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scheme with a large unit size substantially increased average giving.
Did they find the predictable incidence va amount trade off and nonlinearity? How to compare the size of the ask / size of “large” units across these contexts…? I guess there’s were typical lab experiment type stakes?
not yet reviewed by the authors.
I am going through it right now to see whether it makes sense in a general way, and adding a few comments and suggestions and questions. You will also want to selectively and then fully check things in a manual and “fully human” way.
Aside: I suspect hybrid human ai research becomes more common soon, but for now I think people expect full human oversight
eLife Assessment
This study in the Drosophila antennal lobe, which contains multiple non-equivalent sensory channels, provides valuable new insight into how early-life sensory experience can produce lasting, cell-type-specific changes in neural circuit function. The work demonstrates that glial-mediated pruning during a defined developmental window leads to persistent suppression of odor responses in one olfactory neuron type, while sparing another. The evidence is convincing and supported by multiple complementary approaches, although some mechanistic interpretations remain speculative and would benefit from additional functional testing.
Reviewer #1 (Public review):
Summary:
This study builds on earlier work showing that early-life odor exposure can trigger glial-mediated pruning of specific olfactory neuron terminals in Drosophila. Moving from indirect to direct functional imaging, the authors show that pruning during a narrow developmental window leads to long-lasting suppression of odor responses in one neuron type (Or42a) but not another (Or43b). The combination of calcium and voltage imaging with connectomic analysis is a strength, though the voltage imaging results are less straightforward to interpret and may not reflect synaptic output changes alone.
Strengths:
Biologically, one of the main strengths of this work is the direct comparison between two odor-responsive OSN types that differ in their long-term adaptation to early-life odor exposure. While Or42a OSNs undergo pruning and remain persistently suppressed into late adulthood, Or43b OSNs, which also respond to the same odor, show little lasting change. This contrast not only underscores the cell-type specificity of critical-period plasticity but also points to a potential role of inhibitory network architecture in determining susceptibility. The persistence of the Or42a suppression well beyond the developmental window provides compelling evidence that early glia-mediated pruning can imprint a stable, life-long functional state on selected sensory channels. By situating these functional outcomes within the context of detailed connectomic data, the study offers a framework for linking structural connectivity to long-term sensory coding stability or vulnerability.
Comments on revised version:
I thank the authors for their careful revision and thoughtful responses to the reviewers' comments. The revised manuscript addresses my previous concerns in a satisfactory manner, and the interpretation of the findings has been appropriately clarified and balanced. I have no further major comments.
Reviewer #2 (Public review):
Recent work from the authors identified the synaptic changes and glial reaction that occurs during exposure of a Drosophila odorant receptor neuron population to continued exposure of a stimulating odorant. This work markedly advanced our understanding of cellular response to critical periods. This current Advance manuscript carries that work forward and examines the non-autonomous responses to constant odorant exposure. The authors discover that the changes to ORN populations are not accompanied by changes to either PN dendrite or PN axon volume, nor are they concurrent with changes in postsynaptic PN structures. These changes are, however, notable accompanied by changes in Ca2+ and voltage responses in ORNs. Importantly, this set of responses is specific for the Or42a ORNs (that are highly sensitive to the odorant in question, ethyl butyrate) and not the Or43b ORNs (which respond to ethyl butyrate, but not as drastically). Finally, the authors include connectomics analyses showing that Or43b and Or42a ORNs differ in their synaptic input/output relationships.
This is an excellent use of the Advance mechanism for the journal as these are important follow-up findings for the parent story. The non-autonomous effects (or lack thereof) on PNs is an important part of the story as is the functional response of Or42a ORNs and the differing response of similarly (but not identically) sensitive Or43b ORNs. The experiments are well conceived, controlled, and conducted. Where the story falters a bit, though, is with the connectomics analysis. The authors show distinct differences between Or43b and Or42b ORN input output relationships and suggest that those differences may underlie the differences observed in their response to ethyl butyrate exposure during the critical period. This is certainly a possibility, but as it stands now, it is too disconnected to offer significant proof. There would have to be additional experiments to address this. Right now, the inclusion of the connectomics work feels like a distraction at best, and a complete non sequitur at worst. To be clear, the connectomics work is well done and I have no issues with its validity, but is not helpful to the central thesis of the work. I would suggest the authors either remove it entirely or strongly rethink how it fits into the paper.
Comments on revised version:
I appreciate the consideration of my comments and the authors' responses. The additional data on PN synapse number is intriguing (and welcome) as is the text discussing potential postsynaptic compensatory mechanisms. I respect the authors' decision in retaining the connectivity analysis, but despite the textual changes, I still feel that it is peripherally related to the main thesis of the work and would best be omitted from the paper and included in a separate, more relevant study. Ultimately, though, that is their choice.
Author response:
The following is the authors’ response to the original reviews.
Public Reviews:
Reviewer #1 (Public review):
Summary:
This study builds on earlier work showing that early-life odor exposure can trigger glial-mediated pruning of specific olfactory neuron terminals in Drosophila. Moving from indirect to direct functional imaging, the authors show that pruning during a narrow developmental window leads to long-lasting suppression of odor responses in one neuron type (Or42a) but not another (Or43b). The combination of calcium and voltage imaging with connectomic analysis is a strength, though the voltage imaging results are less straightforward to interpret and may not reflect synaptic output changes alone.
Strengths:
Biologically, one of the main strengths of this work is the direct comparison between two odor-responsive OSN types that differ in their long-term adaptation to early-life odor exposure. While Or42a OSNs undergo pruning and remain persistently suppressed into late adulthood, Or43b OSNs, which also respond to the same odor, show little lasting change. This contrast not only underscores the cell-type specificity of critical-period plasticity but also points to a potential role of inhibitory network architecture in determining susceptibility. The persistence of the Or42a suppression well beyond the developmental window provides compelling evidence that early glia-mediated pruning can imprint a stable, life-long functional state on selected sensory channels. By situating these functional outcomes within the context of detailed connectomic data, the study offers a framework for linking structural connectivity to long-term sensory coding stability or vulnerability.
Weaknesses:
The narrative begins with the absence of changes in PN dendrites and axons. While this establishes specificity, it is a relatively weak starting point compared to the novel OSN functional results.
We agree that switching the order of Figures 1 and 2 recontextualizes the negative PN morphology findings to make their significance more clear, especially with the addition of PN odour-evoked activity data (see Figures 2A, B of the revised manuscript).
Calcium imaging with GCaMP, though widely used, is an indirect measure of synaptic function, and reduced signals could reflect changes in non-synaptic calcium influx as well as release probability. The interpretation of the voltage imaging results is also unclear: if suppression were solely due to impaired synaptic release, one might expect action potential-evoked voltage signals to remain unchanged. The reported changes raise the possibility of deficits in action potential initiation or propagation, which would shift the mechanistic explanation.
Although it is true that non-synaptic Ca<sup>2+</sup<> influx could contribute to odour-evoked signals in OSN axon terminals, it seems likely to be a relatively small contribution when compared to Ca<sup>2+</sup> influx via voltage-gated Ca<sup>2+</sup> channels at the active zone. Given the observation that synaptic markers are eliminated during this form of critical period plasticity and remain decreased even after OSNs regrow their terminals days later (consistent with our observed continued decrease in odour-evoked responses), the most parsimonious explanation is that we are seeing a reduction in synaptic Ca<sup>2+</sup> influx. We cannot dismiss the possibility that there is a decreased voltage signal arising from fewer action potentials being elicited by the odour stimulation. However, the reduction in voltage signal must arise at least in part from the observed reduction in Ca<sup>2+</sup> influx. We have therefore provided additional text to this effect in the results section.
The difference between Or42a and Or43b OSNs is attributed to varying inhibitory input densities from connectome data, but this remains speculative without functional tests such as manipulating GABA receptor expression in OSNs. In Or43b, there is essentially no strong phenotype, making it premature to ascribe the absence of suppression solely to inhibitory connectivity.
We have tempered our conclusions to posit additional mechanisms that could explain the more mild pruning that occurs for Or43b OSNs. While the pruning phenotype for Or43b OSNs is not as strong as Or42a, it is not absent. To further explore the contribution of inhibition as a candidate mechanism underlying differences in susceptibility of Or42a and Or43b to this form of critical period plasticity we compared the relative impact of knocking down expression of GABA-A receptor (called “rdl”) in Or42a and Or43b OSNs. Consistent with the degree of pruning being regulated inhibition, knocking down expression of rdl enhanced pruning for both Or42a and Or43b OSNs. However, because the magnitude of the enhancement was similar between both OSN types, we agree with the reviewer that inhibitory connectivity cannot be the sole mechanism that explains the difference and have therefore tempered our language appropriately.
Finally, the study does not connect circuit-level changes to behavioral outcomes; assays of odor-guided attraction or discrimination could place the findings in an organismal context.
We agree that behavioral assays will be a critical component for understanding the functional consequences of this form of critical period plasticity. However, the goal of this study was to extend our prior work to determine the longevity and selectivity of the critical period pruning. Behavioral assays testing the consequences of this form of early life plasticity will be a component of future studies.
Some introduction material overlaps with the authors' 2024 paper, and the novelty of the present study could be signposted more clearly.
We have included text to highlight the novelty of the present study.
Reviewer #2 (Public review):
Recent work from the authors identified the synaptic changes and glial reaction that occur during exposure of a Drosophila odorant receptor neuron population to continued exposure of a stimulating odorant. This work markedly advanced our understanding of cellular response to critical periods. This current Advance manuscript carries that work forward and examines the non-autonomous responses to constant odorant exposure. The authors discover that the changes to ORN populations are not accompanied by changes to either PN dendrite or PN axon volume, nor are they concurrent with changes in postsynaptic PN structures. These changes are, however, notable, accompanied by changes in Ca2+ and voltage responses in ORNs. Importantly, this set of responses is specific to the Or42a ORNs (that are highly sensitive to the odorant in question, ethyl butyrate) and not the Or43b ORNs (which respond to ethyl butyrate, but not as drastically). Finally, the authors include connectomics analyses showing that Or43b and Or42a ORNs differ in their synaptic input/output relationships.
