Antibiotic Resistance Profiles to Determine Sources of Fecal Contamination in a Rural Virginia Watershed

Abstract

Antibiotic resistance analysis (ARA) was used to determine if enterococci of human origin were present in a stream (Spout Run) that passes through a rural nonsewered community (Millwood, VA). Millwood consists of 82 homes, all served by individual septic systems, and Spout Run drains a 5,800-ha karst topography watershed that contains large populations of livestock and wildlife. Periodic monitoring by state regulatory officials had resulted in Spout Run being placed on the Virginia impaired stream list and Millwood being categorized as an at-risk community. Stream samples were collected monthly and analyzed for fecal coliforms and enterococci (May 1999-May 2000); ARA was performed on enterococci stream isolates on a quarterly basis. All 117 stream samples were positive for fecal coliforms, and 32% exceeded the Virginia recreational water standard (1,000 fecal coliforms/100 mL). A library of 1,174 known source Enterococcus isolate antibiotic resistance profiles was constructed, and yielded correct classification rates of 94.6% for 203 human isolates, 93.7% for 734 livestock isolates, and 87.8% for 237 wildlife isolates. Antibiotic resistance analysis of 2,012 enterococcal isolates recovered from stream samples indicated isolates of human origin appeared throughout the stream as it passed through Millwood, with a yearly average of approximately 10% human, 40% wildlife, and 50% livestock. There were no human origin isolates in samples collected upstream from Millwood, and the percent human origin isolates declined downstream from Millwood. While a human signature was found in Spout Run, it was small compared with the proportion of isolates from livestock and wildlife.

Relating Watershed Characteristics to Elevated Stream Escherichia coli Levels in Agriculturally Dominated Landscapes: An Iowa Case Study

“…For example, Animal waste can enter water bodies from leaking or overflowing waste lagoons or through runoff from fields amended with animal manure [1][2][3][4]. In many watersheds, wildlife such as geese and deer are a significant source of E. coli [5][6][7]. E. coli is also associated with point-source discharges of human wastes such as those which occur at wastewater treatment plants [8].…”

Microbial Source Tracking in a Watershed Dominated by Swine

“…A subset of the host-origin isolates were also identified by API 20E patterns (bioMerieux sa®, France). Details for collection, transport, dilutions, and plating of fecal samples have been previously described [32]. A total of 1,937 E. coli isolates (10-12 isolates/fecal sample) from the nine known hosts were used to develop the database.…”

“…Any isolates which failed to grow on the control plates were excluded from the analysis. The details of the ARA procedure have been described and are the same as that used in the method comparison studies [23,32,36]. …”

“…The ARA approach is based on the premise that fecal indicator bacteria (FIB) from hosts exposed to antibiotics will develop resistance to those antibiotics, and on the hypothesis that this selective pressure would be a mechanism for discriminating among fecal indicator bacteria from a variety of hosts [30][31][32][33][34][35]. A library or database composed of antibiotic resistance profiles (ARP) of FIB from known host sources is developed and serves as a point of reference for identifying unknown source isolates from water sources.…”

Stability of Antibiotic Resistance Patterns in Agricultural Pastures: Lessons from Kentucky, USA

“…Phenotypic MST techniques such as antibiotic resistance analysis (ARA) have had moderate levels of success in small and relatively simple aquatic systems to differentiate human and nonhuman sources of pollution, or two-way level of classification (Carson et al, 2001;Guan et al, 2002;Graves et al, 2002;Hagedorn et al, 1999;Hartel et al, 2002;Harwood et al, 2000;Ritchey and Coyne, unpublished data;Ritchey and Coyne, 2009;Wiggins et al, 1999). Correct classification rates of ≥50% including five or more sources are considered useful by resource managers and rates of 60-70% are very useful (Harwood et al, 2000).…”