Physiological and Metabolic Responses of Thellungiella salsuginea to Osmotic Stress
Bibliographic record
Abstract
Abiotic stresses such as extreme temperatures, drought and high salinity severely compromise plant productivity, and have placed selective pressure for the acquisition of traits enabling plants to adjust to and recover from these unfavorable environmental conditions. Thellungiella salsuginea is a plant that is native to highly saline and semiarid environments and exhibits an exceptional ability to tolerate abiotic stress. In this thesis, I report on laboratory and field studies aimed at identifying traits that allow Thellungiella to tolerate harsh environmental conditions. It was found that Thellungiella accumulates organic solutes in response to abiotic stress. Transcript and metabolite profiling approaches were used to identify metabolic pathways important for the accumulation of compatible organic solutes in Thellungiella in response to sub-optimal environmental conditions. The relative abundance of transcripts encoding enzymes associated with the biosynthesis of compatible organic solutes such as proline or galactinol showed stress-responsive increases in cabinet-grown material and these metabolites were accumulated in salt or drought treated plants, respectively. However, proline and galactinol were found to be of low relative abundance in leaves of field plants. In contrast, several carbohydrates including sucrose, glucose, and fructose made a greater relative contribution to the field plant profiles suggesting that carbohydrates play an important role in plant abiotic stress tolerance during growth under field conditions. The identification of stress-specific metabolic changes can be used to identify important biochemical traits underlying environmental stress tolerance in Thellungiella. This information can be used to improve the tolerance of stress -sensitive crops (including a related crucifer species, canola) that are grown in areas where persistent droughts, saline soils and early or late frosts frequently occur.
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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 | 0.001 |
| 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".