This is an excellent use of the Advance mechanism for the journal, as these are important follow-up findings for the parent story. The non-autonomous effects (or lack thereof) on PNs is an important part of the story, as is the functional response of Or42a ORNs and the differing response of similarly (but not identically) sensitive Or43b ORNs. The experiments are well-conceived, controlled, and conducted. Where the story falters a bit, though, is with the connectomics analysis. The authors show distinct differences between Or43b and Or42b ORN input-output relationships, and suggest that those differences may underlie the differences observed in their response to ethyl butyrate exposure during the critical period. This is certainly a possibility, but as it stands now, it is too disconnected to offer significant proof. There would have to be additional experiments to address this. Right now, the inclusion of the connectomics work feels like a distraction at best, and a complete non sequitur at worst. To be clear, the connectomics work is well done and I have no issues with its validity, but it is not helpful to the central thesis of the work. I would suggest the authors either remove it entirely or strongly rethink how it fits into the paper.
We have tempered our stated interpretations of the connectivity analysis and include new experiments examining the impact of GABA signaling on pruning. We have therefore opted to retain the connectivity analysis as we feel that it has been better integrated into the overall narrative of the paper.
Major Concerns:
(1) The examination of PN axon terminals in the MB and LH is interesting, but it is only one possibility. Oftentimes, the volume of neurons remains constant with perturbation, while the synapse number is affected. Figure 1C and E would be greatly helped by examining synapse number (via Brp or Brp-Short) in the PN axons.
We agree that the counting synapse number would provide greater resolution information about synapse function relative to axon volume and have added this analysis to what is now Figure 2.
(2) The use of dlg1[4K] is a strong use of a new tool, but the result is surprising. The presynaptic ORN synapse number onto the PNs is notably changed, but that is not reflected in a postsynaptic PSD-95 change. That suggests a compensatory mechanism that the authors might explore. A good proportion of PN puncta should be postsynaptic to those ORNs, so why aren't they adjusted?
We agree that this result suggests that a compensatory mechanism may be present. We have therefore added new text to point out this observation and potential explanation.
Recommendations for the authors:
Reviewer #1 (Recommendations for the authors):
The interpretation of the voltage imaging results would benefit from clarification. If these signals are reduced because of upstream action potential changes rather than synaptic release, this should be explicitly discussed and illustrated with representative raw traces for both OSN types. The proposed link between inhibitory connectivity and selective vulnerability could be tested more directly, for example, by manipulating GABA receptor function in OSNs.
We have now tested the link between inhibitory connectivity and susceptibility to glial pruning by testing the effects of GABA receptor knockdown in either Or42a or Or43b OSNs (fully described above).
Adding an intermediate post-exposure time point for Or42a responses could help resolve whether suppression is immediate or develops over time.
The suppression of Or42a odour-evoked responses is present immediately after the 2 day exposure period and responses remain suppressed until 25 days post-eclosion, indicating that the suppression is immediate and sustained. We therefore respectfully disagree that another physiological time point will help resolve whether the suppression is immediate or develops over time.
In terms of presentation, the introduction could be tightened to reduce overlap with the 2024 paper, figures should have clear axis labels and consistent terminology for neuron types and glomeruli, and a schematic summarising key inhibitory connections for Or42a vs. Or43b would aid clarity
We have now streamlined the introduction, improved clarity on axis labels and checked for consistency of terminology.
Minor Concerns:
(1) The dlg1[4K] is made with a V5 epitope but the authors have it labeled mCD8::GFP in Figure 1F. This is likely a typo and should be corrected.
This typo has now been corrected.
(2) Can the responses be separated in Figures 2A, C, and E? It is difficult to see the differences in oil and EB exposure. This would make it much more straightforward to tell the difference if both traces were clearly visible.
Overlaying the averaged response traces for in Figure 2C, E and G (now Figures 1C, E and G) enables the reader to make direct visual comparisons between the responses of OSNs from flies in each condition to both mineral oil and ethylbutyrate. Separating the individual traces would make it much more difficult to make these comparisons.
Our closest comparison keeps the monetary ask fixed at CHF 150:
Closest comparison to what?
that a well-designed unit-donation scheme can increase giving, especially with a large unit size. Their full treatment does more than report a unit cost: it reframes the decision as choosing physical units and can restrict choices to a unit grid
This needs expansion or clarification. Perhaps in a footnote or tooltip’s
It does not show that private philanthropy can replace government aid, or identify how to build political support for official development assistance
This not that ai language. Adjust
eLife Assessment
This is an important study that provides evidence that GATA6-dependent programming of peritoneal macrophages helps to regulate lipid metabolism and influences eosinophil survival. The evidence linking GATA6 deficiency to altered lipid profiles and eosinophil accumulation is solid, although the proposed mechanistic pathway connecting sphingolipid remodelling, LTE4 production and eosinophil survival currently remains incomplete. Strengthening these links and/or appropriately calming the conclusions would help to increase the impact of the study.
Reviewer #1 (Public review):
Summary:
Recent findings have established that macrophage function is tailored to individual tissues through upregulation of tissue-specific transcription factors in response to local microenvironmental signals. However, how these transcriptional pathways affect macrophage lipid metabolism and the importance of this for homeostasis of neighbouring immune cells remains relatively uncharted. One exemplary pathway is the specific expression of GATA-6 by macrophages within the serous cavities that is triggered by local retinoic acid production. Here, Czubala et al have used mice with macrophage-specific deletion of GATA6 (GATA-6KO-mye) to study the importance of tissue-specific macrophage programming in regulating the macrophage and tissue lipidome and the functional importance of this for the regulation of eosinophil numbers in the tissue.
Strengths and Weaknesses:
The authors show accumulation of lipid-rich vesicles in the absence of GATA6, which lipidomic analysis suggests are largely comprised of sphingolipids and glycophospholipids. Using published transcriptional data identifies candidate genes in GATA6-deficient cells that may underlie these changes. Manipulating two of these candidate genes, Gba2 and Smpd1, in a macrophage cell line leads to similar changes in sphingolipid composition to those in GATA6-deficient macrophages in vivo, supporting the hypothesis that tissue specialisation of peritoneal macrophages induces transcriptional changes via GATA6 that directly control sphingolipid metabolism. GATA6 deficiency is then shown to affect the oxylipin content of peritoneal macrophages and peritoneal fluid, including higher levels of LTE4 in fluid. Elevated expression of the Ltc4s in GATA6-deficient cells is predicted as the likely mechanism leading to elevated LTE4.
To determine the functional effects of altered lipid metabolism, and specifically LTE4, the authors focus on the elevated accumulation of peritoneal eosinophils previously reported to occur in GATA-6KO-mye mice. They show that eosinophils undergo less apoptosis in these mice and the absence of a measurable increase in known eosinophil chemokines leads them to conclude that eosinophil numbers arise through increased longevity. However, this point remains to be formally demonstrated, and directly measuring the longevity of eosinophils in the cavity would greatly strengthen their conclusions. The authors then examine known regulators of eosinophil survival, IL-5 and GM-CSF. They convincingly demonstrate a role for IL-5 in the regulation of peritoneal eosinophil numbers but conclude that survival factors other than IL-5 and GM-CSF likely control the differential numbers in control and GATA-6KO-mye mice, given IL-5 was observed to be a general survival signal in both genotypes and that no difference in the levels of these growth factors was observed in lavage fluid between genotypes. The authors then blocked production of prostaglandins using the inhibitor indomethacin. This treatment also led to a general reduction in survival and number of eosinophils in both control and GATA-6KO-mye mice, leading to the conclusion that altered prostaglandin production is not the underlying mechanism regulating elevated eosinophil numbers in the absence of GATA6.
One weakness in these conclusions is that if the GATA-6-KO-mye phenotype does lead to increased production of a homeostatic growth factor for eosinophils, then inhibition/blockade of such a factor would be expected to lead to loss of eosinophils in both WT and GATA-6KO-mye mice. Furthermore, cytokines, chemokines, and lipid mediators can be rapidly bound and removed or metabolised in vivo by their receptors, meaning detecting an increase in production in body fluids can be difficult.
Finally, they block production of LTE4 using an inhibitor of the upstream enzyme 5-LO. This treatment reduces eosinophil survival and number in GATA-6KO-mye mice, from which the key conclusion is drawn that elevated LT4E is responsible for the increased survival and accumulation of eosinophils in GATA-6KO-mye mice. The major weakness here is that the equivalent experiment in control mice to determine if inhibition of 5-LO leads to a general reduction in survival/number of eosinophils or if this effect is restricted to the GATA-6KO-mye appears not to have been performed.
Impact and context:
Overall, this study demonstrates key alterations in lipid metabolism and lipid mediator release resulting from loss of GATA6 expression in peritoneal macrophages, and links this to the elevated survival/accumulation of eosinophils that occurs concurrently in GATA6-KO-mye mice. The role of endogenous LTE4 in regulation of eosinophil survival and/or migration into tissues is exciting and opens up a new avenue of research for understanding the importance of this pathway in regulation of eosinophils across tissues and during disease. Furthermore, unlike in the mouse, GATA6-expressing macrophages represent only a minor proportion of macrophages in the human peritoneal cavity, while the dominant GATA6-negative population is more equivalent to the GATA6-KO-mye cells studied here (PMID: 38102487). Hence, the data presented in the current manuscript could have important implications for how eosinophil numbers and lipid metabolism may be regulated by these cells in people.
Reviewer #2 (Public review):
Summary:
This manuscript examines how GATA6-dependent programming of resident peritoneal macrophages regulates their lipidome and, in turn, eosinophil homeostasis, combining lipid imaging, mass spectrometry, transcriptional analysis and in vivo pharmacology. BODIPY/CARS microscopy with targeted lipidomics convincingly shows substantial lipid changes following myeloid GATA6 deficiency, particularly in sphingolipids, with Smpd1 and Gba2 manipulations providing mechanistic support.
