Metabolic and genetic interactions among mutans streptococci

University Of Illinois At Urbana-Champaign, Urbana IL

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Abstract

Project Summary/Abstract Tooth decay (caries) is caused by acid produced primarily by the mutans streptococci, a group of bacteria that colonize the tooth surface. The namesake of the mutans streptococci, S. mutans, is widely studied for its detriments to oral health. Numerous publications have profiled the metabolism, competence, quorum sensing, and acid tolerance of S. mutans, both in vitro and in vivo. The genome of S. mutans is easily manipulated, facilitating our detailed knowledge of the bacterium's genetics. The other mutans streptococci, S. sobrinus, is not genetically facile and is understudied compared to S. mutans. Although S. sobrinus colonization occurs less frequently, its presence is associated with more aggressive caries, especially in children. S. sobrinus clinical isolates are better able to cause caries in rat models, and in vitro cultures of S. sobrinus can tolerate higher acid concentrations than S. mutans. The field's focus on S. mutans has left significant gaps in our knowledge of S. sobrinus, its mechanism of increased cariogenicity, and its interactions with S. mutans. This project aims to systematically expand our knowledge of the mutans streptococci through a combination of experimental and computational techniques. We will: 1) perform a comprehensive genomic, metabolomic, and phenotypic characterization of S. mutans, S. sobrinus, and their interactions; 2) develop genome-scale metabolic models of the bacteria to integrate the datasets and test hypotheses; and 3) experimentally map inter-species genetic interactions to validate and expand our models. Our data-driven, computational approach will enhance our understanding of the similarities and differences among the mutans streptococci. The ability to simulate gene knockouts, environmental changes, and chemical perturbations in silico helps overcome S. sobrinus' resistance to genetic manipulation. The simulations will also create a prioritized ?short- list? of promising drug targets for follow-up experiments. Combining experiments and computation bypasses large, expensive wet-lab screens and provides a faster path to novel treatments for dental caries.