Analysis of Seaweed Extract-induced Transcriptome Leads to Identification of a Negative Regulator of Salt Tolerance in Arabidopsis
Bibliographic record
Abstract
Successful development of plants resistant to salinity stress is problematic as a result of the complex polygenic natures of salt tolerance. Previously, alkaline extracts of the brown seaweed Ascophyllum nodosum have shown promise in enhancing plant tolerance toward abiotic stresses. To understand the underlying molecular mechanisms, the whole genome transcriptome of Arabidopsis undergoing salt stress was analyzed by microarray analysis after treatment with the chemical components of A. nodosum extracts (ANE). Treatment with ANE induced many positive regulators of salt tolerance in addition to downregulating numerous other genes. Using T-DNA insertion mutants within these downregulated genes, we examined the potential for a novel source of enhanced NaCl tolerance through removal of negative regulators of NaCl stress responses within Arabidopsis. Several potential target mutations were identified with enhanced salt-tolerant phenotypes. A T-DNA insertion within the promoter of a putative Pectin Methyl Esterase Inhibitor ( PMEI ) gene ( At1g62760 ) was found to be resistant to salinity stress and was further characterized. This T-DNA insertion mutant was designated as pmei1-1 . The phenotype of pmei1-1 seedlings included increased primary root growth in vitro and improved biomass accumulation under NaCl stress. Additionally, modified transcript levels of dehydration-responsive genes, including RD29A, were observed in pmei1-1 plants. Taken together, these results suggest a role for PMEI as a negative regulator of NaCl resistance and that chemical stress-induced transcriptome analysis may lead to identification of additional novel regulators of abiotic stress tolerance in plants, the use of which would have significant implications for agriculture globally.
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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".