Genome-wide characterization and expression profiling of the MAPKKK genes in <i>Gossypium arboreum</i> L.
Bibliographic record
Abstract
Mitogen-activated protein kinase kinase kinases (MAPKKKs) are important components of MAPK cascades, which have different functions during developmental processes and stress responses. To date, there has been no systematic investigation of this gene family in the diploid cotton Gossypium arboreum L. In this study, a genome-wide survey was performed that identified 78 MAPKKK genes in G. arboreum. Phylogenetic analysis classified these genes into three subgroups: 14 belonged to ZIK, 20 to MEKK, and 44 to Raf. Chromosome location, phylogeny, and the conserved protein motifs of the MAPKKK gene family in G. arboreum were analyzed. The MAPKKK genes had a scattered genomic distribution across 13 chromosomes. The members in the same subfamily shared similar conserved motifs. The MAPKKK expression patterns were analyzed in mature leaves, stems, roots, and at different ovule developmental stages, as well as under salt and drought stresses. Transcriptome analysis showed that 76 MAPKKK genes had different transcript accumulation patterns in the tested tissues and 38 MAPKKK genes were differentially expressed in response to salt and drought stresses. These results lay the foundation for understanding the complex mechanisms behind MAPKKK-mediated developmental processes and abiotic stress-signaling transduction pathways in cotton.
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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) | 0.000 | 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 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".