Reviewer #1 (Public review):

[Editors' note: The revised manuscript addressed the concerns of both reviewers, who have concluded that the manuscript is convincing and important. The manuscript can move towards the Version of Record.]

Summary:

This study is built on the emerging knowledge of trained immunity, where innate immune cells exhibit enhanced inflammatory responses upon challenged by a prior insult. Trained immunity is now a very fast-evolving field and has been explored in diverse disease conditions and immune cell types. Earhart and the team approached the topic from a novel angle and was the first to explore a potential link to the complement system.

The study focused on the central complement protein C3 and investigated how its signalling may modulate immune training in alveolar macrophages. The authors first performed in vivo experiments in C57BL mouse models to observe the presence of enhanced inflammation and C3a in BAL fluid following immune training. These changes were then compared with those from C3-deficient mice, which confirmed the involvement of C3a. This trained immunity was further validated in ex vivo experiments using primary alveolar macrophage, which was blunted in C3-deficiency, and, intriguingly, rescued by adding exogenous C3 protein, but not C3a. The genetic-based findings were supported by pharmacological experiments using the C3aR antagonist SB290157. Mechanistically, transcriptomic analyses suggested the involvement of metabolism-linked, particularly glycolytic, genes, which was in agreement with an upregulation of glycolytic flux in WT but not C3-deficient macrophages.

Collectively, these data suggest that C3, possible through engaging with C3aR, contributes to trained immunity in alveolar macrophages.

Strengths:

The conclusions reached were well supported by in vivo and ex vivo experiments, encompassing both genetic-knockout animal models and pharmacological tools.

The transcriptomic and cell metabolism studies provided valuable mechanistic insights.

Weaknesses:

For the in vivo experiments, the histopathological and other inflammatory markers (Fig 1.) were not directly linked to alveolar macrophages by experimental evidence. Other innate immune cells (e.g. dendritic cells, neutrophils) and endothelial cells could also be involved in immune training and contribute to the pathological outcomes. These cells were not examined or mentioned in the study.

For the ex vivo experiments assessing immune training in alveolar macrophages, only the release of selected inflammatory factors were measured. Macrophage activities constitute multiple aspects (e.g. phagocytosis, ROS production, microbe killing), which should also be considered to better depict the effect of trained immunity.

The proposed mechanism of C3 getting cleaved intracellularly then binding to lysosomal C3aR need to be further supported by experimental evidence.

There was an absence of any validation in human-based models.

Comments on the revised version.

The revised manuscript now encompasses a much wider scope and stronger evidence.

The authors have included the re-analysis of a recently published dataset of human volunteers who received aerosolized BCG exposure compared to saline. Although not proven causality, this data helped strengthen the human relevance of the findings presented in this research and directly rationalized the decision to focus on Ams. The persistence of elevated C3/C3aR1 expression to day 7 further supports the idea that complement‑associated reprogramming is not merely an acute inflammatory phenomenon. Whilst it may be outside of the scope of this current study, it would be helpful to clarify in future studies whether other complement components (C5, factor B, factor D) were also modulated in the dataset, to contextualize whether the response is uniquely centered on C3/C3aR1 or part of a broader complement activation program.

The authors have also expanded the functional characterization of trained alveolar macrophages by including phagocytosis and ROS generation measurements. It is intriguing that HKPA training did not markedly alter the phagocytosis and ROS production by alveolar macrophages relative to the control group, however, C3 deficiency significantly dampened these responses in both trained and untrained groups. This reduction is in congruence with the cytokine release data, but there could be other factors involved.

I appreciate the careful revision and much more expansive mechanistic interpretation regarding intracellular C3aR, and that further studies are underway to better understand the cell type-specific, subcellular localization of C3a-C3aR in alveolar macrophages.

Overall, the revised data interpretation and discussion significantly improved in balance and contextualization of the findings.

Reviewer #2 (Public review):

Earhart et al. investigated the role of the complement system in trained innate immunity (TII) in alveolar macrophages (AM). They used a WT and C3 knockout murine model primed with locally administered heat-killed P. aeruginosa (HKPA). Additionally, they employed ex vivo AM training models using C3 knockout mice, where reconstitution of C3 and blockade of C3R were performed. The study concluded that the C3-C3R axis is essential for inducing TII in macrophages in the ex vivo model. The manuscript is well-written and easy to follow.

Comments on revised version.

My concerns have been addressed, and the provided data is convincing supporting the manuscript's claims.

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Summary:

This study is built on the emerging knowledge of trained immunity, where innate immune cells exhibit enhanced inflammatory responses upon being challenged by a prior insult. Trained immunity is now a very fast-evolving field and has been explored in diverse disease conditions and immune cell types. Earhart and the team approached the topic from a novel angle and were the first to explore a potential link to the complement system.

The study focused on the central complement protein C3 and investigated how its signalling may modulate immune training in alveolar macrophages. The authors first performed in vivo experiments in C57BL mouse models to observe the presence of enhanced inflammation and C3a in BAL fluid following immune training. These changes were then compared with those from C3-deficient mice, which confirmed the involvement of C3a. This trained immunity was further validated in ex vivo experiments using primary alveolar macrophage, which was blunted in C3-deficiency, and, intriguingly, rescued by adding exogenous C3 protein, but not C3a. The genetic-based findings were supported by pharmacological experiments using the C3aR antagonist SB290157. Mechanistically, transcriptomic analyses suggested the involvement of metabolism-linked, particularly glycolytic, genes, which was in agreement with an upregulation of glycolytic flux in WT but not C3-deficient macrophages.

Collectively, these data suggest that C3, possibly through engaging with C3aR, contributes to trained immunity in alveolar macrophages.

Strengths:

The conclusions reached were well supported by in vivo and ex vivo experiments, encompassing both genetic-knockout animal models and pharmacological tools.

The transcriptomic and cell metabolism studies provided valuable mechanistic insights.

We thank the reviewers for acknowledging the importance of the work.

Weaknesses:

For the in vivo experiments, the histopathological and other inflammatory markers (Figure 1) were not directly linked to alveolar macrophages by experimental evidence. Other innate immune cells (eg. dendritic cells, neutrophils) and endothelial cells could also be involved in immune training and contribute to the pathological outcomes. These cells were not examined or mentioned in the study.

We agree with the suggestions from Reviewer 1 that other cell types such as dendritic cells, neutrophils, and endothelial cells can also be involved in immune training. As the focus of this study was on alveolar macrophages, we specifically focused on these cell types. However,

(1) We have re-analyzed a recently published dataset of human volunteers who received aerosolized BCG exposure compared to saline. We observe that by Day 7, aerosolized BCG exposure alters the expression of C3 and C3aR1 in alveolar macrophages in the human bronchoalveolar lavage (BAL) fluid, compared to saline. We have included this new analysis in a revised Figure 1 to clarify why our focus is on investigating the C3-C3aR1 axis in alveolar macrophages.

(2) We have conducted new experiments where we train the mice in vivo, collect the alveolar macrophages, and then provide the second stimulus ex vivo. We observe a similar phenotype in the in vivo trained, ex vivo stimulated alveolar macrophages. We have included this new data in a new Figure S2.

(3) We have updated our Discussion to state “However, we also acknowledge that other immune cells such as dendritic cells and neutrophils, and non-immune cells such as epithelial cells, endothelial cells and fibroblasts can also be involved in immune training (Bigot et al., 2025; Friščić et al., 2021; Moorlag et al., 2020).”

(2) For the ex vivo experiments assessing immune training in alveolar macrophages, only the release of selected inflammatory factors were measured. Macrophage activities constitute multiple aspects (e.g. phagocytosis, ROS production, microbe killing), which should also be considered to better depict the effect of trained immunity.

We agree with the reviewer and have conducted additional experiments to assess immune responses influenced by training in alveolar macrophages. Specifically, we show that in addition to impairing the release of proinflammatory cytokines such as TNFα and IL-6, C3-deficient alveolar macrophages exhibit significantly lower phagocytosis and ROS production compared to WT alveolar macrophages post-training with heat-killed Pseudomonas aeruginosa. Results from these additional experiments have been included in new Figure S2.

(3) The proposed mechanism of C3 getting cleaved intracellularly and then binding to lysosomal C3aR needs to be further supported by experimental evidence. 

The mechanism of C3 being cleaved intracellularly involves serine protease-dependent cleavage of C3 to C3a and has been experimentally demonstrated previously (Liszewski et al. Immunity 2013; Elvington et al. J Clin Invest 2017). A prior report demonstrated that intracellular C3a interacted with a lysosomal C3aR to promote CD4⁺ T cell survival (Liszewski et al. Immunity 2013). Based on the reviewer’s suggestions, we performed confocal microscopy on alveolar macrophages. Although we clearly observed intracellular colocalization of C3a (using a monoclonal antibody to the neo-epitope) with C3aR, we observed only some colocalization with LAMP1, a lysosomal marker (see Author response image 1). Hence, we will refrain from making comments on how C3 binds to lysosomal C3aR intracellularly in alveolar macrophages, as this may be cell type-specific or stimulation-specific. We have now revised the sentence in the manuscript to remove any references to lysosomal C3aR and now state – “Upon internalization, C3 is cleaved to C3a (Elvington et al., 2017), binds to C3aR, and affects cytokine production in CD4⁺ T cells (Liszewski et al., 2013)”. We have not incorporated the Author response image 1 in the main manuscript as we would like to explore this further to precisely define the subcellular localization of C3a-C3aR in alveolar macrophages, but have provided it for the reviewer to explain the basis of the rewording in the revision.

Author response image 1.

C3a-C3aR colocalization in mouse ex vivo cultured alveolar macrophages (mexAM). mexAMs were harvested and cultured as per the protocol from Gorki et al. (2022). Cells were incubated in a Millicell EZ Slide 8-well glass chamber slide overnight to allow for adherence, then fixed, permeabilized, and incubated with anti-C3a conjugated to AF555 (blue, Hycult HM1072), anti-C3aR conjugated to AF647 (red, Hycult HM1123), and anti-LAMP1 (green, Cell Signaling 99437) overnight at 4°C. Slides were washed 3X in PBS (5 min each) and mounted overnight at 4°C in ProLong Diamond Antifade Mountant with DAPI (white). Images were acquired on a Zeiss LSM 880 confocal microscope at 63X. At least 6 cells per condition imaged. Experiments were conducted in duplicate (technical replicates) and repeated (for biological replicates). Scale bar, 2 μm.

(4) There was an absence of any validation in human-based models.

We acknowledge that the observations need to be validated in human-based models. The focus of our manuscript is on training in alveolar macrophages. Unfortunately, we do not have access to an adequate representation of human alveolar macrophages for our ex vivo testing to account for individual-level variation in immune responses. We anticipate this work will form the basis of these future studies. In the interim, we re-analyzed a recently published publicly available dataset of human BAL specimens from human volunteers who underwent aerosolized BCG administration (Marshall et al. Nat Comm 2025). We observe an increase in C3 and C3aR1 expression at Day 2, which persists through Day 7 post-training with aerosolized BCG compared to aerosolized saline specifically in human alveolar macrophages. We have included this data in Revised Figure 1. We also validated C3 uptake in alveolar macrophages using precision-cut lung slices from human donors. We have included this additional data in new Supplementary Figure 3.

Reviewer #2 (Public review):

Earhart et al. investigated the role of the complement system in trained innate immunity (TII) in alveolar macrophages (AM). They used a WT and C3 knockout murine model primed with locally administered heat-killed P. aeruginosa (HKPA). Additionally, they employed ex vivo AM training models using C3 knockout mice, where reconstitution of C3 and blockade of C3R were performed. The study concluded that the C3-C3R axis is essential for inducing TII in macrophages in the ex vivo model. The manuscript is well-written and easy to follow. However, I have the following major concerns.

(1) The secondary challenge to assess the reprogramming of innate cells in the BAL was conducted 14 days after the initial exposure to HKPA. However, no evidence is provided to confirm that homeostasis was re-established following the primary exposure. Demonstrating the resolution of acute inflammation is essential to ensure that the observed responses to the secondary challenge are not confounded by persistent inflammation from the initial exposure.

We thank the reviewer for giving us an opportunity to clarify this point. We have now included additional data from the bronchoalveolar lavage fluid of these mice to show that the levels of protein leaked into the BAL, levels of proinflammatory cytokines (e.g., TNFα, CXCL1) and the neutrophils (all relevant to the acute phase of inflammation) were similar between the untreated and treated wildtype mice. This new data has been included in Figure S1.

(2) In Figure 1D, cytokine production by BAL cells from WT and C3KO mice after HKPA exposure and LPS challenge is shown. However, it is unclear whether the reduced response in trained C3KO mice is due to a defect in trained immunity or an intrinsic inability of C3KO cells to respond to LPS. To clarify this, the response of trained C3KO cells should also be compared to untrained C3KO controls after the LPS challenge. This comparison is necessary to determine if the reduction is specifically related to innate immune memory or a broader impairment in LPS responsiveness. Such control should be included in all ex vivo training and LPS stimulation experiments as well.

We thank the reviewers for their suggestions. We have conducted additional experiments and we observe no significant differences in the BAL cytokine levels between the wildtype and C3-deficient mice post-training in the absence of infection. This new data has been included in Supplementary Figure S1.

Additionally, we came across several manuscripts, including a recent one in eLife as a part of this Series (Gu et al. Elife 2021; Zahalka et al. Mucosal Immunol 2022; Prevel et al Elife 2025) that have done in vivo training followed by an ex vivo challenge. Hence, we have conducted new experiments to compare the response of in vivo HKPA-trained wildtype (WT) and C3-deficient (C3KO) alveolar macrophages compared to untrained AMs after an ex vivo LPS challenge. This new data has been included in Figure S2.

(3) The data presented provide evidence of alterations in the functional and metabolic activities of innate cells in the lung, indicating the induction of innate immune memory in a C3-C3R axis-dependent pathway. However, it remains to be established whether such changes can lead to altered disease outcomes. Therefore, the impact of these changes should be demonstrated, for instance, through an infection model to support the claim made in the study that C3 modulates trained immunity in AMs through C3aR signalling.

We acknowledge this is a Limitation of our manuscript. As this is a Short Report, we focused on how C3, via the C3aR, affects the reprogramming of alveolar macrophages. Recent work demonstrated that systemically administered β-glucan induces peripheral trained immunity and aggravates lung injury (Prével et al., 2025), similar to disease in models of periodontitis and arthritis (Haacke et al., 2025). However, training with β-glucan also reduces bleomycin-induced lung fibrosis (Kang et al., 2024). Hence, our ongoing work involves optimizing relevant intrapulmonary exposures to assess how trained immune responses are modulated by the C3a-C3aR axis. We have included the Reviewer’s critique in our revised Discussion as a limitation, while referencing the abovementioned manuscripts.

(4) Figure 3, panels B and C - stats should be shown for comparing WT-HKPA-trained and C3KO HKPA-trained.

