@vincent : Je te laisse revoir la rédaction de ta partie (cf. le mail que je t'ai envoyé le 19/08).

Cette partie va disparaître sitôt que nous serons certains que tous les projets ont bien été décrits dans cette partie. Attention ! N'effacez pas les sauts de ligne que j'ai ajoutés dans la description de vos projets : ils sont nécessaires pour une mise en page lisible.

@caovy : C'est ici qu'il faut décrire ton projet (et non dans la partie 'fouille'). Pourrais-tu reprendre la structure de description de chaque projet (fournie en début de document) et distribuer les éléments dans cette structure s'il te plaît ? Il me semble que le projet que tu présentes ici est celui qui devrait figurer sur le carnet distam aujourd'hui : c'est un travail fait (ou initié du moins). Donc soit il se poursuit en tant que tel, soit il sert de base à un travail plus large qui inclut ce que tu fais au Luxembourg, en tout état de cause, tu expliques comment ici. Merci !

@Estelle : J'ai mis en forme la présentation du projet (dont il faudra changer le numéro à la fin). Pourras-tu relire l'ensemble pour vérifier qu'il n'y a pas de coquille ?

@lia: J'ai mis en forme la présentation du projet (dont il faudra changer le numéro à la fin). Pourras-tu relire l'ensemble pour vérifier qu'il n'y a pas de coquille ?

@Cecile : J'ai mis en forme la présentation du projet (dont il faudra changer le numéro à la fin). Pourras-tu relire l'ensemble pour vérifier qu'il n'y a pas de coquille ?

@noemie : J'ai mis en forme la présentation du projet (dont il faudra changer le numéro à la fin). Pourras-tu relire l'ensemble pour vérifier qu'il n'y a pas de coquille ?

DOI: 10.1371/journal.pone.0323980

Resource: Bloomington Drosophila Stock Center (RRID:SCR_006457)

Curator: @maulamb

SciCrunch record: RRID:SCR_006457

What is this?

DOI: 10.1101/2025.07.31.667991

Resource: RRID:BDSC_24132

Curator: @maulamb

SciCrunch record: RRID:BDSC_24132

What is this?

DOI: 10.1101/2025.07.31.667991

Resource: RRID:BDSC_30590

Curator: @scibot

SciCrunch record: RRID:BDSC_30590

What is this?

DOI: 10.1158/1078-0432.CCR-24-2200

Resource: (Vector Laboratories Cat# BA-1000, RRID:AB_2313606)

Curator: @scibot

SciCrunch record: RRID:AB_2313606

What is this?

DOI: 10.1126/sciadv.adx6587

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Curator: @scibot

SciCrunch record: RRID:CVCL_0030

What is this?

DOI: 10.1101/2025.08.13.670051

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What is this?

DOI: 10.1101/2025.08.11.669712

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What is this?

DOI: 10.1016/j.immuni.2025.07.005

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What is this?

DOI: 10.1016/j.devcel.2025.07.016

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What is this?

DOI: 10.1016/j.celrep.2025.116157

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What is this?

DOI: 10.1016/j.cell.2025.07.036

Resource: RepeatMasker (RRID:SCR_012954)

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What is this?

DOI: 10.1016/j.cell.2025.07.036

Resource: EDTA (RRID:SCR_022063)

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What is this?

DOI: 10.1016/j.cell.2025.07.034

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What is this?

256 couleurs ? Et ça c'est peu ? Et comment accède-t-on à cette info ?

Il manque une précision ici, je trouve. Ici, on comprend qu'il faut afficher les logos seulement sur la page "À propos", mais dans la solution, les logos sont affichés sur les deux ("Accueil" + "À propos").

It's interesting to see how TV was once considered essential for national unity, but now many news stations tend to be more in support of different political parties. Which often leads to information being heavily censored, misleading information, and disunity.

Review coordinated by Life Science Editors Foundation Reviewed by: Dr. Angela Andersen, Life Science Editors Foundation Potential Conflicts of Interest: None

PUNCHLINE. This preprint identifies CCL2 as a central inflammatory driver of cachexia in autophagy-deficient mice, revealing a novel neuroinflammatory mechanism that suppresses appetite and leads to lethal wasting. In the absence of Atg7, systemic inflammation elevates CCL2, which targets and depletes hypothalamic neurons that promote feeding, specifically those expressing orexin and melanin-concentrating hormone (MCH). This neuronal loss triggers anorexia, metabolic failure, and death. Genetic deletion of Ccl2 or restoration of appetite via leptin deficiency fully rescues survival. However, anti-CCL2 antibodies fail to replicate this protection, highlighting the challenge of targeting cytokines in complex inflammatory states. The findings reframe cachexia as a disorder of brain-centered inflammation, with CCL2 as a key mediator.

BACKGROUND. Autophagy, the lysosomal degradation pathway essential for maintaining cellular homeostasis, is crucial during starvation and in the fed state. Systemic deletion of core autophagy genes like Atg5 or Atg7 in adult mice leads to rapid-onset weight loss, liver inflammation, and neurodegeneration—hallmarks of cachexia, a syndrome common in cancer, chronic kidney disease, and neurodegeneration. Interestingly, restoring autophagy in neurons alone can rescue neonatal lethality, suggesting that brain function is critical to systemic survival. This study addresses a fundamental question: how does autophagy protect against cachexia, and what are the mediators of its failure?

KEY QUESTION ADDRESSED. How does impaired autophagy in the adult organism lead to cachexia, and what is the role of brain inflammation and appetite regulation in this process?

SUMMARY. Mice with inducible, whole-body Atg7 deletion develop lethal cachexia characterized by hypophagia, tissue wasting, and systemic inflammation. Among several cytokines upregulated in these mice, only CCL2 (also known as MCP-1) is essential for driving disease. Genetic deletion of Ccl2 restores feeding, preserves body weight, and rescues survival. The underlying mechanism involves selective loss of hypothalamic neurons that express orexin and MCH—two neuropeptides critical for stimulating appetite and regulating energy balance. Single-nucleus RNA-seq suggests these neurons are absent in Atg7-deficient mice but preserved in Ccl2-deficient animals. Notably, CCL2 appears to originate from fibroblast-like stromal cells in the hypothalamus, highlighting a non-neuronal source of the neuroinflammatory insult. Pharmacologic blockade of CCL2 using a monoclonal antibody fails to rescue the phenotype, emphasizing the need for a deeper understanding of cytokine action in the CNS. Finally, deletion of leptin (ob/ob genotype) also rescues food intake and survival, underscoring anorexia as the critical effector of cachexia lethality in this model.

KEY RESULTS Whole-body Atg7 deletion causes cachexia and inflammation Figures 1A–J, Supp. Fig. S1 Mice exhibit rapid weight loss, muscle and fat depletion, liver inflammation, and elevated circulating cytokines, including CCL2.

CCL2 is essential for lethality in autophagy-deficient mice Figures 2A–B, Supp. Fig. S2 Among multiple cytokines tested (GDF15, CXCL10, CCL2), only Ccl2 deletion fully rescues survival and body composition.

CCL2 deletion restores hepatic function and fasting tolerance Figures 2C–I Ccl2−/−;Atg7Δ/Δ mice maintain glucose levels, survive fasting, and show normal gluconeogenesis, unlike their Atg7Δ/Δ counterparts.

CCL2 drives anorexia in autophagy-deficient mice Figures 3A–H Loss of Ccl2 restores food intake and total energy expenditure (TEE), confirming that CCL2 suppresses appetite.

CCL2 causes hypothalamic neuron loss Figures 4A–G, Supp. Fig. S3 Single-nucleus RNA-seq shows selective depletion of orexin and melanin-concentrating hormone (MCH) neurons—both central to appetite regulation—in Atg7Δ/Δ mice. This neuronal loss is fully reversed by Ccl2 deletion.

CCL2 is expressed in fibroblast cells in the hypothalamus, suggesting the potential source (Figure 4G).

Leptin deficiency restores survival despite persistent CCL2 elevation Figures 5A–I ob/ob;Atg7Δ/Δ mice survive, confirming that appetite suppression, not inflammation or weight loss per se, drives death.

Anti-CCL2 antibody fails to rescue cachexia Supp. Fig. S2G–J A neutralizing antibody against CCL2 (C1142) does not improve survival, highlighting differences between genetic and pharmacologic cytokine suppression.

STRENGTHS Uncovers a specific cytokine-neuron interaction driving lethal anorexia Demonstrates that preserving appetite alone is sufficient for survival Employs rigorous genetic models and multi-omic analysis Challenges the notion that cachexia is a purely metabolic disorder Provides a novel brain-centered mechanistic framework for systemic wasting

FUTURE WORK & EXPERIMENTAL DIRECTIONS Elucidate why CCL2 antibodies fail. Explore CCR2 signaling in the hypothalamus and its contribution to neuron loss Test whether this mechanism operates in more disease-relevant models: * • 5/6 nephrectomy (chronic kidney disease): features systemic inflammation and cachexia * • C26 or Lewis lung carcinoma (cancer cachexia): gold-standard models with upregulated CCL2 and appetite suppression * • SOD1-G93A (ALS model): mimics neuroinflammatory wasting * • Bleomycin-induced pulmonary fibrosis: induces systemic inflammation and muscle loss Use neuron-specific autophagy knockouts to dissect region-specific vulnerability Investigate sex differences and long-term behavioral recovery in Ccl2- or leptin-rescued mice

RELEVANCE TO RECENT LITERATURE This study complements recent work on inflammation-driven neurobehavioral deficits in cachexia: Zhu et al. (2025, Science) identified an IL-6–driven brainstem-to-basal ganglia circuit that suppresses motivation in cancer cachexia. In contrast, this preprint reveals CCL2-mediated destruction of hypothalamic appetite neurons, suggesting multiple inflammatory mediators act through distinct neuroanatomical routes to drive cachexia. Together, these findings support a growing model in which cachexia is driven by neuroimmune signaling.

AUTHORSHIP NOTE. This review was drafted with the assistance of ChatGPT (OpenAI) to organize and articulate key insights. Dr. Angela Andersen checked the final document.

FINAL TAKEAWAY. This preprint redefines cachexia as a neuroinflammatory syndrome, where CCL2—likely produced by fibroblasts in the hypothalamus – leads to the elimination of orexigenic neurons and lethal appetite suppression. By showing that rescuing food intake, rather than reversing metabolic derangements, is sufficient to prevent death, the authors shift our understanding of wasting from a metabolic imbalance to a brain-centered inflammatory pathology. These findings open new paths for preventing cachexia across diseases, but also highlight the complexity of translating cytokine-targeted therapies to the clinic.

The State of the Culture, 2024<br /> by [[Ted Gioia]]<br /> accessed on 2025-08-18T08:53:09

Author response:

The following is the authors’ response to the previous reviews

Reviewer #1 (Public review):

Summary:

The crystal structure of the Sld3CBD-Cdc45 complex presented by Li et al. is a significant contribution that enhances our understanding of CMG formation during the rate-limiting step of DNA replication initiation. This structure provides crucial insights into the intermediate steps of CMG formation, and the particle analysis and model predictions compellingly describe the mechanism of Cdc45 loading. Building upon previously known Sld3 and Cdc45 structures, this study offers new perspectives on how Cdc45 is recruited to MCM DH through the Sld3-Sld7 complex. The most notable finding is the structural rearrangement of Sld3CBD upon Cdc45 binding, particularly the α8-helix conformation, which is essential for Cdc45 interaction and may also be relevant to its metazoan counterpart, Treslin. Additionally, the conformational shift in the DHHA1 domain of Cdc45 suggests a potential mechanism for its binding to Mcm2NTD. Furthermore, Sld3's ssDNA-binding experiments provide evidence of its novel functions in the DNA replication process in yeast, expanding our understanding of its role beyond Cdc45 recruitment.

Strengths:

The manuscript is generally well-written, with a precise structural analysis and a solid methodological section that will significantly advance future studies in the field. The predictions based on structural alignments are intriguing and provide a new direction for exploring CMG formation, potentially shaping the future of DNA replication research. This research also opens up several new opportunities to utilize structural biology to unravel the molecular details of the model presented in the paper.

Weaknesses:

The main weakness of the manuscript lies in the lack of detailed structural validation for the proposed Sld3-Sld7-Cdc45 model, and its CMG bound models, which could be done in the future using advanced structural biology techniques such as single particle cryo-electron microscopy. It would also be interesting to explore how Sld7 interacts with the MCM helicase, and this would help to build a detailed long-flexible model of Sld3-Sld7-Cdc45 binding to MCM DH and to show where Sld7 will lie on the structure. This will help us to understand how Sld7 functions in the complex. Also, future experiments would be needed to understand the molecular details of how Sld3 and Sld7 release from CMG is associated with ssARS1 binding.

The proposals based on this study provide new knowledge of the CMG formation process. We agree that our Sld3-Sld7-Cdc45 model will be further confirmed by cryo-EM. We improved our ssARS1-binding assay and quantified data (See the response to Recommendations for the authors of #3 review).

Reviewer #2 (Public review):

Summary

The manuscript presents valuable findings, particularly in the crystal structure of the Sld3CBD-Cdc45 interaction and the identification of additional sequences involved in their binding. The modeling of the Sld7-Sld3CBD-CDC45 subcomplex is novel, and the results provide insights into potential conformational changes that occur upon interaction. Although the single-stranded DNA binding data from Sld3 of different species is a minor weakness, the experiments support a model in which the release of Sld3 from the complex may be promoted by its binding to origin single-stranded DNA exposed by the helicase.

Strengths

The Sld3CBD-Cdc45 structure is a novel contribution, revealing critical residues involved in the interaction.

The model structures generated from the crystal data are well presented and provide valuable insights into the interaction sequences between Sld3 and Cdc45.

The experiments testing the requirements for interaction sequences are thorough and conducted well, with clear figures supporting the conclusions.

The conformational changes observed in Sld3 and Cdc45 upon binding are interesting and enhance our understanding of the interaction.

The modeling of the Sld7-Sld3CBD-CDC45 subcomplex is a new and valuable addition to the field.

The proposed model of Sld3 release from the complex through binding to single stranded DNA at the origin is intriguing.

Weaknesses

The section on the binding of Sld3 complexes to origin single-stranded DNA is somewhat weakened by the use of Sld3 proteins from different species. The comparisons between Sld3-CBD, Sld3CBD-Cdc45, and Sld7-Sld3CBD-Cdc45 involve complexes from different species, limiting the comparisons' value.

Although the study reveals that Sld3 binds to different residues of Cdc45 than those previously shown to bind Mcm or GINS, the data in the paper do not shed any additional light on how GINS and Sld3 binding to Cdc45 or Mcms. would affect each other. Other previous research has suggested that the binding of GINS and Sld3 to Mcm or Cdc45 may be mutually exclusive. The authors acknowledge that a structural investigation of Sld3, Sld7, Cdc45, and MCM during the stage of GINS recruitment will be a significant goal for future research.

We agree that it is better to use all samples from a source; however, due to limitations in protein expression, we used Sld7-Sld3CBD-Cdc45 from a different source. The two sources used in this study belong to the same family, and the proteins Sld7, Sld3 and Cdc45 share sequence conservation with similar structures predicted by Alphafold3 (RMSD = 0.356, 1.392, and 0.891 for Ca atoms of Sld7CTD, Sld7NTD-Sld3NTD, and Sld3CBD-Cdc45). Such similarity in source and proteins allows us to do the comparison. We also mentioned that a cryo-EM study of Sld3-Sld7-Cdc45-MCM and Sld3-Sld7-CMG structures will be a significant goal for future research in our manuscript.

Reviewer #3 (Public review):

Summary:

The paper by Li et al. describes the crystal structure of a complex of Sld3-Cdc45-binding domain (CBD) with Cdc45 and a model of the dimer of an Sld3-binding protein, Sld7, with two Sld3-CBD-Cdc45 for the tethering. In addition, the authors showed the genetic analysis of the amino acid substitution of residues of Sld3 in the interface with Cdc45 and biochemical analysis of the protein interaction between Sld3 and Cdc45 as well as DNA binding activity of Sld3 to the single-strand DNAs of the ARS sequence.

Strengths:

The authors provided a nice model of an intermediate step in the assembly of an active Cdc45-MCM-GINS (CMG) double hexamers at the replication origin, which is mediated by the Sld3-Sld7 complex. The dimer of the Sld3-Sld7 complexes tethers two MCM hexamers together for the recruitment of GINS-Pol epsilon on the replication origin.

Weaknesses:

The biochemical analysis should be carefully evaluated with more quantitative ways to strengthen the authors' conclusion even in the revised version.

In this revision, we improved our ssARS1-binding assay in more quantitative ways (See the response to Recommendations for the authors).

Reviewer #1 (Recommendations for the authors):

I thank the authors for all their replies to my previous questions and for doing all the necessary corrections. I am satisfied with most of their replies, however, upon second reading I have a few more suggestions which could help to improve the manuscript further and make an impact in the field. My comments are listed below.

(1) In general, the manuscript is well structured, but I feel that it requires professional English correction. In many places it was difficult to understand the sentences and I had to read it several times to understand it. Also, very long sentences should be avoided. The flow should be easy to read and understand, and that is why I feel it requires professional English correction.

Following the comment, we checked English carefully and shortened the very long sentences.

(2) Page 5, line 103, please include molecule after the word complex to make it like- "Only one complex molecule exists within an asymmetric unit."

We revised this sentence (P5/L103).

(3) Line 113- more than the N-terminal half of the protruding long helix α7 113 was disordered in the Sld3CBD-Cdc45 complex. This sentence is not clear. What does it mean more than the N-terminal half? Please rewrite it.

We revised this sentence to give the corresponding residue number “(D219–H231)” (P5/L114).

(4) Page 5, result 2- Conformation changes in Sld3CBD and Cdc45 for binding each other, this section may require a little restructuring. Line 130-131- "Therefore, the helix α8CTP seems to be an intrinsically disordered segment when Sld3 alone but 130 folds into a helix coupled to the binding partner Cdc45 in the Sld3CBD-Cdc45 complex." This statement is the crux of the structural finding and therefore, I feel it should move after the first sentence.

Thank you for your comments. We rewrote this part (P5/L128-131).

(5) Line 121-122: Compared to the isolated form (PDBIDs: 5DGO 121 for huCdc45 [31] and 6CC2 for EhCdc45 [33]) and the CMG form (PDBID: 3JC6. Write it in the same format. Make 6CC2 in bracket like other PDB IDs. Restructure this sentence.

We revised this sentence (P5/122-123).

(6) Line 127-129: This sentence is also not very clear.

We revised this sentence together with above No (4). (P5/L128-131)

(7) In my question 4- "Can authors add a supplementary figure showing the probability of disordernes..."., I meant to use a disorder prediction tool like IUPred for the protein sequences and show that α8 is predicted to be a disordered upon sequence analysis. This will help to show the inherent property of α8 helix, and it could add up to the understanding that a disordered region is being structured in the complex structure.

The structures showed that α8CTP is stabilized by binding with Cdc45, but disordered in Sld3CBD alone, indicating that this part is flexible, like an intrinsically disordered segment. We have deposited the structure to PDB, so predictions like IUPred cannot show meaningful information.

(8) Question 9 regarding Supplementary Figure 8- Please include your statement in the figure legend - "WT Sld3CBD was prepared in a complex with Cdc45, while the mutants of Sld3CBD existed alone, we calculated the elements of secondary structure from the crystal structure of Sld3CBD-Cdc45. The concentration of samples was controlled to the same level for CD measurement."

Following the comment, we optimized the figure legend of Supplementary Figure 8.

(9) Question 13- I understand that negative staining and SEC-SAXS experiments could be very tricky for such protein complexes, which have very long loops and are flexible. Did authors try a GraFix cross-linking before doing the negative staining TEM? If it is not being tried, then it might be a good idea to try it and it may help to get much cleaner particles and easier class averaging. Although I completely understand the technical challenges the authors describe and I agree with them, I still feel that one good experiment that shows this dimer model would be very helpful to strengthen the claim. I am concerned because if people start using a similar DLS experiment to calculate intermolecular distances, citing your paper, in many cases it might be a wrong interpretation. In case the negative staining still does not work, at least discuss your technical challenges in the discussion section and mention that SEC-SAXS showed a similar length of the complex and show the Guinier plot and Porod plots in the supplementary data.

We believe that DLS is one of the methods for analyzing the single particle size. Of course, the confirmation by multiple methods will give compelling evidence. Following the comment, we added SEC-SAXS data in the [Results] (P7/L194-196) (Cdc45 recruitment to MCM DH by Sld3 with partner Sld7) and Supplementary Figure 11. The Sld7-Sld3-Cdc45 forms a flexible, long shape. Each binding domain is rigid but linked by the long loops. The flexibility problems are caused by the long loop linkers, but not by binding. So, we did not try to use the cross-linking method for analysis experiments.  

(10) Page 8, line 221- litter sequence specificity: Correct the word "litter" with little. Also, the word shaped is written as sharped at a few places in the manuscript. Please correct it.

We apologize for making such mistakes. We have modified these words.

(11) Page 9, line 237-238: Would it be possible to add a lane showing Sld7 binding to the ssDNA in figure 4. I recommend showing this to understand the ssDNA binding affinity of Sld7 by itself and it will also help us to compare when it is in complex with Sld3.

Considering that Sld7 on CMG is always a complex with Sld3, the ssDNA binding affinity should use the Sld3-Sld7 complex. Additionally, we attempted to overexpress Sld7, but could not obtain the target protein.

Reviewer #2 (Recommendations for the authors):

Thank you for the improved manuscript. The following sentence is unclear: "Cdc45 binds tighter to long ssDNA (>60 bases) with a litter sequence specificity".

We apologize for making such a mistake. We modified “litter” to “little”.

I found it challenging to understand which species were used while reading the results section and figure legends. I recommend that the authors revise the text in both the results and figure legends to clearly indicate when proteins from different species are being compared. Additionally, it would be valuable to explicitly acknowledge this limitation in the text.

Following the comment, we added a description for using different species in results (P8/L224-225) and figure legends (Supplementary Figure 14). We added more information in the Methods to explain why we used two species for preparing proteins.

Reviewer #3 (Recommendations for the authors):

Major points:

(1) The current title is not appropriate for the general readers. At least, DNA replication or DNA replication initiation should be added and abbreviations such as CBD should be avoided.

Following the comment, we added “DNA replication” into the title. Regarding “CBD”, since the full name of “Cdc45 binding domain” is too long, we continue to use Sld3CBD.

(2) As in my previous review, I asked for quantification of the EMSA assay shown in Figure 4 and Supplemental Figures 13 and 14. Since some signals of the bands are very weak, it is hard to conclude something. Given different protein concentrations used in the experiment, the authors should provide any kinds of value. For example, Sld3CBD-CDC45 shows weaker DNA binding than Sld3CBD alone (line 231). Is this true (or reproducible)? It is hard to conclude without any quantification.

We have repeated the EMSA assay four or more times with different rods of overexpression, purification and DNA synthesis, indicating that the EMSA assay is reproducible. In this revision, we changed the DNA stain and adjusted the ratio between the protein and ssDNA with increasing concentrations. The smeared bands of ssDNA with Sld7–Sld3ΔC–Cdc45 or Sld7–Sld3ΔC exhibit enhanced discernibility, and the ssDNA bands are intense enough for grayscale calculations (Figure 4 in the second revised version). We used a series of t-tests to confirm a significantly ssDNA residual level between Sld3CBD–Cdc45 to Sld3CBD, Sld7–Sld3ΔC–Cdc45, and Sld7–Sld3ΔCS (t-test, ****: P<0.0001). We also carefully controlled the sample amount in the EMAS assay and described it in the [Methods].

Moreover, in this EMSA assay (in Figure 4), the authors suggest that the disappearance of ssDNA bands corresponds with the binding of the protein to the DNA. However, it is also possible that the DNA is degraded. It is very important to show the band of protein-DNA complexes on the gel (a whole gel, not the parts of the gel shown in Figure). Why did the authors use this "insensitive" assay using SyberGreen, not radio-labelled ssDNA?

In this revision, we added a negative control of no ssDNA-binding by using ssARS1-3_3 for all protein samples (Sld3CBD, Sld3CBD–Cdc45, Sld7–Sld3ΔC–Cdc45 and Sld7–Sld3ΔC), which were the same rod of expression and purification for bound to ssARS1s (ssARS1-2 and ssARS1-5) (Figure 4), showing that the disappearance of ssDNA bands is caused by binding to proteins, not degradation. Moreover, this time, by changing the DNA stain and increasing the concentration of the samples, the smeared ssDNA bands exhibit enhanced discernibility in the high molecular weight regions when mixed with Sld7–Sld3ΔC–Cdc45 or Sld7–Sld3ΔC, whereas no bands appeared in the NC (ssARS1-3_1). The positions of smeared ssDNA bonds correspond to those of protein in the protein-stain pages, indicating that ssARS1 were complexed with proteins. Following the comment, we show all bands on the gel in Figure 4 and Supplementary Figure 14. Compared to Sld7–Sld3ΔC–Cdc45 or Sld7–Sld3ΔC, Sld3CBD and ssDNA bonds could not be observed because the pI value of Sld3CBD, which affects the entry of the samples into the gel.

We agree that using radio-labelled ssDNA can obtain a sensitive binding assay. However, current laboratory constraints did not allow us to use radio-labelled ssDNA. Furthermore, considering the characteristics of our target proteins, Sld3CBD, Sld3CBD–Cdc45, Sld7–Sld3ΔC–Cdc45, and Sld7–Sld3ΔC, we planned to perform the binding assay in a more natural state without any modifications, labelling or linkers. Additionally, we have attempted to use ITC experiments but failed in the measurements. Presumably, the conformational flexibility of Sld7-Sld3-Cdc45 and Sld7-Sld3 caused a thermodynamic anomaly.

Minor points:

(1) Line 215, 80b: This should be "80 nucleotides(nt)". Throughout the text, nucleotides is better than base to show the length of ssDNAs.

Thank you for your comments. We modified these words throughout the text.

Reviewer #2 (Public review):

Summary:

This study presents a valuable characterization of the effects of intracranial theta-burst stimulation of the basolateral amygdala on single units spiking activity in several areas in the human brain, associated with memory processing. It is written clearly and concisely, allowing readers to fully understand the analysis used.

The authors used a visual recognition memory task previously employed by their group to characterize the effects of basolateral amygdala stimulation upon memory consolidation (Inman et al, 2018). This current report presents an interesting analysis that complements the results reported in the 2018 paper.

Strengths:

Rare combination of human neurophysiology and behavior -<br /> The type of experiment performed in the manuscript, which contains both neurophysiological data, behavior, and a deep brain stimulation intervention (DBS), is incredibly rare, takes many years to accomplish with tight collaboration between clinical and research teams. Our understanding of spiking dynamics of human neurons is very limited, and this report is an important piece in the puzzle that allows DBS to be used in future interventions that will benefit patients' health.

Multiple brain areas included -<br /> It's important to note that the report analyzes brain areas with which the Amygdala has extensive connections (Fig. 1A) - Hippocampus, OFC, Amygdala, ACC. It seems that neurons in all these areas were modulated by the stimulation, except the ACC, in which firing rates were so low that only a handful of neurons were included in the analysis. This is an important demonstration that low-amplitude stimulation (even when reduced to 0.5mA) can travel far and wide across the human brain.

The experiment is cleverly designed to tease apart responses due to visual stimuli (image presentation) and electrical stimulation. Authors suggest that the units modulated by stimulation are largely distinct from those responsive to image offset during trials without stimulation. The subpopulation that responds strongly also tends to have a higher baseline firing rate. It's important to add that the chosen modulation index is more likely to be significant in neurons with higher firing rates (Figure S8). The authors discuss the tradeoff of using a nonparametric modulation index for vs. other methods (for example, percent change in trial-averaged firing rate from baseline).

Author response:

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

Reviewer #1 (Public review):

“This is an exploratory study that doesn't explore quite enough. Critically, the authors make a point of mentioning that neuronal firing properties vary across cell types, but only use baseline firing rate as a proxy metric for cell type. This leaves several important explorations on the table, not limited to the following:”

1a: “Do waveform shape features, which can also be informative of cell type, predict the effect of stimulation?”

To address this question, we modeled our approach to cell type classification after Peyrache et al. 2012. More specifically, we extracted two features from the mean unit waveforms—the valley-to-peak time (VP) and the peak half-width (PHW). These features were then used to classify units into two distinct clusters (k-means, clusters = 2, based on a strong prior from existing literature), representing putative excitatory and inhibitory neurons. Our approach recapitulated many of the same observations in Peyrache et al. 2012, namely (1) identification of two clusters (low PHW/VP: inhibitory, high PHW/VP: excitatory), (2) an ~80/20 ratio of excitatory/inhibitory neurons, and (3) greater baseline firing rates in the inhibitory vs. excitatory neurons. However, we did not observe a preferential modulation of one cell type compared to another (see newly created Figure 4). A description of this analysis and its takeaways has been incorporated into the manuscript.

Change to Text:

Created Figure 4 (Separation of presumed excitatory and inhibitory neurons by waveform morphology).

Caption: (A) Two metrics were calculated using the averaged waveforms for each detected unit: the valley-to-peak width (VP) and peak half-width (PHW). (B) Scatterplot of the relationship between VP and PHW; note that units with identical metrics are overlaid. Using k-means clustering, we identified two distinct response clusters, representing presumed excitatory (E, blue) and inhibitory (I, red) neurons. The units from which the example waveforms were taken are outlined in black. Probability distributions for each metric are shown along the axes. (C) Total number of units within each cluster, separated by region. (D) Comparison of baseline firing rates, separated by cluster. (E) Percent of modulated units in each cluster. * p < 0.05, NS = not significant.

Added a description of clustering methodology to lines 132-137: “We calculated two metrics from the averaged waveform from each detected unit: the valley-to-peak-width (VP) and the peak half-width (PHW) (Figure 4A); previously, these two properties of waveform morphology have been used to discriminate pyramidal cells (excitatory) from interneurons (inhibitory) in human intracranial recordings (Peyrache et al., 2012). Next, we performed k-means clustering (n = 2 clusters) on the waveform metrics, in line with previous approaches to cell type classification.

Added a section in the Results titled “Theta Burst Stimulation Modulates Excitatory and Inhibitory Neurons Equally”. Lines 370-378: “Using k-means clustering, we grouped neurons into two distinct clusters based on waveform morphology, representing neurons that were presumed to be excitatory (E) and inhibitory (I) (Figure 4B). Inhibitory (fast-spiking) neurons exhibited shorter waveform VP and PHW, compared with excitatory (regular-spiking) neurons (I cluster centroid: VP = 0.50ms, PHW = 0.51ms; E cluster centroid: VP = 0.32ms, PHW = 0.31ms), and greater baseline firing rates (U(N_I = 23, N<_E = 133) = 1074.50, p = 0.023) (Figure 4D). Although we observed a much greater proportion of excitatory vs. inhibitory neurons (E: 85.3%, I: 14.7%), stimulation appeared to affect excitatory and inhibitory neurons equally, suggesting that one cell type is not preferentially activated over another (Figure 4E).

Modified discussion of the effects of stimulation on different cell types. Lines 475-483: “…To test these hypotheses directly, we clustered neurons into presumed excitatory and inhibitory neurons based on waveform morphology. In doing so, we observed ~85% excitatory and ~15% inhibitory neurons, which is very similar what has been reported previously in human intracranial recordings (Cowan et al. 2024, Peyrache et al., 2012). Interestingly, stimulation appeared to modulate approximately the same proportion of neurons for each cell type (~30%), despite the differently-sized groups. Recent reports, however, have suggested that the extent to which electrical fields entrain neuronal spiking, particularly with respect to phase-locking, may be specific to distinct classes of cells (Lee et al., 2024).”

1b:  “Is the autocorrelation of spike timing, which can be informative about temporal dynamics, altered by stimulation? This is especially interesting if theta-burst stimulation either entrains theta-rhythmic spiking or is more modulatory of endogenously theta-modulated units.”

The reviewer is correct in suggesting that rate-modulation represents only one of many possible ways by which exogenous theta burst stimulation may influence neuronal activity. Indeed, intracranial theta burst stimulation has previously been shown to evoke theta-frequency oscillatory responses in local field potentials (Solomon et al. 2021), and other forms of stimulation (i.e., transcranial alternating current stimulation) may modulate the rhythm, rather than the rate, of neuronal spiking (Krause et al. 2019).

To investigate whether stimulation altered rhythmicity in neuronal firing, we contrasted the spike timing autocorrelograms, as suggested. More specifically, we computed the pairwise differences in spike timing for each trial, separating spikes into the same pre-, during-, and post-stimulation epochs described in the manuscript (bin size = 5 ms, max lag = 250 ms), grouped neurons by whether they were modulated, and then contrasted the differences in the latencies of the peak normalized autocorrelation value between epochs. Only neurons with a firing rate of ≥ 1 Hz (n = 70/203, 34.5%) were included in this analysis since sparse firing resulted in noisy autocorrelation estimates. Subsequent statistical testing of the peak latency differences between pre-/during- and pre-/post-stimulation did not reveal any group-level differences (Mann-Whitney U tests, p > 0.05). Thus, we were not able to identify neuronal responses suggestive of altered rhythmicity (see Figure S5). A description of this analysis and its takeaways has been incorporated into the manuscript.

Of note, there are two elements of the data that constrain our ability to detect modulation in the rhythm of firing. First, the baseline activity recorded across neurons modulated by stimulation was relatively low (i.e., median firing rate = 1.77 Hz). Second, stimulation often resulted in a suppression, rather than an enhancement, of firing rate. Taken together, the sparse firing afforded limited opportunity to characterize changes to subtle patterns of spiking. 

Change to Text:

Created Figure S5 (Analysis of modulation in spiking rhythmicity)

Caption: (A) Representative autocorrelograms ACG) for a single neuron. The pairwise differences in spike timing were computed for each trial and epoch (bin size = 5 ms, max lag = 250 ms), then smoothed with a Gaussian kernel. The peak in the normalized ACG across trials was computed for each epoch. (B) Kernel density estimate of the peak ACG lag, separated by epoch. (C) The peak ACG lags were split by whether the neuron was modulated (Mod) or unaffected by stimulation (NS = not significant) for each of the two contrasts: pre- vs. during-stim (left) and pre- vs. post-stim (right).

Details about the autocorrelation methodology have been incorporated. Lines 166-172: “To investigate whether stimulation altered rhythmicity in neuronal firing, we analyzed the spike timing autocorrelograms. More specifically, we computed the pairwise differences in spike timing for each trial (bin size = 5 ms, max lag = 250 ms) and then contrasted the differences in the latencies of the peak normalized autocorrelation value between epochs (pre-, during-, post-stimulation). Only neurons with a firing rate of ≥ 1 Hz (n = 70/203, 34.5%) were included in this analysis since sparse firing resulted in noisy autocorrelation estimates.

The results from contrasting the autocorrelograms are now mentioned briefly. Lines 297-298: “Stimulation, however, did not appear to alter the rhythmicity in neuronal firing, as measured by spiking autocorrelograms (Figure S5).”

1c: “The authors reference the relevance of spike-field synchrony (30-55 Hz) in animal work, but ignore it here. Does spike-field synchrony (comparing the image presentation to post-stimulation) change in this frequency range? This does not seem beyond the scope of investigation here.”

We agree that a further characterization of spike-field and spike-phase relationships may provide rich insights into more complex regional and interregional dynamics that may be altered by stimulation. Given that many metrics are biased by sample size (e.g., number of spikes), which can vary considerably, computing the pairwise phase consistency (PPC) between spikes and LFP is a preferred metric (Vinck et al. 2010). Although PPC is unbiased, its variance nonetheless increases considerably with low spike counts; pooling spike counts across trials, however, decouples the temporal relationship between spiking and the LFP phase for each trial, confounding results and yielding an unstable estimate.

To determine whether such an analysis is indeed possible, we calculated the percentage of stimulation trials with ≥ 10 spikes in both the 1s pre- and post-stimulation epochs (a relatively low threshold for inclusion). Only a very small proportion of the total number of trials across all neurons met this criterion (2.5%). Thus, because of the sparse spiking in our data, we are unable to reliably characterize spike-field or spike-phase modulation in detected neurons.

Change to Text:

In the manuscript, we have added a description of why our data is not well-suited to investigate these relationships.

Lines 532-538: “The present study did not investigate interactions between spiking activity and local field potentials because neuronal spiking was sparse at baseline and often further suppressed by stimulation; only a very small proportion of the total number of trials across all neurons exhibited ≥ 10 spikes in both the 1s pre- and post-stimulation epochs (~2.5%). Although certain metrics are not biased by sample size (e.g., pairwise phase consistency), low spike counts can dramatically affect variance and, therefore, result in unstable estimates (Vinck et al., 2011).

1d: “How does multi-unit activity respond to stimulation? At this somewhat low count of neurons (total n=156 included) it would be valuable to provide input on multi-unit responses to stimulation as well.”

We thank the reviewer for this suggestion. We have incorporated an analysis of multiunit activity (MUA), which similarly identifies robust modulation via permutation-based statistical testing and characterizes the different profiles of responses (i.e., increased vs. decreased MUA threshold crossings pre- vs. post-stimulation).

Change to Text:

Created Figure S8 (Analysis of multiunit activity response to stimulation)

Caption: (A) Example trace of multiunit activity (MUA) in one channel during a single stimulation trial. Threshold crossings are highlighted with a pink dot overlaid on the MUA signal with a corresponding hash below. (B) The percentage of channels with significantly modulated MUA, separated by the direction of effect. (C) The percentage of channels with significantly modulated MUA, separated by direction effect and region. Inc (red; post > pre) vs. Dec (blue; post < pre). HIP = hippocampus, OFC = orbitofrontal cortex, AMY = amygdala, ACC = anterior cingulate cortex. *** p < 0.001, NS = not significant.

Details about the MUA methodology have been incorporated. Lines 174-180: “Finally, we measured modulation in multiunit activity (MUA) by filtering the microleectrode signals in a 300-3,000 Hz window and counting the number of threshold crossings. Thresholds were determined on a per-channel basis and defined as -3.5 times the root mean square of the signal during the baseline period; activity during stimulation was excluded since stimulation artifact is difficult to separate from MUA in the absence of spike sorting.

MUA results are now incorporated. Lines 365-367: “Additional characterization of MUA revealed a dominant signature of increased activity post- vs. pre-stimulation, in line with these trends observed at the single-neuron level (Figure S8).”

1e: “Several intracranial studies have implicated proximity to white matter in determining the effects of stimulation on LFPs; do the authors see an effect of white matter proximity here?”

