Comparative transcriptome profiling reveals the reprogramming of gene networks under arsenic stress in Indian mustard
Bibliographic record
Abstract
Arsenic is a widespread toxic metalloid that is classified as a class I carcinogen known to cause adverse health effects in humans. In the present study, we investigated arsenic accumulation potential and comparative gene expression in Indian mustard. The amount of arsenic accumulated in shoots varied in the range of 15.99-1138.70 mg/kg on a dry weight basis among five cultivars. Comparative expression analysis revealed 10 870 significantly differentially expressed genes mostly belonging to response to stress, metabolic processes, signal transduction, transporter activity, and transcription regulator activity to be up-regulated, while most of the genes involved in photosynthesis, developmental processes, and cell growth were found to be down-regulated in arsenic-treated tissues. Further, pathway analysis using the KEGG Automated Annotation server (KAAS) revealed a large-scale reprogramming of genes involved in genetic and environmental information processing pathways. Top pathways with maximum KEGG orthology hits included carbon metabolism (2.5%), biosynthesis of amino acids (2.1%), plant hormone signal transduction (1.4%), and glutathione metabolism (0.6%). A transcriptomic investigation to understand the arsenic accumulation and detoxification in Indian mustard will not only help to improve its phytoremediation efficiency but also add to the control measures required to check bioaccumulation of arsenic in the food chain.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".