Functional characterization of wheat, fusarium head blight resistance (QTL) «Fhb1» based on non-target metabolomics and proteomics
Bibliographic record
Abstract
Fusarium head blight (FHB) caused by Fusarium graminearum is a dreadful disease of wheat (Triticum aestivum L.). Host resistance to FHB in wheat is quantitatively inherited. Though more than 100 QTLs have been identified, only a few have been validated. However, the resistance mechanisms governed by these QTLs are poorly understood. A type II FHB resistance QTL Fhb1 is the most consistent and largest effect QTL in wheat against FHB spread in wheat. Non-targeted metabolic and proteomic profiling of wheat near isogenic lines (NILs) with resistant and susceptible Fhb1 alleles was used to functionally characterize Fhb1 using a high resolution LC-MS. The Fhb1 from a moderately resistant cultivar Nyubai was associated with cell wall thickening, mainly at the rachis, due to deposition of hydroxycinnamic acid amides (HCAAs), phenolic glucosides and flavonoids. A hypothetical protein coding gene (GenBank: CBH32656.1) near Fhb1 locus was putatively identified as hydroxycinnamoyl transferase, which catalyzes the biosynthesis of HCAAs. Deoxynivalenol (DON) accumulation was high in both the NILs, eliminating DON detoxification as a mechanism associated with Fhb1 (Chapter III). For additional confirmation, the Fhb1 resistant allele, from a highly FHB resistant cultivar Sumai-3 was profiled. Even though the DON accumulation was low in resistant NIL, the detoxification of DON by host UDP-glycosyltransferase was moderately high in both the NILs, with no significant difference. Interestingly, unlike in the resistant NIL, constitutively present glycerophospholipids were absent in the susceptible NIL following pathogen inoculation due to degradation of membrane. Membrane degradation was caused due to programmed cell death as evidenced by DNA laddering in the susceptible NIL. A locus TAA_ctg0954b.00390.1 was identified as an Fhb1 candidate gene that contains a calmodulin binding motif and two nucleolar localization signal motifs and hence re-annotated as calmodulin binding protein (TaCaMBP_Fhb1). The TaCaMBP_Fhb1 is induced following pathogen infection, binds to Ca2+ bound calmodulin, and triggers Ca2+ signalling cascade including transcriptional activation of endonucleases that cleaves the genomics DNA and cause programmed cell death. The resistant allele of TaCaMBP_Fhb1 lacks part of the promoter region and is non-functional in triggering Ca2+ signalling. While the susceptible allele of TaCaMBP_Fhb1, with functional promoter region is capable of triggering Ca2+ signalling and programmed cell death. The necrotrophic pathogen F. graminearum feeds on the dead tissue, multiply in the host and produce more DON, following a repeated cycle in the susceptible genotype (Chapter IV). The wheat resistance mechanisms against FHB were further confirmed, based on metabolic profiling of rachis, from a resistant cultivar Sumai-3 and a susceptible cultivar Roblin, for resistance against spread of a trichothecene producing (Wild: FgTri5+) and a trichothecene non- producing (mutant: FgTri5-) isolates of F. graminearum. The wild isolate repressed several host resistance mechanisms in both the cultivars due to production of DON. The FHB resistance to spread in Sumai-3 was mainly because of increased cell wall thickening, especially at rachis, due to deposition of lignin, HCAAs and flavonoids, and partially, due to induced RR metabolites which in turn reduced the fungal biomass and toxin biosynthesis. The resistance was not attributed to DON detoxification by UDP-glycosyltransferase, as it was not significant in both the cultivars confirming our previous studies (Chapter V). The resistant alleles of two Fhb1 candidate genes, identified in this study, can be suitably stacked into genome of elite cultivars to enhance FHB resistance in wheat.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".