@article{kumaraswamy_mass_2011,
	title = {Mass {Spectrometry} {Based} {Metabolomics} to {Identify} {Potential} {Biomarkers} for {Resistance} in {Barley} against {Fusarium} {Head} {Blight} ({Fusarium} graminearum)},
	volume = {37},
	issn = {1573-1561},
	url = {https://doi.org/10.1007/s10886-011-9989-1},
	doi = {10/b4954n},
	abstract = {Resistance in Triticeae to fusarium head blight (FHB) is quantitatively inherited. Metabolomics as a tool was used to better understand the mechanisms of resistance and to identify potential FHB resistance biomarker metabolites in barley. Five FHB-resistant two-row barley genotypes (CIho 4196, Zhedar-1, Zhedar-2, Fredrickson, and Harbin-2r) and one FHB-susceptible genotype (CH 9520–30) were each inoculated with either pathogen-suspension or mock-solution. Disease severity, quantified as the proportion of spikelets diseased, varied among genotypes, being the greatest in CH 9520–30. Spikelets were sampled, metabolites extracted with aqueous methanol, and analyzed using an LC-ESI-LTQ-Orbitrap system. A pair wise, resistant vs. susceptible, t-test identified 1774 significant treatment peaks. Canonical discriminant analysis of peak abundance allowed the genotypes to be sorted into three clusters: (i) CH9520-30, (ii) Harbin-2r, (iii) the remaining four genotypes. The t-test was further used to identify resistance-related (RR) and pathogenesis-related (PR) metabolites. The pathogen-produced virulence factor deoxynivalenol (DON), and its detoxification product, DON-3-O-glucoside (D3G) were designated as resistance indicator (RI) metabolites. Metabolites (RR, PR, or RI) occurring in at least two resistant genotypes, showing a two-fold or greater abundance in resistant vs. susceptible lines, and also known to have plant defense functions were selected as potential FHB resistance biomarker metabolites. These included phenylalanine, p-coumaric acid, jasmonate, linolenic acid, total DON produced (TDP), and the proportion of DON converted to D3G (PDC). Total DON was the lowest in CIho 4196, while PDC was the highest in Zhedar-2. The application of RR, PR, and RI metabolites as potential biomarkers to enhance resistance is discussed.},
	language = {en},
	number = {8},
	urldate = {2021-06-08},
	journal = {Journal of Chemical Ecology},
	author = {Kumaraswamy, Kenchappa G. and Kushalappa, Ajjamada C. and Choo, Thin M. and Dion, Yves and Rioux, Sylvie},
	month = aug,
	year = {2011},
	pages = {846--856},
}

{"_id":"dhctceQ7x385HizvM","bibbaseid":"kumaraswamy-kushalappa-choo-dion-rioux-massspectrometrybasedmetabolomicstoidentifypotentialbiomarkersforresistanceinbarleyagainstfusariumheadblightfusariumgraminearum-2011","author_short":["Kumaraswamy, K. G.","Kushalappa, A. C.","Choo, T. M.","Dion, Y.","Rioux, S."],"bibdata":{"bibtype":"article","type":"article","title":"Mass Spectrometry Based Metabolomics to Identify Potential Biomarkers for Resistance in Barley against Fusarium Head Blight (Fusarium graminearum)","volume":"37","issn":"1573-1561","url":"https://doi.org/10.1007/s10886-011-9989-1","doi":"10/b4954n","abstract":"Resistance in Triticeae to fusarium head blight (FHB) is quantitatively inherited. Metabolomics as a tool was used to better understand the mechanisms of resistance and to identify potential FHB resistance biomarker metabolites in barley. Five FHB-resistant two-row barley genotypes (CIho 4196, Zhedar-1, Zhedar-2, Fredrickson, and Harbin-2r) and one FHB-susceptible genotype (CH 9520–30) were each inoculated with either pathogen-suspension or mock-solution. Disease severity, quantified as the proportion of spikelets diseased, varied among genotypes, being the greatest in CH 9520–30. Spikelets were sampled, metabolites extracted with aqueous methanol, and analyzed using an LC-ESI-LTQ-Orbitrap system. A pair wise, resistant vs. susceptible, t-test identified 1774 significant treatment peaks. Canonical discriminant analysis of peak abundance allowed the genotypes to be sorted into three clusters: (i) CH9520-30, (ii) Harbin-2r, (iii) the remaining four genotypes. The t-test was further used to identify resistance-related (RR) and pathogenesis-related (PR) metabolites. The pathogen-produced virulence factor deoxynivalenol (DON), and its detoxification product, DON-3-O-glucoside (D3G) were designated as resistance indicator (RI) metabolites. Metabolites (RR, PR, or RI) occurring in at least two resistant genotypes, showing a two-fold or greater abundance in resistant vs. susceptible lines, and also known to have plant