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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

基本信息

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
项目类别：
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

项目摘要

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，而不是社区分类组成，作为最强大的描述微生物区系与其宿主的关系，并作为一个潜在的干预点，在调节人类健康。用于探索新测序的宏基因组的现有计算工具严重依赖于序列同源性，并且尚未利用来自数千个公开可用的功能实验结果的信息。同样，没有以前的方法提供了基因组规模的计算工具，用于生物学假设的产生，关于特定的分子之间的相互作用的微生物区系和与人类宿主。本研究的目的是发展计算方法来解释微植物群落的功能活动：1。整合分类学、宏基因组学和元转录组学数据集的功能信息。我们将开发方法，通过整合来自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种表示。2.确定种间功能活动的基因组预测因子，包括宿主/微生物相互作用和社区范围内的监管反馈点。我们将通过计算筛选微生物组测定，以确定社区中多种生物体的分子相互作用和调控基序。3.将这些技术作为公开可用、可访问和可解释的工具来实施。我们将提供免费提供的，开源的，可下载的和基于网络的实现这一方法的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，计算工具将有必要总结它们的全局功能活动和系统水平的调控相互作用。从长远来看，通过提供在分子水平上了解人类微生物组的方法，我们希望能够将其作为诊断指标和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存在于人体内和人体上的微生物，其中许多是有益的，其中一些可能是有害的。虽然我们现在可以收集关于这些微生物的细胞行为及其与人类相互作用的数据，但需要计算工具来解释这些信息。通过开发新的软件来研究这些微生物群落，我们希望最终能够检测出它们何时可能引起疾病，并修改它们的组成以改善人类健康。

项目成果

期刊论文数量（18）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Toward an efficient method of identifying core genes for evolutionary and functional microbial phylogenies.

DOI：
10.1371/journal.pone.0024704
发表时间：
2011
期刊：
PloS one
影响因子：
3.7
作者：
Segata N;Huttenhower C
通讯作者：
Huttenhower C

Compact graphical representation of phylogenetic data and metadata with GraPhlAn.

DOI：
10.7717/peerj.1029
发表时间：
2015
期刊：
PeerJ
影响因子：
2.7
作者：
Asnicar F;Weingart G;Tickle TL;Huttenhower C;Segata N
通讯作者：
Segata N

Complex host genetics influence the microbiome in inflammatory bowel disease.

DOI：
10.1186/s13073-014-0107-1
发表时间：
2014
期刊：
Genome medicine
影响因子：
12.3
作者：
Knights D;Silverberg MS;Weersma RK;Gevers D;Dijkstra G;Huang H;Tyler AD;van Sommeren S;Imhann F;Stempak JM;Huang H;Vangay P;Al-Ghalith GA;Russell C;Sauk J;Knight J;Daly MJ;Huttenhower C;Xavier RJ
通讯作者：
Xavier RJ

Chapter 12: Human microbiome analysis.