How well do indicator bacteria estimate Salmonella in freshwater streams?
Timothy M. Smith, Zsofia Jakab, Sarah F. Lucento, David W. Buckalew
Department of Biological and Environmental Sciences
Longwood University
Farmville, VA 23909
LONGWOOD UNIVERSITY
Department of Biological and Environmental Sciences
Methods
Bacterial Isolation and Enumeration
Bacterial counts from both the Appomattox River and Green Creek sites reveal significant (p<0.05) and
linear relationships between bacterial indicator and Salmonella. The relationship between EC and Sal
counts for APP2 and GRE16 produced R2 values of 0.458 and 0.338, respectively (Fig’s 4 and 5) and
Pearson correlation coefficients of 0.722 and 0.471, respectively (Table 2). These relationships were
not observed between the Sayler’s Creek bacterial counts (see Fig 6 and Table 2).
Water samples were collected from three locations: Appomattox River (APP2), Sayler’s Creek
(SAY5), and Green Creek (GRE16). All samples were processed for Salmonella and for Total
Coliform (TC) and E. coli (EC).
Salmonella enrichment and analysis: Membrane filtration
Figure 4. Comparison of numbers of E.coli and Salmonella from the
same water samples obtained from APP2 collection site.
Filter membrane with Salmonella growth
+
Table 2. Pearson correlations comparing Salmonella counts with both
Coliform and E.coli counts in warm weather, cold weather, and
composite samples.
-
+
500
Pearson r coeff.
Cold months
+
+
Escherichia coli (EC), Klebsiella spp., Enterobacter spp.,
Appomattox River
Warm months
Sal vs Coliform
-0.010
0.075
0.279
Sal vs EC
0.692
0.700
0.722
+
400
Green Creek
and Citrobacter spp.
Sal vs Coliform
0.185
0.740
0.423
Sal vs EC
0.414
0.568
0.471
Courtesy of Oxoid™ website
Membrane labeled (+) for Salmonella spp. and (-) for others
Introduction
MPN for E. coli counting fluorescent MUG +
Statistical Analyses and Data presentation
Coliforms (TC)
E.coli (EC)
3878.7 ± 3675
1202.5 ± 1686.2
349.8 ± 486.6
2836.2 ± 1696.4
4850 ± 5094
3882.3 ± 3209
803.7 ± 1139
1551.8 ± 1873
1237.7 ± 1885.2
136.2 ± 96.8
599.5 ± 754.7
307.9 ± 186.6
400
300
200
60%
50%
APP Sal. Counts
40%
APP E. coli Counts
30%
APP Coliform Counts
600
300
Sampling event
SAY Sal. Counts
40%
SAY E. coli Counts
30%
SAY Coliform Counts
11 13 15 17 19 21 23 25 27 29 31
Sample event
Proportion of total bacterial count (%)
Proportion of total bacterial count (%)
50%
9
6000
9000
12000
15000
5000
10000
15000
20000
25000
30000
Salmonella counts (#/100 mL)
• The ecology and environmental survival characteristics of bacterial, viral, and parasitic
enteropathogens vary suggesting that no single indicator organism or group can consistently
predict the presence of all enteric pathogens.
• Fecal indicator bacteria (Coliforms, Fecal Coliforms, E.coli, and Enterococci) have been used to
assess biological quality of environmental and potable water since the early 20th Century and
they have adequately withstood the test of time.
Figure 3. Proportional view of TC vs EC vs Sal counts per sample from
the Green Creek sampling site; 30 total samples
60%
7
0
3000
• Microbial monitoring using only fecal indicator bacteria may not be sufficient for each particular
pathogen, but they may have a high degree of predictive value if relationships are examined
with respect to specific pathogen and environment.
10%
100%
5
900
20%
1 2 3 4 5 6 7 8 9 1011121314151617181920212223242526272829
70%
3
Salmonella vs E.coli counts
1200
• The relationship between any one group of free-living bacteria and any other within the
external environment cannot be perfectly linear as there exist a constellation of functional
parameters relating to differential survivorship.
70%
80%
1
1500
• EC concentrations are generally 1 order of magnitude less than Salmonella concentrations, but
as E.coli increases, so does Salmonella.
Green Creek: Ratios of
Indicator bacteria vs Salmonella
0%
1800
• Although not all of our data show positive correlations between fecal indicator bacteria and Sal
species, the majority of our samples revealed a positive correlation between numbers of EC and
numbers of Sal in the watershed of the upper Appomattox River.
80%
90%
10%
2100
90%
Figure 2. Proportional view of TC vs EC vs Sal counts per sample from
the Sayler’s Creek sampling site; 31 total samples
20%
y = 0.0553x + 240.18
R² = 0.3375
Discussion
100%
100%
Department of Biological and Environmental Sciences
Salmonella vs E.coli counts
Salmonella counts (#/100 mL)
0%
Sayler's Creek: Proportion of
Indicator bacteria vs Salmonella
LONGWOOD UNIVERSITY
500
0
App River: Proportion of
Indicator bacteria vs Salmonella
Proportion of bacterial counts (%)
Salmonella (Sal)
2700
2400
600
0
Figure 1. Proportional view of TC vs EC vs Sal counts per sample from
the Appomattox River sampling site; 29 total samples
Norovirus
Graphics courtesy of www.Wikipedia.org
8000
0
For each Salmonella enumeration, the average colony counts of two 1 mL field duplicate samples was taken and
multiplied by 100 to represent the number of suspect Salmonella spp. present per 100 mL standard volume. All
enumerations of TC and EC were also recorded with respect to 100 mL volumes for all samples tested. Bacterial count
data was recorded and illustrated by the use of stacked column graphs (see Fig.’s 1, 2, and 3 below).
