Selective inhibition of CDK7 reveals high-confidence targets and new models for TFIIH function in transcription
Bibliographic record
Abstract
CDK7 associates with the 10-subunit TFIIH complex and regulates transcription by phosphorylating the C-terminal domain (CTD) of RNA polymerase II (RNAPII). Few additional CDK7 substrates are known. Here, using the covalent inhibitor SY-351 and quantitative phosphoproteomics, we identified CDK7 kinase substrates in human cells. Among hundreds of high-confidence targets, the vast majority are unique to CDK7 (i.e., distinct from other transcription-associated kinases), with a subset that suggest novel cellular functions. Transcription-associated factors were predominant CDK7 substrates, including SF3B1, U2AF2, and other splicing components. Accordingly, widespread and diverse splicing defects, such as alternative exon inclusion and intron retention, were characterized in CDK7-inhibited cells. Combined with biochemical assays, we establish that CDK7 directly activates other transcription-associated kinases CDK9, CDK12, and CDK13, invoking a "master regulator" role in transcription. We further demonstrate that TFIIH restricts CDK7 kinase function to the RNAPII CTD, whereas other substrates (e.g., SPT5 and SF3B1) are phosphorylated by the three-subunit CDK-activating kinase (CAK; CCNH, MAT1, and CDK7). These results suggest new models for CDK7 function in transcription and implicate CAK dissociation from TFIIH as essential for kinase activation. This straightforward regulatory strategy ensures CDK7 activation is spatially and temporally linked to transcription, and may apply toward other transcription-associated kinases.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".