These suggestions have been incorporated into Revised Fig 3B and 3C (now Figure 4).

(5) In Figure 4, where the proper untrained C3KO is included, the data presented in Figure 4C show an increase in basal and maximum glycolysis in trained C3KO compared to their untrained control counterparts. Statistical analysis should be provided for this comparison. Based on these data, it appears that metabolic reprogramming occurs even in the absence of C3. Furthermore, C3KO cells intrinsically exhibit reduced glycolytic capacity compared to WT. These observations challenge the conclusions made in the manuscript. Therefore, without the proper control (untrained C3KO) included in all experimental approaches, it is impossible to draw an evidence-based conclusion that the C3-C3R axis plays a role in the induction of innate immune memory.

We have included the statistical comparisons for all the groups in Figure 4C (now Figure 5), as suggested by the reviewer. The data suggests that C3-deficient (C3KO) alveolar macrophages have a blunted metabolic response to training, as compared to C3-sufficient (WT) alveolar macrophages. However, the C3KO cells do not have reduced glycolytic capacity compared to WT in the absence of training. The blunted response in trained C3KO AMs is rescued by exogenous C3, but is then reversed by C3aR antagonism. We have also provided new data/analyses with proper controls (untrained C3KO) in the other Figures (for example, in Figures S1, S2 and 3B&C (now Figure 4)). Taken together, the data would suggest that the effects of C3 in AM reprogramming are C3aR-dependent.

(6) The Results and Discussion sections should be separated, and the results should be thoroughly analyzed in the context of published literature. Separating these sections will allow for a clearer presentation of findings and ensure that the discussion provides a comprehensive interpretation of the data.

We thank the reviewer for this suggestion. The manuscript has been submitted as a Brief Report, and hence, we adhered to the instructions to authors for this format. However, we have added an additional section towards the end of the manuscript based on the Reviewer’s suggestion.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

It is intriguing that whilst there was a significant elevation of C3a in the cell culture medium of alveolar macrophages, the addition of exogenous C3a failed to rescue the phenotypes of C3-deficient alveolar macrophages. Please help discuss this.

Alveolar macrophages secrete both C3 and proteases, which can cleave C3 to C3a in the supernatant. However, we used the addition of exogenous C3a to compare it to the addition of full-length C3. C3 is internalized by multiple cell types, including alveolar macrophages (as demonstrated in new Figure 3 of our Revised Manuscript) as C3(H₂O) in comparison to C3a. Hence, we propose that the internalization of C3(H₂O) provides an intracellular source of C3a (previously reported in Elvington et al. J Clin Invest 2017), which engages with the C3aR to result in alveolar macrophage reprogramming. In comparison, incubating cells with C3a does not exert similar effects. We have included these comments in a separate section towards the end of the manuscript.

Please provide details for the statement "cell-permeable C3aR antagonist (SB290157)" (Figure 3E). Could a paracrine-based mechanism also be at play?

SB290157 does not act selectively on the cell surface, but rather, can also enter cells. Our data, along with previously published reports (Quell et al. J Immunol 2017; Zha et al. Cancer Immunol Res 2019), suggest that C3aR may be intracellular in AMs. However, SB290157 can also block any receptor that may be present on the surface. For this reason, we used exogenous C3a as a way to interrogate surface C3aR signaling, and did not observe significant changes in AM reprogramming with exogenous C3a. However, as this is an indirect approach, we cannot completely rule out a paracrine-based mechanism and have included this limitation in the Discussion section of the revised manuscript.

For Figure 3, please also provide the statistical analysis results for WT versus C3KO HKCA-trained cells. The statistical tests described in the legend for Figure 3D seem to apply to Figure 3E. Please check the labels.

These suggestions have been incorporated into Revised Figure 3 (now Figure 4).

eLife Assessment

This valuable study presents an analysis of the gene regulatory networks that contribute to tumour heterogeneity and tumor plasticity in Ewing sarcoma, with key implications for other fusion-driven sarcomas. The authors employed compelling orthogonal approaches, including single-cell sequencing and xenografts, to reveal the existence and plasticity of specific gene regulatory networks (e.g., TGF-beta signaling) within Ewing sarcoma, as well as significant differences that exist between cell lines and patient tumors.

Reviewer #1 (Public review):

The investigators elegantly utilized single-cell co-assay of RNA and ATAC seq to unveil the heterogeneous gene regulatory networks in Ewing sarcoma. The authors should be commended on their ability to identify multiple unique modules of gene regulation of Ewing sarcoma utilizing complex computational methods between numerous Ewing sarcoma cell lines. Additionally, they complimented their single cell findings with xenografts as well as primary Ewing sarcoma patient tumors - validating the intratumoral heterogeneous gene regulatory networks of Ewing sarcoma. More importantly, they have revealed that exogenous TGF-B may modify these distinct epigenetic and transcriptional signatures within Ewing sarcoma tumors. Overall, the manuscript highlights an important discovery of the heterogenous gene regulatory programming of Ewing sarcoma and further highlights the role that TGFB plays within the tumor microenvironment of Ewing sarcoma. There are some areas of ambiguity that require clarification to increase the impact of the manuscript.

Comments on the latest revision:

The responses to my review were appropriate and my comments were all addressed.

Reviewer #2 (Public review):

Summary:

This work by Waltner, et. al. provides a comprehensive single cell multiomics analysis of plasticity in gene regulatory networks present in Ewing sarcoma using single cell RNA-sequencing (scRNA-seq) and single cell assay for transposase accessible chromatin with sequencing (scATAC-seq). They find that Ewing sarcoma cell lines models have distinct patterns of chromatin accessibility compared to non-Ewing sarcoma models, and that there is significant variability across Ewing sarcoma cell lines, and sometimes within a single cell line. These differences across models are linked to 3 distinct gene regulatory modules, 2 of which are present across the range of model systems studied here. The first modules present across models is activated when the fusion is expressed and includes genes enriched for the known EWSR1::FLI1 response element, GGAA microsatellites along with other neural crest transcription factors. The other module primarily consists of genes repressed by EWSR1::FLI1, which are activated in EWSR1::FLI1-low states. Interestingly, EWSR1::FLI1-low cells have already been tied to more migratory and metastatic phenotypes and the data here suggest these cells are more responsive to external signals from TGF-β and this may be mediated through FOSL2-mediated gene regulation. This is a technically rigorous study, with a variety of different analytical techniques used to address similar questions and this approach elevates confidence in the answers provided. This is further strengthened by the diverse set of model systems used, including patient-derived cell lines, cell line xenograft models, patient-derived xenografts, mining available single cell data from patient samples, and validation of the gene modules identified in a larger set of patient microarray samples. In whole, this study provides a valuable resource for understanding heterogeneity, plasticity, and gene expression networks in Ewing sarcoma. This may be a useful resource for future studies of metastatic disease and provide a framework for similar questions in other fusion-driven sarcomas.

Comments on revised version.

The authors have addressed comments from my prior review. Thank you!

Author response:

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

Public Reviews:

We thank both reviewers for their thoughtful and constructive evaluations of our manuscript. We are pleased that the reviewers recognize the value of our multimodal single-cell approach and the diversity of model systems used to define gene regulatory networks underlying heterogeneity in Ewing sarcoma.

Reviewer #1 (Public review):

The investigators elegantly utilized a single-cell co-assay of RNA and ATAC seq to unveil the heterogeneous gene regulatory networks in Ewing sarcoma. The authors should be commended on their ability to identify multiple unique modules of gene regulation of Ewing sarcoma utilizing complex computational methods between numerous Ewing sarcoma cell lines. Additionally, they complemented their single-cell findings with xenografts as well as primary Ewing sarcoma patient tumors - validating the intratumoral heterogeneous gene regulatory networks of Ewing sarcoma. More importantly, they have revealed that exogenous TGF-β may modify these distinct epigenetic and transcriptional signatures within Ewing sarcoma tumors. Overall, the manuscript highlights an important discovery of the heterogenous gene regulatory programming of Ewing sarcoma and further highlights the role that TGFB plays within the tumor microenvironment of Ewing sarcoma. There are some areas of ambiguity that require clarification to increase the impact of the manuscript.

We appreciate Reviewer 1's positive assessment of our work and their recognition of the importance of identifying heterogeneous gene regulatory programs in Ewing sarcoma, including the role of TGF-β in the tumour microenvironment. We have addressed the areas of ambiguity noted by the reviewer, including clarifying cluster assignments and the selection of k=3 for module identification, adding statistical comparisons to relevant figures, correcting figure cross-references, and improving figure labeling for clarity. We have also added higher-resolution images of the spatial profiling data and highlighted relevant correlations between CHLA9 and CHLA10 clusters. We believe these revisions improve the clarity and rigour of the manuscript.

Reviewer #2 (Public review):

Summary:

This work by Waltner et. al. provides a comprehensive single-cell multiomics analysis of plasticity in gene regulatory networks present in Ewing sarcoma using single-cell RNA-sequencing (scRNA-seq) and single-cell assay for transposase accessible chromatin with sequencing (scATAC-seq). They find that Ewing sarcoma cell line models have distinct patterns of chromatin accessibility compared to non-Ewing sarcoma models, and that there is significant variability across Ewing sarcoma cell lines, and sometimes within a single cell line. These differences across models are linked to 3 distinct gene regulatory modules, 2 of which are present across the range of model systems studied here. The first modules present across models are activated when the fusion is expressed and include genes enriched for the known EWSR1::FLI1 response element, GGAA microsatellites, along with other neural crest transcription factors. The other module primarily consists of genes repressed by EWSR1::FLI1, which are activated in EWSR1::FLI1-low states. Interestingly, EWSR1::FLI1-low cells have already been tied to more migratory and metastatic phenotypes, and the data here suggest these cells are more responsive to external signals from TGF-β, and this may be mediated through FOSL2-mediated gene regulation. While there are some minor additional validation studies that can be performed to strengthen a few individual analyses, this is a technically rigorous study, with a variety of different analytical techniques used to address similar questions, and this approach elevates confidence in the answers provided. This is further strengthened by the diverse set of model systems used, including patient-derived cell lines, cell line xenograft models, patient-derived xenografts, mining available single-cell data from patient samples, and validation of the gene modules identified in a larger set of patient microarray samples. In whole, this study provides a valuable resource for understanding heterogeneity, plasticity, and gene expression networks in Ewing sarcoma. This may be useful for future studies of metastatic disease and may also provide a framework for similar questions in other fusion-driven sarcomas.

Strengths:

There are a few core strengths in this study. First is the number and diversity of Ewing sarcoma models studied, spanning commonly used cell lines, patient-derived xenografts, and patient samples. The second is the large array of rigorous and orthogonal approaches used to uncover the identity and function of various gene modules. This includes an array of informatics techniques, as well as specific modulation of cell line models in culture. A third is confirmation that different gene expression programs are present in the same tumor using spatial transcriptomic analysis. Lastly, the authors have made all of their data and code accessible, enabling continued use of this dataset as a resource for others.

Weaknesses:

As highlighted by the authors, this study is somewhat limited by the small number of single-cell data from patient samples that are publicly available. Much of the analysis comes from cell lines. Additionally, they focus only on one type of signal that may modulate cell plasticity, and there are likely to be many others. Lastly, there are a few weak spots in the data. Some of this likely arises from the underlying complexity of the data, the generally sparse nature of scATAC data, and the biological heterogeneity present in the cell lines studied. The most pronounced weakness was in the analysis of transcription factors that dictate gene expression in the distinct modules, as well as the response to TGF-β. While some specific transcription factors showed module-specific expression consistent with the computational prediction in Figure 2, others did not likely due to additional factors not tested here. Likewise, the same transcription factors did not always show consistent enrichment in the gene modules that responded to TGF-β treatment when analyzed across cell lines. On the whole, these are relatively minor weaknesses and do not diminish the value of this study.

We thank Reviewer 2 for their thorough and balanced assessment. We agree that the study's strengths lie in the breadth of model systems and orthogonal analytical approaches, and we appreciate the reviewer's acknowledgement that the identified weaknesses are relatively minor.

In response to the reviewer's suggestions, we have made several substantive improvements. First, we have included a new Western blot panel in Figure 1 showing EWS::FLI1 protein levels across cell lines, which provides important context for interpreting the long-read fusion transcript detection data. Second, we have revised text throughout the Results section to improve precision — in particular, clarifying that our chromatin analyses assess accessibility at published EWS::FLI1 binding sites rather than binding per se, and ensuring that our stated hypotheses match the metrics presented in the corresponding figures. Third, we have improved figure color schemes and labeling to aid interpretation and corrected errors in panel labeling.

Regarding the reviewer's observation about transcription factor enrichment patterns across cell lines (particularly RUNX3 in the TGF-β response analysis and FOSL2’s modest expression at the protein level in CHLA9), we acknowledge that not all TFs showed perfectly consistent module-specific behaviour across every cell line. As the reviewer notes, this likely reflects the underlying biological complexity and additional regulatory factors not tested here. We have made attempts to temper our language accordingly.

We believe that the revised manuscript, with its additional experimental data, improved figures, and clarified text, addresses the concerns raised by both reviewers and strengthens the overall impact of our findings.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

Specific comments:

(1) Figure 1A: Suggest adding cell labels on the UMAP plot - difficult to tell with different colors - which cluster is which cell line.

We have added labels to Figure 1A (UMAP plot) as suggested.

(2) Figure 1B: Although this may have been slightly addressed later in the manuscript, since CHLA9 and 10 are from the same patient, are the authors surprised to see the differences in EWS::FLI1 motif accessibility, or are these findings further reinforcement of inherent heterogeneity? Should one expect the ChromVAR deviation z-score at least to overlap between CHLA9 and 10, since they're from the same patient, if not, can the authors explain why?

We were indeed surprised to see differences in EWS::FLI motif accessibility inferred from our data particularly from the isogenic lines CHLA9 and 10. While it is impossible to know for sure, we suspect that some treatment related changes increased accessibility in CHLA10. However, when considering global accessibility signatures, a subset of cells from CHLA10 (C23) was most correlated with signatures from CHLA9. We have addressed this by stating “Unsurprisingly, of all cell lines, CHLA10 C23 cells exhibited the greatest correlation to profiles from CHLA9,” in the 11th paragraph of the results section.

(3) Figure 1B: Can the authors comment on the differences in trends of fusion transcript to chromatin enrichment (e.g., A673 and TC71 inverse trends vs. other cell lines that are direct positive or negative trends).

We are hesitant to draw too many conclusions from transcript counting vs chromatin enrichment given some outliers, but in general, we found that high-fusion transcribing cell lines A4573, SKNMC, RDES were associated with module 3, while the lower transcribing cells CHLA9/10, TC32 and PDX305 were associated with module 2. We did not choose to emphasize this correlation too strongly in the paper as there may be other factors such as additional mutations (such as BRAF^V600E in A673) that may have either been present in the original tumour or after serial passaging that may play a role.

