulting in the phagocytosis of apoptotic neurons. In addition, the knockdown of DNT-2/Spz5 throughout development causes an increase in apoptotic debris in the brain, which can lead to neurodegeneration. Indeed Figure 3H shows that an adult-specific knockdown of DNT-2 using DNT2-GAL4 causes an increase in Dcp1 signal in many neurons and not just TH neurons.

Comments on revisions:

The authors have made some changes in the text to tone down their claims. They have also provided additional images to support their work. However, requested controls are not provided, and new experiments are not added to address reviewer concerns.

Reviewer #2 (Public review):

This paper examines how structural plasticity in neural circuits, particularly in dopaminergic systems, is regulated by Drosophila neurotrophin-2 (DNT-2) and its receptors, Toll-6 and Kek-6. The authors show that these molecules are critical for modulating circuit structure, dopaminergic neuron survival, synaptogenesis, and connectivity. They demonstrate that the loss of DNT-2 or Toll-6 function leads to the loss of dopaminergic neurons, reduced dendritic arborization, and synaptic impairment, whereas overexpression of DNT-2 increases dendritic complexity and synaptogenesis. Additionally, DNT-2 and Toll-6 influence dopamine-dependent behaviors, including locomotion and long-term memory, suggesting a link between DNT-2 signaling, structural plasticity, and behavior.

A major strength of this study is the impressive cellular resolution achieved. By focusing on specific dopaminergic neurons, such as the PAM and PPL1 clusters, and using a range of molecular markers, the authors were able to clearly visualize intricate details of synapse formation, dendritic complexity, and axonal targeting within defined circuits. Given the critical role of dopaminergic pathways in learning and memory, this approach provides a valuable foundation for exploring the role of DNT-2, Toll-6, and Kek-6 in experience-dependent structural plasticity. While the manuscript hints at a connection to experience-induced plasticity, the study does not establish a direct causal link between neurotrophin signaling and experience-driven changes. To support this idea, it would be necessary to observe experience-induced structural changes and demonstrate that downregulation of DNT-2 signaling prevents these changes. The closest attempt in this study was the artificial activation of DNT-2 neurons using TrpA1, which resulted in overgrowth of axonal arbors and an increase in synaptic sites in both DNT-2 and PAM neurons. However, whether the observed structural changes were dependent on DNT-2 signaling remains unclear.

In conclusion, this study demonstrates that DNT-2 and its receptors play a role in regulating the structure of dopaminergic circuits in the adult fly brain. Whether DNT-2 signaling contributes to experience-dependent structural plasticity within these circuits remains an exciting open question and warrants further investigation.

Comments on revisions:

I appreciate the authors' responses to my previous comments and have no further suggestions.

Reviewer #3 (Public review):

Summary:

The authors used the model organism Drosophila melanogaster to show that the neurotrophin Toll-6 and its ligands, DNT-2 and kek-6, play a role in maintaining the number of dopaminergic neurons and modulating their synaptic connectivity. This supports previous findings on the structural plasticity of dopaminergic neurons and suggests a molecular mechanism underlying this plasticity.

Strengths:

The experiments are overall very well designed and conclusive. Methods are in general state-of-the-art, the sample sizes are sufficient, the statistical analyses are sound, and all necessary controls are at place. The data interpretation is straight forwards, and the relevant literature is taken into consideration. Overall, the manuscript is solid and presents novel, interesting and important findings.

Weaknesses:

There are three technical weaknesses that could perhaps be improved.

First, the model of reciprocal, inhibitory feedback loops (figure 2F) is speculative. On the one hand, glutamate can act in flies as excitatory or inhibitory transmitter (line 157!), and either situation can be the case here. On the other hand, it is not clear how an increase or decrease in cAMP level translates into transmitter release. One can only conclude that two type of neurons potentially influence each other.

Second, the quantification of bouton volumes (no y-axis label in Figure 5 C and D!) and dendrite complexity are not convincingly laid out. Here, the reader expects fine-grained anatomical characterizations of the structures under investigation, and a method to precisely quantify the lengths and branching patterns of individual dendritic arborizations as well as the volume of individual axonal boutons.

Third, figure 1C shows two neurons with the goal of demonstrating between-neuron variability. It is not convincingly demonstrated that the two neurons are actually of the very same type of neuron in different flies, or two completely different neurons.

Review of the revised manuscript:

The authors have addressed some points of concern raised by the reviewers. I would like to emphasize that I find the overall research study highly interesting and important.

Author response:

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

Reviewer #1 (Public review):

Summary:

Sun et al. are interested in how experience can shape the brain and specifically investigate the plasticity of the Toll-6 receptor-expressing dopaminergic neurons (DANs). To learn more about the role of Toll-6 in the DANs, the authors examine the expression of the Toll-6 receptor ligand, DNT-2. They show that DNT-2 expressing cells connect with DANs and that loss of function of DNT-2 in these cells reduces the number of PAM DANs, while overexpression causes alterations in dendrite complexity. Finally, the authors show that alterations in the levels of DNT-2 and Toll-6 can impact DAN-driven behaviors such as climbing, arena locomotion, and learning and long-term memory.

Strengths:

The authors methodically test which neurotransmitters are expressed by the 4 prominent DNT-2 expressing neurons and show that they are glutamatergic. They also use Trans-Tango and Bac-TRACE to examine the connectivity of the DNT-2 neurons to the dopaminergic circuit and show that DNT-2 neurons receive dopaminergic inputs and output to a variety of neurons including MB Kenyon cells, DAL neurons, and possibly DANS.

We are very pleased that Reviewer 1 found our connectivity analysis a strength.

Weaknesses:

(1) To identify the DNT-2 neurons, the authors use CRISPR to generate a new DN2-GAL4.

They note that they identified at least 12 DNT-2 plus neurons. In Supplementary Figure 1A, the DNT-2-GAL4 driver was used to express a UAS-histoneYFP nuclear marker. From these figures, it looks like DNT-2-GAL4 is labeling more than 12 neurons. Is there glial expression?

Indeed, we claimed that DNT-2 is expressed in at least 12 neurons (see line 141, page 6 of original manuscript), which means more than 12 could be found. The membrane tethered reporters we used – UAS-FlyBow1.1, UASmcD8-RFP, UAS-MCFO, as well as UAS-DenMark:UASsyd-1GFP – gave a consistent and reproducible pattern. However, with DNT-2GAL4>UAS-Histone-YFP more nuclei were detected that were not revealed by the other reporters. We have found also with other GAL4 lines that the patterns produced by different reporters can vary. This could be due to the signal strength (eg His-YFP is very strong) and perdurance of the reporter (e.g. the turnover of His-YFP may be slower than that of the other fusion proteins).

We did not test for glial expression, as it was not directly related to the question addressed in this work.

(2) In Figure 2C the authors show that DNT-2 upregulation leads to an increase in TH levels using q-RT-PCR from whole heads. However, in Figure 3H they also show that DNT-2 overexpression also causes an increase in the number of TH neurons. It is unclear whether TH RNA increases due to expression/cell or the number of TH neurons in the head.

Figure 3H shows that over-expression of DNT-2 FL increased the number of Dcp1+ apoptotic cells in the brain, but not significantly (p=0.0939). The ability of full-length neurotrophins to induce apoptosis and cleaved neurotrophins promote cell survival is well documented in mammals. We had previously shown that DNT-2 is naturally cleaved, and that over-expression of DNT-2 does not induce apoptosis in the various contexts tested before (McIlroy et al 2013 Nature Neuroscience; Foldi et al 2017 J Cell Biol; Ulian-Benitez et al 2017 PLoS Genetics). Similarly, throughout this work we did not find DNT-2FL to induce apoptosis.

Instead, in Figure 3G we show that over-expression of DNT-2FL causes a statistically significant increase in the number of TH+ cells. This is an important finding that supports the plastic regulation of PAM cell number. We thank the Reviewer for highlighting this point, as we had forgotten to add the significance star in the graph. In this context, we cannot rule out the possibility that the increase in TH mRNA observed when we over-express DNT-2FL could not be due to an increase in cell number instead. Unfortunately, it is not possible for us to separate these two processes at this time. Either way, the result would still be the same: an increase in dopamine production when DNT-2 levels rise.

We have now edited the abstract lines 38-39 adding that “By contrast, over-expressed DNT-2 increased DAN cell number,…”, within the main text in Results page 10 lines 259-265 and in the Discussion section page 15 lines 391, 393-396.

(3) DNT-2 is also known as Spz5 and has been shown to activate Toll-6 receptors in glia (McLaughlin et al., 2019), resulting in the phagocytosis of apoptotic neurons. In addition, the knockdown of DNT-2/Spz5 throughout development causes an increase in apoptotic debris in the brain, which can lead to neurodegeneration. Indeed Figure 3H shows that an adult specific knockdown of DNT-2 using DNT2-GAL4 causes an increase in Dcp1 signal in many neurons and not just TH neurons.

Indeed, we did find Dcp1+ TH-negative cells too (although not widely throughout the brain), although this is not shown in the images of Figure 3H where we showed only TH+ Dcp+ cells.

That is not surprising, as DNT-2 neurons have large arborisations that can reach a wide range of targets; DNT-2 is secreted, and could reach beyond its immediate targets; Toll-6 is expressed in a vast number of cells in the brain; DNT-2 can bind promiscuously at least also Toll-7 and other Keks, which are also expressed in the adult brain (Foldi et al 2017 J Cell Biology; Ulian-Benitez et al 2017 PLoS Genetics; Li et al 2020 eLife). Together with the findings by McLaughlin et al 2019, our findings further support the notion that DNT-2 is a neuroprotective factor in the adult brain. It will be interesting to find out what other neuron types DNT-2 maintains.

We have made some edits on these points in page 10 lines 259-265.

We would like to thank Reviewer 1 for their positive comments on our work and their interesting and valuable feedback.

Reviewer #2 (Public review):

This paper examines how structural plasticity in neural circuits, particularly in dopaminergic systems, is regulated by Drosophila neurotrophin-2 (DNT-2) and its receptors, Toll-6 and Kek-6. The authors show that these molecules are critical for modulating circuit structure and dopaminergic neuron survival, synaptogenesis, and connectivity. They show that loss of DNT-2 or Toll-6 function leads to loss of dopaminergic neurons, dendritic arborization, and synaptic impairment, whereas overexpression of DNT-2 increases dendritic complexity and synaptogenesis. In addition, DNT-2 and Toll-6 modulate dopamine-dependent behaviors, including locomotion and long-term memory, suggesting a link between DNT-2 signaling, structural plasticity, and behavior.

A major strength of this study is the impressive cellular resolution achieved. By focusing on specific dopaminergic neurons, such as the PAM and PPL1 clusters, and using a range of molecular markers, the authors were able to clearly visualize intricate details of synapse formation, dendritic complexity, and axonal targeting within defined circuits. Given the critical role of dopaminergic pathways in learning and memory, this approach provides a good opportunity to explore the role of DNT-2, Toll-6, and Kek-6 in experience-dependent structural plasticity. However, despite the promise in the abstract and introduction of the paper, the study falls short of establishing a direct causal link between neurotrophin signaling and experience-induced plasticity.

Simply put, this study does not provide strong evidence that experience-induced structural plasticity requires DNT-2 signaling. To support this idea, it would be necessary to observe experience-induced structural changes and demonstrate that downregulation of DNT-2 signaling prevents these changes. The closest attempt to address this in this study was the artificial activation of DNT-2 neurons using TrpA1, which resulted in overgrowth of axonal arbors and an increase in synaptic sites in both DNT-2 and PAM neurons. However, this activation method is quite artificial, and the authors did not test whether the observed structural changes were dependent on DNT-2 signaling. Although they also showed that overexpression of DNT-2FL in DNT-2 neurons promotes synaptogenesis, this phenotype was not fully consistent with the TrpA1 activation results (Figures 5C and D).

In conclusion, this study demonstrates that DNT-2 and its receptors play a role in regulating the structure of dopaminergic circuits in the adult fly brain. However, it does not provide convincing evidence for a causal link between DNT-2 signaling and experience-dependent structural plasticity within these circuits.

We would like to thank Reviewer 2 for their very positive assessment of our approach to investigate structural circuit plasticity. We are delighted that this Reviewer found our cellular resolution impressive. We are also very pleased that Reviewer 2 found that our work demonstrates that DNT-2 and its receptors regulate the structure of dopaminergic circuits in the adult fly brain. This is already a very important finding that contributes to demonstrating that, rather than being hardwired, the adult fly brain is plastic, like the mammalian brain. Furthermore, it is remarkable that this involves a neurotrophin functioning via Toll and kinase-less Trks, opening an opportunity to explore whether such a mechanism could also operate in the human brain.

We are very pleased that this Reviewer acknowledges that this work provides a good opportunity to explore the role of DNT-2, Toll-6, and Kek-6 in experience-dependent structural plasticity. We provide a molecular mechanism and proof of principle, and we demonstrate a direct link between the function of DNT-2 and its receptors in circuit plasticity. We also showed a link of DNT-2 to neuronal activity, as neuronal activity increased the production of DNT-2GFP, induced the cleavage of DNT-2 and a feedback loop between DNT-2 and dopamine, and both neuronal activity and increased DNT-2 levels promoted synaptogenesis.

As the Reviewer acknowledges this approach provides a good opportunity to explore the role of DNT-2, Toll-6, and Kek-6 in experience-dependent structural plasticity. Finding out the direct link in response to lived experience is a big task, beyond the scope of this manuscript, and we will be testing this with future projects. Nevertheless, it is important to place our findings within this context together with the link to mammalian neurotrophins (as explained in the discussion), as it is here where the findings have deep and impactful implications.

To accommodate the criticism of this Reviewer, we have now toned down our narrative. This does not diminish the importance of the findings, it makes the argument more stringent. Please see edits in: Abstract page 2 lines 42-44; and Discussion page 22 line 586 – which were the only points were a direct claim had been made.

We would like to thank Reviewer 2 for the positive and thoughtful evaluation of our work, and for their feedback.

Reviewer #3 (Public review):

Summary:

The authors used the model organism Drosophila melanogaster to show that the neurotrophin Toll-6 and its ligands, DNT-2 and kek-6, play a role in maintaining the number of dopaminergic neurons and modulating their synaptic connectivity. This supports previous findings on the structural plasticity of dopaminergic neurons and suggests a molecular mechanism underlying this plasticity.

Strengths:

The experiments are overall very well designed and conclusive. Methods are in general state-of-the-art, the sample sizes are sufficient, the statistical analyses are sound, and all necessary controls are in place. The data interpretation is straightforward, and the relevant literature is taken into consideration. Overall, the manuscript is solid and presents novel, interesting, and important findings.

We are delighted that Reviewer 3 found our work solid, novel, interesting and with important findings. We are also very pleased that this Reviewer found that all necessary controls have been carried out.

Weaknesses:

There are three technical weaknesses that could perhaps be improved.

First, the model of reciprocal, inhibitory feedback loops (Figure 2F) is speculative. On the one hand, glutamate can act in flies as an excitatory or inhibitory transmitter (line 157), and either situation can be the case here. On the other hand, it is not clear how an increase or decrease in cAMP level translates into transmitter release. One can only conclude that two types of neurons potentially influence each other.

Thank you for pointing out that glutamate can be inhibitory. In response, we have removed the word ‘excitatory’ from the only point it had been used in the text: page 7 line 167.

In mammals, the neurotrophin BDNF has an important function in glutamatergic synapses, thus we were intrigued by a potential evolutionary conservation. Our evidence that DNT-2A neurons could be excitatory is indirect, yet supportive: exciting DNT-2 neurons with optogenetics resulted in an increase in GCaMP in PAMs (data not shown); over-expression of DNT-2 in DNT-2 neurons increased TH mRNA levels; optogenetic activation of DNT-2 neurons results in the Dop2R-dependent downregulation of cAMP levels in DNT-2 neurons. Dop2R signals in response to dopamine, which would be released only if dopaminergic neurons had been excited. Accordingly, glutamate released from DNT-2 neurons would have been rather unlikely to inhibit DANs.

cAMP is a second messenger that enables the activation of PKA. PKA phosphorylates many target proteins, amongst which are various channels. This includes the voltage gated calcium channels located at the synapse, whose phosphorylation increases their opening probability. Other targets regulate synaptic vesicle release. Thus, a rise in cAMP could facilitate neurotransmitter release, and a downregulation would have the opposite effect. Other targets of PKA include CREB, leading to changes in gene expression. Conceivably, a decrease in PKA activity could result in the downregulation of DNT-2 expression in DNT-2 neurons. This negative feedback loop would restore the homeostatic relationship between DNT-2 and dopamine levels.

We agree with this Reviewer that whereas our qRT-PCR data show that over-expression of DNT-2 increases TH mRNA levels, this does not demonstrate that originates from PAM neurons. Similarly, although our EPAC data imply that dopamine must be released from DANs and received by DNT-2 neurons to explain those data, the evidence did not include direct visualisation of dopamine release in response to DNT-2 neuron activation. To accommodate these criticisms, we have edited the summary Figure 2E adding question marks to indicate inference points and page 9 line 221.

Our data indeed demonstrate that DNT-2 and PAM neurons influence each other, not potentially, but really. We have provided data that: DNT-2 and PAMs are connected through circuitry; that the DNT-2 receptors Toll-6 and kek-6 are expressed in DANs, including in PAMs; that alterations in the levels of DNT-2 (both loss and gain of function) and loss of function for the DNT-2 receptors Toll-6 and Kek-6 alter PAM cell number, alter PAM dendritic complexity and alter synaptogenesis in PAMs; alterations in the levels of DNT-2, Toll-6 and kek-6 in adult flies alters dopamine dependent behaviours of climbing, locomotion in an arena and learning and long-term memory. These data firmly demonstrate that the two neuron types DNT-2 and PAMs influence each other.

We have also shown that over-expression of DNT-2 in DNT-2 neurons increases TH mRNA levels, whereas activation of DNT-2 neurons decreases cAMP levels in DNT-2 neurons in a dopamine/Dop2R-dependent manner. These data show a functional interaction between DNT-2 and PAM neurons.

Second, the quantification of bouton volumes (no y-axis label in Figure 5 C and D!) and dendrite complexity are not convincingly laid out. Here, the reader expects fine-grained anatomical characterizations of the structures under investigation, and a method to precisely quantify the lengths and branching patterns of individual dendritic arborizations as well as the volume of individual axonal boutons.

Figure 5C, D do contain Y-axis labels, all our graphs in main manuscript and in supplementary files contain Y-axis labels.

In fact, we did use a method to precisely quantify the lengths and branching patterns of individual dendritic arborisations, volume of individual boutons and bouton counting. These analyses were carried out using Imaris software. For dendritic branching patterns, the “Filament Autodetect” function was used. Here, dendrites were analysed by tracing semi-automatically each dendrite branch (ie manual correction of segmentation errors) to reconstruct the segmented dendrite in volume. From this segmented dendrite, Imaris provides measurements of total dendrite volume, number and length of dendrite branches, terminal points, etc. For bouton size and number, we used the Imaris “Spot” function. Here, a threshold is set to exclude small dots (eg of background) that do not correspond to synapses/boutons. All samples and genotypes are treated with the same threshold, thus the analysis is objective and large sample sizes can be analysed effectively. We had already provided a description of the use of Imaris in the methods section.

We have now exapanded the protocol on how we use Imaris to analyse dendrites and synapses, in: Materials and Methods section, page 28 lines 756-768 and page 29 lines 778-799.

Third, Figure 1C shows two neurons with the goal of demonstrating between-neuron variability. It is not convincingly demonstrated that the two neurons are actually of the very same type of neuron in different flies or two completely different neurons.

We thank Reviewer 3 for raising this interesting point. It is not possible to prove which of the four DNT-2A neurons per hemibrain, which we visualised with DNT-2>MCFO, were the same neurons in every individual brain we looked at. This is because in every brain we have looked at, the soma of the neurons were not located in exactly the same location. Furthermore, the arborisation patterns are also different and unique, for each individual brain. Thus, there is natural variability in the position of the soma and in the arborisation patterns. Such variability presumably results from the combination of developmental and activity-dependent plasticity. Importantly, for every staining we carried out using DNT-2GAL4 and various membrane reporters and MCFO clones, we never found two identical DNT-2 neuron profiles.

To increase the evidence in support of this point, we have now expanded Figure 1, adding one more image of DNT-2>FlyBow (Figure 1A) and two more images of DNT-2>MCFO (Figure 1D). In total, seven images in Figure 1 and two further images in Figure 5A demonstrate the variability of DNT-2 neurons.

We would like to thank Reviewer 3 for the very positive evaluation of our work and the interesting and valuable feedback.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors): 

In the fly list, several fly lines are missing references and sources. 

Apologies for this over-sight, this has now been corrected.

We thank Reviewer 1 for their effort and time to scrutinise our work, and for their very positive and helpful feedback.

Reviewer #2 (Recommendations for the authors):

(1) Here I provide some more specific comments that I hope will help the authors further improve the study.

(2) L148: "single neuron clones revealed variability in the DNT-2A". How do the authors know that they are labeling the same subtype of DNT-2A neurons? 

There are four anterior DNT-2A cells per hemibrain, that project from the SOG area to the SMP. It is not possible to verify that every time we look at exactly the same neuron, because the exact position of the somas and the arborisation patterns vary from brain to brain. We know this from two sources of data: (1) when using DNT-2GAL4 to visualise the expression of membrane reporters (e.g. UAS-FlyBow, UAS-mCD8-GFP, UAS-CD8-RFP) no brain ever showed a pattern identical to that of another brain, neither in the exact position of the somas nor in the exact arborisation patterns. (2) When we generated DNT-2>MCFO clones to visualise 1-2 cells at a time, no single neuron or 2-neuron clones ever showed an identical pattern. The most parsimonious interpretation is that the exact location of the somas and the exact arborisation patterns vary across individual flies. Developmental variability in neuronal patterns has also been reporter by Linneweber et al (2020) Science.

To make our evidence more compelling, and in response to this Reviewer’s query, we have now added further images. Please find in revised Figure 1 A,B three examples of three different brains expressing DNT-2>FlyBow1.1. In Figure 1D, two more examples (altogether 4) of DNT-2>MCFO clones. Here it is clear to see that no neuron shape is identical to that of others, demonstrating variability in individual fly brains. We now show four images in Figure 1 and two more in Figure 5A that demonstrate the variability of DNT-2A neurons.

(3) Figure 1E: Are all DNT-2A neurons positive for vGlut and Dop2R? This figure shows only two DNT-2A neurons. 

Yes, all four DNT-2A neurons per hemibrain are vGlut positive and we have now added more images to Supplementary Figure S1A (right), also showing that presynaptic DNT-2A endings at SMP also coincide with a vGlut+ domain (Figure S1A left).

Yes, all all four DNT-2A neurons per hemibrain are Dop2R positive and we have now added more images to Supplementary Figure S1B.

(4) L156: Glutamate is generally considered to be inhibitory in the adult fly brain. More evidence is needed before the authors can claim that "DNT-2A neurons are excitatory glutamatergic neurons". 

Thank you for pointing this out. Although our data do not conclusively demonstrate it, they are consistent with DNT-2A neurons being excitatory. BDNF is most commonly released from glutamatergic neurons in mammals, its release is activity-dependent and leads to formation and stabilisation of synapses.  The phenotypes we have observed are consistent with this and reveal functional evolutionarily conservation: (1) exciting DNT-2 neurons with TrpA1 results in increased production and cleavage of DNT-2GFP and de novo synaptogenesis; (2) over-expression of DNT-2 in the adult induces de novo synaptogenesis; (3) down-regulation or loss of DNT-2 and its receptors Toll-6 and Kek-6 impair synaptogenesis. Furthermore, we show that DNT-2 dependent synaptogenesis is between DNT-2 and dopaminergic neurons, which are involved in the control of locomotion, reward learning and long-term memory, and dopamine itself is required for such behaviour. Consistently with this we found that: (1) over-expression of DNT-2 increases TH mRNA levels, which would lead to the up-regulation of dopamine production; (2) exciting DNT-2 neurons increases locomotion speed in an arena; (3) knock-down of DNT-2 and its receptors decreases locomotion, whereas over-expression of DNT-2 increases locomotion; (4) over-expression of DNT-2 increases learning and long-term memory. Finally, in a previous version in bioRxiv, we also showed using optogenetics and calcium imaging that exciting DNT-2 neurons induced GCaMP signalling in their output PAM neurons, and in this version we show that exciting DNT-2 neurons regulates cAMP in DNT-2 neurons via dopamine-release dependent feedback. Altogether, the most parsimonious interpretation of these data is that vGlut+ DNT-2 neurons are excitatory.

In any case, to address this reviewer’s point, we have now removed the word ‘excitatory’ from page 7 line 167.

(5) Figure 1H, I: A more detailed description of the Toll-6 and Kek-6 expressing neurons will be helpful. Are they expressed in specific types of PAM and PPL1 DANs? The legend in Figure S2 mentions labeling in γ2α′1 zones, but it seems to be more than that.

This information had been already provided, presumable this Reviewer overlooked this. This was already described in great detail by comparing our microscopy data with the single cell RNA-seq data available through Fly Cell Atlas (https://flycellatlas.org) and Scope (https://scope.aertslab.org/#/b77838f4-af3c-4c37-8dd9-cf7a41e4b034/*/welcome).

Please see our previously submitted Table S1 “Expression of Tolls, keks and Toll downstream adaptors in cells related to DNT-2A neurons”.

(6) Figure S3 should be controls for Figure 2A. It is incorrectly labeled as controls for Figure 3A. 

Thank you for pointing out this typo, this has now been corrected.

(7) L197: The authors state, "This showed that DNT-2 could stimulate dopamine production in neighboring DANs". However, the results do not fully support this conclusion because the experiments measure overall TH levels in the brain, not specifically in neighboring DANs. The observed effect could be indirect via other neurons. 

Indeed, we have now edited the text to: “This showed that DNT-2 could stimulate dopamine production”: page 8 line 208.

(8) Figure 3: If Toll-6 is expressed in specific subtypes of PAM DANs, are they the dying cells when Toll-6 was knocked down? I think the paper will be significantly improved if the authors provide a more in-depth analysis of the phenotype. Also, permissive temperature controls are missing for the experiments in (E)-(H). Permissive controls are essential to confirm that the observed effects are due to adult-specific RNAi knockdown.

Current tools do not enable us to visualise Toll-6+ neurons at the same time as manipulating DNT-2 neurons and at the same time as monitoring Dcp1. Stainings with Dcp1 in the adult brain are not trivial. Thus, we cannot guarantee this. However, Toll-6 is the preferential receptor for DNT-2, and given that apoptosis increases when we knock-down DNT-2, the most parsimonious interpretation is that the dying cells bear the DNT-2 receptor Toll-6. Even if DNT-2 can promiscuously bind other Toll receptors, the simplest way to interpret these data remains that DNT-2 promotes cell survival by signalling via its receptors, as no other possible route is known to date. This would be consistent with all other data in this figure.

We thank this Reviewer for the feedback on the controls. Unfortunately, these are not trivial experiments, they require considerable time, effort, dedication and skill. This manuscript has already taken 5 years of daily hard work. We no longer have the staff (ie the first author left the lab) nor resources to dedicate to address this point.

(9) Figure 4B: This phenotype in DNT-2 mutants is very striking. Did the neurons still survive and did their axonal innervation in the lobes remain intact?

Homozygous DNT-2 mutants are viable and have impair climbing, as we had already shown in Figure 7C.

(10) L261: The authors mention that "PAM-β2β′2 neurons express Toll-6 (Table S1)". However, I cannot find this information in Table S1. 

Unfortunately, I cannot identify the source of that statement at present and the first authors has left the lab. In any case, although the fact that knocking down Toll-6 in these neurons causes a phenotype means they must, it does not directly prove it. We have now corrected this to: “PAM-b2b'2 neuron dendrites overlap axonal DNT2 projections”, page 11 line 280.

(11) Figure 4C, D: What about their synaptogenesis? Do they agree with the result in Figure 4B? 

This was not tested at the time. Unfortunately, these are not trivial experiments and require considerable time, effort, dedication and skill. Addressing this point experimentally is not possible for us at this point. In any case, given the evidence we already provide, it is highly unlikely they would alter the interpretation of our findings and the value of the discoveries already provided.

(12) L270: The authors state: "To ask whether DNT-2 might affect axonal terminals, we tested PPL1 axons." However, it is unclear why the focus was shifted to PPL1 neurons when similar analyses could have been performed on PAM DANs for consistency. In addition, it would be beneficial to assess dendritic arbor complexity and synaptogenesis in PPL1-γ1-pedc neurons to provide a more comprehensive comparison between PPL1 and PAM DANs. Performing parallel analyses on both neuron types would strengthen the study by providing insight into the generality and specificity of DNT-2 in different dopaminergic circuits. 

The question we addressed with Figure 4 was whether the DNT-2 and its receptors could modify axons, dendrites and synapses, ie all features of neuronal plasticity. The reason we used PPL1-g1-pedc to analyse axonal terminals was because of their morphology, which offered a clearer opportunity to visualise axonal endings than PAMs did. An exhaustive analysis of PPL1-g1-pedc is beyond the scope of this work and not the central focus.

(13) Figure 4G lacks a permissive temperature control, which is essential to confirm that the observed effects are due to adult-specific RNAi knockdown. 

We thank this Reviewer for this feedback, which we will bear in mind for future projects.

(14) Figure 5A requires quantification and statistical comparison.

We thank this Reviewer for this feedback. We did consider this, but the data are too variable to quantify and we decided it was best to present it simply as an observation, interesting nonetheless. This is consistent as well with the data in Figure 1, which we have now expanded with this revision, which show the natural variability in DNT-2 neurons.

(15) Figure 5B: Many green signals in the control image are not labeled as PSDs, raising concerns about the accuracy of the image analysis methods used for synapse identification. While I trust that the authors have validated their analysis approach, it would strengthen the study if they provided a clearer description or evidence of the validation process. 

This was done using the Imaris “Spot function”, in volume. A threshold is set to exclude spots due to GFP background and select only synaptic spots. The selection of spots and quantification are done automatically by Imaris. All spots below the threshold are excluded, regardless of genotype and experimental conditions, rendering the analysis objective. We have now provided a detailed description of the protocol in the Materials and Methods section: page 29 lines 778-799.

(16) Figure 5C lacks genotype controls (i.e., DNT2-GAL4-only and UAS-TrpA1-only). These controls are essential because elevated temperatures alone, without activation of DNT2 neurons, could potentially increase Syt-GCaMP production, leading to an increase in the number of Syt+ synapses. Including these controls would help ensure that the observed effects are truly due to the activation of DNT2 neurons and not temperature-related artifacts. 

We thank this Reviewer for this feedback, which we will bear in mind for future projects.

(17) L314-316: The authors state, "Here, the coincidence of... revealed that newly formed synapses were stable." I think this statement needs to be toned down because there is no evidence that these pre- and post-synaptic sites are functionally connected. 

The Reviewer is correct that our data did not visualise together, in the same preparation and specimen, both pre- and post-synaptic sites. Still, given that PAMs have already been proved by others to be required for locomotion, learning and long-term memory, our data strongly suggest that synapses between them at the SMP are functionally connected.

Nevertheless, as we do not provide direct cellular evidence, we have now edited the text to tone down this claim: “Here, the coincidence of increased pre-synaptic Syt-GFP from PAMs and post-synaptic Homer-GFP from DNT-2 neurons at SMP suggests that newly formed synapses could be stable”, page 13 line 351.

(18) Figure 5D lacks permissive temperature controls. Also, the DNT-2FL overexpression phenotypes are different from the TpA1 activation phenotypes. The authors may want to discuss this discrepancy. 

Regarding the controls, these are not appropriate for this data set. These data were all taken at a constant temperature of 25°C, there were no shifts, and therefore do not require a permissive temperature control. We thank this Reviewer for drawing our attention to the fact that we made a mistake drawing the diagram, which we have now corrected in Figure 5D.

Regarding the discrepancy, this had already been discussed in the Discussion section of the previously submitted version, page 19 Line 509-526. Presumably this Reviewer missed this before.

(19) Figure 6A, B lack permissive temperature controls. These controls are important if the authors want to claim that the behavioral defects are due to adult-specific manipulations. In addition, there is no statistical difference between the PAM-GAL4 control and the RNAi knockdown group. The authors should be careful when stating that climbing was reduced in the RNAi knockdown flies (L341-342). 

We thank this Reviewer for this feedback, which we will bear in mind for future projects.

Point taken, but climbing of the tubGAL80ts, PAM>Toll-6RNAi flies was significantly different from that of the UAS-Toll-6RNAi/+ control.

(20) Figure 6C: It seems that the DAN-GAL4 only control (the second group) also rescued the climbing defect. The authors may want to clarify this point. 

The phenotype for this genotype was very variable, but certainly very distinct from that of flies over-expressing Toll-6[CY].

We thank Reviewer 2 for their very thorough analysis of our paper that has helped improve the work.

Reviewer #3 (Recommendations for the authors): 

Overall, the manuscript reports highly interesting and mostly very convincing experiments. 

We are very grateful to this Reviewer for their very positive evaluation of our work.

Based on my comments under the heading "public review", I would like to suggest three possible improvements. 

First, the quantification of structural plasticity at the sub-cellular level should be explained in more detail and potentially improved. For example, 3D reconstructions of individual neurons and quantification of the structure of boutons and dendrites could be undertaken. At present, it is not clear how bouton volumes are actually recorded accurately. 

Thank you for the feedback. The analyses of dendrites and synapses were carried out in 3D-volumes using Imaris “Filament” module and “Spot function”, respectively. Dendrites are analysed semi-automatically, ie correcting potential branching errors of Imaris, and synapses are counted automatically, after setting appropriate thresholds. Details have now been expanded in the Materials and Sections section: page 28 lines 756-768 and page 29 lines 780-799.

We would also like to thank Imaris for enabling and facilitating our remote working using their software during the Covid-19 pandemic, post-pandemic lockdowns and lab restrictions that spanned for over a year.

Second, the variability between DNT-2A-positive neurons with increasing sample size compared to a control (DNT-2A-negative neurons) should be demonstrated. Figure 2C does currently not present convincing evidence of increased structural variability. 

It is unclear what data the Reviewer refers to. Figure 2C shows qRT-PCR data, and it does not show structural variability, which instead is shown with microscopy. If it is the BacTrace data in Figure 2B, the controls had been provided and the data were unambiguous. If Reviewer means Figure 1C, it is unclear why DNT-2GAL4-negative flies are needed when the aim was to visualise normal (not genetically manipulated) DNT-2 neurons. Thus, unfortunately we do not understand what the point is here.

The observation that DNT-2 neurons are very variable, naturally, is highly interesting, and presumably this is what drew the attention of Reviewer 3. We agree that showing further data in support of this is interesting and valuable. Thus, in response to this Reviewer’s comment we have now increased the number of images that demonstrate variability of DNT-2 neurons:

(1) We have added an extra image, altogether providing three images in new Figure 1A showing three different individual brains stained with DNT-2GAL4>UAS-FlyBow1.1. These show common morphology and features, but different location of the somas and distinct detailed arborisation patterns. Two more images using DNT-2GAL4 are provided in Figure 5A.

(2) We have now added two further MCFO images, altogether showing four examples where the somas are not always in the same location and the axons arborise consistently at the SMP, but the detailed projections are not identical: new Figure 1D.

These data compellingly show natural variability in DNT-2 neuron morphology.

Third, I propose to simplify the feedback model (Figure 2F) to be less speculative. 

Indeed, some details in Figure 2F are speculative as we did not measure real dopamine levels. Accordingly, we have now edited this diagram, adding question marks to indicate speculative inference, to distinguish from the arrows that are grounded on the data we provide.

Accordingly, we have also edited the text in:

- page 9, lines 221: “Altogether, this shows that DNT-2 up-regulated TH levels (Figure 2E), and presumably via dopamine release, this inhibited cAMP in DNT-2A neurons (Figure 2F)”.

- page 20, lines 515: “Importantly, we showed that activating DNT-2 neurons increased the levels and cleavage of DNT-2, up-regulated DNT-2 increased TH expression, and this initial amplification resulted in the inhibition of cAMP signalling via the dopamine receptor Dop2R in DNT-2 neurons.”

As minor points: 

(1) Appetitive olfactory learning is based on Tempel et al., (1983); Proc Natl Acad Sci U S A. 1983 Mar;80(5):1482-6. doi: 10.1073/pnas.80.5.1482. This paper should perhaps be cited. 

Thank you for bringing this to our attention, we have now added this reference to page 14 line 394.

(2) Line 34: I would add ..."ligand for Toll-6 AND KEK-6,". 

Indeed, thank you, now corrected.

(3) Line 39: DNT-2-POSITIVE NEURONS. 

Now corrected, thank you.

(4) The levels of TH mRNA were quantified. Why not TH or dopamine directly using antibodies, ELISA, or HPLC? After all, later it is explicitly written that DNT modulates dopamine levels (line 481)! 

We thank this Reviewer for this suggestion. We did try with HPLC once, but the results were inconclusive and optimising this would have required unaffordable effort by us and our collaborators. Part of this work spanned over the pandemic and subsequent lockdowns and lab restrictions to 30% then 50% lab capacity that continued for one year, making experimental work extremely challenging. Although we were unable to carry out all the ideal experiments, the DNT-2-dependent increase in TH mRNA coupled with the EPAC-Dop2R data provided solid evidence of a DNT-2-dopamine link.

(5) Line 271: The PPL1-g1-pedc neuron has mainly (but not excusively) a function in short-term memory! 

They do, but others have also shown that PPL1-g1-pedc neurons have a gating function in long-term memory (Placais et al 2012; Placais et al 2017; Huang et al 2024) and are required for long-term memory (Adel and Griffith 2020; Boto et al 2020).

(6) Line 401: Reward learning requires PAM neurons. PPL1 neurons are required for aversive learning. 

Indeed, PPL1 neurons are required for aversive learning, but they also have a gating function in long-term memory common for both reward and aversive learning (Adel and Griffith, 2020 Neurosci Bull; Placais et al, 2012 Nature Neuroscience; Placais et al 2017 Nature Communications; Huang et al 2024 Nature).

Overall, the manuscript presents extremely interesting, novel results, and I congratulate the authors on their findings. 

We would like to thank this Reviewer for taking the time to scrutinise our work, their helpful feedback that has helped us improve the work and for their interest and positive and kind works.

Author response:

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

eLife Assessment

The work is important and of potential value to areas other than the bone field because it supports a role and mechanism for beta-catenin that is novel and unusual. The findings are significant in that they support the presence of another anabolic pathway in bone that can be productively targeted for therapeutic goals. The data for the most part are convincing. The work could be strengthened by better characterizing the osteoclast KO of Malat1 related to the Lys cre model and by including biochemical markers of bone turnover from the mice.

We thank the editors and reviewers for their time and their positive and insightful comments. We are pleased that the editors and reviewers were very enthusiastic, as stated in their Strength comments. We have performed experiments and addressed all of the points raised by the reviewers. We have revised the manuscript accordingly and the reviewers’ points are specifically addressed below. 

Public Reviews:

Reviewer #1 (Public Review):

Summary

The authors were trying to discover a novel bone remodeling network system. They found that an IncRNA Malat1 plays a central role in the remodeling by binding to β-catenin and functioning through the β-catenin-OPG/Jagged1 pathway in osteoblasts and chondrocytes. In addition, Malat1 significantly promotes bone regeneration in fracture healing in vivo. Their findings suggest a new concept of Malat1 function in the skeletal system. One significantly different finding between this manuscript and the competing paper pertains to the role of Malat1 in osteoclast lineage, specifically, whether Malat1 functions intrinsically in osteoclast lineage or not.

Strengths:

This study provides strong genetic evidence demonstrating that Malat1 acts intrinsically in osteoblasts while suppressing osteoclastogenesis in a non-autonomous manner, whereas the other group did not utilize relevant conditional knockout mice. As shown in the results, Malat1 knockout mouse exhibited abnormal bone remodeling and turnover. Furthermore, they elucidated molecular function of Malat1, which is sufficient to understand the phenotype in vivo.

We are grateful to the reviewer for highlighting the novelty, strengths and significance of our work.

Weaknesses:

Discussing differences between previous paper and their status would be highly informative and beneficial for the field, as it would elucidate the solid underlying mechanisms.

These points have been fully addressed in the point-to-point response below.

Reviewer #2 (Public Review):

Summary:

The authors investigated the roles of IncRNA Malat1 in bone homeostasis which was initially believed to be non-functional for physiology. They found that both Malat1 KO and conditional KO in osteoblast lineage exhibit significant osteoporosis due to decreased osteoblast bone formation and increased osteoclast resorption. More interestingly they found that deletion of Malat1 in osteoclast lineage cells does not affect osteoclast differentiation and function. Mechanistically, they found that Malat1 acts as a co-activator of b-Catenin directly regulating osteoblast activity and indirectly regulating osteoclast activity via mediating OPG, but not RANKL expression in osteoblast and chondrocyte. Their discoveries establish a previously unrecognized paradigm model of Malat1 function in the skeletal system, providing novel mechanistic insights into how a lncRNA integrates cellular crosstalk and molecular networks to fine-tune tissue homeostasis, and remodeling.

Strengths:

The authors generated global and conditional KO mice in osteoblast and osteoclast lineage cells and carefully analyzed the role of Matat1 with both in vivo and in vitro systems. The conclusion of this paper is mostly well supported by data.

We are grateful to the reviewer for highlighting the novelty, strengths and significance of our work.

Weaknesses:

More objective biological and biochemical analyses are required.

These points have been fully addressed in the point-to-point response below.

Reviewer #3 (Public Review):

Summary:

In this manuscript, Qin and colleagues study the role of Malat1 in bone biology. This topic is interesting given the role of lncRNAs in multiple physiologic processes. A previous study (PMID 38493144) suggested a role for Malat1 in osteoclast maturation. However, the role of this lncRNA in osteoblast biology was previously not explored. Here, the authors note osteopenia with increased bone resorption in mice lacking Malat1 globally and in osteoblast lineage cells. At the mechanistic level, the authors suggest that Malat1 controls beta-catenin activity. These results advance the field regarding the role of this lncRNA in bone biology.

Strengths:

The manuscript is well-written and data are presented in a clear and easily understandable manner. The bone phenotype of osteoblast-specific Malat1 knockout mice is of high interest. The role of Malat1 in controlling beta-catenin activity and OPG expression is interesting and novel.

We are grateful to the reviewer for highlighting the novelty, strengths and significance of our work.

Weaknesses:

The lack of a bone phenotype when Malat1 is deleted with LysM-Cre is of interest given the previous report suggesting a role for this lncRNA in osteoclasts. However, to interpret the findings here, the authors should investigate the deletion efficiency of Malat1 in osteoclast lineage cells in their model. The data in the fracture model in Figure 8 seems incomplete in the absence of a more complete characterization of callus histology and a thorough time course. The role of Malat1 and OPG in chondrocytes is unclear since the osteocalcin-Cre mice (which should retain normal Malat1 levels in chondrocytes) have similar bone loss as the global mutants.

These points have been fully addressed in the point-to-point response below.

Recommendations for the authors:

Reviewing Editor (Recommendations For The Authors):

There are several suggestions for improving the manuscript, and we hope that you will review the recommendations carefully and make changes to the paper to address the concerns raised. Suggestions have been made to better characterize the osteoclast KO of Malat1 related to the Lys cre model as well as suggestions to include biochemical markers of bone turnover from your mice.

These points have been fully addressed in the point-to-point response below.

Reviewer #1 (Recommendations For The Authors):

(1) Replicate numbers in Figure 3 should be noted.

We thank the reviewer for this point. The experiments in Fig. 3 have been replicated three times, which is now noted in the figure legend.

(2) It is novel to identify OPG expression in chondrocytes. More discussion is expected.

Yes, a paragraph regarding this point has been added to the Discussion section.  

Reviewer #2 (Recommendations For The Authors):

(1) It is better to show serum osteoblast bone formation marker and osteoclast resorption marker, such as P1NP and CTx, in both Malat1 KO and osteoblast conditional KO mice.

We thank the reviewer for this important point. Since CTx values are often influenced by food intake, we measured serum TRAP levels, which also reflect changes in osteoclastic bone resorption. We have observed that the serum osteoblastic bone formation marker P1NP was decreased, while osteoclastic bone resorption marker TRAP was increased, in both Malat1^-/- and Malat1^ΔOcn mice. These changes in serum biochemical markers of bone turnover are consistent with the bone phenotype caused by Malat1 deficiency. The new data are shown in Fig.1i, Fig. 2e, and Fig.5b.    

(2) in vitro osteoblast differentiation assay is required to further confirm Malat1 regulates osteoblast differentiation.

We thank the reviewer for this suggestion. As recommended, we have performed in vitro osteoblast differentiation multiple times using calvarial cells, a commonly used system in the field. However, we observed big variability in the culture results across different experimental batches, whether conducted by different scientists or the same individual. This variability is likely due to differences in the purity of the cultured cells, as literature shows that the current culture system in the field contains a mixture of tissue cells, including not only osteoblasts but also other cells, such as stromal and hematopoietic lineage cells (DOI: 10.1002/jbmr.4052). We hope to test osteoblast differentiation using a purer culture system once it becomes available in the field. In contrast, our in vivo data, indicated by multiple parameters, show consistent osteoblast and bone formation phenotypes across a large number of mice. Therefore, the in vivo results in our study strongly support our conclusion regarding Malat1's role in osteoblastic bone formation.

(3) The authors found that Matat1 regulates osteoclast activity through OPG expression not only in osteoblasts, but also in chondrocytes and concluded that chondrocyte is involved in the crosstalk with osteoclast lineage cells in marrow. This is a very novel finding. Do the authors have any in vivo data to support this point, such as deleting Malat1 in chondrocyte lineage cells with chondrocyte-specific Cre?

We appreciate the reviewer for highlighting our novel findings and providing valuable suggestions. Given the considerable time required to generate chondrocyte-specific conditional KO mice, we plan to thoroughly investigate the crosstalk between chondrocytes and osteoclasts via Malat1 in vivo in our next project.

Reviewer #3 (Recommendations For The Authors):

(1) Ideally would show male and female data side by side in the main text figures

We thank the reviewer for this suggestion. The male and female data are now displayed side by side in Fig. 1b. 

(2) The sample size for the in vivo datasets is quite large. A power calculation should be provided to better understand how the authors decided to analyze so many mice.

Due to staff turnover during the pandemic, the first authors and several co-authors were involved in breeding the mice and collecting and analyzing bone samples. To avoid bias in sample selection, we pooled all the samples, resulting in a highly consistent phenotype across mice. This robust approach further strengthens our conclusion. 

(3) The candidate gene approach to look at beta-catenin is a bit random, it would be ideal to assess Malat1 binding proteins in osteoblasts in an unbiased way. Also, does Malat1 bind bcatenin in other cell types? The importance of this point is further underscored by ref 47 which indicates that Malat binds TEAD3.

As β-catenin is a key regulator in osteoblasts, we believe that studying the interaction between β-catenin and Malat1 is not random. Instead, this approach is well-founded and based on established knowledge in the field (as discussed below). In parallel, we are investigating genome-wide Malat1-bound targets beyond β-catenin, which will be reported in future studies. 

More detailed points have been discussed in the manuscript: 

Given that we identified Malat1 as a critical regulator in osteoblasts, we sought to investigate the mechanisms underlying the regulation of osteoblastic bone formation by Malat1. β-catenin is a central transcriptional factor in canonical Wnt signaling pathway, and plays an important role in positively regulating osteoblast differentiation and function (28-33). Upon stimulation, most notably from canonical Wnt ligands, β-catenin is stabilized and translocates into the nucleus, where it interacts with coactivators to activate target gene transcription. Previous reports observed a link between Malat1 and β-catenin signaling pathway in cancers (34,35), but the underlying molecular mechanisms in terms of how Malat1 interacts with β-catenin and regulates its nuclear retention and transcriptional activity are unclear. 

