Response to Classmate I agree with your point about fear shaping U.S. policy. NSC-68 makes it seem like the government believed any weakness would help the Soviet Union. I think that fear made military spending seem necessary, even if it also created new problems later.

I think Eisenhower was warning that war and defense spending could become normal in American life. His tone feels serious, like he knew the Cold War had created a system that might be hard to control later

Eisenhower Question Eisenhower warning about the “military-industrial complex” stands out because he was a military leader himself. Why do you think he waited until his farewell address to give such a serious warning?

This shows how the Mukurtu archive was not just about locking information away. It was more about deciding who should have access based on cultural responsibilities and community rules, instead of treating everything like public internet content.

This line points out a common belief that everything online should be open and accessible. The article uses it to question that idea, showing that it doesn’t really work when applied to Indigenous knowledge, since that kind of information isn’t meant for everyone to access or share freely.

Janie and Tea Cake arrive in the Everglades where they start to work and live together. They enjoy life experiencing with the community and the other workers.

The love/ attraction that was once their in between them is now gone

Why does he want her to have no right to her own emotions? He is a very controlling guy and doesn’t like the fact that she is independent and doesn’t need him.

Joe is very physically abusive to Janie. For some reason, his pent up frustrations with jealousy towards other men, he lets it out on Janie. Or it might be already in his nature to be so abusive. No wonder Janie had 3 husbands.

He is very jealous and controlling which abuses to problems in their marriage.

In this chapter Janie and Tea Cake arrive in the Everglades where they start to work and live together. They enjoy life experiencing with the community and the other workers. This chapter showed Janie a happy life.

he was sincerely laughing. Maybe a part of him did want to be a part of them but his ego and sense of duty forbade him not to which is why he looks down on others who don't have that sense as well.

This is very sad.. this is how women would get treated…

We see Janie want to help the mule and when her husband buys it she has hope that her husband cares. But much like the mule Jody just feeds the mule and like Janie Jody just feeds her and dose not let her have an emotional connection or a life making her realize this marriage is not a good one.

It shows how he’s very strict of Janie and how he got angry at the fact that some other guy was touching her hair so because of that he got more strict.

This shows that Joe was very restrictive and controlling over Janie.

The mule finally died and they found it lying on its back under a tree.

It really shows how Jody controls Janie through public humiliation. The porch conversations seem like harmless fun but when Jody forces Janie to stay silent and work the store while he jokes with the men it's clear he sees her as a possession. The most painful part is when Janie realizes she can't even laugh at the "mule" jokes without his approval her spirit is being tamed in plain sight

Janie is jealous of people that are just sitting outside the store because she starting to feel trapped and no control over her own life

Joe isn’t as nice as he seemed in the beginning. He isn’t letting Janie speak what she wants.

Janie is starting to feel trapped in her marriage because Joe controls everything she does and doesn’t let her be herself. The mule also kind of represents Janie since it’s treated badly and has no freedom, just like how she feels.

This shows how compassionate Janie is, while everyone is teasing the mule Janie is speaking out for the mule and defending it

He doesn’t care about Janie’s feelings but when someone else gives her attention, even without her knowing, he gets controlling.

ink here between burges and the pre-raphaelites - gothic revival was all interconnected!

good link to castell coch with the hint of arabic that's all around it!!

travel and industrialisation facilitating this

link to the reasoning for the frenchy vibes of the turrets?

hence why he excavated castell coch and was able to reconstruct it as historically acurate as he could

SLAYYYY industrialisation brought the pair togtehr, but it was their own convictions that enabled them to take the gothiv style

Good link for stained-glass becoming an artistic medium that could be accesible to all!

This is quite good for stained-glass and stuff!!! It shows how the pre-raphaelite form was seen to be the most pious, it brought people back to the awe and reverence of the faith that appeared to be present in medieval england!

cool, did he make himself an arthurian hero? St lucian appears to be an arthur-esque hero??

Chivalry of gothic revival

Good quote to show how the gothic revival was something that was so old it could not be shaken by the changes of industrialisation

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

This paper describes the localisation of DNA repair proteins, which carry out their DNA repair function in the nucleus, to the cytoplasmic Golgi apparatus. Using the Human Protein Atlas to identify candidates, the authors use antibody localisation to show that a significant number of DNA repair proteins also localise at the Golgi. It appears that proteins involved in common DNA repair pathways localise to common regions of the Golgi. The Golgi-nucleus distribution of the DNA repairs proteins changes upon DNA damage, indicating a dynamic relationship. The authors focus on the DNA repair protein RAD51C and show that its loss from the Golgi and translocation to the nucleus upon DNA damage is mediated by the ATM kinase. Anchoring at the Golgi is shown to be mediated by the golgin giantin. A functional role for giantin in DNA repair is shown in knockdown studies, supporting a mechanism whereby Golgi anchoring of RAD51C, and possibly other DNA repair proteins, by giantin, is required to maintain proper control of DNA repair. The data are clear and support the authors' conclusions. The data are carefully quantified throughout. I found the text easy to read.

• Major points:*

• 1.) To validate the Golgi localisation, KD using siRNA was used. It was deemed that a signal reduction of 25% was enough to indicate specific antibody labelling. This seems like a low number, and not very stringent. For some of the hits, expressing tagged versions of the proteins would greatly strengthen the Golgi assignment. This may not be possible for all, but for RAD51C would seem an important experiment. *

Response: We thank the reviewer for raising the important issue of antibody validation stringency. We agree that for a single-candidate study, a larger reduction after knockdown would generally be preferable. In our case, the 25% cutoff was used only in the primary high-content screening step as part of an intentionally inclusive two-stage workflow, for the following reasons:

First, because this dataset is generated in a screening format across hundreds of targets, knockdown-efficiency, protein turnover, and the relative size of the Golgi associated pool are unknown and highly variable between genes. For many proteins the Golgi pool represents a small fraction of total cellular signal, and a modest change in total abundance can translate into a smaller absolute change in the Golgi ROI after segmentation, background subtraction, and imaging noise. We therefore selected a permissive cutoff to reduce false negatives and ensure we did not systematically miss candidates with slower turnover, partial knockdown, or small Golgi pools. This strategy is consistent with large scale subcellular mapping efforts, including the Human Protein Atlas, where genetic depletion by siRNA is used as a key validation pillar for immunofluorescence localization and is combined with additional validation strategies when deeper confidence is required (Stadler et al, 2012). Furthermore, it is important to note that this validation was performed in a high-content screening format in which fixation, permeabilisation, antibody concentration, and blocking conditions were kept uniform across all candidates rather than optimised for each individual antibody. In standard single-target immunofluorescence experiments, these parameters would be titrated to maximise signal-to-noise for the specific antibody and antigen in question. Under non-optimised screening conditions, the absolute magnitude of signal change upon knockdown is inherently attenuated compared to what would be expected from a purpose-optimised assay. We therefore consider a 25% reduction threshold under these uniform, non-optimised screening conditions to be a meaningful and appropriately calibrated criterion.

Second, we wish to clarify that the primary intent of our screen was not to validate the Golgi-nuclear localisation of any single protein in isolation, but rather to identify whether entire functional pathways are represented at the two organelles. This is precisely why the bioinformatic network analysis was performed as an integral part of the workflow, and not as an afterthought. The finding that the validated hit list is significantly enriched for coherent functional clusters, most notably a network spanning multiple core DNA repair pathways (HR, MMR, BER, MMEJ) serves as an in silico validation of the dataset as a whole. The emergence of pathway-level organisation, with proteins from the same repair pathways co-associating, localising to the same Golgi sub-compartments, and redistributing in the same direction upon genotoxic stimuli, provides biological coherence that goes beyond what individual antibody validation can offer, and substantially reduces the likelihood that the Golgi signal represents a collection of unrelated false positives.

Third, our mechanistic conclusions do not rely on the 25% screening threshold. For RAD51C, we used multiple orthogonal validation approaches, including independent antibodies recognizing distinct RAD51C epitopes and genetic depletion, supported by biochemical evidence.

In response to this comment, we have provided the full screening validation dataset as source data (Supplementary____Table S1), including intensity changes for the candidates, so that readers can inspect the distributions and apply their own thresholds. We have also clarified in the Results section the rationale behind our screening strategy (lines 128-139) and the role of the bioinformatic network analysis as an integral validation step (lines 141-156).

Turning to the specific suggestion of tagged RAD51C, we fully agree that tagged proteins can provide valuable orthogonal validation. We attempted endogenous tagging using CRISPR-mediated homologous recombination but were unable to obtain viable colonies following editing, consistent with the essential role of RAD51C in homologous recombination. We also attempted ectopic expression of tagged RAD51C but were unable to obtain constructs that preserved physiological expression levels, maintained robust cell viability or produced interpretable localization. This difficulty is not unique to our laboratory: colleagues working on RAD51 paralog complexes have reported that tagging or overexpression of RAD51C perturbs both its localisation and its ability to form functional paralog complexes (Greenhough et al, 2023; Rawal et al, 2023; Somyajit et al, 2015; Berti et al, 2020) all use purified complexes or untagged proteins for functional assays. We discussed these challenges extensively with experts in the DNA damage repair field at several international meetings (EMBO Sounio, Keystone Symposia, German DNA Repair Society). For these reasons, we relied on orthogonal approaches that do not require tagging (genetic depletion plus independent antibodies, and biochemical fractionation) to support the Golgi localization claim. We agree with the reviewer that this represents a limitation of this study, and we addressed these concerns in the discussion of our revised manuscript (lines 630-641).

*2.) The total signal should be quantified for each DNA repair protein upon genotoxic stress, in addition to the Golgi to nucleus ratio. For many of the proteins it looks like the total signal goes down, which could influence interpretation. *

Response: __We thank the reviewer for this important point. We wish to clarify that our imaging pipeline uses marker-based segmentation throughout, the Golgi compartment is segmented using GM130 and the nucleus using Hoechst, as unsegmented whole-cell masks without organelle markers yield unreliable intensity measurements in this experimental setup. True total cellular signal is therefore not directly accessible in this dataset. In the revised manuscript we provide the absolute fluorescence intensities for both the Golgi and nuclear compartments separately. In addition, we now include total (Golgi + nuclear) intensity measurements for each protein (__Supplementary Figures 3D, 4D, __and 5E__) as the most reliable proxy for overall protein distribution. These data are presented alongside the redistribution ratio to enable comprehensive interpretation.

As the reviewer correctly notes, a subset of proteins shows a reduction in total signal after treatment, particularly with doxorubicin. This is consistent with known effects of doxorubicin-induced DNA damage on cellular proteostasis, including widespread ubiquitination and suppression of protein translation (Halim et al, 2018). Several DDR regulators are subject to ubiquitin-dependent turnover following genotoxic stress, such as CHK1 (Zhang et al, 2005). More broadly, ubiquitin and proteasome mediated regulation is an integral component of the DNA damage response and can affect the abundance and detectability of DDR factors (Brinkmann et al, 2015). Changes in abundance are therefore an expected biological feature of the response. For this reason, we used the Golgi-to-nucleus ratio as the primary redistribution readout, as it captures relative compartmental partitioning independently of changes in total protein levels.

*3.) The study would benefit from live imaging of the Golgi to nucleus translocation of RAD51C. This would give a better indication of dynamics. *

__Response: __We agree that live imaging would directly visualize the dynamics of RAD51C redistribution between the Golgi and the nucleus. This was indeed one of our initial goals following the identification of the Golgi-associated RAD51C pool. However, as described above in our response to Major Comment 1, live imaging requires a fluorescently tagged RAD51C construct, and all tagging strategies we attempted, both endogenous CRISPR-mediated tagging and ectopic expression, failed to yield cell lines with robust signal while preserving physiological behaviour. This appears to be a broader challenge for highly conserved and functionally constrained DNA repair proteins, and is not unique to our laboratory.

Given these constraints, we focused on tag-independent approaches: multiple independent RAD51C antibodies combined with genetic depletion controls, quantitative fixed-cell time courses, and biochemical fractionation. These orthogonal datasets together support compartment-specific changes over time in a manner consistent with redistribution. We have clarified this limitation explicitly in the manuscript and avoided any wording that could be interpreted as implying direct single-molecule tracking in live cells. We present this as an important avenue for future work, contingent on the development of viable RAD51C-expressing cell lines (lines 630-641).

*4.) The double depletion experiments suggest a functional relationship between giantin and RAD51C. But they do not formally show it. Experiments to more directly address the functional role of the interaction between these two proteins would strengthen the study. *

Response: We agree with the reviewer that double depletion alone cannot formally prove that the physical Giantin-RAD51C interaction is the sole determinant of the observed DDR phenotypes. However, we would like to highlight the breadth of evidence we have assembled in support of this functional relationship:

• Physical interaction between endogenous Giantin and RAD51C demonstrated by colocalisation (Figure 4F-G) and co-immunoprecipitation (Figure 4H-I).
• Damage-induced dissociation of the Giantin-RAD51C complex that is prevented by ATM inhibition or Importazole treatment, directly linking the interaction to the DDR signalling axis (Figure 3K-P)
• Premature nuclear accumulation of RAD51C upon Giantin depletion, producing aberrant nuclear foci lacking canonical HR markers and impaired ATM signalling (Figure 4B-E & J-M)
• DR-GFP reporter assay confirming that Giantin depletion reduces HR efficiency to approximately 60% of control, consistent with the reduction previously reported in the genome-wide HR screen (Adamson et al. 2012) and validating the functional significance of Giantin in HR (Figure 5L).
• Partial rescue of ATM phosphorylation, genomic instability and proliferation phenotypes by RAD51C co-depletion, arguing for RAD51C as a functionally relevant conduit of the Giantin-dependent phenotype (Figures 5M-5P). These observations are further supported by the established literature on RAD51C function, its roles in CHK2 phosphorylation, replication fork stabilisation, and RAD51 filament formation (Badie et al, 2009; Somyajit et al, 2015; Prakash et al, 2022) providing a mechanistically coherent framework in which mislocalisation of RAD51C, whether directly or indirectly through Giantin, leads to dysregulation of DDR signalling and repair capacity, as we directly demonstrate with the HR efficiency assay.

Nonetheless, we fully agree that the most direct proof of the functional relevance of the physical Giantin-RAD51C interaction would come from separation-of-function experiments, ideally using an interaction-deficient Giantin mutant or an RAD51C variant unable to bind Giantin. We wish to be transparent that both approaches face substantial technical barriers in this system. RAD51C tagging consistently compromised cell viability and protein function, precluding the generation of interaction-deficient variants at physiological expression levels. Engineering an interaction-deficient Giantin mutant presents an independent challenge: Giantin is one of the largest Golgi matrix proteins (~376 kDa), composed almost entirely of extended coiled-coil domains that are resistant to structural prediction, and identifying a discrete RAD51C interaction interface without disrupting broader scaffolding function would require a dedicated structural and biochemical programme. We have framed these explicitly as the most important future priorities in the Discussion (lines 555-564), rather than over-interpreting the current data.

*5.) The Kaplan-Meier plots in Fig S9 seems to be quite selective in that only breast cancer is shown. Does giantin reduction correlate with poor prognosis in other cancers? *

__Response: __We thank the reviewer for this suggestion. We initially focused on breast cancer because RAD51C is a clinically established hereditary breast and ovarian cancer susceptibility gene (Meindl et al, 2010; Ghannoum et al, 2023), providing direct clinical context for a study centred on RAD51C dynamics and genome stability. We agree however that restricting the survival analysis to a single cancer type can appear selective.

To address this directly, we expanded the in-silico survival analysis of Giantin (GOLGB1) using GEPIA2 (Tang et al, 2019) across all available TCGA cohorts (overall survival, median cutoff, FDR correction). In the pooled pan-cancer analysis, higher GOLGB1 expression is significantly associated with improved overall survival (HR(high) = 0.75, p = 6.6 × 10⁻¹⁵). When stratified by tumour type, the majority of individual associations do not reach statistical significance. The two most robust statistically significant associations are kidney renal clear cell carcinoma (KIRC; HR(high) = 0.57, p = 3.4 × 10⁻⁴), where high GOLGB1 expression is associated with improved survival, and lower-grade glioma (LGG; HR(high) = 1.5, p = 0.036), where the association is in the opposite direction. A significant association is also observed in thymoma (THYM; HR(high) = 7.3, p = 0.031), though this should be interpreted with caution given the small cohort size (n = 59). Notably, the breast cancer association observed in the KM Plotter analysis (HR = 0.71, p = 1.8 × 10⁻¹¹; n = 4,929) does not reach significance in the TCGA BRCA cohort (HR = 1.1, p = 0.68; n = 1,070), most likely reflecting the substantially smaller sample size of the TCGA cohort, which is approximately 4.6-fold smaller and therefore underpowered to detect a modest effect. These context-dependent associations are consistent with the tumour-type-specific roles of Golgi scaffolding proteins and are discussed accordingly in the revised manuscript.

In the revised manuscript we have retained the original breast cancer Kaplan-Meier plots and supplemented them with a pan-cancer survival map across all TCGA cohorts (lines 611-625; Figure S9G) and a summary table (Supplementary Table 3) reporting hazard ratios, sample sizes, and p-values for each tumour type, allowing readers to assess the clinical relevance of GOLGB1 expression.

*Minor points: There are a few grammatical errors here and there. The figures do not appear in the correct order in the text, which makes the early parts of the paper a bit difficult to follow. Some of the figures don't seem to clearly match the text. For example, it is mentioned that RAD51C labelling was done with 3 different antibodies. I could not find this data. *

Response: __We thank the reviewer for these helpful observations. In the revised manuscript we have (i) carefully proofread the text and corrected grammatical errors throughout; (ii) revised the Results section to ensure that figures and supplementary figures are cited in sequential order and that each panel is explicitly introduced before being discussed, improving readability in the early sections. and (iii) corrected figure callouts to ensure they match the text. In particular, the statement that RAD51C labeling was performed with three different antibodies has been linked to the corresponding figure panels in the Results section. Antibody identifiers, sources, and dilutions are clearly reported in the Methods and in the table in __Supplementary Table S1.

__ Reviewer #1 (Significance (Required)):__

*This paper is novel and should be of significant interest to the field. It has important implications for how we think about the Golgi apparatus, and for how DNA repair pathways may be controlled. The pattern is clearly complex, with many DNA repair proteins localising to the Golgi, and some showing opposite dynamics. However, by focussing on RAD51C and giantin, the paper nicely demonstrates a novel mechanism for controlling DNA repair by these proteins. *

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

Background - Eukaryotic cells rely on tightly regulated DNA repair pathways to preserve genome stability under the constant threat of both endogenous and exogenous genotoxic stress. While the nucleus, and to a lesser extent the mitochondria, is the primary site where DNA damage is detected and repaired, accumulating evidence indicates that extranuclear organelles, particularly the Golgi apparatus, play a surprisingly important role in modulating stress signaling, proteostasis, and the trafficking/activation of key DNA repair factors.

• Emerging evidence has shown that genotoxic stress can result in a major remodeling of the Golgi apparatus; however, the crosstalk between the Golgi and the nucleus, and its contribution to the DNA damage response, remains poorly defined. The present study offers timely insight by examining the spatiotemporal behavior of DNA repair proteins that shuttle between the Golgi and the nucleus, and how this trafficking contributes to the maintenance of genomic stability.*

Main findings - The authors employed the Human Protein Atlas (HPA) project to shortlist proteins that might link Golgi-nuclear function and validated each candidate using an siRNA-mediated antibody-validation pipeline, thereby identifying 163 proteins that localize to both the Golgi and the nucleus. Bioinformatic analysis of these candidates revealed a significant enrichment for DNA damage response (DDR) regulators, including multiple factors from core DNA repair pathways, suggesting that a portion of the DDR machinery may reside in the Golgi at steady state. Interestingly, the authors observed that dual-localizing DDR proteins undergo lesion-specific redistribution between the Golgi and the nucleus in response to specific types of DNA injuries. For instance, BER and MMEJ proteins shifted from nucleus to Golgi in response to doxorubicin, whereas MMR and HR proteins redistributed from Golgi to nucleus. This trend was reversed with H2O2 or KBrO3 treatments.

• To gain further insight into the link between the DDR and Golgi-nuclear communication, the authors focused on the HR factor RAD51C, which also plays a key role during the replicative stress response. The authors noticed that RAD51 is significantly associated with the Golgi, in addition to its known nuclear pool. Interestingly, they demonstrated that doxorubicin triggers the ATM-dependent release of this Golgi-tethered RAD51C pool and its Importin-β-mediated import into the nucleus, where it forms repair-associated foci. They further identified Giantin as the Golgi scaffold that anchors RAD51C at steady state in this subcellular compartment and showed that its depletion leads to premature nuclear accumulation of RAD51C, formation of aberrant RAD51C foci lacking canonical HR markers, reduced ATM activation, elevated genomic instability, and increased cell proliferation. *

Together, this study revealed an underappreciated and functionally meaningful spatiotemporal level of regulation within the DDR, suggesting that the Golgi, rather than functioning solely as a trafficking organelle, acts as a platform that anchors, releases, and temporally controls the availability of key DNA repair factors in response to genotoxic stress. In particular, the authors demonstrated that the timely and regulated release of RAD51C from the Golgi is essential for maintaining genome stability and is dependent on canonical DDR signaling pathways, including ATM activation and Importin-β-mediated nuclear import.

• Overall Critique - This manuscript offers a novel and compelling perspective on the regulation of the DDR by positioning the Golgi as an active participant in the spatiotemporal control of DNA repair factors. By integrating multiple experimental layers, including a systematic localization screening, a sub-Golgi mapping, several dynamic redistribution assays, and functional perturbation read-outs, the authors built a strong and coherent case for a biologically meaningful Golgi-nucleus communication axis during the DDR. Therefore, the study is timely and highly relevant for the DNA repair field, with broader implications for our understanding of how subcellular organelles coordinate genome maintenance and cellular homeostasis.

While the manuscript is clearly written and the figures are coherent and supportive of the main findings of the study, several issues should be addressed to ensure full interpretability and reproducibility.

Major Comments*

*1. Limited use of agents causing genotoxic stress - The authors report intriguing lesion-specific shifts in Golgi-nuclear redistribution, yet much of the mechanistic work relies heavily on doxorubicin, a pleiotropic drug that induces diverse forms of DNA damage beyond DSBs. Expanding the core analysis of the study to include a broader panel of mechanistically defined genotoxins (e.g., etoposide, camptothecin, neocarzinostatin, or ionizing radiation) would substantially strengthen the conclusion that the trafficking patterns reflect damage-type specificity rather than drug-specific off-target effects. Such broader analysis would also clarify whether Golgi-nucleus communication responds differentially to replication-associated breaks, Topo II-dependent lesions, oxidative stress, or crosslinks. *

__Response: __We thank the reviewer for this important point. We would first note that while doxorubicin is indeed pleiotropic, its primary and best-established mechanism of action is the poisoning of Topoisomerase II, leading to DNA double-strand breaks, a mechanism it shares with etoposide (van der Zanden et al, 2021; Thorn et al, 2011). The additional effects of doxorubicin, including reactive oxygen species generation and chromatin remodelling, are well-documented but secondary to this DSB-inducing activity, as we note in the revised manuscript. Nonetheless the goal of this study was not to comprehensively map lesion-specific trafficking for every DDR protein, but rather to establish the existence of a dynamic Golgi-nucleus redistribution axis and then focus mechanistically on the validated targets, in this case RAD51C. The lesion-dependent redistribution patterns are therefore presented as an initial, hypothesis-generating observation emerging from our screening and characterisation framework. A systematic, lesion-by-lesion dissection of redistribution kinetics across the broader DDR network would represent a substantial additional study and is beyond the scope of the present work.

Importantly, our key mechanistic observations for RAD51C are not restricted to doxorubicin. We tested a panel of genotoxic agents covering mechanistically distinct lesion classes: camptothecin (CPT; Topoisomerase I-associated replication breaks), etoposide (ETO; Topoisomerase II-dependent DSBs), and mitomycin C (MMC; interstrand crosslinks) (Figures S8A-S8I). Across all DSB-inducing agents, RAD51C consistently redistributed from the Golgi to the nucleus, demonstrating that this response is not a doxorubicin-specific off-target effect. Notably, RAD51C did not redistribute in response to oxidative lesions induced by hydrogen peroxide or potassium bromate, consistent with its established role in homologous recombination and DSB repair rather than oxidative damage pathways, as discussed in the manuscript. This lesion-type selectivity provides additional evidence that the Golgi-nuclear redistribution we observe is a biologically specific response rather than a non-selective stress effect.

*2. Functional implications of RAD51C redistribution for HR efficiency - Although the study convincingly demonstrates a release of RAD51C from the Golgi and its subsequent nuclear foci formation, it remains unclear how this redistribution influences HR efficiency. Incorporating a functional HR assay (e.g., DR-GFP reporter, RAD51 filament assembly, or fork protection assays) would help determine whether Golgi-anchored RAD51C release is directly required for HR or instead primarily modulates upstream DDR signaling. *

Response: __We thank the reviewer for this important suggestion. We have performed DR-GFP reporter assays to directly assess HR efficiency following Giantin and RAD51C depletion. Depletion of Giantin reduced HR efficiency to approximately 60% of control levels, and RAD51C depletion to approximately 40%, consistent with the HR reduction previously reported in the genome-wide HR screen (Adamson et al, 2012). Co-depletion of Giantin and RAD51C reduced HR to levels comparable to RAD51C depletion alone, suggesting that the effect of Giantin on HR is mediated primarily through RAD51C, consistent with RAD51C being the key effector of the Giantin-dependent spatial regulatory mechanism we describe. These data are included in the revised manuscript (__lines 455-465; Figure 5L).

*In addition, the manuscript does not fully reconcile how Golgi-tethering of RAD51C fits with its well-established nuclear roles during replication stress, where timely availability of RAD51C is essential for fork stabilization and restart. *

Response: __We agree that the nuclear function of RAD51C during replication stress is well established and important to reconcile with our findings. Our imaging data consistently show a detectable nuclear RAD51C population at steady state across all cell lines examined, and we do not propose that RAD51C is exclusively Golgi-localised. We suggest that the two pools serve distinct functional purposes: the constitutive nuclear pool supports ongoing replication fork stabilisation and restart, processes that require RAD51C availability independently of acute DNA damage, while the Golgi-tethered fraction represents a damage-responsive reserve that is released acutely upon DSB induction in an ATM-dependent manner. We wish to be transparent that this two-pool model is speculative at present, formally distinguishing the contributions of each pool would require direct labelling of the Golgi-anchored fraction, which was not technically feasible in this system as discussed above. Nonetheless, this model is consistent with established principles of signal-responsive protein sequestration in cell biology, and is directly supported by our Giantin depletion data: premature release of the Golgi pool leads to aberrant nuclear RAD51C foci lacking canonical HR markers and impaired ATM signalling, demonstrating that unscheduled nuclear accumulation is actively detrimental rather than simply redundant. We have added a paragraph to the revised Discussion explicitly framing the two-pool distinction as a working model and identifying direct pool-identity tracking as an important future direction (__lines 566-587).

*3. Specificity of Giantin-related phenotypes - The phenotypes observed upon Giantin depletion (e.g., increased micronuclei, comet tail moments, impaired ATM signaling, and elevated proliferation) could partially reflect a global dysfunction of the Golgi rather than RAD51C-specific tethering defects. Although co-depletion of RAD51C provides partial rescue, additional controls examining Golgi integrity, trafficking competence, or rescue with siRNA-resistant Giantin would help confirm specificity and distinguish direct from indirect effects. *

__Response: __We thank the reviewer for raising this important concern, which was a central consideration throughout our investigation. We address it through three complementary lines of evidence.

First, regarding Golgi structural integrity and trafficking competence: as previously reported, Giantin depletion has not been associated with strong Golgi fragmentation or major morphological alterations (Koreishi et al, 2013; Bergen et al, 2017; Stevenson et al, 2021), and we observed no significant Golgi fragmentation upon Giantin knockdown in our system. Consistent with the literature, Giantin has been implicated in specific cargo trafficking, most notably collagen secretion, rather than general secretory pathway function (Stevenson et al, 2021). To directly confirm that general Golgi trafficking competence was preserved in our experimental system, we performed the VSV-G-YFP trafficking assay (Presley et al, 1997), a well-established functional readout of general secretory trafficking. Giantin depletion did not result in a significant change in trafficking efficiency compared to control siRNA (Rebuttal Figure 1), consistent with the literature and arguing against a general collapse of Golgi function as the basis for the phenotypes observed.

Rebuttal ____Figure 1. VSV-G-YFP trafficking assay.

(A) Representative images of cells treated with control siRNA or giantin siRNA. Nuclei are stained with Hoechst. Total VSV-G-YFP (YFP-tsO45G) signal is shown together with antibody staining against VSV-G in non-permeabilized cells to assess cell surface levels. Scale bars, 10 μm.

(B) Quantification of VSV-G trafficking from two independent biological replicates.

Second, the phenotypes are RAD51C-dependent and not a generic Golgi dysfunction: the genomic instability and DDR signalling defects we observe upon Giantin depletion are not phenocopied by GMAP210 depletion, another Golgin family member, indicating that the phenotypes are not a generic consequence of Golgin loss. Critically, we now directly demonstrate using the DR-GFP reporter assay that Giantin depletion reduces HR efficiency to approximately 60% of control, and that co-depletion of RAD51C produces no further reduction beyond RAD51C depletion alone, consistent with RAD51C epistasis over Giantin for HR capacity (Figure 5L). This functional epistasis, together with the physical interaction between Giantin and RAD51C by co-immunoprecipitation, their co-localisation within the same Golgi sub-compartment, and the partial rescue of ATM phosphorylation, micronuclei formation and proliferation phenotypes upon RAD51C co-depletion, provides a coherent mechanistic chain linking Giantin specifically to RAD51C-dependent DDR outcomes. While we cannot formally exclude indirect contributions from other Giantin-associated factors, none of our observations are consistent with the phenotype arising from non-specific Golgi perturbation.

