Decoding Strain-Level Variation in the Human Microbiome

人体是各种微生物的宿主。 我们的微生物细胞统称为人类微生物群，是人类健康的重要因素，不仅通过引起疾病，还通过促进健康。 必须了解微生物群，以对抗抗生素耐药性，阻止自身免疫性疾病的增加，并提供精准医疗。 微生物还携带有关我们祖先和环境暴露的信息。 宏基因组测序允许研究人员探索那里有什么以及他们正在做什么。 但该领域直到最近才开始将宏基因组数据的差异与特定微生物联系起来，因为菌株水平的分析在计算和统计上都具有挑战性。 该项目的目标是开发有效和准确的计算方法，用于在菌株水平上研究人类微生物组，以便阐明我们微生物细胞的全部变异程度及其与我们生物学的关联。 发现的新关联可用于识别诊断和个性化治疗的微生物生物标志物，或设计微生物组靶向药物，益生元和益生菌。 开发的工具也将有助于表征土壤和海洋等环境中微生物的菌株水平变化。 研究人员将在研究生教学中使用该项目的网络基础设施和发现，通过公共媒体进行宣传和交流。该项目将为微生物组研究创建新的统计方法，模型和软件，从而能够表征鸟枪宏基因组中微生物菌株的基因拷贝数和单核苷酸变体。 研究人员假设，菌株水平的分析将揭示宿主和微生物之间的神秘多样性和关联，而忽略菌株之间基因含量差异的方法会错过这些多样性和关联。 研究人员发现，来自不同人的常见人类相关细菌菌株之间的基因含量存在巨大差异（50%共享基因），这无疑会产生功能性后果。 因此，微生物物种对于精确医学和微生物组的进化研究来说不是足够的生物标志物，因为一个人的菌株可能不包含途径（例如，对于致病性或药物代谢）在同一物种的另一菌株中鉴定。 通过量化微生物组基因组多样性的新工具，将分析数千个公开可用的宏基因组，以全面评估人类相关微生物的全球种群结构。 通过比较具有不同功能能力的物种之间的生物地理学，并将其与人类特征和人口统计学相关联，目的是发现微生物如何适应和影响不同的人类宿主。

The human body is host to a diverse array of microorganisms. Collectively known as the human microbiota, our microbial cells are a significant factor in human health, not only through causing disease but also by promoting wellness. The microbiota must be understood to fight antibiotic resistance, to stop the rise of autoimmune diseases, and to deliver precision medicine. Microbes also carry information about our ancestry and environmental exposures. Metagenomic sequencing allows researchers to explore what is there and what they are doing. But the field has only recently begun to link differences in metagenomic data to specific microbes, because strain-level analysis is computationally and statistically challenging. The goal of this project is to develop efficient and accurate computational methods for studying the human microbiome at the strain level, so that the full extent of variation in our microbial cells and its association with our biology can be elucidated. The novel associations discovered could be used to identify microbial biomarkers for diagnosis and personalized treatments or to design microbiome targeted drugs, prebiotics, and probiotics. The tools developed will also be useful for characterizing strain-level variation in microbes from environments such as soils and oceans. The investigators will use the cyberinfrastructure and discoveries from this project in graduate teaching, for outreach and communication through public media.This project will create new statistical methods, models, and software for microbiome research that enable characterization of gene copy number and single nucleotide variants of the microbial strains in shotgun metagenomes. The investigators hypothesize that strain-level analysis will reveal cryptic diversity and associations between host and microbes, which are missed by approaches that ignore differences in gene content amongst strains. The researchers have discovered massive differences in gene content (50% shared genes) between strains of common human-associated bacteria from different people, which no doubt has functional consequences. Microbial species therefore are not sufficient biomarkers for precision medicine and evolutionary studies of the microbiome, because a person's strain may not harbor the pathways (e.g., for pathogenicity or drug-metabolism) identified in another strain of the same species. With novel tools for quantifying microbiome genomic diversity, thousands of publicly available metagenomes will be analyzed to comprehensively assess the global population structure of human-associated microbes. By comparing this biogeography across species with different functional capabilities and correlating it with human traits and demography, the aim is to discover how microbes adapt to and affect diverse human hosts.