Analysis of metabolome and microbiome revealed the resistance mechanisms in sugarcane cultivars with high resistance to pokkah boeng disease
Bibliographic record
Abstract
Abstract Background Endophytes are reported to play a role in resistance against plant pathogens. Understanding the metabolite-mediated endophytic microbiota composition in plants provides insights to improve plant stress resistance. In this study, via metabolome and microbiome analyses, we aimed to elucidate the resistance mechanism of sugarcane cultivars with high resistance to sugarcane pokkah boeng disease (PBD). The endophytic microbial composition and metabolites in the stems of various sugarcane cultivars with high resistance (HR) or high susceptibility (HS) to PBD were analyzed. Results The results revealed that the endophytic fungi with biocontrol effects such as Shinella , Dechloromonas , and Microbacter were significantly enriched, and the abundance of pathogenic fungi such as Fusarium , Ramichloridium , Scleroramularia , Phaeosphaeriopsis , Sarocladium , Zygophiala , Gibberella , Pseudocercospora , Cyphellophora , Monocillium , Apiotrichum , Microsphaeropsis , and Scleroramularia significantly reduced in the stems of HR cultivars. Additionally, six metabolites [citric acid, isocitrate, malic acid, PC(16:0/0:0), phosphocholine, and lysoPC(16:0)] were significantly related to the endophytes in the stems of HR cultivars. Conclusions These results suggested that more abundance of antagonistic microbes and highly active metabolic functions of endophytes in the HR cultivars were the important mechanisms underlying their higher resistance to PBD. Graphical abstract
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".