Banana fruit <scp>NAC</scp> transcription factor <scp>MaNAC5</scp> cooperates with <scp>MaWRKYs</scp> to enhance the expression of pathogenesis‐related genes against <scp> <i>C</i> </scp> <i>olletotrichum musae</i>
Bibliographic record
Abstract
Plants respond to pathogen attack by the modulation of a large set of genes, which are regulated by different types of transcription factor (TF). NAC (NAM/ATAF/CUC) and WRKY are plant-specific families of TFs, and have received much attention as transcriptional regulators in plant pathogen defence. However, the cooperation between NAC and WRKY TFs in the disease response remains largely unknown. Our previous study has revealed that two banana fruit WRKY TFs, MaWRKY1 and MaWRKY2, are involved in salicylic acid (SA)- and methyl jasmonate (MeJA)-induced resistance against Colletotrichum musae via binding to promoters of pathogenesis-related (PR) genes. Here, we found that MaNAC1, MaNAC2 and MaNAC5 were up-regulated after C. musae infection, and were also significantly enhanced by SA and MeJA treatment. Protein-protein interaction analysis showed that MaNAC5 physically interacted with MaWRKY1 and MaWRKY2. More importantly, dual-luciferase reporter (DLR) assay revealed that MaNAC5, MaWRKY1 and MaWRKY2 were transcriptional activators, and individually or cooperatively activated the transcriptional activities of MaPR1-1, MaPR2, MaPR10c and MaCHIL1 genes. Collectively, our results indicate that MaNAC5 cooperates with MaWRKY1 and MaWRKY2 to regulate the expression of a specific set of PR genes in the disease response, and to contribute at least partially to SA- and MeJA-induced pathogen resistance.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".