Novel Approach to Classify Plants Based on Metabolite-Content Similarity

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.

Comparing Medicinal Uses of Cochlospermaceae throughout Its Geographic Range with Insights from Molecular Phylogenetics

“…Diversity 2018, 10, x FOR PEER REVIEW 11 of 20 produce similar secondary metabolites (bioactive compounds) because they share the same or similar metabolic pathways. This predictive nature of phylogenies can be used for drug discovery and to guide studies that provide additional support of effectiveness of target species [23,28,29,114]. Figure 8.…”

Prediction of Medicinal Properties Using Mathematical Models and Computation, and Selection of Plant Materials

“…However, in the practical application, the approach is far from perfection and many researchers are still trying on other compound types. One such instance is that the application of graph-clustering algorithm on the metabolite content of the plant led to the successful classification of 217 plants in Japan (Liu et al, 2017). The approach provides successful result even in incomplete metabolite data by obtaining consistent relationship between plant clusters and known evolutional relationship of plants.…”

Implementations of the chemical structural and compositional similarity metric in R and Python

“…metabolomics profiles to compare pairs of samples [16,15,8]. Among these, the chemical structural and compositional similarity (CSCS) as proposed by Sedio and collaborators [19,17,18], accounts for the chemical structural similarity across metabolites by integrating the similarity of their MS/MS fragmentation patterns through the cosine score.…”