defense functions were selected as potential FHB resistance biomarker metabolites. These included phenylalanine, p-coumaric acid, jasmonate, linolenic acid, total DON produced (TDP), and the proportion of DON converted to D3G (PDC). Total DON was the lowest in CIho 4196, while PDC was the highest in Zhedar-2. The application of RR, PR, and RI metabolites as potential biomarkers to enhance resistance is discussed.","language":"en","number":"8","urldate":"2021-06-08","journal":"Journal of Chemical Ecology","author":[{"propositions":[],"lastnames":["Kumaraswamy"],"firstnames":["Kenchappa","G."],"suffixes":[]},{"propositions":[],"lastnames":["Kushalappa"],"firstnames":["Ajjamada","C."],"suffixes":[]},{"propositions":[],"lastnames":["Choo"],"firstnames":["Thin","M."],"suffixes":[]},{"propositions":[],"lastnames":["Dion"],"firstnames":["Yves"],"suffixes":[]},{"propositions":[],"lastnames":["Rioux"],"firstnames":["Sylvie"],"suffixes":[]}],"month":"August","year":"2011","pages":"846–856","bibtex":"@article{kumaraswamy_mass_2011,\n\ttitle = {Mass {Spectrometry} {Based} {Metabolomics} to {Identify} {Potential} {Biomarkers} for {Resistance} in {Barley} against {Fusarium} {Head} {Blight} ({Fusarium} graminearum)},\n\tvolume = {37},\n\tissn = {1573-1561},\n\turl = {https://doi.org/10.1007/s10886-011-9989-1},\n\tdoi = {10/b4954n},\n\tabstract = {Resistance in Triticeae to fusarium head blight (FHB) is quantitatively inherited. Metabolomics as a tool was used to better understand the mechanisms of resistance and to identify potential FHB resistance biomarker metabolites in barley. Five FHB-resistant two-row barley genotypes (CIho 4196, Zhedar-1, Zhedar-2, Fredrickson, and Harbin-2r) and one FHB-susceptible genotype (CH 9520–30) were each inoculated with either pathogen-suspension or mock-solution. Disease severity, quantified as the proportion of spikelets diseased, varied among genotypes, being the greatest in CH 9520–30. Spikelets were sampled, metabolites extracted with aqueous methanol, and analyzed using an LC-ESI-LTQ-Orbitrap system. A pair wise, resistant vs. susceptible, t-test identified 1774 significant treatment peaks. Canonical discriminant analysis of peak abundance allowed the genotypes to be sorted into three clusters: (i) CH9520-30, (ii) Harbin-2r, (iii) the remaining four genotypes. The t-test was further used to identify resistance-related (RR) and pathogenesis-related (PR) metabolites. The pathogen-produced virulence factor deoxynivalenol (DON), and its detoxification product, DON-3-O-glucoside (D3G) were designated as resistance indicator (RI) metabolites. Metabolites (RR, PR, or RI) occurring in at least two resistant genotypes, showing a two-fold or greater abundance in resistant vs. susceptible lines, and also known to have plant defense functions were selected as potential FHB resistance biomarker metabolites. These included phenylalanine, p-coumaric acid, jasmonate, linolenic acid, total DON produced (TDP), and the proportion of DON converted to D3G (PDC). Total DON was the lowest in CIho 4196, while PDC was the highest in Zhedar-2. The application of RR, PR, and RI metabolites as potential biomarkers to enhance resistance is discussed.},\n\tlanguage = {en},\n\tnumber = {8},\n\turldate = {2021-06-08},\n\tjournal = {Journal of Chemical Ecology},\n\tauthor = {Kumaraswamy, Kenchappa G. and Kushalappa, Ajjamada C. and Choo, Thin M. and Dion, Yves and Rioux, Sylvie},\n\tmonth = aug,\n\tyear = {2011},\n\tpages = {846--856},\n}\n\n\n\n","author_short":["Kumaraswamy, K. G.","Kushalappa, A. C.","Choo, T. M.","Dion, Y.","Rioux, S."],"key":"kumaraswamy_mass_2011","id":"kumaraswamy_mass_2011","bibbaseid":"kumaraswamy-kushalappa-choo-dion-rioux-massspectrometrybasedmetabolomicstoidentifypotentialbiomarkersforresistanceinbarleyagainstfusariumheadblightfusariumgraminearum-2011","role":"author","urls":{"Paper":"https://doi.org/10.1007/s10886-011-9989-1"},"metadata":{"authorlinks":{}}},"bibtype":"article","biburl":"https://bibbase.org/zotero/upscpub","dataSources":["9cGcv2t8pRzC92kzs","fvfkWcShg3Mybjoog","Tu3jPdZyJF3j547xT","3zTPPmKj8BiTcpc6C"],"keywords":[],"search_terms":["mass","spectrometry","based","metabolomics","identify","potential","biomarkers","resistance","barley","against","fusarium","head","blight","fusarium","graminearum","kumaraswamy","kushalappa","choo","dion","rioux"],"title":"Mass Spectrometry Based Metabolomics to Identify Potential Biomarkers for Resistance in Barley against Fusarium Head Blight (Fusarium graminearum)","year":2011}