Functional activity and inter-organismal interactions in the human microbiome

人类微生物组的功能活动和有机体间相互作用

• 批准号: 8537085
• 负责人: Curtis Huttenhower
• 金额: $ 13.99万
• 依托单位: HARVARD SCHOOL OF PUBLIC HEALTH
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-27 至 2015-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8537085
• 关键词: Behavior; Binding; Bioinformatics; Biological; Biological Assay; Cells; Communities; Complement; Computer software; Computing Methodologies; DNA; DNA Sequence; Data; Data Collection; Data Set; Databases; Descriptor; Diagnostic; Disease; Environment; Feedback; Future; Gene Expression; Generations; Genes; Genome; Genomics; Health; Human; Human Microbiome; Human body; Individual; Internet; Intervention; Machine Learning; Maps; Mentors; Metabolic; Metagenomics; Methodology; Methods; Microbe; Modeling; Molecular; Online Systems; Organism; Pathway Analysis; Pathway interactions; Process; Proteins; Recombinant DNA; Research Personnel; Resources; Role; Sequence Homology; Signaling Molecule; System; Systems Biology; Taxon; Techniques; Technology; Testing; Tissues; base; computerized tools; functional genomics; improved; member; metagenome; metagenomic sequencing; microbial; microbial community; microbiome; microorganism; novel; open source; public health relevance; repository; tool; transcriptomics; Behavior; Binding; Bioinformatics; Biological; Biological Assay; Cells; Communities; Complement; Computer software; Computing Methodologies; DNA; DNA Sequence; Data; Data Collection; Data Set; Databases; Descriptor; Diagnostic; Disease; Environment; Feedback; Future; Gene Expression; Generations; Genes; Genome; Genomics; Health; Human; Human Microbiome; Human body; Individual; Internet; Intervention; Machine Learning; Maps; Mentors; Metabolic; Metagenomics; Methodology; Methods; Microbe; Modeling; Molecular; Online Systems; Organism; Pathway Analysis; Pathway interactions; Process; Proteins; Recombinant DNA; Research Personnel; Resources; Role; Sequence Homology; Signaling Molecule; System; Systems Biology; Taxon; Techniques; Technology; Testing; Tissues; base; computerized tools; functional genomics; improved; member; metagenome; metagenomic sequencing; microbial; microbial community; microbiome; microorganism; novel; open source; public health relevance; repository; tool; transcriptomics

DESCRIPTION (provided by applicant): High-throughput sequencing has provided a tool capable of observing the human microbiome, but characterizing the biological roles and metabolic potential of these microbial communities remains a significant challenge. Increasing evidence points to the functional activity of gene products, rather than community taxonomic composition, as the most robust descriptor of the microflora's relationship with its host and as a potential point of intervention in modulating human health. Existing computational tools for exploring a newly sequenced metagenome rely heavily on sequence homology and do not yet leverage information from the thousands of publicly available functional experimental results. Likewise, no previous methods have provided genome-scale computational tools for biological hypothesis generation regarding specific molecular interactions among the microflora and with a human host. This proposal aims to develop computational methodology to interpret the functional activity of microfloral communities: 1. Integrate functional information from taxonomic, metagenomic, and metatranscriptomic datasets. We will develop methodology to unify these three representations of microbiome composition by incorporating information from large scale functional genomic data collections. 2. Identify genomic predictors of inter-species functional activity, including host/microflora interactions and points of community-wide regulatory feedback. We will computationally screen microbiome assays for molecular interactions and regulatory motifs spanning multiple organisms in the community. 3. Implement these technologies as publicly available, accessible, and interpretable tools. We will provide freely available, open source, downloadable and web-based implementations of this methodology for use by the bioinformatic and biological communities. As high-throughput sequencing becomes more widely used to study microbial communities in the human microbiome and in the environment, computational tools will be necessary to summarize their global functional activity and systems-level regulatory interactions. In the long term, by providing methodology to understand the human microbiome at the molecular level, we hope to enable its future use as a diagnostic indicator and as a point of intervention to improve human health. PUBLIC HEALTH RELEVANCE: DNA sequencing technology has recently allowed us to examine the microorganisms naturally residing in and on the human body, many of which are beneficial and some of which can be harmful. Although we can now gather data on the cellular behavior of these microbes and on their interactions with human beings, computational tools are needed to interpret this information. By developing new software to study these communities of microorganisms, we hope to eventually be able to detect when they may be causing disease and modify their composition to improve human health.

描述（由申请人提供）：高通量测序提供了一种能够观察人类微生物组的工具，但是表征这些微生物群落的生物学作用和代谢潜力仍然是一个重大挑战。越来越多的证据表明，基因产物的功能活性，而不是社区分类的组成，是微生物与宿主关系的最强大描述符，也是调节人类健康的干预措施的潜在点。现有用于探索新测序元基因组的计算工具在很大程度上依赖于序列同源性，并且尚未利用数千个公开可用的功能实验结果中的信息。同样，没有以前的方法为生物学假设产生基因组规模计算工具，涉及微生物和人类宿主之间的特定分子相互作用。该建议旨在开发计算方法来解释微核群落的功能活动：1。从分类学，元基因组和元文字数据集中整合功能信息。我们将开发方法来通过合并大规模功能基因组数据收集的信息来统一微生物组组成的这三个表示。 2。确定种间功能活性的基因组预测因子，包括宿主/微生物相互作用和社区范围的调节反馈点。我们将在计算上筛选微生物组的分子相互作用和跨越社区中多种生物的调节基序。 3。将这些技术实施为公开，可访问和可解释的工具。我们将提供此方法的免费，开源，可下载和基于Web的实现，以供生物信息学和生物社区使用。随着高通量测序越来越广泛地用于研究人类微生物组和环境中的微生物群落，计算工具将是必要的，以总结其全球功能活动和系统级别的监管相互作用。从长远来看，通过提供方法来了解分子水平的人类微生物组，我们希望能够将其作为诊断指标的未来使用，并作为改善人类健康的干预点。 公共卫生相关性：DNA测序技术最近使我们能够检查自然居住在人体和人体上的微生物，其中许多是有益的，其中一些可能是有害的。尽管我们现在可以收集有关这些微生物的细胞行为及其与人类的相互作用的数据，但仍需要计算工具来解释此信息。通过开发新软件来研究这些微生物社区，我们希望最终能够检测到它们何时可能引起疾病并修改其成分以改善人类健康。