Ref47 tested Malat1 binding to Tead3 in osteoclasts. However, a key difference between our findings and those of Ref47 is that both our in vitro and in vivo data, using myeloid osteoclastspecific conditional Malat1 KO mice, do not support an intrinsically significant role for Malat1 in osteoclasts. 

(4) The statement on page 6 concluding that Malat acts as a scaffold to tether β-catenin in the nucleus is not supported by data in Fig 3d demonstrating that b-catenin nucleus translocation in response to Wnt3a is similar in control and Malat-deficient cells.

The experiment in Fig. 3d is not designed to demonstrate Malat1 and β-catenin binding, but it is essential as the result rules out the possibility that Malat1 may affect β-catenin nuclear translocation. Moreover, we have utilized two robust approaches, CHIRP and RIP, to demonstrate that Malat1 acts as a scaffold to tether β-catenin in the nucleus (Fig. 3a, b, c, Supplementary Fig. 3). 

(5) Figure 4e: can the authors show Malat deletion efficiency in the LysM-Cre model? This is important in light of the negative data in this figure and ref 47 which claims an osteoclast intrinsic role for Malat

We thank the reviewer for this suggestion. The deletion efficiency of Malat1 in the LysM-Cre mice is very high (>90%). This data is now presented in Fig. 4e. 

(6) Figure 5: since the magnitude of the effects on osteoclasts at the histology level are mild, it would be nice to also look at serum markers of bone resorption (CTX)

The magnitude of osteoclast changes at the histological level in Fig. 5 is not mild in our view, as we observe 25-30% changes with statistical significance in the osteoclast parameters of Malat1ΔOcn mice. Since CTx values are often influenced by food intake, we measured serum TRAP levels, which reflect changes in osteoclastic bone resorption. As shown in Fig.5b, serum TRAP levels are significantly elevated in Malat1^ΔOcn mice compared to control mice.

(7) Data showing chondrocytic expression of OPG is not as novel as the authors claim. Should think about growth plate versus articular sources of OPG. Growth plate chondrocytes express OPG to regulate osteoclasts in the primary spongiosa which resorb mineralized cartilage.

In the present study, we do not focus on comparing the sources of OPG from the chondrocytes in the growth plate versus articular cartilage. The novelty of our work lies in the discovery that Malat1 links chondrocyte and osteoclast activities through the β-catenin-OPG/Jagged1 axis. This Malat1-β-catenin-OPG/Jagged1 axis represents a novel mechanism regulating the crosstalk between chondrocytes and osteoclasts. 

(8) The relevance of the chondrocyte role of Malat is unclear since the bone phenotype in global and osteocalcin-Cre mice is similar.

Bone mass was decreased by 20% in Malat1^ΔOcn mice, while a 30% reduction was observed in global KO (Malat1^-/-) mice. This difference indicates potential contributions from other cell types, such as chondrocytes, and our results in Fig. 6 further support the impact of chondrocytes in Malat1's regulation of bone mass. We plan to thoroughly investigate the crosstalk between chondrocytes and osteoclasts via Malat1 in vivo in our next project.

(9) Fracture data in Figure 8 seems incomplete, it would be ideal to support micro CT with histology and look at multiple time points.

We thank the reviewer for this suggestion. We have performed histological analysis of our samples, and found that Malat1 promotes bone healing in the fracture model (Fig. 8f), which is consistent with our μCT data.

eLife Assessment

This is an important and convincing dataset shedding new light on a role for Malat1 in osteoblast physiology. The work is of value to areas other than the bone field because it supports a role and mechanism for beta-catenin that is novel and unusual. The findings are significant in that they support the presence of another anabolic pathway in bone that can be productively targeted for therapeutic goals. Revisions further improved the paper and addressed the reviewers' concerns.

Reviewer #2 (Public review):

Summary:

The authors investigated the roles of IncRNA Malat1 in bone homeostasis which was initially believed to be non-functional for physiology. They found that both Malat1 KO and conditional KO in osteoblast lineage exhibit significant osteoporosis due to decreased osteoblast bone formation and increased osteoclast resorption. More interestingly, they found that deletion of Matat1 in osteoclast lineage cell does not affect osteoclast differentiation and function. Mechanistically, they found that Malat1 acts as an co-activator of b-Catenin directly regulating osteoblast activity and indirectly regulating osteoclast activity via mediating OPG, but not RANKL expression in osteoblast and chondrocyte. Their discoveries establish a previous unrecognized paradigm model of Malat1 function in the skeletal system, providing novel mechanistic insights into how a lncRNA integrates cellular crosstalk and molecular networks to fine tune tissue homeostasis, remodeling.

Strengths:

The authors generated global and conditional KO mice in osteoblast and osteoclast lineage cells and carefully analyzed the role of Matat1 with both in vivo and in vitro system. The conclusion of this paper is mostly well supported by data.

Comments on revised version:

The authors have addressed all my concerns.

Reviewer #3 (Public review):

Summary:

In this manuscript, Qin and colleagues study the role of Malat1 in bone biology. This topic is interesting given the role of lncRNAs in multiple physiologic processes. A previous study (PMID 38493144) suggested a role for Malat1 in osteoclast maturation. However, the role of this lncRNA in osteoblast biology was previously not explored. Here, the authors note osteopenia with increased bone resorption in mice lacking Malat1 globally and in osteoblast lineage cells. At the mechanistic level, the authors suggest that Malat1 controls beta-catenin activity. These result advance the field regarding the role of this lncRNA in bone biology.

Strengths:

The manuscript is well-written and data are presented in a clear and easily understandable manner. The bone phenotype of osteoblast-specific Malat1 knockout mice is of high interest. The role of Malat1 in controlling beta-catenin activity and OPG expression is interesting and novel.

Weaknesses:

The lack of a bone phenotype when Malat1 is deleted with LysM-Cre is of interest given the previous report suggesting a role for this lncRNA in osteoclasts, especially in light of satisfactory deletion efficiency in this model. The data in the fracture model in Figure 8 is enhanced with quantitative data. The role of Malat1 and OPG in chondrocytes is unclear since the osteocalcin-Cre mice (which should retain normal Malat1 levels in chondrocytes) have similar bone loss as the global mutants.

Comments on revised version:

All previous comments have been addressed in a satisfactory manner.

eLife Assessment

This auhors present findings on the role of the sirtuins SIRT1 and SIRT3 during Salmonella Typhimurium infection. This valuable study increases our understanding of the mechanisms used by this pathogen to interact with its host and may have implications for other intracellular pathogens. The reviewers disagreed on the strength of the evidence to support the claims. Although one reviewer found the strength of the evidence convincing, the other found that it was incomplete, and that the main claims are only partially supported, as can be seen from the public reviews.

Reviewer #2 (Public review):

Dipasree Hajra et al demonstrated that Salmonella was able to modulate the expression of Sirtuins (Sirt1 and Sirt3) and regulate the metabolic switch in both host and Salmonella, promoting its pathogenesis. The authors found Salmonella infection induced high levels of Sirt1 and Sirt3 in macrophages, which were skewed toward the M2 phenotype allowing Salmonella to hyper-proliferate. Mechanistically, Sirt1 and Sirt3 regulated the acetylation of HIF-1alpha and PDHA1, therefore mediating Salmonella-induced host metabolic shift in the infected macrophages. Interestingly, Sirt1 and Sirt3-driven host metabolic switch also had an effect on the metabolic profile of Salmonella. Counterintuitively, inhibition of Sirt1/3 led to increased pathogen burdens in an in vivo mouse model. Overall, this is a well-designed study.

The revised manuscript has addressed all of the previous comments. The re-analysis of flow cytometry and WB data by authors makes the results and conclusion more complete and convincing.

Reviewer #3 (Public review):

Summary:

In this paper Hajra et al have attempted to identify the role of Sirt1 and Sirt3 in regulating metabolic reprogramming and macrophage host defense. They have performed gene knock down experiments in RAW macrophage cell line to show that depletion of Sirt1 or Sirt3 enhances the ability of macrophages to eliminate Salmonella Typhimurium. However, in mice inhibition of Sirt1 resulted in dissemination of the bacteria but the bacterial burden was still reduced in macrophages. They suggest that the effect they have observed is due to increased inflammation and ROS production by macrophages. They also try to establish a weak link with metabolism. They present data to show that the switch in metabolism from glycolysis to fatty acid oxidation is regulated by acetylation of Hif1a, and PDHA1.

Strengths:

The strength of the manuscript is that the role of Sirtuins in host-pathogen interactions have not been previously explored in-depth making the study interesting. It is also interesting to see that depletion of either Sirt1 or Sirt3 result in a similar outcome.

Weaknesses:

The major weakness of the paper is the low quality of data, making it harder to substantiate the claims. Also, there are too many pathways and mechanisms being investigated. It would have been better if the authors had focussed on either Sirt1 or Sirt3 and elucidated how it reprograms metabolism to eventually modulate host response against Salmonella Typhimurium. Experimental evidences are also lacking to prove the proposed mechanisms. For instance they show correlative data that knock down of Sirt1 mediated shift in metabolism is due to HIF1a acetylation but this needs to be proven with further experiments.

Author response:

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

Reviewer #2 (Public review):

Dipasree Hajra et al demonstrated that Salmonella was able to modulate the expression of Sirtuins (Sirt1 and Sirt3) and regulate the metabolic switch in both host and Salmonella, promoting its pathogenesis. The authors found Salmonella infection induced high levels of Sirt1 and Sirt3 in macrophages, which were skewed toward the M2 phenotype allowing Salmonella to hyper-proliferate. Mechanistically, Sirt1 and Sirt3 regulated the acetylation of HIF-1alpha and PDHA1, therefore mediating Salmonella-induced host metabolic shift in the infected macrophages. Interestingly, Sirt1 and Sirt3-driven host metabolic switch also had an effect on the metabolic profile of Salmonella. Counterintuitively, inhibition of Sirt1/3 led to increased pathogen burdens in an in vivo mouse model. Overall, this is a well-designed study.<br /> The revised manuscript has addressed all of the previous comments. The re-analysis of flow cytometry and WB data by authors makes the results and conclusion more complete and convincing.

We are immensely grateful to the reviewer for improving the strength of the manuscript by providing insightful comments and for appreciating the work.

Reviewer #3 (Public review):

Summary:

In this paper Hajra et al have attempted to identify the role of Sirt1 and Sirt3 in regulating metabolic reprogramming and macrophage host defense. They have performed gene knock down experiments in RAW macrophage cell line to show that depletion of Sirt1 or Sirt3 enhances the ability of macrophages to eliminate Salmonella Typhimurium. However, in mice inhibition of Sirt1 resulted in dissemination of the bacteria but the bacterial burden was still reduced in macrophages. They suggest that the effect they have observed is due to increased inflammation and ROS production by macrophages. They also try to establish a weak link with metabolism. They present data to show that the switch in metabolism from glycolysis to fatty acid oxidation is regulated by acetylation of Hif1a, and PDHA1.

Strengths:

The strength of the manuscript is that the role of Sirtuins in host-pathogen interactions have not been previously explored in-depth making the study interesting. It is also interesting to see that depletion of either Sirt1 or Sirt3 result in a similar outcome.

Weaknesses:

The major weakness of the paper is the low quality of data, making it harder to substantiate the claims. Also, there are too many pathways and mechanisms being investigated. It would have been better if the authors had focussed on either Sirt1 or Sirt3 and elucidated how it reprograms metabolism to eventually modulate host response against Salmonella Typhimurium. Experimental evidences are also lacking to prove the proposed mechanisms. For instance they show correlative data that knockdown of Sirt1 mediated shift in metabolism is due to HIF1a acetylation but this needs to be proven with further experiments.

As the public review of the reviewer remains unaltered as the previous version without further recommendations for authors, we are sticking to our former author’s response. We respect the reviewer’s opinion and thank the reviewer for the critical analysis of our work.

---------

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

Reviewer #2 (Public Review):

Dipasree Hajra et al demonstrated that Salmonella was able to modulate the expression of Sirtuins (Sirt1 and Sirt3) and regulate the metabolic switch in both host and Salmonella, promoting its pathogenesis. The authors found Salmonella infection induced high levels of Sirt1 and Sirt3 in macrophages, which were skewed toward the M2 phenotype allowing Salmonella to hyper-proliferate. Mechanistically, Sirt1 and Sirt3 regulated the acetylation of HIF-1alpha and PDHA1, therefore mediating Salmonella-induced host metabolic shift in the infected macrophages. Interestingly, Sirt1 and Sirt3-driven host metabolic switch also had an effect on the metabolic profile of Salmonella. Counterintuitively, inhibition of Sirt1/3 led to increased pathogen burdens in an in vivo mouse model. Overall, this is a well-designed study.

Comments on revised version:

The authors have performed additional experiments to address the discrepancy between in vitro and in vivo data. While this offers some potential insights into the in vivo role of Sirt1/3 in different cell types and how this affects bacterial growth/dissemination, I still believe that Sirt1/3 inhibitors could have some effect on the gut microbiota contributing to increased pathogen counts. This possibility can be discussed briefly to give a better scenario of how Sirt1/3 inhibitors work in vivo. Additionally, the manuscript would improve significantly if some of the flow cytometry analysis and WB data could be better analyzed.

We are highly grateful for your valuable and insightful comments. Thank you for appreciating the merit of our manuscript. As rightly pointed out by the eminent reviewer, we acknowledge the probable link of Sirtuin on gut microbiota and its effect on increased bacterial loads as indicated by previous literature studies (PMID: 22115311, PMID: 19228061). These reports suggested that a low dose of Sirt1 activator, resveratrol treatment in rats for 25 days treatment under 5% DSS induced colitis condition led to alterations in gut microbiota profile with increased lactobacilli and bifidobacteria alongside reduced abundance of enterobacteria. This study correlates with our study wherein we have detected enhanced Salmonella (belonging to Enterobacteriaceae family) loads under both Sirt1/3 in vivo knockdown condition or inhibitor-treated condition in C57BL/6 mice and reduced burden under Sirt-1 activator treatment SRT1720.

As per your valid suggestion, we have discussed this possibility in our discussion section. (Line- 541-548).

We have incorporated the suggestions for the improvement in the analysis of WB data and flow cytometry.

Reviewer #3 (Public Review):

Summary:

In this paper Hajra et al have attempted to identify the role of Sirt1 and Sirt3 in regulating metabolic reprogramming and macrophage host defense. They have performed gene knock down experiments in RAW macrophage cell line to show that depletion of Sirt1 or Sirt3 enhances the ability of macrophages to eliminate Salmonella Typhimurium. However, in mice inhibition of Sirt1 resulted in dissemination of the bacteria but the bacterial burden was still reduced in macrophages. They suggest that the effect they have observed is due to increased inflammation and ROS production by macrophages. They also try to establish a weak link with metabolism. They present data to show that the switch in metabolism from glycolysis to fatty acid oxidation is regulated by acetylation of Hif1a, and PDHA1.

Strengths:

The strength of the manuscript is that the role of Sirtuins in host-pathogen interactions has not been previously explored in-depth making the study interesting. It is also interesting to see that depletion of either Sirt1 or Sirt3 results in a similar outcome.

Weaknesses:

The major weakness of the paper is the low quality of data, making it harder to substantiate the claims. Also, there are too many pathways and mechanisms being investigated. It would have been better if the authors had focussed on either Sirt1 or Sirt3 and elucidated how it reprograms metabolism to eventually modulate host response against Salmonella Typhimurium. Experimental evidence is also lacking to prove the proposed mechanisms. For instance they show correlative data that knock down of Sirt1 mediated shift in metabolism is due to HIF1a acetylation but this needs to be proven with further experiments.

We appreciate the reviewer’s critical analysis of our work. In the revised manuscript, we aimed to eliminate the low-quality data sets and have tried to substantiate them with better and conclusive ones, as directed in the recommendations for the author section. We agree with the reviewer that the inclusion of both Sirtuins 1 and 3 has resulted in too many pathways and mechanisms and focusing on one SIRT and its mechanism of metabolic reprogramming and immune modulation would have been a less complicated alternative approach. However, as rightly pointed out, our work demonstrated the shared and few overlapping roles of the two sirtuins, SIRT1 and SIRT3, together mediating the immune-metabolic switch upon Salmonella infection. As per the reviewer’s suggestion, we have performed additional experiments with HIF-1α inhibitor treatment in our revised manuscript to substantiate our correlative findings on SIRT1-mediated regulation of host glycolysis (Fig.7G). We wanted to clarify our claim in this regard. Our results suggested that loss of SIRT1 function triggered increased host glycolysis alongside hyperacetylation of HIF-1α. HIF-1α is reported to be one of the important players in glycolysis regulation (Kierans SJ, Taylor CT. Regulation of glycolysis by the hypoxia-inducible factor (HIF): implications for cellular physiology. J Physiol. 2021;599(1):23-37. doi:10.1113/JP280572.) and additionally, SIRT1 has been shown to regulate HIF-1α acetylation status (Lim JH, Lee YM, Chun YS, Chen J, Kim JE, Park JW. Sirtuin 1 modulates cellular responses to hypoxia by deacetylating hypoxia-inducible factor 1 alpha. Mol Cell. 2010;38(6):864-878. doi:10.1016/j.molcel.2010.05.023.) Further, ectopic expression of SIRT1 has been demonstrated to reduce glycolysis by negatively regulating HIF-1α. (Wang Y, Bi Y, Chen X, et al. Histone Deacetylase SIRT1 Negatively Regulates the Differentiation of Interleukin-9-Producing CD4(+) T Cells. Immunity. 2016;44(6):1337-1349. doi:10.1016/j.immuni.2016.05.009). We have subsequently shown in Fig. 7G, that the increase in host glycolysis upon SIRT knockdown in the infected macrophages gets lowered upon HIF-1α inhibitor treatment, suggesting that one of the mechanisms of SIRT-mediated regulation of host glycolysis is via regulation of HIF-1α. However, this warrants further future mechanistic research.

Recommendations for the authors:

Reviewer #2 (Recommendations For The Authors):

(1) Figures 8I-S: are only viable cells used for analysis? Please provide gating strategy used for these analyses.

(2) Many changes seen in WB seem to be marginal. Since the authors used densitometric plot to quantify the band intensities, I expect these experiments were repeated at least three times. Please indicate the number of repeats. For instance, Figures 7C, 7I (UI SCR vs UI shSIRT3), 7J, show marginal changes or no changes. What do other WB images look like? Are they more convincing than the ones currently shown? Please provide them in the response letter.

(3) Figure 7C: label is a bit misleading. Please relabel the figure title to Acetylated HIF vs total levels

(4) Figure 7J: which band is AcPDHA1?

(1) We are highly apologetic for not clarifying our gating strategy for the analysis.

We initially gated the viable splenocyte population based on Forward scatter (FSC) and Side Scatter (SSC). This gated population was further subjected to gating based on cell FSC-H (height) versus FSC-A (area). Subsequently, the population was gated as per SSC-A and GFP (expressed by intracellular bacteria) based on the autofluorescence exhibited by the uninfected control (Fig. 8I-J).

Author response image 1.

UNINFECTED

Author response image 2.

VEHICLE CONTROL INFECTED

Author response image 3.

EX-527 INFECTED

Author response image 4.

3TYP INFECTED

Author response image 5.

SRT 1720 INFECTED

For gating different cell types such as F4/80 (PE) positive population in Fig. 8K-L, the viable cell population was gated based on SSC-A versus PE-A to gate the macrophage population. These macrophage populations were gated further based on GFP (Salmonella) + population to obtain the percentage of macrophage population harboring GFP+ bacteria. Similar strategies were followed for other cell types as depicted in Fig. 8M-S, Fig. S8.

(2) We agree with the reviewer’s concern with the marginal changes in the western blots (Figures 7C, 7I (UI SCR vs UI shSIRT3), 7J). As per the suggestions, we have provided the alternate blot images and have indicated the number of repeats in the manuscript. The alternate blot images are provided herewith:

Author response image 6.

Alternate blot images for Fig. 7B-C

Author response image 7.

Alternate blot images for Fig. 7I, J

(1) We are highly thankful to the reviewer for recommending this suggestion. We have made the necessary modifications of relabelling Fig. C to Acetylated HIF-1α over total HIF-1α as per the suggestion.

(2) 7J Acetylated PDHA1 has been duly pointed as per the suggestion. We are extremely apologetic for the inconvenience caused.

Author response image 8

Reviewer #3 (Recommendations For The Authors):

The authors have done some work to improve the manuscript. However, the data presented lacks clarity.

Fig 4B: I still do not see a change in Ac p65 in the less saturated blot. It looks reduced as the band is distorted. I am not sure how this could be quantified.

Fig S2 b-actin bands are hyper saturated, and it is not possible to decipher the knockdown efficiency. It is probably better to provide a ponceau staining similar to S2C. The band intensity values are out of place.

Fig 5F HADHA blot: Lane 1 expression appears to be significantly higher than lane 3, but the values mentioned do not match the intensity of the bands.

It is hard to interpret the authors' claim that the shift in metabolism is HIF1a-dependent.

Fig 7B: I would expect HIF1a acetylation to be increased in UI ShSIRT1 compared to UI SCR. The blot shows reduced HIF1a acetylation.

Fig 7D: SIRT1 immunoprecipitates with HIF1a equally under all conditions. Is this what the authors expect? Labelling of the blots are not clear. It looks like the bottom SIRT1 blot is from Beads IgG control.

Fig 7H: How does PDHA1 interact with SIRT3 so strongly in shSIRT3 cells (lane 2)?

Authors have mentioned in their response that a knockdown of 40% has been achieved in the uninfected but the blot does not reflect that. SIRT3 expression seems to be more in the knockdown.

Blots are also not labelled properly especially Input. The lanes are not marked.

We thank the reviewer for acknowledging the improvements in the revised version and for suggesting further clarifications and improvements.

We have tried to incorporate the specified modifications to the best of our abilities in the revised manuscript.

We are highly apologetic for the inconclusive blot image in the figure 4B. We have provided an alternative blot image with better clarity for Fig.4B used for quantification analysis.

Author response image 9.

 

As per the reviewer’s valuable suggestions, we have provided the ponceau image in the Fig. S2B.

We thank the reviewers for rightly pointing out the discrepancy in the band intensity quantification in the Fig. 5F. We have re-evaluated the intensities on imageJ and have provided with the correct band intensities. We are highly apologetic for the inaccuracies.

As per the reviewer’s previous suggestion, we have performed additional experiments with HIF-1α inhibitor treatment in our revised manuscript to substantiate our correlative findings on SIRT1-mediated regulation of host glycolysis (Fig.7G). We wanted to clarify our claim in this regard. Our results suggested that loss of SIRT1 function triggered increased host glycolysis alongside hyperacetylation of HIF-1α. HIF-1α is reported to be one of the important players of glycolysis regulation (Kierans SJ, Taylor CT. Regulation of glycolysis by the hypoxia-inducible factor (HIF): implications for cellular physiology. J Physiol. 2021;599(1):23-37. doi:10.1113/JP280572.) and additionally, SIRT1 has been shown to regulate HIF-1α acetylation status (Lim JH, Lee YM, Chun YS, Chen J, Kim JE, Park JW. Sirtuin 1 modulates cellular responses to hypoxia by deacetylating hypoxia-inducible factor 1alpha. Mol Cell. 2010;38(6):864-878. doi:10.1016/j.molcel.2010.05.023.) Further, ectopic expression of SIRT1 has been demonstrated to reduce glycolysis by negatively regulating HIF-1α. (Wang Y, Bi Y, Chen X, et al. Histone Deacetylase SIRT1 Negatively Regulates the Differentiation of Interleukin-9-Producing CD4(+) T Cells. Immunity. 2016;44(6):1337-1349. doi:10.1016/j.immuni.2016.05.009). We have subsequently shown in Fig. 7G, that the increase in host glycolysis upon SIRT knockdown in the infected macrophages gets lowered upon HIF-1α inhibitor treatment, suggesting that one of the mechanisms of SIRT-mediated regulation of host glycolysis is via regulation of HIF-1α. However, this warrants further future mechanistic research.

We agree with the reviewer’s claim of increased HIF-1α acetylation in the UI sh1 versus UI SCR. The apparent reduced acetylation depicted in UI sh1 in Fig. 7B could be attributed to lower HIF-1α levels in the UI sh1 compared to UI SCR. Therefore, we have provided an alternate blot image that been used for quantification in Fig. 7C (Author response image 6).

To answer the reviewer’s question in Fig. 7D, we have noticed more or less equal degree of immunoprecipitation of HIF-1α under pull down of HIF-1α in all the sample cohorts under conditions of SIRT1 inhibitor treatment. However, we have observed reduced interaction of HIF-1α with SIRT1 in the infected sample upon SIRT1 inhibitor treatment.

We thank the reviewers for suggesting improvements in the blot labelling and for raising this concern. We have corrected the blot labelling to avoid the previous confusion.

We appreciate the reviewer’s concern and therefore we have provided an alternate blot image for Fig. 7H which might address the previous stated concern wherein we have achieved an enhanced SIRT3 knockdown percentage.

We are extremely apologetic for the improper labelling of the Input blot with unmarked lanes. We have addressed this issue by labelling the lanes in the input section of the blots.

eLife Assessment

This study presents valuable quantitative insights into the prevalence of functionally clustered synaptic inputs on neuronal dendrites. The simple analytical calculations and computer simulations provide solid support for the main arguments. The findings can lead to a more detailed understanding of how dendrites contribute to the computation of neuronal networks.

Joint Public Review:

Summary:

If synaptic input is functionally clustered on dendrites, nonlinear integration could increase the computational power of neural networks. But this requires the right synapses to be located in the right places. This paper aims to address the question of whether such synaptic arrangements could arise by chance (i.e. without special rules for axon guidance or structural plasticity), and could therefore be exploited even in randomly connected networks. This is important, particularly for the dendrites and biological computation communities, where there is a pressing need to integrate decades of work at the single-neuron level with contemporary ideas about network function.

Using an abstract model where ensembles of neurons project randomly to a postsynaptic population, back-of-envelope calculations are presented that predict the probability of finding clustered synapses and spatiotemporal sequences. Using data-constrained parameters, the authors conclude that clustering and sequences are indeed likely to occur by chance (for large enough ensembles), but require strong dendritic nonlinearities and low background noise to be useful.

Strengths:

- The back-of-envelope reasoning presented can provide fast and valuable intuition. The authors have also made the effort to connect the model parameters with measured values. Even an approximate understanding of cluster probability can direct theory and experiments towards promising directions, or away from lost causes.

- I found the general approach to be refreshingly transparent and objective. Assumptions are stated clearly about the model and statistics of different circuits. Along with some positive results, many of the computed cluster probabilities are vanishingly small, and noise is found to be quite detrimental in several cases. This is important to know, and I was happy to see the authors take a balanced look at conditions that help/hinder clustering, rather than just focus on a particular regime that works.

- This paper is also a timely reminder that synaptic clusters and sequences can exist on multiple spatial and temporal scales. The authors present results pertaining to the standard `electrical' regime (~50-100 µm, <50 ms), as well as two modes of chemical signaling (~10 µm, 100-1000 ms). The senior author is indeed an authority on the latter, and the simulations in Figure 5, extending those from Bhalla (2017), are unique in this area. In my view, the role of chemical signaling in neural computation is understudied theoretically, but research will be increasingly important as experimental technologies continue to develop.

(Editors' note: the paper has been through two rounds of revisions and the authors are encouraged to finalise this as the Version of Record. The earlier reviews are here: https://elifesciences.org/reviewed-preprints/100664v2/reviews)

Author response:

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

Reviewer #1 (Public Review):

In this revision, the authors significantly improved the manuscript. They now address some of my concerns. Specifically, they show the contribution of end-effects on spreading the inputs between dendrites. This analysis reveals greater applicability of their findings to cortical cells, with long, unbranching dendrites than other neuronal types, such as Purkinje cells in the cerebellum.

They now explain better the interactions between calcium and voltage signals, which I believe improve the take-away message of their manuscript. They modified and added new figures that helped to provide more information about their simulations.

However, some of my points remain valid. Figure 6 shows depolarization of ~5mV from -75. This weak depolarization would not effectively recruit nonlinear activation of NMDARs. In their paper, Branco and Hausser (2010) showed depolarizations of ~10-15mV.

More importantly, the signature of NMDAR activation is the prolonged plateau potential and activation at more depolarized resting membrane potentials (their Figure 4). Thus, despite including NMDARs in the simulation, the authors do not model functional recruitment of these channels. Their simulation is thus equivalent to AMPA only drive, which can indeed summate somewhat nonlinearly.

In the current study, we used short sequences of 5 inputs, since the convergence of longer sequences is extremely unlikely in the network configurations we have examined. This resulted in smaller EPSP amplitudes of ~5mV (Figure 6 - Supplement 2A, B). Longer sequences containing 9 inputs resulted in larger somatic depolarizations of ~10mV (Figure 6 - Supplement 2E, F). Although we had modified the (Branco, Clark, and Häusser 2010) model to remove the jitter in the timing of arrival of inputs and made slight modifications to the location of stimulus delivery on the dendrite, we saw similar amplitudes when we tested a 9-length sequence using (Branco, Clark, and Häusser 2010)’s published code (Figure 6 - Supplement 2I, J). In all the cases we tested (5 input sequence, 9 input sequence, 9 input sequence with (Branco, Clark, and Häusser 2010) code repository), removal of NMDA synapses lowered both the somatic EPSPs (Figure 6 - Supplement 2C,D,G,H,K,L) as well as the selectivity (measured as the difference between the EPSPs generated for inward and outward stimulus delivery) (Figure 6 Supplement 2M,N,O). Further, monitoring the voltage along the dendrite for a sequence of 5 inputs showed dendritic EPSPs in the range of 20-45 mV (Figure 6 - Supplement 2P, Q), which came down notably (10-25mV) when NMDA synapses were abolished (Figure 6 - Supplement 2R, S). Thus, even sequences containing as few as 5 inputs were capable of engaging the NMDA-mediated nonlinearity to show sequence selectivity, although the selectivity was not as strong as in the case of 9 inputs.

Reviewer #1 (Recommendations for the authors):

Minor points:

Figure 8, what does the scale in A represent? I assume it is voltage, but there are no units. Figure 8, C, E, G, these are unconventional units for synaptic weights, usually, these are given in nS / per input.

We have corrected these. The scalebar in 8A represents membrane potential in mV. The units of 8C,E,G are now in nS.

Reviewer #2 (Public Review):

Summary:

If synaptic input is functionally clustered on dendrites, nonlinear integration could increase the computational power of neural networks. But this requires the right synapses to be located in the right places. This paper aims to address the question of whether such synaptic arrangements could arise by chance (i.e. without special rules for axon guidance or structural plasticity), and could therefore be exploited even in randomly connected networks. This is important, particularly for the dendrites and biological computation communities, where there is a pressing need to integrate decades of work at the single-neuron level with contemporary ideas about network function.

Using an abstract model where ensembles of neurons project randomly to a postsynaptic population, back-of-envelope calculations are presented that predict the probability of finding clustered synapses and spatiotemporal sequences. Using data-constrained parameters, the authors conclude that clustering and sequences are indeed likely to occur by chance (for large enough ensembles), but require strong dendritic nonlinearities and low background noise to be useful.

Strengths:

(1) The back-of-envelope reasoning presented can provide fast and valuable intuition. The authors have also made the effort to connect the model parameters with measured values. Even an approximate understanding of cluster probability can direct theory and experiments towards promising directions, or away from lost causes.

(2) I found the general approach to be refreshingly transparent and objective. Assumptions are stated clearly about the model and statistics of different circuits. Along with some positive results, many of the computed cluster probabilities are vanishingly small, and noise is found to be quite detrimental in several cases. This is important to know, and I was happy to see the authors take a balanced look at conditions that help/hinder clustering, rather than to just focus on a particular regime that works.

(3) This paper is also a timely reminder that synaptic clusters and sequences can exist on multiple spatial and temporal scales. The authors present results pertaining to the standard `electrical' regime (~50-100 µm, <50 ms), as well as two modes of chemical signaling (~10 µm, 100-1000 ms). The senior author is indeed an authority on the latter, and the simulations in Figure 5, extending those from Bhalla (2017), are unique in this area. In my view, the role of chemical signaling in neural computation is understudied theoretically, but research will be increasingly important as experimental technologies continue to develop.

Weaknesses:

(1) The paper is mostly let down by the presentation. In the current form, some patience is needed to grasp the main questions and results, and it is hard to keep track of the many abbreviations and definitions. A paper like this can be impactful, but the writing needs to be crisp, and the logic of the derivation accessible to non-experts. See, for instance, Stepanyants, Hof & Chklovskii (2002) for a relevant example.

It would be good to see a restructure that communicates the main points clearly and concisely, perhaps leaving other observations to an optional appendix. For the interested but time-pressed reader, I recommend starting with the last paragraph of the introduction, working through the main derivation on page 7, and writing out the full expression with key parameters exposed. Next, look at Table 1 and Figure 2J to see where different circuits and mechanisms fit in this scheme. Beyond this, the sequence derivation on page 15 and biophysical simulations in Figures 5 and 6 are also highlights.

We appreciate the reviewers' suggestions. We have tightened the flow of the introduction. We understand that the abbreviations and definitions are challenging and have therefore provided intuitions and summaries of the equations discussed in the main text.

Clusters calculations

Our approach is to ask how likely it is that a given set of inputs lands on a short segment of dendrite, and then scale it up to all segments on the entire dendritic length of the cell.

Thus, the probability of occurrence of groups that receive connections from each of the M ensembles (PcFMG) is a function of the connection probability (p) between the two layers, the number of neurons in an ensemble (N), the relative zone-length with respect to the total dendritic arbor (Z/L) and the number of ensembles (M).

Sequence calculations

Here we estimate the likelihood of the first ensemble input arriving anywhere on the dendrite, and ask how likely it is that succeeding inputs of the sequence would arrive within a set spacing.

Thus, the probability of occurrence of sequences that receive sequential connections (PcPOSS) from each of the M ensembles is a function of the connection probability (p) between the two layers, the number of neurons in an ensemble (N), the relative window size with respect to the total dendritic arbor (Δ/L) and the number of ensembles (M).

(2) I wonder if the authors are being overly conservative at times. The result highlighted in the abstract is that 10/100000 postsynaptic neurons are expected to exhibit synaptic clustering. This seems like a very small number, especially if circuits are to rely on such a mechanism. However, this figure assumes the convergence of 3-5 distinct ensembles. Convergence of inputs from just 2 ense mbles would be much more prevalent, but still advantageous computationally. There has been excitement in the field about experiments showing the clustering of synapses encoding even a single feature.

We agree that short clusters of two inputs would be far more likely. We focused our analysis on clusters with three of more ensembles because of the following reasons:

(1) The signal to noise in these clusters was very poor as the likelihood of noise clusters is high.

(2) It is difficult to trigger nonlinearities with very few synaptic inputs.

(3) At the ensemble sizes we considered (100 for clusters, 1000 for sequences), clusters arising from just two ensembles would result in high probability of occurrence on all neurons in a network (~50% in cortex, see p_CMFG in figures below.). These dense neural representations make it difficult for downstream networks to decode (Foldiak 2003).

However, in the presence of ensembles containing fewer neurons or when the connection probability between the layers is low, short clusters can result in sparse representations (Figure 2 - Supplement 2). Arguments 1 and 2 hold for short sequences as well.

(3) The analysis supporting the claim that strong nonlinearities are needed for cluster/sequence detection is unconvincing. In the analysis, different synapse distributions on a single long dendrite are convolved with a sigmoid function and then the sum is taken to reflect the somatic response. In reality, dendritic nonlinearities influence the soma in a complex and dynamic manner. It may be that the abstract approach the authors use captures some of this, but it needs to be validated with simulations to be trusted (in line with previous work, e.g. Poirazi, Brannon & Mel, (2003)).

We agree that multiple factors might affect the influence of nonlinearities on the soma. The key goal of our study was to understand the role played by random connectivity in giving rise to clustered computation. Since simulating a wide range of connectivity and activity patterns in a detailed biophysical model was computationally expensive, we analyzed the exemplar detailed models for nonlinearity separately (Figures 5, 6, and new figure 8), and then used our abstract models as a proxy for understanding population dynamics. A complete analysis of the role played by morphology, channel kinetics and the effect of branching requires an in-depth study of its own, and some of these questions have already been tackled by (Poirazi, Brannon, and Mel 2003; Branco, Clark, and Häusser 2010; Bhalla 2017). However, in the revision, we have implemented a single model which incorporates the range of ion-channel, synaptic and biochemical signaling nonlinearities which we discuss in the paper (Figure 8, and Figure 8 Supplement 1, 2,3). We use this to demonstrate all three forms of sequence and grouped computation we use in the study, where the only difference is in the stimulus pattern and the separation of time-scales inherent in the stimuli.

(4) It is unclear whether some of the conclusions would hold in the presence of learning. In the signal-to-noise analysis, all synaptic strengths are assumed equal. But if synapses involved in salient clusters or sequences were potentiated, presumably detection would become easier? Similarly, if presynaptic tuning and/or timing were reorganized through learning, the conditions for synaptic arrangements to be useful could be relaxed. Answering these questions is beyond the scope of the study, but there is a caveat there nonetheless.

We agree with the reviewer. If synapses receiving connectivity from ensembles had stronger weights, this would make detection easier. Dendritic spikes arising from clustered inputs have been implicated in local cooperative plasticity (Golding, Staff, and Spruston 2002; Losonczy, Makara, and Magee 2008). Further, plasticity related proteins synthesized at a synapse undergoing L-LTP can diffuse to neighboring weakly co-active synapses, and thereby mediate cooperative plasticity (Harvey et al. 2008; Govindarajan, Kelleher, and Tonegawa 2006; Govindarajan et al. 2011). Thus if clusters of synapses were likely to be co-active, they could further engage these local plasticity mechanisms which could potentiate them while not potentiating synapses that are activated by background activity. This would depend on the activity correlation between synapses receiving ensemble inputs within a cluster vs those activated by background activity. We have mentioned some of these ideas in a published opinion paper (Pulikkottil, Somashekar, and Bhalla 2021). In the current study, we wanted to understand whether even in the absence of specialized connection rules, interesting computations could still emerge. Thus, we focused on asking whether clustered or sequential convergence could arise even in a purely randomly connected network, with the most basic set of assumptions. We agree that an analysis of how selectivity evolves with learning would be an interesting topic for further work.

References

• Bhalla, Upinder S. 2017. “Synaptic Input Sequence Discrimination on Behavioral Timescales Mediated by Reaction-Diffusion Chemistry in Dendrites.” Edited by Frances K Skinner. eLife 6 (April):e25827. https://doi.org/10.7554/eLife.25827.

• Branco, Tiago, Beverley A. Clark, and Michael Häusser. 2010. “Dendritic Discrimination of Temporal Input Sequences in Cortical Neurons.” Science (New York, N.Y.) 329 (5999): 1671–75. https://doi.org/10.1126/science.1189664.

• Foldiak, Peter. 2003. “Sparse Coding in the Primate Cortex.” The Handbook of Brain Theory and Neural Networks. https://research-repository.st-andrews.ac.uk/bitstream/handle/10023/2994/FoldiakSparse HBTNN2e02.pdf?sequence=1.

• Golding, Nace L., Nathan P. Staff, and Nelson Spruston. 2002. “Dendritic Spikes as a Mechanism for Cooperative Long-Term Potentiation.” Nature 418 (6895): 326–31. https://doi.org/10.1038/nature00854.

• Govindarajan, Arvind, Inbal Israely, Shu-Ying Huang, and Susumu Tonegawa. 2011. “The Dendritic Branch Is the Preferred Integrative Unit for Protein Synthesis-Dependent LTP.” Neuron 69 (1): 132–46. https://doi.org/10.1016/j.neuron.2010.12.008.

• Govindarajan, Arvind, Raymond J. Kelleher, and Susumu Tonegawa. 2006. “A Clustered Plasticity Model of Long-Term Memory Engrams.” Nature Reviews Neuroscience 7 (7): 575–83. https://doi.org/10.1038/nrn1937.

• Harvey, Christopher D., Ryohei Yasuda, Haining Zhong, and Karel Svoboda. 2008. “The Spread of Ras Activity Triggered by Activation of a Single Dendritic Spine.” Science (New York, N.Y.) 321 (5885): 136–40. https://doi.org/10.1126/science.1159675.

• Losonczy, Attila, Judit K. Makara, and Jeffrey C. Magee. 2008. “Compartmentalized Dendritic Plasticity and Input Feature Storage in Neurons.” Nature 452 (7186): 436–41. https://doi.org/10.1038/nature06725.

• Poirazi, Panayiota, Terrence Brannon, and Bartlett W. Mel. 2003. “Pyramidal Neuron as Two-Layer Neural Network.” Neuron 37 (6): 989–99. https://doi.org/10.1016/S0896-6273(03)00149-1.

• Pulikkottil, Vinu Varghese, Bhanu Priya Somashekar, and Upinder S. Bhalla. 2021. “Computation, Wiring, and Plasticity in Synaptic Clusters.” Current Opinion in Neurobiology, Computational Neuroscience, 70 (October):101–12. https://doi.org/10.1016/j.conb.2021.08.001.

eLife Assessment

This study presents valuable quantitative insights into the prevalence of functionally clustered synaptic inputs on neuronal dendrites. The simple analytical calculations and computer simulations provide solid support for the main arguments. The findings can lead to a more detailed understanding of how dendrites contribute to the computation of neuronal networks.

Reviewer #1 (Public review):

In the current manuscript, the authors use theoretical and analytical tools to examine the possibility of neural projections to engage ensembles of synaptic clusters in active dendrites. The analysis is divided into multiple models that differ in the connectivity parameters, speed of interactions and identity of the signal (electric vs. second messenger). They first show that random connectivity almost ensures the representation of presynaptic ensembles. As expected, this convergence is much more likely for small group sizes and slow processes, such as calcium dynamics. Conversely, fast signals (spikes and postsynaptic potentials) and large groups are much less likely to recruit spatially clustered inputs. Dendritic nonlinearity in the postsynaptic cells was found to play a highly important role in distinguishing these clustered activation patterns, both when activated simultaneously and in sequence. The authors tackled the difficult issue of noise, showing a beneficiary effect when noise 'happen' to fill in gaps in a sequential pattern but degraded performance at higher background activity levels. Last, the authors simulated selectivity to chemical and electrical signals. While they find that longer sequences are less perturbed by noise, in more realistic activation conditions, the signals are not well resolved in the soma.

While I think the premise of the manuscript is worth exploring, I have a number of reservations regarding the results.

(1) In the analysis, the authors made a simplifying assumption that the chemical and electrical processes are independent. However, this is not the case; excitatory inputs to spines often trigger depolarization combined with pronounced calcium influx; this mixed signaling could have dramatic implications on the analysis, particularly if the dendrites are nonlinear (see below)<br /> (2) Sequence detection in active dendrites is often simplified to investigating activation in a part of or the entirety of individual branches. However, the authors did not do that for most of their analysis. Instead, they treat the entire dendritic tree as one long branch and count how many inputs form clusters. I fail to see why the simplification is required and suspect it can lead to wrong results. For example, two inputs that are mapped to different dendrites in the 'original' morphology but then happen to fall next to each other when the branches are staggered to form the long dendrites would be counted as neighbors.<br /> (3) The simulations were poorly executed. Figures 5 and 6 show examples but no summary statistics. The authors emphasize the importance of nonlinear dendritic interactions, but they do not include them in their analysis of the ectopic signals! I find it to be wholly expected that the effects of dendritic ensembles are not pronounced when the dendrites are linear.

To provide a comprehensive analysis of dendritic integration, the authors could simulate more realistic synaptic conductances and voltage-gated channels. They would find much more complicated interactions between inputs on a single site, a sliding temporal and spatial window of nonlinear integration that depends on dendritic morphology, active and passive parameters and synaptic properties. At different activation levels, the rules of synaptic integration shift to cooperativity between different dendrites and cellular compartments, further complicated by nonlinear interactions between somatic spikes and dendritic events.

While it is tempting to extend back-of-the-napkin calculations of how many inputs can recruit nonlinear integration in active dendrites, the biological implementation is very different from this hypothetical. It is important to consider these questions, but I am not convinced that this manuscript adequately addressed the questions it set out to probe, nor does it provide information that was unknown beforehand.

Update after the first revision:

In this revision, the authors significantly improved the manuscript. They now address some of my concerns. Specifically, they show the contribution of end-effects on spreading the inputs between dendrites. This analysis reveals greater applicability of their findings to cortical cells, with long, unbranching dendrites than other neuronal types, such as Purkinje cells in the cerebellum.

They now explain better the interactions between calcium and voltage signals, which I believe improve the take-away message of their manuscript. They modified and added new figures that helped to provide more information about their simulations.<br /> However, some of my points remain valid. Figure 6 shows depolarization of ~5mV from -75. This weak depolarization would not effectively recruit nonlinear activation of NMDARs. In their paper, Branco and Hausser (2010) showed depolarizations of ~10-15mV. More importantly, the signature of NMDAR activation is the prolonged plateau potential and activation at more depolarized resting membrane potentials (their Figure 4). Thus, despite including NMDARs in the simulation, the authors do not model functional recruitment of these channels. Their simulation is thus equivalent to AMPA only drive, which can indeed summate somewhat nonlinearly.

Reviewer #2 (Public review):

Summary:

If synaptic input is functionally clustered on dendrites, nonlinear integration could increase the computational power of neural networks. But this requires the right synapses to be located in the right places. This paper aims to address the question of whether such synaptic arrangements could arise by chance (i.e. without special rules for axon guidance or structural plasticity), and could therefore be exploited even in randomly connected networks. This is important, particularly for the dendrites and biological computation communities, where there is a pressing need to integrate decades of work at the single-neuron level with contemporary ideas about network function.

Using an abstract model where ensembles of neurons project randomly to a postsynaptic population, back-of-envelope calculations are presented that predict the probability of finding clustered synapses and spatiotemporal sequences. Using data-constrained parameters, the authors conclude that clustering and sequences are indeed likely to occur by chance (for large enough ensembles), but require strong dendritic nonlinearities and low background noise to be useful.

Strengths:

- The back-of-envelope reasoning presented can provide fast and valuable intuition. The authors have also made the effort to connect the model parameters with measured values. Even an approximate understanding of cluster probability can direct theory and experiments towards promising directions, or away from lost causes.

- I found the general approach to be refreshingly transparent and objective. Assumptions are stated clearly about the model and statistics of different circuits. Along with some positive results, many of the computed cluster probabilities are vanishingly small, and noise is found to be quite detrimental in several cases. This is important to know, and I was happy to see the authors take a balanced look at conditions that help/hinder clustering, rather than just focus on a particular regime that works.

- This paper is also a timely reminder that synaptic clusters and sequences can exist on multiple spatial and temporal scales. The authors present results pertaining to the standard `electrical' regime (~50-100 µm, <50 ms), as well as two modes of chemical signaling (~10 µm, 100-1000 ms). The senior author is indeed an authority on the latter, and the simulations in Figure 5, extending those from Bhalla (2017), are unique in this area. In my view, the role of chemical signaling in neural computation is understudied theoretically, but research will be increasingly important as experimental technologies continue to develop.

Weaknesses:

- The paper is mostly let down by the presentation. In the current form, some patience is needed to grasp the main questions and results, and it is hard to keep track of the many abbreviations and definitions. A paper like this can be impactful, but the writing needs to be crisp, and the logic of the derivation accessible to non-experts. See, for instance, Stepanyants, Hof & Chklovskii (2002) for a relevant example.

It would be good to see a restructure that communicates the main points clearly and concisely, perhaps leaving other observations to an optional appendix. For the interested but time-pressed reader, I recommend starting with the last paragraph of the introduction, working through the main derivation on page 7, and writing out the full expression with key parameters exposed. Next, look at Table 1 and Figure 2J to see where different circuits and mechanisms fit in this scheme. Beyond this, the sequence derivation on page 17 and biophysical simulations in Figures 5 and 6 are also highlights.

