Impact/Purpose:

If this project results in a reliable tool for distinguishing sources of fecal indicator bacteria in natural waters, it will be used by state water quality agencies in developing appropriate TMDLs and in implementation plans for reducing numbers of these bacteria to acceptable levels.

Description:

The primary objective of the proposed study is to apply secretory immunoglobulin A (sIgA) analysis to surface waters in eastern Mississippi and to clarify the source(s) of pollution entering the Wolf and Jordan River watersheds. The method would attempt to determine if bovine fecal pollution is the primary source of contamination or whether bovine waste contamination is secondary to other sources of pollution (human, chickens, wildlife, etc.). The study would consist of two sub-objectives. First, protocol described above and which was applied earlier to field studies of human pollution would be used to trace the concentrations of bovine sIgA in each watershed. Second, research targeting improved bovine sIgA detection by amplified ELISA methods would be conducted. To complement these investigations, the fecal bacteria, Escherichia coli (E. coli) enterococci , will be isolated and subjected to methods that will discriminate among E. coli from different hosts. These methods will include pulsed field gel electrophoresis, ribotyping, amplified fragment length polymorphism (AFLP) and random amplification of polymorphic DNA. The initial goal of the proposed project is to establish a collection of E. coli and enterococcal isolates from known animal and environmental sources in Mississippi (e.g., humans, swine, cattle, deer, etc) and to build a database (often referred to as a "library") of DNA fingerprints from these isolates. These fingerprints will later serve as reference for comparison of E. coli and enterococci collected from Mississippi coastal watersheds to identify possible sources of contamination. The size of the library can be estimated from the potential contribution of each animal type, but at this time no one knows how large a library must be to create a statistically valid analysis of a single, unknown isolate.

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