Integration of meta-QTL discovery with omics: Towards a molecular breeding platform for improving wheat resistance to Fusarium head blight

Zheng, Tao; Chen, Hua; Li, Lei; Sun, Zhengxi; Yuan, Mingxuan; Bai, Guihua; Humphreys, Gavin; Li, Tao

doi:10.1016/j.cj.2020.10.006

Cited by 57 publications

(72 citation statements)

References 58 publications

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“…These two proteins showed the highest abundances in the less susceptible cv. Renan, as well as four other proteins (TraesCS3B01G362600.1 in Cv1 and TraesCS5A01G226400.1, TraesCS5B01G416700.1, and TraesCS6A01G059600.1 in Cv + T4) coded by genes previously identified to be localized in high-confidence meta-QTL regions involved in wheat resistance to FHB (Zheng et al, 2020). This assumption can also be argued by the three homologs of candidate susceptibility genes showing differences in abundance between wheat cultivars (Supplementary Table 9).…”

Section: Basal Proteome Differences Could Drive Fhb Susceptibility In Wheatmentioning

confidence: 83%

“…In the past decades, substantial efforts have been devoted to the identification of resistance sources to FHB in wheat (Buerstmayr et al, 2020;Ma et al, 2020). Over the last 20 years, the mapping of several association panels has made possible the identification of more than 620 resistance quantitative trait loci (QTLs), delineating 77 meta-QTLs distributed over all the chromosomes of bread wheat (Steiner et al, 2017(Steiner et al, , 2019Venske et al, 2019;Zheng et al, 2020). Although many determinants were already described as associated with wheat FHB resistance, the specific molecular mechanisms responsible for FHB resistance remain poorly understood (Wang et al, 2019), as exemplified by the QTL Fhb1 characterized as the most stable and efficient locus for wheat resistance to FHB (Bai et al, 1999;Ollier et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

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Proteomics-Based Data Integration of Wheat Cultivars Facing Fusarium graminearum Strains Revealed a Core-Responsive Pattern Controlling Fusarium Head Blight

et al. 2021

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Fusarium head blight (FHB), mainly occurring upon Fusarium graminearum infection in a wide variety of small-grain cereals, is supposed to be controlled by a range of processes diverted by the fungal pathogen, the so-called susceptibility factors. As a mean to provide relevant information about the molecular events involved in FHB susceptibility in bread wheat, we studied an extensive proteome of more than 7,900 identified wheat proteins in three cultivars of contrasting susceptibilities during their interaction with three F. graminearum strains of different aggressiveness. No cultivar-specific proteins discriminated the three wheat genotypes, demonstrating the establishment of a core proteome regardless of unequivocal FHB susceptibility differences. Quantitative protein analysis revealed that most of the FHB-induced molecular adjustments were shared by wheat cultivars and occurred independently of the F. graminearum strain aggressiveness. Although subtle abundance changes evidenced genotype-dependent responses to FHB, cultivar distinction was found to be mainly due to basal abundance differences, especially regarding the chloroplast functions. Integrating these data with previous proteome mapping of the three F. graminearum strains facing the three same wheat cultivars, we demonstrated strong correlations between the wheat protein abundance changes and the adjustments of fungal proteins supposed to interfere with host molecular functions. Together, these results provide a resourceful dataset that expands our understanding of the specific molecular events taking place during the wheat–F. graminearum interaction.

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Section: Basal Proteome Differences Could Drive Fhb Susceptibility In Wheatmentioning

confidence: 83%

Section: Introductionmentioning

confidence: 99%

Proteomics-Based Data Integration of Wheat Cultivars Facing Fusarium graminearum Strains Revealed a Core-Responsive Pattern Controlling Fusarium Head Blight

et al. 2021

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“…Meta-QTL analysis [ 31 ] has been widely used for combining data from independent QTL mapping studies, identify the occurrence of QTL hotspots in a consensus genetic linkage map, and narrowing the QTL genetic confidence intervals [ 32 , 33 , 34 , 35 , 36 , 37 ]. QTL meta-analysis has been performed in wheat for several traits, including heading and maturity [ 36 , 38 , 39 ].…”

Section: Introductionmentioning

confidence: 99%

Physical Mapping of QTL in Four Spring Wheat Populations under Conventional and Organic Management Systems. I. Earliness

