Integrative meta-analysis of transcriptomic responses to abiotic stress in cotton
Bibliographic record
Abstract
Abiotic environmental stresses are important factors that limit the growth, fiber yield, and quality of cotton. In this study, an integrative meta-analysis and a system-biology analysis were performed to explore the underlying transcriptomic mechanisms that are critical for response to stresses. From the meta-analysis, it was observed that a total of 1465 differentially expressed genes (DEGs) between normal and stress conditions. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis revealed that DEGs were significantly enriched in the ubiquitin-dependent process, biosynthesis of secondary metabolites, plant hormone, and signaled transduction. The results also indicated that some of DEGs were assigned to transcription factors (TFs). A total of 148 TFs belonged to 25 conserved families were identified that among them S1Fa-like, ERF, NAC, bZIP families, were the most abundant groups. Moreover, we searched in upstream regions of DEGs for over-represented DNA motifs and were able to identify 11 conserved sequence motifs. The functional analysis of these motifs revealed that they were involved in regulation of transcription, DNA replication, cytoskeleton organization, and translation. Weighted gene co-expression network analysis (WGCNA) uncovered 12 distinct co-expression modules. Four modules were significantly associated with genes involved in response to stress and cell wall organization. The network analysis also identified hub genes such as RTNLB5 and PRA1, which may be involved in regulating stress response. The findings could help to understand the mechanisms of response to abiotic stress and introduce candidate genes that may be beneficial to cotton plant breeding programs.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".