Third, Giantin may play a broader role in connecting DDR signalling to cytoplasmic and Golgi-resident processes, beyond RAD51C tethering alone: we consider this a feature of the biology rather than a confound. Golgins are well established as multi-cargo scaffolding platforms, and Giantin in particular occupies a strategic position where several processes converge: the tethering of DDR factors, the regulation of damage-induced signalling cascades, and the directional trafficking of repair factors between compartments. This would explain why Giantin depletion produces a phenotype that extends beyond what RAD51C co-depletion alone can fully rescue, and is consistent with the pathway-level coherence we observe across our screen. Understanding the full complement of Giantin-associated DDR interactions represents one of the most compelling directions emerging from this work.

In response to this comment, we have expanded the Discussion (lines 545-565) to explicitly propose that Giantin functions as a broader organisational node coordinating multiple DDR factors, while our data specifically and consistently implicate RAD51C as a primary conduit.

*4. Positioning of ATM in the Golgi-nuclear signaling - While ATM inhibition prevents RAD51C release, its spatial and mechanistic basis of this regulation remains obscure. It is not clear whether ATM acts locally at the Golgi, through cytoplasmic pools, or indirectly via nuclear feedback signaling. Clarifying or discussing this point in more depth would improve the mechanistic coherence of the proposed model. *

__Response: __We thank the reviewer for raising this important mechanistic question. The spatial basis of ATM action at the Golgi is indeed an emerging and exciting area of cell biology. A growing body of evidence demonstrates that ATM associates with the Golgi membrane through binding to phosphatidylinositol-4-phosphate (PI4P), and that this Golgi-resident pool modulates the magnitude and kinetics of the nuclear DDR (Ovejero et al, 2023). Importantly, the most recent work in this area demonstrates that Golgi-associated ATM is not merely a passive reservoir but is enzymatically active and capable of phosphorylating Golgi-resident substrates (Soulet et al, 2026), providing a compelling mechanistic basis for how damage-induced ATM signalling could reach the Golgi to license RAD51C release.

To directly examine whether ATM localises to the Golgi in our system and whether its activation state changes upon DNA damage, we performed a biochemical Golgi enrichment assay using the Minute{trade mark, serif} Golgi Apparatus EnrichmentKit (Cat #: GO-037) to examine ATM distribution across cis- and trans-Golgi fractions. Fraction purity was validated using GM130 (cis-Golgi), TGN46 (trans-Golgi), and HSP60 (membrane fraction) (Rebuttal Figure 2A). This analysis revealed that ATM is detectable in the total membrane fraction and enriched in the cis-Golgi fraction under basal conditions (Rebuttal Figure 2A). Under normal physiological conditions, activated ATM (pATM) was absent from Golgi-enriched fractions (Rebuttal Figure 2B), but was detectable in the cis-Golgi fraction following doxorubicin-induced genotoxic stress (Rebuttal Figure 2C). While these observations are preliminary and require further validation, they are consistent with the emerging literature and raise the intriguing possibility that ATM is recruited to and activated at the Golgi in a damage-dependent manner, where it could act locally to license RAD51C release.

Rebuttal Figure 2. Biochemical Golgi fractionation confirms ATM enrichment in cis-Golgi compartments.

*Western blot of HeLa-K fractions enriched for cis- and trans-Golgi membranes, probing for (A) ATM under basal conditions, and (B and C) pATM under basal conditions and (B) pATM (C) after treatment with DOX (40 μM) (markers: GM130 for cis-Golgi, TGN46 for trans-Golgi, HSP60 for membrane fraction (MEM). *

We consider the precise spatial and mechanistic dissection of ATM signalling at the Golgi and its relationship to nuclear feedback, one of the most exciting directions to emerge from this work, and one that we hope our study has helped to open. We have expanded the Discussion (lines 525-543) accordingly to place our findings in the context of the emerging Golgi-ATM literature and to frame this as an important unresolved question for future investigation.

*5. RAD51C is examined in silo, without consideration for the BCDX2 complex - RAD51C is exclusively analyzed in isolation, despite its well-established function as part of the BCDX2 paralog complex (RAD51B-RAD51C-RAD51D-XRCC2). Because RAD51C does not normally operate as a standalone factor, it is unclear why only RAD51C, among all paralogs, would be subjected to Golgi tethering, ATM-dependent release, and Importin-β-driven nuclear import. This raises important mechanistic questions: Are other BCDX2 members also Golgi-associated? Do they undergo similar trafficking dynamics? Does Golgi tethering selectively regulate RAD51C, or does the complex translocate together? Addressing these points would greatly strengthen the biological plausibility and mechanistic coherence of the proposed model. *

Response: We thank the reviewer for raising this important point. We fully agree that RAD51C functions as a core component of the BCDX2 (RAD51B-RAD51C-RAD51D-XRCC2) and CX3 (RAD51C-XRCC3) paralog complexes, and that its canonical roles in HR and replication fork protection occur within these assemblies. Our decision to focus on RAD51C was driven by the screening data: of the DDR proteins identified, RAD51C displayed the most robust Golgi-associated pool, the clearest damage-induced redistribution dynamics, and a tractable anchoring interaction with Giantin that could be interrogated biochemically.

We would also note that extending this analysis to other RAD51 paralogs is not straightforward with current tools. The available commercial antibodies against RAD51B, RAD51D and XRCC2 perform poorly in immunofluorescence applications, and most localisation studies for these proteins have relied on overexpression of tagged constructs, a strategy that, as discussed above, risks perturbing both localisation and complex assembly. The lack of reliable antibodies for endogenous paralog detection at the resolution required for Golgi localisation analysis represents a genuine technical barrier that we encountered directly during this study.

Whether Golgi association and ATM-dependent release involve RAD51C alone or extend to other BCDX2 or CX3 members is therefore a genuinely open and important question. We note that our co-immunoprecipitation data were performed on total cell lysate and cannot distinguish whether the Golgi-associated RAD51C is complexed with other paralogs or represents a monomeric subpopulation. Golgins are well established as multi-cargo scaffolding platforms, and it is entirely plausible that Giantin organises a broader paralog module rather than tethering RAD51C as an isolated subunit. A systematic analysis of RAD51 paralogs for Golgi localisation and lesion-dependent trafficking enabled by improved reagents such as proximity labelling or endogenous tagging approaches compatible with essential proteins would determine whether the BCDX2 complex translocates as a unit or whether individual subunits are differentially regulated, with potentially distinct consequences for HR fidelity. We have revised the manuscript accordingly and identify this as an explicit priority for future work in the revised Discussion (lines 583-602).

Minor Comments

1. Pathway-specific sub-Golgi localization patterns - The finding that DDR proteins map to distinct cis/trans Golgi subdomains is an interesting and potentially important observation. However, the dataset is limited to 15 proteins, making the proposed pathway-level trends (e.g., HR factors enriched in cis-Golgi; BER/MMEJ factors enriched in trans-Golgi) preliminary. Strengthening this conclusion by increasing the number of DDR proteins analyzed would help determine whether sub-Golgi compartmentalization contributes meaningfully to DNA repair pathway regulation.

Response: We thank the reviewer for this constructive suggestion. We agree that extending sub-Golgi mapping to a larger number of DDR proteins would be valuable, and we present the current dataset explicitly as a first, hypothesis-generating map rather than a definitive pathway atlas.

We would like to highlight, however, that the value of this observation lies not simply in the number of proteins mapped, but in the biological coherence of the patterns that emerge. The finding that proteins from the same repair pathway tend to occupy the same Golgi sub-compartment: BER and MMEJ factors enriching in the trans-Golgi, HR factors in the medial/cis-Golgi, and that this sub-compartmental positioning correlates with the direction of their redistribution upon genotoxic stress, is a pattern that would be unlikely to arise by chance across 15 independently validated proteins. This internal consistency argues that the sub-Golgi organisation reflects genuine pathway-level biology rather than noise, even if the dataset is not yet exhaustive. Together with the bioinformatic network analysis, which independently supports pathway-level clustering across the broader validated hit list, these observations reinforce each other as complementary layers of evidence.

2. Is the Golgi-released RAD51C indeed the pool that enters the nucleus? The major assumption of the study is that the RAD51C population released from the Golgi upon DNA damage is the same pool that subsequently accumulates in the nucleus to form repair foci. While the imaging and fractionation data are consistent with this model, the study does not directly track or distinguish Golgi-derived RAD51C from cytoplasmic or pre-existing nuclear pools. Without a method to specifically label, pulse-chase, or track the Golgi-anchored fraction, it remains formally possible that nuclear RAD51C originates from other subcellular reservoirs.

__Response: __We thank the reviewer for highlighting this important mechanistic point, which we agree cannot be fully resolved with the current dataset. Several independent lines of evidence are nonetheless consistent with a model in which the Golgi-associated pool contributes directly to damage-induced nuclear accumulation.

• Our time-resolved imaging demonstrates a reciprocal decrease at the Golgi and a concurrent increase in the nucleus following genotoxic stress, consistent with redistribution rather than independent compartment-specific changes (Figures 3E-3I).
• Biochemical fractionation provides an orthogonal readout of the same reciprocal shift under identical conditions (Figures 3J and S6D).
• ATM inhibition simultaneously prevents Golgi loss and blunts nuclear accumulation, while Importin-β perturbation blocks nuclear entry, together supporting an active and regulated translocation route (Figures 3K-3P).
• Giantin depletion, which releases the Golgi-tethered RAD51C pool prematurely, leads to aberrant nuclear RAD51C foci lacking canonical HR markers and impaired ATM signalling, strongly supporting that the Golgi-tethered fraction has functional consequences in the nucleus consistent with it being the relevant pool (Figures 4B-4E and 4J-4M).
• In the revised manuscript we have included cytoplasmic RAD51C signal quantification across the doxorubicin time course (Figure 3H). The cytoplasmic signal shows only a moderate and gradual reduction that is kinetically distinct from the sharp Golgi decrease and does not precede the nuclear increase. This pattern is inconsistent with a large pre-existing cytoplasmic reservoir driving the nuclear accumulation; if the cytoplasmic pool were the primary source, one would expect a rapid and prominent cytoplasmic decrease coinciding with or preceding nuclear accumulation, which we do not observe. Instead, the data are more consistent with rapid transit of Golgi-released RAD51C through the cytoplasm rather than stable cytoplasmic accumulation prior to nuclear entry. We acknowledge that definitive pool-identity tracking would require spatially restricted labelling approaches such as Giantin-proximal TurboID or photoactivatable tagging strategies, which are precluded by the technical constraints on RAD51C tagging described above. We have revised the manuscript to avoid overstatement on this point and identify these approaches as important future directions (lines 297-305 & lines 715-719).

Reviewer #2 (Significance (Required)):

General assessment - This study presents a novel and conceptually compelling view of the DNA damage response (DDR) by positioning the Golgi apparatus as an active regulator of the spatiotemporal availability of DNA repair factors. The strongest aspects of the work include its integration of a systematic immune-localization screening, a sub-Golgi compartment mapping, dynamic redistribution assays, and functional perturbations to build a coherent model of Golgi-nucleus communication during genotoxic stress. The mechanistic focus on RAD51C provides a clear case study linking organelle-level regulation to genome stability.

• Advance - To my knowledge, this is the first comprehensive demonstration that the Golgi can serve as a spatiotemporal coordination node for DDR proteins, including those involved in HR. The identification of a substantial pool of RAD51C, and reportedly other DDR factors, anchored within specific Golgi subdomains represents a significant conceptual advance. The demonstration that Golgi-tethered RAD51C is released in an ATM-dependent manner and subsequently participates in nuclear foci formation suggests a previously unrecognized organelle-level regulatory checkpoint in genome maintenance. This work therefore extends current models of the DDR by revealing a layer of intracellular coordination that bridges classical nuclear pathways with cytoplasmic organelle function.*

• Audience - This study will be of strong interest to a specialized audience in the fields of DNA repair, genome stability, and cell biology, particularly those studying the spatial organization of repair pathways and intracellular stress signaling. It will also appeal to researchers investigating organelle biology, intracellular trafficking, and the broader coordination of cytoplasmic and nuclear responses to stress. Beyond these communities, the work may be relevant to cancer, as it suggests new mechanisms by which organelle perturbations or Golgi-associated scaffolding proteins could influence therapeutic responses or genomic instability.

Reviewer expertise - Field of expertise: DNA repair, genome stability, organelle biology, cancer cell biology.*

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

*This study investigates the communication between the Golgi complex and the nucleus of the cell, which remains a largely unexplored field. The authors used publicly available siRNA and antibody data from the Human Protein Atlas as a basis for finding overlap between the proteomes of the two cellular compartments. In validating the data from the HPA, the study finds a novel cluster of DNA repair proteins present in the Golgi, which they validate and resolve to sub-compartmental localization. To do so they use immunofluorescence (IF) localization on ¬cis- and trans-Golgi cisternae marked by GM130 and TGN46, respectively. The authors find that many of the fully validated proteins present in both the nucleus and Golgi redistribute between the Golgi and the nucleus dependent on the protein and the type of DNA lesion. They focused on RAD51C, a recombination factor. They show that RAD51C resides in both the ¬cis- and trans- subsections prior to damage and responds to DNA damage in an ATM-dependent manner via release of a Golgi-based pool bound to Giantin, which is then imported into the nucleus via Importin-β. Knockdown experiments showed that Giantin regulates RAD51C spatially and temporally. The work reveals a dynamic interchange of proteins between the Golgi and nucleus that controls cell functions beyond the classic secretory, membrane trafficking, and PTM roles of the Golgi. The authors build on prior work on Golgi impacts on DDR, offering an alternative cellular compartment for storage of DDR factors prior to damage. Overall, the data is timely and relevant, as it finds new roles for the Golgi in DNA damage response (DDR) regulation. The data is largely convincing and well controlled. The IF data is presented in black and white single channels and merged in color, which allows good comparison of the different protein stains. The scope of the initial screen of HPA antibodies and Golgi/Nuclear dual proteomes is impressive, and the overlap of DDR proteins is characterized for fifteen different proteins at a sub-compartmental level. The focus on RAD51C as a member of the HR pathway was a strong choice, and the study presents interesting information on its regulation by Golgi complex members, as well as a feedback look with pATM. The possibility of the Golgi storing specific DDR factors in specific compartments is well-supported and intriguing. There are a few major and minor points that should strengthen the paper and improve clarity prior to publication. *

Major Comments:

*1. Much of the strength of the IF data is lost in the choice of scale for presentation of the data. In almost all cases, enlarged sections should be shown of the areas currently indicated by arrow, in all channels. This is done well in Figure 3A, where an area of the Golgi is enlarged and the overlap of RAD51C in the GM130-marked Golgi is clearly visible in the merged channel, even when printed out. I would highly recommend including the white box and enlarged in all images and channels, while keeping the representative fields as is (e.g. if the image is 40mm, draw a 7mm box around representative cells/Golgi, and enlarge to 15mm in the bottom left). This change should be made to F1E, F2F, F3E, F3J, and F3M, as well as having enlarged figures in the corners in all supplementary data IF figures. Where possible, a fully enlarged image of the bounding box could also be included. Some of the IF data would be strengthened by using the nuclei stain to draw a masking outline to include in the black and white channels, to clearly delaminate what is Golgi-localized and what is nuclear. *

Response: We thank the reviewer for this helpful suggestion and fully agree that enlarged insets substantially improve the visibility of Golgi-localised signal, particularly when figures are printed. We share the reviewer's view that alternative display formats with larger insets would be preferable, and we have implemented enlarged boxed regions wherever space constraints permitted.

Specifically, we have added boxed regions with enlarged insets to Figure 1E, all panels of Figure 3. For Figure 2, the number of conditions and proteins displayed simultaneously within the constraints of standard journal figure dimensions made it impractical to include enlarged insets for all panels without reducing the overall field size to the point of losing contextual information. We have nonetheless improved the visibility of the Golgi signal in Figure 2 as much as possible within these constraints, and note that the final figure layout will be further optimised in line with the journal's specific formatting guidelines. In addition, all figures have been provided as high-resolution image files to allow electronic magnification, enabling readers to inspect the Golgi-localised signal in detail beyond what is visible in the printed version.

Regarding the use of nuclear outline masks in single-channel images, we tested this approach but found that given the number of structures present within each field, including Golgi stacks, nuclear foci, and cytoplasmic signal, overlaying nuclear outlines on individual channels added visual complexity that made the images harder rather than easier to interpret. As an alternative, we have included a full-colour merged panel, when possible, which we consider a cleaner way to delineate nuclear versus Golgi-localised signal and allows the reader to directly compare compartment-specific distributions across channels.

• 1. *There is a lack of consistency in the representative images shown by IF. For example, Figure 1 gives the impression of very little RAD51C in the nucleus but this is rightly shown to not be the case in Supp. Fig 2A. The same is true of the various images of LIG1. The authors should use representative data that better reflects the distribution of the proteins being studied and maintain consistency across images. If there is a lot of variation in staining patterns, the authors should show images and percentages corresponding to the variations especially for the key gene studied, RAD51C.

Response: We agree and have replaced the representative IF panels for RAD51C and LIG1 with images that better reflect the quantified distributions across biological replicates. The revised panels were selected to match the quantified compartment intensities shown in the accompanying graphs rather than representing outlier cells. We would also note that the apparent discrepancy between Figure 1E and Supplementary Figure S2A partly reflects a difference in imaging conditions: Supplementary Figure S2A __and __Figure 2F were acquired directly from the high-content screening pipeline under uniform, non-optimised antibody and fixation conditions at widefield resolution, whereas Figure 1E shows representative single optical section confocal images acquired after candidate identification with antibody conditions optimised for each individual protein. The improved signal-to-noise in the optimised confocal images more faithfully captures the dual Golgi and nuclear localisation of RAD51C, and the apparent difference between the two image sets is therefore expected rather than inconsistent. We have updated the figure legends to clarify the imaging modality and conditions for each panel. Furthermore, the quantified distribution of RAD51C across Golgi, nuclear and cytoplasmic compartments across multiple cell lines is shown in Figure 3B and 3D, providing a population-level representation of the dual localisation that complements the representative images shown in Figure 1E.

• 1. *The initial screening by siRNA-mediated knockdown pipeline that validated and confirmed dual Golgi and nuclear localization of 163 of the 329 dual-localization HPA proteins does not have any data included. This seems like a very large amount of data to gloss over and not include even as supplementary data. This should be included as source data, and discussion of the in-text information should be strengthened. The data included with the networking of these validated proteins is strong, but the process of elimination and validation has not been shown. In addition, the antibody information included in the supplementary data does not include dilution factors or blocking factors is not included, which would be beneficial to future studies to include.

Response: We agree and have addressed this in full. We note that the HPA antibody validation data, including immunofluorescence images and siRNA knockdown results, are publicly available for inspection on the Human Protein Atlas website (www.proteinatlas.org) for the majority of candidates, providing an independent layer of verification. In the revised submission, we additionally provide the complete siRNA-mediated validation dataset generated in our laboratory as source data (Table S1; lines 1025-1041), including for each candidate the HPA antibody identifier, gene symbol, Ensembl ID, antibody staining pattern, siRNA identifier, cell number per replicate, and normalised Golgi and nuclear signal ratios for both experimental replicates. This allows readers to inspect the validation metrics directly and apply alternative thresholds if desired. We have also expanded the antibody information to include diluent conditions (4% FBS in 0.1% Triton-X100 for all HPA antibodies used at 2 μg/ml in the screening pipeline), enabling reproducibility and reuse of the dataset by the community.

• 1. *The authors should expand upon the paragraph lines 155-162 to include more discussion on Figure S2A and S2B. The expanse of this data is some of the strongest in the paper, and it should be further discussed in-text. Also, the rationale behind the choice in the specific proteins that are included in these analysis / figures is not always clear in -text, and more attention should be spent on the narrowing down of the analysis to the final proteins. This is also especially important as many of the DDR proteins chosen are not the most common DDR proteins. Also note in text that the Golgi marker GM130 (presumably) was used for the screening, which means that some proteins which are only localizing to the TGN46 trans Golgi might have been lost in the validation step (or, explain why this is not the case).

Response: __We expanded the Results text (__lines 141-163) to discuss Figures S2A and S2B in more depth and clarified the rationale for selecting the final set of DDR proteins taken forward, including considerations of pathway representation, bioinformatic annotations, literature-described roles in DNA repair. We would also note that the identity of the DDR proteins identified in this screen was determined by the HPA dataset and the unbiased validation pipeline rather than by prior assumptions about which repair factors would be present at the Golgi. The presence of less commonly studied DDR factors is therefore a direct reflection of the screen output, and we consider this one of the strengths of the approach.

We would also like to address the reviewer's concern about potential GM130-based bias directly: at the widefield or confocal resolution used in the high-content screening pipeline, the Golgi apparatus appears as a single perinuclear structure and cis- and trans-Golgi subdomains cannot be resolved. GM130 was therefore used purely as a segmentation marker to define the Golgi compartment as a whole rather than to selectively label the cis-Golgi cisternae. The resulting Golgi mask captures signals from the entire Golgi ribbon, including trans-Golgi regions, meaning that proteins with exclusively trans-Golgi localisation would not have been systematically excluded at the screening stage. Sub-compartmental resolution of cis versus trans localisation was only possible in subsequent analyses using nocodazole-dispersed mini-stacks imaged by confocal microscopy with co-staining for both GM130 and TGN46.

*5. The relationship between Giantin loss, increased cell proliferation, and elevated endogenous DNA damage as it relates to RAD51C remains insufficiently resolved and requires further clarification. Several of the proliferation assays used are not optimal for addressing changes in cell growth. For example, Figure 5O appears to quantify cell numbers by counting fields from IF images, which is an unconventional approach. This should be done by growth curves, luminescent viability or colony formation assays. In addition, this point will be greatly strengthened by performing rescue experiments for Giantin directly (instead of co-depletion as a means of rescue) and/or using a mutant of RAD51C that does not bind to Giantin. If these additional experiments are beyond the current scope, the conclusions should be softened in the discussion. *

Response: We thank the reviewer for raising these important points, which we address in turn:

Giantin-RAD51C relationship and mechanistic interpretation. __We acknowledge that establishing the full causal chain between Giantin loss, RAD51C mislocalisation, elevated endogenous DNA damage and increased cell proliferation is challenging within the scope of a single study, and we discuss this openly in the Discussion (__lines 555-564). Our evidence collectively includes: physical interaction between endogenous Giantin and RAD51C by co-immunoprecipitation (Figures 4H and 4I), premature nuclear accumulation of RAD51C upon Giantin depletion (Figures 4B-4E and 4J-4M), new additional experiment showing direct reduction of HR efficiency in the DR-GFP assay (Figure 5L), impaired ATM signalling (Figures 5J and 5M), elevated genomic instability (Figures 5A-5E), and epistatic rescue by RAD51C co-depletion (Figures 5M-5P). These observations are further contextualised by the established literature on RAD51C function: RAD51C is known to regulate CHK2 phosphorylation and cell cycle checkpoint signalling (Badie et al, 2009), stabilise replication forks (Somyajit et al, 2015), and promote RAD51 filament formation required for DSB repair (Prakash et al, 2015). Dysregulation of these functions through Giantin-dependent mislocalisation provides a mechanistically coherent explanation for the elevated genomic instability and altered proliferation we observe, and is entirely consistent with our model. Together, the experimental evidence and the published biology of RAD51C support a model in which Giantin spatially regulates RAD51C to maintain proper DDR signalling and HR capacity.

We agree that separation-of-function tools would further strengthen this model and identify these as important future priorities. We wish to note however that both approaches face substantial technical barriers in this system. As described in our response to Reviewer 1 Major Comment 1, RAD51C tagging, whether by CRISPR-mediated endogenous editing or ectopic expression, consistently compromised cell viability and protein function, precluding the generation of interaction-deficient variants at physiological expression levels. Engineering an interaction-deficient Giantin mutant presents an independent and considerable challenge: Giantin is one of the largest Golgi matrix proteins (~376 kDa), composed almost entirely of extended coiled-coil domains that are intrinsically difficult to model structurally, and identifying a discrete interaction interface with RAD51C without disrupting the broader scaffolding function of the protein would require a dedicated structural and biochemical programme. We therefore consider these important but substantial future directions rather than straightforward experimental additions to the current study.

Proliferation assays. Colony formation assays provide a rigorous readout of long-term proliferative capacity, and these data are presented for single knockdown conditions in Figures 5F-5I. The cell number quantification in Figure 5P was specifically included to assess the double knockdown of Giantin and RAD51C simultaneously, a condition not covered by the colony formation assay. We respectfully note that automated fluorescence microscopy-based nuclear counting is a well-established approach for measuring cell proliferation in siRNA screening contexts. Nuclear counting from high-content imaging has been used as a direct readout of cell growth and proliferation in RNAi screens (Boutros et al, 2004; Martin et al, 2014; Garvey et al, 2016; Mikheeva et al, 2024), and has been shown to produce results comparable to or superior to conventional viability assays including MTT and flow cytometry-based methods (Mikheeva et al, 2024). We have nonetheless clarified in the revised figure legend that Figure 5P reports relative cell number quantified by automated nuclear counting from high-content imaging fields as a secondary concordant measure alongside the colony formation data, rather than a standalone proliferation assay.

*6. It is unclear from the discussion and from presented data whether proteins are directly transported between the Golgi and the nucleus, or whether they go into the cytoplasm for a transient period, presumably when they could interact with Importin β. There is also some data where cytoplasm signal could be quantified to address this (Figure 3E-I). *

Response: We thank the reviewer for this mechanistic point. In the revised manuscript we have included cytoplasmic RAD51C signal quantification alongside Golgi and nuclear measurements for the doxorubicin time course (lines 297-305; Figure 3H). The cytoplasmic signal shows a moderate and gradual reduction distinct in both magnitude and kinetics from the sharp Golgi decrease, consistent with a transient cytoplasmic intermediate rather than a stable pool. Regarding the identity of the translocating pool, two observations directly support a Golgi origin. First, Importazole treatment prevents RAD51C release from the Golgi following genotoxic stress and simultaneously reduces nuclear RAD51C foci formation, demonstrating that Importin-β-mediated import is required both for Golgi clearance and for productive nuclear accumulation. Second, Giantin depletion which prematurely releases the Golgi-tethered pool, leads to aberrant nuclear RAD51C foci, directly linking the Golgi-anchored fraction to nuclear accumulation. Together these data support a model in which Golgi-resident RAD51C transits through the cytoplasm for Importin-β-mediated nuclear import. We acknowledge that without direct labelling of the Golgi-anchored fraction, the precise contribution of each subcellular pool to the nuclear accumulation cannot be fully resolved with the current dataset. We discuss the development of appropriate tagging strategies as an important future direction to dissect the dynamics of this process in further detail.

*7. Statistical analysis on experiments with more than two samples need to be performed with ANOVA and a follow up post-hoc test, not with two-tailed unpaired Student's t-test, which only compares the control and each individual sample. This type of analysis inflates the Type 1 error rates (false positives) in your datasets. For example, the two-tailed unpaired Student's t-test is appropriate in Figure 2F-H, but not in Figure 3 when the samples are timepoints. In this case, a One-way ANOVA with Tukey's post-hoc test (if you want to show all coparisons), or Bonferroni/Sidak if you only need to compare several samples). *

Response: We agree with the reviewer and thank them for highlighting this important statistical issue. We have revised the statistical analysis for all experiments involving more than two groups to avoid inflation of Type I error rates caused by multiple pairwise Student's t tests. Specifically, for Figures 3F-I, 4C-E, and Figure 5, the data were reanalysed using one way ANOVA followed by the appropriate multiple comparisons post hoc test. The Methods section and corresponding figure legends have been updated to clearly state the statistical tests used for each dataset.

Minor Comments: General 1. Throughout the text, the reference to many figures and supplementary figures in the same sentence, with little discussion of the data therein makes it hard to follow. In-text referencing is particularly confusing in the section "Dual-localising DDR proteins dynamically redistribute between the Golgi and nucleus in response to specific types of DNA injuries," where the reader is switching between multiple figures and supplementary figures.

__Response: __We thank the reviewer for this helpful comment. In the revised manuscript, we have improved the readability of the text and revised the figure references to make them clearer. We hope these revisions make the manuscript easier to follow and allow readers to better inspect the figures.

1. In figures that display technical replicates as individual data points, consider distinguishing each replicate by using different marker shapes (e.g., repeat 1 = upright triangle; repeat 2 = inverted triangle; repeat 3 = diamond). This would provide additional clarity regarding the consistency and repeatability of each technical repeat.

__Response: __We thank the reviewer for this suggestion. We have updated the data presentation to distinguish biological replicates using different marker shapes in datasets where replicate tracking is of particular relevance to the interpretation. For datasets where individual replicate values are already clearly separable, we have maintained the existing presentation to avoid unnecessary visual complexity.

1. Make sure all western blot data includes the marker size (F3C and F5L has none, F4H/I have size of proteins not size of markers).

__Response: __We added missing marker sizes to our western blot data in the revised manuscript.

1. Be consistent with use of capitalization in figure legends and graph/figure labels.

__Response: __We made sure that the capitalisation is consistent in figure legends, graph and figure legends in the revised manuscript.

Figure 2

In Figure 2A, please include in the figure itself that GM130 is the cis Golgi, and TGN46 is the trans Golgi (Figures should not be dependent on the text for full understanding).

__Response: __We revised Figure 2A and 2C to label GM130 as cis-Golgi and TGN46 as trans-Golgi within the figure, making it self-explanatory.

1. Why are LRIG2 and LRRIQ3 not included in the 2E cis vs trans Golgi data, when all other proteins from F1D are included? Include, or comment on in-text.

__Response: __Both LRIG2 and LRRIQ3 are included in 2E in both the original and revised manuscript.

1. Be sure to include scale bar data in each figure legend (F2A-E is currently missing it), and include updated scales included in the enlarged data.

__Response: __Scale bar data is now included in each figure legend in the revised manuscript.

1. In Figure 2F, make sure that the merged green channel is presented at the same intensity as it is in the single black and white channel, as the green looks very overexposed in several of the merged (CCAR1 DMSO merged is the most noticeable).

__Response: __We agree and thank you for pointing this out. We have now revised the images and corrected the issue by updating all image panels in the figure.

1. In Figure 2G, include the grey label in the figure legend.

__Response: __We thank the reviewer for this comment. The grey label has now been included in the figure legend in the revised manuscript.