- The analysis supporting the claim that strong nonlinearities are needed for cluster/sequence detection is unconvincing. In the analysis, different synapse distributions on a single long dendrite are convolved with a sigmoid function and then the sum is taken to reflect the somatic response. In reality, dendritic nonlinearities influence the soma in a complex and dynamic manner. It may be that the abstract approach the authors use captures some of this, but it needs to be validated with simulations to be trusted (in line with previous work, e.g. Poirazi, Brannon & Mel, (2003)).

- It is unclear whether some of the conclusions would hold in the presence of learning. In the signal-to-noise analysis, all synaptic strengths are assumed equal. But if synapses involved in salient clusters or sequences were potentiated, presumably detection would become easier? Similarly, if presynaptic tuning and/or timing was reorganized through learning, the conditions for synaptic arrangements to be useful could be relaxed. Answering these questions is beyond the scope of the study, but there is a caveat there nonetheless.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

In the current manuscript, the authors use theoretical and analytical tools to examine the possibility of neural projections to engage ensembles of synaptic clusters in active dendrites. The analysis is divided into multiple models that differ in the connectivity parameters, speed of interactions, and identity of the signal (electric vs. second messenger). They first show that random connectivity almost ensures the representation of presynaptic ensembles. As expected, this convergence is much more likely for small group sizes and slow processes, such as calcium dynamics. Conversely, fast signals (spikes and postsynaptic potentials) and large groups are much less likely to recruit spatially clustered inputs. Dendritic nonlinearity in the postsynaptic cells was found to play a highly important role in distinguishing these clustered activation patterns, both when activated simultaneously and in sequence. The authors tackled the difficult issue of noise, showing a beneficiary effect when noise 'happens' to fill in gaps in a sequential pattern but degraded performance at higher background activity levels. Last, the authors simulated selectivity to chemical and electrical signals. While they find that longer sequences are less perturbed by noise, in more realistic activation conditions, the signals are not well resolved in the soma.

While I think the premise of the manuscript is worth exploring, I have a number of reservations regarding the results.

(1) In the analysis, the authors made a simplifying assumption that the chemical and electrical processes are independent. However, this is not the case; excitatory inputs to spines often trigger depolarization combined with pronounced calcium influx; this mixed signaling could have dramatic implications on the analysis, particularly if the dendrites are nonlinear (see below)

We thank the reviewer for pointing out that we were not entirely clear about the strong basis upon which we had built our analyses of nonlinearity. In the previous version we had relied on published work, notably (Bhalla 2017), which does include these nonlinearities. However, we agree it is preferable to unambiguously demonstrate all the reported selectivity properties in a single model with all the nonlinearities discussed. We have now done so. This is now reported in the paper:

“A single model exhibits multiple forms of nonlinear dendritic selectivity

We implemented all three forms of selectivity described above, in a single model which included six voltage and calcium-gated ion channels, NMDA, AMPA and GABA receptors, and chemical signaling processes in spines and dendrites. The goal of this was three fold: To show how these nonlinear operations emerge in a mechanistically detailed model, to show that they can coexist, and to show that they are separated in time-scales. We implemented a Y-branched neuron model with additional electrical compartments for the dendritic spines (Methods). This model was closely based on a published detailed chemical-electrical model (Bhalla 2017). We stimulated this model with synaptic input corresponding to the three kinds of spatiotemporal patterns described in figures Figure 8 - Supplement 1 (sequential synaptic activity triggering electrical sequence selectivity), Figure 8 - Supplement 2 (spatially grouped synaptic stimuli leading to local Ca4_CaM activation), and Figure 8 - Supplement 3 (sequential bursts of synaptic activity triggering chemical sequence selectivity). We found that each of these mechanisms show nonlinear selectivity with respect to both synaptic spacing and synaptic weights. Further, these forms of selectivity coexist in the composite model (Figure 8 Supplements 1, 2, 3), separated by the time-scales of the stimulus patterns (~ 100 ms, ~ 1s and ~10s respectively). Thus mixed signaling in active nonlinear dendrites yields selectivity of the same form as we explored in simpler individual models. A more complete analysis of the effect of morphology, branching and channel distributions deserves a separate in-depth analysis, and is outside the scope of the current study.”

(2) Sequence detection in active dendrites is often simplified to investigating activation in a part of or the entirety of individual branches. However, the authors did not do that for most of their analysis. Instead, they treat the entire dendritic tree as one long branch and count how many inputs form clusters. I fail to see why simplification is required and suspect it can lead to wrong results. For example, two inputs that are mapped to different dendrites in the 'original' morphology but then happen to fall next to each other when the branches are staggered to form the long dendrites would be counted as neighbors.

We have added the below section within the main text in the section titled “Grouped Convergence of Inputs” to address the effect of branching.

“End-effects limit convergence zones for highly branched neurons

Neurons exhibit considerable diversity with respect to their morphologies. How synapses extending across dendritic branch points interact in the context of a synaptic cluster/group, is a topic that needs detailed examination via experimental and modeling approaches. However for the sake of analysis, we present calculations under the assumption that selectivity for grouped inputs might be degraded across branch points.

Zones beginning close to a branch point might get interrupted. Consider a neuron with B branches. The length of the typical branch would be L/B. As a conservative estimate if we exclude a region of length Z for every branch, the expected number of zones that begin too close to a branch point is

                                                                    [Equation 3]

For typical pyramidal neurons B~50, so Eend ~ 0.05 for values of Z of ~10 µm. Thus pyramidal neurons will not be much affected by branching effects, Profusely branching neurons like Purkinje cells have B~900 for a total L of ~7800 µm, (McConnell and Berry, 1978), hence Eend ~1 for values of Z of ~10 µm. Thus almost all groups in Purkinje neurons would run into a branch point or terminal. For the case of electrical groups, this estimate would be scaled by a factor of 5 if we consider a zone length of 50 µm. However, it is important to note that these are very conservative estimates, as for clusters of 4-5 inputs, the number of synapses available within a zone are far greater (~100 synapses within 50 µm).”

(3) The simulations were poorly executed. Figures 5 and 6 show examples but no summary statistics.

We have included the summary statistics in Figure 5F and Figure 6E. The statistics for both these panels were generated by simulating multiple spatiotemporal combinations of ectopic input in the presence of different stimulus patterns for each sequence length.

The authors emphasize the importance of nonlinear dendritic interactions, but they do not include them in their analysis of the ectopic signals! I find it to be wholly expected that the effects of dendritic ensembles are not pronounced when the dendrites are linear.

We would like to clarify that both Figures 5 and 6 already included nonlinearities. In Figure 5, the chemical mechanism involving the bistable switch motif is strongly selective for ordered inputs in a nonlinear manner. A separate panel highlighting this (Panel C) has now been included in Figure 5. This result had been previously shown in Figure 3I of (Bhalla 2017). We have reproduced it in Figure 5C.

The published electrical model used in Figure 6 also has a nonlinearity which predominantly stems from the interaction of the impedance gradient along the dendrite with the voltage dependence of NMDARs. Check Figure 4C,D of (Branco, Clark, and Häusser 2010).

To provide a comprehensive analysis of dendritic integration, the authors could simulate more realistic synaptic conductances and voltage-gated channels. They would find much more complicated interactions between inputs on a single site, a sliding temporal and spatial window of nonlinear integration that depends on dendritic morphology, active and passive parameters, and synaptic properties. At different activation levels, the rules of synaptic integration shift to cooperativity between different dendrites and cellular compartments, further complicated by nonlinear interactions between somatic spikes and dendritic events.

We would like to clarify two points. First, the key goal of our study was to understand the role played by random connectivity in giving rise to clustered computation. In this revision we provide simulations to show the mechanistic basis for the nonlinearities, and then abstracted these out in order to scale the analysis to networks. These nonlinearities were taken as a given, though we elaborated previous work slightly in order to address the question of ectopic inputs. Second, in our original submission we relied on published work for the estimates of dendritic nonlinearities. Previous work from (Poirazi, Brannon, and Mel 2003; Branco, Clark, and Häusser 2010; Bhalla 2017) have already carried out highly detailed realistic simulations, and in some cases including chemical and electrical nonlinearities as the reviewer mentions (Bhalla 2017). Hence we did not feel that this needed to be redone.

In this resubmission we have addressed the above and two additional concerns, namely whether the different forms of selectivity can coexist in a single model including all these nonlinearities, and whether there is separation of time-scales. The answer is yes to both. The outcome of this is presented in Figure 8 and the associated supplementary figures, and all simulation details are provided on the github repository associated with this paper. A more complete analysis of interaction of multiple nonlinearities in a detailed model is material for further study.

While it is tempting to extend back-of-the-napkin calculations of how many inputs can recruit nonlinear integration in active dendrites, the biological implementation is very different from this hypothetical. It is important to consider these questions, but I am not convinced that this manuscript adequately addressed the questions it set out to probe, nor does it provide information that was unknown beforehand.

We developed our analysis systematically, and perhaps the reviewer refers to the first few calculations as back-of-the-napkin. However, the derivation rapidly becomes more complex when we factor in combinatorics and the effect of noise. This derivation is in the supplementary material. Furthermore, the exact form of the combinatorial and noise equations was non-trivial to derive and we worked closely with the connectivity simulations (Figures 2 and 4) to obtain equations which scale across a large parameter space by sampling connectivity for over 100000 neurons and activity over 100 trials for each of these neurons for each network configuration we have tested.

the biological implementation is very different from this hypothetical.

We do not quite understand in what respect the reviewer feels that this calculation is very different from the biological implementation. The calculation is about projection patterns. In the discussion we consider at length how our findings of selectivity from random projections may be an effective starting point for more elaborate biological connection rules. We have added the following sentence:

“We present a first-order analysis of the simplest kind of connectivity rule (random), upon which more elaborate rules such as spatial gradients and activity-dependent wiring may be developed.”

In case the reviewer was referring to the biological implementation of nonlinear integration, we treat the nonlinear integration in the dendrites as a separate set of simulations, most of which are closely based on published work (Bhalla 2017). We use these in the later sections of the paper to estimate selectivity terms, which inform our final analysis.

In the revision we have worked to clarify this progression of the analysis. As indicated above, we have also made a composite model of all of the nonlinear dendritic mechanisms, chemical and electrical, which underlie our analysis.

nor does it provide information that was unknown beforehand.

We conducted a broad literature survey and to the best of our knowledge these calculations and findings have not been obtained previously. If the reviewer has some specific examples in mind we would be pleased to refer to it.

Reviewer #2 (Public Review):

Summary:

If synaptic input is functionally clustered on dendrites, nonlinear integration could increase the computational power of neural networks. But this requires the right synapses to be located in the right places. This paper aims to address the question of whether such synaptic arrangements could arise by chance (i.e. without special rules for axon guidance or structural plasticity), and could therefore be exploited even in randomly connected networks. This is important, particularly for the dendrites and biological computation communities, where there is a pressing need to integrate decades of work at the single-neuron level with contemporary ideas about network function.

Using an abstract model where ensembles of neurons project randomly to a postsynaptic population, back-of-envelope calculations are presented that predict the probability of finding clustered synapses and spatiotemporal sequences. Using data-constrained parameters, the authors conclude that clustering and sequences are indeed likely to occur by chance (for large enough ensembles), but require strong dendritic nonlinearities and low background noise to be useful.

Strengths:

(1) The back-of-envelope reasoning presented can provide fast and valuable intuition. The authors have also made the effort to connect the model parameters with measured values. Even an approximate understanding of cluster probability can direct theory and experiments towards promising directions, or away from lost causes.

(2) I found the general approach to be refreshingly transparent and objective. Assumptions are stated clearly about the model and statistics of different circuits. Along with some positive results, many of the computed cluster probabilities are vanishingly small, and noise is found to be quite detrimental in several cases. This is important to know, and I was happy to see the authors take a balanced look at conditions that help/hinder clustering, rather than to just focus on a particular regime that works.

(3) This paper is also a timely reminder that synaptic clusters and sequences can exist on multiple spatial and temporal scales. The authors present results pertaining to the standard `electrical' regime (~50-100 µm, <50 ms), as well as two modes of chemical signaling (~10 µm, 100-1000 ms). The senior author is indeed an authority on the latter, and the simulations in Figure 5, extending those from Bhalla (2017), are unique in this area. In my view, the role of chemical signaling in neural computation is understudied theoretically, but research will be increasingly important as experimental technologies continue to develop.

Weaknesses:

(1) The paper is mostly let down by the presentation. In the current form, some patience is needed to grasp the main questions and results, and it is hard to keep track of the many abbreviations and definitions. A paper like this can be impactful, but the writing needs to be crisp, and the logic of the derivation accessible to non-experts. See, for instance, Stepanyants, Hof & Chklovskii (2002) for a relevant example.

It would be good to see a restructure that communicates the main points clearly and concisely, perhaps leaving other observations to an optional appendix. For the interested but time-pressed reader, I recommend starting with the last paragraph of the introduction, working through the main derivation on page 7, and writing out the full expression with key parameters exposed. Next, look at Table 1 and Figure 2J to see where different circuits and mechanisms fit in this scheme. Beyond this, the sequence derivation on page 15 and biophysical simulations in Figures 5 and 6 are also highlights.

We appreciate the reviewers' suggestions. We have tightened the flow of the introduction. We understand that the abbreviations and definitions are challenging and have therefore provided intuitions and summaries of the equations discussed in the main text.

Clusters calculations

“Our approach is to ask how likely it is that a given set of inputs lands on a short segment of dendrite, and then scale it up to all segments on the entire dendritic length of the cell.

Thus, the probability of occurrence of groups that receive connections from each of the M ensembles (PcFMG) is a function of the connection probability (p) between the two layers, the number of neurons in an ensemble (N), the relative zone-length with respect to the total dendritic arbor (Z/L) and the number of ensembles (M).”

Sequence calculations

“Here we estimate the likelihood of the first ensemble input arriving anywhere on the dendrite, and ask how likely it is that succeeding inputs of the sequence would arrive within a set spacing.

Thus, the probability of occurrence of sequences that receive sequential connections (PcPOSS) from each of the M ensembles is a function of the connection probability (p) between the two layers, the number of neurons in an ensemble (N), the relative window size with respect to the total dendritic arbor (Δ/L) and the number of ensembles (M).”

(2) I wonder if the authors are being overly conservative at times. The result highlighted in the abstract is that 10/100000 postsynaptic neurons are expected to exhibit synaptic clustering. This seems like a very small number, especially if circuits are to rely on such a mechanism. However, this figure assumes the convergence of 3-5 distinct ensembles. Convergence of inputs from just 2 ense mbles would be much more prevalent, but still advantageous computationally. There has been excitement in the field about experiments showing the clustering of synapses encoding even a single feature.

We agree that short clusters of two inputs would be far more likely. We focused our analysis on clusters with three of more ensembles because of the following reasons:

(1) The signal to noise in these clusters was very poor as the likelihood of noise clusters is high.

(2) It is difficult to trigger nonlinearities with very few synaptic inputs.

(3) At the ensemble sizes we considered (100 for clusters, 1000 for sequences), clusters arising from just two ensembles would result in high probability of occurrence on all neurons in a network (~50% in cortex, see p_CMFG in figures below.). These dense neural representations make it difficult for downstream networks to decode (Foldiak 2003).

However, in the presence of ensembles containing fewer neurons or when the connection probability between the layers is low, short clusters can result in sparse representations (Figure 2 - Supplement 2). Arguments 1 and 2 hold for short sequences as well.

(3) The analysis supporting the claim that strong nonlinearities are needed for cluster/sequence detection is unconvincing. In the analysis, different synapse distributions on a single long dendrite are convolved with a sigmoid function and then the sum is taken to reflect the somatic response. In reality, dendritic nonlinearities influence the soma in a complex and dynamic manner. It may be that the abstract approach the authors use captures some of this, but it needs to be validated with simulations to be trusted (in line with previous work, e.g. Poirazi, Brannon & Mel, (2003)).

We agree that multiple factors might affect the influence of nonlinearities on the soma. The key goal of our study was to understand the role played by random connectivity in giving rise to clustered computation. Since simulating a wide range of connectivity and activity patterns in a detailed biophysical model was computationally expensive, we analyzed the exemplar detailed models for nonlinearity separately (Figures 5, 6, and new figure 8), and then used our abstract models as a proxy for understanding population dynamics. A complete analysis of the role played by morphology, channel kinetics and the effect of branching requires an in-depth study of its own, and some of these questions have already been tackled by (Poirazi, Brannon, and Mel 2003; Branco, Clark, and Häusser 2010; Bhalla 2017). However, in the revision, we have implemented a single model which incorporates the range of ion-channel, synaptic and biochemical signaling nonlinearities which we discuss in the paper (Figure 8, and Figure 8 Supplement 1, 2,3). We use this to demonstrate all three forms of sequence and grouped computation we use in the study, where the only difference is in the stimulus pattern and the separation of time-scales inherent in the stimuli.

(4) It is unclear whether some of the conclusions would hold in the presence of learning. In the signal-to-noise analysis, all synaptic strengths are assumed equal. But if synapses involved in salient clusters or sequences were potentiated, presumably detection would become easier? Similarly, if presynaptic tuning and/or timing were reorganized through learning, the conditions for synaptic arrangements to be useful could be relaxed. Answering these questions is beyond the scope of the study, but there is a caveat there nonetheless.

We agree with the reviewer. If synapses receiving connectivity from ensembles had stronger weights, this would make detection easier. Dendritic spikes arising from clustered inputs have been implicated in local cooperative plasticity (Golding, Staff, and Spruston 2002; Losonczy, Makara, and Magee 2008). Further, plasticity related proteins synthesized at a synapse undergoing L-LTP can diffuse to neighboring weakly co-active synapses, and thereby mediate cooperative plasticity (Harvey et al. 2008; Govindarajan, Kelleher, and Tonegawa 2006; Govindarajan et al. 2011). Thus if clusters of synapses were likely to be co-active, they could further engage these local plasticity mechanisms which could potentiate them while not potentiating synapses that are activated by background activity. This would depend on the activity correlation between synapses receiving ensemble inputs within a cluster vs those activated by background activity. We have mentioned some of these ideas in a published opinion paper (Pulikkottil, Somashekar, and Bhalla 2021). In the current study, we wanted to understand whether even in the absence of specialized connection rules, interesting computations could still emerge. Thus, we focused on asking whether clustered or sequential convergence could arise even in a purely randomly connected network, with the most basic set of assumptions. We agree that an analysis of how selectivity evolves with learning would be an interesting topic for further work.

References

Bhalla, Upinder S. 2017. “Synaptic Input Sequence Discrimination on Behavioral Timescales Mediated by Reaction-Diffusion Chemistry in Dendrites.” Edited by Frances K Skinner. eLife 6 (April):e25827. https://doi.org/10.7554/eLife.25827.

Branco, Tiago, Beverley A. Clark, and Michael Häusser. 2010. “Dendritic Discrimination of Temporal Input Sequences in Cortical Neurons.” Science (New York, N.Y.) 329 (5999): 1671–75. https://doi.org/10.1126/science.1189664.

Foldiak, Peter. 2003. “Sparse Coding in the Primate Cortex.” The Handbook of Brain Theory and Neural Networks. https://research-repository.st-andrews.ac.uk/bitstream/handle/10023/2994/FoldiakSparse HBTNN2e02.pdf?sequence=1.

Golding, Nace L., Nathan P. Staff, and Nelson Spruston. 2002. “Dendritic Spikes as a Mechanism for Cooperative Long-Term Potentiation.” Nature 418 (6895): 326–31. https://doi.org/10.1038/nature00854.

Govindarajan, Arvind, Inbal Israely, Shu-Ying Huang, and Susumu Tonegawa. 2011. “The Dendritic Branch Is the Preferred Integrative Unit for Protein Synthesis-Dependent LTP.” Neuron 69 (1): 132–46. https://doi.org/10.1016/j.neuron.2010.12.008.

Govindarajan, Arvind, Raymond J. Kelleher, and Susumu Tonegawa. 2006. “A Clustered Plasticity Model of Long-Term Memory Engrams.” Nature Reviews Neuroscience 7 (7): 575–83. https://doi.org/10.1038/nrn1937.

Harvey, Christopher D., Ryohei Yasuda, Haining Zhong, and Karel Svoboda. 2008. “The Spread of Ras Activity Triggered by Activation of a Single Dendritic Spine.” Science (New York, N.Y.) 321 (5885): 136–40. https://doi.org/10.1126/science.1159675.

Losonczy, Attila, Judit K. Makara, and Jeffrey C. Magee. 2008. “Compartmentalized Dendritic Plasticity and Input Feature Storage in Neurons.” Nature 452 (7186): 436–41. https://doi.org/10.1038/nature06725.

Poirazi, Panayiota, Terrence Brannon, and Bartlett W. Mel. 2003. “Pyramidal Neuron as Two-Layer Neural Network.” Neuron 37 (6): 989–99. https://doi.org/10.1016/S0896-6273(03)00149-1.

Pulikkottil, Vinu Varghese, Bhanu Priya Somashekar, and Upinder S. Bhalla.     2021.

“Computation, Wiring, and Plasticity in Synaptic Clusters.” Current Opinion in Neurobiology, Computational Neuroscience, 70 (October):101–12. https://doi.org/10.1016/j.conb.2021.08.001.

eLife Assessment

This important study combines compelling experiments with optogenetic actuation and convincing theory to understand how signalling proteins control the switch between cell protrusion and retraction, two essential processes in single cell migration. The authors examine the importance of the basal concentration and recruitment dynamics of a guanine exchange factor (GEF) on the activity of the downstream effectors RhoA and Cdc42, which control retraction and protrusion. The experimental and theoretical evidence provides a model of RhoA's involvment in both protrusion and retraction and shows that these complex processes are highly dependent on the concentration and activity dynamics of the components.

Reviewer #1 (Public review):

De Seze et al. investigated the role of guanine exchange factors (GEFs) in controlling cell protrusion and retraction. In order to causally link protein activities to the switch between the opposing cell phenotypes, they employed optogenetic versions of GEFs which can be recruited to the plasma membrane upon light exposure and activate their downstream effectors. Particularly the RhoGEF PRG could elicit both protruding and retracting phenotypes. Interestingly, the phenotype depended on the basal expression level of the optoPRG. By assessing the activity of RhoA and Cdc42, the downstream effectors of PRG, the mechanism of this switch was elucidated: at low PRG levels, RhoA is predominantly activated and leads to cell retraction, whereas at high PRG levels, both RhoA and Cdc42 are activated but PRG also sequesters the active RhoA, therefore Cdc42 dominates and triggers cell protrusion. Finally, they create a minimal model that captures the key dynamics of this protein interaction network and the switch in cell behavior.

The conclusions of this study are strongly supported by data, harnessing the power of modelling and optogenetic activation. The minimal model captures well the dynamics of RhoA and Cdc42 activation and predicts that by changing the frequency of optogenetic activation one can switch between protruding and retracting behaviour in the same cell of intermediate optoPRG level. The authors are indeed able to demonstrate this experimentally albeit with a very low number of cells. A major caveat of this study is that global changes due to PRG overexpression cannot be ruled out. Also, a quantification of absolute protein concentration, which is notoriously difficult, would be useful to put the level of overexpression here in perspective with endogenous levels. Furthermore, it remains unclear whether in cases of protein overexpression in vivo such as cancer, PRG or other GEFs can activate alternative migratory behaviours.

Previous work has implicated RhoA in both protrusion and retraction depending on the context. The mechanism uncovered here provides a convincing explanation for this conundrum. In addition to PRG, optogenetic versions of two other GEFs, LARG and GEF-H1, were used which produced either only one phenotype or less response than optoPRG, underscoring the functional diversity of RhoGEFs. The authors chose transient transfection to achieve a large range of concentration levels and, to find transfected cells at low cell density, developed a small software solution (Cell finder), which could be of interest for other researchers.

Reviewer #2 (Public review):

Summary:

This manuscript builds from the interesting observation that local recruitment of the DHPH domain of the RhoGEF PRG can induce local retraction, protrusion, or neither. The authors convincingly show that these differential responses are tied to the level of expression of the PRG transgene. This response depends on the Rho-binding activity of the recruited PH domain and is associated with and requires (co?)-activation of Cdc42. This begs the question of why this switch in response occurs. The use a computational model to predict that the timing of protein recruitment can dictate the output of the response in cells expressing intermediate levels and found that, "While the majority of cells showed mixed phenotypes irrespectively of the activation pattern, in few cells (3 out of 90) we were able to alternate the phenotype between retraction and protrusion several times at different places of the cell by changing the frequency while keeping the same total integrated intensity (Figure 6F and Supp Movie)."

Comments on the revised manuscript:

The authors have addressed the previous points and they have convincingly demonstrated that an optogenetically recruited PRG-GEF acts, as expected, as a GEF for RhoA. However, if this fragment is strongly over-expressed, it activates Cdc42, instead of RhoA. This appears to be due to sequestration of active RhoA by the overexpressed PRG-GEF.

Author response:

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

Public Review:

Reviewer #2 (Public Review): 

Regarding reviewer #2 public review, we update here our answers to this public review with new analysis and modification done in the manuscript. 

This manuscript is missing a direct phenotypic comparison of control cells to complement that of cells expressing RhoGEF2-DHPH at "low levels" (the cells that would respond to optogenetic stimulation by retracting); and cells expressing RhoGEF2-DHPH at "high levels" (the cells that would respond to optogenetic stimulation by protruding). In other words, the authors should examine cell area, the distribution of actin and myosin, etc in all three groups of cells (akin to the time zero data from figures 3 and 5, with a negative control). For example, does the basal expression meaningfully affect the PRG low-expressing cells before activation e.g. ectopic stress fibers? This need not be an optogenetic experiment, the authors could express RhoGEF2DHPH without SspB (as in Fig 4G). 

Updated answer: We thank reviewer #2 for this suggestion. PRG-DHPH overexpression is known to affect the phenotype of the cell as shown in Valon et al., 2017. In our experiments, we could not identify any evidence of a particular phenotype before optogenetic activation apart from the area and spontaneous membrane speed that were already reported in our manuscript (Fig 2E and SuppFig 2). Regarding the distribution of actin and myosin, we did not observe an obvious pattern that will be predictive of the protruding/retracting phenotype. Trying to be more quantitative, we have classified (by eye, without knowing the expression level of PRG nor the future phenotype) the presence of stress fibers, the amount of cortical actin, the strength of focal adhesions, and the circularity of cells. As shown below, when these classes are binned by levels of expression of PRG (two levels below the threshold and two above) there is no clear determinant. Thus, we concluded that the main driver of the phenotype was the PRG basal expression rather than any particularity of the actin cytoskeleton/cell shape.

Author response image 1.

Author response image 2.

Relatedly, the authors seem to assume ("recruitment of the same DH-PH domain of PRG at the membrane, in the same cell line, which means in the same biochemical environment." supplement) that the only difference between the high and low expressors are the level of expression. Given the chronic overexpression and the fact that the capacity for this phenotypic shift is not recruitmentdependent, this is not necessarily a safe assumption. The expression of this GEF could well induce e.g. gene expression changes. 

Updated answer: We agree with reviewer #2 that there could be changes in gene expression. In the next point of this supplementary note, we had specified it, by saying « that overexpression has an influence on cell state, defined as protein basal activity or concentration before activation. »  We are sorry if it was not clear, and we changed this sentence in the revised manuscript (in red in the supp note). 

One of the interests of the model is that it does not require any change in absolute concentrations, beside the GEF. The model is thought to be minimal and fits well and explains the data with very few parameters. We do not show that there is no change in concentration, but we show that it is not required to invoke it. We revised a sentence in the new version of the manuscript to include this point.

Additional answer: During the revision process, we have been looking for an experimental demonstration of the independence of the phenotypic switch to any change in global gene expression pattern due to the chronic overexpression of PRG. Our idea was to be in a condition of high PRG overexpression such that cells protrude upon optogenetic activation, and then acutely deplete PRG to see if cells where then retracting. To deplete PRG in a timescale that prevent any change of gene expression, we considered the recently developed CATCHFIRE (PMID: 37640938) chemical dimerizer. We designed an experiment in which the PRG DH-PH domain was expressed in fusion with a FIRE-tag and co-expressing the FIRE-mate fused to TOM20 together with the optoPRG tool. Upon incubation with the MATCH small molecule, we should be able to recruit the overexpressed PRG to the mitochondria within minutes, hereby preventing it to form a complex with active RhoA in the vicinity of the plasma membrane. Unfortunately, despite of numerous trials we never achieved the required conditions: we could not have cells with high enough expression of PRGFIRE-tag (for protrusive response) and low enough expression of optoPRG (for retraction upon PRGFIRE-tag depletion). We still think this would be a nice experiment to perform, but it will require the establishment of a stable cell line with finely tuned expression levels of the CATCHFIRE system that goes beyond the timeline of our present work.      

Concerning the overall model summarizing the authors' observations, they "hypothesized that the activity of RhoA was in competition with the activity of Cdc42"; "At low concentration of the GEF, both RhoA and Cdc42 are activated by optogenetic recruitment of optoPRG, but RhoA takes over. At high GEF concentration, recruitment of optoPRG lead to both activation of Cdc42 and inhibition of already present activated RhoA, which pushes the balance towards Cdc42."

These descriptions are not precise. What is the nature of the competition between RhoA and Cdc42? Is this competition for activation by the GEFs? Is it a competition between the phenotypic output resulting from the effectors of the GEFs? Is it competition from the optogenetic probe and Rho effectors and the Rho biosensors? In all likelihood, all of these effects are involved, but the authors should more precisely explain the underlying nature of this phenotypic switch. Some of these points are clarified in the supplement, but should also be explicit in the main text. 

Updated answer: We consider the competition between RhoA and Cdc42 as a competition between retraction due to the protein network triggered by RhoA (through ROCK-Myosin and mDia-bundled actin) and the protrusion triggered by Cdc42 (through PAK-Rac-ARP2/3-branched Actin). We made this point explicit in the main text.  

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):  

Major 

- why this is only possible for such few cells. Can the authors comment on this in the discussion? Does the model provide any hints? 

As said in our answer to the public comment or reviewer #1, we think that the low number of cells being able to switch can be explained by two different reasons: 

(1) First, we were looking for clear inversions of the phenotype, where we could see clear ruffles in the case of the protrusion, and clear retractions in the other case. Thus, we discarded cells that would show in-between phenotypes, because we had no quantitative parameter to compare how protrusive or retractile they were. This reduced the number of switching cells 

(2) Second, we had a limitation due to the dynamic of the optogenetic dimer used here. Indeed, the control of the frequency was limited by the dynamic of unbinding of the optogenetic dimer. This dynamic of recruitment (~20s) is comparable to the dynamics of the deactivation of RhoA and Cdc42. Thus, the differences in frequency are smoothed and we could not vary enough the frequency to increase the number of switches. Thanks to the model, we can predict that increasing the unbinding rate of the optogenetic tool (shorter dimer lifetime) should allow us to increase the number of switching cells. 

We have added a sentence in the discussion to make this second point explicit.

- I would encourage the authors to discuss this molecular signaling switch in the context of general design principles of switches. How generalizable is this network/mechanism? Is it exclusive to activating signaling proteins or would it work with inhibiting mechanisms? Is the competition for the same binding site between activators and effectors a common mechanism in other switches? 

The most common design principle for molecular switches is the bistable switch that relies on a nonlinear activation (for example through cooperativity) with a linear deactivation. Such a design allows the switch between low and high levels. In our case, there is no need for a non-linearity since the core mechanism is a competition for the same binding site on active RhoA of the activator and the effectors. Thus, the design principle would be closer to the notion of a minimal “paradoxical component” (PMID: 23352242) that both activate and limit signal propagation, which in our case can be thought as a self-limiting mechanism to prevent uncontrolled RhoA activation by the positive feedback. Yet, as we show in our work, this core mechanism is not enough for the phenotypic switch to happen since the dual activation of RhoA and Cdc42 is ultimately required for the protrusion phenotype to take over the retracting one. Given the particularity of the switch we observed here, we do not feel comfortable to speculate on any general design principles in the main text, but we thank reviewer #1 for his/her suggestion.

- Supplementary figures - there is a discrepancy between the figures called in the text and the supplementary files, which only include SF1-4. 

We apologize for this error and we made the correction. 

- In the text, the authors use Supp Figure 7 to show that the phenotype could not be switched by varying the fold increase of recruitment through changing the intensity/duration of the light pulse. Aside from providing the figure, could you give an explanation or speculation of why? Does the model give any prediction as to why this could be difficult to achieve experimentally (is the range of experimentally feasible fold change of 1.1-3 too small? Also, could you clarify why the range is different than the 3 to 10-fold mentioned at the beginning of the results section? 

We thank the reviewer for this question, and this difference between frequency and intensity can be indeed understood in a simple manner through the model. 

All the reactions in our model were modeled as linear reactions. Thus, at any timepoint, changing the intensity of the pulse will only change proportionally the amount of the different components (amount of active RhoA, amount of sequestered RhoA, and amount of active Cdc42). This explains why we cannot change the balance between RhoA activity and Cdc42 activity only through the pulse strength. We observed the same experimentally: when we changed the intensity of the pulses, the phenotype would be smaller/stronger, but would never switch, supporting our hypothesis on the linearity of all biochemical reactions. 

On the contrary, changing the frequency has an effect, for a simple reason: the dynamics of RhoA and Cdc42 activation are not the same as the dynamics of inhibition of RhoA by the PH domain (see

Figure 4). The inhibition of RhoA by the PH is almost instantaneous while the activation of RhoGTPases has a delay (sets by the deactivation parameter k_2). Intuitively, increasing the frequency will lead to sustained inhibition of RhoA, promoting the protrusion phenotype. Decreasing the frequency – with a stronger pulse to keep the same amount of recruited PRG – restricts this inhibition of RhoA to the first seconds following the activation. The delayed activation of RhoA will then take over. 

We added two sentences in the manuscript to explain in greater details the difference between intensity and frequency.  

Regarding the difference between the 1.3-3 fold and the 3 to 10 fold, the explanation is the following: the 3 to 10 fold referred to the cumulative amount of proteins being recruited after multiple activations (steady state amount reached after 5 minutes with one activation every 30s); while the 1.3-3 fold is what can be obtained after only one single pulse of activation.  

- The transient expression achieves a large range of concentration levels which is a strength in this case. To solve the experimental difficulties associated with this, i.e. finding transfected cells at low cell density, the authors developed a software solution (Cell finder). Since this approach will be of interest for a wide range of applications, I think it would deserve a mention in the discussion part. 

We thank the reviewer for his/her interest in this small software solution.

We developed the description of the tool in the Method section. The Cell finder is also available with comments on github (https://github.com/jdeseze/cellfinder) and usable for anyone using Metamorph or Micromanager imaging software. 

Minor 

- Can the authors describe what they mean with "cell state"? It is used multiple times in the manuscript and can be interpreted as various things. 

We now explain what we mean by ‘cell state’ in the main text :

“protein basal activities and/or concentrations - which we called the cell state”

- “(from 0% to 45%, Figure 2D)", maybe add here: "compare also with Fig. 2A". 

We completed the sentence as suggested, which clarifies the data for the readers.

- The sentence "Given that the phenotype switch appeared to be controlled by the amount of overexpressed optoPRG, we hypothesized that the corresponding leakiness of activity could influence the cell state prior to any activation." might be hard to understand for readers unfamiliar with optogenetic systems. I suggest adding a short sentence explaining dark-state activity/leakiness before putting the hypothesis forward. 

We changed this whole beginning of the paragraph to clarify.

- Figure 2E and SF2A. I would suggest swapping these two panels as the quantification of the membrane displacement before activation seems more relevant in this context. 

We thank reviewer #1 for this suggestion and we agree with it (we swapped the two panels)

- Fig. 2B is missing the white frames in the mixed panels. 

We are sorry for this mistake, we changed it in the new version.  

- In the text describing the experiment of Fig. 4G, it would again be helpful to define what the authors mean by cell state, or to state the expected outcome for both hypotheses before revealing the result.

We added precisions above on what we meant by cell state, which is the basal protein activities and/or concentrations prior to optogenetic activation. We added the expectation as follow: 

To discriminate between these two hypotheses, we overexpressed the DH-PH domain alone in another fluorescent channel (iRFP) and recruited the mutated PH at the membrane. “If the binding to RhoA-GTP was only required to change the cell state, we would expect the same statistics than in Figure 2D, with a majority of protruding cells due to DH-PH overexpression. On the contrary, we observed a large majority of retracting phenotype even in highly expressing cells (Figure 4G), showing that the PH binding to RhoA-GTP during recruitment is a key component of the protruding phenotype.”

- Figure 4H,I: "of cells that overexpress PRG, where we only recruit the PH domain" doesn't match with the figure caption. Are these two constructs in the same cell? If not please clarify the main text. 

We agree that it was not clear. Both constructs are in the same cell, and we changed the figure caption accordingly.  

- "since RhoA dominates Cdc42" is this concluded from experiments (if yes, please refer to the figure) or is this known from the literature (if yes, please cite). 

The assumption that RhoA dominates Cdc42 comes from the fact that we see retraction at low PRG concentration. We assumed that RhoA is responsible for the retraction phenotype. Our assumption is based on the literature (Burridge 2004 as an example of a review, confirmed by many experiments, such as the direct recruitment of RhoA to the membrane, see Berlew 2021) and is supported by our observations of immediate increase of RhoA activity at low PRG. We modified the text to clarify it is an assumption.

- Fig. 6G  o left: is not intuitive, why are the number of molecules different to start with? 

The number of molecules is different because they represent the active molecules: increasing the amount of PRG increases the amount of active RhoA and active Cdc42. We updated the figure to clarify this point.

o right: the y-axis label says "phenotype", maybe change it to "activity" or add a second y-axis on the right with "phenotype"? 

We updated the figure following reviewer #1 suggestion.

- Discussion: "or a retraction in the same region" sounds like in the same cell. Perhaps rephrase to state retraction in a similar region? 

Sorry for the confusion, we change it to be really clear: “a protrusion in the activation region when highly expressed, or a retraction in the activation region when expressed at low concentrations.”

Typos: 

- "between 3 and 10 fold" without s. 

- Fig. 1H, y-axis label. 

- "whose spectrum overlaps" with s. 

- "it first decays, and then rises" with s. 

- Fig 4B and Fig 6B. Is the time in sec or min? (Maybe double-check all figures). 

- "This result suggests that one could switch the phenotype in a single cell by selecting it for an intermediate expression level of the optoPRG.". 

- "GEF-H1 PH domain has almost the same inhibition ability as PRG PH domain". 

We corrected all these mistakes and thank the reviewer for his careful reading of the manuscript.

Reviewer #2 (Recommendations For The Authors): 

Likewise, the model assumes that at high PRG GEF expression, the "reaction is happening far from saturation ..." and that "GTPases activated with strong stimuli -giving rise to strong phenotypic changes- lead to only 5% of the proteins in a GTP-state, both for RhoA and Cdc42". Given the high levels of expression (the absolute value of which is not known) this assumption is not necessarily safe to assume. The shift to Cdc42 could indeed result from the quantitative conversion of RhoA into its active state. 

We agree with the reviewer that the hypothesis that RhoA is fully converted into its active state cannot be completely ruled out. However, we think that the two following points can justify our choice.

- First, we see that even in the protruding phenotype, RhoA activity is increasing upon optoPRG recruitment (Figure 3). This means that RhoA is not completely turned into its active GTP-loaded state. The biosensor intensity is rising by a factor 1.5 after 5 minutes (and continue to increase, even if not shown here). For sure, it could be explained by the relocation of RhoA to the place of activation, but it still shows that cells with high PRG expression are not completely saturated in RhoA-GTP. 

- We agree that linearity (no saturation) is still an hypothesis and very difficult to rule out, because it is not only a question of absolute concentrations of GEFs and RhoA, but also a question of their reaction kinetics, which are unknow parameters in vivo. Yet, adding a saturation parameter would mean adding 3 unknown parameters (absolute concentrations of RhoA, as well as two reaction constants). The fact that there are not needed to fit the complex curves of RhoA as we do with only one parameter tends to show that the minimal ingredients representing the interaction are captured here.  

The observed "inhibition of RhoA by the PH domain of the GEF at high concentrations" could result from the ability of the probe to, upon membrane recruitment, bind to active RhoA (via its PH domain) thereby outcompeting the RhoA biosensor (Figure 4A-C). This reaction is explicitly stated in the supplemental materials ("PH domain binding to RhoA-GTP is required for protruding phenotype but not sufficient, and it is acting as an inhibitor of RhoA activity."), but should be more explicit in the main text. Indeed, even when PRG DHPH is expressed at high concentrations, it does activate RhoA upon recruitment (figure 3GH). Not only might overexpression of this active RhoA-binding probe inhibit the cortical recruitment of the RhoA biosensor, but it may also inhibit the ability of active RhoA to activate its downstream effectors, such as ROCK, which could explain the decrease in myosin accumulation (figure 3D-F). It is not clear that there is a way to clearly rule this out, but it may impact the interpretation. 

This hypothesis is actually what we claim in the manuscript. We think that the inhibition of RhoA by the PH domain is explained by its direct binding. We may have missed what Reviewer #2 wanted to say, but we think that we state it explicitly in the main text :

“Knowing that the PH domain of PRG triggers a positive feedback loop thanks to its binding to active RhoA 18, we hypothesized that this binding could sequester active RhoA at high optoPRG levels, thus being responsible for its inhibition.”

And also in the Discussion:

“However, this feedback loop can turn into a negative one for high levels of GEF: the direct interaction between the PH domain and RhoA-GTP prevents RhoA-GTP binding to effectors through a competition for the same binding site.”

We may have not been clear, but we think that this is what is happening: the PH domain prevents the binding to effectors and decreases RhoA activity (as was shown in Chen et al. 2010).  

The X-axis in Figure 4C time is in seconds not minutes. The Y-axis in Figure 4H is unlabeled. 

We are sorry for the mistake of Figure 4C. We changed the Y-axis in the Figure 4h.  

Although this publication cites some of the relevant prior literature, it fails to cite some particularly relevant works. For example, the authors state, "The LARG DH domain was already used with the iLid system" and refers to a 2018 paper (ref 19), whereas that domain was first used in 2016 (PMID 27298323). Indeed, the authors used the plasmid from this 2016 paper to build their construct. 

We thank the reviewer for pointing out this error, we have corrected the citation and put the seminal one in the revised version.

An analogous situation pertains to previous work that showed that an optogenetic probe containing the DH and PH domains in RhoGEF2 is somewhat toxic in vivo (table 6; PMID 33200987). Furthermore, it has previously been shown that mutation of the equivalent of F1044A and I1046E eliminates this toxicity (table 6; PMID 33200987) in vivo. This is particularly important because the Rho probe expressing RhoGEF2-DHPH is in widespread usage (76 citations in PubMed). The ability of this probe to activate Cdc42 may explain some of the phenotypic differences described resulting from the recruitment of RhoGEF2-DHPH and LARG-DH in a developmental context (PMID 29915285, 33200987). 

We thank reviewer #2 for these comments, and added a small section in the discussion, for optogenetic users: 

This underlines the attention that needs to be paid to the choice of specific GEF domains when using optogenetic tools. Tools using DH-PH domains of PRG have been widely used, both in mammalian cells and in Drosophila (with the orthologous gene RhoGEF2), and have been shown to be toxic in some contexts in vivo 28. Our study confirms the complex behavior of this domain which cannot be reduced to a simple RhoA activator.   

Concerning the experiment shown in 4D, it would be informative to repeat this experiment in which a non-recruitable DH-PH domain of PRG is overexpressed at high levels and the DH domain of LARG is recruited. This would enable the authors to distinguish whether the protrusion response is entirely dependent on the cell state prior to activation or the combination of the cell state prior to activation and the ability of PRG DHPH to also activate Cdc42. 

We thank the reviewer for his suggestion. Yet, we think that we have enough direct evidence that the protruding phenotype is due to both the cell state prior to activation and the ability of PRG DHPH to also activate Cdc42. First, we see a direct increase in Cdc42 activity following optoPRG recruitment (see Figure 6). This increase is sustained in the protruding phenotype and precedes Rac1 and RhoA activity, which shows that it is the first of these three GTPases to be activated. Moreover, we showed that inhibition of PAK by the very specific drug IPA3 is completely abolishing only the protruding phenotype, which shows that PAK, a direct effector of Cdc42 and Rac1, is required for the protruding phenotype to happen. We know also that the cell state prior to activation is defining the phenotype, thanks to the data presented in Figure 2. 

We further showed in Figure 1 that LARG DH-PH domain was not able to promote protrusion. The proposed experiment would be interesting to confirm that LARG does not have the ability to activate another GTPase, even in a different cell state with overexpressed PRG. However, we are not sure it would bring any substantial findings to understand the mechanism we describe here, given the facts provided above.  

Similarly, as PRG activates both Cdc42 and Rho at high levels, it would be important to determine the extent to which the acute Rho activation contributes to the observed phenotype (e.g. with Rho kinase inhibitor). 

We agree with the reviewer that it would be interesting to know whether RhoA activation contributes to the observed phenotype, and we have tried such experiments. 

For Rho kinase inhibitor, we tried with Y-27632 and we could never prevent the protruding phenotype to happen. However, we could not completely abolish the retracting phenotype either (even when the effect on the cells was quite strong and visible), which could be due to other effectors compensating for this inhibition. As RhoA has many other effectors, it does not tell us that RhoA is not required for protrusion. 

We also tried with C3, which is a direct inhibitor of RhoA. However, it had too much impact on the basal state of the cells, making it impossible to recruit (cells were becoming round and clearly dying. As both the basal state and optogenetic activation require the activation of RhoA, it is hard to conclude out of experiments where no cell is responding. 

The ability of PRG to activate Cdc42 in vivo is striking given the strong preference for RhoA over Cdc42 in vitro (2400X) (PMID 23255595). Is it possible that at these high expression levels, much of the RhoA in the cell is already activated, so that the sole effect that recruited PRG can induce is activation of Cdc42? This is related to the previous point pertaining to absolute expression levels.  

As discussed before, we think that it is not only a question of absolute expression levels, but also of the affinities between the different partners. But Reviewer #2 is right, there is a competition between the activation of RhoA and Cdc42 by optoPRG, and activation of Cdc42 probably happens at higher concentration because of smaller effective affinity.

Still, we know that activation of the Cdc42 by PRG DH-PH domain is possible in vivo, as it was very clearly shown in Castillo-Kauil et al., 2020 (PMID 33023908). They show that this activation requires the linker between DH and PH domain of PRG, as well as Gαs activation, which requires a change in PRG DH-PH conformation. This conformational switch does not happen in vitro, which might explain why the affinity against Cdc42 was found to be very low. 

Minor points 

In both the abstract and the introduction the authors state, "we show that a single protein can trigger either protrusion or retraction when recruited to the plasma membrane, polarizing the cell in two opposite directions." However, the cells do not polarize in opposite directions, ie the cells that retract do not protrude in the direction opposite the retraction (or at least that is not shown). Rather a single protein can trigger either protrusion or retraction when recruited to the plasma membrane, depending upon expression levels. 

We thank the reviewer for this remark, and we agree that we had not shown any data supporting a change in polarization. We solved this issue, by showing now in Supplementary Figure 1 the change in areas in both the activated and in the not activated region. The data clearly show that when a protrusion is happening, the cell retracts in the non-activated region. On the other hand, when the cell retracts, a protrusion happens in the other part of the cell, while the total area is staying approximately constant. 

We added the following sentence to describe our new figure:

Quantification of the changes in membrane area in both the activated and non-activated part of the cell (Supp Figure 1B-C) reveals that the whole cell is moving, polarizing in one direction or the other upon optogenetic activation.

While the authors provide extensive quantitative data in this manuscript and quantify the relative differences in expression levels that result in the different phenotypes, it would be helpful to quantify the absolute levels of expression of these GEFs relative to e.g. an endogenously expressed GEF. 

