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.

Public Health Relevance

Sewage contains many pathogens that can cause waterborne illness such as gastroenteritis. Humans may be exposed to waterborne pathogens through contamination of drinking water or from swimming at polluted recreational beaches. Current methods to detect fecal pollution do not distinguish the source (human or non-human), nor do they indicate the presence of disease- causing organisms. This project will produce a suite of qPCR based assays and a sequence based source identification pipeline to identify and quantify health risk from untreated sewage in surface water. Overall, our work will improve detection and tracking waterborne disease threats.

National Institute of Health (NIH)
National Institute of Allergy and Infectious Diseases (NIAID)
Research Project (R01)
Project #
Application #
Study Section
Bacterial Pathogenesis Study Section (BACP)
Program Officer
Glock, Jonathan A
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
University of Wisconsin Milwaukee
Graduate Schools
United States
Zip Code
Roguet, Adélaïde; Eren, A Murat; Newton, Ryan J et al. (2018) Fecal source identification using random forest. Microbiome 6:185
Dila, Deborah K; Corsi, Steven R; Lenaker, Peter L et al. (2018) Patterns of Host-Associated Fecal Indicators Driven by Hydrology, Precipitation, and Land Use Attributes in Great Lakes Watersheds. Environ Sci Technol :
McLellan, Sandra L; Sauer, Elizabeth P; Corsi, Steve R et al. (2018) Sewage loading and microbial risk in urban waters of the Great Lakes. Elementa (Wash D C) 6:
Feng, Shuchen; Bootsma, Melinda; McLellan, Sandra L (2018) Human-Associated Lachnospiraceae Genetic Markers Improve Detection of Fecal Pollution Sources in Urban Waters. Appl Environ Microbiol 84:
Green, Hyatt C; Fisher, Jenny C; McLellan, Sandra L et al. (2015) Identification of Specialists and Abundance-Occupancy Relationships among Intestinal Bacteria of Aves, Mammalia, and Actinopterygii. Appl Environ Microbiol 82:1496-1503
Fisher, Jenny C; Eren, A Murat; Green, Hyatt C et al. (2015) Comparison of Sewage and Animal Fecal Microbiomes by Using Oligotyping Reveals Potential Human Fecal Indicators in Multiple Taxonomic Groups. Appl Environ Microbiol 81:7023-33
Eren, A Murat; Sogin, Mitchell L; Morrison, Hilary G et al. (2015) A single genus in the gut microbiome reflects host preference and specificity. ISME J 9:90-100
Fisher, Jenny C; Newton, Ryan J; Dila, Deborah K et al. (2015) Urban microbial ecology of a freshwater estuary of Lake Michigan. Elementa (Wash D C) 3:
Newton, Ryan J; McLellan, Sandra L; Dila, Deborah K et al. (2015) Sewage reflects the microbiomes of human populations. MBio 6:e02574
Ponce-Terashima, Rafael; Koskey, Amber M; Reis, Mitermayer G et al. (2014) Sources and distribution of surface water fecal contamination and prevalence of schistosomiasis in a Brazilian village. PLoS Negl Trop Dis 8:e3186

Showing the most recent 10 out of 20 publications