1. In Figure 2G-H, the method of data presentation in the graphs coupled with the statistical analysis is confusing and should be expanded upon in the legend.

__Response: __We agree that the amount of data presented may appear overwhelming. In the revised figure, we have adjusted the placement of the statistical annotations to improve clarity. Also, we improved the figure legend, to make the figure easier to read and interpret.

Figure 3

Figure E/F/G: Is there cytoplasmic quantification as well? Your rationale is that the Golgi RAD51C goes into the nucleus, but via the cytoplasm (due to Importin β import); do you see the cytoplasmic levels increase? Or is it too dilute to notice a difference? At least, this omission needs to be mentioned in-text.

Figure H/I also include the quantification of the cytoplasmic fraction. It is mentioned in-text on line 272, but not quantified. This comes up as a big question: Do the proteins go directly between the Golgi and nucleus, or do they go through the cytoplasm?

__Response: __We thank the reviewer for both of these related points. As described in our response to Major Comment 6 above, we have added cytoplasmic RAD51C signal quantification to the doxorubicin time course in the revised manuscript (Figure 3H) and discuss the implications for the proposed translocation route.

Figure 3A, 3E, and if the data is present for 3J and 3M, could all benefit from using the nuclei staining as a mask to draw an outline around the nucleus in the other channels, and then show a merge in full color instead of a nuclei-only channel. Also note from the major comments, that this data especially is so small to see without enlarged images.

__Response: __We thank the reviewer for this suggestion. Regarding nuclear outline masks, we tested this approach but found that the number of structures present in each field, including Golgi stacks, nuclear foci and cytoplasmic signal, made overlaid outlines visually confusing rather than clarifying. We have instead included a full-colour merged panel in Figure 3E, which we consider a cleaner way to distinguish nuclear from Golgi-localised signal while preserving the spatial context of the data.

Regarding image size, we have added enlarged insets to Figures 3E, 3J and 3M in the revised manuscript. We have chosen to display multiple cells per panel rather than a single enlarged cell in order to capture the heterogeneity of the cell population, which we consider important for an accurate representation of the data. All figures have been provided as high-resolution image files to allow electronic magnification, enabling detailed inspection of the signal beyond what is visible in the printed version. We acknowledge that the constraints of standard journal figure dimensions limit how large individual panels can be, and the final layout will be optimised in line with the journal's formatting guidelines.

*In-text discussion of the results from Figure 3 has an in-depth discussion of the NLS and NES in RAD51C, but this is not followed up on with site-directed mutagenesis or any data; perhaps move this to the discussion instead of results section. *

__Response: __We have removed the discussion of the NLS and NES from the Results section.

Figure 4

Comments from earlier figures hold, with size of enlarged events and using the nuclei as an outline in the single channels. E.g. Figure 4F arrows appear to point to nothing at the chosen scale. The zoom in 4G is insufficient, as the chosen feature is so small it is not even visible in full fields.

__Response: __We thank the reviewer for this comment. The arrows in Figure 4F indicate individual nocodazole-dispersed Golgi mini-stacks, which are displayed at higher magnification in Figure 4G. The full field in Figure 4F is intentionally shown to illustrate the degree of Golgi dispersion achieved by nocodazole treatment, a context that may be unfamiliar to readers outside the Golgi field, before zooming into a single representative mini-stack in Figure 4G for the cisternal localisation analysis.

• Figure 4H and 4I need to show the size of the markers *

__Response: __The size of the markers are now included in the revised manuscript.

*The representative image in 4L for siGiantin pATM has no pATM foci, while the quantification in 4M has a reduction from ~50% to ~25%, so this image is not representative of this data, or the data quantification is not as strong as the actual data. *

__Response: __We thank the reviewer for this observation. We wish to clarify that the quantification in Figure 4M reports the mean percentage of RAD51C foci co-localising with pATM across the entire cell population from three independent biological replicates. A reduction from ~50% to ~25% therefore reflects a population-level shift in co-localisation frequency, not that every individual cell shows exactly 25% co-localisation. Given the inherent cell-to-cell variability in foci number and co-localisation, individual cells will span a range of values around this mean, and the representative image shown in Figure 4L reflects one such cell.

Figure 5

*Figure 5A has overexposure of the nuclei stain in order to visualize micronuclei. Readjust the levels, and enlarge the images for better visualization. (is this DAPI-stained? Please label). *

__Response: __The display levels of the nuclear stain in Figure 5A are intentionally set to allow visualisation of micronuclei, which are significantly dimmer than the main nucleus and would not be detectable at display settings optimised for the primary nuclear signal. This is standard practice in micronuclei quantification studies and is necessary to accurately identify and score these structures. The nuclear stain is Hoechst 33342, and this has been explicitly labelled in the revised figure legend.

*Figure 5A-C: Figure 5A does not show siRAD51, but it is included in the DMSO only graph. Please either show RAD51 data in 5A and 5C, or do not include in 5B. If the DMSO and ETO experiments were performed separately and that accounts for this discrepancy, then show separately. *

__Response: __We thank the reviewer for this observation. The siRAD51C condition is included in Figure 5B as an internal positive control, consistent with its well-established role in genome stability. RAD51C depletion combined with etoposide treatment resulted in severe cellular toxicity and insufficient cell numbers for reliable quantification, and this condition was therefore excluded from Figure 5C. This has been clarified in the revised figure legend.

*Figure 5M the white label is difficult to see in the green box. *

__Response: __We have updated the label colour in Figure 5M to improve visibility against the green background in the revised manuscript.

* Supplementary Figures*

Consider reordering/ subdividing supplementary figures for ease of reference during reading.

Response: We thank the reviewer for this suggestion. The current supplementary figure structure was intentionally designed to minimise the total number of supplementary figures and maintain a logical correspondence with the main figures, avoiding a situation where readers need to navigate an extensive supplementary section, a concern the reviewer raised regarding figure presentation. We believe the current organisation achieves a reasonable balance between completeness and accessibility.

SF1 and SF2A: Include enlarged boxes or full images so that data is visible.

__Response: __As described in our response to Major Comment 1, all figures have been provided as high-resolution image files to allow electronic magnification. Space constraints within standard journal figure dimensions preclude the addition of enlarged insets to all supplementary panels without substantially reducing the contextual field of view.

*SF3A, SF4A, and SF5A: Include enlarged images, include nuclei marker if possible (otherwise, the nuclear intensity is not proven nuclear). *

Response: We appreciate the suggestion, but adding enlarged insets and nuclei markers to all panels in Figures S3A, S4A and S5A would disproportionately increase the length and complexity of the supplementary section, making it harder rather than easier to navigate. The nuclear intensity measurements are derived from automated segmentation of the Hoechst channel using CellProfiler, which reliably defines nuclear boundaries independently of the antibody channel, and are therefore not dependent on visual confirmation of nuclear localisation in each representative image.

*SF3B-C, SF4B-C, and SF5 B-D: Change the data presentation in the same method as changed for F2G-H. *

Response: We have updated the figure legends for Figures S3B-C, S4B-C and S5B-D to improve readability.

SF3D: List proteins in the same order as in B and C.

Response: The proteins in Figure S3D are listed in the same order as in Figures S3B and S3C.

SF6D: Label M N and C more clearly. Include size labels.

Response: We have added clearer labels for the membrane (M), nuclear (N) and cytoplasmic (C) fractions and included molecular weight size markers in the revised Figure S6D.

*SF7A-B: Include enlarged. *

Response: We respectfully note that the purpose of Figures S7A-B is to display the overall cellular response to inhibitor treatments across the cell population, rather than to highlight specific subcellular structures. Enlarged insets would reduce the number of cells visible per panel and would not add scientific value in this context. The Golgi and nuclear signals are clearly visible at the chosen magnification.

*SF8: Include arrows as in previous experiments, include enlarge. *

Response: Arrows have been added to Figure S8 to indicate Golgi and nuclear RAD51C signal, consistent with the annotation style used in the main figures. The images already show two representative cells per condition to maximise the visible detail at the chosen scale.

*SF9G: G is labelled, but not included. *

Response: Figure S9G has been added in the revised manuscript, showing the pan-cancer overall survival map for GOLGB1 expression across all TCGA cohorts generated using GEPIA2. The figure legend has been updated accordingly.

*Reviewer #3 (Significance (Required)): *

* The work finds new roles for the Golgi in regulation of DNA damage responses and the screen could be an important dataset (but results need to be made available) for the DNA repair community. The scope of the initial screen of HPA antibodies and Golgi/Nuclear dual proteomes is impressive, and the overlap of DDR proteins is characterized for fifteen different proteins at a sub-compartmental level. The work provides important insights into RAD51C regulation, however, there are key mechanistic insights and control experiments missing from the studies involving RAD51C and Giantin, dampening its impact. The idea of an alternative cellular compartment for storage of DDR factors prior to damage is interesting, and suggests the spatial regulation of specific lesion responses are stored in specific sub-compartments of the Golgi, which could contribute to repair regulation.*

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Referee #3

Evidence, reproducibility and clarity

This study investigates the communication between the Golgi complex and the nucleus of the cell, which remains a largely unexplored field. The authors used publicly available siRNA and antibody data from the Human Protein Atlas as a basis for finding overlap between the proteomes of the two cellular compartments. In validating the data from the HPA, the study finds a novel cluster of DNA repair proteins present in the Golgi, which they validate and resolve to sub-compartmental localization. To do so they use immunofluorescence (IF) localization on ¬cis- and trans-Golgi cisternae marked by GM130 and TGN46, respectively. The authors find that many of the fully validated proteins present in both the nucleus and Golgi redistribute between the Golgi and the nucleus dependent on the protein and the type of DNA lesion. They focused on RAD51C, a recombination factor. They show that RAD51C resides in both the ¬cis- and trans- subsections prior to damage and responds to DNA damage in an ATM-dependent manner via release of a Golgi-based pool bound to Giantin, which is then imported into the nucleus via Importin-β. Knockdown experiments showed that Giantin regulates RAD51C spatially and temporally. The work reveals a dynamic interchange of proteins between the Golgi and nucleus that controls cell functions beyond the classic secretory, membrane trafficking, and PTM roles of the Golgi. The authors build on prior work on Golgi impacts on DDR, offering an alternative cellular compartment for storage of DDR factors prior to damage. Overall, the data is timely and relevant, as it finds new roles for the Golgi in DNA damage response (DDR) regulation. The data is largely convincing and well controlled. The IF data is presented in black and white single channels and merged in color, which allows good comparison of the different protein stains. The scope of the initial screen of HPA antibodies and Golgi/Nuclear dual proteomes is impressive, and the overlap of DDR proteins is characterized for fifteen different proteins at a sub-compartmental level. The focus on RAD51C as a member of the HR pathway was a strong choice, and the study presents interesting information on its regulation by Golgi complex members, as well as a feedback look with pATM. The possibility of the Golgi storing specific DDR factors in specific compartments is well-supported and intriguing. There are a few major and minor points that should strengthen the paper and improve clarity prior to publication.

Major Comments:

1. Much of the strength of the IF data is lost in the choice of scale for presentation of the data. In almost all cases, enlarged sections should be shown of the areas currently indicated by arrow, in all channels. This is done well in Figure 3A, where an area of the Golgi is enlarged and the overlap of RAD51C in the GM130-marked Golgi is clearly visible in the merged channel, even when printed out. I would highly recommend including the white box and enlarged in all images and channels, while keeping the representative fields as is (e.g. if the image is 40mm, draw a 7mm box around representative cells/Golgi, and enlarge to 15mm in the bottom left). This change should be made to F1E, F2F, F3E, F3J, and F3M, as well as having enlarged figures in the corners in all supplementary data IF figures. Where possible, a fully enlarged image of the bounding box could also be included. Some of the IF data would be strengthened by using the nuclei stain to draw a masking outline to include in the black and white channels, to clearly delaminate what is Golgi-localized and what is nuclear.
2. There is a lack of consistency in the representative images shown by IF. For example, Figure 1 gives the impression of very little RAD51C in the nucleus but this is rightly shown to not be the case in Supp. Fig 2A. The same is true of the various images of LIG1. The authors should use representative data that better reflects the distribution of the proteins being studied and maintain consistency across images. If there is a lot of variation in staining patterns, the authors should show images and percentages corresponding to the variations especially for the key gene studied, RAD51C.
3. The initial screening by siRNA-mediated knockdown pipeline that validated and confirmed dual Golgi and nuclear localization of 163 of the 329 dual-localization HPA proteins does not have any data included. This seems like a very large amount of data to gloss over and not include even as supplementary data. This should be included as source data, and discussion of the in-text information should be strengthened. The data included with the networking of these validated proteins is strong, but the process of elimination and validation has not been shown. In addition, the antibody information included in the supplementary data does not include dilution factors or blocking factors is not included, which would be beneficial to future studies to include.
4. The authors should expand upon the paragraph lines 155-162 to include more discussion on Figure S2A and S2B. The expanse of this data is some of the strongest in the paper, and it should be further discussed in-text. Also, the rationale behind the choice in the specific proteins that are included in these analysis / figures is not always clear in -text, and more attention should be spent on the narrowing down of the analysis to the final proteins. This is also especially important as many of the DDR proteins chosen are not the most common DDR proteins. Also note in text that the Golgi marker GM130 (presumably) was used for the screening, which means that some proteins which are only localizing to the TGN46 trans Golgi might have been lost in the validation step (or, explain why this is not the case).
5. The relationship between Giantin loss, increased cell proliferation, and elevated endogenous DNA damage as it relates to RAD51C remains insufficiently resolved and requires further clarification. Several of the proliferation assays used are not optimal for addressing changes in cell growth. For example, Figure 5O appears to quantify cell numbers by counting fields from IF images, which is an unconventional approach. This should be done by growth curves, luminescent viability or colony formation assays. In addition, this point will be greatly strengthened by performing rescue experiments for Giantin directly (instead of co-depletion as a means of rescue) and/or using a mutant of RAD51C that does not bind to Giantin. If these additional experiments are beyond the current scope, the conclusions should be softened in the discussion.
6. It is unclear from the discussion and from presented data whether proteins are directly transported between the Golgi and the nucleus, or whether they go into the cytoplasm for a transient period, presumably when they could interact with Importin β. There is also some data where cytoplasm signal could be quantified to address this (Figure 3E-I).
7. Statistical analysis on experiments with more than two samples need to be performed with ANOVA and a follow up post-hoc test, not with two-tailed unpaired Student's t-test, which only compares the control and each individual sample. This type of analysis inflates the Type 1 error rates (false positives) in your datasets. For example, the two-tailed unpaired Student's t-test is appropriate in Figure 2F-H, but not in Figure 3 when the samples are timepoints. In this case, a One-way ANOVA with Tukey's post-hoc test (if you want to show all coparisons), or Bonferroni/Sidak if you only need to compare several samples).

Minor Comments:

General

1. Throughout the text, the reference to many figures and supplementary figures in the same sentence, with little discussion of the data therein makes it hard to follow. In-text referencing is particularly confusing in the section "Dual-localising DDR proteins dynamically redistribute between the Golgi and nucleus in response to specific types of DNA injuries," where the reader is switching between multiple figures and supplementary figures.
2. In figures that display technical replicates as individual data points, consider distinguishing each replicate by using different marker shapes (e.g., repeat 1 = upright triangle; repeat 2 = inverted triangle; repeat 3 = diamond). This would provide additional clarity regarding the consistency and repeatability of each technical repeat.
3. Make sure all western blot data includes the marker size (F3C and F5L has none, F4H/I have size of proteins not size of markers).
4. Be consistent with use of capitalization in figure legends and graph/figure labels.

Figure 2

1. In Figure 2A, please include in the figure itself that GM130 is the cis Golgi, and TGN46 is the trans Golgi (Figures should not be dependent on the text for full understanding).
2. Why are LRIG2 and LRRIQ3 not included in the 2E cis vs trans Golgi data, when all other proteins from F1D are included? Include, or comment on in-text.
3. Be sure to include scale bar data in each figure legend (F2A-E is currently missing it), and include updated scales included in the enlarged data.
4. In Figure 2F, make sure that the merged green channel is presented at the same intensity as it is in the single black and white channel, as the green looks very overexposed in several of the merged (CCAR1 DMSO merged is the most noticeable).
5. In Figure 2G, include the grey label in the figure legend.
6. In Figure 2G-H, the method of data presentation in the graphs coupled with the statistical analysis is confusing and should be expanded upon in the legend.

Figure 3

1. Figure E/F/G: Is there cytoplasmic quantification as well? Your rationale is that the Golgi RAD51C goes into the nucleus, but via the cytoplasm (due to Importin β import); do you see the cytoplasmic levels increase? Or is it too dilute to notice a difference? At least, this omission needs to be mentioned in-text.
2. Figure H/I also include the quantification of the cytoplasmic fraction. It is mentioned in-text on line 272, but not quantified. This comes up as a big question: Do the proteins go directly between the Golgi and nucleus, or do they go through the cytoplasm?
3. Figure 3A, 3E, and if the data is present for 3J and 3M, could all benefit from using the nuclei staining as a mask to draw an outline around the nucleus in the other channels, and then show a merge in full color instead of a nuclei-only channel. Also note from the major comments, that this data especially is so small to see without enlarged images.
4. In-text discussion of the results from Figure 3 has an in-depth discussion of the NLS and NES in RAD51C, but this is not followed up on with site-directed mutagenesis or any data; perhaps move this to the discussion instead of results section.

Figure 4

1. Comments from earlier figures hold, with size of enlarged events and using the nuclei as an outline in the single channels. E.g. Figure 4F arrows appear to point to nothing at the chosen scale. The zoom in 4G is insufficient, as the chosen feature is so small it is not even visible in full fields.
2. Figure 4H and 4I need to show the size of the markers
3. The representative image in 4L for siGiantin pATM has no pATM foci, while the quantification in 4M has a reduction from ~50% to ~25%, so this image is not representative of this data, or the data quantification is not as strong as the actual data.

Figure 5

1. Figure 5A has overexposure of the nuclei stain in order to visualize micronuclei. Readjust the levels, and enlarge the images for better visualization. (is this DAPI-stained? Please label).
2. Figure 5A-C: Figure 5A does not show siRAD51, but it is included in the DMSO only graph. Please either show RAD51 data in 5A and 5C, or do not include in 5B. If the DMSO and ETO experiments were performed separately and that accounts for this discrepancy, then show separately.
3. Figure 5M the white label is difficult to see in the green box.

Supplementary Figures

1. Consider reordering/ subdividing supplementary figures for ease of reference during reading.
2. SF1 and SF2A: Include enlarged boxes or full images so that data is visible.
3. SF3A, SF4A, and SF5A: Include enlarged images, include nuclei marker if possible (otherwise, the nuclear intensity is not proven nuclear).
4. SF3B-C, SF4B-C, and SF5 B-D: Change the data presentation in the same method as changed for F2G-H.
5. SF3D: List proteins in the same order as in B and C.
6. SF6D: Label M N and C more clearly. Include size labels.
7. SF7A-B: Include enlarged.
8. SF8: Include arrows as in previous experiments, include enlarge.
9. SF9G: G is labelled, but not included.

Significance

The work finds new roles for the Golgi in regulation of DNA damage responses and the screen could be an important dataset (but results need to be made available) for the DNA repair community. The scope of the initial screen of HPA antibodies and Golgi/Nuclear dual proteomes is impressive, and the overlap of DDR proteins is characterized for fifteen different proteins at a sub-compartmental level. The work provides important insights into RAD51C regulation, however, there are key mechanistic insights and control experiments missing from the studies involving RAD51C and Giantin, dampening its impact. The idea of an alternative cellular compartment for storage of DDR factors prior to damage is interesting, and suggests the spatial regulation of specific lesion responses are stored in specific sub-compartments of the Golgi, which could contribute to repair regulation.

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Referee #2

Evidence, reproducibility and clarity

Background - Eukaryotic cells rely on tightly regulated DNA repair pathways to preserve genome stability under the constant threat of both endogenous and exogenous genotoxic stress. While the nucleus, and to a lesser extent the mitochondria, is the primary site where DNA damage is detected and repaired, accumulating evidence indicates that extranuclear organelles, particularly the Golgi apparatus, play a surprisingly important role in modulating stress signaling, proteostasis, and the trafficking/activation of key DNA repair factors.

Emerging evidence has shown that genotoxic stress can result in a major remodeling of the Golgi apparatus; however, the crosstalk between the Golgi and the nucleus, and its contribution to the DNA damage response, remains poorly defined. The present study offers timely insight by examining the spatiotemporal behavior of DNA repair proteins that shuttle between the Golgi and the nucleus, and how this trafficking contributes to the maintenance of genomic stability.

Main findings - The authors employed the Human Protein Atlas (HPA) project to shortlist proteins that might link Golgi-nuclear function and validated each candidate using an siRNA-mediated antibody-validation pipeline, thereby identifying 163 proteins that localize to both the Golgi and the nucleus. Bioinformatic analysis of these candidates revealed a significant enrichment for DNA damage response (DDR) regulators, including multiple factors from core DNA repair pathways, suggesting that a portion of the DDR machinery may reside in the Golgi at steady state. Interestingly, the authors observed that dual-localizing DDR proteins undergo lesion-specific redistribution between the Golgi and the nucleus in response to specific types of DNA injuries. For instance, BER and MMEJ proteins shifted from nucleus to Golgi in response to doxorubicin, whereas MMR and HR proteins redistributed from Golgi to nucleus. This trend was reversed with H2O2 or KBrO3 treatments.

To gain further insight into the link between the DDR and Golgi-nuclear communication, the authors focused on the HR factor RAD51C, which also plays a key role during the replicative stress response. The authors noticed that RAD51 is significantly associated with the Golgi, in addition to its known nuclear pool. Interestingly, they demonstrated that doxorubicin triggers the ATM-dependent release of this Golgi-tethered RAD51C pool and its Importin-β-mediated import into the nucleus, where it forms repair-associated foci. They further identified Giantin as the Golgi scaffold that anchors RAD51C at steady state in this subcellular compartment and showed that its depletion leads to premature nuclear accumulation of RAD51C, formation of aberrant RAD51C foci lacking canonical HR markers, reduced ATM activation, elevated genomic instability, and increased cell proliferation.

Together, this study revealed an underappreciated and functionally meaningful spatiotemporal level of regulation within the DDR, suggesting that the Golgi, rather than functioning solely as a trafficking organelle, acts as a platform that anchors, releases, and temporally controls the availability of key DNA repair factors in response to genotoxic stress. In particular, the authors demonstrated that the timely and regulated release of RAD51C from the Golgi is essential for maintaining genome stability and is dependent on canonical DDR signaling pathways, including ATM activation and Importin-β-mediated nuclear import.

Overall Critique - This manuscript offers a novel and compelling perspective on the regulation of the DDR by positioning the Golgi as an active participant in the spatiotemporal control of DNA repair factors. By integrating multiple experimental layers, including a systematic localization screening, a sub-Golgi mapping, several dynamic redistribution assays, and functional perturbation read-outs, the authors built a strong and coherent case for a biologically meaningful Golgi-nucleus communication axis during the DDR. Therefore, the study is timely and highly relevant for the DNA repair field, with broader implications for our understanding of how subcellular organelles coordinate genome maintenance and cellular homeostasis.

While the manuscript is clearly written and the figures are coherent and supportive of the main findings of the study, several issues should be addressed to ensure full interpretability and reproducibility.

Major Comments

1. Limited use of agents causing genotoxic stress - The authors report intriguing lesion-specific shifts in Golgi-nuclear redistribution, yet much of the mechanistic work relies heavily on doxorubicin, a pleiotropic drug that induces diverse forms of DNA damage beyond DSBs. Expanding the core analysis of the study to include a broader panel of mechanistically defined genotoxins (e.g., etoposide, camptothecin, neocarzinostatin, or ionizing radiation) would substantially strengthen the conclusion that the trafficking patterns reflect damage-type specificity rather than drug-specific off-target effects. Such broader analysis would also clarify whether Golgi-nucleus communication responds differentially to replication-associated breaks, Topo II-dependent lesions, oxidative stress, or crosslinks.
2. Functional implications of RAD51C redistribution for HR efficiency - Although the study convincingly demonstrates a release of RAD51C from the Golgi and its subsequent nuclear foci formation, it remains unclear how this redistribution influences HR efficiency. Incorporating a functional HR assay (e.g., DR-GFP reporter, RAD51 filament assembly, or fork protection assays) would help determine whether Golgi-anchored RAD51C release is directly required for HR or instead primarily modulates upstream DDR signaling.

In addition, the manuscript does not fully reconcile how Golgi-tethering of RAD51C fits with its well-established nuclear roles during replication stress, where timely availability of RAD51C is essential for fork stabilization and restart. 3. Specificity of Giantin-related phenotypes - The phenotypes observed upon Giantin depletion (e.g., increased micronuclei, comet tail moments, impaired ATM signaling, and elevated proliferation) could partially reflect a global dysfunction of the Golgi rather than RAD51C-specific tethering defects. Although co-depletion of RAD51C provides partial rescue, additional controls examining Golgi integrity, trafficking competence, or rescue with siRNA-resistant Giantin would help confirm specificity and distinguish direct from indirect effects. 4. Positioning of ATM in the Golgi-nuclear signaling - While ATM inhibition prevents RAD51C release, its spatial and mechanistic basis of this regulation remains obscure. It is not clear whether ATM acts locally at the Golgi, through cytoplasmic pools, or indirectly via nuclear feedback signaling. Clarifying or discussing this point in more depth would improve the mechanistic coherence of the proposed model. 5. RAD51C is examined in silo, without consideration for the BCDX2 complex - RAD51C is exclusively analyzed in isolation, despite its well-established function as part of the BCDX2 paralog complex (RAD51B-RAD51C-RAD51D-XRCC2). Because RAD51C does not normally operate as a standalone factor, it is unclear why only RAD51C, among all paralogs, would be subjected to Golgi tethering, ATM-dependent release, and Importin-β-driven nuclear import. This raises important mechanistic questions: Are other BCDX2 members also Golgi-associated? Do they undergo similar trafficking dynamics? Does Golgi tethering selectively regulate RAD51C, or does the complex translocate together? Addressing these points would greatly strengthen the biological plausibility and mechanistic coherence of the proposed model.

Minor Comments

1. Pathway-specific sub-Golgi localization patterns - The finding that DDR proteins map to distinct cis/trans Golgi subdomains is an interesting and potentially important observation. However, the dataset is limited to 15 proteins, making the proposed pathway-level trends (e.g., HR factors enriched in cis-Golgi; BER/MMEJ factors enriched in trans-Golgi) preliminary. Strengthening this conclusion by increasing the number of DDR proteins analyzed would help determine whether sub-Golgi compartmentalization contributes meaningfully to DNA repair pathway regulation.
2. Is the Golgi-released RAD51C indeed the pool that enters the nucleus? The major assumption of the study is that the RAD51C population released from the Golgi upon DNA damage is the same pool that subsequently accumulates in the nucleus to form repair foci. While the imaging and fractionation data are consistent with this model, the study does not directly track or distinguish Golgi-derived RAD51C from cytoplasmic or pre-existing nuclear pools. Without a method to specifically label, pulse-chase, or track the Golgi-anchored fraction, it remains formally possible that nuclear RAD51C originates from other subcellular reservoirs.

Significance

General assessment - This study presents a novel and conceptually compelling view of the DNA damage response (DDR) by positioning the Golgi apparatus as an active regulator of the spatiotemporal availability of DNA repair factors. The strongest aspects of the work include its integration of a systematic immune-localization screening, a sub-Golgi compartment mapping, dynamic redistribution assays, and functional perturbations to build a coherent model of Golgi-nucleus communication during genotoxic stress. The mechanistic focus on RAD51C provides a clear case study linking organelle-level regulation to genome stability.

Advance - To my knowledge, this is the first comprehensive demonstration that the Golgi can serve as a spatiotemporal coordination node for DDR proteins, including those involved in HR. The identification of a substantial pool of RAD51C, and reportedly other DDR factors, anchored within specific Golgi subdomains represents a significant conceptual advance. The demonstration that Golgi-tethered RAD51C is released in an ATM-dependent manner and subsequently participates in nuclear foci formation suggests a previously unrecognized organelle-level regulatory checkpoint in genome maintenance. This work therefore extends current models of the DDR by revealing a layer of intracellular coordination that bridges classical nuclear pathways with cytoplasmic organelle function.

Audience - This study will be of strong interest to a specialized audience in the fields of DNA repair, genome stability, and cell biology, particularly those studying the spatial organization of repair pathways and intracellular stress signaling. It will also appeal to researchers investigating organelle biology, intracellular trafficking, and the broader coordination of cytoplasmic and nuclear responses to stress. Beyond these communities, the work may be relevant to cancer, as it suggests new mechanisms by which organelle perturbations or Golgi-associated scaffolding proteins could influence therapeutic responses or genomic instability.

Reviewer expertise - Field of expertise: DNA repair, genome stability, organelle biology, cancer cell biology.

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Referee #1

Evidence, reproducibility and clarity

This paper describes the localisation of DNA repair proteins, which carry out their DNA repair function in the nucleus, to the cytoplasmic Golgi apparatus. Using the Human Protein Atlas to identify candidates, the authors use antibody localisation to show that a significant number of DNA repair proteins also localise at the Golgi. It appears that proteins involved in common DNA repair pathways localise to common regions of the Golgi. The Golgi-nucleus distribution of the DNA repairs proteins changes upon DNA damage, indicating a dynamic relationship. The authors focus on the DNA repair protein RAD51C and show that its loss from the Golgi and translocation to the nucleus upon DNA damage is mediated by the ATM kinase. Anchoring at the Golgi is shown to be mediated by the golgin giantin. A functional role for giantin in DNA repair is shown in knockdown studies, supporting a mechanism whereby Golgi anchoring of RAD51C, and possibly other DNA repair proteins, by giantin, is required to maintain proper control of DNA repair.

The data are clear and support the authors' conclusions. The data are carefully quantified throughout. I found the text easy to read.

