Plant Phenotype Demarcation Using Nontargeted LC-MS and GC-MS Metabolite Profiling
The characterization of the metabolome is a critical aspect in basic research and plant breeding. In this work, the Putative application of metabolomics for phenotyping closely related genotypes has been tested. Crude extracts were profiled by LC-MS and GC-MS, and mass data extraction was performed...
| Autores principales: | , , , |
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| Formato: | article |
| Lenguaje: | Inglés |
| Publicado: |
2017
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| Acceso en línea: | http://hdl.handle.net/20.500.11939/4383 |
| _version_ | 1855032111675736064 |
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| author | Arbona, Vicent Iglesias, Domingo J. Talón, Manuel Gómez-Cadenas, Aurelio |
| author_browse | Arbona, Vicent Gómez-Cadenas, Aurelio Iglesias, Domingo J. Talón, Manuel |
| author_facet | Arbona, Vicent Iglesias, Domingo J. Talón, Manuel Gómez-Cadenas, Aurelio |
| author_sort | Arbona, Vicent |
| collection | ReDivia |
| description | The characterization of the metabolome is a critical aspect in basic research and plant breeding. In this work, the Putative application of metabolomics for phenotyping closely related genotypes has been tested. Crude extracts were profiled by LC-MS and GC-MS, and mass data extraction was performed with XCMS software. Result validation was achieved with principal component analysis (PCA). The ability of the profiling methodologies to discriminate plant genotypes was assessed after hierarchical clustering analysis (HCA). Cluster robustness was assessed by a multiscale bootstrap resampling method. A better performance of LC-MS profiling over GC-MS was evidenced in terms of phenotype demarcation after PCA and HCA, Citrus demarcation was similarly achieved independently of the environmental conditions used to grow plants. In addition, when all different locations were pooled in a single experimental design, it was still possible to differentiate the three closely related genotypes. The presented methodology provides a fast and nontargeted workflow as a powerful tool to discriminate related plant phenotypes. The novelty of the technique relies on the use of mass signals as markers for phenotype demarcation independent of putative metabolite identities and the relatively simple analytical strategy that can be applicable to a wide range of plant matrices with no previous optimization. |
| format | article |
| id | ReDivia4383 |
| institution | Instituto Valenciano de Investigaciones Agrarias (IVIA) |
| language | Inglés |
| publishDate | 2017 |
| publishDateRange | 2017 |
| publishDateSort | 2017 |
| record_format | dspace |
| spelling | ReDivia43832025-04-25T14:43:00Z Plant Phenotype Demarcation Using Nontargeted LC-MS and GC-MS Metabolite Profiling Arbona, Vicent Iglesias, Domingo J. Talón, Manuel Gómez-Cadenas, Aurelio The characterization of the metabolome is a critical aspect in basic research and plant breeding. In this work, the Putative application of metabolomics for phenotyping closely related genotypes has been tested. Crude extracts were profiled by LC-MS and GC-MS, and mass data extraction was performed with XCMS software. Result validation was achieved with principal component analysis (PCA). The ability of the profiling methodologies to discriminate plant genotypes was assessed after hierarchical clustering analysis (HCA). Cluster robustness was assessed by a multiscale bootstrap resampling method. A better performance of LC-MS profiling over GC-MS was evidenced in terms of phenotype demarcation after PCA and HCA, Citrus demarcation was similarly achieved independently of the environmental conditions used to grow plants. In addition, when all different locations were pooled in a single experimental design, it was still possible to differentiate the three closely related genotypes. The presented methodology provides a fast and nontargeted workflow as a powerful tool to discriminate related plant phenotypes. The novelty of the technique relies on the use of mass signals as markers for phenotype demarcation independent of putative metabolite identities and the relatively simple analytical strategy that can be applicable to a wide range of plant matrices with no previous optimization. 2017-06-01T10:09:59Z 2017-06-01T10:09:59Z 2009 AUG 26 2009 article Arbona, Vicent, Iglesias, D.J., Talón, M., Gomez-Cadenas, Aurelio (2009). Plant Phenotype Demarcation Using Nontargeted LC-MS and GC-MS Metabolite Profiling. Journal of Agricultural and Food Chemistry, 57(16), 7338-7347. 0021-8561 http://hdl.handle.net/20.500.11939/4383 10.1021/jf9009137 en openAccess Impreso |
| spellingShingle | Arbona, Vicent Iglesias, Domingo J. Talón, Manuel Gómez-Cadenas, Aurelio Plant Phenotype Demarcation Using Nontargeted LC-MS and GC-MS Metabolite Profiling |
| title | Plant Phenotype Demarcation Using Nontargeted LC-MS and GC-MS Metabolite Profiling |
| title_full | Plant Phenotype Demarcation Using Nontargeted LC-MS and GC-MS Metabolite Profiling |
| title_fullStr | Plant Phenotype Demarcation Using Nontargeted LC-MS and GC-MS Metabolite Profiling |
| title_full_unstemmed | Plant Phenotype Demarcation Using Nontargeted LC-MS and GC-MS Metabolite Profiling |
| title_short | Plant Phenotype Demarcation Using Nontargeted LC-MS and GC-MS Metabolite Profiling |
| title_sort | plant phenotype demarcation using nontargeted lc ms and gc ms metabolite profiling |
| url | http://hdl.handle.net/20.500.11939/4383 |
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