Wheat transcriptome profiling reveals abscisic and gibberellic acid treatments regulate early-stage phytohormone defense signaling, cell wall fortification, and metabolic switches following Fusarium graminearum-challenge
Bibliographic record
Abstract
BACKGROUND: Treatment of wheat with the phytohormones abscisic acid (ABA) and gibberellic acid (GA) has been shown to affect Fusarium head blight (FHB) disease severity. However, the molecular mechanisms underlying the elicited phenotypes remain unclear. Toward addressing this gap in our knowledge, global transcriptomic profiling was applied to the FHB-susceptible wheat cultivar 'Fielder' to map the regulatory responses effected upon treatment with ABA, an ABA receptor antagonist (AS6), or GA in the presence or absence of Fusarium graminearum (Fg) challenge. RESULTS: Spike treatments resulted in a total of 30,876 differentially expressed genes (DEGs) identified in 'Fielder' (26,004) and the Fg (4872) pathogen. Topology overlap and correlation analyses defined 9689 wheat DEGs as Fg-related across the treatments. Further enrichment analyses demonstrated that these included expression changes within 'Fielder' defense responses, cell structural metabolism, molecular transport, and membrane/lipid metabolism. Dysregulation of ABA and GA crosstalk arising from repression of 'Fielder' FUS3 was noted. As well, expression of a putative Fg ABA-biosynthetic cytochrome P450 was detected. The co-applied condition of Fg + ABA elicited further up-regulation of phytohormone biosynthesis, as well as SA and ET signaling pathways and cell wall/polyphenolic metabolism. In contrast, co-applied Fg + GA mainly suppressed phytohormone biosynthesis and signaling, while modulating primary and secondary metabolism and flowering. Unexpectedly, co-applied Fg + AS6 did not affect ABA biosynthesis or signaling, but rather elicited antagonistic responses tied to stress, phytohormone transport, and FHB disease-related genes. CONCLUSIONS: Observed exacerbation (misregulation) of classical defense mechanisms and cell wall fortifications upon ABA treatment are consistent with its ability to promote FHB severity and its proposed role as a fungal effector. In contrast, GA was found to modulate primary and secondary metabolism, suggesting a general metabolic shift underlying its reduction in FHB severity. While AS6 did not antagonize traditional ABA pathways, its impact on host defense and Fg responses imply potential for future investigation. Overall, by comparing these findings to those previously reported for four additional plant genotypes, an additive model of the wheat-Fg interaction is proposed in the context of phytohormone responses.
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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".