Microbial Community Profiles Identify New Indicators of Waterborne Pathogens

项目摘要 水中传播的人类病原体导致中国多达1900万例胃肠炎 在美国，每年有220万人死亡，主要影响五岁以下儿童。粪便污染 将病原体引入地表水和地下水，用于饮用水、家庭需要和娱乐。 传统的粪便污染指标大肠杆菌和肠球菌缺乏宿主来源的特异性 (动物或人类)，通常与致病生物体的存在相关性较差。未经治疗 污水很可能携带人类病原体；因此，新的替代指标是 具体到人类来源，将是对人类健康风险的更可靠评估。我们已经产生了 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65个以上的候选人类粪便污染的替代指标。在……里面 在这项工作中，我们将精准最敏感和具体的人类粪便污染指标，并制定 将严格验证灵敏度和特异度的一套定量分析方法。我们将利用 我们开发了新的计算工具来创建一个序列分类管道，它将允许用户 从序列数据中识别粪便污染源。我们将为关系生成经验数据 用于定量微生物风险评估的未经处理污水中病原体和新指示剂之间的关系 (QMRA)框架，该框架将为相关人类特定指标的浓度提供指导方针 暴露后可接受的风险阈值。指标和病原体的生态将在 一个明确的分水岭。我们将跟踪病原体和指示剂在污水溢出期间的腐烂情况。 弥散在密歇根湖的质量(羽流)。我们还将利用一项大型研究，病原体 已在多个上游河流站点测量了浓度。影响排污指标的因素很多 以及分水岭尺度上的病原体关系。我们将使用Logistic和多元线性回归来 检查这些因素(造成污水污染的人数、 社区)，以及指标浓度和水文参数，以确定 病原体浓度。这些计算将阐明指标的生态和 病原体可能是不同的。重要的是，这个项目将为不同的团体提供培训 在跨学科环境中的本科生、研究生和博士后 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十字路口。

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.