Major points:

1. To validate the Golgi localisation, KD using siRNA was used. It was deemed that a signal reduction of 25% was enough to indicate specific antibody labelling. This seems like a low number, and not very stringent. For some of the hits, expressing tagged versions of the proteins would greatly strengthen the Golgi assignment. This may not be possible for all, but for RAD51C would seem an important experiment.
2. The total signal should be quantified for each DNA repair protein upon genotoxic stress, in addition to the Golgi to nucleus ratio. For many of the proteins it looks like the total signal goes down, which could influence interpretation.
3. The study would benefit from live imaging of the Golgi to nucleus translocation of RAD51C. This would give a better indication of dynamics.
4. The double depletion experiments suggest a functional relationship between giantin and RAD51C. But they do not formally show it. Experiments to more directly address the functional role of the interaction between these two proteins would strengthen the study.
5. The Kaplan-Meier plots in Fig S9 seems to be quite selective in that only breast cancer is shown. Does giantin reduction correlate with poor prognosis in other cancers?

Minor points: There are a few grammatical errors here and there. The figures do not appear in the correct order in the text, which makes the early parts of the paper a bit difficult to follow. Some of the figures don't seem to clearly match the text. For example, it is mentioned that RAD51C labelling was done with 3 different antibodies. I could not find this data.

Significance

This paper is novel and should be of significant interest to the field. It has important implications for how we think about the Golgi apparatus, and for how DNA repair pathways may be controlled. The pattern is clearly complex, with many DNA repair proteins localising to the Golgi, and some showing opposite dynamics. However, by focussing on RAD51C and giantin, the paper nicely demonstrates a novel mechanism for controlling DNA repair by these proteins.

Growing up she usually never made much money but now she can see how much money she really has

Janie was proud of Joe and knew the town needed his help.

Jody builds a house symbolizing his authority and success of being different from the towns people

She believes that she is better than the people around her because they are lazy

Janie had lost all hope in anything with joe becoming more controlling.

As time passes they got older and Joe becomes more abusive to Janie and criticized her and is very mean to her well for the first time Jainen stands up for herself and Joe hits hits her and becomes more abusive it's not love anymore and the town notices

Hes so insecure about himself that he starts projecting all his insecurity unto her so that way no one will really look at him. The more people there are, the mor he does this so there’s more eyes on her.

After coming to terms that the fact that she was out of love she starts increasingly talking back. As she gets older she realizes that her husband is sick and wants to leave.

Janie breaks from staying silent to her husband, Jody Starks after years of abuse. He insults her in the store and Janie humiliates him. Janie is tired of having to deal with Jody.

She decided to hit jani as he was made fun of and the way or tone that jani speaked to him as she finally had enough of it

Despite how Janie wants to act or live freely she can’t as shes being restricted and controlled by Jody.

Janie was not happy with Joe. She didn’t want to argue and didn’t have any motivation to go against Joe.

She starts hating him for the way he treating here.

She wishes her life was different because of how horrible Jody’s made it.

Joe in this chapter is growing tired of Janie and is treating her bad. He keeps getting angry and isn’t afraid to hit her in front of people anymore.

When she got to be 17 everything changed in her life

Jody was way to controlling and Janie stopped caring

This would mean that blocks come from emotions and your inhibition stopping you from writing.

I used to not realize when I was blocked and just thought I didn't enjoy writing.

This is interesting and makes me think of other instances outside of writing where I may experience that paradoxical reluctance.

eLife Assessment

This important study has demonstrated that MORC2 undergoes phase separation in cells and established multiple interactions responsible for the phase separation. Upon revision, the data generally provide solid support to the claim that MORC2 condensates are functionally relevant in gene regulation and begins to demonstrate the importance of the physical properties of biological condensates. Nevertheless, there remains some weakness in the connection between condensates and function.

Reviewer #1 (Public review):

This work demonstrates that MORC2 undergoes phase separation (PS) in cells to form nuclear condensates, and the authors demonstrate convincingly the interactions responsible for this phase separation. Specifically, the authors make good use of crystallography and NMR to identify multiple protein:protein interactions and use EMSA to confirm protein:DNA interactions. These interactions work together to promote in vitro and in cell phase separation and boosted ATPase activity by the catalytic domain of MORC2.

Moreover, the authors show solid evidence supporting their important claim that MORC2 PS is important for MORC2-mediated gene regulation. Exploring causal links between PS and function is an important need in the phase separation field, particularly as regards the role of condensates in gene regulation, and is a non-trivial matter. It is crucial and challenging to properly explore the alternative possibility that soluble complexes, existing in the same conditions as phase-separated condensates, are the functional species. The authors have attempted to address this concern by manipulating the physical nature of the MORC2 condensates using a killswitch (KS) peptide (MORC2 +KS), finding that reducing condensates dynamics results in a cellular phenotype very similar to that of the phase separation-deficient MORC2 condensates. While not fully ruling out the alternative, soluble-complex hypothesis, this experiment suggests that function is indeed localized inside the MORC2 condensates, and that perturbing the condensate can be functionally equivalent to removing condensate formation.

The authors also look at several disease related mutants of MORC2. While most of these do not seem to have an obvious connection to the phase separation data, it is quite interesting that one mutant, E236G, displays similar intra-condensate dynamics compared to MORC2 +KS, strengthening the claim that MORC2 phase separation is important for function and suggesting that the observations in this paper may indeed have some disease relevance.

Strengths

Static light scattering and crystallography are nicely used to demonstrate the dimerization of MORC2FL and to discover the structure of the CC3 domain dimer, presumably responsible for the dimerization of MORC2FL (Figure 1).

Extensive use of deletion mutants in multiple cell lines is used to identify regions of MORC2 that are important for forming condensates in the nucleus: the IBD, IDR, and CC3 domains are found to both be essential for condensate formation, while the CW domain plays an unknown role in condensate morphology (Figure 3). The authors use NMR to further identify that the IBD domain seems to interact with the first third of the centrally located IDR, termed IDRa, but not with the latter two thirds of the IDR domain (Figure 4). This leads them to propose that phase separation is the product of IDB:IDRa interaction, CC3 dimerization, and an unknown but important role for the CW domain.

Based on the observation that removal of the NLS resulted in diffuse cytoplasmic localization, they hypothesized that DNA may play an important role in MORC2 PS. EMSA was used to demonstrate interaction between DNA and several MORC2 domains: CC1, CC2, IDR, and TCD-CC3-IBD. Further in vitro microscopy with purified MORC2 showed that DNA addition significantly reduces MORC2 saturation concentration (Figure 5).

These assays convincingly demonstrate that MORC2 phase separates in cells and identifies the protein domains and interactions responsible for this phenomenon.

Weaknesses

The connection between condensates and function, while improved from the original manuscript, still has some weak points.

The central experiment demonstrating that MORC2 condensates mediate function takes the form of RNA-Seq in MORC2 KO HeLa cells (Figure 6), rescued with WT, condensate-deficient mutants, and a KS peptide mutant that reduces dynamics by increasing homotypic protein interactions. The observation that rescuing with MORC2 +KS is ineffective, in a manner similar to rescue with condensate-deficient MORC2 mutants, suggests that unperturbed condensates are important for function. An alternative possibility, however, is that condensates are non-functional bystanders, and that the increased homotypic interactions present in MORC2 +KS result in stronger MORC2 +KS recruitment to condensates, reducing the pool of functional, dilute phase MORC2 +KS and squashing function via sequestration. Similar ideas have been explored by others for transcription factors (e.g. Chong et al, Mol Cell, 2022). This possibility is neither discussed nor ruled out. The absence of microscopy data showing similar localization of MORC2 and MORC2 +KS (particularly the amount of diffuse MORC2 outside condensates) amplifies this concern.

The RNA-Seq data presented in Figure 6h also has some concerning qualities. Inter-replicate variability is higher than ideal, particularly for MORC2 deltaCC3. This may be a product of the transient transfection system used for these experiments, which inherently results in stochasticity. Specific sets of genes regulated by MORC2 are consistent with the main conclusion (Figure 6i, individual genes in 6h, showing that all mutants are more similar to one another than to WT MORC2), but global transcription shifts seem quite different between MORC2 condensate-deficient mutants and MORC2 +KS (Figure 6h heatmap), suggesting much more than simple condensate disruption is taking place. Together, this weakens the conclusion that MORC2 condensates are the functional form of MORC2.

Reviewer #2 (Public review):

Summary:

The study by Zhang et al. focuses on how condensation of a chromatin-associated protein MORC2 regulates gene expression. Their study shows that MORC2 forms dynamic nuclear condensates in cells. In vitro, MORC2 phase separation is driven by dimerization and multivalent interactions involving the C-terminal domain but interplay with other parts of MORC2 too. A key finding is that the intrinsically disordered region (IDR) of MORC2 exhibits strong DNA binding. They report that DNA binding enhances MORC2's phase separation and its ATPase activity, offering new insights into how MORC2 contributes to chromatin organization and gene regulation. Authors correlate MORC2's condensate forming ability and material properties with its gene silencing function using a few variants. Moreover, they investigate the effect of disease-linked mutations in the N-terminal domain of MORC2 on its ability to form cellular condensates, ATPase activity and DNA-binding. Their work implies that proper material properties of MORC2 condensates may be important to their biological function.

Strengths:

The authors determined a 3.1 Å resolution crystal structure of the dimeric coiled-coil 3 (CC3) domain of MORC2, revealing a hydrophobic interface that stabilizes dimer formation. They present extensive evidence that MORC2 phase separates across multiple contexts, including in vitro, in cellulo, and in vivo. Through systematic cellular screening, they identified the C-terminal domain of MORC2 as a key driver of condensate formation. Biophysical and biochemical analyses further show that the IDR within the C-terminal domain interacts with the C-terminal end region (IBD) and also exhibit strong DNA-binding capacity (using 601 DNA), both of which promote MORC2 phase separation. Together, this study emphasizes that interactions mediated by multiple domains-CC3, IDR, and IBD- drives MORC2 phase separation. Additionally, the work uses a unique kill-switch peptide fused to the MORC2 sequence to disrupt its material properties -- this permits the authors to examine the link between material properties and transcription function. The study is overall strengthened by (1) the combination of variants tested both in vitro and in cellulo, and (2) the systematic examination of domain contributions that highlight the multivalent interactions at play mediating MORC2 condensation.

Weaknesses:

The employed MORC2 variants have enabled the beginning of an investigation linking condensation and biological function, but more work will be needed to really dissect the contribution of condensation to DNA-binding, ATPase activity, and gene silencing. A systematic investigation of differential material properties on MORC2 condensates will be needed to assess the link to biological function, especially as the authors' work is reminiscent of how the liquidity of Caulobacter crescentus PopZ condensates tunes bacterial fitness.

Reviewer #3 (Public review):

Summary:

The manuscript by Zhang et al. demonstrates that MORC2 undergoes liquid-liquid phase separation (LLPS) to form nuclear condensates critical for transcriptional repression. Using a combination of in vitro LLPS assays, cellular studies, NMR spectroscopy, and crystallography, the authors show that a dimeric scaffold formed by CC3 drives phase separation, while multivalent interactions between an intrinsically disordered region (IDR) and a newly defined IDR-binding domain (IBD) further promote condensate formation. Notably, LLPS enhances MORC2 ATPase activity in a DNA-dependent manner and contributes to transcriptional regulation, establishing a functional link between phase separation, DNA binding, and transcriptional control.

Strengths:

The manuscript is well organized and logically structured. It provides valuable mechanistic insights into MORC2 function, and the majority of the conclusions are well supported by the data presented.

Author response:

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

Reviewer #1 (Public review):

Summary:

This work demonstrates that MORC2 undergoes phase separation (PS) in cells to form nuclear condensates, and the authors demonstrate convincingly the interactions responsible for this phase separation. Specifically, the authors make good use of crystallography and NMR to identify multiple protein: protein interactions and use EMSA to confirm protein: DNA interactions. These interactions work together to promote in vitro and in cell phase separation and boost ATPase activity by the catalytic domain of MORC2.

However, the authors have very weak evidence supporting their potentially valuable claim that MORC2 PS is important for the appropriate gene regulatory role of MORC2 in cells. Exploring causal links between PS and function is an important need in the phase separation field, particularly as regards the role of condensates in gene regulation, and is a non-trivial matter. Any study with convincing data on this matter will be very important. For this reason, it is crucial to properly explore the alternative possibility that soluble complexes, existing in the same conditions as phase-separated condensates, are the functional species. It is also critical to keep in mind that, while a specific protein domain may be essential for PS, this does not mean its only important function pertains to PS.

In this study, the authors do not sufficiently explore the role that soluble MORC2 complexes may play alongside MORC2 condensates. Neither do they include enough data to solidly show that domain deletion leads to phenotypes via a loss of phase separation per se, rather than the loss of phase separation being a microscopically visible result, not cause, of an underlying shift in protein function. For these reasons, the authors' conclusions regarding the functional role of MORC2 condensates are based on incomplete data. This also dampens the utility of this work as a whole, since the very nice work detailing the mechanism of MORC2 PS is not paired with strong data showing the importance of this observation.

We thank the reviewer for this thoughtful and constructive critique. We agree that establishing a causal link between phase separation (PS) and biological function—particularly in transcriptional regulation—is a central and non-trivial challenge in the condensate field. We also appreciate the reviewer’s emphasis on two critical alternative interpretations: (i) that soluble MORC2 complexes, rather than condensates, may represent the primary functional species, and (ii) that loss of phase separation upon domain deletion could reflect a downstream consequence of altered protein function rather than its cause.

To address these concerns, we have performed a series of new experiments specifically designed to decouple condensate formation, and condensate dynamics, thereby allowing us to more rigorously interrogate the functional relevance of MORC2 condensates.

First, to overcome the limitation of domain deletions which may affect MORC2 function beyond phase separation we introduced a micropeptide-based kill switch (KS) to the C terminus of MORC2. This strategy has recently emerged as a powerful approach to selectively reduce condensate dynamics without disrupting protein expression, folding, or domain architecture [1]. Importantly, unlike CC3 or IDRa deletions, MORC2+KS robustly form nuclear condensates but exhibits markedly reduced internal dynamics, as demonstrated by FRAP analyses showing minimal fluorescence recovery after photo bleaching (Fig. 6a-c). This strategy therefore allows us to perturb condensate material properties independently of MORC2 domain integrity.

Second, we systematically compared the transcriptional consequences of rescuing MORC2-knockout HeLa cells with MORC2FL, condensation-deficient mutants (ΔCC3 and ΔIDRa), and the dynamics-defective MORC2+KS (Fig. 6d). Despite being expressed at substantially higher levels than MORC2FL (Fig. 6e), all three mutants showed a striking and consistent failure to restore MORC2-dependent transcriptional regulation (Fig. 6f-h). This effect was particularly pronounced for transcriptionally repressed genes, including two sets of high-confidence MORC2 targets reported in prior studies (Fig. 6i and Fig.S10). These findings demonstrate that neither increased protein abundance nor the mere presence of condensate-like structures alone is sufficient to restore MORC2 function.

Third, our data instead support a model in which both soluble MORC2 complexes and dynamic MORC2 condensates are required for full transcriptional regulation activity. While soluble MORC2 is likely involved in target recognition and complex assembly, our results indicate that proper condensate formation—and critically, condensate dynamics—are essential for effective transcriptional repression and activation. The inability of the MORC2+KS mutant to rescue transcriptional defects, despite intact condensate formation, points away from a model in which MORC2 condensates represent only microscopically visible byproducts of MORC2 activity.

We believe these new data strengthen the manuscript by pairing the detailed mechanistic dissection of MORC2 phase separation with direct functional evidence, enhancing the conceptual impact and biological significance of the study.

Strengths:

Static light scattering and crystallography are nicely used to demonstrate the dimerization of MORC2FL and to discover the structure of the CC3 domain dimer, presumably responsible for the dimerization of MORC2FL (Figure 1).

Extensive use of deletion mutants in multiple cell lines is used to identify regions of MORC2 that are important for forming condensates in the nucleus: the IBD, IDR, and CC3 domains are found to be essential for condensate formation, while the CW domain plays an unknown role in condensate morphology (Figure 3). The authors use NMR to further identify that the IBD domain seems to interact with the first third of the centrally located IDR, termed IDRa, but not with the latter two-thirds of the IDR domain (Figure 4). This leads them to propose that phase separation is the product of IDB:IDRa interaction, CC3 dimerization, and an unknown but important role for the CW domain.

Based on the observation that removal of the NLS resulted in diffuse cytoplasmic localization, they hypothesized that DNA may play an important role in MORC2 PS. EMSA was used to demonstrate interaction between DNA and several MORC2 domains: CC1, CC2, IDR, and TCD-CC3-IBD. Further in vitro microscopy with purified MORC2 showed that DNA addition significantly reduces MORC2 saturation concentration (Figure 5).

These assays convincingly demonstrate that MORC2 phase separates in cells, and identify the protein domains and interactions responsible for this phenomenon, with the notable caveat that the role of the CW domain here is left unexplored.

We appreciate the reviewer for their positive and detailed assessment of the strengths of our study. Our understanding of the CW domain’s function remains preliminary. Although we observed that the CW domain can influence condensate size, the IDR, IBD, and CC3 domains constitute the core structural elements driving phase separation. Consequently, the CW domain was not a primary focus of the current study. Nonetheless, investigating its functional contributions represents an interesting avenue for future work.

Weaknesses:

Although the authors demonstrated phase separation of MORC2FL, their evidence that this plays a functional role in the cell is incomplete.

Firstly, looking at differentially upregulated genes under MORC2FL overexpression, the authors acknowledge that only 10% are shared with differentially regulated genes identified in other MORC2FL overexpression studies (Figure 6c, d). No explanation is given for why this overlap is so low, making it difficult to trust conclusions from this data set.

We thank the reviewer for raising this important concern. In response, we have improved the quality and robustness of our RNA-seq analysis by repeating the experiments with optimized sample handling and increased sequencing depth. Using this updated dataset, we identified a considerably higher overlap between MORC2-regulated genes in our study and those reported previously.

Specifically, we observed 84 overlapping genes with the study by Nikole L. Fendler et al. [2], corresponding to approximately 32% of the MORC2-regulated genes reported in that work (Fig. 6i). In addition, we identified 102 overlapping genes with the dataset reported by Iva A. Tchasovnikarova et al. [3], representing approximately 22% of the genes identified in that study (Fig. S10b).

We note that complete concordance with previous reports is not expected, given substantial differences in experimental design. For example, Fendler et al. employed a doxycycline-inducible MORC2 expression system [2], whereas our study relies on transient overexpression in MORC2-knockout HeLa cells. In contrast, Tchasovnikarova et al. compared transcriptomes between MORC2 knockout and wild-type cells [3], rather than MORC2 rescue conditions. Moreover, RNA-seq results are inherently influenced by cell line batch variability, sequencing depth, and analysis pipelines, all of which differ across studies.

Taken together, we consider an overlap in the range of ~20–30% to be reasonable and biologically meaningful in the context of these experimental differences, and we believe that the revised RNA-seq data provide a more reliable foundation for our conclusions regarding MORC2-dependent transcriptional regulation.

Secondly, of the 21 genes shared in this study and in earlier studies, the authors note that the differential regulation is less pronounced when a phase-separation-deficient MORC2 mutant is overexpressed, rather than MORC2FL (Figure 6e). This is taken as evidence that phase separation is important for the proper function of MORC2. However, no consideration is made for the alternative possibility that the mutant, lacking the CC3 dimerization domain, may result in non-functional complexes involving MORC2, eliminating the need for a PS-centric conclusion. To take the overexpression data as solid evidence for a functional role of MORC2 PS, the authors would need to test the alternative, soluble complex hypothesis. Furthermore, there seems to be low replicate consistency for the MORC2 mutant condition (Figure S6a), with replicate 3 being markedly upregulated when compared to replicates 1 and 2.

We thank the reviewer for raising these important concerns. In the revised manuscript, we have substantially strengthened both the experimental evidence and the data presentation to directly address the alternative “soluble complex” interpretation as well as the issue of replicate consistency. Specifically, we now provide data that clarify the functional impact of phase-separation-deficient MORC2 mutants and explicitly show replicate-level RNA-seq analyses. The Fig. 6 and Fig. S10support these improvements and enhance both the robustness and transparency of our transcriptional analyses. Collectively, these revisions directly address the reviewer’s concerns regarding the functional interpretation of MORC2 phase separation.

Thirdly, the authors close by examining the in-cell PS capabilities and ATPase activity of several disease-associated mutants of MORC2 (Figure 7). However, the relevance of these mutants to the past 6 figures is unclear. None of these mutations is in regions identified as important for PS. Two of the mutations result in a higher percentage of the cell population being condensate-positive, but this is not seemingly connected to ATPase activity, as only one of these two mutants has increased ATPase activity. Figure 7 does not add any support to the main hypotheses in the paper, and nowhere in the paper do the authors investigate the protein regions where the mutations in Figure 7 are found.

We thank the reviewer for raising this point regarding Fig. 7. At the current stage, the results for disease-associated mutations are primarily descriptive. While we observed that certain mutations clustered at the N-terminus can affect MORC2 condensate formation, ATPase activity, and DNA binding, we did not identify a mechanistic explanation for these correlations. Notably, the T424R mutation, previously reported to significantly enhance ATPase activity [4], also increased both intracellular condensate formation and in vitro DNA binding in our experiments. In contrast, other mutations did not show such consistent effects. Previous studies have established that MORC2’s ATP-binding and DNA-binding activities are independent [4]. Our results further suggest that MORC2’s phase separation behavior is independent of both ATP and DNA binding affinity, although existing evidence hints at potential cross-regulatory interactions among these three functions.

We would also like to emphasize an additional observation that may help contextualize the relevance of N-terminal mutations. Although deletion of the MORC2 N-terminus does not prevent the remaining C-terminal region from forming nuclear condensates, these C-terminal condensates exhibit a marked loss of fluorescence recovery in FRAP assays (Fig. S11). This finding suggests that while the N-terminus is not strictly required for condensate assembly, it plays an important role in regulating condensate fluidity. Accordingly, disease-associated mutations distributed across the N-terminal region may influence MORC2 function by modulating condensate material properties rather than condensate formation per se. Based on this hypothesis, we evaluated the fluidity of condensates formed by the E236G and T424R mutants. FRAP measurements indicated substantially reduced fluorescence recovery in E236G, whereas T424R exerted minimal effects (Fig. 7e, f).

Overall, our interpretation of the results in Fig. 7 is still at a preliminary stage. Nevertheless, the role of the MORC2 N-terminus in modulating condensate fluidity, together with the observed impairment caused by the E236G mutation, appears to be robust, although the underlying mechanism remains to be elucidated. We have incorporated additional discussion on this point and consider it an important direction for future study.

Reviewer #1 (Recommendations for the authors):

(1) Why does MORC2 overexpression lead to changes in gene regulation that are so different from past MORC2 overexpression studies? This is unsettling to me.

(2) Likewise, why is replicate 3 for the MORC2ΔCC3 variant so different from replicates 1 and 2? Perhaps repeating this experiment would be helpful, both for showing better repeatability and perhaps as regards pulling out a stronger phenotype.

We have repeated the experiments and obtained improved data quality.

(3) A better explanation of the relevance of Figure 7 to the story of the rest of the paper, especially the phase-separation of MORC2, would be important to improving this paper.

We thank the reviewer for this suggestion. We have performed additional experiments and expanded the discussion.

(4) Are expression levels of mutant proteins in Figure 7 uniform between mutants? If not, is it possible that expression levels might account for the difference in condensate-positive cells between mutants?

We cannot fully exclude the possibility that differences in expression levels may contribute to the observed differences among mutants. In our experiments, equal amounts of plasmid DNA were used for transfection across all conditions. Although we did not directly quantify post-transfection protein expression levels by immunoblotting or similar approaches, even if certain mutations were to affect protein expression, it would be technically challenging to further optimize the strategy to fully normalize expression levels across mutants.

Importantly, we note that MORC2 does not form condensates in all transfected cells, even when EGFP fluorescence indicates robust expression levels that are comparable to, or even exceed, those observed in condensate-positive cells. This observation suggests that high expression alone is not sufficient to drive MORC2 phase separation in cells. Therefore, we do not favor the interpretation that the E236K and T424R mutations enhance MORC2 condensation simply by increasing MORC2 protein expression levels.

Minor:

(1) I would suggest considering using the term "dynamic" rather than "liquid-like", as FRAP is technically a measurement of the dynamicity of a protein within a volume, rather than a measurement of the actual fluidity of that volume.

We thank the reviewer for this helpful suggestion. We agree that FRAP measurements primarily report protein mobility and condensate dynamics rather than the physical fluidity of the condensates. We have therefore revised the manuscript to replace “liquid-like” with “dynamic” where conclusions are based on FRAP analyses.

(2) A further investigation of the role of the CW domain would be very interesting, since it clearly has a major role in condensate morphology. Perhaps CW confers important heterotypic interactions which contribute to compositional control of the MORC2 condensates, and thus function and morphology? However, due to the complexity of this specific question and the potentially marginal improvement offered by this paper, I do not think this is a critical addition.

We thank the reviewer for this insightful suggestion. We have noted this possibility in the Discussion as an important avenue for future investigation.

(3) Why is TCD not tested alone by EMSA for affinity to DNA in Figure 5?

Our inference regarding the DNA-binding capacity of the TCD domain was based on comparative EMSA analyses. Specifically, we found that the TCD–CC3–IBD fragment was able to bind DNA, whereas the CC3–IBD fragment alone showed no detectable DNA binding. From this comparison, we inferred that the TCD domain is responsible for the observed DNA-binding activity.

Because the TCD domain does not affect MORC2 condensate formation, it was not a central focus of the present study, which primarily aims to elucidate the mechanisms underlying MORC2 phase separation and its functional relevance. For this reason, we did not further test TCD alone by EMSA in Figure 5.

Reviewer #2 (Public review):

Summary:

The study by Zhang et al. focuses on how phase separation of a chromatin-associated protein MORC2, could regulate gene expression. Their study shows that MORC2 forms dynamic nuclear condensates in cells. In vitro, MORC2 phase separation is driven by dimerization and multivalent interactions involving the C-terminal domain. A key finding is that the intrinsically disordered region (IDR) of MORC2 exhibits strong DNA binding. They report that DNA binding enhances MORC2's phase separation and its ATPase activity, offering new insights into how MORC2 contributes to chromatin organization and gene regulation. The authors try to correlate MORC2's condensate-forming ability with its gene silencing function, but this warrants additional controls and validation. Moreover, they investigate the effect of disease-linked mutations in the N-terminal domain of MORC2 on its ability to form cellular condensates, ATPase activity, and DNA-binding, though the findings appear inconclusive in the manuscript's current form.

Thank you for your thorough and constructive review of our manuscript. In response to the concerns raised regarding the functional relevance of MORC2 condensate formation, we have redesigned and expanded the experiments presented in Fig. 6 and Fig. S6 to directly link MORC2’s condensate-forming capacity with its transcriptional regulatory function. These new experiments provide additional controls and validation, strengthening the causal relationship between MORC2 condensate dynamics and gene regulation.

At the current stage, the results for disease-associated mutations are descriptive. While we observed that certain mutations clustered at the N-terminus can affect MORC2 condensate formation, ATPase activity, and DNA binding, we did not identify a mechanistic explanation for these correlations. Notably, the T424R mutation, previously reported to significantly enhance ATPase activity [4], also increased both intracellular condensate formation and in vitro DNA binding in our experiments. In contrast, other mutations did not show such consistent effects. Previous studies have established that MORC2’s ATP-binding and DNA-binding activities are independent [4]. Our results further suggest that MORC2’s phase separation behavior is also independent of both ATP and DNA binding, although existing evidence hints at potential cross-regulatory interactions among these three functions.

Strengths:

The authors determined a 3.1 Å resolution crystal structure of the dimeric coiled-coil 3 (CC3) domain of MORC2, revealing a hydrophobic interface that stabilizes dimer formation. They present extensive evidence that MORC2 undergoes liquid-liquid phase separation (LLPS) across multiple contexts, including in vitro, in cellulo, and in vivo. Through systematic cellular screening, they identified the C-terminal domain of MORC2 as a key driver of condensate formation. Biophysical and biochemical analyses further show that the IDR within the C-terminal domain interacts with the C-terminal end region (IBD) and also exhibits strong DNA-binding capacity, both of which promote MORC2 phase separation. Together, this study emphasizes that interactions mediated by multiple domains-CC3, IDR, and IBD- drives MORC2 phase separation. Finally, the authors quantified the effect of removing the CC3 on the upregulation and downregulation of target gene expression.

We thank the reviewer for their appreciation of the key findings presented in this manuscript.

Weaknesses:

Though the findings appear compelling in isolation, the study lacks discussion on how its findings compare with previous studies. Particularly in the context of MORC2-DNA binding, there are previous studies extensively exploring MORC2-DNA binding (Tan, W., Park, J., Venugopal, H. et al. Nat Commun 2025), and its effect on ATPase activity (ref 22). The contradictory results in ref 22 about the impact of DNA-binding on ATPase activity, and ATPase activity on transcriptional repression, warrant proper discussion. The authors performed extensive in-cellulo screening for the investigation of domain contribution in MORC2 condensate formation, but the study does not consider/discuss the possibility of some indirect contributions from the complex cellular environment. Alternatively, the domain-specific contributions could be quantified in vitro by comparing phase diagrams for their variants. While the basis of this study is to investigate the mechanism of MORC2 condensate-mediated gene silencing, the findings in Figure 6 appear incomplete because the CC3 deletion not only affects phase separation of MORC2 but also dimerization. Furthermore, their investigation on disease-linked MORC2 mutations appears very preliminary and inconclusive because there are no obvious trends from the data. Overall, the discussion appears weak as it is missing references to previous studies and, most importantly, how their findings compare to others'.

We thank the reviewer for their careful assessment of MORC2’s DNA-binding properties and its relationship with ATPase and transcriptional activities. We would like to offer the following clarifications to address these concerns, which will also be incorporated into the Discussion section of the revised manuscript.

First, recent work by Tan et al. [5] similarly identified multiple DNA-binding sites in MORC2, consistent with our findings, though there are discrepancies in the precise binding regions. In particular, they reported that isolated CC1 and CC2 domains do not bind 60 bp dsDNA, which contrasts with our observations. We attribute this difference to the types of DNA used in the assays. In our study, we employed 601 DNA, a defined nucleosome-positioning sequence, which differs substantially from randomly designed short dsDNA. For instance, prior work by Christopher H. Douse et al. [54] also confirmed that MORC2’s CC1 domain can bind 601 DNA.

