Multi-omic Functional Integration Using Networks

项目总结/摘要 人类相关的微生物群最近已被确定为健康的关键决定因素， 疾病它已经引起了医学学科的广泛兴趣，并有望成为新的微生物靶向药物。 可以有效地将微生物群转变为健康促进状态的疗法。微生物群以 其异常的复杂性，复杂的代谢网络控制着微生物的共生和竞争， 交互.同时使用多组学技术来表征微生物群已经被广泛应用。 被研究界广泛认为是一种强大的方法，因为它可以揭示 微生物群的各个组成部分之间以及微生物群的组成和功能之间的联系， 靶向治疗发展的需要。然而，这种方法的成功取决于 发展计算和统计方法，以确定高阶相互作用， 与影响疾病风险和桥接多个高维组学数据相关的微生物群 溪流我们的目标是通过开发、评估、应用和分发一个新的 一套工具，用于对人类微生物群研究的组学数据进行有意义的分析和整合。我们 我们将围绕两种最强大和最广泛使用的技术来开发我们的工具， 表征人类相关的微生物群：（1）基于序列的微生物分类学分析， 表征微生物群落组成和（2）基于光谱的非靶向代谢组学分析 用于表征微生物群落功能。我们将首先开发和评估一种方法， 利用微生物代谢途径的公开数据库的多组学数据流。接下来我们 将开发一种方法，通过以下透镜， 微生物群功能概况。然后，我们将应用我们的方法来鉴定微生物群落及其 在大型队列中与临床终点相关的表型。最后，我们的方法将被发布到 人类微生物群研究社区作为一个开源软件包。我们提出的工作将建立 分析工具，执行多个组学数据流的功能集成，并评估复杂的 与临床结果的关系。这代表了一个新的框架，用于确定微生物-健康 协会及其职能基础的方式，包括这一系统的复杂性。的 这项工作的意义在于它的潜力，以帮助翻译实验和人类受试者的研究， 将微生物群转化为靶向治疗，使微生物群转向健康促进状态。

PROJECT SUMMARY / ABSTRACT The human-associated microbiota has recently been established as a critical determinant of health and disease. It has attracted wide interest across medical disciplines with the promise of novel microbiota-targeted therapies that can effectively shift the microbiota toward a health-promoting state. The microbiota is noted for its exceptional complexity, with intricate metabolic networks governing microbial symbiotic and competitive interactions. The simultaneous use of multiple ‘omics technologies for characterizing the microbiota has been recognized broadly by the research community as a powerful approach because it can expose the interactions between the various components of the microbiota and link microbiota composition and function, a key requirement of targeted therapy development. However, the success of this approach depends on the development of computational and statistical methods for identifying the high-order interactions in the microbiota that are relevant for influencing disease risk and for bridging multiple high-dimensional ‘omics data streams. We aim to address this methodological gap by developing, evaluating, applying and distributing a new set of tools for performing meaningful analysis and integration of ‘omics data for human microbiota studies. We will frame the development of our tools around two of the most powerful and widely-used technologies for characterizing the human-associated microbiota: (1) sequence-based microbial taxonomic profiling for characterizing microbial community composition and (2) spectroscopy-based untargeted metabolomic profiling for characterizing microbial community function. We will first develop and evaluate a method for integrating multi-omic data streams that leverages publicly available databases of microbial metabolic pathways. Next, we will develop a method for mapping associations between composition and clinical outcomes through the lens of microbiota functional profiles. We will then apply our methods for identifying microbial communities and their phenotypes associated with clinical endpoints in a large cohort. Finally, our methods will be released to the human microbiota research community as an open-source software package. The work we propose will build analytic tools that perform functional integration of multiple ‘omics data streams and evaluate complex relationships with clinical outcomes. This represents a novel framework for identifying microbiota-health associations and their functional underpinnings in a manner that embraces the complexity of this system. The significance of this work lies in its potential to help translate experimental and human subjects studies of the microbiota into targeted therapies that shift the microbiota toward a health-promoting state.