Conditional, Temperature-Induced Proteolytic Regulation of Cyanobacterial RNA Helicase Expression
Bibliographic record
Abstract
Conditional proteolysis is a crucial process regulating the abundance of key regulatory proteins associated with the cell cycle, differentiation pathways, or cellular response to abiotic stress in eukaryotic and prokaryotic organisms. We provide evidence that conditional proteolysis is involved in the rapid and dramatic reduction in abundance of the cyanobacterial RNA helicase, CrhR, in response to a temperature upshift from 20 to 30°C. The proteolytic activity is not a general protein degradation response, since proteolysis is only present and/or functional in cells grown at 30°C and is only transiently active at 30°C. Degradation is also autoregulatory, since the CrhR proteolytic target is required for activation of the degradation machinery. This suggests that an autoregulatory feedback loop exists in which the target of the proteolytic machinery, CrhR, is required for activation of the system. Inhibition of translation revealed that only elongation is required for induction of the temperature-regulated proteolysis, suggesting that translation of an activating factor was already initiated at 20°C. The results indicate that Synechocystis responds to a temperature shift via two independent pathways: a CrhR-independent sensing and signal transduction pathway that regulates induction of crhR expression at low temperature and a CrhR-dependent conditional proteolytic pathway at elevated temperature. The data link the potential for CrhR RNA helicase alteration of RNA secondary structure with the autoregulatory induction of conditional proteolysis in the response of Synechocystis to temperature upshift.
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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".