Second, in the study by Fendler et al. [2], DNA binding was reported to reduce MORC2’s ATPase activity—an observation that appears inconsistent with the results presented in our Fig. 5j. A critical distinction between the two studies lies in the experimental systems used: Fendler et al. [2] employed MORC2 constructs and 35 bp double-stranded DNA (dsDNA), whereas our experiments utilized full-length MORC2 and 601 bp DNA (a sequence with high nucleosome assembly potential). These differences including the absence of potentially regulatory C-terminal regions in the truncated construct and the varying length/structural properties of the DNA substrates introduce variables that substantially complicate direct comparative analysis of ATPase activity outcomes.

Separately, Douse et al. [4] demonstrated that the efficiency of HUSH complex-dependent epigenetic silencing decreases as MORC2’s ATP hydrolysis rate increases, implying an inverse relationship between ATPase activity and silencing function. Notably, our current work has not established a direct mechanistic link between MORC2 phase separation and its ATPase activity. Thus, we refrain from inferring that the effect of MORC2 phase separation on transcriptional repression is mediated through modulation of its ATPase function this remains an important question to address in future studies.

Finally, we have redesigned and expanded the experiments presented in Fig. 6 and Fig. S6 to directly link MORC2’s condensate-forming capacity with its transcriptional regulatory function.

Reviewer #2 (Recommendations for the authors):

Major concerns:

(1) Unaddressed discrepancies with the previous study:

(a) Inadequate discussion of Reference 22 and apparent contradictions. Notably, Reference 22 provides evidence for reduced ATPase activity upon DNA binding, in contrast to the current study's observations. Moreover, Reference 22 demonstrates that ATP hydrolysis (ATPase activity) is inversely associated with MORC2-mediated gene silencing, whereas this study concludes that 'the silencing function of MORC2 requires its ATPase activity'. These apparent contradictions warrant a more thorough discussion to reconcile the differences, including potential mechanistic explanations and experimental context that could account for the discrepancies. Additionally, the authors should discuss potential reasons why Ref. 22 may not have observed phase separation during MORC2 biophysical analysis. For instance, in Ref. 22, SEC-MALS was performed at 2 mg/mL (~16 µM) MORC2 FL in the presence of 150 mM NaCl, conditions that could influence phase behavior based on the current manuscript's results. Addressing whether differences in protein construct, buffer composition, or experimental design might account for this discrepancy would strengthen the discussion.

We thank the reviewer for pointing out the apparent discrepancies between our results and those reported in Ref. 22. We agree that these differences warrant explicit discussion, and we have revised the Discussion accordingly to clarify the experimental and conceptual distinctions between the two studies.

First, regarding the effect of DNA binding on ATPase activity, Ref. 22 examined MORC2 ATPase activity under conditions where MORC2 does not undergo detectable phase separation, whereas our ATPase assays were performed under conditions in which MORC2 readily forms condensates in the presence of DNA. We therefore propose that the observed increase in ATPase activity in our study may reflect a distinct biochemical regime in which phase separation and/or high local protein concentration modulates enzymatic activity. Importantly, our data do not exclude the possibility that DNA binding per se can inhibit ATPase activity under non-condensing conditions, as reported in Ref. 22.

Second, with respect to transcriptional repression, Ref. 22 reported an inverse correlation between ATP hydrolysis and MORC2-mediated silencing, whereas our study finds that ATPase activity is required for efficient repression. We suggest that these observations are not necessarily contradictory but may reflect different regulatory layers of MORC2 function. Specifically, ATP binding and hydrolysis may be required for MORC2 structural remodeling and chromatin engagement, while excessive or dysregulated ATP hydrolysis could impair stable silencing complexes, as suggested previously [4]. We now explicitly discuss this possibility in the revised manuscript.

Finally, we appreciate the reviewer’s suggestion regarding the absence of phase separation in Ref. 22. Indeed, SEC-MALS experiments in Ref. 22 were conducted at ~16 µM MORC2 in the presence of 150 mM NaCl (the purification condition is 500 mM NaCl, 10% glycerol), conditions that based on our phase diagrams—are close to or above the saturation concentration but also strongly influenced by ionic strength. This combination of factors explains why the UV peak from SEC-MALS is not indicative of a homogeneous sample [3].

(b) The DNA binding capacity of individual MORC2 domains was tested in Fig. 5. IDR appears to be the strongest DNA binder among others. Is this the effect of IDR being isolated from the rest of the protein? A recent paper (Tan, W., Park, J., Venugopal, H. et al. Nat Commun 2025) also investigated DNA binding capacity of different regions of MORC2 using hydrogen-deuterium exchange experiments and EMSA. Interestingly, it can be seen in Figure S9 that the DNA binding capacity of different regions changes when compared together to when in isolation (MORC2 1-603 vs 1-265; 1-495; 496-603). In line with the above, MORC2 IDR's interaction with DNA warrants additional investigation, taking the system as a whole to avoid misinterpretation arising from non-specific interactions.

We appreciate the reviewer’s insightful comments regarding domain-specific DNA binding and the potential caveats of studying isolated regions. In Figure 5, our EMSA analyses show that the isolated IDR exhibits the strongest DNA-binding signal among the tested fragments. We agree that this observation may, at least in part, reflect the removal of structural or regulatory constraints imposed by the full-length protein.

Consistent with the reviewer’s point, Tan et al. [5] demonstrated that DNA-binding behavior of MORC2 regions differs when analyzed in isolation versus in the context of larger constructs. We have now incorporated this comparison into the Discussion and explicitly note that DNA binding by the IDR should be interpreted as a contextual and potentially cooperative property rather than an autonomous function.

Importantly, our conclusions do not rely on the IDR acting as an independent DNA-binding module in vivo. Rather, we propose that the IDR contributes to DNA engagement and phase behavior within the architectural framework of full-length MORC2. We now emphasize this limitation and highlight the need for future studies that probe DNA binding in the context of intact MORC2 or minimally perturbed constructs.

(2) MORC2 DNA binding impacting phase separation and ATPase activity:

While it is clear that MORC2: DNA interaction facilitates MORC2 phase separation, the impact on ATPase activity is not conclusive. First, they observe an opposite trend (compared to ref. 22) for DNA binding on MORC2's ATPase activity. Secondly, it is not clear if the increase in ATPase activity is mediated by DNA binding or phase separation. The ATPase activity was measured at 1 µM MORC2 protein concentration in the presence of DNA, where MORC2 appears to phase separate. To draw more definitive conclusions, additional controls are necessary. Specifically, a phase separation-deficient mutant (from this study) and a DNA-binding-deficient mutant (see ref. 22) should be included to disentangle the contributions of DNA binding and phase separation to ATPase activity. The choice of ATP-binding-deficient mutant N39A as a negative control seems inconclusive in this regard. Additionally, why is there an increase in ATP hydrolysis rate for the ATP-binding-deficient mutant in the presence of DNA, resulting in ATP hydrolysis rates similar to WT MORC2? This raises further questions about the underlying mechanism.

We agree with the reviewer that disentangling the contributions of DNA binding and phase separation to ATPase activity is challenging and that our current data do not fully resolve this issue. As noted, ATPase assays were performed at protein concentrations (1 µM) where MORC2 undergoes DNA-induced phase separation, making it difficult to distinguish whether enhanced ATP hydrolysis arises directly from DNA binding or indirectly from condensate formation.

We acknowledge that inclusion of additional mutants such as phase separation deficient or DNA-binding deficient variants would provide a more definitive mechanistic separation of these effects. However, generating and validating such mutants in a manner that preserves overall protein integrity is beyond the scope of the current study. Accordingly, we have revised the text to present our findings more cautiously and to frame the observed ATPase enhancement as a correlation rather than a causal mechanism.

Regarding the ATP-binding–deficient N39A mutant, we agree that its behavior in the presence of DNA raises interesting mechanistic questions. We now explicitly note this unexpected observation and discuss possible explanations, including partial ATP binding, altered oligomeric states, or indirect effects mediated by condensate formation.

(3) Dissecting the domain-specific contribution in MORC2 phase separation:

(a) While in cellulo data indicate that the presence of IDR, NLS, CC3, and IBD is all essential for MORC2 condensate formation, it is not clear if this is the effect of the complex cellular environment or whether it is intrinsic for MORC2 phase separation ability. In lines 256-259, the authors suggest IDRa interaction with IBD may serve as a nucleation mechanism for LLPS. In other places, it has been mentioned that CC3 dimerization acts as a scaffold for condensate formation. It is not clear if all of these are essential for MORC2 phase separation, or one of them is essential while the other domain(s) facilitates the phase separation. Though Figure 3 provides a qualitative overview of the contribution of different regions in MORC2 phase separation in cellulo-influenced by the complex cellular environment and substrate interactions, the absolute domain contribution in phase separation would be better studied in vitro by quantitatively comparing phase diagrams (for example, c-sat vs temperature) of different domain deletion constructs.

We thank the reviewer for highlighting the distinction between intrinsic phase separation propensity and cellular context dependent effects. Our in cellular screening was designed to identify regions required for condensate formation under physiological conditions, where chromatin, binding partners, and macromolecular crowding are present. We agree that this approach does not directly quantify the intrinsic phase separation contribution of individual domains.

While CC3 dimerization, IDR–IBD interactions, and nuclear localization all contribute to condensate formation, our data do not imply that these elements are mechanistically equivalent. Rather, we propose that CC3 provides a structural scaffold, while IDR-mediated interactions lower the energetic barrier for condensation. We have revised the manuscript to clarify this hierarchical model and to avoid implying that all domains contribute equally or independently.

We agree that quantitative in vitro phase diagrams would provide valuable insight into intrinsic domain contributions. Whereas the MORC2ΔCC3-IBD (1–900) and CC3-IBD (900-1032) fragment fails to induce phase separation, the IDR mix CC3–IBD fragment drives robust phase separation; additionally, phase separation is entirely abrogated in the absence of domain–domain interactions. These observations collectively verify that phase separation is contingent on specific domain combinations and their interactions.

(b) Similarly, for line 228-231: 'Notably, condensates formed exclusively in the nucleus and not in the cytoplasm of transfected HeLa cells, suggesting that chromatin-associated nuclear factors, such as DNA, may contribute to the nucleation or stabilization of MORC2 condensates.' This is an important observation made by the authors. Since MORC2 readily phase separates in vitro under physiological conditions, it is important to discuss why MORC2 does not make condensates in the cytoplasm (in the case of MORC2deltaNLS). In this regard, how does the concentration of overexpressed EGFP-MORC2 constructs compare with in vitro tested droplets of MORC2?

We thank the reviewer for highlighting this important conceptual point. Although MORC2 readily undergoes phase separation in vitro under physiological buffer conditions, the absence of condensate formation in the cytoplasm of cells expressing MORC2ΔNLS underscores the importance of the nuclear environment in promoting MORC2 assembly.

The cytoplasm differs fundamentally from the nucleus not only in overall molecular composition but also in the availability of high-valency scaffolds such as chromatin. We propose that chromatin-associated components, particularly DNA, provide a platform that locally concentrates MORC2 and increases its effective valency, thereby facilitating nucleation or stabilization of condensates in the nucleus. In contrast, the cytoplasm lacks such scaffolds, even when MORC2 is expressed at appreciable levels. In cultured cells, MORC2 is seldom observed in the cytoplasm. While specific experimental contexts may facilitate its cytoplasmic localization, such observations are rarely reported [6]. In transfection-based systems, MORC2 predominantly displays droplet-like behavior in the nucleus. Notably, in endogenous EGFP–MORC2 chimeric mice, we detected punctate MORC2 structures in the neuronal cytoplasm of the brain and spinal cord. The functional significance and biophysical state of cytoplasmic MORC2 remain largely unexplored.

With respect to protein concentration, while EGFP-MORC2 is robustly expressed in cells, direct comparison between cellular expression levels and the protein concentrations used in vitro is inherently challenging. Importantly, in vitro phase separation is driven by bulk protein concentration under defined conditions, whereas in cells, effective local concentration and interaction valency are strongly shaped by spatial confinement and chromatin association. We have revised the manuscript text to emphasize this distinction and to avoid interpreting nuclear specificity as a purely concentration-dependent phenomenon.

(c) Lines 227-228: '... CW domain restricts condensate overgrowth or fusion', this inference is based on CTDdeltaCW puncta being larger in size (Figure 3a). However, in Figure 4h MORC2deltaIDRb and MORC2deltaIDRc also result in larger puncta. Making a final conclusion that the CW domain restricts condensate overgrowth or fusion warrants additional investigation.

We thank the reviewer for pointing out the limitation of our original conclusion. We agree that the enlarged puncta in both CTDΔCW (Figure 3a) indicate that condensate size regulation involves the CW domain was insufficiently rigorous.

Re-analysis of existing data identifies clear phenotypic disparities between the mutants: MORC2ΔIDRb/ΔIDRc mutants show two distinct phenotypes (reduced puncta number with enlarged size, or unchanged puncta number with uniform enlargement), and their total puncta area per cell is comparable to the WT. By contrast, CTDΔCW mutants display markedly larger puncta relative to the WT. Based on this distinction, we have revised our conclusion to a more cautious formulation: "These observations suggest that the CW domain may participate in regulating initial nucleation size and the exact molecular mechanisms require further investigation."

(4) MORC2 condensate-mediated gene silencing:

This is one of the key investigations of this study where the authors evaluate the ability of MORC2 condensates to regulate gene silencing (transcriptional repression). The major concern here is that the authors are drawing their conclusion based on a CC3 domain deletion mutant of MORC2 and comparing it with wild-type MORC2. Notably, the CC3 domain is responsible for MORC2 dimerization, and as the authors quote, 'The dimeric assembly of CC3 is essential for maintaining the structural integrity of the protein', the absence of CC3 would have a direct impact on its function (such as ATPase activity). With these considerations, it is not clear whether the effect of CC3 domain deletion on gene regulation is an effect of no phase separation or a consequence of loss of function. This necessitates additional validation by including other controls, such as IBD domain deletion mutant, IDRa domain deletion mutant, where the phase separation is impeded without affecting dimerization.

We appreciate the reviewer’s concern regarding the interpretation of CC3 deletion experiments. We agree that CC3 deletion affects both dimerization and phase separation, complicating attribution of gene regulatory effects solely to condensate formation. Our intention was not to claim that loss of repression arises exclusively from impaired phase separation, but rather to demonstrate that disrupting condensate-dynamic capacity correlates with impaired silencing.

To directly address these concerns, we have performed a series of new experiments specifically designed to decouple condensate formation, condensate dynamics, and protein abundance, thereby allowing us to more rigorously interrogate the functional relevance of MORC2 condensates.

First, to overcome the limitation of domain deletions which may affect MORC2 function beyond phase separation we introduced a micropeptide-based kill switch (KS) to the C terminus of MORC2. This strategy has recently emerged as a powerful approach to selectively reduce condensate dynamics without disrupting protein expression, folding, or domain architecture [1]. Importantly, unlike CC3 or IDRa deletions, MORC2+KS robustly form nuclear condensates but exhibits markedly reduced internal dynamics, as demonstrated by FRAP analyses showing minimal fluorescence recovery after photo bleaching (Fig. 6a-c). This strategy therefore allows us to perturb condensate material properties independently of MORC2 domain integrity.

Second, we systematically compared the transcriptional consequences of rescuing MORC2-knockout HeLa cells with MORC2FL, condensation-deficient mutants (ΔCC3 and ΔIDRa), and the dynamics-defective MORC2+KS (Fig. 6d). Despite being expressed at substantially higher levels than MORC2FL (Fig. 6e), all three mutants showed a striking and consistent failure to restore MORC2-dependent transcriptional regulation (Fig. 6f-h). This effect was particularly pronounced for transcriptionally repressed genes, including two sets of high-confidence MORC2 targets reported in prior studies (Fig. 6i and Fig. S10). These findings demonstrate that neither increased protein abundance nor the mere presence of condensate-like structures alone is sufficient to restore MORC2 function.

Third, our data instead support a model in which both soluble MORC2 complexes and dynamic MORC2 condensates are required for full transcriptional activity. While soluble MORC2 is likely involved in target recognition and complex assembly, our results indicate that proper condensate formation and critically, condensate dynamics are essential for effective transcriptional repression and activation. The inability of the MORC2+KS mutant to rescue transcriptional defects, despite intact condensate formation, points away from a model in which MORC2 condensates represent only microscopically visible byproducts of MORC2 activity.

We believe these new data strengthen the manuscript by pairing the detailed mechanistic dissection of MORC2 phase separation with direct functional evidence, enhancing the conceptual impact and biological significance of the study.

(5) Uncertain impact of pathogenic MORC2 mutations:

Line 356-365: While the statements such as "disease-associated mutations primarily affect enzymatic and phase behaviors rather than DNA affinity" and "these findings provide mechanistic insight into how specific mutations may contribute to distinct pathological outcomes" are conceptually compelling, the data presented in Figure 7b-d do not appear to fully support these conclusions. For many of the mutants, the differences from WT across key parameters-condensation, ATPase activity, and DNA binding-are either modest or statistically insignificant. As such, drawing a unified mechanistic conclusion from these datasets may overstate what the data actually support.

We agree that the effects of disease-associated MORC2 mutations described in Fig. 7 are modest and, in some cases, statistically insignificant. Our intention was to document observable trends rather than to propose a unified mechanistic framework. We have revised the manuscript to temper these conclusions and to emphasize the descriptive nature of these data.

(6) Important conceptual clarifications:

(a) Intrinsically disordered regions (IDRs) are not synonymous with phase separation. As the authors show, it is a combination of IDR-mediated interactions and CC3 dimerization that contributes towards the phase separation of MORC2. While IDRs can act as scaffolds for multivalent weak interactions that may promote biomolecular condensate formation, many IDRs serve other roles-such as mediating transient interactions, signaling, or regulatory functions-without undergoing phase separation. Researchers should avoid generalizing the assumption that the mere presence of IDRs in a protein implies its ability for phase separation. In this regard, authors should consider restructuring some of their generalized statements: Line 87-88: 'Recent studies suggest that intrinsically disordered regions (IDRs) can drive liquid-liquid phase separation (LLPS)' and Line 159-161: 'we noticed a long unstructured region at its C-terminus (Fig. S1b), a characteristic often associated with proteins capable of phase separation'.

We agree that IDRs are not synonymous with phase separation and have revised the Introduction to avoid generalized statements. The revised text now emphasizes that IDRs can contribute to phase separation in a context-dependent manner and act in concert with structured oligomerization domains such as CC3-IBD.

(b) Liquid-liquid phase separation: I would suggest switching the phrase to just phase separation. The rationale is that the in vitro studies of MORC2 (FRAP, droplet imaging) do not show liquid-like behavior, but perhaps liquid-solid. The FRAP studies suggest liquid-like behavior for some of the constructs. Given the differences in viscoelastic properties across the in vitro and in cellulo studies, it is better to generalize to "phase separation". Movies for droplet fusion and FRAP, wherever applicable, would be much appreciated. As the nature of in vitro MORC2 droplets appears different than in cells, movie representations of the above would enable readers to better assess the viscoelastic nature of the droplets (whether liquid, gel, etc).

We appreciate the reviewer’s insight regarding the viscoelastic properties of MORC2. Our experimental data indeed show a disparity in dynamics between the two environments: while in vitro MORC2-FL condensates exhibit relatively low internal mobility, the in cellulo MORC2-FL puncta display high dynamics, characterized by rapid internal recovery in FRAP assays and droplet fusion events (Fig. S2f).

This contrast suggests that the intracellular microenvironment plays a critical role in regulating the material state of MORC2 condensates. Consequently, we have focused on providing in vivo fusion data, as we believe in vitro characterizations (such as fusion or FRAP under various artificial conditions) may not faithfully represent the physiological behavior of MORC2. We have revised the manuscript to use the more general term “phase separation” or “condensation” and have added a discussion on these limitations to avoid overinterpreting the material properties observed in vitro.

(7) Methods:

(a) Figure 6 S2b: If phase separation occurs at, say, 1.8 µM protein concentration, this indicates that the protein has reached its saturation concentration (c-sat). Beyond c-sat, any additional protein should partition into the dense phase, while the concentration of the dilute phase remains constant. However, in this figure, the dilute phase concentration appears to increase with increasing total protein concentration, which is inconsistent with expected phase separation behavior. As the methods section does not have any sub-section for the sedimentation assay, it becomes difficult to understand how this experiment was performed, whether there is any technical discrepancy in the way soluble and pellet fractions were handled and processed for loading onto the gels. This is also the case with Figure 3d.

We thank the reviewer for carefully examining the sedimentation assay and for raising this important conceptual point. We agree that, for an ideal two-phase system at thermodynamic equilibrium, the concentration of the dilute phase is expected to remain constant once the saturation concentration (c-sat) is reached.

In our study, the sedimentation assay was used as an operational readout to assess concentration-dependent partitioning rather than to quantitatively define equilibrium phase boundaries. The assay involves centrifugation-based separation of supernatant and pellet fractions followed by SDS–PAGE analysis, and therefore does not necessarily report the equilibrium concentrations of coexisting dilute and dense phases. In particular, this approach can be influenced by incomplete physical separation of phases, kinetic trapping, and redistribution of material during handling, especially in systems where condensate maturation or internal reorganization occurs on longer timescales.

Consequently, the apparent increase in the supernatant fraction with increasing total protein concentration likely stems from kinetic limitations and inherent technical constraints of the sedimentation assay, rather than a genuine deviation from classical phase separation behavior. These caveats are now explicitly clarified in the Methods section, with similar limitations of centrifugation-based assays for defining equilibrium phase behavior of biomolecular condensates reported previously.

(b) Figure 4: The NMR comparisons appear to be primarily qualitative, lacking quantitative analyses such as chemical shift perturbation (CSP) and intensity ratio plots, which would offer deeper mechanistic insights. The NMR spectra detailing interactions among the IDR domains need to be quantified.

We thank the reviewer for the suggestion. We have now performed quantitative CSP analyses for the NMR data shown in Fig. 4, and the corresponding CSP plots have been added to the revised manuscript (Fig. S7).

As expected for interactions mediated by intrinsically disordered regions involved in phase separation, the observed CSPs are generally small. Notably, the CSP profile of IDRa closely matches that observed for the full-length IDR, whereas IDRb and IDRc show minimal perturbations. These results indicate that the interaction is primarily mediated by IDRa, with little contribution from the remaining regions.

Peak intensity analyses were also examined but did not reveal additional residue-specific trends. Together, the quantitative CSP data support our conclusion that the interaction is weak, dynamic, and region-specific, consistent with an IDR-driven, phase-separation-related mechanism. We add this statement in method: CSPs were calculated in Hz at 600 MHz using the following equation:

Minor comments:

(1) Line 59-60: The Authors mention the HUSH-complex and then the MORC protein family, but do not discuss the relation between the two.

We thank the reviewer for this comment. We have revised the Introduction to explicitly state that MORC2 may serve as a component of the HUSH complex and to clarify the functional relationship between MORC family proteins and HUSH-mediated transcriptional repression.

(2) Line 74: 'Despite their structural similarities...', similarities between what all?

We agree that this statement was ambiguous. We have revised the text to explicitly specify that the comparison refers to structural similarities among MORC family members.

(3) Line 75: 'MORC-mediated repression remains...', this is the first time the word 'repression' is mentioned in the text and directly as an outstanding question.

We have revised the Introduction to introduce the concept of transcriptional repression earlier and to provide appropriate context before posing it as an outstanding question.

(4) The third paragraph does address issues in comments 1 and 3 to some extent, but the introduction needs some restructuring to provide a proper flow of information.

We agree that the Introduction required restructuring. We have revised this section to improve logical flow, better integrate prior studies, and more clearly articulate the motivation and scope of the present work.

(5) Line 83-85: How does the presence of IDRs suggest potential regulatory mechanisms?

We have revised this sentence to clarify that IDRs may contribute to regulatory mechanisms by enabling multivalent and dynamic interactions, rather than implying that IDRs inherently confer regulatory function or phase separation capability.

(6) Line 106-107: 'To determine whether MORC2 has N- and C-terminal dimerization interfaces similar to those...', reference 14 has already established that CC3 (denoted as CC4 in ref 14) is responsible for dimerization. Consider acknowledging their work in this regard?

We thank the reviewer for this reminder. We have now explicitly acknowledged Ref. 14, which previously established the role of CC3 (denoted CC4 in that study) in MORC2 dimerization.

(7) Lines 117-122: Are the authors comparing morphology from negative stain EM with AlphaFold predicted structure (Figure S1a and S1b)? If so, providing a zoomed-in inset from Figure S1a would be helpful.

Yes, the comparison was intended to relate the negative-stain EM morphology to the AlphaFold-predicted architecture. We have added a zoomed-in inset in Fig. S1a to facilitate clearer comparison.

(8) Line 152-153: '...even under varying physiological conditions', what are these varying conditions? Are the authors trying to point towards any of their specific results?

We have revised this phrase to explicitly refer to variations in salt concentration and protein concentration tested in our in vitro assays.

(9) Line 154-155: 'The dimeric assembly of CC3 is essential for maintaining the structural integrity of the protein', if it has been established, then please provide a reference.

We thank the reviewer for this suggestion. For MORC family proteins, C-terminal coiled-coil–mediated dimerization is necessary for correct homodimer formation and functional stability (Xie et al., 2019, Cell Commun Signal. 17:160, Ref 14 in the revised manuscript).

(10) Line 159-161: 'we noticed a long unstructured region at its C-terminus (Figure S1b), a characteristic often associated with proteins capable of phase separation25.', again authors are generalizing a statement which is, in most cases, context-dependent. For example, ref 25 mentions that unstructured regions or IDRs serve as a scaffold for multivalent interactions.

We agree with the reviewer and have revised this sentence to avoid generalization. The revised text now emphasizes that IDRs may facilitate multivalent interactions in a context-dependent manner, rather than being intrinsically indicative of phase separation. Additionally, we have explicitly cited the mechanistic insight from Reference 25 that IDRs serve as scaffolds for multivalent interactions, to strengthen the logical link between the structural feature and its potential functional relevance.

(11) Methods section for NMR (Line 665-667) mentions that nucleotides were added to a final concentration of 10 mM. There is no figure or section for MORC2 NMR with added nucleotides/DNA.

We thank the reviewer for pointing this out. The nucleotide (ATP) addition was part of preliminary NMR trials and is not directly associated with the figures presented. We have deleted this in the Methods section to avoid confusion.

(12) Line 285-294: Authors compare the effect of DNA binding on the phase separation of both MORC2FL and MORC2 CTDdeltaCW and conclude that DNA-induced condensation is primarily mediated through interactions with the IDR-NLS region. This appears not to be backed by proper control experiments. The authors do not show whether DNA binding mediates any phase separation for the isolated NTD or not? Similarly, what is the effect of DNA binding on MORC2 deltaIDR?

We thank the reviewer for this insightful comment and agree that additional controls are essential for rigorously dissecting the contribution of DNA binding to MORC2 phase separation. Our interpretation that DNA-enhanced condensation is primarily mediated through the IDR–NLS region was based on comparative analyses of MORC2FL and MORC2 CTDΔCW, together with EMSA results demonstrating that DNA binding activity is conferred by the IDR–NLS–containing region. We acknowledge, however, that DNA binding alone is not sufficient to infer phase separation behavior.

To address this point, we have performed additional analyses using the isolated NTD’ (residues 1–536) and MORC2 ΔIDR–NLS mutants (Fig. S6). The isolated NTD’ exhibited detectable DNA binding [4] but did not undergo DNA-induced condensation under conditions while MORC2FL or MORC2 CTDΔCW (residues 537-1032) readily formed condensates, indicating that DNA binding by itself is insufficient to drive phase separation. In parallel, MORC2 ΔIDR–NLS mutants showed severely compromised solubility and stability in vitro, which limited their quantitative characterization in phase separation assays. Nevertheless, under the conditions tested, these mutants did not display DNA-enhanced condensation comparable to MORC2FL.

Taken together, these observations support a model in which the IDR–NLS region plays a critical role in coupling DNA binding to condensation, while additional domains are required to sustain robust phase separation. We have revised the manuscript text to clarify the experimental scope and to avoid overinterpreting the contribution of DNA binding in the absence of fully reconstituted control systems.

(13) How did the authors assign the backbone amide NMR chemical shifts for MORC2?

Backbone assignments of MORC2 IBD (1004-1032) were obtained using SOFAST versions of standard triple-resonance experiments, including HNCACB and CBCACONH, recorded at 298 K. Residual assignment ambiguities were resolved using [15] N-edited HMQC-NOESY-HMQC spectra.

(14) Line 256: 'The partial compaction of IDRa...', what does the author mean here with 'partial compaction'? How did they measure compaction here?

Regarding the term “partial compaction” mentioned previously, we apologize for the typographical error this phrase was erroneously used in place of “key component”.

(15) Line 312-315: Why is there even a MORC2 readout for MORC2 KO cells with only EGFP? Also, the authors suggest that IDR deletion may impair mRNA stability or transcription; however, the expression levels of MORC2 deltaIDR and MORC2 deltaCC3 do not appear drastically different in Figure 3a.

We thank the reviewer for raising these points. The apparent MORC2 signal in MORC2 knockout cells transfected with EGFP alone is due to the presence of residual MORC2 mRNA. Although CRISPR–Cas9–mediated knockout introduces a frameshift that prevents MORC2 protein expression, the mRNA can still be detected by RNA-seq. This is because nonsense-mediated decay (NMD), which targets transcripts with premature stop codons for degradation, is not always 100% efficient. Therefore, some MORC2 transcripts remain and produce detectable RNA-seq reads, even though no functional protein is expressed.

Regarding the apparent discrepancy in expression levels, Fig. 3a displays only EGFP-positive cells, within which the fluorescence intensity of MORC2ΔIDR and MORC2ΔCC3 appears comparable to that of WT MORC2. However, the overall fraction of EGFP-positive cells is markedly reduced for these mutants compared to WT. Thus, while expression levels among successfully transfected cells are similar, fewer cells express detectable levels of the ΔIDR or ΔCC3 constructs across the total population. We therefore interpret this reduction in EGFP-positive cell fraction as reflecting impaired expression efficiency of these mutants, potentially arising from altered transcriptional output, mRNA stability, or protein stability. We have revised the manuscript text to clarify this distinction and to avoid overinterpreting the underlying mechanism in the absence of direct measurements.

