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KMID : 0545120110210010005
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Journal of Microbiology and Biotechnology
2011 Volume.21 No. 1 p.5 ~ p.13
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Chemotaxonomy of Trichoderma spp. Using Mass Spectrometry-Based Metabolite Profiling
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Kang Dae-Jung
Kim Ji-Young
Choi Jung-Nam
Liu Kwang-Hyeon
Lee Choong-Hwan
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Abstract
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In this study, seven Trichoderma species (33 strains) were classified using secondary metabolite profile-based chemotaxonomy. Secondary metabolites were analyzed by liquid chromatography-electrospray ionization tandem mass spectrometry (LC-ESI-MS-MS) and multivariate statistical methods. T. longibrachiatum and T. virens were independently clustered based on both internal transcribed spacer (ITS) sequence and secondary metabolite analyses. T. harzianum formed three subclusters in the ITS-based phylogenetic tree and two subclusters in the metabolitebased dendrogram. In contrast, T. koningii and T. atroviride strains were mixed in one cluster in the phylogenetic tree, whereas T. koningii was grouped in a different subcluster from T. atroviride and T. hamatum in the chemotaxonomic tree. Partial least-squares discriminant analysis (PLS-DA) was applied to determine which metabolites were responsible for the clustering patterns observed for the different Trichoderma strains. The metabolites were hetelidic acid, sorbicillinol, trichodermanone C, giocladic acid, bisorbicillinol, and three unidentified compounds in the comparison of T. virens and T. longibrachiatum; harzianic acid, demethylharzianic acid, homoharzianic acid, and three unidentified compounds in T. harzianum I and II; and koninginin B, E, and D, and six unidentified compounds in T. koningii and T. atroviride. The results of this study demonstrate that secondary metabolite profiling-based chemotaxonomy has distinct advantages relative to ITSbased classification, since it identified new Trichoderma clusters that were not found using the latter approach.
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KEYWORD
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Trichoderma, chemotaxonomy, mass spectrometry, multivariate analysis, metabolomics
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