Overexpression of <i>ZmSTOP1-A</i> Enhances Aluminum Tolerance in Arabidopsis by Stimulating Organic Acid Secretion and Reactive Oxygen Species Scavenging
Bibliographic record
Abstract
Abstract Aluminum (Al) toxicity and low pH are major factors limiting plant growth in acidic soils. Sensitive to Proton Rhizotoxicity 1 (STOP1) transcription factor respond to these stresses by regulating the expression of multiple Al- or low pH-responsive genes. ZmSTOP1-A, a STOP1-like protein from maize ( Zea mays ), was localized to the nucleus and had transactivation activity. ZmSTOP1-A was expressed moderately in both roots and shoots of maize seedlings, but was not induced by Al stress or low pH. Overexpression of ZmSTOP1-A in Arabidopsis Atstop1 mutant partially restored Al tolerance and completely low pH tolerance with respect to root growth. Regarding Al tolerance, ZmSTOP1-A/Atstop1 plants showed clear upregulation of organic acid transporter genes, and leading to increased organic acid secretion and reduced Al accumulation in roots. Besides, the antioxidant enzyme activity in roots and shoots of ZmSTOP1-A/Atstop1 plants was significantly enhanced, ultimately alleviating Al toxicity via scavenging reactive oxygen species. Similarly, ZmSTOP1-A could directly activate ZmMATE1 expression in maize, positively correlated with the number of Al-responsive GGNVS cis -element in the ZmMATE1 promoter. Our results revealed that ZmSTOP1-A antagonizes Al toxicity by enhancing organic acid secretion and reactive oxygen species scavenging in Arabidopsis, demonstrating that it is an important transcription factor conferring Al tolerance. Our findings help to comprehensively elucidate the role of STOP1-like transcript factor in enabling plants to detoxifying Al.
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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 | 0.001 |
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".