We agree with the reviewer comment, and we also wanted to have an idea of the absolute level of expression of GEFs present in these cells to be able to relate fluorescent intensities with absolute concentrations. We tried different methods, especially with the purified fluorescent protein, but having exact numbers is a hard task.

We ended up quantifying the amount of fluorescent protein within a stable cell line thanks to ELISA and comparing it with the mean fluorescence seen under the microscope. 

We estimated that the switch concentration was around 200nM, which is 8 times more than the mean endogenous concentration according to https://opencell.czbiohub.org/, but should be reachable locally in wild type cell, or globally in mutated cancer cells. 

Given the numerical data (mostly) in hand, it would be interesting to determine whether RhoGEF2 levels, cell area, the pattern of actin assembly, or some other property is most predictive of the response to PRG DHPH recruitment. 

We think that the manuscript made it clear that the concentration of PRG DHPH is almost 100% predictive of the response to PRG DHPH. We believe that other phenotypes such as the cell area or the pattern of actin assembly would only be consequences of this. Interestingly, as experimentators we were absolutely not able to predict the behavior by only seeing the shape of the cell, event after hundreds of activation experiments, and we tried to find characteristics that would distinguish both populations with the data in our hands and could not find any.

There is some room for general improvement/editing of the text. 

We tried our best to improve the text, following reviewers suggestions.

Author response:

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

We are thankful to the reviewers and the editor for their detailed feedback, insightful suggestions, and thoughtful assessment of our work. The revised manuscript has taken into account all the comments of the three reviewers. We have also undertaken additional analyses and added materials in response to reviewer suggestions. In brief:

(1) We have conducted a more in-depth analysis of frequency domain HRV metrics to better depict the change of autonomic tone.

(2) We have revised the manuscript to provide justifications for the chosen taVNS protocol and to clearly articulate the objectives of the current study.

(3) In response to comments from reviewer #2, we have included two new tables that present the absolute changes in cardiovascular metrics, clinical characteristics for the two trial arms, and effects of taVNS adjusted for age.

Other significant amendments include:

(1) An expanded discussion linking our findings to the existing literature on the effects of taVNS on cardiovascular function, biomarkers for taVNS response, the safety of taVNS, and the dose-response relationship of taVNS.

(2) Revision to the Method section to provide details of QT interval calculation.

Reviewer #1 (Public Review):

The authors report the results of a randomized clinical trial of taVNS as a neuromodulation technique in SAH patients. They found that taVNS appears to be safe without inducing bradycardia or QT prolongation. taVNS also increased parasympathetic activity, as assessed by heart rate variability measures. Acute elevation in heart rate might be a biomarker to identify SAH patients who are likely to respond favorably to taVNS treatment. The latter is very important in light of the need for acute biomarkers of response to neuromodulation treatments.

Comments:

(1) Frequency domain heart rate variability measures should be analyzed and reported. Given the short duration of the ECG recording, the frequency domain may more accurately reflect autonomic tone.

We sincerely appreciate this encouraging summary of our paper. We have analyzed and reported frequency-domain heart rate variability measures, including the relative power of the high-frequency band (0.15–0.4 Hz) and the relative power of the low-frequency band (0.04 – 0.15). We showed the distribution of the two frequency-domain HRV measures in supplementary Figure 2C-D. For 24-hour ECG recording, we found that the change in the relative high-frequency power from Day 1 was not significantly different between the treatment groups. As both high-frequency band and low-frequency band power are relative to the total power, the comparison of the relative power of the low-frequency band between groups would be the opposite of the relative power of the high-frequency band. As both time-domain and frequency-domain HRV measures can reflect the autonomic tone, we performed factor analysis to identify the parasympathetic activity component (Figure 2D). Comparing the change in parasympathetic activity component and relative high-frequency power, we observed similarities and discrepancies. Specifically, both the change in parasympathetic activity component and the change in relative high-frequency power were higher in the taVNS group at the early phase (Day 2 - 4).

We also observed higher high-frequency power in the Sham group at the later phase. If the factor analysis successfully isolates the parasympathetic activity, there should be other factors than the parasympathetic activity affecting the relative power of the high-frequency band. One such factor is the respiration rate. The high-frequency range is between 0.15 to 0.4 Hz, corresponding to respiration's frequency range of approximately 9 to 24 breaths per minute. If the respiration rate increases and exceeds 24 breaths per minute, the respiratory-driven HRV might occur at a frequency higher than the typical high-frequency band. Given that the respiration rate was higher in the taVNS treatment group, a compensatory mechanism to ensure oxygen delivery (Figure 4E), we hypothesized that observed lower high-frequency power in the taVNS treatment group compared to sham at later phases is a result of increased respiration rate in the taVNS treatment group. Indeed, we found the normalized high-frequency power is higher when RR is less than 25 bpm compared to when RR > 25 bpm (Cohen’s d = 0.85, Supplementary Figure 3A). Moreover, an increase in RR in the taVNS treatment group is associated with a decrease in high-frequency power (Supplementary Figure 3B). These control analyses underscored the necessity of performing factor analysis to robustly measure parasympathetic activities and confirm that taVNS treatment mitigated the sympathetic overactivation during the early phase.

We have now discussed the results of frequency-domain HRV measures in the Discussion section: taVNS and autonomic system (p23): “A key metric that reflects this restored sympathovagal balance is the increase in heart rate variability (Figure 3F). Specifically, the factor analysis showed that the parasympathetic activity was significantly higher in the taVNS treatment group. This difference was most pronounced during the early phase, particularly between Days 2 and 4 following SAH. In addition to analyzing the correlation between the parasympathetic activity factor and established HRV measures that reflect parasympathetic activity such as RMSSD and pNNI_50 (Figure 3C), we also examined changes in a frequency-domain HRV measure—the relative power of the high-frequency band (0.15–0.4 Hz)—to validate the accuracy of the factor analysis. the relative power of the high-frequency band is widely used to indicate respiratory sinus arrhythmia, a process primarily driven by the parasympathetic nervous system (Supplementary Figure 2). We found that both the change in parasympathetic activity factor and relative high-frequency power were higher in the taVNS group at the early phase (Day 2 - 4). Conversely, we observed higher high-frequency power in the Sham group during the later phase. If the factor analysis successfully isolates the parasympathetic activity, there should be other factors than the parasympathetic activity affecting the relative power of the high-frequency band. One such factor is the respiration rate. The high-frequency range is between 0.15 to 0.4 Hz, corresponding to respiration's frequency range of approximately 9 to 24 breaths per minute. If the respiration rate increases and exceeds 24 breaths per minute, the respiratory-driven HRV might occur at a frequency higher than the typical high-frequency band. Given that the respiration rate was higher in the taVNS treatment group, a compensatory mechanism to ensure oxygen delivery (Figure 4E), we hypothesized that observed lower high-frequency power in the taVNS treatment group compared to sham at later phases is a result of increased respiration rate in the taVNS treatment group. Indeed, we found the normalized high-frequency power is higher when RR is less than 25 bpm compared to when RR > 25 bpm (Cohen’s d = 0.85, Supplementary Figure 3A). Moreover, an increase in RR in the taVNS treatment group is associated with a decrease in high-frequency power (Supplementary Figure 3B). These control analyses underscored the necessity of performing factor analysis to robustly measure parasympathetic activities and confirm that taVNS treatment mitigated the sympathetic overactivation during the early phase.”

We have also reported the changes in the relative power of the high-frequency band between the two treatment groups in Supplementary Figure 6. We did not find a significant change in relative high-frequency band power between the treatment groups (Treatment – pre-treatment difference: p = 0.74, Cohen’s d = -0.08, N(Sham) = 199, N(taVNS) = 188, Mann-Whitney U test). We reported these results in the Results section: Acute effects of taVNS on cardiovascular function (p18): “There were no significant differences in changes in corrected QT interval or heart rate variability, as measured by RMSSD, SDNN, and relative power of high-frequency band between treatment groups (Figure 5D and E and Supplementary Figure 6).”

How was the "dose" chosen (20 minutes twice daily)?

The choice of a 20-minute taVNS session twice daily was informed by findings from Addorisio et al. (2019), where the authors administered 5-minute taVNS twice daily to patients with rheumatoid arthritis for two days. They found that the circulating c-reactive protein (CRP) levels significantly reduced after two days of treatment but returned to baseline at the second clinical assessment by day 7. Given the high inflammatory state associated with subarachnoid hemorrhage (SAH) and our intention to maintain a steady reduction in inflammation, we extended the duration of taVNS to 20 minutes per session. We have clarified this stimulation schedule's rationale in the Results section (p5-6): “This treatment schedule was informed by findings from Addorisio et al., where a 5-minute taVNS protocol was administered twice daily to patients with rheumatoid arthritis for two days.29 Their study found that circulating c-reactive protein (CRP) levels significantly reduced after 2 days of treatment but returned to baseline at the second clinical assessment by day 7. Given the high inflammatory state associated with SAH and our intention to maintain a steady reduction in inflammation, we decided to extend the treatment duration to 20 minutes per session.”

Addorisio, Meghan E., et al. "Investigational treatment of rheumatoid arthritis with a vibrotactile device applied to the external ear." Bioelectronic Medicine 5 (2019): 1-11.

The use of an acute biomarker of response is very important. A bimodal response to taVNS has been previously shown in patients with atrial fibrillation (Kulkarni et al. JAHA 2021).

Thank you for this valuable insight and for bringing the study by Kulkarni et al. to our attention. Their study showed that the response to Low-Level Tragus Stimulation (LLTS) varied among patients with atrial fibrillation, which can be predicted by acute P-wave alternans (PWA) to some degree. We have discussed the implication of the bimodal response to taVNS in the Discussion section (p26-27): “Kulkarni et al. showed that the response to low-level tragus stimulation (LLTS) varied among patients with atrial fibrillation.49 Similarly, in our study, not all patients in the taVNS treatment group showed a reduction in mRS scores (improved degree of disability or dependence). This differential response may be inherent to taVNS and potentially influenced by factors such as anatomical variations in the distribution of the vagus nerve at the outer ear. These findings underscore the importance of using acute biomarkers to guide patient selection and optimize stimulation parameters. Furthermore, we found that increased heart rate was a potential acute biomarker for identifying SAH patients who are most likely to respond favorably to taVNS treatment. Translating this finding into clinical practice will require further research to elucidate the mechanisms by which an acute increase in heart rate may predict the outcomes of patients receiving taVNS, including its relationship with neurological evaluations, vasospasm, echocardiography, and inflammatory markers.”

Reviewer #2 (Public Review):

Summary:

This study investigated the effects of transcutaneous auricular vagus nerve stimulation (taVNS) on cardiovascular dynamics in subarachnoid hemorrhage (SAH) patients. The researchers conducted a randomized clinical trial with 24 SAH patients, comparing taVNS treatment to a Sham treatment group (20 minutes per day twice a day during the ICU stay). They monitored electrocardiogram (ECG) readings and vital signs to assess acute as well as middle-term changes in heart rate, heart rate variability, QT interval, and blood pressure between the two groups. The results showed that repetitive taVNS did not significantly alter heart rate, corrected QT interval, blood pressure, or intracranial pressure. However, it increased overall heart rate variability and parasympathetic activity after 5-10 days of treatment compared to the sham treatment. Acute taVNS led to an increase in heart rate, blood pressure, and peripheral perfusion index without affecting corrected QT interval, intracranial pressure, or heart rate variability. The acute post-treatment elevation in heart rate was more pronounced in patients who showed clinical improvement. In conclusion, the study found that taVNS treatment did not cause adverse cardiovascular effects, suggesting it is a safe immunomodulatory treatment for SAH patients. The mild acute increase in heart rate post-treatment could potentially serve as a biomarker for identifying SAH patients who may benefit more from taVNS therapy.

Strengths:

The paper is overall well written, and the topic is of great interest. The methods are solid and the presented data are convincing.

Weaknesses:

(1) It should be clearly pointed out that the current paper is part of the NAVSaH trial (NCT04557618) and presents one of the secondary outcomes of that study while the declared first outcomes (change in the inflammatory cytokine TNF-α in plasma and cerebrospinal fluid between day 1 and day 13, rate of radiographic vasospasm, and rate of requirement for long-term CSF diversion via a ventricular shunt) are available as a pre-print and currently under review (doi: 10.1101/2024.04.29.24306598.). The authors should better stress this point as well as the potential association of the primary with the secondary outcomes.

Thank you for this valuable suggestion. The current study indeed focuses on the trial’s secondary outcomes. The main objective is to evaluate the cardiovascular safety of the taVNS protocol and to provide insights that will inform the application of taVNS in SAH patients. Following your comments, we have clarified this in the Introduction section (p6): “The current study is part of the NAVSaH trial (NCT04557618) and focuses on the trial’s secondary outcomes, including heart rate, QT interval, HRV, and blood pressure.32 This interim analysis aims to evaluate the cardiovascular safety of the taVNS protocol and to provide insights that will inform the application of taVNS in SAH patients. The primary outcomes of this trial, including change in the inflammatory cytokine TNF-α and rate of radiographic vasospasm, are available as a pre-print and currently under review.26”

The negative association between HRV and inflammatory cytokines has been reported in numerous studies such as (Williams et al., Brain, Behavior, and Immunity, 2019; Haensel et al., Psychoneuroendocrinology. 2008). There are some studies suggesting that increased sympathetic tone following SAH is associated with vasospasm (Bjerkne Wenneberg, S. et al., Acta Anaesthesiologica Scandinavica. 2020; Megjhani et al., Neurocrit Care. 2020). Based on the literature, we compared the effects of taVNS on primary and secondary outcomes. The findings from the two parallel analyses are consistent: taVNS treatment reduced pro-inflammatory cytokines and increased HRV. Furthermore, the analyses of the primary outcomes revealed a reduction in the presence of any radiographic vasospasm in the taVNS treatment group compared to the sham. We have now integrated these findings and discussed them in the Discussion section (p25-26): “Given the negative association between pro-inflammatory markers and HRV, our finding that HRV was higher in the taVNS treatment group aligns with the findings of primary outcomes of this clinical trial, which showed that taVNS treatment reduced pro-inflammatory cytokines, including tumor necrosis factor-alpha (TNF-α) and interleukin-6.26,52 The consistency between these findings strengthens the evidence supporting the anti-inflammatory effects of taVNS. In addition, the sympathetic predominance following SAH is implicated in an increased risk of delayed cerebral vasospasm, which is most commonly detected 5-7 days after SAH.12 Given that taVNS treatment mitigated the sympathetic overactivation before the typical onset of cerebral vasospasm, it could potentially reduce the severity of this complication.”

(2) The references should be implemented particularly concerning other relevant papers (including reviews and meta-analysis) of taVNS safety, particularly from a cardiovascular standpoint, such as doi: 10.1038/s41598-022-25864-1 and doi: 10.3389/fnins.2023.1227858).

Thank you for providing the relevant papers. We have provided these references in the Introduction section to provide a more comprehensive background of our study (p6): “While some animal studies have reported a potential risk of bradycardia and decreased blood pressure associated with vagus nerve stimulation, two reviews of human studies have considered the cardiovascular effects of taVNS generally safe, with adverse effects reported only in patients with pre-existing heart diseases. 21,22,23

(3) The dose-response issue that affects both VNS and taVNS applications in different settings should be mentioned (doi: 10.1093/eurheartjsupp/suac036.) as well as the need for more dose-finding preclinical as well as clinical studies in different settings (the best stimulation protocol is likely to be disease-specific).

Overall, the present work has the important potential to further promote the usage of taVNS even on critically ill patients and might set the basis for future randomized studies in this setting

Thank you for this valuable insight. Scientific understanding of the dose-response relationship and determining optimal parameters tailored to specific disease contexts has been recognized as an important part of taVNS research and, more generally, in the electrical neuromodulation field. Studies in this direction are often complex and time-intensive due to the multitude of possible parameter combinations. As such, most taVNS studies opted to use parameters that have been established in previous studies. For example, 20 Hz taVNS is extensively used as a therapeutic intervention in stroke (Matyas Jelinek ,2024, https://www.sciencedirect.com/science/article/pii/S0014488623003138). As we pioneer the application of taVNS as an immunomodulation technique in SAH patients, we also adopt parameters reported in similar studies, aiming to provide a basis for future preclinical and clinical studies of taVNS in this patient population. As you noted, the effects of taVNS are dose-dependent, necessitating systematic exploration of the parameter space, including frequency, intensity, and duration. Our findings of the acute biomarker (heart rate) hold the promise of close-loop taVNS. We have now emphasized the importance of investigating how parameters/dose affect taVNS’s effects on immune function and cardiovascular function in SAH patients (p28): “As we pioneer the application of taVNS as an immunomodulation technique in SAH patients, we adopt parameters (20 Hz, 0.4 mA) reported in similar studies.55 The current study provides a basis for future preclinical and clinical studies of taVNS in this patient population. To build on our findings, a systematic evaluation of the relationship between parameters such as frequency, intensity, and duration and taVNS’s effects on the immune system and cardiovascular function is necessary to establish taVNS as an effective therapeutic option for SAH patients.56”

Reviewer #2 (Recommendations For The Authors):

The paper is overall well written, and the topic is of great interest. The reviewer has some major comments:

(1) It should be clearly pointed out that the current paper is part of the NAVSaH trial and presents one of the secondary outcomes of that study while the declared first outcomes (change in the inflammatory cytokine TNF-α in plasma and cerebrospinal fluid between day 1 and day 13, rate of radiographic vasospasm, and rate of the requirement for long-term CSF diversion via a ventricular shunt) are available as a pre-print and currently under review (doi: 10.1101/2024.04.29.24306598.).

We have revised the manuscript following your comment. Please see comment Reviewer 2 Public Review and our response.

The authors should assess the relationship between the impact of taVNS on inflammatory markers in plasma and in cerebrospinal fluid and the autonomic responses. The association between inflammatory markers and noninvasive autonomic markers as well as sympathovagal balance should also be assessed. Specifically, the authors should try to assess whether the acute post-treatment elevation in heart rate was more pronounced in patients who experienced a more pronounced reduction in inflammatory biomarkers. Indeed, since all patients in the current study received the same dose of taVNS (20 Hz frequency, 250 μs pulse width, and 0.4 mA intensity), while in several cardiovascular studies (doi: 10.1016/j.jacep.2019.11.008, doi: 10.1007/s10286-023-00997-z) the intensity (amplitude) of taVNS was differentially set based on the subjective pain/sensory threshold, that might be a marker of acute afferent neuronal engagement.

We agree that analyzing the change in cardiovascular metrics and changes in inflammatory markers is an important next step. In particular, testing whether the acute elevation in heart rate correlates with changes in inflammatory markers could further establish heart rate as a biomarker to guide patient selection and optimize stimulation parameters. (Please refer to comment 1.3 and our responses). However, in this paper, the primary objective is the cardiovascular safety of the current taVNS protocol in SAH patients. This association between inflammatory markers and autonomic responses extends beyond the scope of the current manuscript and would be more appropriately addressed in a separate publication.

Previous literature has shown a negative association between HRV and inflammatory markers in SAH patients (for example, Adam, J., 2023). It is reasonable to postulate that taVNS modulates the immune system and the autonomic system synergistically. We found that parasympathetic tone was higher in the taVNS treatment group, with the most notable differences observed between Days 2 and 4 following SAH (Figure 3F). In a separate study of the primary outcomes of this trial (Huguenard et al., 2024), serum levels of IL-6 (pro-inflammation cytokine) were also significantly lower in the taVNS treatment group on Day 4 (Figure 3A, in our preprint, https://doi.org/10.1101/2024.04.29.24306598).

We appreciate your input regarding the potential mechanism behind acute heart rate changes. In this trial, all patients who were able to engage in verbal communication were asked if they felt any prickling or pain during all sessions. We confirmed that the current stimulation setting was sub-perception in all trialed patients, making it unlikely that the observed heart rate increase was due to pain or sensory perception. Our current hypothesis is that successful activation of the afferent vagal pathway by taVNS increased arousal, resulting in increased heart rate. We have revised the Discussion section based on your insight (p29): “All patients who were capable of verbal communication were asked if they felt any prickling or pain during all sessions. We confirmed that the current taVNS protocol is below the perception threshold for all trialed patients. Altogether, successful activation of the afferent vagal pathway by taVNS increased arousal, resulting in increased heart rate.50,51”

Huguenard, A. L. et al. Auricular Vagus Nerve Stimulation Mitigates Inflammation and Vasospasm in Subarachnoid Hemorrhage: A Randomized Trial. (2024) doi:10.1101/2024.04.29.24306598.

Adam, J., Rupprecht, S., Künstler, E. C. S. & Hoyer, D. Heart rate variability as a marker and predictor of inflammation, nosocomial infection, and sepsis – A systematic review. Autonomic Neuroscience vol. 249 103116 (2023).

A new table should be provided with the mean (or median) values of the two arms of the population (taVNS and sham) including baseline clinical characteristics, comorbidities (mean age, % of female, % with known hypertension, diabetes, etc), ongoing medications (% on beta-betablockers, etc), and pre, during and post-treatment absolute values (expressed as mean or median depending on the distribution) of the studied parameters (QT and QTc absolute values, heart rate, SDNN, etc) in order for the reader to have a better understanding of how SAH affects these parameters. Absolute changes in the abovementioned parameters should also be presented in the table. For instance, the reported absolute increase in heart rate, based on Figure 5, panel C, seems very modest, below 2 bpm. This is very important to underlying for several reasons, including the fact that the evaluation of the impact of treatment on heart rate variability as assessed in the time domain might be influenced by concomitant changes in heart rate due to the nonlinearity of neural modulation of sinus node cycle length. Indeed, time-domain indexes of HRV intrinsically increase when heart rate decreases in a nonlinear way, while frequency domain indexes (e.g. the low frequency/high frequency (LF/HF) ratio), appear to be devoid of intrinsic rate-dependency (doi: 10.1016/s0008-6363(01)00240-1).

Thank you for your suggestion. We have added the new table to the manuscript. In this table, we include clinical characteristics, the median of absolute values of cardiovascular metrics from 24-hour ECG recording, and the median absolute changes in these metrics for both arms. We believe that absolute values of cardiovascular metrics from 24-hour ECG recording are more informative about how SAH affects these parameters than metrics for the pre-, during-, and post-treatment periods.

In Result (p7), we have added: “Supplementary Table 3 shows the clinical characteristics of the two treatment groups.” In Result, Acute effect of taVNS on cardiovascular function (p20), we have added: “Supplementary Table 3 summarizes the absolute changes in cardiovascular metrics for the treatment groups.”

Thank you for raising the concern about HRV and providing the reference. We have now reported frequency domain indexes in our results: relative power of high-frequency power, which is negatively correlated with the LF/HF ratio. The high-frequency power is used to capture sinus arrhythmia, reflecting the parasympathetic modulation of the heart. Although the frequency domain metrics might be less susceptible to the rate-dependency (doi: 10.1016/s0008-6363(01)00240-1), there are circumstances when the frequency domain metrics might not accurately reflect the autonomic tone (Please see Reviewer 1 Publice Review and our responses).

An attempt to correct the effect of taVNS on the evaluated autonomic parameters according to age should be provided, considering that there were no age limits and parasympathetic indexes, particularly at the sinus node level, are known to decrease with age, particularly for those older than 65 years.

Thank you for the suggestion. We were aware of the influence of age on cardiac heart rate and heart rate variability. In our initial analysis, we compared the change in autonomic parameters from day 1 within each subject across the two treatment groups. This approach controls for individual differences, including those due to age. In addition to your comment, age is a risk factor for subarachnoid hemorrhage. Older individuals often face an increased risk of poor outcomes. To further verify if age influences autonomic changes following SAH, we performed ANCOVA on autonomic function parameters with age included as a covariate. This analysis showed that age was negatively correlated with changes in heart rate, SDNN, and RMSSD from Day 1, but not with changes in QT intervals. After adjusting for age, we found that RMSSD changes and SDNN changes were significantly higher in the taVNS treatment group, while QTc changes were significantly lower in this group. These results align with the main findings (Figures 2 and 3). In addition, autonomic changes following SAH may be influenced by age. Specifically, lower RMSSD and SDNN in older individuals suggest a greater shift toward sympathetic predominance following SAH. We have now reported these results in Supplementary Table 4 and discussed their implication in the Discussion section (p28): “To control for individual differences, including those due to age, our study compared the change in cardiovascular parameters from Day 1 within each subject across treatment groups. To further verify if age influences autonomic changes following SAH, we performed ANCOVA on autonomic function parameters with age included as a covariate. This analysis showed that age was negatively correlated with changes in heart rate, SDNN, and RMSSD from Day 1 but not with changes in QT intervals. After adjusting for age, we found that RMSSD changes and SDNN changes were significantly higher, while QTc changes were significantly lower in the taVNS treatment group (Supplementary Table 4). These results align with the conclusion that repetitive taVNS treatment increased HRV and was unlikely to cause bradycardia or QT prolongation. In addition, autonomic changes following SAH may be influenced by age. Specifically, lower RMSSD and SDNN in older individuals suggest a greater shift toward sympathetic predominance following SAH (Supplementary Table 4).”

The results of the current study should be discussed considering what was previously demonstrated concerning the cardiovascular effects of taVNS (doi: 10.3389/fnins.2023.1227858).

We appreciate the suggestion to consider previous findings on the cardiovascular effects of taVNS. However, it is important to note that most studies investigating the cardiovascular effects of taVNS involve healthy individuals, whereas our study focuses on SAH patients who are critically ill. Given the influence of SAH on cardiovascular parameters, we should be cautious when generalizing our findings to the broader population. Previous studies involving stroke populations have reported cardiovascular parameters descriptively as part of their safety assessments (doi: 10.1155/2020/8841752). Our study is currently the only one systematically investigating the cardiovascular safety of taVNS in SAH patients. Furthermore, the review paper (doi: 10.3389/fnins.2023.1227858) includes a highly heterogeneous mix of studies, such as auricular acupressure, auricular acupuncture, and electrical stimulation applied to different parts of the ear. For the subset of studies involving electrical stimulation, there is considerable variation in the parameters used, with frequencies ranging from 0.5 Hz to 100 Hz, currents from 0.1 mA to 45 mA, and durations spanning from 20 minutes to 168 days. These variations make direct comparisons with our findings challenging.

It looks like QT measurements were performed automatically. It should be specified which method was used for the measurements (threshold, tangent, or superimposed method?).

In our study, QT intervals were measured based on thresholding after wavelet transforming the ECG signals (Martínez, J. P., IEEE Transactions on Biomedical Engineering, 2004, doi: 10.1109/TBME.2003.821031). The local maxima of the wavelet transform correspond to significant changes in the ECG signal, such as the rapid upward or downward deflections associated with the QRS complex. The algorithm searches modulus maxima, that is, peaks of wavelet transform coefficients that exceed specific thresholds, to identify the QRS complex. R peaks are found as the zeros crossing between the positive-negative modulus maxima pair. After localizing the R peak, the Q onset is detected as the beginning of the first modulus maximum before the modulus maximum pair created by the R wave. To identify the T wave, the algorithm searches for local maxima in the absolute wavelet transform in a search window defined relative to the QRS complex. Thresholding is used to identify the offset of the T wave. Please refer to comments 3.4 and 3.5 and our responses for details. We have clarified the method for measuring QT in the Method section (p35): “This algorithm identifies the QRS complex by searching for modulus maxima, which are peaks in the wavelet transform coefficients that exceed specific thresholds. The onset of the QRS complex is determined as the beginning of the first modulus maximum before the modulus maximum pair created by the R wave. To identify the T wave, the algorithm searches for local maxima in the absolute wavelet transform in a search window defined relative to the QRS complex. Thresholding is used to identify the offset of the T wave.”

QTc dispersion was not evaluated, and this should be listed as a limitation of the current study.

We have added this limitation in the Discussion section: Limitations and outlook (p31): “The current study did not explore the effects of taVNS on less commonly used cardiovascular metrics, such as QTc dispersion.”

It has been recently suggested (doi: 10.1016/j.brs.2018.12.510) that QTc, as a potential indirect marker of HRV, might be used as a biomarker for VNS response in the treatment of resistant depression. The author should try to assess whether in the current study baseline QTc before taVNS is associated with outcome and with taVNS response.

Thank you for the suggestion. The conference abstract in the provided doi stated that QTc as an indirect marker of HRV before implantation was correlated with changes in the depression rating scale. The mechanism seems to be that QTc has information about the pathophysiology of the depression (10.1097/YCT.0000000000000684). The current study focused on the comparison between taVNS treatment and sham treatment. Our future study will further test if SAH patients’ response to taVNS can be predicted by baseline QTc.

The dose-response issue that affects both VNS and taVNS in different settings should be mentioned (doi: 10.1093/eurheartjsupp/suac036.) as well as the need for more dose-finding preclinical as well as clinical studies in different settings (the best stimulation protocol is likely to be disease-specific).

Please refer to our responses to comment 3.

Minor Comments

Some typos or commas instead of affirmative points and vice versa.

Thank you for pointing this out. We have carefully proofread the manuscript and made the necessary corrections to ensure proper punctuation and grammar throughout.

Table 1: why age is expressed as a range for each person?

MedRxiv asks authors to remove all identifying information. Precise ages are direct identifiers, as opposed to age ranges. We have now revised the age column to ‘decade of life’ in the updated table. We believe this modification reduces confusion while adhering to MedRxiv’s guidelines.

Although already reported in the study protocol (doi: 10.1101/2024.03.18.24304239), the heart rate limits for inclusion should be reported (sustained bradycardia on arrival with a heart rate < 50 beats per minute for > 5 minutes, implanted pacemaker or another electrical device).

We have now added the specific inclusion and exclusion criteria in the Method details section (p33): “Inclusion criteria were: (1) Patients with SAH confirmed by CT scan; (2) Age > 18; (3) Patients or their legally authorized representative are able to give consent. Exclusion criteria were: (1) Age < 18; (2) Use of immunosuppressive medications; (3) Receiving ongoing cancer therapy; (4) Implanted electrical device; (5) Sustained bradycardia on admission with a heart rate < 50 beats per minute for > 5 minutes; (6) Considered moribund/at risk of imminent death.”

Why did the authors choose a taVNS schedule of two times per day of 30 minutes each as compared for instance to one hour per day? Please comment on that also referring to other taVNS studies in the acute setting such as the one by Dasari T et al (doi: 10.1007/s10286-023-00997-z.) where taVNS was applied for 4 hours twice daily. For instance, Yum Kim et al (doi: 10.1038/s41598-022-25864-1) recently reported in a systematic review and meta-analysis of taVNS, safety, that repeated sessions and sessions lasting 60 min or more were shown to be more likely to lead to adverse events.

The International Consensus-Based Review and Recommendations for Minimum Reporting Standards in Research on Transcutaneous Vagus Nerve Stimulation should be referred to and contextualized (doi: 10.3389/fnhum.2020.568051).

Thank you for raising this question and providing relevant references. We have reviewed the proposed checklist for minimum reporting items in taVNS research (10.3389/fnhum.2020.568051) and have ensured that our manuscript complies with the recommended reporting items.

The current taVNS schedule was based on findings from Addorisio et al. (2019). We have revised the manuscript to clarify the rationale behind the current taVNS protocol. Please refer to our response to comment 1.2. The two studies mentioned in the comments were published after our trial was designed and initiated (https://clinicaltrials.gov/study/NCT04557618). Based on the meta-analysis by Yum Kim et al., the short duration of treatment sessions might explain the cardiovascular safety of the current taVNS protocol. We are also currently assessing the effects of our taVNS protocol on inflammatory markers.

Reviewer #3 (Public Review):

Summary:

The authors aimed to characterize the cardiovascular effects of acute and repetitive taVNS as an index of safety. The authors concluded that taVNS treatment did not induce adverse cardiovascular effects, such as bradycardia or QT prolongation.

Strengths:

This study has the potential to contribute important information about the clinical utility of taVNS as a safe immunomodulatory treatment approach for SAH patients.

Weaknesses:

A number of limitations were identified:

(1) A primary hypothesis should be clearly stated. Even though the authors state the design is a randomized clinical trial, several aspects of the study appear to be exploratory. The method of randomization was not stated. I am assuming it is a forced randomization given the small sample size and approximately equal numbers in each arm.

Thank you for the suggestion. The current study is part of the NAVSaH trial (NCT04557618), aiming to define the effects of taVNS on inflammatory markers, vasospasm, hydrocephalus, and continuous physiology data. This study focuses on the effects of repetitive and acute taVNS on continuous physiology data to evaluate the cardiovascular safety of the current taVNS protocol. The primary hypothesis tested in our study is that repetitive taVNS increased HRV but did not cause bradycardia and QT prolongation. Following your comments, we have clarified this in the Introduction section (p6): “This interim analysis aims to evaluate the cardiovascular safety of the taVNS protocol and to provide insights that will inform the application of taVNS in SAH patients. The primary outcomes of this trial, including change in the inflammatory cytokine TNF-α and rate of radiographic vasospasm, are available as a pre-print and currently under review.26 Based on a meta-analysis, repeated sessions lasting 60 min or more are likely to lead to aversive effects; therefore, we hypothesized that repetitive taVNS increased HRV but did not cause bradycardia and QT prolongation.23”

(2) The authors "first investigated whether taVNS treatment induced bradycardia or QT prolongation, both potential adverse effects of vagus nerve stimulation. This analysis showed no significant differences in heart rate calculated from 24-hour ECG recording between groups." A justification should be provided for why a difference is expected from 20 minutes of taVNS over a period of 24 hours. Acute ECG changes are a concern for increasing arrhythmic risk, for example, due to cardiac electrical restitution properties.

A human study (Clancy, L. A. et al., Brain Stimulation, 2017, https://doi.org/10.1016/j.brs.2014.07.031) has found that 15-min taVNS led to reduced sympathetic activity measured by low-frequency/high-frequency (LF/HF) ratio. The sympathetic activity remained lower than baseline levels during the recovery period, suggesting potential long-term effects of taVNS on cardiovascular function. In addition, the repetitive taVNS treatment in this clinical trial was intended to maintain a steady low-inflammatory state. Given the potential life-threatening implications of bradycardia and QT prolongation in these critically ill patients, we deemed it crucial to evaluate heart rate and QT interval both acutely and from 24-hour ECG monitoring. We have now provided the justification in the Result section (p11): “A study has shown that 15 minutes of taVNS reduced sympathetic activity in healthy individuals, with effects that persist during the recovery period.33 This finding suggests that taVNS may exert long-term effects on cardiovascular function. Therefore, we investigated whether repetitive taVNS treatment affects heart rate and QT interval, key indicators of bradycardia or QT prolongation, using 24-hour ECG recording.”

An additional value of analyzing 24-hour ECG recording is that we can detect bradycardia or QT prolongation that happen outside the period of the stimulation, which could caused by repetitive taVNS. To this end, we reanalyzed the data and calculated the percentage of prolonged QT intervals using 500ms criterion (Giudicessi, J. R., Noseworthy, P. A. & Ackerman, M. J. The QT Interval. Circulation, 2019). When comparing the percentage of prolonged QT intervals between the treatment groups, we found that changes in prolonged QT intervals percentage from Day 1 were higher in the Sham group (Figure 3F, Mann–Whitney U test, N(taVNS) = 94, N(Sham)=95, p-value < 0.001, Cohen’s d = -0.72). We have now reported the results in the Result section (p11): “To ensure that repetitive taVNS did not lead to QT prolongation happening outside the period of stimulation, we calculated the percentage of prolonged QT intervals. Prolonged QT intervals were defined as corrected QT interval >= 500 ms. We found that changes in prolonged QT intervals percentage from Day 1 were higher in the Sham group (Figure 3F, Mann–Whitney U test, N(taVNS) = 94, N(Sham)=95, p-value < 0.001, Cohen’s d = -0.72).

The concern regarding acute ECG changes related to increased arrhythmic risk is valid. We have improved the reasoning behind analyzing acute ECG change, which now reads (p20): “Assessing the acute effect of taVNS on cardiovascular is crucial for its safe translation into clinical practice. We compared the acute change of heart rate, corrected QT interval, and heart rate variability between treatment groups, as abrupt changes in the pacing cycle may increase the risk of arrhythmias.”

(3) More rigorous evaluation is necessary to support the conclusion that taVNS did not change heart rate, HRV, QTc, etc. For example, shifts in peak frequencies of the high-frequency vs. low-frequency power may be effective at distinguishing the effects of taVNS. Further, compensatory sympathetic responses due to taVNS should be explored by quantifying the changes in the trajectory of these metrics during and following taVNS.

We appreciate your concerns regarding the potential effects on the autonomic system associated with taVNS treatment. We would like to clarify that the primary objective of our study was to evaluate the cardiovascular safety of the taVNS protocol in SAH, with a specific focus on detecting any acute changes in heart rate and QT interval. As you highlighted, such acute ECG changes are a concern for increasing arrhythmic risk. By directly studying the trend of heart rate, HRV, and QT over the acute treatment periods, we found no significant change in these metrics between treatment groups. In addition, these metrics remained within 0.5 standard deviations of their daily fluctuations during and following taVNS treatment (Figure 5 and Supplementary Figure 6). These findings support the conclusion that the current protocol is unlikely to cause cardiac complications.

In response to your suggestion to conduct a more rigorous analysis, particularly concerning peak frequencies within the high-frequency (HF) and low-frequency (LF) bands, we pursued this analysis to explore more nuanced effects of taVNS on the autonomic system. We compared the shifts in peak frequencies within these bands between the treatment groups and found no significant changes that would suggest a sympathetic or parasympathetic shift following acute taVNS.

In detail, we have made the following revisions following your comments:

(1) We have clarified the motivation behind studying the acute change of cardiac metrics following taVNS treatment – monitoring the cardiovascular safety of current taVNS protocol in SAH patients (p18): please refer to response to comment 3.2.

(2) We compared the peak frequencies of the high-frequency and low-frequency bands following taVNS. added the results in the supplementary materials:

We note that neurophysiology underlying peak frequencies has not been thoroughly studied in the literature compared to the LF-band power or HF-band power. Therefore, we report this result as an exploratory analysis.

(3) We have added the changes of QTc during and following taVNS in Figure 5 and showed that they were within 0.5 standard deviations of their daily fluctuations during and following taVNS treatment. We have now shown the changes of HRV during and following taVNS in Supplementary Figure 6 A-D. We added the change of high-frequency power following Reviewer #1’s comment 1.1. Overall, our results suggest that repetitive taVNS increased parasympathetic activities, while there is no evidence that acute taVNS significantly affected heart rate or QT.

(4) The authors do not state how the QT was corrected and at what range of heart rates. Because all forms of corrections are approximations, the actual QT data should be reported along with the corrected QT.

The corrected QT interval (QTc) estimates the QT interval at a standard heart rate of 60 bpm. In practice, we removed RR intervals outside of the 300 – 2000 ms range. Further, we removed ectopic beats, defined as RR intervals differing by more than 20% from the one proceeding. We used the Bazett formula to correct the QT intervals: . We have now clarified how QT was corrected in the Method section – Data processing (p35-36): “R-peaks were detected as local maxima in the QRS complexes. P-waves, T-waves, and QRS waves were delineated based on the wavelet transform (Figure 2A-C).34  RR intervals were preprocessed to exclude outliers, defined as RR intervals greater than 2 s or less than 300 ms. RR intervals with > 20% relative difference to the previous interval were considered ectopic beats and excluded from analyses. After preprocessing, RR intervals were used to calculate heart rate, heart rate variability, and corrected QT (QTc) based on Bazett's formula: .44 The corrected QT interval (QTc) estimates the QT interval at a standard heart rate of 60 bpm.”

We have reported the actual QT data in the Result section (p10 and p 19):” Moreover, changes in corrected QT interval from Day 1 were significantly higher in the Sham group compared to the taVNS group (Figure 3B, Mann–Whitney U test, N(taVNS) = 94, N(Sham)=95, p-value < 0.001, Cohen’s d = -0.57). Similarly, uncorrected QT intervals from Day 1 were higher in the Sham group (Supplementary Figure 10A, Cohen’s d = -0.42).”

“Supplementary Figure 10B-C shows the acute changes in uncorrected QT interval.”

(5) The QT extraction method needs to be more robust. For example, in Figure 2C, the baseline voltage of the ECG is shifting while the threshold appears to be fixed. If indeed the threshold is not dynamic and does not account for baseline fluctuations (e.g., due to impedance changes from respiration), then the measures of the QT intervals were likely inaccurate.

A robust method to estimate the QT interval is essential in our study. To this end, we used the state-of-the-art method to calculate QT intervals. We first applied a 0.5 Hz fifth-order high-pass Butterworth filter and a 60 Hz powerline filter on the ECG recording. The high-pass filtering is used to correct potential baseline fluctuations. Subsequently, a wavelet-based algorithm was used to delineate the QRS complex and T wave (Martínez, J. P., IEEE Transactions on Biomedical Engineering, 2004). In short, this algorithm identifies QRS based on modulus maxima of the wavelet transform of ECG signals. After localizing the R peak, the Q onset is detected as the beginning of the first modulus maximum before the modulus maximum pair created by the R wave. The detection is performed on wavelet transform at a small scale rather than on the original signal, minimizing the effect of baseline shift (see III Detection methods, (5), Cuiwei Li et al., IEEE TBME, 1995, Detection of ECG Characteristic Points Using Wavelet Transforms). T wave is detected similarly based on wavelet transform. Please refer to our response to comment 2.9.

Martínez, J. P., Almeida, R., Olmos, S., Rocha, A. P., & Laguna, P. (2004). A wavelet-based ECG delineator: evaluation on standard databases. IEEE Transactions on Biomedical Engineering, 51(4), 570-581.

In Figure 2C, the purple and green lines take the value of 1 at the QRS onset or the T wave offset; otherwise, 0, which might appear to be a threshold. We have now used verticle lines to denote the detected QRS onsets and T wave offsets. Please see below for a comparison of the annotation:

We have clarified the details of extracting QT intervals from ECG recordings in the Method section (p31): “To calculate cardiac metrics, we first applied a 0.5 Hz fifth-order high-pass Butterworth filter and a 60 Hz powerline filter on ECG data to reduce artifacts. 35 We detected QRS complexes based on the steepness of the absolute gradient of the ECG signal using the Neurokit2 software package.35 R-peaks were detected as local maxima in the QRS complexes. P waves, T waves, and QRS complexes were delineated based on the wavelet transform of the ECG signals proposed by Martinez J. P. et al. (Figure 2A-C).36 This algorithm identifies the QRS complex by searching for modulus maxima, which are peaks in the wavelet transform coefficients that exceed specific thresholds. The onset of the QRS complex is determined as the beginning of the first modulus maximum before the modulus maximum pair created by the R wave. To identify the T wave, the algorithm searches for local maxima in the absolute wavelet transform in a search window defined relative to the QRS complex. Thresholding is used to identify the offset of the T wave.”

We have modified Figure 2C for better clarity:

More statistical rigor is needed. For example, in Figure 2D, the change in heart rate for days 5-7, 8-10, and 11-13 is clearly a bimodal distribution and as such, should not be analyzed as a single distribution. Similarly, Figure 2E also shows a bimodal distribution. Without the QT data, it is unclear whether this is due to the application of the heart rate correction method.

Thank you for raising this concern. Several factors could contribute to the observed distribution of changes in heart rate for days 5-7, 8-10, and 11-13, as shown in Figure 2D. One such factor is the smaller sample size in the later days. The mean duration of hospitalization for the 24 subjects included in this study was 11.29 days, with a standard deviation of 6.43, respectively. Other factors include variations in medical history, SAH pathology, and clinical outcomes during hospitalization. Further analysis revealed that heart rate was lower in patients with improved mRS scores (Supplementary Figure 4B), suggesting that clinical outcomes might impact changes in heart rate. Understanding the association between cardiovascular metrics and clinical assessments, such as vasospasm and inflammation, could help decide whether future taVNS trials should control for these factors when evaluating the effects of taVNS on cardiovascular function. We are currently continuing to recruit SAH patients in this clinical trial, and we plan to perform such analyses in future studies.

In the manuscript, we reported the effect size between the treatment groups for days 5-7, 8-10, and 11-13. This should be interpreted in conjunction with the characteristics of the distribution. To provide a rigorous interpretation of our results, we have now discussed these considerations in the discussion section (p28): “We noticed a high variance of change in heart rate for days 5 – 7, 8 – 10, and 11 – 13 for both treatment groups (Figure 2D). This may be due to the small sample size in the later days, given that the mean duration of hospitalization for the 24 subjects included in this study was 11.3 days with a standard deviation of 6.4. Differences in medical history and clinical outcomes during hospitalization may also explain the variance of change in heart rate for the later days. For example. heart rate was lower in patients with improved mRS scores (Supplementary Figure 4B). Understanding the association between cardiovascular metrics and clinical assessments, such as vasospasm and inflammation, could help decide whether future taVNS trials should control for these factors when evaluating the effects of taVNS on cardiovascular function.”

To test our hypothesis that repetitive taVNS does not induce significant heart rate change, we performed a two-tailed equivalence test of heart rate change between the two treatment groups, including data from days 2-13 (Figure 2D, left panel). To verify the validity of this approach, we calculated the Bimodality Coefficient (BC) and performed the Dip Test for unimodality for the distribution of heart rate change for the two treatment groups. The Bimodality Coefficient (BC) is a measure that combines skewness and kurtosis to assess whether a distribution is bimodal or unimodal. A BC value greater than 0.555 typically indicates a bimodal distribution, whereas a BC value less than or equal to 0.555 suggests an unimodal distribution. The Dip Test is a statistical test that assesses the unimodality of a distribution. A non-significant p-value (p-value ≥ 0.05) indicates that the distribution is likely unimodal. This analysis suggests that the distributions of heart rate changes in both treatment groups (days 2 - 13) are unimodal (BC = 0.457 and p = 0.374 for the taVNS treatment group; BC = 0.421 and p = 0.656 for the sham treatment group). This finding provides justification for our statistical approaches.

Figure 3A shows a number of outliers. A SDNN range of 200 msec should raise concern for a non-sinus rhythm such as arrhythmia or artifact, instead of sinus arrhythmia. Moreover, Figure 3B shows that the Sham RMSSD data distribution is substantially skewed by the presence of at least 3 outliers, resulting in lower RMSSD values compared to taVNS. What types of artifact or arrhythmia discrimination did the authors employ to ensure the reported analysis is on sinus rhythm? The overall results seem to be driven by outliers.

Mild cardiac abnormalities are common in SAH patients. Therefore, change in cardiovascular metrics was expected to differ from healthy individuals, which makes studying the cardiovascular effect on taVNS extremely important in this context. Following your comment, we investigated whether the large SDNN change was due to arrhythmia or artifacts. Except for a single instance where one subject exhibited an SDNN change of 200 ms on a particular day, all other SDNN changes were less than 150 msec. We identified the subject and day associated with the largest SDNN change, which is Day 7. As shown in Author response image 1A and B, SDNN of this subject increased on day 7 while the heart rate (HR) of this subject decreased. Changes in HRV were inversely related to HR changes, suggesting shifts in sympathetic and parasympathetic tone. We checked the ECG recording and the extracted NN intervals (processed RR intervals) on that day. The NN intervals are more variate on day 7 compared to day 1 (Author response image 1C and D). To determine whether the significant variance observed between 5:01 am and 5:02 am was due to arrhythmia or artifacts, we closely examined the corresponding ECG signals (Author response image 1E and F). Based on our analysis, the elevated SDNN is unlikely to be attributed to artifacts.

Author response image 1.

Similarly, we identified the subjects and days corresponding to the most prominent RMSSD decrease in the sham treatment group. We verified the ECG quality for this subject and the accuracy of RR interval identification, and that there was no significant cardiovascular event during the subject’s stay in the ICU. Based on the inclusion and exclusion criteria defined in our protocol (Huguenard A et al.m PLOS ONE, 2024), we did not exclude these data from our analysis.

