Bacterial Source Tracking Methodologies

Salisbury University

Elichia A. Venso

Mark F. Frana

Bacterial Source Tracking (BST)

Determination of sources of fecal bacteria Determination of sources of fecal bacteria from environmental water samples (1995)from environmental water samples (1995)

Several different techniques are currently Several different techniques are currently

being used for BST – “Tool Box”being used for BST – “Tool Box”

Many current techniques are “library-based” Many current techniques are “library-based”

Used for TMDL determination in MD, VAUsed for TMDL determination in MD, VA

BST TechnologyBST Technology GenotypicGenotypic

• Pulsed-Field Gel Pulsed-Field Gel Electrophoresis Electrophoresis (Simmons/Herbein/Hagedorn – Va. Tech.)(Simmons/Herbein/Hagedorn – Va. Tech.)

• Ribotyping Ribotyping (Samadpour – U. of Wash., (Samadpour – U. of Wash., NOAA Oxford Lab)NOAA Oxford Lab)

• Randomly Amplified Randomly Amplified Polymorphic DNA Polymorphic DNA

(RAPD)/PCR (RAPD)/PCR (Sadowsky – U. of Minn.)(Sadowsky – U. of Minn.)

• Microarray Microarray (Soule, Wash. St. Univ.)(Soule, Wash. St. Univ.)

RAPD on E. Coli DNA

BST TechnologyBST Technology

• Biochemical (Phenotypic)Biochemical (Phenotypic) Antibiotic Resistance Analysis (ARA)Antibiotic Resistance Analysis (ARA)

• (Hagedorn - Va. Tech/ Kator – VIMS/ Wiggins – (Hagedorn - Va. Tech/ Kator – VIMS/ Wiggins – James Madison, NOAA Oxford Lab)James Madison, NOAA Oxford Lab)

Coliphage Coliphage • (Geoff Scott, Jill Stewart – NOAA)(Geoff Scott, Jill Stewart – NOAA)

Carbon Source Utilization ProfilesCarbon Source Utilization Profiles• (Hagedorn – Va. Tech)(Hagedorn – Va. Tech)

• Chemical (Non bacterial)Chemical (Non bacterial) Human/Non humanHuman/Non human Optical Brightener DetectionOptical Brightener Detection Caffeine DetectionCaffeine Detection

Fecal Indicators (Microbial)Fecal Indicators (Microbial)• Fecal Coliforms (Fecal Coliforms (E. coli E. coli ))

• Fecal Streptococci Fecal Streptococci ((Enterococcus Enterococcus spp.)spp.)

• Bacteriodes fragilisBacteriodes fragilis

Enterococcus faecalis

E. coli

Bacteriodes fragilis

General Project Overview:General Project Overview:Library-Based Library-Based

Isolate/grow indicator organism from:Isolate/grow indicator organism from:• Known sources (scat) for “library”Known sources (scat) for “library”• Unknown sources from water samples Unknown sources from water samples

Analyze all isolated organisms using BST Analyze all isolated organisms using BST method of choicemethod of choice

Predict probable sources of fecal indicator in Predict probable sources of fecal indicator in water by comparing to librarywater by comparing to library

Antibiotic Resistance AnalysisAntibiotic Resistance Analysis 35 combinations of 12 different 35 combinations of 12 different

antibiotics at 1-4 concentrations eachantibiotics at 1-4 concentrations each

Inoculate antibiotic-containing plate Inoculate antibiotic-containing plate with 48 isolates of known/unknownwith 48 isolates of known/unknown

Incubate for 24 h at 35 Incubate for 24 h at 35 00CC

Read plates Read plates • 1=isolate grew (resistant)1=isolate grew (resistant)• 0=isolate did not grow (susceptible)0=isolate did not grow (susceptible) 250,000 data points250,000 data points

Control Plate

Cephalothin 50 µg/ml

Vancomycin2.5 µg/ml

Probable Source CategoriesProbable Source Categories

Pets – public education, outreachPets – public education, outreach

Human – infrastructure/septic system Human – infrastructure/septic system repair and/or replacementrepair and/or replacement

Livestock – adherence to BMPsLivestock – adherence to BMPs

Wildlife – no management intendedWildlife – no management intended

Statistical Methods in ARAStatistical Methods in ARA

Discriminant analysis – most often Discriminant analysis – most often usedused

Logistic regression Logistic regression Classification Tree Software - SUClassification Tree Software - SU

• Developed at Stanford UniversityDeveloped at Stanford University• CARTCART®®

PredictivePredictive Uses all of data to maximize predictive Uses all of data to maximize predictive

abilityability Maximizes correct classificationMaximizes correct classification

Classification: LibraryClassification: Library

Rates of Correct Classification by Category

Pet 93%

Human 97%

Livestock 93%

Wildlife 71%

RCC = number of correctly predicted species / Total number predicted.

Rock Creek Watershed Probable Bacterial Pollution Sources

Pet17%

Human10%

Livestock28%

Wildlife37%

Unknown8%

Figure 5. Probable Sources by Date for Rock CreekStation NBR0002

0

5

10

15

20

25

30

To

tal I

sola

tes

unknown 1 2 1 7 2 2

wildlife 7 7 9 4 2 1 1 8 8 11 11

livestock 15 7 7 2 1 8 7 8 9 6

human 2 3 2 1 12 1 4 1

pet 1 4 3 9 2 1 3 3

NOV 19 02 DEC 03 02 JAN 07 03 MAR 04 03 APR 22 03 MAY 06

03JUN 03 03 JUL 08 03 AUG 05 03 SEP 09 03 OCT 07 03

Conclusions:Conclusions: Several successful methods exist for BST Several successful methods exist for BST

analysis.analysis.

Each method has known advantages and Each method has known advantages and disadvantages.disadvantages.

ARA is the current method of choice at Salisbury ARA is the current method of choice at Salisbury University for the State of Maryland.University for the State of Maryland.

Statistical analysis of correct rates of Statistical analysis of correct rates of classification support the validity of this classification support the validity of this approach.approach.

AcknowledgementsAcknowledgements

Technical and Regulatory Services Technical and Regulatory Services Administration, Maryland Department of Administration, Maryland Department of the Environmentthe Environment

Richard A. Henson School of Science Richard A. Henson School of Science and Technology, Salisbury Universityand Technology, Salisbury University

Price Associates, Inc., White Plains, NYPrice Associates, Inc., White Plains, NY