Differential responses of Coffea arabica L. leaves and roots to chemically induced systemic acquired resistance
Bibliographic record
Abstract
Coffea arabica is susceptible to several pests and diseases, some of which affect the leaves and roots. Systemic acquired resistance (SAR) is the main defence mechanism activated in plants in response to pathogen attack. Here, we report the effects of benzo(1,2,3)thiadiazole-7-carbothioic acid-S-methyl ester (BTH), a SAR chemical inducer, on the expression profile of C. arabica. Two cDNA libraries were constructed from the mRNA isolated from leaves and embryonic roots to create 1587 nonredundant expressed sequence tags (ESTs). We developed a cDNA microarray containing 1506 ESTs from the leaves and embryonic roots, and 48 NBS-LRR (nucleotide-binding site leucine-rich repeat) gene fragments derived from 2 specific genomic libraries. Competitive hybridization between untreated and BTH-treated leaves resulted in 55 genes that were significantly overexpressed and 16 genes that were significantly underexpressed. In the roots, 37 and 42 genes were over and underexpressed, respectively. A general shift in metabolism from housekeeping to defence occurred in the leaves and roots after BTH treatment. We observed a systemic increase in pathogenesis-related protein synthesis, in the oxidative burst, and in the cell wall strengthening processes. Moreover, responses in the roots and leaves varied significantly.
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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) | 0.000 | 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".