We thank the reviewer for the interesting question. Subsequent characterization revealed only small differences in the proximity of stimulation contacts to white matter (range 1.5-8.0 mm), likely because the chosen target (i.e., basolateral amygdala) has several nearby white matter structures (e.g., stria terminalis). Nonetheless, we performed a linear regression between the proximity to white matter and the stimulation-induced effect on behavior (stimulation vs. no-stimulation d’ difference), the results of which indicate no clear association (p > 0.05; see Figure S9). Critically, this is not to suggest that white matter proximity has no interaction with the reported behavioral effects, but rather, that we could not identify such an association within our data.

Change to Text:

Created Figure S9 (The effect of stimulation proximity to white matter and distance to recorded neurons).

Caption: (A) Kernel density estimate of the Euclidean distance from stimulation contacts to nearest WM structure (in mm); hash marks represent individual observations. (B) The change in memory performance (Δd’) was linearly regressed onto the distance from the stimulated contacts to white matter.

The following has been added to lines 405-426: “Proximity to white matter has been shown to influence the effects of stimulation on behavior and the strength of evoked responses (Mankin et al., 2021; Mohan et al., 2020; Paulk et al., 2022). Across all stimulated contacts, we observed only small differences in the proximity of stimulation contacts to white matter (median = 4.5 mm, range = 1.5-8.0 mm), likely because the chosen target (i.e., basolateral amygdala) has several nearby white matter structures (e.g., stria terminalis). Nonetheless, we performed a linear regression between the proximity to white matter and the stimulation-induced effect on behavior (stimulation vs. no-stimulation d’ difference), the results of which indicate no clear association (p > 0.05; see Figure S9).

Comment 2: “It is a little confusing to interpret stimulation-induced modulation of neuronal spiking in the absence of stimulation-induced change in behavior. How do the authors findings tell us anything about the neural mechanisms of stimulation-modulated memory if memory isn't altered? In line with point #1, I would suggest a deeper dive into behavior (e.g. reaction time? Or focus on individual sessions that do change in Figure 4A?) to make a stronger statement connecting the neural results to behavioral relevance.”

We agree that the connection between the observed stimulation-induced neuronal modulation and effects on behavior is unclear and has proven challenging to elucidate. Per the reviewer’s suggestion, we further focused our analyses on the neuronal modulation effects in the individual sessions that resulted in a robust change in memory performance (stimulation vs. no-stimulation d’ difference threshold of ± 0.5, based on a moderate effect size for Cohen’s d); both a positive and negative threshold were used to capture robust changes in memory performance associated with firing rate modulation, whether enhancement or suppression. To this end, we contrasted the proportion of modulated neurons in the sessions where stimulation resulted in a robust behavioral change (Δd’) with those that did not (~d’). We did not observe a difference in the proportions between groups when collapsed across all sampled regions, or when separately evaluated (Fisher’s exact tests, p > 0.05; see Figure 5C).

Given that this approach did not further clarify the connection between our neural and behavioral results, we believe it is most appropriate to deemphasize claims in the manuscript regarding the potential insights for behavioral modulation (e.g., memory enhancement), and have done so.

Change to Text:

Toned down reference to the memory-related effects of stimulation in the abstract by removing the following lines from the abstract: “Previously, we demonstrated that intracranial theta burst stimulation (TBS) of the basolateral amygdala (BLA) can enhance declarative memory, likely by modulating hippocampal-dependent memory consolidation…” and “…and motivate future neuromodulatory therapies that aim to recapitulate specific patterns of activity implicated in cognition and memory.”

Changed Figure 4 to Figure 5

Created Figure 5C (Interaction between behavioral effects and neuronal modulation)(C)  Change in recognition memory performance was split into two categories using a d’ difference threshold of ± 0.5: responder (positive or negative; Δd’, pink) and non-responder (~d’, grey). Individual d’ scores are shown (left) with points colored by outcome category; dotted lines demarcate category boundaries, and the grey-shaded region represents negligible change. The number of sessions within each outcome category (middle) and the proportion of modulated units as a function of outcome category, separated by region (right). NS = not significant.

The description of the behavioral results has been updated. Lines 394-403: “At the level of individual sessions, we observed enhanced memory (Δd’ > +0.5) in 36.7%, impaired memory (Δd’ < -0.5) in 20.0%, and negligible change (-0.5 ≤ Δd’ ≤ 0.5) in 43.3% when comparing performance between the stim and no-stim conditions; a threshold of Δd’ ± 0.5 was chosen for this classification based on the defined range of a “medium effect” for Cohen’s d. To test our hypothesis that neuronal modulation would be associated with changes in memory performance, we combined the sessions that resulted in either memory enhancement or impairment and contrasted the proportion of modulated units across regions sampled. We did not, however, observe a meaningful difference in the proportion of modulated units when grouped by behavioral outcome (all contrasts p > 0.05) (Figure 5C).

Lines 213-214 and 394-397 have been edited to reflect a change in the d’ threshold used for categorizing behavioral results (from Δd’ ± 0.2 to Δd’ ± 0.5).

Comment 3: “It is not clear to me why the assessment of firing rates after image onset and after stim offset is limited to one second - this choice should be more theoretically justified, particularly for regions that spike as sparsely as these.”

We thank the reviewer for this question and acknowledge that no clear justification was provided for this decision in the manuscript. Our decision to limit each of the analysis epochs to 1s was chosen for two reasons. First, the maximum possible length of the during-stimulation epoch was 1 s (stim on for 1 s). Although the pre- and post-stimulation epochs could be extended without issue, we were concerned that variable time windows could introduce a bias, for instance, resulting in different variances between epochs. Second, we anticipated, both from empirical observations and prior literature, that the neural response following stimulation or task features (e.g., image onset/offset) was likely to be transient, rather than sustained for a period of many seconds. By keeping the windows short, we ensured that our approach to detecting modulation (i.e., contrasting trial-wise spike counts between each pair of epochs) captured the intended effect rather than random noise. We have incorporated a discussion of this rationale in the Peri-Stimulation Modulation Analyses section.

Change to Text:

Lines 156-158 have been added: “Each epoch was constrained to 1 s to ensure that subsequent firing rate contrasts were unbiased and to capture potential transient effects (e.g., image onset/offset).”

Comment 4: “This work coincides with another example of human intracranial stimulation investigating the effect on firing rates (doi: https://doi.org/10.1101/2024.11.28.625915). Given how incredibly rare this type of work is, I think the authors should discuss how their work converges with this work (or doesn't).”

Thank you for bringing this highly relevant work to our attention. We were unaware of this recent preprint and have incorporated a discussion of its main findings into the manuscript.

Change to Text:

New citations: van der Plas et al. 2024 (bioRxiv), Cowan et al. 2024 (bioRxiv)

The discussion of related studies has been updated. Lines 447-457: “Few studies, however, have characterized the impact of electrical stimulation via macroelectrodes on the spiking activity of human cortical neurons, none of which involve intracranial theta burst stimulation. One study reported a long-lasting reduction in neural excitability among parietal neurons, with variable onset time and recovery following continuous transcranial TBS in non-human primates (Romero et al., 2022). In a similar vein, it was recently shown that human neurons are largely suppressed by single-pulse electrical stimulation (Cowan et al., 2024; Plas et al., 2024). Other emerging evidence suggests that transcranial direct current stimulation may entrain the rhythm rather than rate of neuronal spiking (Krause et al., 2019) and that stimulation-evoked modulation of spiking may meaningfully impact behavioral performance on cognitive tasks (Fehring et al., 2024).”

Comment 5: “What information does the pseudo-population analysis add? It's not totally clear to me.”

We recognize the need to further contextualize the motivation for the exploratory pseudo-population analysis and appreciate the reviewer for bringing the lack of detail to our attention. In brief, the analysis allowed us to observe trends in activity across populations of neurons, which, in principle, are not visible by characterizing modulation solely in discrete neurons. Additional details have been incorporated into the manuscript, as suggested.

Change to Text:

Additional justification has been incorporated in the description of the methodology. Lines 185-187: “…This approach enables the identification of dominant patterns of coordinated neural activity that may not be apparent when examining individual neurons in isolation.”, lines 192-194: “…By collapsing across subjects into a common pseudo-population, this analysis provides a mesoscale view of how stimulation modulates shared activity patterns across anatomically distributed neural populations.”

A summary interpretation has been added to the paragraph describing the results. Lines 326-328: “Taken together, these analyses reveal global structure in the state space of responses to BLA stimulation within hippocampal circuits.”

Reviewer #2 (Public review):

Comment 1 “Authors suggest that the units modulated by stimulation are largely distinct from those responsive to image offset during trials without stimulation. The subpopulation that responds strongly also tends to have a higher baseline of firing rate. It's important to add that the chosen modulation index is more likely to be significant in neurons with higher firing rates.”

This is an important point that was not previously addressed in our manuscript. We suspect there are likely two factors at play worth considering with respect to our chosen nonparametric modulation index: neurons with lower activity require smaller changes in spike counts to be significantly modulated (easier to flip ranks), and neurons with higher activity empirically exhibit greater absolute shifts in the number of spikes. Our further use of permutation testing, while mitigating false positives, may also somewhat constrain the ability to detect modulation in sparsely active neurons. Nonetheless, given that many trials entailed few or no spikes, we believe this approach is preferable to alternatives that may be more susceptible to noise (e.g., percent change in trial-averaged firing rate from baseline).

To better understand the tradeoffs with detection probability, we performed a sensitivity analysis. We generated synthetic data with different baseline firing rates (0.1-5.0 Hz) and effect sizes (± 0.1-0.7 Hz) and simulated the likelihood of detection with our given modulation index across neurons. The results of the simulation support the notion that the probability of detecting modulation is lower for sparsely active neurons (Figure S8C). Further discussion of this consideration for the chosen modulation index, as well as details regarding the sensitivity analysis, have been incorporated into the manuscript.

Change to Text:

Created Figure S7C (Detection probability analysis)

Caption: The same permutation-based analyses reported in the manuscript were repeated under different control conditions… (C) Visualization of the predicted probability of detecting modulation across synthetic neurons with variable firing rates and modulation effect sizes; FR = firing rate.

Lines 223-224 have been added to the Methods section titled “Firing Rate Control Analyses”: “We performed a series of control analyses to test whether our approach to firing rate detection was robust…”

A description of the simulation has been incorporated into the same section as above. Lines 234-237: “Finally, to better understand the tradeoffs with our statistical approach, we generated synthetic data with different baseline firing rates (0.1-5.0 Hz) and effect sizes (± 0.1-0.7 Hz), then simulated the likelihood of detecting modulation across variable conditions (Figure S7C).”

The description of the results from the control analyses has been updated. Lines 330-339: “Finally, we performed three supplementary analyses to evaluate the robustness of our approach to detecting firing rate modulation: a sensitivity analysis assessing the proportion of modulated units at different firing rate thresholds for inclusion/exclusion, a data dropout analysis designed to control for the possibility that non-physiological stimulation artifacts may preclude the detection of temporally adjacent spiking, and a synthetic detection probability analysis. These results recapitulate our observation that units with higher baseline firing are most likely to exhibit modulation (though the probability of detecting modulation is lower for sparsely active neurons) and suggest that suppression in firing rate is not solely attributable to amplifier saturation following stimulation (Figure S7).

Comment 2: “Readers can benefit from understanding with more details the locations chosen for stimulation - in light of previous studies that found differences between effects based on proximity to white matter (For example - PMID 32446925, Mohan et al, Brain Stimul. 2020 and PMID 33279717 Mankin et al Brain Stimul. 2021).”

This has been addressed in the above response to Reviewer’s 1 comment 1.1e.

Change to Text:

See changes related to Reviewer 1 comment 1.1e.

Comment 3: “Missing information in the manuscript…”

3a: “Images of stimulation anatomical locations for all subjects included in this study. Ideally information about the impedance of the contacts to be able to calculate the actual current used.”

As requested, we have provided an image from the coronal T1 MRI sequence, which highlights the position of the stimulated contacts for each of the 16 patients. Though we did not measure the impedances directly, the stimulation was current-controlled, which ensured that the desired current and charge density were consistent regardless of the tissue or electrode impedance.

Change to Text:

Created Figure S1 (Anatomical location of stimulated electrodes).

Caption: A coronal slice from the T1-weighted MRI scan is shown for each patient who participated in the study (n = 16). Electrode contacts within the same plane of the image are shown with blue circles, and the bipolar pair of stimulated contacts within the basolateral amygdala is highlighted in red.

Lines 144-145 have been edited to reflect that the delivered stimulation was current-controlled: “Specifically, we administered current-controlled, charge-balanced, …”

3b: “The studied population is epilepsy patients, and the manuscript lacks description of their condition, proximity to electrodes included in the study to pathological areas, and the number of units from each patient/hemisphere.”

We agree that additional information regarding patient demographics, experimental details, and clinical characteristics would further contextualize this unique patient population. A new table has been included, which contains the following information: patient ID, sex, age, # experimental session, # SEEG leads (and # microelectrodes), # detected units (L vs. R hemisphere), and suspected seizure onset zone.

Change to Text:

Created Table S1 (Patient demographics and clinical characteristics).

Lines 258-259 have been added: “…(see Table S1 for patient demographics).”

3c: “I haven't seen any comments on code availability (calculating modulation indices and statistics) and data sharing.”

For clarification, a section titled Resource Availability is already appended to the end of the manuscript following the Conclusion, which describes the data and code availability.

Change to Text:

None

3d: “Small comment - Figure legend 3E - Define gray markers (non-modulated units?)”

Thank you for highlighting this omission. We have updated the relevant figure caption.

Change to Text:

The following has been added to the Figure 3 caption: “…whereas units without a significant change in activity are shown in grey.”

Reviewer #2 (Public review):

Summary:

Soham Mukhopadhyay et al. investigated the protein folding of the secretome from gall-forming microbes using the AI-based structure-modeling tool AlphaFold2. Their study analyzed six gall-forming species, including two Plasmodiophorid species and four others spanning different kingdoms, along with one non-gall-forming Plasmodiophorid species, Polymyxa betae. The authors found no effector fold specifically conserved among gall-forming pathogens, leading to the conclusion that their virulence strategies are likely achieved through diverse mechanisms. However, they identified an expansion of the Ankyrin repeat family in two gall-forming Plasmodiophorid species, with a less pronounced presence in the non-gall-forming Polymyxa betae. Additionally, the study revealed that known effectors such as CCG and AvrSen1 belong to sequence-unrelated but structurally similar (SUSS) effector clusters.

Strengths:

(1) The bioinformatics analyses presented in this study are robust, and the AlphaFold2-derived resources deposited in Zenodo provide valuable resources for researchers studying plant-microbe interactions. The manuscript is also logically organized and easy to follow.

(2) The inclusion of the non-gall-forming Polymyxa betae strengthens the conclusion that no effector fold is specifically conserved in gall-forming pathogens and highlights the specific expansion of the Ankyrin repeat family in gall-forming Plasmodiophorids.

(3) Figure 4a and 4b effectively illustrate the SUSS effector clusters, providing a clear visual representation of this finding.

(4) Figure 1 is a well-designed, comprehensive summary of the number and functional annotations of putative secretomes in gall-forming pathogens. Notably, it reveals that more than half of the analyzed effectors lack known protein domains in some pathogens, yet some were annotated based on their predicted structures, despite the absence of domain annotations.

Weaknesses:

(1) The effector families discussed in this paper remain hypothetical in terms of their functional roles, which is understandable given the challenges of demonstrating their functions experimentally. However, this highlights the need for experimental validation as a next step.

Authors' response: Thank you. Yes, there is a lot of work to do in the coming years.

Reviewer's response: Incorporating experimental validation substantially strengthened the manuscript. Did you try the AlphaFold-Multimer prediction of the interaction between PBTT_00818 and the GroES-like protein? Does the model indicate a high-confidence interface?

(2) Some analyses, such as those in Figure 4e, emphasize motifs derived from sequence alignments of SUSS effector clusters. Since these effectors are sequence-unrelated, sequence alignments might be unreliable. It would be more rigorous to perform structure-based alignments in addition to sequence-based ones for motif confirmation. For instance, methods described in Figure 3E of de Guillen et al. (2015, https://doi.org/10.1371/journal.ppat.1005228) or tools like Foldseek could be useful for aligning structures of multiple sequences.

Authors' response: In Fig. 4e, we highlight the conserved cysteine residues. While there is no clearly conserved overall motif, the figure illustrates that despite the high sequence divergence, the key cysteines involved in disulfide-bridge formation are consistently conserved across the sequences.

Reviewer's response: Understood. Nevertheless, if a reliable sequence alignment can indeed be generated, I would interpret this to mean that the CCG effectors constitute a highly diversified family rather than being truly sequence unrelated. By comparison, members of the MAX effector family share a common fold, yet their sequences are so divergent that sequence alignment is impossible.

(3) When presenting AlphaFold-generated structures, it is essential to include confidence scores such as pLDDT and PAE. For example, in Figure 1D of Derbyshire and Raffaele (2023, https://doi.org/10.1038/s41467-023-40949-9), the structural representations were colored red due to their high pLDDT scores, emphasizing their reliability.

Authors' response: Thank you for the observation. Due to the restrictive parameters used in our analysis, over 90 % of the structure would appear red. For this reason, we chose not to include the color scale, as it would not provide additional informative value in this context.

Reviewer's response: Understood.

Author response:

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

Reviewer #1 (Public review):

Summary:

This manuscript presents a comprehensive structure-guided secretome analysis of gall-forming microbes, providing valuable insights into effector diversity and evolution. The authors have employed AlphaFold2 to predict the 3D structures of the secretome from selected pathogens and conducted a thorough comparative analysis to elucidate commonalities and unique features of effectors among these phytopathogens.

Strengths:

The discovery of conserved motifs such as 'CCG' and 'RAYH' and their central role in maintaining the overall fold is an insightful finding. Additionally, the discovery of a nucleoside hydrolase-like fold conserved among various gall-forming microbes is interesting.

Weaknesses:

Important conclusions are not verified by experiments.

Thank you very much. There are many aspects of this study that could be further validated, each potentially requiring years of work. Therefore, we chose to focus on two specific hypotheses: are AlphaFol-Multimer predictions accurate? Can ANK target more than one host protein? Particularly, we focused on the identification of putative targets for one of the ankyrin repeat proteins, PBTT_00818 (Fig. 6). Using one-by-one yeast two-hybrid (Y2H) assays, we tested the AlphaFold-Multimer prediction of an interaction between PBTT_00818 and MPK3. The interaction did not occur in yeast, suggesting it might not take place under those conditions.

This negative result led us to perform a Y2H screen using an Arabidopsis cDNA library, which identified a GroES-like protein, highly expressed in roots, as a potential target of the ANK effector. Surprisingly, both the PBTT_00818–MPK3 and PBTT_00818–GroES-like protein interactions were later confirmed in planta using BiFC assays. These findings suggest two key points: (1) AlphaFold predictions can be accurate for ANK proteins, and (2) ANK domains, known for mediating protein-protein interactions, may enable these effectors to target multiple host proteins.

Although the precise biological implications remain unclear, it is possible that ANK proteins act as scaffolds or adaptors for other effectors during infection. The validations presented here open exciting avenues for further research into the role of ANK proteins in Plasmodiophorid pathogenesis and gall formation. This is presented in the corrected preprint and Fig. 7, Table S12, Fig. S7-S8.

Reviewer #2 (Public review):

Summary:

Soham Mukhopadhyay et al. investigated the protein folding of the secretome from gall-forming microbes using the AI-based structure modeling tool AlphaFold2. Their study analyzed six gall-forming species, including two Plasmodiophorid species and four others spanning different kingdoms, along with one non-gall-forming Plasmodiophorid species, Polymyxa betae. The authors found no effector fold specifically conserved among gall-forming pathogens, leading to the conclusion that their virulence strategies are likely achieved through diverse mechanisms. However, they identified an expansion of the Ankyrin repeat family in two gall-forming Plasmodiophorid species, with a less pronounced presence in the non-gall-forming Polymyxa betae. Additionally, the study revealed that known effectors such as CCG and AvrSen1 belong to sequence-unrelated but structurally similar (SUSS) effector clusters.

Strengths:

(1) The bioinformatics analyses presented in this study are robust, and the AlphaFold2-derived resources deposited in Zenodo provide valuable resources for researchers studying plant-microbe interactions. The manuscript is also logically organized and easy to follow.

(2) The inclusion of the non-gall-forming Polymyxa betae strengthens the conclusion that no effector fold is specifically conserved in gall-forming pathogens and highlights the specific expansion of the Ankyrin repeat family in gall-forming Plasmodiophorids.

(3) Figure 4a and 4b effectively illustrate the SUSS effector clusters, providing a clear visual representation of this finding.

(4) Figure 1 is a well-designed, comprehensive summary of the number and functional annotations of putative secretomes in gall-forming pathogens. Notably, it reveals that more than half of the analyzed effectors lack known protein domains in some pathogens, yet some were annotated based on their predicted structures, despite the absence of domain annotations.

Weaknesses:

(1) The effector families discussed in this paper remain hypothetical in terms of their functional roles, which is understandable given the challenges of demonstrating their functions experimentally. However, this highlights the need for experimental validation as a next step.

Thank you. Yes, there is a lot of work to do in the coming years.

(2) Some analyses, such as those in Figure 4e, emphasize motifs derived from sequence alignments of SUSS effector clusters. Since these effectors are sequence-unrelated, sequence alignments might be unreliable. It would be more rigorous to perform structure-based alignments in addition to sequence-based ones for motif confirmation. For instance, methods described in Figure 3E of de Guillen et al. (2015, https://doi.org/10.1371/journal.ppat.1005228) or tools like Foldseek could be useful for aligning structures of multiple sequences.

In Fig. 4e, we highlight the conserved cysteine residues. While there is no clearly conserved overall motif, the figure illustrates that despite the high sequence divergence, the key cysteines involved in disulfide bridge formation are consistently conserved across the sequences.

(3) When presenting AlphaFold-generated structures, it is essential to include confidence scores such as pLDDT and PAE. For example, in Figure 1D of Derbyshire and Raffaele (2023, https://doi.org/10.1038/s41467-023-40949-9), the structural representations were colored red due to their high pLDDT scores, emphasizing their reliability.

Thank you for the observation. Due to the restrictive parameters used in our analysis, over 90% of the structure would appear red. For this reason, we chose not to include the color scale, as it would not provide additional informative value in this context.

Reviewer #1 (Recommendations for the authors):

Experimental validation of the significance of 'CCG' and 'RAYH' motifs would further strengthen this study.

Regarding the Mig1-like protein in Ustilago maydis, the presence of four conserved cysteine residues that are pivotal for maintaining the stability of its folded structure raises an intriguing question. Specifically, while many Mig cluster effectors contain four cysteine residues that form two conserved disulfide bridges, this structure is notably absent in the Mig protein itself. The author has speculated that these four cysteine residues form two conserved disulfide bonds, which are crucial for the stability of Mig protein folding. However, this hypothesis remains unvalidated. To test this prediction, it would be prudent to simulate mutations in the cysteine residues corresponding to the disulfide bonds in Mig and employ molecular dynamics simulations to assess the stability of folding before and after the mutation.

Mig-1 does contain the four conserved cysteine residues responsible for forming disulfide bridges. However, due to the high divergence among Mig-1-like sequences, the alignment software was unable to properly align all the cysteine residues. As a result, Mig-1 may appear to lack these conserved cysteines in the alignment, although they are indeed present upon individual inspection. This is an area that research groups working with U. maidis as a model could explore further to expand our understanding of this effector family.

Could you please clarify why talking about Ankyrins and LRR in Arabidopsis thaliana (line 252)? Additionally, what are the structural and functional differences between the LRR sequences of P. brassicae and those of the host plants?

This sentence refers to the identification of the ANK motif in P. brassicae and S. spongospora, not in Arabidopsis thaliana. While the hydrophobic core of the ANK domains appears conserved between the host and the pathogen, the surface residues are highly polymorphic.

The evidence supporting the interaction between the ANK effector and Arabidopsis immunity-related proteins, as validated using AlphaFold-Multimer, is currently limited. To enhance the reliability of these data, it is advisable for the author to select several pairs of proteins predicted to interact for further experimental verification.

We conducted a large-scale yeast two-hybrid (Y2H) screen using the ANK domain effector PBTT_00818, which was selected due to its high iPTM+pTM score. The Y2H interactions were subsequently validated through BiFC assays. Our results show that PBTT_00818 interacts with Arabidopsis MPK3 in the nucleus, consistent with predictions from the AlphaFold2-multimer model. In addition, PBTT_00818 was also found to target AT3G56460, a GroES-like zinc-binding alcohol dehydrogenase, also localized in the nucleus.

While the manuscript is well-composed, certain sections could be enhanced for clarity and readability. For example, the discussion section could be expanded to include a more in-depth analysis of the implications of the findings for understanding the virulence mechanisms of gall-forming microbes. Additionally, a comparison of the findings with previous studies on related pathogens would provide a more comprehensive perspective.

Certain sections of the discussion have been expanded. However, we chose to focus on the novel aspects of the study and to avoid comparisons with other plant pathogens, as those mechanisms are already well known and extensively studied. Studies using AlphaFold in plant pathology are also limited.

*Reviewer #2 (Recommendations for the authors):*

The results of clustering analyses are highly dependent on the chosen thresholds. Given that the authors provide clear and well-designed visualizations of SUSS effectors in Figures 4a and 4b, applying the same presentation methods to Figures 5a and 5b could make these analyses more convincing.

We were able to generate the all-vs-all matrix for Figures 4a and 4b because it involved only 13 proteins. However, Figure 5b includes over 40 effectors, making it impractical to visualize the data in the same way. Instead, we presented the sequence-based clusters as nodes and connected them based on structural similarity.

Ceci est un test

Phrase à revoir

Rob Nixon, Slow Violence and the Environmentalism of the Poor (Cambridge, MA: Harvard University Press, 2011).

Me llama la atención como se da a entender que no solo basta con tener los "ingredientes" necesarios para la vida, también se necesita una fuente de energía para que la vida florezca, dando a entender que todo fue un proceso que necesitó un impulso en su inicio

En este párrafo me llamó la atención esta pregunta y decidí indagar un poco. Según lo que investigué, pudo haber sido un proceso aleatorio, sin embargo, estos 20 aminoácidos fueron los que proporcionaron una base más solida para la vida, ya que, el código genético que traduce los genes en proteínas está limitados a estos 20 aminoácidos y habría tenido mayor dificultad si se hubieran incluido más; trayendo mutaciones fatales para la vida.

esta investigación es que nos recuerda que la vida en la Tierra quizá no empezó únicamente aquí, sino que fue el resultado de una colaboración cósmica. Los rayos gamma, que normalmente asociamos con radiación peligrosa, en realidad pudieron ser la chispa que transformó simples moléculas en aminoácidos dentro de los asteroides. Es como si el universo mismo hubiera hecho una especie de laboratorio natural, preparando los ingredientes de la vida y enviándolos a nuestro planeta en forma de meteoritos.

Nixon, Rob. Slow Violence and the Environmentalism of the Poor, Rob Nixon. Cambridge, MA: Harvard University Press, 2011.

Nixon, Rob. Slow Violence and the Environmentalism of the Poor, Rob Nixon. Cambridge, MA: Harvard University Press, 2011.

Necesito que realicen un resumen del siguiente texto

Al menos destaca la revista de la Red de Humanidades Digitales: https://revistas.uned.es/index.php/RHD/index

No existían para 2017, ya en 2025 hay algunas como es el caso de la que se imparte en la UAQuerétaro y en el Tec de Monterrey.

• Informativo nº 688
• 15 de março de 2021.
• RECURSOS REPETITIVOS
• Processo: REsp 1.769.306-AL, Rel. Min. Benedito Gonçalves, Primeira Seção, por unanimidade, julgado em 10/03/2021. (Tema 1009).

Ramo do Direito DIREITO ADMINISTRATIVO

Tema - Servidor público. Devolução de valores recebidos. Artigo 46, caput, da Lei n. 8.112/1990. Revisão da tese definida no Tema repetitivo 531/STJ. Ausência de alcance nos casos de pagamento indevido decorrente de erro de cálculo ou operacional da administração pública. Possibilidade de devolução. Salvo inequívoca presença da boa-fé objetiva. Tema 1009.

DESTAQUE - Os pagamentos indevidos aos servidores públicos decorrentes de erro administrativo (operacional ou de cálculo), não embasado em interpretação errônea ou equivocada da lei pela Administração, estão sujeitos à devolução, ressalvadas as hipóteses em que o servidor, diante do caso concreto, comprova sua boa-fé objetiva, sobretudo com demonstração de que não lhe era possível constatar o pagamento indevido.

INFORMAÇÕES DO INTEIRO TEOR - A controvérsia consiste em definir se a tese firmada no Tema 531/STJ seria igualmente aplicável aos casos de erro operacional ou de cálculo, para igualmente desobrigar o servidor público, de boa-fé, a restituir ao erário a quantia recebida a maior.

• No julgamento do Recurso Especial Repetitivo n. 1.244.182/PB (Tema 531/STJ), definiu-se que quando a Administração Pública interpreta erroneamente uma lei, resultando em pagamento indevido ao servidor, de boa-fé, cria-se uma falsa expectativa de que os valores recebidos são legais e definitivos, impedindo, assim, que ocorra desconto dos mesmos, o que está em conformidade com a Súmula 34 da Advocacia Geral da União - AGU.

• Assim, acerca da impossibilidade de devolução ao erário de valores recebidos indevidamente por servidor público, de boa-fé, em decorrência de equívoco na interpretação de lei pela Administração Pública, constata-se que o tema está pacificado.

• O artigo 46, caput, da Lei n. 8.112/1990 estabelece a possibilidade de reposições e indenizações ao erário. Trata-se de disposição legal expressa, plenamente válida, embora com interpretação dada pela jurisprudência com alguns temperamentos, especialmente em observância aos princípios gerais do direito, como boa-fé, a fim de impedir que valores pagos indevidamente sejam devolvidos ao erário.

• Diferentemente dos casos de errônea ou má aplicação de lei, onde o elemento objetivo é, por si, suficiente para levar à conclusão de que o beneficiário recebeu o valor de boa-fé, assegurando-lhe o direito da não devolução do valor recebido indevidamente, na hipótese de erro material ou operacional deve-se analisar caso a caso, de modo a averiguar se o servido tinha condições de compreender a ilicitude no recebimento dos valores, de modo a se lhe exigir comportamento diverso, diante do seu dever de lealdade para com a Administração Pública.

• Impossibilitar a devolução dos valores recebidos indevidamente por erro perceptível da Administração Pública, sem a análise do caso concreto da boa-fé objetiva, permitiria o enriquecimento sem causa por parte do servidor, em flagrante violação do artigo 884 do Código Civil.

• Por tudo isso, não há que se confundir erro na interpretação de lei com erro operacional, de modo àquele não se estende o entendimento fixado no Recurso Especial Repetitivo n. 1.244.182/PB, sem a observância da boa-fé objetiva do servidor público, o que possibilita a restituição ao Erário dos valores pagos indevidamente decorrente de erro de cálculo ou operacional da Administração Pública.

• Isto é, se houve erro de interpretação ou de aplicação de lei; não deve haver devolução;

• Se se tratar de erro de cálculo ou operacional, deve haver devolução.

= Honionicity

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for = wikify myself

CSP vs Actor model for concurrency -

my comment

Revolutionizing DevOps with Ansible:

The Future of Automated Infrastructure

my comment on this

DOI: 10.1002/fft2.70078

Resource: Bloomington Drosophila Stock Center (RRID:SCR_006457)

Curator: @maulamb

SciCrunch record: RRID:SCR_006457

What is this?

DOI: 10.3390/nu17152434

Resource: (Vector Laboratories Cat# BA-2000, RRID:AB_2313581)

Curator: @scibot

SciCrunch record: RRID:AB_2313581

What is this?

DOI: 10.3390/cells14151150

Resource: (DSHB Cat# BA-F8, RRID:AB_10572253)

Curator: @scibot

SciCrunch record: RRID:AB_10572253

What is this?

DOI: 10.1371/journal.pone.0327344

Resource: Zebrafish International Resource Center (RRID:SCR_005065)

Curator: @scibot

SciCrunch record: RRID:SCR_005065

What is this?

DOI: 10.1038/s42003-025-08544-4

Resource: (Vector Laboratories Cat# BA-1000, RRID:AB_2313606)

Curator: @scibot

SciCrunch record: RRID:AB_2313606

What is this?

Aunque los biocombustibles suelen considerarse una opción ecológica, su producción a partir de cultivos agrícolas provoca más daños que beneficios: favorece la deforestación, incrementa las emisiones de CO₂ de manera indirecta, encarece los alimentos y amenaza a los ecosistemas. Solo tendrían un verdadero valor sustentable si se elaboran con residuos o mediante tecnologías que no compitan con la agricultura ni demanden grandes extensiones de tierra.

another citation:

Niles MT, Schimanski LA, McKiernan EC, Alperin JP (2020) Why we publish where we do: Faculty publishing values and their relationship to review, promotion and tenure expectations. PLoS ONE 15(3): e0228914. https://doi.org/10.1371/journal.pone.0228914

• 1. Indeferimento do pedido de aditamento da inicial para incluir as alterações trazidas pela Lei Estadual nº 12.978/2005. A jurisprudência desta Corte é no sentido de que o aditamento à inicial somente é possível nas hipóteses em que a inclusão da nova impugnação (i) dispense a requisição de novas informações e manifestações; e (ii) não prejudique o cerne da ação, o que não ocorre no presente caso. Precedente.

(ADI 1926, Relator(a): ROBERTO BARROSO, Tribunal Pleno, julgado em 20-04-2020, PROCESSO ELETRÔNICO DJe-136 DIVULG 01-06-2020 PUBLIC 02-06-2020)

• 1. Entendimento desta CORTE no sentido de que o aditamento da inicial só é possível, observados os princípios da economia e da celeridade processuais, quando a inclusão de nova impugnação dispensa a requisição de novas informações. No presente caso, não é possível tal aditamento com a finalidade de corrigir vício relativo à legislação não impugnada do complexo normativo. 4. Agravo Regimental a que se nega provimento.

(ADI 4.265, Plenário, Rel. Min. Alexandre de Moraes, j. em 09.04.2018)

Taxa de segurança preventiva relativa a eventos não gratuitos e a emissão de certidões para defesa de direitos - ADI 3.717/PR Relator: Ministro Nunes Marques

RESUMO - É constitucional a instituição de taxa por serviços prestados por órgãos de segurança pública relativos (i) à segurança preventiva em eventos esportivos e de lazer com cobrança de ingresso, bem como (ii) à emissão de certidões e atestados, desde que não se destinem à defesa de direitos ou ao esclarecimento de interesse pessoal (CF/1988, art. 5º, XXXIV, b).

• Conforme jurisprudência desta Corte (1), o serviço de segurança pública e as atividades a ela inerentes, como policiamento ostensivo e vigilância, não podem ser financiados mediante taxas, dada a impossibilidade de que sua prestação ocorra de forma individualizada. Assim, por constituir serviço geral e indivisível, prestado a toda a coletividade, este deve ser remunerado por meio de impostos. Contudo, há situações em que os serviços, apesar de prestados por órgãos de segurança pública, são efetivamente oferecidos de modo específico e divisível. Nesse contexto, prestações oferecidas atipicamente pelos órgãos de segurança pública e que são usufruídas de modo particular pelos administrados podem ser custeadas por meio de taxas (2).

• Na espécie, a operação logística necessária para garantir a segurança em eventos de grande porte, com finalidade lucrativa, não pode ser imputada à sociedade como um todo através de um financiamento indistinto, arrecadado pelo poder público via impostos. Também não é cabível partilhar, entre toda a sociedade, os custos de serviços prestados pelos órgãos da Administração Policial Militar estadual para fornecimento, entre outros, de “cópias (xerox) autenticadas (por folha)”, “diárias/permanência de veículos apreendidos nas unidades policiais militares”, fotografias e inscrição em cursos e exames. Por expressa vedação constitucional (3), a cobrança de taxa não é válida apenas para o fornecimento de certidões e atestados direcionados à defesa de direitos ou ao esclarecimento de interesse pessoal (4). Com base nesses entendimentos, o Plenário, por unanimidade, julgou parcialmente procedente a ação para (i) declarar a inconstitucionalidade dos itens 1.1.1 e 1.2 (1.2.1 a 1.2.5) da tabela anexa à Lei nº 10.236/1992 do Estado do Paraná; e (ii) dar interpretação conforme aos itens 2.1 e 2.3 da mesma lista, no sentido de impossibilitar a cobrança de taxa para emissão de certidões/atestados solicitados com o propósito de defender direitos e esclarecer situações de interesse pessoal.

• Também não é cabível partilhar, entre toda a sociedade, os custos de serviços prestados pelos órgãos da Administração Policial Militar estadual para fornecimento, entre outros, de “cópias (xerox) autenticadas (por folha)”, “diárias/permanência de veículos apreendidos nas unidades policiais militares”, fotografias e inscrição em cursos e exames. Por expressa vedação constitucional (3), a cobrança de taxa não é válida apenas para o fornecimento de certidões e atestados direcionados à defesa de direitos ou ao esclarecimento de interesse pessoal (4). Com base nesses entendimentos, o Plenário, por unanimidade, julgou parcialmente procedente a ação para (i) declarar a inconstitucionalidade dos itens 1.1.1 e 1.2 (1.2.1 a 1.2.5) da tabela anexa à Lei nº 10.236/1992 do Estado do Paraná; e (ii) dar interpretação conforme aos itens 2.1 e 2.3 da mesma lista, no sentido de impossibilitar a cobrança de taxa para emissão de certidões/atestados solicitados com o propósito de defender direitos e esclarecer situações de interesse pessoal.

• Informativo nº 857
• 12 de agosto de 2025.
• PRIMEIRA TURMA
• Processo: REsp 1.931.196-RS, Rel. Ministro Paulo Sérgio Domingues, Primeira Turma, por unanimidade, julgado em 5/8/2025.

Ramo do Direito DIREITO PROCESSUAL CIVIL, DIREITO TRIBUTÁRIO

TemaPaz, Justiça e Instituições Eficazes <br /> Execução fiscal. Juntada de título executivo relativo a terceiro. Emenda à inicial. Art. 240, § 1º, do CPC/2015. Retroação da interrupção da prescrição à data da propositura da ação. Impossibilidade.

Destaque - Não é possível considerar como válida, para fins de interrupção da prescrição, a propositura de execução fiscal fundada em certidão de dívida ativa (CDA) de contribuinte diverso.

Informações do Inteiro Teor - A controvérsia consiste em decidir se a emenda à inicial pela juntada do título executivo incorreto afasta a regra do art. 240, § 1º, do Código de Processo Civil (CPC), segundo a qual a interrupção da prescrição, operada pelo despacho que ordena a citação, retroage à data da propositura da ação.

