The Oomycete Microbe-Associated Molecular Pattern, Arachidonic Acid, and an <i>Ascophyllum nodosum</i>-Derived Plant Biostimulant Induce Defense Metabolome Remodeling in Tomato
Bibliographic record
Abstract
, a plant biostimulant that contains AA, can also prime plants for defense against pathogen challenges. A previous parallel study comparing the transcriptomes of AA- and ANE-root-treated tomatoes demonstrated significant overlap in transcriptional profiles, a shared induced resistance phenotype, and changes in the accumulation of various defense-related phytohormones. In this work, untargeted metabolomic analysis via liquid chromatography-mass spectrometry was conducted to investigate the local and systemic metabolome-wide remodeling events elicited by AA and ANE root treatment in tomatoes. Our study demonstrated AA and ANE's capacity to locally and systemically alter the metabolome of tomatoes with enrichment of chemical classes and accumulation of metabolites associated with defense-related secondary metabolism. AA- and ANE-root-treated plants showed enrichment of fatty acyl-glycosides and strong modulation of hydroxycinnamic acids and derivatives. Identification of specific metabolites whose accumulation was affected by AA and ANE treatment revealed shared metabolic changes related to ligno-suberin biosynthesis and the synthesis of phenolic compounds. This study highlights the extensive local and systemic metabolic changes in tomatoes induced by treatment with a fatty acid MAMP and a seaweed-derived plant biostimulant with implications for induced resistance and crop improvement.
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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".