Strengths:
The authors also connect these changes to eosinophil biology, confirming increased peritoneal eosinophils in Gata6-deficient mice with reduced apoptosis (via two methods) rather than increased production. Testing of alternative explanations (chemokines, IL-5, prostaglandins, 12/15-LOX products) strengthens the argument by narrowing candidate mechanisms. The identification of increased LTE4 is notable as it correlates with eosinophil abundance, and zileuton reduces LTE4, eosinophil numbers, and increases apoptosis. This supports a role for 5-LOX/cysteinyl-leukotriene signalling.
Weaknesses:
The principal weakness is specificity: zileuton affects the broader leukotriene pathway, not LTE4 alone, so correlation with LTE4 doesn't establish causality. This matters more given the LTE4 receptor remains unidentified (to the best of my knowledge). Similarly, the proposed transcellular biosynthesis mechanism (ImmGen data suggesting complementary enzyme expression across cell types converting LTC4 to LTE4) is inferential; direct evidence of cellular source and transfer is lacking.
Design limitations include reliance on pooled animals in some lipidomic measurements, small replicate numbers, and a stronger eosinophil phenotype in females that shifts subsequent analysis toward females. Indeed, this sex dependence deserves more discussion given it limits generalisability.
Overall, this is a technically strong, conceptually interesting study. The core conclusions, that GATA6-dependent regulation of the macrophage lipidome and a role for cystl Lts in eosinophil survival, are well supported. The more specific claim that LTE4 is the causal factor via a defined transcellular pathway is plausible but not yet firmly established. Experimental strengthening or moderated claims would improve the study.
Reviewer #3 (Public review):
Summary:
The authors sought to define how GATA6-dependent programming of resident peritoneal macrophages regulates lipid metabolism and, in turn, eosinophil survival. By integrating a myeloid-restricted GATA6-deficiency model with cellular phenotyping, lipidomic analyses, and measurements of lipid mediators, the study attempts to connect macrophage transcriptional identity to sphingolipid and cysteinyl leukotriene pathways that may shape eosinophil persistence. The work also appears intended to provide a mechanistic bridge between prior observations from this group and others regarding GATA6-positive macrophages, lipid metabolism, and eosinophil homeostasis.
Strengths:
(1) The study addresses an important and understudied question: how tissue-resident macrophage identity controls the local lipid environment and thereby influences eosinophil survival.
(2) The use of a genetically defined GATA6-deficiency model provides a biologically relevant framework for testing the contribution of macrophage programming.
(3) The lipidomic data broaden the analysis beyond a single mediator and identify coordinated changes in sphingolipids and glycerophospholipids that may generate useful hypotheses for the field.
(4) The finding that GATA6 deficiency promotes eosinophil survival is clear, potentially important, and consistent with prior work cited by the authors.
(5) The study is performed by a knowledgeable team and brings together macrophage biology, eosinophil biology, and lipid metabolism in a way that is likely to interest several research communities.
Weaknesses:
(1) The central mechanistic chain-GATA6 deficiency leading to altered sphingolipid abundance, altered LTE4 production, and consequently increased eosinophil survival-is not fully demonstrated. The data support associations among these features, but the causal order remains uncertain.
(2) The cited literature linking sphingolipid and cysteinyl leukotriene biosynthesis does not substitute for direct testing in this model. Perturbation or rescue experiments targeting sphingolipid synthesis and cysteinyl leukotriene production would be needed to establish necessity and directionality.
(3) The broader lipidomic changes complicate the emphasis on sphingolipids. Because glycerophospholipids are also increased, the phenotype may reflect more extensive membrane-lipid remodeling, altered phospholipase activity, changes in the Lands cycle, or shifts in free fatty-acid availability.
(4) The manuscript would benefit from a clearer distinction between observations made directly in GATA6-deficient peritoneal macrophages and mechanistic inferences extrapolated from prior studies.
(5) The physiological and pathological relevance is not yet sufficiently established. It remains unclear whether enhanced eosinophil survival translates into altered eosinophil accumulation, activation, or tissue injury during inflammatory disease in the peritoneal cavity or lung.
Author response:
We would like to thank all the reviewers and the editors for their considerate evaluation of our study.
We are pleased that overall the reviewers were positive about the bulk of our study establishing a role of tissue macrophage programming/specialisation in regulating the macrophage lipidome, in the peritoneum, including the exemplar sphingolipid class. The reviewers raise understandable issues about the specificity of the available inhibitory compounds, such as zileuton meaning that conclusive statements about the role of LTE4 are not possible.
In a revised manuscript, we will address all points but predominantly focus on the second aspect of the study, ensuring that reviewers comments are addressed appropriately, detailing and weaknesses, or ambiguities, with our study. This will include, but will not be limited to:
- Further commentary on the regulation of eosinophil numbers within the tissue;
- Addressing the specificity of zileuton and the implications of this for interpretation of our results with respect to eosinophil biology;
- More careful framing of the transcellular biosynthesis potential;
- A detailed discussion of sex dependency with regard to eosinophil numbers in general and any potential effect on the reported Gata6-dependent phenomenon;
We are grateful for the constructive comments.
https://bafybeibgznbox2gpx5lkttywunvabt6afdrlp5yhv6k5tt6xwx7prert6m.ipfs.inbrowser.link
not one of them ever offered me the least abuse of unchastity to me, in word or action.
Goes to show the difference between the natives and the colonials. Natives didn't rape, colonials did.
if I were willing, he and his squaw would run away, and go home along with me.
Why the sudden change in heart? Why the desire to go with her?
but afterwards they assented to it, and seemed much to rejoice in it; some asked me to send them some bread, others some tobacco, others shaking me by the hand, offering me a hood and scarfe to ride in; not one moving hand or tongue against it
They sought out the things they needed for survival and for enjoyment and they thought she would/could give it to them.
Regardless of what commitments we make, the public deserves to know what is going on.
Common Good Lens: Using the common good lens, we need to think about the outcomes for all members of the community. I believe that pacing is still the correct thing to do. Of course, it will only work if all AI companies agree to it. If there are some that do not agree, they could use AI to do harm to some people. No matter which lens you use, there needs to be care taken to make sure that we do not allow AI to grow faster than we can control and understand it. There must be some sort of third party monitoring.
Carefully wielded, AI can be the latest in a long line of technological miracles that have uplifted and ennobled humanity.
Utilitarian Lens: Pacing AI could definitely delay many potential benefits of AI. Economic, medical, and others. However, pacing AI can limit or prevent a lot of risks. I believe one of the biggest risks we are taking is losing control of the current AI systems. To AI itself, but more importantly to those who wish to do harm to others. Using the Utilitarian Lens, we must think about what would create the best outcome for the most people. A cost/benefit analysis would be very beneficial to make this decision. Not just financial cost but the cost of slowing progress on so many fronts. On the other hand, pacing will only truly be helpful if there are agreements in place from all AI companies to do the same. Especially from countries that would like to get ahead of the US and cause us harm. Whether it is economic harm or biological harm or something else, we may not be able to recover. Amodei states "it's my worry that in 6-12 months such a swarm could be capable of taking over the entire internet" shows how big of a risk we would be taking by continuing on our current course. We need to take the time to make sure AI is safe for the public.
My sisters and I had bonded in the kitchen, spending visits preparing elaborate dishes together for hours.
This sentence reminds me of when my cousin and I would visit our grandma and would spend the whole time in the kitchen making different desserts and meals for her to try.
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On behalf of the authors, I thank the reviewers for their critical reading of the manuscript. We really appreciate the care and attention they have applied to their reading and reports.
An initial comment may add some context - in accordance with German law, I (AFS) had to retire from my position at Dresden University in October 2023. I am still working however have very little capacity to add new experiments to the manuscript. Consequently my response to the reviewers is somewhat more critical than the normal concessions and acquiescence that are usually adopted.
Major comments Figures 2D, 2E, 3C: there are no panels showing the efficiency of Bod1 or Bod1L immunoprecipitation. The immunoblots seem to indicate that either Bod1 or Bod1L precipitate a substantial fraction of Setd1a and a much smaller fraction of Setd1b, although it is impossible to tell without the blots of Bod1/Bod1L. The idea that Setd1a is primarily associated with Bod1L vs Bod1 is presumed in the rest of the manuscript (likely based on previous results in other cell lines) but is not strongly supported by these figures.
Response: The protein complex and interaction data is based on AP-MS (affinity purification-mass spectrometry) and the results are presented as Volcano plots, which is the standard and most accessible format. AP-MS acquires candidate data because some bona-fide interactions will be missed and spurious interactions will be included, especially when the threshold of significance is lowered. To validate the candidate data from tagged SETD1A and SETD1B we used
(a) reciprocal AP-MS with tagged BOD1L, BOD1 and CXXC1. These results secured the primary conclusion regarding the associations of BOD1L and BOD1 with SETD1A and SETD1B (as well as other subunits). I should add that we show – for the first time – functional evidence that BOD1L is a subunit of the SETD1A complex (Figure 5).
(b) immunoprecipitations to confirm selected interactions. As stated in the legend of Figure 2, 10% total extract are shown as controls. This control allows the reader to evaluate the efficiency of the associated protein in the IP. For example, (Fig. 2D), clearly BOD1L associates much more with SETD1A than with SETD1B. Nevertheless, the association with SETD1B was detected and reciprocally confirmed (Fig. 3C). Another example, (Fig. 2E), clearly BPTF interacts with BOD1L at notable efficiency but not with SETD1A. There are also additional IPs in the supplement that add further confidence.
Additionally, AP-MS with three SETD1A deletion mutants (Fig. 4) adds supporting evidence to the conclusions drawn from Figures 2 and 3. The implication that BOD1L and BOD1 interact with X3, which in Figure 4 is a negative result and therefore – because negative results in AP-MS cannot be used to draw conclusions – we explicitly tested the proposition that BOD1L and BOD1 interact with X3 to secure the conclusion (Fig 4D).
