An official website of the United States government
Abstract Introducing and characterizing variation through mutagenesis plus functional genomics can accelerate resistance breeding as well as our understanding of crop plant immunity. To reveal new germplasm resources for fungal disease resistance breeding in elite durum wheat, we challenged the diverse alleles in a sequenced and cataloged ethyl methanesulfonate mutagenized population of elite tetraploid wheatTriticum turgidumsubsp.durumcv ‘Kronos’ with stripe rust. We screened 2,000 mutant lines and identified sixteen enhanced disease resistance (EDR) lines with persistent resistance to stripe rust over four years of field testing. To find broad-spectrum resistance, we challenged these lines with other major biotrophic and necrotrophic pathogens, including those causing Septoria tritici blotch, tan spot, Fusarium head blight and leaf rust. Enhanced resistance to multiple fungi was found in 13 of 16 EDR lines. Five EDR lines showed spontaneous lesion formation in the absence of pathogens, providing new mutant resources to study plant stress response in the absence of the confounding effects of pathogen infection. We mapped exome capture sequencing data of the EDR lines to a recently released long-read Kronos genome to aid in the identification of causal mutations. We located an EDR resistance locus to an 175 Mb interval on chromosome 1B. Importantly, these phenotypically characterized EDR lines are newly described durum germplasm coupled with improved functional genomics resources that are readily available for both wheat fungal resistance breeding and basic plant immunity research.
more »
« less
Non-invasive diagnosis of wheat stripe rust progression using hyperspectral reflectance
Wheat stripe rust (WSR), a fungal disease capable of inflicting severe crop loss, threatens most of global wheat production. Breeding for genetic resistance is the primary defense against stripe rust infection. Further development of rust-resistant wheat varieties depends on the ability to accurately and rapidly quantify rust resilience. In this study we demonstrate the ability of visible through shortwave infrared reflectance spectroscopy to effectively provide high-throughput classification of wheat stripe rust severity and identify important spectral regions for classification accuracy. Random forest models were developed using both leaf-level and canopy-level hyperspectral reflectance observations collected across a breeding population that was scored for WSR severity using 10 and 5 severity classes, respectively. The models were able to accurately diagnose scored disease severity class across these fine scoring scales between 45-52% of the time, which improved to 79-96% accuracy when allowing scores to be off-by-one. The canopy-level model demonstrated higher accuracy and distinct spectral characteristics relative to the leaf-level models, pointing to the use of this technology for field-scale monitoring. Leaf-level model performance was strong despite clear variation in scoring conducted between wheat growth stages. Two approaches to reduce predictor and model complexity, principal component dimensionality reduction and backward feature elimination, were applied here. Both approaches demonstrated that model classification skill could remain high while simplifying high-dimensional hyperspectral reflectance predictors, with parsimonious models having approximately 10 unique components or wavebands. Through the use of a high-resolution infection severity scoring methodology this study provides one of the most rigorous tests of the use of hyperspectral reflectance observations for WSR classification. We demonstrate that machine learning in combination with a few carefully-selected wavebands can be leveraged for precision remote monitoring and management of WSR to limit crop damage and to aid in the selection of resilient germplasm in breeding programs.
