A COMPARATIVE ANALYSIS OF DETECTION TECHNIQUES FOR SALMONELLA IN
SURFACE WATER
By
Matthew T. Flood

A THESIS
Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of
Fisheries and Wildlife - Master of Science
2014

ABSTRACT
A COMPARATIVE ANALYSIS OF DETECTION TECHNIQUES FOR SALMONELLA IN
SURFACE WATERS
By
Matthew T. Flood
Surface waters provide a challenge for pathogen detection due to changing environmental
conditions and microbial populations that may affect method chemistry. Due to the importance
of these pathogens with regard to human health, rapid detection and screening of water samples
is necessary to determine the risks of potential pathogens present at any given time. The ANSR
assay, using Nicking Enzyme Amplification Reaction (NEAR™) technology, originally
developed for food safety, was tested for its effectiveness in detecting Salmonella in surface
waters. The main objective of this study was to determine if the ANSR assay is appropriate for
detection of Salmonella in surface waters and comparable to the current culture-based industry
standard USEPA 1682 method. These methods were tested by comparing their ability to detect
seeded and naturally-occurring Salmonella in surface waters from around Michigan and northern
Ohio. The ANSR assay and the USEPA 1682 method found 84.4% and 31.4% of samples
positive for naturally-occurring Salmonella, respectively. The ANSR assay has shown the ability
to detect Salmonella in surface waters at least as well as the USEPA 1682 method, but because
of its much higher potential for false positives and its low precision and accuracy rates, it should
be used in conjunction with other detection methods to confirm positive results. This assay
shows potential for use as a rapid screening tool for surface water samples in order to prioritize
in depth testing at sites, however what appears to be false-positives will need to be reduced.

Copyright by
MATTHEW T. FLOOD
2014

ACKNOWLEDGEMENTS

I would like to thank my advisor, Dr. Joan B. Rose, for her help and support in the
development and execution of this project, and throughout my time at MSU. I am grateful to my
committee members, Dr. Mohamed Faisal and Dr. Lois G. Wolfson, for their guidance during
my Master’s research. I would like to thank the Rose Lab members for their support and help in
carrying out this project, especially Rebecca L. Ives, M.S. without who’s help this project would
not have been possible. I would to also thank Neogen, specifically Dr. Paul Norton, Dr. Mark
Mozola, and Lisa Pinkava for allowing me access to their laboratories and equipment without
which I would not have been able to complete this project. And I would like to thank NSF
International, specifically Dr. Ratul Saha and Dr. Robert Donofrio for their assistance in
determining a suitable molecular assay for use in sample confirmation, providing access to their
laboratories and equipment, and their generous donation of a molecular assay kit and supplies.

iv

TABLE OF CONTENTS
LIST OF TABLES ...................................................................................................................... vii
LIST OF FIGURES ..................................................................................................................... ix
KEY TO ABBREVIATIONS .................................................................................................... xii
CHAPTER 1. THE IMPORTANCE OF SALMONELLA IN WATER .................................. 1
1. General Information on Salmonella ................................................................................. 1
1.1 Salmonella Taxonomy ...................................................................................................... 1
1.2 Clinical importance ........................................................................................................... 6
1.3 Multiple Drug Resistance ................................................................................................. 7
2. Salmonella in Water.............................................................................................................. 8
2.1 Survival in Water .............................................................................................................. 8
2.2 Waterborne Outbreaks of Salmonella ............................................................................... 9
2.2.1 Waterborne outbreaks in developed countries ........................................................... 9
2.2.2 Underdeveloped Nations .......................................................................................... 15
2.3 The role of water in foodborne outbreaks ....................................................................... 17
3. Research Objectives ............................................................................................................ 18
4. Methods of Detection ......................................................................................................... 20
4.1 Culture-based Detection of Salmonella .......................................................................... 20
4.2 Non-culture-based Detection of Salmonella ................................................................... 22
Chapter 2. Materials and Methods............................................................................................ 25
1. Study Sites............................................................................................................................ 25
1.1 Survey 1 Study Sites ....................................................................................................... 25
1.2 Survey 2 Study Sites ....................................................................................................... 27
2. Samples ................................................................................................................................ 30
2.1 Sample Transport and Preparation .................................................................................. 30
3. Salmonella Analysis ............................................................................................................ 30
3.1 USEPA 1682 method protocol........................................................................................ 31
3.2 ANSR™ Assay protocol .................................................................................................. 31
3.3 Recovery efficiency comparison..................................................................................... 32
3.4 Initial nonselective enrichment media comparison......................................................... 33
3.5 Independent molecular confirmation of Survey 2 samples............................................. 33
3.5.1 Sample preparation and storage for independent molecular confirmation .............. 33
3.5.2 DNA extraction ........................................................................................................ 33
3.5.3 Molecular detection assay ........................................................................................ 34
3.6 Statistical Analysis .......................................................................................................... 35
Chapter 3. Results ....................................................................................................................... 38
1. Survey 1 Results .................................................................................................................. 38
1.1 Naturally occurring Salmonella ...................................................................................... 38
1.2 Recovery efficiency comparison..................................................................................... 53
2. Survey 2 Results .................................................................................................................. 59
2.1 Initial nonselective enrichment comparison ................................................................... 59
2.2 Survey 2 detection method comparison .......................................................................... 63
3. Independent Genetic Confirmation and Combined Survey Results .............................. 68
v

Chapter 4. Discussion ................................................................................................................. 73
APPENDIX .................................................................................................................................. 82
REFERENCES ............................................................................................................................ 90

vi

LIST OF TABLES

Table 1. Salmonella species and subspecies....................................................................................3
Table 2. Waterborne Salmonella outbreaks in the U.S. 1971 to 2008...........................................12
Table 3. Salmonella water-related disease (WRD) outbreaks in Italy 1998 - 2005......................14
Table 4. Surface water sampling site conditions and watershed inputs.........................................29
Table 5. Microbial DNA qPCR array cycling protocol for use with Qiagen’s Microbial DNA
qPCR custom array kit...................................................................................................................35
Table 6. Percent positive and MPN/100ml levels for naturally occurring Salmonella in surface
waters for the USEPA 1682 method and the ANSR™ assay in Survey 1.....................................38
Table 7. Positive samples of naturally occurring Salmonella in surface waters for the USEPA
1682 method in Survey 1...............................................................................................................39
Table 8. Positive samples of naturally occurring Salmonella in surface water for the ANSR™
assay in Survey 1...........................................................................................................................39
Table 9. Naturally occurring and seeded samples found positive for Salmonella by the USEPA
1682 method and the ANSR™ assay during Survey 1...................................................................40
Table 10. MPN/100ml levels for Salmonella in water samples found to be positive by both the
USEPA 1682 method and the ANSR™ assay during Survey 1.....................................................41
Table 11. Survey 1. MPN/100ml values for naturally occurring Salmonella in surface water
samples...........................................................................................................................................42
Table 12. Recovery efficiency results for the USEPA 1682 method and the ANSR™ assay during
Survey 1.........................................................................................................................................54
Table 13. Overall recovery efficiency comparison for N=24 samples in Survey 1.......................56
Table 14. MPN/100ml values for naturally occurring Salmonella using the USEPA 1682 method
with two different initial enrichment medias (TSB & Enrichment Media #3) during Survey 2...60
Table 15. MPN/100ml values and total percent of samples found positive for naturally occurring
Salmonella by the USEPA 1682 method and the ANSR™ assay during Survey 2.......................64
Table 16. MPN/100ml values for naturally occurring Salmonella in water samples found to be
positive by both the USEPA 1682 method and
ANSR™ assay during Survey 2.....................................................................................................65
vii