Consequently, with respect, we do not agree with the following comment by reviewer 1 -
- although it is impossible to tell without the blots of Bod1/Bod1L. The idea that Setd1a is primarily associated with Bod1L vs Bod1 is presumed in the rest of the manuscript (likely based on previous results in other cell lines) but is not strongly supported by these figures.
__ T__he data presented clearly allows reasonable evaluation of yield and consequently conclusions about the BOD1L and SET1A interactions. The primary association of BOD1L with SETD1A is established by the data presented, as is the secondary associations between BOD1 and SETD1A, as well as BOD1L and SETD1B.
Figure 4: the SETD1A-X1 and X3 internal deletion lines show many interesting interactions not detected in the wild-type line, notably with CPSF components. What is the significance of this?
AP-MS explores the proteome and a number of intriguing associations can be found in addition to the most robust biochemical interactions. In this manuscript, we present some exploration of the intriguing extras but remain focused on the SETD1A and B complexes. We and others have published on the connection between the yeast Set1C and yeast CPSF, but exploring that issue connection is beyond the experimental and conceptual themes of this manuscript, especially considering other comments by the reviewers about reducing the manuscript.
Related to Figure 4: Why were none of the functional genomics experiments described in Figures 5-7 performed on the Setd1a-X3 deletion line given that the authors had it in hand? This would have been a logical complement to the Bod1L deletion experiment and further addressed the issue of functional partnership between Setd1a and Bod1/L. One could make a similar point regarding Bod1-it is unclear why (given the co-IP and AP-MS data in Figures 2 and 3) a deletion line of Bod1 was not analyzed in parallel. There was convincing rationale in the Hoshii 2024 paper to focus on Setd1a/Bod1L in that system; the rationale for doing this here is less clear.
We thank the reviewer for this suggestion. Indeed this is a good experiment that emerges from the data we are presenting (and not from Hoshii et al 2024). We take this excellent suggestion as an indication that our manuscript has presented the evidence sufficiently well to permit the reviewer to make this suggestion, which is worth pursing in a new project. However the manuscript is already replete with progress. It is worth mentioning that in response to an earlier round of reviewing elsewhere, we added the experiment that is now Figure 7. This process of adding further experiments in response to thoughtful reviewing comes at the risk of promoting further good suggestions, which are constructive and welcome but at some point progress should be published.
Figure 5G: This figure does not seem to include a control in which wild-type ESCs are treated with tamoxifen in parallel with the Flp Bod1L line.
There is no published or conceptual reason to include a control for the induction of DNA damage by tamoxifen in wild type cells. My lab pioneered ligand inducible conditional mutagenesis (Logie C and Stewart AF. 1995 Ligand-regulated site-specific recombination. PNAS 92, 5940-5944) including tamoxifen inducible conditional mutagenesis in mice (Schwenk F, Kühn R, Angrand P-O, Rajewsky K and Stewart AF. 1998 Temporally and spatially regulated somatic mutagenesis in mice. Nucleic Acids Res. 26, 1427-1432) and we have published advice for the appropriate controls for tamoxifen induced conditional mutagenesis (Anastassiadis et al 2010, Methods Enzymol, 477, 109-23). All experiments involving tamoxifen induction of conditional mutagenesis were thoroughly accompanied by appropriate controls. In the case of Fig. 5G, administration of tamoxifen to ESCs had no detectable effect on DNA damage as evaluated by p-H2AX or p-ATM staining – as expected and therefore not shown.
Figure 6: This figure should include Venn diagrams that clearly show the overlap between genes affected by Bod1L removal compared to Setd1a.
In Figure 6B, the overlap is clearly illustrated in a colour presentation that we think is superior to presenting these data as a Venn diagram. These data are also presented in different formats - in the Supplement Fig. 6D and the most significant DNA repair genes are listed in Table 1 again presented in an overlapping format.
Related to Figure 6/7: These figures should include analysis of the H3K4me3 ChIP-seq data in Figure 5 specifically at Bod1L-regulated DEGs.
We now present a new Supplemental Figure 6E and include the statement - ‘Increased H3K4me3 peaks were also observed at the promoters of the DNA repair genes that showed decreased expression after loss of BOD1L (Supplemental Figure 6E).’ in the text. In other words, elevated H3K4me3 is also observed on the DNA repair genes that show decreased expression.
Discussion: The authors should note that the direct role of Setd1a at DNA breaks is proposed to rely on enzymatic activity (Higgs et al 2018, Bayley et al 2022).
A sentence regarding SETD1A enzyme activity in DNA repair has been included in the Discussion.
Minor comments
__ Figure 5: panel arrangement is confusing. __The arrangement of Figure 5 has been improved.
__Hoshii et al, 2024 is not listed in the references. __This omission has been corrected
__Reviewer #1 (Significance (Required)): ____*
The key advance in this work is demonstration that Bod1L removal affects expression of DNA repair genes and increases DNA damage in ES cells. This seems to occur independently of changes in H3K4me3 (although specific analysis of H3K4me3 at these genes was not performed). *__
We now include analysis of H3K4me3 at promoters of genes downregulated after loss of Bod1L (new Supplemental Figure 6E). As with all active promoters, H3K4me3 is also elevated on these genes.
Removal of Setd1a had similar effects. The work seems to support previous reports in leukemia cell lines for specific effects of Setd1a/Bod1L on DNA repair that is not related to Setd1a enzymatic activity (Hoshii et al 2018, 2024 in the manuscript)(and contrasts with findings in U2Os cells that do implicate enzymatic activity and emphasize a direct role at replication forks rather than in transcription; Higgs et al 2018 and Bayley et al 2022 in the manuscript). This is a modest but important conceptual advance in thinking about Bod1L and COMPASS complex functions in transcription and DNA damage repair. Whether these functions are specific to Bod1L vs Bod1 in ESCs, or to Setd1a over Setd1b, in ESCs, was not addressed.
In the Introduction we highlighted the difference between Setd1a and b in ESCs. We previously published that Setd1a is essential whereas Setd1b is not, and Setd1b fails to rescue the loss of Setd1a in ESCs when (over)expressed from the Setd1a promoter (Bledau et al, 2014). Furthermore the preferential association of BOD1 with SETD1B rather than SETD1A can be concluded from the data provided. These considerations support the conclusion that SETD1A and BOD1L are specifically regulating DNA repair gene expression.
The audience for this work will be chromatin/epigenetics experts. My expertise is in the field of chromatin and epigenetics, and I have studied histone modification function extensively in the context of transcription.
As a final comment to reviewer 1 – thankyou for your thoughtfulness but please allow a comment on the term ‘COMPASS’, which is not used in our manuscript. COMPASS is a confusing and ambiguous term. It means ‘COMPlex ASsociated with yeast Set1’ and was first used to describe the incomplete yeast Set1 complex. Concomitantly, my group published the complete complex, termed Set1C, along with the first biochemical proof that it was an H3K4 methyltransferase (the first bona fide H3K4 methyltransferase, and the second bona fide histone methyltransferase). The following year, the second COMPASS publication reported the full complex by including the missing subunit and also biochemical proof of H3K4 methyltransferase enzyme activity. Subsequently the term ‘COMPASS’ has been applied to the Trithorax and MLL complexes, which share half of the Set1C/COMPASS complex – these four subunits are highly conserved in eukaryotes – but also include another 4+ subunits unrelated to the other half of Set1C/COMPASS, which are also are different between the Trithorax/MLL1,2 and Trithorax related/MLL3,4 complexes. So it is imprecise and confusing – especially for the majority of bioscientists who do not have histone methylation expertise - to name these partially related but distinct complexes, ‘COMPASS’. The highly conserved 4 subunits of these complexes have been more precisely termed ‘WRAD’ (after the mammalian names, WDR5, RBBP5, ASH2L, DPY30). WRAD, as opposed to COMPASS, is unambiguous and does not require specialist insider knowledge to unravel the confusion. Therefore the term ‘WRAD’ is used to refer to the conserved quartet in the manuscript.
__*Reviewer #2 (Evidence, reproducibility and clarity (Required)):
This manuscript investigates H3K4me-methylating complexes in mouse embryonic stem cells with a specific focus on the SETD1A complex and its binding partners. The authors define the interaction between SETD1A and BOD1L and characterize a requirement for BOD1L in maintaining the expression of DNA repair genes in mESCs. BOD1L has previously been implicated in regulating the replication fork (Bayley et al., Mol Cell, 2022 and others), however the authors propose another role for BOD1L in restraining H3K4me3 at TSS-proximal nucleosomes which impacts gene expression of DNA repair genes. *__
With respect, we do not suggest that the restraining action of BOD1L on H3K4me3 has any impact on gene expression. In contrast, we note that elevated H3K4me3 at TSS-proximal nucleosomes does not correlate with changes in gene expression.
However, a number of aspects of this model require additional support. Furthermore, while there are some new insights gained from the experiments performed, the data presentation makes the impact of the studies difficult to interpret. Specific concerns are outlined in further detail below: ____ 1. A key approach used through the manuscript is AP-MS experiments to determine protein interactors of SETD1A, SETD1B, and other complex subunits. It appears these experiments were generally performed in triplicate, however, there should be more discussion of what thresholds were used to quantify interactors. The methods states that if 2 unique peptides were identified, though it is very difficult to tell from the volcano plots why some proteins are labelled and named as interactors and others are not discussed. Furthermore, the volcano plots are generally difficult to read and do not lend themselves well to comparisons between different experiments. Another format in addition to potential volcano plots, such as a heatmap, would improve the readability and provide a better method of visualizing the quantitative results of these experiments.
Our presentation of AP-MS data in Volcano plots is conventional. As mentioned above (reviewer 1, response 1), AP-MS analyses present candidate data that requires further support, which we supplied for the conclusions we draw, as detailed above.
There is very little discussion of the additional interactors identified, outside of expected components, in the AP-MS experiments described in Figures 2 and 3. The authors state that they pursued additional experiments but that there was not productive data. It is unclear what this means as to whether these are not legitimate interactors or if there were other technical challenges. There is some discussion of OGT and BPTF, but it is not particularly informative. I think further clarification and/or characterization on the other interactors would be useful to be able to interpret the validity of the data presented in the AP-MS volcano plots____.
