Mining the effects of a Bacillus sp. olive tree endophyte-derived lipopeptide extract on the metabolism of Colletotrichum acutatum applying GC/MS and 1H NMR metabolomics
Bibliographic record
Abstract
The issues that the plant protection sector is facing dictate the need for the discovery of improved sources of bioactivity as plant protection products. Within this context, endophytes have become the focus of the research based on their capacity to synthesize compounds with unique bioactivity. Here, the effect of a previously isolated lipopeptide extract (LP) of a Bacillus sp. olive tree endophyte on the metabolism of Colletotrichum acutatum [ 1 ] was investigated ( Figure 1 ). In the analyses, GC/EI/MS and NMR platforms were employed performing metabolomics. A large portion of the fungal metabolome was recorded, including various carboxylic, amino, and fatty acids, carbohydrates and phenolic compounds. Based on multivariate analysis, metabolites-biomarkers of the toxicity of the applied LP were discovered; α,α -trehalose, L-proline, and phenylacetate were amongst the metabolites with the highest leverage on the observed toxicity, that also, play central role in fungal metabolism. The latter, is associated to the pathogenicity of the fungus, indicating an antipathogenic activity of the LP. Pipeline of the metabolomics study. Publication History Article published online: 13 December 2021 © 2021. Thieme. All rights reserved. Georg Thieme Verlag Rüdigerstraße 14, 70469 Stuttgart, Germany
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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 | 0.000 | 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".