A Global Perspective of the Genetic Basis for Carbonyl Stress Resistance
Bibliographic record
Abstract
The accumulation of protein adducts caused by carbonyl stress (CS) is a hallmark of cellular aging and other diseases, yet the detailed cellular effects of this universal phenomena are poorly understood. An understanding of the global effects of CS will provide insight into disease mechanisms and can guide the development of therapeutics and lifestyle changes to ameliorate their effects. To identify cellular functions important for the response to carbonyl stress, multiple genome-wide genetic screens were performed using two known inducers of CS. We found that different cellular functions were required for resistance to stress induced by methylglyoxal (MG) and glyoxal (GLY). Specifically, we demonstrate the importance of macromolecule catabolism processes for resistance to MG, confirming and extending known mechanisms of MG toxicity, including modification of DNA, RNA, and proteins. Combining our results with related studies that examined the effects of ROS allowed a comprehensive view of the diverse range of cellular functions affected by both oxidative and carbonyl stress. To understand how these diverse cellular functions interact, we performed a quantitative epistasis analysis by creating multimutant strains from those individual genes required for glyoxal resistance. This analysis allowed us to define novel glyoxal-dependent genetic interactions. In summary, using multiple genome-wide approaches provides an effective approach to dissect the poorly understood effects of glyoxal in vivo. These data, observations, and comprehensive dataset provide 1) a comprehensive view of carbonyl stress, 2) a resource for future studies in other cell types, and 3) a demonstration of how inexpensive cell-based assays can identify complex gene-environment toxicities.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".