Influence of transcription elongation rate on gene expression in the fission yeast Schizosaccharomyces pombe
Bibliographic record
Abstract
Although it has become clear that transcription is intimately coupled to gene expression, how transcription elongation influences the transcriptome remains poorly understood. Some studies have shown that mutations in particular transcription factors impact transcription elongation leading to the potential overexpression of some genes, and in extreme situations, oncogenes which are associated with cancer. However, what if we approached this problem another way? How would gene expression be affected if transcription elongation rate was slowing down? To examine those consequences, we used genome editing in the fission yeast Schizosaccharomyces pombe to generate rpb1 mutants that produce a RNA polymerase II complex that transcribes slower than the wild type RNAPII. Using RNA-seq, differential gene expression analysis revealed a list of genes significantly up-regulated in conjunction with stress responses and a list of genes significantly down-regulated in conjunction with non-coding RNAs. Moreover, after merging those data with published genome-wide alternative polyadenylation sites, we discovered a more efficient function of the proximal polyadenylation sites in the slow mutant. Together, these results suggest a focus on tgp1 whose protein product is a phosphate transmembrane transporter, and which is known to be regulated by a cis long non-coding RNA. Several ChIP-qPCR assays have allowed us to gather mechanistic evidence about the link between a slow transcription elongation rate and tgp1 overexpression.
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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".