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Henriksson, T.

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Henriksson
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Henriksson, T.

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    Exploring GWAS and genomic prediction to improve Septoria tritici blotch resistance in wheat
    (Nature Publishing Group, 2023) Zakieh, M.; Alemu, A.; Henriksson, T.; Pareek, N.; Singh, P.K.; Chawade, A.
    Publication
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    Phenotyping Fusarium head blight through seed morphology characteristics using RGB imaging
    (Frontiers, 2022) Leiva, F.; Zakieh, M.; Alamrani, M.; Dhakal, R.; Henriksson, T.; Singh, P.K.; Chawade, A.
    Publication
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    QTL mapping and transcriptome analysis to identify differentially expressed genes induced by Septoria tritici blotch disease of wheat
    (MDPI, 2019) Odilbekov, F.; Xinyao He; Armoniené, R.; Saripella, G.V.; Henriksson, T.; Singh, P.K.; Chawade, A.
    Resistance to Septoria tritici blotch (STB) is an economically important trait in many wheat-breeding programs across the world. Several quantitative trait loci (QTL) for STB resistance were identified in wheat but due to the dynamic pathogen population it is necessary to continuously identify new resistance genes/QTL and determine the underlying resistance mechanism. In this work, we integrated QTL mapping and transcriptome profiling to identify candidate genes underlying QTL associated with STB resistance in bread wheat at the seedling stage. The results revealed four QTL on chromosomes 1BS, 1BL, 3AS and 3DL for STB resistance. Among these, two QTL on 2BL and 3DL were mapped for chlorosis, necrosis and pycnidia while the other two on 1BS and 3AS were associated with necrosis and pycnidia. Among the four identified QTL, genes were identified in three QTL (1BS, 2BL and 3DL). In total, 238 differentially expressed genes (DEGs) were localized in 1BS, 16 DEGs in 2BL and 80 DEGs in 3DL QTL region respectively. F-box protein, NBS-LRR disease resistance genes and receptor-like protein kinase were the most over-represented. The results emphasize the importance of integrating QTL and transcriptome analysis to accelerate the identification of key genes underlying the traits of interest.
    Publication
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    MARPLE, a point-of-care, strain-level disease diagnostics and surveillance tool for complex fungal pathogens
    (BioMed Central, 2019) Radhakrishnan, G.V.; Cook, N.M.; Bueno Sancho, V.; Lewis, C. M.; Persoons, A.; Debebe, A.; Heaton, M.; Davey, P.E.; Abeyo Bekele Geleta; Alemayehu, Y.; Badebo, A.; Barnett, M.; Bryant, R.; Chatelain, J.; Xianming Chen; Suomeng Dong; Henriksson, T.; Holdgate, S.; Justesen, A.F.; Kalous, J.; Zhensheng Kang; Laczny, S.; Legoff, J.P.; Lesch, D.; Richards, T.; Randhawa, H.S.; Thach, T.; Meinan Wang; Hovmoller, M.S.; Hodson, D.P.; Saunders, D.G.O.
    Background: Effective disease management depends on timely and accurate diagnosis to guide control measures. The capacity to distinguish between individuals in a pathogen population with specific properties such as fungicide resistance, toxin production and virulence profiles is often essential to inform disease management approaches. The genomics revolution has led to technologies that can rapidly produce high-resolution genotypic information to define individual variants of a pathogen species. However, their application to complex fungal pathogens has remained limited due to the frequent inability to culture these pathogens in the absence of their host and their large genome sizes. Results: Here, we describe the development of Mobile And Real-time PLant disEase (MARPLE) diagnostics, a portable, genomics-based, point-of-care approach specifically tailored to identify individual strains of complex fungal plant pathogens. We used targeted sequencing to overcome limitations associated with the size of fungal genomes and their often obligately biotrophic nature. Focusing on the wheat yellow rust pathogen, Puccinia striiformis f.sp. tritici (Pst), we demonstrate that our approach can be used to rapidly define individual strains, assign strains to distinct genetic lineages that have been shown to correlate tightly with their virulence profiles and monitor genes of importance. Conclusions: MARPLE diagnostics enables rapid identification of individual pathogen strains and has the potential to monitor those with specific properties such as fungicide resistance directly from field-collected infected plant tissue in situ. Generating results within 48 h of field sampling, this new strategy has far-reaching implications for tracking plant health threats.
    Publication