(4) Figure S1D: Please make the pt. smaller in order to better visualize the differences and highlight the stark differences of the ChromVAR binding site score.

We have adjusted the point size of the embeddings in Fig S1D to enhance visualization.

(5) Figure S1D: Is it surprising to see a large proportion of PDX305 and minor proportions of CHLA10 and CHLA9 with low ChromVAR binding site score?

Our analyses indeed show that PDX305, CHLA9, and CHLA10 utilise fusion-repressed gene programs, so lower enrichment of EWS: FLI1 microsatellites is consistent with our findings.

(6) Figure 2A: Unclear how k = 3 was ultimately selected - was it purely a visualization of how well separated each of the cell lines is? Can the authors further clarify how k = 3 was determined to be the most optimal? Is there a UMAP that demonstrated a difference in clustering between groups 1, 2, and 3? Is there a threshold cut-off to assign groups into 1, 2, or 3?

Thank you for pointing out this omission. In Fig 1 we show that unsupervised clustering of DA peaks across cell lines grouped EwS cell lines into 2 clusters. When using peak-2-gene linkages (Fig 2), we chose a k =3 to explore the potential genes/CREs explaining the grouping found in Fig 1 because A673 was a clear outlier. We have added the following sentence to the results section paragraph 6. “We selected k = 3 to extend the two-cluster structure observed in Fig. 1F–G, reasoning that A673 represented a clear outlier whose distinct regulatory program would be obscured at lower k.”

(7) Figure 2G: Understanding the substantial heterogeneity of each cell and TF binding motifs - given that CHLA9 is considered group 2, is it unexpected that FOSL2 was not highly expressed compared to the other EwS cell lines within group 2: (TC32, CHAL10, PDX305).

Thank you for astutely pointing out that CHLA9 did defy the trend for FOSL2 protein expression compared with other group 2 lines. We specifically did not comment on this finding in the manuscript as we could not account for its status as an outlier. We address this in the public response above.

(8) Figure S3B: Did the authors perform a similar computational analysis (performed for Figure 4), looking specifically at CHLA9 (Clusters 24 and 25) to determine if there are any overlaps with CHLA10 cluster 23's pathway activity/MSigDB/GO: Biological process terms? If there are potential overlaps, can the authors potentially infer tumor clonal evolution from CHLA9 to CHLA10?

While we didn’t do pathway analysis, Figure S3 shows strong pearson correlation of C23 from CHLA10 with both C24 and C25 from CHLA9. We have highlighted this with a red box in the figure to make this more obvious to the reader.

(9) Figure 4: Given the heterogeneity of CHLA-10. Did the authors observe any differences in morphology within CHLA-10 between the predominant modules (modules 2 and 3), given such stark transcriptional heterogeneity?

We did not observe any morphologic differences within CHLA10 but acknowledge this would be an interesting avenue of further investigation.

(10) Figure 5B: Can the authors also plot out module 3 gene expression to see if the cluster enrichment is unique from module 2 gene expression with TGFB1 and vehicle?

We thank the reviewer for this suggestion and have replaced Fig S4B with violin plots so the enrichments are clearer. Module 3 expression in cluster 3 cells from CHLA10 is among the lowest in the cell line.

(11) Figure 5H: Can the authors provide a higher zoom/resolution of the H&E stain of the ROIs in order to see if there are indeed more stroma/fibrosis in ROI9 and ROI10, and if there are differences in tumor cell morphology within different ROIs that harbor different modules?

We have now included higher resolution and magnification of IHC panels in Figure 5H. These images are included as Supplemental Fig 4C. It is notable that although no dramatic differences in tumour cell morphology are visualized, ROI9 and ROI10 comprise small islands of viable tumour surrounded by necrosis.

(12) Figure 6: Within Volchenboum/Lawlor's dataset, of the 46 clinically annotated primary tumors, 10 of the COG samples contained substantial stromal elements, while all the tumors in the European cohort were >70% viable tumors. Can the authors separate out the stromal-rich (n = 10) samples and analyze the 36 tumor-enriched samples to see if the survival curve is the same as what is shown in Figure 6G/H and S4E?

We thank the reviewer for this suggestion. We observed no differences when stratifying the patients as outlined. Indeed, in the original Volchenboum et al, manuscript it was demonstrated that no prognostic gene signature was identifiable when the stromal-rich tumours were removed from the cohort.

(13) Figure 6: Additionally, can the authors run a similar computational analysis to determine the predominant modules within bulk sequencing of the Volchenboum/Lawlor dataset between each tumor?

The computational approach deconvolution was developed for use with bulk RNA-seq and is based on count data. The linear mixture model at the heart of deconvolution methods requires that the measured signal is proportional to abundance across the full range, and Affymetrix microarray data violate that assumption in a gene-specific, nonlinear way that can't be fully corrected post hoc.

(14) Page 15, line 2: Wrong GSE data cited - currently cited as GSE61357 - should be GSE63157 instead.

This has been corrected, thank you.

(15) Please add statistical comparisons for Figure 3E-H.

We thank the reviewer for highlighting this omission. We used the software package ggpubr to perform Wilcoxon rank-sum tests comparing mean module scores between conditions. We have added statistical labels to the plots. All comparisons were statistically to the level indicated in the figure.

(16) Page 11 Line 21: Figure S3 D-E is not about EMT, migration, and TGFB signaling - I believe the authors are referring to Figures 4D-E

Thank you, we have corrected these errors

Reviewer #2 (Recommendations for the authors):

(1) Data

(a) In Figure 1 and the associated text, there is an analysis of cells expressing EWSR1::FLI1 performed using a locus-specific amplification and long-range sequencing. On page 7, lines 1-5, there is some discussion about how some of these track with overall transcript levels, while others don't. Additionally, a very low fraction of cells is shown to be EWSR1::FLI1 positive. This analysis might also be strengthened by a Western blot to show protein levels and how they vary across the cell lines, which may help explain additional differences in the data. The Abcam antibody ab133485 works well for western blotting of EWSR1::FLI1. While this isn't additional single-cell data, the percentage of cells with transcript detected is not really equivalent to the total expression level. This seems particularly valuable to do, as prior publications (Pishas, et. al., Mol. Cancer. Ther., 2018) show that of the cell lines tested here, TC32 has relatively high EWSR1::FLI1 protein levels, while A673 has relatively low expression. This contrasts with the percent of positive cells here.

We thank the reviewer for this suggestion and have included a new panel, Figure 1E containing the western results for cells harvested in log-phase growth.

(b) Figures 5D-F are not obviously referenced in the section about Figure 5 (page 12 line 4 through pg. 13 line 20). One question to pose to the authors is how to interpret the data here for TC71 in light of the fact that they were relatively insensitive to TGF-β. This shows up obviously in the Western blot in 5G.

Thank you, for drawing attention to this omission. We have corrected the figure reference to include Figure 5D-F. In regards to our interpretation of TC71’s lack of response to TGF-B, we direct the authors attention to our statement: “The muted response of the TC71 cell line (module-3 dominant) to the influence of TGF-b (Fig. 5A-C, Fig. S4B) suggests that pre-existing transcriptional states may condition the sensitivity to TGF-β signalling.”

(c) For the discussion of Figure 5, the authors say on page 12, line 21, that "RUNX3 was enriched in non-responsive clusters." I'm not entirely convinced that the data support this statement as written. This is true in A673s, but there appears to be no difference between the maximally responsive and maximally non-responsive clusters in CHLA10. TC71 was not particularly responsive, but showed the opposite effect.

We agree this was not clearly written. We have de-emphasized RUNX3 findings here. Our new conclusion in the results section paragraph 15 is: “Correlation analyses revealed a reciprocal enrichment pattern of many key TFs from Figure 2, where correlation of FOSL2 (but not RUNX3) gene expression and accessibility was generally highest in TGF-β responsive clusters.”

Can data for A673 cells be included for Figure 5G? Like CHLA10, this cell line had the pattern of FOSL2 enrichment that is concordant with that described in the text.

We thank the reviewer for this suggestion and have included A673 in the western blot.

(2) Figures

(a) In Figure 2A, it is very difficult to distinguish the colors for CHLA9 and TC32 in the left panel. Can the color scheme here be changed to make these easier to distinguish?

We thank the reviewer for pointing this out and we have added labels to the UMAP. We hope this makes the visualization more obvious.

(b) Similarly, the KLF4 and SP1 lines in Figure 2D are a little close and might benefit from having more distinct colors.

We thank the reviewer for pointing this out. We have changed the KLF4 to a green hue.

(c) The lower panel of Figure 5I has 2 samples labeled "11" and no sample labeled "12".

Thank you for catching this error. It has been corrected.

(3) Text

(a) One section early in the response was a little bit confusing and could benefit from some revision to improve clarity. On page 6, lines 9-11, this reads a little bit like they looked at cell-line specific EWSR1::FLI1 binding, but that wasn't the assay that was performed. Perhaps there is a better way to describe this than simply "enrichment of EWS::FLI1 sites."

We thank the reviewer for their efforts to improve clarity of our work.

We changed the following sentence in the 2nd paragraph of the result section:

"We visualized the enrichment of EWS::FLI1 sites across all cell lines and discovered distinct EwS cell-line specific usage (Fig. 1C & Fig. S1D)."

And revised to:

"We then assessed chromatin accessibility at these published EWS::FLI1 binding sites across all cell lines and discovered that accessibility at these loci varied in a cell-line-specific manner (Fig. 1C & Fig. S1D)."

(b) Then at the start of the next paragraph (page 6 line 12), the authors talk about "differences in EWS::FLI1 motif and binding site enrichment" and my first thoughts were whether this was differences in which sites were bound or differences in the strength of enrichment. More precise language would be helpful.

Here in the 3rd paragraph of the result section we changed:

"Given the differences in EWS::FLI1 motif and binding site enrichment"

To:

"Given the heterogeneity in the magnitude of EWS::FLI1 motif enrichment"

(c) Related to the comment about EWSR1::FLI1 positive cells vs. protein levels above, the hypothesis on page 6, line 13 says that there was a hypothesis that different Ewing sarcoma lines have different levels of EWSR1::FLI1 transcript. But the metric shown in Figure 2D is the percentage of cells with detectable transcript, not transcript levels. Be specific about what the data are showing here.

We agree with the need to improve clarity. In the 3rd paragraph, we changed: "we hypothesized that EwS cell lines have different levels of EWS::FLI1 transcript."

To:

"We hypothesized that EwS cell lines differ in the proportion of cells expressing high levels of the EWS::FLI1 fusion transcript."

Please remove this part.

no comment

no comment

no comment

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Reply to the reviewers

We thank the two reviewers for their constructive comments regarding our manuscript. Below is our point-by-point response (un-bold) to each reviewer’s comments together with the experiments we propose to carry-out in order to strengthen our main conclusions.

Reviewer #1

In this study, the authors investigate the effects of pharmacological inhibition of the spliceosome using the SF3B1 inhibitor pladienolide B in models of platinum-resistant non-small cell lung cancer (NSCLC). Using a combination of cell lines, platinum-resistant derivatives, and patient-derived xenograft (PDX) models, the authors show that spliceosome inhibition sensitizes platinum-resistant tumors to treatment and leads to increased DNA damage accumulation and impaired DNA damage response signaling. Transcriptomic analyses indicate that transcripts encoding DNA damage regulators are particularly sensitive to alternative splicing perturbations, and selected mechanistic experiments suggest involvement of specific regulators such as MLH3. The study further explores links between splicing inhibition, transcriptional activity, and cell cycle progression. Overall, the manuscript presents extensive datasets across multiple experimental systems and provides strong evidence that spliceosome inhibition can sensitize platinum-resistant tumors to DNA damage. However, several aspects of the mechanistic interpretation, data consistency, and presentation require clarification or strengthening to fully support the central claims.

We thank the reviewer for his/her positive comments on our work and we agree that the manuscript requires clarification to support our main claims.

Major comments* *

Conceptual clarity and synthesis of mechanistic model

The manuscript presents multiple mechanistic observations-including altered splicing of DNA repair genes, increased DNA damage accumulation, transcriptional perturbation, and cell cycle changes-but these are not integrated into a coherent conceptual framework. While it is reasonable that not all mechanistic details are fully resolved, the current presentation leaves the reader uncertain about the relative contributions of these processes. A clearer synthesis of the proposed mechanism, possibly including a summary model figure, would substantially improve the conceptual clarity of the study.

__We thank the reviewer for his/her comment. We acknowledge that our study provides multiple mechanistic observations. However, all these observations converge towards a more global mechanism by which pladienolide B induces cell death in NSCLC. Hence, we demonstrate that pladienolide B, which targets the SF3B1 protein, a core component of the spliceosome machinery, induces a massive shutdown of the DNA Damage Response signaling pathway, both at the transcriptional and splicing levels, which leads to enhanced genomic instability and cell death notably in NSCLC cells with acquired resistance to platinum salts. More specifically, we identified ATR, DNA-PKcs and MLH3 as novel targets of SF3B1 in NSCLC cell lines, as well as more importantly, in NSCLC Patient-Derived Xenografts. To our knowledge, and as also highlighted by the reviewer, this is the first evidence that pladienolide B slows-down tumor growth by negatively impacting DNA Damage Response in patient-derived xenografts. We agree with the reviewer that providing a summary model figure would improve the clarity of the study. We will provide such a graphical abstract in our revised manuscript. __

Biological specificity of platinum-resistant cell sensitivity

A central premise of the study is that platinum-resistant cells exhibit enhanced sensitivity to spliceosome inhibition. However, in several experiments (e.g., cell cycle analysis in Figure 2A), similar responses to pladienolide B appear to occur in both platinum-sensitive and resistant cells. This observation complicates the interpretation that resistant cells exhibit uniquely distinct vulnerability. The authors should clarify how these findings align with the proposed model and more explicitly distinguish shared versus resistance-specific responses.

__We thank the reviewer for this remark. In this study, we did not want to claim that platinum-salts resistant cells exhibit unique vulnerability to pladienolide B as we fully agree with the reviewer that pladienolide B also exhibits cytotoxic effects in parental sensitive cells, although with a delayed kinetic. We acknowledge that the way we introduced our results section in the former manuscript could have contributed to such a misunderstanding. Rather, we propose a model in which pladienolide B induces cell death in NSCLC cells by massively impairing the expression of key components of the DNA damage and repair signaling pathways, at the transcriptional and/or splicing level. As NSCLC cells with acquired resistance to platinum salts are likely more dependent to these pathways for their survival than parental cells, this explains enhanced susceptibility of these resistant cells to pladienolide B-induced cell death. We think that these results highlight spliceosome targeting compounds as an alternative therapeutic strategy in NSCLC patients who escape chemotherapy. According to the remark of the reviewer, we will modify the way we introduce our results and we will clarify all these points in the discussion section of the revised manuscript. __

Heterogeneity in PDX responses and lack of platinum-sensitive controls

The PDX experiments represent a major strength of the study. However, resistant tumors display heterogeneous responses to pladienolide treatment, suggesting the presence of additional determinants of sensitivity.