Author response image 1.

EGFP, EGFP–MORC2 (FL), EGFP–MORC2 (ΔCC3), and EGFP–MORC2 (ΔIDR) were re-expressed in MORC2-knockout HeLa cells. Confocal imaging revealed that full-length MORC2 formed condensates in the nucleus, whereas mutants lacking either the CC3 or IDR domain failed to exhibit such behavior. Notably, under identical experimental conditions, we observed a marked reduction in the transfection efficiency of the EGFP-MORC2 (ΔIDR) construct. In contrast to the other variants, EGFP signals for ΔIDR were detectable in only a small fraction of the total cell population, despite consistent DNA loading and protocol synchronization. This observation suggests that the IDR might be required not only for biomolecular condensation but also for maintaining the steady-state levels of the MORC2 mRNA/protein or overall cellular fitness.

(16) Line 330: 'MORC2 deltaCC3 failed to repress any of the 18 downregulated targets...'. This does not appear to be entirely true as repression of some targets (LBH, TGFB2, GADD45A) are closer to MORC2 FL than the EGFP control.

We thank the reviewer for pointing out this inconsistency and for highlighting the need for precise wording. We have updated the dataset and revised the text to describe the results more accurately. We now describe that the mutants impair MORC2FL-mediated transcriptional regulation, consistent with the overall trend observed across these target genes.

(17) Line 347-350: Based on the percent of cells with condensates, the authors conclude that CMT2Z-linked E236G and SMA-linked T424R mutants promote MORC2 phase separation. Again, the effect of these mutations on MORC2 condensation in cells may be direct or indirect. This can be investigated by comparing the in vitro effect of these mutations on MORC2 phase separation.

We thank the reviewer for raising this important point and fully agree that the effects of disease-associated MORC2 mutations on condensate formation in cells may arise from either direct alteration in intrinsic phase separation propensity or indirect influences mediated by the cellular environment.

In our study, disease-associated MORC2 mutants were assessed for condensate formation in HEK293F cells. Attempts were made to characterize these mutants in vitro; however, the E236G mutant exhibited markedly reduced solubility and stability upon purification, which precluded reliable in vitro phase separation analysis. We therefore evaluated the impact of E236G in cells and found that this mutation significantly impaired the dynamics of nuclear MORC2 condensates. For the T424R mutant, we note that its intracellular condensates displayed FRAP recovery kinetics comparable to those of WT MORC2, suggesting broadly similar dynamic properties of the assemblies formed in cells, but not necessarily implying a direct enhancement of intrinsic phase separation.

In light of these considerations, we have revised the text in Lines 347–350 to avoid attributing a direct causal role of these mutations in promoting MORC2 phase separation. Instead, we now describe the observed increase in the fraction of cells containing condensates as a descriptive cellular correlation. We further emphasize that systematic in vitro characterization of disease-associated MORC2 mutants will be required to distinguish direct from indirect effects and represents an important direction for future investigation.

(18) The discussion section lacks referencing to individual figures in the results section as well as previous literature.

We agree with the reviewer that the Discussion would benefit from clearer integration with both the Results figures and prior literature. In the revised manuscript, we have substantially restructured the Discussion to explicitly reference key figures when interpreting experimental findings and to more clearly distinguish conclusions drawn from specific datasets. In addition, we have expanded citations to previous studies where relevant, particularly in the context of MORC2 DNA binding, ATPase regulation, chromatin association, and disease-linked mutations. These revisions aim to better situate our findings within the existing literature and to guide readers more clearly between experimental observations and their interpretation.

Reviewer #3 (Public review):

Summary:

The manuscript by Zhang et al. demonstrates that MORC2 undergoes liquid-liquid phase separation (LLPS) to form nuclear condensates critical for transcriptional repression. Using a combination of in vitro LLPS assays, cellular studies, NMR spectroscopy, and crystallography, the authors show that a dimeric scaffold formed by CC3 drives phase separation, while multivalent interactions between an intrinsically disordered region (IDR) and a newly defined IDR-binding domain (IBD) further promote condensate formation. Notably, LLPS enhances MORC2 ATPase activity in a DNA-dependent manner and contributes to transcriptional regulation, establishing a functional link between phase separation, DNA binding, and transcriptional control. Overall, the manuscript is well-organized and logically structured, offering mechanistic insights into MORC2 function, and most conclusions are supported by the presented data. Nevertheless, some of the claims are not sufficiently supported by the current data and would benefit from additional evidence to strengthen the conclusions.

Thank you for your insightful review and constructive suggestions, which have been invaluable in refining our manuscript.

The following suggestions may help strengthen the manuscript:

Major comments:

(1) The central model proposes that multivalent interactions between the IDR and IBD promote MORC2 LLPS. However, the characterization of these interactions is currently limited. It is recommended that the authors perform more systematic analyses to investigate the contribution of these interactions to LLPS, for example, by in vitro assays assessing how the IDR or IBD individually influence MORC2 phase separation.

We appreciate the reviewer’s insightful comment regarding the characterization of IDR–IBD interactions. In this study, we combined NMR spectroscopy, domain deletion analysis (in vivo), and in vitro phase separation assays to demonstrate that interactions between the IDR and IBD contribute to MORC2 condensate formation. To systematically assess the individual contributions of the IDR and IBD to MORC2 phase separation, we performed in vitro reconstitution assays using purified domain constructs (Fig. S6). Neither the isolated IDR nor the IBD alone exhibited phase separation under buffer conditions approximating the physiological environment, indicating that each domain is individually insufficient to drive condensation. Upon the addition of 10% PEG8000, phase separation was selectively observed for the IDR but not for the IBD, suggesting that the IDR possesses an intrinsic propensity for phase separation that can be enhanced by crowding molecular. Importantly, when the IDR and IBD were mixed, phase separation was robustly induced, supporting a model in which cooperative inter-domain interactions between the IDR and IBD promote MORC2 condensation. In the absence of PEG, no phase separation was observed for the IDR–IBD mixture. These observations imply that IDR–IBD interactions cannot drive phase separation on their own, but require cooperation with CC3-mediated dimerization to achieve this process, which is the central point we wish to emphasize.

(2) The authors mention that DNA binding can promote MORC2 LLPS. It is recommended that they generate a phase diagram to systematically assess how DNA influences phase separation.

We agree that constructing a full phase diagram would provide a more systematic evaluation of the effect of DNA on MORC2 phase separation. In the current study, we assessed DNA-dependent condensation across multiple protein and DNA concentrations, which consistently showed that DNA enhances MORC2 phase separation. At low protein concentration (0.5 µM), phase separation requires sufficient DNA, whereas increasing either DNA or protein concentration promotes liquid droplet formation. At high DNA and protein concentrations, amorphous structures dominate, indicating a transition away from dynamic assemblies. We have clarified this point in the Results and Discussion sections and now note that a comprehensive phase diagram analysis represents an important direction for future work.

(3) The authors use the N39A mutant as a negative control to study the effect of DNA binding on ATP hydrolysis. Given that N39A is defective in DNA binding, it could also be employed to directly test whether DNA binding influences MORC2 phase separation.

We thank you for your constructive suggestions. The purified wild-type MORC2(1–603) exhibited weak but detectable ATPase activity, whereas the N39A mutant was completely inactive [5]. Based on this characteristic, the N39A mutant was used as a negative control for the ATP-binding-deficient mutant in this study [3]. However, no evidence has been provided to demonstrate that the N39A mutant is defective in DNA binding. Importantly, both our results and previous studies [5-6] indicate that MORC2 engages DNA via multiple domains, suggesting that a single-point mutation is unlikely to significantly compromise its overall DNA-binding capacity.

(4) Many of the cellular and in vitro LLPS experiments employ EGFP fusions. The authors should evaluate whether the EGFP tag influences MORC2 phase separation behavior.

We appreciate the reviewer’s concern regarding the potential influence of the EGFP tag. The use of EGFP fusions in our study was primarily to maintain consistency with the in-cell experiments. Importantly, we confirmed that EGFP alone does not undergo phase separation in cells, and this observation is consistent with previous studies [7]. Additionally, in vitro phase separation of MORC2 was independently validated using Cy3–labeled CTD (Fig. S5), which recapitulated the condensate formation seen with EGFP-fused protein. Together, these results indicate that the EGFP tag does not significantly influence MORC2 phase separation, supporting the validity of our conclusions.

Reviewer #3 (Recommendations for the authors):

(1) The authors claim to have obtained nucleic acid-free protein, but no data are provided to support this assertion. It is recommended that they include appropriate validation to confirm the absence of nucleic acids.

We thank the reviewer for highlighting this point. To validate that the purified MORC2 protein is indeed free of nucleic acid contamination, we have additional experimental evidence (e.g., A260/280 measurements, agarose gel analysis, or EMSA in Fig. 5), which has been added to the Methods section and Table S2.

Note: Agarose gel analysis for MORC2 constructs to confirm the absence of nucleic acids. The pET32 vector as the positive control, the protein preparation for analysis is 0.05 mg. E means E. coli and H means HEK293F.

(2) The FRAP recovery curves are not normalized to 0, making comparison difficult. The authors should normalize the post-bleach intensity to 0 and re-plot the curves to allow a more standard interpretation of mobile fractions.

We agree with the reviewer and have now normalized the FRAP recovery curves by setting the post-bleach intensity to 0. The revised plots are presented in the Figures (2f, j, l; 6c, 7f), allowing for more direct comparison of mobile fractions across different conditions.

(3) The HSQC spectra for IBD appear inconsistent: the peak positions in Fig. 4C do not align with those shown in panels D-F. The authors should verify the spectral assignments and ensure consistency across figures.

We thank the reviewer for pointing this out. The apparent inconsistency arose from the fact that different spectral regions were displayed in Fig. 4c versus Fig. 4d-f for visualization purposes, which may have given the impression of mismatched peak positions. The spectral assignments themselves are consistent across all panels.

To avoid confusion, we have now adjusted the spectral window shown in Fig. 4c to match that used in Fig. 4d-f. The revised figure ensures consistent presentation of the same spectral region across all panels.

Reference:

(1) Zhang, Y., Stöppelkamp, I., Fernandez-Pernas, P. et al. Probing condensate microenvironments with a micropeptide killswitch. Nature 643, 1107–1116 (2025).

(2) Fendler NL, Ly J, Welp L, et al. Identification and characterization of a human MORC2 DNA binding region that is required for gene silencing. Nucleic Acids Res.53(4):gkae1273 (2025).

(3) Tchasovnikarova, I., Timms, R., Douse, C. et al. Hyperactivation of HUSH complex function by Charcot–Marie–Tooth disease mutation in MORC2. Nat Genet 49, 1035–1044 (2017).

(4) Douse, C. H. et al. Neuropathic MORC2 mutations perturb GHKL ATPase dimerization dynamics and epigenetic silencing by multiple structural mechanisms. Nat Commun 9, 651 (2018).

(5) Tan, W., Park, J., Venugopal, H. et al. MORC2 is a phosphorylation-dependent DNA compaction machine. Nat Commun 16, 5606 (2025).

(6) Sánchez-Solana B, Li DQ, Kumar R. Cytosolic functions of MORC2 in lipogenesis and adipogenesis. Biochim Biophys Acta. 1843(2):316-326 (2014).

(7) Li, C.H., Coffey, E.L., Dall’Agnese, A. et al. MeCP2 links heterochromatin condensates and neurodevelopmental disease. Nature 586, 440–444 (2020).

Janie didn’t want to get married but her Nanny thought it was the best for her and she should be happy but instead has an attitude.

That it was the not a good environmrent

1:20:00 50 years ago, religious fanatics conspired to fight the "war on drugs".<br /> today, the same religious fanatics conspire to fight the "war on meat"...<br /> human stupidity really is a curse on this planet >: (

It really shows how Jody controls Janie through public humiliation. The porch conversations seem like harmless fun but when Jody forces Janie to stay silent and work the store while he jokes with the men it's clear he sees her as a possession. The most painful part is when Janie realizes she can't even laugh at the "mule" jokes without his approval her spirit is being tamed in plain sight

testing

Quicksand A quicksand pit covers the ground in roughly a 10-foot-square area and is usually 10 feet deep. When a creature enters the area, it sinks 1d4 + 1 feet into the quicksand and becomes restrained. At the start of each of the creature’s turns, it sinks another 1d4 feet. As long as the creature isn’t completely submerged in quicksand, it can escape by using its action and succeeding on a Strength check. The DC is 10 plus the number of feet the creature has sunk into the quicksand. A creature that is completely submerged in quicksand can’t breathe (see the suffocation rules in the Player’s Handbook).

A creature can pull another creature within its reach out of a quicksand pit by using its action and succeeding on a Strength check. The DC is 5 plus the number of feet the target creature has sunk into the quicksand.

пример3

BULLSHIT should be p<0.001

spyware industry

Interpersonal Computing

https://hyp.is/ikhoBELqEfGJV98MPQslOg/x.com/JonErlichman/status/1280665777025187841

Steve Job on InterPersonal Computing = as the next (r)evolutional step beond Personal Computing -

eLife Assessment

This study addresses an important question in gustatory neuroscience by developing a machine-learning classifier to identify distinct ingestive orofacial movement subtypes from electromyographic recordings and relating their dynamics to population-level activity in the gustatory cortex. The evidence that transitions in cortical ensemble firing are temporally associated with reorganization of ingestive movement patterns is convincing, though some aspects of the behavioral classification and neural analyses require further validation and clarification. The work provides a technically innovative framework for linking neural state dynamics to the motor expression of taste-guided decisions.

Reviewer #1 (Public review):

Summary:

This study investigates how ingestive behaviors are reflected in muscle activity and how these behaviors relate to neural dynamics in the brain. By combining muscle recordings with computational analysis, the authors identify patterns of mouth movements and show that these change over time and align with changes in brain activity. The work suggests that ingestion is not defined by a single action but by coordinated changes across multiple behaviors.

Strengths:

(1) Addresses an important and underexplored question about how ingestive behavior is organized.

(2) Combines behavioral, physiological, and computational approaches creatively.

(3) Provides a novel framework for quantifying complex ingestive movements.

(4) Demonstrates a clear temporal relationship between behavior and brain activity.

Weaknesses

(1) Behavioral labels rely on video-based scoring, which may not fully capture subtle or hidden movements.

(2) The relationship between brain activity and behavior is correlational, but sometimes interpreted more strongly.

(3) The manuscript could be clearer and more accessible to readers outside the field.

Reviewer #2 (Public review):

Summary:

In this study, Baas-Thomas et al. aim to study the neural mechanisms underlying ingestive versus rejection responses to taste stimuli by developing an EMG-based approach to identify ingestion-related orofacial movements. Whereas prior work has focused primarily on detecting rejection-related gapes, the authors introduce a machine-learning classifier that uses waveform features extracted from anterior digastric (AD) EMG signals to detect mouth- and tongue-movement (MTM) events associated with ingestion. Clustering analyses further suggest that ingestive behavior consists of multiple MTM subtypes whose relative frequencies vary across trial time and taste conditions. Finally, simultaneous recordings indicate that shifts in MTM expression follow transitions in gustatory cortex (GC) population dynamics into palatability-related firing states, supporting a role for cortical ensemble activity in coordinating ingestive motor responses.

Strengths:

Overall, the scientific question addressed in this study is well motivated. A mechanistic understanding of ingestive decision-making requires a precise characterization of the motor patterns that implement ingestion, and these behaviors have remained insufficiently resolved in prior work. The authors take a reasonable and technically innovative approach by leveraging AD EMG recordings to classify distinct orofacial movement patterns. The extracted waveform features appear effective in separating gapes from ingestion-related mouth-tongue movements, and clustering analyses further suggest the presence of distinguishable MTM subtypes that show meaningful temporal structure and neural correlates. Taken together, the work provides a potentially useful framework for linking gustatory cortical dynamics to the motor expression of taste-guided decisions.

A particularly valuable aspect of this work is the attempt to move beyond a binary characterization of ingestive behavior and instead identify multiple subtypes of ingestion-related movements. This finer behavioral resolution has the potential to provide a more realistic account of how complex consummatory actions are organized. More broadly, the effort to relate structured behavioral motifs to population-level neural dynamics is conceptually interesting and could prove useful for future studies seeking to connect circuit dynamics with the motor implementation of motivated behaviors.

Weaknesses:

(1) I have several concerns regarding the methodological comparisons used to establish the superiority of the proposed XGBoost classifier. In particular, the comparison between the XGBoost classifier and previously used QDA approaches (Figure 3) may not be entirely well-matched. The QDA framework was originally designed primarily to detect gape events and does not explicitly assign labels to MTM movements. As a result, the apparent advantage of XGBoost in identifying MTMs may partly reflect differences in task formulation rather than intrinsic differences in classification performance. From visual inspection, gape detection performance appears broadly comparable across methods.

A more informative benchmark would involve comparing XGBoost to an extended pipeline in which QDA-based gape detection is combined with a secondary movement-detection stage, distinguishing MTMs from periods of no movement. Such a comparison would better isolate the contribution of classifier architecture per se. Without this control analysis, the strength of the claim that XGBoost provides superior performance for behavioral decoding remains somewhat uncertain.

(2) The presentation of the neural ensemble analyses is considerably less comprehensive and intuitive than that of the behavioral analyses. The manuscript would benefit from more direct visualization of inferred neural state transitions. For example, plotting predicted neural states in a manner analogous to the behavioral states illustrated in Figure 6B would improve interpretability and help readers understand how neural dynamics relate temporally to behavioral changes.

In addition, the interpretation that GC ensemble dynamics drive behavioral state transitions may require further clarification. If GC activity plays a causal role in initiating behavioral changes, one might expect a consistent brain-to-behavior lag across changepoints. However, Figure 6 appears to show such lag primarily at the second transition but not at the first. This raises questions about how uniformly the proposed causal interpretation applies across state boundaries, and additional analysis or discussion is needed.

(3) The neural ensemble analyses primarily focus on constructing higher-level behavioral state variables rather than directly testing how individual movement subtypes relate to neural activity. The behavioral interpretation of the inferred state structure, therefore, remains somewhat unclear. While this approach is consistent with previous work from the authors and with broader state-transition frameworks of gustatory processing, it is not immediately obvious that this is the most informative level of analysis for the present dataset.

In particular, it would strengthen the manuscript to examine whether GC neurons or ensembles also encode lower-level motor structure, such as the occurrence of gapes or specific MTM subtypes. Demonstrating selective or mixed encoding across hierarchical levels (movement motifs versus abstract behavioral states) would help clarify the functional interpretation of the reported neural dynamics. At present, the manuscript largely assumes that GC activity reflects higher-order behavioral states without directly testing alternative representational possibilities.

(4) Because direct behavioral ground truth for intra-oral ingestive movements is difficult to obtain, MTM subtypes are inferred primarily through clustering of EMG waveform features. Although the authors demonstrate statistical separability and cross-session stability of these clusters, it remains unclear whether they correspond to discrete motor programs or instead reflect a structured partitioning of a continuous behavioral space shaped by feature selection and preprocessing choices. Perhaps some additional robustness analyses or convergent validation (e.g., alternative clustering methods, feature perturbation tests, or stronger neural and behavioral dissociations) would help clarify the biological significance of the inferred subtype structure.

Reviewer #3 (Public review):

Summary:

This study examines how ingestive-related orofacial movements relate to ensemble dynamics in gustatory cortex (GC) during taste processing. Previous work has shown that GC activity evolves through a sequence of population states following taste delivery, culminating in a transition to palatability-related firing that precedes rejection-related orofacial movements (e.g., gaping). Importantly, perturbing GC activity around the time of this transition alters the timing of gaping, suggesting that these ensemble dynamics play a functional role in linking taste evaluation to behavioral responses. The present study asks whether similar neural dynamics are also associated with ingestive-related orofacial movements that occur during the consumption of palatable stimuli.

To address this question, the authors develop a machine-learning classifier to identify distinct orofacial movements from anterior digastric EMG recordings. Using a set of labeled EMG waveforms obtained from video-scored trials, a gradient-boosted (XGBoost) classifier is trained to detect gapes, mouth/tongue movements (MTMs), and periods of no movement. Applying this classifier to a larger EMG dataset reveals that ingestive-related MTMs cluster into three distinct movement subtypes whose frequencies change systematically within trials.

The authors then relate these behavioral dynamics to previously described GC ensemble transitions identified using changepoint modeling. They report that changes in MTM subtype frequencies tend to occur shortly after the transition to palatability-related activity in GC. These results suggest that GC population dynamics are temporally associated not only with rejection-related behaviors but also with ingestive motor patterns that occur as animals prepare to consume palatable tastants.

Strengths:

The study introduces an innovative framework for extracting intricate orofacial movement information from EMG recordings. The machine-learning classifier provides a scalable method for identifying specific orofacial movements and performs better than previously published algorithms designed for gape detection. This approach allows the authors to examine movement microstructure at a temporal resolution that cannot be achieved with video scoring in freely moving animals.

A second strength is the integration of orofacial movement analysis with neural population dynamics. By relating EMG-derived movement subtypes to ensemble state transitions in GC, the study builds on a substantial body of work examining the temporal evolution of taste responses in cortex. The finding that ingestive-related movement dynamics occur shortly after the emergence of palatability-related firing provides an interesting extension of previous observations linking GC state transitions to rejection behavior.

The manuscript is also commendable in its commitment to data accessibility. By providing clear information about how the datasets can be accessed and making training data for the classifier publicly available, the authors make it possible for other researchers to examine the analytical pipeline and apply similar approaches to their own datasets. This transparency provides a useful framework for extending and building upon the methods presented here.

Weaknesses:

Some aspects of the EMG-based movement classification pipeline warrant careful interpretation. The training dataset used for classifier development is relatively small and is derived from a subset of trials in which mouth movements were clearly visible in video recordings. While the classifier performs well on this labeled dataset, it is not entirely clear how representative these labeled examples are of the full range of EMG signals present in the larger dataset.

The interpretation of the three identified MTM subtypes also remains somewhat tentative. The study convincingly demonstrates that distinct waveform-defined clusters exist in the EMG data, but the functional significance of these clusters as ingestive "behaviors" is less clear. As acknowledged by the authors, the specific roles of these movement patterns in the ingestion process remain speculative.

Finally, several conclusions in the Discussion rely on relatively strong mechanistic language when describing the relationship between GC dynamics and ingestive behavior. The data clearly demonstrate a temporal association between GC state transitions and changes in the frequencies of the different MTM subtypes. However, the results primarily support the interpretation that similar cortical dynamics are associated with ingestive and rejection-related behaviors rather than definitively establishing that these behaviors "are governed by the same underlying neural mechanisms".

Author response:

Public Reviews:

Reviewer #1 (Public review):

(1) Behavioral labels rely on video-based scoring, which may not fully capture subtle or hidden movements.

This is very true; certainly, this work is only a starting point. But the techniques used for this manuscript, despite starting with video-based scoring, specifically did allow us to differentiate behaviors that were too subtle to recognize in the video. For the revision, we will describe how this work leads to future studies in which we will be able to explore other means of collecting behavioral labels, potentially directly from simultaneous recordings of multiple muscles.

(2) The relationship between brain activity and behavior is correlational, but sometimes interpreted more strongly.

We will comb through the manuscript and make edits to be more precise and technically correct in presenting this relationship, and clarify that our suggestion of a causal link is only indirect and related to previous work (Mukherjee et al. 2019).

(3) The manuscript could be clearer and more accessible to readers outside the field.

We will edit the manuscript in multiple places to make technical and field-specific aspects more accessible. As part of this, in appreciation of Reviewer 2’s comments, we will take additional care to elaborate on and clarify our need and interpretation of SHAP values and classifier structure.

Reviewer #2 (Public review):

(1) I have several concerns regarding the methodological comparisons used to establish the superiority of the proposed XGBoost classifier. In particular, the comparison between the XGBoost classifier and previously used QDA approaches (Figure 3) may not be entirely well-matched. The QDA framework was originally designed primarily to detect gape events and does not explicitly assign labels to MTM movements. As a result, the apparent advantage of XGBoost in identifying MTMs may partly reflect differences in task formulation rather than intrinsic differences in classification performance. From visual inspection, gape detection performance appears broadly comparable across methods.

A more informative benchmark would involve comparing XGBoost to an extended pipeline in which QDA-based gape detection is combined with a secondary movement-detection stage, distinguishing MTMs from periods of no movement. Such a comparison would better isolate the contribution of classifier architecture per se. Without this control analysis, the strength of the claim that XGBoost provides superior performance for behavioral decoding remains somewhat uncertain.

The revision will further clarify that, as the reviewer notes, the primary improvement in XGB classification compared to QDA (in multi-class aggregated metrics) comes specifically from its ability to classify MTMs, and that for gapes, both QDA and XGB perform on par. We will be more explicit about the fact that our goal in constructing the classifier is not to compare “classifier architecture”—not to find the very best classifier possible—but rather to take the next step by generating an instance of a classifier that performs demonstrably better on aggregated orofacial movements. We will update the manuscript to be more clear in our claims in this regard, and how the current XGB classifier can, once validated, be bootstrapped by future techniques (possibly using more informative data sources) to more fully characterize orofacial movements.

(2) The presentation of the neural ensemble analyses is considerably less comprehensive and intuitive than that of the behavioral analyses. The manuscript would benefit from more direct visualization of inferred neural state transitions. For example, plotting predicted neural states in a manner analogous to the behavioral states illustrated in Figure 6B would improve interpretability and help readers understand how neural dynamics relate temporally to behavioral changes.

In addition, the interpretation that GC ensemble dynamics drive behavioral state transitions may require further clarification. If GC activity plays a causal role in initiating behavioral changes, one might expect a consistent brain-to-behavior lag across changepoints. However, Figure 6 appears to show such lag primarily at the second transition but not at the first. This raises questions about how uniformly the proposed causal interpretation applies across state boundaries, and additional analysis or discussion is needed.

We are happy to update the figures (likely by adding another panel to Figure 6) to clearly show inference of neural state transitions, in a manner similar to how we have shown behavioral state transitions in Fig. 6B. In addition, we will do a more comprehensive job of describing and referencing earlier work in which we have unpacked these analyses in greater detail—work that makes it clear why we would predict a lag-relationship for one set of change points and not the other.

(3) The neural ensemble analyses primarily focus on constructing higher-level behavioral state variables rather than directly testing how individual movement subtypes relate to neural activity. The behavioral interpretation of the inferred state structure, therefore, remains somewhat unclear. While this approach is consistent with previous work from the authors and with broader state-transition frameworks of gustatory processing, it is not immediately obvious that this is the most informative level of analysis for the present dataset.

In particular, it would strengthen the manuscript to examine whether GC neurons or ensembles also encode lower-level motor structure, such as the occurrence of gapes or specific MTM subtypes. Demonstrating selective or mixed encoding across hierarchical levels (movement motifs versus abstract behavioral states) would help clarify the functional interpretation of the reported neural dynamics. At present, the manuscript largely assumes that GC activity reflects higher-order behavioral states without directly testing alternative representational possibilities.

The reviewer makes a good point. While previous work from the lab (Li et al. 2016) has assessed the relationship of GC activity with both the onset of gaping (i.e., the behavioral state transition) and individual gapes and found only a relationship with onset of gaping (findings that we now explicitly describe in the revision), we have not performed a similar analysis for MTMs. We will do so and add it to the paper.

(4) Because direct behavioral ground truth for intra-oral ingestive movements is difficult to obtain, MTM subtypes are inferred primarily through clustering of EMG waveform features. Although the authors demonstrate statistical separability and cross-session stability of these clusters, it remains unclear whether they correspond to discrete motor programs or instead reflect a structured partitioning of a continuous behavioral space shaped by feature selection and preprocessing choices. Perhaps some additional robustness analyses or convergent validation (e.g., alternative clustering methods, feature perturbation tests, or stronger neural and behavioral dissociations) would help clarify the biological significance of the inferred subtype structure.

We admit (in fact, we have done so in the text) that we are not yet to the point of being able to “split hairs” to this degree (although we, like R2, see that as a goal). In the meantime, we will expand the section of Results text in which we describe the fact that the clustering of behaviors is observed both in “waveform space” (Fig. 4E was generated using standardized waveforms) and “feature space” (Fig. 4 B,C, and F), and that as such the clusters are NOT simply a partitioning of continuous, unimodal behavioral space. We will report convergent results from alternative (k-means) clustering methods to further support that conclusion. Finally, we will describe (in the Discussion section) ways to more rigorously test and extend this claim in future work.

Reviewer #3 (Public review):

Some aspects of the EMG-based movement classification pipeline warrant careful interpretation. The training dataset used for classifier development is relatively small and is derived from a subset of trials in which mouth movements were clearly visible in video recordings. While the classifier performs well on this labeled dataset, it is not entirely clear how representative these labeled examples are of the full range of EMG signals present in the larger dataset.

Very good point. We will update the text to note this qualification to the reader. We will also, however, highlight the fact that our focus on a highly reliable and representative (i.e., agreed upon by 2 independent, blind scorers) subset of labels allows us to perform more targeted analyses and make more targeted interpretation in our results. And we will also be more pointed in the revision, as we have noted above, about the fact that this work is only scratching the surface of what can be accomplished in this domain, and that future work will involve STARTING with the waveforms that aren't accounted for in terms of gapes and MTMs.

The interpretation of the three identified MTM subtypes also remains somewhat tentative. The study convincingly demonstrates that distinct waveform-defined clusters exist in the EMG data, but the functional significance of these clusters as ingestive "behaviors" is less clear. As acknowledged by the authors, the specific roles of these movement patterns in the ingestion process remain speculative.

We share R3’s desire for clarity on this point—we do not wish to imply that we understand more than we understand—and will be sure to fine-tune our language to make clearer and more explicit the fact that the distinction in the roles of the MTM subtypes in ingestion at this point remains speculative.

