Lipid metabolism and MAPK-ICE1 cascade play crucial roles in cold tolerance of banana
Bibliographic record
Abstract
Abstract Low temperature is a major environmental factor that limits the growth, yield, and geographical distribution of Cavendish and Dajiao bananas ( Musa spp.). Dajiao bananas exhibit a significantly higher cold tolerance than the Cavendish cultivar. However, the underlying mechanisms involved in cold tolerance regulation in Dajiao plants have not been determined. In this study, we investigated the mechanisms underlying the differences in cold tolerance at the cellular level between the cold-sensitive Cavendish and cold-tolerant Dajiao banana types through comparative metabolomics and transcriptomics analyses in calli exposed to cold treatment under dark conditions. A higher accumulation of lipids was observed in Dajiao calli cells compared to Cavendish cells under cold stress. After cold treatment, 4,626 and 5,516 differentially expressed genes (DEGs) were identified in Cavendish and Dajiao banana cells, respectively. By integrating the transcriptomic and metabolomic datasets, we discovered that the linoleic acid and α-linoleic acid metabolism and the Abscisic acid (ABA)-independent Mitogen-activated protein kinase (MAPK) cascade-Inducer of CBF expression 1 (ICE1) signal transduction pathway (including Mitogen-activated protein kinase kinase kinase 1 [ MEKK1 ], MAPK5 , ICE1 , and Cold-regulated 47 [ COR47 ]) played crucial roles in the cold tolerance of Dajiao bananas. Our study provides new insights into cold response regulation and novel cold tolerance mechanisms, providing valuable leads and targets for the genetic improvement of cold tolerance in bananas.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".