The candidate data obtained by AP-MS analyses include a core of reliable interactions as well as other less robust identifications that may be true or false positives. In this manuscript, we focused on the reliable and verified interactions, and mentioned notable additions, which we hope will assist further investigations. Further proteomic exploration is beyond the scope of this manuscript.
In Figure 4, AP-MS is used to characterize interactors of different deletion mutants of SETD1A. The expression of the deletion mutants should be shown by western or another approach to see how these compare to wildtype. In addition, the interaction is further probed in cells by a co-IP approach using an overexpression construct of the X3 region of SETD1A. Since the authors have the deletion mutant, this could be used in a co-IP experiment in addition to exogenous expression of just the X3 fragment. This would allow a direct comparison with WT SETD1A and other mutants. Also, to further support the specificity of the interaction show in Fig 4D, a similar experiment could be performed with other regions of SETD1A (or B), such as a the X1 or X2 regions.
We are not certain about these comments. The deletion mutants were examined by AP-MS, which is effectively superior to a co-IP, and retrieved most of the expected proteins. If we had pursued unexpected proteins identified in the mutant AP-MS, then Western (or similar) analysis of expression levels of the SETD1A mutants would be important. However we obtained reciprocal confirmation of the primary result, which is better than a co-IP with Western. Detailed analyses with other X regions are beyond the focus of this work.
Figure 5C and this H3K4me3 chip results in the BOD1L mutant cells would be further supported by showing the levels of SETD1A (and other H3K4 methylating enzymes) in these cells lines to better support the conclusion that BOD1L is directly restricting SETD1A activity at chromatin____.
In both yeast and in vitro, elevated H3K4me3 by Set1C without Shg1 is not due to elevated Set1 expression or changes of the Set1 complex, (other than loss of Shg1; Roguev et al, 2001, Kim et al, 2013). Concordantly, we show that SETD1A without X3 (i.e. without BOD1L) still retrieves the rest of the SETD1A-Complex (Figure 4B). Furthermore, Setd1a is expressed from its endogenous promoter to ensure physiological expression level.
Figure 5F shows growth curves of WT and BOD1 mutant ESCs. However, this data requires statistical analysis to make an accurate comparison between cell lines. Furthermore, the mutant cell lines in particular would benefit from showing at least one additional time point if feasible. In addition, the authors state that this is likely representative of increased cell death in the mutant cells, however this is not directly tested in this experiment.
Figure 5F shows straightforward growth curves of ESCs wt, heterozygous or homozygous Bod1l mutants. Please note that the figure includes the growth curves of two independent heterozygous and homozygous Bod1l ES cell lines thereby presenting reproducibility for the impaired growth.
The volcano plots in Figure 6 for the RNA-seq analysis are also difficult to interpret. Another data presentation method should also be used to be able to compare between experiments- this is not that feasible with the method and labeling of the data here, and the quantitative aspect of this data is not fully realized using this approach.
Figure 6A represents the RNA-seq data in Volcano plots, which is complemented by the dot plots of Figure 6B and the listing of genes in Table 1. RNA-seq data is difficult to present in visually accessible figures and we think that the presentations in Figure 6 are effective, because this visualization allows the estimation of effect size as well as p-values. These data are also presented in a different format in the Supplement Figure 6D.
The authors propose that the role of BOD1L DNA damage repair in ESCs is two fold in ESCs-one is a direct role at replication forks, and a second is its role in regulation of DNA repair genes with SETD1A. This may be the case, but the data provided here do not show that there is a direct role for BOD1L in regulating these genes. Additional experiments showing chIP or CUT&RUN of BOD1L and or SETD1A-dependent H3K4methyl species would provide more evidence that these DNA repair gene expression changes are directly due to BOD1L's role. It is also possible the gene expression changes are an indirect consequence of it's role at replication forks, but this is not really addressed. Furthermore, the model proposed in Figure 8 is difficult to understand and does not clearly represent the data in places (for example, the impact on H3K4methylation).
Our conclusion regarding the two-fold role of BOD1L is based on (a) the data of others regarding protection of the replication fork; (b) our verification that BOD1L is a component of the SETD1A complex; (c) the known role of SETD1A as the major H3K4 trimethyltransferase at active promoters; (d) the observation that conditional mutagenesis of Bod1l predominantly leads to decreased mRNAs that encode for various components of DNA repair pathways. If the loss of BOD1L led only to DNA damage and not gene expression changes due to compromised action of the SETD1A complex, then a loss of expression of DNA damage genes would not be expected. In particular, double strand DNA damage elevates the expression of Xrcc4, Xrcc5 and Rad51c however these mRNAs are strongly down-regulated when Bod1l (or Setd1a) is lost.
These points constitute a strong basis for our conclusion of a two-fold role for BOD1L and these points have been strengthened in the revised manuscript.
Regarding the reviewers comments –
This may be the case, but the data provided here do not show that there is a direct role for BOD1L in regulating these genes. Additional experiments showing chIP or CUT&RUN of BOD1L and or SETD1A-dependent H3K4methyl species would provide more evidence that these DNA repair gene expression changes are directly due to BOD1L's role.
The discussion section could be significantly streamlined and focused more directly on the content of the manuscript. There are a number of different areas covered in detail that go beyond what is needed for the discussion of the paper and are distracting and confusing. For example, the discussion of the work of Hoshii et al on page 13 is highly relevant, but it could be shortened to focus on the most relevant data from the 2024 paper. (Although I could not find this paper in the reference list, but assume it is this one:https://pubmed.ncbi.nlm.nih.gov/38989615/). Other areas that seems somewhat tangential include the discussion of the potential PP2A interaction on page 14, which is not focused on in the manuscript. Also, the broader question of the role of H3K4 methylation in transcription is covered in some detail and this could be shortened to discuss in a more straightforward manner the potential implications of this study on our understanding of H3K4methyl marks in transcription.
With due respect, we disagree. A shorter, less informative and less thoughtful discussion would probably have been criticized as insufficient. The reviewer is expressing an opinion. We think that we have succinctly presented the complexities of H3K4 methylation in transcription and our brief comment about PP2A may assist further research.
Reviewer #2 (Significance (Required)): There are some new insights provided into the relationship between SETD1A/SETD1B and the BOD1 and BOD1L components of the complex, however, the overall advances of this manuscript are relatively limited. A number of additional experiments are required to advance this work beyond what is already known in the field, and the specificty of their conclusions needs additional support. This limits the overall impact of this study* *
We thank the reviewer for acknowledging that there are some new insights. We have a different opinion regarding their impact on current knowledge. The SET1Complex and H3K4 methylation lies at the centre of epigenetic. Any progress is vitally important.
__*Reviewer #3 (Evidence, reproducibility and clarity (Required)):
This manuscript reveals that BOD1L, as a subunit of the SETD1A complex, plays a key role in embryonic stem cell survival by maintaining the expression of DNA repair genes to protect cells from the accumulation of DNA damage. The study finds that BOD1L interacts with the X3 helix of SETD1A through its Shg1 homology region, and that loss of BOD1L leads to elevated H3K4me2/3 levels (consistent with the conserved function of Shg1 in yeast), downregulation of DNA repair gene expression, accumulation of DNA damage, and cell death. While the findings possess a certain degree of novelty, there are some logical issues that require further revision.__ ** 1.The authors conclude that "BOD1L maintains DNA repair gene expression through the SETD1A complex," but the current evidence is merely correlational. However, does the downregulation of DNA repair genes directly lead to DNA damage accumulation and cell death? Does BOD1L's own function in replication fork protection (Higgs et al., 2015) also contribute to this phenotype? The authors mention this point in the discussion, but the experiments do not distinguish between these two functions. *
Please see the comments above responding to reviewer 2, point 7. The manuscript presents strong evidence that the conclusion is not ‘merely coincidental’. We addressed the question ‘____Does BOD1L’s own function ____in replication fork protection (Higgs et al., 2015) also contribute to this phenotype? ‘ in the experiment of Figure 7 and not just mentioned in the discussion.
It is recommended to supplement with rescue experiments: in BOD1L-deficient cells, complement with wild-type BOD1L and mutants (e.g., lacking the X3 binding domain) to examine DNA repair gene expression, DNA damage accumulation, and cell death.
As noted in our response to reviewer 1 point 3, we thank the reviewer for this constructive suggestion for further experiments that ideally will be in another manuscript.
2.Figure 5C shows that BOD1L deletion leads to increased H3K4me3, whereas SETD1A deletion results in decreased H3K4me3. However, RNA-seq reveals substantial overlap in the downregulated genes between the two conditions (Figure 6B). Based on this, the authors infer that "H3K4me3 is not essential for DNA repair gene expression." This inference is reasonable, but one possibility needs to be excluded: whether the increase in H3K4me3 caused by BOD1L deletion occurs at non-target genes (specifically, DNA damage repair genes). It is recommended to perform H3K4me3 ChIP-qPCR in BOD1L-deficient cells to validate changes at the promoter regions of key DNA repair ____genes.
This comment is similar to a point made by reviewer 1 point 6. The analysis is now included in Supplement Figure 6E.
3.Figure 5G uses γH2AX and pATM staining to detect DNA damage, but the type of DNA damage (double-strand breaks, single-strand breaks, replication fork stalling, etc.) and its extent have not been quantified. It is recommended to supplement with: (1) a neutral comet assay to detect double-strand breaks, or an alkaline comet assay to detect total DNA damage; (2) an analysis of replication fork stability (e.g., a DNA fiber assay) to distinguish between BOD1L's replication fork protection function and its transcriptional regulatory role.
With respect, further DNA damage assays will not distinguish between BOD1L's replication fork protection function and its transcriptional regulatory role.