• No caso, por ocasião do ajuizamento da execução fiscal, a Fazenda Nacional procedeu à juntada de título executivo cujo sujeito passivo não era a parte executada, ou seja, referente a empresa distinta. Intimado, o ente fazendário apresentou o documento correto, prosseguindo, assim, a tramitação do processo.

• Inicialmente, afasta-se a aplicação da Súmula n. 392 do Superior Tribunal de Justiça, que admite a substituição da certidão de dívida ativa (CDA) até a prolação da sentença de embargos, nos casos de correção de erro material ou formal, vedada a modificação do sujeito passivo da execução. Isso porque, na hipótese, não se discute a validade da CDA em si, mas sim a juntada equivocada de título executivo alheio à parte executada, o que comprometeria a regularidade da petição inicial.

• A correção desse vício atrai a incidência do Código de Processo Civil, aplicável de forma subsidiária à execução fiscal, nos termos do art. 1º da Lei n. 6.830/1980.

• Assim, é cabível a aplicação do art. 321 do CPC para permitir a emenda da petição inicial, com a correção dos documentos que a instruem. Apenas em caso de inércia da parte após a intimação é que se justifica o indeferimento da inicial.

• Contudo, embora haja previsão legal para a correção de defeitos ou irregularidades na petição inicial, para o Superior Tribunal de Justiça, quando a petição inicial é protocolada em desacordo com o disposto no art. 319 do CPC, de modo a impedir o desenvolvimento válido e regular do processo, a interrupção da prescrição, nos termos do art. 240, § 1º, do CPC, somente retroage à data da emenda da inicial (AgInt no REsp n. 1.749.085/DF, rel. Ministro Marco Buzzi, Quarta Turma, julgado em 2/10/2023, DJe de 5/10/2023; e AgInt no REsp n. 1.746.781/PE, rel. Ministro Napoleão Nunes Maia Filho, Primeira Turma, julgado em 25/5/2020, DJe de 28/5/2020).

• Aplica-se esse entendimento ao caso, visto que seria completamente inviável o prosseguimento da execução fiscal cujo sujeito passivo do título executivo não correspondesse à parte executada.

• SEGUNDA TURMA

• Processo: AREsp 2.523.152-CE, Rel. Ministro Francisco Falcão, Segunda Turma, por unanimidade, julgado em 21/5/2024, DJe 23/5/2024.

• Ramo do Direito <br /> DIREITO ADMINISTRATIVO, DIREITO PROCESSUAL CIVIL, DIREITO TRIBUTÁRIO

TemaPaz, Justiça e Instituições Eficazes <br /> Embargo à execução. Desistência do embargado. Adesão ao REFIS. Previsão de pagamento de honorários. Nova cobrança. Bis in idem.

DESTAQUE - Havendo a previsão de pagamento, na esfera administrativa, dos honorários advocatícios, na ocasião da adesão do contribuinte ao Programa de Parcelamento Fiscal, a imposição de pagamento da verba honorária, quando da extinção da execução fiscal, configura bis in idem, sendo vedada nova fixação da verba.

INFORMAÇÕES DO INTEIRO TEOR - Havendo a previsão de pagamento, na esfera administrativa, dos honorários advocatícios, na ocasião da adesão do contribuinte ao Programa de Parcelamento Fiscal, a imposição de pagamento da verba honorária, quando da extinção da execução fiscal, configura bis in idem, sendo vedada nova fixação da verba. Tal entendimento, inclusive, foi cristalizado no enunciado do Tema repetitivo n. 400/STJ.

• Nesse mesmo sentido, destaca-se: [...] V. Na esteira do entendimento firmado nesta Corte, em regra, a desistência da Ação Anulatória ou dos Embargos à Execução, decorrente da adesão do contribuinte ao Programa de Parcelamento, não implica o afastamento da condenação aos honorários advocatícios. [...] VI. Todavia, a jurisprudência desta Corte orienta-se no sentido de que, havendo a previsão de pagamento, na esfera administrativa, dos honorários advocatícios, quando da adesão do contribuinte ao Programa de Parcelamento Fiscal, a imposição de pagamento da verba honorária, quando da extinção da Execução Fiscal, configura bis in idem. [...] (AgInt no REsp n. 1.994.559/MG, relatora Ministra Assusete Magalhães, Segunda Turma, julgado em 14/11/2022, DJe de 22/11/2022).

• Informativo nº 856
• 5 de agosto de 2025.

• PRIMEIRA TURMA

• Processo: REsp 2.032.814-RS, Rel. Ministro Gurgel de Faria, Rel. para acórdão Ministro Paulo Sérgio Domingues, Primeira Turma, por maioria, julgado em 10/6/2025, DJEN 30/6/2025.

Ramo do Direito DIREITO TRIBUTÁRIO

TemaPaz, Justiça e Instituições Eficazes <br /> Transação tributária. Renúncia para fins de adesão. Silêncio da legislação. Condenação em honorários advocatícios com base no art. 90 do CPC/2015. Não cabimento. Princípios da segurança jurídica, da boa-fé, da proteção e da confiança. Violação.

Destaque - A cobrança, pela Fazenda Pública, de honorários advocatícios sem previsão na legislação que instituiu as condições da transação tributária viola os princípios da segurança jurídica, da boa-fé do administrado e da proteção da confiança.

Informações do Inteiro Teor - Discute-se, no caso, se a parte recorrida, que renunciou ao direito pleiteado na presente ação como condição para aderir à transação tributária prevista na Lei n. 13.988/2020, deve ser condenada ao pagamento de honorários advocatícios, com base no artigo 90 do Código de Processo Civil/2015, aplicado subsidiariamente.

• Dentre os compromissos a serem assumidos pelo administrado/contribuinte na celebração da transação, previstos no art. 3º da Lei n. 13.988/2020, está a renúncia do direito objeto do litígio, independentemente de qual ação judicial está sendo utilizada para discutir o valor cobrado pelo fisco. O parágrafo 1º deixa evidente que o administrado/contribuinte deve aceitar todas as condições estabelecidas na Lei e sua regulamentação, confessando o débito.

• Diferentemente de outros acordos que possam ser realizados, os dispositivos legais transcritos deixam clara a supremacia da Fazenda Nacional na celebração da transação, ao fixar suas condições no edital que a parte aderirá ou não. Não há negociação e sim o aceite ou não pelo administrado/contribuinte das condições impostas, ou seja, não há horizontalidade na relação.

• Por sua vez, quanto à incidência dos honorários advocatícios na renúncia, pelo contribuinte, das ações judiciais nas quais o valor transacionado está sendo discutido a Lei n. 13.988/2020 é omissa. Assim, essa é a questão que se coloca: realizada a adesão do contribuinte à transação, em caso de silêncio da respectiva lei regente, deve ser aplicado subsidiariamente o CPC/2015, como lei geral, para arbitramento de honorários quando da renúncia ao direito em que se fundam ações judiciais em andamento?

• A transação apresenta verdadeira novação em relação ao crédito tributário que estava sendo discutido judicialmente. Toma-se o valor do crédito, divide-se pelo número de parcelas, e eis o valor que será cobrado do contribuinte.

• Não é possível admitir que, após a transação, se venha a incluir no montante transacionado novos valores não previstos na lei que a instituiu nem no edital com o qual o contribuinte concordou. A cobrança de honorários advocatícios não previstos no instrumento de transação - elaborado pela própria Fazenda Nacional - viola os princípios da boa-fé e da não-surpresa.

• Nessa esteira de raciocínio está o venire contra factum proprium, implícito na cláusula geral da boa-fé objetiva, pois não há previsão de honorários na lei que rege a matéria nem na Portaria da transação elaborada pela própria Fazenda Nacional. Assim, não cabe a ela requerer ao Poder Judiciário que supra uma lacuna que ela mesma criou.

• Não se trata aqui de negar vigência ao art. 90 do CPC/2015, que versa sobre a incidência de honorários sucumbenciais em caso de renúncia ao direito sobre o qual se funda a ação. É que a renúncia, em geral, é o ato unilateral da parte, a qual havia ingressado em Juízo e, por qualquer razão, desejou deixar de litigar. Aplica-se a regra geral do CPC/2015.

• Contudo, no caso da transação tributária, o negócio jurídico realizado tem todas as suas condições estabelecidas na nova lei que a instituiu. E elas estão todas previstas no artigo 3º da Lei n. 13.988/2020 e respectivas regulamentações.

• No tocante ao princípio da segurança jurídica, o Superior Tribunal de Justiça possui entendimento de que: "a proteção da confiança no âmbito tributário, uma das faces do princípio da segurança jurídica, prestigiado pelo CTN, deve ser homenageada, sob pena de olvidar-se a boa-fé do contribuinte, que aderiu à política fiscal de inclusão social, concebida mediante condições onerosas para o gozo da alíquota zero de tributos." (REsp 1.928.635/SP, rel. Ministra Regina Helena Costa, Primeira Turma, julgado em 10/8/2021, DJe de 16/8/2021).

• Aqui a renúncia não é totalmente voluntária. É uma condição para a realização da transação a que o contribuinte aderiu, como a própria Fazenda Nacional alega em seu recurso especial. Por isso, somente podem ser incluídos no instrumento de transação as verbas expressamente previstas na legislação que a permitiu.

• Ou seja, a situação foge ao que ordinariamente se encontra, e não se pode aplicar a regra do CPC/2015 de forma subsidiária. Aplica-se o art. 171 do Código Tributário Nacional: somente valem as condições expressas na lei.

• Desse modo, sem previsão na legislação que instituiu as condições da transação, a Fazenda Pública não pode cobrar honorários sem violar os princípios da segurança jurídica, da boa-fé do administrado e da proteção da confiança. O silêncio da norma quanto à aplicação de honorários advocatícios não permite a aplicação do artigo 90 do CPC/2015 ao caso, pelas razões já expostas.

• Sendo assim, o fato de a Lei n. 13.988/2020 e a Portaria PGFN n. 14.402/2020 silenciarem a respeito da inclusão de honorários sucumbenciais por ocasião da renúncia em ações em andamento não constitui uma omissão a ser suprida pela aplicação subsidiária do CPC/2015. É um silêncio deliberado, que leva à aplicação da lei especial, o art. 171 do CTN e a lei específica que regula a transação e exclui a aplicação da lei geral.

• Informativo nº 857
• 12 de agosto de 2025.
• QUARTA TURMA
• Processo: REsp 2.200.180-SP, Rel. Ministro Antonio Carlos Ferreira, Quarta Turma, por unanimidade, julgado em 5/8/2025.

Ramo do Direito DIREITO PROCESSUAL CIVIL

TemaPaz, Justiça e Instituições Eficazes <br /> Cumprimento de sentença. Adjudicação de bens. Penhora prévia. Necessidade. Devido processo legal.

Destaque - A penhora é ato processual prévio e necessário à adjudicação de bens.

Informações do Inteiro Teor - A controvérsia jurídica diz respeito à possibilidade de se deferir a adjudicação de bem no processo de execução sem sua penhora prévia e formal.

• Os artigos 523, § 3º e 825, inciso I, do Código de Processo Civil estabelecem que a penhora é ato processual prévio e necessário à adjudicação de bens. Essa sequência lógica e cronológica decorre da própria natureza da execução forçada e do sistema de expropriação nela previsto.

• A exigência da penhora prévia como pressuposto para a adjudicação não representa mera formalidade processual, mas concretiza a garantia fundamental do devido processo legal prevista no art. 5º, LIV, da Constituição Federal, segundo o qual " ninguém será privado da liberdade ou de seus bens sem o devido processo legal".

• Dessa forma, a sequência procedimental estabelecida pelo legislador processual (penhora-avaliação-expropriação) reforça o comando constitucional, estruturando um processo executivo que equilibra a efetividade da tutela jurisdicional com as garantias do executado.

• A penhora, nessa perspectiva constitucional, representa uma etapa processual qualificada, que não pode ser suprimida por decisão judicial sem que isso implique violação à própria garantia do devido processo legal.

• A supressão da penhora viola, portanto, não apenas as disposições infraconstitucionais que regulam o procedimento executivo, mas também o núcleo essencial da garantia constitucional do devido processo legal, na medida em que permite a privação de bens do executado sem a observância do procedimento legalmente estabelecido.

• A inobservância deste pressuposto processual caracteriza nulidade absoluta, dispensando a comprovação de dano efetivo. Nesse contexto, o prejuízo é presumido ex lege, uma vez que vulnera princípios fundamentais como a segurança jurídica e o devido processo legal.

• Por fim, é relevante observar que a necessidade da penhora antecedente não se restringe à adjudicação, mas constitui requisito inafastável em qualquer modalidade de expropriação prevista no art. 825 do CPC, seja ela a adjudicação (inciso I), a alienação (inciso II) ou a apropriação de frutos e rendimentos (inciso III).

Est-ce qu'on devrait enlever le mot « financiers »? Est-ce que la divulgation des frais de voyage et d'accueil est considéré comme financier?

Gest. princ.- Financial politiques, assurance qualité et délégations... selon PC411... mais on dirait que le début n'est pas traduit dans PC411 (Financial politiques)

Gestionnaire principal des finances - Modernisation de la gestion financière et systèmes... Selon PC411

Est-ce qu'il faut indiquer : Bureau de la.... ou juste Fonction du contrôleur?

SVP me revenir si oui afin que j'ajuste mon bloc de signature dans mes courriels.

Est-ce qu'il faut indiquer : Bureau de la.... ou juste Fonction du contrôleur?

SVP me revenir si oui afin que j'ajuste mon bloc de signature dans mes courriels.

Il manque la traduction de : We look forward to collaborating with you. (voir version anglaise)... à la fin du paragraphe

directives? Dans la version anglaise on a dit directives

Should this paragraph have a alignment ''justified''?

Can we update the link to the following so it goes directly on the directive and not the Financial Policies portal :

extension://bjfhmglciegochdpefhhlphglcehbmek/pdfjs/web/viewer.html?file=https%3A%2F%2Fintranet2%2Fmedia%2F6961987%2Fdirective-on-collecting-sales-taxes-v10.pdf

Chapitre très intéressant ! Par contre, mon cerveau était en feu dans la partie des "ancres" :)

Bon bah du coup, il ne reste plus qu'à ajouter cet attribut à toutes les balises "a" de la formation :)

Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

Learn more at Review Commons

Reply to the reviewers

Manuscript number: RC-2025-02879 Corresponding author(s): Matteo Allegretti; Alia dos Santos

1. General Statements

In this study, we investigated the effects of paclitaxel on both healthy and cancerous cells, focusing on alterations in nuclear architecture. Our novel findings show that:

• Paclitaxel-induced microtubule reorganisation during interphase alters the perinuclear distribution of actin and vimentin. The formation of extensive microtubule bundles, in paclitaxel or following GFP-Tau overexpression, coincides with nuclear shape deformation, loss of regulation of nuclear envelope spacing, and alteration of the nuclear lamina.

• Paclitaxel treatment reduces Lamin A/C protein levels via a SUN2-dependent mechanism. SUN2, which links the lamina to the cytoskeleton, undergoes ubiquitination and consequent degradation following paclitaxel exposure.

• Lamin A/C expression, frequently dysregulated in cancer cells, is a key determinant of cellular sensitivity to, and recovery from, paclitaxel treatment.

Collectively, our data support a model in which paclitaxel disrupts nuclear architecture through two mechanisms: (i) aberrant nuclear-cytoskeletal coupling during interphase, and (ii) multimicronucleation following defective mitotic exit. This represents an additional mode of action for paclitaxel beyond its well-established mechanism of mitotic arrest.

We thank the reviewers for their time and constructive feedback. We have carefully considered all comments and have carried out a full revision. The updated manuscript now includes additional data showing:

• Overexpression of microtubule-associated protein Tau causes similar nuclear aberration phenotypes to paclitaxel. This supports our hypothesis that increased microtubule bundling directly leads to nuclear disruption in paclitaxel during interphase.

• Paclitaxel's effects on nuclear shape and Lamin A/C and SUN2 expression levels occur independently of cell division.

• Reduced levels of Lamin A/C and SUN2 upon paclitaxel treatment occur at the protein level via ubiquitination of SUN2.

• The effects of paclitaxel on the nucleus are conserved in breast cancer cells.

Full Revision

We have also edited our text and added further detail to clarify points raised by the reviewers. We believe that our revised manuscript is overall more complete, solid and compelling thanks to the reviewers' comments.

1. Point-by-point description of the revisions

Reviewer #1 Evidence, reproducibility and clarity

This description of the down-regulation of the expression of lamin A/C upon treatment with paclitaxel and its sensitivity to SUN2 is quite interesting but still somehow preliminary. It is unclear whether this effect involves the regulation of gene expression, or of the stability of the proteins. How SUN2 mediates this effect is still unknown.

We thank the reviewer for this valuable comment. To elucidate the mechanism behind the decrease in Lamin A/C and SUN2 levels, we have now performed several additional experiments. First, we performed RT-qPCR to quantify mRNA levels of these genes, relative to the housekeeping gene GAPDH (Supplementary Figure 3B and O). The levels of SUN2 and LMNA mRNA remained the same between control and paclitaxel-treated cells, indicating that this effect instead occurs at the protein level. We have also tested post-translational modifications as a potential regulatory mechanism for Lamin A/C and SUN2. In addition to the phosphorylation of Ser404 which we had already tested (Supplementary Figure 3C), we have now included additional Phos-tag gel and Western blotting data showing that the overall phosphorylation status of Lamin A/C is not affected by paclitaxel (Supplementary Figure 3E and F). We also pulled-down Lamin A/C from cell lysates and then Western blotted for polyubiquitin and acetyl-lysine, which showed that the ubiquitination and acetylation states of Lamin A/C are also not affected by paclitaxel (Supplementary Figure 3G-I). However, Western blots for polyubiquitin of SUN2 pulled down from cell lysates showed that paclitaxel treatment results in significant SUN2 ubiquitination (Figure 3M and N). Therefore, we propose that the downregulation of SUN2 following paclitaxel treatment occurs by ubiquitin-mediated proteolysis.

The roles of free tubulins and polymerized microtubules, and thus the potential role of paclitaxel, need to be uncovered.

We addressed this important point by using an alternative method to stabilise/bundle microtubules in interphase, namely by overexpressing GFP-Tau, as suggested by reviewer 2. Following GFP- Tau overexpression, large microtubule bundles were observed throughout the cytoplasm (Figure 4A), and this resulted in a significant decrease in nuclear solidity (Figure 4B). Furthermore, in cells where microtubule bundles extensively contacted the nucleus, the nuclear lamina became unevenly distributed and appeared patchy (Figure 4C). This supports our hypothesis that the aberrations to nuclear shape and Lamin A/C localisation in paclitaxel-treated cells are due to the presence of microtubules bundles surrounding the nucleus.

The doses of paclitaxel at which occur the effects described in the paper are not fully consistent with all the conclusions. Most experiments have been done at 5 nM. However, at this dose the effect of lamin A/C over or down expression on the growth (differences in the slopes of the curves in Figure 4A) are not fully convincing and not fully consistent with the clear effect on viability as well (in addition, duration of treatments before assessing vialbility are not specified). At 1 nM, cell growth is reduced and the rescuing effect of lamin over-expression is much more clear (Fig 4A), and the nucleus deformation clear (Fig 2A) but this dose has no effect on lamin A/C expression (Fig 3C), which questions how lamins impact nucleus shape and cell survival. Cytoskeleton reorganisation in these conditions is not described although it could clarify the respective role of force production (suggested in figure 1) and nuclei resistance (shown in figure 2) in paclitaxel sensitivity.

We thank the reviewer for raising this important point. We have addressed this by conducting additional repeats for the cell confluency measurements to increase the statistical power of our experiments (Figure 5A). Our data now show that GFP-lamin A/C had a statistically significant effect on rescuing cell growth at both 1 nM and 5 nM paclitaxel, while Lamin A/C knockdown exacerbated the inhibition of cell growth at 5 nM paclitaxel but not 1 nM paclitaxel (Figure 5A). In addition, we note that the duration of paclitaxel treatment before assessing viability was specified in the figure legend: "Bar graph comparing cell viability between wild-type (red), GFP-Lamin A/C overexpression (green), and Lamin A/C knockdown (blue) cells following 20 h incubation in 0, 1, 5, or 10 nM paclitaxel." We also repeated cell viability analysis after 48 h incubation in paclitaxel instead of 20 h to allow for a longer time for differences to take effect (Figure 5B).

We also added figures showing the cytoskeletal reorganisation at both 1 and 10 nM in addition to 0 and 5 nM (Supplementary Figure 1A) showing that microtubule bundling and condensation of actin into puncta correlated with increased paclitaxel concentration. Vimentin colocalised well with microtubules at all concentrations.

We have also included in our results section further clarification for the use of 5nM paclitaxel in this study. The new section reads as follows: "Experiments were performed at 5 nM paclitaxel (with additional experiments to determine dose relationships at 1 and 10 nM) because this aligns with previous studies7,14,24. Furthermore, previous analysis of patient plasma reveals that typical concentrations are within the low nanomolar range8, and concentrations of 5-10 nM are required in cell culture to reach the same intracellular concentrations observed in vivo in patient tumours9. This aligns with in vitro cytotoxic studies of paclitaxel in eight human tumour cell lines which show that paclitaxel's IC50 ranges between 2.5 and 7.5 nM41."

Finally, although the absence of role of mitotic arrest is clear from the data, the defective reorganisation of the nucleus after mitosis still suggest that the effect of paclitaxel is not independent of mitosis.

We thank the reviewer for pointing out the need for clarification in the wording of our manuscript. We have reworded the title and relevant sections of our abstract, introduction, and discussion to make it clearer that the effects of paclitaxel on the nucleus are due to a combination of aberrant nuclear cytoskeletal coupling during interphase and multimicronucleation following mitotic slippage. We have also added additional data in support of the effect of paclitaxel on nuclear architecture during interphase. For this, we used serum-starved cells (which divide only very slowly such that the majority of cells do not pass through mitosis during the 16 h incubation in paclitaxel [Supplementary Figure 2D]). Our new data confirmed that paclitaxel's effects on nuclear solidity, and Lamin A/C and SUN2 proteins levels can occur independently of cell division (Figure 2C; Figure 3H-J). Finally, when we overexpressed GFP-Tau (as discussed above) we observed similar aberrations to nuclear solidity and Lamin A/C localisation. This indicates that these effects occur due to microtubule bundling in interphase, especially as in our study GFP-Tau did not lead to multimicronucleation or appear to affect mitosis (Figure 4).

Below are the main changes to the text regarding the interphase effect of paclitaxel:

• Title: "Paclitaxel compromises nuclear integrity in interphase through SUN2-mediated cytoskeletal coupling"

• Abstract: "Overall, our data supports nuclear architecture disruption, caused by both aberrant nuclear-cytoskeletal coupling during interphase and exit from defective mitosis, as an additional mechanism for paclitaxel beyond mitotic arrest."

• Introduction: "Here we propose that cancer cells have increased vulnerability to paclitaxel both during interphase and following aberrant mitosis due to pre-existing defects in their NE and nuclear lamina."

• Discussion: "Overall, our work builds on previous studies investigating loss of nuclear integrity as an anti-cancer mechanism of paclitaxel separate from mitotic arrest14,20,21. We propose that cancer cells show increased sensitivity to nuclear deformation induced by aberrant nuclear-cytoskeletal coupling and multimicronucleation following mitotic slippage. Therefore, we conclude that paclitaxel functions in interphase as well as mitosis, elucidating how slowly growing tumours are targeted."

minor: a more thorough introduction of known data about dose response of cells in culture and in vivo would help understanding the range of concentrations used in this study.

As mentioned above, we have now included additional information in our Results section to clarify our paclitaxel dose range: "Experiments were performed at 5 nM paclitaxel (with additional experiments to determine dose relationships at 1 and 10 nM) because this aligns with previous studies7,14,24. Furthermore, previous analysis of patient plasma reveals that typical concentrations are within the low nanomolar range8, and concentrations of 5-10 nM are required in cell culture to reach the same intracellular concentrations observed in vivo in patient tumours9. This aligns with in vitro cytotoxic studies of paclitaxel in eight human tumour cell lines which show that paclitaxel's IC50 ranges between 2.5 and 7.5 nM41."

Significance

In this manuscript, Hale and colleagues describe the effect of paclitaxel on nucleus deformation and cell survival. They showed that 5nM of paclitaxel induces nucleus fragmentation, cytoskeleton reorganisation, reduced expression of LaminA/C and SUN2, and reduced cell growth and viability. They also showed that these effects could be at least partly compensated by the over-expression of lamin A/C. As fairly acknowledged by the authors, the induction of nuclear deformation in paclitaxel-treated cells, and the increased sensitivity to paclitaxel of cells expressing low level of lamin A/C are not novel (reference #14). Here the authors provided more details on the cytoskeleton changes and nuclear membrane deformation upon paclitaxel treatment. The effect of lamin A/C over and down expression on cell growth and survival are not fully convincing, as further discussed below. The most novel part is the observation that paclitaxel can induce the down-regulation of the expression of lamin A/C and that this effect is mediated by SUN2.

We appreciate the reviewer's summary and thank them for their time. We believe our comprehensive revisions have addressed all comments, strengthening the manuscript and making it more robust and compelling.

Reviewer #2 Evidence, reproducibility and clarity This study investigates the effects of the chemotherapeutic drug paclitaxel on nuclear-cytoskeletal coupling during interphase, claiming a novel mechanism for its anti-cancer activity. The study uses hTERT-immortalized human fibroblasts. After paclitaxel exposure, a suite of state- of-the-art imaging modalities visualizes changes in the cytoskeleton and nuclear architecture. These include STORM imaging and a large number of FIB-SEM tomograms.

We thank the reviewer for the summary and for highlighting our efforts in using the latest imaging technical advances.

Major comments:

The authors make a major claim that in addition to the somewhat well-described mechanism of paclitaxel on mitosis, they have discovered 'an alternative, poorly characterised mechanism in interphase'.

However, none of the data proves that the effects shown are independent of mitosis. To the contrary, measurements are presented 48 hours after paclitaxel treatment starts, after which it can be assumed that 100% of cells have completed at least one mitotic event. The appearance of micronuclei evidences this, as discussed by the authors shortly. It looks like most of the results shown are based on botched mitosis or, more specifically, errors on nuclear assembly upon exit from mitosis rather than a specific effect of paclitaxel on interphase. The readouts the authors show just happen to be measurements while the cells are in interphase.

Alternative hypotheses are missing throughout the manuscript, and so are critical controls and interpretations.

We thank the reviewer for highlighting the lack of clarity in our wording. We have revised the title, abstract and relevant sections of the introduction and discussion to clarify our message that the effects of paclitaxel on the nucleus arise from a combination of aberrant nuclear-cytoskeletal coupling during interphase and multimicronucleation following exit from defective mitosis. We have also included additional data where we used slow-dividing, serum-starved cells (under these conditions, the majority of cells do not undergo mitosis during the 16 h incubation in paclitaxel [Supplementary Figure 2D]). Our new data show that even in these cells there is a clear effect of paclitaxel on nuclear solidity, and Lamin A/C and SUN2 protein levels, further supporting our hypothesis that these phenotypes can occur independently of cell division (Figure 2C; Figure 3H-J). Furthermore, we performed additional experiments where we used overexpression of GFP-Tau as an alternative method of stabilising microtubules in interphase and observed similar aberrations to nuclear solidity and Lamin A/C localisation. As GFP-Tau overexpression did not lead to micronucleation or appear to affect mitosis, these data support the hypothesis that nuclear aberrations occur due to microtubule bundling in interphase (Figure 4). We discuss these experiments in more detail below. Finally, we have reworded the introduction to better introduce alternative hypotheses and mechanisms for paclitaxel's activity.

The authors claim that 'Previously, the anti-cancer activity of paclitaxel was thought to rely mostly on the activation of the mitotic checkpoint through disruption of microtubule dynamics, ultimately resulting in apoptosis.' The authors may have overlooked much of the existing literature on the topic, including many recent manuscripts from Xiang-Xi Xu's and another lab.

We would like to note that the paper from Xiang-Xi Xu's lab (Smith et al, 2021) was cited in our original manuscript (reference 14 in both the original and revised manuscripts). We have now also included additional review articles from the Xiang-Xi Xu lab (PMID:36368286 20 and PMID: 35048083 21). Furthermore, we have clarified the wording in both the introduction and discussion to better reflect the current understanding of paclitaxel's mechanism and alternative hypotheses.

The data, e.g. in Figure 1, does not hold up to the first alternative hypothesis, e.g. that paclitaxel stabilizes microtubules and that excessive mechanical bundling of microtubules induces major changes to cell shape and mechanical stress on the nucleus. Even the simplest controls for this effect (the application of an alternative MT stabilizing drug or the overexpression of an MT stabilizer, e.g., tau).

We thank the reviewer for suggesting this control experiment using the microtubule stabiliser Tau. We have now included these experiments in the revised version of the manuscript (Figure 4). The overexpression of GFP-Tau supports our hypothesis that cytoskeletal reorganisation in paclitaxel exerts mechanical stress on the nucleus during interphase, resulting in nuclear deformation and aberrations to the nuclear lamina. In particular, GFP-Tau overexpression resulted in large microtubule bundles throughout the cytoplasm (Figure 4A). Notably, in cells where these bundles extensively contacted the nucleus, we observed a significant decrease in nuclear solidity (Figure 4B) accompanied by changes in nuclear lamina organisation, including a patchy lamina phenotype, similar to that induced by paclitaxel (Figure 4C).

The focus on nuclear lamina seems somewhat arbitrary and adjacent to previously published work by other groups. What would happen if the authors stained for focal adhesion markers? There would probably be a major change in number and distribution. Would the authors conclude that paclitaxel exerts a specific effect on focal adhesions? Or would the conclusion be that microtubule stabilization and the following mechanical disruption induce pleiotropic effects in cells? Which effects are significant for paclitaxel function on cancer cells?

We thank the reviewer for raising important points regarding the specificity of paclitaxel's effects. We agree that microtubule stabilisation can induce myriad cellular changes, including alterations to focal adhesions and other cytoskeletal components. Our focus on Lamin A/C and nuclear morphology is grounded both in the established clinical relevance of nuclear mechanics in cancer and builds on mechanistic work from other groups.

Lamin A/C expression is commonly altered in cancer, and nuclear morphology is frequently used in cancer diagnosis35. Lamin A/C also plays a crucial role in regulating nuclear mechanics32 and, importantly, determines cell sensitivity to paclitaxel14. However, the mechanism by which Lamin A/C determines sensitivity of cancer cells to paclitaxel is unclear.

Our data are consistent with Lamin A/C being a determinant of paclitaxel survival sensitivity. We also provide evidence that paclitaxel itself reduces Lamin A/C protein levels and disrupts its organisation at the nuclear envelope. We directly link these effects to microtubule bundling around the nucleus and degradation of force-sensing LINC component SUN2, highlighting the importance of nuclear architecture and mechanics to overall cellular function. Furthermore, we show that recovery from paclitaxel treatment depends on Lamin A/C expression levels. This has clinical relevance, as unlike cancer cells, healthy tissue with non-aberrant lamina would be able to selectively recover from paclitaxel treatment.

Minor comments:

While I understand the difficulty of the experiments and the effort the authors have put into producing FIB-SEM tomograms, I am not sure they are helping their study or adding anything beyond the light microscopy images. Some of the images may even be in the way, such as supplementary Figure 6, which lacks in quality, controls, and interpretation. Do I see a lot of mitochondria in that slice?

We agree with the reviewer that Supplementary Figure 6 does not add significant value to the manuscript and thank the reviewer for pointing this out. We have removed it from the manuscript accordingly.

I may have overlooked it, but has the number of cells from which lamellae have been produced been stated?

We thank the reviewer for pointing out the missing information. For our cryo-ET experiments, we collected data from 9 lamellae from paclitaxel-treated cells and 6 lamellae from control cells, with each lamella derived from a single cell. This information has now been added to the figure legend (Figure 2F).

Significance

The significance of studying the effect of paclitaxel, the most successful chemotherapy drug, should be broad and of interest to basic researchers and clinicians.

As outlined above, I believe that major concerns about the design and interpretation of the study hamper its significance and advancements.

We appreciate the reviewer's concerns and have performed major revisions to strengthen the significance of our study. Specifically, we conducted two key sets of experiments to validate our original conclusions: serum starvation to control for the effects of cell division, and overexpression of the microtubule stabiliser Tau to demonstrate that paclitaxel can affect the nucleus via its microtubule bundling activity in interphase.

By elucidating the mechanistic link between microtubule stabilisation and nuclear-cytoskeletal coupling, our findings contribute to our understanding of paclitaxel's multifaceted actions in cancer cells.

My areas of expertise could be broadly defined as Cell Biology, Cytoskeleton, Microtubules, and Structural Biology.

Reviewer #3 Evidence, reproducibility and clarity The manuscript presents interesting new ideas for the mechanism of an old drug, taxol, which has been studied for the last 40 years.

We thank the reviewer for the positive feedback.

Although similar ideas are published, which may be suitable to be cited? • Paclitaxel resistance related to nuclear envelope structural sturdiness. Smith ER, Wang JQ, Yang DH, Xu XX. Drug Resist Updat. 2022 Dec;65:100881. doi: 10.1016/j.drup.2022.100881. Epub 2022 Oct 15. PMID: 36368286 Review. • Breaking malignant nuclei as a non-mitotic mechanism of taxol/paclitaxel. Smith ER, Xu XX. J Cancer Biol. 2021;2(4):86-93. doi: 10.46439/cancerbiology.2.031. PMID: 35048083 Free PMC article.

We thank the reviewer for bringing to our attention these important review articles. In our initial manuscript, we only cited the original paper (14, also reference 14 in the original manuscript). We have now included citations to the suggested publications (20,21).

We would also like to emphasise how our manuscript distinguishes itself from the work of Smith et al.14,20,21:

• Cell-type focus: In their study 14, Smith et al. examined the effect of paclitaxel on malignant ovarian cancer cells and proposed that paclitaxel's effects on the nucleus are limited to cancer cells. However, our data extends these findings by demonstrating paclitaxel's effects in both cancerous and non-cancerous backgrounds.

• Cytoskeletal reorganisation: Smith et al. show reorganisation of microtubules in paclitaxel-treated cells14. Our data show re-organisation of other cytoskeletal components, including F-actin and vimentin.

• Multimicronucleation: Smith et al. propose that paclitaxel-induced multimicronucleation occurs independently of cell division14. Although we observe progressive nuclear abnormalities during interphase over the course of paclitaxel treatment, our data do not support this conclusion; we find that multimicronucleation occurs only following mitosis.

• Direct link between microtubule bundling and nuclear aberrations: We show that nuclear aberrations caused by paclitaxel during interphase (distinct from multimicronucleation) are directly linked to microtubule bundling around the nucleus, suggesting they result from mechanical disruption and altered force propagation.

• Lamin A/C regulation: Consistent with Smith et al.14, we show that Lamin A/C depletion leads to increased sensitivity to paclitaxel treatment. However, we further demonstrate that paclitaxel itself leads to reduced levels of Lamin A/C and that this effect occurs independently of mitosis and is mediated via force-sensing LINC component SUN2. Upon SUN2 knockdown, Lamin A/C levels are no longer affected by paclitaxel treatment.

• Recovery: Finally, our work reveals that cells expressing low levels of Lamin A/C recover less efficiently after paclitaxel removal. This might help explain how cancer cells could be more susceptible to paclitaxel.

Only one cell line was used in all the experiments? "Human telomerase reverse transcriptase (hTERT) immortalised human fibroblasts" ? The cells used are not very relevant to cancer cells (carcinomas) that are treated with paclitaxel. It is not clear if the observations and conclusions will be able to be generalized to cancer cells.

We thank the reviewer for this comment. Our initial study aimed to understand the effects of paclitaxel on nuclear architecture in non-aberrant backgrounds. To show that the observed effects of paclitaxel are also applicable to cancer cells, we have now repeated our main experiments using MDA-MB-231 human breast cancer cells (Supplementary Figure 1B; Supplementary Figure 3P-T). Similar to our findings in human fibroblasts, paclitaxel treatment of MDA-MB-231 led to cytoskeletal reorganisation (Supplementary Figure 1B), a decrease in nuclear solidity (Supplementary Figure 3P), aberrant (patchy) localisation of Lamin A/C (Supplementary Figure 3Q), and a reduction in Lamin A/C and SUN2 levels (Supplementary Figure 3R-T).

"Fig. 1. (B) STORM imaging of α-tubulin immunofluorescence in cells fixed after 16 h incubation in control media or 5 nM paclitaxel. Lower panels show α-tubulin clusters generated with HDBSCAN analysis. Scale bars = 10 μm." It needs explanation of what is meaning of the different color lines in the lower panels, just different filaments?

We have added further detail to the figure legend for clarification: "Lower panels show α-tubulin clusters generated with HDBSCAN analysis. Different colours distinguish individual α-tubulin clusters, representing individual microtubule filaments or filament bundles."

Generally, the figures need additional description to be clear.

We have added further clarification and detail to our figure legends.

"Figure 3 - Paclitaxel results in aberrations to the nuclear lamina." The sentence seems not to be well constructed. "Paclitaxel treatment causes ..."?

We changed this sentence to: "Figure 3 - Paclitaxel treatment results in aberrant organisation of the nuclear lamina and decreased Lamin A/C levels via SUN2."

Lamin A and C levels are different in different images (Fig. 3B, H): some Lamin A is higher, and sometime Lamin C is higher? This may possibly due to culture condition or subtle difference in sample handling?.

We thank the reviewer for pointing this out and we agree that the ratio of Lamin A to Lamin C can vary with culture conditions. To confirm that paclitaxel treatment reduces total Lamin A/C levels regardless of this ratio, we repeated the Western blot analysis in three additional biological replicates using cells in which Lamin C levels exceeded Lamin A levels. These experiments confirmed a comparable decrease in total Lamin A/C levels. Figure 3B and 3C have been updated accordingly.

Also, the effect on Lamin A/C and SUN2 levels are not significant of robust.

Decreased Lamin A/C and SUN2 levels following paclitaxel treatment were consistently seen across three or more biological repeats (Figure 3B-C), and this could be replicated in a different cell type (MDA-MB-231) (Supplementary Figure 3R-T). Furthermore, Western blotting results are consistent with the patchy Lamin A/C distribution observed using confocal and STORM following paclitaxel treatment (Figure 3A; Supplementary Figure 3A), where Lamin A/C appears to be absent from discrete areas of the lamina.

Any mechanisms are speculated for the reason for the reduction?

