Updating the Breeding Philosophy of Wheat to Fusarium Head Blight (FHB): Resistance Components, QTL Identification, and Phenotyping—A Review

Mesterházy, Ákos

doi:10.3390/plants9121702

Cited by 56 publications

(66 citation statements)

References 92 publications

(177 reference statements)

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“…Practical identification of varieties with greater and lower levels of resistance to FHB followed shortly after the different types of resistance were recognized [ 241 ]. Resistance is quantitative in nature and QTLs for resistance have been localized to all wheat chromosomes [ 29 ]. Although a series of seven major QTLs ( fhb1–7 ) have been identified and mapped, none of them provide complete resistance and their mode of action is not well understood even if the protein they encode is known.…”

Section: Pre-harvestmentioning

confidence: 99%

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Key Global Actions for Mycotoxin Management in Wheat and Other Small Grains

Leslie

Moretti

Mesterházy

et al. 2021

Toxins

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Mycotoxins in small grains are a significant and long-standing problem. These contaminants may be produced by members of several fungal genera, including Alternaria, Aspergillus, Fusarium, Claviceps, and Penicillium. Interventions that limit contamination can be made both pre-harvest and post-harvest. Many problems and strategies to control them and the toxins they produce are similar regardless of the location at which they are employed, while others are more common in some areas than in others. Increased knowledge of host-plant resistance, better agronomic methods, improved fungicide management, and better storage strategies all have application on a global basis. We summarize the major pre- and post-harvest control strategies currently in use. In the area of pre-harvest, these include resistant host lines, fungicides and their application guided by epidemiological models, and multiple cultural practices. In the area of post-harvest, drying, storage, cleaning and sorting, and some end-product processes were the most important at the global level. We also employed the Nominal Group discussion technique to identify and prioritize potential steps forward and to reduce problems associated with human and animal consumption of these grains. Identifying existing and potentially novel mechanisms to effectively manage mycotoxin problems in these grains is essential to ensure the safety of humans and domesticated animals that consume these grains.

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Section: Pre-harvestmentioning

confidence: 99%

“…The amount of toxin contamination can be considerable, but is not necessarily directly related to the severity of the disease outbreak. Disease resistance of wheat to different Fusarium species is governed by species-non-specific QTLs [ 27 , 28 , 29 ]. Most lines have no more than moderate resistance to FHB.…”

Section: Introductionmentioning

confidence: 99%

Key Global Actions for Mycotoxin Management in Wheat and Other Small Grains

Leslie

Moretti

Mesterházy

et al. 2021

Toxins

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“…Typical R genes such as NBS-LRR, receptor like proteins (RLP) and receptor like kinases (RLK) have been cloned since their phenotypes can be easily observed ( Dangl and Jones, 2001 ). For some diseases such as sclerotinia diseases in rapeseed, sunflower and soybean and fusarium head blight in cereal crops, no vertical, qualitative or race-specific resistance has been identified so no typical R genes for these diseases has been cloned ( Behla et al, 2017 ; Mesterhazy, 2020 ). In this study, the horizontal resistance of BLMR2 has been stressed since it conferred intermediate resistance under field conditions ( Dandena et al, 2019 ) and showed intermediate resistance to all isolated tested under controlled environmental conditions.…”

Section: Discussionmentioning

confidence: 99%

Genetic Analysis of a Horizontal Resistance Locus BLMR2 in Brassica napus

et al. 2021

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Leptosphaeria maculans causes blackleg disease in Brassica napus. The blackleg disease is mainly controlled by resistance genes in B. napus. Previous studies have shown that the blackleg resistant BLMR2 locus that conferred horizontal resistance under field conditions, is located on chromosome A10 of B. napus. The purpose of this study is to fine map this locus and hence identify a candidate gene underlying horizontal resistance. The spectrum of resistance to L. maculans isolates of the resistance locus BLMR2 was analyzed using near isogenic lines, resistant, and susceptible cultivars. The results showed that this locus was horizontally resistant to all isolates tested. Sequence characterized amplified regions (SCAR), simple sequence repeats (SSR), and single nucleotide polymorphism (SNP) markers were developed in the chromosome region of BLMR2 and a fine genetic map was constructed. Two molecular markers narrowed BLMR2 in a 53.37 kb region where six genes were annotated. Among the six annotated genes, BnaA10g11280D/BnaA10g11290D encoding a cytochrome P450 protein were predicted as the candidate of BLMR2. Based on the profiling of pathogen induced transcriptome, three expressed genes in the six annotated genes were identified while only cytochrome P450 showed upregulation. The candidate corresponds to the gene involved in the indole glucosinolate biosynthesis pathway and plant basal defense in Arabidopsis thaliana. The molecular markers identified in this study will allow the quick incorporation of the BLMR2 allele in rapeseed cultivars to enhance blackleg resistance.

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“…The symptoms can be mixed with maturation, especially with crops such as oats, and the disease can be quite unevenly spread in fields because it may spread from infested seeds or overwintered crop residue. Plant breeders must therefore rely on inoculated disease nurseries to get evenly distributed infections, and they also need to include analyses made for grain samples [95] to screen resistance to FHB. These analyses, such as the determination of mycotoxin content by enzyme-linked immunosorbent assays (ELISA), are expensive, and alternative methods like NIR spectroscopy are being considered for analysing the yield samples or for making field phenotyping more reliable by chlorophyll fluorescence [96] or hyperspectral sensors [97], and RGB [98].…”

Section: Differences With Other Uav Imaging Applicationsmentioning

confidence: 99%

Reference Measurements in Developing UAV Systems for Detecting Pests, Weeds, and Diseases

et al. 2021

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The development of UAV (unmanned aerial vehicle) imaging technologies for precision farming applications is rapid, and new studies are published frequently. In cases where measurements are based on aerial imaging, there is the need to have ground truth or reference data in order to develop reliable applications. However, in several precision farming use cases such as pests, weeds, and diseases detection, the reference data can be subjective or relatively difficult to capture. Furthermore, the collection of reference data is usually laborious and time consuming. It also appears that it is difficult to develop generalisable solutions for these areas. This review studies previous research related to pests, weeds, and diseases detection and mapping using UAV imaging in the precision farming context, underpinning the applied reference measurement techniques. The majority of the reviewed studies utilised subjective visual observations of UAV images, and only a few applied in situ measurements. The conclusion of the review is that there is a lack of quantitative and repeatable reference data measurement solutions in the areas of mapping pests, weeds, and diseases. In addition, the results that the studies present should be reflected in the applied references. An option in the future approach could be the use of synthetic data as reference.

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Updating the Breeding Philosophy of Wheat to Fusarium Head Blight (FHB): Resistance Components, QTL Identification, and Phenotyping—A Review

Cited by 56 publications

References 92 publications

Key Global Actions for Mycotoxin Management in Wheat and Other Small Grains

Key Global Actions for Mycotoxin Management in Wheat and Other Small Grains

Genetic Analysis of a Horizontal Resistance Locus BLMR2 in Brassica napus

Reference Measurements in Developing UAV Systems for Detecting Pests, Weeds, and Diseases

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