Mechanisms of magnesium amelioration of aluminum toxicity in soybean at the gene expression level
Bibliographic record
Abstract
Micromolar concentration of magnesium (Mg) in culture solution is known to ameliorate aluminum (Al) toxicity in soybean and other leguminous species. To advance the understanding of this phenomenon at the level of gene expression in soybean, we undertook a comparative transcriptome analysis using DNA microarrays and Al-tolerant and Al-sensitive genotypes treated with Al ions alone or Al plus Mg ions. We observed a more rapid alteration of transcription for Al-tolerant than Al-sensitive soybean after introduction of Mg into Al-containing medium, but at 72 h, far more genes were altered (both upregulated and downregulated) in the Al-sensitive line, reflecting the known greater saving effect of Mg for Al-sensitive than Al-tolerant lines. Mg appears to ameliorate Al toxicity in the sensitive genotype by the dual mechanisms of (i) specifically increasing the expression level of several genes that are upregulated in the Al-treated, Al-tolerant genotype in the absence of Mg and (ii) possibly saving energy by decreasing expression of most genes relative to expression under Al stress. Mg-mediated reduction in gene expression also appears to be an important mechanism of Mg protection of the Al-tolerant genotype.
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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".