SAY 5 site (n=31)
Caliciviridae
6000
3000
y = 0.0031x + 301.72
R² = 0.0035
100
GRE 16 site (n=30)
Picornaviridae
4000
Figure 5. Comparison of numbers of E.coli and Salmonella from the same
water samples obtained from GRE16 collection site.
E. coli counts (#/100 mL)
E.coli counts (#/100 mL)
MPN for total coliforms counting
chromogenic ONPG +
APP 2 site (n=29)
Reoviridae
2000
Linear regression Sal vs EC:
Green Creek
700
Cryptosporidium
Rotavirus
100
800
Pooled data (n=90)
Hepatitis A
0.131
Linear regression Sal vs EC:
Sayler’s Creek
Table 1. Overview of data set: Pooled and site-by-site means ± std. dev.
Viral pathogens: Coxsackievirus
0.256
Figure 6. Comparison of numbers of E.coli and Salmonella from the same
water samples obtained from SAY5 collection site.
Table 1 provides both pooled and composite averages for each of the three sampling sites. Figures
1, 2, and 3 illustrate the proportion of each bacterial group per sample date at each of the three
sampling sites – APP2 (Fig 1; n=29), Say5 (Fig. 2; n=31), and GRE16 (Fig. 3; n=30).
Entamoeba
0.132
150
Salmonella counts (#/100 mL)
Results
Protozoan pathogens: Giardia
Sal vs EC
200
0
Bacterial pathogens:
Listeria
0.169
Sal vs E.coli counts
250
0
Common human pathogens transferred via water:
Campylobacter
-0.292
300
50
Since all Salmonella – indicator comparisons (e.g., Sal vs TC and Sal vs EC) at each sample site were significantly
different by Student t-test comparisons(p<0.05), a Pearson r correlation combined with a linear regression analysis
was performed to determine the degree of correlation between counts of Salmonella spp. and indicator bacteria
across the 18 months of the study.
Salmonella*
-0.061
350
Total Coliform and E. coli enumeration: Colilert defined substrates medium
Use of ‘total coliform’ and ‘fecal coliform/thermotolerant coliform’ bacteria as
environmental risk indicators for the presence of fecal-associated pathogens has been used
since the early 20th Century (Eijkman, 1904; Leiter, 1929). The most recent USEPA guideline
(2012) for water monitoring recommends the use of these indicator bacteria since “it is
difficult, time-consuming, and expensive to test for specific pathogens”. While some
studies suggest the relationship between coliforms and pathogen is somewhat clear and
positive for protozoan pathogens ( Hogan et al., 2012 ), for human viruses (McQuaig et al.,
2012 ), and for bacterial pathogens (Efstratiou et al., 1998) others show a weak to no
correlation (DePaola et al., 2010; Schriewer et al., 2010).
The questions we have addressed include: How effective are indicator bacteria such as total
coliforms and/or E. coli in predicting the counts of potential pathogens, specifically
Salmonella species, in freshwater streams in south-central Virginia? We chose Salmonella
as it is considered the cause of the largest number of enteric infections worldwide.
Sayler's Creek
Sal vs Coliform
y = 0.0347x + 32.323
R² = 0.4583
450
Composite
E. coli counts (#/100 mL)
Indicator Bacteria used for assessing water quality:
Linear regression Sal vs EC:
Appomattox River
90%
80%
70%
60%
50%
GRE Sal. Counts
40%
GRE E. coli Counts
30%
GRE Coliform Counts
20%
10%
0%
1 2 3 4 5 6 7 8 9 101112131415161718192021222324252627282930
Sampling event
Literature cited
DePaola, A. et al. 2010. Bacterial and viral pathogens in live oysters: 2007 United States Market survey. AEM. 76: 2754-2768.
Eijkman, E. 1904. Die Garungsprobe be 46 als Hilfsmittel bei der Trinkwasseruntersuchung. Zentralbl. Bakteriol. Parasitenkd. Infectionskr.
Hyg. Abt. 1 Orig. 37: 742-752.
Hogan, J.N. et al. 2012. Longitudinal Poisson regression to evaluate the epidemiology of Cryptosporidium, Giardia, and fecal indicator
bacteria in coastal California wetlands. AEM. 78: 3606-3613.
Leiter, W.L. 1929. The Eijkman fermentation test as an aid in the detection of fecal organisms in water. Amer. J. Hyg. 9: 705-734.
McQuaig, S. et al. 2012. Association of fecal indicator bacteria with human viruses and microbial source tracking markers at coastal beaches
impacted by non-point source pollution. AEM. 78: 6423-6432.
Schriewer, A. 2010. Presence of Bacteroidales as a predictor of pathogens in surface waters of the central California Coast. AEM. 76: 58025814.
USEPA 2012. Water monitoring and assessment 5.11 Fecal Bacteria. See: http://water.epa.gov/type/rsl/monitoring/vms511.cfm