How well do indicator bacteria estimate Salmonella in freshwater streams? Timothy M. Smith, Zsofia...
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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 Introduction 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 20 th 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. Methods Bacterial Isolation and Enumeration 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 Results 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). Total Coliform and E. coli enumeration: Colilert defined substrates medium + + + + + - - Membrane labeled (+) for Salmonella spp. and (-) for others Statistical Analyses and Data presentation 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). 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. Discussion • 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. • EC concentrations are generally 1 order of magnitude less than Salmonella concentrations, but as E.coli increases, so does Salmonella. • 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. • 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 20 th Century and they have adequately withstood the test of time. • 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. 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 R 2 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). LONGWOOD UNIVERSITY Department of Biological and Environmental Sciences Indicator Bacteria used for assessing water quality: Escherichia coli (EC), Klebsiella spp., Enterobacter spp., and Citrobacter spp. Common human pathogens transferred via water: Bacterial pathogens: Salmonella* Campylobacter Listeria Protozoan pathogens: Giardia Entamoeba Cryptosporidium Viral pathogens: Coxsackievirus Hepatitis A Rotavirus Norovirus Picornaviridae Reoviridae Caliciviridae Why test for indic ators of water qua l ity? Filter membrane with Salmonella growth MPN for total coliforms counting chromogenic ONPG + MPN for E. coli counting fluorescent MUG + Salmonella (Sal) Coliforms (TC) E.coli (EC) Pooled data (n=90) 3878.7 ± 3675 1202.5 ± 1686.2 349.8 ± 486.6 APP 2 site (n=29) 2836.2 ± 1696.4 803.7 ± 1139 136.2 ± 96.8 GRE 16 site (n=30) 4850 ± 5094 1551.8 ± 1873 599.5 ± 754.7 SAY 5 site (n=31) 3882.3 ± 3209 1237.7 ± 1885.2 307.9 ± 186.6 1 4 7 10 13 16 19 22 25 28 31 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Sayler's Creek: Proportion of Indicator bacteria vs Salmonella SAY Sal. Counts SAY E. coli Counts SAY Coliform Counts Sample event Proportion of total bacterial count (%) 1 7 4 10 13 16 19 22 25 28 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Green Creek: Ratios of Indicator bacteria vs Salmonella GRE Sal. Counts GRE E. coli Counts GRE Coliform Counts Sampling event Proportion of total bacterial count (%) 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% App River: Proportion of Indicator bacteria vs Salmonella APP Sal. Counts APP E. coli Counts Sampling event Proportion of bacterial counts (%) Table 1. Overview of data set: Pooled and site-by-site means ± std. dev. Figure 1. Proportional view of TC vs EC vs Sal counts per sample from the Appomattox River sampling site; 29 total samples Figure 2. Proportional view of TC vs EC vs Sal counts per sample from the Sayler’s Creek sampling site; 31 total samples LONGWOOD UNIVERSITY Department of Biological and Environmental Sciences Figure 3. Proportional view of TC vs EC vs Sal counts per sample from the Green Creek sampling site; 30 total samples Pearson r coeff. Appomattox River Warm months Cold months Composite Sal vs Coliform -0.010 0.075 0.279 Sal vs EC 0.692 0.700 0.722 Green Creek Sal vs Coliform 0.185 0.740 0.423 Sal vs EC 0.414 0.568 0.471 Sayler's Creek Sal vs Coliform -0.061 -0.292 0.169 Sal vs EC 0.132 0.256 0.131 0 3000 6000 9000 12000 15000 0 100 200 300 400 500 600 700 800 f(x) = 0.00307816099171286 x + 301.715193015871 R² = 0.00352401633744748 Linear regression Sal vs EC: Sayler’s Creek Salmonella vs E... Salmonella counts (#/100 mL) E.coli counts (#/100 mL) 0 5000 10000 15000 20000 25000 30000 0 300 600 900 1200 1500 1800 2100 2400 2700 3000 f(x) = 0.0552517740778595 x + 240.177769600642 R² = 0.337547935777933 Linear regression Sal vs EC: Green Creek Salmonella vs E.coli counts Linear (Salmonella vs E.coli counts) Linear (Salmonella vs E.coli counts) Salmonella counts (#/100 mL) E. coli counts (#/100 mL) 0 1000 2000 3000 4000 5000 6000 7000 8000 0 50 100 150 200 250 300 350 400 450 500 f(x) = 0.0346969185938749 x + 32.3225950538281 R² = 0.458329908216839 Linear regression Sal vs EC: Appomattox River Sal vs E.coli counts Linear (Sal vs E.coli counts) Linear (Sal vs E.coli counts) Salmonella counts (#/100 mL) E. coli counts (#/100 mL) Courtesy of Oxoid™ website Graphics courtesy of www.Wikipedia.org Figure 4. Comparison of numbers of E.coli and Salmonella from the same water samples obtained from APP2 collection site. Figure 5. Comparison of numbers of E.coli and Salmonella from the same water samples obtained from GRE16 collection site. Figure 6. Comparison of numbers of E.coli and Salmonella from the same water samples obtained from SAY5 collection site. Table 2. Pearson correlations comparing Salmonella counts with both Coliform and E.coli counts in warm weather, cold weather, and composite samples. 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: 5802-5814. USEPA 2012. Water monitoring and assessment 5.11 Fecal Bacteria. See: http://water.epa.gov/type/rsl/monitoring/vms511.cfm
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Transcript of How well do indicator bacteria estimate Salmonella in freshwater streams? Timothy M. Smith, Zsofia...
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 SciencesLongwood UniversityFarmville, VA 23909
IntroductionUse 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.