__We thank the rewiever for this remark. In this study, we used seven distinct NSCLC PDXs We initially selected these PDXs based on their low responsive rate to cisplatin rather than their mutational status. As discussed in the discussion section, we did not find any common mutation(s) that could predict the differential response of these PDXs to pladienolide B. We only noticed that the LCIM10 PDX, which is the most responsive to pladienolide B, exhibits ATRX mutation. ATRX has been shown to protect stalled replication forks from collapsing. As we found that pladienolide B induces early replicative stress in NSCLC cells, it is tempting to speculate that ATRX mutation might interfere with the replicative stress response and potentiates pladienolide B’s cytotoxic effects. Noteworthy, LCIM10 PDX also displayed higher basal levels of both P-DNA-PKcs(Ser2056) and P-ATR(Thr1989) proteins as compared to LCF26, ML1 and LCIM1 PDXs that were less responsive to pladienolide B (data to be added in the revised version of the manuscript as supplementary data). Therefore, and although this remains to be further clarified, this suggests that NSCLC patients with higher basal level of replicative stress, such as those who escape chemotherapy, might be more susceptible to SF3B1 inhibition. __

Including platinum-sensitive PDX tumors, if available, would provide valuable baseline comparison and strengthen interpretation of resistance-specific effects. If not feasible, the limitations should be acknowledged and discussed.

To our knowledge, and as also mentioned by the reviewer, our study provides the first demonstration of the effects of pladienolide B on the growth of NSCLC PDXs. We think that these results pave the way for further investigations in additional NSCLC PDXs and we agree with the reviewer that adding platinum-sensitive PDX tumors would be interesting. However, due to cost limitations and time constraints, we will be unable to repeat them for this specific study. Based on the results we already obtained in 7 platinum salts-resistant PDXs as regard to their response heterogeneity, one might also speculate that the comparisons between numerous sensitive and resistant PDXs should be complicated as sensitive PDXs might also display distinct mutational status. According to the remark of the reviewer, we will discuss these aspects in the discussion section of the revised manuscript.

Consistency between pharmacological inhibition and genetic depletion

In Figure 4, the authors compare pladienolide treatment with SF3B1 knockdown to demonstrate target specificity. However, the effects observed with the two perturbations are not entirely consistent-for example, pladienolide affects phosphorylation of DNA-PKcs, while SF3B1 knockdown appears to produce broader effects at both protein and mRNA levels. Additionally, differences are observed between resistant cell lines in the response to SF3B1 knockdown. These discrepancies should be addressed and discussed, as they may reflect mechanistic differences between acute pharmacological inhibition and genetic depletion.

__We thank the reviewer for this remark and we agree that acute pharmacological inhibition using pladienolide B and genetic depletion using SF3B1 siRNA might produce distinct effects as they do not exhibit the same mechanism of action. Hence, pharmacological inhibitors target the protein while siRNA targets mRNA with effects depending on the basal mRNA level/stability. This could explain why we did not exactly observe the same effects on ATR/DNA-PKcs mRNA and protein levels using both approachs in H460/A549 parental and resistant cells (Fig 3-4). For pladienolide B treatment, the effects were analyzed at “early” timepoint [i.e. after 4-6 hours treatment (Fig 3a-d)] or at a “later timepoint” [i.e. after 24-48 hours treatment (Fig 3e-f)]. At early timepoint, pladienolide B induced replicative stress that correlated with DNA-PKcs phosphorylation, while at later timepoints it decreased ATR and DNA-PKcs mRNA/protein levels. The biological consequences of SF3B1 knock-down were analyzed after 72 hours of transfection in resistant cells. This could explain why SF3B1 knock-down appears to produce broader effects at both protein and mRNA levels. However, we acknowledge the existence of differences between SF3B1 knocked-down-H460 and -A549 cells as regard to the downregulation of ATR and DNA-PKcs that occurs at the mRNA and/or protein level depending on the cell line. Again, this could depend on the time as well as the efficiency of SF3B1 knock-down which was more prominent in H460 resistant cells compared to A549 cells. Nevertheless, and despite these mechanistic differences in cell lines and between pladienolide B and SF3B1 siRNA, our results identify ATR and DNA-PKcs as novel targets of SF3B1 in NSCLC cell lines, including cells with acquired resistance to cisplatin, as well as more importantly in NSCLC PDXs (Fig 9c). To our knowledge, this is the first evidence that pladienolide B or SF3B1 knock-down negatively targets ATR or DNA-PKcs in solid tumors. Owing to the crucial role played by both kinases in the maintenance of genomic stability in cancer cells, we think that this result is of importance. Nevertheless, as suggested by the reviewer, we propose to acknowledge and discuss more in details the discrepancies between cellular models and pladienolide B / SF3B1 knock-down in the revised version of the manuscript. __

Selection and interpretation of splicing-sensitive transcripts

Transcriptomic analyses in Figure 5 identify both shared and differential splicing changes between sensitive and resistant cells. However, much of the analysis focuses on transcripts that are commonly affected in both conditions, rather than those uniquely altered in resistant cells. Given that the central phenotype is resistance-specific sensitivity, transcripts uniquely mis-spliced in resistant cells may represent more informative candidates. The authors should clarify the rationale behind focusing on shared events and discuss the implications of resistance-specific versus common splicing changes.

We thank the reviewer for this remark. Indeed, after obtaining RNA-Seq data, we initially looked for genes which differential expression and/or splicing upon pladienolide B treatment could only be observed in resistant cells but not parental ones. We focused first on the 121 genes belonging to the DNA repair pathways full network (WikiPathway WP4946) since Gene-Ontology analyses based on RNA-Seq data demonstrated enrichment of genes involved in DNA metabolic process, which includes DNA repair, among genes down-regulated after pladienolide B treatment (Fig S3). The focus on DNA repair was also justified by our observation showing accumulation of DNA double strand breaks upon pladienolide B treatment in H460 resistant cells (Fig 2d-f). Doing this comparison, we showed that pladienolide B regulates the expression of 41 (33%) and 47 (39%) genes of this network in H460S and H460R cells respectively, ____which were mostly down-regulated in both H460S (33/41) and H460R (33/47) cells (Table 3). Fifteen genes involved in all DNA repair processes were found to be specifically down-regulated in H460R cells upon pladienolide B treatment, including PARP-1. As a whole, these results demonstrated that pladienolide B down-regulates the expression of numerous DNA repair genes in both NSCLC parental and resistant cells. As discussed above, we propose that the enhanced sensitivity of resistant cells to pladienolide B is related to their increased dependency for survival to functional DNA repair pathways.

When differentially spliced genes were considered, and focusing on exon skipping events, as they were the more prominent (Fig 5e), we found that pladienolide B regulates the splicing of 107 and 87 genes of the WikiPathway WP4946 in H460 parental and resistant cells, respectively (Table 5). Forty five genes were predicted to be regulated in both cell lines. Only 3 genes, namely DCLRE1C, POLD3 and PNKP, were predicted to be differentially spliced upon pladienolide B treatment in H460R cells only. Trying to increase the number of genes to study, we extended our analysis to genes belonging to another DNA repair database (Human DNA Repair Genes, Resources from Wood laboratory, UT MD Anderson), and we found six additional genes, namely MLH3, MSH5, RAD54L, EME1, SETMAR and SMC6, that were also predicted to be differentially spliced upon pladienolide B treatment in H460R cells only. However, four of these exon skipping events (i.e. PNKP-Ex9, DCLRE1C-Ex11, SETMAR-Ex2, SMC6-Ex6) were not validated and we did not observe clear difference between H460 resistant and parental cells for the others (Fig 5g and Fig S6). Skipping of MLH3-Ex8 was the sole event displaying a slight difference between both cell lines, mainly in term of kinetic of recovery. This is why we decided to further analyze this specific splicing event. Noteworthy, we focused only on genes involved in DNA damage and repair signaling pathways. Therefore, we cannot exclude that genes involved in other biological processes might be differentially transcribed or spliced in response to pladienolide B in H460 parental and resistant cells.

Transient nature of splicing effects

The authors report transient alternative splicing effects upon prolonged pladienolide treatment. This observation is counterintuitive, as continued spliceosome inhibition might be expected to produce cumulative splicing defects. While the authors reference studies showing that transient inhibition can produce lasting effects, the current observations involve continuous exposure. This apparent discrepancy should be clarified and discussed.

We thank the reviewer for this remark. We agree with him/her that the transient effect of pladienolide B on most of the splicing events we studied was unexpected as pladienolide B treatment was prolonged. We do not have a clear explanation for that. One possibility is that pladienolide B is not stable in the cell culture supernatant and is degraded rapidly. Another, not exclusive, possibility relies on the structure of studied transcripts. As discussed in the discussion section, it is possible that the nature of the transcripts involved in DNA damage response/DNA repair which have a long length, a large number of small exons per transcript, and an elevated number of introns could explain why they recover very rapidly from pladienolide B inhibition. Alternatively, upon SF3B1 inhibition, compensatory regulations by other splicing factors might occur. We will discuss these aspects in the revised version of the manuscript.

Use of unrelated cell lines in reporter assays

The DNA damage reporter assays (Figure 6A-D) appear to be performed in cell lines not directly linked to platinum sensitivity or resistance. Given the central importance of resistance-specific responses, repeating key reporter assays in both sensitive and resistant paired models would strengthen the conclusions.

__We initially engineered these cellular models to assess the role of SRSF2, a splicing factor, in DNA repair ____(Khalife M. et al., NAR Cancer, 2025). In these models derived of either H1299 or A549 NSCLC cell lines, DNA double strand breaks (DSBs) are produced after cleavage by the SceI enzyme and the efficiency of repair is assessed based on the expression of either GFP (for homologous recombination) or CD4 (for c-NHEJ). These cellular models were difficult to engineer and to work with as they require a first stable transfection with either PBL174 pDR-GFP (for HR analysis) or PBL230 (for c-NHEJ) plasmid followed by a second transient transfection with the PLBL133 plasmid that encodes SceI enzyme. As an example, we tried to generate stable A549 cells with PBL174 plasmid but we never succeeded. The reverse was true for H1299 cells and transfection with PBL230. In addition, during the time course of this project, we also tried to transiently transfect plasmid encoding MLH3 or MLH3 protein devoid of exon 8-encoding amino acids in H460 or A549 resistant cells but we never obtained good efficiency of transfection, although we tested several transfection reagents. So, it looks like that resistant cells are hardly transfectable. This is why repeating these experiments in resistant models will not be possible. However, and although we agree with the reviewer that the cell lines we used were not directly linked to platinum sensitivity or resistance, the idea behind these experiments was to test whether pladienolide B could have a general impact on DNA repair by homologous recombination or c-NHEJ using these SceI-induced DSBs systems that are already widely used in the DNA repair field. As shown in Fig 6b and 6d, pladienolide B prevented DNA repair in these cellular models, confirming that it widely negatively impacts DNA repair pathways. __

Interpretation of MLH3 splicing results

In Figure 7, differences between pharmacological inhibition and SF3B1 knockdown in MLH3 exon 8 regulation are not entirely consistent.

We do not strictly agree with this comment. __As also discussed above, the differences seen between pharmacological inhibition of SF3B1 using pladienolide B and SF3B1 knock-down in term of MLH3-exon 8 regulation could be related to differences in term of mechanism of action and/or the fact that the effects of SF3B1 knockdown were analyzed after 72 hours treatment (as we obtained the best knock-down efficiency at this time point) while those of pladienolide B were studied between 6 to 48 hours treatment. However, as illustrated in Fig 7a and 7c (right panel), we showed that both pladienolide B and SF3B1 knock-down promote MLH3-exon 8 exclusion in H460 and A549 resistant cells. The effects of SF3B1 knock-down were less pronounced in A549R cells which could be consistent with the decreased efficiency of SF3B1 knockdown as depicted in Fig 7c (left panel). Pladienolide B also promoted MLH3-Ex8 exclusion in H460 and A549 parental cells but the recovery was faster in parental cells as compared to resistant cells (Fig 7a). __

Furthermore, inclusion levels of regulated and non-regulated exons appear similarly correlated with SF3B1 expression, potentially weakening the argument for exon-specific regulation. These observations should be clarified.

__As regard to MLH3-exon 5 exclusion, we observed its exclusion in A549 parental and resistant cells upon pladienolide B treatment, while this was not observed in H460 parental and resistant cells (Fig 7a-b). In SF3B1 knocked-down H460R and A549R cells, the exclusion of MLH3-exon 5 was seen in A549R cells. When analyzing MLH3 exon 8 or exon 5 usage in lung adenocarcinoma patients (Fig 7e), we agree with the reviewer that there was a significant correlation between SF3B1 mRNA level and MLH3 exons 5 and 8 usage. Therefore, these and our data indicate that both MLH3 exons 5 and 8 could be regulated by SF3B1 in NSCLC although differences might occur depending on the cell line. To make this point clearer, we will clarify the text of the results for Figure 7 and our conclusion. __

Combination treatment logic

In Figure 8, co-treatment experiments with pladienolide B and cisplatin are performed primarily in resistant cells. Performing similar experiments in platinum-sensitive cells would provide an important reference point to distinguish additive versus resistance-specific effects.

__We thank the reviewer for this important remark. The objective of figure 8 was to investigate whether pladienolide B that induces a shutdown of numerous DNA damage response-related genes could resensitize NSCLC cells with acquired resistance to cisplatin-induced apoptosis. The results presented in Figures 8a and 8b show that this is indeed the case. However, and according to the remark of the reviewer, we propose to illustrate in the revised version of the manuscript the results of the co-treatment experiments in sensitive parental cells also. Indeed, we already had the results for H460 parental cells. They did not show any additive or synergistic effects of the combination in these cells. Rather adding pladienolide B to cisplatin tended to decrease apoptosis as compared to cisplatin alone. We will reiterate these experiments in A549 cells. If confirmed, and to a translational point of view, these results would support the idea that treating NSCLC patients who relapse from chemotherapy with a combination of platinum salts and pladienolide B could provide therapeutic benefits, whereas NSCLC patients that primary respond to platinum salts could less benefit from this combination. __

Minor comments

• In Figure 1, pladienolide treatment in platinum-sensitive cells appears to plateau at approximately 50% cell killing. Extending the concentration range may help clarify whether maximal efficacy was reached.

__ We agree with this remark. We will reiterate our MTS experiments increasing the dose of pladienolide B. __

In Figure 1H, the difference between 2.5 mg/kg and 5 mg/kg pladienolide in PDX models appears disproportionately large relative to the dose change. This should be discussed or experimentally clarified.

We agree with the reviewer but these are the results we obtained. In Figure 1h, we illustrated the probability of progression based on Relative Tumor Volume (RTV) = 2. The difference between the two doses was less, although remaining significant, when considering RTV = 4 as a marker of progression (Fig S2d).