Table 17. Samples found positive for naturally occurring Salmonella by the USEPA 1682
method and the ANSR™ assay, and confirmed with qPCR during Survey 2................................69
Table 18. Survey 1. ANSR™ assay samples analyzed by qPCR...................................................70
Table 19. Survey 1 and Survey 2 combined results for method comparison................................71
Table A1. Studies on Salmonella occurrence in surface waters....................................................83

viii

LIST OF FIGURES

Figure 1. Salmonella serovars reported as causes of outbreaks of human illnesses around the
world from 1992 – 2008..................................................................................................................4
Figure 2. Salmonella serovars reported as causes of cases of human illness around the world
from 1992 – 2008............................................................................................................................5
Figure 3. Map of Survey 1 sampling sites.....................................................................................26
Figure 4. Map of Survey 2 sampling sites.....................................................................................28
Figure 5. Log transformed MPN/100ml values for River Raisin that were obtained from the
USEPA 1682 method in Survey 1.................................................................................................43
Figure 6. Log transformed MPN/100ml values for Grand River that were obtained from the
USEPA 1682 method in Survey 1.................................................................................................43
Figure 7. Log transformed MPN/100ml values for Red Cedar River that were obtained from the
USEPA 1682 method in Survey 1.................................................................................................44
Figure 8. Log transformed MPN/100ml values for Farm Canal 1 that were obtained from the
USEPA 1682 method in Survey 1.................................................................................................44
Figure 9. Log transformed MPN/100ml values for River Raisin that were obtained from the
ANSRTM assay in Survey 1............................................................................................................45
Figure 10. Log transformed MPN/100ml values for Grand River that were obtained from the
ANSRTM assay in Survey 1............................................................................................................45
Figure 11. Log transformed MPN/100ml values for Red Cedar River that were obtained from the
ANSRTM assay in Survey 1............................................................................................................46
Figure 12. Log transformed MPN/100ml values for Farm Canal 1 that were obtained from the
ANSRTM assay in Survey 1............................................................................................................46
Figure 13. Log transformed average MPN/100ml values for each sampling site obtained from the
USEPA 1682 method in Survey 1.................................................................................................47
Figure 14. Log transformed average MPN/100ml values for each sampling site obtained from the
ANSRTM assay in Survey 1............................................................................................................47
Figure 15. Comparison of log transformed average MPN/100ml values of Salmonella using the
USEPA 1682 method and the ANSRTM assay in Survey 1...........................................................48

ix

Figure 16. Comparison of log transformed MPN/100ml values of Salmonella for River Raisin
using the USEPA 1682 method and the ANSRTM assay in Survey 1............................................49
Figure 17. Comparison of log transformed MPN/100ml values of Salmonella for Grand River
using the USEPA 1682 method and the ANSRTM assay in Survey 1............................................49
Figure 18. Comparison of log transformed MPN/100ml values of Salmonella for Red Cedar
River using the USEPA 1682 method and the ANSRTM assay in Survey 1..................................50
Figure 19. Comparison of log transformed MPN/100ml values of Salmonella for Farm Canal 1
using the USEPA 1682 method and the ANSRTM assay in Survey 1............................................50
Figure 20. Log transformed MPN/100ml values for naturally occurring Salmonella in all samples
examined during Survey 1.............................................................................................................52
Figure 21. Comparison of Log transformed MPN/100ml values of seeded Salmonella for River
Raisin using the USEPA 1682 method and the ANSRTM assay in Survey 1.................................57
Figure 22. Comparison of Log transformed MPN/100ml values of seeded Salmonella for Grand
River using the USEPA 1682 method and the ANSRTM assay in Survey 1..................................57
Figure 23. Comparison of Log transformed MPN/100ml values of seeded Salmonella for Red
Cedar River using the USEPA 1682 method and the ANSRTM assay in Survey 1.......................58
Figure 24. Comparison of Log transformed MPN/100ml values of seeded Salmonella for Farm
Canal 1 using the USEPA 1682 method and the ANSRTM assay in Survey 1..............................58
Figure 25. Log transformed MPN/100ml values for naturally occurring Salmonella during initial
enrichment media comparison in Survey 2....................................................................................62
Figure 26. Survey 2. Log transformed MPN/100ml values for naturally occurring Salmonella
using the USEPA 1682 method and the ANSRTM assay...............................................................66
Figure 27. Survey 2. average Log MPN/100ml values for the USEPA 1682 method and the
ANSRTM assay...............................................................................................................................67
Figure A1. River Raisin MPN/100ml values for Survey 1 obtained from the USEPA 1682
method............................................................................................................................................86
Figure A2. Grand River MPN/100ml values for Survey 1 obtained from the USEPA 1682
method............................................................................................................................................86
Figure A3. Red Cedar River MPN/100ml values for Survey 1 obtained from the USEPA 1682
method............................................................................................................................................87

x

Figure A4. Farm Canal 1 MPN/100ml values for Survey 1 obtained from the USEPA 1682
method............................................................................................................................................87
Figure A5. River Raisin MPN/100ml values for Survey 1 obtained from the ANSR™ assay......88
Figure A6. Grand River MPN/100ml values for Survey 1 obtained from the ANSR™ assay.......88
Figure A7. Red Cedar River MPN/100ml values for Survey 1 obtained from the ANSR™
assay...............................................................................................................................................89
Figure A8. Farm Canal 1 MPN/100ml values for Survey 1 obtained from the ANSR™ assay.....89

xi

KEY TO ABBREVIATIONS

ANSR

Amplified Nucleic Single Temperature Reaction

AOAC

The Association of Analytical Communities

AWWA

American Water Works Association

CFU

Colony Forming Unit

CDC

Centers for Disease Control and Prevention

DL

Detection Limit

DNA

Deoxyribonucleic Acid

ELISA

Enzyme-linked Immunosorbant Assay

GFP

Green fluorescent protein

IAC

Internal Amplification Control

ISO

International Organization for Standardization

LIA

Lysine Iron Agar

MDR

Multiple Drug Resistant

MPN

Most Probable Number

MSRV

Modified Semisolid Rappaport-Vassiliadis Medium

NEARTM

Nicking Enzyme Amplification Reaction

ND

Non-detect

NSF Int.

National Sanitation Foundation International

OPR

Ongoing Precision Recovery

OTUs

Operational Taxonomic Units

PCR

Polymerase Chain Reaction

xii

qPCR

Quantitative Polymerase Chain Reaction

rt-PCR

Real-time Quantitative Polymerase Chain Reaction

RNA

Ribonucleic Acid

rRNA

Ribosomal Ribonucleic Acid

UK

United Kingdom

US

United States of America

USEPA

US Environmental Protection Agency

VBNC

Viable but not culturable

WHO

World Health Organization

WRD

Water-related Disease

XLD

Xylose-lysine Desoxycholate Agar

xiii

CHAPTER 1. THE IMPORTANCE OF SALMONELLA IN WATER
1. General Information on Salmonella
1.1 Salmonella Taxonomy
Salmonella are Gram-negative, facultative anaerobic non-spore forming bacillus shaped
bacteria, which belong to the family Enterobacteriaceae. The genus Salmonella consists of two
recognized species, Salmonella enterica and Salmonella bongori (Table 1). Salmonella enterica
is further divided into six subspecies: enterica (subsp. I), salamae (subsp. II), arizonae (subsp.
IIIa), diarizonae (subsp. IIIb), houtenae (subsp. IV), and indica (subp. VI) (Table 1). These
subspecies are further divided into over 2500 serovars based on their unique combinations of
somatic O and flagellar H1 and H2 antigens (Table 1) (Popoff et al., 2004, Grimont and Weill,
2007, Su and Chiu, 2007, Chen et al. 2013). When referring to serovars it is not necessary to
name the species and subspecies of Salmonella, instead only the genus and the non-italicized
capitalized serovar name are required (Grimont and Weill, 2007). For example, Salmonella
enterica enterica (subsp. I) Typhimurium would be Salmonella Typhimurium. This is accepted
due to the uniqueness of each serovar name, which are not repeated within either species, or
subspecies of Salmonella. The subspecies Salmonella enterica (subsp. I) contains most of these
serovars (Grimont and Weill, 2007, Chen et al. 2013). Many Salmonella serovars are known to
be enteric pathogens with most residing in the Salmonella enterica subspecies enterica. The most
common of these serovars and arguably the most important in regards to human health are S.
Enteritidis and S. Typhimurium with both combined being responsible for over 60% of all
Salmonella outbreaks causing human illness and 58% of all reported cases of human illness
caused by Salmonella (Figure 1, Figure 2) (Doyle et al., 2009, Bellido-Blasco and Arnedo-Pena,
2011). The Salmonella species bongori does not have any subspecies, but includes 22 serovars
1

(Grimont and Weill, 2007). Most serovars of Salmonella possess peritrichous flagella allowing
for motility. Though motility is a crucial criterion for the identification of Salmonella spp. many
serovars have been found to be non-motile (Bugarel et al., 2012).