We have now included additional data which aims to shed light on the mechanism behind the decrease in Lamin A/C and SUN2 levels following paclitaxel treatment. We found that SUN2 is selectively degraded during paclitaxel treatment. Immunoprecipitation of SUN2 followed by Western blotting against Polyubiquitin C showed increased SUN2 ubiquitination in paclitaxel (Figure 3M and N). Furthermore, in our original manuscript, we showed that Lamina A/C levels remained unaltered during paclitaxel treatment in cells where SUN2 had been knocked down. We propose that changes in microtubule organisation affect force propagation to Lamin A/C specifically via SUN2 and that this leads to Lamina A/C removal and depletion. Future work will be needed to fully understand this mechanism.

In addition to the findings described above, we report no significant changes in mRNA levels for LMNA or SUN2 in paclitaxel (Supplementary Figure 3B and O). Phos-tag gels followed by Western blotting analysis for Lamin A/C also did not detect changes to the overall phosphorylation status of Lamin A/C due to paclitaxel treatment. This is in agreement with our initial data showing no changes to Lamin A/C Ser 404 phosphorylation levels (Supplementary Figure 3E and F). Finally, Lamin A/C immunoprecipitation experiments followed by Western blotting for Polyubiquitin C and acetyl-lysine showed no significant changes in the ubiquitination and acetylation state of Lamin A/C in paclitaxel-treated cells (Supplementary Figure 3G-I).

Also, the about 50% reduction in protein level is difficult to be convincing as an explanation of nuclear disruption.

The nuclear lamina and LINC complex proteins play a critical role in regulating nuclear integrity, stiffness and mechanical responsiveness to external forces28,31-33,54,75, as well as in maintaining the nuclear intermembrane distance69,74. In particular, SUN-domain proteins physically bridge the nuclear lamina to the cytoskeleton through interactions with Nesprins, thereby preserving the perinuclear space distance30,69,74. Mutations in Lamins have been shown to disrupt chromatin organization, alter gene expression, and compromise nuclear structural integrity, and experiments with LMNA knockout cells reveal that nuclear mechanical fragility is closely coupled to nuclear deformation47. Furthermore, nuclear-cytoskeletal coupling is essential during processes such as cell migration, where cells undergo stretching and compression of the nucleus; weakening or loss of the lamina in such cases compromises cell movement47,73. In our work, we show that alterations to nuclear Lamin A/C and SUN2 by paclitaxel treatment coincide with nuclear deformations (Figure 2A-D, F, G; Figure 3A-D, F, G; Supplementary Figure 3A, P-T) and that these deformations are reversible following paclitaxel removal (Supplementary Figure 4B-D). Our experiments also demonstrate that Lamin A/C expression levels significantly influence cell growth, cell viability, and cell recovery in paclitaxel (Figure 5). Therefore, drawing on current literature and our results, we propose that, during interphase, paclitaxel induces severe nuclear aberrations through the combined effects of: i) increased cytoskeletal forces on the NE caused by microtubule bundling; ii) loss of ~50% Lamin A/C and SUN2; iii) reorganisation of nucleo-cytoskeletal components.

Significance

The manuscript presents interesting new ideas for the mechanism of an old drug, taxol, which has been studied for the last 40 years.

The data may be improved to provide stronger support.

Additional cell lines (of cancer or epithelial origin) may be repeated to confirm the generality of the observation and conclusions.?

We thank the reviewer for the feedback and valuable suggestions. In response, we have included experiments using human breast cancer cell line MDA-MB-231 to further corroborate our findings and interpretations. We believe these additions have improved the clarity, robustness and impact of our manuscript, and we are grateful for the reviewer's contributions to its improvement.

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

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Referee #1

Evidence, reproducibility and clarity

This description of the down-regulation of the expression of lamin A/C upon treatment with paclitaxel and its sensitivity to SUN2 is quite interesting but still somehow preliminary. It is unclear whether this effect involves the regulation of gene expression, or of the stability of the proteins. How SUN2 mediates this effect is still unknown. The roles of free tubulins and polymerized microtubules, and thus the potential role of paclitaxel, need to be uncovered.

The doses of paclitaxel at which occur the effects described in the paper are not fully consistent with all the conclusions. Most experiments have been done at 5 nM. However, at this dose the effect of lamin A/C over or down expression on the growth (differences in the slopes of the curves in Figure 4A) are not fully convincing and not fully consistent with the clear effect on viability as well (in addition, duration of treatments before assessing vialbility are not specified). At 1 nM, cell growth is reduced and the rescuing effect of lamin over-expression is much more clear (Fig 4A), and the nucleus deformation clear (Fig 2A) but this dose has no effect on lamin A/C expression (Fig 3C), which questions how lamins impact nucleus shape and cell survival. Cytoskeleton reorganisation in these conditions is not described although it could clarify the respective role of force production (suggested in figure 1) and nuclei resistance (shown in figure 2) in paclitaxel sensitivity.

Finally, although the absence of role of mitotic arrest is clear from the data, the defective reorganisation of the nucleus after mitosis still suggest that the effect of paclitaxel is not independent of mitosis.

minor: a more thorough introduction of known data about dose response of cells in culture and in vivo would help understanding the range of concentrations used in this study.

Significance

In this manuscript, Hale and colleagues describe the effect of paclitaxel on nucleus deformation and cell survival. They showed that 5nM of paclitaxel induces nucleus fragmentation, cytoskeleton reorganisation, reduced expression of LaminA/C and SUN2, and reduced cell growth and viability. They also showed that these effects could be at least partly compensated by the over-expression of lamin A/C. As fairly acknowledged by the authors, the induction of nuclear deformation in paclitaxel-treated cells, and the increased sensitivity to paclitaxel of cells expressing low level of lamin A/C are not novel (reference #14). Here the authors provided more details on the cytoskeleton changes and nuclear membrane deformation upon paclitaxel treatment. The effect of lamin A/C over and down expression on cell growth and survival are not fully convincing, as further discussed below. The most novel part is the observation that paclitaxel can induce the down-regulation of the expression of lamin A/C and that this effect is mediated by SUN2.

DOI: 10.1083/jcb.201804018

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DOI: 10.1083/jcb.201804018

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DOI: 10.1083/jcb.201804018

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DOI: 10.1038/s41467-025-56346-3

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What is this?

Creo que vale decir que la mayoría de los proyectos que se han adjudicado el InES, así como las universidades que tienen programas de ciencia abierta (como la UC) siguen enfocados en el vicio que mencionaba anteriormente. Publicaciones abiertas, pero sin profundizar en la cultura de la OS con los demás pasos que involucra.

Me da la impresión que este debiera ser el objetivo fuerte a impulsar dentro del ecosistema de la ciencia abierta. Digo, sobre el que más trabajo falta por hacer. Se podrían citar los ejercicios de replicación de Breznau. No necesariamente acá, sino más adelante al momento de habalr sobre el caso de Chile y latinoamérica.

Acá sería bueno señalar que en términos generales, la mayoría de los logros en ciencias sociales en América Latina se concentran en el crecimiento de fuentes y publicaciones abiertas como Scielo o Redalyc: https://www.ouvrirlascience.fr/latin-america-could-become-a-world-leader-in-non-commercial-open-science/

Sería bueno incluir la fuente acá: https://www.science.org/content/article/university-california-boycotts-publishing-giant-elsevier-over-journal-costs-and-open o https://www.nature.com/articles/d41586-019-00758-x

Author response:

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

Reviewer #1 (Public review):

Summary:

In this study by Li et al., the authors re-investigated the role of cDC1 for atherosclerosis progression using the ApoE model. First, the authors confirmed the accumulation of cDC1 in atherosclerotic lesions in mice and humans. Then, in order to examine the functional relevance of this cell type, the authors developed a new mouse model to selectively target cDC1. Specifically, they inserted the Cre recombinase directly after the start codon of the endogenous XCR1 gene, thereby avoiding off-target activity. Following validation of this model, the authors crossed it with ApoE-deficient mice and found a striking reduction of aortic lesions (numbers and size) following a high-fat diet. The authors further characterized the impact of cDC1 depletion on lesional T cells and their activation state. Also, they provide in-depth transcriptomic analyses of lesional in comparison to splenic and nodal cDC1. These results imply cellular interactions between lesion T cells and cDC1. Finally, the authors show that the chemokine XCL1, which is produced by activated CD8 T cells (and NK cells), plays a key role in the interaction with XCR1-expressing cDC1 and particularly in the atherosclerotic disease progression.<br /> Strengths:

The surprising results on XCL1 represent a very important gain in knowledge. The role of cDC1 is clarified with a new genetic mouse model.

Thank you

Weaknesses:

My criticism is limited to the analysis of the scRNAseq data of the cDC1. I think it would be important to match these data with published data sets on cDC1. In particular, the data set by Sophie Janssen's group on splenic cDC1 might be helpful here (PMID: 37172103; https://www.single-cell.be/spleen_cDC_homeostatic_maturation/datasets/cdc1). It would be good to assign a cluster based on the categories used there (early/late, immature/mature, at least for splenic DC).

Thank you very much for your help. Using the scRNA seq data of Xcr1⁺ cDC1 sorted from ApoE^–/– mice, we re-annotated the populations, following the methodology proposed by Sophie Janssen's group. These results are presented in Figure S9 and Figure S10 and described in detail in the Results and Discussion section.

Please refer to the Results section from line 264 to 284: “Using the scRNA seq data of Xcr1⁺ cDC1 sorted from hyperlipidemic mice, we annotated the 10 populations as shown in Figure S9A, following the methodology from a previous study [41]. Ccr7⁺ mature cDC1s (Cluster 3, 7 and 9) and Ccr7- immature cDC1s (remaining clusters) were identified across cDC1 cells sorted from aorta, spleen and lymph nodes (Figure S9B). Further stratification based on marker genes reveals that Cluster 10 is the pre-cDC1, with high expression level of CD62L (Sell) and low expression level of CD8a (Figure S9C). Cluster 6 and 8 are the proliferating cDC1s, which express high level of cell cycling genes Stmn1 and Top2a (Figure S9D). Cluster 1 and 4 are early immature cDC1s, and cluster 2 and 5 are late immature cDC1s, according to the expression pattern of Itgae, Nr4a2 (Figure S9E). Cluster 9 cells are early mature cDC1s, with elevated expression of Cxcl9 and Cxcl10 (Figure S9F). Cluster 3 and 7 as late mature cDC1s, characterized by the expression of Cd63 and Fscn1 (Figure S9G). As shown in Figure 5C and Figure S9, the 10 populations displayed a major difference of aortic cDC1 cells that lack in pre-cDC1s (cluster 10) and mature cells (cluster 3, 7 and 9). Interestingly, in hyperlipidemic mice splenic cDC1 possess only Cluster 3 as the late mature cells while the lymph node cDC1 cells have two late mature populations namely Cluster 3 and Cluster 7. In further analysis, we also compared splenic cDC1 cells from HFD mice to those from ND mice. As shown in Figure S10, HFD appears to impact early immature cDC1-1 cells (Cluster 1) and increases the abundance of late immature cDC1 cells (Cluster 2 and 5), regardless of the fact that all 10 populations are present in two origins of samples. We also found that Tnfaip3 and Serinc3 are among the most upregulated genes, while Apol7c and Tifab are downregulated in splenic cDC1 cells sorted from HFD mice”.  

Please refer to the Discussion section from line 380 to 385: “Based on the maturation analysis of the cDC1 scRNA seq data [41], our findings suggest that the aortic cDC1 cells display a major difference from those of spleen and lymph nodes by lacking the mature clusters, whereas lymph node cDC1 cells contain an additional Fabp5⁺ S100a4⁺ late mature Cluster. Our results also suggest that hyperlipidemia contributes to alteration in early immature cDC1 and in the abundance of late immature cDC1 cells, which was associated with dramatic change in gene expression of Tnfaip3, Serinc3, Apol7c and Tifab”.

Reviewer #2 (Public review):

This study investigates the role of cDC1 in atherosclerosis progression using Xcr1Cre-Gfp Rosa26LSL-DTA ApoE-/- mice. The authors demonstrate that selective depletion of cDC1 reduces atherosclerotic lesions in hyperlipidemic mice. While cDC1 depletion did not alter macrophage populations, it suppressed T cell activation (both CD4+ and CD8+ subsets) within aortic plaques. Further, targeting the chemokine Xcl1 (ligand of Xcr1) effectively inhibits atherosclerosis. The manuscript is well-written, and the data are clearly presented. However, several points require clarification:

(1) In Figure 1C (upper plot), it is not clear what the Xcr1 single-positive region in the aortic root represents, or whether this is caused by unspecific staining. So I wonder whether Xcr1 single-positive staining can reliably represent cDC1. For accurate cDC1 gating in Figure 1E, Xcr1+CD11c+ co-staining should be used instead.

The observed false-positive signal in the wavy structures within immunofluorescence Figure 1C (upper panel) results from the strong autofluorescence of elastic fibers, a major vascular wall component (alongside collagen). This intrinsic property of elastic fibers is a well-documented confounder in immunofluorescence studies [A, B].

In contrast, immunohistochemistry (IHC) employs an enzymatic chromogenic reaction (HRP with DAB substrate) that generates a brown precipitate exclusively at antigen-antibody binding sites. Importantly, vascular elastic fibers lack endogenous enzymatic activity capable of catalyzing the DAB reaction, thereby preventing this source of false positivity in IHC.

Given that Xcr1 is exclusively expressed on conventional type 1 dendritic cells [C], and considering that IHC lacks the multiplexing capability inherent to immunofluorescence for antigen co-localization, single-positive Xcr1 staining reliably identifies cDC1s in IHC results.

[A] König, K et al. “Multiphoton autofluorescence imaging of intratissue elastic fibers.” Biomaterials vol. 26,5 (2005): 495-500. doi:10.1016/j.biomaterials.2004.02.059

[B] Andreasson, Anne-Christine et al. “Confocal scanning laser microscopy measurements of atherosclerotic lesions in mice aorta. A fast evaluation method for volume determinations.” Atherosclerosis vol. 179,1 (2005): 35-42. doi:10.1016/j.atherosclerosis.2004.10.040

[C] Dorner, Brigitte G et al. “Selective expression of the chemokine receptor XCR1 on cross-presenting dendritic cells determines cooperation with CD8+ T cells.” Immunity vol. 31,5 (2009): 823-33. doi:10.1016/j.immuni.2009.08.027

(2) Figure 4D suggests that cDC1 depletion does not affect CD4+/CD8+ T cells. However, only the proportion of these subsets within total T cells is shown. To fully interpret effects, the authors should provide:

(a) Absolute numbers of total T cells in aortas.

(b) Absolute counts of CD4+ and CD8+ T cells.

Thanks for your suggestions. We agree that assessing both proportions and absolute numbers in Figure 4 provides a more complete picture of the effects of cDC1 depletion on T cell populations. Furthermore, we also add the absolute count of cDC1 cells and total T cells, and CD44 MFI (mean fluorescence intensity) in CD4⁺ and CD8⁺ T cells in Figure 4, and supplemented corresponding textual descriptions in the revised manuscript.

Please refer to the Results section from line 183 to 187: “Subsequently, we assessed T cell phenotype in the two groups of mice. While neither the frequencies nor absolute counts of aortic CD4⁺ and CD8⁺ T cells differed significantly between two groups of mice (Figure 4D-F), CD69 frequency and CD44 MFI (Mean Fluorescence Intensity), the T cell activation markers, were significantly reduced in both CD4⁺ and CD8⁺ T cells from Xcr1⁺ cDC1 depleted mice compared to controls (Figure 4G and H)”.

(3) How does T cell activation mechanistically influence atherosclerosis progression? Why was CD69 selected as the sole activation marker? Were other markers (e.g., KLRG1, ICOS, CD44) examined to confirm activation status?

We sincerely appreciate these insightful comments. As extensively documented in the literature, activated effector T cells (both CD4+ and CD8+) critically promote plaque inflammation and instability through their production of pro-inflammatory cytokines (particularly IFN-γ and TNF-α), which drive endothelial activation, exacerbate macrophage inflammatory responses, and impair smooth muscle cell function [A].

In our study, we specifically investigated the role of cDC1 cells in atherosclerosis progression. Our key findings demonstrate that cDC1 depletion attenuates T cell activation (as shown by reduced CD69/CD44 expression) and that this reduction in activation is functionally linked to the observed decrease in atherosclerosis burden in our model. 

Regarding CD44 as an activation marker, we performed quantitative analyses of CD44 mean fluorescence intensity (MFI) in aortic T cells (Figure 4). Importantly, the MFI of CD44 was significantly lower on both CD4+ and CD8+ T cells from Xcr1^Cre-Gfp Rosa26^LSL-DTA ApoE^–/– mice compared to the control ApoE^–/– mice (data shown below), which is consistent with the result of CD69 in Figure 4. We added the related description in the Result section.

Please refer to the Results section from line 185 to 187 “CD69 frequency and CD44 MFI (Mean Fluorescence Intensity), the T cell activation markers, were significantly reduced in both CD4+ and CD8+ T cells from Xcr1+ cDC1 depleted mice compared to controls (Figure 4G and H)”.

Similarly, MFI of CD44 was significantly lower on both CD4⁺ and CD8⁺ T cells from Xcl1^–/– ApoE^–/– mice compared to the control ApoE^–/– mice (data shown below), which is consistent with the result of CD69 in Figure 7. We also added the related description in the Result section.

Please refer to the Results section from line 308 to 309 “Crucially, CD69⁺ frequency and CD44 MFI remained comparable in both aortic CD4⁺ and CD8⁺ T cells between two groups (Figure 7D-F).”

[A] Hansson, Göran K, and Andreas Hermansson. “The immune system in atherosclerosis.” Nature immunology vol. 12,3 (2011): 204-12. doi:10.1038/ni.2001

(4) Figure 7B: Beyond cDC1/2 proportions within cDCs, please report absolute counts of: Total cDCs, cDC1, and cDC2 subsets. Figure 7D: In addition to CD4+/CD8+ T cell proportions, the following should be included:

(a) Total T cell numbers in aortas

(b) Absolute counts of CD4+ and CD8+ T cells.

Thanks for your suggestions. We have now included in Figure 7 the absolute counts of cDC, cDC1, and cDC2 cells, along with CD4⁺ and CD8⁺ T cells in aortic tissues. Additionally, we provide the corresponding CD44 mean fluorescence intensity (MFI) measurements for both CD4⁺ and CD8⁺ T cell populations. We added the related description in the Result section.

Please refer to the Results section from line 303 to 311: “The flow cytometric results illustrated that both frequencies and absolute counts of Xcr1⁺ cDC1 cells in the aorta were significantly reduced, but cDCs and cDC2 cells from Xcl1^–/– ApoE^–/– were comparable with that from ApoE^–/– (Figure 7A-C). Moreover, in both lymph node and spleen, the absolute numbers of pDC, cDC1 and cDC2 from Xcl1^–/– ApoE^–/– were comparable with that from ApoE^–/– (Figure S11). Crucially, CD69⁺ frequency and CD44 MFI remained comparable in both aortic CD4⁺ and CD8⁺ T cells between two groups (Figure 7D-F). However, aortic CD8⁺ T cells exhibited reduced frequency and absolute count, while CD4⁺ T cells showed increased frequency but unchanged counts in Xcl1^–/– ApoE^–/– mouse versus controls (Figure 7G and H).”

(5) cDC1 depletion reduced CD69+CD4+ and CD69+CD8+ T cells, whereas Xcl1 depletion decreased Xcr1+ cDC1 cells without altering activated T cells. How do the authors explain these different results? This discrepancy needs explanation.

We sincerely appreciate your professional and insightful comments regarding the mechanistic relationship between cDC1 depletion and T cell activation. Direct cDC1 depletion in the Xcr1^Cre-Gfp Rosa26^LSL-DTA ApoE^–/– micmodel removes both recruited and tissue-resident cDC1s, eliminating their multifunctional roles in antigen presentation, co-stimulation and cytokine secretion essential for T cell activation. In contrast, Xcl1 depletion reduces, but does not eliminate cDC1 migration into plaques. Furthermore, alternative chemokine axes (e.g., CCL5/CCR5, CXCL9/CXCR3, BCL9/BCL9L) may partially rescue cDC1 recruitment [13, 68, 69], and non-cDC1 APCs (e.g., monocytes, cDC2s) may compensate for T cell activation [55, 70]. We emphasize that Xcl1 depletion specifically failed to alter T cell activation in hyperlipidemic ApoE^–/– mice. However, its impact may differ in other pathophysiological contexts due to compensatory mechanisms. We thank you again for highlighting this nuance, which strengthens our mechanistic interpretation. We have added these points to the discussion section and included new references.

Please refer to the Discussion section from line 407 to 413: “Notably, while complete ablation of Xcr1⁺ cDC1s impaired T cell activation, reduction of Xcr1⁺ cDC1 recruitment via Xcl1 deletion did not significantly compromise this process. This discrepancy may arise through compensatory mechanisms: alternative chemokine axes (e.g., CCL5/CCR5, CXCL9/CXCR3, BCL9/BCL9L) may partially rescue Xcr1⁺ cDC1 homing [13, 68, 69], while non-cDC1 antigen-presenting cells (e.g., monocytes, cDC2s) may sustain T cell activation [55, 70]. Furthermore, tissue-specific microenvironment factors could potentially modulate its role in other diseases.”. [13] Eisenbarth, S C. “Dendritic cell subsets in T cell programming: location dictates function.” Nature reviews. Immunology vol. 19,2 (2019): 89-103. doi:10.1038/s41577-018-0088-1 [55] Brewitz, Anna et al. “CD8+ T Cells Orchestrate pDC-XCR1+ Dendritic Cell Spatial and Functional Cooperativity to Optimize Priming.” Immunity vol. 46,2 (2017): 205-219. doi:10.1016/j.immuni.2017.01.003 [68] de Oliveira, Carine Ervolino et al. “CCR5-Dependent Homing of T Regulatory Cells to the Tumor Microenvironment Contributes to Skin Squamous Cell Carcinoma Development.” Molecular cancer therapeutics vol. 16,12 (2017): 2871-2880. doi:10.1158/1535-7163.MCT-17-0341.[69] He F, Wu Z, Liu C, Zhu Y, Zhou Y, Tian E, et al. Targeting BCL9/BCL9L enhances antigen presentation by promoting conventional type 1 dendritic cell (cDC1) activation and tumor infiltration. Signal Transduct Target Ther. 2024;9(1):139. Epub 2024/05/30. doi: 10.1038/s41392-024-01838-9. PubMed PMID: 38811552; PubMed Central PMCID: PMCPMC11137111.[70] Böttcher, Jan P et al. “Functional classification of memory CD8(+) T cells by CX3CR1 expression.” Nature communications vol. 6 8306. 25 Sep. 2015, doi:10.1038/ncomms9306.

Reviewer #1 (Recommendations for the authors):

(1) Line 32 - The authors might want to add that the mouse model leads to a "constitutive" depletion of cDC1.

Thanks for your advice, we have revised the sentence as follows.

Please refer to the Results section from line 31 to 33: “we established Xcr1^Cre-Gfp Rosa26^LSL-DTA ApoE^–/– mice, a novel and complex genetic model, in which cDC1 was constitutively depleted in vivo during atherosclerosis development”.

(2) Line 187-188: The authors claim that T cell activation was "inhibited" if cDC1 was depleted. The data shows that the T cells were less activated, but there is no indication of any kind of inhibition; this should be corrected.

Thanks for your advice, we have revised the sentence as follows.

Please refer to the Results section from line 183 to 187: “Subsequently, we assessed T cell phenotype in the two groups of mice. While neither the frequencies nor absolute counts of aortic CD4⁺ and CD8⁺ T cells differed significantly between two groups of mice (Figure 4D-F), CD69 frequency and CD44 MFI (Mean Fluorescence Intensity), the T cell activation markers, were significantly reduced in both CD4⁺ and CD8⁺ T cells from Xcr1⁺ cDC1 depleted mice compared to controls (Figure 4G and H)”.

(3) Why are some splenic DC clusters absent in LNs and vice versa? This is not obvious to this reviewer and should at least be discussed.

We appreciate the insightful question regarding the absence of certain splenic DC clusters in LNs. This phenomenon in Figure 5 aligns with the 'division of labor' paradigm in dendritic cell biology: tissue microenvironments evolve specialized DC subsets to address local immunological challenges. The absence of universal clusters reflects functional adaptation, not technical artifacts. We acknowledge that this tissue-specific heterogeneity warrants further discussion and have expanded our analysis to address this point in the discussion part of our manuscript.

Please refer to the Discussion section from line 375 to 385: “This pronounced tissue-specific compartmentalization of Xcr1⁺ cDC1 subsets may related to multiple mechanisms including developmental imprinting that instructs precursor differentiation into transcriptionally distinct subpopulations [62], and microenvironmental filtering through organ-specific chemokine axes (e.g., CCL2/CCR2 in spleen) selectively recruits receptor-matched subsets [63, 64]. This spatial specialization optimizes pathogen surveillance for local immunological challenges. Based on the maturation analysis of the cDC1 scRNA seq data [41], our findings suggest that the aortic cDC1 cells display a major difference from those of spleen and lymph nodes by lacking the mature clusters, whereas lymph node cDC1 cells contain an additional Fabp5⁺ S100a4⁺ late mature Cluster. Our results also suggest that hyperlipidemia contributes to alteration in early immature cDC1 and in the abundance of late immature cDC1 cells, which was associated with dramatic change in gene expression of Tnfaip3, Serinc3, Apol7c and Tifab”.

[62]. Liu Z, Gu Y, Chakarov S, Bleriot C, Kwok I, Chen X, et al. Fate Mapping via Ms4a3-Expression History Traces Monocyte-Derived Cells. Cell. 2019;178(6):1509-25 e19. Epub 2019/09/07. doi: 10.1016/j.cell.2019.08.009. PubMed PMID: 31491389.

[63]. Bosmans LA, van Tiel CM, Aarts S, Willemsen L, Baardman J, van Os BW, et al. Myeloid CD40 deficiency reduces atherosclerosis by impairing macrophages' transition into a pro-inflammatory state. Cardiovasc Res. 2023;119(5):1146-60. Epub 2022/05/20. doi: 10.1093/cvr/cvac084. PubMed PMID: 35587037; PubMed Central PMCID: PMCPMC10202633.

[64]. Mildner A, Schonheit J, Giladi A, David E, Lara-Astiaso D, Lorenzo-Vivas E, et al. Genomic Characterization of Murine Monocytes Reveals C/EBPbeta Transcription Factor Dependence of Ly6C(-) Cells. Immunity. 2017;46(5):849-62 e7. Epub 2017/05/18. doi: 10.1016/j.immuni.2017.04.018. PubMed PMID: 28514690.

[41]. Bosteels V, Marechal S, De Nolf C, Rennen S, Maelfait J, Tavernier SJ, et al. LXR signaling controls homeostatic dendritic cell maturation. Sci Immunol. 2023;8(83):eadd3955. Epub 2023/05/12. doi: 10.1126/sciimmunol.add3955. PubMed PMID: 37172103.

(4) The authors should discuss how XCL1 could impact lesional cDC1 and T cell abundance. Notably, preDCs do not express XCR1, and T cells express XCL1 following TCR activation. Is there a recruitment or local proliferation defect of cDC1 in the absence of XCL1? Could there also be a role for NK cells as a potential source of XCL1?

We appreciate your insightful questions regarding the differential effects of Xcl1 on cDC1s and T cells. Xcl1 primarily mediates the recruitment of mature cDC1s. Our data demonstrate that Xcl1 deletion significantly reduces aortic cDC1 abundance, which correlates with a concomitant decrease in CD8⁺ T cell numbers within the aorta. These findings strongly suggest that the Xcl1-Xcr1 axis plays a regulatory role in T cell accumulation in aortic plaques.

Consistent with prior studies [A, B], cDC1 recruitment can occur in the absence of Xcl1 which echoes our findings that cDC1 cells were still found in Xcl1 knockout aortic plaque but in lower abundance. It is very true that further studies are required to address how the Xcl1 dependent and independent cDC1 cells activate T cells and if they possess capability of proliferation in tissue differentially. We have added these points in discussion section.

Please refer to the Discussion section from line 407 to 415: “Notably, while complete ablation of Xcr1⁺ cDC1s impaired T cell activation, reduction of Xcr1⁺ cDC1 recruitment via Xcl1 deletion did not significantly compromise this process. This discrepancy may arise through compensatory mechanisms: alternative chemokine axes (e.g., CCL5/CCR5, CXCL9/CXCR3, BCL9/BCL9L) may partially rescue Xcr1⁺ cDC1 homing [13, 68, 69], while non-cDC1 antigen-presenting cells (e.g., monocytes, cDC2s) may sustain T cell activation [55, 70]. Furthermore, tissue-specific microenvironment factors could potentially modulate its role in other diseases. In summary, our findings identify Xcl1 as a potential therapeutic target for atherosclerosis therapy, though its cellular origins and regulation of lesional Xcr1⁺ cDC1 and T cells dynamics require further studies”.

In literatures, Xcl1 are expressed in NK cells and subsects of T cells, and NK cells can be a potential source of Xcl1 during atherosclerosis which deserve further investigations [A, C, D].

[A] Böttcher, Jan P et al. “NK Cells Stimulate Recruitment of cDC1 into the Tumor Microenvironment Promoting Cancer Immune Control.” Cell vol. 172,5 (2018): 1022-1037.e14. doi:10.1016/j.cell.2018.01.004

[B] He, Fenglian et al. “Targeting BCL9/BCL9L enhances antigen presentation by promoting conventional type 1 dendritic cell (cDC1) activation and tumor infiltration.” Signal transduction and targeted therapy vol. 9,1 139. 29 May. 2024, doi:10.1038/s41392-024-01838-9

[C] Woo, Yeon Duk et al. “The invariant natural killer T cell-mediated chemokine X-C motif chemokine ligand 1-X-C motif chemokine receptor 1 axis promotes allergic airway hyperresponsiveness by recruiting CD103+ dendritic cells.” The Journal of allergy and clinical immunology vol. 142,6 (2018): 1781-1792.e12. doi:10.1016/j.jaci.2017.12.1005

[D] Winkels, Holger et al. “Atlas of the Immune Cell Repertoire in Mouse Atherosclerosis Defined by Single-Cell RNA-Sequencing and Mass Cytometry.” Circulation research vol. 122,12 (2018): 1675-1688. doi:10.1161/CIRCRESAHA.117.312513

Reviewer #2 (Recommendations for the authors):

There is a logical error in line 298. I suggest revising to: "Collectively, these data suggest that Xcl1 promotes atherosclerosis by recruiting Xcr1+ cDC1 cells, which subsequently drive T cell activation in lesions."

Thanks for your advice. Since Xcl1 deficiency reduced both the frequencies and absolute counts of Xcr1+ cDC1 and CD8+ T cells in lesions without affecting T cell activation, we revised the sentence as you suggested.

Please refer to the Results section from line 314 to 315: “Collectively, these data suggest that Xcl1 promotes atherosclerosis by recruiting Xcr1⁺ cDC1 cells, and facilitating CD8⁺ T cell accumulation in lesions”.

Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

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Reply to the reviewers

Manuscript number: RC-2025-02946

Corresponding author(s): Margaret, Frame

Roza, Masalmeh

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Reviewer #1

Evidence, reproducibility and clarity

Review of Masalmeh et al. Title: "FAK modulates glioblastoma stem cell energetics..."

Previous studies have implicated FAK and the related tyrosine kinase PYK2 in glioblastoma growth, cell migration, and invasion. Herein, using a murine stem cell model of glioblastoma, the authors used CRISPR to inactivate FAK, FAK-null cells selected and cloned, and lentiviral re-expression of murine FAK in the FAK-null cells (termed FAK Rx) was accomplished. FAK-/- cells were shown to possess epithelial characteristics whereas FAK Rx cells expressed mesenchymal markers and increased cell migration/invasion in vitro. Comparisons between FAK-/- and FAK Rx cells showed that FAK re-expressed increased mitochondrial respiration and amino acid uptake. This was associated with FAK Rx cells exhibiting filamentous mitochondrial morphology (potentially an OXPHOS phenotype) and decreased levels of MTFR1L S235 phosphorylation (implicated in mito morphology fragmentation). Mito and epithelial cell morphology of FAK-/- cells was reversed by treatment with Rho-kinase inhibitors that also increased mito metabolism and cell viability. Last, FAK-dependent glioblastoma tumor growth was shown by comparisons of FAK-/- and FAK Rx implantation studies.

The studies by Masalmeh provide interesting findings associating FAK expression with changes in mitochondrial morphology, energy metabolism, and glutamate uptake. According to the authors model, FAK expression is supporting a glioblastoma stem cell like phenotype in vitro and tumor growth in vivo. What remains unclear is the mechanistic connection to cell changes and whether or not these are be dependent on intrinsic FAK activity or as the Frame group has previously published, potentially FAK nuclear localization. The associations with MTFR1L phosphorylation and effects by Rho kinase inhibition are likely indirect and remind this reviewer of long-ago studies with FAK-null fibroblasts that exhibit epithelial characteristics, still express PYK2, exhibited elevated RhoA GTPase activity. Some of these phenotypes were linked to changes in RhoGEF and RhoGAP signaling with FAK and/or Pyk2. At a minimum, it would be informative to know whether Pyk2 signaling is relevant for observed phenotypes and whether the authors can further support their associations with FAK-targeted or FAK-Pyk2-targeted inhibitors or PROTACs.

Some questions that would enhance potential impact. 1. Cell generation. Please describe the analysis of FAK-/- clones in more detail. The "low viability" phenotype needs further explanation with regard to clonal expansion and growth characteristics?

Response:

• We included a better description and a supplementary figure in our revised manuscript to indicate that we have examined several FAK -/- clones and confirmed that our observations were not due to clonal variation; multiple clones displayed similar morphological changes (Figure S1D). We also show that the elongated mesenchymal-like morphology was observed at 48 h after nucleofecting the cells with the FAK‑expressing vector, before beginning G418 selection to enrich for cells expressing FAK (Figure S1C). We also included experiments to acutely modulate FAK signalling (detaching and seeding cells on fibronectin) (Figure S2D, E, F and Figure S3) to exclude the possibility that the profound effects are due to protocols/selection we used for generating FAK-deleted cells.
• Regarding the term "low viability", we have clarified in the text that there is no significant difference in cell number (Figure S1A) or 'cell viability' when it is assessed by trypan blue exclusion (a non-mitochondria-dependent read-out) (Figure S1B) between FAK-expressing FAK Rx and FAK-/- cells cultured for three days under normal conditions. Therefore, we agree the term 'cell viability' in this context could be confusing and have replace "cell viability" with "metabolic activity as measured by Alamar Blue." in Figure 1D and Figure 5B, and the corresponding text in the original manuscript. This wording more accurately reflects the data.

Figure 1F: need further support of MET change upon FAK KO and EMT reversion.

Response: We have added a heatmap (Figure S1E) illustrating the changes in protein expression of core-enriched EMT/MET genes products (by proteomics) after FAK gene deletion (EMT genes as defined in Howe et al., 2018) ; this strengthens the conclusion that the MET reversion morphological phenotype is accompanied by recognised MET protein changes.

Fig. 2: Need further support if FAK effects impact glycolysis or oxidative phosphorylation in particular as implicated by the stem cell model.

Response: We show that FAK impacts both glycolysis (Figure 2A, 2E, and 2F) and mitochondrial oxidative phosphorylation on the basis of the oxygen consumption rate (OCR) (Figure 2B, and 2D), showing both are contributing pathways to FAK-dependent energy production. We have clarified this in the text.

Is there a combinatorial potential between FAKi and chemotherapies used for glioblastoma. Need to build upon past studies.

Response: Yes, previous studies suggest that inhibiting FAK can sensitize GBM cells to chemotherapy (Golubovskaya et al., 2012; Ortiz-Rivera et al., 2023). We have included a paragraph in the discussion section to make sure this is clearer. Although it is not the subject of this study, we appreciate it is useful context.

The notation of changes in glucose transporter expression should be followed up with regard to the potential that FAK-expressing cells may have different uptake of carbon sources and other amino acids. Altered uptake could be one potential explanation for increase glycolysis and glutamine flux.

Response: We agree with the reviewer that glucose uptake could be contributing and we include data that 2 glucose transporters are indeed FAK-regulated namely Glucose transporter 1 (GLUT1, encoded by Slc2a1 gene) and Glucose transporter 3 (GLUT 3, encoded by Slc2a3 gene) (shown in Figure S2B and C).

It would be helpful to support the confocal microscopy of mitos with EM.

Response:

We are concerned (and in our experience) that Electron microscopy (EM) may introduce artefacts during sample preparation. In contrast, immunofluorescence sample preparation is less susceptible to artefacts. The SORA system we used is not a conventional point-scanning confocal microscope, but is a super-resolution module based on a spinning disk confocal platform (CSU-W1; Yokogawa) using optical pixel reassignment with confocal detection. This method enhances resolution in all dimensions with resolution in our samples measured at 120nm. This has been instructive in defining a new level of changes in mitochondrial morphology upon FAK gene deletion.

Lack of FAK expression with increased MTFR1 phosphorylation is difficult to interpret.

Response: We do not directly show that this phosphorylation event is causal in our experiments; however, we think it important to document this change since it has been published that phosphorylation of MTFR1 has been causally linked to the mitochondrial morphology we observed in other systems (Tilokani et al., 2022).

Need to have better support between loss of FAK and the increase in Rho signaling. Use of Rho kinase inhibitors is very limited and the context to FAK (and or Pyk2) remains unclear. Past studies have linked integrin adhesion to ECM as a linkage between FAK activation and the transient inhibition of RhoA GTP binding. Is integrin signaling and FAK involved in the cell and metabolism phenotypes in this new model?

Response: To better support the antagonistic effect of FAK on Rho-kinase (ROCK) signalling, we included a new experiment in which the integrin-FAK signalling pathway has been disrupted by treating FAK WT cells with an agent that causes detachment from the substratum, Accutase, and growing the cells in suspension in laminin-free medium. We present ROCK activity data, as judged by phosphorylated MLC2 at serine 19 (pMLC2 S19), relating this to induced FAK phosphorylation at Y397 (a surrogate for FAK activity) that is supressed after integrin disengagement. These measurements have been compared with conditions whereby integrin-FAK signalling is activated by growing the cells on laminin coated surfaces. We observed a time-dependent decrease in pFAK(Y397) levels (normalised to total FAK) in suspended cells compared to those spread on laminin, while pMLC2(S19) levels increased in a reciprocal manner over time in detached cells relative to spread cells (S4A and B). There is therefore an inverse relationship between integrin-FAK signalling and ROCK-MLC2 activity, consistent with findings from FAK gene deletion experiments. In the former case, we do not rely on gene deletion cell clones.