In Figure 5, differential expression and splicing analyses are presented using multiple cutoffs (e.g., log₂FC > 0.4 and >1; ΔPSI thresholds). This introduces redundancy and may obscure key findings. A single well-justified cutoff would improve clarity.

We agree with the reviewer that in initial Figure 5 we provided graphs illustrating different analysis thresholds based on our transcriptomic analyses. We will select one cut-off for Differentially Expressed Genes [absolute Log2Fold Change ≥ 0.4 and p ≤ 0.05 (Fig 5a)] and Differentially Spliced Genes [absolute percent splice in (PSI) ≥ 0.2 and p ≤ 0.05 (Fig 5e)]. We will remove Fig 5b and 5d for more clarity.

Several isoform-specific RT-PCR gels are difficult to interpret due to low image clarity. Improving gel presentation or focusing on key timepoints would strengthen data readability.

We will improve gel presentation.

Some figure panels appear redundant, showing similar datasets under different analysis thresholds.

We agree with the reviewer that in Figure 5 we provided graphs illustrating different analysis thresholds based on our transcriptomic analyses. We will select one cut-off for Differentially Expressed Genes [absolute Log2Fold Change ≥ 0.4 and p ≤ 0.05] and for Differentially Spliced Genes [absolute percent splice in (PSI) ≥ 0.2 and p ≤ 0.05]. We will remove Fig 5b and 5d for more clarity.

A graphical summary model illustrating the proposed mechanism would improve reader comprehension.

We agree with the reviewer. We will provide such a graphical abstract in the revised version of our manuscript.

In Figure 3D, representative images of γH2AX foci appear visually similar across conditions, whereas quantification shows large differences. The authors should ensure that representative images accurately reflect quantified trends and clarify selection criteria for displayed images.

We agree with the reviewer. We will select additional images illustrating more the differences we highlighted after quantification of more than 500 nuclei (Figure 3D, right panel).

Reviewer #1 (Significance (Required)):

This study represents a comprehensive investigation of spliceosome inhibition as a therapeutic strategy to overcome platinum resistance in NSCLC. The use of resistant cell lines, PDX models, and functional reporters provides strong experimental depth. The most compelling aspects of the study include the demonstration that spliceosome inhibition enhances DNA damage accumulation and sensitizes resistant tumors to platinum-based therapies. However, several mechanistic interpretations require clarification, and data presentation could be streamlined to improve logical coherence.

We thank the reviewer for his/her constructive remarks. We hope that our answers and the additional works we now propose to carry-out in order to revise the manuscript will get agreement to him/her and will strengthen our main claims

Advance

The work provides evidence that targeting spliceosome function-specifically via SF3B1 inhibition-can sensitize platinum-resistant tumors to DNA-damaging agents. To my knowledge, this represents one of the first comprehensive demonstrations that spliceosome-targeting compounds can effectively overcome acquired platinum resistance in solid tumor models. The study also contributes to the emerging understanding that DNA damage response transcripts may represent particularly sensitive targets of splicing perturbation.

Audience

The study will be of interest to researchers in RNA biology, cancer therapeutics, DNA damage response, and translational oncology. It is particularly relevant to scientists investigating therapeutic vulnerabilities in drug-resistant cancers and those exploring RNA processing as a therapeutic target.

Expertise I have expertise in RNA biology, alternative splicing and cancer models. My expertise is more limited in pharmacological dosing strategies and some aspects of in vivo xenograft modeling.

Keywords: RNA biology, alternative splicing, spliceosome function, cancer biology, DNA damage response, transcriptomics

Reviewer #2 (Evidence, reproducibility and clarity (Required)):

Summary

In the manuscript by Jamal-El-Hussein et al., the authors demonstrated that pladienolide B, an inhibitor of splicing factor 3B subunit 1 (SF3B1), inhibits cell viability in NSCLC cells and PDXs with resistance to platinum-based chemotherapy. Mechanistically, they identified that pladienolide B regulates splicing events of genes associated with DNA damage signaling and repair, specifically through exon skipping of MLH3, thus increasing vulnerability to chemotherapy. This study provides therapeutic insights into combining pladienolide B with chemotherapy for overcoming therapy resistance.

__Major comments __

The authors showed that both H460R and A549R cell lines are sensitive to pladienolide B; however, they exhibit distinct molecular responses. For example, SF3B1 knockdown downregulates the mRNA expression of ATR and PRKDC in H460R cells, while the expression of these genes remains unchanged in A549R cells (Fig. 4b). Furthermore, exon 8 skipping of MLH3 is not observed in A549R cells with pladienolide B treatment (Fig. 7a). These discrepancies suggest that the two cell lines respond to pladienolide B through different mechanisms. The authors should also perform a bulk RNA-seq on A549 cell line to better understand the different behavior of the two cell lines.

We disagree with the remark regarding Fig 7a as this figure demonstrated MLH3 exon 8 skipping in A549R cells also after 6 and 24 hours pladienolide B treatment. __Although we agree with the reviewer that some differences exist in term of molecular mechanisms regulated by pladienolide B or SF3B1 knock-down in H460R and A549R cell lines, we identified in this study ATR, DNA-PKcs and MLH3 as novel targets of SF3B1 in both cell lines as well as more importantly in NSCLC PDXs. To our knowledge, this is the first evidence that SF3B1 inhibition negatively impacts ATR or DNA-PKcs and regulates MLH3 alternative splicing in solid tumors. In addition, we found a significant correlation between SF3B1 and DNA-PKCs or ATR protein levels in 77 NSCLC cell lines (Fig 4c) which supports a close relationship between these proteins in lung cancer. As also discussed in the response to reviewer 1, the differences between pladienolide B and SF3B1 knock-down could be related to the distinct timepoints at which we analyzed their effects as well as to the different mechanisms of action between pharmacological and siRNA inhibition. Nevertheless, we propose to acknowledge and discuss more in details the discrepancies between cellular models and pladienolide B or SF3B1 knock-down in the revised version of the manuscript. __

The authors' central claim is that platinum-based chemotherapy-resistant cells are more sensitive to pladienolide B treatment.

__We thank the reviewer for this remark. In this study, we did not want to claim that platinum-salts resistant cells exhibit unique vulnerability to pladienolide B as we fully agree with the reviewer that pladienolide B also exhibits cytotoxic effects in parental sensitive cells, although with a delayed kinetic. We acknowledge that the way we introduced our results section in the former manuscript could have contributed to such a misunderstanding. Rather, we propose a model in which pladienolide B induces cell death in NSCLC cells by massively impairing the expression of key components of the DNA damage and repair signaling pathways, at the transcriptional and/or splicing level. As NSCLC cells with acquired resistance to platinum salts are likely more “addict” to these pathways for their survival than parental cells, this might explain enhanced susceptibility of these resistant cells to pladienolide B-induced cell death. We think that these results highlight spliceosome targeting compounds as an alternative therapeutic strategy in NSCLC patients who escape chemotherapy. According to the remark of the reviewer, we will modify the way we introduce our results and we will clarify all these points in the discussion section of the revised manuscript. __

In their bulk RNA-seq analysis, the authors selected genes commonly regulated by pladienolide B in both parental and resistant cells for further validation. This approach raises a critical concern: the observed sensitivity to pladienolide B may already be present in parental cells rather than representing a mechanism uniquely acquired by the resistant cells. To substantiate their central claim, the authors should analyze the differentially expressed genes between parental and resistant cells to identify resistance-specific molecular alterations that may confer enhanced sensitivity to pladienolide B, thereby providing a more mechanistically rigorous basis for their conclusions.

We thank the reviewer for this remark and we agree with him/her. Indeed, after obtaining RNA-Seq data, we initially looked for genes which differential expression and/or splicing upon pladienolide B treatment could be only observed in resistant cells but not parental ones. We initially focused on the 121 genes belonging to the DNA repair pathways full network (WikiPathway WP4946) since Gene-Ontology analyses demonstrated enrichment of genes involved in DNA metabolic process, which includes DNA repair, among genes down-regulated after pladienolide B treatment (Fig S3). The focus on DNA repair was also justified by our observation showing accumulation of DNA double strand breaks upon pladienolide B treatment in H460 resistant cells (Fig 2d-f). Doing this comparison, we showed that pladienolide B regulates the expression of 41 (33%) and 47 (39%) genes of this network in H460S and H460R cells respectively, ____which were mostly down-regulated in both H460S (33/41) and H460R (33/47) cells (Table 3). Fifteen genes involved in all DNA repair processes were found to be specifically down-regulated in H460R cells upon pladienolide B treatment, including PARP-1. As a whole, these results demonstrated that pladienolide B down-regulates the expression of numerous DNA repair genes in both NSCLC parental and resistant cells. However, and as discussed above, we propose that the enhanced sensitivity of resistant cells to pladienolide B is related to their increased dependency for their survival to functional DNA repair pathways.

__When differentially spliced genes were considered, and focusing on exon skipping events, as they were the more prominent (Fig 5e), we found that pladienolide B regulates the splicing of 107 and 87 genes of the WikiPathway WP4946 in H460 parental and resistant cells, respectively (Table 5). Forty five genes were predicted to be regulated in both cell lines. Only 3 genes, namely DCLRE1C, POLD3 and PNKP, were predicted to be differentially spliced upon pladienolide B treatment in H460R cells only. Trying to increase the number of genes to study, we extended our analysis to genes belonging to another DNA repair database (Human DNA Repair Genes, Resources from Wood laboratory, UT MD Anderson), and we found six additional genes, namely MLH3, MSH5, RAD54L, EME1, SETMAR and SMC6, that were also predicted to be differentially spliced in H460R cells only. However, four of these exon skipping events (i.e. PNKP-Ex9, DCLRE1C-Ex11, SETMAR-Ex2, SMC6-Ex6) were not validated and we did not observe clear difference between H460 resistant and parental cells for the others (Fig 5g and Fig S6). Skipping of MLH3-Ex8 was the sole event displaying a slight difference between both cell lines, mainly in term of kinetic of recovery. This is why we decided to further analyze this specific splicing event. Noteworthy, we focused only on genes involved in DNA damage and repair signaling pathways. Therefore, we cannot exclude that genes involved in other biological processes might be differentially transcribed or spliced in response to pladienolide B in H460 parental and resistant cells. __

The authors conclude that pladienolide B treatment correlates with activation of DNA-PKcs signaling followed by a shutdown of ATR and DNA-PKcs pathways. However, the data presented do not fully support this interpretation. P-ATR levels are already elevated in resistant cells and remain unchanged following pladienolide B treatment (Fig. 3a). However, prolonged pladienolide B treatment leads to decreased total ATR protein and mRNA expression (Fig. 3e-f), suggesting that pladienolide B maintains an initial constitutive ATR activation followed by transcriptional downregulation. Since pladienolide B is a splicing inhibitor, the authors should determine whether ATR and DNA-PKcs mRNA downregulation is a direct consequence of aberrant splicing of their transcripts, or a non-specific effect of prolonged cellular toxicity. To strengthen their mechanistic conclusions, the authors should perform time-course experiments to establish the temporal relationship between these signaling events, analyze splicing changes specifically in ATR and DNA-PKcs transcripts (are they in the differential genes from the bulk RNA-seq analysis?), and also check the downstream targets of the ATR and DNA-PKcs signaling.

__We thank the reviewer for his/her comment. In our RNA-Seq analyses, we did not recover PRKDC among the differential genes expressed or spliced upon pladienolide B treatment whatever the cell line. However, PRKDC was also not in the full RNA-Seq data list of not significant genes. Therefore, it remains unclear whether PRKDC splicing could account for the decrease of PRKDC mRNA level upon pladienolide B treatment. Concerning ATR, it was not in the RNA-Seq data list of the genes significantly up- or down-regulated upon pladienolide B treatment in either H460 parental or resistant cells. However, transcriptomic analyses were performed after 8 hours pladienolide B treatment while the decrease of ATR mRNA was observed after 24 hours (Fig 3f). Regarding splicing, ATR was predicted to be spliced, skipping of exon 30, upon pladienolide B treatment in both H460 parental and resistant cells. We validated this splicing event after 8 hours treatment with pladienolide B in both H460 cellular models but we did not analyze this splicing event at later timepoints, nor in the A549 parental or resistant cells. Therefore, and according to the remarks of the reviewer, we propose to deepen the temporal relationships between all these signaling events by performing time-course experiments for analysis of ATR exon 30 splicing by RT-PCR, ATR and PRKDC mRNA levels by RT-qPCR, and expression of downstream targets of ATR and DNA-PKcs, such as P-CHK1(Ser345) or P-RPA32(Ser4/8) by immunoblotting. __

The authors report that skipping of exon 8 of MLH3 leads to the complete absence of the protein (Fig. 7d). However, this observation needs further clarification, as at least two alternative explanations exist. First, the antibody used to detect MLH3 may specifically recognize an epitope encoded by exon 8 or downstream exons, in which case the loss of signal would reflect antibody incompatibility rather than true protein absence.

__We thank the reviewer for this remark. The anti-MLH3 antibody recognizes the C-terminal part of the MLH3 full-length protein (between amino acids 1228-1453). MLH3 exon 8 is 72 base pair and does not encode for the amino acids recognized by the anti-MLH3 antibody. In ENSEMBL, the MLH3-201 transcript encodes for the full-length protein (1453 amino acids) and the MLH3-202 transcript encodes for a MLH3 protein (1429 amino acids) devoid of the amino acids encoded by exon 8. Nevertheless, the two products have the same C-terminus recognized by the anti-MLH3 antibody used in this study. So, the loss of the signal depicted in Figure 7d is not due to antibody incompatibility. __

Second, skipping of exon 8 may introduce a premature stop codon, triggering nonsense-mediated mRNA decay (NMD) and consequent loss of the transcript. To distinguish between these possibilities, the authors should perform qPCR using primers targeting sequences both upstream and downstream of the skipped exon, as well as consider NMD inhibition experiments, to clarify whether the observed protein loss occurs at the transcriptional or translational level.

