Computational approach to the systematic prediction of glycolytic abilities: looking into human microbiota

Abstract

Glycoside hydrolases are responsible for the enzymatic deconstruction of complex carbohydrates. Most of the families are known to conserve the catalytic machinery and molecular mechanisms. This work introduces a new method to predict glycolytic abilities in sequenced genomes and thus, gain a better understanding of how to target specific carbohydrates and identify potentially interesting sources of specialised enzymes. Genome sequences are aligned to those of organisms with expertly curated glycolytic abilities. Clustering of homology scores helps identify organisms that share common abilities and the most promising organisms regarding specific glycolytic abilities. The method has been applied to members of the bacterial families Ruminococcaceae (39 genera), Eubacteriaceae (11 genera) and Lachnospiraceae (59 genera), which hold major representatives of the human gut microbiota. The method predicted the potential presence of glycoside hydrolases in 1701 species of these genera. Here, the validity and practical usefulness of the method is discussed based on the predictions obtained for members of the genus Ruminococcus. Results were consistent with existing literature and offer useful, complementary insights to comparative genomics and physiological testing. The implementation of the Gleukos web portal (http://sing-group.org/gleukos) offers a public service to those interested in targeting microbial carbohydrate metabolism for biotechnological and health applications.