Comparative transcriptome analysis of yeast strains carrying<i>slt2</i>,<i>rlm1,</i>and<i>pop2</i>deletions
Bibliographic record
Abstract
The function of the genes SLT2 (encoding the Mpk1 protein), RLM1, and POP2 have previously been related to several stress responses in yeasts. DNA arrays have been used to identify differences among the transcriptomes of a Saccharomyces cerevisiae wild type strain and its derivative Δslt2, Δrlm1, and Δpop2 mutants. Correspondence analyses indicate that the vast majority of genes that show lower expression in Δrlm1 also show lower expression in Δslt2. In contrast, there is little overlap between the results of the transcriptome analyses of the Δpop2 strain and the Δslt2 or Δrlm1 strains. The DNA array data were validated by reverse Northern blotting and chromatin immunoprecipitation (ChIp). ChIp assays demonstrate Rlm1p binding to specific regions of the promoters of two genes that show expression differences between the Δrlm1 mutant and wild type strains. Interestingly, RLM1 deletion decreases the transcription of SLT2, encoding the Mpk1p kinase that phosphorylates Rlm1p, suggesting a feedback control in the signal transduction pathway. Also, deletion of RLM1 causes a decrease in the mRNA level of KDX1, which is paralogous to SLT2. In contrast, deletion of POP2 is accompanied by an increase of both SLT2 and KDX1 levels. We show that SLT2 mRNA increase in the Δpop2 strain is due to a decrease in RNA turnover, consistent with the expected loss of RNA-deadenylase activity in this strain.
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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 | 0.000 |
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".