Global Gene Expression Responses to Waterlogging in Roots and Leaves of Cotton (Gossypium hirsutum L.)
Bibliographic record
Abstract
Waterlogging stress causes yield reduction in cotton (Gossypium hirsutum L.). A major component of waterlogging stress is the lack of oxygen available to submerged tissues. While changes in expressed protein, gene transcription and metabolite levels have been studied in response to low oxygen stress, little research has been done on molecular responses to waterlogging in cotton. We assessed cotton growth responses to waterlogging and assayed global gene transcription responses in root and leaf cotton tissues of partially submerged plants. Waterlogging caused significant reductions in stem elongation, shoot mass, root mass and leaf number, and altered the expression of 1,012 genes (4% of genes assayed) in root tissue as early as 4 h after flooding. Many of these genes were associated with cell wall modification and growth pathways, glycolysis, fermentation, mitochondrial electron transport and nitrogen metabolism. Waterlogging of plant roots also altered global gene expression in leaf tissues, significantly changing the expression of 1,305 genes (5% of genes assayed) after 24 h of flooding. Genes affected were associated with cell wall growth and modification, tetrapyrrole synthesis, hormone response, starch metabolism and nitrogen metabolism The implications of these results for the development of waterlogging-tolerant cotton are discussed.
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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".