Genetic Engineering of Glycinebetaine Production toward Enhancing Stress Tolerance in Plants: Metabolic Limitations
Bibliographic record
Abstract
Glycinebetaine (betaine) affords osmoprotection in bacteria, plants and animals, and protects cell components against harsh conditions in vitro. This and a compelling body of other evidence have encouraged the engineering of betaine production in plants lacking it. We have installed the metabolic step for oxidation of choline, a ubiquitous substance, to betaine in three diverse species, Arabidopsis, Brassica napus, and tobacco (Nicotiana tabacum), by constitutive expression of a bacterial choline oxidase gene. The highest levels of betaine in independent transgenics were 18.6, 12.8, and 13 micromol g(-1) dry weight, respectively, values 10- to 20-fold lower than the levels found in natural betaine producers. However, choline-fed transgenic plants synthesized substantially more betaine. Increasing the choline supplementation further enhanced betaine synthesis, up to 613 micromol g(-1) dry weight in Arabidopsis, 250 micromol g(-1) dry weight in B. napus, and 80 micromol g(-1) dry weight in tobacco. These studies demonstrate the need to enhance the endogenous choline supply to support accumulation of physiologically relevant amounts of betaine. A moderate stress tolerance was noted in some but not all betaine-producing transgenic lines based on relative shoot growth. Furthermore, the responses to stresses such as salinity, drought, and freezing were variable among the three species.
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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) | 0.001 | 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".