Proteasomal Regulation of Mammalian SPT16 in Controlling Transcription
Bibliographic record
Abstract
ranscription), an essential and evolutionarily conserved heterodimer from yeast to humans, controls transcription and is found to be upregulated in various cancers. However, the basis for such upregulation is not clearly understood. Our recent results deciphering a new ubiquitin-proteasome system regulation of the FACT subunit SPT16 in orchestrating transcription in yeast hint at the involvement of the proteasome in controlling FACT in humans, with a link to cancer. To test this, we carried out experiments in human embryonic kidney (HEK293) cells, which revealed that human SPT16 undergoes ubiquitylation and that its abundance is increased following inhibition of the proteolytic activity of the proteasome, thus implying proteasomal regulation of human SPT16. Furthermore, we find that the increased abundance/expression of SPT16 in HEK293 cells alters the transcription of genes, including ones associated with cancer, and that the proteasomal degradation of SPT16 is impaired in kidney cancer (Caki-2) cells to upregulate SPT16. Like human SPT16, murine SPT16 in C2C12 cells also undergoes ubiquitylation and proteasomal degradation to regulate transcription. Collectively, our results reveal a proteasomal regulation of mammalian SPT16, with physiological relevance in controlling transcription, and implicate such proteasomal control in the upregulation of SPT16 in cancer.
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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".