Iron-Superoxide Dismutase Expression in Transgenic Alfalfa Increases Winter Survival without a Detectable Increase in Photosynthetic Oxidative Stress Tolerance
Bibliographic record
Abstract
To determine whether overexpression of Fe-superoxide (SOD) dismutase would increase superoxide-scavenging capacity and thereby improve the winter survival of transgenic alfalfa (Medicago sativa L.) plants, two genotypes were transformed with the vector pEXSOD10, which contains a cDNA for Arabidopsis Fe-SOD with a chloroplast transit peptide and cauliflower mosaic virus 35S promoter. A novel Fe-SOD was detected by native PAGE in both greenhouse- and field-grown transgenic plants, but activity varied among independent transgenic plants. The increased Fe-SOD activity was associated with increased winter survival over 2 years in field trials, but not with oxidative stress tolerance as measured by resistance of leaves to methyl viologen, a superoxide generator. Total shoot dry matter production over 2 harvest years was not associated with Fe-SOD activity. There was no detectable difference in the pattern of primary freezing injury, as shown by vital staining, nor was there additional accumulation of carbohydrates in field-acclimated roots of the transgenic alfalfa plants. We did not detect any difference in growth of one transgenic plant with high Fe-SOD activity compared with a non-transgenic control. Therefore, the improvement in winter survival did not appear to be a consequence of improved oxidative stress tolerance associated with photosynthesis, nor was it a consequence of a change in primary freezing injury. We suggest that Fe-SOD overexpression reduced secondary injury symptoms and thereby enhanced recovery from stresses experienced during winter.
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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) | 0.001 | 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".