4.Figure 2E shows that BOD1L interacts with BPTF independently of SETD1A. However, the functional significance of this interaction has not been further explored. BPTF is a subunit of the NURF chromatin remodeling complex, and its interaction with BOD1L may be involved in DNA repair or transcriptional regulation. It is recommended to supplement with: (1) examining changes in BPTF chromatin binding in BOD1L-deficient cells (BPTF ChIP-seq); (2) investigating whether BPTF knockdown affects DNA repair gene expression or the DNA damage response.
The manuscript is not about BPTF, rather we present a complementary observation to assist further research.
5.The study demonstrates that BOD1L binding to the X3 helix inhibits the methylation activity of the SET domain, but the molecular mechanism remains unclear. The authors propose hypotheses in the Discussion, such as "monomer vs dimer" or "allosteric regulation," but direct biophysical evidence to explain how this long-range regulation is achieved is lacking.
The interaction between BOD1L and SETD1A is presented and the implications are discussed in the context of existing information on the Set1 complexes. Further work – indeed a completely new project - is required to explore the implications of the findings we report.
6.The experiment observed that BOD1 can also bind to the X3 site of SETD1B, and the two proteins share structural similarity. Although the text mentions that BOD1L is the major subunit in ESCs, it does not sufficiently explore whether BOD1 exerts partial compensatory effects in the absence of BOD1L, or the logic underlying their specific switching in different tissues.
We agree – the manuscript does not explore whether BOD1 exerts partial compensatory effects in the absence of BOD1L. That would also be another project. Also we have not included speculations about ‘the logic underlying their specific switching in different tissues. ____‘
Minor suggestion____：____
1.The RNA-seq experiments were performed with biological duplicates (two samples per condition), it is generally recommended to have at least three biological replicates to ensure statistical power.
As specified in the M&M, the primary RNA-seq experiments were performed with biological duplicates in parallel in three closely related ESC culture conditions and the conclusions are drawn from these six overlapping datasets. The other RNA-seq experiment was performed in triplicates, as was an RNA-seq experiment using ESCFCS conditions, that was not included but delivered the same results as presented here.
2.In the co-IP experiments shown in Figure 2D and 2E, there is a lack of quantification or internal controls.
As mentioned in response to reviewer 1, point 1, the controls are included and quantification can be estimated from the figures. Further data are provided in Supplement Figure 1.__3.The peak calling parameters and statistical methods for the ChIP-seq analysis were not described in detail. __Now included in the M&M.
4.The description of the BOD1L-BPTF interaction results (Figure 2E) in the main text is too brief, and the conditions and controls for the IP experiment are not specified.
The text referring to BPTF has been expanded and is now –
Therefore, we examined the interaction with BPTF in more detail. By immunoprecipitation using BOD1L-VENUS expressed from a Bod1l BAC transgene, the interaction between BOD1L and BPTF was confirmed. However, immunoprecipitation using BPTF-VENUS expressed from a Bptf BAC transgene retrieved the NURF subunit SNF2L/SMARCA1 (Supplemental Fig. S1) but failed to retrieve SETD1A (Fig. 2E) indicating that BOD1L independently interacts with both SETD1A-C and BPTF, and that BPTF interacts with BOD1L independently of its interaction with NURF.
5.The discussion section is somewhat lengthy and contains speculative content (such as the discussion on OGT and MLL complexes). Although interesting, these points are not strongly related to the core findings of this study and could be streamlined.
The Discussion is a little less than 1300 words.
6.Page8 "Bod1 esiRNAi knock-down" should be ""Bod1 esiRNA knock-down".
Corrected.
Reviewer #3 (Significance (Required)): Should be revised.
Revisons suggested by the reviewers have been incorporated.
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This manuscript reveals that BOD1L, as a subunit of the SETD1A complex, plays a key role in embryonic stem cell survival by maintaining the expression of DNA repair genes to protect cells from the accumulation of DNA damage. The study finds that BOD1L interacts with the X3 helix of SETD1A through its Shg1 homology region, and that loss of BOD1L leads to elevated H3K4me2/3 levels (consistent with the conserved function of Shg1 in yeast), downregulation of DNA repair gene expression, accumulation of DNA damage, and cell death. While the findings possess a certain degree of novelty, there are some logical issues that require further revision.
1.The authors conclude that "BOD1L maintains DNA repair gene expression through the SETD1A complex," but the current evidence is merely correlational. However, does the downregulation of DNA repair genes directly lead to DNA damage accumulation and cell death? Does BOD1L's own function in replication fork protection (Higgs et al., 2015) also contribute to this phenotype? The authors mention this point in the discussion, but the experiments do not distinguish between these two functions. It is recommended to supplement with rescue experiments: in BOD1L-deficient cells, complement with wild-type BOD1L and mutants (e.g., lacking the X3 binding domain) to examine DNA repair gene expression, DNA damage accumulation, and cell death. 2.Figure 5C shows that BOD1L deletion leads to increased H3K4me3, whereas SETD1A deletion results in decreased H3K4me3. However, RNA-seq reveals substantial overlap in the downregulated genes between the two conditions (Figure 6B). Based on this, the authors infer that "H3K4me3 is not essential for DNA repair gene expression." This inference is reasonable, but one possibility needs to be excluded: whether the increase in H3K4me3 caused by BOD1L deletion occurs at non-target genes (specifically, DNA damage repair genes). It is recommended to perform H3K4me3 ChIP-qPCR in BOD1L-deficient cells to validate changes at the promoter regions of key DNA repair genes. 3.Figure 5G uses γH2AX and pATM staining to detect DNA damage, but the type of DNA damage (double-strand breaks, single-strand breaks, replication fork stalling, etc.) and its extent have not been quantified. It is recommended to supplement with: (1) a neutral comet assay to detect double-strand breaks, or an alkaline comet assay to detect total DNA damage; (2) an analysis of replication fork stability (e.g., a DNA fiber assay) to distinguish between BOD1L's replication fork protection function and its transcriptional regulatory role. 4.Figure 2E shows that BOD1L interacts with BPTF independently of SETD1A. However, the functional significance of this interaction has not been further explored. BPTF is a subunit of the NURF chromatin remodeling complex, and its interaction with BOD1L may be involved in DNA repair or transcriptional regulation. It is recommended to supplement with: (1) examining changes in BPTF chromatin binding in BOD1L-deficient cells (BPTF ChIP-seq); (2) investigating whether BPTF knockdown affects DNA repair gene expression or the DNA damage response. 5.The study demonstrates that BOD1L binding to the X3 helix inhibits the methylation activity of the SET domain, but the molecular mechanism remains unclear. The authors propose hypotheses in the Discussion, such as "monomer vs dimer" or "allosteric regulation," but direct biophysical evidence to explain how this long-range regulation is achieved is lacking. 6.The experiment observed that BOD1 can also bind to the X3 site of SETD1B, and the two proteins share structural similarity. Although the text mentions that BOD1L is the major subunit in ESCs, it does not sufficiently explore whether BOD1 exerts partial compensatory effects in the absence of BOD1L, or the logic underlying their specific switching in different tissues.
Minor suggestion
1.The RNA-seq experiments were performed with biological duplicates (two samples per condition), it is generally recommended to have at least three biological replicates to ensure statistical power. 2.In the co-IP experiments shown in Figure 2D and 2E, there is a lack of quantification or internal controls. 3.The peak calling parameters and statistical methods for the ChIP-seq analysis were not described in detail. 4.The description of the BOD1L-BPTF interaction results (Figure 2E) in the main text is too brief, and the conditions and controls for the IP experiment are not specified. 5.The discussion section is somewhat lengthy and contains speculative content (such as the discussion on OGT and MLL complexes). Although interesting, these points are not strongly related to the core findings of this study and could be streamlined. 6.Page8 "Bod1 esiRNAi knock-down" should be ""Bod1 esiRNA knock-down".
Should be revised.
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This manuscript investigates H3K4me-methylating complexes in mouse embryonic stem cells with a specific focus on the SETD1A complex and its binding partners. The authors define the interaction between SETD1A and BOD1L and characterize a requirement for BOD1L in maintaining the expression of DNA repair genes in mESCs. BOD1L has previously been implicated in regulating the replication fork (Bayley et al., Mol Cell, 2022 and others), however the authors propose another role for BOD1L in restraining H3K4me3 at TSS-proximal nucleosomes which impacts gene expression of DNA repair genes. However, a number of aspects of this model require additional support. Furthermore, while there are some new insights gained from the experiments performed, the data presentation makes the impact of the studies difficult to interpret. Specific concerns are outlined in further detail below:
There are some new insights provided into the relationship between SETD1A/SETD1B and the BOD1 and BOD1L components of the complex, however, the overall advances of this manuscript are relatively limited. A number of additional experiments are required to advance this work beyond what is already known in the field, and the specificty of their conclusions needs additional support. This limits the overall impact of this study
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Summary
This manuscript describes functional characterization of Bod1 family proteins (particularly Bod1L) and their interaction with COMPASS family histone methyltransferase complexes. Bod1 proteins are conserved through evolution and related to yeast Shg1, which binds to the yeast COMPASS via a conserved domain and negatively regulates H3K4me3 levels. Here the authors performed IP-MS of Bod1L, Bod1, Setd1a, and Setd1b in engineered mouse embryonic stem cells and confirmed presence of Bod1 and Bod1L in both Setd1a and Setd1b-containing COMPASS complexes. AlphaFold modeling predicted an interaction between the Bod1/L Shg domain and a conserved helix in Setd1a/b which was validated in stable Setd1a deletion ESC lines and with isolated Sed1a/b fragments. Functional analysis of Bod1L and Setd1a deletion lines confirmed that Bod1L negatively regulated H3K4me3 levels. Moreover, the knockouts caused parallel effects on the expression of DNA repair genes and both apparently enhanced levels of baseline DNA damage, in agreement with findings in leukemia cell lines. This argues that Setd1a/Bod1L regulates DNA repair gene expression independently of H3K4me3.