Significance

The studies by Masalmeh provide interesting findings associating FAK expression with changes in mitochondrial morphology, energy metabolism, and glutamate uptake. According to the authors model, FAK expression is supporting a glioblastoma stem cell like phenotype in vitro and tumor growth in vivo. What remains unclear is the mechanistic connection to cell changes and whether or not these are be dependent on intrinsic FAK activity or as the Frame group has previously published, potentially FAK nuclear localization. The associations with MTFR1L phosphorylation and effects by Rho kinase inhibition are likely indirect and remind this reviewer of long-ago studies with FAK-null fibroblasts that exhibit epithelial characteristics, still express PYK2, exhibited elevated RhoA GTPase activity. Some of these phenotypes were linked to changes in RhoGEF and RhoGAP signaling with FAK and/or Pyk2. At a minimum, it would be informative to know whether Pyk2 signaling is relevant for observed phenotypes and whether the authors can further support their associations with FAK-targeted or FAK-Pyk2-targeted inhibitors or PROTACs.

__Response: __

Deleting the gene encoding FAK in mouse embryonic fibroblasts leads to elevated Pyk2 expression (Sieg, 2000). However, in the GBM stem cell model we used here, Pyk2 was not expressed (determined by both transcriptomics and proteomics). We have included Figure S1E to show that PYK2 expression was undetectable in FAK -/- and FAK Rx cells at the RNA level (Figure S1F). We conclude that there is no compensatory increase in Pyk2 upon FAK loss in these cells. In the transformed neural stem cell model of GBM, we do not consistently or robustly detect nuclear FAK.

Review #2

Masalmeh and colleagues employ a neural stem/progenitor cell-based glioma model (NPE cells) to investigate the role of Focal Adhesion Kinase (FAK) in GBM, with a focus on potential links between the regulation of morphological/adhesive and metabolic GBM cell properties. For this, the authors employ wt cells alongside newly generated FAK-KO and -reexpressing cells, as well as pharmacological interventions to probe the relevance of specific signaling pathways. The authors´ main claims are that FAK crucially modulates glioma cell morphology, cell-cell and cell-substrate interactions and motility, as well as their metabolism, and that these effects translate to changes to relevant in vivo properties such as invasion and tumor growth.

My main issues are with the model chosen by the authors.

As per the methods section, generation of FAK-KO and -"Rx" NPE cells entailed protracted selection/expansion processes, which may have resulted in inadvertent selection for cellular/molecular properties unrelated to the desired one (loss or gain of FAK expression) and which may have had cascading effects on NPE cells. The authors nonetheless repeatedly claim the parameters they quantify, such as mitochondrial or cytoskeletal properties or metabolic features, to have directly resulted from FAK loss or reintroduction. Examples of such causal inferences are to be found in lines 123, 134/135, 165, 181. Such causal claims are, in my view, unsupported.

Acute perturbation of FAK expression/activity, genetically or pharmacologically, followed by a rapid assessment of the processes under investigation, would be needed to begin to assess causality, even if acute genetic perturbations may be technically challenging as sufficient gene expression reduction or restoration to physiologically relevant levels may be hard to achieve.

Response:

We would like to first comment on the model we used here, which we think will clarify the validity of our approach. The model is a transformed stem cell model of GBM that was published in (Gangoso et al., Cell, 2021) and is now used regularly in the GBM field. As mentioned in the response to Reviewer 1, we have added text (page 4 and 5 in the revised manuscript) and a new supplementary figure (Figure S1D) clarifying that the morphological changes we observed were consistent across multiple FAK -/- clones, showing this was not due to any inter-clonal variability. We also added images showing that the morphological changes were apparent at 48 h after nucleofecting FAK -/- cells with the FAK‑expressing vector specifically (not the empty vector), prior to starting G418 selection to enrich for FAK‑expressing cells (Figure S1C), addressing the worry that clonal variation and selection was the cause of the FAK-dependent phenotypes we observed. We believe that our model provides a type of well controlled, clean genetic cancer cell system of a type that is commonly used in cancer cell biology, allowing us to attribute phenotypes to individual proteins.

We have also carried out a more acute treatment by using the FAK inhibitor VS4718 to perturb FAK kinase activity and assessed the effects on glycolysis and glutamine oxidation after 48h treatment (Figure S2D, E and F). We found that treating the transformed neural stem cells (parental population) with FAK inhibitor (300nM VS4718) decreases glucose incorporation into glycolysis intermediates and glutamine incorporation into TCA cycle intermediates, consistent with a role for FAK's kinase activity in maintaining glycolysis and glutamine oxidation.

The employed pharmacological modulation of ROCK activity is the only approach that, given the presumably acute nature of the treatment, may have allowed the authors to probe the proposed functional links. The methods section of the manuscript does not however comprise details as to the duration of these treatments, which leaves open the possibility of long-term treatment having been carried out (data shown in Figure 5B refers to 72hr treatment).

__Response: __

We have added the duration of the treatment to the Methods section and Figure Legends, to clarify that cells were treated with ROCK inhibitors for 24h, before assessing the effects on mictochondria (Figure 4C, D, S4C and D) and glutamine oxidation (Figure 5A, and S5). For metabolic activity by AlamarBlue assay, cells were treated with ROCK inhibitors for 72h (Figure 5B).

Even in the case of ROCK inhibitor experiments, it is however unclear if and how the effects on cell morphology and adhesion, mitochondrial organization and metabolic activity may be connected to each other and, if at all, to FAK expression.

Given the above uncertainties due to the nature of the model and experimental approaches, it is hard to assess the reliability and thus the relevance of the findings.

Response:

FAK suppresses ROCK activity (as judged by pMLC2 S19, Figure 4A and B). Treating FAK -/- cells with two different ROCK inhibitors restored mesenchymal-like cell morphology, mitochondrial morphology and glutamine oxidation. As mentioned above, to strengthen our evidence for the antagonistic role of FAK in ROCK-MLC2 signalling, we have now introduced an experiment whereby integrin-FAK signalling was disrupted through treatment with a detachment agent (Accutase), and subsequently maintaining the cells in suspension in laminin-free medium. We assessed pMLC2 S19 levels (a measure of ROCK activity) relating this to FAK phosphorylation that is supressed after integrin disengagement. These results were evaluated relative to spread wild type cells growing on laminin where Integrin-FAK signalling was active (Figure S4A and B). We observed an inverse relationship between Integrin-FAK signalling and ROCK-MLC2 activity in keeping with our conclusions (Figure 4A and B).

Experimental support for the ability of cell-substrate interaction modulation to concomitantly impact cellular metabolism and motility/invasion would be significant both in terms of advancing our understanding of glioma cell biology and of its translational potential, but the evidence being provided is at best compatible with the proposed model.

Response: We carried out a new experiment to support the ability of cell-substrate interaction modulation to impact metabolism; specifically, we inhibited cell-substrate interactions by plating the cells on Poly-2-hydroxyethyl methacrylate (Poly 2-HEMA)-coated dishes. This suppressed FAK phosphorylation at Y397, as expected, with concomitant reduction in glutamine utilisation in the TCA cycle (Figure S3A, B and C).

My background/expertise is in developmental and adult neurogenesis, in vivo modelling of gliomagenesis and cell fate control/reprogramming, with a focus on molecular mechanisms of differentiation and quantitative aspects of lineage dynamics; molecular details of the control of cellular metabolism, cell-cell adhesion and cytoskeletal dynamics are not core expertise of mine.

We appreciate this reviewer's expertise are not necessarily in the cancer cell biology and genetic intervention aspects of our study. We hope that the explanations we have provided satisfy the reviewer that our conclusions are valid.

no será mejor agregar en el mismo esquema cuáles son dimensiones y cuáles subdimensiones?

párrafo 2 de introducción, y falta agregar lo del OCS

mejor incluir la descripción en párrafo arriba

Author response:

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

Reviewer #1 (Public Review):

Major points

(1) The authors discovered a novel regulation of the Hippo-YAP pathway by SARS-CoV-2 infection but did not address the pathological significance of this finding. It remains unclear why YAP downstream gene transcription needs to be inhibited in response to SARS-CoV-2 infection. Is this inhibition crucial for the innate immune response to SARS-CoV-2? The authors should re-analyze their snRNA-seq and bulk RNA-seq data described in Figure 1 to determine whether any of the affected YAP downstream genes are involved in this process.

We appreciate the reviewer’s suggestion to clarify the pathological significance of YAP pathway inhibition in SARS-CoV-2 infection. To address this, we re-analyzed our snRNA-seq and bulk RNA-seq datasets to determine whether YAP target genes overlap with known mediators of the innate immune response. As described in Fig. 1C, bulk RNA-seq revealed decreased expression of multiple YAP downstream targets linked to innate immune regulation (e.g., Thbs1, Ccl2, Axl, and Csf1) in SARS-CoV-2–infected cells in vitro.

snRNA-seq of alveolar type I (AT1) cells from COVID-19 patients revealed a more complex landscape: While we observed reduced YAP activity overall (Fig. 1G), multiple YAP target genes involved in innate immunity and cytokine signaling were paradoxically elevated (Supplemental Fig. 1E). Several factors likelt explain these conflicting observations: 1. In the lung, AT1 cells (which are critical for gas exchange) may cell specifically respond to virus infection by upregulating genes related to immune response by other signaling pathway(s); 2. In vivo, SARS-CoV-2 infection triggers a surge in cytokines, chemokines, and other local factors that can differentially modulate YAP binding sites and thus affect its downstream targets, a complexity not fully captured in vitro; 3. YAP is highly sensitive to mechanical signals and tissue architecture. The 3D structure of altered cell–cell junctions in infected lung tissue, and fluid shear stress in the alveolar space could shape YAP target gene transcription differently from simplified monolayer cell cultures.

We have expanded the results section of the new version to include the above points. We also acknowledge that ongoing and future work is needed to delineate the exact molecular and tissue-specific pathways through which YAP inhibition confers a potential advantage in combating SARS-CoV-2.

(2) The authors concluded that helicase activity is required for NSP13-induced inhibition of YAP transcriptional activity based on mutation studies (Figure 3B). This finding is somewhat confusing, as K131, K345/K347, and R567 are all essential residues for NSP13 helicase activity while mutating K131 did not affect NSP13's ability to inhibit YAP (Figure 3B). Additionally, there are no data showing exactly how NSP13 inhibits the YAP/TEAD complex through its helicase function. This point was also not reflected in their proposed working model (Figure 4H).

We appreciate the reviewer’s concerns regarding the helicase‐dependent inhibition of YAP by NSP13, particularly the roles of K131, K345/K347, and R567. Based on published structural and biochemical studies, each of these residues uniquely supports helicase function (1): K131 is crucial for stabilizing the NSP13 stalk region by interacting with S424. Substituting K131 with alanine (K131A) reduces helicase efficiency but does not completely abolish it; K345/K347 are key DNA‐binding residues, and mutating both (K345A/K347A) largely prevents NSP13 from binding DNA, thus eliminating unwinding. R567 is critical for ATP hydrolysis, and the R567A mutant retains DNA binding capacity but fails to unwind it. In Fig. 3B, K131A suppresses YAP transactivation to nearly the same extent as wild‐type NSP13, suggesting that partial helicase activity is sufficient for complete YAP/TEAD inhibition. Conversely, the K345A/K347A and R567A mutants show markedly diminished repression, underscoring the importance of DNA binding and ATP hydrolysis.

As the new Fig. 4J illustrates, NSP13 must bind DNA and hydrolyze ATP to unwind nucleic acids. This helicase‐dependent process likely enables NSP13 to remodel chromatin structure by binding TEAD and properly organize YAP repressors at YAP/TEAD complex to prevent YAP/TEAD transactivation. In support of this mechanism, the K345A/K347A mutant, unable to anchor to DNA, fails to repress YAP and slightly increases YAP‐driven transcription (Fig. 3B), presumably by mislocalizing YAP repressors. Likewise, the ATPase‐dead R567A can bind DNA but does not unwind and remodel chromatin to recruit YAP repressors, resulting in a loss of YAP suppression (Fig. 3B and 3F). Our revised model demonstrates that both DNA binding and ATP‐dependent unwinding are essential for NSP13 to suppress YAP transcriptional activity. We have updated the results, discussion, and model accordingly.

(3) The proposed model that NSP13 binds TEAD4 to recruit repressor proteins and inhibits YAP/TEAD downstream gene transcription (Figure 4H) needs further characterization. Second, NSP13 is a DNA-binding protein, and its nucleic acid-binding mutant K345A/K347A failed to inhibit YAP transcriptional activity (Figure 3B). The authors should investigate whether NSP13 could bind to the TEAD binding sequence or the nearby sequence on the genome to modulate TEAD's DNA binding ability. Third, regarding the identified nuclear repressors, the authors should validate the interaction of NSP13 with the ones whose loss activates YAP transcriptional activity (Figure 4G). Lastly, why can't NSP13 bind TEAD4 in the cytoplasmic fractionation if both NSP13 and TEAD4 are detected there (Figure 3B)? This finding indicates their interaction is not a direct protein-protein interaction but is mediated by something in the nucleus, such as genomic DNA.

(1) Low TEAD expression in HEK293T cells: Our IP-MS experiments were performed in HEK293T cells, which, according to the Human Protein Atlas, express TEAD1–4 at comparatively low levels (TEAD1: 16.5, TEAD2: 16.4, TEAD3: 4.9, TEAD4: 38.7 nTPM). In contrast, HeLa cells, where we successfully validated NSP13-mediated YAP suppression (Fig. 4H, Supplementary Fig.5B-D), show higher expression of these TEAD isoforms (TEAD1: 97.1, TEAD2: 27.3, TEAD3: 12.2, TEAD4: 48.1 nTPM). Therefore, insufficient TEAD abundance in HEK293T cells may limit the sensitivity needed to detect TEAD–NSP13 interactions in our proteomic screens.

(2) Transience and potential DNA dependence: Our co-immunoprecipitation (co-IP) experiments (Fig. 4B, Supplementary Fig.4C-E) indicated that NSP13–TEAD4 binding is low-affinity. Under standard IP-MS conditions (which typically do not include chemical cross-linkers or nucleic acids to stabilize transient complexes), weak or short-lived interactions can be lost during washes or sample processing.

(3). Additional supporting evidence: We carefully checked our IP-MS data and found that the well-known TEAD binding proteins, including CTBP1/2 and GATA4, were pulled down, suggesting TEAD’s absence does not rule out an NSP13–TEAD association.

(3a) We acknowledge that our NSP13 immunoprecipitation–mass spectrometry (IP-MS) did not identify any TEAD proteins (Fig. 4G and IP-MS tables). Several factors likely contributed to this outcome:

(3b) We sincerely appreciate the reviewer’s insightful suggestion. While we agree that mapping NSP13 occupancy at individual TEAD-binding motifs is valuable, we respectfully consider this to be beyond the scope of the current study. Biochemical and structural work on coronavirus NSP13 shows that it recognizes nucleic‑acid substrates primarily through their 5′ single‑stranded overhang and duplex architecture, not through a defined base sequence(2, 3). Accordingly, our data (Fig. 3B and 3F) indicate that DNA binding ability, rather than recognition of a specific motif, enables NSP13 to perform its helicase activity in proximity to TEAD and recruit repressors. Moreover, the DNA‑binding mutant K345A/K347A and the ATPase‑dead mutant R567A both fail to suppress YAP/TEAD transcription despite retaining the ability to interact with TEAD (Fig. 3B). These loss‑of‑function phenotypes demonstrate that NSP13’s chromatin engagement and unwinding activity, rather than sequence‑restricted targeting, are essential for repression. For these reasons, motif‑specific binding assays were not pursued in this revision, but we clarified in the discussion that NSP13’s DNA engagement is likely structural or TEAD-dependent, rather than sequence‑directed. We also highlighted this as an important avenue for future investigation.

(3c) To validate the NSP13 interacting proteins from our IP-MS data, we generated plasmids expressing several candidates (CCT3, SMARCD1, EIF4A1, LMNA, TTF2, and YY2) and performed co-IP assays. As predicted, we confirmed the robust interaction between NSP13 and TEAD (Supplemental Fig. 5E). However, these putative nuclear repressors exhibited weak binding to NSP13 compared with TEAD4, suggesting that NSP13 associates with them indirectly, possibly as part of a larger multiprotein complex or depending on the chromatin structure, rather than via direct protein–protein interaction (Fig. 4J).

(3d) We appreciate the reviewer’s question. To investigate whether their association might be DNA‐dependent, we performed co‐IP experiments using nuclear lysates in the presence or absence of various nucleases: Universal Nuclease (which degrades all forms of DNA and RNA), DNase I (which cleaves both single‐ and double‐stranded DNA), and RNase H (which selectively cleaves the RNA strand in RNA/DNA hybrids). Our findings revealed that nucleic acid removal did not disrupt the NSP13/TEAD4 interaction (Supplemental Fig.4E), indicating that their binding is not solely mediated by DNA or RNA.

Reviewer #2 (Public Review):

Specific comments and suggestions for improvement of the manuscript:

(1) NSP13 has been reported to block, in a helicase-dependent manner, episomal DNA transcription (PMID: 37347173), raising questions about the effects observed on the data shown from the HOP-Flash and 8xGTIIC assays. It would be valuable to demonstrate the specificity of the proposed effect of NSP13 on TEAD activation by YAP (versus broad effects on reporter assays) and also to show that NSP13 reduces the function of endogenous YAP-TEAD transcriptional activity (i.e., does ectopic NSP13 expression reduce the expression of YAP induced TEAD target genes in cells).

We appreciate the reviewer’s comments and have carefully revisited the conclusions from the published paper(4) (PMID: 37347173), which reported that NSP13 suppresses episomal DNA transcription, as evidenced by reduced Renilla luciferase (driven by the herpes simplex virus thymidine kinase promoter) and GFP expression upon co‐expression with NSP13. For our experiments, we used a dual‐luciferase assay with Renilla luciferase (under the same promoter) as an internal control. After re-examining our raw Renilla luciferase data (now provided in the supplemental Excel file “Supporting data value”), we found that while 100 ng of NSP13 did not affect Renilla luciferase levels, 400 ng of NSP13 reduced them by approximately 50% relative to the YAP5SA‐only group (Supplemental Fig.2B, Fig.3C-D). We observed a similar reduction with NSP13 truncation mutants—an outcome not fully consistent with the published study (Supplemental Fig.3D, PMID: 37347173). However, unlike their finding of robust episomal DNA suppression, our data indicate that the K345A/K347A mutant of NSP13, which lacks DNA‐binding ability, completely lost its suppressive effect (Fig.3B).

We performed additional Notch reporter assays to address the concern that NSP13 might nonspecifically inhibit episomal DNA transcription (including the HOP‑Flash and 8×GTIIC reporters). These experiments revealed that co‑expression of NSP13 with NICD (Notch intracellular domain) does not suppress Notch signaling (Supplemental Fig. 2C), indicating that NSP13 does not globally block all reporter systems. To evaluate whether NSP13 reduces endogenous YAP‑TEAD activity, we transiently overexpressed NSP13 WT and its R567A mutant in HeLa cells. However, bulk RNA‑seq and qPCR analyses did not reveal a clear decrease in YAP target genes, possibly due to the low transfection efficiency (< 50%, Supplemental Fig.4D). Interestingly, we observed that YAP5SA was predominantly retained in the nucleus upon NSP13 or R567A co‑expression, suggesting that NSP13 (or together with its interacting partners) restricts YAP5SA cytoplasmic shuttling. Future studies will involve stable cell lines expressing NSP13 WT or R567A to better characterize the mechanisms driving YAP5SA nuclear retention and clarify how NSP13 specifically suppresses YAP activity.

(2) While the IP-MS experiment may have revealed new regulators of TEAD activity, the data presented are preliminary and inconclusive. No interactions are validated and beyond slight changes in TEAD reporter activity following knockdown, no direct links to YAP-TEAD are demonstrated, and no link to NPS13 was shown. Also, no details are provided about the methods used for the IP-MS experiment, raising some concerns about potential false positive associations within the data.

We appreciate the reviewer’s feedback regarding our IP-MS findings and acknowledge that additional validation is required to establish definitive links between the identified putative regulators, YAP-TEAD, and NSP13. We have taken the following steps (and plan further experiments) to address these concerns:

(2a) Co-IP validation: Same with the answer for Reviewer #1 (3c), we generated plasmids expressing several top candidate interactors from the IP-MS data (CCT3, SMARCD1, EIF4A1, LMNA, TTF2, and YY2) and performed direct co-IP assays in a more controlled setting. The results indicated that these putative NSP13 interactors had weaker binding compared to TEAD4, implying that NSP13 may associate with them as part of a larger complex or depending on the chromatin structure rather than through a direct protein–protein interaction (Fig. 4J).

(2b) qPCR validation: Beyond reporter assays for evaluating YAP transactivation after the candidate YAP suppressor knockdown (Fig. 4H and Supplemental Fig. 5C), we performed qPCR to detect YAP activation on endogenous YAP-TEAD target genes (e.g., CTGF CYR61, and AMOTL2) after CCT3 knockdown. Expression of CTGF and CYR61 was higher compared to control (Supplemental Fig. 5D), strengthening the case for an interaction relevant to YAP-TEAD signaling.

(2c) To investigate how NSP13‐interacting proteins link to the YAP/TEAD complex, we examined the IP‑MS dataset and identified several well‐known YAP and TEAD binding partners, including CTBP1/2 (TEAD‐binding), GATA4 (TEAD‐binding), and multiple 14‐3‐3 isoforms (YWHAZ/YWHAB/YWHAH/YWHAQ, YAP binding). These findings suggest that NSP13 may form a larger nuclear complex with YAP/TEAD and associated cofactors. In the future, we will determine whether these putative TEAD regulators also interact with NSP13 under various conditions (e.g., in the presence or absence of DNA) and whether co‐expression of NSP13 influences their association with YAP or TEAD. This approach will clarify how NSP13 might leverage these factors to regulate YAP‐TEAD function.

(2e) For the mass spectrometry experiments, HEK293T cells were transfected with Flag‐YAP1, HA‐NSP13, or Flag‐YAP1 + HA‐NSP13 according to the manufacturer’s standard protocols. After nuclear extraction and lysis, the supernatant was incubated with HA magnetic beads to immunoprecipitate (IP) NSP13. The IP samples were subsequently analyzed by mass spectrometry to identify NSP13‐associated proteins (Fig. 4F). Each experimental condition was performed in duplicate to ensure reproducibility. We included an appropriate negative control (Flag‐YAP1) and stringent data‐filtering criteria to minimize false positives. We apologize for not including these details in our original Methods section; in this revised manuscript, we have fully described the number of replicates, the controls used, and our data analysis pipelines.

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Reviewer #1 (Evidence, reproducibility and clarity (Required)):

This manuscript addresses the question of whether inhibitors of the phosphatases Eya1-4 and of the kinase PLK1 provide an effective therapeutic approach to a range of cancers. Both Eyas and PLK1 have well documented roles in development, and have been implicated in a subset of tumors. Moreover, the authors have previously shown that PLK1 is a substrate of Eya phosphatase activity. Building on these previous findings, the authors assess the possibility of combining an Eya inhibitor, benzarone, with a PLK1 inhibitor, BI2536.

There are several concerns with the study: 1. The authors suggest that these two drugs are synergistic. Synergy is usually taken as indicative of a greater than additive effect of the two drugs. The ZIP synergy score tested here indicates that the combination of the two drugs has a synergy score between 0 and 10 (figure1, and figure 5). According to "Synergy Finder", "A ZIP synergy score of greater than 10 often indicates a strong synergistic effect, while a score less than -10 suggests a strong antagonistic effect. Scores between -10 and 10 are typically considered additive or near-additive." The data in figure 2 on mitotic cell fraction and on cell death also seems to be more of an additive effect of the two drugs than synergy. The data in figure 3 are also additive effects on RAD51. Therefore a conclusion that "These data indicate that the drug combination was broadly synergistic" seems unwarranted.

There is a general lack of nomenclature standardisation for defining synergy. Furthermore, multiple synergy models exist, with discrepancies between them. However, as the reviewer states, the prevailing view is that synergy is a combination effect that is stronger than the additive effect of the two drugs. Synergy scores derived from dose-response matrices using different synergy scoring models with scores that fall above 5 are considered truly synergistic (Malyutina A et al., 2019). To strengthen our conclusion of synergy between PLK1 and EYA inhibitors, we have calculated synergy scores using additional synergy models for both benzarone + BI2536 and benzarone + volasertib in H4 and T98G cell lines. Specifically, we find robust synergy (>5) using ZIP, HSA and Bliss calculations with the Benzarone + BI2536 drug combination in H4 cells and with Benzarone + Volasertib in H4 and T98G cells. Synergy scores for Benzarone + BI2536 fell just below 5 in T98G cells. These data are now included in Supplemental Fig S1G of the revised manuscript.

The discovery of synergistic drug combinations can be further strengthened by evaluating synergy across multiple cellular models. In this study, we have tested a total of 27 different cancer models that universally support synergy.

Regarding the phenotypic outcomes (mitotic cell fraction, cell death, RAD51 foci), we agree that the observed effects are additive. This is consistent with overall synergistic effects on viability being caused by a combination of additive mechanistic effects. We have amended the text in the revised manuscript to clarify this point.

There was no statistical difference in the synergy scores of the "high expressing" versus "low expressing cells". So the conclusion that the drug combination "t was effective at lower doses in cell lines with high levels of EYA1 and/or EYA4" seems unwarranted based on the data. Moreover, since there was no statistical difference in synergy between high and low expressing cells, stating that "the potential utility of the combination treatment depends on the specific overexpression of EYA1 and/or EYA4 in cancer cells," seems unwarranted by the data.

Synergy scores quantify the interaction between drugs, but do not capture absolute treatment effectiveness or dose sensitivity, both of which are crucial for therapeutic considerations. We have included the following sentence in the revised manuscript to clarify this distinction: “While synergy scores did not significantly differ between high and low EYA expressors, high EYA1/4 expression was associated with increased sensitivity to the combination treatment at lower doses, as evidenced by decreased cell viability.” We have also amended the conclusions in the Abstract and Discussion to reflect that the potential utility of the combination therapy in EYA1/4-high cancers is supported by potency rather than synergy scores alone.

Benzarone and benzbromarone and their derivatives have been shown to bind and inhibit Eya phosphatases, albeit at fairly high doses. However, these two compounds also have a number of other, unrelated targets. The only demonstration that Eyas are a target of benzarone in this study are the CETSA data in supplemental figure 1. The data here seem to represent an n of 1, with no error bars shown. Even more importantly, there is no control. Looking at the blot of actin, it seems as if there may be a benzarone- temperature effect on this protein as well. It would be very helpful to show some evidence that knockdown of Eya similarly synergizes with the PLK1 inhibitor, show data that benzarone is in fact inhibiting Eya activity in these cells by looking at known targets (ie the carboxyterminal tyrosine of H2AX), and other evidence of specificity.

The specificity of benzarone to the EYA proteins has been demonstrated previously using both in vitro phosphatase assays and the assessment of EYA-mediated pathways (Tadjuidje et al., 2012; Wang et al., 2021; Nelson et al., 2024). These publications have been cited in the manuscript. In addition, benzarone produces phenotypes consistent with the known functions of the EYAs (ie, reduction of PLK1 activity, reduction in RAD51 foci, G2/M arrest, and apoptosis). To further validate EYA target specificity, we have performed viability assays on control and EYA4-depleted HeLa, H4 and T98G cells in response to BI2536 treatment, demonstrating EYA4 depletion-mediated sensitization to BI2536. These data are now included in Fig 1H of the revised manuscript.

To strengthen our CETSA data, we have now included: (i) densitometry of actin, demonstrating a lack of benzarone-temperature effect, (ii) CETSA analysis for an additional cell line (T98G), demonstrating enhanced thermal stability of the EYAs in the presence of benzarone, and (iii) CETSA analysis of an additional protein (BUB1) to demonstrate target specificity. These data are now included in Supplemental Fig S1E and F of the revised manuscript.

The proteomic and transcriptomic data of cell lines that were vulnerable to the combination of BI2536 and benzarone implicate overall changes in chromatin with sensitivity. These findings call into question the idea that these two compounds are acting selectively on PLK1 and Eyas. The authors don't really provide any model for explaining this correlation of Nurd complex components with targeting Eyas and PLK1.

The proteomic and transcriptomic data demonstrate that sensitivity to the combination treatment is associated with higher expression of NuRD complex members and other chromatin regulators. This suggests that cell lines with certain chromatin configurations might be more susceptible to the combined inhibition of PLK1 and EYA. This does not undermine the demonstrated on-target effects of the two compounds, but rather suggests a potential contextual dependence of drug efficacy on chromatin state. Our data thereby implicate NuRD complex expression as a predictive biomarker for tumours that are likely to respond to EYA and PLK1 combination therapy. This has now been clarified in the discussion section of the revised manuscript.

Specificity of antibodies: I would like to see validation of the Eya antibodies, given the difficulty with such reagents in the field.

All EYA antibodies have now been validated by western blot analysis following siRNA-mediated depletion. These data are presented in Supplemental Fig S1A of the revised manuscript.

Reviewer #1 (Significance (Required)):

New therapies targeting glioblastoma would be welcome. It is not clear that the combination tested here is an effective approach to therapy. It would be necessary to know the targets of the combination and understand the mechanism so that the approach could be pursued further,

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

This study explores the sensitivity of cancer cell lines, particularly GBM cells, to dual inhibition of EYA and PLK1, aiming to uncover the connection between these pathways and the cancer stem cell state. Additionally, it investigates whether the NuRD complex modulates GBM cell responses to EYA and PLK1 inhibition. While the findings are interesting, further clarification is needed to establish the mechanistic links between EYA, PLK1, and NuRD, as well as a stronger rationale for their targeted inhibition in GBM therapy- this can be better clarified.

Some key comments and recommendations: The findings demonstrate that the combination of Benzarone (EYAi) and volasertib (PLKi) significantly reduced cell proliferation in H4 and T98G GBM cell lines, both of which show high expression of EYA. In contrast, the low EYA-expressing A172 cells exhibited limited response. A possible explanation is the inherently slower proliferation rate of A172 cells, which may reduce their dependence on G2/M arrest, thereby diminishing the impact of PLK1i. Does A172 line show a similar growth or cell division rate to H4 and T98G lines.

A172 cells have a slower proliferation rate than H4 or T98G cells, which may diminish their response to EYA/PLK1 inhibitors. However, in this study we have tested a total of 15 cancer cell lines and 12 GBM stem cell line models. No clear correlation between cell growth rate and sensitivity was observed. As a specific example, the low EYA expressing SJSA-1 cell line has a high proliferation rate but is a low responder to EYA1/PLK1 inhibitors.

Additionally, although protein expression levels of EYA were assessed across these cell lines, the activity and expression levels of PLK1 were not fully characterized. Since PLK1 is a crucial regulator of mitotic entry and DNA damage repair, its activity across cell lines may contribute to the observed variations in drug sensitivity. Could the authors investigate levels of PLK in these cell lines?

To address this point, we compared PLK1 expression levels across the panel of cancer cell lines used in our study. These data are now included in Supplemental Fig S1D of the revised manuscript, and show that PLK1 levels are comparable across the cell lines, indicating that baseline PLK1 abundance does not fully explain the observed differential sensitivity.

The study describes the combination treatment as synergistic in H4 and T98G cells, however this synergy is unclear in Fig 2A and Supplemental Fig S2A. The data suggest that H4 and T98G cells exhibit sensitivity to either EYA or PLK1 inhibition alone, with combined treatment showing enhanced effects rather than synergy. This distinction is evident as BI2536 alone induces robust G2/M arrest with decreased G1 and S phase cells. To validate these findings, combination treatment should be tested in additional GBM cell lines. Additionally, repeating FUCCI cell cycle assays in A172 and H4 cells, particularly in H4, where increased γH2AX and phospho-H3 were detected in response to individual inhibitors, would provide more definitive insights into treatment-induced cell cycle dynamics.

We agree that several of the phenotypic outcomes, for example G2/M arrest (Fig 2A) and micronuclei formation (Supplemental Fig S2A), produce additive rather than synergistic effects in the combination treated cells. The major claim of the study is that the combination treatment results in potent loss of cell viability in EYA1/EYA4 overexpressing cancer cell models. This is consistent with a combination of additive mechanistic effects causing overall synergistic effects on cancer cell viability. We have clarified this point in the revised manuscript.

We have previously struggled to get adequate FUCCI sensor expression in H4 cells. However, to address this point, we have quantified cell cycle phase distribution in H4 cells treated with benzarone, BI2536, and the drug combination, using our quantitative image-based cytometry data (Fig 3A, B). These data demonstrate an accumulation of H4 cells in G2/M following combination treatment, consistent with the FUCCI data from T98G cells. Cell cycle dynamics of H4 cells are now included in Supplemental Fig S2A of the revised manuscript.

A notable inconsistency: Figure 1 utilizes volasertib, whereas Figure 2 employs BI2536. Given that both inhibitors target PLK1 why these specific inhibitors were chosen for each experiment.

This is not the case. To clarify, BI2536 is used in both Fig 1 and 2. Volasertib is used in Supplemental Fig S1 to reproduce the synergy matrix, thereby demonstrating consistent results with a second PLK1 inhibitor.

The observation of increased Rad52 foci and sister chromatid exchange (SCE) upon EYA and PLK1 inhibition (Figure 3) is interesting. These findings suggest that dual inhibition impairs homologous recombination (HR), reinforcing the role of EYA and PLK1 in maintaining genomic stability.

We agree.

Figure 4 suggests that SJH1 cells, with low EYA expression, exhibit increased sensitivity to EYA inhibition - does this cell line show high expression of PLK or NuRD?

To clarify, Fig 4 shows that SJH1 cells, which display moderate levels of EYA expression, are highly sensitive to EYA/PLK1 inhibition. Consistent with the observed positive correlation between NuRD protein expression and EYA/PLK1 inhibitor sensitivity, SJH1 cells exhibit the highest levels of NuRD components relative to the other GBM stem cell lines. Expression levels of NuRD components across the slightly sensitive, moderately sensitive, and highly sensitive GBM stem cell lines from publicly available proteomic data and western blot analysis have now been included in Supplemental Fig S5A and B of the revised manuscript, further demonstrating this positive correlation.

It seems like EYA1 (HW1) and EY4 (SB2B and PB1) expression levels are better predictors of sensitivity to treatment, but not EYA2 and 3 (which is high in H4)- can the authors comment on this?

Overall, EYA1 and EYA4 expression levels are the major predictors of EYA/PLK1 inhibitor sensitivity in both the cancer cell lines (Fig 1) and the GBM stem cell models (Fig 4). EYA3 levels are also positively associated with sensitivity in the GBM stem cell models, but not in the cancer cell lines. Despite being consistently high, EYA2 expression levels were not associated with sensitivity in either model. These intricacies are likely to reflect functional differences between the proteins, and their ability to form different sub-complexes with each other. We have now clarified these points in the discussion of the revised manuscript.

Reviewer #2 (Significance (Required)):

It remains unclear whether NuRD complex involvement is independent of EYA expression levels. Since EYA and PLK1 regulate cell cycle progression and DNA repair, further investigation is needed to delineate their connection to NuRD-mediated chromatin remodeling and differentiation programs. Overall, this study provides some interesting evidence for targeting transcriptional and mitotic vulnerabilities in GBM but requires further validation of synergistic mechanisms, differential inhibitor effects, and NuRD complex involvement in regulating the EYA-PLK1 axis.

Reviewer #3 (Evidence, reproducibility and clarity (Required)):

This manuscript extends the findings of the interactions between EYA family members and PLK1. The idea to combine EYA inhibitors and PLK1 inhibitors is a thoughtful approach. The effects on proliferation and DNA damage are useful. This effort is a combination of preclinical efforts and some mechanistic efforts and will require additional efforts to support the conclusions drawn.

Major concerns: 1. The preclinical studies will absolutely require in vivo studies. All brain tumor treatments are limited by delivery across the blood-brain barrier. It is critical to have intracranial survival studies to support the significance of the findings.

In this study, we have focused on in vitro models including cancer cell lines, GBM stem cell models and 3D tumor spheroids, to establish proof-of-principle as well as mechanistic insight for combined EYA/PLK1 inhibition. We recognize that blood-brain-barrier penetration and therapeutic efficacy in vivo are key translational steps; however, we feel that benzarone is a suboptimal drug candidate for in vivo evaluation. Future development of second-generation EYA inhibitors with higher potency, improved selectivity, and better blood-brain-barrier permeability, is currently underway by ourselves and other groups. These compounds are likely to be more suitable for future in vivo studies, including pharmacokinetic profiling, blood-brain-barrier penetration assays, and orthotopic intracranial tumour models to assess their therapeutic potential more rigorously.

Likewise, cancer stem cell studies require in vivo studies.

As outlined above, we feel that in vivo studies fall beyond the scope of this study.

The proper studies of sphere formation would include in vitro limiting dilution assays. I would suggest greater depth in stem cell and differentiation marker studies to understand what the connection to stemness is.

The limiting dilution assay is used to measure the self-renewal potential of cancer stem cells, and would be used in this context to determine whether the treatments impact cellular differentiation. This is not the focus of this study. Rather, we are interested in comparing drug sensitivity in cancer stem cells versus differentiated cancer cells. Nevertheless, this is a great suggestion for future investigation as part of a more detailed evaluation of stemness and how these drugs and drug combinations impact self-renewal.

DNA damage responses differ between cancer stem cells and differentiated tumor cells. I would suggest comparison of effects between matched cells with different cell states.

We agree that cancer stem cells and their differentiated counterparts often display distinct DNA damage responses. We have tried to mimimise the impact of these differences on the overall conclusions by using multiple cancer cell lines and GBM stem models. To address this comment, we performed western blot analysis of DNA damage response proteins in matched PB1 stem cells and differentiated cells, demonstrating comparable expression of DNA damage response proteins. These data have now been included in Supplemental Fig S5C of the revised manuscript.

While the inhibitors used may have general specificity for the molecular targets, I would suggest that the authors use genetic loss-of-function and gain-of-function studies to validate the findings. It is particularly important because the primary targets do not predict treatment responses. I would suggest that rescues with PLK1 phosphorylation mutants would be helpful.

Our data demonstrate that EYA expression levels are predictive of treatment response in both cancer cell lines and GBM stem cell models. To further validate EYA target specificity, we have used a genetic loss-of-function approach. Specifically, we performed viability assays on control and EYA4-depleted HeLa, H4 and T98G cells in response to BI2536 treatment, demonstrating EYA4 depletion-mediated sensitization to BI2536. These data are now included in Fig 1H of the revised manuscript.