Huguenard A, Tan G, Johnson G, Adamek M, Coxon A, et al. (2024) Non-invasive Auricular Vagus nerve stimulation for Subarachnoid Hemorrhage (NAVSaH): Protocol for a prospective, triple-blinded, randomized controlled trial. PLOS ONE 19(8): e0301154. https://doi.org/10.1371/journal.pone.0301154

To ensure accurate inferences about sympathetic and parasympathetic tone from these cardiovascular metrics, we have rigorously refined our methodologies, including correcting RR intervals outliers, correcting ectopic peaks, using state-of-art algorithms to identify QRS complex, P wave, and T wave (please refer to response to comment 3.5), and performing factor analysis. In addition, no significant cardiac complications have been reported by the attending physicians for the subjects included in this study. Nonetheless, it is important to note that ECG patterns in patients with SAH differ from those in healthy individuals, potentially impacting the accuracy of R peak identification. For example, one identified R peak (out of 73) was Q peak (F in the above figure). The pathology associated with SAH complicates the precise calculation of cardiovascular metrics and the interpretation of the results. We are committed to continually improving our methodologies for assessing autonomic function in SAH patients. We have now discussed these limitations in the Discussion section (p31-32): “Mild cardiac abnormalities are common in SAH patients5, complicating the precise calculation of cardiovascular metrics from ECG signals and the interpretation of the results. Systematic verification of methods for calculating cardiovascular metrics to ensure their applicability in SAH patients is crucial.”

The above concern will also affect the power analysis, which was reported by authors to have been performed based on the t-test assuming the medium effect size, but the details of sample size calculations were not reported, e.g., X% power, t-test assumed Bonferroni correction in the power analysis, etc.

Thank you for raising this concern. The current study is part of the NAVSaH trial (NCT04557618), focusing on the trial’s secondary outcomes (Please refer to comment 2.1 and our responses). The main objective of this interim analysis is to evaluate the cardiovascular safety of the current taVNS protocol. Goal enrollment for the pilot NAVSaH trial is 50 patients, based on power calculations to detect significant differences in inflammatory cytokines, radiographic vasospasm, and chronic hydrocephalus. The detailed power analysis is described in the protocol (Huguenard A et al.m PLOS ONE, 2024):

“Under a 2-by-2 repeated measures design consisting of two groups of patients, each measured at two time points, our goal is to compare the change across time in the taVNS group to the change across time in the Sham group. Based upon previous work from Koopman et al. [67], we assume our study will observe 1.1 standardized inflammatory cytokines mean change difference between the two groups. Using a two-sided, two-sample t-test, assuming both time points have equal variance and there is a weak correlation (i.e., 0.15) between measurement pairs, a sample size of 25 in each group achieves at least 80% power to detect a standardized difference of 1.1 in mean changes, with a significance level (alpha) of 0.05 [68].

Based on our preliminary data, we assume this study will observe 25% and 55% severe vasospasm in the taVNS and Sham groups, respectively. Under a design with 2 repeated measurements (i.e., 2 raters), assuming a compound symmetry covariance structure with a Rho of 0.2, at a significance level (alpha) of 0.05, a sample size of 25 in each group achieves at least 80% power when the null proportion is 0.55, and the alternative proportion is 0.25 [69–71].

As previously described, LV et al. [8] studied the relationship between cytokine levels and clinical endpoints in SAH, including hydrocephalus. From their outcomes, we predict a needed enrollment of approximately 50 to detect these endpoints. From our own preliminary data, with an incidence of chronic hydrocephalus 0% in treated patients and 28.6% in control (despite grade of hemorrhage), alpha = 0.05 and power = 0.80, the projected sample size to capture that change is approximately 44 patients.”

In this study, we used power analysis to report the achieved power of insignificant findings. For example, a Mann-Whitney U test on heart rate change between the treatment groups revealed no significant differences. We then used power analysis to calculate the achieved power. We have added the details of power analysis in the Method section (p34): “We calculated the achieved power of tests on heart rate change between the treatment groups assuming a medium effect size (Cohen’s d of 0.5) and a Type I error probability (a) of 0.05. Given that the Mann-Whitney U test is a non-parametric counterpart to the t-test and that the asymptotic relative efficiency of the U test relative to the t-test is 0.95 with normal distributions, we estimated the achieved power based on the power of a two-sample t-test, which is 0.93. We have clarified this in the introduction section and in the method section (p6 and p38):

“The current study is part of the NAVSaH trial (NCT04557618) and focuses on the trial’s secondary outcomes, including heart rate, QT interval, HRV, and blood pressure.30 This interim analysis aims to evaluate the cardiovascular safety of the taVNS protocol and to provide insights that will inform the application of taVNS in SAH patients. The primary outcomes of this trial, including change in the inflammatory cytokine TNF-α and rate of radiographic vasospasm, are available as a pre-print and currently under review.24”

“In this study, we reported the statistical power achieved for tests that yielded non-significant results. The achieved power is calculated based on a two-sample t-test assuming a medium effect size (Cohen’s d of 0.5) and a Type I error probability (a) of 0.05.”

If the study was designed to show a cardiovascular effect, I am surprised that N=10 per group was considered to be sufficiently powered given the extensive reports in the literature on how HRV measures (except when pathologically low) vary within individuals. Moreover, HRV measures are especially susceptible to noise, artifacts, and outliers.

If the study was designed to show a lack of cardiovascular effect (as the conclusions and introduction seem to suggest), then a several-fold larger sample size is warranted.

The primary goal of this study is to assess the cardiovascular safety of the current taVNS protocol in SAH patients (please refer to comments 2.1 and 3.8 and our responses). More specifically, we want to assess whether the current taVNS protocol is associated with bradycardia or QT prolongation. The data in this study included ECG signals and vital signals from 24 subjects recruited between 2021 and 2024. The total number of days in the ICU is 271 days, which corresponds to 542 taVNS/sham treatment sessions. These data allow us to detect significant cardiovascular effects of acute taVNS with high power. For example, the comparison of heart rate from pre- to post-treatment sessions between treatment groups had power > 99% (N1 = 188, N2 = 199, assuming 0.05 type I error probability, medium effect size two sample t-test).

To safely conclude that there is no significant cardiovascular effect of repetitive taVNS on any given day following SAH, we would need to perform statistical tests between treatment groups on Day 1, Day 2, and Day N. In this context, 64 subjects per treatment group are required to achieve 80% power assuming medium effect size and 0.05 type I error probability (two-sample t-test). We have acknowledged this limitation in the Discussion section. Thank you for raising this concern!

The results reported in this study treat each day as an independent sample for several reasons. First, heart rate and HRV metrics exhibited great daily variations (Figure in comment 3.7, for example). Their value on one day was not predictive of the metrics on another day, which could be due to medications, interventions, or individualized SAH recovery process during the patient’s stay in the ICU. Second, SAH patients in the ICU often experience rapid/daily changes in clinical status, including fluctuations in intracranial pressure, blood pressure, neurological status, and other vital signs. Also, the recovery process from SAH is highly individualized, with different patients exhibiting distinct trajectories of recovery or complications. Day-to-day cardiovascular function changes varied as the patient recovered or encountered setbacks. Moreover, we verified ECG signal quality, corrected outliers and artifacts in ECG processing, and employed a state-of-the-art QRS delineation method (Please refer to comment 3.5). All these ensure the accuracy of our reported results.

The revised Discussion section now reads (31): ” Our study considers each day as an independent sample for the following considerations: 1. heart rate and HRV metrics exhibited great daily variations. Their value on one day was not predictive of the metrics on another day, which could be due to medications, interventions, or individualized SAH recovery process during the patient’s stay in the ICU. 2. SAH patients in the ICU often experience daily changes in clinical status, including fluctuations in intracranial pressure, blood pressure, neurological status, and other vital signs. 3. Day-to-day cardiovascular function changes varied as the patient recovered or encountered setbacks. To conclusively establish that there is no significant cardiovascular effect of repetitive taVNS on any given day following SAH, we would need to perform statistical tests between treatment groups for each day. In this context, 64 subjects per treatment group are required to achieve 80% power assuming medium effect size and 0.05 type I error probability (two-sample t-test).”

eLife Assessment

The authors provide a solid set of data supporting the safety of transcutaneous auricular vagal nerve stimulation on cardiovascular parameters in the acute setting of critically ill patients presenting with subarachnoid hemorrhage. This important study also suggests a promising effect on autonomic balance.

Reviewer #2 (Public review):

Summary:

This study investigated the effects of transcutaneous auricular vagus nerve stimulation (taVNS) on cardiovascular dynamics in subarachnoid hemorrhage (SAH) patients. The researchers conducted a randomized clinical trial with 24 SAH patients, comparing taVNS treatment to a Sham treatment group (20 minutes per day twice a day during the ICU stay). They monitored electrocardiogram (ECG) readings and vital signs to assess acute as well as middle -term changes in heart rate, heart rate variability, QT interval, and blood pressure between the two groups. The results showed that repetitive taVNS did not significantly alter heart rate, corrected QT interval, blood pressure, or intracranial pressure. However, it increased overall heart rate variability and parasympathetic activity after 5-10 days of treatment compared to the sham treatment. Acute taVNS led to an increase in heart rate, blood pressure, and peripheral perfusion index without affecting corrected QT interval, intracranial pressure, or heart rate variability. The acute post-treatment elevation in heart rate was more pronounced in patients who showed clinical improvement. In conclusion, the study found that taVNS treatment did not cause adverse cardiovascular effects, suggesting it as a safe immunomodulatory treatment for SAH patients. The mild acute increase in heart rate post-treatment could potentially serve as a biomarker for identifying SAH patients who may benefit more from taVNS therapy.

Strengths:

The paper is overall well written, and the topic is of great interest. The methods are solid and the presented data are convincing.

Comments on revisions:

The main previous weaknesses of the paper have now been fixed.

Reviewer #3 (Public review):

Summary:

The authors characterized the cardiovascular effects of acute and repetitive taVNS as an index of safety and concluded that taVNS treatment does not induce adverse cardiovascular effects such as bradycardia or QT prolongation.

Strengths:

This study contributes important information about the clinical utility of taVNS as a safe immunomodulatory treatment approach for SAH patients.

Comments on revised version:

A number of limitations were identified previously: https://elifesciences.org/reviewed-preprints/100088/reviews#peer-review-2. These major concerns were largely addressed by the authors.

Author response:

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

Reviewer #1:

Reviewer #1 was very appreciative of our results and commented “This is a novel result in ferredoxin and a significant contribution to the field”. We are very honored and pleased.

Reviewer #2:

(1) Changing the nomenclature of the models investigated to include the oxidation state being discussed. As they are now (CM, CMNA, etc), multiple re-reads were required to ascertain which redox state was being discussed for a particular model in a given section of the text. Appending "Ox" or "Red" for oxidized or reduced would be sufficient. 

As you indicated there are several nomenclatures to distinguish the model systems in the text. On the other hand, the main issue discussed in the text is the ionization potential (IP), which is calculated by the difference in energies between oxidized and reduced states for each model. In other words, a discussion of the IP value on each model includes both the “Ox” and “Red” energies. In order to clarify the relationship between the nomenclature of models and redox states, we added sentences below.

“Note that the IP value is obtained for each model by calculating both the Ox and Red state energies of the model.” (lines 195-196).

On the other hand, we must specify the charge state when the geometry optimization is performed for CM and CMH models. Therefore, we revised the sentence as follows.

“The decrease in |IP| value indicates that the relative stability of the Red state is suppressed compared with the CMH but is significantly larger than the CM, suggesting the importance of the protonation of Asp64 (Fig. S2B). 

To consider the effect of the structural change caused by the redox on the IP, geometrical optimization of the 4Fe-4S core was performed for the CM (Red) and CMH (Red) models using the same level of theory to the single-point calculations. The optimized Cartesian coordinates are summarized in Table S3. As illustrated in Fig. S2A, the IP values of CM and CMH change from –3.27 to –2.38 eV (|DIP| = 0.89 eV), and from –1.06 to –0.19 eV (|DIP| = 0.87 eV), respectively, before and after the geometrical optimization.” (lines 224-232)

(2) In addition to the very thorough DFT investigation of the different spin and charge combinations, did the authors try a broken-symmetry calculation to obtain the ground state description of the FeS cluster? Given the ubiquity of this approach in other FeS cluster studies, it was surprising that this approach was not taken here. Granted, the DFT investigation of each possible combination is sufficiently thorough and need not be redone. 

Thank you for your comments. A term “spin-unrestricted method”, which is used in the manuscript in the text is synonym of “broken-symmetry method”. In order to emphasize this, we revised the manuscript as follows. 

“All calculations were performed by using the spin-unrestricted (broken-symmetry) hybrid DFT method with the B3LYP functional set. As the basis set, 6-31G* and 6-31+G* were used for [Fe, C, N, O, H] and [S] atoms, respectively, for the IP calculations.” (Line 451)

(3) Line 161 "an" to "a" 

We corrected the mistake. Thank you so much. (Line 161)

(4) Figure 4A seems a bit odd. Why do the traces eclipse the y-axis? And the traces between 330 and 370 nm are much noisier and appear thicker than the rest of the plot. Is this an issue with the monochromator grating used in wavelength selection? Reducing the thickness of the individual traces may help the data presentation in this figure. Also, the arrows on the plot have an opaque white background. Can this be removed so that the arrows do not eclipse the traces in the plot? 

The spectrum in the Fig.4A seemed to be odd. The spectral figure has been revised to improve its appearance. (We have also corrected E53A in Figure 5B.) This reviewer also pointed out that “the traces between 330 and 370 nm are much noisier”. We are struggling with the noise caused by the grating (or the motor malfunction) of the monochromator as you pointed out. Once the monochromator is repaired and a smooth spectrum is obtained, we will upload further revisions.

(5) Figure S9 is a very nice schematic illustrating the general findings of the study. Can this be moved to the main text?

Thank you for your helpful comment. Accordingly, the Fig.9S and its legend are moved to the main text. (Lines 675-680)

eLife Assessment

Ferredoxins are ubiquitous electron transfer proteins that drive essential metabolic processes across all domains of life. This fundamental contribution to the field provides the first description of how specific amino acids, though a series of hydrogen bonds, control the ability of iron-sulfur clusters in ferrodoxins to accept and donate electrons. The evidence supporting the conclusions is compelling as is the combined use of neutron crystallography with X-ray crystallography and classical spectral/redox studies.

Reviewer #1 (Public review):

Summary:

The authors introduced neutron crystallography coupled with room temperature X-ray crystallography to exam the redox properties of the BtFt [4Fe-4S] cluster expressed in E. coli. Neutron structure allowed the authors to exam the influence of Asp64 on the redox properties of the [4Fe-4S] cluster. The neutron structure also allowed for the identification of the hydrogen network around the [4Fe-4S] structure. This work was followed by density functional theory calculation to examine different redox states which also pointed to the role of Asp64 in affecting or dictating redox function of the [4Fe-4S] cluster. Based on the DFT work the authors examine the redox properties under oxic and anoxic conditions in wild type enzymes and in a D64N mutant again showing the role of Asp64 on the redox kinetics and redox potential of the [4Fe-4S] cluster. Lastly, the authors examined similar [4Fe-4S] ferredoxins from several organisms and with a Asp64 or Glu64 observed a similar role of Asp64 on the low potential state of the [4Fe-4S] cluster. The major conclusion of the study was to identify the role of specific amino acids, in this case Asp64, in controlling the redox state and kinetics of [4Fe-4S] clusters. The authors also demonstrate the strength of neutron crystallography when combined with classical X-ray crystallography and classical spectral/redox studies.

Strengths:

In general, the experimental design is logical and the results are convincing demonstrating the role of Asp64 on the redox properties of [4Fe-4S] clusters in ferredoxins.

Weaknesses:

The role(s) of coordinating amino acids on the redox properties of a functional group is not surprising, this reviewer believes this is a novel result in ferredoxins and does make a nice contribution to the field.

Reviewer #2 (Public review):

In this study, Wada et al. investigate the low potential ferredoxin from Bacillus thermoproteolyticus (BtFd) using a combination of neutron crystallography, x-ray crystallography, DFT and spectroscopy to determine the influence of hydrogen bonding networks on the redox potential of ferredoxin's 4Fe-4S cluster. The use of neutron diffraction allowed the authors to probe the precise location of hydrogens around the 4Fe-4S cluster, which was not possible from prior studies, even with the previously reported high-resolution (0.92 Å) structure of BtFd. This allowed the authors to revise prior models of the proposed H bonding network theorized from earlier x-ray crystallography studies ( for example, showing that there is not in fact a H bond formed between the Thr63-O𝛾1 and the [4Fe-4S]-S4 atoms). With this newly described H-bonding network established, the electronic structure of the 4Fe-4S cluster was then investigated using DFT methodology, revealing a startling role of the deprotonated surface residue Asp64, which bears substantial electronic density in the LUMO which is otherwise localized to the 4Fe-4S cluster. While aspartate is usually deprotonated at physiological pH, the authors provide compelling evidence that this aspartate has a much higher pKa than is usual, and is able to act as a protonation-dependent switch which controls the stability of the reduced state of the 4Fe-4S cluster, and thus the redox potential.

The findings of this study and the conclusions drawn from them are well supported by the data and computational work. Their findings have implications for similar control mechanisms in other, non-ferredoxin 4Fe-4S bearing electron transport proteins which have yet to be explored, providing great value to the metalloprotein community. One change that the authors may consider to enhance the clarity of the manuscript regards the nomenclature used for the varying models discussed (CM, CMNA, CMH and so forth). It would be beneficial to the reader if the nomenclature included the redox state (ox. vs red.) of the model in the model's name.

Comments on revisions:

I'm satisfied with their revisions, it looks good to me.

eLife Assessment

This manuscript presents important finding regarding the regulation of a key stem cell population, namely muscle stem cells (or "satellite cells"). The evidence presented is convincing that Scx, a marker for tendon, is expressed in some myogenic cells and is essential for adult muscle regeneration.

Reviewer #1 (Public review):

This manuscript by Bai et al concerns the expression of Scleraxis (Scx) by muscle satellite cells (SCs) and the role of that gene in regenerative myogenesis. The authors report the expression of this gene associated with tendon development in satellite cells. Genetic deletion of Scx in SCs impairs muscle regeneration, and the authors provide evidence that SCs deficient in Scx are impaired in terms of population growth and cellular differentiation. Overall, this report provides evidence of the role of this gene, unexpectedly, in SC function and adult regenerative myogenesis.

There are a few points of concern.

(1) From the data in Figure 1, it appears that all of the SCs, assessed both in vitro and in vivo, express Scx. The authors refer to a scRNA-seq dataset from their lab and one report from mdx mouse muscle that also reveal this unexpected gene expression pattern. Has this been observed in many other scRNA-seq datasets? If not, it would be important to discuss potential explanations as to why this has not been reported previously.

(2) A major point of the paper, as illustrated in Fig. 3, is that Scx-neg SCs fail to produce normal myofibers and renewed SCs following injury/regeneration. They mention in the text that there was no increased PCD by Caspase staining at 5 DPI. A failure of cell survival during the process of SC activation, proliferation, and cell fate determination (differentiation versus self-renewal) would explain most of the in vivo data. As such, this conclusion that would seem to warrant a more detailed analysis in terms of at least one or two other time points and an independent method for detecting dead/dying cells (the in vitro data in Fig. 4F is also based on assessment of activated Caspase to assess cell death). The in vitro data presented later in Fig. S4G,H do suggest an increase in cell loss during proliferative expansion of Scx-neg SCs. To what extent does cell loss (by whatever mechanism of cell death) explain both the in vivo findings of impaired regeneration and even the in vitro studies showing slower population expansion in the absence of Scx?

(3) I'm not sure I understand the description of the data or the conclusions in the section titled "Basement membrane-myofiber interaction in control and Scx cKO mice". Is there something specific to the regeneration from Scx-neg myogenic progenitors, or would these findings be expected in any experimental condition in which myogenesis was significantly delayed, with much smaller fibers in the experimental group at 5 DPI?

(4) The data presented in Fig. 4B showing differences in the purity of SC populations isolated by FACS depending on the reporter used are interesting and important for the field. The authors offer the explanation of exosomal transfer of Tdt from SCs to non-SCs. The data are consistent with this explanation, but no data are presented to support this. Are there any other explanations that the authors have considered and that could be readily tested?

(5) The Cut&Run data of Fig. 6 certainly provide evidence of direct Scx targets, especially since the authors used a novel knock-in strain for analyses. The enrichment of E-box motifs provides support for the 207 intersecting genes (scRNA-seq and Cut&Run) being direct targets. However, the rationale elaborated in the final paragraph of the Results section proposing how 4 of these genes account for the phenotypes on the Scx-neg cells and tissues is just speculation, however reasonable. These are not data, and these considerations would be more appropriate in the Discussion in the absence of any validation studies.

Comments on revisions:

The authors have adequately addressed all of the concerns I raised regarding the original submission. I have no further issues to be addressed.

Reviewer #2 (Public review):

Summary:

Scx is a well-established marker for tenocytes, but the expression in myogenic-lineage cells was unexplored. In this study, the authors performed lineage-trace and scRNA-seq analyses and demonstrated that Scx is expressed in activated SCs. Further, the authors showed that Scx is essential for muscle regeneration using conditional KO mice and identified the target genes of Scx in myogenic cells, which differ from those of tendons.

Strengths:

Sometimes, lineage-trace experiments cause mis-expression and do not reflect the endogenous expression of the target gene. In this study, the authors carefully analyzed the unexpected expression of Scx in myogenic cells using some mouse lines and scRNA-seq data.

Weaknesses:

Scx protein expression has not been verified.

Comments on revisions:

The authors sincerely addressed all concerns, excluding the protein expression of Scx. There is convincing evidence from other experiments that indirectly indicate the protein expression of Scx. In addition, the importance of this study is solid. So, this reviewer doesn't require the authors to make more revisions.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

This manuscript by Bai et al concerns the expression of Scleraxis (Scx) by muscle satellite cells (SCs) and the role of that gene in regenerative myogenesis. The authors report the expression of this gene associated with tendon development in satellite cells. Genetic deletion of Scx in SCs impairs muscle regeneration, and the authors provide evidence that SCs deficient in Scx are impaired in terms of population growth and cellular differentiation. Overall, this report provides evidence of the role of this gene, unexpectedly, in SC function and adult regenerative myogenesis.

We appreciate the comments and thank her/him for the support.

There are a few minor points of concern.

(1) From the data in Figure 1, it appears that all of the SCs, assessed both in vitro and in vivo, express Scx. The authors refer to a scRNA-seq dataset from their lab and one report from mdx mouse muscle that also reveals this unexpected gene expression pattern. Has this been observed in many other scRNA-seq datasets? If not, it would be important to discuss potential explanations as to why this has not been reported previously.

Thanks for this question regarding data in Fig.1. We did initially use immunofluorescence staining of Pax7 and GFP on muscle sections and primary myoblast cultures prepared from Tg-ScxGFP mice to conclude that Scx was expressed in satellite cells (SCs). In addition to the cited mdx RNA-seq data, we have included a re-analysis of a published scRNA-seq data set in Fig.2E (Dell'Orso et al., Development, 2019), and our own scRNA-seq data (Fig.S5D, F). We have now re-examined an additional scRNA-seq data set of TA muscles at various regeneration time points (De Micheli et al., Cell Rep. 2020), in which Scx expression was detected in MuSC progenitors and mature muscle cells. We have added the De Micheli et al. reference and the re-analysis of that scRNA-seq data set for Scx expression as an additional panel in Fig. 2E, with accompanying text (p. 7, ln. 4-6). Thus, our immunostaining results are consistent with scRNA-seq data from our and two other independent scRNA-seq data sets.

We think that Scx expression in the adult myogenic lineage was not previously reported mainly because its expression level was low, and might be dismissed as spurious detection. Additionally, detecting such low expression levels requires sophisticated detection methods with high capture efficiency. Previous studies have noted limitations in transcript capture or transcription factor dropout in 10x Genomics-based datasets (Lambert et al., Cell, 2018; Pokhilko et al., Genome Res., 2021). The most likely and straightforward reason is that Scx was simply not a focus in prior studies amid so many other genes of interest. We have now added this last explanation in the text (p.7, ln. 8-9), following the re-analyses of Scx expression in published scRNA-seq data sets.

(2) A major point of the paper, as illustrated in Fig. 3, is that Scx-neg SCs fail to produce normal myofibers and renewed SCs following injury/regeneration. They mention in the text that there was no increased PCD by Caspase staining at 5 DPI. A failure of cell survival during the process of SC activation, proliferation, and cell fate determination (differentiation versus self-renewal) would explain most of the in vivo data. As such, this conclusion would seem to warrant a more detailed analysis in terms of at least one or two other time points and an independent method for detecting dead/dying cells (the in vitro data in Fig. 4F is also based on an assessment of activated Caspase to assess cell death). The in vitro data presented later in Fig. S4G, H do suggest an increase in cell loss during proliferative expansion of Scx-neg SCs. To what extent does cell loss (by whatever mechanism of cell death) explain both the in vivo findings of impaired regeneration and even the in vitro studies showing slower population expansion in the absence of Scx?

We appreciate these constructive suggestions. Based on the number of available control and cKO animals, we were limited to one additional time point at 3 dpi to assess PCD by TUNEL in vivo. We were disappointed again to find no appreciable levels of PCD at 3 dpi by TUNEL (new Fig.S4I), thus no quantifications were included. We also re-did the in vitro experiment using purified SCs and monitored PCD by staining for cleaved Caspase-3 using a validated tube of antibodies (positive staining after 6 h of treatment by 1 mM staurosporine of control and ScxcKO cells; included as new Fig. S4J and legend). We were pleased to find an increase of cleaved Caspase3 stained cells, i.e. PCD, of Scx-cKO SCs at day 4 in culture, compared to that of the control. We have now replaced the old Fig. 4F with new Fig.4F and 4G to document PCD. We also provided new text/legend for these new data (p.10. ln. 2-10; new legend for Fig. 4F and 4G).

(3) I'm not sure I understand the description of the data or the conclusions in the section titled "Basement membrane-myofiber interaction in control and Scx cKO mice". Is there something specific to the regeneration from Scx-neg myogenic progenitors, or would these findings be expected in any experimental condition in which myogenesis was significantly delayed, with much smaller fibers in the experimental group at 5 DPI?

We very much appreciate this comment. We agree that there is unlikely anything specific about the regeneration from Scx-negative myogenic progenitors. Unfilled or empty ghost fibers (basement membrane remnant) are expected due to small fiber and poor regeneration in the ScxcKO mice at 5 dpi. We have removed the subtitle and changed the content to an expected consequence rather than something special (p. 8, ln. 19-22).

(4) The data presented in Fig. 4B showing differences in the purity of SC populations isolated by FACS depending on the reporter used are interesting and important for the field. The authors offer the explanation of exosomal transfer of Tdt from SCs to non-SCs. The data are consistent with this explanation, but no data are presented to support this. Are there any other explanations that the authors have considered and that could be readily tested?

Thanks for highlighting this phenomenon. We struggled with the SC purity issue for a long time. The project started with using the R26RtdT reporter for tdT’s paraformaldehyde  resistant strong fluorescence (fixation) to aid visualization in vivo. Later, when we used the tdT signal to purify SCs by FACS, we found that only 80% sorted tdT+ cells are Pax7+. We then switched to the R26RYFP reporter, from which we achieved much higher purity (95%) of SCs (Pax7+) by FACS. As such, we also repeated and confirmed many in vivo experimental results using the R26RYFP reporter (included in the manuscript). Due to the low purity of tdT+SCs by FACS, we discontinued that mouse colony after we confirmed the superior utility of the R26RYFP reporter for SC isolation.

We sincerely apologize for not being able to conduct further testable experiments on this intriguing phenomenon. However, this issue has since been addressed and published by Murach et al., iScience, (2021). Like our experience, they found non-satellite mononuclear cells with tdT fluorescence after TMX treatment when SCs were isolated via FACS. To determine this was not due to off-target recombination or a technical artifact from tissue processing, they conducted extensive analyses. They found that the tdT+ mononuclear cells included fibrogenic cells (fibroblasts and FAPs), immune cells/macrophages, and endothelial cells. Additionally, they confirmed the significant potential of extracellular vesicle (EV)-mediated cargo transfer, which facilitates the transfer of full-length tdT transcript from lineage-marked Pax7+ cells to those mononuclear cells. We have modified the text to emphasize and acknowledge their contribution to this important point, and explained the difference between YFP and tdT reporter alleles in more detail (p.9, ln. 11-17).

(5) The Cut&Run data of Fig. 6 certainly provide evidence of direct Scx targets, especially since the authors used a novel knock-in strain for analyses. The enrichment of E-box motifs provides support for the 207 intersecting genes (scRNA-seq and Cut&Run) being direct targets. However, the rationale elaborated in the final paragraph of the Results section proposing how 4 of these genes account for the phenotypes on the Scx-neg cells and tissues is just speculation, however reasonable. These are not data, and these considerations would be more appropriate in the Discussion in the absence of any validation studies.

We agree with this comment and have moved speculations into the Discussion (p. 15, ln. 4-15, and from p. 18, ln. 4 to p. 19, ln. 4).

Reviewer #2 (Public Review):

Summary:

Scx is a well-established marker for tenocytes, but the expression in myogenic-lineage cells was unexplored. In this study, the authors performed lineage-trace and scRNA-seq analyses and demonstrated that Scx is expressed in activated SCs. Further, the authors showed that Scx is essential for muscle regeneration using conditional KO mice and identified the target genes of Scx in myogenic cells, which differ from those of tendons.

Strengths:

Sometimes, lineage-trace experiments cause mis-expression and do not reflect the endogenous expression of the target gene. In this study, the authors carefully analyzed the unexpected expression of Scx in myogenic cells using some mouse lines and scRNA-seq data.

We appreciate the comments and thank her/him for noting the strengths of our manuscript.

Weaknesses:

Scx protein expression has not been verified.

We are aware of this weakness. We had previously used Western blotting (WB) using cultured SCs from control and ScxcKO mice, but did not detect endogenous Scx protein even in the control. In response to this comment, we have re-done several WB experiments using new lysates from control and ScxcKO SCs and two commercial antibodies: anti-Scx antibody 1 from Abcam (ab58655) and anti-Scx antibody 2 from Invitrogen (PA5-23943). These antibodies have been reported to detect endogenous Scx protein in tendon cells in Spang et al., BMC Musculoskelet Disord (2016) and  Bochon et al., Int J Stem Cells (2021). Despite our best efforts, we were not able to detect a reliable Scx band. We have also conducted immunofluorescence using these two antibodies. Still, we failed to detect a difference of staining signals between control and cKO SCs using these antibodies. Lastly, we conducted immunofluorescence using the ScxTy1 myoblasts and we did not find the staining signal coinciding with the Ty1 signal (by double staining). We have been very frustrated by not knowing what caused this technical difficulty in our hands. Given that these were negative data, we did not include them. However, we do hope that the combined data from scRNA-seq, ScxCreERT2 lineage-tracing, Tg-ScxGFP expression, and ScxTy1 knock-in together are deemed sufficient to make up for the deficiency of data for endogenous Scx protein in regenerative myogenic cells.

Response to Recommendations for the Authors:

Reviewer #1 (Recommendations For The Authors):

p. 8: The text refers to Fig. 3I, but this should be Fig. 3H.

We apologize for the confusion. Please note that by keeping all 14 dpi data in the same row, we placed Fig.3I at an unconventional/unexpected position, i.e., next to 3D &3E, and above 3F-H. We were aware that this unconventional placement could cause confusion, and it did. With that said, we have now re-arranged the subfigures (same data content) so that the updated Fig.3 contains subfigures in the expected and proper spatial order. We double-checked the figure referral in the text (p. 8, ln. 16-17) and the text is correct – just that the original Fig.3I should have been at the original Fig.3H position and that is now corrected.

Reviewer #2 (Recommendations For The Authors):

(1) Given that Scx binds to the E-box and regulates gene expression, it is of interest to know the relevance between MyoD and Scx. If possible, the reviewer recommends to include some discussions.

Thanks for the comment. MyoD1 is a well-known transcript factor regulating myogenesis, whereas Scx is primarily studied in tenocytes and other connective tissues. We agree that our new findings deserve a discussion regarding the relevance between MyoD1 and Scx.  We have added a description of their differences in the discussion and two new references (p.19, ln. 7-17).

(2) Considering that Scx is a transcriptional factor, it is interesting that Scx-GFP was not detected in the nuclei of regenerated myofibers. Could the subcellular localization of Scx-GFP provide some insights into the function of Scx as a transcription factor during muscle regeneration?

Tg-ScxGFP is a transgenic line generated by random insertion into the genome (Pryce et al., 2007; cited). The plasmid used for transgenesis was constructed by replacing most of Scx’s first exon with GFP, and including ~ 9Kb flanking regulatory sequences. As such, the ScxGFP is not a fusion gene, but rather that the GFP expression is regulated by Scx promoter and enhancer(s). This GFP reporter lacks a nuclear localization signal (NLS), hence it is mainly detected in the cytoplasm; some nuclear signal is detected, presumably due to GFP’s small size permitting passive diffusion into the nucleus. Thus, the GFP signal is used as a reporter for Scx expression, but GFP subcellular localization does not provide insight into Scx function per se. Conversely, ScxTy1/Ty1 is a knock-in allele created by fusing a triple-Ty1 tag (3XTy1) to the C-terminus of Scx, and we observed that Ty1 is located in the nucleus by the immunofluorescent staining. We used the Ty1 epitope to carry out CUT&RUN experiments to gain insight to the function of Scx as a transcription factor.

(3) Fig1D The number of arrows in the Merge image is not matched with others. In addition, the star mark in the Pax7 image is likely an error.

Apologies. We have now corrected these errors in the revised Fig.1D.

(4) FigS1A Is there only one myofiber shown in the dashed line in this image? It is unclear why only this myofiber is surrounded by the dashed line.

The dashed line encircles a single fiber because it was not visible in the provided image. However, there are 3 fibers in this image. Because we did not immuno-stain for myofibers here, we circled one fiber for illustration. For clarity, we brightened the background (of the entire original images) so the background signals from myofiber boundaries are discernable without outlines.

(5) FigS1B There was no overlapped DAPI staining in the Myogenin+ cell. DAPI-staining should be present in Myogenin+ cells because myogenin is located in the nucleus.

Fig.S1B is immuno-staining for MyoD , and we marked one MyoD+DAPI+GFP+ cell/nucleus. Fig.S1C is immune-staining for Myogenin, and we also marked one (cell/nucleus) that is triple positive.

(6) The position of the asterisk for the ScxGFP in FigS1D is misaligned. In addition, the position is not matched with Fig1C. Because all myofibers are Scx-positive, it is strange that only one myofiber has an asterisk. The reviewer suggests removing the mark.

Thank you for pointing out these errors. We have now corrected the misalignment and removed the unnecessary asterisk.

Author response:

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

eLife Assessment 

This study presents valuable experimental and numerical results on the motility of a magnetotactic bacterium living in sedimentary environments, particularly in environments of varying magnetic field strengths. The evidence supporting the claims of the authors is solid, although the statistical significance comparing experiments with the numerical work is weak. The study will be of interest to biophysicists interested in bacterial motility. 

We thank the reviewers and editors for their careful reading and the constructive comments. With respect to the statement about weak statistical significance, we think that this statement mixes two separate issues, the significance of the difference between experiments at 0 and 50µT and the comparison of experiments with simulations. We have amended our manuscript to address both points as described below. The difference between the experiments at 0 and 50µT is indeed significant, and the discrepancy between experiments and simulations can be explained by unavoidable differences in the way we quantify bacterial throughput.

Public Reviews: 

Reviewer #1 (Public Review): 

Summary: 

The authors present experimental and numerical results on the motility Magnetospirillum gryphiswaldense MSR-1, a magnetotactic bacterium living in sedimentary environments. The authors manufactured microfluidic chips containing three-dimensional obstacles of irregular shape, that match the statistical features of the grains observed in the sediment via microcomputer tomography. The bacteria are furthermore subject to an external magnetic field, whose intensity can be varied. The key quantity measured in the experiments is the throughput ratio, defined as the ratio between the number of bacteria that reach the end of the microfluidic channel and the number of bacteria entering it. The main result is that the throughput ratio is non-monotonic and exhibits a maximum at magnetic field strength comparable with Earth's magnetic field. The authors rationalize the throughput suppression at large magnetic fields by quantifying the number of bacteria trapped in corners between grains. 

Strengths: 

While magnetotactic bacteria's general motility in bulk has been characterized, we know much less about their dynamics in a realistic setting, such as a disordered porous material. The micro-computer tomography of sediments and their artificial reconstruction in a microfluidic channel is a powerful method that establishes the rigorous methodology of this work. This technique can give access to further characterization of microbial motility. The coupling of experiments and computer simulations lends considerable strength to the claims of the authors, because the model parameters (with one exception) are directly measured in the experiments. 

Weaknesses: 

The main weakness of the manuscript pertains to the discussion of the statistical significance of the experimental throughput ratio. Especially when comparing results at zero and 50 micro Tesla. The simulations seem to predict a stronger effect than seen in the experiments. The authors do not address this discrepancy. 

We thank the reviewer for their positive assessment and the detailed constructive remarks. 

The increase in bacterial throughput between 0 and 50 µT is indeed more pronounced in the simulations than in the experiments, partly due to the fact that there is considerably more variability in the experimental data. We did two things to address this issue: (1) We performed additional statistical test addressing the difference between the experimental results at 0 and 50 µT. Indeed, the difference is only weakly significant (in contrast to the difference of either to 500µT). The increase is however consistent with the observation in the absence of obstacles in the channel, where we see a monotonous increase from 0 to 500 µT (Supp. Figure S5). We have added the test results in the caption of Fig. 3. (2) To address the difference between simulations and experiments, we added a section in Methods on how we determine the throughput and a short discussion in the Results section. The key points are that the initial condition is different in simulations and experiments and that the throughput is therefore quantified differently. This difference is due to experimental limitations: we cannot track bacteria through the whole channel and we wanted to avoid pushing them into the channel with fluid flow to avoid effects of flow on the results. As a consequence, bacteria continue to enter the IN region of the channel from the inlet during the experiment, while in the simulation, they all start at the beginning of the channel simultaneously. We expect this to mostly affect the case with diffusive transport (B=0).

Reviewer #2 (Public Review): 

Summary: 

simulation study of magnetotactic bacteria in microfluidic channels containing sediment-mimicking obstacles. The obstacles were produced based on micro-computer tomography reconstructions of bacteria-rich sediment samples. The swimming of bacteria through these channels is found experimentally to display the highest throughput for physiological magnetic fields. Computer simulations of active Brownian particles, parameterized based on experimental trajectories are used to quantify the swimming throughput in detail. Similar behavior as in experiments is obtained, but also considerable variability between different channel geometries. Swimming at strong field is impeded by the trapping of bacteria in corners, while at weak fields the direction of motion is almost random. The trapping effect is confirmed in the experiments, as well as the escape of bacteria with reducing field strength. 

Strengths: 

This is a very careful and detailed study, which draws its main strength from the fruitful combination of the construction of novel microfluidic devices, their use in motility experiments, and simulations of active Brownian particles adapted to the experiment. Based on their results, the authors hypothesize that magnetotactic bacteria may have evolved to produce magnetic properties that are adapted to the geomagnetic field in order to balance movement and orientation in such crowded environments. They provide strong arguments in favor of such a hypothesis. 

Weaknesses: 

Some of the issues touched upon here have been studied also in other articles. It would be good to extend the list of references accordingly and discuss the relation briefly in the text. 

We thank the reviewer for the constructive comments. We answer to the point concerning previous literature in the response to the recommendations below.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors): 

Here follows a list of points the authors should address. 

(1) Are additional experiments feasible to decrease the statistical noise present in Fig. 3c? At the very least, the authors should discuss the statistical significance of the results at 50 muT vis-a-vis 0 T. 

See our response to Strengths/Weaknesses above

(2) The experimental setup is not immediately clear. I think that adding a panel from Fig. S1 (or a sketch thereof) would help clarify, especially in relation to the entry zone and end zone. 

We are not sure what you mean. Fig. 3A already contains exactly such a panel. We have however added another supplementary figure that shows an additional detailed view of the setup (Fig. S3). In addition, we revised several figures: We have replaced Fig. S1 with a better version and exchanged the schematic view of the obstacle channel in Fig 1, removing the additional inlets that were not used in this study (also in Fig 3A), Instead we added a comment in Methods explaining their presence. Hopefully this makes the setup clear.

(3) It should be also stated that there is no external flow imposed on the channel. 

We have added such a statement in the description of the experiment (in section 2.2 Swimming of magnetotactic bacteria through sediment-mimicking obstacle channels.  

(4) Fig. 3c and Fig. 6c are seemingly showing the same quantity (or closely related ones). The authors should use the same symbol and give an explicit mathematical definition. 

The two quantities are not exactly the same, as we cannot directly quantify the flux of bacteria through the channel in our experiments. On the one hand, we cannot track bacteria through the whole channel, on the other hand, the initial conditions are not exactly the same as in the simulations. In the simulations all bacteria start at the same time at the entrance to the channel. In the experiments, they enter from the inlet and do so at different times (pushing them in with fluid flow would be possible, but carries the risk of perturbing the results due to induced flow through the channel). We have added a new section in the Methods section that explains this difference and describes the procedure used to obtain the throughput from the experiments in detail. We have also added a corresponding comment in the Result section, where the simulations are compared with the experiments. 

Minor issues: 

- Figures have different styles that should be unified. For example, the panel labels sometimes have round brackets and sometimes they don't.

See above

- Page 6, (muCT) should have the Greek letter mu 

Thanks, corrected.

- Fig. 3a is not very clear; see my point 2 above. 

See above

Reviewer #2 (Recommendations For The Authors): 

I have only a few comments and questions, which the authors should address: 

(1) The observed exponential dependence of decay time on the "well" depth could be related to the exponential density distribution of active particles in a gravitational field, which has been derived previously. Might be interesting to discuss such a possible connection. 

Thank you for the suggestion, the two cases are indeed somewhat analogous with behaviors reminiscent of thermal processes with an effective temperature. Such a description is however not generally possible (even for sedimentation, only some features are described). We plan to address in future work whether it can be made more quantitative in our case of escape from the corner traps. We have included a short discussion of the analogy in the section on trapping and escape. 

(2) The authors should consider the following relevant references, and discuss them briefly in their manuscript:

- Sedimentation, trapping, and rectification of dilute bacteria J Tailleur, ME Cates EPL 86, 60002 (2009) 

- Human spermatozoa migration in microchannels reveals boundary-following navigation P Denissenko, V Kantsler, DJ Smith, J Kirkman-Brown Proc. Natl. Acad. Sci. USA 109, 8007-8010 (2012) 

- Wall accumulation of self-propelled spheres J Elgeti, G Gompper Europhysics Letters 101, 48003 (2013) 

- Wall entrapment of peritrichous bacteria: a mesoscale hydrodynamics simulation study SM Mousavi, G Gompper, RG Winkler Son Maber 16 (20), 4866-4875 (2020) 

- A Geometric Criterion for the Optimal Spreading of Active Polymers in Porous Media C Kurzthaler, S Mandal, T Bhabacharjee, H Löwen, SS Daba, HA Stone Nat. Commun. 12, 7088 (2021) 

- Run-to-Tumble Variability Controls the Surface Residence Times of E. coli Bacteria G Junot, T Darnige, A Lindner, VA Martinez, J Arlt, A Dawson, WCK Poon, H Auradou, E Clement Phys. Rev. Leb. 128, 248101 (2022) 

- Dynamics and phase separation of active Brownian particles on curved surfaces and in porous media P Iyer, RG Winkler, DA Fedosov, G Gompper Phys. Rev. Research 5, 033054 (2023) 

We agree that there is a lot of literature on these aspects, specifically interaction of self-propelled objects with walls and motion of swimmers through porous media. We have slightly extended our overview of previous literature in the introduction and included most of these references.

eLife Assessment

This study presents valuable experimental and numerical results on the motility of a magnetotactic bacterium living in sedimentary environments, particularly in environments of varying magnetic field strengths. The evidence supporting the claims of the authors is compelling and the study will be of specific relevance to biophysicists interested in bacterial motility.

Reviewer #1 (Public review):

Summary:

The authors present experimental and numerical results on the motility Magnetospirillum gryphiswaldense MSR-1, a magnetotactic bacterium living in sedimentary environments. The authors manufactured microfluidic chips containing three-dimensional obstacles of irregular shape, that match the statistical features of the grains observed in the sediment via micro-computer tomography. The bacteria are furthermore subject to an external magnetic field, whose intensity can be varied. The key quantity measured in the experiments is the throughput ratio, defined as the ratio between the number of bacteria that reach the end of the microfluidic channel and the number of bacteria entering it. The main result is that the throughput ratio is non-monotonic and exhibits a maximum at magnetic field strength comparable with Earth's magnetic field. The authors rationalize the throughput suppression at large magnetic fields by quantifying the number of bacteria trapped in corners between grains.

Strengths:

While magnetotactic bacteria general motility in bulk has been characterized, we know much less about their dynamics in a realistic setting, such as a disordered porous material. The micro-computer tomography of sediments and their artificial reconstruction in a microfluidic channel is a powerful method that establishes the rigorous methodology of this work. This technique can give access to further characterization of the microbial motility. The coupling of experiments and computer simulations lends considerable strength to the claims of the authors, because the model parameters (with one exception) are directly measured in the experiments.

Weaknesses:

The main weakness of the manuscript pertains to the comparison between simulations and experiments due to limitations in the tracking of bacteria in the experiments.

Impact:

Building on the present work, and refining the experimental setup may shed light on the microbial interactions in an environment such as soil which deserves further studies.

Reviewer #2 (Public review):

Summary:

The manuscript reports results of a combined experimental and simulation study of magnetotactic bacteria in microfluidic channels containing sediment-mimicking obstacles. The obstacles were produced based on micro-computer tomography reconstructions of bacteria-rich sediment samples. The swimming of bacteria through these channels is found experimentally to display the highest throughput for physiological magnetic fields. Computer simulations of active Brownian particles, parameterized based on experimental trajectories are used to quantify the swimming throughput in detail. Similar behavior as in experiments is obtained, but also considerable variability between different channel geometries. Swimming at strong field is impeded by the trapping of bacteria in corners, while at weak fields the direction of motion is almost random. The trapping effect is confirmed in the experiments, as well as the escape of bacteria with reducing field strength.

Strengths:

This is a very careful and detailed study, which draws its main strength from the fruitful combination of construction of novel microfluidic devives, their use in motility experiments, and simulations of active Brownian particles adapted to the experiment.<br /> Based on their results, the authors hypothesize that magnetotactic bacteria may have evolved to produce magnetic properties that are adapted to the geomagnetic field in order to balance movement and orientation in such crowded environments. They provide strong arguments in favor of such a hypothesis.

Weaknesses:

Some of the issues touched upon here have been studied also in other articles. It would be good to extend the list of references accordingly and discuss the relation briefly in the text.

Comments on revisions:

In their rebuttal letter, the authors have responded in detail to all points raised in my previous report. They have revised their manuscript accordingly.

eLife Assessment

This paper provides fundamental insights into the control of Salmonella within human macrophages, with convincing evidence that Salmonella can replicate in the macrophage cytosol in the absence of inflammasome signaling. This paper, which improves our understanding of how the immune system fights bacterial infections, will be of broad interest to cell biologists, immunologists and microbiologists.

Reviewer #1 (Public review):

Summary:

In this excellent manuscript by Egan et al., the authors very carefully dissect the roles of inflammasome components in restricting Salmonella Typhimurium (STm) replication in human macrophages. They show that caspase-1 is essential to mediating inflammasome responses and that caspase-4 contributes to bacterial restriction at later time points. The authors show very clear roles for the host proteins that mediate terminal lysis, gasdermin D and ninjurin-1. The unique finding in this study is that in the absence of inflammasome responses, Salmonella hypereplicates within the cytosol of macrophages. These findings suggest that caspase-1 and possibly caspase-4 play roles in restricting the replication of Salmonella in the cytosol as well as in the Salmonella containing vacuole.