2

Species
Subspecies

enterica (I)

Number of Serovars
Human Health
Significance
Preferred Host
Characteristics
Dulcitol
ONPG (2h)
Malonate
Gelatinase
Sorbitol
Grown with KCN
L(+)-tartratea
Galacturonate
γ-glutamyltransferase
β-glucuronidase
Mucate
Salicine
Lactose
Lysed by phage O1

1,531
****

Table 1. Salmonella species and subspecies
S. enterica
salamae
arizonae (IIIa) diarizonae (IIIb)
(II)
505
99
336
*

*

*

Warm-blooded animals
+
–
–
–
+
–
+
–
+b
v
+
–
–
+

+
–
+
+
+
–
–
+
+
v
+
–
–
+

S. bongori
houtenae (IV)

indica (VI)

73

13

22

*

*

*

Cold-blooded animals and the environment
–
+
+
+
+
–
–
–
–
–
+
–

–
+
+
+
+
–
–
+
+
+
– (70%)

– (75%)
–

–
+(75%)
+

–
–
–
+
+
+
–
+
+
–
–
+
–
–

a d-tartrate
b Typhimurium v, Dublin + 90% or more positive reactions
- 90% or more negative reactions
v different reactions given by different
Combined and updated from: (Minor et al., 1982, Minor et al., 1986, Popoff et al., 2004, Grimont and Weill, 2007)

3

v
v
–
+
–
–
–
+
+
v
+
–
v
+

+
+
–
–
+
+
–
+
+
–
+
–
–
v

Figure 1. Salmonella serovars reported as causes of outbreaks of human illness around the
world from 1992 - 2008

Other Serotypes (17%)

Paratyphi (1%)
Montevideo (1%)

Typhimurium (39%)

Thompson (1%)
Typhi (2%)
I monophasic (2%)
Agona (2%)
Javiana (2%)
Saintpaul (2%)
Oranienburg (2%)

Newport (4%)

Enteritidis (25%)

Total outbreaks N = 492
**Figure 1. reproduced
eproduced from Doyle et al., 2009

4

Figure 2. Salmonella serovars reported as causes of cases of human illness around the
world from 1992 - 2008

Other Serotypes
17%

Enteritidis
37%

Agona
2%
Muenchen
2%
Newport
4%
Javiana
3%
Oranienburg
5%
Hadar
5%
Saintpaul
4%

Typhimurium
21%

Total cases N = >55,739
**Figure 1 reproduced
eproduced from Doyle et al., 2009

5

1.2 Clinical importance
Salmonella spp. are the disease-causing agents of salmonellosis in humans and, to this
day, cause significant worldwide public health concerns. Salmonellosis has three clinically
distinguishable forms: gastroenteritis, enteric fever, and septicemia. Serovars of salmonella that
cause these illnesses are put into two categories or groups, typhoid and nontyphoid Salmonella.
Salmonella Typhi and S. Paratyphi A, B, and C are referred to as typhoid Salmonella due to their
roles as the causes of typhoid fever. All other Salmonella serovars being considered nontyphoid
Salmonella. Both gastroenteritis and enteric fever caused by Salmonella spp. are still of concern
today even in developed countries (Dale et al., 2007, Blasi et al., 2008, Berg, 2008, Kozlica et
al., 2010, Levantesi et al., 2012, Chen et al. 2013). Salmonella infection is difficult to control due
to the bacterium’s widespread distribution, high tolerance to environmental stress, multiple drug
resistance, and adaptability (Runkel et al., 2013, Chen et al. 2013). Both groups of Salmonella,
typhoid and nontyphoid, are known to be transmitted through the fecal-oral route of infection.
While the causes of typhoid and paratyphoid fever (S. Typhi and S. Paratyphi A, B, and C) are
host-specific to humans, nontyphoid Salmonella serovars are known to be zoonotic pathogens
and are of importance in both humans and animals.
Majowicz et al. (2010) reported that the global burden of nontyphoid Salmonella
gastroenteritis had an average incidence of 1.14 episodes/100 person-years each year with a total
of 93.8 million cases and 155,000 deaths. Incidences of disease were highest in Asia, which had
an average of 4.72 episodes/100 person-years each year with a total of 83.4 million cases and
137,700 deaths (Majowicz et al., 2010). In the US, there are approximately 1 million cases of
salmonellosis each year with approximately 19,000 of those cases requiring hospitalization
(CDC, 2011). In the US, while there has been a 25% reduction in food-related illness caused by

6

pathogens such as Escherichia coli and Campylobacter in the last decade, there have been no
significant changes in the incidence of salmonellosis (Osterholm, 2011).
Typhoid and enteric fevers caused by S. Typhi and S. Paratyphi, while not common in
developed nations, are still a cause for concern in less developed countries around the world.
Kindhuaser (2003) reported an annual incidence of 17 million cases of typhoid and paratyphoid
fever worldwide. Salmonella serovars Typhi and Paratyphi are strictly human pathogens and
human feces are the main source of typhoid fever transmission. Thus water that has been
contaminated with sewage and feces can be a major source of infection (Levantesi et al., 2012).
1.3 Multiple Drug Resistance
Multiple drug resistant (MDR) Salmonella is an issue of emerging concern due to the
increasing frequency of MDR strains in recent years (Lightfoot, 2004, Pond, 2005, Lynch et al.,
2009). Antibiotic resistance is seen in both typhoid and nontyphoid Salmonella (Flor et al., 2011,
Chen et al., 2013). Antimicrobial resistance among salmonellae rose from 20-30% in the early
1990s to 70% in various countries by the year 2000, with resistance rates varying among
serovars (Su et al., 2004, Lauderdale et al., 2006, Parry and Threlfall, 2008, Lee et al., 2009). For
example, two serovars that are both globally distributed but show different levels of resistance
are S. Typhimurium and S. Enteritidis (Su et al., 2004., Helms et al., 2005). Salmonella
Typhimurium has a much higher rate of resistance than S. Enteritidis (which is relatively more
susceptible to antimicrobial agents), with S. Typhimurium definitive phage type (DT) 104
emerging as a global health problem since the 1980s (Su et al., 2004., Helms et al., 2005).
Multidrug-resistant nontyphoid Salmonella strains are usually resistant to ampicillin,
chloramphenicol/florfenicol, streptomycin/spectinomycin, sulfonamides, and tetracyclines
(Helms et al., 2005). Resistance to extended spectrum cephalosporins, trimethoprim,

7

fluoroquinolones, ciprofloxacin, ceftriaxone and carbapenems have also been documented in
nontyphoid Salmonella strains (Angulo et al., 2000, Guerra et al., 2002, Chiu et al., 2004, Jean et
al. 2005, Lee et al., 2009, Hall, 2010, Su et al., 2011, Su et al., 2012). Similar patterns of
resistance are prevalent in S. Typhi and S. Paratyphi, both of which exhibit resistance to
chloramphenicol, beta-lactams, quinolones, azithromycin, ampicillin, trimethoprimsulphamethoxazole, and ciproflaxin (Kumar et al., 2008, Chuang et al., 2009, Molloy et al.,
2010, Zaki and Karande, 2011). As these MDR strains continue to disseminate there is an
increased risk of nontyphoid and typhoid disease complications in both developed and
underdeveloped nations.
2. Salmonella in Water
2.1 Survival in Water
Although reservoirs of Salmonella include both domestic and wild animals, survival and
persistence in the environment have been shown to be important in the spread of Salmonella.
While S. Typhi and S. Paratyphi are not as widely disseminated in nature as nontyphoid
Salmonella serovars due to their restrictive host requirements (i.e., humans), water still plays a
crucial role in the spread of typhoid and paratyphoid fever. Nontyphoid Salmonella is much
more widely distributed in the environment perhaps because it is known to be a zoonotic disease
(Percival and Williams, 2014). Water is often overlooked when it comes to Salmonella
outbreaks, except for S. Typhi, which is known to spread through shared water sources. Most
cases of salmonellosis (95%) are attributed to food contamination, although contaminated waters
sources including drinking, surface, and groundwater have been implicated in human illness as
well (Doyle et al., 2009, Percival and Williams, 2014).

8

2.2 Waterborne Outbreaks of Salmonella
Waterborne outbreaks of Salmonella are associated with poor-quality water due to
inadequate or lack of treatment of a contaminated source or fecal contamination of the
distribution system such as a sewage cross connection (Angulo et al., 1997). Infections by
waterborne Salmonella can occur through drinking or swimming in contaminated water
(O’Reilly et al., 2007, Hsu et al., 2011). In addition foodborne outbreaks may occur as a result of
food that has been washed or come in contact with contaminated water (CDC, 2012a).
2.2.1 Waterborne outbreaks in developed countries
Present day waterborne outbreaks of Salmonella that occur in developed countries are
mostly associated with nontyphoid Salmonella serovars (Levantesi et al., 2012). Historically,
outbreaks caused by typhoid Salmonella serovars were present, but proper water treatment,
through the use of municipal and wastewater treatment systems during the second half of the 20th
century, has greatly contributed to reduction in waterborne outbreaks and the spread of typhoid
fever in most industrialized countries, although in the US, 300-400 cases of typhoid fever are
still reported each year (Smith et al. 2006, CDC, 2008, Lynch et al., 2009). In the US, sporadic
cases of typhoid and paratyphoid fever still occur and are often a result of people becoming
exposed during foreign travel in areas where the disease is more common. In Italy, the Indian
subcontinent, and southeast Asia, typhoid fever remains endemic (DeRoeck et al., 2007, Rizzo et
al., 2008, Lynch et al., 2009).
In the US, waterborne outbreaks have been most commonly associated with the
Salmonella serovars Typhimurium, Enteritidis, Javiana, Newport, Bareilly, and Weltervreden
(Covert, 1999, Craun et al., 2004). Between the years 1971 and 2000, 15 drinking-wateroutbreaks of nontyphoid Salmonella occurred (6% of the total were considered to be zoonotic

