Abstract

High-throughput metabolite analysis is very important for biologists to identify the functions of genes. A mutation in a gene encoding an enzyme is expected to alter the level of the metabolites which serve as the enzyme’s reactant(s) (also known as substrate) and product(s). To find the function of a mutated gene, metabolite data from a wild-type organism and a mutant are compared and candidate reactants and products are identified. The screening principle is that the concentration of reactants will be higher and the concentration of products will be lower in the mutant than in wild type. This is because the mutation reduces the reaction between the reactant and the product in the mutant organism. Based upon this principle, we suggest a method to screen metabolite pairs for candidate reactant-product pairs. Metrics are defined that quantify the effect of a mutation on each potential reaction, represented by a metabolite pair. For reactions catalyzed by well-characterized enzymes, one or more biologically functioning reactant-product pairs are known. Knowledge of the functional reactant-product pairs informs the development of the metrics. The goal is for ranking of the metrics for all possible pairs to reflect the likelihood that a particular metabolite pair is a functional reactant-product pair.

Keywords

Lipid experiment; Pathway analysis; Reactant-product lipid pairs; Metabolome; Statistic distribution

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Apr 29th, 3:30 PM

EXPLORATION OF REACTANT-PRODUCT LIPID PAIRS IN MUTANT-WILD TYPE LIPIDOMICS EXPERIMENTS

High-throughput metabolite analysis is very important for biologists to identify the functions of genes. A mutation in a gene encoding an enzyme is expected to alter the level of the metabolites which serve as the enzyme’s reactant(s) (also known as substrate) and product(s). To find the function of a mutated gene, metabolite data from a wild-type organism and a mutant are compared and candidate reactants and products are identified. The screening principle is that the concentration of reactants will be higher and the concentration of products will be lower in the mutant than in wild type. This is because the mutation reduces the reaction between the reactant and the product in the mutant organism. Based upon this principle, we suggest a method to screen metabolite pairs for candidate reactant-product pairs. Metrics are defined that quantify the effect of a mutation on each potential reaction, represented by a metabolite pair. For reactions catalyzed by well-characterized enzymes, one or more biologically functioning reactant-product pairs are known. Knowledge of the functional reactant-product pairs informs the development of the metrics. The goal is for ranking of the metrics for all possible pairs to reflect the likelihood that a particular metabolite pair is a functional reactant-product pair.