We have previously performed comprehensive rescue experiments with PLK1 phosphorylation mutants (Fig 5C–K; Nelson et al., Nat. Commun. 2024). These experiments demonstrated that cell death in response to EYA depletion or inhibition is attributable to the phosphorylation status of pY445 on PLK1, with an accumulation of Y445 phosphorylation reducing PLK1 activity and functionality, culminating in the potent induction of mitotic cell death.

Figure 5 should be performed with several lines across different response groups.

Our study currently includes cell viability and proliferation data from multiple models including 15 cancer cell lines and 12 GBM stem cell line models, spanning different EYA expression levels, and displaying varying sensitivities to both single agents and the EYA/PLK1 combination treatment. We then narrowed the number of models significantly for follow-up analysis. In Fig 5, we selected the highly sensitive PB1 GBM stem cell line based on its ability to form and grow as spheroids. While we appreciate the suggestion to expand these analyses to additional lines, we would like to respectfully decline growing additional spheroids at this time due to limitations inherent in the expansion of these models. We believe that the current dataset adequately demonstrates the reproducibility and relevance of our findings across different response groups.

The molecular associations are currently just associations. I would suggest greater analysis using genetic manipulation to test causation.

To address this concern, we have performed additional experiments using siRNA-mediated knockdown of EYA4 in HeLa, H4 and T98G cells. These experiments demonstrate that depletion of EYA4 sensitizes cells to PLK1 inhibition, mimicking the effects observed with pharmacological EYA inhibition. These data have been included in Fig 1H of the revised manuscript, and provide additional functional evidence supporting a causal relationship between EYA activity and sensitivity to PLK1 inhibition.

Figure 6 should be better developed to include protein testing and validation.

To address this point, expression levels of NuRD components have been compared using publicly available proteomic datasets and western blot analysis across the slightly sensitive, moderately sensitive and highly sensitive GBM stem cell lines, supporting a positive correlation with sensitivity. These data have been included in Supplemental Fig S5A and B of the revised manuscript.

Reviewer #3 (Significance (Required)):

This is a modest advance in understanding how EYA family members may function with PLK1.

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Referee #3

Evidence, reproducibility and clarity

This manuscript extends the findings of the interactions between EYA family members and PLK1. The idea to combine EYA inhibitors and PLK1 inhibitors is a thoughtful approach. The effects on proliferation and DNA damage are useful. This effort is a combination of preclinical efforts and some mechanistic efforts and will require additional efforts to support the conclusions drawn.

Major concerns:

1. The preclinical studies will absolutely require in vivo studies. All brain tumor treatments are limited by delivery across the blood-brain barrier. It is critical to have intracranial survival studies to support the significance of the findings.

2. Likewise, cancer stem cell studies require in vivo studies.

3. The proper studies of sphere formation would include in vitro limiting dilution assays. I would suggest greater depth in stem cell and differentiation marker studies to understand what the connection to stemness is.

4. DNA damage responses differ between cancer stem cells and differentiated tumor cells. I would suggest comparison of effects between matched cells with different cell states.

5. While the inhibitors used may have general specificity for the molecular targets, I would suggest that the authors use genetic loss-of-function and gain-of-function studies to validate the findings. It is particularly important because the primary targets do not predict treatment responses. I would suggest that rescues with PLK1 phosphorylation mutants would be helpful.

6. Figure 5 should be performed with several lines across different response groups.

7. The molecular associations are currently just associations. I would suggest greater analysis using genetic manipulation to test causation.

8. Figure 6 should be better developed to include protein testing and validation.

Significance

This is a modest advance in understanding how EYA family members may function with PLK1.

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

This study explores the sensitivity of cancer cell lines, particularly GBM cells, to dual inhibition of EYA and PLK1, aiming to uncover the connection between these pathways and the cancer stem cell state. Additionally, it investigates whether the NuRD complex modulates GBM cell responses to EYA and PLK1 inhibition. While the findings are interesting, further clarification is needed to establish the mechanistic links between EYA, PLK1, and NuRD, as well as a stronger rationale for their targeted inhibition in GBM therapy- this can be better clarified.

Some key comments and recommendations:

• The findings demonstrate that the combination of Benzarone (EYAi) and volasertib (PLKi) significantly reduced cell proliferation in H4 and T98G GBM cell lines, both of which show high expression of EYA. In contrast, the low EYA-expressing A172 cells exhibited limited response. A possible explanation is the inherently slower proliferation rate of A172 cells, which may reduce their dependence on G2/M arrest, thereby diminishing the impact of PLK1i. Does A172 line show a similar growth or cell division rate to H4 and T98G lines.

• Additionally, although protein expression levels of EYA were assessed across these cell lines, the activity and expression levels of PLK1 were not fully characterized. Since PLK1 is a crucial regulator of mitotic entry and DNA damage repair, its activity across cell lines may contribute to the observed variations in drug sensitivity. Could the authors investigate levels of PLK in these cell lines?

• The study describes the combination treatment as synergistic in H4 and T98G cells, however this synergy is unclear in Figure 2A and EV 2A. The data suggest that H4 and T98G cells exhibit sensitivity to either EYA or PLK1 inhibition alone, with combined treatment showing enhanced effects rather than synergy. This distinction is evident as BI2536 alone induces robust G2/M arrest with decreased G1 and S phase cells. To validate these findings, combination treatment should be tested in additional GBM cell lines. Additionally, repeating FUCCI cell cycle assays in A172 and H4 cells, particularly in H4, where increased γH2AX and phospho-H3 were detected in response to individual inhibitors, would provide more definitive insights into treatment-induced cell cycle dynamics.

• A notable inconsistency: Figure 1 utilizes volasertib, whereas Figure 2 employs BI2536. Given that both inhibitors target PLK1 why these specific inhibitors were chosen for each experiment.

• The observation of increased Rad52 foci and sister chromatid exchange (SCE) upon EYA and PLK1 inhibition (Figure 3) is interesting. These findings suggest that dual inhibition impairs homologous recombination (HR), reinforcing the role of EYA and PLK1 in maintaining genomic stability.

• Figure 4 suggests that SJH1 cells, with low EYA expression, exhibit increased sensitivity to EYA inhibition - does this cell line show high expression of PLK or NuRD?

• It seems like EYA1 (HW1) and EY4 (SB2B and PB1) expression levels are better predictors of sensitivity to treatment, but not EYA2 and 3 (which is high in H4)- can the authors comment on this?

Significance

It remains unclear whether NuRD complex involvement is independent of EYA expression levels. Since EYA and PLK1 regulate cell cycle progression and DNA repair, further investigation is needed to delineate their connection to NuRD-mediated chromatin remodeling and differentiation programs.

Overall, this study provides some interesting evidence for targeting transcriptional and mitotic vulnerabilities in GBM but requires further validation of synergistic mechanisms, differential inhibitor effects, and NuRD complex involvement in regulating the EYA-PLK1 axis.

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

This manuscript addresses the question of whether inhibitors of the phosphatases Eya1-4 and of the kinase PLK1 provide an effective therapeutic approach to a range of cancers. Both Eyas and PLK1 have well documented roles in development, and have been implicated in a subset of tumors. Moreover, the authors have previously shown that PLK1 is a substrate of Eya phosphatase activity. Building on these previous findings, the authors assess the possibility of combining an Eya inhibitor,benzarone, with a PLK1 inhibitor, BI2536.

There are several concerns with the study:

1. The authors suggest that these two drugs are synergistic. Synergy is usually taken as indicative of a greater than additive effect of the two drugs. The ZIP synergy score tested here indicates that the combination of the two drugs has a synergy score between 0 and 10 (figure1, and figure 5) . According to "Synergy Finder" , "A ZIP synergy score of greater than 10 often indicates a strong synergistic effect, while a score less than -10 suggests a strong antagonistic effect. Scores between -10 and 10 are typically considered additive or near-additive." The data in figure 2 on mitotic cell fraction and on cell death also seems to be more of an additive effect of the two drugs than synergy. The data in figure 3 are also additive effects on RAD51. Therefore a conclusion that "These data indicate that the drug combination was broadly synergistic" seems unwarranted. Indeed, the data form

2. There was no statistical difference in the synergy scores of the "high expressing" versus "low expressing cells". So the conclusion that the drug combination "t was effective at lower doses in cell lines with high levels of EYA1 and/or EYA4" seems unwarranted based on the data. Moreover, since there was no statistical difference in synergy between high and low expressing cells, stating that "the potential utility of the combination treatment depends on the specific overexpression of EYA1 and/or EYA4 in cancer cells," seems unwarranted by the data.

3. Benzarone and benzbromarone and their derivatives have been shown to bind and inhibit Eya phosphatases, albeit at fairly high doses. However, these two compounds also have a number of other, unrelated targets. The only demonstration that Eyas are a target of benzarone in this study are the CETSA data in supplemental figure 1. The data here seem to represent an n of 1, with no error bars shown. Even more importantly, there is no control. Looking at the blot of actin, it seems as if there may be a benzarone- temperature effect on this protein as well. It would be very helpful to show some evidence that knockdown of Eya similarly synergizes with the PLK1 inhibitor, show data that benzarone is in fact inhibiting Eya activity in these cells by looking at known targets (ie the carboxyterminal tyrosine of H2AX), and other evidence of specificity.

4. The proteomic and transcriptomic data of cell lines that were vulnerable to the combination of BI2536 and benzarone implicate overall changes in chromatin with sensitivity. These findings call into question the idea that these two compounds are acting selectively on PLK1 and Eyas. The authors don't really provide any model for explaining this correlation of Nurd complex components with targeting Eyas and PLK1.

5. Specificity of antibodies: I would like to see validation of the Eya antibodies, given the difficulty with such reagents in the field.

Significance

New therapies targeting glioblastoma would be welcome. It is not clear that the combination tested here is an effective approach to therapy. It would be necessary to know the targets of the combination and understand the mechanism so that the approach could be pursued further,

Lehet akkor ezt is nyelvesíteni kéne Elszámolópartner -r . HA mindenhol DEAG/REAG akkor maradjon

Vous avez expliqué (dans le texte ou dans la vidéo) qu'on pouvait "mettre l'indentation" avec la touche tab. Mais ça ne positionne pas le curseur de notre alinéa directement là où nous sommes sensés écrire (selon le nombre de parents). J'ai trouvé sur internet un raccourci permettant de corriger l'organisation de tous nos alinéas : Alt + Maj + F

Evitam apoptose

Dit is een test

Reviewer #4 (Public review):

Summary:

In this paper, Derkaloustian et al. look at the important topic of what affects fine touch perception. The observations that there may be some level of correlation with instabilities are intriguing. They attempted to characterize different materials by counting the frequency (occurrence #, not of vibration) of instabilities at various speeds and forces of a PDMS slab pulled lengthwise over the material. They then had humans perform the same vertical motion to discriminate between these samples. They correlated the % correct in discrimination with differences in frequency of steady sliding over the design space as well as other traditional parameters such as friction coefficient and roughness.

The authors pose an interesting hypothesis and make an interesting observation about the occurrences of instability regimes in different materials while in contact with PDMS, which is interesting for the community to see in publication. It should be noted however that the finger is complex, and there are many factors that may be over simplified, and perhaps even incorrect, with the use of the PDMS finger. There are trends, such as the trend of surfaces that are more similar in PDMS friction coefficient being easier to discriminate than those with more different PDMS friction coefficient, that contradict multiple other papers in the literature (Fehlberg et al., 2024; Smith and Scott, 1996). This may be due to the PDMS finger not being representative of the real finger conditions. A measurement of friction and the instabilities with a human finger, or demonstration that the PDMS finger is producing the same results (friction coefficient, instabilities, etc.) as a human finger, is needed.

Strengths:

The strength of this paper is in its intriguing hypothesis and important observation that instabilities may contribute to what humans are detecting as differences in these apparently similar samples.

Weaknesses:

There is are significant weaknesses in the representativeness of the PDMS finger, the vertical motion, and the speed of sliding to real human exploration. The real finger has multiple layers with different moduli. In fact, the stratum corneum cells, which are the outer layer at the interface and determine the friction, have much higher modulus than PDMS. In addition, the flat contact area can cause shifting of contact points. Both can contribute to making the PDMS finger have much more stick slip than a real finger. In fact, if you look at the regime maps, there is very little space that has steady sliding. This does not represent well human exploration of surfaces. We do not tend to use force and velocity that will cause extensive stick slip (frequent regions of 100% stick slip) and, in fact, the speeds used in the study are on the slow side, which also contributes to more stick slip. At higher speeds and lower forces, all of the materials had steady sliding regions. Further, on these very smooth surfaces, the friction and stiction are more complex and cannot dismiss considerations such as finger material property change with sweat pore occlusion and sweat capillary forces. Also, the vertical motion of both the PDMS finger and the instructed human subjects is not the motion that humans typically use to discriminate between surfaces.

This all leads to the critical question, why is the friction, normal force, and velocity not measured during the measured human exploration using the real human finger? An alternative would be showing that the PDMS finger reproduces the results of the human finger. I have checked the author's previous papers with this setup and did not find one that showed that the PDMS finger produced the same results as a human finger (Carpenter et al., 2018; Dhong et al., 2018; Nolin et al., 2022, 2021). The reviewer is not asking to do a more detailed psychophysical study with a decision-making model. All that is being asked is to use a human finger for the friction coefficient and instability measurements at typical human forces and speeds, or at least doing these measurements with both for one or two samples to show that the PDMS finger produces the same results as a human finger. The authors posed an extremely interesting hypothesis that humans may alter their speed to feel the instability transition regions. This is something that could be measured with a real finger but is not likely to be correlated accurately enough to match regime boundaries determined with such a simplified artificial finger.

References

Carpenter CW, Dhong C, Root NB, Rodriquez D, Abdo EE, Skelil K, Alkhadra MA, Ramírez J, Ramachandran VS, Lipomi DJ. 2018. Human ability to discriminate surface chemistry by touch. Mater Horiz 5:70-77. doi:10.1039/C7MH00800G<br /> Dhong C, Kayser LV, Arroyo R, Shin A, Finn M, Kleinschmidt AT, Lipomi DJ. 2018. Role of fingerprint-inspired relief structures in elastomeric slabs for detecting frictional differences arising from surface monolayers. Soft Matter 14:7483-7491. doi:10.1039/C8SM01233D<br /> Fehlberg M, Monfort E, Saikumar S, Drewing K, Bennewitz R. 2024. Perceptual Constancy in the Speed Dependence of Friction During Active Tactile Exploration. IEEE Transactions on Haptics 17:957-963. doi:10.1109/TOH.2024.3493421<br /> Nolin A, Licht A, Pierson K, Lo C-Y, Kayser LV, Dhong C. 2021. Predicting human touch sensitivity to single atom substitutions in surface monolayers for molecular control in tactile interfaces. Soft Matter 17:5050-5060. doi:10.1039/D1SM00451D<br /> Nolin A, Pierson K, Hlibok R, Lo C-Y, Kayser LV, Dhong C. 2022. Controlling fine touch sensations with polymer tacticity and crystallinity. Soft Matter 18:3928-3940. doi:10.1039/D2SM00264G<br /> Smith AM, Scott SH. 1996. Subjective scaling of smooth surface friction. Journal of Neurophysiology 75:1957-1962. doi:10.1152/jn.1996.75.5.1957

Author response:

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

Reviewer #1 (Public Review):

Summary:

In this manuscript, the authors used a coarse-grained DNA model (cgNA+) to explore how DNA sequences and CpG methylation/hydroxymethylation influence nucleosome wrapping energy and the probability density of optimal nucleosomal configuration. Their findings indicate that both methylated and hydroxymethylated cytosines lead to increased nucleosome wrapping energy. Additionally, the study demonstrates that methylation of CpG islands increases the probability of nucleosome formation.

Strengths:

The major strength of this method is that the model explicitly includes elastic constraints on the positions of phosphate groups facing a histone octamer, as DNA-histone binding site constraints. The authors claim that their model enhances the accuracy and computational efficiency and allows comprehensive calculations of DNA mechanical properties and deformation energies.

Weaknesses:

A significant limitation of this study is that the parameter sets for the methylated and hydroxymethylated CpG steps in the cgNA+ model are derived from all-atom molecular dynamics (MD) simulations that suggest that both methylated and hydroxymethylated cytosines increase DNA stiffness and nucleosome wrapping energy (P´erez A, et al. Biophys J. 2012; Battistini, et al. PLOS Comput Biol. 2021). It could predispose the coarse-grained model to replicate these findings. Notably, conflicting results from other all-atom MD simulations, such as those by Ngo T in Nat. Commun. 2016, shows that hydroxymethylated cytosines increase DNA flexibility, contrary to methylated cytosines. If the cgNA+ model was trained on these later parameters or other all-atom force fields, different conclusions might be obtained regarding the effects of methylated and hydroxymethylation on nucleosome formation.

Despite the training parameters of the cgNA+ model, the results presented in the manuscript indicate that methylated cytosines increase both DNA stiffness and nucleosome wrapping energy. However, when comparing nucleosome occupancy scores with predicted nucleosome wrapping energies and optimal configurations, the authors find that methylated CGIs exhibit higher nucleosome occupancies than unmethylated ones, which seems to contradict their findings from the same paper which showed that increased stiffness should reduce nucleosome formation affinity. In the manuscript, the authors also admit that these conclusions “apparently runs counter to the (perhaps naive) intuition that high nucleosome forming affinity should arise for fragments with low wrapping energy”. Previous all-atom MD simulations (P´erez A, et al. Biophys J. 2012; Battistini, et al. PLOS Comput Biol. 202; Ngo T, et al. Nat. Commun. 20161) show that the stiffer DNA upon CpG methylation reduces the affinity of DNA to assemble into nucleosomes or destabilizes nucleosomes. Given these findings, the authors need to address and reconcile these seemingly contradictory results, as the influence of epigenetic modifications on DNA mechanical properties and nucleosome formation are critical aspects of their study. Understanding the influence of sequence-dependent and epigenetic modifications of DNA on mechanical properties and nucleosome formation is crucial for comprehending various cellular processes. The authors’ study, focusing on these aspects, will definitely garner interest from the DNA methylation research community.

Training the cgNA+ model on alternative MD simulation datasets is certainly of interest to us. However, due to the significant computational cost, this remains a goal for future work. The relationship between nucleosome occupancy scores and nucleosome wrapping energy is still debated, with conflicting findings reported in the literature, as noted in our Discussion section. Interestingly, we find that our predicted log probability density of DNA spontaneously acquiring a nucleosomal configuration is a better indicator of nucleosome occupancy than our predicted DNA nucleosome wrapping energy.

Reviewer #2 (Public Review):

Summary:

This study uses a coarse-grained model for double-stranded DNA, cgNA+, to assess nucleosome sequence affinity. cgNA+ coarse-grains DNA on the level of bases and accounts also explicitly for the positions of the backbone phosphates. It has been proven to reproduce all-atom MD data very accurately. It is also ideally suited to be incorporated into a nucleosome model because it is known that DNA is bound to the protein core of the nucleosome via the phosphates.

It is still unclear whether this harmonic model parametrized for unbound DNA is accurate in describing DNA inside the nucleosome. Previous models by other authors, using more coarse-grained models of DNA, have been rather successful in predicting base pair sequence-dependent nucleosome behavior. This is at least the case as far as DNA shape is concerned whereas assessing the role of DNA bendability (something this paper focuses on) has been consistently challenging in all nucleosome models, to my knowledge.

It is thus of major interest whether this more sophisticated model is also more successful in handling this issue. As far as I can tell the work is technically sound and properly accounts for not only the energy required in wrapping DNA but also entropic effects, namely the change in entropy that DNA experiences when going from the free state to the bound state. The authors make an approximation here which seems to me to be a reasonable first step.

Of interest is also that the authors have the parameters at hand to study the effect of methylation of CpG-steps. This is especially interesting as it allows us to study a scenario where changes in the physical properties of base pair steps via methylation might influence nucleosome positioning and stability in a cell-type-specific way.

Overall, this is an important contribution to the question of how the sequence affects nucleosome positioning and affinity. The findings suggest that cgNA+ has something new to offer. But the problem is complex, also on the experimental side, so many questions remain open.

Strengths:

The authors use their state-of-the-art coarse-grained DNA model which seems ideally suited to be applied to nucleosomes as it accounts explicitly for the backbone phosphates.

Weaknesses:

(1) According to the abstract the authors consider two “scalar measures of the sequence-dependent propensity of DNA to wrap into nucleosomes”. One is the bending energy and the other, is the free energy. Specifically in the latter, the authors take the difference between the free energies of the wrapped and the free DNA. Whereas the entropy of the latter can be calculated exactly, they assume that the bound DNA always has the same entropy (independent of sequence) in its more confined state. The problem is the way in which this is written (e.g. below Eq. 6) which is hard to understand. The authors should mention that the negative of Eq. 6 is what physicists call free energy, namely especially the free energy difference between bound and free DNA.

We have included the necessary clarifications in the revised manuscript, below Eq. 6.

(2) In Eq. 5 the authors introduce penalty coefficients c_i. They write that values are “set by numerical experiment to keep distances ... within the ranges observed in the PDB structure, while avoiding sterical clashes in DNA.” This is rather vague, especially since it is unclear to me what type of sterical clashes might occur. Figure 1 shows then a comparison between crystal structures and simulated structures. They are reasonably similar but standard deviations in the fluctuations of the simulation are smaller than in the experiments. Why did the authors not choose smaller c_i-values to have a better fit? Do smaller values lead to unwanted large fluctuations that would lead to steric clashes between the two DNA turns? I also wonder what side views of the nucleosomes look like (experiments and simulations) and whether in this side view larger fluctuations of the phosphates can be observed in the simulation that would eventually lead to turn-turn clashes for smaller c_i-values.

The side view plots of the experimental and predicted nucleosome structures are now added to Supplementary material (Figure S8). Indeed, smaller c_i values lead to steric clashes between the two turns of DNA – this is now specified in the Methods section. A possible improvement of our optimisation method and a direction of future work would be adding a penalty which prevents steric clashes to the objective function. Then the c_i values could be reduced to have bigger fluctuations that are even closer to the experimental structures. We added this explanation to the Results section.

Reviewer #3 (Public Review):

Summary:

In this study, the authors utilize biophysical modeling to investigate differences in free energies and nucleosomal configuration probability density of CpG islands and nonmethylated regions in the genome. Toward this goal, they develop and apply the cgNA+ coarse-grained model, an extension of their prior molecular modeling framework.

Strengths:

The study utilizes biophysical modeling to gain mechanistic insight into nucleosomal occupancy differences in CpG and nonmethylated regions in the genome.

Weaknesses:

Although the overall study is interesting, the manuscripts need more clarity in places. Moreover, the rationale and conclusion for some of the analyses are not well described.

We edited the manuscript according to the reviewer’s suggestions and hopefully improved its readability.

Reviewer #1 (Recommendations For The Authors):

(1) The cgNA+ model parameters are derived from all-atom molecular dynamics (MD) simulations, yet there is no consensus within all-atom MD simulations regarding the impact of CpG methylation on DNA mechanical properties. The authors could consider fitting the coarsegrained model with a different all-atom force field to verify whether the conclusions regarding the effects of methylation and hydroxymethylation on DNA nucleosome wrapping energies still hold. For further details on MD simulations related to CpG methylation effects, the authors are advised to consult the review paper by Li et al. (2022) titled “DNA methylation: Precise modulation of chromatin structure and dynamics” published in Current Opinion in Structural Biology.

Parametrizing the cgNA+ model using MD simulations with various force fields is certainly of interest to us. However, due to the computational cost involved, it remains a goal for future work.

(2) Beyond DNA mechanical properties, which are directly linked to nucleosome wrapping energies in this study, the authors might also consider other factors such as geometric properties that could influence nucleosome formation. This approach might help the authors to reconcile the observed higher nucleosome occupancy scores for methylated CpGs. The authors are encouraged to review the aforementioned paper for additional experimental and MD simulation studies that could support this perspective.

Geometric properties of DNA are directly incorporated into our method through the cgNA+ model equilibrium shape prediction µ. We compute the mechanical energy needed deform µ to a nucleosomal configuration. Notably, the equilibrium shape µ is sensitive to methylation, as demonstrated in Figure 3.

(3) There are some issues with citation accuracy in the manuscript. For instance, in the Discussion section, the authors attribute a statement to Collings et al. and Anderson (2017), claiming that “methylated regions, known to have high wrapping energy, are among the highest nucleosome occupied elements in the genome.” However, upon reviewing this paper, it appears that it does not make any claims about the high wrapping energy of methylated regions.

The paragraph is now edited and a separate citation, P´erez et al. (2012), is given for the statement that methylation regions have high wrapping energy.

Reviewer #2 (Recommendations For The Authors):

Please improve the readability by:

(1) making clear that -ln ρ in Eq. 6 on page 4 is actually the free energy. Also, the word entropy comes too late (on page 7) where the best explanation of Eq. 6 is presented.

We added a comment about -ln ρ being the free energy after Eq. 6 and also included an equation, relating ln ρ and entropy.

(2) page 12 and 13 show two sets of experimental data. They are quite different from each other. When reading this, I wondered why there is this difference. But only on page 16, you explain that these are different cell types. The difference should be explained already when the papers are introduced on page 12.

A corresponding sentence already appeared in page 12: “The observations about nucleosome occupancy should be regarded as preliminary, and be treated with caution, as they are based on experimental data obtained for the cancerous HeLa cells Schwartz et al. (2019) and human genome embryonic stem cells Yazdi et al. (2015)”. Now we also added this information to the first paragraph of the subsection for clarity.

Finally, I add here some general thoughts that came up when reading the paper, comparing your findings with earlier findings in the field. This is not a strict one-to-one comparison and thus does not have to find its way into this manuscript but might give ideas for future studies. Experiments suggest that nucleosomes prefer DNA with a high content of C’s and G’s. Figure 2 does not look at the GC content but at the number of CpG’s. But in any case, let’s use this as a proxy for GC content. Figure 2a suggests that there is not a strong dependence of the bending energy on the number CpG steps. This is consistent with earlier work with the rigid basepair model which shows the same behavior for GC content (for both MD and crystal parametrizations). Figure 2c (related to the negative free energy) shows that with an increasing number of CpG steps the propensity to bind goes down. This suggests that the entropic cost to confine CpG-rich DNA increases, which in turn reflects that these DNA stretches are softer. This is rather interesting since in the case of the rigid basepair model this effect is observed only when stiffnesses are extracted from crystal data not MD data (however, this refers again to CG content). This might indicate a difference between the rigid bp model and cgNA+ which will be interesting to study in the future. Interesting is also the effect of CpG methylation. The stiffer methylated steps lead to an increase in the energy with the number of such steps (Figure 2a). The entropic cost for binding is thus expected to be smaller and this is indeed observed in Figure 2c when compared to the non-methylated steps.

We thank the reviewer for this comment. As for the GC content, the energy and lnp plots are indeed very similar to those in Figure 2.

Reviewer #3 (Recommendations For The Authors):

(1) The formulation of the cgNA+ model in the method section was not easy to follow and can be described better to improve clarity.

We have revised the model description and hope that its clarity has been improved

(2) The authors mention utilizing 100 human genome sequences with 100 configurations from DB. It would be helpful to clarify the source of these 100 human genome sequences. Are these 100 distinct regions on the human reference genome, or are they from a specific dataset or database?

We now include an explanation about the origin of sequences: “The human genome sequences are a random subset of our sequence sample for the CGI and NMI intersection in the Chromosome 1, but the following observations remain unchanged for sequence samples from different genomic regions.”

(3) The authors mention the lack of tail unwrapping in their model. It would be beneficial to understand the magnitude of this issue and its potential impact on the overall results. How significant is the lack of unwrapping events in their current model?

We observed the unwrapping of approximately five base-pairs at each end of our predicted nucleosome configurations, in comparison to the experimental configurations (Figure 1). This issue could be solved by adding additional constraints at the ends of the 147 bp sequence. The wrapping energy would increase marginally, as only about 10 of 147 bp would be affected. We added this remark to the main text.

(4) Observations from Figure 3 are not described properly. Are these differences statistically significant? Why is twist higher for CpG sites but lower for a roll?

We added an explanation of how the statistics was computed into the caption of Figure 3. In fact, we didn’t use statistical estimates here, but generated all the possible cases and computed the exact statistics (for the given set of our model parameters). Regarding the changes in twist and roll, we have added the following comment on page 7: “The ground state changes resulting from cytosine modifications – primarily characterized by an average increase in roll and a decrease in twist – may be linked to steric hindrance caused by the cytosine 5-substituent (Battistini et al. (2021)). Notably, the negative coupling between twist and roll has already been observed in X-ray crystallography data (Olson et al. (1998)).”

(5) Figure 4 does not clarify the authors’ conclusion of higher stiffness for ApT and TpA dinucleotides. The authors should provide further explanation for this observation.

We revised the text to clarify that the statement regarding ApT and TpA being the most stiff and the most flexible dinucleotides is not a conclusion derived from Figure 4, but rather from earlier work that we cite.

(6) In Figure 7, the authors note that methylated CGIs have higher nucleosome occupancy on average than unmethylated sequences. Is this observation statistically significant?

We observe that methylated sequences have a higher average occupancy than unmethylated sequences in Yazdi et al. data, when the CpG count falls into the intervals from 5 to 14 and from 15 to 24. For each of the two intervals this difference is statistically significant: the permutation test, used due to the lack of normality, yields a p-value of 0.0001 for both cases. The differences in mean scores shown in Figure 8 are also statistically significant. Such test results are expected, given the large sample sizes and the observed differences in means, therefore we prefer not to include this discussion in main text.

(7) The authors note that their analyses to correlate nucleosome occupancy profile with the methylation state of underlying sequences are preliminary, as different cell lines were used to perform these analyses. Given this inconsistency, it needs to be clarified why this analysis was performed and what the takeaway is.

We added the following comment at the end of the Results section: “Although comparing data from different cell lines is not optimal, to the best of our knowledge, no publicly available methylation and nucleosome occupancy data exist for the entire human genome within the same cell type. Nevertheless, since the lowest log probability densities in the human genome are predicted for CpG-rich sequences regardless of their methylation state (Figure 2d), and the same holds for both sets of the nucleosome occupancy scores (Figure 7), we conclude that the lowest occupancies occur for sequences with the lowest log probability densities.”

DOI: 10.1101/2025.08.05.668642

Resource: None

Curator: @scibot

SciCrunch record: RRID:Addgene_86851

What is this?

En fait, c'est comme s'il y avait des balises "imp 1", "imp 2" et "imp 3" et que le navigateur change de style selon le chiffre après le "imp", c'est ça ? Ce que je ne comprends pas, c'est que, si "strong" ne signifie pas, pour le navigateur, "mettre en gras", dans ce cas, il choisit aléatoirement entre "surligner", "italique" et "gras" ? Mais dans ce cas, pourquoi des balises spécifiques pour chacun des styles ?

Reviewer #2 (Public review):

Summary:

In this manuscript, the authors investigate the functional requirements for glutamine and glutaminolysis in antibody responses. The authors first demonstrate that the concentrations of glutamine in lymph nodes are substantially lower than in plasma, and that at these levels, glutamine is limiting for plasma cell differentiation in vitro. The authors go on to use genetic mouse models in which B cells are deficient in glutaminase 1 (Gls), the glucose transporter Slc2a1, and/or mitochondrial pyruvate carrier 2 (Mpc2) to test the importance of these pathways in vivo.

Interestingly, deficiency of Gls alone showed clear antibody defects when ovalbumin was used as the immunogen, but not the hapten NP. For the latter response, defects in antibody titers and affinity were observed only when both Gls and either Mpc2 or Slc2a1 were deleted. These latter findings form the basis of the synthetic auxotrophy conclusion. The authors go on to test these conclusions further using in vitro differentiations, Seahorse assays, pharmacological inhibitors, and targeted quantification of specific metabolites and amino acids. Finally, the authors document reduced STAT3 and STAT1 phosphorylation in response to IL-21 and interferon (both type 1 and 2), respectively, when both glutaminolysis and mitochondrial pyruvate metabolism are prevented.

Strengths:

(1) The main strength of the manuscript is the overall breadth of experiments performed. Orthogonal experiments are performed using genetic models, pharmacological inhibitors, in vitro assays, and in vivo experiments to support the claims. Multiple antigens are used as test immunogens--this is particularly important given the differing results.

(2) B cell metabolism is an area of interest but understudied relative to other cell types in the immune system.

(3) The importance of metabolic flexibility and caution when interpreting negative results is made clear from this study.

Weaknesses:

(1) All of the in vivo studies were done in the context of boosters at 3 weeks and recall responses 1 week later. This makes specific results difficult to interpret. Primary responses, including germinal centers, are still ongoing at 3 weeks after the initial immunization. Thus, untangling what proportion of the defects are due to problems in the primary vs. memory response is difficult.

(2) Along these lines, the defects shown in Figure 3h-i may not be due to the authors' interpretation that Gls and Mpc2 are required for efficient plasma cell differentiation from memory B cells. This interpretation would only be correct if the absence of Gls/Mpc2 leads to preferential recruitment of low-affinity memory B cells into secondary plasma cells. The more likely interpretation is that ongoing primary germinal centers are negatively impacted by Gls and Mpc2 deficiency, and this, in turn, leads to reduced affinities of serum antibodies.

(3) The gating strategies for germinal centers and memory B cells in Supplemental Figure 2 are problematic, especially given that these data are used to claim only modest and/or statistically insignificant differences in these populations when Gls and Mpc2 are ablated. Neither strategy shows distinct flow cytometric populations, and it does not seem that the quantification focuses on antigen-specific cells.

(4) Along these lines, the conclusions in Figure 6a-d may need to be tempered if the analysis was done on polyclonal, rather than antigen-specific cells. Alum induces a heavily type 2-biased response and is not known to induce much of an interferon signature. The authors' observations might be explained by the inclusion of other ongoing GCs unrelated to the immunization.

Prezados autores, lido o texto há por destacar o seguinte: - o texto é relevante e aponta para uma temática pouco explorada em Moçambique; - Possui uma descrição clara do objecto e indica seu foco, relevância e inserção na perspectiva teórico metodológica adoptada; - É rico na descrição e atualização autoral. Entretanto, alguns aspectos podem merecer reparo: - A necessidade de uma correção linguística e gramatical; - A necessidade de, logo na introdução, no âmbito da busca por mapear o contexto da fala, afinar o foco para a proposta da pesquisa. Por exemplo, evitando grande menção feita sobre o fotojornalismo. Att., Roberto Chaua

húzzuk ki még nem létezik

• 「できる」が反復。
• 「〜することができる」は「〜できる」と短く言い換えられる (ref: https://ics.media/entry/19096/#%E5%86%97%E9%95%B7%E3%81%AA%E8%A1%A8%E7%8F%BE%E3%82%92%E4%BD%BF%E3%81%A3%E3%81%A6%E3%81%84%E3%81%AA%E3%81%84%E3%81%8B)
• 知れない→しれない （ref: https://e-kotobano.com/713/#toc2 ）

その他含め↓とかどうでしょう

筆者自身も手の届くところから貢献を始められるかもしれない

As television was introduced to families, it created a new way for families to interact, whether it be positive or negative.

Párrafo interesante

Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

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Reply to the reviewers

We would like to thank the reviewers for taking the time to review our manuscript and for providing valuable comments on how to improve it. We are pleased to see that both reviewers recognize the novelty and importance of our study, its conceptual advance and potential clinical significance. They also noted the novelty and value of our functional mechanistic approach using epigenetic editing. Below, we provide a point-by-point response to their questions and points raised. The changes introduced in response to their feedback are highlighted in yellow in the revised manuscript file.

Point-by-point description of the revisions

__Reviewer #1 (Evidence, reproducibility and clarity (Required)): __

Summary This study by Prada et al. aimed to explore DNA methylation and gene expression in primary EpCAMhigh/PDPNlow cells, consisting of for (probably) the largest part of AT2 cells, to understand the molecular mechanisms behind the impaired regeneration of alveolar epithelial progenitor cells in COPD. They found that higher or lower promoter methylation in COPD-associated cells was inversely correlated with changes in gene expression, with interferon signaling emerging as one of the most upregulated pathways in COPD. IRF9 was identified as the master regulator of interferon signaling in COPD. Targeted DNA demethylation of IRF9 in an A549 cell line resulted in a robust activation of its downstream target genes, including OAS1, OAS3, PSMB8, PSMB9, MX2 and IRF7, demonstrating that demethylation of IRF9 is sufficient to activate the IFN signaling pathway, validating IRF9 as a master regulator of IFN signaling in (alveolar) epithelial cells.

Major comments:

• To remove airways (and blood vessels) completely from the lung tissue is difficult, if not impossible. This means that the assumption that the sorted EpCAMpos/PDPNlow cells primarily consisted of AT2 cells remains valid only if a quantitative analysis is conducted on the proportion of HT2-280pos cells in all samples in cytospins to exclude any significant contamination from bronchial epithelial cells. If authors cannot demonstrate >95% pure HT-280-positive cells, then the key conclusions suggesting that the epigenetic regulation of the IFN pathway might be crucial in AT2 progenitor cell regeneration could also potentially apply to bronchial progenitor cells. In addition, if >95% purity cannot be demonstrated, the data should be adjusted to account for differences in cell type composition.

__Response: __

We thank the reviewer for raising this important point. Although, as pointed out by the reviewer, we cannot guarantee that our sorted cells do not contain a minor contamination from respiratory / terminal bronchial cells, we carefully selected donors, tissue regions, and sorting strategy to ensure the highest possible enrichment of AT2 cells, as we explain below. We have now expanded the methods and results section and covered this point in the manuscript discussion.

• The lung tissue pieces we received were distal, as evidenced by the presence of pleura. We collected representative tissue pieces for histology to validate sample quality. Our protocol includes a dissection of all visible airways and vessels using a dissecting microscope, which were cryopreserved separately from distal parenchyma. Hence, the starting material for tissue dissociation was depleted from airways and vessels. The importance of vessel/airway removal for enrichment of distal alveolar cells was established by Tata's group (PMID: 35712012).
• We selected the AT2 sorting protocol (EpCAMpos/PDPNlow) based on previous publications that used tissue from both healthy and COPD lungs to separate AT2 cells from AT1 and airway basal cells, as AT1 and basal cells are both PDPNhigh (PMID: 22033268, PMID: 23117565; PMID: 35078977). This protocol was favoured due to the lack of information about HT2-280 expression and distribution in COPD lungs.
• The sort quality for each sample was assessed by the FACS analysis (back sorting) of the sorted cells, where we observed 95-97% purity (EpCAMpos/PDPNlow, __ 1G __shown below). In addition, we validated the sorting protocol and high AT2 enrichment from both no COPD and COPD tissues by immunostaining the FACS-sorted cells with HT2-280, an AT2 marker widely used in the field (strategy suggested by the reviewer) and observed that close to 100% of cells were positive for this marker (__Fig. 1H __shown below). However, we could not do it retrospectively for those patients, where we didn't have enough material. Sorting primary AT2 from small tissue pieces is challenging, and we need at least 20.000 cells to obtain high-quality methylation & RNA-seq data.