Major comments
Figures 2D, 2E, 3C: there are no panels showing the efficiency of Bod1 or Bod1L immunoprecipitation. The immunoblots seem to indicate that either Bod1 or Bod1L precipitate a substantial fraction of Setd1a and a much smaller fraction of Setd1b, although it is impossible to tell without the blots of Bod1/Bod1L. The idea that Setd1a is primarily associated with Bod1L vs Bod1 is presumed in the rest of the manuscript (likely based on previous results in other cell lines) but is not strongly supported by these figures. Figure 4: the SETD1A-X1 and X3 internal deletion lines show many interesting interactions not detected in the wild-type line, notably with CPSF components. What is the significance of this? Related to Figure 4: Why were none of the functional genomics experiments described in Figures 5-7 performed on the Setd1a-X3 deletion line given that the authors had it in hand? This would have been a logical complement to the Bod1L deletion experiment and further addressed the issue of functional partnership between Setd1a and Bod1/L. One could make a similar point regarding Bod1-it is unclear why (given the co-IP and AP-MS data in Figures 2 and 3) a deletion line of Bod1 was not analyzed in parallel. There was convincing rationale in the Hoshii 2024 paper to focus on Setd1a/Bod1L in that system; the rationale for doing this here is less clear. Figure 5G: This figure does not seem to include a control in which wild-type ESCs are treated with tamoxifen in parallel with the Flp Bod1L line. Figure 6: This figure should include Venn diagrams that clearly show the overlap between genes affected by Bod1L removal compared to Setd1a. Related to Figure 6/7: These figures should include analysis of the H3K4me3 ChIP-seq data in Figure 5 specifically at Bod1L-regulated DEGs.<br /> Discussion: The authors should note that the direct role of Setd1a at DNA breaks is proposed to rely on enzymatic activity (Higgs et al 2018, Bayley et al 2022)
Minor comments
Figure 5: panel arrangement is confusing Hoshii et al, 2024 is not listed in the references
The key advance in this work is demonstration that Bod1L removal affects expression of DNA repair genes and increases DNA damage in ES cells. This seems to occur independently of changes in H3K4me3 (although specific analysis of H3K4me3 at these genes was not performed). Removal of Setd1a had similar effects. The work seems to support previous reports in leukemia cell lines for specific effects of Setd1a/Bod1L on DNA repair that is not related to Setd1a enzymatic activity (Hoshii et al 2018, 2024 in the manuscript)(and contrasts with findings in U2Os cells that do implicate enzymatic activity and emphasize a direct role at replication forks rather than in transcription; Higgs et al 2018 and Bayley et al 2022 in the manuscript). This is a modest but important conceptual advance in thinking about Bod1L and COMPASS complex functions in transcription and DNA damage repair. Whether these functions are specific to Bod1L vs Bod1 in ESCs, or to Setd1a over Setd1b, in ESCs, was not addressed. The audience for this work will be chromatin/epigenetics experts. My expertise is in the field of chromatin and epigenetics, and I have studied histone modification function extensively in the context of transcription.
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Application Security Overview Propelo.Tech® helps organizations secure modern applications, APIs, containers, Infrastructure as Code (IaC), and CI/CD pipelines throughout the software development lifecycle. As enterprises adopt cloud-native architectures, automation pipelines, containerized workloads, and distributed application environments, security must be embedded directly into development and operational workflows rather than treated as a separate process. Our Application Security services help organizations reduce vulnerabilities, strengthen software integrity, improve governance, and maintain secure application delivery across development, staging, and production environments. We provide continuous security testing, runtime monitoring, DevSecOps integration, vulnerability management, and operational visibility to help organizations maintain secure and resilient application ecosystems. Our Approach Our approach integrates security directly into software delivery pipelines, infrastructure automation, and operational workflows. We implement application security testing, IaC validation, container scanning, dependency analysis, runtime monitoring, and continuous vulnerability management throughout the software lifecycle to help identify and remediate risks early. By combining proactive threat detection, governance-driven controls, secure development practices, and continuous observability, we help organizations improve application security while maintaining development agility, operational consistency, and deployment efficiency. Schedule Let’s Connect Key Offerings Static and dynamic application security testing API and web application security Container and Kubernetes security Infrastructure as Code (IaC) security validation CI/CD security integration and DevSecOps Dependency analysis and SBOM visibility Runtime application monitoring and protection Secrets management and access controls Vulnerability management and remediation support Secure software delivery governance
As AI becomes embedded in healthcare and enterprise applications, application security needs to address more than traditional software vulnerabilities. Protecting APIs, access controls, sensitive data, model interactions, and application behaviour is essential for maintaining secure and trustworthy AI-enabled systems.
AI Application Security Overview AI-powered applications introduce new security risks across APIs, prompts, models, orchestration workflows, and runtime environments. Our AI Application Security services help organizations protect AI, ML, and LLM-powered applications from threats such as prompt injection, model abuse, adversarial attacks, unauthorized access, data leakage, and API exploitation. The services secure AI applications across inference layers, pipelines, orchestration systems, cloud-native deployments, and runtime environments helping organizations reduce misuse risks, strengthen AI governance, and maintain secure, resilient AI operations across cloud, hybrid, and on-premises environments. Our Approach Our approach combines Zero Trust architecture, secure AI development practices, runtime monitoring, policy enforcement, and continuous threat detection to secure AI applications throughout their lifecycle. Layered security controls are implemented across APIs, prompts, orchestration workflows, model access, and application runtimes to detect anomalous behavior, abuse patterns, prompt manipulation attempts, and unauthorized interactions. By integrating AI security into development pipelines, infrastructure operations, governance frameworks, and deployment workflows, organizations can maintain secure, observable, and production-ready AI environments with continuous monitoring, auditability, and proactive risk management. Schedule Let’s Connect Key Offerings AI application runtime security Prompt injection protection and guardrails AI API security and access control AI threat monitoring and anomaly detection Secure AI deployment and governance AI workflow and orchestration security Model access governance and policy enforcement AI risk assessments and security reviews Audit logging and AI observability Outcomes Reduced AI application misuse and exploitation risks Improved protection against prompt injection and adversarial attacks Better visibility into AI application behavior and risk Stronger governance across AI-enabled systems Improved security posture for AI-driven business operations Increased confidence deploying enterprise AI applications
AI monitoring is becoming an increasingly practical tool for care homes, particularly for supporting resident safety, identifying potential risks earlier, and reducing administrative pressure on staff. The key is ensuring these technologies complement—not replace—the judgement and human connection of care professionals.
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Resource: RRID:Addgene_12259
Curator: @sonofthor
SciCrunch record: RRID:Addgene_12259
12260
DOI: 10.1021/acssynbio.1c00606
Resource: RRID:Addgene_12260
Curator: @sonofthor
SciCrunch record: RRID:Addgene_12260
61427
DOI: 10.1021/acssynbio.1c00606
Resource: RRID:Addgene_157860
Curator: @sonofthor
SciCrunch record: RRID:Addgene_157860
1000000055
DOI: 10.1021/acssynbio.1c00606
Resource: RRID:Addgene_1000000055
Curator: @sonofthor
SciCrunch record: RRID:Addgene_1000000055
100000024
DOI: 10.1021/acssynbio.1c00212
Resource: RRID:Addgene_1000000024
Curator: @sonofthor
