Investigating the drought-stress response of hybrid poplar genotypes by metabolite profiling
Bibliographic record
Abstract
Drought stress is perhaps the most commonly encountered abiotic stress plants experience in the natural environment, and it is one of the most important factors limiting plant productivity. Here, we employed untargeted metabolite profiling to examine four drought-stressed hybrid poplar (Populus spp.) genotypes for their metabolite content, using gas chromatography coupled to mass spectrometry. The primary objective of these analyses was to characterize the metabolite profile of poplar trees to assess relative drought resistance and to investigate the underlying biochemical mechanisms employed by the genotypes to combat drought. Metabolite profiling identified key metabolites that increased or decreased in relative abundance upon exposure to drought stress. Overall, amino acids, the antioxidant phenolic compounds catechin and kaempferol, as well as the osmolytes raffinose and galactinol exhibited increased abundance under drought stress, whereas metabolites involved in photorespiration, redox regulation and carbon fixation showed decreased abundance under drought stress. One clone in particular, Okanese, displayed unique responses to the imposed drought conditions. This clone was found to have higher leaf water potential, but lower growth rate relative to the other clones tested. Okanese also had lower accumulation of osmolytes such as raffinose, galactinol and proline, but higher overall levels of antioxidants such as catechin and dehydroascorbic acid. As such, it was proposed that osmotic adjustment as a mechanism for drought avoidance in this clone is not as well developed in comparison with the other clones investigated in this study, and that a possible alternative mechanism for the enhanced drought avoidance displayed by Okanese may be due to differential allocation of resources or better retention of water.
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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) | 0.000 | 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 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".