Methods
Bacterial Isolation and Enumeration
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
Results
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).
Total Coliform and E. coli enumeration: Colilert defined substrates medium
+
+
++
+-
-
Membrane labeled (+) for Salmonella spp. and (-) for others
Statistical Analyses and Data presentation
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).
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.
Discussion
• 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.
• EC concentrations are generally 1 order of magnitude less than Salmonella concentrations, but as E.coli increases, so does Salmonella.
• 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.
• 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.
• 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.
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).
LONGWOOD UNIVERSITYDepartment of Biological and Environmental Sciences
Indicator Bacteria used for assessing water quality: Escherichia coli (EC), Klebsiella spp., Enterobacter spp.,
and Citrobacter spp.
Common human pathogens transferred via water:Bacterial pathogens: Salmonella* Campylobacter Listeria
Protozoan pathogens: Giardia Entamoeba Cryptosporidium
Viral pathogens: Coxsackievirus Hepatitis A Rotavirus Norovirus
PicornaviridaeReoviridae Caliciviridae
Why te
st for i
ndicators
of water q
uality?
Filter membrane with Salmonella growth
MPN for total coliforms counting chromogenic ONPG + MPN for E. coli counting fluorescent MUG +
Salmonella (Sal) Coliforms (TC) E.coli (EC)
Pooled data (n=90) 3878.7 ± 3675 1202.5 ± 1686.2 349.8 ± 486.6
APP 2 site (n=29) 2836.2 ± 1696.4 803.7 ± 1139 136.2 ± 96.8
GRE 16 site (n=30) 4850 ± 5094 1551.8 ± 1873 599.5 ± 754.7
SAY 5 site (n=31) 3882.3 ± 3209 1237.7 ± 1885.2 307.9 ± 186.6
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 310%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Sayler's Creek: Proportion ofIndicator bacteria vs Salmonella
SAY Sal. CountsSAY E. coli CountsSAY Coliform Counts
Sample event
Prop
ortio
n of
tota
l bac
teria
l cou
nt (%
)
1 3 5 7 9 11 13 15 17 19 21 23 25 27 290%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Green Creek: Ratios ofIndicator bacteria vs Salmonella
GRE Sal. CountsGRE E. coli CountsGRE Coliform Counts
Sampling event
Prop
ortio
n of
tota
l bac
teria
l cou
nt (%
)
1 3 5 7 9 11 13 15 17 19 21 23 25 27 290%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
App River: Proportion ofIndicator bacteria vs Salmonella
APP Sal. Counts
APP E. coli Counts
APP Coliform Counts
Sampling event
Prop
ortio
n of
bac
teria
l cou
nts (
%)
Table 1. Overview of data set: Pooled and site-by-site means ± std. dev.Figure 1. Proportional view of TC vs EC vs Sal counts per sample from the Appomattox River sampling site; 29 total samples
Figure 2. Proportional view of TC vs EC vs Sal counts per sample from the Sayler’s Creek sampling site; 31 total samples
LONGWOOD UNIVERSITYDepartment of Biological and Environmental Sciences
Figure 3. Proportional view of TC vs EC vs Sal counts per sample from the Green Creek sampling site; 30 total samples
Pearson r coeff.
Appomattox River Warm monthsCold months
Composite
Sal vs Coliform -0.010 0.075 0.279
Sal vs EC 0.692 0.700 0.722
Green Creek
Sal vs Coliform 0.185 0.740 0.423
Sal vs EC 0.414 0.568 0.471
Sayler's Creek
Sal vs Coliform -0.061 -0.292 0.169
Sal vs EC 0.132 0.256 0.131
0 3000 6000 9000 12000 150000
100
200
300
400
500
600
700
800
f(x) = 0.00307816099171286 x + 301.715193015871R² = 0.00352401633744748
Linear regression Sal vs EC:Sayler’s Creek
Salmonella vs E.coli counts
Salmonella counts (#/100 mL)
E.co
li co
unts
(#/1
00 m
L)
0 5000 10000 15000 20000 25000 300000
300
600
900
1200
1500
1800
2100
2400
2700
3000
f(x) = 0.0552517740778595 x + 240.177769600642R² = 0.337547935777934
Linear regression Sal vs EC:Green Creek
Salmonella vs E.coli countsLinear (Salmonella vs E.coli counts)Linear (Salmonella vs E.coli counts)
Salmonella counts (#/100 mL)
E. c
oli c
ount
s (#
/100
mL)
0 1000 2000 3000 4000 5000 6000 7000 80000
50
100
150
200
250
300
350
400
450
500
f(x) = 0.0346969185938749 x + 32.3225950538281R² = 0.458329908216839
Linear regression Sal vs EC: Appomattox River
Sal vs E.coli counts
Linear (Sal vs E.coli counts)
Linear (Sal vs E.coli counts)
Salmonella counts (#/100 mL)
E. c
oli c
ount
s (#
/100
mL)
Courtesy of Oxoid™ website
Graphics courtesy of www.Wikipedia.org
Figure 4. Comparison of numbers of E.coli and Salmonella from the same water samples obtained from APP2 collection site.
Figure 5. Comparison of numbers of E.coli and Salmonella from the same water samples obtained from GRE16 collection site.
Figure 6. Comparison of numbers of E.coli and Salmonella from the same water samples obtained from SAY5 collection site.
Table 2. Pearson correlations comparing Salmonella counts with both Coliform and E.coli counts in warm weather, cold weather, and composite samples.
Literature citedDePaola, 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: 5802-
5814.USEPA 2012. Water monitoring and assessment 5.11 Fecal Bacteria. See: http://water.epa.gov/type/rsl/monitoring/vms511.cfm
