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Modeling Sources of Variation in Interaction Networks Using a C. Elegans Microbiome Model

使用线虫微生物组模型对交互网络中的变异源进行建模

基本信息

批准号：
2014173
负责人：
Nic Vega
金额：
$ 42万
依托单位：
Emory University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-09-01 至 2024-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2014173&HistoricalAwards=false
关键词：
Modeling Sources Variation Interaction Networks

项目摘要

Host-associated microbiomes are shaped by networks of interactions among microbes and between microbes and the host. It is not possible to directly measure these interactions, requiring inference from co-occurrence data. Such inferences are prone to high rates of false positives and negatives, and the complications found in real data - in particular demographic noise, higher-order and frequency-dependent interactions among microbes, and variation across hosts - are not well understood. In this project a direct test will be developed using the utility of co-occurrence-based interaction networks in understanding host-associated microbial communities where these biologically realistic complications are present. Further, the PIs will test the common implicit assumption that variation in individual hosts within a population does not fundamentally alter the inter-species interactions in a microbiome, such that the same interaction network can be used to understand the microbiome. Educational and outreach activities are specifically designed to excite and engage students at all levels in biophysics and theoretical biology. The proposed work will actively engage undergraduate students from the Biology and Physics programs in independent biophysics research. Further, quantitative laboratory modules are being developed for the Biology and Physics curricula at Emory to familiarize students with core concepts in population dynamics and to introduce the concepts of variation and hidden states in biological data.The PIs will use the nematode worm Caenorhabditis elegans and an eight-species bacterial consortium from the worm's native microbiome. The worm is a powerful model system for understanding microbial community assembly, where bottom-up assembly and quantification microbiome composition in large numbers of homozygous individual hosts is possible on short experimental time scales, allowing rapid generation of large, high-quality data sets describing microbiome composition and variation. Using this tractable system, the PIs will obtain microbiome composition data from large numbers of individual hosts, directly parameterizing interactions among microbes (competition, facilitation etc.) and between individual microbial species and the host (e.g. colonization rates and densities in the intestine). Using these data, they will fit and constrain neutral and near-neutral stochastic models of community assembly to: 1) determine the interactions shaping these systems, 2) measure the effects of higher-order and frequency-dependent interactions on inferred networks, and 3) test the assumption that microbial interaction networks are conserved across hosts. The project will use a tractable experimental system as a tool to understand a complex network inference problem on noisy data with hidden underlying states, a problem that is highly relevant for real- world understanding of microbiome data and other data from systems where interactions are not directly measurable, and is therefore relevant for the Physics of Living Systems program. The combined expertise of the PIs will allow rigorous analysis of the technical problem while maintaining a firm connection of these analyses and conclusions to the biological reality.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

宿主相关微生物组是由微生物之间以及微生物与宿主之间的相互作用网络形成的。不可能直接测量这些相互作用，需要从共现数据中进行推断。这样的推断容易出现高比例的假阳性和假阴性，而在真实的数据中发现的复杂性--特别是人口统计学噪声、微生物之间的高阶和频率依赖性相互作用以及宿主之间的变异--还没有得到很好的理解。在这个项目中，将开发一个直接的测试，使用基于共现的相互作用网络的效用，了解这些生物现实的并发症存在的宿主相关的微生物群落。此外，PI将测试常见的隐含假设，即群体中个体宿主的变化不会从根本上改变微生物组中的物种间相互作用，因此可以使用相同的相互作用网络来理解微生物组。教育和推广活动是专门设计的，以激发和吸引各级学生在生物物理学和理论生物学。拟议的工作将积极吸引生物学和物理学专业的本科生参与独立的生物物理学研究。此外，正在为埃默里大学的生物学和物理学课程开发定量实验室模块，以使学生熟悉人口动态学的核心概念，并介绍生物数据中的变异和隐藏状态的概念。蠕虫是一个强大的模型系统，用于了解微生物群落组装，其中自下而上的组装和定量微生物组组成在大量纯合个体宿主中是可能的，在短的实验时间尺度上，允许快速生成描述微生物组组成和变异的大型高质量数据集。使用这个易于处理的系统，PI将从大量个体宿主中获得微生物组组成数据，直接参数化微生物之间的相互作用（竞争，促进等）。以及个体微生物物种与宿主之间的差异（例如肠道中的定殖率和密度）。使用这些数据，他们将拟合和约束社区组装的中性和近中性随机模型，以：1）确定塑造这些系统的相互作用，2）测量高阶和频率依赖性相互作用对推断网络的影响，以及3）测试微生物相互作用网络在宿主中保守的假设。该项目将使用一个易于处理的实验系统作为工具，以了解具有隐藏底层状态的噪声数据的复杂网络推理问题，该问题与真实的世界对微生物组数据和来自相互作用不可直接测量的系统的其他数据的理解高度相关，因此与生命系统物理学计划相关。PI的综合专业知识将允许对技术问题进行严格的分析，同时保持这些分析和结论与生物现实的牢固联系。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。