Sumoylation of the Transcriptional Intermediary Factor 1β (TIF1β), the Co-repressor of the KRAB Multifinger Proteins, Is Required for Its Transcriptional Activity and Is Modulated by the KRAB Domain
Bibliographic record
Abstract
Small ubiquitin-related modifier (SUMO) has emerged as a key post-translational modulator of protein functions. Here we show that TIF1beta, a developmental regulator proposed to act as a universal co-repressor for the large family of KRAB domain-containing zinc finger proteins, is a heavily SUMO-modified substrate. A combined analysis of deletion and punctual mutants identified TIF1beta as a multilysine acceptor for SUMO which specifically targets six lysine residues (Lys(554), Lys(575), Lys(676), Lys(750), Lys(779), and Lys(804)) within the TIF1beta C-terminal repressive region. Reporter gene assays indicate that TIF1beta requires SUMO-modification for its repressive activity. Indeed, sumoylation-less mutants failed to recapitulate TIF1beta-dependent repression. TIF1beta homodimerization properties and interaction with the KRAB domain are preserved in the mutants with lysine to arginine substitutions as confirmed by in vivo bioluminescence resonance energy transfer (BRET). Using histone deacetylase (HDAC) inhibitors, we also demonstrate that TIF1beta sumoylation is a prerequisite for the recruitment of HDAC and that TIF1beta SUMO-dependent repressive activity involves both HDAC-dependent and HDAC-independent components. Finally, we report that, in addition to relying on the integrity of its PHD finger and on its self-oligomerization, TIF1beta sumoylation is positively regulated by its interaction with KRAB domain-containing proteins. Altogether, our results provide new mechanistic insights into TIF1beta transcriptional repression and suggest that KRAB multifinger proteins not only recruit TIF1beta co-repressor to target genes but also increase its repressive activity through enhancement of its sumoylation.
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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) | 0.001 | 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".