hnRNP K Coordinates Transcriptional Silencing by SETDB1 in Embryonic Stem Cells
Bibliographic record
Abstract
Retrotransposition of endogenous retroviruses (ERVs) poses a substantial threat to genome stability. Transcriptional silencing of a subset of these parasitic elements in early mouse embryonic and germ cell development is dependent upon the lysine methyltransferase SETDB1, which deposits H3K9 trimethylation (H3K9me3) and the co-repressor KAP1, which binds SETDB1 when SUMOylated. Here we identified the transcription co-factor hnRNP K as a novel binding partner of the SETDB1/KAP1 complex in mouse embryonic stem cells (mESCs) and show that hnRNP K is required for ERV silencing. RNAi-mediated knockdown of hnRNP K led to depletion of H3K9me3 at ERVs, concomitant with de-repression of proviral reporter constructs and specific ERV subfamilies, as well as a cohort of germline-specific genes directly targeted by SETDB1. While hnRNP K recruitment to ERVs is dependent upon KAP1, SETDB1 binding at these elements requires hnRNP K. Furthermore, an intact SUMO conjugation pathway is necessary for SETDB1 recruitment to proviral chromatin and depletion of hnRNP K resulted in reduced SUMOylation at ERVs. Taken together, these findings reveal a novel regulatory hierarchy governing SETDB1 recruitment and in turn, transcriptional silencing in mESCs.
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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 | 0.001 |
| 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".