9

pathogen waterborne outbreaks) (Table 2) (CDC, 1978, CDC, 1979, CDC, 1983, CDC, 1985,
CDC, 1988, CDC, 1996, CDC, 1998, CDC, 2002, 2006, CDC, 2011). Eleven were associated
with community water systems and groundwater (Craun et al., 2004). In 1993, a significant
waterborne outbreak of Salmonella Typhimurium occurred in Gideon, Missouri, where 650
people were infected, 15 were hospitalized, and 7 died (Angulo et al., 1997). However, between
2000 and 2006, waterborne outbreaks of Salmonella were rare in the US (Yoder et al., 2004,
CDC, 2006, Yoder et al., 2008). Two outbreaks occurred in 2008, demonstrating that waterborne
outbreaks continue to occur in areas where there are inadequate water supply systems or
deficiencies in water treatment (Berg, 2008, Kozlica et al., 2010). From March to April of 2008,
a waterborne outbreak of Salmonella occurred in Alamosa, Colorado, where the drinking water
was not chlorinated prior to the outbreak, but the city had been in compliance with all healthbased drinking water standards (Berg, 2008). There were 442 reported illnesses (122 were
laboratory-confirmed) and one death reported but the epidemiological estimates suggested up to
1,300 people fell ill (Berg, 2008). Four months later, in August of 2008, an outbreak of
Salmonella I 4,[5],12:i- was identified in a rural community in Tennessee where an untreated
private spring-fed water system was contaminated (five cases) (Kozlica et al., 2010). The use of
private wells and septic systems have been identified as important risk factors for sporadic
Salmonella infections in children (Denno et al., 2009).
Between 1995 and 2005, there was only one CDC-reported waterborne outbreak of
Salmonella, which was linked to recreational water exposure in the United States (CDC, 1998,
Pond, 2005). The scarcity of recreational waterborne Salmonella outbreaks seems to indicate that
recreational waters are insignificant as the cause of Salmonella infections, but this is not always
the case. Three Washington State county health departments have identified both aquatic and

10

marine recreational waters as important risk factors for sporadic Salmonella infections in
children (Denno et al., 2009).

11

Table 2. Waterborne Salmonella outbreaks in the U.S. 1971 to 2008
Year
Etiological Agent
Outbreaks
Cases Type of System§ Deficiency a Location
1978
Salmonella
1
78
Com
1978
Salmonella
1
11
Ncom
1979
Salmonella
1
69
Ncom
1983
Salmonella
2
1150
Com
1985
Salmonella Typhi
1
60
Com
4
VI
1986
Salmonella, Mixed
1
9
Com
2
MS
1986
Salmonella
1
61
Com
4
UT
1993
Salmonella Typhimurium
1
625
Com
4
MO
1995
Salmonella Java
1
3
Rec
GA
1999
Salmonella Typhimurium
1
124
Com
3
MO
2000
Salmonella Bareilly
1
84
Ind
5
Multistate
2004
Salmonella Typhimurium
1
70
Ncom
3,4
MT
2007
Salmonella Newport
1
2
Ind
2
TN
2007
Mixed agents*
1
229
Ncom
2
WI
2008
Salmonella Typhimurium
1
1,300
Com
4
CO
2008
Salmonella serovar I 4,5,12:i:1
5
Ncom
2
TN
* Agents included Salmonella, Norovirus genogroup I, and Campylobacter
§ Community Water System, Ncom = Noncommunity Water System, Ind = Individual Water System, Rec = Recreational
Waters
a Treatment deficiency codes: 2 - Untreated groundwater intended for drinking, 3 - Treatment deficiency, 4 Distribution system deficiency, includes storage, 5 - Legionella in drinking water system

12

Australia has reported that of 10 observed drinking waterborne outbreaks, from 2001 to
2007, 50% were caused by Salmonella spp. making it the most common pathogen in drinking
water outbreaks during this period (Dale et al., 2007). These outbreaks were associated with both
typhoid and nontyphoid Salmonella spp. Unlike the community water outbreaks seen in the U.S.,
waterborne outbreaks of Salmonella in Australia were related to contaminated waters from bore
holes, storage tanks, and roof-collected rain water (Taylor et al., 2000, Ashbolt and Kirk, 2006,
Franklin et al., 2009).
European reporting of waterborne outbreaks is not as clearly defined as in the US,
because drinking water is categorized as a food in Europe, precluding the definitive identification
of specific waterborne outbreaks. In addition, country-specific reporting is limited. Two country
-specific reviews have been conducted in recent years in Italy and in the UK (Smith et al., 2006,
Blasi et al., 2008). Between 1998 and 2005 Salmonella spp. were identified in 21.5% of the total
water-related disease (WRD) outbreaks in Italy (Blasi et al., 2008). This may be an
underestimation of the true disease burden due to underreporting as seen in Table 3. While in
England and Wales, between 1992 and 2003, Salmonella spp. were not associated with any
waterborne outbreaks (Smith et al., 2006).

13

Table 3. Salmonella water-related disease (WRD) outbreaks in Italy 1998 - 2005
Epidemic Focus
Drinking Water
Agricultural
Shelfish
Total WRD Outbreaks
30
9
147
Total WRD Cases
1,017
32
1,497

Total
186
2,546

Salmonella WRD Outbreaks
Salmonella WRD Cases

11 (36.7%)*
63 (6.2%)*

2 (22.2%)*
6 (18.8%)*

27 (18.4%)*
232 (15.5%)*

40 (21.5%)
301 (11.8%)

Unidentified WRD Outbreaks
Unidentified WRD Cases

1 (3.3%)*
723 (71.1%)*

3 (33.3%)*
9 (28.1%)*

80 (54.4%)*
680 (45.4%)*

84 (45.2%)
1,412 (55.5%)

% of Total WRD Outbreaks

5.9%

0.01%

14.5%

21.50%

33.3%
26.1%

40.3%
28.4%

39.20%
25.7%

% of Total Identified WRD Outbreak
37.9%
% of Total Identified WRD Cases
21.4%
* Percentages Reflect proportion of outbreaks/cases within each focues
Data obtained from: (Blasi et al., 2008)

14

According to the CDC, approximately 42,000 nontyphoid Salmonella infections and 400
deaths are reported each year in the US (CDC, 2012b). While many infections are mild in nature
and thus left undiagnosed and unreported, waterborne Salmonella outbreaks are still of concern.
It is estimated that the actual incidence of infection is at least 29 times higher than what is
reported (CDC, 2012b). With the large number of Salmonella infections occurring in both
humans and animals within the US, it is clear that human sewage and manure are sources of
pathogens contaminating water supplies. As water infrastructures continue to age there is an
increased probability of waterborne outbreaks occurring. Because poor and failing water
infrastructure leads to increased waterborne outbreaks, significant investments in current water
infrastructure will need to be made. For example, in the US the American Water Works
Association (AWWA) has estimated that over the next 20 years more than $1 trillion will need to
be invested to both repair and upgrade current water infrastructure (AWWA, 2012). The EPA
has also expressed the need for substantial investment in water infrastructure over the next two
decades (EPA, 2013).
2.2.2 Underdeveloped Nations
Underdeveloped nations face an even greater risk of waterborne Salmonella outbreaks,
especially with regard to typhoid Salmonella serovars (Swaddiwudhipong & Kanlayanaphotporn,
2001, Srikantiah et al., 2006, Lynch et al., 2009, Kim, 2010). This risk is directly related to the
absence of proper water infrastructure, with the World Health Organization (WHO), reporting
2.5 billion people still lacking access to improved sanitation and 768 million people lacking
access to an improved drinking water source as of 2011 (WHO, 2013). The dissemination of
MDR S. Typhi also poses an increased risk in underdeveloped nations, given that with MDR

