Identification of Rpl29 as a major substrate of the lysine methyltransferase Set7/9
Bibliographic record
Abstract
Set7/9 (also known as Set7, Set9, Setd7, and Kmt7) is a lysine methyltransferase that catalyzes the methylation of multiple substrates, including histone H3 and non-histone proteins. Although not essential for normal development and physiology, Set7/9-mediated methylation events play important roles in regulating cellular pathways involved in various human diseases, making Set7/9 a promising therapeutic target. Multiple Set7/9 inhibitors have been developed, which exhibit varying degrees of potency and selectivity in vitro. However, validation of these compounds in vivo has been hampered by the lack of a reliable cellular biomarker for Set7/9 activity. Here, we report the identification of Rpl29, a ribosomal protein abundantly expressed in all cell types, as a major substrate of Set7/9. We show that Rpl29 lysine 5 (Rpl29K5) is methylated exclusively by Set7/9 and can be demethylated by Lsd1 (also known as Kdm1a). Rpl29 is not a core component of the ribosome translational machinery and plays a regulatory role in translation efficiency. Our results indicate that Rpl29 methylation has no effect on global protein synthesis but affects Rpl29 subcellular localization. Using an Rpl29 methylation-specific antibody, we demonstrate that Rpl29K5 methylation is present ubiquitously and validate that (R)-PFI-2, a Set7/9 inhibitor, efficiently reduces Rpl29K5 methylation in cell lines. Thus, Rpl29 methylation can serve as a specific cellular biomarker for measuring Set7/9 activity.
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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) | 0.001 | 0.001 |
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".