Protein Carbonylation: Emerging Roles in Plant Redox Biology and Future Prospects
Bibliographic record
Abstract
Plants are sessile in nature and they perceive and react to environmental stresses such as abiotic and biotic factors. These induce a change in the cellular homeostasis of reactive oxygen species (ROS). ROS are known to react with cellular components, including DNA, lipids, and proteins, and to interfere with hormone signaling via several post-translational modifications (PTMs). Protein carbonylation (PC) is a non-enzymatic and irreversible PTM induced by ROS. The non-enzymatic feature of the carbonylation reaction has slowed the efforts to identify functions regulated by PC in plants. Yet, in prokaryotic and animal cells, studies have shown the relevance of protein carbonylation as a signal transduction mechanism in physiological processes including hydrogen peroxide sensing, cell proliferation and survival, ferroptosis, and antioxidant response. In this review, we provide a detailed update on the most recent findings pertaining to the role of PC and its implications in various physiological processes in plants. By leveraging the progress made in bacteria and animals, we highlight the main challenges in studying the impacts of carbonylation on protein functions in vivo and the knowledge gap in plants. Inspired by the success stories in animal sciences, we then suggest a few approaches that could be undertaken to overcome these challenges in plant research. Overall, this review describes the state of protein carbonylation research in plants and proposes new research avenues on the link between protein carbonylation and plant redox biology.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".