Deciphering Aphanomyces euteiches-pea-biocontrol bacterium interactions through untargeted metabolomics
Bibliographic record
Abstract
Aphanomyces euteiches causes root rot in pea, leading to significant yield losses. However, the metabolites involved in this pathosystem have not been thoroughly studied. This study aimed to fill this gap and explore mechanisms of bacterial suppression of A. euteiches via untargeted metabolomics using pea grown in a controlled environment. Chemical isotope labeling (CIL), followed by liquid chromatography-mass spectrometry (LC-MS), was used for metabolite separation and detection. Univariate and multivariate analyses showed clear separation of metabolites from pathogen-treated pea roots and roots from other treatments. A three-tier approach positively or putatively identified 5249 peak pairs or metabolites. Of these, 403 were positively identified in tier 1; 940 were putatively identified with high confidence in tier 2. There were substantial changes in amino acid pool, and fatty acid and phenylpropanoid pathway products. More metabolites, including salicylic and jasmonic acids, were upregulated than downregulated in A. euteiches-infected roots. 1-aminocyclopropane-1-carboxylic acid and 12-oxophytodienoic acid were upregulated in A. euteiches + bacterium-treated roots compared to A. euteiches-infected roots. A great number of metabolites were up- or down-regulated in response to A. euteiches infection compared with the control and A. euteiches + bacterium-treated plants. The results of this study could facilitate improved disease management.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".