Bacterial Source Tracking Methodologies Salisbury University Elichia A. Venso Mark F. Frana Mark Frana and I are Co-PIs at SU’s BST Library. We put in our first BST grant in 1999 and became the lab for Kathy Brohawn’s at MDE’s first BST lab that same summer. We have been conducting BST studies continuously since then. We’re MD’s BST lab for data that is used for TMDL determination. This picture is of the only public beach in Salisbury, which had been closed in the mid 1990s due to bacterial contamination. Most believed it was the resident Canada geese that were the culprits. In 2000, SU conducted a study at this pond to see whate the sources were. We found that the main probable source was dog – the geese contributed little to the bacterial load. That result indicated an entirely different abatement effort would be required than would have been for geese. Thus, the benefit of Bacterial Source Tracking.

Bacterial Source Tracking (BST) Determination of sources of fecal bacteria from environmental water samples (1995) Several different techniques are currently being used for BST – “Tool Box” Many current techniques are “library-based” Used for TMDL determination in MD, VA BST is based on the assumption that fecal indicator organisms differ genetically and/or phenotypically as a result of host differences. The first use of host specificity of bacteria was first investigated by George Simmons, VA. Tech, in the mid 1990s, when he used PFGE to identify raccon as the source of contamination for a shellfish harvester in VA. They threw netting over the area near the beach where raccoons defecated and the bacterial levels in the water dropped. Six years later, In 2001, EPA published a protocol for pathogen TMDL development.

BST Technology Genotypic Pulsed-Field Gel Electrophoresis (Simmons/Herbein/Hagedorn – Va. Tech.) Ribotyping (Samadpour – U. of Wash., NOAA Oxford Lab) Randomly Amplified Polymorphic DNA (RAPD)/PCR (Sadowsky – U. of Minn.) Microarray (Soule, Wash. St. Univ.) Image Source: http://images.google.com/images?q=E.+coli&ndsp=20&svnum=10&hl=en&rls=WZPA,WZPA:2006-37,WZPA:en&start=40&sa=N Google images [Available 03.07.2007] RAPD on E. Coli DNA

BST Technology Biochemical (Phenotypic) Chemical (Non bacterial) Antibiotic Resistance Analysis (ARA) (Hagedorn - Va. Tech/ Kator – VIMS/ Wiggins – James Madison, NOAA Oxford Lab) Coliphage (Geoff Scott, Jill Stewart – NOAA) Carbon Source Utilization Profiles (Hagedorn – Va. Tech) Chemical (Non bacterial) Human/Non human Optical Brightener Detection Caffeine Detection Image source: Google images [Available 03.07.2007]

Fecal Indicators (Microbial) Fecal Coliforms (E. coli ) Fecal Streptococci (Enterococcus spp.) Bacteriodes fragilis E. coli E. Coli Image Source: http://images.google.com/images?q=E.+coli&svnum=10&hl=en&rls=WZPA,WZPA:2006-37,WZPA:en&start=20&sa=N&ndsp=20 Google images [Available 03.07.2007] Enterococcus Source: http://images.google.com/images?q=enterococcus&ndsp=20&svnum=10&hl=en&rls=WZPA,WZPA:2006-37,WZPA:en&start=60&sa=N Google images [Available 03.07.2007] Image source: http://www.sourcemolecular.com/_images/bacteroidetes.jpg Google images [Available 03.07.2007] Bacteriodes fragilis Enterococcus faecalis

General Project Overview: Library-Based Isolate/grow indicator organism from: Known sources (scat) for “library” Unknown sources from water samples Analyze all isolated organisms using BST method of choice Predict probable sources of fecal indicator in water by comparing to library Water and scat collected year around because of temporal variability. Water samples for SU collected monthly at routine monitoring stations in the watershed. Prediction uses statistical methodology.

Antibiotic Resistance Analysis 35 combinations of 12 different antibiotics at 1-4 concentrations each Inoculate antibiotic-containing plate with 48 isolates of known/unknown Incubate for 24 h at 35 0C Read plates 1=isolate grew (resistant) 0=isolate did not grow (susceptible)  250,000 data points Now an overview of ARA, which is the BST method of choice at SU for the high through-put studies for TMDL determination for MD. ARA is the work-horse for this type of investigation. We try to isolate 24 isolates per water sample and 8 isolates per scat sample. Each isolate has 35 responses to the antibiotics and those responses make up the profile for each scat and water sample. The scat bacteria profiles make up the library.

Control Plate Cephalothin 50 µg/ml Vancomycin 2.5 µg/ml

Probable Source Categories Pets – public education, outreach Human – infrastructure/septic system repair and/or replacement Livestock – adherence to BMPs Wildlife – no management intended Categories based on potential management remediation/abatement and on the assumption that these different categories of bacterial hosts different in their resistance to antibiotics.

Statistical Methods in ARA Discriminant analysis – most often used Logistic regression Classification Tree Software - SU Developed at Stanford University CART® Predictive Uses all of data to maximize predictive ability Maximizes correct classification We have an article in last May’s issue of Applied and Environmental Microbiology describing our use of this software.

Classification: Library Rates of Correct Classification by Category Pet 93% Human 97% Livestock 93% Wildlife 71% RCC = number of correctly predicted species / Total number predicted. We look at the library’s ability to classify the known isolates when a subset is treated as unknown. We assume the RCC for water sources will be at approximately the same as for the library.

The final results.

We can also determine sources by monitoring site or sites by date or season. Here we have representation for one monitoring site for each of the sampling dates. We can see the variation in sources over time. Just look at April, May or June. This information may be important to mitigate contamination.

Conclusions: Several successful methods exist for BST analysis. Each method has known advantages and disadvantages. ARA is the current method of choice at Salisbury University for the State of Maryland. Statistical analysis of correct rates of classification support the validity of this approach.

Acknowledgements Technical and Regulatory Services Administration, Maryland Department of the Environment Richard A. Henson School of Science and Technology, Salisbury University Price Associates, Inc., White Plains, NY