Multi-omics insights into the positive role of strigolactone perception in barley drought response
Bibliographic record
Abstract
Abstract Background Drought is a major environmental stress that affects crop productivity worldwide. Although previous research demonstrated links between strigolactones (SLs) and drought, here we used barley (Hordeum vulgare) SL-insensitive mutant hvd14 (dwarf14) to scrutinize the SL-dependent mechanisms associated with water deficit response. Results We have employed a combination of transcriptomics, proteomics, phytohormonomics analyses, and physiological data to unravel differences between wild-type and hvd14 plants under drought. Our research revealed that drought sensitivity of hvd14 is related to weaker induction of abscisic acid-responsive genes/proteins, lower jasmonic acid content, higher reactive oxygen species content, and lower wax biosynthetic and deposition mechanisms than wild-type plants. In addition, we identified a set of transcription factors (TFs) that are exclusively drought-induced in the wild-type barley. Conclusions Critically, we resolved a comprehensive series of interactions between the drought-induced barley transcriptome and proteome responses, allowing us to understand the profound effects of SLs in alleviating water-limiting conditions. Several new avenues have opened for developing barley more resilient to drought through the information provided. Moreover, our study contributes to a better understanding of the complex interplay between genes, proteins, and hormones in response to drought, and underscores the importance of a multidisciplinary approach to studying plant stress response mechanisms.
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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 | 0.000 | 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) | 0.018 | 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".