Iron Availability Influences Protein Carbonylation in Arabidopsis thaliana Plants
Bibliographic record
Abstract
Protein carbonylation is an irreversible form of post-translational modification triggered by reactive oxygen species in animal and plant cells. It occurs either through the metal-catalyzed oxidation of Lys, Arg, Pro, and Thr side chains or the addition of α, β-unsaturated aldehydes and ketones to the side chains of Cys, Lys, and His. Recent genetic studies concerning plants pointed to an implication of protein carbonylation in gene regulation through phytohormones. However, for protein carbonylation to stand out as a signal transduction mechanism, such as phosphorylation and ubiquitination, it must be controlled in time and space by a still unknown trigger. In this study, we tested the hypothesis that the profile and extent of protein carbonylation are influenced by iron homeostasis in vivo. For this, we compared the profile and the contents of the carbonylated proteins in the Arabidopsis thaliana wild-type and mutant-deficient in three ferritin genes under normal and stress conditions. Additionally, we examined the proteins specifically carbonylated in wild-type seedlings exposed to iron-deficient conditions. Our results indicated that proteins were differentially carbonylated between the wild type and the triple ferritin mutant Fer1-3-4 in the leaves, stems, and flowers under normal growth conditions. The profile of the carbonylated proteins was also different between the wild type and the ferritin triple mutant exposed to heat stress, thus pointing to the influence of iron on the carbonylation of proteins. Consistent with this, the exposure of the seedlings to iron deficiency and iron excess greatly influenced the carbonylation of certain proteins involved in intracellular signal transduction, translation, and iron deficiency response. Overall, the study underlined the importance of iron homeostasis in the occurrence of protein carbonylation in vivo.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".