Label-free quantitative proteomic analysis of alfalfa in response to microRNA156 under high temperature
Bibliographic record
Abstract
Abstract Background Abiotic stress, including heat, is one of the major factors that affect alfalfa growth and forage yield. The small RNA, microRNA156 (miR156), regulates multiple traits in alfalfa during abiotic stress. The aim of this study was to explore the role of miR156 in regulating heat response in alfalfa at the protein level. Results In this study, we compared an empty vector control and miR156 overexpressing (miR156OE) alfalfa plants after exposing them to heat stress (40 °C) for 24 h. We measured physiological parameters of control and miR156OE plants under heat stress, and collected leaf samples for protein analysis. A higher proline and antioxidant contents were detected in miR156OE plants than in controls under heat stress. Protein samples were analyzed by label-free quantification proteomics. Across all samples, a total of 1878 protein groups were detected. Under heat stress, 45 protein groups in the empty vector plants were significantly altered ( P < 0.05; |log 2 FC| > 2). Conversely, 105 protein groups were significantly altered when miR156OE alfalfa was subjected to heat stress, of which 91 were unique to miR156OE plants. The identified protein groups unique to miR156OE plants were related to diverse functions including metabolism, photosynthesis, stress-response and plant defenses. Furthermore, we identified transcription factors in miR156OE plants, which belonged to squamosa promoter binding-like protein, MYB, ethylene responsive factors, AP2 domain, ABA response element binding factor and bZIP families of transcription factors. Conclusions These results suggest a positive role for miR156 in heat stress response in alfalfa. They reveal a miR156-regulated network of mechanisms at the protein level to modulate heat responses in alfalfa.
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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".