Semagn

Iqbal

Chen

et al. 2021

Plants

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In previous studies, we reported quantitative trait loci (QTL) associated with the heading, flowering, and maturity time in four hard red spring wheat recombinant inbred line (RIL) populations but the results are scattered in population-specific genetic maps, which is challenging to exploit efficiently in breeding. Here, we mapped and characterized QTL associated with these three earliness traits using the International Wheat Genome Sequencing Consortium (IWGSC) RefSeq v2.0 physical map. Our data consisted of (i) 6526 single nucleotide polymorphisms (SNPs) and two traits evaluated at five conventionally managed environments in the ‘Cutler’ × ‘AC Barrie’ population; (ii) 3158 SNPs and two traits evaluated across three organic and seven conventional managements in the ‘Attila’ × ‘CDC Go’ population; (iii) 5731 SilicoDArT and SNP markers and the three traits evaluated at four conventional and organic management systems in the ‘Peace’ × ‘Carberry’ population; and (iv) 1058 SNPs and two traits evaluated across two conventionally and organically managed environments in the ‘Peace’ × ‘CDC Stanley’ population. Using composite interval mapping, the phenotypic data across all environments, and the IWGSC RefSeq v2.0 physical maps, we identified a total of 44 QTL associated with days to heading (11), flowering (10), and maturity (23). Fifteen of the 44 QTL were common to both conventional and organic management systems, and the remaining QTL were specific to either the conventional (21) or organic (8) management systems. Some QTL harbor known genes, including the Vrn-A1, Vrn-B1, Rht-A1, and Rht-B1 that regulate photoperiodism, flowering time, and plant height in wheat, which lays a solid basis for cloning and further characterization.

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“…Fhb1 has the greatest effect on type II resistance to FHB among all QTL identified so far [ 32 ]. The single QTL of Fhb1 explains 15% to 30% of phenotypic variation [ 33 ]. Therefore, we used the effects of Fhb1 on disease severity and DON content to evaluate the practicability of the two inoculation methods.…”

Section: Discussionmentioning

confidence: 99%

Two Different Inoculation Methods Unveiled the Relative Independence of DON Accumulation in Wheat Kernels from Disease Severity on Spike after Infection by Fusarium Head Blight

Wang

Chen

et al. 2021

Toxins

Self Cite

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Fusarium head blight (FHB) causes wheat yield loss and mycotoxin (deoxynivalenol, DON) accumulation in wheat kernel. Developing wheat cultivars with overall resistance to both FHB spread within a spike and DON accumulation in kernels is crucial for ensuring food security and food safety. Here, two relatively novel inoculation methods, bilateral floret inoculation (BFI) and basal rachis internode injection (BRII), were simultaneously employed to evaluate disease severity and DON content in kernels in a segregating population of recombinant inbred lines (RILs) developed from Ning 7840 (carrying Fhb1) and Clark (without Fhb1). Under both inoculation methods, four contrasting combinations of disease severity and DON content were identified: high severity/high DON (HSHD), high severity/low DON (HSLD), low severity/high DON (LSHD) and low severity/low DON (LSLD). Unexpectedly, the BRII method clearly indicated that disease severity was not necessarily relevant to DON concentration. The effects of Fhb1 on disease severity, and on DON concentrations, agreed very well across the two methods. Several lines carrying Fhb1 showed extremely higher severity and (or) DON content under both inoculation methods. The “Mahalanobis distance” (MD) method was used to rate overall resistance of a line by inclusion of both disease severity and DON content over both methods to select LSLD lines.

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Integration of meta-QTL discovery with omics: Towards a molecular breeding platform for improving wheat resistance to Fusarium head blight

Cited by 57 publications

References 58 publications

Proteomics-Based Data Integration of Wheat Cultivars Facing Fusarium graminearum Strains Revealed a Core-Responsive Pattern Controlling Fusarium Head Blight

Proteomics-Based Data Integration of Wheat Cultivars Facing Fusarium graminearum Strains Revealed a Core-Responsive Pattern Controlling Fusarium Head Blight

Physical Mapping of QTL in Four Spring Wheat Populations under Conventional and Organic Management Systems. I. Earliness

Two Different Inoculation Methods Unveiled the Relative Independence of DON Accumulation in Wheat Kernels from Disease Severity on Spike after Infection by Fusarium Head Blight

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