Physiological and molecular analysis of polyethylene glycol‐induced reduction of aluminium accumulation in the root tips of common bean (<i>Phaseolus vulgaris</i>)
Bibliographic record
Abstract
• Aluminium (Al) toxicity and drought are two major stress factors limiting common bean (Phaseolus vulgaris) production on tropical acid soils. Polyethylene glycol (PEG) treatment reduces Al uptake and Al toxicity. • The effect of PEG 6000-induced osmotic stress on the expression of genes was studied using SuperSAGE combined with next-generation sequencing and quantitative reverse transcription-polymerase chain reaction (qRT-PCR) for selected genes. • Less Al stress in PEG-treated roots was confirmed by decreased Al-induced up-regulation of MATE and ACCO genes. The withdrawal of PEG from the Al treatment solution restored the Al accumulation and reversed the expression of MATE and ACCO genes to the level of the treatment with Al alone. Using SuperSAGE, we identified 611 up- and 728 down-regulated genes in PEG-treated root tips, and the results were confirmed by qRT-PCR using 46 differentially expressed genes. Among the 12 genes studied in more detail, XTHa and BEG (down-regulated by PEG) and HRGP, bZIP, MYB and P5CS (up-regulated by PEG) recovered completely within 2 h after removal of PEG stress. • The results suggest that genes related to cell wall assembly and modification, such as XTHs, BEG and HRGP, play important roles in the PEG-induced decrease in cell wall porosity, leading to reduced Al accumulation in root tips.
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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".