Transcriptional analysis of different mulberry cultivars in response to <i>Ralstonia solanacearum</i>
Bibliographic record
Abstract
Bacterial wilt caused by Ralstonia solanacearum is a major disease of the mulberry (Morus atropurpurea Roxb.), resulting in severe yield and quality losses. However, little is known about the molecular mechanisms of resistance. Using the RNA sequencing technique, we identified early transcriptional changes in resistant (KQ10 and YS283) and susceptible (YSD10) mulberry cultivars in response to R. solanacearum infection. We observed that 798 genes were differentially and specifically regulated in both resistant cultivars but not in the susceptible cultivar after infection with R. solanacearum, including 502 upregulated and 296 downregulated genes. Among the differentially expressed genes, 31 encode transcription factors and 48 encode protein kinases. Interestingly, we found that a large number of genes (61) associated with cell-wall modification were differentially and specifically regulated in the resistant cultivars. These genes could be divided into 10 major groups. The largest group is the glucosyltransferase family, followed by the pectinesterase inhibitor, glucanase, and glycoprotein families, suggesting that cell-wall modifications may play an important role in resistance levels in mulberry. This transcriptional analysis paves the way for elucidating the molecular mechanisms of the resistance response to R. solanacearum in mulberry.
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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) | 0.001 | 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".