Finally, several conclusions in the Discussion rely on relatively strong mechanistic language when describing the relationship between GC dynamics and ingestive behavior. The data clearly demonstrate a temporal association between GC state transitions and changes in the frequencies of the different MTM subtypes. However, the results primarily support the interpretation that similar cortical dynamics are associated with ingestive and rejection-related behaviors rather than definitively establishing that these behaviors "are governed by the same underlying neural mechanisms".

We will soften our language to clarify which of our Discussion suggestions are speculation, highlighting for the reader the fact that our data, while consistent with evidence suggesting a causal link between the GC transition and gaping (Li et al., 2016; Mukherjee et al., 2019), do not prove a causal neural-behavioral link for MTMs.

References:

Li, Jennifer X., et al. “Sensory Cortical Activity Is Related to the Selection of a Rhythmic Motor Action Pattern.” The Journal of Neuroscience, vol. 36, no. 20, May 2016, pp. 5596–607. DOI.org (Crossref), https://doi.org/10.1523/JNEUROSCI.3949-15.2016.

Mukherjee, Narendra, et al. “Impact of Precisely-Timed Inhibition of Gustatory Cortex on Taste Behavior Depends on Single-Trial Ensemble Dynamics.” eLife, edited by Laura L. Colgin et al., vol. 8, June 2019, p. e45968. eLife, https://doi.org/10.7554/eLife.45968.

eLife Assessment

This fundamental study provides a major contribution to our understanding of Amyotrophic Lateral Sclerosis (ALS) pathogenesis by utilizing a primate model that overcomes the historical limitations of rodent paradigms. By demonstrating the retrograde and trans-synaptic spread of pathological TDP-43 from the periphery to the spinal cord and motor cortex, the authors propose a new model for the disease spreading. The evidence supporting these findings is compelling, characterized by rigorous post-mortem histological observations. This work will be of profound interest to neuroscientists and translational researchers seeking to decode the mechanisms of systemic disease progression in ALS.

Reviewer #1 (Public review):

Summary:

The authors have used a macaque (two animals only) to follow the migration of 'seeded' TDP43 protein in neuronal pathways - thus mimicking the spread of ALS in the human CNS. Previous experiments in rodents failed to demonstrate this, posing interesting and important biological differences, possibly related to the UMN-LMN system in higher order apes and humans.

Strengths:

An important step forward.

Weaknesses:

No weaknesses were identified by this reviewer. Only 2 animals were used, but that is appropriate given the sensate status of the macaque. In the opinion of this reviewer, the results are entirely convincing.

Reviewer #2 (Public review):

Summary:

There are astonishingly few papers trying to reproduce the process of initiation and spreading that Braaks studies have suggested and postulated. The authors should be applauded for pioneering such a difficult experiment. They overexpressed the TDP-43 protein in the motor neuron pool of the brachioradialis muscle and showed that by this technique, motor neurons in this pool died, and the muscle got denervated. They had evidence of a spreading process from the spinal cord to the cortex, demonstrated by showing widespread deposits of phosphorylated TDP-43 bilaterally in the cervical cord and the motor cortex. By their experiment, they created a dying-backwards model, not a model of corticofugal spread, like that shown by Braak. No muscle weakness was observed, not even in the brachioradialis.

Strengths:

The strength of this innovative study is the fact that this spreading experiment uses the phylogenetically young connectome of primates (macaques). They also made the thought-provoking observation of spreading from the cord to the motor cortex, not the corticofugal spread model observed by Heiko Braak. This is thought-provoking because this enables the observer to compare their model with the findings in humans.

Weaknesses:

The following aspects are not a weakness but need to be better explained for the interested reader - and potentially improved in future studies for which the authors laid the foundation:

(1) Why do the authors use the brachioradialis motor neuron pool to overexpress TDP-43? More is known about other muscles and how they are embedded in the motor connectome of primates. Why not the biceps brachii or the hand extensors or - even better - the small muscles of the hand? These are known to be strongly monosynaptically connected with the motor cortex. The authors should explain this. I am unclear if there was a specific reason which I did not see or understand. In my view, the brachioradialis is not the best representative of the primate connectome, for example, to examine this model and compare it with the corticofugal spread.

(2) In the Braaks experiment, only (seemingly soluble) non-phoshorylated TDP-43 "crossed" synapses. Phosphorylated TDP-43 did not do this. The authors of this study saw phosphorylated TDP43 in motor neurons and the cortex. Is there any potential explanation for how it crosses synapses? If it really does, there is an obvious difference to the human situation which needs to be emphasized and explained (in the future).

(3) There were significant deposits of phosphorylated TDP-43 in oligodendrocytes in humans. Whilst I understand that one experiment cannot solve every question - I am curious about whether the authors saw anything in oligodendrocytes?

(4) Which was the pattern of damage? Of course, this pattern is not likely to have a monosynaptic pattern - like in humans........but was there a pattern? Did it have a physiologically meaningful basis? Was there any relation to the corticofugal monosynaptic pattern? What are the differences? The authors speak of "multiple waves". Does this mean that if this were a corticofugal model, for example, oculomotor neurons would also degenerate?

Reviewer #3 (Public review):

Summary:

In this paper by Jones and colleagues, a non-human primate model is described in which wild-type TDP-43 is expressed in the cervical spinal cord. This gave rise to loss of motor neurons in the ventral horn at that level in the cervical spinal cord. MRI of the muscles allowed to see increased intensity in the mostly affected brachioradialis muscle, suggesting this muscle becomes denervated. At the neuropathological level, TDP-43 and pTDP-43 staining in the cytoplasm is increased, not only at the specific level of the cervical spinal cord, but also at a distance.

Strengths:

A clear strength is the state-of-the art focal expression of the TDP-43 transgene at a focal site in the cervical spinal cord. This is achieved by combining a general expression of a flipped loxP flanked TDP-43 vector using AAV9 intrathecal administration, followed by an intramuscular AAV2 hSyn CRE-TdTomato vector in the brachioradialis muscle in order to induce focal recombination and expression of TDP-43 in motor neurons innervating this muscle on one side.

Another strength is the non-human primate background, which is much closer to the human situation.

Weaknesses:

Given the complexity and cost of the model, the n is very low.

The design of the experiments and the results shown about the toxicity induced by this focal TDP-43 expression do not allow us to conclude that it is a good model for ALS for several reasons. It is not clear that the TDP-43 overexpression results in spreading weakness or in spreading motor neuron loss. The neuropathological changes described suggest that there is a kind of stress response, which extends to regions away from the site of primary damage, but more is needed to provide convincing evidence that there is spreading of disease pathology reminiscent of human ALS.

Reviewer #4 (Public review):

Summary:

In this manuscript, the authors present data describing the development of a model of ALS in rhesus macaques. They use a viral intersectional model to overexpress TDP-43 in a population of motor neurons and then study the spread of the pathology about 7 months later. They demonstrate that both the cervical spinal cord and motor cortex (new and old M1) are full of TDP-43, suggesting that the pathology spreads from the single motor pool to presumably related neurons.

Strengths:

This is a super-important study in two main ways:

(1) This could be the birth of a really important model, one that is really needed for making progress in understanding ALS and the development of therapeutics. There are shortfalls with all the rodent models. Models dependent on cell cultures are superb for understanding cell-autonomous processes, but miss out on connectivity, particularly the long-range connectivity. Organoids may ultimately prove to be beneficial, but they would need cortex, spinal cord, and muscle, and translatability from them is not assured. So a NHP model is needed, and this may be it. Furthermore, the Methods are meticulously described and will undoubtedly facilitate reproducibility.

(2) The concept of the spread of pathology has been proposed for some time, I think, based initially on the detailed clinical observations of Ravits and colleagues. The authors have looked at this directly and provide supporting evidence for this interesting hypothesis. They show spread locally and contralaterally in the spinal cord (although a figure would be nice) and to the motor cortex.

Taking only these 2 points into account is more than sufficient for me to be enthusiastic about this work.

Weaknesses:

I'd like to make a couple of points that if addressed, could, in my view, help the authors strengthen this work.

(1) We don't know how many MNs were transduced by the rAAV. There was no tdTom expression, for whatever reason. The authors show an image of a control experiment with a single MN transduced, but there should be a red motor pool, at least in the control experiments. The impression that I get is that very few were transduced, and, in my mind, this makes the findings even more interesting - maybe you don't need many "starter" MNs.

(2) Continuing on this point, this leads the authors to conclude that all BR MNs have died. They support this by the reduced MN count (see point 3). Firstly, do we know how many BR MNs there are in the rhesus macaque, and does the reduction seen correspond to this number? Secondly, and more importantly, the muscle looks normal on MRI at 28 weeks - it does not look like a denervated muscle. The authors state that it has maybe been reinnervated, but by what, if all the BR MNs are dead? This does not seem like a plausible explanation to me. Muscle histology, NMJs, and fibre typing would have been useful to understand what's going on with the MNs. (And electrophysiology would have been wonderful, but beyond the scope of this study.)

(3) Some MN biologists, like me, fuss a lot about how to count MNs, which is almost as difficult as counting the number of angels on the head of a pin. Every method has its problems. Focusing on the two methods here: (a) ChAT immunohistochemistry is pretty good in healthy states, but we don't know what happens to ChAT expression in different diseases, particularly when you have a new model. If its expression is decreased, then it is not a good marker for MNs; (b) Identifying MNs based on the size and morphology of neurons in the ventral horn is also insufficient. For example, ~30% of neurons in a typical pool are small gamma MNs, and a significant proportion (depending on the muscle) of the remainder will be small alpha MNs. So what one is counting is, at best, the large alpha MNs, not all the MNs in a pool. And in ALS, it's these largest MNs that are affected at the earliest stages. The small ones might be fine. So results will be skewed. (Hence, it would be interesting to see if the muscle had a higher proportion of Type I fibres after being reinnervated by S-type MNs.)

(4) Statistics. These are complex experiments looking at the spread of a disease. The experimental unit is therefore the monkey, n=2. In each monkey, multiple sections are analysed, which are key technical replicates and often summative. For example, do we care about the average cell number in Figures 4D, E, 5 I, J or 6G, H, or rather the total cell number? Do the error bars mean anything? To be clear, I am by no means minimising the importance of the overall convincing findings. But I do not think this statistical analysis is particularly meaningful.

Author response:

Public Reviews:

Reviewer #1 (Public review): 

Summary: 

The authors have used a macaque (two animals only) to follow the migration of 'seeded' TDP43 protein in neuronal pathways - thus mimicking the spread of ALS in the human CNS. Previous experiments in rodents failed to demonstrate this, posing interesting and important biological differences, possibly related to the UMN-LMN system in higher order apes and humans. 

Strengths: 

An important step forward. 

Weaknesses: 

No weaknesses were identified by this reviewer. Only 2 animals were used, but that is appropriate given the sensate status of the macaque. In the opinion of this reviewer, the results are entirely convincing. 

Reviewer #2 (Public review): 

Summary: 

There are astonishingly few papers trying to reproduce the process of initiation and spreading that Braaks studies have suggested and postulated. The authors should be applauded for pioneering such a difficult experiment. They overexpressed the TDP-43 protein in the motor neuron pool of the brachioradialis muscle and showed that by this technique, motor neurons in this pool died, and the muscle got denervated. They had evidence of a spreading process from the spinal cord to the cortex, demonstrated by showing widespread deposits of phosphorylated TDP-43 bilaterally in the cervical cord and the motor cortex. By their experiment, they created a dying-backwards model, not a model of corticofugal spread, like that shown by Braak. No muscle weakness was observed, not even in the brachioradialis. 

Strengths: 

The strength of this innovative study is the fact that this spreading experiment uses the phylogenetically young connectome of primates (macaques). They also made the thought-provoking observation of spreading from the cord to the motor cortex, not the corticofugal spread model observed by Heiko Braak. This is thought-provoking because this enables the observer to compare their model with the findings in humans. 

Weaknesses: 

The following aspects are not a weakness but need to be better explained for the interested reader - and potentially improved in future studies for which the authors laid the foundation: 

(1) Why do the authors use the brachioradialis motor neuron pool to overexpress TDP-43? More is known about other muscles and how they are embedded in the motor connectome of primates. Why not the biceps brachii or the hand extensors or - even better - the small muscles of the hand? These are known to be strongly monosynaptically connected with the motor cortex. The authors should explain this. I am unclear if there was a specific reason which I did not see or understand. In my view, the brachioradialis is not the best representative of the primate connectome, for example, to examine this model and compare it with the corticofugal spread. 

The brachioradialis muscle was chosen primarily for reasons of animal welfare; our concern when designing the experiments was that the muscle we chose for injection might become very wasted and weak before the experiment had been completed. If we had injected a hand muscle, this would have affected manipulation, feeding and grooming behaviours, whereas had we injected biceps brachii or forearm extensors, this would have affected more important behaviours requiring strength for body support in the home cage (e.g. climbing, swinging, etc.). The advantage of choosing brachioradialis is that there is some functional redundancy; in macaques, compared to biceps brachii, brachioradialis has a relatively minor role in elbow flexion and supination of the forearm. We therefore reasoned that there should be physiological compensation for any weakness in brachioradialis, and thus minimal effects on normal behaviour.

A secondary practical consideration was the importance of good quality MR imaging of the injected muscle and the positioning of the focussing coil; because of the physical constraints related to the monkey sitting in our narrow-bore scanner, the forearm muscles were the optimal choice. 

With reference to the ‘primate connectome’, whilst hand muscles are known to have strong cortico-motoneuronal connections, we have shown previously that monosynaptic corticomotoneuronal connections are as strong in muscles innervated by the deep radial nerve (like brachioradialis) as in intrinsic hand muscles (Witham et al, 2016).

Finally, for the purposes of these experiments, all we required was a method for inoculating TDP-43 into a motor neuron pool within the spinal cord, without direct surgical trauma to the spinal cord. Our aim was to test the hypothesis that extracellular TDP-43 is sufficient to cause spreading neuronal changes in macaque, similar to those observed in human ALS/MND; our aim was not to replicate the actual pattern of human MND observed clinically.

These points will be addressed in a revised version of the manuscript. 

(2) In the Braaks experiment, only (seemingly soluble) non-phoshorylated TDP-43 "crossed" synapses. Phosphorylated TDP-43 did not do this. The authors of this study saw phosphorylated TDP43 in motor neurons and the cortex. Is there any potential explanation for how it crosses synapses? If it really does, there is an obvious difference to the human situation which needs to be emphasized and explained (in the future). 

To clarify, there was no evidence of phosphorylated TDP-43 crossing synapses. It is more likely that excess non-phosphorylated TDP-43 crossed synapses, and that this then subsequently led to TDP-43 phosphorylation.  

(3) There were significant deposits of phosphorylated TDP-43 in oligodendrocytes in humans. Whilst I understand that one experiment cannot solve every question - I am curious about whether the authors saw anything in oligodendrocytes? 

We have not looked at this.

(4) Which was the pattern of damage? Of course, this pattern is not likely to have a monosynaptic pattern - like in humans........but was there a pattern? Did it have a physiologically meaningful basis? Was there any relation to the corticofugal monosynaptic pattern? What are the differences? The authors speak of "multiple waves". Does this mean that if this were a corticofugal model, for example, oculomotor neurons would also degenerate? 

The description of ‘multiple waves’ in paragraph 2 of the discussion section is entirely hypothetical, based on the assumption that there are different mechanisms by which TDP-43 spreads through the nervous system, from slow local spread by diffusion to more rapid long-range axonal spread to widely separated regions. For the neuropathological staging analysis, we therefore looked at different brain regions (hypoglossal nuclei, reticular formation, inferior olives, frontal cortex, temporal cortex and hippocampal formation). This analysis only showed loss of motor neurons in the spinal cord ipsilateral to the side of the muscle injections, in segments consistent with the location of brachioradialis motoneurons. We did not demonstrate a ‘pattern of damage’ as described in humans in our experiments because this is a pre-symptomatic pre-clinical model, with no established ‘damage’ from each wave. We speculate that this is because animals were terminated too early in the disease process.

However, whilst there was no established neuronal degeneration outside the cervical spinal cord, the observation that there were more pTDP-43 positive Betz cells in left (contralateral to the brachioradialis injection) New M1 than Old M1 (see Figure 6I and J) would support spread via monosynaptic connections to motoneurons; New M1 is where most monosynaptic cortico-motoneuronal connections originate.

Reviewer #3 (Public review): 

Summary: 

In this paper by Jones and colleagues, a non-human primate model is described in which wild-type TDP-43 is expressed in the cervical spinal cord. This gave rise to loss of motor neurons in the ventral horn at that level in the cervical spinal cord. MRI of the muscles allowed to see increased intensity in the mostly affected brachioradialis muscle, suggesting this muscle becomes denervated. At the neuropathological level, TDP-43 and pTDP-43 staining in the cytoplasm is increased, not only at the specific level of the cervical spinal cord, but also at a distance. 

Strengths: 

A clear strength is the state-of-the art focal expression of the TDP-43 transgene at a focal site in the cervical spinal cord. This is achieved by combining a general expression of a flipped loxP flanked TDP-43 vector using AAV9 intrathecal administration, followed by an intramuscular AAV2 hSyn CRE-TdTomato vector in the brachioradialis muscle in order to induce focal recombination and expression of TDP-43 in motor neurons innervating this muscle on one side. 

Another strength is the non-human primate background, which is much closer to the human situation. 

Weaknesses: 

Given the complexity and cost of the model, the n is very low. 

As is common in most studies in non-human primates, we have carried out all statistical analysis within one animal (e.g. the comparison of motoneuron numbers between left and right cord). We then show that results are reproducible in two animals. Although the number of animals is lower than in a typical rodent study, we see this as an advantage of the model, adhering to the 3Rs principle of ‘reduction’.

The design of the experiments and the results shown about the toxicity induced by this focal TDP-43 expression do not allow us to conclude that it is a good model for ALS for several reasons. It is not clear that the TDP-43 overexpression results in spreading weakness or in spreading motor neuron loss. The neuropathological changes described suggest that there is a kind of stress response, which extends to regions away from the site of primary damage, but more is needed to provide convincing evidence that there is spreading of disease pathology reminiscent of human ALS. 

As already noted in our response to Reviewer 2 (point 1), animal welfare is an important consideration when designing these complex experiments in primates. We could not therefore justify allowing the animals to survive until extensive wasting and weakness were evident, recapitulating the human disease. 

The model developed in these experiments is therefore a pre-symptomatic pre-clinical model, in which animals are terminated before pathology leading to widespread motor neuron loss is evident. At post mortem we do have evidence of motor neuron loss in the segments supplying brachioradialis (C4-C8).

Stress of various forms, including blunt trauma (e.g. Anderson et al, 2021), stab/electrode insertion injury (e.g. Zambusi et al, 2022), chemical (e.g. arsenite) exposure (e.g. Huang et al, 2024), or hypoxia (Marcus et al, 2021) can result in pathological nucleocytoplasmic translocation of TDP-43. In our model, there was no direct trauma to the brain or spinal cord ante mortem, excluding one major cause of tissue stress. Hypoxia during the process of euthanasia is possible, but we would expect there would not be enough time before death for this to manifest as TDP-43 translocation. In the literature TDP-43 translocation due to stress is diffuse; we have demonstrated that in our model the TDP-43 pathology is not diffuse but selective. For example, there was no evidence of disease in the oculomotor nuclei; in the primary motor cortex (M1) there are significantly more pathological changes in the evolutionarily younger ‘NewM1’ compared to the neighbouring ‘OldM1’.

It is therefore improbable that our findings could be explained by ‘a kind of stress response’. Our findings are better explained by spread of the TDP-43 protein.

Reviewer #4 (Public review): 

Summary: 

In this manuscript, the authors present data describing the development of a model of ALS in rhesus macaques. They use a viral intersectional model to overexpress TDP-43 in a population of motor neurons and then study the spread of the pathology about 7 months later. They demonstrate that both the cervical spinal cord and motor cortex (new and old M1) are full of TDP-43, suggesting that the pathology spreads from the single motor pool to presumably related neurons. 

Strengths: 

This is a super-important study in two main ways: 

(1) This could be the birth of a really important model, one that is really needed for making progress in understanding ALS and the development of therapeutics. There are shortfalls with all the rodent models. Models dependent on cell cultures are superb for understanding cell-autonomous processes, but miss out on connectivity, particularly the long-range connectivity. Organoids may ultimately prove to be beneficial, but they would need cortex, spinal cord, and muscle, and translatability from them is not assured. So a NHP model is needed, and this may be it.

Furthermore, the Methods are meticulously described and will undoubtedly facilitate reproducibility. 

(2) The concept of the spread of pathology has been proposed for some time, I think, based initially on the detailed clinical observations of Ravits and colleagues. The authors have looked at this directly and provide supporting evidence for this interesting hypothesis. They show spread locally and contralaterally in the spinal cord (although a figure would be nice) and to the motor cortex. 

Taking only these 2 points into account is more than sufficient for me to be enthusiastic about this work. 

Weaknesses: 

I'd like to make a couple of points that if addressed, could, in my view, help the authors strengthen this work. 

(1) We don't know how many MNs were transduced by the rAAV. There was no tdTom expression, for whatever reason. The authors show an image of a control experiment with a single MN transduced, but there should be a red motor pool, at least in the control experiments. The impression that I get is that very few were transduced, and, in my mind, this makes the findings even more interesting - maybe you don't need many "starter" MNs. 

Unfortunately, we cannot know how many motoneurons were transduced.

However, the reviewer may be correct, that it is actually only a small fraction of the brachioradialis pool. This is supported by the evidence for rather focal denervation seen on MRI.

(2) Continuing on this point, this leads the authors to conclude that all BR MNs have died. They support this by the reduced MN count (see point 3). Firstly, do we know how many BR MNs there are in the rhesus macaque, and does the reduction seen correspond to this number? Secondly, and more importantly, the muscle looks normal on MRI at 28 weeks - it does not look like a denervated muscle. The authors state that it has maybe been reinnervated, but by what, if all the BR MNs are dead? This does not seem like a plausible explanation to me. Muscle histology, NMJs, and fibre typing would have been useful to understand what's going on with the MNs. (And electrophysiology would have been wonderful, but beyond the scope of this study.) 

To clarify, we did not conclude that all brachioradialis motor neurons had died, rather that all transfected brachioradialis motor neurons pool had died. As noted above, when these cells die and the muscle is denervated, the MRI signal changes occupy only a small volume of the muscle and are transient. We would not expect to see long-term MRI changes in muscle anatomy after this limited denervation-reinnervation event. 

Analysis of muscle histology, including fibre typing, is outwith the scope of this initial paper reporting the model; we hope that this will form the basis of a future publication.

(3) Some MN biologists, like me, fuss a lot about how to count MNs, which is almost as difficult as counting the number of angels on the head of a pin. Every method has its problems. Focusing on the two methods here: (a) ChAT immunohistochemistry is pretty good in healthy states, but we don't know what happens to ChAT expression in different diseases, particularly when you have a new model. If its expression is decreased, then it is not a good marker for MNs; (b) Identifying MNs based on the size and morphology of neurons in the ventral horn is also insufficient. For example, ~30% of neurons in a typical pool are small gamma MNs, and a significant proportion (depending on the muscle) of the remainder will be small alpha MNs. So what one is counting is, at best, the large alpha MNs, not all the MNs in a pool. And in ALS, it's these largest MNs that are affected at the earliest stages. The small ones might be fine. So results will be skewed. (Hence, it would be interesting to see if the muscle had a higher proportion of Type I fibres after being reinnervated by S-type MNs.) 

This is an interesting point, and we agree that each method used to quantify MN number carries its own limitations. The problem of MN identification is heightened in a MND-like pathological state, especially when considering evidence of reduced ChAT activity in spinal motoneurons in end-stage disease in post mortem human samples (Oda et al, 1995), and more recent evidence from Casas et al. (2013), who demonstrated early presymptomatic reduction in ChAT expression in SOD1G93A mice. It is important to note that this was a modest reduction, not complete abolition of signal (76% of control levels). ChAT immunoreactivity was still present and motor neurons were still identifiable as ChAT-positive at this pre-clinical stage of disease. As counts in our study were performed based on detecting ChAT in cells, it seems unlikely that we would miss cells. However, we cannot rule this out. If indeed this did occur, it would mean that the reduced motoneuron counts which we observed reflect not only cell death, but also profound motoneuron dysfunction which is presumably the proximal precursor to cell death.

We acknowledge that size-based criteria applied to ChAT-positive neurons will preferentially capture large alpha motor neurons, and that gamma motor neurons and small alpha motor neurons are likely underrepresented in our counts. Our counts therefore reflect the large alpha motor neuron population rather than the total motor neuron pool. We believe that this is not a critical limitation in the context of the present study. Large alpha motor neurons are the population of primary pathological interest in ALS and related MND, being the earliest and most severely affected subtype. The selective vulnerability of fast-fatigable large alpha motor neurons in ALS is well established, and their preferential loss is the defining feature of disease progression in both human post mortem tissue and rodent models (Lalancette-Hébert et al., 2016). In this respect, our size threshold selects for precisely the population whose degeneration is most relevant to the disease phenotype we are modelling. 

We intend to include comments on these important points in the revised version of the manuscript.

In response to the final point regarding muscle histology and proportions of Type I fibres, as stated above, reporting of muscle histology, including fibre typing, is planned for a separate publication.

(4) Statistics. These are complex experiments looking at the spread of a disease. The experimental unit is therefore the monkey, n=2. In each monkey, multiple sections are analysed, which are key technical replicates and often summative. For example, do we care about the average cell number in Figures 4D, E, 5 I, J or 6G, H, or rather the total cell number? Do the error bars mean anything? To be clear, I am by no means minimising the importance of the overall convincing findings. But I do not think this statistical analysis is particularly meaningful. 

Here, the experimental unit is the tissue slice, mounted on a slide for histological analysis, and not the monkey. All statistical comparisons are made within a single animal. We then show that the findings can be replicated in two animals, both of which show significant results. This is standard approach taken in primate neuroscience, given the need to reduce animal numbers to the minimum consistent with producing convincing results.

This work has been peer reviewed in GigaScience (see https://doi.org/10.1093/gigascience/giag039), which carries out open, named peer-review. These reviews are published under a CC-BY 4.0 license and were as follows:

Reviewer 2:

General Comments This manuscript introduces a tool named HVRLocator, designed to address the issue of missing or non-standard metadata in 16S rRNA sequencing data found in public databases such as the SRA. The tool identifies amplicon regions by aligning sequences to a reference genome and attempts to detect the presence of primers using a machine learning model. This is a subject with significant practical value, particularly for conducting large-scale meta-analyses. However, there are still many issues regarding methodological rigor, the depth of validation, and comparisons with existing tools that require further clarification by the authors. Major Comments 1. Concerns regarding the singularity of the reference sequence The authors mention aligning sequences to a single Escherichia coli (J01859.1) reference genome to determine start and end positions. Is a single E. coli reference sufficient to cover Archaea or bacterial phyla that are distantly related to Proteobacteria, which may be present in environmental samples (e.g., soil, ocean)? For taxa with significant length variations or insertions/deletions (Indels), could forced alignment to the E. coli reference lead to misjudgment of start/end positions? Have the authors evaluated the impact on accuracy if a more universal reference database (such as representative sequences from SILVA or Greengenes) were used? 2. Rationality of the primer detection model (Random Forest based on Quality Scores) The authors developed a Random Forest model to predict primer presence by analyzing the quality score distribution of the first 1,000 reads. Primer detection is typically based on the sequence itself rather than quality scores. Can the authors explain why quality scores were chosen as features? Sequencing quality scores are influenced by technical factors such as sequencer status, reagent batches, and run cycles, which have no direct biological correlation with the presence of primers. Is there a risk that this model is "overfitting" specific sequencing platforms or datasets? Since the reads are already downloaded, why not directly use degenerate primer sequence matching (e.g., using Cutadapt or SeqKit logic) to determine primer presence? This seems to be a more direct and accurate method. 3. Verification of accuracy claims In the validation section, the authors claim to achieve 100% accuracy on certain datasets. In bioinformatics tool development, a claim of 100% accuracy is often a red flag. Have the authors manually checked those samples marked as "correct" by the model that might suffer from edge effects or borderline cases? 4. Dataset imbalance in the Random Forest model For the Random Forest model, the authors used 882 samples with primers and 8,940 samples without primers for training. Such an extremely imbalanced dataset, even with stratified sampling, may cause the model to be biased towards the majority class. 5. Comparison with existing tools The manuscript mentions that no tool has been designed for this specific purpose, but this may overlook some existing general-purpose tools or scripts. Many pipelines (such as certain plugins in QIIME 2, USEARCH, etc.) possess functionalities to identify primers or evaluate amplicon regions. The authors should discuss how their tool compares to these existing workflows. Minor Comments 1. Confusion regarding processing speed metrics The abstract mentions a processing speed of "0.147 samples per minute", but later the text mentions "6.5 samples per minute" and "one sample every 0.147 minutes". There is confusion regarding units and values in these three descriptions (is it samples per minute or minutes per sample?). Please unify and correct these data to ensure consistency. 2. Usage of fastq-dump The use of fastq-dump is mentioned. The SRA Toolkit's fastq-dump is relatively slow and has largely been superseded by fasterq-dump for efficiency. Why did the authors not use the more efficient fasterq-dump? 3. Definition of "Standardized metadata" The term "standardized metadata" is used frequently. Please explicitly define what constitutes "standard" metadata in the context of this tool within the text. 4. Robustness and error handling The results section mentions that some samples failed due to "NCBI portal-related issues". Does this imply the tool lacks breakpoint resumption or retry mechanisms? Given that network fluctuations are common during large-scale downloads, how is the tool's robustness demonstrated? 5. Output confidence intervals The output file contains "TRUE/FALSE" and a probability score. For samples where the probability score is at a critical threshold (e.g., around 0.5), does the tool provide an "uncertain" tag, or does it force a classification? It is suggested to add an indicator for ambiguous ranges.

