1. Novel Approach to Classify Plants Based on Metabolite-Content Similarity
- Author
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Takaaki Nishioka, Altaf-Ul-Amin, Ming Huang, Shigehiko Kanaya, Kang Liu, and Azian Azamimi Abdullah
- Subjects
0301 basic medicine ,Support Vector Machine ,Article Subject ,Ecological environment ,Metabolite ,Statistics as Topic ,lcsh:Medicine ,Computational biology ,Biology ,General Biochemistry, Genetics and Molecular Biology ,03 medical and health sciences ,chemistry.chemical_compound ,Similarity (network science) ,Botany ,Metabolome ,Cluster Analysis ,Herbivore ,General Immunology and Microbiology ,lcsh:R ,General Medicine ,Plants ,Hierarchical clustering ,030104 developmental biology ,chemistry ,Network clustering ,Adaptation ,Research Article - Abstract
Secondary metabolites are bioactive substances with diverse chemical structures. Depending on the ecological environment within which they are living, higher plants use different combinations of secondary metabolites for adaptation (e.g., defense against attacks by herbivores or pathogenic microbes). This suggests that the similarity in metabolite content is applicable to assess phylogenic similarity of higher plants. However, such a chemical taxonomic approach has limitations of incomplete metabolomics data. We propose an approach for successfully classifying 216 plants based on their known incomplete metabolite content. Structurally similar metabolites have been clustered using the network clustering algorithm DPClus. Plants have been represented as binary vectors, implying relations with structurally similar metabolite groups, and classified using Ward’s method of hierarchical clustering. Despite incomplete data, the resulting plant clusters are consistent with the known evolutional relations of plants. This finding reveals the significance of metabolite content as a taxonomic marker. We also discuss the predictive power of metabolite content in exploring nutritional and medicinal properties in plants. As a byproduct of our analysis, we could predict some currently unknown species-metabolite relations.
- Published
- 2017
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