Metabolite profiling coupled with statistical analyses for potential high-throughput screening of quantitative resistance to fusarium head blight in wheat

Hamzehzarghani, Habiballah; Paranidharan, V.; Abu-Nada, Y.; Kushalappa, Ajjamada C.; Dion, Yves; Rioux, S.; Comeau, A.; Yaylayan, Varoujan A.; Marshall, William D.

doi:10.1080/07060660809507493

Cited by 34 publications

(39 citation statements)

References 37 publications

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“…Some partial ecometabolomic studies of polar metabolites have shown that plant defensive responses involve the induction of diverse polar secondary metabolites such as p-and m-coumaric acid, inositol, caffeic acid, indolic derivates, phenylpropanoids and flavonoids, and some non-polar metabolites such as phosphatidyl glycerol, aromatic compounds and fatty acids (Bednarek et al 2005;Allwood et al 2006;Strack et al 2006;Jobic et al 2007;Cao et al 2008;Hamzehzarghani et al 2008;Muth et al 2009;Abdel-Farid et al 2009;Lima et al 2010) and also the decrease of other metabolites such as GABA, fructose and sucrose involved in plant growth and primary metabolism (Jobic et al 2007;Abdel-Farid et al 2009) (Table 2). Similarly, higher contents of some amino and organic acids, carbohydrates and mainly of phenolic compounds have been observed to be constitutive defenses in fungusresistent subspecies of Vitis vinifera when compared with susceptible subspecies of Vitis vinifera (Figueiredo et al 2008;Ali et al 2009).…”

Section: Plant-fungusmentioning

confidence: 99%

Ecological metabolomics: overview of current developments and future challenges

2011

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Ecometabolomics, which aims to analyze the metabolome, the total number of metabolites and its shifts in response to environmental changes, is gaining importance in ecological studies because of the increasing use of new technical advances, such as modern HNMR spectrometers and GC-MS coupled to bioinformatic advances. We review here the state of the art and the perspectives of ecometabolomics. The studies available demonstrate ecometabolomic techniques have great sensitivity in detecting the phenotypic mechanisms and key molecules underlying organism responses to abiotic environmental changes to biotic interactions. But such studies are still scarce, and in most cases they are limited to the direct effects of a single abiotic factor or of biotic interactions between two trophic levels under controlled conditions. Several exciting challenges remain to be achieved through the use of ecometabolomics in field conditions, involving more than two trophic levels, or combining the effects of abiotic gradients with intra-and inter-specific relationships. The coupling of ecometabolomic studies with genomics, transcriptomics, ecosystem stoichiometry, community biology and biogeochemistry may provide a further step forward in many areas of ecological sciences, including stress responses, species lifestyle, life history variation, population structure, trophic interaction, nutrient cycling, ecological niche and global change.

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Section: Plant-fungusmentioning

confidence: 99%

Ecological metabolomics: overview of current developments and future challenges

2011

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“…The RR metabolite concentration is inversely related to the fungal biomass in a resistant genotype, so most of these have plant origin. Also, irrespective of variation in genetic background several RR metabolites in wheat against FHB have been identified (Hamzehzarghani et al 2008a). However, these studies were based on gas chromatography and mass spectrometry (GC/MS) where several of the RR metabolites such as glucosinolates, terpenoids, flavonoids, etc.…”

Section: Introductionmentioning

confidence: 98%

“…Metabolomics has been used to phenotype resistance in wheat against Gibberella zeae and several pathogenesis related (Hamzehzarghani et al 2005) and resistance related metabolites have been identified in different genotypes (Hamzehzarghani et al 2008a;Paranidharan et al 2008). The pathogenicity related (PR) metabolites are those that are over expressed following pathogen inoculation, while the resistance related (RR) metabolites are those that have higher concentration in a resistant genotype compared to a susceptible genotype.…”

Section: Introductionmentioning

confidence: 99%

Metabolomics technology to phenotype resistance in barley against Gibberella zeae

et al. 2011

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The mechanisms of resistance in barley to fusarium head blight (FHB), caused by Gibberella zeae are complex. Metabolomics technology was explored to phenotype resistance. Spikelets of barley genotypes with contrasting levels of resistance to FHB, mock inoculated or with the pathogen, were extracted with aqueous methanol and the metabolites were analyzed using liquid chromatography and hybrid mass spectrometry. Peaks were de-convoluted using XCMS and annotated using CAMERA and IntelliXtract bioinformatics tools. A t-test, of a total of 1608 purified peaks, selected 626 metabolites with significant treatment effects, of which 161 were identified as resistance related (RR) metabolites. A total of 53 metabolites, that are RR or pathogenicity related (PR), were assigned with putative compound names. These mainly belonged to three metabolic pathways: fatty acid (jasmonic acid, methyl jasmonate, 9,10-dihydroisojasmonate, linolenic acid, linoleic acid, traumatic acid), phenylpropanoid (p-coumaric acid, caffeyl alcohol, dimethoxy-4-phenylcoumarin, rosmarinic acid, diphyllin, 5-methoxypodophyllotoxin) and flavonoid (naringenin, catechin, quercetin, and alpinumisoflavone). A few PR/RR metabolites significantly reduced mycelial growth of G. zeae in vitro.

