The University of Iowa

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The University of Iowa Center for Global and
Regional Environmental Research Seed Grant
2002-2003
Principal Investigators Mary Skopec, IDNR
Geological Survey Nancy Hall, University Hygienic
Laboratory Karen Owens, University Hygienic
Laboratory
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Bacteria Source Tracking in the Upper Iowa River
Watershed

• What we know about bacteria in Iowa streams?
• What is bacteria source tracking?
• Why the Upper Iowa Watershed?
• What is E. coli bacteria?
• What is DNA Ribotyping?
• Results
• Discussion

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What do we know about bacteria in streams?

• Bacteria levels are highly variable
• Rainfall affects bacteria levels
• Bacteria levels vary seasonally
• Sources of bacteria likely vary seasonally
• Many potential sources of bacteria in a watershed

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Sources of fecalmaterial in water

• Leaking sewage lagoons
• Malfunctioning septic systems
• Sewage treatment plant discharges
• Dirty diapers
• Boating or swimming fecal accidents
• Overflowing manure lagoons
• Manure spills
• Runoff from fields after manure application
• Storm water runoff from lands with wildlife or
  pet droppings
• Fecal material expelled by animals standing in
  the water
• Swimmers

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What is bacteria source tracking?

• Using bacterial method to determine sources of
  fecal bacteria in the environment
• Different methods available
• Genotypic methods
• Phenotypic methods

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Why is bacteria source tracking needed in Iowa?

• Better target BMPs for watershed projects to
  reduce a bacteria problem, need to know where
  its coming from
• Improve remediation efforts at state beaches

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Why the Upper Iowa watershed?

• Site of an active watershed group

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Why the Upper Iowa watershed (cont.)?

• Elevated bacteria identified as water quality
  concern

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Why the Upper Iowa watershed (cont.)?

• Interest locally in identifying bacteria sources

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Upper Iowa Watershed

• 1,005 mi2 watershed
• NE Iowa and SE Minnesota
• Great recreational value

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Coldwater Creek (9)
Silver Creek near Cresco (8 and 801)
Silver Creek near Waukon (27)
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Sample Collection

• Water Samples
• Weekly samples from each watershed
• Fecal Samples
• Collected from known sources

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Water E. coli Isolates

• 50 E. coli samples
• Isolated from
• Silver Creek 27 - 12
• Silver Creek 8 - 13
• Silver Creek 801 - 14
• Cold Water Creek - 11

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Fecal Sample Collection

• Most samples were collected in spring of 2003

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Escherichia coli (E.coli)

• Common inhabitant of human and animal intestines
• Predominant fecal coliform bacterium
• Indicator of fecal pollution
• Presence indicates disease-producing organisms
  may be present
• Presence does not determine source

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E. coli Methods - Isolation
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Ribotyping of E.coli

• Automatically generates genetic fingerprint
• Useful epi tool for tracking outbreaks
• Useful tool for ident. human and non-human
  pollution

UHLs Riboprinter
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Ribotyping Process

• Purification
• Identification
• Harvesting

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Ribotyping Process
E. coli cells are lysed
releasing DNA
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Ribotyping Process
DNA is cut into fragments using special
restriction enzymes
Fragments separated by size through
electrophoresis
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Ribotyping Process

• Fragment pattern is transferred to membrane,
  mixed with DNA probe and chemiluminescent
  chemicals to produce a visible band pattern

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Data Normalization
Sample lanes
Algorithms
RiboPrint patterns for 8 lanes of data
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Each unique pattern is assigned a unique
designation
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Different RiboPrint Patterns for 4 Hog and 4 Cow
Isolates
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Ribotyping - Source TrackingFirst - Building the
Libraries

• Known E.coli riboprint patterns from different
  species from the Upper Iowa
• Import these patterns into BioNumerics
• Patterns grouped into various libraries
• Perform band matching analysis
• Statistics
• Cluster verification (Jackknife test)
• Discriminate analysis

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Analysis by BioNumerics Software(Applied Math,
Belgium)

• Integrated software package
• Relational database with analysis and clustering
  modules
• UHL currently has software (PulseNet)
• Library development
• Database sharing capabilities

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Band Assignment and Quantification
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Cluster Verification for 5 Groups
ARCC 69 p 30
Cluster Verification for 3 Groups (CAH)
ARCC 81 p 31
Cluster Verification for 2 Groups
ARCC 91 p 32
Recent Published Ribotyping Studies
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Ribotyping - Source TrackingSecond Unknown
Identification

• Compare unknown E.coli patterns with known E.coli
  pattern groups to determine probable source
• Statistics
• Curve-based Pearson Correlation
• Calculation of Quality quotient

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Ribotyping - Source TrackingSecond Unknown
Identification

• Criteria for Good Identification
• ? Similarity coefficient 90
• (linear relationship between 2 entries)
• ? Quality quotient/factor
• High probability is A or B
• (how well it fits in the group, taking into
  consideration the internal spread)

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Interpretation Guidelines

• Interpretation based solely on the match
• Not quantitative - identifications not
  proportional to source contribution
• Sampling bias/small sample size/both in E.coli
  and sample number
• (As total of E.coli in sample ?, the
  probability of identifying all waste sources ?)

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510 ave. (3 samples, 5 isolates -1)
27 (1 sample, 2 isolates-7)
770 ave. (3 samples, 5 isolates-
E.coli Results per season
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9200 (1 sample)
450 ave. (3 samples, 4 isolates-
50 ave. (1 sample, 2 isolates-4)
1000 ave. (3 samples)
E.coli Results per season
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9100 ave. (2 samples)
100 (1 sample, 2 isolates-2)
400 ave. (2 samples)
750 (1 sample)
E.coli Results per season
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27 ave. (3 samples, 6 isolates-22)
4600 ave. (2 samples)
150 (1 sample)
E.coli Results per season
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Observations

• Sources identified matched watershed surveys
• Cattle in streams
• Failing private septic systems

Photos by Pat Kambesis, W. Kentucky University
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In Summary

• Theres still no magic bullet
• All source tracking methods have their strengths
  and limitations
• All source tracking methods need better
  quantitative criteria
• These methods continue to evolve and look very
  promising to differentiate human and animal
  pollution sources

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In Summary (continued)

• Toolbox approach advocated
• watershed evaluation
• key monitoring parameters
• Snap-shot sampling (HOT SPOTS)
• strategic monitoring sites
• Use 1 tracking tools (e.g. microbial chemical)

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Future Studies

• Lake Darling
• Implementing Toolbox approach
• Smaller watershed
• Limited number of sources
• Collection over longer period of time
• Will Upper Iowa database be valid in another
  geographic area a year later?

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Presentation and Reportavailable at

• ftp//ftp.igsb.uiowa.edu/pub/Download/EOBrien/Sour
  ceTracking/
• or
• http//wqm.igsb.uiowa.edu/publications/presentatio
  ns/presentations.htm

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