• AT2 marker genes (ABCA3, LPCAT1, LAMP3 and the surfactant genes SFTPA2, SFTPB and SFTPC) were among the top highly expressed genes in our RNA-seq data and were not significantly changed in COPD (please see expression data in __ S2A__ in the manuscript, and below for convenience), as well as Table 6, providing further evidence that the sorted cells carry a strong AT2 transcriptional signature. Fig. 1G* FACS plot examples showing the analysis of sorted AT2 cells (back sorting) from control (blue) and COPD (green) donors displayed over total cell lung suspensions (grey) H Representative IF staining of HT2-280 expression in sorted AT2 cells from no COPD (top) and COPD (bottom) donors. Nuclei (blue) were stained with DAPI, scale bars=20µm __Fig. S2A __Normalized read counts from RNA-seq data for AT2-specific genes in sorted AT2 cells from each donor (dots). Data points represent normalised counts from no COPD (blue), COPD I (light green) and COPD II-IV (dark green). Group median is shown as a black bar. *

• In agreement with a previous study which profiled bulk AT2 using expression arrays (PMID: 23117565), we also observed upregulation of IFN signaling pathway in COPD AT2s. The enrichment of IFNα/β signature was also observed in COPD in the inflammatory AT2 cluster (iAT2) in a recent scRNA-seq study (PMID: 36108172). As part of the revision, we compared the IFN gene signature identified in our bulk AT2 RNA-seq with a recent scRNA-seq study (published after the submission of our manuscript, PMID: 39147413) that profiled EpCAMpos cells from COPD and non-smoker donor lungs. We observed an upregulation of our IFN signature genes in AT2 in COPD (mostly in AT2c and rbAT2 subsets), suggesting that similar signatures were observed in COPD AT2s in this dataset as well (please see __ S4E-F__ below). ____Figure S4E Expression values for the indicated genes of the IFN pathway from an external scRNA-seq dataset of AT2 cells from COPD patients and healthy controls (Hu et al, 2024). Y-axis shows log-normalized gene expression levels. F. Combined gene set score of the genes shown in (E) in different subsets of AT2 cells from Hu et al, 2024. The IFN signature genes were identified in our integrative analysis of TWGBS and RNA-seq in sorted AT2 cells.

• We have also carefully examined DNA methylation profiles across all samples. The density plots of our T-WGBS DNA methylation data are very similar among the individual samples in all 3 groups, indicating that the sorted cells consist mostly of a single cell type, as there are no obvious intermediate (25-75%) methylation peaks, as observed in cell mixtures ( 2A and the panel below). No reference DNA methylation profiles are available for respiratory or terminal bronchial cells; hence, we cannot compare how epigenetically different these cells would be from AT2 nor perform a deconvolution for potential minor contamination with distal airway cells. *Figure: DNA methylation density plots of sorted EpCAMpos/PDPNneg cells from no COPD (blue, n=3), COPD I (light green, n=3) and COPD II-IV (dark green, n=5) showing a homogeneous methylation pattern and low abundance at intermediate (25%-75%) methylation values across all profiled samples, indicating that the sorted cells were mostly of a single cell type. *

• We have now added a sentence to the limitations section of the discussion to cover that point specifically. CHANGES IN THE MANUSCRIPT:

AT2 cells were isolated by fluorescence-activated cell sorting (FACS) from cryopreserved distal lung parenchyma, depleted of visible airways and vessels of three no COPD controls, three COPD I and five COPD II-IV patients as previously described (24, 52, 53)

The isolated cells were positive for HT2-280, a known AT2 marker (54)*, as confirmed by immunofluorescence (Fig. 1H), validating the identity and high enrichment of the isolated AT2 populations. ** *

*Known AT2-specific genes, including ABCA3, LAMP3 and surfactant genes (SFTPA2, SFTPB and SFTPC) were among the top highly expressed genes and were not significantly changed in COPD AT2s (Fig. S2A, Table 6), further confirming the AT2-characteristic transcriptional signature of our isolated cells. *

However, 5-AZA is a global demethylating agent, and the observed effects may not be direct. To validate the epigenetic regulation of central AT2 pathways further, we took advantage of locus-specific epigenetic editing technology *(73). We focused on the IFN pathway because it was the most significantly enriched Gene Ontology (GO) term in our integrative analysis of TWGBS and RNA-seq data. Several IFN pathway members had associated hypomethylated DMRs within promoter-proximal regions and concomitant increased gene expression (Fig. 4C and S2C). Additionally, we confirmed the elevated expression of IFN-related genes with associated DMRs identified in our study in AT2 cells and AT2 cell subclusters from a recently published scRNA-seq cohort (74) (Fig. S4E-F). *

We observed upregulation of multiple IFN genes in AT2 in COPD, consistent with a previous expression array study (24). IFNα/β signaling was also enriched in COPD patients in the inflammatory AT2 cluster (iAT2) in a recent scRNA-seq study (84) and our INF signature genes were also upregulated in AT2c and AT2rb subsets in COPD, identified by another scRNA-seq study recently (74)*. ** *

Finally, despite careful removal of airways from distal lung tissue using a dissecting microscope, we cannot exclude the presence of some terminal/respiratory bronchiole cells in our FACS-isolated EpCAMpos/PDPNlow population. Recent scRNA-seq studies provided an unprecedented resolution and identified several epithelial subpopulations and transitional cells residing in the terminal/respiratory bronchioles and alveoli, including respiratory airway secretory cells (93), terminal airway-enriched secretory cells (28), terminal bronchiole-specific alveolar type-0 (AT0) (70), and emphysema-specific AT2 cells (74). These cells may contribute to alveolar repair in healthy and COPD lungs; however, with our bulk DNA methylation and RNA-seq study, we are unable to resolve all these subpopulations. Future development of single-cell methylation and non-reference-based algorithms for DNA methylation deconvolution will enable deeper epigenetic phenotyping of specific AT2 and bronchiolar cell subsets.

(Methods) Validation of IFN gene upregulation in a published scRNA-seq dataset

scRNA-seq data from (74), generously provided by M. Köningshoff, were processed using the default Seurat workflow (117). Expression of IFN-related genes was extracted and plotted as log-normalised gene expression levels in AT2 cells from control and COPD donors. Seurat's AddModuleScore() function was used to compute a gene set score for a custom IFN program using the genes listed in __Fig. S4E __and to analyse the IFN gene set scores in AT2 cell subclusters identified in (74). Briefly, average gene expression scores were computed for the gene set of interest, and the expression of control features (randomly selected) was subtracted as described in (118).

Fig. S4E and F: E. Expression values for the indicated genes of the IFN pathway from an external scRNA-seq dataset of AT2 cells from COPD patients and healthy controls (74). Y-axis shows log-normalized gene expression levels. F. Combined gene set score of the genes shown in (E) in different subsets of AT2 cells from (74). The IFN signature genes were identified in our integrative analysis of TWGBS and RNA-seq in sorted AT2 cells.

• The overrepresentation of several keratins (KRT5, KRT14, KRT16, KRT17), mucins (MUC12, MUC13, MUC16, MUC20) and the transcription factor FoxJ1 is now attributed by the authors to a possible dysregulation of AT2 identity and differentiation in COPD (lines 282 - 284) where they cite refs 28, 69, 70. Authors try to support this with IF double stains for KRT5 and HT-280 to identify co-expression of KRT5 and HT2-280 in lung tissue (Figure S2H). However, the evidence for the co-expression of both markers could be presented more convincingly.

__Response: __

We found the potential co-expression of airway and alveolar markers in COPD lungs interesting and hence included it in the original manuscript. The initial discovery came from our bulk RNA-seq data, where we observed upregulation of several genes typically found in more proximal airways in COPD (mentioned above by the reviewer). Of note, some of them (e.g., FoxJ1) are expressed at very low levels. Following reviewer's comments, to validate possible colocalization of AT2 and airway markers on protein level, we performed further IF analysis. We took Z-stack images to demonstrate the co-localization of HT2-280 and Krt5 more convincingly and co-stained the same tissue regions with SCGB3A2 (a TASC/distal airway cell marker, PMID 36796082). Even though these are rare events, we were able to reproduce the existence of HT2-280/Krt5 positive, SCGB3A2 negative cells in the alveoli of COPD patients on the protein level (__Fig. S2H __and panels below). Although interesting, we decided to keep this finding in the supplement and did not include it in the discussion to focus the story on the epigenetic regulation of the IFN pathway, which is the main discovery of our study. We will investigate this observation in future studies.

Figure S2H and here: Examples of HT2-280/Krt5 double positive cells. Top, immunofluorescence staining of the alveolar region of a COPD II donor showing the existence of AT2 cells (HT2-280 positive (red), which are SCGB3A2 negative (green, left) but KRT5 positive (green, right). In conclusion, double-positive HT2-280/KRT5 cells are rare but present in the alveoli of COPD patients. Magnification: 20x. Scale bar: 50 µm. Bottom, Z-stack images highlighting HT2-280 (red) and KRT5 (green) double-positive cells at 63x magnification. Scale bar: 5 µm.

CHANGES IN THE MANUSCRIPT:

In addition, we observed an upregulation of several keratins (KRT5, KRT14, KRT16, KRT17) and mucins (MUC12, MUC13, MUC16, MUC20), suggesting a potential dysregulation of alveolar epithelial cell differentiation programs in COPD (Table 6, Fig. S2F). Immunofluorescence staining confirmed the presence of KRT5-positive cells in the distal lung in COPD and identified cells positive for both KRT5 and HT2-280 (Fig. S2H). Collectively, these results indicate a dysregulation of stemness and identity in the alveolar epithelial cells in COPD.

Fig. S2H legend: The zoomed-in panel (right corner, bottom) demonstrates the presence of rare HT2-280/KRT5 double-positive cells in the alveoli of COPD patients.* Slides were counterstained with DAPI, scale bars = 50µm, 20µm or 5µm, as displayed in images. *

• Double staining for KRT5 and HT2-280 did highlight the proximity of both cell types in lung tissue, underscoring the challenge of removing airways (including the smaller and terminal bronchi) from the tissue. In addition, HT-280/KRT5 co-expression is not consistent with recent studies from refs 28, 69, 70 where other markers for distal airway cell transition, such as SCGB3A2 and BPIFB1, have been demonstrated, which were not investigated in this study.

Response:

We provided a general overview of the different signatures observed in our data, but we could not validate every deregulated pathway or gene. We include the relevant tables detailing all differentially expressed genes and differentially methylated regions to enable and encourage the community to follow up on the data in subsequent studies.

As demonstrated above, we detect the co-occurrence of HT2-280/KRT5 staining on the protein level in the same cells in the alveoli of COPD patients. We would like to emphasize that alveolar epithelial cell identity in CODP lungs has not been investigated in detail on the protein or RNA level, and HT2-280/KRT5 co-expression/co-localization has not been directly tested in the studies mentioned by the reviewer since, among other reasons, the gene encoding HT2-280 has not been identified. Notably, a recent study (published after the submission of our manuscript) focusing on enriched epithelial cells from the distal lungs of COPD patients (PMID 35078977), identified an emphysema-specific AT2 subtype co-expressing the AT2 marker SFTPC and distal airway cell transition marker SCGB3A2, indicating that disease-specific AT2 populations with possible co-occurrence of AT2 and airway markers exist. In our dataset, SCGB3A2 was not deregulated (log2 fold change=0.22, adj p-value= 0.47), as shown in Table 6, and the HT2-280/Krt5 positive cells were negative for SCGB3A2 in our IF staining (see above).

BPIFB1 is one of the antimicrobial peptides genes with an associated DMR and is significantly upregulated in COPD cells in our study (log2 fold change=1.17, adj p-value=0.0016), as shown in the supplementary figure Fig S4C and here below for convenience.

Figure S4C Fold-change in gene expression of BPIFB1 in AT2 cells in COPD (RNA-seq) and A549 cells treated with 0.5µM AZA (RT-qPCR) compared to control samples. Left, RNA-seq data from AT2 cells (no COPD, blue, n=3; COPD II-IV, green, n=5). Right, A549 treated with AZA (orange, n=3) compared to control DMSO-treated cells (grey, n=3). The group median is shown as a black bar.

• The small (and not evenly divided) sample size of both COPD and non-COPD specimens may lead to a higher risk for false positive results as adjustments for multiple testing typically rely on the number of comparisons, and small sample sizes may not provide enough data points to adequately control for this.

__Response: __

We acknowledge the problem of testing for multiple traits with relatively small numbers of samples. The availability of donor tissue, especially from non-COPD and COPD-I donors, was limited, and we applied very strict donor matching and quality control criteria for sample inclusion to avoid additional variability and confounding factors. The importance of strict quality control in selecting appropriate control samples was highlighted in our previous study (PMID: 33630765), where we demonstrated that approximately 50% of distal lung tissue from cancer patients with normal spirometry has pathological changes. Hence, we believe that the quality of the tissue was paramount to the reliability of the data. Strict quality control and sample matching for multiple parameters, including age, BMI, smoking status and smoking history (critical for DNA methylation studies), and cancer type (for background tissue), is a key strength of our approach, but it inevitably limited our sample size.

First, all samples were cryopreserved and then processed in parallel in groups of 1 non-COPD and 2-3 COPD samples. This process included tissue dissociation, FACS sorting, back sorting (always), and immunofluorescence staining (when enough material was available). Cell pellets were stored at -80{degree sign}C until the entire cohort was ready for sequencing. This was done to limit the potential variation introduced by processing and sorting. RNA and DNA isolations were performed in parallel for all the sorted cell pellets, which were then sequenced as a single batch.

During data analysis, we applied stringent cutoffs for DMR detection to reduce the risk of false positives due to multiple comparisons and a small sample size. Specifically, we filtered for regions with at least 10% methylation difference and containing at least 3 CpGs. Additionally, we applied a non-parametric Wilcoxon test using average DMR methylation levels to remove potentially false-positive regions, as the t-statistic is not well suited for non-normally distributed values, as expected at very low/high (close to 0% / 100%) methylation levels. A significance level of 0.1 has been used. Therefore, we are confident that the rigorous analysis and strict criteria applied in this study allowed us to detect trustworthy DMRs that we could further functionally validate using epigenetic editing. All the details of the DMR analysis are provided in the methods section. To address this point and limitation, we have added the following paragraphs in the discussion section of the manuscript:

CHANGE IN THE MANUSCRIPT:

*The strengths of our study include the use of purified human alveolar type 2 epithelial progenitor cells from a well-matched and carefully validated cohort of human samples, including mild and severe COPD patients, providing high relevance to human COPD. *

However, we acknowledge several limitations of our study that warrant further investigation. First, the sample size was small. The use of strict quality criteria for donor selection limited the available samples, particularly for the ex-smoker control group. This resulted in an unequal distribution of COPD and control samples. This impacts the power of statistical analysis, particularly in the WGBS analysis, where millions of regions genome-wide are tested. Nevertheless, the clear negative correlation between promoter methylation and corresponding gene expression highlights the robustness of the DMR selection. Additionally, we were able to experimentally validate interferon-associated DMRs using epigenetic editing, highlighting the power of integrated epigenetic profiling in identifying disease-relevant regulators.

__Minor suggestions for improvement __

__Introduction __ • In general, refer to the actual experimental studies rather than review papers where appropriate.

Response:

We have now carefully checked all the references and amended them to refer to experimental studies when required.

• Clearly specify whether a study was conducted in mice or humans, as this distinction is crucial for understanding the relevance of the findings to COPD.

__Response: __

All our experiments were performed with human lung cells and tissues. No mouse samples were used. As suggested, we have now clearly stated that our study was performed using human tissue samples and cells in different parts of the manuscript, including the discussion, where we now explicitly highlight the strengths and limitations of our study.

CHANGES IN THE MANUSCRIPT:

...we generated whole-genome DNA methylation and transcriptome maps of sorted human primary alveolar type 2 cells (AT2) at different disease stages.

However, the regulatory circuits that drive aberrant gene expression programs in human AT2 cells in COPD are poorly understood

Therefore, we set out to profile DNA methylation of human AT2 cells at single CpG-resolution across COPD stages.

...*suggesting that aberrant epigenetic changes may drive COPD phenotypes in human AT2. *

To identify genome-wide DNA methylation changes associated with COPD in purified human AT2 cells...

The similarity of the methylation and gene expression profiles in the PCAs suggested that epigenetic and transcriptomic changes in human AT2 cells during COPD might be interrelated ...

*In this work, we demonstrate that genome-wide DNA methylation changes occurring in human AT2 cells may drive COPD pathology by dysregulating key pathways that control inflammation, viral immunity and AT2 regeneration. *

*Using high-resolution epigenetic profiling, we uncovered widespread alterations of the DNA methylation landscape in human AT2 cells in COPD that were associated with global gene expression changes. *

*Currently, it is unclear how cigarette smoking leads to changes in DNA methylation patterns in human AT2 *

The strengths of our study include the use of purified human alveolar epithelial progenitor cells from a well-matched and carefully validated cohort of human samples, including mild and severe COPD patients, providing high relevance to human COPD.

__Methods __ • Line 473, here is meant 3 ex-smoker controls instead of smoker controls?

__Response: __

All donors (no COPD and COPD) used in our study are ex-smokers. Matching the samples with regard to smoking status and history is critical for epigenetic studies, as cigarette smoke profoundly affects DNA methylation genome-wide (PMID: 38199042, PMID: 27651444). This has now been clarified in the revised manuscript.

CHANGE IN THE MANUSCRIPT____:

Of note, we included only ex-smokers in our profiling to avoid acute smoking-induced inflammation as a confounding factor (50)*. *

Importantly, we matched the smoking status and smoking history of all donors, which is key in epigenetic studies, as cigarette smoking profoundly impacts the DNA methylation landscape of tissues (96).

In total, 3 ex-smoker controls (no COPD), 3 mild COPD donors ex-smokers (GOLD I, COPD I) and 5 moderate-to-severe COPD donors ex-smokers (GOLD II-IV, COPD II-IV) were profiled (Fig. 1A-C, Table 1)

__Discussion __ • A list of limitation should be added to the discussion. One is the use of the alveolar cell line A549, which produces mucus, a characteristic more commonly associated with bronchial epithelial cells. (ref 43)l530:

__Response: __

The profiling was performed using purified primary human alveolar epithelial progenitor cells. For technical reasons, A549 cells were only used for validation of the results using epigenetic editing. The A549 phenotype depends on the growth medium used, in our case, Ham's F-12 medium, which is recommended for long-term A549 culture and promotes multilamellar body formation and differentiation toward an AT2-like phenotype (PMID: 27792742)__. __We are developing epigenetic editing technology for use in primary lung cells; however, the approach currently relies on the high efficiency of transient transfections, which cannot yet be achieved with primary adult AT2 cells. We were positively surprised by how well the methylation data obtained from patient AT2s translated into mechanistic insights when using A549 cells, despite being a cancer cell line. This suggests that the fundamental mechanisms of epigenetic regulation of IRF9 and the IFN signaling pathway are conserved between A549 and primary AT2 cells.

• Another limitation to consider is that cells were isolated primarily from individuals with lung cancer, except for patients with COPD stage IV. In particular as COPD stage II and IV samples were taken together. And discuss the small and unevenly divided sample size

__Response: __

We thank the reviewer for bringing up this important point, which we carefully considered when designing our study. To match our samples across the cohort, all the no-COPD, COPD I, and two of the COPD II-IV samples were obtained from cancer resections. In addition to other characteristics, like age, BMI and smoking status, we also matched the donors by cancer type (all profiled donors had squamous cell carcinoma). We collected lung tissue as far away from the carcinoma as possible and sent representative pieces for histological analysis by an experienced lung pathologist to confirm the absence of visible tumours. In addition, to ensure that our data represents COPD-relevant signatures, we intentionally included samples from three COPD donors undergoing lung resections (without a cancer background) in the profiling.

Following the reviewer's suggestion, to investigate the potential impact of non-cancer samples on driving the observed differences, we carefully checked the PCAs for both DNA methylation and RNA-seq. We could not identify a clear separation of no-cancer COPD samples from the cancer COPD samples (or other cancer samples) in any examined PCs, indicating no cofounding effect of cancer background in the samples. We observed that one sample contributing to PC2 is a non-cancer sample, but this was a rather sample-specific effect, as the other two non-cancer samples clustered together with the other severe COPD samples with a cancer background. Notably, in our DNA methylation data, we do not observe typical features of cancer methylomes, like global loss of DNA methylation or aberrant methylation of CpG islands (e.g., in tumour suppressor genes) (see Fig 2A), further suggesting that we do not "pick up" confounding cancer signatures in our data.

Following the comments from both reviewers, to clarify that point, we added the information about cancer and non-cancer samples to the PCA figures for DNA methylation (new Fig. 2B) and RNA-seq (new Fig. 3A) data in the revised manuscript, as shown below

CHANGE IN THE MANUSCRIPT____:

COPD samples from donors with a cancer background clustered together with the COPD samples from lung resections, confirming that we detected COPD-relevant signatures (Fig. 2B).

Fig.2B* Principal component analysis (PCA) of methylation levels at CpG sites with > 4-fold coverage in all samples. COPD I and COPD II-IV samples are represented in light and dark green triangles, respectively, and no COPD samples as blue circles. COPD samples without a cancer background are displayed with a black contour. The percentage indicates the proportion of variance explained by each component. *

Unsupervised principal component analysis (PCA) on the top 500 variable genes revealed a clear influence of the COPD phenotype in separating no COPD and COPD II-IV samples, as previously observed with the DNA methylation analysis, irrespective of the cancer background of COPD samples (Fig.3A, Fig. S2B).

*Principal component analysis (PCA) of 500 most variable genes in RNA-seq analysis. PCA 1 and 2 are shown in Fig.3A, PCA 1 and 4 in Fig.S2B. COPD I and COPD II-IV samples are represented in light and dark green triangles, respectively, and no COPD samples as blue circles. COPD samples without a cancer background are displayed with a black contour. The percentage indicates the proportion of variance explained by each component. *

__Response: __

We thank the reviewer for suggestions on how to improve the discussion of our manuscript. We have now added a strength/limitation section to our discussion and included the points suggested by both reviewers.

CHANGE IN THE MANUSCRIPT____:

The strengths of our study include the use of purified human alveolar epithelial progenitor cells from a well-matched and carefully validated cohort of human samples, including mild and severe COPD patients, providing high relevance to human COPD. Importantly, we matched the smoking status and smoking history of all donors, which is key in epigenetic studies, as cigarette smoking profoundly impacts the DNA methylation landscape of tissues (96). With the first genome-wide high-resolution methylation profiles of isolated cells across COPD stages, we offer novel insights into the epigenetic regulation of gene expression in epithelial progenitor cells in COPD, expanding our understanding of how alterations in regulatory regions and specific genes could contribute to disease development. We identified IRF9 as a key IFN transcription factor regulated by DNA methylation. Notably, by targeting IRF9 through epigenetic modifications, we modulated the activity of the IFN pathway, which plays a crucial role in the immune response and lung tissue regeneration. Epigenetic editing techniques could offer a novel therapeutic strategy for COPD by downregulating IFN pathway activation and promoting the regeneration of epithelial progenitor cells in the lungs. Further preclinical and clinical studies are needed to validate the efficacy and safety of epigenetic editing approaches in COPD treatment (33)*. *

*However, we acknowledge several limitations to our study that warrant further investigation. First is the small sample size and replication difficulty due to the lack of available data, common challenges for studies working with sparse human material and hard-to-purify cell populations. The use of strict quality criteria in donor selection limited the available samples, especially for the ex-smoker control group, leading to an unequal distribution of COPD and control samples. Overall, this impacts the power of statistical analysis, especially in the WGBS analysis, where millions of regions genome-wide are tested. Nevertheless, the clear negative correlation of promoter methylation to the corresponding gene expression highlights the robustness of the DMR selection. Furthermore, we could experimentally validate interferon-associated DMRs using epigenetic editing, highlighting the power of integrated epigenetic profiling for the discovery of disease-relevant regulators. *

Overall, we detected a higher number of correlated DMR-DEG associations using our simple promoter-proximal linkage compared to the GeneHancer approach. Assigning enhancers to their target genes with high confidence is a complex and challenging task. Enhancers are often located far from the genes they regulate and can interact with their target genes through three-dimensional chromatin loops. Furthermore, enhancers can operate in a highly context-dependent manner, with the same enhancer regulating different genes depending on the cell type, developmental stage, or environmental signals. Determining which enhancer is active under specific conditions remains a hurdle in the field, especially since the AT2-specific chromatin profiles of enhancer marks are not yet available.

In addition, while WGBS provides unprecedented resolution and high coverage of the DNA methylation sites across the genome, it does not allow distinguishing 5-methylcytosine from 5-hydroxymethylcytosine. Therefore, we cannot exclude that some methylated sites we detected are 5-hydroxymethylated. However, as 5-hydroxymethylcytosine is present at very low levels in the lung tissue (97)*, its effect is likely marginal. *

Finally, despite careful removal of airways from distal lung tissue using a dissecting microscope, we cannot exclude the presence of some terminal/respiratory bronchiole cells in our FACS-isolated EpCAMpos/PDPNlow population. Recent scRNA-seq studies provided an unprecedented resolution and identified several epithelial subpopulations and transitional cells residing in the terminal/respiratory bronchioles and alveoli, including respiratory airway secretory cells (93), terminal airway-enriched secretory cells (28), terminal bronchiole-specific alveolar type-0 (AT0) (70), and emphysema-specific AT2 cells (74). These cells may contribute to alveolar repair in healthy and COPD lungs; however, with our bulk DNA methylation and RNA-seq study, we are unable to resolve all these subpopulations. Future development of single-cell methylation and non-reference-based algorithms for DNA methylation deconvolution will enable deeper epigenetic phenotyping of specific AT2 and bronchiolar cell subsets.

__References __ • Check references. For instance, there is no reference in the text to ref 43.

• Align format of references

__Response: __

We thank the reviewer for spotting this inconsistency. We have carefully checked and aligned the format of all references. The (old) reference 43 is now mentioned in the discussion part.

__Reviewer #1 (Significance (Required)): __

The strength of this study lies in its focus on the molecular mechanisms underlying the impaired regeneration of epithelial progenitor cells in COPD. The discovery of IRF9, which regulates IFN signaling and is prominently upregulated in COPD, together with the convincing validation of the epigenetic control of the IFN pathway by targeted DNA demethylation of the IRF9 gene, adds significant value to the COPD research field.

Main limitations of the study are the relatively small sample size of both COPD and non-COPD specimens and the claim that the sorted EpCAMpos/PDPNlow cells primarily consisted of AT2 cells.

__- Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field. __

The nature and significance of the advance in epigenetic editing of IRF9 in COPD can be described as both conceptual and potentially clinical:

Conceptual Advance: The epigenetic editing of IRF9 enhances our understanding of the molecular mechanisms underlying COPD pathogenesis. By targeting IRF9 through epigenetic modifications, researchers were able to modulate the activity of the IFN pathway, which plays a crucial role in the immune response and lung tissue regeneration. This approach offers insights into the epigenetic regulation of gene expression in epithelial progenitor cells in COPD and expands our understanding of how alterations in specific gene methylation could contribute to disease progression.

Clinical Significance: The potential clinical significance of epigenetic editing of IRF9 lies in its implications for COPD therapy. If successful, epigenetic editing techniques could offer a novel therapeutic strategy for COPD by downregulating IFN pathway activation and promoting regeneration of epithelial progenitor cells in the lungs. Obviously, further preclinical and clinical studies are needed to validate the efficacy and safety of epigenetic editing approaches in COPD treatment.

__Response: __We thank the reviewer for recognising the importance of our study, its conceptual advance and potential clinical significance. We are pleased to see that the reviewer highlights the promise of epigenetic editing in both furthering our basic understanding of molecular mechanisms of chronic diseases and its future potential as a therapeutic strategy.

__- Place the work in the context of the existing literature (provide references, where appropriate). __ Few experimental papers have been published on epigenetic editing in lung diseases, with limited research available beyond the study referenced in citation 43. Song J, Cano-Rodriquez D, Winkle M, Gjaltema RA, Goubert D, Jurkowski TP, Heijink IH, Rots MG, Hylkema MN. Targeted epigenetic editing of SPDEF reduces mucus production in lung epithelial cells. Am J Physiol Lung Cell Mol Physiol. 2017 Mar 1;312(3):L334-L347. doi: 10.1152/ajplung.00059.2016. Epub 2016 Dec 23. PMID: 28011616.

Response:

We thank the reviewer for recognising the uniqueness and novelty of our study and the lack of research on the functional understanding of DNA methylation in the context of lung and lung diseases.

- State what audience might be interested in and influenced by the reported findings.

This study is of broad interest to researchers investigating the pathogenesis and treatment of COPD.

__- Define your field of expertise with a few keywords to help the authors contextualize your point of view. __

Expertise in: Lung pathology, Immunology, COPD, Epigenetics

- Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate. Less expertise in: Epigenetic Editing

__Reviewer #2 (Evidence, reproducibility and clarity (Required)): __

__Summary: __

This study aim to understand the molecular mechanisms underlying dysfunction in AT2 cells in COPD, by profiling bulk genome wide DNA methylation using Tagmentation-based whole-genome bisulfite sequencing (T-WGBS) and RNA sequencing in selectively sorted primary AT2 cells. The study stands out in it's sequencing breadth and use of an incredibly difficult cell population, and has the potential to add substantially to our mechanistic understanding of epigenetic contributions to COPD. A further highlight is the concluding aspect of the study where the authors undertook targeted modification of specific CpG methylation, provided direct, site-specific evidence for transcriptional regulation by CpG methylation.

Response:

We thank the reviewer for recognizing the conceptual and methodological advance of our study and for noting the value of our functional mechanistic approach.

__Major comments: __

The authors clearly show that there is DNA methylation alteration in AT2 cells from COPD individuals that links functional to gene expression at some level. However, I think the statement "to identify genome-wide changes associated with COPD development and progression..." and similar other references to disease development understanding is not accurate given the DNA methylation primary comparison is between control and moderate to severe COPD, with no temporal detail or evidence that they drive progression rather than are a result of COPD development. The paragraph starting on line 186 where this is a addressed to some extent is quite vague and doesn't really provide confidence that DNAm dysregulation occurs at an early stage in this context. This can be addressed by changing the focus/style of the text.

__Response: __

Thank you for raising this point. We agree with the reviewer that our cross-sectional study describes the association of methylation changes with either COPD I or more established disease (COPD II-IV) and that the observed changes may be either the driver or a result of COPD development. This has been clarified in the revised manuscript, and we removed the statements about disease initiation and progression. This is an important point; hence, we added an extra line to the discussion to make that clear.

__CHANGE IN THE MANUSCRIPT____: __

Therefore, we set out to profile DNA methylation of human AT2 cells at single CpG-resolution across COPD stages to identify epigenetic changes associated with disease and combine this with RNA-seq expression profiles.

To identify epigenetic changes associated with COPD, we collected lung tissue from patients with different stages of COPD,

....to identify methylation changes associated with mild disease, we included TWGBS data from AT2 isolated from COPD I patients (n=3) in the analysis.

Currently, we do not know whether the identified DNA methylation changes are the cause or the consequence of the disease process and not much is known about the correlation of DNA methylation with disease severity.

*However, our study is cross-sectional, our cohort included only 3 COPD I donors, and we did not have any follow-up data on the patients, so future large-scale profiling of mild disease (or even pre-COPD cohorts) in an extended patient cohort will be crucial for a better understanding of early disease and its progression trajectories. *

__Results comments and suggestions: __

For the integrated analysis, there is a focus on DMRs in promoters with very little analysis on other regions. The paragraph starting on line 317 describes some analysis on enhancers but is very brief, doesn't include information on how many/which DMRs were included, making it hard to interpret the impact of the 147 DMRs and 93 genes identified - is this nearly all DMRs and genes analysed or very few? A comparison to the promoter analysis would be of interest. Especially as the targeted region followed up with lovely functional assessment in the last sections is a gene body DMR, not a promoter DMR.

__Response: __

We thank the reviewer for pointing out the importance of changes in enhancers. We agree that extending the enhancer analysis is very interesting. However, assigning enhancers to their target genes with high confidence is a complex and challenging task. Enhancers are often located far from the gene they regulate, sometimes spanning hundreds of kilobases. They can interact with their target genes through three-dimensional chromatin loops, potentially bypassing nearby genes to activate more distant ones, making it difficult to confidently link specific enhancers to their target genes. Furthermore, enhancers can operate in a highly context-dependent manner. The same enhancer can regulate different genes depending on the cell type, developmental stage, or environmental signals. Another challenge is that enhancers often work in clusters or "enhancer landscapes," where multiple enhancers contribute to the regulation of a single gene. Disentangling the contribution of individual enhancers within such clusters and determining which enhancer is active under specific conditions remains an ongoing hurdle in the field, especially since the AT2-specific chromatin profiles of enhancer marks are not yet available.

One approach we tried to account for more distal regulatory regions was to assign DMRs to the nearest gene with a maximum distance of up to 100 kb using GREAT (Genomic Regions Enrichment of Annotations Tool) and simultaneously perform gene enrichment analysis of the associated genes. The old Figure S1C (now S1D) shows the top 10 enriched terms of either hyper- or hypomethylated DMRs, and Table 4 shows the full list of enriched terms. However, in this analysis, we did not integrate the results of the RNA-seq analysis. To demonstrate that we can correlate methylation with gene expression associations in this analysis, we then took a closer look at the WNT/b-catenin pathway, which contains 147 DMRs associated with 93 genes from the respective pathway (old Figure S3D, now S3G). Here, we showed that distal DMRs up to 100 kb away from the TSS show a high correlation with gene expression. We are including the two figures below for convenience:

*Left panels, functional annotation of genes located next to hypermethylated (top) and hypomethylated (bottom) DMRs using GREAT. Hits were sorted according to the binominal adjusted p-value and the top 10 hits are shown. The adjusted p-value is indicated by the color code and the number of DMR associated genes is indicated by the node size. Right panel, scatter plot showing distal DMR-DEG pairs associated with Wnt-signaling. Pairs were extracted from GREAT analysis (hypermethylated, DMR-DEG distance Following the reviewer's suggestion, we have now extended the enhancer analysis using the GeneHancer database, the most comprehensive, integrated resource of enhancer/promoter-gene associations. We used the GeneHancer version 5.14, which annotates 392,372 regulatory genomic elements (GeneHancer element) on the hg19 reference genome. Of the 25,028 DMRs, 18,289 DMRs (73% of all DMRs) coincided with at least one GeneHancer element, resulting in 19,661 DMR-GeneHancer associations. Next, we extracted the GeneHancer elements associated with protein-coding or long-non-coding RNAs genes, which left us with 2,144 DMR-GeneHancer associations. Next, we used only high-scoring gene GeneHancer associations ("Elite"), leaving 1,485 DMR-GeneHancer associations. Of those, we selected the GeneHancer elements, which are linked to genes differentially expressed in our RNA-seq analysis resulting in a final table of 376 DMR-GeneHancer associations (Table 9 DMR_DEG_GeneHancer, Tab 2). Similar to the promoter-proximal analysis, we analysed the correlation of expression and methylation changes of the DMR-GeneHancer associations, demonstrating a high number of negatively and positively correlated events (Fig.S3D). Finally, we performed the gene enrichment analysis for positively and negatively correlating genes. We detected significant GO term enrichments only for negatively correlating genes (Fig.S3E and Table 10_Enrichment_results, Tab2).

CHANGE IN THE MANUSCRIPT

To harness the full resolution of our whole-genome DNA methylation data, we extended the analysis beyond promoter-proximal regions and assessed how epigenetic changes in distal regulatory regions (enhancers) may relate to transcriptional differences in COPD. As the assignment of enhancer elements to the corresponding genes is challenging, we tried two different approaches. First, we used the GeneHancer database (72) to link DMRs to regulatory genomic elements (GeneHancer element). Of the 25,028 DMRs, 18,289 DMRs (73%) coincided with at least one GeneHancer element. Of those 2,144 DMR-GeneHancer associations were linked either to protein-coding or lncRNA genes. Next, we filtered for high-scoring gene GeneHancer associations ("Elite"), leaving 1,485 DMR-GeneHancer Elite associations. Of those, we selected the GeneHancer elements, which are linked to genes differentially expressed in our RNA-seq analysis, resulting in 376 DMR-GeneHancer associations (Table 9). Similar to the promoter-proximal analysis, we assessed the correlation of expression and methylation changes of the DMR-GeneHancer associations, demonstrating a high proportion of negatively and positively correlated events (Fig. S3E). Finally, we performed gene enrichment analysis for positively and negatively correlated genes. We detected significant GO term enrichments for negatively correlating genes only (Fig. S3F and Table 10), with the most pronounced term "regulation of tumor necrosis factor". In an alternative approach, we linked proximal and distal (within 100 kb from TSS) DMRs to the next gene using GREAT (57) (Fig S1C, Table 4) *and calculated Spearman correlation between DMRs and associated DEGs__. 147 DMRs were associated with high correlation rates with 93 genes from the WNT/β-catenin pathway (Fig. S3G)__, suggesting that DNA methylation may also drive the expression of genes of the WNT/β-catenin family. *

Figure S3E and F: E. Spearman correlation between gene expression and DMR methylation of DMRs assigned to gene regulatory elements using the GeneHancer database. F. GO-Term over-representation analysis of DEGs negatively correlated to DMRs in gene regulatory elements. The adjusted p-value is indicated by the color code and the percentage number of associated DEGs is indicated by the node size.

(Methods) For enhancer analysis, the GeneHancer database version 5.14, which annotates 392,372 regulatory genomic elements (GeneHancer element) on the hg19 reference genome, was used (72). Of the 25,028 DMRs 18,289 DMRs coincided with at least one GeneHancer element, resulting in 19,661 DMR-GeneHancer associations. Next, the GeneHancer elements were filtered for association with protein-coding or long-non-coding RNAs genes and high-scoring gene GeneHancer associations ("Elite"), leaving 1,485 DMR-GeneHancer associations. Of those, the GeneHancer elements were selected, which are linked to differentially expressed genes in COPD resulting in a final table of 376 DMR-GeneHancer associations. Similar to the promoter-proximal analysis, the Spearman correlation of expression and methylation changes of the DMR-GeneHancer associations was assessed. GO gene enrichment analysis for positively and negatively correlating genes was done using Metascape (111).

A comparison to the promoter analysis would be of interest.