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“…The 2DL-QTL has also been identified in the Chinese landrace Wangshuibai (Mardi et al 2005), but not in Sumai3. Some of the RR-metabolites identified in this study in the NIL with a FHB resistance QTL on 2DL chromosome had also been identified as metabolites related to the resistance function, based on the factor analysis in our previous study on Sumai3, even though the QTL on chromosome 2DL was not reported (Hamzehzarghani et al 2005) and also in other cultivars with and without this QTL (Hamzehzarghani et al 2008). Some of these were phenylpropanoid-related metabolites, signal compounds such as myo-inositol, fatty acids of the octadecanoid/hexadecanoid pathway, and amino acids.…”

Section: Discussionmentioning

confidence: 99%

“…A greater abundance of several fatty acids, particularly stearic and palmitic acids, aromatic compounds such as p-and m-coumaric acids, and sugars such as myo-inositol was associated with the resistant cultivar Sumai3. Metabolic profiles of six wheat cultivars varying in type II resistance to FHB yielded a total of 214 metabolites, of which 13 and 18 were identified as resistance related (significantly higher abundance in the resistant) constitutive (RRC) and resistance related induced (RRI) metabolites, respectively (Hamzehzarghani et al 2008). Similarly, a nuclear magnetic resonance based metabolite profiling study also identified glucose, carbohydrates and aromatic compounds related to resistance (Browne and Brindle 2007).…”

mentioning

confidence: 98%

Metabolic profiling to discriminate wheat near isogenic lines, with quantitative trait loci at chromosome 2DL, varying in resistance to fusarium head blight

Hamzehzarghani¹,

Paranidharan²,

Abu-Nada³

et al. 2008

Can. J. Plant Sci.

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. 2008. Metabolic profiling to discriminate wheat near isogenic lines, with quantitative trait loci at chromosome 2DL, varying in resistance to fusarium head blight. Can. J. Plant Sci. 88: 789Á797. The resistance in wheat to fusarium head blight (FHB) is controlled by several quantitative trait loci (QTLs), which are mainly expressed as two different types of resistance. The objective of this study was to assess the potential of a metabolomics approach to identify resistance-related metabolites associated with a QTL that confers resistance to FHB. Two near isogenic lines (NIL), with alternate alleles for the FHB resistance/ susceptibility QTL on chromosome 2DL, were grown under greenhouse conditions and spikelets were inoculated with F. graminearum. Metabolites were extracted from the rachis and spikelets using a mixture of methanol-water and chloroform, and subsequently analyzed using GC/MS. Compound identification and quantification were achieved using AMDIS, GMD and NIST libraries, and MET-IDEA as the software platform. A total of 182 components were detected. A t-test of the quantities of these metabolites identified 27 resistance-related (RR) metabolites, including 22 constitutive (RRC) and 8 induced (RRI), with three common metabolites. Canonical discriminant analysis was used to classify treatments and to identify the associated metabolic functions. The putative metabolic pathways linking the RR-metabolites identified here are discussed. Key words: Functional genomics, metabolomics, fusarium head blight, Triticum aestivum, Fusarium graminearumHamzehzarghani, H., Paranidharan, V., Abu-Nada, Y., Kushalappa, A. C., Mamer, O. et Somers, D. 2008. Discrimination des ligne´es de ble´quasi isoge´niques pre´sentant des locus quantitatifs de re´sistance variable a`la bruˆlure de l'e´pi cause´e par fusarium sur le chromosome 2DL par profilage du me´tabolisme. Can. J. Plant Sci. 88: 789Á797. Plusieurs locus quantitatifs codent la re´sistance du ble´a`la bruˆlure de l'e´pi cause´e par Fusarium (BEF). Ces locus expriment surtout deux formes de re´sistance. L'e´tude devait servir a`e´valuer si on peut identifier les me´tabolites lie´s a`cette re´sistance et les associer a`un des locus quantitatifs qui codent la re´sistance a`la BEF par la me´tabolomique. À cette fin, les auteurs ont cultive´en serre deux ligne´es presque isoge´niques mais pre´sentant des alle`les diffe´rents aux locus quantitatifs de re´sistance/sensibilite´a`la BEF sur le chromosome 2DL, puis ont inocule´les e´pillets avec F. graminearum. Ils ont extrait les me´tabolites du rachis et des e´pillets avec un me´lange d'eau-me´thanol et de chloroforme, puis analyse´la solution par CG-SM. Les compose´s ont e´teí dentifie´s et quantifie´s graˆce aux re´pertoires AMDIS, GMD et NIST sur la plateforme logicielle MET-IDEA. En tout, 182 compose´s ont e´te´de´cele´s. Le test t effectue´sur leur quantite´a permis d'en identifier 27 lie´s a`la re´sistance, dont 22 constitutifs et 8 induits, y compris 3 communs. Les traitements ont e´te´classe´s par analyse de discri...

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Metabolite profiling coupled with statistical analyses for potential high-throughput screening of quantitative resistance to fusarium head blight in wheat

Cited by 34 publications

References 37 publications

Ecological metabolomics: overview of current developments and future challenges

Ecological metabolomics: overview of current developments and future challenges

Metabolomics technology to phenotype resistance in barley against Gibberella zeae

Metabolic profiling to discriminate wheat near isogenic lines, with quantitative trait loci at chromosome 2DL, varying in resistance to fusarium head blight

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