15

typhoid serovars have been reported in multiple drinking water outbreaks in Asia (Mermin et al.,
1999, Swaddiwudhipong & Kanlayanaphotporn, 2001, Lewis et al., 2005, Kim, 2010).
Typhoid Salmonella serovars remain major public health concerns around the world, but
especially in both the Indian subcontinent and southeast Asia where they remain endemic
allowing epidemics to continue to occur (DeRoeck et al., 2007). The main sources of typhoid and
paratyphoid fever epidemics in Asia are contaminated drinking waters, which originate from a
variety of sources including well water, unboiled spring water, and piped municipal water
(Mermin et al., 1999, Swaddiwudhipong & Kanlayanaphotporn, 2001, Kim et al., 2003, Lewis et
al., 2005, Bhunia et al., 2009, Farooqui et al., 2009). In 1997, a contaminated municipal water
system in Dushanbe, Tajikistan led to an enormous epidemic of typhoid fever in which 8,901
cases were reported (Mermin et al., 1999). An overwhelming number (93%) of Salmonella Typhi
isolates recovered from this outbreak showed resistance to seven different antibiotics (i.e.
ampicillin, streptomycin, chloramphenicol, nalidixic acid, sulfisoxazole, tetracycline, and
trimethoprim-sulfamethoxazole) (Mermin et al., 1999). Multiple drug resistance in Salmonella
Typhi outbreaks is not limited to any one country in Asia. In 1999, a waterborne outbreak of
MDR S. Typhi occurred in the Poppra District of the Tak Province in Thailand when the
village’s spring water became contaminated (Swaddiwudhipong & Kanlayanaphotporn, 2001).
In 2000, an outbreak of MDR S. Typhi occurred in Mumbai, India when a well water source
became contaminated (Misra et al., 2005). The largest single-point source outbreak of MDR S.
Typhi occurred in 2002 in Bharatpur, Nepal when the only municipal water supply became
contaminated, resulting in 5,963 cases of typhoid fever (Lewis et al., 2005). In 2004, a
community water outbreak of MDR S. Typhi occurred in a small village near Karachi, Pakistan,
in which over 300 people became ill, due to contamination of the village’s well (Farooqui et al.,

16

2009). Most recently, Singla et al. (2013) reported an outbreak of MDR S. Typhi in a suburban
area of the city of Chandigarh, India. While 90% of the known morbidity and mortality caused
by typhoid fever occurs in Asia (Crump et al., 2004), it remains a major disease burden in Africa
as well.
Salmonella Typhi is a major cause of enteric disease in Africa, but due to the absence of
comprehensive surveillance studies and reliable measures of the incidence of disease occurring,
the actual burden of typhoid fever is unknown (Crump et al., 2004). Much of Africa (e.g., the
Venda region in South Africa) still lacks clean potable water and proper sanitization (Obi et al.,
2004, Potgieter et al., 2005), which imposes an increased risk of waterborne infections and
outbreaks. While little waterborne outbreak information is available for Africa, some studies
have linked Salmonella to incidences of disease caused by contaminated water sources
(Oguntoke et al., 2009, Bessong et al., 2009). Multiple Drug Resistant nontyphoid Salmonella
serovars are reported in many African countries to be the most common cause of bacteraemia in
children. Although the route of transmission is uncertain, clustering of cases during the rainy
season suggests a possible waterborne/water-associated transmission route (Suresh & Smith,
2004, Gordon & Graham, 2008).
2.3 The role of water in foodborne outbreaks
The role of water in foodborne outbreaks is another confounding issue with the detection
and reporting of waterborne Salmonella outbreaks. Contaminated water may play an important
role in foodborne outbreaks (Berger et al., 2010), but the outbreaks are often attributed to the
food product consumed and not the source of contamination itself. According to the CDC
(2012a), the quality of the water that comes into contact with fresh fruits and vegetables is a
critical factor influencing the potential for outbreaks to occur. This is particularly important since

17

large portions of surface waters, which are used for irrigation, are contaminated with effluent
(e.g., 71% of irrigation waters in the UK) (Tyrrel et al., 2006). In the US, the CDC has reported
52 foodborne outbreaks of Salmonella since 2006, 13 of which were associated with fresh fruits
or vegetables (CDC, 2014). According to the CDC (2012a), a number of foodborne outbreaks
associated with fresh fruits and vegetables were linked to contaminated water. In 2002 and then
again in 2005, during the investigation of two Salmonella outbreaks linked to tomatoes, after
tracing the outbreaks back to Virginia, the outbreak serovar responsible in both outbreaks, S.
Newport, was isolated from pond water used for irrigation (Greene et al., 2008). These two
outbreaks and subsequent isolation of the S. Newport serovar from the same pond water three
years apart shows that if the original source of contamination (e.g., the pond water) is not
properly dealt with then the risk of outbreaks reoccurring increases substantially (Greene et al.,
2008). This suggests that there may be an unknown number of Salmonella outbreaks that are due
to contaminated water and are never reported because health officials cease investigations of
outbreaks once a common food product has been identified, even though the food product itself
may not be the original source of contamination. This represents an area of study that requires
further investigation to fully understand the role of contaminated water in foodborne outbreaks.
3. Research Objectives
While many countries have established some guidelines for Salmonella in drinking water
supplies, information and criteria for Salmonella in environmental waters are limited (Hsu et al.,
2011). As incidences of waterborne and water-related Salmonella outbreaks increase, interest in
pathogen detection and ecology in recreational, waste, and groundwater have grown. The current
USEPA 1682 method for Salmonella detection (USEPA, 2006) for waste and surface waters in
the US is based on most probable number (MPN) values obtained by enrichment of multiple

18

volumes/dilutions. This culture-based method is time consuming, labor intensive, and can be
nonspecific for pathogenic Salmonella species, subtypes, and serovars without additional testing.
Novel alternative methods that combine culture and genetic approaches that rapidly detect live
organisms (results within 24 hours) are emerging with application to food but have not been
evaluated for water. This study sought to determine whether a novel isothermal Salmonella spp.
detection assay originally developed for food safety, which uses nicking enzyme amplification
reaction (NEAR™) technology (Van Ness et al., 2003) allowing for rapid (<1 hr) total sample
processing times and relatively low costs (~$10 /sample), is appropriate for the detection of
Salmonella spp. in surface waters. This study also sought to determine whether this novel assay
is comparable to the current culture-based USEPA 1682 method for Salmonella detection in
surface and wastewaters. The causes of differences, if any, between the USEPA 1682 method
and the novel isothermal Salmonella spp. detection assay are also of interest.
The objectives of this research were to:
•

Compare the US EPA 1682 method and a novel isothermal method for Salmonella spp.
detection in surface and wastewater.
o Compare recovery efficiencies of the two methods in a paired set of sample
matrices.
o Determine the impact of the initial enrichment media being used in the USEPA
1682 method on detection of Salmonella spp. in water.
o Determine the false positive and negative rates associated with the two methods
based on genetic species identification in water samples.

•

Determine the applicability of this new isothermal method in Salmonella detection in
surface waters.

19

4. Methods of Detection
Salmonella detection methods are generally divided into two categories, culture-based
and non-culture-based with many of these methods developed for the food industry first, and
then later applied to environmental samples. While currently culture-based methods are preferred
by regulatory agencies, due to their ability to show the presence of live possibly-infectious
organisms, non-culture-based methods are of great interest to the water quality community due to
their normally high specificity, rapid detection of Salmonella DNA targets, and their ability to
process a much larger number of samples in a significantly smaller period of time. However,
these non-culture-based methods do not allow for their users to claim any risk to human health
based on their results because they cannot show any evidence that the Salmonella DNA that they
are detecting is coming from live possibly-infectious organisms. It is precisely because of this
that water quality regulatory agencies are unwilling to accept these methods results without
evidence of live organisms. This need to show the presence of live organisms has led to the
combination of culture-based and non-culture-based detection methods with an attempt to
shorten the amount of time spent on culturing the organisms by testing suspected Salmonella
isolates with molecular targets. For example, the ANSR™ assay for Salmonella detection
developed by the Neogen Corp. utilizes a preenrichment step prior to analysis to allow for the
growth of possible Salmonella isolates (Mozola et al., 2013). Nevertheless, if specific serovar
identification is required, currently the only definitive way to identify all Salmonella serovars is
through serotyping (Grimont and Weills, 2007).
4.1 Culture-based Detection of Salmonella
Culture-based detection methods for Salmonella all follow a general pattern for detection
and identification. First samples are grown in a non-selective media (e.g., buffered peptone water