Response:

We detected more highly correlated (|correlation coefficient| > 0.5) DMR-DEG associations using our simple promoter proximal linkage (n=643) in comparison with the GeneHancer approach comprising annotated enhancer elements (n=327/2,144). Gene enrichment results pointed to the interferon pathway, which we could confirm using epigenetic editing. This pathway was not present in the GeneHancer analysis, indicating that regulation of the IFN pathway may be controlled by proximal elements.

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Overall, we detected a higher number of correlated DMR-DEG associations using our simple promoter-proximal linkage compared to the GeneHancer approach. Assigning enhancers to their target genes with high confidence is a complex and challenging task. Enhancers are often located far from the genes they regulate and can interact with their target genes through three-dimensional chromatin loops. Furthermore, enhancers can operate in a highly context-dependent manner, with the same enhancer regulating different genes depending on the cell type, developmental stage, or environmental signals. Determining which enhancer is active under specific conditions remains a hurdle in the field, especially since the AT2-specific chromatin profiles of enhancer marks are not yet available.

Especially as the targeted region followed up with lovely functional assessment in the last sections is a gene body DMR, not a promoter DMR.

Response:

We thank the reviewer for bringing up that point. To clarify, we defined the promoter regions for the analysis as regions located {plus minus} 6 kb (upstream and downstream) from the transcriptional start site (TSS). Since the term "promoter" often refers to the region upstream of the transcriptional start site, its use may have been misleading. For clarity, we changed the text correspondingly to __promoter proximal methylation __and explained in the methods how the regions for analysis were defined.

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"DMR association per gene promoter" was changed to "Gene promoter proximal DMRs"

Fig. S3B: "DMR in promoter" was changed to "promoter proximal DMR(s)"

"by DNA methylation changes in promoters" was changed to "by DNA methylation changes in promoter proximity"

"regulated by promoter methylation" was changed to "regulated by promoter-proximal methylation"

"analysis of the promoter DMRs" was changed to "analysis of the promoter-proximal DMRs"

"between promoter methylation" was changed to "between promoter proximal methylation"

Cytoscape was used to analyse negatively or positively correlated DMR DEG pairs. ClueGO (v2.5.6) analysis was conducted using all DEG associated with a promoter proximal DMR (+/- 6 kb from TSS) and the Spearman correlation coefficient 0.5 (112).

• Lines 299-301 - I'm not sure the graph in Fig S3A support the conclusion that there was a preferential negative relationship between DNAm and gene expression. Looks like there are a substantial number of cases where a positive relationship is observed and this needs to be acknowledged.

Response:

In this part, we refer to Fig S3C. In the left panel, downregulated genes clearly show higher counts for the hypermethylated DMRs, whereas the hypomethylated DMRs are enriched at upregulated genes (right panel), indicating a preference for negative correlation: lower methylation, higher gene expression. If there were no preference, we would expect a 50:50 ratio of hypo- and hypermethylated DMRs, and we observed a 77:23 ratio. Nevertheless, we agree that there is a substantial number of cases (n=151) with a high positive correlation, which we now highlight in the text. For clarity, we also modified the figure legend to indicate that a stacked histogram is represented in the panel.

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L303: Interestingly, 23.5% of the identified DMR DEG pairs (n=151) showed a positive correlation between gene expression and DNA methylation.

*Figure legend in Fig. S3C was changed to: C Stacked histogram showing location of hyper- and hypomethylated DMRs relative to the TSS of DEGs in downregulated (left) and upregulated (right) genes. *

• Line 307 - what are the "analysed DEGs"? Are they the methylation associated genes?

Response:

Those are the DEGs we identified in RNA-seq analysis. To clarify, we changed the text to "identified DEGs".

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• "analysed DEGs" was changed to "identified DEGs"*

• Line 307-309 - "Among the analyzed DEGs, 76.5% (492) displayed a negative correlation (16.8% of the total DEGs), indicating a possible direct regulation by DNA methylation, while 23.5% (151) showed a positive correlation between gene expression and DNA methylation" - are the authors suggesting the positive correlation doesn't indicate direct regulation?

__Response: __

Thank you for highlighting this point. We did not intend to suggest that negative correlation indicates direct regulation, while positive correlation suggests a lack thereof. To clarify that point, we have reformulated this sentence.

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Among the identified DEGs, 76.5% (n=492) displayed a negative correlation (16.8% of the total DEGs), consistent with a repressive role of promoter DNA methylation. Interestingly, 23.5% of the identified DEG (n=151) showed a positive correlation between gene expression and DNA methylation.

• Line 313 - why did the authors focus on only negatively correlated genes to identify their top dysregulated pathway of IFN signalling? Why not do pathway analysis on the DNAm associated genes separately to identify DNAm associated pathways?

Response:

We have also performed a pathway enrichment analysis using the positively correlated genes but did not identify any significantly enriched pathways/process/terms. When we examined the top hit of the gene set enrichment analysis, the interferon signaling pathway, we observed only negatively correlated DMR gene associations (Fig. 5B). Therefore, we decided to use only the negatively correlated DMRs, as using all correlated genes would give a higher background and dilute our results.

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Cytoscape was used to analyse negatively or positively correlated DMR DEG pairs. ClueGO (v2.5.6) analysis was conducted using all DEG associated with a promoter proximal DMR (+/- 6 kb from TSS) and the Spearman correlation coefficient 0.5 (113).

• A comparison of the gene expression data with previous data in AT2 cell/single cell data would strengthen the gene expression section.

__Response: __

We compared our gene expression signatures with the study of Fujino et al., who profiled sorted AT2 cells (EpCAMhighPDPNlow) from COPD/controls using expression arrays (PMID: 23117565). Consistent with our study, the authors also observed the upregulation of interferon signalling (among other pathways) in COPD AT2s. However, no raw data was available in the published manuscript for a more in-depth analysis.

Several recent scRNA-seq studies identified transcriptional signatures of COPD and control cells (e.g., PMIDs: 36108172, 35078977, 36796082, 39147413__). However, most studies did not match the smoking status of the control and COPD donors and looked at the whole lung tissue, with limited power to detect gene expression changes in distal alveolar cells. It is difficult to directly compare our data to the gene expression data from non-smokers vs COPD patients, as cigarette smoking profoundly remodels the epigenome and transcriptional signatures of cells. In addition, differences in technologies and depth of sequencing make such comparisons challenging. However, one study (PMID: 36108172) performed scRNA-seq analysis on 3 non-smokers, 4 ex-smokers and 7 COPD ex-smoker lungs. Despite relatively limited coverage of epithelial cells in the dataset (We also compared the main AT2 IFN signature identified in the integration of our DNA methylation in promoter-proximal regions and RNA-seq with a recent study (published after the submission of our manuscript, PMID: 39147413) that profiled EpCAMpos cells from COPD and control lungs (non-smokers) using scRNA-seq. We observed an upregulation of our IFN signature genes in AT2 in COPD (specifically in AT2-c and rbAT2 subsets), suggesting that similar signatures were observed in this dataset as well. However, ex-smokers were not included in this study, making direct comparisons difficult. We have now included the panels shown below as __Figure S4E and S4F:

Figure S4E and F: Expression values for the indicated genes of the IFN pathway from an external scRNA-seq dataset of AT2 cells from COPD patients and healthy controls (74). Y-axis shows log-normalized gene expression levels. F. Combined gene set score of the genes shown in (E) in different subsets of AT2 cells from (74)*. The IFN signature genes were identified in our integrative analysis of TWGBS and RNA-seq in sorted AT2 cells. *

CHANGES IN THE MANUSCRIPT:

However, 5-AZA is a global demethylating agent, and the observed effects may not be direct. To validate the epigenetic regulation of central AT2 pathways further, we took advantage of locus-specific epigenetic editing technology (73). We focused on the IFN pathway because it was the most significantly enriched Gene Ontology (GO) term in our integrative analysis of TWGBS and RNA-seq data. Several IFN pathway members had associated hypomethylated DMRs within promoter-proximal regions and concomitant increased gene expression (Fig. 4C and Fig.S2C). Additionally, we confirmed the elevated expression of IFN-related genes with associated DMRs identified in our study in AT2 cells and AT2 cell subclusters from a recently published scRNA-seq cohort (74)* (Fig. S4E-F). *

(Methods) Validation of IFN gene upregulation in a published scRNA-seq dataset

scRNA-seq data from (74), generously provided by M. Köningshoff, were processed using the default Seurat workflow (117). Expression of IFN-related genes was extracted and plotted as log-normalised gene expression levels in AT2 cells from control and COPD donors. Seurat's AddModuleScore() function was used to compute a gene set score for a custom IFN program using the genes listed in __Fig. S4E __and to analyse the IFN gene set scores in AT2 cell subclusters identified in (74). Briefly, average gene expression scores were computed for the gene set of interest, and the expression of control features (randomly selected) was subtracted as described in (118).

Fig. S4 E and F. E. Expression values for the indicated genes of the IFN pathway from an external scRNA-seq dataset of AT2 cells from COPD patients and healthy controls (74). Y-axis shows log-normalized gene expression levels. F. Combined gene set score of the genes shown in (E) in different subsets of AT2 cells from (74). The IFN signature genes were identified in our integrative analysis of TWGBS and RNA-seq in sorted AT2 cells. __ __

• The paragraph starting on line 173 feels a little redundant when we know there is RNA available to test if the differential DNAm links to altered gene expression - this selected of example regions/genes would be better placed after the gene expression has been reported, at which point you could say whether the linked genes displayed altered transcription.

Response:

The current structure (with DNA methylation, followed by RNA-seq and integration) is intentional and serves several important purposes. As this is the first genome-wide high-resolution COPD DNA methylation study of AT2, we aimed to describe the methylation landscape independently of gene expression (noting the limitation of current understanding of how DNA methylation regulates expression). This early focus on DMRs lays clear groundwork by highlighting potential regulatory elements and pathways that could be disrupted, independent of or even before corroborative transcriptional data. Additionally, positioning these examples early in the narrative helps to frame subsequent gene expression analyses. Once RNA data are introduced later, the reader can directly compare the methylation patterns with transcriptional outcomes, thereby enhancing the overall story. In other words, by first showcasing disease-relevant methylation changes, we underscore a hypothesis that these epigenetic modifications are functionally meaningful. The later integration of gene expression data then serves as a confirmatory or complementary layer, rather than the sole basis for inferring biological significance. This is important as we still do not fully understand the function of DNA methylation outside promoters, and its role is also important for splicing, 3D genome organisation, non-coding RNA regulation, enhancer regulation, etc.

• Similarly, the TF enrichment analysis is great but maybe would have added value to be done on DNA regions later shown to be linked to differential expression - was there different enrichment at DNA regions that are vs are not associated with altered expression? And could you test in vitro whether changing methylation of DNA (maybe a blunt too like 5-aza would be ok) alters TF binding (cut+run/ChIP?). Furthermore, it would be interesting to understand the TF sensitivity analysis within the context of positive versus negative DNA methylation:gene expression correlations.

Response:

As suggested by the reviewer, we now performed the TF enrichment analysis using the DMRs with a high correlation (|correlation coefficient|>0.5) between methylation and expression (Figure S3D) and expanded the method section to include TF analysis. We observed ETS domain motifs enriched at hypomethylated regions. They prefer unmethylated DNA (MethylMinus) and are therefore expected to bind with higher affinity to the respective DMRs in COPD. We agree with the reviewer that further verifying altered TF binding using cut&run or ChIP assays would be very interesting, but it is out of the scope of this manuscript. Such analysis is technically very challenging to perform with low numbers of primary AT2 cells and will be the focus of our follow-up mechanistic studies.

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Additionally, motif analysis of DMRs that were highly correlated (|Spearman correlation coefficient| > 0.5) with DEGs revealed a prominent enrichment of the cognate motif for ETS family transcription factors, such as ELF5, SPIB, ELF1 and ELF2 at hypomethylated DMRs (Fig. S3D). Interestingly, SPIB was shown to facilitate the recruitment of IRF7, activating interferon signaling (71)*, and our WGBS data uncovers SPIB motifs at hypomethylated DMRs, which aligns with its binding preferences at unmethylated DNA (methyl minus, Fig. S3D). *

Figure S3D: Enrichment of methylation-sensitive binding motifs at hypo- (right) and hypermethylated (left) DMRs, using DMRs with a high correlation (|Spearman correlation coefficient| > 0.5) between methylation and gene expression. Methylation-sensitive motifs were derived from Yin et al (64). Transcription factors, whose binding affinity is impaired upon methylation of their DNA binding motif, are shown in red (Methyl Minus), and transcription factors, whose binding affinity upon CpG methylation is increased, are shown in blue (Methyl Plus).

(Methods) To obtain information about methylation-dependent binding for transcription factor motifs which are enriched at DMRs, the results of a recent SELEX study (64)* were integrated into the analysis. They categorised transcription factors based on the binding affinity of their corresponding DNA motif to methylated or unmethylated motifs. Those whose affinity was impaired by methylation were categorised as MethylMinus, while those whose affinity increased were categorised as MethylPlus. A motif database of 1,787 binding motifs with associated methylation dependency was constructed. The log odds detection threshold was calculated for the HOMER motif search as follows. Bases with a probability > 0.7 got a score of log(base probability/0.25); otherwise, the score was set to 0. The final threshold was calculated as the sum of the scores of all bases in the motif. Motif enrichment analysis was carried out against a sampled background of 50,000 random regions with matching GC content using the findMotifsGenome.pl script of the HOMER software suite, omitting CG correction and setting the generated SELEX motifs as the motif database. *

__Methods: __ • The authors should include more detail of the TWGBS rather than directing the reader to a previous publication. Also DNA concentration post bisulfite conversion would be a useful metric to provide.

__Response: __

Following the suggestion, we have now expanded the details of TWGBS in the methods part of the manuscript. Due to limited space, we did not include a detailed protocol but instead referred to a published step-by-step protocol (55). Of note, we do not measure DNA concentration post-bisulfite conversion but consistently use the starting input of 30 ng of genomic DNA across all samples.

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(Methods): 15 pg of unmethylated DNA phage lambda was spiked in as a control for bisulfite conversion. Tagmentation was performed in TAPS buffer using an in-house purified Tn5 assembled with load adapter oligos (55) at 55 {degree sign}C for 8 min. Tagmentation was followed by purification using AMPure beads, oligo replacement and gap repair as described (55). Bisulfite treatment was performed using EZ DNA Methylation kit (Zymo) following the manufacturer's protocol.

*The T-WGBS library preparations were performed for all donors in parallel and sequenced in a single batch to minimize batch effects and technical variability. *

• Differential DNA methylation analysis: It is stated that DNA regions had to contain 3 CpG sites but was this within a defined DNA size range?

Response:

The maximum distance between individual CpGs within DMR was set to 300 bp. To clarify, we added that information to the methods part.

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*"regions with at least 10% methylation difference and containing at least 3 CpGs with a maximum distance of 300 bp between them. *

• Refence genome only provided for RNAseq not TWGBS?

__Response: __We used hg19 as the reference genome. The information on the reference genome for DNA methylation analysis was provided in the methods L574 (original manuscript_: "The reads were aligned to the transformed strands of the hg19 reference genome using BWA MEM")

• The tables do not appear in the PDF and I struggled to tally to the "Dataset" files provided if that is what they were referring to?

Response:

Full tables (uploaded as Datasets in the manuscript central due to their size) were uploaded together with the manuscript files. They are quite large and will not convert to pdf, so they may not have been included in the merged pdf file. We assume that they should be available to the reviewers with the other files and will clarify that with the editorial staff in the resubmission cover letter.

• For the gene expression analysis, can it be made clearer that a full analysis was done on COPD I samples. It is a little confusing to the reader as this was not done for DNAm so might be assumed the same targeted analysis on only genes found to be differentially expressed between control and COPD II-IV, but that cannot be the case as an overlap of COPD1 vs COPD II-IV genes if provided. For this overlap, do genes show the same effect direction?

__Response: __

To clarify, for the RNA-seq analysis, we performed DEG analysis for no-COPD versus COPD II-IV, as well as no-COPD versus COPD I. We then took all differentially expressed genes (presented in the Venn diagram) and plotted them for all samples as a heatmap. To split the genes into groups displaying similar effect directions, we applied a clustering approach and identified 3 main signatures. Cluster 3 primarily comprises genes unique to COPD I samples, which are associated with the adaptive immune system and hemostasis (Fig. 4E). In the other two clusters, we mainly observe a transitioning pattern from control to severe COPD samples, correlating with the FEV1 values of the patients. This has now been clarified in the manuscript.

• Replication is difficult on these studies as the samples are so difficult to come by. Also limited by sample size for the same reason. It doesn't mean the study is not worth doing and the data are still valuable. However, it may be pertinent to include technical validation of a few regions of interest, acknowledge the limitation (along side strengths) in the discussion, and perhaps provide actual p value rather than blanket Response:

We thank the reviewer for acknowledging the replication challenges for studies working with sparse human material and hard-to-purify cell populations. Following the reviewer's suggestion, we have now included a strengths and limitations section in the discussion where we summarised the points highlighted by both reviewers.

Regarding technical validation, we would like to note that the whole genome bisulfite sequencing (WGBS) technology, as well as the tagmentation-based WGBS (T-WGBS), have been validated in the past few years in several publications (e.g., PMID: 24071908) and shown to yield reliable DNA methylation quantification in comparison to other technologies (PMID: 27347756). For us, technical validation using alternative methods (e.g. bisulfite sequencing or pyrosequencing) is difficult as it requires significantly more input DNA than the low-input T-WGBS we have performed and obtaining sufficient amounts of material from primary human AT2 cells (especially from severe COPD) is not possible with the size of tissue we can access. However, while establishing the T-WGBS for this project, we initially validated our approach using Mass Array, a sequencing-independent method. For this, we performed T-WGBS on the commercially available smoker and COPD lung fibroblasts and selected 9 regions with different methylation levels for validation using a Mass Array. We obtained an excellent correlation between both methods, providing technical validation of T-WGBS and our analysis workflow. This validation was published in our earlier manuscript (PMID: 37143403), but we provided the data below for convenience.

Scatter plots showing correlation of average methylation obtained with T-WGBS and Mass Array from COPD and smoker fibroblasts. Each dot represents one region with varying methylation levels. The blue diagonal represents the linear regression. Shaded areas are confidence intervals of the correlation coefficient at 95%. Correlation coefficients and P values were calculated by the Pearson correlation method.

To enable further validation and follow-up by the community, we included the full list of DMRs, associated p-values and additional information for DNA methylation analysis (DMR width, n.CpGs, MethylDiff, etc) in Table 3 (Table_3_wgbs_dmr_info.xlsx) and the information about DEGs from RNA-seq in Table 6 (Table_6_RNAseq_DEG_info.xlsx).

• It isn't clear to me if DNA and RNA are from the same cells? The results say "cells matching those used for T-WGBS" but the methods suggest separate extractions so not the same cells? If they are not the same cells a comment on the implications of this should be included in the discussion for example, potentially some differences in cell type composition, storage time etc.

Response:

Lung tissue samples were freshly cryopreserved, and H&E slides derived from exemplary pieces of the tissue analyzed. Once we had a group of at least 3 samples comprising one non-COPD and 2 COPD samples, we processed them in parallel to limit sorting variation between control and disease samples. The sorted cells were counted, aliquoted and pelleted at 4{degree sign}C before flash freezing and storing at -80{degree sign}C. The storage time of the cell pellets varied between the donors. RNA and DNA were isolated from cell pellets collected from the same FACS sorting experiment; therefore, we do not expect differences in cell type composition. In addition, RNA and DNA isolation were performed for all sorted pellets in parallel. All library preparations for TWGBS and RNA-seq were performed for all donors in parallel and sequenced in a single batch to minimise batch effects and technical variability. This has now been clarified in the methods part of the manuscript.

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To minimize potential technical bias, samples from no COPD and COPD donors were processed in parallel in groups of 3 (one no COPD and 2 COPD samples).

RNA and genomic DNA for RNA-seq and TWGBS were isolated from identical aliquots of sorted cell pellets.

Genomic DNA was extracted from 1-2x104 sorted alveolar epithelial cells isolated from cryopreserved lung parenchyma from 11 different donors in parallel using QIAamp Micro Kit

The TWGBS library preparations were performed for all donors in parallel and sequenced in a single batch to minimize batch effects and technical variability.* *

RNA was isolated from flash-frozen pellets of 2x104 sorted AT2 cells from 11 different donors in parallel.

The RNA-seq library preparation for all donors was performed in parallel and all samples were sequenced in a single batch to minimize batch effects and technical variability.

• Line 193 the authors say "Since DMRs were overrepresented at cis-regulatory sites...." - "cis" needs to be defined. If you link DNAm regions to gene via "closest gene" does this not automatically mean you're outputs will be cis? Just needs better definition/explanation.

Response:

The term "cis‐regulatory sites" in our manuscript is intended to denote regulatory elements-such as enhancers, promoters, and other nearby control regions-that reside on the same chromosome and close to the genes they regulate. While it's true that linking a DMR to its closest gene captures a cis association, our phrasing emphasises that the DMRs are enriched specifically at these functional regulatory elements (Fig. 2E) rather than being randomly distributed. This usage aligns with established conventions in the field. To avoid any misunderstandings, we have now changed the term to gene regulatory sites.

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*We changed the "cis-regulatory sites" to "gene regulatory sites" *

__Minor comments: __

Line 157: "we identified site-specific differences....". Change to region specific?

Response:

This has now been corrected as suggested.

Line 102-103: needs a reference for the statement "Alterations in DNA methylation patterns have been implicated......"

Response:

Following the reviewer's suggestion, we added the relevant references (34-36) to this statement.

Line 266 - what does "strong dysregulation" mean? Large fold change, very significant?

Response:

We removed the word "strong" from this sentence.

Lines 423-425 - statement needs a reference

Response:

Following the reviewer's suggestion, we added the relevant reference to this statement.

Line 428 - word missing between "epigenetic , we"?

Response:

This has now been corrected. The text reads: "Through treatment with a demethylating drug and targeted epigenetic editing, we demonstrated the ability to modulate..."

Prior studies are well references, text and figures are clear and accurate.

__Reviewer #2 (Significance (Required)): __

This study has several strengths:

1) Sample collection and characterisation. AT2 cells are incredibly hard to come by and the authors should be commended to generating the samples. However, proximity to cancer is always a potential issue, especially in epigenetic studies. Is it feasible to include any analysis to show the samples derived from those with cancer don't drive the changes observed? Even a high level PCA or an edit of fig 2A with non-cancer in a different colour in supplemental - looks like there is one outlier, is that a non-cancer? Or a correlation of change in beta between control and cancer/COPD and control and non-cancer:COPD (for want a better phrase!). just an indicator that the non-cancer COPD samples are not driving differences.

Response:

We thank the reviewer for highlighting the value of generating data from hard-to-work-with AT2 populations and bringing up the important point of cancer proximity, which we considered very carefully when designing our study. To match our samples across the cohort, all the no-COPD, COPD I, and two of the COPD II-IV distal lung samples were obtained from cancer resections. In addition to other characteristics, like age, BMI and smoking status, we also matched the donors by cancer type (all profiled donors had squamous cell carcinoma). We collected lung tissue as far away from the carcinoma as possible and sent representative pieces for histological analysis by an experienced lung pathologist to confirm the absence of visible tumours. In addition, to ensure that our data represents COPD-relevant signatures, we intentionally included samples from three COPD donors undergoing lung resections (without a cancer background) in the profiling.

Following the reviewer's suggestion, to investigate the potential impact of non-cancer samples on driving the observed differences, we carefully checked the PCAs for both DNA methylation and RNA-seq. We could not identify a clear separation of no-cancer COPD samples from the cancer COPD samples (or other cancer samples) in any examined PCs, indicating no cofounding effect of cancer samples. We observed that one sample contributing to PC2 is a non-cancer sample, but this was a rather sample-specific effect, as the other two non-cancer samples clustered together with the other severe COPD samples with a cancer background. Notably, in our DNA methylation data, we do not observe typical features of cancer methylomes, like global loss of DNA methylation or aberrant methylation of CpG islands (e.g., in tumour suppressor genes) (see Fig. 2A), further suggesting that we do not "pick up" confounding cancer signatures in our data.

Following the comments from both reviewers, to clarify that point, we added the information about cancer and non-cancer samples to the PCA figures for DNA methylation (new Fig. 2B) and RNA-seq (new Fig. 3A) data in the revised manuscript, as shown below

CHANGE IN THE MANUSCRIPT____:

COPD samples from donors with a cancer background clustered together with the COPD samples from lung resections, confirming that we detected COPD-relevant signatures (Fig. 2B).

Fig. 2B.* Principal component analysis (PCA) of methylation levels at CpG sites with > 4-fold coverage in all samples. COPD I and COPD II-IV samples are represented in light and dark green triangles, respectively, and no COPD samples as blue circles. COPD samples without a cancer background are displayed with a black contour. The percentage indicates the proportion of variance explained by each component. *

Unsupervised principal component analysis (PCA) on the top 500 variable genes revealed a clear influence of the COPD phenotype in separating no COPD and COPD II-IV samples, as previously observed with the DNA methylation analysis, irrespective of the cancer background of COPD samples (Fig.3A, Fig. S2B).

*Principal component analysis (PCA) of 500 most variable genes in RNA-seq analysis. PCA 1 and 2 are shown in Fig.3A, PCA 1 and 4 in Fig.S2B. COPD I and COPD II-IV samples are represented in light and dark green triangles, respectively, and no COPD samples as blue circles. COPD samples without a cancer background are displayed with a black contour. The percentage indicates the proportion of variance explained by each component. *

2) This is the first time DNAm has been profiled in AT2 cells. It is incredibly difficult, valuable and novel data that will increase the fields capability technically, their understanding of functional mechanisms and potential translation considerably. It's audience will be primarily translational respiratory however the fundamental science aspect of gene expression regulation by DNA methylation with have wider reach across developmental and disease science.

Response:

We thank the reviewer for recognising the uniqueness and novelty of our study and highlighting the value and potential impact of our datasets for the lung field.

3) the functional analysis using targeted CRISPR-Cas9 is very well done and adds impact.

Response:

We thank the reviewer for recognising the strengths and added value of the functional analysis using epigenetic editing.

__Potential weaknesses/areas for development __

I feel the main weakness is the in the section integrating DNA methylation and gene expression. The rationale for a focus on various aspects, for example inversely related DNAm/gene expression pairs, the IFN pathway and IRF9, are not clear. Also further understanding of the differences between DNAm associated genes and non-DNAm associated genes could be expanded, at the pathway level, TF regulation level, effect size level (are DNAm associated changes to gene expression larger, enriched for earlier differential expression)

Response:

Our rationale for focusing on the inversely related DNAm/gene expression pairs in promoter proximal is purely data-driven, as they represent the biggest group in our data (Fig. 4A-B). Among those negatively correlated genes, we observed the strongest enrichment for the IFN pathway (Fig. C), making it an obvious, data-driven target for further studies. The negative correlation of expression and methylation for IFN pathway genes could be validated in 5-AZA assays in A549 cells (Fig. 5A). Next, we made an interaction network analysis showing IRF9 and STAT2 as master regulators (Fig. 5B) of the negatively correlated IFN genes. As IRF9 itself displayed a negative correlation between DNA methylation and expression (Fig. 5C), we used the associated DMR for further epigenetic editing (Fig. 5D-E). We performed the additional requested analyses of the enhancer-associated changes and genes, as described above. We fully agree with the reviewer that our data sets are a great resource and can be further used to elaborate on other relationships of DNA methylation and RNA expression or other pathways, but this is out of the scope of this study. To enable further studies by the research community, we provide all necessary information about DMRs and DEGs in the associated supplementary tables and the raw data through the EGA, as well as the CRISPRa editing assay.

The authors could comment on potential masking of differences between 5hmC and mC and the implications it may have

Response:

We thank the reviewer for bringing up this important point. Indeed, bisulfite sequencing cannot differentiate between methylated and hydroxymethylated cytosines; hence, some of the methylated sites may be hydroxymethylated. However, the overall levels of hydromethylation in differentiated adult tissues are very low (except for the brain), orders of magnitude lower compared to DNA methylation. Following the reviewer's suggestion, we have added a sentence in the limitation section of the discussion to clarify that point.

__CHANGE IN THE MANUSCRIPT: __

In addition, while WGBS provides unprecedented resolution and high coverage of the DNA methylation sites across the genome, it does not allow distinguishing 5-methylcytosine from 5-hydroxymethylcytosine. Therefore, we cannot exclude that some methylated sites we detected are 5-hydroxymethylated. However, the 5-hydroxymethylcytosine is present at very low levels in the lung tissue (97)*. ** *

Furthermore, while the rationale for looking at DMRs is clear, especially given the sample number, I am interested to understand what proportion of the assayed CpGs "fit" within the cut off stipulations of the DMR analysis - that is, is their potentially COPD effects at sparse CpG regions/individual CpG sites that are not being identified. A comment on this would be useful and seems the strength of profiling genome wide. I'm happy genome wide is beneficial it just feels a little circular that the authors have chosen whole genome to avoid the bias of the Illumina array and a focus on promotors, but have primarily reported promoter DNAm. This caught my attention again in the discussion where the authors state that cis-regulatory regions were also identified in their fibroblast data .....is this finding a factor of the analysis performed? (also a comparison of regions Identified in AT2 cells versus fibroblasts would be really interesting for a future paper)

Response:

We decided to focus our analysis on regions rather than individual CpG sites when looking at differential methylation, as DNA methylation is spatially correlated, and methylation changes in larger regions are more likely to have a biological function. Extending the analysis to single CpG sites would require a higher number of samples for a reliable analysis compared to the DMR analysis (as mentioned by the reviewer).

Of note, we addressed the platform comparison between Illumina array technology and WGBS in our previous fibroblast study (PMID: 37143403), where we compared our WGBS data with the published 450k array data of COPD parenchymal fibroblasts (Clifford et al., 2018). We observed only a marginal overlap between the CpGs from our DMRs and the CpGs probes available on the array (which was due to the differences in technologies used and the limited coverage of the 450K array in comparison to our genome-wide approach, in which we covered 18 million CpGs). Out of the 6279 DMRs identified in our fibroblast study, only 1509 DMRs overlapped with at least one CpG probe on the 450K array, and after removing low-quality CpGs from the array data, only 1419 DMRs were left. This comparison highlighted the increased resolution of the WGBS compared to Illumina arrays.

The reason why we focused on promoter proximal DMRs are the following: 1) the assignment of the enhancer elements in AT2 to the corresponding gene is still too inaccurate in the absence of AT2 specific enhancer chromatin maps 2) regulation at enhancers by DNA methylation might be more complex and might change (increase or attenuate) binding affinities of certain transcription factors (Fig.2H), which might lead to gene expression changes or 3) methylation changes might be an indirect effect of differential TF binding PMID: 22170606). However, we agree with the reviewer that despite these limitations, expanding the analysis beyond promoters adds value to the manuscript; hence, as described above, we expanded the analysis of non-promoter regions, including enhancers, in the revised manuscript.

We thank the reviewer for the suggestion to compare the regions identified in AT2 cells and fibroblasts in a future paper.

My expertise:Respiratory, cell biology, epigenetics.

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Referee #2

Evidence, reproducibility and clarity

Summary:

This study aim to understand the molecular mechanisms underlying dysfunction in AT2 cells in COPD, by profiling bulk genome wide DNA methylation using Tagmentation-based whole-genome bisulfite sequencing (T-WGBS) and RNA sequencing in selectively sorted primary AT2 cells. The study stands out in it's sequencing breadth and use of an incredibly difficult cell population, and has the potential to add substantially to our mechanistic understanding of epigenetic contributions to COPD. A further highlight is the concluding aspect of the study where the authors undertook targeted modification of specific CpG methylation, provided direct, site-specific evidence for transcriptional regulation by CpG methylation.

Major comments:

The authors clearly show that there is DNA methylation alteration in AT2 cells from COPD individuals that links functional to gene expression at some level. However, I think the statement "to identify genome-wide changes associated with COPD development and progression..." and similar other references to disease development understanding is not accurate given the DNA methylation primary comparison is between control and moderate to severe COPD, with no temporal detail or evidence that they drive progression rather than are a result of COPD development. The paragraph starting on line 186 where this is a addressed to some extent is quite vague and doesn't really provide confidence that DNAm dysregulation occurs at an early stage in this context. This can be addressed by changing the focus/style of the text.

Results comments and suggestions:

For the integrated analysis, there is a focus on DMRs in promoters with very little analysis on other regions. The paragraph starting on line 317 describes some analysis on enhancers but is very brief, doesn't include information on how many/which DMRs were included, making it hard to interpret the impact of the 147 DMRs and 93 genes identified - is this nearly all DMRs and genes analysed or very few? A comparison to the promoter analysis would be of interest. Especially as the targeted region followed up with lovely functional assessment in the last sections is a gene body DMR, not a promoter DMR.

• Lines 299-301 - I'm not sure the graph in Fig S3A support the conclusion that there was a preferential negative relationship between DNAm and gene expression. Looks like there are a substantial number of cases where a positive relationship is observed and this needs to be acknowledged.

• Line 307 - what are the "analysed DEGs"? Are they the methylation associated genes?

• Line 307-309 - "Among the analyzed DEGs, 76.5% (492) displayed a negative correlation (16.8% of the total DEGs), indicating a possible direct regulation by DNA methylation, while 23.5% (151) showed a positive correlation between gene expression and DNA methylation" - are the authors suggesting the positive correlation doesn't indicate direct regulation?

• Line 313 - why did the authors focus on only negatively correlated genes to identify their top dysregulated pathway of IFN signalling? Why not do pathway analysis on the DNAm associated genes separately to identify DNAm associated pathways?

• A comparison of the gene expression data with previous data in AT2 cell/single cell data would strengthen the gene expression section.

• The paragraph starting on line 173 feels a little redundant when we know there is RNA available to test if the differential DNAm links to altered gene expression - this selected of example regions/genes would be better placed after the gene expression has been reported, at which point you could say whether the linked genes displayed altered transcription.

• Similarly, the TF enrichment analysis is great but maybe would have added value to be done on DNA regions later shown to be linked to differential expression - was there different enrichment at DNA regions that are vs are not associated with altered expression? And could you test in vitro whether changing methylation of DNA (maybe a blunt too like 5-aza would be ok) alters TF binding (cut+run/ChIP?). Furthermore it would be interesting to understand the TF sensitivity analysis within the context of positive versus negative DNA methylation:gene expression correlations.

Methods:

• The authors should include more detail of the TWGBS rather than directing the reader to a previous publication. Also DNA concentration post bisuphite conversion would be a useful metric to provide.

• Differential DNA methylation analysis: It is stated that DNA regions had to contain 3 CpG sites but was this within a defined DNA size range?

• Refence genome only provided for RNAseq not TWGBS?

• The tables do not appear in the PDF and I struggled to tally to the "Dataset" files provided if that is what they were referring to?

• For the gene expression analysis, can it be made clearer that a full analysis was done on COPD I samples. It is a little confusing to the reader as this was not done for DNAm so might be assumed the same targeted analysis on only genes found to be differentially expressed between control and COPD II-IV, but that cannot be the case as an overlap of COPD1 vs COPD II-IV genes if provided. For this overlap, do genes show the same effect direction?

• Replication is difficult on these studies as the samples are so difficult to come by. Also limited by sample size for the same reason. It doesn't mean the study is not worth doing and the data are still valuable. However, it may be pertinent to include technical validation of a few regions of interest, acknowledge the limitation (along side strengths) in the discussion, and perhaps provide actual p value rather than blanket < p 0.1, seems very lenient but may all be super significant (this may already be in the tables I wasn't able to find).

• It isn't clear to me if DNA and RNA are from the same cells? The results say "cells matching those used for T-WGBS" but the methods suggest separate extractions so not the same cells? If they are not the same cells a comment on the implications of this should be included in the discussion for example, potentially some differences in cell type composition, storage time etc.

• Line 193 the authors say "Since DMRs were overrepresented at cis-regulatory sites...." - "cis" needs to be defined. If you link DNAm regions to gene via "closest gene" does this not automatically mean you're outputs will be cis? Just needs better definition/explanation.

Minor comments:

• Line 157: "we identified site-specific differences....". Change to region specific?

• Line 102-103: needs a reference for the statement "Alterations in DNA methylation patterns have been implicated......"

• Line 266 - what does "strong dysregulation" mean? Large fold change, very significant?

• Lines 423-425 - statement needs a reference

• Line 428 - word missing between "epigenetic , we"?

• Prior studies are well references, text and figures are clear and accurate.

Significance

This study has several strengths:

1) Sample collection and characterisation. AT2 cells are incredibly hard to come by and the authors should be commended to generating the samples. However, proximity to cancer is always a potential issue, especially in epigenetic studies. Is it feasible to include any analysis to show the samples derived from those with cancer don't drive the changes observed? Even a high level PCA or an edit of fig 2A with non-cancer in a different colour in supplemental - looks like there is one outlier, is that a non-cancer? Or a correlation of change in beta between control and cancer/COPD and control and non-cancer:COPD (for want a better phrase!). just an indicator that the non-cancer COPD samples are not driving differences.

2) This is the first time DNAm has been profiled in AT2 cells. It is incredibly difficult, valuable and novel data that will increase the fields capability technically, their understanding of functional mechanisms and potential translation considerably. It's audience will be primarily translational respiratory however the fundamental science aspect of gene expression regulation by DNA methylation with have wider reach across developmental and disease science.

3) the functional analysis using targeted CRISPR-Cas9 is very well done and adds impact.

Potential weaknesses/areas for development:

I feel the main weakness is the in the section integrating DNA methylation and gene expression. The rationale for a focus on various aspects, for example inversely related DNAm/gene expression pairs, the IFN pathway and IRF9, are not clear. Also further understanding of the differences between DNAm associated genes and non-DNAm associated genes could be expanded, at the pathway level, TF regulation level, effect size level (are DNAm associated changes to gene expression larger, enriched for earlier differential expression) The authors could comment on potential masking of differences between 5hmC and mC and the implications it may have

Furthermore, while the rationale for looking at DMRs is clear, especially given the sample number, I am interested to understand what proportion of the assayed CpGs "fit" within the cut off stipulations of the DMR analysis - that is, is their potentially COPD effects at sparse CpG regions/individual CpG sites that are not being identified. A comment on this would be useful and seems the strength of profiling genome wide. I'm happy genomewide is beneficial it just feels a little circular that the authors have chosen whole genome to avoid the bias of the Illumina array and a focus on promotors, but have primarily reported promoter DNAm. This caught my attention again in the discussion where the authors state that cis-regulatory regions were also identified in their fibroblast data ..... is this finding a factor of the analysis performed? (also a comparison of regions Id'ed in AT2 cells versus fibroblasts would be really interesting for a future paper)

My expertise: Respiratory, cell biology, epigenetics.