Translational suppression via IFG-1/eIF4G confers resistance to stress-induced RNA alternative splicing in <i>Caenorhabditis elegans</i>
Bibliographic record
Abstract
SUMMARY Splicing of pre-mRNA is an essential process for dividing cells and splicing defects have been linked to aging and various chronic diseases. Environmental stress has recently been shown to alter splicing fidelity and molecular mechanisms that protect against splicing disruption remains unclear. Using an in vivo RNA splicing reporter, we performed a genome-wide RNAi screen in Caenorhabditis elegans and found that protein translation suppression via silencing of the conserved initiation factor 4G (IFG-1/eIF4G) protects against cadmium-induced splicing disruption. Transcriptome analysis of an ifg-1 deficient mutant revealed an overall increase in splicing fidelity and resistance towards cadmium-induced alternative splicing compared to the wild-type. We found that the ifg-1 mutant up-regulates >80 RNA splicing regulatory genes that are controlled by the TGF-β transcription factor SMA-2. The extended lifespan of the ifg-1 mutant is partially reduced upon sma-2 depletion and completely nullified when core spliceosome genes including snr-1, snr-2 , and uaf-2 are knocked down. Together, these data describe a molecular mechanism that provides resistance towards stress-induced alternative splicing and demonstrate an essential role for RNA homeostasis in promoting longevity in a translation-compromised mutant.
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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".