more »
« less
- Award ID(s):
- 1954556
- PAR ID:
- 10579950
- Publisher / Repository:
- Frontiers in Plant Science
- Date Published:
- Journal Name:
- Frontiers in Plant Science
- Volume:
- 15
- ISSN:
- 1664-462X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Abstract Hyperspectral imaging allows for rapid, non-destructive and objective assessments of crop health. Narrowband-hyperspectral data was used to select wavelength regions that can be exploited to identify wheat infected with soil-borne mosaic virus. First, leaf samples were scanned in the lab to investigate spectral differences between healthy and diseased leaves, including non-symptomatic and symptomatic areas within a diseased leaf. The potential of 84 commonly used vegetation indices to find infection was explored. A machine-learning approach was used to create a classification model to automatically separate pixels into symptomatic, non-symptomatic and healthy classes. The success rate of the model was 69.7% using the full spectrum. It was very encouraging that by using a subset of only four broad bands, sampled to simulate a data set from a much simpler and less costly multispectral camera, accuracy increased to 71.3%. Next, the classification models were validated on field data. Infection in the field was successfully identified using classifiers trained on the entire spectrum of the hyperspectral data acquired in a lab setting, with the best accuracy being 64.9%. Using a subset of wavelengths, simulating multispectral data, the accuracy dropped by only 3 percentage points to 61.9%. This research shows the potential of using lab scans to train classifiers to be successfully applied in the field, even when simultaneously reducing the hyperspectral data to multispectral data.more » « less
-
null (Ed.)Finding trees that are resistant to pathogens is key in preparing for current and future disease threats such as the invasive white pine blister rust. In this study, we analyzed the potential of using hyperspectral imaging to find and diagnose the degree of infection of the non-native white pine blister rust in southwestern white pine seedlings from different seed-source families. A support vector machine was able to automatically detect infection with a classification accuracy of 87% (κ = 0.75) over 16 image collection dates. Hyperspectral imaging only missed 4% of infected seedlings that were impacted in terms of vigor according to expert’s assessments. Classification accuracy per family was highly correlated with mortality rate within a family. Moreover, classifying seedlings into a ‘growth vigor’ grouping used to identify the degree of impact of the disease was possible with 79.7% (κ = 0.69) accuracy. We ranked hyperspectral features for their importance in both classification tasks using the following features: 84 vegetation indices, simple ratios, normalized difference indices, and first derivatives. The most informative features were identified using a ‘new search algorithm’ that combines both the p-value of a 2-sample t-test and the Bhattacharyya distance. We ranked the normalized photochemical reflectance index (PRIn) first for infection detection. This index also had the highest classification accuracy (83.6%). Indices such as PRIn use only a small subset of the reflectance bands. This could be used for future developments of less expensive and more data-parsimonious multispectral cameras.more » « less
-
Abstract The wheat wild relativeAegilops tauschiiwas previously used to transfer theLr42leaf rust resistance gene into bread wheat.Lr42confers resistance at both seedling and adult stages, and it is broadly effective against all leaf rust races tested to date.Lr42has been used extensively in the CIMMYT international wheat breeding program with resulting cultivars deployed in several countries. Here, using a bulked segregant RNA-Seq (BSR-Seq) mapping strategy, we identify three candidate genes forLr42. Overexpression of a nucleotide-binding site leucine-rich repeat (NLR) gene AET1Gv20040300 induces strong resistance to leaf rust in wheat and a mutation of the gene disrupted the resistance. TheLr42resistance allele is rare inAe. tauschiiand likely arose from ectopic recombination. Cloning ofLr42provides diagnostic markers and over 1000 CIMMYT wheat lines carryingLr42have been developed documenting its widespread use and impact in crop improvement.more » « less
-
null (Ed.)Abstract Key message The first cytological characterization of the 2N v S segment in hexaploid wheat; complete de novo assembly and annotation of 2N v S segment; 2N v S frequency is increasing 2N v S and is associated with higher yield. Abstract The Aegilops ventricosa 2N v S translocation segment has been utilized in breeding disease-resistant wheat crops since the early 1990s. This segment is known to possess several important resistance genes against multiple wheat diseases including root knot nematode, stripe rust, leaf rust and stem rust. More recently, this segment has been associated with resistance to wheat blast, an emerging and devastating wheat disease in South America and Asia. To date, full characterization of the segment including its size, gene content and its association with grain yield is lacking. Here, we present a complete cytological and physical characterization of this agronomically important translocation in bread wheat. We de novo assembled the 2N v S segment in two wheat varieties, ‘Jagger’ and ‘CDC Stanley,’ and delineated the segment to be approximately 33 Mb. A total of 535 high-confidence genes were annotated within the 2N v S region, with > 10% belonging to the nucleotide-binding leucine-rich repeat (NLR) gene families. Identification of groups of NLR genes that are potentially N genome-specific and expressed in specific tissues can fast-track testing of candidate genes playing roles in various disease resistances. We also show the increasing frequency of 2N v S among spring and winter wheat breeding programs over two and a half decades, and the positive impact of 2N v S on wheat grain yield based on historical datasets. The significance of the 2N v S segment in wheat breeding due to resistance to multiple diseases and a positive impact on yield highlights the importance of understanding and characterizing the wheat pan-genome for better insights into molecular breeding for wheat improvement.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3389/fpls.2024.1429879