Strengths:

(1) The genetic and biochemical approaches have shown for the first time in human macrophages that the caspase-1-GSDMD-NINJ1 axis is very important for restricting intracellular STm replication. In addition, they demonstrate a later role for Casp4 in control of intracellular bacterial replication.

(2) In addition, they show that in macrophages deficient in the caspase-1-GSDMD-NINJ1 axis that STm are found replicating in the cytosol, which is a novel finding. The electron microscopy is convincing that STm are in the cytosol.

(3) The authors go on to use a chloroquine resistance assay to show that inflammasome signaling also restricts STm within SCVs in human macrophages.

(4) Finally, they show that the Type 3 Secretion System encoded on Salmonella Pathogenicity Island 1 contributes to STm's cytosolic access in human macrophages.

Weaknesses:

(1) Their results with human macrophages suggest that there are differences between murine and human macrophages in inflammasome-mediated restriction of STm growth. For example, Thurston et al. showed that in murine macrophages that inflammasome activation controls the replication of mutant STm that aberrantly invades the cytosol, but only slightly limits replication of WT STm. In contrast, here the authors found that primed human macrophages rely on caspase-1, gasdermin D and ninjurin-1 to restrict WT STm. I wonder if the priming of the human macrophages in this study could account for the differences in these studies. Along those lines, do the authors see the same results presented in this study in the absence of priming the macrophages with Pam3CSK4. I think that determining whether the control of intracellular STm replication is dependent on priming is very important. Another difference with the Thurston et al. paper is the way that the STm inoculum was prepared - stationary phase bacteria that were opsonized. Could this also account for differences between the two studies rather than differences between murine and human macrophages in inflammasome-dependent control of STm?

(2) The authors show that the pore-forming proteins GSDMD and Ninj1 contribute to control of STm replication in human macrophages. Is it possible that leakage of gentamicin from the media contributes to this control?

(3) One major question that remains to be answered is whether casp-1 plays a direct role in the intracellular localization of STm. If the authors quantify the percentage of vacuolar vs. cytosolic bacteria at early time points in WT and casp-1 KO macrophages, would that be the same in the presence and absence of casp-1? If so, then this would suggest that there is a basal level of bacterial-dependent lysis of the SCV and in WT macrophages the presence of cytosolic PAMPS trigger cell death and bacteria can't replicate in the cytosol. However, in the inflammasome KO macrophages, the host cell remains alive and bacteria can replicate in the cytosol.

Comments on revisions:

The authors have addressed my previous concerns. The addition of the statements indicating the limitations of the study are an important addition.

Reviewer #2 (Public review):

Summary:

This work addresses the question of how human macrophages restrict intracellular replication of Salmonella.

Strengths:

Through a series of genetic knockouts and using specific inhibitors, Egan et al. demonstrated that the inflammasome components caspase-1, caspase-4, gasdermin D (GSDMD), and the final lytic death effector ninjurin-1 (NINJ1) are required for control of Salmonella replication in human macrophages. Interestingly, caspase-1 proved crucial in restricting Salmonella early during infection, whereas caspase-4 was essential in the later stages of infection. Furthermore, using a chloroquine resistance assay and state-of-the-art microscopy, the authors found that NAIP receptor and caspase-1 mostly regulate replication of cytosolic bacteria, with smaller, yet significant, impact on the vacuolar bacteria.

The finding that inflammasomes are critical in the restriction of replication of intracellular Salmonella in human macrophages contrasts with the published minimal role of inflammasomes in restriction of replication of intracellular Salmonella in murine macrophages. Some of these differences could be due to differences in the methodologies used in the two studies. However, the findings suggest yet another example of interspecies and intercellular differences in regulation of bacterial infections by the immune system.

Comments on revisions:

The authors may wish to comment that the measurements of released cytokines by ELISA do not discriminate between active and full-length forms of the cytokines.

Reviewer #3 (Public review):

The manuscript by Egan and coworkers investigates how Caspase-1 and Caspase-4 mediated cell death affects replication of Salmonella in human THP-1 macrophages in vitro.

Overall evaluation:

Strength of the study include the use of human cells, which exhibit notable differences (e.g., Caspase 11 vs Caspase-4/5) compared to commonly used murine models. Furthermore, the study combines inhibitors with host and bacterial genetics to elucidate mechanistic links.

Comments on revisions:

The authors have addressed my comments regarding the previous submission.

Author response:

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

Reviewer #1: 

(1) Their results with human macrophages suggest that there are differences between murine and human macrophages in inflammasome-mediated restriction of STm growth. For example, Thurston et al. showed that in murine macrophages that inflammasome activation controls the replication of mutant STm that aberrantly invades the cytosol, but only slightly limits replication of WT STm. In contrast, here the authors found that primed human macrophages rely on caspase-1, gasdermin D and ninjurin-1 to restrict WT STm. I wonder if the priming of the human macrophages in this study could account for the differences in these studies. Along those lines, do the authors see the same results presented in this study in the absence of priming the macrophages with Pam3CSK4. I think that determining whether the control of intracellular STm replication is dependent on priming is very important.

We thank the Reviewer for their careful attention to our manuscript and for their thoughtful comments. We have addressed this question about the impact of priming by repeating the bacterial intracellular burden assays in unprimed WT and CASP1-/- THP-1 cells. We have added additional figures to the manuscript to address this: Figure 1 – Figure Supplement 3. Under unprimed conditions, CASP1-/- cells still harbored significantly higher bacterial burdens at 6 hpi and a significant fold-increase in bacterial CFUs compared to WT cells. These results suggest that the caspase-1-mediated restriction of intracellular Salmonella replication in human macrophages is independent of priming. 

(2) Another difference with the Thurston et al. paper is the way that the STm inoculum was prepared - stationary phase bacteria that were opsonized. Could this also account for differences between the two studies rather than differences between murine and human macrophages in inflammasome-dependent control of STm?

We thank the Reviewer for this excellent suggestion. To address this possibility, we repeated the bacterial intracellular burden assays in WT and CASP1-/- THP-1 cells using stationary phase bacteria. We infected WT and CASP1-/- THP-1 cells with stationary phase Salmonella, and we subsequently assayed for intracellular bacterial burdens. These data have now been added to the manuscript in Figure 1 – Figure Supplement 4. Interestingly, we did not observe any fold-change in the bacterial colony forming units in both the WT and CASP1-/- THP-1 cells for the stationary phase Salmonella. These data indicate that by 6 hours postinfection, Salmonella do not replicate efficiently in human macrophages unless grown under SPI-1-inducing conditions. Furthermore, these results suggest that differences in how the Salmonella inoculum is prepared may contribute to the discrepancies between our study and previous studies, as noted by the Reviewer. 

(3) The authors show that the pore-forming proteins GSDMD and Ninj1 contribute to control of STm replication in human macrophages. Is it possible that leakage of gentamicin from the media contributes to this control?

Response: We thank the Reviewer for their insightful comment. We have addressed this question on the impact of gentamicin by repeating the bacterial intracellular burden assays using a lower concentration of gentamicin in combination with extensively washing the cells with RPMI media to remove the gentamicin. WT and CASP1-/- THP-1 cells were infected with WT Salmonella. Then, at 30 minutes post-infection, cells were treated with 25 μg/ml of gentamicin to kill any extracellular bacteria. At 1 hour post-infection (hpi), the cells were washed for a total of five times with fresh RPMI to remove the gentamicin, and then the media was replaced with fresh media containing no gentamicin. In parallel, we also treated cells with 100 μg/ml of gentamicin at 30 minutes post-infection, washed the cells five times with fresh RPMI at 1 hpi to remove the gentamicin, and then replaced the media with fresh media containing 10 μg/ml of gentamicin. This data has now been included in the manuscript as Figure 1 – Figure Supplement 5. We observed similar levels in the intracellular bacterial burdens at 1 hpi and 6 hpi and a fold-increase in bacterial colony forming units in CASP1-/- cells compared to WT cells across both gentamicin conditions, suggesting that gentamicin appears to not contribute to the intracellular control of Salmonella replication in human macrophages. Of note, we also tried repeating the bacterial intracellular burden assays without gentamicin, using only washes to remove extracellular at 1 hpi; however, under these experimental conditions, we observed high levels of extracellular Salmonella. Therefore, we relied on using a lower concentration of gentamicin to kill extracellular Salmonella in conjunction with extensive washing to remove the gentamicin for the remainder of the infection. 

(4) One major question that remains to be answered is whether casp-1 plays a direct role in the intracellular localization of STm. If the authors quantify the percentage of vacuolar vs. cytosolic bacteria at early time points in WT and casp-1 KO macrophages, would that be the same in the presence and absence of casp-1? If so, then this would suggest that there is a basal level of bacterial-dependent lysis of the SCV and in WT macrophages the presence of cytosolic PAMPS trigger cell death and bacteria can't replicate in the cytosol. However, in the inflammasome KO macrophages, the host cell remains alive and bacteria can replicate in the cytosol.

We thank this Reviewer for raising this important point. We have addressed this experimentally by quantifying the percentage of vacuolar vs. cytosolic Salmonella at 2 hpi in WT, NAIP-/-, and CASP1-/- THP-1 cells using a chloroquine (CHQ) resistance assay. This data has now been included in the manuscript in the new Figure 5A. The original subfigures of Figure 5 have consequently been rearranged. We did not observe any significant differences in vacuolar and cytosolic bacterial burdens at this early time point in WT, NAIP-/-, and CASP1-/- THP-1 cells. As noted by the Reviewer, these results suggest that the basal level of bacterialdependent lysis of the SCV in human macrophages is not dependent on caspase-1 or NAIP. 

Reviewer #3: 

(1) The main weaknesses of the study are the inherent limitations of tissue culture models. For example, to study interaction of Salmonella with host cells in vitro, it is necessary to kill extracellular bacteria using gentamicin. However, since Salmonella-induced macrophage cell death damages the cytosolic membrane, gentamicin can reach intracellular bacteria and contribute to changes in CFU observed in tissue culture models (major point 1). This can result in tissue culture "artefacts" (i.e., observations/conclusions that cannot be recapitulated in vivo). For example, intracellular replication of Salmonella in murine macrophages requires T3SS-2 in vitro, but T3SS-2 is dispensable for replication in macrophages of the spleen in vivo (Grant et al., 2012).  

We thank the Reviewer for their helpful comments and insightful suggestions. We have addressed some of the concerns about gentamicin in our response to Reviewer #1 above. To address the Reviewer’s concerns further, we have included language to acknowledge the limitations of our study based on the artefacts of tissue culture models in our Discussion section: “In this study, we utilized tissue culture models to examine intracellular Salmonella replication in human macrophages. These in vitro systems allow for precise control of experimental conditions and, therefore, serve as powerful tools to interrogate the molecular mechanisms underlying inflammasome responses and Salmonella replication in both immortalized and primary human cells. Still, there are limitations of tissue culture models, as they lack the inherent complexity of tissues and organs in vivo. To assess whether our findings reflect Salmonella dynamics in the mammalian host, it will be important to complement our studies and extend the implications of our work using approaches that model more complex systems, such as organoids or organ explant models co-cultured with immune cells, and in vivo techniques, such as humanized mouse models.”

(2) In Figure 1: are increased CFU in WT vs CASP1-deficient THP-1 cells due to Caspase 1 restricting intracellular replication or due to Caspase-1 causing pore formation to allow gentamicin to enter the cytosol thereby restricting bacterial replication? The same question arises about Caspase-4 in Figure 2, where differences in CFU are observed only at 24h when differences in cell death also become apparent. The idea that gentamicin entering the cytosol through pores is responsible for controlling intracellular Salmonella replication is also consistent with the finding that GSDMD-mediated pore formation is required for restricting intracellular Salmonella replication (Figure 3). Similarly, the finding that inflammasome responses primarily control Salmonella replication in the cytosol could be explained by an intact SCV membrane protecting Salmonella from gentamicin (Figure 5). 

We thank the Reviewer for highlighting this important point regarding gentamicin.

We have addressed this question in our response above to Review #1 and in Figure 1 – Figure Supplement 5. We observed caspase-1-mediated restriction of Salmonella in human macrophages even when cells were treated with a lower concentration of gentamicin (25 μg/ml) for 30 minutes and then extensively washed with RPMI media to remove any gentamicin for the remainder of the infection. These data suggest that gentamicin is likely not responsible for controlling intracellular Salmonella in human macrophages.

eLife Assessment

This manuscript applies a theoretical analysis to two published datasets on yeast and bacterial evolution to compare different ways of quantifying fitness. It makes an important advance by clarifying how discrepancies can arise by using different approaches and provides recommendations for best practices. While the evidence is solid, some improvements in the presentation of the data and a greater focus on the causes of the discrepancies between the various fitness estimates would strengthen the paper further.

Reviewer #1 (Public review):

The authors point out that the fitness estimates obtained from different experimental assays (monoculture, pairwise competition, or bulk competition) are not generally equivalent, not even with regard to the fitness ranking of different genotypes. Using a computational model based on experimentally measured growth phenotypes for knockout strains in yeast, as well as data from Lenski's Long Term Evolution Experiment (LTEE), they derive a set of best practice rules aimed at extracting the optimal amount of information from such experiments.

The study is very complete on a technical level and I have no suggestions for further analyses. However, I feel the readability and the conceptual focus of the manuscript could be significantly improved by rearranging the material with regard to the contents of the main text vs. the Methods and the Supplement. Detailed recommendations:

(1) Regarding readability, the large number of references to material in the Methods and Supplement fragment the main text and make it difficult to follow.

(2) Conceptually, it seems to me that the current presentation obscures the reasons why we should care about fitness in the first place. In the first paragraph of Results, the authors define fitness "as any number that is sufficient to predict the genotype's relative abundance x(t) over a short-time horizon". To me, this seems like an extremely narrow and not very interesting definition. Instead, I view fitness as an intrinsic property of a genotype that allows us to predict its performance<br /> under a range of conditions, including in particular conditions that are different from the experimental setup that was used to obtain the fitness estimates. The latter viewpoint is well expressed in Supplementary Section S1, where the authors discuss the notion of fitness potential. I would recommend to move at least part of this discussion to the main text. By comparison, the arguments in favor of the logit encoding that currently opens the Results session are rather straightforward and could be shortened significantly.

(3) Similarly, the modeling strategy used in this work is quite subtle and needs to be explained more fully in the main text. The authors use growth traits (lag time, growth rate, and yield) extracted from monoculture experiments on a yeast knockout collection and feed them into a specific mathematical model to simulate pairwise and bulk competition scenarios. Since a key claim of the work is that monoculture experiments are generally poor predictors of competitive fitness, the basis for this conclusion and the assumptions on which it is based need to be described clearly in the main text. In the current version of the manuscript, this information has<br /> been largely relegated to the Methods section.

Reviewer #2 (Public review):

Summary:

The manuscript "Quantifying microbial fitness in high-throughput experiments" provides a comprehensive analysis of the various approaches to quantifying fitness in microbial evolution, focusing on three primary factors: encoding of relative abundance, time scale of measurement, and the choice of reference subpopulation. The authors systematically explore how these choices impact fitness statistics and provide recommendations aimed at standardizing practices in the field. This manuscript aims to highlight the impact of differing fitness definitions and the methodologies utilized for analysis and how that can significantly alter interpretations of mutant fitness, affecting evolutionary predictions and the overall understanding of genetic interactions in the experiments. Although this manuscript focuses on a critical issue in the quantification of fitness in high throughput experiments, it heavily relies on only one experimental dataset (Warringer et al 2003) and one organism i.e, Yeast (Saccharomyces cerevisiae) grown in a defined medium, the environmental influence is not completely captured. While the theoretical framework is strong, more experimental examples with more organisms (i.e., more datasets) in their analysis and comparison would enhance the manuscript, especially its conclusion.

Strengths:

The choices for quantifying fitness in evolution experiments are critical and highly relevant given the increasing prevalence of high-throughput experiments in evolutionary biology. The authors methodically categorize fitness statistics and their implications, providing clarity on a complex subject. This structured approach aids in understanding the nuances of fitness measurement. The manuscript effectively highlights how different choices in fitness measurement can influence fitness rankings and the understanding of epistasis, which is important for modeling evolutionary dynamics.

Weaknesses:

The theoretical framework is robust, but the manuscript could benefit from more empirical examples to illustrate how different fitness quantification methods lead to varied conclusions in experiments. The discussion on the choice of reference subpopulation could be expanded with the influence of the environment or the condition. Different types of reference groups might yield different implications for fitness calculations, and further elaboration would enhance this section. The authors overgeneralize some findings; for instance, the implications of fitness measurement choices could vary significantly across different microbes or experimental conditions. A more detailed discussion would strengthen the conclusion.

Overall, this manuscript is a significant contribution to the field of evolutionary biology, addressing a critical issue in the quantification of fitness but lacks more experimental support to make it a wider claim. By systematically exploring the factors that influence fitness measurements, the authors provide valuable insights that can guide future research - the framework is computationally thorough but needs a more detailed explanation of concepts instead of generalizing. Further work is needed, particularly to incorporate empirical examples and expand certain discussions to include environmental variation and their impact, which would improve clarity and applicability.

Reviewer #3 (Public review):

Summary:

The authors present analyses of different fitness measures derived from empirical data from yeast knock-out mutants and the long-term evolution experiment (LTEE) with Escherichia coli to explore discrepancies and identify preferred methods to estimate relative fitness in high-throughput experiments. Their work has three components. They first discuss the different "encodings" of relative abundance data and conclude that logit transformations are preferred because they transform nonlinear abundance trajectories into linear trajectories with greater predictive power. Next, they compare per-generation with per-growth cycle relative fitness estimates inferred from simulations of pairwise competitions based on published growth traits for the yeast strains and on published pairwise competition measurements for the LTEE data. Both data sets show quantitative and qualitative (i.e. rank order) discrepancies of estimates across different time scales, which are highlighted by considering possible underlying causes (i.e. trade-offs between growth traits) and consequences (i.e. epistasis among mutations affecting different growth traits). Finally, the authors compare simulated pairwise and bulk (i.e. where many mutants compete during a growth cycle in a single environment) competition assays based on the yeast knock-out mutants and demonstrate an optimal ratio of collective mutants to wild-type strains that minimizes both sampling error and overestimation of fitness estimates when compared with pairwise competitions.

Strengths:

The study deals with a highly relevant topic. Fitness is central to general evolutionary theory, but also poorly defined and implies different traits for different organisms and conditions. For microbes, which are often used in evolution experiments, high-throughput experiments may yield different measures to quantify abundance over time, from individual growth traits to bulk competition experiments. Hence, it is relevant to consider discrepancies among those measures and identify preferred measures with respect to predicting population dynamics and evolutionary processes. The present study contributes to this aim by (i) making readers aware of differences among commonly used fitness estimates, (ii) showing that simulated (yeast) and calculated (E. coli) competitive fitness may differ across time scales, and (iii) showing that bulk competitions may yield relative fitness estimates that are systematically higher than pairwise competitions. The study is rather thorough on the theory side, with extensive derivations and analyses of various fitness measures using their resource competition model in the Supplementary Information. The study ends with a few practical recommendations for preferred methods to infer relative fitness estimates, that may be useful for experimentalists and stimulate further investigations.

Weaknesses:

The study has several limitations. Perhaps the most apparent limitation is the lack of a clear answer to the question of which fitness measure is best "in the light of first principles". The authors show clear discrepancies between fitness estimates across different time scales or using different reference genotypes in bulk competition and provide useful recommendations based on practical considerations (e.g. using pairwise competitions as the "golden standard"), but it remains unclear whether these measures provide the greatest value for the questions researchers may want to answer with them (e.g. predict shifts in genotype frequencies).

A second limitation is that the authors analyse fitness differences arising solely from resource competition, whereas microbes often interact via other mechanisms, e.g. the production of anticompetitior toxins, cross-feeding of metabolites, or lack of growth to enhance their persistence in stress conditions. Without simulations of these processes, understanding discrepancies among fitness measures is necessarily limited. In addition, the analysis of trade-offs between growth traits causing these discrepancies during resource competition seems confounded by biases in measurement error or parameter estimation, at least for growth rate and lag time (Figure 2B), where the replicate estimates for the wildtype show a similar negative correlation.

Third, the study does not validate relative fitness predictions from growth traits (as is done for the yeast mutants) with measured relative fitness estimates using competition assays, while such data are available, e.g. for the LTEE. This would strengthen their inferences about preferred fitness measures.

Fourth, the analysis of epistasis between mutations affecting different growth traits (shown in Figure 3) based on the LTEE data could be better introduced and analysed more comprehensively. Now, the examples given in panels C-F seem rather idiosyncratic and readers may wonder how general these consequences of using fitness estimates based on different time scales are.

Finally, the study is generally less accessible to experimentalists due to the extensive and principled treatment of specific population dynamic models and fitness inferences. This may distract from the overarching aim to identify fitness measures that are most accurate and useful for predictions of population dynamics and evolutionary processes. In this light, the motivation for the initial discussion of the importance of how to best encode relative abundance (Figure 1) is unclear. Also, the conclusion, that logit encoding is preferred, because it linearizes logistic growth dynamics and "improves the quality of predictions", is not further motivated. Experimentalists using non-linear models to infer fitness from growth curves or competition assays may miss the relevance of this discussion.

Author response:

We thank all three reviewers and the editors for their detailed comments on our manuscript.  The two main themes of this feedback concern the paper’s generality and its presentation.  Reviewers #2 and #3 raise questions about how the discrepancies in fitness statistics we report will be realized across organisms, environments, and in models with interactions beyond resource competition (e.g., toxicity or cross-feeding).  All reviewers and the editors have also expressed the need for the presentation to be improved, including a broader introduction to the concept of fitness (Reviewer #1), a clearer explanation of our model (Reviewer #1), better explanations of how quantifying fitness answers key biological questions (Reviewer #3), and improvements to the most technical sections to ensure accessibility to experimentalists (Reviewer #3).

In light of these comments, we wish to clarify that the goal of this paper is to provide a proof-of-principle for how different choices in quantifying fitness can lead to different analysis outcomes.  Since the focus of this paper is on the theoretical concepts, we focus on a few example data sets and a simple model to demonstrate the existence of these discrepancies.  While other organisms and environments, especially with more complex growth dynamics and interactions, could certainly have additional or different discrepancies in fitness statistics, we believe the simplicity of our approach is valuable because it demonstrates that even basic features of microbial growth (common across systems) with realistic parameter values are sufficient to cause significant differences in fitness depending on these quantification choices.  We agree with the reviewers that a systematic documentation of how these fitness discrepancies are empirically realized is important, but we believe that question is best explored in separate future works that can focus fully on this empirical rather than theoretical question.

We plan to revise the manuscript in several ways, following the suggestions of the three reviewers and the editor.  First, we will better articulate the main goal and conclusions of this manuscript, especially its generality and limitations.  Second, we will work to streamline and clarify several points in the main text identified by the reviewers to make it more accessible and useful to a broader audience, especially experimentalists who routinely measure fitness in their work.  We are grateful to the reviewers and the editor for their time and effort in assessing the manuscript, and we look forward to providing an updated version that addresses these concerns.

eLife Assessment

This study provides valuable insights into the efficacy and safety of pyrotinib as an extended adjuvant therapy following trastuzumab-based treatment in patients with high-risk HER2-positive breast cancer. The strength of evidence is solid, supported by the multicenter phase II trial design, which included a substantial number of patients across 23 centers in China. However, the single-arm study design without a control group limits the ability to draw definitive conclusions about the comparative effectiveness of pyrotinib.

Reviewer #1 (Public review):

Summary:

This study introduces a novel therapeutic strategy for patients with high-risk HER2-positive breast cancer and demonstrates that the incorporation of pyrotinib into adjuvant trastuzumab therapy can improve invasive disease-free survival.

Strengths:

The study features robust logic and high-quality data. Data from 141 patients across 23 centers were analyzed, thereby effectively mitigating regional biases and endowing the research findings with high applicability.

Weaknesses:

(1) Introduction and Discussion: Update the literature regarding the efficacy of pyrotinib combined with trastuzumab in treating HER2-positive advanced breast cancer.

(2) Did all the data have a normal distribution? Expand the description of statistical analysis.

(3) The novelty and innovative potential of your manuscript compared to the published literature should be described in more detail in the abstract and discussion section.

(4) Figure legend should provide a bit more detail about what readers should focus on.

(5) P-values should be clarified for the analysis.

(6) The order (A, B, and C) in Figure 3 should be labeled in the upper left corner of the Figure.

Reviewer #2 (Public review):

In this manuscript, Cao et al. evaluated the efficacy and safety of 12 months pyrotinib after trastuzumab-based adjuvant therapy in patients with high-risk, HER2-positive early or locally advanced breast cancer. Notably, the 2-year iDFS rate reached 94.59% (95% CI: 88.97-97.38) in all patients, and 94.90% (95% CI: 86.97-98.06) in patients who completed 1-year treatment of pyrotinib. This is an interesting and uplifting results, given that in ExteNET study, the 2-year iDFS rate was 93.9% (95% CI 92·4-95·2) in the 1-year neratinib group, and the 5-year iDFS survival was 90.2%, and 1-year treatment of neratinib in ExteNET study did not translate into OS benefit after 8-year follow-up. In this case, readers will be eagerly anticipating the long-term follow-up results of the current PERSIST study, as well as the results of the phase III clinical trial (NCT03980054).

I have the following comments:

(1) The introduction of the differences between pyrotinib and neratinib in terms of mechanism, efficacy, resistance, etc. is supposed to be included in the text so that authors could better highlight the clinical significance of the current trial.

(2) Please make sure that a total of 141 patients were enrolled in the study, 38 patients had a treatment duration of less than or equal to 6 months, and a total of 92 and 31 patients completed 1-year and 6-month treatment of extended adjuvant pyrotinib, respectively, which means 7 patients had a treatment duration of fewer than 6 months.

(3) The previous surgery history should be provided, and how many patients received lumpectomy, and mastectomy.

eLife Assessment

This useful modeling study alters a previous model of the intact cat spinal locomotor network to simulate a lateral hemi-section of the spinal cord. The modeling and experimental work described provide solid evidence that this model is capable of qualitatively predicting alterations to the swing and stance phase durations during locomotion at different speeds on intact or split-belt treadmills, but a revision of the figures to overlay the model predictions with the experimental data would facilitate the assessment of this qualitative agreement. This paper will interest neuroscientists studying vertebrate motor systems, including researchers investigating motor dysfunction after spinal cord injury.

Reviewer #1 (Public review):

Summary:

This study adapts a previously published model of the cat spinal locomotor network to make predictions of how phase durations of swing and stance at different treadmill speeds in tied-belt and split-belt conditions would be altered following a lateral hemisection. The simulations make several predictions that are replicated in experimental settings.

Strengths:

(1) Despite only altering the connections in the model, the model is able to replicate very well several experimental findings. This provides strong validation for the model and highlights its utility as a tool to investigate the operations of mammalian spinal locomotor networks.

(2) The study provides insights about interactions between the left and right sides of the spinal locomotor networks, and how these interactions depend on the mode of operation, as determined by speed and state of the nervous system.

(3) The writing is logical, clear, and easy to follow.

Weaknesses:

(1) Could the authors provide a statement in the methods or results to clarify whether there were any changes in synaptic weight or other model parameters of the intact model to ensure locomotor activity in the hemisected model?

(2) The authors should remind the reader what the main differences are between state-machine, flexor-driven, and classical half-center regimes (lines 77-79).

(3) There may be changes in the wiring of spinal locomotor networks after the hemisection. Yet, without applying any sort of plasticity, the model is able to replicate many of the experimental data. Based on what was experimentally replicated or not, what does the model tell us about possible sites of plasticity after hemisection?

(4) Why are the durations on the right hemisected (fast) side similar to results in the full spinal transected model (Rybak et al. 2024)? Is it because the left is in slow mode and so there is not much drive from the left side to the right side even though the latter is still receiving supraspinal drive, as opposed to in the full transection model? (lines 202-203).

(5) There is an error with probability (line 280).

Reviewer #2 (Public review):

This is a nice article that presents interesting findings. One main concern is that I don't think the predictions from the simulation are overlaid on the animal data at any point - I understand the match is qualitative, which is fine, but even that is hard to judge without at least one figure overlaying some of the data. Second is that it's not clear how the lateral coupling strengths of the model were trained/set, so it's hard to judge how important this hemi-split-belt paradigm is. The model's predictions match the data qualitatively, which is good; but does the comparison using the hemi-split-belt paradigm not offer any corrections to the model? The discussion points to modeling plasticity after SCI, which could be good, but does that mean the fit here is so good there's no point using the data to refine?

The manuscript is well-written and interesting. The putative neural circuit mechanisms that the model uncovers are great, if they can be tested in an animal somehow.

Page 2, lines 75-6: Perhaps it belongs in the other paper on the model, but it's surprising that in the section on how the model has been revised to have different regimes of operation as speed increases, there is no reference to a lot of past literature on this idea. Just one example would be Koditschek and Full, 1999 JEB Figure 3, where they talk about exactly this idea, or similarly Holmes et al., 2006 SIAM review Figure 7, but obviously many more have put this forward over the years (Daley and Beiwener, etc). It's neat in this model to have it tied down to a detailed neural model that can be compared with the vast cat literature, but the concept of this has been talked about for at least 25+ years. Maybe a review that discusses it should be cited?

Page 2, line 88: While it makes sense to think of the sides as supraspinal vs afferent driven, respectively, what is the added insight from having them coupled laterally in this hemisection model? What does that buy you beyond complete transection (both sides no supra) compared with intact? I can see how being able to vary cycle frequencies separately of the two limbs is a good "knob" to vary when perturbing the system in order to refine the model. But there isn't a ton of context explaining how the hemi-section with split belt paradigm is important for refining the model, and therefore the science. Is it somehow importantly related to the new "regimes" of operation versus speed idea for the model?

Page 5, line 212: For the predictions from the model, a lot depends on how strong the lateral coupling of the model is, which, in turn, depends on the data the model was trained on. Were the model parameters (especially for lateral coupling of the limbs) trained on data in a context where limbs were pushed out of phase and neuronal connectivity was likely required to bring the limbs back into the same phase relationship? Because if the model had no need for lateral coupling, then it's not so surprising that the hemisected limbs behave like separate limbs, one with surpaspinal intact and one without.

Page 8, line 360: The discussion of the mechanisms (increased influence of afferents, etc) that the model reveals could be causing the changes is exciting, though I'm not sure if there is an animal model where it can be tested in vivo in a moving animal.

Page 9, line 395: There are some interesting conclusions that rely on the hemi-split-belt paradigm here.

eLife Assessment

This fundamental article significantly advances our understanding of FGF signalling, and in particular, highlights the complex modifications affecting this pathway. The evidence for the authors' claims is convincing, combining state-of-the-art conditional gene deletion in the mouse lens with histological and molecular approaches. This work should be of great interest to molecular and developmental biologists beyond the lens community. The manuscript itself deserves minor editorial improvements, in particular, the literature on FGFR and SHC should be expanded in the introduction and discussed in more detail in the discussion.

Reviewer #1 (Public review):

Summary:

This manuscript uses the eye lens as a model to investigate basic mechanisms in the Fgf signaling pathway. Understanding Fgf signaling is of broad importance to biologists as it is involved in the regulation of various developmental processes in different tissues/organs and is often misregulated in disease states. The Fgf pathway has been studied in embryonic lens development, namely with regards to its involvement in controlling events such as tissue invagination, vesicle formation, epithelium proliferation, and cellular differentiation, thus making the lens a good system to uncover the mechanistic basis of how the modulation of this pathway drives specific outcomes. Previous work has suggested that proteins, other than the ones currently known (e.g., the adaptor protein Frs2), are likely involved in Fgfr signaling. The present study focuses on the role of Shp2 and Shc1 proteins in the recruitment of Grb2 in the events downstream of Fgfr activation.

Strengths:

The findings reveal that the juxtamembrane region of the Fgf receptor is necessary for proper control of downstream events such as facilitating key changes in transcription and cytoskeleton during tissue morphogenesis. The authors conditionally deleted all four Fgfrs in the mouse lens that resulted in molecular and morphological lens defects, most importantly, preventing the upregulation of the lens induction markers Sox2 and Foxe3 and the apical localization of F-actin, thus demonstrating the importance of Fgfrs in early lens development, i.e. during lens induction. They also examined the impact of deleting Fgfr1 and 2, on the following stage, i.e. lens vesicle development, which could be rescued by expressing constitutively active KrasG12D. By using specific mutations (e.g. Fgfr1ΔFrs lacking the Frs2 binding domain and Fgfr2LR harboring mutations that prevent binding of Frs2), it is demonstrated that the Frs2 binding site on Fgfr is necessary for specific events such as morphogenesis of lens vesicle. Further, by studying Shp2 mutations and deletions, the authors present a case for Shp2 protein to function in a context-specific manner in the role of an adaptor protein and a phosphatase enzyme. Finally, the key surprising finding from this study is that downstream of Fgfr signaling, Shc1 is an important alternative pathway - in addition to Shp2 - involved in the recruitment of Grb2 and in the subsequent activation of Ras. The methodologies, namely, mouse genetics and state-of-the-art cell/molecular/biochemical assays are appropriately used to collect the data, which are soundly interpreted to reach these important conclusions. Overall, these findings reveal the flexibility of the Fgf signaling pathway and its downstream mediators in regulating cellular events. This work is expected to be of broad interest to molecular and developmental biologists.

Weaknesses:

A weakness that needs to be discussed is that Le-Cre depends on Pax6 activation, and hence its use in specific gene deletion will not allow evaluation of the requirement of Fgfrs in the expression of Pax6 itself. But since this is the earliest Cre available for deletion in the lens, mentioning this in the discussion would make the readers aware of this issue. Referring to Jag1 among "lens-specific markers" (page 5) is debatable, suggesting changing to the lines of "the expected upregulation of Jag1 in lens vesicle". The Abstract could be modified to clearly convey the existing knowledge gap and the key findings of the present study. As it stands now, it is a bit all over the place. Some typos in the manuscript need to be fixed, e.g. "...yet its molecular mechanism remains largely resolved" - unresolved? "...in the development lens" - in the developing lens? In Figure 4 legend, "(B) Grb2 mutants Grb2 mutants displayed...", etc.

Reviewer #2 (Public review):

Summary:

I have reviewed a manuscript submitted by Wang et al., which is entitled "Shc1 cooperates with Frs2 and Shp2 to recruit Grb2 in FGF-induced lens development". In this paper, the authors first examined lens phenotypes in mice with Le-Cre-mediated knockdown (KD) of all four FGFR (FGFR1-4), and found that pERK signals, Jag1, and foxe3 expression are absent or drastically reduced, indicating that FGF signaling is essential for lens induction. Next, the authors examined lens phenotypes of FGFR1/2-KD mice and found that lens fiber differentiation is compromised and that proliferative activity and cell survival are also compromised in lens epithelium. Interestingly, Kras activation rescues defects in lens growth and lens fiber differentiation in FGFR1/2-KD mice, indicating that Ras activation is a key step for lens development. Next, the authors examined the role of Frs2, Shp2, and Grb2 in FGF signaling for lens development. They confirmed that lens fiber differentiation is compromised in FGFR1/3-KD mice combined with Frs2-dysfunctional FGFR2 mutants, which is similar to lens phenotypes of Grb2-KD mice. However, lens defects are milder in mice with Shp2YF/YF and Shp2CS mutant alleles, indicating that the involvement of Shp2 is limited for the Grb2 recruitment for lens fiber differentiation. Lastly, the authors showed new evidence on the possibility that another adapter protein, Shc1, promotes Grb2 recruitment independent of Frs2/Shp2-mediated Grb2 recruitment.

Strengths:

Overall, the manuscript provides valuable data on how FGFR activation leads to Ras activation through the adapter platform of Frs2/Shp2/Grb2, which advances our understanding of complex modification of the FGF signaling pathway. The authors applied a genetic approach using mice, whose methods and results are valid to support the conclusion. The discussion also well summarizes the significance of their findings.

Weaknesses:

The authors eventually found that the new adaptor protein Shc1 is involved in Grb2 recruitments in response to FGF receptor activation. however, the main data for Shc1 are histological sections and statistical evaluation of lens size. So, my major concern is that the authors need to provide more detailed data to support the involvement of Shc1 in Grb2 recruitment of FGF signaling for lens development.

Reviewer #3 (Public review):

Summary:

The manuscript entitled "Shc1 cooperates with Frs2 and Shp2 to recruit Grb2 in FGF-induced lens development" by Wang et al., investigates the molecular mechanism used by FGFR signaling to support lens development. The lens has long been known to depend on FGFR signaling for proper development. Previous investigations have demonstrated that FGFR signaling is required for embryonic lens cell survival and for lens fiber cell differentiation. The requirement of FGFR signaling for lens induction has remained more controversial as deletion of both Fgfr1 and Fgfr2 during lens placode formation does not prevent the induction of definitive lens markers such as FOXE3 or αA-crystallin. Here the authors have used the Le-Cre driver to delete all four FGFR genes from the developing lens placode demonstrating a definitive failure of lens induction in the absence of FGFR signaling. The authors focused on FGFR1 and FGFR2, the two primary FGFRs present during early lens development, and demonstrated that lens development could be significantly rescued in lenses lacking both FGFR1 and FGFR2 by expressing a constitutively active allele of KRAS. They also showed that the removal of pro-apoptotic genes Bax and Bak could also lead to a substantial rescue of lens development in lenses lacking both FGFR1 and FGFR2. In both cases, the lens rescue included both increased lens size and the expression of genes characteristic of lens cells.

Significantly the authors concentrated on the juxtamembrane domain, a portion of the FGFRs associated with FRS2. Previous investigations have demonstrated the importance of FRS2 activation for mediating a sustained level of ERK activation. FRS2 is known to associate both with GRB2 and SHP2 to activate RAS. The authors utilized a mutant allele of Fgfr1, lacking the entire juxtamembrane domain (Fgfr1ΔFrs), and an allele of Fgfr2 containing two-point mutations essential for Frs2 binding (Fgfr2LR). When combining three floxed alleles and leaving only one functional allele (Fgfr1ΔFrs or Fgfr2LR) the authors got strikingly different phenotypes. When only the Fgfr1ΔFrs allele was retained, the lens phenotype matched that of deleting both Fgfr1 and Fgfr2. However, when only the Fgfr2LR allele was retained the phenotype was significantly milder, primarily affecting lens fiber cell differentiation, suggesting that something other than FRS2 might be interacting with the juxtamembrane domain to support FGFR signaling in the lens. The authors also deleted Grb2 in the lens and showed that the phenotype was similar to that of the lenses only retaining the Fgfr2LR allele, resulting in a failure of lens fiber cell differentiation and decreased lens cell survival. However, mutating the major tyrosine phosphorylation site of GRB2 did not affect lens development. The author additionally investigated the role of SHP2 lens development by making by either deleting SHP2 or by making mutations in the SHP2 catalytic domain. The deletion of the SHP2 phosphatase activity did not affect lens development as severely as the total loss of SHP2 protein, suggesting a function for SHP2 outside of its catalytic activity. Although the loss of Shc1 alone has only a slight effect on lens size and pERK activation in the lens, the authors showed that the loss of Shc1 exacerbated the lens phenotype in lenses lacking both Frs2 and Shp2. The authors suggest that SHC1 binds to the FGFR juxtamembrane domain allowing for the recruitment of GRB2 independently of FRS2.

Strengths:

(1) The authors used a variety of genetic tools to carefully dissect the essential signals downstream of FGFR signaling during lens development.

(2) The authors made a convincing case that something other than FRS2 binding mediates FGFR signaling in the juxtamembrane domain.

(3) The authors demonstrated that despite the requirement of both the adaptor function and phosphatase activity of SHP2 are required for embryonic survival, neither of these activities is absolutely required for lens development.

(4) The authors provide more information as to why FGFR loss has a phenotype much more severe than the loss of FRS2 alone during lens development.

(5) The authors followed up their work analyzing various signaling molecules in the context of lens development with biochemical analyses of FGF-induced phosphorylation in murine embryonic fibroblasts (MEFs).

(6) In general, this manuscript represents a Herculean effort to dissect FGFR signaling in vivo with biochemical backing with cell culture experiments in vitro.

Weaknesses:

(1) The authors demonstrate that the loss of FGFR1 and FGFR2 can be compensated by a constitutive active KRAS allele in the lens and suggest that FGFRs largely support lens development only by driving ERK activation. However, the authors also saw that lens development was substantially rescued by preventing apoptosis through the deletion of BAK and BAX. To my knowledge, the deletion of BAK and BAX should not independently activate ERK. The authors do not show whether ERK activation is restored in the BAK/BAX deficient lenses. Do the authors suggest the FGFR3 and/or FGFR4 provide sufficient RAS and ERK activation for lens development when apoptosis is suppressed? Alternatively, is it the survival function of FGFR-signaling as much as a direct effect on lens differentiation?

(2) The authors make the argument that deleting all four FGFRs prevented lens induction but that the deletion of only FGFR1 and FGFR2 did not. Part of this argument is the retention of FOXE3 expression, αA-crystallin expression, and PROX1 expression in the FGFR1/2 double mutants. However, in Figure 1E, and Figure 1F, the staining of the double mutant lens tissue with FOXE3, αA-crystallin, and PROX1 is unconvincing. However, the retention of FOXE3 expression in the FGFR1/FGFR2 double mutants was previously demonstrated in Garcia et al 2011. Also, there needs to be an enlargement or inset to demonstrate the retention of pSMAD in the quadruple FGFR mutants in Figure 1D.

(3) Do the authors suggest that GRB2 is required for RAS activation and ultimately ERK activation? If so, do the authors suggest that ERK activation is not required for FGFR-signaling to mediate lens induction? This would follow considering that the GRB2 deficient lenses lack a problem with lens induction.

(4) The increase in p-Shc is only slightly higher in the Cre FGFR1f/f FGFR2r/LR than in the FGFR1f/Δfrs FGFR2f/f. Can the authors provide quantification?

(5) The authors have not shown directly that Shc1 binds to the juxtamembrane region of either Fgfr1 or Fgfr2.

(6) The authors have used the Le-Cre strain for all of their lens deletion experiments. Previous work has documented that the Le-Cre transgene can cause lens defects independent of any floxed alleles in both homozygous and hemizygous states on some genetic backgrounds (Dora et al., 2014 PLoS One 9:e109193 and Lam et al., Human Genomics 2019 13(1):10. Are the controls used in these experiments Le-Cre hemizygotes?

eLife Assessment

This useful study highlights the largely redundant role of the decapping activators Edc3 and Scd6 in orchestrating post-transcriptional programs to modulate metabolic responses to nutrients in yeast. The authors provide solid evidence for their conclusions, employing a variety of mutants in conjunction with a battery of transcriptome-wide analyses. This study could be further strengthened by direct biochemical validation of the functional interactions observed by systems biology approaches.

Reviewer #1 (Public review):

Summary:

mRNA decapping and decay factors play critical roles in post-transcriptionally regulating gene expression. Here, Kumar and colleagues investigate how deleting two yeast decapping enhancer proteins (Edc3 and Scd6), either alone or in tandem, influences the transcriptome. Using RNA-Seq and ribosome profiling, they come to the conclusion that these factors generally act in a redundant fashion, with a mutant lacking both proteins showing an increased abundance of select mRNAs. As these upregulated transcripts are also upregulated in mutants lacking the decapping enzyme, Dcp2, and show no increases in transcription of their cognate genes, they come to the conclusion that this is at the level of mRNA decapping and decay. Their ribosome profiling data also led them to conclude that Scd6 and Edc3 display functional redundancy and cooperativity with Dhh1/Pat1 in repressing the translation of specific transcripts. Finally, as their data suggest that Scd6 and Edc3 repress mRNAs coding for proteins involved in cellular respiration, as well as proteins involved in the catabolism of alternative carbon sources, they go on to show that these decapping activators play a role in repressing oxidative phosphorylation.

Strengths:

Overall, this manuscript is well-written and contains a large amount of high-quality data and analyses. At its core, it helps to shed light on the overlapping roles of Edc3 and Scd6 in sculpting the yeast transcriptome.

Weaknesses:

(1) While the data presented makes conclusions about mRNA stability based on corresponding ChIP-Seq analyses and analyzing other mutants (e.g. Dcp2 knockout), at no point is mRNA stability actually ever directly assessed. This direct assessment, even for select transcripts, would further strengthen their conclusions.

(2) Scd6 and Edc3 show a high level of functional redundancy, as demonstrated by the double mutant. As these proteins form complexes with other decapping factors/activators, I'm curious if depleting both proteins in the double mutant destabilizes any of these other factors. Have the authors ever assessed the levels of other key decapping factors in the double mutants (i.e. Dhh1, Pat1, Dcp2...etc)? I wonder if depleting both proteins leads to a general destabilization of key complexes. It would also be interesting to see if depleting Edc3 or Scd6 leads to a concomitant increase in the other protein as a compensatory mechanism.

(3) While not essential, it would be interesting if the authors carried out add-back experiments to determine which domain within Scd6/Edce3 plays a critical role in enforcing the regulation that they see. Their double mutant now puts them in a perfect position to carry out such experiments.

Reviewer #2 (Public review):

Summary:

This manuscript by Kumar and Zhang presents compelling evidence that Edc3 and Scd6 decapping activators present a high degree of redundancy that can only be overcome by a double mutant. In addition, the authors provide strong evidence of these complexes in regulating starvation-induced pathways as evidenced by measurements of mitochondrial membrane potential, metabolomics, and analysis of the flux of Krebs cycle intermediates.

Strengths:

Kumar and Zhang et al provide multiple sources of evidence of the direct mechanism of Edc3 and Scd6 function, by using and comparing different approaches such as mRNA-seq, ribosome occupancies, and translational efficiencies. By extensive analysis, the authors show that this complex can also regulate genes outside the Environmental Stress Response (non-iESR), which are significantly up-regulated in all three mutants. Remarkably, the gene ontology analysis of these non-iESR genes identifies enrichment for mitochondrial proteins that are implicated in the Krebs cycle. Overall, this study adds novel mechanistic insight into how nutrients control gene expression by modulating decapping and translational repression.

Weaknesses:

The authors show very nicely in Figure S1A that growth phenotypes from scd6Δedc3∆ can be rescued by transformation of EDC3 (pLfz614-7) or SCD6 (pLfz615-5). The manuscript might benefit from using these rescue strategies in the analysis performed (e.g. RNA-seq, ribosome occupancies, and translational efficiencies). Also, these rescue assays could provide a good platform to further characterise the protein-protein interactions between Edc3, Scd6, and Dhh1.

Reviewer #3 (Public review):

Summary:

In this paper, Kumar et al aimed to investigate the roles of two decapping activators, Edc3 and Scd6, in regulating mRNA decay and translation in yeast. Previous research suggested limited individual roles for these proteins in mRNA decay. The authors hypothesized that Edc3 and Scd6 act redundantly and explored how these proteins interact with two other factors involved in mRNA decapping and translational repression, with Dhh1 and Pat1, particularly in response to nutrient availability. The study aims to identify mRNAs targeted by these proteins for degradation and translation repression and assess their impact on metabolic pathways including mitochondrial function and alternative carbon source utilization.

Strengths:

The paper has several strengths including the comprehensive approach taken by the authors using multiple experimental techniques (RNA-seq, ribosome profiling, Western blotting, TMT-MS, and polysome profiling) to examine both mRNA abundance and translation efficiency, thereby providing multiple lines of evidence to support their conclusions. The authors demonstrate clear redundancy of the factors by using single and double mutants for Edc3 and Scd6 and their global approach enables an understanding of these factors' roles across the yeast transcriptome. The work connects post-transcriptional processes to nutrient-dependent gene regulation, providing insights into how cells adapt to changes in their environment. The authors demonstrate the redundant roles of Edc3 and Scd6 in mRNA decapping and translation repression. Their RNA-seq and ribosome profiling results convincingly show that many mRNAs are derepressed only in the double mutants, confirming their hypothesis of redundancy. Furthermore, the functional cooperation between Edc3/Scd6 and Dhh1/Pat1 in regulating specific metabolic pathways, like mitochondrial function and carbon source utilization, is supported by the data. The results therefore support the authors' conclusions that these decapping factors work together to fine-tune gene expression in response to nutrient availability.