__As shown in Figure 8f, we demonstrated by RT-qPCR that pladienolide B alone or the combination of pladienolide B with cisplatin does not negatively impact MLH3 mRNA level in both H460R and A549R cells. These results were confirmed in a time-course experiment of pladienolide B treatment performed in H460 and A549 parental and resistant cells, as well as in NSCLC PDXs. Similar results were obtained in H460R or A549R cells deprived of SF3B1. These new data will be added in the revised version of the manuscript. The couple of primers we used for MLH3 amplification was located downstream of exon 8, respectively on constitutive MLH3-exon 9 (forward primer) and MLH3-exon 10 (reverse primer). These results indicate that pladienolide B regulates MLH3 splicing but not MLH3 total mRNA level. Considering NMD, in the FASTER DB database, none of the MLH3 transcripts devoid of exon 8 are predicted to be degraded by NMD. So we do not think that pladienolide B-induced MLH3 exon 8 skipping promotes the synthesis of transcripts recognized by the NMD machinery. As discussed above, the anti-MLH3 antibody does not allow to distinguish between the full length MLH3 protein and the MLH3 product encoded by transcript devoid of exon 8. In addition, only 24 amino acids (around 2-3KDa) differentiate both products which could render difficult their specific detection in SDS-PAGE. So, we speculate that the decrease of MLH3 signal detected by immunoblotting in pladienolide B-treated and SF3B1 knocked-down cells is mostly related to the decrease of MLH3 full-length protein due to the decreased level of MLH3 transcript retaining exon 8 and encoding MLH3 full-length protein. Alternatively, and not exclusively, MLH3 product devoid of exon 8 might also be less stable. __

The difference shown in Fig. 6b after pladienolide B treatment decreases from 2.2% to 1%. This raises concern about whether the observed difference reflects a true biological effect or is confounded by technical limitations such as low transfection efficiency. The authors should consider optimizing their transfection conditions to achieve a more robust and convincing result.

We agree with the reviewer’s comment. However, using this SceI-inducible system to analyze DNA double strand breaks repair by homologous recombination, it is very frequent to have only a very low percentage of cells able to perform homologous recombination thereby expressing the GFP protein ____(as examples: Yoshino Y et al., Sci Reports, 2019; Brustel et al., Sci Rep., 2018; Croglio et al., Oncotarget, 2016; Mamouni et al., Mol Cell Biol., 2014). This is why the difference is low between each condition but it is significant. Indeed, these engineered cellular models are not easy to manipulate as we first need to obtain stable clones having incorporated the PBL174 pDR-GFP-plasmid and then to transiently transfect them using a second plasmid encoding the SceI enzyme which creates DNA Double Strand Breaks. The efficiency of the second round of transfection might therefore be decreased as the cells already experienced a first round of transfection.

__Minor comments __

The abbreviation "S" in H460S and A549S cells is not defined in the manuscript. As this designation is used throughout the text, the authors should clarify what "S" denotes upon its first appearance.

__We thank the reviewer for this remark. We will correct the text. __

The current presentation of Figure S1a does not clearly demonstrate that different NSCLC cell lines exhibit differential sensitivity to pladienolide B. The authors should calculate and report IC50 values for each cell line to enable a more rigorous and quantitative comparison of their respective dose-response relationships.

We thank the reviewer for this remark and agree with it. We will calculate and report in the revised version of Fig S1a the IC50 for pladienolide B for each cell line.

In Figure 4c, two dashed black lines are present in the plot but are not described or explained in the figure legend or the main text.

We thank the reviewer for this remark. The two dashed black lines represent the upper and lower boundaries of the 95% confidence interval for the fitted linear regression line. We have now clarified their meaning in the revised figure legend and indicated section of the main text also.

In Figure 5a, the authors combine the downregulated and upregulated genes in a single Venn diagram. This approach may obscure biologically meaningful differences, as overlapping genes between conditions could reflect opposing directions of regulation. The authors should separate upregulated and downregulated genes into distinct Venn diagrams to provide a more accurate and interpretable comparison.

We thank the reviewer for this remark and agree with it. Hence, in Fig 5a-b and Fig S3, we already highlighted the number of genes down-regulated or up-regulated upon pladienolide B treatment in either H460 parental and resistant cells using bar graphs. We will provide new Venn diagrams separating up-regulated and down-regulated genes for both cell lines.

In the figure legend of Fig. 6a, the panel is incorrectly described as a "quantification." As the panel depicts a schematic representation of the experimental construct rather than numerical data, the term "illustration" or "schematic" would be more accurate and should be used instead.

We thank the reviewer for this remark and we agree with it. We will modify the legend of Figure 6a accordingly.

Reviewer #2 (Significance (Required)):

This manuscript provides evidence that pladienolide B can overcome chemotherapy resistance in NSCLC by modulating splicing events of genes associated with DNA damage signaling and repair. Although the underlying mechanism requires further elucidation, this study offers valuable mechanistic insights into how aberrant splicing regulates therapy resistance, with potential implications for the development of novel therapeutic strategies targeting splicing factors in chemotherapy-resistant cancers.

My research field is in tumor heterogeneity and tumor microenvironment.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

Referee #2

Evidence, reproducibility and clarity

Summary

In the manuscript by Jamal-El-Hussein et al., the authors demonstrated that pladienolide B, an inhibitor of splicing factor 3B subunit 1 (SF3B1), inhibits cell viability in NSCLC cells and PDXs with resistance to platinum-based chemotherapy. Mechanistically, they identified that pladienolide B regulates splicing events of genes associated with DNA damage signaling and repair, specifically through exon skipping of MLH3, thus increasing vulnerability to chemotherapy. This study provides therapeutic insights into combining pladienolide B with chemotherapy for overcoming therapy resistance.

Major comments

1. The authors showed that both H460R and A549R cell lines are sensitive to pladienolide B; however, they exhibit distinct molecular responses. For example, SF3B1 knockdown downregulates the mRNA expression of ATR and PRKDC in H460R cells, while the expression of these genes remains unchanged in A549R cells (Fig. 4b). Furthermore, exon 8 skipping of MLH3 is not observed in A549R cells with pladienolide B treatment (Fig. 7a). These discrepancies suggest that the two cell lines respond to pladienolide B through different mechanisms. The authors should also perform a bulk RNA-seq on A549 cell line to better understand the different behavior of the two cell lines.
2. The authors' central claim is that platinum-based chemotherapy-resistant cells are more sensitive to pladienolide B treatment. However, in their bulk RNA-seq analysis, the authors selected genes commonly regulated by pladienolide B in both parental and resistant cells for further validation. This approach raises a critical concern: the observed sensitivity to pladienolide B may already be present in parental cells rather than representing a mechanism uniquely acquired by the resistant cells. To substantiate their central claim, the authors should analyze the differentially expressed genes between parental and resistant cells to identify resistance-specific molecular alterations that may confer enhanced sensitivity to pladienolide B, thereby providing a more mechanistically rigorous basis for their conclusions.
3. The authors conclude that pladienolide B treatment correlates with activation of DNA-PKcs signaling followed by a shutdown of ATR and DNA-PKcs pathways. However, the data presented do not fully support this interpretation. P-ATR levels are already elevated in resistant cells and remain unchanged following pladienolide B treatment (Fig. 3a). However, prolonged pladienolide B treatment leads to decreased total ATR protein and mRNA expression (Fig. 3e-f), suggesting that pladienolide B maintains an initial constitutive ATR activation followed by transcriptional downregulation. Since pladienolide B is a splicing inhibitor, the authors should determine whether ATR and DNA-PKcs mRNA downregulation is a direct consequence of aberrant splicing of their transcripts, or a non-specific effect of prolonged cellular toxicity. To strengthen their mechanistic conclusions, the authors should perform time-course experiments to establish the temporal relationship between these signaling events, analyze splicing changes specifically in ATR and DNA-PKcs transcripts (are they in the differential genes from the bulk RNA-seq analysis?), and also check the downstream targets of the ATR and DNA-PKcs signaling.
4. The authors report that skipping of exon 8 of MLH3 leads to the complete absence of the protein (Fig. 7d). However, this observation needs further clarification, as at least two alternative explanations exist. First, the antibody used to detect MLH3 may specifically recognize an epitope encoded by exon 8 or downstream exons, in which case the loss of signal would reflect antibody incompatibility rather than true protein absence. Second, skipping of exon 8 may introduce a premature stop codon, triggering nonsense-mediated mRNA decay (NMD) and consequent loss of the transcript. To distinguish between these possibilities, the authors should perform qPCR using primers targeting sequences both upstream and downstream of the skipped exon, as well as consider NMD inhibition experiments, to clarify whether the observed protein loss occurs at the transcriptional or translational level.
5. The difference shown in Fig. 6b after pladienolide B treatment decreases from 2.2% to 1%. This raises concern about whether the observed difference reflects a true biological effect or is confounded by technical limitations such as low transfection efficiency. The authors should consider optimizing their transfection conditions to achieve a more robust and convincing result.

Minor comments

1. The abbreviation "S" in H460S and A549S cells is not defined in the manuscript. As this designation is used throughout the text, the authors should clarify what "S" denotes upon its first appearance.
2. The current presentation of Figure S1a does not clearly demonstrate that different NSCLC cell lines exhibit differential sensitivity to pladienolide B. The authors should calculate and report IC50 values for each cell line to enable a more rigorous and quantitative comparison of their respective dose-response relationships.
3. In Figure 4c, two dashed black lines are present in the plot but are not described or explained in the figure legend or the main text.
4. In Figure 5a, the authors combine the downregulated and upregulated genes in a single Venn diagram. This approach may obscure biologically meaningful differences, as overlapping genes between conditions could reflect opposing directions of regulation. The authors should separate upregulated and downregulated genes into distinct Venn diagrams to provide a more accurate and interpretable comparison.
5. In the figure legend of Fig. 6a, the panel is incorrectly described as a "quantification." As the panel depicts a schematic representation of the experimental construct rather than numerical data, the term "illustration" or "schematic" would be more accurate and should be used instead.

Significance

This manuscript provides evidence that pladienolide B can overcome chemotherapy resistance in NSCLC by modulating splicing events of genes associated with DNA damage signaling and repair. Although the underlying mechanism requires further elucidation, this study offers valuable mechanistic insights into how aberrant splicing regulates therapy resistance, with potential implications for the development of novel therapeutic strategies targeting splicing factors in chemotherapy-resistant cancers.

My research field is in tumor heterogeneity and tumor microenvironment.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

Referee #1

Evidence, reproducibility and clarity

In this study, the authors investigate the effects of pharmacological inhibition of the spliceosome using the SF3B1 inhibitor pladienolide B in models of platinum-resistant non-small cell lung cancer (NSCLC). Using a combination of cell lines, platinum-resistant derivatives, and patient-derived xenograft (PDX) models, the authors show that spliceosome inhibition sensitizes platinum-resistant tumors to treatment and leads to increased DNA damage accumulation and impaired DNA damage response signaling. Transcriptomic analyses indicate that transcripts encoding DNA damage regulators are particularly sensitive to alternative splicing perturbations, and selected mechanistic experiments suggest involvement of specific regulators such as MLH3. The study further explores links between splicing inhibition, transcriptional activity, and cell cycle progression.

Overall, the manuscript presents extensive datasets across multiple experimental systems and provides strong evidence that spliceosome inhibition can sensitize platinum-resistant tumors to DNA damage. However, several aspects of the mechanistic interpretation, data consistency, and presentation require clarification or strengthening to fully support the central claims.

Major comments

Conceptual clarity and synthesis of mechanistic model

The manuscript presents multiple mechanistic observations-including altered splicing of DNA repair genes, increased DNA damage accumulation, transcriptional perturbation, and cell cycle changes-but these are not integrated into a coherent conceptual framework. While it is reasonable that not all mechanistic details are fully resolved, the current presentation leaves the reader uncertain about the relative contributions of these processes. A clearer synthesis of the proposed mechanism, possibly including a summary model figure, would substantially improve the conceptual clarity of the study.

Biological specificity of platinum-resistant cell sensitivity

A central premise of the study is that platinum-resistant cells exhibit enhanced sensitivity to spliceosome inhibition. However, in several experiments (e.g., cell cycle analysis in Figure 2A), similar responses to pladienolide B appear to occur in both platinum-sensitive and resistant cells. This observation complicates the interpretation that resistant cells exhibit uniquely distinct vulnerability. The authors should clarify how these findings align with the proposed model and more explicitly distinguish shared versus resistance-specific responses.

Heterogeneity in PDX responses and lack of platinum-sensitive controls

The PDX experiments represent a major strength of the study. However, resistant tumors display heterogeneous responses to pladienolide treatment, suggesting the presence of additional determinants of sensitivity. Including platinum-sensitive PDX tumors, if available, would provide valuable baseline comparison and strengthen interpretation of resistance-specific effects. If not feasible, the limitations should be acknowledged and discussed.

(OPTIONAL - would strengthen study but may require substantial additional work.)

Consistency between pharmacological inhibition and genetic depletion

In Figure 4, the authors compare pladienolide treatment with SF3B1 knockdown to demonstrate target specificity. However, the effects observed with the two perturbations are not entirely consistent-for example, pladienolide affects phosphorylation of DNA-PKcs, while SF3B1 knockdown appears to produce broader effects at both protein and mRNA levels. Additionally, differences are observed between resistant cell lines in the response to SF3B1 knockdown. These discrepancies should be addressed and discussed, as they may reflect mechanistic differences between acute pharmacological inhibition and genetic depletion.

Selection and interpretation of splicing-sensitive transcripts

Transcriptomic analyses in Figure 5 identify both shared and differential splicing changes between sensitive and resistant cells. However, much of the analysis focuses on transcripts that are commonly affected in both conditions, rather than those uniquely altered in resistant cells. Given that the central phenotype is resistance-specific sensitivity, transcripts uniquely mis-spliced in resistant cells may represent more informative candidates. The authors should clarify the rationale behind focusing on shared events and discuss the implications of resistance-specific versus common splicing changes.

Transient nature of splicing effects

The authors report transient alternative splicing effects upon prolonged pladienolide treatment. This observation is counterintuitive, as continued spliceosome inhibition might be expected to produce cumulative splicing defects. While the authors reference studies showing that transient inhibition can produce lasting effects, the current observations involve continuous exposure. This apparent discrepancy should be clarified and discussed.

Use of unrelated cell lines in reporter assays

The DNA damage reporter assays (Figure 6A-D) appear to be performed in cell lines not directly linked to platinum sensitivity or resistance. Given the central importance of resistance-specific responses, repeating key reporter assays in both sensitive and resistant paired models would strengthen the conclusions.

(OPTIONAL - likely moderate experimental effort.)

Interpretation of MLH3 splicing results

In Figure 7, differences between pharmacological inhibition and SF3B1 knockdown in MLH3 exon 8 regulation are not entirely consistent. Furthermore, inclusion levels of regulated and non-regulated exons appear similarly correlated with SF3B1 expression, potentially weakening the argument for exon-specific regulation. These observations should be clarified.

Combination treatment logic

In Figure 8, co-treatment experiments with pladienolide B and cisplatin are performed primarily in resistant cells. Performing similar experiments in platinum-sensitive cells would provide an important reference point to distinguish additive versus resistance-specific effects.

(OPTIONAL - moderate experimental effort.)