This work has been peer reviewed in GigaScience (see https://doi.org/10.1093/gigascience/giag039), which carries out open, named peer-review. These reviews are published under a CC-BY 4.0 license and were as follows:

Reviewer 1:

The manuscript presents GEfetch2R, an R package (with a Docker image) that fetches scRNA-seq and bulk RNA-seq data from multiple repositories, loads the data into R objects, and benchmarks format-conversion tools. The problem addressed is real and important; the implementation appears practical and well documented. I see strong potential for adoption. Major comments

1) Robust cross-repository support for .RData files While GEfetch2R lists rdata among supported extensions for Zenodo and HCA, many GEO submissions and other archives still provide processed data exclusively as .RData, often bundling matrices and metadata in heterogeneous objects. Please add an explicit, repository-agnostic .RData ingestion path with: (i) automatic object introspection, (ii) standardized extraction of matrices/metadata, (iii) graceful fallbacks with clear diagnostics for non-standard objects, and (iv) reproducible examples. This materially increases real-world coverage.

2) Large-scale, automated evaluation on ~100 scRNA-seq datasets Beyond the single COVID-19 application and the conversion benchmark, please include a systematic "fetch success-rate" study across ~100 GEO scRNA-seq datasets. Provide a Dockerized workflow (publicly available) that periodically attempts end-to-end retrieval (raw / count / processed) and reports success/failure rates stratified by repository and file type, with resource/time footprints and categorized failure causes. Given heterogeneous deposition practices, even ~50% overall success would be informative.

3)Another very important point is to provide a Dockerfile together with the Docker. Minor revisions

"altas" → atlas (COVID-19 section title/caption).

"Count maatrix" → Count matrix (Figure 3 caption/table column).

"PanglanDB" → PanglaoDB (tables).

Consistency: keep SeuratObject (not "Seurat object"); keep rds lowercase;

如果只是单纯的拿到输入和输出，那可以 。 但是一定不能让agent 拿到测试数据。 一旦通过测试数据，构建pattern ， 优化迭代就会出问题。

实验通过 Harbor 统筹调度全流程：自动启动 Daytona 沙箱环境、对接智能体运行循环，并完成结果校验与分数评定。 这里两个英文值得看看是啥？ 回头过来看

这里具体怎么微调的呢

系统提示词， 工具 ， 整体的 workflow ； 这是harness 的工作范畴。 给了一个定义

This work has been peer reviewed in GigaScience (see https://doi.org/10.1093/gigascience/giag040), which carries out open, named peer-review. These reviews are published under a CC-BY 4.0 license and were as follows:

Reviewer 2:

General Comments This manuscript introduces a tool named HVRLocator, designed to address the issue of missing or non-standard metadata in 16S rRNA sequencing data found in public databases such as the SRA. The tool identifies amplicon regions by aligning sequences to a reference genome and attempts to detect the presence of primers using a machine learning model. This is a subject with significant practical value, particularly for conducting large-scale meta-analyses. However, there are still many issues regarding methodological rigor, the depth of validation, and comparisons with existing tools that require further clarification by the authors. Major Comments 1. Concerns regarding the singularity of the reference sequence The authors mention aligning sequences to a single Escherichia coli (J01859.1) reference genome to determine start and end positions. Is a single E. coli reference sufficient to cover Archaea or bacterial phyla that are distantly related to Proteobacteria, which may be present in environmental samples (e.g., soil, ocean)? For taxa with significant length variations or insertions/deletions (Indels), could forced alignment to the E. coli reference lead to misjudgment of start/end positions? Have the authors evaluated the impact on accuracy if a more universal reference database (such as representative sequences from SILVA or Greengenes) were used? 2. Rationality of the primer detection model (Random Forest based on Quality Scores) The authors developed a Random Forest model to predict primer presence by analyzing the quality score distribution of the first 1,000 reads. Primer detection is typically based on the sequence itself rather than quality scores. Can the authors explain why quality scores were chosen as features? Sequencing quality scores are influenced by technical factors such as sequencer status, reagent batches, and run cycles, which have no direct biological correlation with the presence of primers. Is there a risk that this model is "overfitting" specific sequencing platforms or datasets? Since the reads are already downloaded, why not directly use degenerate primer sequence matching (e.g., using Cutadapt or SeqKit logic) to determine primer presence? This seems to be a more direct and accurate method. 3. Verification of accuracy claims In the validation section, the authors claim to achieve 100% accuracy on certain datasets. In bioinformatics tool development, a claim of 100% accuracy is often a red flag. Have the authors manually checked those samples marked as "correct" by the model that might suffer from edge effects or borderline cases? 4. Dataset imbalance in the Random Forest model For the Random Forest model, the authors used 882 samples with primers and 8,940 samples without primers for training. Such an extremely imbalanced dataset, even with stratified sampling, may cause the model to be biased towards the majority class. 5. Comparison with existing tools The manuscript mentions that no tool has been designed for this specific purpose, but this may overlook some existing general-purpose tools or scripts. Many pipelines (such as certain plugins in QIIME 2, USEARCH, etc.) possess functionalities to identify primers or evaluate amplicon regions. The authors should discuss how their tool compares to these existing workflows. Minor Comments 1. Confusion regarding processing speed metrics The abstract mentions a processing speed of "0.147 samples per minute", but later the text mentions "6.5 samples per minute" and "one sample every 0.147 minutes". There is confusion regarding units and values in these three descriptions (is it samples per minute or minutes per sample?). Please unify and correct these data to ensure consistency. 2. Usage of fastq-dump The use of fastq-dump is mentioned. The SRA Toolkit's fastq-dump is relatively slow and has largely been superseded by fasterq-dump for efficiency. Why did the authors not use the more efficient fasterq-dump? 3. Definition of "Standardized metadata" The term "standardized metadata" is used frequently. Please explicitly define what constitutes "standard" metadata in the context of this tool within the text. 4. Robustness and error handling The results section mentions that some samples failed due to "NCBI portal-related issues". Does this imply the tool lacks breakpoint resumption or retry mechanisms? Given that network fluctuations are common during large-scale downloads, how is the tool's robustness demonstrated? 5. Output confidence intervals The output file contains "TRUE/FALSE" and a probability score. For samples where the probability score is at a critical threshold (e.g., around 0.5), does the tool provide an "uncertain" tag, or does it force a classification? It is suggested to add an indicator for ambiguous ranges.

This work has been peer reviewed in GigaScience (see https://doi.org/10.1093/gigascience/giag040), which carries out open, named peer-review. These reviews are published under a CC-BY 4.0 license and were as follows:

Reviewer 1:

Metabarcoding data are accumulating rapidly. This paper makes a very valuable contribution to the automated extraction and curation of metabarcoding data and should be of great value in facilitating the re-use of existing data and the construction of custom databases based on these. I have not tested or tried to install the software myself, as the manuscript provided sufficient detail to enable me to assess the tool

General comments

The manuscript is written entirely in terms of "bacteria" and aligns amplicons to an E. coli model sequence. This is reasonable, but there should certainly be some acknowledgement of Archaea and ideally some mention of Eukaryotes too. These are probably things for the discussion section of this manuscript, but the authors may wish to consider whether a future version of the program could contain options to use model Archaea and Eukaryote sequences as alternatives to the E. coli model. It would also be helpful to assess how the program with its E. coli model deals with sequence data from Archaea, Eukaryotes (including mitochondria) and bacteria that are very divergent from E. coli. The methods section does not contain details of software used to generate the figures, or whether these figures are produced by "the pipeline" or by separate analysis of the .txt file that the pipeline produces. I suspect that it is that latter, in which case making the authors should make the scripts used available - as well as providing complete documentation of what has been done, this is likely to increase use made of the tool. And it would be helpful to include an output file in the supplementary materials

Specific comments

Line 64 "however the integration of these data in light of processing metadata" - not clear

Line 67-8 "though bacterial diversity increases linearly with amplicon length". Needs re-wording. The number of ASVs will increase with amplicon length, but the actual bacterial diversity in a sample is constant.

Line 79 "Wasimuddin and colleagues" should be "Wasimuddin et al". More generally, check that citations conform with journal house style

Line 79-82 "For example, Wasimuddin and colleagues [8] found that compared to three other primer sets targeting different regions, the primer pair targeting the V4 hypervariable region of the 16S rRNA gene produced the highest estimates of species richness and diversity across various sample types" There are three issues here: 1) different primer pairs vary in their coverage and bias, so different primers targeting the same variable region will produce different numbers of ASVs 2) Even with complete coverage and the absence of bias, different variable regions will generate different numbers of ASVs as a result of differences in length and rate of evolution between variable regions (and differences in the number of ASVs that are clustered into OTUs at a particular sequence similarity threshold 3) The relationship between ASVs or OTUs and "species" is not straightforward (Edgar, 2018). At minimum "species" should be replace with ASV or OTU (whichever Wasimuddin et al used)

Line 89-90 "as bacterial diversity and taxonomic resolution linearly increase with target sequence length [12]." Overlaps with statement made in line 67-8, and the same issue applies here.

Lines 167-170. The output file contains (amongst other things) "Predicted HV region Start/End: Predicted hypervariable (HV) region based on the median alignment start and end positions across all reads, inferred from literature on conserved and hypervariable regions of the 16S rRNA gene (Brosius et al., 1978; Yang et al., 2016)". This implies that the program predicts a single variable region for each study - I am not clear what this column will contain for amplicons that contain more than one variable region, although columns 11-19 indicate that the program identifies the presence/absence of each of the 9 HV regions. My guess is that the authors are using "HV region" in two different sense: 1) Its usual meaning of one region out of V1 to V9 2) The sequence from the beginning of the first of the nine variable regions the amplicon includes to the end of the last. It would also be helpful to indicate whether the sequence positions here are relative to the E coli model or refer to sequence positions in the amplicon

Edgar, R. C. (2018). Updating the 97% identity threshold for 16S ribosomal RNA OTUs. Bioinformatics, 34(14), 2371-2375. doi:10.1093/bioinformatics/bty113

This work has been peer reviewed in GigaScience (see https://doi.org/10.1093/gigascience/giag036), which carries out open, named peer-review. These reviews are published under a CC-BY 4.0 license and were as follows:

Reviewer 2:

I had the privilege of reviewing the manuscript titled "MicroFinder: conserved gene-set mapping and assembly ordering for manual insertion of bird microchromosomes" by Mathers et al. The manuscript presents a conserved gene set linked to bird microchromosomes for identifying putative contigs/scaffolds. Subsequently, microchromosomes contigs/scaffolds can be made into their corresponding chromosome models using orthogonal evidence from HiC data. MicroFinder utilises the current knowledge of microchromosome conservation across birds. This approach is similar to assembly evaluation method using BUSO genes.

One of the major limitation of the manuscript is the lack of validation or supportive evidence to show that manual curation results after applying MicroFinder hints are valid and robust. Authors can perform local synteny or chromosome scale alignments analyses and conservation property evaluation to demonstrate that results of assembly curation are valid. Authors can also report metrics of HiC contact maps before and after curation for inter and intra chromosomes contacts to demonstrate improvements. If this is not done, authors may have to remove results and methods corresponding to manual curation so as to focus on genes that are found in "putative" microchromosomes.

Manuscript is generally well written with some minor concerns. Analyses presented are generally robust.

It was confusing to read the difference between micro and dot chromosomes. I encourage authors to avoid "dot" chromosome term. Although it has been used in literature in the past, we can do without that term. There is no strong evidence to suggest if micro and dot chromosomes have any significant functional or system level differences. Best to avoid the term.

If authors insist on using the dot nomenclature, a justification and explanation would be required with clear definitions for both. Also, the name of the workflow may need to change as well. I leave it up to authors to make that call.

Similarly I encourage authors avoid using the term shrapnel for small unplaced contigs. Just use small unplaced contigs instead.

Finding section contains a lot of information that belongs in methods section. For example line numbers 109-117 122-125 135-137 154-156 160-164 167-172 187-192. Please revise the text so that findings section doesn't have any methods description.

A definition of what is a orthogroup and fuzzy orthogroup is required.

Result/findings section needs significant improvements. Authors have relegated much of the results to tables in supplementary information. I insist that authors summarise those results in a meaningful descriptive way and refer to supplementary information for extra details.

Lines 176-177 mentions about the manual curation of micro chromosomes. I would like to see the rules and decisions that were employed to join or break or reorder contigs/scaffolds into a chromosome model.

Authors have mentioned that 216kb-4.3mb of additional content per assembly was added. This is incorrect as the sequence content was already present in the assembly. It is just reorganised into microchromosome scaffolds. Please correct the text to say that unplaced scaffolds are organised into putative microchromosomes.

Lines 108-199 mentions about errors in original assembly. A description about the type of errors would be required.

Authors should discuss the property of eagles, falcons and parrots with rearranged/fused micro chromosomes. The proposed method may not be effective in such instances.

Authors suggest the use of 5Mbp cut off. However, in instances where a micro chromosome is incorrectly placed with a macro- chromosome may miss these instances. Authors discuss this as paralog or misalignment related issues. I suggest that authors provide a metric for the success/failure of identifying genes similar to BUSCO. Authors can run the software on all available bird genomes to define the property of such metric for each gene. Result section can explain proportions of 9400 found on macro vs micro. Proportions of 14k fuzzy genes on micro vs macro, their copy status. 9400 + 14514 doesn't add up to 16,589 orthogroup. Something is not clearly described about those numbers. Please improve the text to make meaningful assessments of conserved gene sets on Microchromosomes for it to be useful for the research community.

Methods: Lines 233-234: what is taxon in this context? Please clarify. There is also a mention of taxa with missing data. What data were missing? Please clarify.

Lines 236-237: do authors mean that chromosomes identified by the submitter of primary assembly? Please clarify.

For each species, authors should refer to refseq version of the assembly for posterity as well. Common names of species may be useful too for broad readership.

Line 254: please modify the section header to remove assembly version as they are not useful

Methods describing the orthogroup clustering should include details about how alignments were filtered and processed. This is currently missing.

Significance of phylogenetic analyses in the context of manuscript is not very clear. May be remove that section. Perhaps authors can utilise the phylogenetic distance as a way to discuss how conserved gene sets are behaving between species based on distance.

Results section can include run time and compute resource usage metrics for others to estimate resource requirements for such analyses.

Updated assemblies can be submitted to NCBI. Authors should consider this.

This work has been peer reviewed in GigaScience (see https://doi.org/10.1093/gigascience/giag036), which carries out open, named peer-review. These reviews are published under a CC-BY 4.0 license and were as follows:

Reviewer 1:

I am very happy to see that MicroFinder is going to be published! Last year I used it very often to curated the bird assemblies. I found no major issues, but only the minor one.

The only crucial (but still technical issue) is that your protein dataset is from dot microchromosomes, i.e. not from the all microchromosomes. So I highly recommend to use "dot microchromosomes" where relevant including the title of the manuscript.

Minor issues:

row 19 (Abstract background) change "major goal" to a softer statement. Generation of the assemblies is a very important task of bioiversity genomics but not a major one

row 54-55 Do you imply that typical bird genome contains 37-41 chromosome pairs? There are a lot of birds with lower number of chromosome, so i am not sure that it is typical.. Also a reference to publication from 1981 looks outdated

row 109 - why only eleven assemblies were selected?

row 111 - 112 Please, highlight how many orders/families were not covered

rows 129 - 137 This lines are in some contradiction with all the text including the abstract. Your dataset is focused on a dot chromosomes and not on the all microchromosomes. I suggest to replace "microchromosomes" nearly everywhere to "dot microchromosomes" including the title

row 173 - 185 I am very skeptical about expanding the results obtained on a single genome assembly to the whole family, especially if remember that your dataset covers less than a half of bird orders. My experience with Microfinder tells that sometimes it select contigs/scaffold belonging to macrochromosomes. However, not many and they are usually short. Please, soften statements

row 429 Reference 13 is in French and doesn't have an English translation of the title

foundation drawing of castell coch

DOI: 10.1167/iovs.67.4.33

Resource: (MMRRC Cat# 034748-UCD,RRID:MMRRC_034748-UCD)

Curator: @AleksanderDrozdz

SciCrunch record: RRID:MMRRC_034748-UCD

What is this?

DOI: 10.1084/jem.20240801

Resource: RRID:MMRRC_065809-JAX

Curator: @AleksanderDrozdz

SciCrunch record: RRID:MMRRC_065809-JAX

What is this?

I chose this source because it makes invisible disability easier to understand. ME/CFS can seriously affect a person even if other people cannot see it, and symptoms can change over time. That connects well to the chapter’s point that not all disabilities are obvious from the outside.

This source grabs my attention simply because of how common it is in society, especially amongst men. I feel like the general idea of color blindness is so interestingly perceived but also having it holds back from many careers in life. This disability most commonly defines men.

I liked this definition because it makes disability feel less like a personal defect and more like a mismatch between people and the way society is designed. That stood out to me because it shifts the focus from “what is wrong with this person” to “what assumptions are built into this space or system.”

This was an extremely interesting comment to reflect on simply because it is so true. I don't think I've thought about it in that was before, in terms of the fact that anything in society can pose as an issue or plan someone into the "disability" category .

This sentence outlines the key foundational elements of pronunciation that beginners need to learn first.

• Steak, mash and sausage rolls dipped in ketchup provided by the local fancy restaurant for my birthday, which I can share with Rupert and Superted

definition of benthic: the lowest ecological zone in a body of water, including the seafloor, sediment surface, and sub-surface layers

Jerry Lewis "Typewriter skit" in Who's Minding the Store (1963)

Reducing duplication and message loss helps maintain accurate and consistent data transfer. This ensures that the received data matches what was sent. It improves the reliability and correctness of communication.

Synchronization checkpoints allow communication to resume from a specific point after interruption. This minimizes data loss and avoids restarting the entire process. It enhances reliability in data transmission.

Token-based control ensures that only one device transmits data at a time. This reduces the chances of data collision and confusion during communication. It improves overall efficiency and clarity in data exchange.

Proper session termination ensures that all data is successfully transmitted before closing the connection. It prevents incomplete transfers and data loss. This leads to a clean and reliable end to communication.

Checkpoints help maintain proper sequence and structure of data during transmission. They allow systems to resume communication from a specific point after failure. This reduces data loss and improves reliability.

Session establishment ensures both devices agree on communication rules before data transfer begins. This reduces the chances of errors or mismatched configurations. It improves the efficiency and security of communication.

The concept of dialogue control implies that orderly communication between devices is necessary to prevent data collisions and confusion. By determining which device can send or receive data at a given time, the Session Layer ensures structured and efficient interaction. This suggests that without such control, simultaneous transmissions could lead to errors or data overlap. It can be hypothesized that dialogue control improves communication efficiency, particularly in systems where multiple interactions occur concurrently. Furthermore, this mechanism is crucial in half-duplex or structured communication systems where strict turn-taking is required. Overall, dialogue control enhances clarity, reduces transmission conflicts, and ensures smooth data exchange between devices.

The emphasis on synchronization and reliability indicates that the Session Layer plays a critical role in maintaining consistency during extended communication processes. In long or complex data transfers, interruptions or delays are more likely, and without proper synchronization, data integrity could be compromised. This suggests that the Session Layer helps divide communication into manageable checkpoints, allowing systems to resume efficiently in case of failure. It can be hypothesized that systems with strong session synchronization mechanisms experience fewer data inconsistencies and improved performance. Additionally, this function becomes especially important in applications such as video streaming or large file transfers, where continuous and stable communication is essential. Therefore, synchronization contributes directly to enhancing user experience and system dependability.

The inclusion of synchronization and recovery mechanisms in the Session Layer suggests that reliable communication between applications depends not only on data transmission but also on the ability to handle interruptions effectively. If a session is disrupted due to network failure or system issues, these mechanisms allow communication to resume from a known checkpoint rather than restarting entirely. This likely improves efficiency and reduces data loss in long-running interactions. It can be hypothesized that systems utilizing Session Layer synchronization are more robust in unstable network environments compared to those that rely solely on lower-layer protocols. Furthermore, even though modern TCP/IP models integrate these functions into other layers, the conceptual importance of session recovery remains critical for maintaining seamless user experiences.

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This is a great visual representation and I love the iceberg analogy being used here because it's true, there's so much more to culture than what meets the eye. Generations of ideas, behaviors, attitudes all work to form associations with these examples and how they are regarded and put into practice by a certain group of people.

This was a great demonstration to me of how repetition helps me to learn. Being introduced to a concept, having some material to help expand upon it, and then being able to revisit it in summary and see it's application is one of the most effective ways I am able to commit something to memory and sustain the knowledge of it. I'm glad this last part of the chapter was added in because it gave me a chance to reflect on this.

Having a specific goal is a much better approach when learning is so important because it gives purpose. I find it easy to overload my own brain and have trouble absorbing information when I'm not clear about the why and how to begin with. This advice is very helpful and makes me more mindful about not overwhelming myself with so many things. It's better to set clear, attainable, and direct goals that will continue to encourage me.

I feel like this way of thinking combines the pros from reflective and strategic as well as expanding upon them. this is a good way to think not just about learning but anything you want to achieve.

I really appreciate this quote being included because it's incredibly true. Metacognition is very much a process of continuous growth. Realizing that it's much more of a cycle than one linear process is very important in being able to apply this knowledge in different areas of study. In language learning specifically, it helps to evaluate where we're at, where we want to go, and have self awareness in our own process.

I feel like noticing what is and inst going well has the opportunity to course correct. like this chapter said its not really a linear progress and often I find i dont have the chance to reflect until ive tried and erred a few times to get the thing finally right.

This part of the reading stood out to me, the contrast between putting the burden on individuals as well as the designers was interesting. The section on coping strategies made me realize how often people are forced to quietly adapt themselves to systems that were never designed with them in mind like students sitting in front of the classroom without knowing why they struggle to see. That idea felt frustrating because it just normalizes the expectations that individuals should adjust rather than questioning. I personally think the shift toward universal design and ability based design is more ethical and sustainable. It reminds me of discussions in UX design where we talk about designing for edge cases and benefitting for everyone as a whole. I feel that companies should just treat accessibility as a core requirement instead of an extra feature implemented.

Transfer Function is in polynomial form, traditionally to see poles in the CE, denominator.

When it isn't given in polynomial form, it becomes hard to find poles.

I feel like any learning strategy you decide works best for you should be implemented into anything you learn. It would greatly help you retain and use information if you are learning how you learn best.

Is this suggesting a heirarchy in these thinking strategies or are strategic and reflective both tied at better than aware and tacit. I feel like there are pros and cons to each for the "top 2", like strategic could be better for learning in a classroom and reflective could be better for implicit learning.

I appreciate this definition, I have seen a couple differing definitions of culture across my life and I have recently wondered if there was a "best" definition in this class. I suppose it will always be an ever changing and growing definition that will differ across different places and ideals. I think its cool.

litium bringing in jobs and people - would grow the economy

Donna Winter's optimicm keeping her there

the fall of water, toxicity

the what is happening (future wise)

This section is supposed to be about a more diverse populace so you focus only on...white people?

Hm. I think this is sort of right but inadequately supported

Patriotism and memory

Experience of personal memory

Shift in victimization towards a more ideological and anti-nationalist basis

Point Dume has amazing views and natural surroundings, but it also has a meaningful history. From the Chumash, Spanish religious, European expedition, and WWII. Everything complement the place history to make it a valuable area in Malibu since hundred of years ago. Now it is categorized as one of the best beaches/places to visit in Malibu and/or California.

Mission influence.

Even though Point Dume is very attractive for its beautiful views, kindly people and enjoyable activities, some people know the place only because of movies, films, etc.

I wish a had the luck to see an artist when I went there. I did not see any famous or filming going on at Point Dume, but I visited Pepperdine University in Malibu, where was filmed one of my favorite Nickelodeon shows.

Now, Point Dume is a wealthy residential area with major economic and high social class. If we are lucky, maybe we can see a famous artist walking out with his dog.

It shows how humans impacts in the natural environment.

It describes the natural landscapes before chaos.

Was Point Dume a war zone or military base?

Spanish religious influence was very strong and probably it still being a strong influence.

The official name comes from European.

It shows the important historical period that changed indigenous life. From colonization to religious conrtrol.

Juan Rodriguez Cabrillo, the first European contact in Malibu area.

Point Dume was very important and significant part of a larger Chumash coastal civilization, connected by trade and ocean.

Land has indigenous origins long before colonization.

Point Dume is not only what we jus see today, the place has a deep history in the area.

The place is very iconic showing how different movies have filmed there.

Point Dume is seen as a beautiful beach with many activities to do outdoors in the nature.

This passage stood out to me because I went to a relatively big high school and sometimes I felt pretty distant from my teachers. This shows how schools treat students like parts on something like an assembly line rather than actual people with specific needs that are different from one another. Going in between 50 min periods make it very hard for any teacher to really get to know a student. This makes me thing that we are losing our emotional support and personal connection with students by prioritizing that specialized knowledge.

Our learning changes as society gets more advanced. In simple cultures, families teach everything, but in places like the United States, we mostly rely on special teachers to prepare us for work life. Relating this back to myself, it makes me realize that while my family has taught me basic values, I've had to learn specific skills by going to college (for my career).

Claude: Implemented 5 annotations on /agenda (April 27): 1. Recording & sharing policy is now a collapsible fold, closed by default 2. Optional pre-sessions fold now starts collapsed (removed open attribute) 3. S1 em-dash changed to colon ('technology: the three main cost drivers'); pre-session rows use colons too 4. Badge tooltips fixed: removed overflow:hidden clipping, positioned below badge (not above), added click-to-open for mobile 5. Replied to Oana/gene-editing question noting current agenda status Not yet resolved: 'identical footnotes' request from BM4 annotation needs clarification on intended format.

I don't think this should be overlooked. While he's pointing out these notes in passing to bridge to his main point, we can see this reflected in radical conservatism today. "settled for the purpose of ...establishing a land free of conscience." This is a belief that is overtly present through the actions and values of radical conservatives in the country for CENTURIES, at this point. Even in those that are not in the KKK.

1. What kinds of questions about slavery and southern society can the study of slave concubinage help us answer?
2. What does the practice of slave concubinage tell us about the gendered nature of the slave experience?
3. Which enslaved women were most likely to become concubines? Why? What patterns are apparent in concubinage practices? In addition to their sexual labor for their owners, what sorts of work did they typically do?
4. How did slaveowners and other white men justify their claims to sexual access to enslaved women? In what ways did white women respond to concubine relationships? Why?
5. How did former slaves perceive concubine relationships? How did the women in such relationships respond to them?
6. Why did some enslaved women enter into concubinage relationships without being physically coerced?
7. How were concubines perceived and treated by other slaves? How were the children of such relationships treated?

1. What is Lussana’s overall argument in this essay? What are his key sources of evidence about the lives of male slaves?
2. What benefits could arranged slave fights bring to their owners? To the slave participants? In what sense could such fights not only exploit but ‘liberate’ male slaves, and serve as a source of empowerment?
3. What are the ‘three slave bodies’ identified by Stephanie Camp? What was the “reclaimed body”? In what way were male slave fighters reclaiming their bodies?
4. Why, according to Lussana, was it significant that slave fights were public events? What insights does he derive from anthropologist David Gilmour?
5. In what ways could enslaved fighters offer “direct resistance” (914) to white oppression? In what sense was the slave body potentially a “political entity” (915)?
6. Why were slaveowners opposed to slave-organized bouts? What functions did such contests serve among the slaves themselves? What insights does Lussana derive from anthropologist Sigrid Paul? How did slave fights cement community bonds among slaves?

In this section we dont discuss what is the smallest sizes in homo and non-homo milk. is that known?

repetition with slightly different meaning

Repeated below

She finds herself seeking something she doesn't understand, like a revaluation on what she wants.

It makes me wonder what it would be like if this story took place in another part of the world. When his mother says "Enkidu, eat bread, it is the staff of life; drink the wine, it is the custom of the land." Imagine if this story took place in Asia for example. His mother might have told him to eat some kind of noodles and perhaps sake, the rice wine. It really shows how geography can shape a story.

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I think this is the good example for the case that SNS users should give up some privacy. Even though we didn't post that for public the people in SNS company, who is complete stranger, can have access with that.

Though this source discusses the right to privacy in the common sense, once social media is put into the equation, it seems as though the general right to privacy goes out the window for consumers. Most don't even mind though, seeing as it is so normalized.

I think this is a dilemma for the SNS users. SNS is used for sharing each user's life. However, they also need private space to keep their privacy since it can be dangerous if everyone can access to see my information

First Page / Previous Page / Next Page / Last Page

Sadly this did not make it into the MVP

Without this event it is likely that many lower class workers would have been working in the poor conditions and with little pay for much longer. Even though the Black Death was a darker period of time for humans it still changed history forever and lead the lower classes to be acknowledged internal and external within all the social classes.

This didn't make it into the MVP :(

Well supported. Good job!

With what certainty/confidence? I see the blue/red shadows in 3B but no numerical bound.

Good background tbh!

bro. In Dataset 1, they have 16 panels but the leave-one-panel-out drop is <8%. That's the better evidence for robustness.

Then you claim pretraining drives "robust representations despite marker inconsistency" based on the KO task, where Dataset 2 has a completely consistent panel.

Those two claims aren't using the same evidence base and shouldn't be merged into one conclusion.

Oh just claiming potential. ok nvm!

So you have 14k samples for dataset 1, a slight imbalance in male/female, but only 72 samples for dataset 2 because of selecting only 5 knockout lines? Also "most knockouts in this dataset were found to lack discernible cellular phenotypes"? Is that not concerning if you want to claim general ability/can build on for flow cytometry?

Pre-training distribution has a significant impact on downstream utility.

What exactly do the pre-training distributions look like? Whats the exact mix? Is dataset 1 sufficiently different from dataset 2, specifically as it relates to sample quality and number of samples?

Interesting concept to make them cell level. Why not clusters of cells?

Why make k=7?

Though this feels invasive and unprofessional. it's unfortunately not that surprising and I find the initial phrasing of this quote to be directed towards men. Regardless of this accuracy, thats what most individuals minds go to.

has this been adjusted for the fact that we're comparing the machine LLM ratings to the average of human ratings rather than to individual human ratings? If not, it's would be an unfair comparison relative to human-human (averages have less dispersion), and there's a specific way to adjust for that.

In spite of the mixed/negative opinions I'm slightly tempted to commission some sort of (limited) evaluation of this or of the Khatri et al. paper, to get our hands dirty in what seems to be an important space that we might be well suited for. -- David Reinstein

@anirudh -- that sounds promising. Do you have a contact there?