Exploring the Niche Space of Human Microbiome Functions through Convex Geometry and Evolutionary Genomics

人类微生物群是生活在我们体内和身体上的大量微生物的集合。虽然研究人员才刚刚开始了解这些微生物在人类生物学中扮演的复杂角色，但很明显，微生物区系的特定变化与宿主的疾病有关，有时还会导致或治愈疾病。到目前为止，大多数微生物组研究都集中在描述不同身体部位(例如，肠道、口腔、肘部皮肤)或跨疾病组的单一部位的群落的分类组成。该项目的目标是将微生物组研究从“谁在那里？”的描述中转移出来。对“他们在做什么”的描述。为了做到这一点，研究人员将开发新的方法来分析鸟枪式元基因组数据，这是从群落中的各种微生物中提取并测序的DNA的汇集样本。因为这些序列代表了许多生物体基因组的短片段，所以元基因组学提供了微生物群落的蛋白质图谱的快照。这些丰富的数据蕴含着巨大的希望，但也给数据分析带来了许多挑战。为了应对这些挑战，研究人员将首先设计和验证一条生物信息学管道，将元基因组序列分类为蛋白质家族。该工具的关键组件将是隐马尔可夫模型，该模型描述了每一种已知微生物蛋白质的进化概况，从而能够准确地表征样本中存在的蛋白质功能。其次，他们将推导出新的随机模型，以预测在给定关于患者人口统计学和临床特征的数据的情况下，蛋白质家族在元基因组样本中的出现情况。这些模型基于生态学和凸几何的概念，将允许研究人员估计、绘制和统计比较高维表型空间中蛋白质生态位的形状。最后，他们将制作一个医学利基地图集，通过一个公众可访问的、用户友好的可视化工具和数据库，将蛋白质分布与疾病状态联系起来。该项目将为微生物组研究、药物开发和生物勘探产生新的数学理论和新的计算工具。除了对微生物组研究的直接影响外，我们的数学结果还将用于其他领域的空间建模，如生态学、社会学和流行病学。该项目的目标是开发计算资源和随机模型，以揭示个人健康与生活在其体内和身体上的微生物活动之间的复杂关系。这项研究将生成一个医学生态位地图集，它将绘制具有不同临床特征(例如疾病、饮食或治疗)的个体的微生物蛋白质功能分布。就像地理地图集使外行人能够接触到制图专业知识一样，我们的利基地图集将允许研究人员、学生、临床医生和任何好奇的人从计算机终端轻松探索人类微生物组的功能能力。它的蛋白质生态位地图将直观地显示每种微生物蛋白质可能出现的患者特征范围，以及这些范围在不同疾病状态下的差异。我们将利用这一能力作为研究生教学工具，通过公共媒体进行推广和交流，并建立实验合作，以测试我们产生的关于微生物组蛋白在人类疾病(如炎症性肠病)中的作用的假设。这些研究将旨在确定新的疾病生物标志物，包括可用于在目前难以早期诊断的患者亚群中诊断疾病发作的微生物组蛋白。利基地图集还将使基于个人-S微生物组的知识开发个性化治疗和预防成为可能。总而言之，这些免费获取的资源将极大地拓宽获取尖端药物的途径。

The human microbiome is the vast collection of microorganisms living in and on our bodies. While researchers are only just beginning to understand the complex roles that these microbes play in human biology, it is clear that specific changes in microbial flora are associated with and sometimes cause or cure disease in the host. Most microbiome research to date has focused on describing the taxonomic composition of communities in different body sites (e.g., gut, mouth, elbow skin) or in a single site across disease groups. The goal of this project is to move microbiome research from descriptions of "who is out there?" towards characterizations of "what they are doing?". To do so the investigators will develop new methodology for analyzing shotgun metagenomic data, which is a pooled sample of DNA extracted and sequenced from the various microbes in a community. Because the sequences represent short segments of the genomes of many organisms, metagenomics provides a snapshot of the protein repertoire of a microbiome community. This rich data holds great promise and also presents many challenges for data analysis. To meet these challenges, the investigators will first design and validate a bioinformatics pipeline to classify metagenomic sequences into protein families. The key component of this tool will be hidden Markov models describing the evolutionary profile of every known microbial protein, enabling accurate characterization of the protein functions present in a sample. Second, they will derive novel stochastic models to predict the occurrence of protein families in a metagenomic sample given data about the demographic and clinical characteristics of a patient. These models, based on concepts from ecology and convex geometry, will allow the investigators to estimate, draw, and statistically compare the shapes of protein niches in high-dimensional phenotype space. Finally, they will produce a medical Niche Atlas that will link protein distributions to disease states via a publicly accessible, user-friendly visualization tool and database. This project will produce new mathematical theory and novel computational tools for microbiome research, drug development, and bioprospecting. In addition to immediate impacts on microbiome research, our mathematical results will be useful for spatial modeling in other fields, such as ecology, sociology, and epidemiology.The aim of this project is to develop computational resources and stochastic models that will shed light on the complex relationship between the health of an individual and the activities of the microbes living in and on his/her body. This research will generate a medical Niche Atlas that will map the distributions of microbial protein functions across individuals with different clinical characteristics (e.g., diseases, diets, or treatments). Just as a geographic atlas makes cartographic expertise accessible to a layperson, our Niche Atlas will allow researchers, students, clinicians, and any curious person to easily explore the functional capabilities of the human microbiome from a computer terminal. Its protein niche maps will visually display the range of patient characteristics at which each microbial protein is likely to occur and how these ranges differ across disease states. We will leverage this capability as a graduate teaching tool, for outreach and communication through public media, and to establish experimental collaborations to test the hypotheses we generate about the roles of microbiome proteins in human diseases, such as inflammatory bowel disease. These investigations will aim to identify new disease biomarkers, including microbiome proteins that can be used to diagnose onset of disease in patient subpopulations where early diagnosis is currently difficult. The Niche Atlas will also enable development of personalized treatments and preventions based on knowledge of an individual?s microbiome. Together, these freely accessible resources will significantly broaden access to cutting-edge medicine.