TaWRKY70 transcription factor in wheat QTL-2DL regulates downstream metabolite biosynthetic genes to resist Fusarium graminearum infection spread within spike
Bibliographic record
Abstract
A semi-comprehensive metabolomics was used to identify the candidate metabolites and genes to decipher mechanisms of resistance in wheat near-isogenic lines (NILs) containing QTL-2DL against Fusarium graminearum (Fg). Metabolites, with high fold-change in abundance, belonging to hydroxycinnamic acid amides (HCAAs): such as coumaroylagmatine, coumaroylputrescine and Fatty acids: phosphatidic acids (PAs) were identified as resistance related induced (RRI) metabolites in rachis of resistant NIL (NIL-R), inoculated with Fg. A WRKY like transcription factor (TF) was identified within the QTL-2DL region, along with three resistance genes that biosynthesized RRI metabolites. Sequencing and in-silico analysis of WRKY confirmed it to be wheat TaWRKY70. Quantitative real time-PCR studies showed a higher expression of TaWRKY70 in NIL-R as compared to NIL-S after Fg inoculation. Further, the functional validation of TaWRKY70 based on virus induced gene silencing (VIGS) in NIL-R, not only confirmed an increased fungal biomass but also decreased expressions of downstream resistance genes: TaACT, TaDGK and TaGLI1, along with decreased abundances of RRI metabolites biosynthesized by them. Among more than 200 FHB resistance QTL identified in wheat, this is the first QTL from which a TF was identified, and its downstream target genes as well as the FHB resistance functions were deciphered.
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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".