Microbial Community Profiles Identify New Indicators of Waterborne Pathogens

项目摘要 人类病原体的水媒传播是造成2010年多达1900万例胃肠炎的原因。 全球每年有220万人死亡，主要影响5岁以下的儿童。粪便污染 将病原体引入用于饮用水、家庭需求和娱乐的地表水和地下水。 粪便污染的传统指标大肠杆菌（E.大肠杆菌）和肠球菌缺乏宿主来源的特异性 （动物或人类）的感染，并且通常与致病生物体的存在相关性很差。未处理 污水携带人类病原体的可能性很高;因此， 对人类来源的具体评估将是对人类健康风险的更可靠评估。我们已经生成 来自美国71个城市和多个城市污水中微生物群落的高分辨率16S rRNA基因图谱 动物物种，确定了超过65种人类粪便污染替代指标的候选者。在 这项工作，我们将查明人类粪便污染的最敏感和具体的指标，并制定一个 一套定量分析，将严格验证灵敏度和特异性。我们将利用 我们开发了一种新的计算工具来创建一个序列分类管道， 从序列数据中识别粪便污染源。我们将生成关系的经验数据 未处理污水中的病原体和新指标之间的关系，用于定量微生物风险评估 （QMRA）框架，该框架将为与人类相关的特定指标的集中提供指导 达到可接受的风险阈值。指示物和病原体的生态学将在 一个明确的分水岭我们将遵循病原体和指标的衰减，在污水溢出的水 质量（羽流），因为它分散在密歇根湖。我们还将利用一项大型研究， 在多个上游河流站点测量了浓度。许多因素影响污水指标 和病原体之间的关系我们将使用逻辑回归和多元线性回归， 研究这些因素（造成污水污染的人数、 以及指标浓度和水文参数，以确定 病原体浓度这些计算将揭示的情况下，生态指标和 病原体可能不同。重要的是，该项目将为不同群体提供培训， 本科生，研究生和博士后在一个跨学科的环境，是在 微生物生态学、遗传学、微生物学、水文学、传染病和公共卫生的交叉点。

Project Summary Waterborne transmission of human pathogens is responsible for up to 19 million cases of gastroenteritis in the US each year and 2.2 million deaths globally, primarily affecting children under five years old. Fecal pollution introduces pathogens into surface and groundwater used for drinking water, household needs, and recreation. Traditional indicators of fecal pollution Escherichia coli (E. coli) and enterococci lack specificity for host source (animal or human) and generally correlate poorly with the presence of disease-causing organisms. Untreated sewage has a high likelihood of carrying human pathogens; therefore, new alternative indicators that are specific for human sources would be a more reliable assessment of human health risk. We have generated high-resolution 16S rRNA gene profiles of microbial communities in sewage from 71 US cities and multiple animal species, which identified more than 65 candidates for alternative indicators of human fecal pollution. In this work, we will pinpoint the most sensitive and specific indicators for human fecal pollution and develop a suite of quantitative assays that will be rigorously validated for sensitivity and specificity. We will draw upon the novel computational tools we have developed to create a sequence classification pipeline that will allow users to identify fecal pollution sources from sequence data. We will generate empirical data for relationships between pathogens and new indicators in untreated sewage to use in a quantitative microbial risk assessment (QMRA) framework, which would inform guidelines for concentrations of human specific indicators that relate to acceptable risk thresholds following exposure. The ecology of indicators and pathogens will be compared in a defined watershed. We will follow the decay of pathogens and indicators during sewage overflows in a water mass (plume) as it disperses in Lake Michigan. We will also leverage a large study where pathogen concentrations have been measured at multiple upstream river sites. Many factors influence sewage indicators and pathogen relationships at the watershed scale. We will use logistic and multiple linear regression to examine these factors (number of people contributing to sewage contamination, disease incidence in the community), as well as indicator concentrations and hydrological parameters, to determine predictors for pathogen concentrations. These calculations will shed light on instances where the ecology of indicators and pathogens might be different. Importantly, this project will provide training for a diverse body of undergraduates, graduate students, and postdocs within an interdisciplinary environment that is at the crossroads of microbial ecology, genetics, microbiology, hydrology, infectious disease, and public health.