Transcriptome-IPMS analysis reveals a tissue-dependent miR156/SPL13 regulatory mechanism in alfalfa drought tolerance
Bibliographic record
Abstract
BACKGROUND: We previously reported on the interplay between miR156/SPL13 and WD40-1/DFR to improve response to drought stress in alfalfa (Medicago sativa L.). Here we aimed to investigate whether the role of miR156/SPL13 module in drought response is tissue-specific, and to identify SPL13-interacting proteins. We analyzed the global transcript profiles of leaf, stem, and root tissues of one-month old RNAi-silenced SPL13 (SPL13RNAi) alfalfa plants exposed to drought stress and conducted protein-protein interaction analysis to identify SPL13 interacting partners. RESULT: Transcript analysis combined with weighted gene co-expression network analysis showed tissue and genotype-specific gene expression patterns. Moreover, pathway analysis of stem-derived differentially expressed genes (DEG) revealed upregulation of genes associated with stress mitigating primary and specialized metabolites, whereas genes associated with photosynthesis light reactions were silenced in SPL13RNAi plants. Leaf-derived DEG were attributed to enhanced light reactions, largely photosystem I, II, and electron transport chains, while roots of SPL13RNAi plants upregulated transcripts associated with metal ion transport, carbohydrate, and primary metabolism. Using immunoprecipitation combined with mass spectrometry (IPMS) we showed that SPL13 interacts with proteins involved in photosynthesis, specialized metabolite biosynthesis, and stress tolerance. CONCLUSIONS: We conclude that the miR156/SPL13 module mitigates drought stress in alfalfa by regulating molecular and physiological processes in a tissue-dependent manner.
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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 | 0.001 |
| 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".