Overlapping Local and Systemic Defense Induced by an Oomycete Fatty Acid MAMP and Brown Seaweed Extract in Tomato
Bibliographic record
Abstract
Eicosapolyenoic fatty acids are integral components of oomycete pathogens that can act as microbe-associated molecular patterns to induce disease resistance in plants. Defense-inducing eicosapolyenoic fatty acids include arachidonic acid (AA) and eicosapentaenoic acid and are strong elicitors in solanaceous plants, with bioactivity in other plant families. Similarly, extracts of a brown seaweed, Ascophyllum nodosum, used in sustainable agriculture as a biostimulant of plant growth, may also induce disease resistance. A. nodosum, similar to other macroalgae, is rich in eicosapolyenoic fatty acids, which comprise as much as 25% of total fatty acid composition. We investigated the response of roots and leaves from AA or a commercial A. nodosum extract (ANE) on root-treated tomatoes via RNA sequencing, phytohormone profiling, and disease assays. AA and ANE significantly altered transcriptional profiles relative to control plants, inducing numerous defense-related genes with both substantial overlap and differences in gene expression patterns. Root treatment with AA and, to a lesser extent, ANE also altered both salicylic acid and jasmonic acid levels while inducing local and systemic resistance to oomycete and bacterial pathogen challenge. Thus, our study highlights overlap in both local and systemic defense induced by AA and ANE, with potential for inducing broad-spectrum resistance against pathogens. [Formula: see text] Copyright © 2023 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license .
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".