Gene Coexpression Analysis Identifies Genes Associated with Chlorophyll Content and Relative Water Content in Pearl Millet
Bibliographic record
Abstract
Pearl millet is a significant crop that is tolerant to abiotic stresses and is a staple food of arid regions. However, its underlying mechanisms of stress tolerance are not fully understood. Plant survival is regulated by the ability to perceive a stress signal and induce appropriate physiological changes. Here, we screened for genes regulating physiological changes such as chlorophyll content (CC) and relative water content (RWC) in response to abiotic stress by using “weighted gene coexpression network analysis” (WGCNA) and clustering changes in physiological traits, i.e., CC and RWC associated with gene expression. Genes’ correlations with traits were defined in the form of modules, and different color names were used to denote a particular module. Modules are groups of genes with similar patterns of expression, which also tend to be functionally related and co-regulated. In WGCNA, the dark green module (7082 genes) showed a significant positive correlation with CC, and the black (1393 genes) module was negatively correlated with CC and RWC. Analysis of the module positively correlated with CC highlighted ribosome synthesis and plant hormone signaling as the most significant pathways. Potassium transporter 8 and monothiol glutaredoxin were reported as the topmost hub genes in the dark green module. In Clust analysis, 2987 genes were found to display a correlation with increasing CC and RWC. Furthermore, the pathway analysis of these clusters identified the ribosome and thermogenesis as positive regulators of RWC and CC, respectively. Our study provides novel insights into the molecular mechanisms regulating CC and RWC in pearl millet.
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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".