Genome-Wide Identification of Cytokinin Response Factors (CRFs) Involved in Stress Responses in Banana (Musa acuminata)
Bibliographic record
Abstract
Cytokinin response factors (CRFs), belonging to AP2/ERF transcription factor family, play pivotal roles in regulating plant growth, hormone signaling, and stress responses. While CRF genes have been functionally characterized in multiple plant species, their systematic analysis in banana (Musa spp.), a globally important tropical fruit crop, remains unexplored. In this study, we identified eight putative MaCRF genes in the wild banana Musa acuminata ssp. malaccensis var. Pahang. Through comprehensive bioinformatic analyses, we characterized the MaCRF family and investigated their expression profiles across diverse tissues and under various biotic and abiotic stresses. Intriguingly, MaCRF4 exhibited contrasting expression patterns in response to Fusarium oxysporum f. sp. cubense tropical race 4 (Foc TR4) infection. MaCRF4 was strongly induced in the susceptible cultivar Z1 but transiently upregulated at early stages followed by downregulation in the resistant cultivar Z8. Furthermore, MaCRF3 and MaCRF4 were markedly induced by osmotic stress, low temperature, salinity, and ABA treatment. Our findings provide the systematic characterization of the MaCRF family in banana and offer valuable insights for future functional studies aimed at enhancing stress tolerance through molecular breeding strategies.
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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 | 0.001 | 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".