Metabolic and genetic interactions among mutans streptococci

项目摘要/摘要 龋齿是由主要由变形链球菌产生的酸引起的，变形链球菌是一组 在牙齿表面定植。变形链球菌的同名者，变形链球菌，因其 对口腔健康有害。许多出版物都描述了新陈代谢，能力，群体感应， 以及变形链球菌在体外和体内的耐酸性。变形链球菌的基因组很容易被操纵， 促进了我们对细菌遗传学的详细了解。另一种变形链球菌，远缘链球菌，是 基因上不灵活，与变形链球菌相比，研究不足。虽然远缘链霉菌的定殖化 在较少的情况下，它的存在与更具侵袭性的龋齿有关，特别是在儿童中。远缘链霉菌 临床分离株在大鼠模型中更能引起龋齿，而远缘链球菌的体外培养可以耐受。 酸浓度高于变形链球菌。该领域对变形链球菌的关注在我们的 远缘链球菌的知识，其致龋性增加的机制，及其与变形链球菌的相互作用。 这个项目旨在系统地扩展我们对变形链球菌的知识，通过结合 实验和计算技术。我们将：1)执行全面的基因组、代谢和 变形链球菌和远缘链球菌的表型特征及其相互作用；2)建立基因组规模 细菌的新陈代谢模型，以整合数据集和检验假设；以及3)实验地图 物种间的遗传交互作用来验证和扩展我们的模型。我们的数据驱动、计算型 这种方法将增强我们对变形链球菌之间的异同的理解。 在硅胶中模拟基因敲除、环境变化和化学扰动的能力是有帮助的 克服远缘链霉菌对基因操作的抗性。模拟还将创建一个按优先顺序排列的“空头” 有希望进行后续实验的药物靶点清单。实验和计算相结合的旁路 大型、昂贵的湿实验室筛查，并提供了一条更快的途径来治疗龋齿的新方法。

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.