Weaknesses:

The limitations of the study include the use of indirect evidence to support claims that Edc3 and Scd6 recruit Dhh1 to the Dcp2 complex, which is inferred from correlations in mRNA abundance and ribosome profiling data rather than direct biochemical evidence. Also, there is limited exploration of other signals as the study is focused on glucose availability, and it is unclear whether the findings would apply broadly across different environmental stresses or metabolic pathways.

Nonetheless, the study provides new insights into how mRNA decapping and degradation are tightly linked to metabolic regulation and nutrient responses in yeast. The RNA-seq and ribosome profiling datasets are valuable resources for the scientific community, providing quantitative information on the role of decapping activators in mRNA stability and translation control.

eLife Assessment

By exploring the conservation and functional diversity of RIPK3 and related RHIM-containing proteins across vertebrates, this important work sheds light on the evolutionary dynamics of these key immune pathways. The evidence supporting the claims is overall solid, although thorough documentation of the evolutionary analysis (e.g. in the 'Phylogenetic analysis' section), and additional work beyond human HEK293 cells, would strengthen the functional validation in support of the study's conclusions.

Reviewer #1 (Public review):

The manuscript titled "Evolutionary and Functional Analyses Reveal a Role for the RHIM in Tuning RIPK3 Activity Across Vertebrates" by Fay et al. explores the function of RIPK gene family members across a wide range of vertebrate and invertebrate species through a combination of phylogenomics and functional studies. By overexpressing these genes in human cell lines, the authors examine their capacity to activate NF-κB and induce cell death. The methods employed are appropriate, with a thorough analysis of gene loss, positive selection, and functionality. While the study is well-executed and comprehensive, its broader relevance remains limited, appealing mainly to specialists in this specific field of research. It misses the opportunity to extract broader insights that could extend the understanding of these genes beyond evolutionary conservation, particularly by employing evolutionary approaches to explore more generalizable functions.

Major comments:

The main issue I encounter is distinguishing between what is novel in this study and what has been previously demonstrated. What new insights have been gained here that are of broader relevance? The discussion, which would be a good place to do so, is very speculative and has little to do with the actual results. Throughout the manuscript, there is little explanation of the study's importance beyond the fact that it was possible to conduct it. Is the evolutionary analysis being used to advance our understanding of gene function, or is the focus merely on how these genes behave across different species? The former would be exciting, while the latter feels less impactful.

Reviewer #2 (Public review):

Summary:

By combining bioinformatical and experimental approaches, the authors address the question of why several vertebrate lineages lack specific genes of the necroptosis pathway or those that regulate the interplay between apoptosis and necroptosis. The lack of such genes was already known from previous publications, but the current manuscript provides a more in-depth analysis and also uses experiments in human cells to address the question of the functionality of the remaining genes and pathways. A particular focus is placed on RIPK3/RIPK1 and their dual roles in inducing NFkB and/or necroptosis.

Strengths:

The well-documented bioinformatical analyses provide a comprehensive data basis of the presence/absence of RIP-kinases, other RHIM proteins, apoptosis signaling proteins (FADD, CASP8, CASP10), and some other genes involved in these pathways. Several of these genes are known to be missing in certain animal lineages, which raises the question of why their canonical binding partners are present in these species. By expressing several such proteins (both wildtype and mutants destroying particular interaction regions) in human cells, the authors succeed in establishing a general role of RIPK3 and RIPK1 in NFkB activation. This function appears to be better conserved and more universal than the necroptotic function of the RHIM proteins. The authors also scrutinize the importance of the kinase function and RHIM integrity for these separate functionalities.

Weaknesses:

A major weakness of the presented study is the experimental restriction to human HEK293 cells. There are several situations where the functionality of proteins from distant organisms (like lampreys or even mussels) in human cells is not necessarily indicative of their function in the native context. In some cases, these problems are addressed by co-expressing potential interaction partners, but not all of these experiments are really informative.

A second weakness is that the manuscript addresses some interesting effects only superficially. By using host cells that are deleted for certain signaling components, a more focussed hypothesis could have been tested.

Thus, while the aim of the study is mostly met, it could have been a bit more ambitious. The limited conclusions drawn by the authors are supported by convincing evidence. I have no doubts that this study will be very useful for future studies addressing the evolution of necroptosis and its regulation by NFkB and apoptosis.

Reviewer #3 (Public review):

This important study provides insights into the functional diversification of RIP family kinase proteins in vertebrate animals. The provided results, which combine bioinformatic and experimental analyses, will be of interest to specialists in both immunology and evolutionary biology. However, the computational part of the methodology is insufficiently covered in the paper and the experimental results would benefit from including data for additional species.

(1) In the Methods section concerning gene loss analysis, the authors refer to the 'Phylogenetic analysis' section for details of RIPK sequence acquisition and alignment procedure. This section is missing from the manuscript as provided. In its absence, it is hard for the reviewer to provide relevant comments on gene presence/absence analysis.

(2) In the same section, the authors state that gene sequences were filtered and grouped based on the initial gene tree pattern (lines 448-449). How exactly did the authors filter the non-RIP kinases and other irrelevant homologs from the gene trees? Did they consider the reciprocal best (BLAST) hit approach or similar approaches for orthology inference? Did they also encounter potential pseudogenes of genes marked as missing in Figure 1C? Will the gene trees mentioned be available as supplementary files?

(3) The authors state the presence of additional RIPK2 paralog in non-therian vertebrates. The ramifications of this paralog loss in therians are not discussed in the text, although RIPK2 is also involved in NF-kB activation. In addition, the RIPK2B gene loss pattern is shunned from Figure 1C to Supplementary Figure 4, despite posing comparable interest to the reader.

(4) The authors present evidence for (repeated) positive selection in both RIPK1 and RIPK3 in bats; however, neither bat RIPK1/3 orthologs nor bat-specific RHIM tetrad variants (IQFG, IQLG) are considered in the experimental part of the work.

(5) The authors present gene presence/absence patterns for zebra mussels as an outgroup of vertebrate species analyzed. From the evolutionary perspective, adding results for a closer invertebrate group, such as lancelets, tunicates, or echinoderms, would be beneficial for reconstructing the evolutionary progression of RIPK-mediated immune functions in animals.

(6) In the broader sense, the list of non-mammalian species included in the study is not explained or substantiated in the text. What was the rationale behind selecting lizards, turtles, and lampreys for experimental assays? Why was turtle RIPK3 but not turtle RIPK1CT protein used for functional tests? Which results do the authors expect to observe if amphibian or teleost RIPK1/3 are included in the analysis, especially those with divergent tetrad variants?

(7) For lamprey RIPK3, the observed NF-kB activity levels still remain lower than those of mammalian and reptilian orthologs even after catalytic tetrad modification. In the same way, switching human RIPK3 catalytic tetrad to that of lamprey does not result in NF-kB activation. What are the potential reasons for the observed difference? Does it mean that lamprey's RIPK3 functions in NF-kB activation are, at least partially, delegated to RIPK1?

(8) In lines 386-388, the authors state that 'only non-mammalian RIPK1CT proteins required the RHIM for maximal NF-kB activation', which is corroborated by results in Figure 4B. The authors further associate this finding with a lack of ZBP1 in the respective species (lines 388-389). However, non-squamate reptiles seem to retain ZBP1, as suggested by Supplementary Table 1. Given that, do the authors expect to observe RHIM-independent (maximal) NF-kB activation in turtles and crocodilians or respective RIPK1CT-transfected cells?

eLife Assessment

This important study reports numerous attempts to replicate reports on transgenerational inheritance of a learned behavior - pathogen avoidance - in C. elegans. While the authors observe parental effects that are limited to a single generation (also called intergenerational inheritance), the authors failed to find any evidence for transmission over multiple generations, or transgenerational inheritance. The experiments presented are meticulously described, making for compelling evidence that in the authors' hands transgenerational inheritance cannot be observed. There remains the possibility that subtle differences in culture conditions or lab environment explain the failure to reproduce previous observations, with updates to the paper having further reduced the probability that this applies here. And even if this were the case, it would imply that the original results were not very robust. Given the prominence of the original reports of transgenerational inheritance, the present study is of broad interest to anyone studying genetics, epigenetics, or learned behavior.

Reviewer #1 (Public review):

Summary:

The authors report an inability to reproduce a transgenerational memory of avoidance of the pathogen PA14 in C. elegans. Instead, the authors demonstrate intergenerational inheritance for a single F1 generation, in embryos of mothers exposed to OP50 and PA14, where embryos isolated from these mothers by bleaching are capable of remembering to avoid PA14 in a manner that is dependent on systemic RNAi proteins sid-1 and sid-2. This could reflect systemic sRNAs generated by neuronal daf-7 signaling that are transmitted to F1 embryos. The authors note that transgenerational memory of PA14 was reported by the Murphy group at Princeton, but that environmental or strain variation (worms or bacteria) might explain the single generation of inheritance observed at Harvard. The Hunter group tried different bacterial growth conditions and different worm growth temperatures for independent PA14 strains, which they show to be strongly pathogenic. However, the authors could not reproduce a transgenerational effect at Harvard. This paper honestly alters expectations and indicates that the model that avoidance of PA14 is remembered for multiple generations is not robust enough to be replicated in all laboratories.<br /> Overall, this paper that demonstrates that one model for transgenerational inheritance in C. elegans not robust. The author do demonstrate an avoidance memory for F1 embryos that could be a maternal effect, and the authors confirm that this is mediated by a systemic small RNA response. There are several points in the manuscript where a more positive tone might be helpful.

Strengths:

The authors note that the high copy number daf-7::GFP transgene used by the Murphy group displayed variable expression and evidence for somatic silencing or transgene breakdown in the Hunter lab, as confirmed by the Murphy group. The authors nicely use single copy daf-7::GFP to show that neuronal daf-7::GFP is elevated in F1 but not F2 progeny with regards to memory of PA14 avoidance, speaking to an intergenerational phenotype.

The authors nicely confirm that sid-1 and sid-2 are generally required for intergenerational avoidance of F1 embryos of moms exposed to PA14. However, these small RNA proteins did not affect daf-7::GFP elevation in the F1 progeny. This result is unexpected given previous reports that daf-7::GFP is not elevated in F1 progeny of sid mutants.

The authors studied antisense small RNAs that change in Murphy data sets, identifying 116 mRNAs that might be regulated by sRNAs in response to PA14. The authors show that the maco-1 gene, putatively targeted by piRNAs according to the Kaletsky 2020 paper, displays few siRNAs that change in response to PA14. The authors conclude that the P11 ncRNA of PA14, which was proposed to promote interkingdom RNA communication by the Murphy group, may not affect maco-1 expression in C. elegans, although they did not formally demonstrate this. The authors define 8 genes based on their analysis of sRNAs and mRNAs that might promote resistance to PA14, but they do not further characterize these genes' role in pathogen avoidance. Others might wish to consider following up on these genes and their possible relationship with P11.

Weaknesses:

This very thorough and interesting manuscript is at times pugnacious.

Please explain more clearly what is High Growth media for E. coli in the text and methods, conveying why it was used by the Murphy lab, and if Normal Growth or High Growth is better for intergenerational heritability assays.

Comments on revisions:

The authors have done a reasonable job cordially revising this manuscript, and the authors have addressed most reviewer concerns. It is likely that the P11 gene was in some of the PA14 Pseudomonas strains tested, as one was kindly provided by the Murphy group

Reviewer #2 (Public review):

This paper examines the reproducibility of results reported by the Murphy lab regarding transgenerational inheritance of a learned avoidance behavior in C. elegans. It has been well established by multiple labs that worms can learn to avoid the pathogen pseudomonas aeruginosa (PA14) after a single exposure. The Murphy lab has reported that learned avoidance is transmittable to 4 generations and dependent on a small RNA expressed by PA14 that elicits the transgenerational silencing of a gene in C. elegans. The Hunter lab now reports that although they can reproduce inheritance of the learned behavior by the first generation (F1), they cannot reproduce inheritance in subsequent generations.

This is an important study that will be useful for the community. Although they fail to identify a "smoking gun", the study examine several possible sources for the discrepancy, and their findings will be useful to others interested in using these assays. The preference assay appears to work in their hands in as much as they are able to detect the learned behavior in the P0 and F1 generations, suggesting that the failure to reproduce the transgenerational effect is not due to trivial mistakes in the protocol. The authors provide a full protocol and highlight key deviations from the Murphy lab protocol. The authors provide good evidence that no single protocol modification was sufficient on its own to explain the divergent results. It remains possible that protocol differences affected the assay cumulatively or that other uncontrolled factors were responsible. Nevertheless, the authors provide good evidence that the trans-generational effect reported by the Murphy lab lacks experimental robustness, calling into question its ecological relevance in the wild.

Reviewer #3 (Public review):

Summary:

It has been previously reported in many high-profile papers, that C. elegans can learn to avoid pathogens. Moreover, this learned pathogen avoidance can be passed on to future generations - up to the F5 generation in some reports. In this paper, Gainey et al. set out to replicate these findings. They successfully replicated pathogen avoidance in the exposed animals, as well as a strong increase in daf-7 expression in ASI neurons in F1 animals, as determined by a daf-7::GFP reporter construct. However, they failed to see strong evidence for pathogen avoidance or daf-7 overexpression in the F2 generation. The failure of replication is the major focus of this work.<br /> Given their failure to replicate these findings, the authors embark on a thorough test of various experimental confounders that may have impacted their results. They also re-analyze the small RNA sequencing and mRNA sequencing data from one of the previously published papers and draw some new conclusions, extending this analysis.

Strengths:

• The authors provide a thorough description of their methods, and a marked-up version of a published protocol that describes how they adapted the protocol to their lab conditions. It should be easy to replicate the experiments.<br /> • The authors test source of bacteria, growth temperature (of both C. elegans and bacteria), and light/dark husbandry conditions. They also supply all their raw data, so that sample size for each testing plate can be easily seen (in the supplementary data). None of these variations appears to have a measurable effect on pathogen avoidance in the F2 generation, with all but one of the experiments failing to exhibit learned pathogen avoidance.<br /> • The small RNA seq and mRNA seq analysis is well performed and extends the results shown in the original paper. The original paper did not give many details of the small RNA analysis, which was an oversight. Although not a major focus of this paper, it is a worthwhile extension on the previous work.<br /> • It is rare that negative results such as these are accessible. Although the authors were unable to determine the reason that their results differ from those previously published, it is important to document these attempts in detail, as has been done here. Behavioral assays are notoriously difficult to perform and public discourse around these attempts may give clarity to the difficulties faced by a controversial field.

Weaknesses:

• Although the "standard" conditions have been tested over multiple biological replicates, many of the potential confounders that may have altered the results have been tested only once or twice. For example, changing the incubation temperature to 25{degree sign}C was tested in only two biological replicates (Exp 5.1 and 5.2) - and one of these experiments actually resulted in apparent pathogen avoidance inheritance in the F2 generation (but not in the F1). An alternative pathogen source was tested in only one biological replicate (Exp 3). Given the variability observed in the F2 generation, increasing biological replicates would have added to the strengths of the report.<br /> • A key difference between the methods used here and those published previously, is an increase in the age of the animals used for training - from mostly L4 to mostly young adults. I was unable to find a clear example of an experiment when these two conditions were compared, although the authors state that it made no difference to their results.<br /> • The original paper reports a transgenerational avoidance effect up to the F5 generation. Although in this work the authors failed to see avoidance in the F2 generation, it would have been prudent to extend their tests for more generations in at least a couple of their experiments to ensure that the F2 generation was not an aberration (although this reviewer acknowledges that this seems unlikely to be the case).

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The authors report an inability to reproduce a transgenerational memory of avoidance of the pathogen PA14 in C. elegans. Instead, the authors demonstrate intergenerational inheritance for a single F1 generation, in embryos of mothers exposed to OP50 and PA14, where embryos isolated from these mothers by bleaching are capable of remembering to avoid PA14 in a manner that is dependent on systemic RNAi proteins sid-1 and sid-2. This could reflect systemic sRNAs generated by neuronal daf-7 signaling that are transmitted to F1 embryos. The authors note that transgenerational memory of PA14 was reported by the Murphy group at Princeton, but that environmental or strain variation (worms or bacteria) might explain the single generation of inheritance observed at Harvard. The Hunter group tried different bacterial growth conditions and different worm growth temperatures for independent PA14 strains, which they showed to be strongly pathogenic. However, the authors could not reproduce a transgenerational effect at Harvard. This important data will allow members of the scientific community to focus on the robust and reproducible inheritance of PA14 avoidance transmitted to F1 embryos of mothers exposed to PA14, which the authors demonstrate depends on small RNAs in a manner that is downstream of or in parallel to daf-7. This paper honestly and importantly alters expectations and questions the model that avoidance of PA14 is mediated by a bacterial ncRNA whose siRNAs target a C. elegans gene. Instead, endogenous C. elegans sRNAs that affect pathogen response may be the culprit that explains sRNA-mediated avoidance.

Overall, this is an important paper that demonstrates that one model for transgenerational inheritance in C. elegans is not reproducible. This is important because it is not clear how many of the reported models of transgenerational inheritance reported in C. elegans are reproducible. The authors do demonstrate a memory for F1 embryos that could be a maternal effect, and the authors confirm that this is mediated by a systemic small RNA response. There are several points in the manuscript where a more positive tone might be helpful.

We would like to correct the statement made in the second to last sentence. The demonstration of an F1 response to PA14 was first reported by Moore et al., (2019) and then by Pereira et al., (2020) using a different behavioral assay. We merely confirmed these results in our hands, and confirmed the observation, first reported by Kaletsky et al., (2020), that sid-1 and sid-2 are required for this F1 response; although we did find that sid-1 and sid-2 are not required for the PA14-induced increase in daf-7p::gfp expression in ASI neurons in the F1 progeny of trained adults, which had not been addressed in the published work.

Yes, the intergenerational F1 response could be a maternal effect, but the in utero F1 embryos and their precursor germ cells were directly exposed to PA14 metabolites and toxins (non-maternal effect) as well as any parental response, whether mediated by small RNAs, prions, hormones, or other unknown information carriers. While the F1 aversion response does require sid-1 and sid-2, we would not presume that the substrate is therefore an RNA molecule, particularly because the systemic RNAi response supported by sid-1 and sid-2 is via long double-stranded RNA. To date, no evidence suggests that either protein transports small RNAs, particularly single-stranded RNAs.

Strengths:

The authors note that the high copy number daf-7::GFP transgene used by the Murphy group displayed variable expression and evidence for somatic silencing or transgene breakdown in the Hunter lab, as confirmed by the Murphy group. The authors nicely use single copy daf-7::GFP to show that neuronal daf-7::GFP is elevated in F1 but not F2 progeny with regards to the memory of PA14 avoidance, speaking to an intergenerational phenotype.

The authors nicely confirm that sid-1 and sid-2 are generally required for intergenerational avoidance of F1 embryos of moms exposed to PA14. However, these small RNA proteins did not affect daf-7::GFP elevation in the F1 progeny. This result is unexpected given previous reports that single copy daf-7::GFP is not elevated in F1 progeny of sid mutants. Because the Murphy group reported that daf-7 mutation abolishes avoidance for F1 progeny, this means that the sid genes function downstream of daf-7 or in parallel, rather than upstream as previously suggested.

The published report (Moore et al., 2019) shows only multicopy daf-7p::gfp results and does not address the daf-7p::gfp response in sid-1 or sid-2 mutants. Thus, our discovery that systemic RNAi, exogenous RNAi, and heritable RNAi mutants don’t disrupt elevated daf-7p::gfp in ASI neurons in the F1 progeny of PA14 trained P0’s is only unexpected with respect to the published models (Moore et al., 2019, Kaletsky et al., 2020).

The authors studied antisense small RNAs that change in Murphy data sets, identifying 116 mRNAs that might be regulated by sRNAs in response to PA14. Importantly, the authors show that the maco-1 gene, putatively targeted by piRNAs according to the Kaletsky 2020 paper, displays few siRNAs that change in response to PA14. The authors conclude that the P11 ncRNA of PA14, which was proposed to promote interkingdom RNA communication by the Murphy group, is unlikely to affect maco-1 expression by generating sRNAs that target maco-1 in C. elegans. The authors define 8 genes based on their analysis of sRNAs and mRNAs that might promote resistance to PA14, but they do not further characterize these genes' role in pathogen avoidance. The Murphy group might wish to consider following up on these genes and their possible relationship with P11.

Weaknesses:

This very thorough and interesting manuscript is at times pugnacious.

We reiterate that we never claimed that Moore et al., (2019) did not obtain their reported results. We simply stated that we could not replicate their results using the published methods and then failed in our search to identify variable(s) that might account for our results. In revising the manuscript, we have striven to make clear, unmuddied statements of facts and state that future investigations may provide independent evidence that supports the original claims and explains our divergent results.

Please explain more clearly what is High Growth media for E. coli in the text and methods, conveying why it was used by the Murphy lab, and if Normal Growth or High Growth is better for intergenerational heritability assays.

We added the standard recipes and the following explanations in the methods section to the revised text.

“NG plates minimally support OP50 growth, resulting in a thin lawn that facilitates visualization of larvae and embryos. HG plates (8X more peptone) support much higher OP50 growth, resulting in a thick bacterial lawn that supports larger worm populations.”

We have also included the following text in our presentation and discussion of the effects of growth conditions on worm choice in PA14 vs OP50 choice assays.

“Furthermore, because OP50 pathogenicity is enhanced by increased E. coli nutritive conditions (Garsin et al., 2003, Shi et al., 2006), the growth of F1-F4 progeny on High Growth (HG) plates (Moore et al., 2019; 2021b), which contain 8X more peptone than NG plates and therefore support much higher OP50 growth levels, immediately prior to the F1-F4 choice assays may further contribute to OP50 aversion among the control animals.”

We don’t know enough to claim that HG or NG media is better than the other for intergenerational assays, but they are different. Thus, switching between the two in a multigenerational experiment likely introduces unknown variability.

Reviewer #2 (Public Review):

This paper examines the reproducibility of results reported by the Murphy lab regarding transgenerational inheritance of a learned avoidance behavior in C. elegans. It has been well established by multiple labs that worms can learn to avoid the pathogen pseudomonas aeruginosa (PA14) after a single exposure. The Murphy lab has reported that learned avoidance is transmittable to 4 generations and dependent on a small RNA expressed by PA14 that elicits the transgenerational silencing of a gene in C. elegans. The Hunter lab now reports that although they can reproduce inheritance of the learned behavior by the first generation (F1), they cannot reproduce inheritance in subsequent generations.

This is an important study that will be useful for the community. Although they fail to identify a "smoking gun", the study examines several possible sources for the discrepancy, and their findings will be useful to others interested in using these assays. The preference assay appears to work in their hands in as much as they are able to detect the learned behavior in the P0 and F1 generations, suggesting that the failure to reproduce the transgenerational effect is not due to trivial mistakes in the protocol. An obvious reason, however, to account for the differing results is that the culture conditions used by the authors are not permissive for the expression of the small RNA by PA14 that the MUrphy lab identified as required for transgenerational inheritance. It would seem prudent for the authors to determine whether this small RNA is present in their cultures, or at least acknowledge this possibility.

We thank the reviewer for raising this issue and have added the following statement to this effect in the revised manuscript.

“We note that previous bacterial RNA sequence analysis identified a small non-coding RNA called P11 whose expression correlates with bacterial growth conditions that induce heritable avoidance (Kaletsky et al., 2020). Critically, C. elegans trained on a PA14 ΔP11 strain (which lacks this small RNA) still learn to avoid PA14, but their F1 and F2-F4 progeny fail to show an intergenerational or transgenerational response (Figure 3L in Kaletsky et al., 2020). The fact that we observed an intergenerational (F1) avoidance response is evidence that our PA14 growth conditions induce P11 expression.”

We believe that this addresses the concern raised here.

The authors should also note that their protocol was significantly different from the Murphy protocol (see comments below) and therefore it remains possible that protocol differences cumulatively account for the different results.

As suggested below, we have added to the supplemental documents the protocol we followed for the aversion assay. In our view, this document shows that our adjustments to the core protocol were minor. Furthermore, where possible, these adjustments were explicitly tested in side-by-side experiments for both the aversion assay and the daf-7p::gfp expression assay and presented in the manuscript.

To discover the source(s) of discrepancy between our results and the published results we subsequently introduced variations to this core protocol to exclude likely variables (worm and bacteria growth temperatures, assay conditions, worm handling methods, bacterial culture and storage conditions, and some minor developmental timing issues). Again, where possible, the effect of variations was tested in side-by-side experiments for both the aversion assay and the daf-7p::gfp expression assay and were presented in or have now been added to the manuscript.

It remains possible that we misunderstood the published Murphy lab protocols, but we were highly motivated to replicate the results so we could use these assays to investigate the reported RNAi-pathway dependent steps, thus we read every published version with extreme care.

Reviewer #3 (Public Review):

Summary:

It has been previously reported in many high-profile papers, that C. elegans can learn to avoid pathogens. Moreover, this learned pathogen avoidance can be passed on to future generations - up to the F5 generation in some reports. In this paper, Gainey et al. set out to replicate these findings. They successfully replicated pathogen avoidance in the exposed animals, as well as a strong increase in daf-7 expression in ASI neurons in F1 animals, as determined by a daf-7::GFP reporter construct. However, they failed to see strong evidence for pathogen avoidance or daf-7 overexpression in the F2 generation. The failure of replication is the major focus of this work.

Given their failure to replicate these findings, the authors embark on a thorough test of various experimental confounders that may have impacted their results. They also re-analyze the small RNA sequencing and mRNA sequencing data from one of the previously published papers and draw some new conclusions, extending this analysis.

Strengths:

(1) The authors provide a thorough description of their methods, and a marked-up version of a published protocol that describes how they adapted the protocol to their lab conditions. It should be easy to replicate the experiments.

As noted above in response to a suggestion by reviewer #2, we have replaced the annotated published protocol with the protocol that we followed. This will aid other groups' attempts to replicate our experimental conditions.

(2) The authors test the source of bacteria, growth temperature (of both C. elegans and bacteria), and light/dark husbandry conditions. They also supply all their raw data, so that the sample size for each testing plate can be easily seen (in the supplementary data). None of these variations appears to have a measurable effect on pathogen avoidance in the F2 generation, with all but one of the experiments failing to exhibit learned pathogen avoidance.

We note that the parallel analysis of daf-7p::gfp expression in ASI neurons was also tested for several of these conditions and also failed to replicate the published findings.

(3) The small RNA seq and mRNA seq analysis is well performed and extends the results shown in the original paper. The original paper did not give many details of the small RNA analysis, which was an oversight. Although not a major focus of this paper, it is a worthwhile extension of the previous work.

(4) It is rare that negative results such as these are accessible. Although the authors were unable to determine the reason that their results differ from those previously published, it is important to document these attempts in detail, as has been done here. Behavioral assays are notoriously difficult to perform and public discourse around these attempts may give clarity to the difficulties faced by a controversial field.

Thank you for your support. Choosing to pursue publication of these negative results was not an easy decision, and we thank members of the community for their support and encouragement.

Weaknesses:

(1) Although the "standard" conditions have been tested over multiple biological replicates, many of the potential confounders that may have altered the results have been tested only once or twice. For example, changing the incubation temperature to 25{degree sign}C was tested in only two biological replicates (Exp 5.1 and 5.2) - and one of these experiments actually resulted in apparent pathogen avoidance inheritance in the F2 generation (but not in the F1). An alternative pathogen source was tested in only one biological replicate (Exp 3). Given the variability observed in the F2 generation, increasing biological replicates would have added to the strengths of the report.

We agree that our study was not exhaustive in our exploration of variables that might be interfering with our ability to detect F2 avoidance. We also note that some of these variables also failed (with many more independent experiments) to induce elevated daf-7p::gfp expression in ASI neurons in F2 progeny. Our goal was not to show that variation in some growth or assay condition would generate reproducible negative results, but the exploration was designed to tweak conditions to enable detection of a robust F2 response. Given the strength of the data presented in Moore et al., (2019) we expected that adjustment of the problematic variable would produce positive results apparent in a single replicate, which could then be followed up. If we had succeeded, then we would have documented the conditions that enabled robust F2 inheritance and would have explored molecular mechanisms that support this important but mysterious process.

(2) A key difference between the methods used here and those published previously, is an increase in the age of the animals used for training - from mostly L4 to mostly young adults. I was unable to find a clear example of an experiment when these two conditions were compared, although the authors state that it made no difference to their results.

We can state firmly that the apparent time delay did not affect P0 learned avoidance (new Figure S1) or, as documented in Table S1, daf-7p::gfp expression in ASI neurons. In our experience, training mostly L4’s on PA14 frequently failed to produce sufficient F1 embryos for both F1 avoidance assays or daf-7p::gfp measurements in ASI neurons and collection of F2 progeny. Indeed, in early attempts to detect heritable PA14 aversion, trained P0 and F1 progeny were not assayed in order to obtain sufficient F2’s for a choice assay. These animals failed to display aversion, but without evidence of successful P0 training or an F1 intergenerational response this was deemed a non-fruitful trouble-shooting approach. We have added supplemental Figure S1 which presents P0 choice assay results from experiments using younger trained animals that failed to produce sufficient F1’s to continue the inheritance experiments.

The different timing at the start of training between the two protocols may reflect the age of the recovered bleached P0 embryos. It is reasonable to assume that bleaching day 1 adults vs day 2 or 3 adults from the P-1 population could shift the average age of recovered P0 embryos by several hours. The Murphy protocol only states that P0 embryos were obtained by bleaching healthy adults. Regardless, if the hypothesis entertained here is true, that a several hour difference in larval/adult age during 24 hours of training affects F2 inheritance of learned aversion but does not affect P0 learned avoidance, then we would argue that this paradigm for heritable learned avoidance, as described in Moore et al., (2019, 2021), is not sufficiently robust for mechanistic investigations.

(3) The original paper reports a transgenerational avoidance effect up to the F5 generation. Although in this work the authors failed to see avoidance in the F2 generation, it would have been prudent to extend their tests for more generations in at least a couple of their experiments to ensure that the F2 generation was not an aberration (although this reviewer acknowledges that this seems unlikely to be the case).

We would point out that we also failed to robustly replicate the F2 response in the daf-7p::gfp expression assays. An F2-specific aberration that affects two different assays seems quite unlikely, and it remains unclear how we would interpret a positive result in F3 and F4 generations without a positive result in the F2 generation. Were we to further extend these investigations, we believe that exploration of additional culture conditions would warrant higher priority than extension of our results to the F3 and F4 generations.

Reviewing Editor Comments:

The reviewers' suggestions for improving the manuscript were mostly minor, to change the wording in some places and to add some more explanation regarding the methods.

What should be highlighted in the section on OP50 growth conditions is that the initial preference for PA14 in the Murphy lab has also been observed by multiple other labs (Bargmann, Kim, Zhang, Abbalay). The fact that this preference was not observed by the Hunter lab is one of several indicators of subtle differences in the environment that might add up to explain the differences in results.

We agree that subtle known and unknown differences in OP50 and PA14 culture conditions can have measurable effects on the detection of PA14 attraction/aversion relative to OP50 attraction/aversion that could obscure or create the appearance of heritable effects between generations. We have added (see below) to the text a fuller description of the variability in the initial or naive preference observed in different laboratories using similar or variant 2-choice assays and culture conditions. It is worth emphasizing that direct comparison of the OP50 growth conditions specified in Moore et al., (2021) frequently revealed a much larger effect on the naïve choice index than is reported between labs (Figure 4).  

“Naïve (OP50 grown) worms often show a bias towards PA14 in choice assays (Zhang et al., 2005; Ha et al., 2010; Moore et al., 2019; Pereira et al., 2020; Lalsiamthara and Aballay, 2022). This response, rather than representing an innate attraction to PA14, likely reflects the context of the worm's recent growth on OP50, a mild C. elegans pathogen (Garigan et al., 2002; Garsin et al., 2003; Shi et al., 2006). Thus, the naïve worms presented with a choice between a recently experienced mild pathogen (OP50) and a novel food choice (PA14) initially choose the novel food instead of the known mild pathogen (OP50 aversion).

In line with our results, some other groups have also reported higher naïve choice index scores (Lee et al., 2017). This variability in naïve choice may reflect differences in growth conditions of either the OP50 or PA14 bacteria. In addition, we note that among the studies that show naïve worm attraction to Pseudomonas (OP50 aversion) there are extensive methodological differences from the methods in Moore et al., (2019; 2021b), including differences in bacterial growth temperature, incubation time, whether the bacteria is diluted or concentrated prior to placement on the choice plates, the concentration of peptone in the choice plates, the length of the choice assay, and the inclusion of sodium azide in the choice assays (Zhang et al., 2005; Ha et al., 2010; Moore et al., 2019; Pereira et al 2020; Lalsiamthara and Aballay, 2022). Thus, the cause of the variability across published reports is not clear.”

Overall, an emphasis on the absence of robustness of the reported results, rather than failure to reproduce them (which can always have many reasons), is appropriate.

We agree that an emphasis on robustness is appropriate and have modified the text throughout the manuscript to shift the emphasis to absence of robustness. This includes a change to the manuscript title, which is now,

“Reported transgenerational responses to Pseudomonas aeruginosa in C. elegans are not robust”

A significant experimental addition would be some attempts to determine whether the bacterial PA14 pathogen in the authors' lab produces the P11 small RNA, which has been proposed to have a causal role in initiating the previously reported transgenerational inheritance.

We acknowledge in the revised manuscript that a subsequent publication (Kaletsky et al., 2020) identified a correlation between PA14 training conditions that induced transgenerational memory and the expression of P11, a P. aeruginosa small non-coding RNA (see our response above to Reviewer #2’s similar query). While testing for the presence of P11 in Harvard culture conditions would be an important assay in any study whose purpose was to investigate the proposed P11-mediated mechanism underlying the transgenerational responses reported by the Murphy Lab, our goal was rather to replicate the robust transgenerational (F2) responses to PA14 training and then to investigate in more detail how sid-1 and sid-2 contribute to transgenerational epigenetic inheritance. Neither sid-1 nor sid-2 are predicted to transport small RNAs or single-stranded RNAs, thus testing for the presence of P11 is less relevant to our goals. Regardless, we note that Figure 3L in Kaletsky et al., (2020) showed that PA14 ΔP11 bacteria failed to induce an F1 avoidance response. Thus, the fact that we observed F1 avoidance implies that our culture conditions successfully induced P11 expression.

Reviewer #1 (Recommendations For The Authors):

The abstract could be more positive by concluding that 'We conclude that this example of transgenerational inheritance lacks robustness but instead reflects an example of small RNA-mediated intergenerational inheritance.'

As recommended, we have added additional clarifying information to the abstract and moderated the conclusion sentence.

“We did confirm that the dsRNA transport proteins SID-1 and SID-2 are required for the intergenerational (F1) inheritance of pathogen avoidance, but not for the F1 inheritance of elevated daf-7 expression. Furthermore, our reanalysis of RNA seq data provides additional evidence that this intergenerational inherited PA14 response may be mediated by small RNAs.”

“We conclude that this example of transgenerational inheritance lacks robustness, confirm that the intergenerational avoidance response, but not the elevated daf-7p::gfp expression in F1 progeny, requires sid-1 and sid-2, and identify candidate siRNAs and target genes that may mediate this intergenerational response.”

Differential expression of sRNAs or mRNAs might be better understood quantitatively by presenting data in scatterplots (Reed and Montgomery 2020) rather than in volcano plots.

We agree and have modified Figure 6A and 6B.

This statement in the main text might be unnecessary, as it affects the tenor of the conclusion of this significant manuscript. 'We note that none of the raw data for the published figures and unpublished replicate experiments . . . this hampered our ability to fully compare'.

We have rewritten this paragraph to focus on our goal: to identify the source of the discrepancy between our results and the published results. We considered discarding this statement but ultimately decided that our inability to directly compare our data to that of previously published work is a shortcoming of our study that deserves to be acknowledged and explained.

“Ideally, we would have compared our results with the published results (Moore et al., 2019), to possibly identify additional experimental parameters for further investigation; for example, a quantitative comparison of naïve choice in the P0 and F1 generations could help to determine the role of bacterial growth in the choice assay response. However, none of the raw data for the published figures and unpublished replicate experiments (Moore et al., 2019) were available on the publisher’s website or provided upon request to the corresponding author. In the absence of a quantitative comparison, it remains possible that an explanation for the discrepancies between our results and those of Moore et al., (2019) has been overlooked.”

The final sentence of the Discussion could be tempered and more positive by stating 'Thus independent reproducibility is of paramount concern, and we have tried to be completely transparent as a model for how heritability research should be conducted within the C. elegans community'.

Thank you. The suggested sentence nicely captures our intention. We now use it, almost verbatim, as our final sentence.

“Thus, independent reproducibility is of paramount concern, and we have tried to be completely transparent as a model for how heritability research should be presented within the C. elegans community.”

Reviewer #2 (Recommendations For The Authors):

Specific comments:

(1) Protocol: It is difficult to assess from the Methods the exact protocol used by the authors to assay food preference. The annotated Murphy protocol is not sufficient. The authors should provide their own protocol - a detailed lab-ready protocol where every step is outlined, and any steps that deviate from the Murphy lab protocol are called out.

Thank you for this excellent suggestion. We now include a protocol that documents the precise steps, timings, and controls that we followed (S1_aversion_protocol). We also include footnotes to both explain the reasons behind particular steps and to document known differences to the published protocol. Given the thoroughness of this suggested approach, we have thus removed the annotated version of Moore et al., (2021) from the revised submission.

(2) The authors imply in the methods that, unlike the Murphy lab, they did NOT use azide in the assay, and instead used 4oC to "freeze" the worms in place - It is not clear whether this method was used throughout all their assays and whether this could be a source of the difference. This change is NOT indicated in the annotated Murphy lab STAR Protocol they provide in the supplement.

We apologize for the lack of clarity. Concerned that azide may be interfering with our ability to detect heritable silencing we tested and then used cold-induced rigor to preserve worm choice in some choice assay results. This was not a change to the core protocol, but a variation used in some assays to determine whether azide could reduce our ability to detect heritable behavioral responses to PA14 exposure. As Moore et al., (2021) show, too much azide can affect measurement of worm choice. Too little or ineffective azide also can affect measurement of worm choice. Azide also affects bacteria (both OP50 and PA14), which could affect the production of molecules that attract or repel worms, much like performing the assay in light vs dark conditions can influence the measured choice index.

In our hands, cold-induced rigor worked well and within biological replicates was indistinguishable from azide (Figure S10). Thus, we include those results in our analysis and now indicate in Tables 2 and S2 and in Figures 1 and 3 which experiments used which method. As suggested, we now provide a detailed protocol that includes a note describing our precise method for cold-induced rigor.

Also, the number of worms used in each assay needs to be specified (same or different from Murphy protocol?), and whether any worms were "censored" as in the Murphy protocol, and if so on what basis.

While we published the exact number of worms scored in each assay (on each plate) it is unknown how this might compare to the results published in Moore et al., (2019), as the number of animals in the presented choice assays (either per plate or per choice) were not reported. Details on censoring, when to exclude data, and additional criteria to abandon an in-progress experiment are now detailed in the protocol (S1_aversion_protocol)

(3) Several instances in the text cite changes in the protocol as producing "no meaningful differences" without referring to a specific experiment that supports that statement (for example, line 399 regarding azide).

We now include data and methods comparing azide and cold-induced rigor (Supplemental document S1_aversion_protocol, Supplemental Figure S10), and data showing the P0 choice index for 48-52 hour post-bleach L4/young adults (Supplemental Figure S1), in addition to the previously noted absence of effects due to differences in embryo bleaching protocols (Figures 2, 3 and Tables 1, 2, S1, and S2).

(4) If the authors want to claim the irreproducibility of the Murphy lab results, they should use the exact protocol used by the Murphy lab in its entirety. It is not sufficient to show that individual changes do not affect the outcome, since the protocol they use appears to include SEVERAL changes which could cumulatively affect the results. If the authors do not want to do this, they should at least acknowledge and summarize in their discussion ALL their protocol changes.

We acknowledge these minor differences between the protocols we followed and the published methods but disagree that they invalidate our results. We transparently present the effect of known minimal protocol changes. We also present analysis of possible invalidating variations (number of animals in a choice assay). We emphasize that in our hands both measures of TEI, the choice assay and measurement of daf-7p::gfp in ASI neurons, failed to replicate the published transgenerational results.

If the protocol is sensitive to how animals are counted, whether bleached embryos are mixed gently or vigorously or a few hours difference in age at training, then in our view this TEI paradigm is not robust.

See also our response to reviewer #3’s public reviews above.

(5) The authors acknowledge that "non-obvious growth culture differences" could account for the different results. In this respect, the Murphy lab has proposed that the transgenerational effect requires a small RNA expressed in PA14. The authors should check that this RNA is expressed in the cultures they grow in their lab and use for their experiments. This could potentially identify where the two protocols diverge.

The bacterial culture conditions and worm training procedures described in Moore et al., (2019) successfully produced trained P0 animals that transmitted a PA14 aversion response to their F1 progeny. In a subsequent publication (Kaletsky et al., 2020), the Murphy lab showed a correlation between the culture conditions that induce heritable avoidance and the expression of P11, a P. aeruginosa small non-coding RNA. As mentioned above in response to Reviewer #2’s public review and the Reviewing Editor’s comments to authors, the Murphy lab showed that PA14 ΔP11 bacteria fail to induce an F1 avoidance response (Figure 3L in Kaletsky et al., (2020)). Thus, the fact that we observed F1 avoidance implies that our culture conditions successfully induced P11 expression. We believe that this addresses the concern raised here. Furthermore, if P11 is not reliably expressed in pathogenic PA14, then the published model is unlikely to be relevant in a natural environment. Again, we thank the reviewer for raising this issue and have added this information to the revised manuscript (see above response to Reviewer #2’s Public Reviews).

(6) Legend to Figure 1: please clarify which experiments were done with which PA14 isolates especially for A-C. What is the origin of the N2 strain used here?

These details from Tables 2 and S2 have been added to Figure 1 panels A-C and Figure 3. Bristol N2, obtained from the CGC (reference 257), was used for aversion experiments.

(7) Growth conditions: "These young adults produced comparable P0 and F1 results (Figure 1, Figure 2, and Figure 3)." It is not clear from the text what specific figure panels need to be compared to examine the effect of the variables described in the text. Please indicate which figure panels should be compared (lines 70-95).

The information for the daf-7p::gfp expression experiments displayed in Figure 1 and Figure 2 is presented in Table 1 and Table S1. The data for P0 aversion training using younger animals is now presented in Figure S1.

Reviewer #3 (Recommendations For The Authors):

While overall I found this easy to follow and well-written, I think the clarity of the figures could be improved by incorporating some of the information from S2 into Figure 3. Besides the figure label listing the experiment (Exp1, Exp2, etc) it would be helpful to add pertinent information about the experiment. For example Exp 1.1 (light, 20{degree sign}C), Exp1.2 (dark, 20{degree sign}C), Exp 5 (25{degree sign}C, light), etc.

Thank you for the suggestion. These details from Tables 2 and S2 have been added to Figures 1 A-C, and 3.

Citations

Moore, R.S., Kaletsky, R., and Murphy, C.T. (2019). Piwi/PRG-1 Argonaute and TGF-beta Mediate Transgenerational Learned Pathogenic Avoidance. Cell 177, 1827-1841 e1812.

Moore, R.S., Kaletsky, R., and Murphy, C.T. (2021). Protocol for transgenerational learned pathogen avoidance behavior assays in Caenorhabditis elegans. STAR Protoc 2, 100384.

Kaletsky, R., Moore, R.S., Vrla, G.D., Parsons, L.R., Gitai, Z., and Murphy, C.T. (2020). C. elegans interprets bacterial non-coding RNAs to learn pathogenic avoidance. Nature 586, 445-451.

Pereira, A.G., Gracida, X., Kagias, K., and Zhang, Y. (2020). C. elegans aversive olfactory learning generates diverse intergenerational effects. J Neurogenet 34, 378-388.

eLife Assessment

In this work, the authors use a Drosophila adult ventral nerve cord injury model extending and confirming previous observations; this important study reveals key aspects of adult neural plasticity. Taking advantage of several genetic reporter and fate tracing tools, the authors provide solid evidence for different forms of glial plasticity, that are increased upon injury. The data on detected plasticity under physiologic conditions and especially the extent of cell divisions and cell fate changes upon injury would benefit from validation by additional markers. The experimental part would improve if strengthened and accompanied by a more comprehensive integration of results regarding glial reactivity in the adult CNS.

Reviewer #1 (Public review):

Summary:

Casas-Tinto et al. present convincing data that injury of the adult Drosophila CNS triggers transdifferentiation of glial cell and even the generation of neurons from glial cells. This observation opens up the possibility to get an handle on the molecular basis of neuronal and glial generation in the vertebrate CNS after traumatic injury caused by Stroke or Crush injury. The authors use an array of sophisticated tools to follow the development of glial cells at the injury site in very young and mature adults. The results in mature adults reveal a remarkable plasticity in the fly CNS and dispels the notion that repair after injury may be only possible in nerve cords which are still developing. The observation of so called VC cells which do not express the glial marker repo could point to the generation of neurons by former glial cells.

Conclusion:

The authors present an interesting story which is technically sound and could form the basis for an in depth analysis of the molecular mechanism driving repair after brain injury in Drosophila and vertebrates.

Strengths:

The evidence for transdifferentiation of glial cells is convincing. In addition, the injury to the adult CNS shows an inherent plasticity of the mature ventral nerve cord which is unexpected.

Weaknesses:

Traumatic brain injury in Drosophila has been previously reported to trigger mitosis of glial cells and generation of neural stem cells in the larval CNS and the adult brain hemispheres. Therefore this report adds to but does not significantly change our current understanding. The origin and identity of VC cells is still unclear. The authors show that VC cells are not GABA- or glutamergic. Yet, there are many other neurotransmitter or neuropetides. It would have been nice to see a staining with another general neuronal marker such as anti-Syt1 to confirm the neuronal identity of Syt1.

Reviewer #2 (Public review):

Summary:

Casas-Tinto et al., provide new insight into glial plasticity using a crush injury paradigm in the ventral nerve cord (VNC) of adult Drosophila. The authors find that both astrocyte-like glia (ALG) and ensheating glia (EG) divide under homeostatic conditions in the adult VNC and identify ALG as the glial population that specifically ramps up proliferation in response to injury, whereas the number of EGs decreases following the insult. Using lineage-tracing tools, the authors interestingly observe interconversion of glial subtypes, especially of EGs into ALGs, which occurs independent of injury and is dependent on the availability of the transcription factor Prospero in EGs, adding to the plasticity observed in the system. Finally, when tracing the progeny of glia, Casas-Tinto and colleagues detect cells of neuronal identity and provide evidence that such glia-derived neurogenesis is specifically favored following ventral nerve cord injury, which puts forward a remarkable way in which glia can respond to neuronal damage.

Strengths:

This study highlights a new facet of adult nervous system plasticity at the level of the ventral nerve cord, supporting the view that proliferative capacity is maintained in the mature CNS and stimulated upon injury.

The injury paradigm is well chosen, as the organization of the neuromeres allows specific targeting of one segment, compared to the remaining intact and with the potential to later link observed plasticity to behavior such as locomotion.

Numerous experiments have been carried out in 7-day old flies, showing that the observed plasticity is not due to residual developmental remodeling or a still immature VNC.

By elegantly combining different methods, the authors show glial divisions including with mitotic-dependent tracing and find that the number of generated glia is refined by apoptosis later on.

The work identifies prospero in glia as an important coordinator of glial cell fate, from development to the adult context, which draws further attention to the upstream regulatory mechanisms.