20

or trypticase soy broth). This is then followed by transferring the enrichment to one or more
selective enrichment medias (e.g., selenite cysteine broth, RV10 broth, Hektoen enteric,
Salmonella-Shigella agar, bismuth sulfite agar, phenol red-brilliant, Rappaport-Vassiliadis broth,
modified semisolid Rappaport-Vassiliadis agar/broth, xylose lysine deoxycholate agar). After
selection, isolates undergo biochemical screening (e.g., urea broth, lysine iron agar, triple sugar
iron agar) and final typing using antisera to confirm specific serovars. If enumeration of samples
is desired, a most probable number (MPN) approach is employed by running replicates of
different sample volumes. The standard method for Salmonella detection in the US is the EPA
1682 method for Salmonella detection in biosolids (USEPA, 2006), which has recently been
modified for use specifically in water, and is now the EPA method 1200: Analytical Protocol for
Non-Typhoidal Salmonella in Drinking and Surface Water (USEPA, 2012). The other standard
method for Salmonella detection, which is used in the food industry, but follows the same
general protocol as the USEPA 1682 and 1200 methods is the ISO International Standard 6579
(2002). The USEPA 1682 method and the ISO International Standard 6579 are both regularly
used for Salmonella detection in fresh and salt waters (Polo et al., 1999, Catalao Dionisio et al.,
2000, Baudart et al., 2000, Martinez-Urtaza et al., 2004, Shellenbarger et al., 2008, Setti et al.,
2009, Haley et al., 2009, Gorski et al., 2011). While these methods provide valuable information
on the presence/absence, identities, and concentrations of viable and potentially infectious
Salmonella spp., they also are both time-consuming and labor-intensive. Depending on the
chosen selective medias and biochemical tests, running these methods can take up to a full 40 h
week to achieve results. In the case of the USEPA 1682 method, specific species, subspecies and
serovar identification is unachievable due to the use of only one polyvalent O antiserum as
instructed in the method without the use of additional antisera or molecular identification. Even

21

so, these methods are often combined with molecular techniques to identify and characterize
Salmonella isolates, such as in Walters et al.’s (2013) recent paper identifying Salmonella
enterica diversity in central California coastal waterways and Palhares et al.’s (2014) paper
examining Salmonella and antimicrobial resistance in an animal-based agricultural river system.
When dealing with constantly changing environmental conditions, the amount of time required
for running these methods is impractical if resampling is needed or risk-based decisions are
being made.
4.2 Non-culture-based Detection of Salmonella
Non-culture-based detection methods for Salmonella present a much faster and less
labor-intensive alternative to culture-based detection methods, but unless used in conjunction
with culture-based methods, they are usually unable to determine if the organism(s) detected are
alive or dead. Numerous non-culture-based methods for Salmonella detection are available with
varying gene targets, and technologies with new methods continue to be investigated. There are
endpoint PCR-based methods for a number of different gene targets including invA, which was
utilized by Rahn et al. in 1992 for Salmonella detection in environmental samples (Ziemer and
Steadham, 2003, Bonetta et al., 2011, Hsu et al., 2011). The 16s rDNA region is also being used
as a gene target (Ziemer and Steadham, 2003, Lin et al., 2004) along with the hilA gene (Ziemer
and Steadham, 2003), the enterotoxin gene stx (Ziemer and Steadham, 2003), repetitive DNA
fragments (Ziemer and Steadham, 2003), a fur-regulated gene iroB (Ziemer and Steadham,
2003), and Salmonella’s histidine transport operon (Ziemer and Steadham, 2003). Endpoint PCR
is highly variable with its sensitivity to detect Salmonella, depending on the gene target chosen,
the number of gene copies per cell, and whether or not a non-selective preenrichment step has
been incorporated into the method’s protocol. PCR with the invA gene has shown to have a

22

highly variable limit of detection, depending on the reaction conditions. After a non-selective
preenrichment step is performed as low as 26 CFU/ml can be detected (Rahn et la., 1992, Fey et
al., 2004, Mogandei et al., 2007).
Due to the complexity of environmental samples with diverse microbial communities
endpoint PCR is often times supplemented by the incorporation of nested primer sets and
multiplex PCR in order to target either multiple gene targets or multiple organisms of interest
(Waage et al., 1999, Touron et al., 2005, Fang et al., 2012, Zhang et al., 2012, Xiao et al., 2013).
Nested PCR has been shown to greatly increase the sensitivity of endpoint PCR allowing as low
as 1 CFU/ml to be detected in environmental water samples with upwards of 103 CFU/ml of
background microflora (Riyaz-Ul-Hassan et al., 2004).
Real-time quantitative PCR (qPCR) has been shown by Shannon et al. (2007) to detect as
low as 100 fg of genomic DNA, or 22 gene copies based on a standard curve using Aeromonas.
Klerks et al. (2004) also showed a quantifiable detection limit of 200 CFU/ml and a qualitative
detection limit of 2 CFU/reaction using three different primer/probe sets for the genes himA,
invA, and spaQ and an internal amplification control (IAC) targeting a green fluorescence
protein (GFP)-coding sequence. Fey et al. (2004) also showed detection limits of 2 and 20
copies/reaction of DNA and RNA respectively, while using a SYBR Green rt-PCR from Qiagen
(Hilden, Germany) using a specific 16s rRNA target and the RNA for invA as a control. More
recently, rt-PCR has been used to targer the bipA gene with similar success to previous gene
targets for Salmonella in water samples. Several commercial rt-PCR kits for Salmonella
detection in food products have been released recently as well, and in 2012 were evaluated by
Margot et al., who showed relatively consistent results with all seven kits which were evaluated
with only four coming up with false- positive for some Citrobacter spp.

23

In 2003, Goodridge et al. compared a rapid MPN-based enzyme-linked immunosorbant
assay (ELISA) method against a traditional MPN method and was able to show no significant
difference between the two. In the last couple of years, reports of the possibility of using gold
nanoparticles for electrochemical detection of Salmonella have been published, but as of yet
have not been applied to water samples.
One of the most promising new non-culture-based technologies for environmental sample
analysis and Salmonella detection specifically is next generation metagenomic sequencing. As
these technologies continue to develop the analysis and identification of entire microbial
communities down to species level or below will become more readily available. Li et al. (2012)
have already used pyrosequencing with the gene targets invA, iroB, hns, hisj, hila, and fimY for
the rapid identification of Salmonella enterica. Ong et al. (2013) recently used Illumina shotgun
16S rRNA sequencing on a diverse microbial community to identify twice as many species
levels operational taxonomic units (OTUs) than previous protocols. As faster and more precise
methods for Salmonella detection are developed, our ability to quickly identify potential risks to
human health from the aquatic environment will also increase allowing for the reduction of
illness from contaminated water sources.

24

Chapter 2. Materials and Methods
1. Study Sites
1.1 Survey 1 Study Sites
The four surface water sites in this survey were chosen for their different characteristics
including average flow rate, geographic locations within Michigan, watershed input, and the
probable sources of fecal pollution input received. These sites included the Red Cedar River, the
Grand River, the River Raisin, and a local farm canal. The Red Cedar River runs through urban
and rural areas and eventually drains into the Grand River, which flows through a large city at
the point of sample collection eventually emptying into Lake Michigan. The River Raisin flows
through largely rural areas and empties into Lake Erie. The farm canal was isolated from any
surface water input other than precipitation. Maps of the hydrology, major watersheds, and
sampling locations were created using ArcGIS 9.3.1 (Figure 3).

25

Figure 3. Map of Survey 1 sampling sites

Red Cedar
River
Grand River

Farm Canal 1

River Raisin

26

1.2 Survey 2 Study Sites
The 21 surface water sites in this survey were chosen from across Michigan and northern
Ohio. These sites were chosen to represent the greatest diversity of flow rate, geographic
location, watershed input, and probable sources of fecal pollution across the Michigan’s Lower
Peninsula and northern Ohio (Table 4). Maps of the hydrology, major watersheds, and sampling
locations were created using ArcGIS 9.3.1 (Figure 4).

27

Figure 4. Map of Survey 2 sampling sites

Saginaw River
Grand River
Shiawassee River
Farm Canal 2

Muskegon River
Grand River

Red Cedar River

Black River

Rouge River
Detroit River

St. Joseph River

Huron River
River Raisin
Ottawa River
Maumee River

Kalamazoo River

River Raisin

28

Table 4. Surface water sampling site conditions and watershed inputs

Sampling Site
Grand River
(Lansing)
Grand River
(Grand Rapids)
Muskegon River
St. Joseph River
Maumee River
River Raisin
(Adrian)
River Raisin
(Monroe)
Kalamazoo River
Huron River
Shiawassee River
Red Cedar River
Rouge River

# of WWTPs % of Rural % Urban
Discharge (ft 3 /s)
discharging into land use in land use in
on day of
surface
water site watershed Wateshed
sampling

Length (mi)

US Watersheds

Drainage area
(mi 2 )