SciCrunch record: RRID:Addgene_1000000024
1000000163
DOI: 10.1021/acsptsci.4c00051
Resource: RRID:Addgene_1000000163
Curator: @sonofthor
SciCrunch record: RRID:Addgene_1000000163
1000000163
DOI: 10.1021/acsptsci.2c00020
Resource: RRID:Addgene_1000000163
Curator: @sonofthor
SciCrunch record: RRID:Addgene_1000000163
https://www.mmrrc.org/catalog/sds.php?mmrrc_id=66947
DOI: 10.1017/S0967199426100598
Resource: RRID:MMRRC_066947-MU
Curator: @AleksanderDrozdz
SciCrunch record: RRID:MMRRC_066947-MU
20923
DOI: 10.1016/j.ymthe.2016.11.012
Resource: RRID:Addgene_20923
Curator: @sonofthor
SciCrunch record: RRID:Addgene_20923
1377638
DOI: 10.1016/j.ymthe.2016.11.012
Resource: RRID:Addgene_13776
Curator: @sonofthor
SciCrunch record: RRID:Addgene_13776
20927
DOI: 10.1016/j.ymthe.2016.11.012
Resource: RRID:Addgene_20927
Curator: @sonofthor
SciCrunch record: RRID:Addgene_20927
20922
DOI: 10.1016/j.ymthe.2016.11.012
Resource: RRID:Addgene_20922
Curator: @sonofthor
SciCrunch record: RRID:Addgene_20922
52961
DOI: 10.1016/j.ymthe.2016.11.012
Resource: RRID:Addgene_52961
Curator: @sonofthor
SciCrunch record: RRID:Addgene_52961
1000000016
DOI: 10.1016/j.ymthe.2016.11.012
Resource: RRID:Addgene_1000000016
Curator: @sonofthor
SciCrunch record: RRID:Addgene_1000000016
1000000136
DOI: 10.1371/journal.pbio.3001877
Resource: RRID:Addgene_1000000136
Curator: @sonofthor
SciCrunch record: RRID:Addgene_1000000136
1000000224
DOI: 10.1016/j.xpro.2024.103383
Resource: RRID:Addgene_1000000224
Curator: @sonofthor
SciCrunch record: RRID:Addgene_1000000224
204333
DOI: 10.1016/j.xpro.2024.103355
Resource: RRID:Addgene_204333
Curator: @sonofthor
SciCrunch record: RRID:Addgene_204333
12259
DOI: 10.1016/j.xpro.2024.103355
Resource: RRID:Addgene_12259
Curator: @sonofthor
SciCrunch record: RRID:Addgene_12259
12260
DOI: 10.1016/j.xpro.2024.103355
Resource: RRID:Addgene_12260
Curator: @sonofthor
SciCrunch record: RRID:Addgene_12260
1000000224
DOI: 10.1016/j.xpro.2024.103355
Resource: RRID:Addgene_1000000224
Curator: @sonofthor
SciCrunch record: RRID:Addgene_1000000224
1000000044
DOI: 10.1016/j.xplc.2026.101837
Resource: RRID:Addgene_1000000044
Curator: @sonofthor
SciCrunch record: RRID:Addgene_1000000044
12251
DOI: 10.1016/j.xgen.2025.100988
Resource: RRID:Addgene_12251
Curator: @sonofthor
SciCrunch record: RRID:Addgene_12251
12259
DOI: 10.1016/j.xgen.2025.100988
Resource: RRID:Addgene_12259
Curator: @sonofthor
SciCrunch record: RRID:Addgene_12259
1000000068
DOI: 10.1016/j.xgen.2025.100988
Resource: RRID:Addgene_1000000068
Curator: @sonofthor
SciCrunch record: RRID:Addgene_1000000068
1000000024
DOI: 10.1016/j.omtm.2017.11.005
Resource: RRID:Addgene_1000000024
Curator: @sonofthor
SciCrunch record: RRID:Addgene_1000000024
1000000155
DOI: 10.1016/j.molmet.2023.101848
Resource: RRID:Addgene_1000000155
Curator: @sonofthor
SciCrunch record: RRID:Addgene_1000000155
RRID: SCR_026360
DOI: 10.1016/j.joule.2026.102669
Resource: Northwestern University Keck Interdisciplinary Surface Science Core Facility (RRID:SCR_026360)
Curator: @nmaralla
SciCrunch record: RRID:SCR_026360
RRID:SCR_017779
DOI: 10.1016/j.joule.2026.102669
Resource: Northwestern University Micro/Nano Fabrication Core Facility (RRID:SCR_017779)
Curator: @nmaralla
SciCrunch record: RRID:SCR_017779
1000000017
DOI: 10.1016/j.jcmgh.2021.04.012
Resource: RRID:Addgene_1000000017
Curator: @sonofthor
SciCrunch record: RRID:Addgene_1000000017
1000000163
DOI: 10.1016/j.isci.2025.113935
Resource: RRID:Addgene_1000000163
Curator: @sonofthor
SciCrunch record: RRID:Addgene_1000000163
218565
DOI: 10.1016/j.isci.2024.109936
Resource: RRID:Addgene_218565
Curator: @sonofthor
SciCrunch record: RRID:Addgene_218565
218564
DOI: 10.1016/j.isci.2024.109936
Resource: RRID:Addgene_218564
Curator: @sonofthor
SciCrunch record: RRID:Addgene_218564
218563
DOI: 10.1016/j.isci.2024.109936
Resource: RRID:Addgene_218563
Curator: @sonofthor
SciCrunch record: RRID:Addgene_218563
1000000036
DOI: 10.1016/j.isci.2024.109936
Resource: RRID:Addgene_1000000036
Curator: @sonofthor
SciCrunch record: RRID:Addgene_1000000036
218577
DOI: 10.1016/j.isci.2024.109936
Resource: RRID:Addgene_218577
Curator: @sonofthor
SciCrunch record: RRID:Addgene_218577
218576
DOI: 10.1016/j.isci.2024.109936
Resource: RRID:Addgene_218576
Curator: @sonofthor
SciCrunch record: RRID:Addgene_218576
218575
DOI: 10.1016/j.isci.2024.109936
Resource: RRID:Addgene_218575
Curator: @sonofthor
SciCrunch record: RRID:Addgene_218575
218574
DOI: 10.1016/j.isci.2024.109936
Resource: RRID:Addgene_218574
Curator: @sonofthor
SciCrunch record: RRID:Addgene_218574
218573
DOI: 10.1016/j.isci.2024.109936
Resource: RRID:Addgene_218573
Curator: @sonofthor
SciCrunch record: RRID:Addgene_218573
218572
DOI: 10.1016/j.isci.2024.109936
Resource: RRID:Addgene_218572
Curator: @sonofthor
SciCrunch record: RRID:Addgene_218572
218571
DOI: 10.1016/j.isci.2024.109936
Resource: RRID:Addgene_218571
Curator: @sonofthor
SciCrunch record: RRID:Addgene_218571
218570
DOI: 10.1016/j.isci.2024.109936
Resource: RRID:Addgene_218570
Curator: @sonofthor
SciCrunch record: RRID:Addgene_218570
218569
DOI: 10.1016/j.isci.2024.109936
Resource: RRID:Addgene_218569
Curator: @sonofthor
SciCrunch record: RRID:Addgene_218569
218568
DOI: 10.1016/j.isci.2024.109936
Resource: RRID:Addgene_218568
Curator: @sonofthor
SciCrunch record: RRID:Addgene_218568
218567
DOI: 10.1016/j.isci.2024.109936
Resource: RRID:Addgene_218567
Curator: @sonofthor
SciCrunch record: RRID:Addgene_218567
1000000163
DOI: 10.1016/j.chembiol.2021.07.004
Resource: RRID:Addgene_1000000163
Curator: @sonofthor
SciCrunch record: RRID:Addgene_1000000163
ATCCCCL-81
DOI: 10.1016/j.celrep.2026.118002
Resource: RRID:CVCL_1K11
Curator: @nmaralla
SciCrunch record: RRID:CVCL_1K11
ATCCTIB-71
DOI: 10.1016/j.celrep.2026.118002
Resource: (ATCC Cat# TIB-71, RRID:CVCL_0493)
Curator: @nmaralla
SciCrunch record: RRID:CVCL_0493
ATCCCCL-2
DOI: 10.1016/j.celrep.2026.118002
Resource: (ATCC Cat# CCL-2.1, RRID:CVCL_1276)
Curator: @nmaralla
SciCrunch record: RRID:CVCL_1276
ATCCCRL-2254
DOI: 10.1016/j.celrep.2026.118002
Resource: (ATCC Cat# CRL-2254, RRID:CVCL_0140)
Curator: @nmaralla
SciCrunch record: RRID:CVCL_0140
ATCCTIB-202
DOI: 10.1016/j.celrep.2026.118002
Resource: (RRID:CVCL_A4CA)
Curator: @nmaralla
SciCrunch record: RRID:CVCL_A4CA
ATCCHB-8065
DOI: 10.1016/j.celrep.2026.118002
Resource: (KCLB Cat# 88065, RRID:CVCL_0027)
Curator: @nmaralla
SciCrunch record: RRID:CVCL_0027
RRID:SCR_002285
DOI: 10.1016/j.celrep.2026.118000
Resource: Fiji (RRID:SCR_002285)
Curator: @nmaralla
SciCrunch record: RRID:SCR_002285
RRID:CVCL_0004
DOI: 10.1016/j.celrep.2026.117999
Resource: (KCB Cat# KCB 90029YJ, RRID:CVCL_0004)
Curator: @nmaralla
SciCrunch record: RRID:CVCL_0004
RRID:CVCL_0007
DOI: 10.1016/j.celrep.2026.117999
Resource: (JCRB Cat# IFO50038, RRID:CVCL_0007)
Curator: @nmaralla
SciCrunch record: RRID:CVCL_0007
ATCCCat#CCL-185
DOI: 10.1016/j.celrep.2026.117968
Resource: (RRID:CVCL_UR31)
Curator: @nmaralla
SciCrunch record: RRID:CVCL_UR31
ATCCCat#TIB-202
DOI: 10.1016/j.celrep.2026.117968
Resource: (RRID:CVCL_A4CA)
Curator: @nmaralla
SciCrunch record: RRID:CVCL_A4CA
ATCCCat#ACS-4500
DOI: 10.1016/j.celrep.2026.117968
Resource: (RRID:CVCL_4V93)
Curator: @nmaralla
SciCrunch record: RRID:CVCL_4V93
ATCCCat#CCL-81
DOI: 10.1016/j.celrep.2026.117968
Resource: RRID:CVCL_1K11
Curator: @nmaralla
SciCrunch record: RRID:CVCL_1K11
ATCCCat#CCL-34
DOI: 10.1016/j.celrep.2026.117968
Resource: (RRID:CVCL_4164)
Curator: @nmaralla
SciCrunch record: RRID:CVCL_4164
ATCCCat#CCL-1
DOI: 10.1016/j.celrep.2026.117968
Resource: (JCRB Cat# IFO50409, RRID:CVCL_0462)
Curator: @nmaralla
SciCrunch record: RRID:CVCL_0462
ATCCCat#HB-8065
DOI: 10.1016/j.celrep.2026.117968
Resource: (KCLB Cat# 88065, RRID:CVCL_0027)
Curator: @nmaralla
SciCrunch record: RRID:CVCL_0027
ATCCCat#HTB-55
DOI: 10.1016/j.celrep.2026.117968
Resource: (ATCC Cat# HTB-55, RRID:CVCL_0609)
Curator: @nmaralla
SciCrunch record: RRID:CVCL_0609
1000000011
DOI: 10.1016/j.canlet.2026.218669
Resource: RRID:Addgene_1000000011
Curator: @sonofthor
SciCrunch record: RRID:Addgene_1000000011
RRID: SCR_015795
DOI: 10.1016/j.brainres.2026.150550
Resource: LI-COR Image Studio Software (RRID:SCR_015795)
Curator: @nmaralla
SciCrunch record: RRID:SCR_015795
RRID: SCR_003070
DOI: 10.1016/j.brainres.2026.150550
Resource: ImageJ (RRID:SCR_003070)
Curator: @nmaralla
SciCrunch record: RRID:SCR_003070
Abstract
DOI: 10.1016/j.autneu.2026.103494
Resource: GraphPad Prism (RRID:SCR_002798)
Curator: @nmaralla
SciCrunch record: RRID:SCR_002798
1000000024
DOI: 10.1007/978-1-4939-7902-8_20
Resource: RRID:Addgene_1000000024
Curator: @sonofthor
SciCrunch record: RRID:Addgene_1000000024
RRID:AB_477010
DOI: 10.1186/s40478-026-02240-y
Resource: (Sigma-Aldrich Cat# G3893, RRID:AB_477010)
Curator: @scibot
SciCrunch record: RRID:AB_477010