Minor comments

• In Figure 1, pladienolide treatment in platinum-sensitive cells appears to plateau at approximately 50% cell killing. Extending the concentration range may help clarify whether maximal efficacy was reached.
• In Figure 1H, the difference between 2.5 mg/kg and 5 mg/kg pladienolide in PDX models appears disproportionately large relative to the dose change. This should be discussed or experimentally clarified.
• In Figure 5, differential expression and splicing analyses are presented using multiple cutoffs (e.g., log₂FC > 0.4 and >1; ΔPSI thresholds). This introduces redundancy and may obscure key findings. A single well-justified cutoff would improve clarity.
• Several isoform-specific RT-PCR gels are difficult to interpret due to low image clarity. Improving gel presentation or focusing on key timepoints would strengthen data readability.
• Some figure panels appear redundant, showing similar datasets under different analysis thresholds.
• A graphical summary model illustrating the proposed mechanism would improve reader comprehension.
• In Figure 3D, representative images of γH2AX foci appear visually similar across conditions, whereas quantification shows large differences. The authors should ensure that representative images accurately reflect quantified trends and clarify selection criteria for displayed images.

Significance

This study represents a comprehensive investigation of spliceosome inhibition as a therapeutic strategy to overcome platinum resistance in NSCLC. The use of resistant cell lines, PDX models, and functional reporters provides strong experimental depth. The most compelling aspects of the study include the demonstration that spliceosome inhibition enhances DNA damage accumulation and sensitizes resistant tumors to platinum-based therapies. However, several mechanistic interpretations require clarification, and data presentation could be streamlined to improve logical coherence.

Advance

The work provides evidence that targeting spliceosome function-specifically via SF3B1 inhibition-can sensitize platinum-resistant tumors to DNA-damaging agents. To my knowledge, this represents one of the first comprehensive demonstrations that spliceosome-targeting compounds can effectively overcome acquired platinum resistance in solid tumor models. The study also contributes to the emerging understanding that DNA damage response transcripts may represent particularly sensitive targets of splicing perturbation.

Audience

The study will be of interest to researchers in RNA biology, cancer therapeutics, DNA damage response, and translational oncology. It is particularly relevant to scientists investigating therapeutic vulnerabilities in drug-resistant cancers and those exploring RNA processing as a therapeutic target.

Expertise

I have expertise in RNA biology, alternative splicing and cancer models. My expertise is more limited in pharmacological dosing strategies and some aspects of in vivo xenograft modeling.

Keywords:

RNA biology, alternative splicing, spliceosome function, cancer biology, DNA damage response, transcriptomics

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Base the decision on the comparison of the verified figures."

The printed total is ₱100 higher than the calculated total."

整个 Flutter 动画的执行过程可以概括为：

Ticker 随屏幕刷新驱动动画。

AnimationController 根据时间产生动画进度（通常为 0～1）。

CurvedAnimation（可选）对动画进度进行曲线映射，使动画具有加速、减速、弹性等效果。

Tween 将映射后的进度转换为实际业务数据（如位置、透明度、颜色、大小等）。

Animation 保存当前计算结果，并通过 addListener() 通知界面刷新。

Widget 在 build() 中读取 animation.value 更新 UI，连续绘制每一帧，从而形成流畅动画。

一句话概括它们的关系：

Ticker 提供每帧时钟 → AnimationController 控制动画进度 → Curve 调整进度变化规律 → Tween 将进度映射为具体属性值 → Animation 保存当前值并通知 UI 刷新，从而实现动画效果。

Flutter 中显式动画的典型使用流程可以概括为：

创建 AnimationController：负责控制动画的时长、播放和停止。

（可选）创建 CurvedAnimation：为动画添加加速、减速等曲线效果。

创建 Tween 并调用 animate()：将 0~1 的动画进度映射到实际需要的数值或对象。

监听动画变化：通过 addListener() 调用 setState()，让界面在每一帧重建。

启动动画：调用 forward()、reverse()、repeat() 等方法。 在 build() 中使用 animation.value 更新 Widget 属性，最终呈现连续的动画效果。

The articles and Stage 4 exercises consistently assess the intended competencies, but most Exercise Bank items do not.

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Compare the values: the log shows 70 hours, while the draft shows 74. Since they do not match, use the log figure.

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整个 Flutter 动画的执行过程可以概括为：

Ticker 随屏幕刷新驱动动画。

AnimationController 根据时间产生动画进度（通常为 0～1）。

CurvedAnimation（可选）对动画进度进行曲线映射，使动画具有加速、减速、弹性等效果。

Tween 将映射后的进度转换为实际业务数据（如位置、透明度、颜色、大小等）。

Animation 保存当前计算结果，并通过 addListener() 通知界面刷新。

Widget 在 build() 中读取 animation.value 更新 UI，连续绘制每一帧，从而形成流畅动画。

一句话概括它们的关系：

Ticker 提供每帧时钟 → AnimationController 控制动画进度 → Curve 调整进度变化规律 → Tween 将进度映射为具体属性值 → Animation 保存当前值并通知 UI 刷新，从而实现动画效果。

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check any differences between the computed and recorded values.

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how much is short lived? 5 min ? 15 min? myb it have answer in the future but i see in many places short-lived without know how much short

what is this?

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"The main estimation strategy is rounding."

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task

What is meant with this?

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"Two geometric properties help determine the correct amount of material."

Summary of, and references to research on, the gigification of labour.

Please use authentic workplace documents, reduce unnecessary explanatory text.( not needed to answer the conversion questions)

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One incorrect reading of a tape measure can waste an entire aluminum bar

PD service won't be enough for support hours, can Sanjer/Pinky identify the service/circumstances and group based on that?

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Writing the rule was only a small part of the job

We need also the option to have timeframe like 24h / 6h and not specific times for the scheduled use case

Maybe it should be 'model' and not models ? :)

Maybe we also need a status? Active / Inactive or something like that So it will be easy to temporarily stop the job from running for some reason instead of deleting it. wdyt?

Maybe this can be optional parameter, so if it is not specified the full model are is returned

If the model is specified in the request how can it get a different source. I think if the bb doesn't fit the model bb it should return an error. wdyt?

Can you please add description for each status, I'm not sure I understand correctly the differences between them.

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A month's worth of work can be shown in one graph.

Numbers arranged over time form a pattern.

suggested shorter version- Pendatun orders brake pads for a fleet garage in Angeles, Pampanga. Each brake pad costs PHP 120. Today's Delivery Receipt lists 7 brake pads. What is the total cost, in pesos?

Onofre works at a barber shop in La Trinidad, Benguet. The hair dryer starts at 40°C on level 1, and each higher dial level increases the temperature by 15°C. The stylist selects level 4. What air temperature, in degrees Celsius, does level 4 provide?- will this question do?

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Each question measures recall,

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He wants to find the total amount spent on the two voucher slips.

questions are aligned

no comment

not supported by safari?

perhaps should use post processing line when the line is changed?

"Re Item 11 (Section 126g)

Section 126g contains a transitional provision whereby, for partnerships applied for by educational institutions prior to the entry into force of this Act, Section 124a(2) in the version applicable up to that point shall continue to apply. The transitional provision applies only to partnerships for which applications have already been submitted. An expansion of the range of services offered by educational institutions is not covered by the grandfathering provision of the transitional provision."

(translation and highlight by commenter)

"Re Item 8 (Section 124a)

With the aim of improving consumer protection and ensuring the quality of academic programmes at university level in Berlin as a centre of science, the possibility of operating other institutions is to be restricted in future to branches of state universities, universities under state sponsorship or state-recognised universities in accordance with Section 124a(1) of the Berlin Higher Education Act (BerlHG) in its current version. With this provision, Berlin is aligning itself with the higher education legislation of other federal states. Concerns raised by the Technical University (TU) regarding its continuing education programmes have not been substantiated."

(translation and highlight by commenter)

DOI: 10.1016/j.ejphar.2026.179151

Resource: RRID:CVCL_8177

Curator: @nmaralla

SciCrunch record: RRID:CVCL_8177

What is this?

DOI: 10.1016/j.devcel.2026.07.001

Resource: Adobe Illustrator (RRID:SCR_010279)

Curator: @nmaralla

SciCrunch record: RRID:SCR_010279

What is this?

DOI: 10.1186/s40478-026-02240-y

Resource: (Sigma-Aldrich Cat# G3893, RRID:AB_477010)

Curator: @scibot

SciCrunch record: RRID:AB_477010

What is this?

DOI: 10.1186/s40478-026-02240-y

Resource: (EnCor Biotechnology Cat# MCA-5C10, RRID:AB_2572311)

Curator: @scibot

SciCrunch record: RRID:AB_2572311

What is this?

DOI: 10.1186/s40478-026-02240-y

Resource: RRID:AB_3678889

Curator: @scibot

SciCrunch record: RRID:AB_3678889

What is this?

DOI: 10.1186/s40478-026-02240-y

Resource: (EnCor Biotechnology Cat# CPCA-GFAP, RRID:AB_2109953)

Curator: @scibot

SciCrunch record: RRID:AB_2109953

What is this?

DOI: 10.1186/s40478-026-02240-y

Resource: (Enzo Life Sciences Cat# BML-FG6090-0100, RRID:AB_2050678)

Curator: @scibot

SciCrunch record: RRID:AB_2050678

What is this?

DOI: 10.1186/s40478-026-02240-y

Resource: RRID:AB_3678888

Curator: @scibot

SciCrunch record: RRID:AB_3678888

What is this?

DOI: 10.1186/s40478-026-02240-y

Resource: RRID:AB_3711244

Curator: @scibot

SciCrunch record: RRID:AB_3711244

What is this?

DOI: 10.1186/s40478-026-02240-y

Resource: (Agilent Cat# Z0334, RRID:AB_10013382)

Curator: @scibot

SciCrunch record: RRID:AB_10013382

What is this?

DOI: 10.1016/j.neuroscience.2026.07.049

Resource: GraphPad Prism (RRID:SCR_002798)

Curator: @scibot

SciCrunch record: RRID:SCR_002798

What is this?

DOI: 10.1016/j.neuroscience.2026.07.049

Resource: ImageJ (RRID:SCR_003070)

Curator: @scibot

SciCrunch record: RRID:SCR_003070

What is this?

DOI: 10.1016/j.neuroscience.2026.07.049

Resource: (Sigma-Aldrich Cat# A5316, RRID:AB_476743)

Curator: @scibot

SciCrunch record: RRID:AB_476743

What is this?

DOI: 10.1016/j.neuroscience.2026.07.049

Resource: (Cell Signaling Technology Cat# 7074, RRID:AB_2099233)

Curator: @scibot

SciCrunch record: RRID:AB_2099233

What is this?

DOI: 10.1016/j.neuroscience.2026.07.049

Resource: (Cell Signaling Technology Cat# 7076, RRID:AB_330924)

Curator: @scibot

SciCrunch record: RRID:AB_330924

What is this?

DOI: 10.1016/j.neuroscience.2026.07.049

Resource: RRID:AB_305132

Curator: @scibot

SciCrunch record: RRID:AB_305132

What is this?

DOI: 10.1016/j.neuroscience.2026.07.049

Resource: RRID:CVCL_C0DX

Curator: @scibot

SciCrunch record: RRID:CVCL_C0DX

What is this?

DOI: 10.1016/j.neuroscience.2026.07.049

Resource: (Cell Signaling Technology Cat# 2876, RRID:AB_2064177)

Curator: @scibot

SciCrunch record: RRID:AB_2064177

What is this?

DOI: 10.1016/j.neuroscience.2026.07.049

Resource: (Cell Signaling Technology Cat# 2772, RRID:AB_10695870)

Curator: @scibot

SciCrunch record: RRID:AB_10695870

What is this?

DOI: 10.1016/j.neuroscience.2026.07.049

Resource: (Cell Signaling Technology Cat# 9662, RRID:AB_331439)

Curator: @scibot

SciCrunch record: RRID:AB_331439

What is this?

DOI: 10.1016/j.neuroscience.2026.07.049

Resource: (Abcam Cat# ab3927, RRID:AB_304167)

Curator: @scibot

SciCrunch record: RRID:AB_304167

What is this?

DOI: 10.1016/j.neuroscience.2026.07.049

Resource: (Cell Signaling Technology Cat# 2978, RRID:AB_2259616)

Curator: @scibot

SciCrunch record: RRID:AB_2259616

What is this?

DOI: 10.1016/j.neuroscience.2026.07.049

Resource: (Thermo Fisher Scientific Cat# PA1-062, RRID:AB_2105077)

Curator: @scibot

SciCrunch record: RRID:AB_2105077

What is this?

DOI: 10.1016/j.neuroscience.2026.07.049

Resource: (Cell Signaling Technology Cat# 9102, RRID:AB_330744)

Curator: @scibot

SciCrunch record: RRID:AB_330744

What is this?

DOI: 10.1016/j.neuroscience.2026.07.049

Resource: (Cell Signaling Technology Cat# 9101, RRID:AB_331646)

Curator: @scibot

SciCrunch record: RRID:AB_331646

What is this?

DOI: 10.1016/j.neuroscience.2026.07.049

Resource: RRID:AB_3665519

Curator: @scibot

SciCrunch record: RRID:AB_3665519

What is this?

DOI: 10.1016/j.neuint.2026.106230

Resource: (Abcam Cat# ab150115, RRID:AB_2687948)

Curator: @scibot

SciCrunch record: RRID:AB_2687948

What is this?

DOI: 10.1016/j.neuint.2026.106230

Resource: (Abcam Cat# ab150113, RRID:AB_2576208)

Curator: @scibot

SciCrunch record: RRID:AB_2576208

What is this?

DOI: 10.1016/j.neuint.2026.106230

Resource: (Sigma-Aldrich Cat# A3854, RRID:AB_262011)

Curator: @scibot

SciCrunch record: RRID:AB_262011

What is this?

DOI: 10.1016/j.neuint.2026.106230

Resource: RRID:AB_10851638

Curator: @scibot

SciCrunch record: RRID:AB_10851638

What is this?

DOI: 10.1016/j.neuint.2026.106230

Resource: (Cell Signaling Technology Cat# 43966, RRID:AB_2799254)

Curator: @scibot

SciCrunch record: RRID:AB_2799254

What is this?

DOI: 10.1016/j.neuint.2026.106230

Resource: RRID:AB_10843103

Curator: @scibot

SciCrunch record: RRID:AB_10843103

What is this?

DOI: 10.1016/j.neuint.2026.106230

Resource: (Abcam Cat# ab55735, RRID:AB_2239227)

Curator: @scibot

SciCrunch record: RRID:AB_2239227

What is this?

DOI: 10.1016/j.neuint.2026.106230

Resource: RRID:AB_2918909

Curator: @scibot

SciCrunch record: RRID:AB_2918909

What is this?

DOI: 10.1016/j.neuint.2026.106230

Resource: RRID:AB_3713001

Curator: @scibot

SciCrunch record: RRID:AB_3713001

What is this?

DOI: 10.1016/j.neuint.2026.106230

Resource: (Santa Cruz Biotechnology Cat# sc-365949, RRID:AB_10917561)

Curator: @scibot

SciCrunch record: RRID:AB_10917561

What is this?

DOI: 10.1016/j.neuint.2026.106230

Resource: (Cell Signaling Technology Cat# 96457, RRID:AB_2800265)

Curator: @scibot

SciCrunch record: RRID:AB_2800265

What is this?