228

Upper Grand

780.24

1,150

5

87.81

8.80%

228

Lower Grand

5,096.44

10,100

5

84.55%

11.58%

227.8
163.6
137

Muskegon
St. Joseph
Maumee

2,650.71
4,710.38
6,330.00

2,150
3,960
2,320

2
3
1

88.58%
89.93%
95.00%

11.58%
5.75%
3.00%

136.6

Raisin

442.11

367

3

90.26%

5.87%

136.6

Raisin

1,060.30

293

3

90.26%

5.87%

1,021.44
877
508.04
342.99
397.96

1,200
337
429
147
52

1
3
4
4
3

88.44%
79.55%
90.01%
87.81
47.16%

7.39%
11.71%
6.42%
8.80%
48.79

700

170,000

3

47.16%

48.79

5,982.65
283.61
153
N/A
N/A

2,500
103
15
N/A
N/A

3
2
N/A
0
0

76.03%
85.76%
87.58%
87.81
84.00%

19.18%
10.37%
8.42%
8.80%
15.00%

128.1
126.2
118.9
50.4
47.4

Kalamazoo
Huron
Shiawassee
Upper Grand
Detroit
Detroit, Lake St. Claire,
Detroit River
28
Clinton River, Rouge
River, Ecorse River
Saginaw River
22
Saginaw
Black River
2.5
Black-Macatawa
Ottawa
20
Ottawa-Stony
Farm Canal 1
N/A
Upper Grand
Farm Canal 2
N/A
Flint
Data Sources: (USGS, 2014, MI DEQ, 2014, OH EPA, 2014)
* This data was not available (N/A) for the specified

29

2. Samples
A total of 45 samples were collected from all study sites. The four sites for Survey 1 were
each sampled six times over the course of four months during the fall of 2012. Samples for
Survey 2 were collected only once from each site in order to facilitate the greatest diversity of
samples available for analysis. Each sample consisted of 2, 1-L sample volumes collected by
grab sampling at each site.
2.1 Sample Transport and Preparation
All samples were collected in sterile 1L sampling bottles and stored on ice during
transport back to the laboratory. Once in the laboratory, samples were stored at 4°C until
analysis, which began as soon as possible within 24 h after sample collection. Sample bottles
were shaken thoroughly before analysis and 1L total was compiled from a mixture of the total 2L
that was sampled. This was then composited and homogenized before analysis.
3. Salmonella Analysis
The two Salmonella detection protocols, using most probable number (MPN)
enumeration, used in this study were a modified version of the USEPA 1682 method, and the
ANSR™ Assay (USEPA, 2006, Mozola et al., 2013). Most probable number (MPN) enumeration
is achieved through the use of qualitative presence/absence results from different dilutions or
volumes of the sample in order to calculate out the most likely concentration of target organisms
is present in the original sample volume. The USEPA 1682 method, consisting of primary
enrichment in a non-selective broth followed by selective enrichment and characterization on
selective indicator agar, was modified for the use in water by removing steps involved with
biosolid dry weight analysis (USEPA, 2012). The ANSR™ Assay consisted of a single 24-h

30

nonselective enrichment step followed by cell lysis and immediate molecular analysis (Mozola et
al., 2013). While most MPN protocols usually involve serial dilutions of a sample, our water
samples were not diluted since Salmonella concentrations in environmental samples are very
low. Instead, we analyzed five replicates of three undiluted sample volumes (20ml, 10ml, 1ml)
for each sample resulting in 15 replicates for each sample. The theoretical detection range for our
methods was 0.65 MPN/100ml to 161 MPN/100ml. Ongoing precision recovery samples, created
using seeded laboratory reagent water, along with positive and negative controls were run
through both protocols for each sample analysis batch.
3.1 USEPA 1682 method protocol
The three undiluted sample volumes (20ml, 10ml, 1ml) were transferred into sterile
containers with Trypticase Soy Broth for primary enrichment. After incubation for 24h ± 2h at
36°C, six discrete drops of 30µl were applied equal distance apart on Modified Semisolid
Rappaport-Vassiliadis (MSRV) medium. The inoculated MSRV plates were incubated for 18h ±
2h at 42°C, then two drops showing motility (a halo forming around the drop) were plated on
separate xylose-lysine desoxycholate agar (XLD). After 24h ± 2h of incubation at 36°C,
presumptive positive colonies were selected and transferred to lysine iron agar (LIA), triple sugar
iron agar (TSI), and urea broth. After a final 24h ± 2h of incubation at 36°C, colonies from either
LIA or TSI were tested against polyvalent O antiserum I and VI (BD, Franklin Lakes, New
Jersey, USA), which detect the most common Salmonella serovars of concern, to confirm the
presence of Salmonella.
3.2 ANSR™ Assay protocol
The three undiluted samples volumes (20ml, 10ml, 1ml) were transferred into sterile
containers with Neogen’s Enrichment Broth #3 (Neogen, Lansing, Michigan, USA) for primary

31

enrichment. All subsequent steps were performed per the manufacturer’s directions. After
incubation at 36°C for 24h ± 2h, 50µl of each replicate were placed in 1.2ml tubes for cell lysis.
A total of 450µl of lysis buffer was added to each tube and then incubated at 80°C for 20 min.
Prior to the addition of sample supernatant, tubes containing ANSR™ lyophilized reagent were
preheated to 56°C for at least 3 min in the ANSR™ reader (Neogen, Lansing, Michigan, USA).
After lysis, 50µl of the lysed sample’s supernatant was transferred to tubes containing the
ANSR™ lyophilized reagent, and homogenized. Reagent tubes were capped, the reader’s lid
closed, and assay run initiated. The presence/absence results for Salmonella were obtained for
each replicate using fluorescent tags and enumerated using MPN methodology. Overall sampling
handling time following enrichment for one sample consisting of the three undiluted sample
volumes (20ml, 10ml, 1ml) and each of their five replicates was approximately 45 min compared
to the approximately 40 hrs needed to run the USEPA 1682 method.
3.3 Recovery efficiency comparison
Volumes of 500ml were split from each sample during Survey 1 and were seeded with
Salmonella Typhimurium ATCC#14028 for the determination of the recovery efficiencies of the
USEPA 1682 method and the ANSR™ assay as described in USEPA 1682 method (USEPA,
2006). Salmonella Typhimurium ATCC#14028 cultures were grown overnight and enumerated
on heart infusion agar (HIA). Samples were seeded with between 2.59x10-1 and 5.31x10-1
CFU/ml. Seeded samples were run through both protocols in order to compare the recovery
efficiency of each. Laboratory reagent water was also seeded to determine the Ongoing Precision
Recovery (OPR) for both the USEPA 1682 method and the ANSR™ assay in order to determine
whether the two methods were performing correctly. Due to the lack of a defined acceptable

32

percent recovery for the ANSR™ assay, values defined by the USEPA for the USEPA 1682
method were used.
3.4 Initial nonselective enrichment media comparison
During Survey 2, a comparison of the effect, if any, of the initial nonselective enrichment
media used with each assay was performed with naturally occurring bacteria. Samples cultured
in each nonselective enrichment media (i.e. TSB and Enrichment Broth #3) were run through the
USEPA 1682 method to determine if any statistically significant difference in the detection of
Salmonella was occurring. A Fisher’s Exact test for independence was performed to determine
the statistical significance with a α-value of 0.05.
3.5 Independent molecular confirmation of Survey 2 samples
Since the ANSR™ assay is currently a novel technology for Salmonella detection and was
not originally developed for environmental water analysis an independent molecular
confirmation of its results was performed. This was necessary in order to strengthen our
confidence in its results when detecting Salmonella in environmental samples in which we did
not have a known concentration of our target organism.
3.5.1 Sample preparation and storage for independent molecular confirmation
After initial enrichment during Survey 2, 10ml of each sample replicate were aliquoted
for later DNA extraction. The 10ml aliquots were centrifuged at 4500xg (4500rcf) for 15
minutes. The supernatant was then removed by aspiration. The remaining cell pellet for each
replicate were then resuspended in 1ml of sterile laboratory reagent water and stored at -80°C
until DNA extraction was performed.
3.5.2 DNA extraction

33

DNA extraction was performed using Qiagen’s QIAmp DNA Mini kit (Qiagen, Hilden,
Germany) as per the manufacturer’s instructions. In a 1.5ml microcentrifuge tube, 20 µl of
proteinase k, 200 µl of lysing buffer, and 200µl of sample were added. The microcentrifuge tube
was then incubated at 56°C for 10 min in a waterbath. After incubation, the microcentrifuge
tubes were centrifuged briefly and then 200 µl of 100% ethanol were added to the sample. This
was then mixed by pulse-vortexing for 15 s and then briefly centrifuged to remove any
remaining droplets of sample from the inside of the tube’s lid. This mixture was then pipetted
into a QIAmp spin column, and centrifuged at 6000 x g for 1 min. The column was then placed
into a clean collection tube, while the filtrate was discarded. Next, 500µl of a wash buffer was
added to the column and centrifuged at 6000 x g for 1 min. This filtrate was again discarded and
the column was placed into a clean collection tube where 500 µl of a second wash buffer was
added to the column and centrifuged at 20,000 x g for 3 min. Finally the column was placed into
a sterile microcentrifuge tube, and was eluted with 200 µl of AE buffer by centrifugation at 6000
x g for 1 min after a 5 min incubation at room temperature. After extraction, DNA samples were
stored at -20°C until analysis.
3.5.3 Molecular detection assay
DNA samples were analyzed for the presence of Salmonella using a proprietary custom
microbial DNA qPCR array from Qiagen (Qiagen, Hilden, Germany). The qPCR array targeted
the 16s rDNA specific for Salmonella enterica (NCBI Tax ID # 28901). The array also included
a proprietary 16s rDNA target for general bacterial detection along with a positive PCR control,
and a no template PCR control for confirmation of the presence of 16s rDNA, that the assay was
working correctly, and the ct value cutoff for positive sample identification respectively.
Samples were transported on ice to National Sanitation Foundation International’s (NSF Int.)