Weaknesses:

The authors observe consistent inter-conversion of EG to ALG glial subtypes that is further stimulated upon injury. The authors conclude that these findings have important consequences for CNS regeneration and potentially for memory and learning. However, it remains somewhat unclear how glial transformation could contribute to regeneration and functional recovery.

The signal of the Fucci cell cycle reporter seems more complex to interpret based on the panels provided compared to the other methods employed by the authors to assess cell divisions.

Elav+ cells originating from glia do not express markers for mature neurons at the analysed time-point. If they will eventually differentiate<br /> or what type of structure is formed by them will have to be followed up in future studies.

Context/Discussion

There is some lack of connecting or later comparing the observed forms of glial plasticity in the VNC with respect to plasticity described in the fly brain.<br /> Highlighting some differences in the reactiveness of glia in the VNC compared to the brain could point to relevant differences in repair capacity in different areas of the CNS.

Based on the assays employed, the study points to a significant amount of glial "identity" changes or interconversions under homeostatic conditions. The potential significance of this rather unexpected "baseline" plasticity in adult tissues is not explicitly pointed out and could improve the understanding of the findings.<br /> Some speculations if "interconversion" of glia is driven by the needs in the tissue could enrich the discussion.

Reviewer #3 (Public review):

In this manuscript, Casas-Tintó et al. explore the role of glial cell in the response to a neurodegenerative injury in the adult brain. They used Drosophila melanogaster as a model organism, and found that glial cells are able to generate new neurons through the mechanism of transdifferentiation in response to injury. This paper provides a new mechanism in regeneration, and gives an understanding to the role of glial cells in the process.

Comments on revisions:

In the previous version of the manuscript, I had suggested several recommendations for the authors. Unfortunately, none of these were addressed in the author's revision.

Author response:

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

Public Reviews:

Reviewer #1:

Summary:

Casas-Tinto et al. present convincing data that injury of the adult Drosophila CNS triggers transdifferentiation of glial cells and even the generation of neurons from glial cells. This observation opens up the possibility of getting a handle on the molecular basis of neuronal and glial generation in the vertebrate CNS after traumatic injury caused by Stroke or Crush injury. The authors use an array of sophisticated tools to follow the development of glial cells at the injury site in very young and mature adults. The results in mature adults revealing a remarkable plasticity in the fly CNS and dispels the notion that repair after injury may be only possible in nerve cords which are still developing. The observation of so-called VC cells which do not express the glial marker repo could point to the generation of neurons by former glial cells.

Conclusion:

The authors present an interesting story that is technically sound and could form the basis for an in-depth analysis of the molecular mechanism driving repair after brain injury in Drosophila and vertebrates.

Strengths:

The evidence for transdifferentiation of glial cells is convincing. In addition, the injury to the adult CNS shows an inherent plasticity of the mature ventral nerve cord which is unexpected.

Weaknesses:

Traumatic brain injury in Drosophila has been previously reported to trigger mitosis of glial cells and generation of neural stem cells in the larval CNS and the adult brain hemispheres. Therefore this report adds to but does not significantly change our current understanding. The origin and identity of VC cells is unclear.

The Reviewer correctly points out that it has been reported that traumatic brain injury trigger generation of neural stem cells. However, according to previous reports, those cells where quiescent Dpn+ neuroblast. We now report that already differentiated adult neuropil glia transdifferentiate into neurons. Which is a new mechanism not previously reported. 

We agree with the reviewer regarding the identity of VC neurons although according to the results of G-TRACE experiments the origin is clear, they originate from neuropil glia (i.e. Astrocyte-like glia and ensheathing glia). We have used a battery of antibodies previously reported to identify specific subtypes of neurons to identify these newly generated neurons (Figure S1). We did not find any other neuronal marker rather than Elav that co-localize with VC cells

Reviewer #2:

Summary:

Casas-Tinto et al., provide new insight into glial plasticity using a crush injury paradigm in the ventral nerve cord (VNC) of adult Drosophila. The authors find that both astrocyte-like glia (ALG) and ensheating glia (EG) divide under homeostatic conditions in the adult VNC and identify ALG as the glial population that specifically ramps up proliferation in response to injury, whereas the number of EGs decreases following the insult. Using lineagetracing tools, the authors interestingly observe the interconversion of glial subtypes, especially of EGs into ALGs, which occurs independent of injury and is dependent on the availability of the transcription factor Prospero in EGs, adding to the plasticity observed in the system. Finally, when tracing the progeny of differentiated glia, Casas-Tinto and colleagues detect cells of neuronal identity and provide evidence that such glia-derived neurogenesis is specifically favored following ventral nerve cord injury, which puts forward a remarkable way in which glia can respond to neuronal damage.

Numerous experiments have been carried out in 7-day-old flies, showing that the observed plasticity is not due to residual developmental remodeling or a still immature VNC.

By elegantly combining different genetic tools, the authors show glial divisions with mitotic-dependent tracing and find that the number of generated glia is refined by apoptosis later on.

The work identifies Prospero in glia as an important coordinator of glial cell fate, from development to the adult context, which draws further attention to the upstream regulatory mechanisms.

We express our gratitude to the reviewer for their keen appreciation of our efforts and their enthusiasm for the outcomes of this research.

Weaknesses:

Although the authors do use a variety of methods to show glial proliferation, the EdU data (Figure 1B) could be more informative (Figure 1B) by displaying images of non-injured animals and providing quantifications or the mention of these numbers based on results previously acquired in the system.

We appreciate the Reviewer’s comment. We believed that adding images of non-injured animals did not add new information as we already quantified the increase of glial proliferation upon injury in Losada-Perez let al. 2021. Besides, the purpose of this experiment was to figure out if dividing cells where Astrocyte-like glia rather than the number of dividing cells. Comparing independent experiments could be tricky but if we compare the quantifications of G2-M glia (repo>fly-Fucci) done in Losada-Perez et al 2021 (fig 1C) with the quantifications of G2-M neuropil glia done in this work (fig 1C) we can see that the numbers are comparable.

The experiments relying on the FUCCI cell cycle reporter suggested considerable baseline proliferation for EGs and ALGs, but when using an independent method (Twin Spot MARCM), mitotic marking was only detected for ALGs. This discrepancy could be addressed by assessing the co-localization of the different glia subsets using the identified driver lines with mitotic markers such as PH3.

In our understanding this discrepancy could be explained by the magnitude of proliferation. The lower proliferation rate of EG (as indicate the fly-fucci experiments) combining with the incomplete efficiency of MARCM clones induction reduces considerably the chances of finding EG MARCM clones. PH3 is a mitotic marker but it is also found in apoptotic cells (Kim and Park 2012. DOI: 10.1371/journal.pone.0044307) however, we stained injured VNCs with anti-Ph3 and found ALG cells positive for PH3 (Author response image 1).

Author response image 1.

 

The data in Figure 1C would be more convincing in combination with images of the FUCCI Reporter as it can provide further information on the location and proportion of glia that enter the cell cycle versus the fraction that remains quiescent.

We added a Figure 1 V2 (version 2) with the suggested images (1-C’).

The analyses of inter-glia conversion in Figure 3 are complicated by the fact that Prospero RNAi is both used to suppress EG - to ALG conversion and as a marker to establish ALG nature. Clarifications if the GFP+ cells still expressed Pros or were classified as NP-like GFP cells are required here.

As described in the text, Pros is a marker for ALG and the results suggest that Prospero expression is required for the EG to ALG transition. We clarified these concepts in the text accordingly. In figure 3 we showed images of NP-like cells originated from EG that are prospero+, and therefore supporting the transdifferentiation from EG to ALG.  

The conclusion that ALG and EG glial cells can give rise to cells of neuronal lineage is based on glial lineage information (GFP+ cells from glial G-trace) and staining for the neuronal marker Elav. The use of other neuronal markers apart from Elav or morphological features would provide a more compelling case that GFP+ cells are mature neurons.

We completely agree with the reviewer's observation regarding the identity of VC neurons. We have used a battery of antibodies previously reported to identify specific subtypes of neurons to identify these newly generated neurons (Figure S1). We did not find any other neuronal marker rather than Elav that colocalize with VC cells

Although the text discusses in which contexts, glial plasticity is observed or increased upon injury, the figures are less clear regarding this aspect. A more systematic comparison of injured VNCs versus homeostatic conditions, combined with clear labelling of the injury area would facilitate the understanding of the panels.

We appreciate the Reviewer’s observation. We have carefully checked all figures and labelled then as “Injured” or “Not Injured”. We added a Figure 2-V2 and a figure 4-V2.

Context/Discussion

The study finds that glia in the ventral cord of flies have latent neurogenic potential. Such observations have not been made regarding glia in the fly brain, where injury is reported to drive glial divisions or the proliferation of undifferentiated progenitor cells with neurogenic potential.

Discussing this different strategy for cell replacement adopted by glia in the VNC and pointing out differences to other modes seems fascinating. Highlighting differences in the reactiveness of glia in the VNC compared to the brain also seems highly relevant as they may point to different properties to repair damage.

Based on the assays employed, the study points to a significant amount of

glial "identity" changes or interconversions, which is surprising under homeostatic conditions. The significance of this "baseline" plasticity remains undiscussed, although glia unarguably show extensive adaptations during nervous system development.

It would be interesting to know if the "interconversion" of glia is determined by the needs in the tissue or would shift in the context of selective ablation/suppression of a glial type.

We deeply appreciate the Reviewer’s enthusiasm on this subject, it is indeed fascinating. We made a reduced discussion in order to fit in the eLife Short report requirements but the specific condition that trigger glial interconversion are of great interest for us. To compromise EG or ALG viability and evaluate the behaviour of glial cells is of great interest for developmental biology and regeneration, but the precise scenario to develop these experiments is not well defined. In this report, we aim to reproduce an injury in Drosophila brain and this model should serve to analyze cellular behaviours. The scenario where we deplete on specific subpopulation of glial cells is conceptually attractive, but far away from the scope of this report.

Reviewer #3:

In this manuscript, Casas-Tintó et al. explore the role of glial cells in the response to a neurodegenerative injury in the adult brain. They used Drosophila melanogaster as a model organism and found that glial cells are able to generate new neurons through the mechanism of transdifferentiation in response to injury.

This paper provides a new mechanism in regeneration and gives an understanding of the role of glial cells in the process.

eLife Assessment

This study provides valuable new information on the role of the endoplasmic reticulum calcium pump, TgSERCA, in the human pathogen Toxoplasma gondii. It is proposed that the endoplasmic reticulum is the major calcium store in these protists and that calcium is directly transported to other organelles via membrane contact sites. While the experimental work is solid and supported by complementary approaches, direct evidence for intra-organellar calcium transport via membrane domains and specific calcium efflux transporters is lacking.

Reviewer #1 (Public review):

Li et al. investigate Ca2+ signaling in T. gondii and argue that Ca2+ tunnels through the ER to other organelles to fuel multiple aspects of T. gondii biology. They focus in particular on TgSERCA as the presumed primary mechanism for ER Ca2+ filling. Although, when TgSERCA was knocked out there was still a Ca2+ release in response to TG present. Overall the Ca2+ signaling data do not support the conclusion of Ca2+ tunneling through the ER to other organelles in fact they argue for direct Ca2+ uptake from the cytosol into the organelles as outlined in the specific points below. The authors show EM membrane contact sites between the ER and other organelles, so Ca2+ released by the ER could presumably be taken up by other organelles but that is not ER Ca2+ tunneling. They clearly show that SERCA is required for T. gondii function. Overall, the data presented to not fully support the conclusions reached.

Reviewer #2 (Public review):

The present study focuses on calcium pools and fluxes in the unicellular parasite Toxoplasma gondii, and in particular on the role of the endoplasmic reticulum (ER) calcium pump TgSERCA in sequestering and redistributing calcium to other intracellular organelles following influx at the plasma membrane. Calcium sequestration by the ER and its interactions with other intracellular organelles, including the concept of tunneling through the ER, have been extensively characterized in mammalian cells and a number of other higher eukaryotes. However, these pathways are still not well understood in many organisms, including protist pathogens such as Toxoplasma. In addition, T. gondii has unique organelles not found in most other organisms, including the apicoplast and the plant-like vacuolar compartment (PLVAC). Moreover, the fact that T. gondii transitions through life cycle stages within and exterior to the host cells, with very different exposures to calcium, adds significance to the current investigation of the role of the ER in redistributing calcium following exposure to physiological levels of extracellular calcium.

The authors have provided significant new information on the T. gondii SERCA, including its ATP- and calcium-dependence, subcellular localization, and role in taking up calcium from the cytosol when cells are exposed to high extracellular calcium. They also use a conditional knockout of TgSERCA to investigate its role in ER calcium store-filling and the ability of other subcellular organelles to sequester and release calcium. These knockout experiments provide important evidence that ER calcium uptake plays a significant role in maintaining the filling state of other intracellular compartments.

While it is clearly demonstrated, and not surprising, that the addition of 1.8 mM extracellular CaCl2 to intact T. gondii parasites preincubated with EGTA leads to an increase in cytosolic calcium and subsequent enhanced loading of the ER and other intracellular compartments, there is a caveat to the quantitation of these increases in calcium loading. The authors rely on the amplitude of cytosolic free calcium increases in response to thapsigargin, GPN, nigericin, and CCCP, all measured with fura2. This likely overestimates the changes in calcium pool sizes because the buffering of free calcium in the cytosol is nonlinear, and fura2 (with a Kd of 100-200 nM) is a substantial, if not predominant, cytosolic calcium buffer. Indeed, the increases in signal noise at higher cytosolic calcium levels (e.g. peak calcium in Figure 1C) are indicative of fura2 ratio calculations approaching saturation of the indicator dye.

Another caveat, not addressed, is that loading of fura2/AM can result in compartmentalized fura2, which might modify free calcium levels and calcium storage capacity in intracellular organelles.

The finding that the SERCA inhibitor cyclopiazonic acid (CPA) only mobilizes a fraction of the thapsigargin-sensitive calcium stores in T. gondii coincides with previously published work in another apicomplexan parasite, P. falciparum, showing that thapsigargin mobilizes calcium from both CPA-sensitive and CPA-insensitive calcium pools (Borges-Pereira et al., 2020, DOI: 10.1074/jbc.RA120.014906). It would be valuable to determine whether this reflects the off-target effects of thapsigargin or the differential sensitivity of TgSERCA to the two inhibitors.

The authors interpret the residual calcium mobilization response to Zaprinast observed after ATc knockdown of TgSERCA (Figures 4E, 4F) as indicative of a target calcium pool in addition to the ER. While this may well be correct, it appears from the description of this experiment that it was carried out using the same conditions as Figure 4A where TgSERCA activity was only reduced by about 50%.

The data in Figures 4A vs 4G and Figures 4B vs 4H indicate that the size of the response to GPN is similar to that with thapsigargin in both the presence and absence of extracellular calcium. This raises the question of whether GPN is only releasing calcium from acidic compartments or whether it acts on the ER calcium stores, as previously suggested by Atakpa et al. 2019 DOI: 10.1242/jcs.223883. Nonetheless, Figure 1H shows that there is a robust calcium response to GPN after the addition of thapsigargin.

An important advance in the current work is the use of state-of-the-art approaches with targeted genetically encoded calcium indicators (GECIs) to monitor calcium in important subcellular compartments. The authors have previously done this with the apicoplast, but now add the mitochondria to their repertoire. Despite the absence of a canonical mitochondrial calcium uniporter (MCU) in the Toxoplasma genome, the authors demonstrate the ability of T. gondii mitochondrial to accumulate calcium, albeit at high calcium concentrations. Although the calcium concentrations here are higher than needed for mammalian mitochondrial calcium uptake, there too calcium uptake requires calcium levels higher than those typically attained in the bulk cytosolic compartment. And just like in mammalian mitochondria, the current work shows that ER calcium release can elicit mitochondrial calcium loading even when other sources of elevated cytosolic calcium are ineffective, suggesting a role for ER-mitochondrial membrane contact sites. With these new tools in hand, it will be of great value to elucidate the bioenergetics and transport pathways associated with mitochondrial calcium accumulation in T. gondi.

The current studies of calcium pools and their interactions with the ER and dependence on SERCA activity in T. gondi are complemented by super-resolution microscopy and electron microscopy that do indeed demonstrate the presence of close appositions between the ER and other organelles (see also videos). Thus, the work presented provides good evidence for the ER acting as the orchestrating organelle delivering calcium to other subcellular compartments through contact sites in T. gondi, as has become increasingly clear from work in other organisms.

Reviewer #3 (Public review):

This manuscript describes an investigation of how intracellular calcium stores are regulated and provides evidence that is in line with the role of the SERCA-Ca2+-ATPase in this important homeostasis pathway. Calcium uptake by mitochondria is further investigated and the authors suggest that ER-mitochondria membrane contact sites may be involved in mediating this, as demonstrated in other organisms.

The significance of the findings is in shedding light on key elements within the mechanism of calcium storage and regulation/homeostasis in the medically important parasite Toxoplasma gondii whose ability to infect and cause disease critically relies on calcium signalling. An important strength is that despite its importance, calcium homeostasis in Toxoplasma is understudied and not well understood.

A difficulty in the field, and a weakness of the work, is that following calcium in the cell is technically challenging and thus requires reliance on artificial conditions. In this context, the main weakness of the manuscript is the extrapolation of data. The language used could be more careful, especially considering that the way to measure the ER calcium is highly artificial - for example utilising permeabilization and over-loading the experiment with calcium. Measures are also indirect - for example, when the response to ionomycin treatment was not fully in line with the suggested model the authors hypothesise that the result is likely affected by other storage, but there is no direct support for that.

Below we provide some suggestions to improve controls, however, even with those included, we would still be in favour of revising the language and trying to avoid making strong and definitive conclusions. For example, in the discussion perhaps replace "showed" with "provide evidence that are consistent with..."; replace or remove words like "efficiently" and "impressive"; revise the definitive language used in the last few lines of the abstract (lines 13-17); etc. Importantly we recommend reconsidering whether the data is sufficiently direct and unambiguous to justify the model proposed in Figure 7 (we are in favour of removing this figure at this early point of our understanding of the calcium dynamic between organelles in Toxoplasma).

Another important weakness is poor referencing of previous work in the field. Lines 248-250 read almost as if the authors originally hypothesised the idea that calcium is shuttled between ER and mitochondria via membrane contact sites (MCS) - but there is extensive literature on other eukaryotes which should be first cited and discussed in this context. Likewise, the discussion of MCS in Toxoplasma does not include the body of work already published on this parasite by several groups. It is informative to discuss observations in light of what is already known.

Author response:

Reviewer #1 (Public review):

Li et al. investigate Ca2+ signaling in T. gondii and argue that Ca2+ tunnels through the ER to other organelles to fuel multiple aspects of T. gondii biology. They focus in particular on TgSERCA as the presumed primary mechanism for ER Ca2+ filling. Although, when TgSERCA was knocked out there was still a Ca2+ release in response to TG present.

Note that we did not knockout SERCA as it is an essential gene so it would not be possible to isolate parasites that do not express SERCA. We created conditional mutants that downregulate the expression of SERCA and some activity is present in the mutant after 24 h of ATc treatment.

Overall the Ca2+ signaling data do not support the conclusion of Ca2+ tunneling through the ER to other organelles in fact they argue for direct Ca2+ uptake from the cytosol.

The authors show EM membrane contact sites between the ER and other organelles, so Ca2+ released by the ER could presumably be taken up by other organelles but that is not ER Ca2+ tunneling.

They clearly show that SERCA is required for T. gondii function.

Overall, the data presented to not fully support the conclusions reached

We agree that the data does not support Ca2+ tunneling as defined and characterized in mammalian cells. In response to this comment, we modified the title and the text accordingly.

However, we think that the study shows far more than just the role of SERCA in T. gondii functions. We argue that the study shows that the ER (through the activity of the SERCA pump) sequesters and re-distributes calcium to other organelles following influx through the PM. The experiments show that the ER is able to take calcium from the cytosol as it enters the parasite through SERCA activity, and this activity is important for the transition of the parasite between various extracellular calcium exposures. We believe that the role of the ER in redistributing calcium following exposure to physiological levels of extracellular calcium is demonstrated in the experiments shown in Figs 1H-I, 4G-H and 5G,H, I, J, K . There are no previous T. gondii studies that address the question of how intracellular stores are filled with calcium, which are essential for the continuation of the lytic cycle, meaning they are essential for the parasitism of T. gondii.

Data argue for direct Ca2+ uptake from the cytosol

The ER most likely takes up calcium from the cytosol following its entry through the PM and redistributes it to the other organelles. We will delete the word “tunneling” and replace it with transfer and re-distribution as they represent our results.

What we think is re-distribution is shown in Figure 1H and I in which the calcium released after GPN and nigericin are enhanced after TG addition. Of note is that there is no experimental evidence that supports the regulation of calcium entry by store depletion (PMID: 24867952), and we do not think that the enhanced response is due to calcium entry.

Figure 4G and H show that knocking down SERCA reduces significantly the response to GPN. Fig 5I shows that the mitochondrial calcium uptake is reduced after the addition of GPN in the knockdown mutant. Fig 2B shows that SERCA can take up calcium at 55 nM calcium while mitochondrial uptake needs higher concentrations (Fig 5B-C). However, higher calcium concentrations could be reached at the microdomains formed around MCS between the ER and mitochondrion. Figure 5E shows that the mitochondrion is not responsive to an increase of cytosolic calcium. This is also shown for the apicoplast in Fig. 7 E and F of the Li et al, Nat Commun 2021 paper.

Reviewer #2 (Public review):

The role of the endoplasmic reticulum (ER) calcium pump TgSERCA in sequestering and redistributing calcium to other intracellular organelles following influx at the plasma membrane.

T. gondii transitions through life cycle stages within and exterior to the host cells, with very different exposures to calcium, adds significance to the current investigation of the role of the ER in redistributing calcium following exposure to physiological levels of extracellular calcium.

They also use a conditional knockout of TgSERCA to investigate its role in ER calcium store-filling and the ability of other subcellular organelles to sequester and release calcium. These knockout experiments provide important evidence that ER calcium uptake plays a significant role in maintaining the filling state of other intracellular compartments.

We thank the reviewer.

While it is clearly demonstrated, and not surprising, that the addition of 1.8 mM extracellular CaCl2 to intact T. gondii parasites preincubated with EGTA leads to an increase in cytosolic calcium and subsequent enhanced loading of the ER and other intracellular compartments, there is a caveat to the quantitation of these increases in calcium loading. The authors rely on the amplitude of cytosolic free calcium increases in response to thapsigargin, GPN, nigericin, and CCCP, all measured with fura2. This likely overestimates the changes in calcium pool sizes because the buffering of free calcium in the cytosol is nonlinear, and fura2 (with a Kd of 100-200 nM) is a substantial, if not predominant, cytosolic calcium buffer. Indeed, the increases in signal noise at higher cytosolic calcium levels (e.g. peak calcium in Figure 1C) are indicative of fura2 ratio calculations approaching saturation of the indicator dye.

We agree about the limitations of using Fura2 but according to the literature (PMID:3838314, fig. 3) Fura2 is suitable for measurements between 100 nM and 1 mM calcium.  The responses in our experiments were within its linear range and the experiments with the SERCA mutant and mitochondrial GCaMPs supports the conclusions of our work.

We agree that the experiment shown in Fig 1C shows a response close to the limit of the linear range of Fura2 and we can provide a more representative trace in the final article. We can include new quantifications and comparisons.

Another caveat, not addressed, is that loading of fura2/AM can result in compartmentalized fura2, which might modify free calcium levels and calcium storage capacity in intracellular organelles.

We are aware of this issue and because of that we have modified our protocol to minimize compartmentalization. We load cells for 26 min at room temperature and keep cells in ice and do not use them for longer that 2-3 hours because we do see evidence of compartmentalization. One evidence of compartmentalization is the increase in the resting calcium concentration.

The finding that the SERCA inhibitor cyclopiazonic acid (CPA) only mobilizes a fraction of the thapsigargin-sensitive calcium stores in T. gondii coincides with previously published work in another apicomplexan parasite, P. falciparum, showing that thapsigargin mobilizes calcium from both CPA-sensitive and CPA-insensitive calcium pools (Borges-Pereira et al., 2020, DOI: 10.1074/jbc.RA120.014906). It would be valuable to determine whether this reflects the off-target effects of thapsigargin or the differential sensitivity of TgSERCA to the two inhibitors.

This is an interesting observation, and we will discuss the result considering the Plasmodium study and include the citation. We will add inhibition curves using the MagFluo protocol and compare CPA and TG.

Figure S1 suggests differential sensitivity, and it shows that thapsigargin mobilizes calcium from both CPA-sensitive and CPA-insensitive calcium pools in T. gondii. Also important is that we used 1 µM TG as we are aware that TG has shown off-target effects at higher concentrations. 

The authors interpret the residual calcium mobilization response to Zaprinast observed after ATc knockdown of TgSERCA (Figures 4E, 4F) as indicative of a target calcium pool in addition to the ER. While this may well be correct, it appears from the description of this experiment that it was carried out using the same conditions as Figure 4A where TgSERCA activity was only reduced by about 50%.

We partially agree as pointed by the reviewer knock down of TgSERCA by only 50% means that the ER still could be targeted by zaprinast and no evidence of another target calcium pool. From the MagFLuo4 experiment (although we are aware that the fluorescence of mag Fluo4 is not linear to calcium), there is SERCA activity after 24 hr of ATc treatment.  However, when adding Zaprinast after TG we see a significant release of calcium which is true for both wild type and conditional knockdowns. Because of this result we proposed that there could be another large neutral calcium pool than the one mobilized by TG. We will address these possibilities in the discussion and interpretation of the result.

The data in Figures 4A vs 4G and Figures 4B vs 4H indicate that the size of the response to GPN is similar to that with thapsigargin in both the presence and absence of extracellular calcium. This raises the question of whether GPN is only releasing calcium from acidic compartments or whether it acts on the ER calcium stores, as previously suggested by Atakpa et al. 2019 DOI: 10.1242/jcs.223883. Nonetheless, Figure 1H shows that there is a robust calcium response to GPN after the addition of thapsigargin.

The results of the experiments did not exclude the possibility that GPN can also mobilize some calcium from the ER besides acidic organelles. We don’t have any evidence to support that GPN can mobilize calcium from the ER either. Based on our unpublished work, we think GPN mainly release calcium from the PLVAC. We will include the mentioned citation and discuss the result considering the possibility that GPN may be acting on the ER.

An important advance in the current work is the use of state-of-the-art approaches with targeted genetically encoded calcium indicators (GECIs) to monitor calcium in important subcellular compartments. The authors have previously done this with the apicoplast, but now add the mitochondria to their repertoire. Despite the absence of a canonical mitochondrial calcium uniporter (MCU) in the Toxoplasma genome, the authors demonstrate the ability of T. gondii mitochondrial to accumulate calcium, albeit at high calcium concentrations. Although the calcium concentrations here are higher than needed for mammalian mitochondrial calcium uptake, there too calcium uptake requires calcium levels higher than those typically attained in the bulk cytosolic compartment. And just like in mammalian mitochondria, the current work shows that ER calcium release can elicit mitochondrial calcium loading even when other sources of elevated cytosolic calcium are ineffective, suggesting a role for ER-mitochondrial membrane contact sites. With these new tools in hand, it will be of great value to elucidate the bioenergetics and transport pathways associated with mitochondrial calcium accumulation in T. gondii.

We thank this reviewer for his/her positive comment. Studies of bioenergetics and transport pathways associated with mitochondrial calcium accumulation is part of our future plans.

The current studies of calcium pools and their interactions with the ER and dependence on SERCA activity in T. gondi are complemented by super-resolution microscopy and electron microscopy that do indeed demonstrate the presence of close appositions between the ER and other organelles (see also videos). Thus, the work presented provides good evidence for the ER acting as the orchestrating organelle delivering calcium to other subcellular compartments through contact sites in T. gondi, as has become increasingly clear from work in other organisms.

Thank you

Reviewer #3 (Public review):

This manuscript describes an investigation of how intracellular calcium stores are regulated and provides evidence that is in line with the role of the SERCA-Ca2+-ATPase in this important homeostasis pathway. Calcium uptake by mitochondria is further investigated and the authors suggest that ER-mitochondria membrane contact sites may be involved in mediating this, as demonstrated in other organisms.

The significance of the findings is in shedding light on key elements within the mechanism of calcium storage and regulation/homeostasis in the medically important parasite Toxoplasma gondii whose ability to infect and cause disease critically relies on calcium signalling. An important strength is that despite its importance, calcium homeostasis in Toxoplasma is understudied and not well understood.

We agree with the reviewer. Thank you

A difficulty in the field, and a weakness of the work, is that following calcium in the cell is technically challenging and thus requires reliance on artificial conditions. In this context, the main weakness of the manuscript is the extrapolation of data. The language used could be more careful, especially considering that the way to measure the ER calcium is highly artificial - for example utilising permeabilization and over-loading the experiment with calcium. Measures are also indirect - for example, when the response to ionomycin treatment was not fully in line with the suggested model the authors hypothesise that the result is likely affected by other storage, but there is no direct support for that.

The MagFluo protocol has been amply used in mammalian cells, DT40 cells and other cells for the characterization of the IP3 receptor response to IP3. We will include and discuss more citations in the revised article. The scheme at the top of the figure shows the protocol used. There is no overloading with calcium because the cells are permeabilized and the concentrations of calcium used are physiological and all experiments were performed at 220 nm calcium which is within the cytosolic levels tolerated by cells. The experiment was done with permeabilized cells because permeabilization allows the indicator to become diluted, the substrate MgATP to reach the membrane of the ER and in addition allows for the exposure to precise concentrations of calcium. MagFluo4 loading is intended for its compartmentalization to all intracellular compartments and the uptake stimulated by MgATP exclusively occurs in the compartment occupied by SERCA. IO is an ionophore that causes calcium release from other stores in addition to the ER and it is expected that will result in a larger release. We must clarify that the experiment shown in Fig. 2 was done to characterize the activity of SERCA and was not aimed at the characterization of the role of SERCA in the parasite. We will explain this result better in the revised version of the article.

Below we provide some suggestions to improve controls, however, even with those included, we would still be in favour of revising the language and trying to avoid making strong and definitive conclusions. For example, in the discussion perhaps replace "showed" with "provide evidence that are consistent with..."; replace or remove words like "efficiently" and "impressive"; revise the definitive language used in the last few lines of the abstract (lines 13-17); etc. Importantly we recommend reconsidering whether the data is sufficiently direct and unambiguous to justify the model proposed in Figure 7 (we are in favour of removing this figure at this early point of our understanding of the calcium dynamic between organelles in Toxoplasma).

We thank the reviewer for the suggestions and will modify the language as suggested.

Fig 7 is only a model and as all models could be incorrect. However, considering this reviewer’s criticism we will replace the model for a simpler one that is less speculative.

Another important weakness is poor referencing of previous work in the field. Lines 248-250 read almost as if the authors originally hypothesised the idea that calcium is shuttled between ER and mitochondria via membrane contact sites (MCS) - but there is extensive literature on other eukaryotes which should be first cited and discussed in this context. Likewise, the discussion of MCS in Toxoplasma does not include the body of work already published on this parasite by several groups. It is informative to discuss observations in light of what is already known.

We added a citation following the sentence mentioned by the reviewer in lines 248-250 (corrected preprint) and will include more in the revised article. We cite several pertinent articles that describe MCS in Toxoplasma (lines 378-380, very few actually). We will make sure not to miss any new articles that could have been recently published. Note that our work is not about describing the presence of MCSs. We are showing transfer of calcium between the ER and mitochondria and we present evidence that supports that it happens through MCSs.

eLife Assessment

This important study examined neuronal activity in the dentate nucleus of the cerebellum when monkeys performed a difficult perceptual decision-making task. The authors provide convincing evidence that the cerebellum represents sensory, motor, and behavioral outcome signals that are sent to the attentional system. This paper is of great general interest in that it shows the involvement of the cerebellum in cognitive processes at the neuronal level.

Reviewer #1 (Public review):

Summary:

Recordings were made from the dentate nucleus of two monkeys during a decision-making task. Correlates of stimulus position and stimulus information were found to varying degrees in the neuronal activities.

Strengths:

A difficult decision-making task was examined in two monkeys.

Weaknesses:

One of the monkeys had difficulty learning the task. The initial version of the manuscript lacked a coherent hypothesis to be tested, although the revision has improved things. In its current form, the manuscript does not provide data regarding the possibility that this part of the brain may have little to do with the task that was being studied. As noted in the response to the reviewer's comments, future studies could address this issue by providing results of additional inactivation experiments.

Reviewer #2 (Public review):

The authors trained monkeys to discriminate peripheral visual cues and associate them with planning future saccades of an indicated direction. At the same time, the authors recorded single-unit neural activity in the cerebellar dentate nucleus. They demonstrated that substantial fractions of DN cells exhibited sustained modulation of spike rates spanning task epochs and carrying information about stimulus, response, and trial outcome. Finally, tracer injections demonstrated this region of the DN projects to a large number of targets including several known to interconnect the visual attention network. The data compellingly demonstrate the authors' central claims, and the analyses are well-suited to support the conclusions. Importantly, the study demonstrates that DN cells convey many motor and nonmotor variables related to task execution, event sequencing, visual attention, and arguably decision-making/working memory.

Author response:

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

Reviewer #1:

- Summary: 

Recordings were made from the dentate nucleus of two monkeys during a decision-making task. Correlates of stimulus position and stimulus information were found to varying degrees in the neuronal activities. 

We agree with this summary.

- Strengths: 

A difficult decision-making task was examined in two monkeys.

We agree with this statement.

- Weaknesses: 

One of the monkeys did not fully learn the task. The manuscript lacked a coherent hypothesis to be tested, and no attempt was made to consider the possibility that this part of the brain may have little to do with the task that was being studied. 

We understand the reviewers concern. It is correct that one of the monkeys (Mi) did not perform at a high level, but it should be noted that both monkeys learned significantly above chance level. Therefore, we would argue that both monkeys in fact did learn the task but Mi’s performance was suboptimal. This difference in the performance levels gave us a rare opportunity to dive deeper into the reasons why some animals perform better than the others and we show that Mi (the lower performing monkey) paid more attention to the outcome of the previous trial – this is evident from our behavioural and decoding models.

We tested the overall hypothesis that neurons of the nucleus dentate can dynamically modulate their activity during a visual attention task, comprising not only sensorimotor but also cognitive attentional components. Many neurons in the dentate are multimodal (Figure 3C-D) which was something that was theorized. One of the specific hypotheses that we tested is that the dentate cells can be direction-selective for both the sensorimotor and cognitive component. Given that many of the recorded cells showed direction-selectivity in their firing rate modulation for gap directions and/or stimulus directions, we provide strong evidence that this hypothesis is correct. We have now spelled out this hypothesis more explicitly in the introduction of the revised version. We now also explain better why we tested this specific hypothesis. Indeed, earlier studies in primates such as those by Herzfeld and colleagues (2018, Nat. Neuro.) and van Es and colleagues (2019, Current Biol) have indicated that direction-selectivity of cerebellar activity may occur in various sensorimotor domains.

We also appreciate the comment of this Reviewer that in our original submission we did not show our attempt to consider the possibility that this part of the brain may have little to do with the task that was being studied. We in fact did consider this possibility in that we successfully injected 3 ml of muscimol (5 μg/ml, Sigma Aldrich) into the dentate nucleus in vivo in one of the monkeys (Mo). This application resulted in a reduction of more than 10% in correct responses of the covert attention task after 45 minutes, whereas the performance remained the same following saline injections. Unfortunately, due to the timing of the experiments and Covid19-related laboratory restrictions we were unable to perform these experiments in the other monkey or repeat them in Mo. We aim to replicate this in future experiments and publish it when we have full datasets of at least two monkeys available. For this paper we have prioritized our tracing experiments, highlighting the connections of the dentate nucleus with attention related areas in brainstem and cortex in both monkeys, following perfusion.

- Perhaps the large differences in performance between the two subjects can be used as a way to interpret the neural data's relationship to behavior, as it provided a source of variance. This is what we would hypothesize if we believed that this area of the brain is playing a significant role in the task. If one animal learns much more poorly, and this region of the brain is important for that behavior, then shouldn't there be clear, interpretable differences in the neural data? 

We thank the Reviewer for this comment. We have added a new Supplementary Figure 2, in which we present the data for both monkeys separately in the revised manuscript. Comparing the two datasets however, we see more commonalities related to the significant learning in both monkeys than differences that might be related to their different levels of learning. We have therefore decided to show the different datasets transparently in the new Supplementary Figure 2, but to stay on the conservative side in our interpretations.

- How should we look for these differences? A number of recent papers in mice have uncovered a large body of data showing that during the deliberation period, when the animal is interpreting a sensory stimulus (often using the whisker system), there is ramping activity in a principal component space among neurons that contribute to the decision. This ramping activity is present (in the PCA space) in the motor areas of the cortex, as well as in the medial and lateral cerebellar nuclei. Perhaps a similar computational approach would benefit the current manuscript. 

We also appreciate this point. We have done the principal component analysis accordingly, and we indeed do find the ramping activity in several components of the dentate activity of both monkeys (Mi and Mo). We have now added a new Supplementary Figure 3 with the first three components of both correct and incorrect trials for Mi and Mo, highlighting their potential contribution.

- What is the hypothesis that is being tested? That is, what do you think might be the function of this region of the cerebellum in this task? It seems to me that we are not entirely in the dark, as previous literature on mice decision-making tasks has produced a reasonable framework: the deliberation period coincides with ramping activity in many regions of the frontal lobe and the cerebellum. Indeed, the ramp in the cerebellum appears to be a necessary condition for the ramp to be present in the frontal lobe. Thus, we should see such ramping activity in this task in the dentate. When the monkey makes the wrong choice, the ramp should predict it. If you don't see the ramping activity, then it is possible that the hypothesis is wrong, or that you are not recording from the right place. 

It is indeed one of our specific hypotheses that the dentate cells can be direction-selective for the preparing cognitive component and/or sensorimotor response. We provide evidence that this hypothesis may be correct when we analyze the regular time response curves (see Figure 2 and the new Supplementary Figure 2 where the data of both monkeys are now presented separately). Moreover, we have now verified this by analysing the ramping curves of PCA space (new Supplementary Figure 3) and firing frequency of DN neurons that modulated upon presentation of the C-stimulus (new Supplementary Figure 4). These figures and findings are now referred to in the main text.

- As this is a difficult task that depends on the ability of the animals to understand the meaning of the cues, it is quite concerning that one of the monkeys performed poorly, particularly in the early sessions. Notably, the disparity between the two subjects is rather large: one monkey at the start of the recordings achieved a performance that was much better than the second monkey did at the end of the recording sessions. You highlighted the differences in performance in Figure 1D and mentioned that you started recording once the animals reached 60% performance. However, this did not make sense to me as the performance of Mi even after the final day of recording did not reach the performance of Mo on the first day of recording. Thus, in contrast to Mo, Mi appeared to be not ready for the task when the recording began.

We understand this point. However, please note that the learning performance of the monkeys concerned retraining sessions after they had had several weeks of vacation. So, even though it is correct that one of the two monkeys had a very good consolidation and started already at a relatively high level on the first retraining session, the other one also started and ended at a level above chance level (the y-axis starts at 0.5). We now highlight this point better in the Results section.

- One objective of having two monkeys is to illustrate that what is true in one animal is also true in the other. In some figures, you show that the neural data are significantly different, while in others you combine them into one. Thus, are you confident that the neural data across the animals should be combined, as you have done in Figure 2? Perhaps you can use the large differences in performance as a source of variance to find meaning in the neural data. 

This is a valid question; as highlighted above, we have now addressed this point in the new Supplementary Figure 2, where the data for both monkeys are presented separately. Given the sample sizes and level of variances, it is in general difficult to draw conclusions about the potential differences and contributions, but the data are sufficiently transparent to observe common trends. With regard to linking differences in the neural data to the differences in performance level, please also consider Figure 4, the new Supplementary Figure 3 (with the ramping PCA component) and new Supplementary Figure 4 (with the additional analysis of the ramping activity of DN neurons that modulated upon presentation of the C-stimulus), which suggests that the ramping stage of Mo starts before that of Mi. This difference highlights the possibility that injecting accelerations of the simple spike modulations of Purkinje cells in the cerebellar hemispheres into the complex of cerebellar nuclei may be instrumental in improving the performance of responses to covert attention, akin to what has been shown for the impact of Purkinje cells of the vestibulocerebellum on eye movement responses to vestibular stimulation (De Zeeuw et al. 1995, J Neurophysiol). This possibility is now also raised in the Discussion.

- How do we know that these neurons, or even this region of the brain, contribute to this task? When a new task is introduced, the contributions of the region of the brain that is being studied are usually established via some form of manipulation. This question is particularly relevant here because the two subjects differed markedly in their performance, yet in Figure 3 you find that a similar percentage of neurons are responding to the various elements of the task.

We appreciate this question. As highlighted above, we are refraining from showing our muscimol manipulation (3 ml of 5 μg/ml muscimol, Sigma Aldrich), as it only concerns 1 successful dataset and 1 control experiment. We hope to replicate this reversible lesion experiment in the future and publish it when we have full new datasets of at least two monkeys available. As explained above, for this paper we have sacrificed both monkeys following a timed perfusion, so as to have similar survival times for the transport of the neuro-anatomical tracer involved.  

- Behavior in both animals was better when the gap direction was up/down vs. left/right. Is this difference in behavior encoded during the time that the animal is making a decision? Are the dentate neurons better at differentiating the direction of the cue when the gap direction is up/right vs. left/right? 

These data have now been included in the new Supplementary Figure 2; we did not observe any significant differences in this respect.

Reviewer #2:

- The authors trained monkeys to discriminate peripheral visual cues and associate them with planning future saccades of an indicated direction. At the same time, the authors recorded single-unit neural activity in the cerebellar dentate nucleus. They demonstrated that substantial fractions of DN cells exhibited sustained modulation of spike rates spanning task epochs and carrying information about stimulus, response, and trial outcome. Finally, tracer injections demonstrated this region of the DN projects to a large number of targets including several known to interconnect the visual attention network. The data compellingly demonstrate the authors' central claims, and the analyses are well-suited to support the conclusions. Importantly, the study demonstrates that DN cells convey many motor and nonmotor variables related to task execution, event sequencing, visual attention, and arguably decision-making/working memory. 

We thank the Reviewer for this positive and constructive feedback.

- The study is solid and I do not have major concerns, but only points for possible improvement. 

We thank the Reviewer for this positive feedback.

- A key feature of this data is the extended changes/ramps in DN output across epochs (Figure 2). Crudely, this presents a challenge for the view that DN output mainly drives motor effectors, as the saccade itself lasts only a tiny fraction of the overall task. Some discussion of this dichotomy in thinking about the function(s) of the cerebellum, vis a vis the multifarious DN targets the authors demonstrate here, etc., would be helpful. 

We agree with the Reviewer and we have expanded our Discussion on this point, also now highlighting the outcome of the new PCA analysis recommended by Reviewer 1 (see the new Supplementary figure Figure 3).

- A high-level suggestion on the data: the presentation of the data focuses (sensibly) on the representation of the stimulus and response epochs (Figures 2-3). Yet, the authors then show that from decoding, it is, in fact, a trial outcome that is best represented in the population (Figure 4). While there is nothing 'wrong' with this, it reads slightly incongruously, and the reader does a bit of a "double take" back to the previous figures to see if they missed examples of the trial-outcome signals, but the previous presentations only show correct trials. Consider adding somewhere in the first 3 main figures some neural data showing comparisons with incorrect trials. This way, the reader develops prior expectations for the outcome decoding result and frame of reference for interpreting it. On a related note, the text contains an earlier introduction of this issue (p24 last sentence) and p25 paragraph 1 cites Figure 3D and 3E for signals "related to the absence of reward" - but the caption says this includes only correct trials? 

We thank the Reviewer for bringing up these points. We have addressed the textual suggestions. Moreover, we have done the PCA analysis suggested by Reviewer 1 for both the correct and incorrect trials (see Supplementary material).

- P29: The discrepancy in retrograde labeling between monkeys (2 orders of magnitude): I realize the authors can't really do anything about this, but the difference is large enough to warrant concerns in the interpretation (how did the tracer spread over the drastically larger area? Isotropically? Could it cross more "hard boundaries" and incorporate qualitatively different inputs/outputs?). A small discussion of possible caveats in interpreting the outcomes would be helpful. 

We fully agree with this comment. As highlighted in the text, in both monkeys we first identified the optimal points for injection in the dentate nucleus electrophysiologically and we used the same pump with the same settings to carry out the injections, but even so the differences are substantial. We suspect that the larger injection might have been caused by an air bubble trapped in the syringe or a deviation in the stock solution, but we can never be sure of that. We have added a potential explanation for the caveat that might have played a role.

- And a list of quick points: 

We have addressed all points listed below; we want to thank the Reviewer for bringing them up.

P3 paragraph 2 needs comma "in daily life,". 

P4 paragraph 2 "C-gap" terminology not previously defined. 

P4 paragraph 2 "animals employed different behavioral strategies". Grammatically, you should probably say "each animal employed a different behavioral strategy," but also scientifically the paragraph doesn't connect this claim to anything about the DN (whereas, e.g., the abstract does make this connection clear). 

P5 paragraph 1 "theca" should be "the". 

P6 paragraph 1 problem with ignashenkova citation insert. 

P10 paragraph 1 I think the spike rate "difference between highest and lowest" is not exactly the same as "variance," you might want to change the terminology. 

P10 paragraph 1 should probably say "To determine if a cell preferentially modulated". 

P10 paragraph 1 last sentence the last clause could be clearer. 

P17 paragraph 2 should be something like "as well as those by Carpenter and..."? 

P20 caption: consider "...directionality in the task: only one C-stim...". 

P20 caption: consider "to the left and right in the [L/R] task...to the top/bottom in the [U/D] task". 

Fig1E and S1 - is there a physical meaning of the "weight" unit, and if none, can this be transformed into a more meaningful unit? 

P21 paragraph 1 consider "activity was recorded for 304 DN neurons...". 

P21 paragraph 1 "correlations with the temporal windows" it's not clear how activity can "correlate" with a time window, consider rephrasing (activity levels changed during these time epochs, depending on stimulus identity). 

P21 paragraph 1 should be "by comparing the number of spikes in a bin...". 

P22 paragraph 2 "when we aligned the neurons to the time of maximum change" needs clarification. The maximum change of what? And per neuron? Across the population? 

P22 paragraph 2 "than that of the facilitating" should be "than did the facilitating units". 

P24 paragraph 1 needs a comma and rewording "Within each direction, trials are sorted by the time of saccade onset". 

P24 paragraph 1 should probably say "Same as in G, but for suppressed cells". 

P24 paragraph 2 should say "more than one task event" not "events". 

P24 paragraph 2 needs a comma "To fully characterize the neural responses, we fitted". 

P25 paragraph 1 should probably say "we sampled from similar populations of DN". 

P34 paragraph 3 consider rephrasing the sentence that contains both "dissociation" and "dissociate". 

P37 last line: consider "coordination of cerebellum and cerebral cortex *in* higher order mental..."? 

P38 paragraph 1 citation needed for "kinematics of goal-directed hand actions of others"? 

P38 paragraph 1 commas probably not needed "map visual input, from high-level visual regions, onto..." 

References

- Herzfeld D.J., Kojima Y, Soetedjo R, Shadmehr R (2018) Encoding of error and learning to correct that error by the Purkinje cells of the cerebellum. Nat Neurosci 21:736–743.

- van Es, D.M., van der Zwaag W., and Knapen T. (2019) Topographic Maps of Visual Space in the Human Cerebellum. Current Biol Volume 29, Issue 10p1689-1694.e3May 20.

- De Zeeuw CI, Wylie DR, Stahl JS, Simpson JI. (1995) Phase relations of Purkinje cells in the rabbit flocculus during compensatory eye movements. J Neurophysiol. Nov;74(5):2051-64. doi: 10.1152/jn.1995.74.5.2051.