DOI: 10.1016/j.neuint.2026.106230

Resource: RRID:AB_805374

Curator: @scibot

SciCrunch record: RRID:AB_805374

What is this?

DOI: 10.1016/j.neuint.2026.106230

Resource: RRID:AB_2608415

Curator: @scibot

SciCrunch record: RRID:AB_2608415

What is this?

DOI: 10.1016/j.neuint.2026.106230

Resource: (Abcam Cat# ab69090, RRID:AB_1523609)

Curator: @scibot

SciCrunch record: RRID:AB_1523609

What is this?

DOI: 10.1016/j.neuint.2026.106230

Resource: (Cell Signaling Technology Cat# 9043, RRID:AB_2665480)

Curator: @scibot

SciCrunch record: RRID:AB_2665480

What is this?

DOI: 10.1016/j.neuint.2026.106230

Resource: (Cell Signaling Technology Cat# 3174, RRID:AB_2162345)

Curator: @scibot

SciCrunch record: RRID:AB_2162345

What is this?

DOI: 10.1016/j.neuint.2026.106230

Resource: (Proteintech Cat# 12601-1-AP, RRID:AB_2278734)

Curator: @scibot

SciCrunch record: RRID:AB_2278734

What is this?

DOI: 10.1016/j.neuint.2026.106230

Resource: (Proteintech Cat# 14241-1-AP, RRID:AB_2107832)

Curator: @scibot

SciCrunch record: RRID:AB_2107832

What is this?

DOI: 10.1016/j.neuint.2026.106230

Resource: RRID:AB_10644161

Curator: @scibot

SciCrunch record: RRID:AB_10644161

What is this?

DOI: 10.1016/j.neuint.2026.106230

Resource: (Cell Signaling Technology Cat# 5664, RRID:AB_10705455)

Curator: @scibot

SciCrunch record: RRID:AB_10705455

What is this?

DOI: 10.1016/j.neuint.2026.106230

Resource: (Cell Signaling Technology Cat# 65915, RRID:AB_2936997)

Curator: @scibot

SciCrunch record: RRID:AB_2936997

What is this?

DOI: 10.1016/j.msea.2026.150834

Resource: Northwestern University Electron Probe Instrumentation Center EPIC Core Facility (RRID:SCR_026361)

Curator: @scibot

SciCrunch record: RRID:SCR_026361

What is this?

DOI: 10.1016/j.molpha.2026.100144

Resource: (Cell Signaling Technology Cat# 9271, RRID:AB_329825)

Curator: @scibot

SciCrunch record: RRID:AB_329825

What is this?

DOI: 10.1016/j.molpha.2026.100144

Resource: (Cell Signaling Technology Cat# 2024, RRID:AB_2116996)

Curator: @scibot

SciCrunch record: RRID:AB_2116996

What is this?

DOI: 10.1016/j.molpha.2026.100144

Resource: (Abcam Cat# ab16051, RRID:AB_443301)

Curator: @scibot

SciCrunch record: RRID:AB_443301

What is this?

DOI: 10.1016/j.molpha.2026.100144

Resource: (Cell Signaling Technology Cat# 3198, RRID:AB_2252325)

Curator: @scibot

SciCrunch record: RRID:AB_2252325

What is this?

DOI: 10.1016/j.molpha.2026.100144

Resource: (Proteintech Cat# 20540-1-AP, RRID:AB_10694569)

Curator: @scibot

SciCrunch record: RRID:AB_10694569

What is this?

DOI: 10.1016/j.molpha.2026.100144

Resource: (Cell Signaling Technology Cat# 3716, RRID:AB_2116962)

Curator: @scibot

SciCrunch record: RRID:AB_2116962

What is this?

DOI: 10.1016/j.molpha.2026.100144

Resource: (Abcam Cat# ab138492, RRID:AB_2861258)

Curator: @scibot

SciCrunch record: RRID:AB_2861258

What is this?

DOI: 10.1016/j.molpha.2026.100144

Resource: (Cell Signaling Technology Cat# 5339, RRID:AB_10626777)

Curator: @scibot

SciCrunch record: RRID:AB_10626777

What is this?

DOI: 10.1016/j.molpha.2026.100144

Resource: (Cell Signaling Technology Cat# 19245, RRID:AB_2734735)

Curator: @scibot

SciCrunch record: RRID:AB_2734735

What is this?

DOI: 10.1016/j.molpha.2026.100144

Resource: (Cell Signaling Technology Cat# 18338, RRID:AB_2798798)

Curator: @scibot

SciCrunch record: RRID:AB_2798798

What is this?

DOI: 10.1016/j.molpha.2026.100144

Resource: RRID:AB_3739910

Curator: @scibot

SciCrunch record: RRID:AB_3739910

What is this?

DOI: 10.1016/j.molpha.2026.100144

Resource: (Cell Signaling Technology Cat# 9272, RRID:AB_329827)

Curator: @scibot

SciCrunch record: RRID:AB_329827

What is this?

DOI: 10.1016/j.molpha.2026.100144

Resource: RRID:AB_1119454

Curator: @scibot

SciCrunch record: RRID:AB_1119454

What is this?

DOI: 10.1016/j.molpha.2026.100144

Resource: (Proteintech Cat# 20536-1-AP, RRID:AB_10700003)

Curator: @scibot

SciCrunch record: RRID:AB_10700003

What is this?

DOI: 10.1016/j.molpha.2026.100144

Resource: (ATCC Cat# CCL-153, RRID:CVCL_0298)

Curator: @scibot

SciCrunch record: RRID:CVCL_0298

What is this?

DOI: 10.1016/j.molcel.2026.07.007

Resource: Nanodrop 8000 Spectrophotometer (RRID:SCR_018600)

Curator: @scibot

SciCrunch record: RRID:SCR_018600

What is this?

DOI: 10.1016/j.molcel.2026.07.007

Resource: NCBI Genome (RRID:SCR_002474)

Curator: @scibot

SciCrunch record: RRID:SCR_002474

What is this?

DOI: 10.1016/j.molcel.2026.07.007

Resource: Cytiva AKTA pure 25 L chromatography system (RRID:SCR_023461)

Curator: @scibot

SciCrunch record: RRID:SCR_023461

What is this?

DOI: 10.1016/j.molcel.2026.07.007

Resource: Foldseek (RRID:SCR_027018)

Curator: @scibot

SciCrunch record: RRID:SCR_027018

What is this?

DOI: 10.1016/j.molcel.2026.07.007

Resource: HH-suite (RRID:SCR_016133)

Curator: @scibot

SciCrunch record: RRID:SCR_016133

What is this?

DOI: 10.1016/j.molcel.2026.07.007

Resource: BioRad ChemiDoc Touch Imaging System (RRID:SCR_021693)

Curator: @scibot

SciCrunch record: RRID:SCR_021693

What is this?

DOI: 10.1016/j.molcel.2026.07.007

Resource: BioTek Synergy H1 Hybrid Multi-Mode Microplate Reader (RRID:SCR_019748)

Curator: @scibot

SciCrunch record: RRID:SCR_019748

What is this?

DOI: 10.1016/j.molcel.2026.07.007

Resource: MAFFT (RRID:SCR_011811)

Curator: @scibot

SciCrunch record: RRID:SCR_011811

What is this?

DOI: 10.1016/j.molcel.2026.07.007

Resource: Thermo Scientific Orbitrap Exploris 480 Mass Spectrometer (RRID:SCR_027000)

Curator: @scibot

SciCrunch record: RRID:SCR_027000

What is this?

DOI: 10.1016/j.molcel.2026.07.007

Resource: Pfam (RRID:SCR_004726)

Curator: @scibot

SciCrunch record: RRID:SCR_004726

What is this?

DOI: 10.1016/j.molcel.2026.07.007

Resource: RRID:SCR_025584

Curator: @scibot

SciCrunch record: RRID:SCR_025584

What is this?

DOI: 10.1016/j.molcel.2026.07.007

Resource: MMseqs2 (RRID:SCR_022962)

Curator: @scibot

SciCrunch record: RRID:SCR_022962

What is this?

DOI: 10.1016/j.molcel.2026.07.007

Resource: BLASTP (RRID:SCR_001010)

Curator: @scibot

SciCrunch record: RRID:SCR_001010

What is this?

DOI: 10.1016/j.molcel.2026.07.007

Resource: RRID:SCR_028511

Curator: @scibot

SciCrunch record: RRID:SCR_028511

What is this?

DOI: 10.1016/j.molcel.2026.07.007

Resource: Bio-Rad TC20 Automated Cell Counter (RRID:SCR_025462)

Curator: @scibot

SciCrunch record: RRID:SCR_025462

What is this?

DOI: 10.1016/j.molcel.2026.07.007

Resource: Amersham Typhoon 5 (RRID:SCR_025702)

Curator: @scibot

SciCrunch record: RRID:SCR_025702

What is this?

DOI: 10.1016/j.molcel.2026.07.007

Resource: (Cell Signaling Technology Cat# 7074, RRID:AB_2099233)

Curator: @scibot

SciCrunch record: RRID:AB_2099233

What is this?

DOI: 10.1016/j.molcel.2026.07.007

Resource: RRID:SCR_021911

Curator: @scibot

SciCrunch record: RRID:SCR_021911

What is this?

DOI: 10.1016/j.molcel.2026.07.007

Resource: (Cell Signaling Technology Cat# 2532, RRID:AB_330331)

Curator: @scibot

SciCrunch record: RRID:AB_330331

What is this?

DOI: 10.1016/j.molcel.2026.07.007

Resource: (RRID:CVCL_0006)

Curator: @scibot

SciCrunch record: RRID:CVCL_0006

What is this?

DOI: 10.1016/j.molcel.2026.07.007

Resource: (Cell Signaling Technology Cat# 3676, RRID:AB_2219397)

Curator: @scibot

SciCrunch record: RRID:AB_2219397

What is this?

DOI: 10.1016/j.molcel.2026.07.007

Resource: (RRID:CVCL_0063)

Curator: @scibot

SciCrunch record: RRID:CVCL_0063

What is this?

DOI: 10.1016/j.molcel.2026.07.007

Resource: (Cell Signaling Technology Cat# 11818, RRID:AB_2687505)

Curator: @scibot

SciCrunch record: RRID:AB_2687505

What is this?

DOI: 10.1016/j.molcel.2026.07.007

Resource: (LI-COR Biosciences Cat# 926-32211, RRID:AB_621843)

Curator: @scibot

SciCrunch record: RRID:AB_621843

What is this?

DOI: 10.1016/j.molcel.2026.07.007

Resource: (LI-COR Biosciences Cat# 926-68070, RRID:AB_10956588)

Curator: @scibot

SciCrunch record: RRID:AB_10956588

What is this?

DOI: 10.1016/j.molcel.2026.07.007

Resource: (Sigma-Aldrich Cat# F1804, RRID:AB_262044)

Curator: @scibot

SciCrunch record: RRID:AB_262044

What is this?

DOI: 10.1016/j.molcel.2026.07.007

Resource: (Thermo Fisher Scientific Cat# 32430, RRID:AB_1185566)

Curator: @scibot

SciCrunch record: RRID:AB_1185566

What is this?

DOI: 10.1016/j.molcel.2026.07.007

Resource: (Sigma-Aldrich Cat# A8592, RRID:AB_439702)

Curator: @scibot

SciCrunch record: RRID:AB_439702

What is this?

DOI: 10.1016/j.molcel.2026.07.007

Resource: Chemdraw (RRID:SCR_016768)

Curator: @scibot

SciCrunch record: RRID:SCR_016768

What is this?

DOI: 10.1016/j.molcel.2026.07.007

Resource: Agilent MassHunter WorkStation - Qualitative Analysis for GC/MS (RRID:SCR_016657)

Curator: @scibot

SciCrunch record: RRID:SCR_016657

What is this?

DOI: 10.1016/j.molcel.2026.07.007

Resource: Thermo Xcalibur (RRID:SCR_014593)

Curator: @scibot

SciCrunch record: RRID:SCR_014593

What is this?

DOI: 10.1016/j.molcel.2026.07.007

Resource: (Santa Cruz Biotechnology Cat# sc-73614, RRID:AB_1131294)

Curator: @scibot

SciCrunch record: RRID:AB_1131294

What is this?

DOI: 10.1016/j.molcel.2026.07.007

Resource: (Cell Signaling Technology Cat# 4968, RRID:AB_2313904)

Curator: @scibot

SciCrunch record: RRID:AB_2313904

What is this?

DOI: 10.1016/j.molcel.2026.07.007

Resource: (Cell Signaling Technology Cat# 2708, RRID:AB_390722)

Curator: @scibot

SciCrunch record: RRID:AB_390722

What is this?

DOI: 10.1016/j.molcel.2026.07.007

Resource: (Cell Signaling Technology Cat# 5125, RRID:AB_1903890)

Curator: @scibot

SciCrunch record: RRID:AB_1903890

What is this?

DOI: 10.1016/j.molcel.2026.07.007

Resource: (Millipore Cat# MABE343, RRID:AB_2566826)

Curator: @scibot

SciCrunch record: RRID:AB_2566826

What is this?

DOI: 10.1016/j.molcel.2026.07.007

Resource: (Cell Signaling Technology Cat# 9204, RRID:AB_2265913)

Curator: @scibot

SciCrunch record: RRID:AB_2265913

What is this?

DOI: 10.1016/j.molcel.2026.07.007

Resource: (Cell Signaling Technology Cat# 9644, RRID:AB_2097841)

Curator: @scibot

SciCrunch record: RRID:AB_2097841

What is this?

DOI: 10.1016/j.molcel.2026.07.007

Resource: TopSpin (RRID:SCR_014227)

Curator: @scibot

SciCrunch record: RRID:SCR_014227

What is this?

DOI: 10.1016/j.molcel.2026.07.007

Resource: LabChart Reader software (RRID:SCR_017551)

Curator: @scibot

SciCrunch record: RRID:SCR_017551

What is this?

DOI: 10.1016/j.molcel.2026.07.007

Resource: HHpred (RRID:SCR_010276)

Curator: @scibot

SciCrunch record: RRID:SCR_010276

What is this?

DOI: 10.1016/j.molcel.2026.07.007

Resource: FastTree (RRID:SCR_015501)

Curator: @scibot

SciCrunch record: RRID:SCR_015501

What is this?

DOI: 10.1016/j.molcel.2026.07.007

Resource: Dali Server (RRID:SCR_013433)

Curator: @scibot

SciCrunch record: RRID:SCR_013433

What is this?

DOI: 10.1016/j.molcel.2026.07.007

Resource: Nikon Eclipse TS100-F Eclipse (RRID:SCR_020324)

Curator: @scibot

SciCrunch record: RRID:SCR_020324

What is this?

DOI: 10.1016/j.molcel.2026.07.007

Resource: Hmmer (RRID:SCR_005305)

Curator: @scibot

SciCrunch record: RRID:SCR_005305

What is this?