SUMO Promotes DNA Repair Protein Collaboration to Support Alterative Telomere Lengthening in the Absence of PML
Bibliographic record
Abstract
Alternative lengthening of telomeres (ALT) pathway maintains telomeres in a significant fraction of cancers associated with poor clinical outcomes. A better understanding of ALT mechanisms can provide a basis for developing new treatment strategies for ALT cancers. SUMO modification of telomere proteins plays a critical role in the formation of ALT telomere-associated PML bodies (APBs), where telomeres are clustered and DNA repair proteins are enriched to promote homology-directed telomere DNA synthesis in ALT. However, whether and how SUMO contributes to ALT beyond APB formation remains elusive. Here, we report that SUMO promotes collaboration among DNA repair proteins to achieve APB-independent telomere maintenance. By using ALT cancer cells with PML protein knocked out and thus devoid of APBs, we show that sumoylation is required for manifesting ALT features, including telomere clustering and telomeric DNA synthesis, independent of PML and APBs. Further, small molecule-induced telomere targeting of SUMO produces signatures of phase separation and ALT features in PML null cells in a manner depending on both sumoylation and SUMO interaction with SUMO interaction motifs (SIMs). Mechanistically, SUMO-induced effects are linked to the enrichment of DNA repair proteins, including Rad52, Rad51AP1, and BLM, to the SUMO-containing telomere foci. Finally, we find that Rad52 can undergo phase separation, enrich SUMO on telomeres, and promote telomere DNA synthesis in collaboration with the BLM helicase in a SUMO-dependent manner. Collectively, our findings suggest that, in addition to forming APBs, SUMO also promotes collaboration among DNA repair proteins to support telomere maintenance in ALT cells. Given the promising effects of sumoylation inhibitors in cancer treatment, our findings suggest their potential use in perturbing telomere maintenance in ALT cancer cells.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".