34

world headquarters in Ann Arbor, Michigan for testing. Samples were run on the array as per the
manufacturer’s instructions. All preparation of the 96 well array plates was performed in the dark
in order to avoid inactivating the mastermix. Array plates and Microbial qPCR mastermix
(Qiagen, Hilden, Germany) were removed from -20°C freezer and allowed to thaw prior to the
addition of reagents. The Microbial qPCR mastermix (Qiagen, Hilden, Germany) remained
covered until its addition to the array plate. Each well received 14 µl of mastermix, 6 µl of
DNAse-free water, and 5 µl of template. Wells were then sealed with optical thin wall 8-cap
strips, and the array plate was placed into the Bio-Rad CFX96 Touch Real-Time PCR Detection
System (Bio-Rad, Hercules, California, USA). Cycling protocol for qPCR is described in Table
5.

Table 5. Microbial DNA qPCR array cycling protocol for use with
Qiagen’s Microbial DNA qPCR custom array kit
Step
Time
Temperature
Cycles
Initial PCR activation
10 min
95°C
1
15 sec
95°C
Denaturation
Slow ramp rate at 1.0°C per sec
40
Annealing and Extension
2 min
60.0°C
Plate Read
A minimum threshold value of 102, and a cutoff ct value of 35 was used to determine
whether a sample was considered positive.
3.6 Statistical Analysis
All statistical analysis was performed using R statistical software (R Core Team, 2012).
For all descriptive statistics including log transformed data in figures, samples with no detectable
levels of Salmonella were assigned a value of 0.325 MPN/100ml (half the lower detection limit)
and samples, which contained levels of Salmonella above the upper detection limit, were
assigned a value of 160.9 MPN/100ml in order to normalize and make figures more readable

35

with a y-intercept at 0. Following log transformed MPN/100ml, samples were plotted with 95%
confidence intervals to examine any observable differences.
Fisher’s Exact test for independence, which can be performed on smaller sample sizes,
was chosen for analysis of the two methods’ presence/absence data due to the restrictions for use
of the Pearson’s chi-squared test. Fisher’s exact test is used to examine contingency between two
categories. It assumes a null hypothesis of independence, so any p-value below α = 0.05
indicates that the two categories’ test results (e.g., presence/absence of Salmonella) are not
significantly different from each other.
A Mann-Whitney U test was used to compare the concentrations of Salmonella detected
by each method. This test allows for fewer assumptions to be made about the data, including
normal distribution, and is an alternative to the paired Student’s t-test with greater efficiency on
non-normal distributions. This test allows for the assessment of paired populations by
determining if their population mean ranks differ significantly. It assumes a null hypothesis that
the two populations are the same. Thus any p-value that fails is above an alpha of 0.05 indicates
that the two populations are not significantly different.
The USEPA 1682 method was used as our “gold standard” method in order to determine
the sensitivity, specificity, precision, and accuracy of the ANSR™ assay, while the results
obtained from qPCR were used to confirm the calculations based on the USEPA 1682 method
and provide a measure to determine the USEPA 1682 method’s sensitivity, specificity, etc. The
following formulas were used:

36

‫‪ ሺܶܲሻ‬ݏ݁ݒ݅ݐ݅ݏ݋ܲ ݁ݑݎܶ‬
‫‪ܰሻ൯‬ܨ‪ ሺ‬ݏ݁ݒ݅ݐܽ݃݁ܰ ݁ݏ݈ܽܨ ‪ ሺܶܲሻ ൅‬ݏ݁ݒ݅ݐ݅ݏ݋ܲ ݁ݑݎܶ‪൫‬‬

‫‪ ሺܶܰሻ‬ݏ݁ݒ݅ݐܽ݃݁ܰ ݁ݑݎܶ‬
‫‪ሺܶܰሻሻ‬ݏ݁ݒ݅ݐܽ݃݁ܰ ݁ݑݎܶ ‪ܲሻ ൅‬ܨ‪ ሺ‬ݏ݁ݒ݅ݐ݅ݏ݋ܲ ݁ݏ݈ܽܨ‪ሺ‬‬

‫ܲܨ‬
‫‪ܲ ൅ ܶܰሻ‬ܨ‪ሺ‬‬

‫‪݁ሻ ൌ‬ݐܴܽ ݁ݒ݅ݐ݅ݏ݋ܲ ݁ݑݎܶ‪ ሺ‬ݕݐ݅ݒ݅ݐ݅ݏ݊݁ܵ‬

‫‪݁ሻ ൌ‬ݐܴܽ ݁ݒ݅ݐܽ݃݁ܰ ݁ݑݎܶ‪ ሺ‬ݕݐ݂݅ܿ݅݅ܿ݁݌ܵ‬

‫‪ܴܲሻ ൌ‬ܨ‪݁ ሺ‬ݐܴܽ ݁ݒ݅ݐ݅ݏ݋ܲ ݁ݏ݈ܽܨ‬

‫‪݁ሻ‬ݐܴܽ ݁ݒ݅ݐ݅ݏ݋ܲ ݁ݑݎܶ‪ ሺ‬ݕݐ݅ݒ݅ݐ݅ݏ݊݁ܵ ‪ܴܰሻ ൌ 1 െ‬ܨ‪݁ ሺ‬ݐܴܽ ݁ݒ݅ݐܽ݃݁ܰ ݁ݏ݈ܽܨ‬

‫‪ሺܶܲ ൅ ܶܰሻ‬‬
‫‪ሻ‬ݏ݁ݒ݅ݐܽ݃݁ܰ ‪ ൅‬ݏ݁ݒ݅ݐ݅ݏ݋ܲ‪ሺ‬‬

‫ܲܶ‬
‫‪ܲሻ‬ܨ ‪ሺܶܲ ൅‬‬

‫‪ ൌ‬ݕܿܽݎݑܿܿܣ‬

‫‪݊ ൌ‬݋݅ݏ݅ܿ݁ݎܲ‬

‫‪37‬‬

Chapter 3. Results
1. Survey 1 Results
1.1 Naturally occurring Salmonella
Salmonella was found in 21% (N = 5) of the total N = 24 samples with an average of 0.99
MPN/100ml using the USEPA 1682 assay (Table 6.). The ANSR™ assay detected Salmonella in
75% (N=18) of the total samples with an average of 3.77 MPN/100ml (Table 6). Sample
MPN/100ml values ranged from < 0.65 to 2.90 MPN/100ml with the USEPA 1682 assay and <
0.65 to 15.29 MPN/100ml with the ANSR™ assay (Table 6). For the USEPA 1682 method,
Table 7 shows each sampling site’s contribution to the total number of samples collected, the
number of positive samples per site, and percent of positive samples for each site. Using the
USEPA 1682 assay, Salmonella was detected in 33.33% of the samples taken from the River
Raisin during Survey 1. Only 16.67% of the samples taken from the Grand River using the
USEPA 1682 assay had detectable levels of Salmonella. The Red Cedar River and Farm Canal 1
also detected Salmonella with the USEPA 1682 method in only 16.67% of each of their samples.

Table 6. Percent positive and MPN/100ml levels for naturally occurring Salmonella in surface water
™
for the USEPA 1682 method and ANSR assay in Survey 1.
EPA 1682
Total % Positive (N =24)
21% (N=5)
Average (MPN/100ml)*
0.99
§
MPN Range (MPN/100ml)
<0.65 – 2.90
* = Average MPN/100ml value calculated without the inclusion of Non-detect values
§ = <0.65 MPN/100ml Represents the lower detection limit for the MPN calculations

38

ANSR ™ assay
75% (N=18)
3.77
§
<0.65 – 15.29

Table 7. Positive samples of naturally occurring Salmonella in surface water for the
USEPA 1682 method in Survey 1.
River Raisin Grand River Red Cedar River Farm Canal 1
6
6
6
6
Samples
Positive Samples (N)
2
1
1
1
% Positive at site
33.33%
16.67%
16.67%
16.67%
Total % Positive (N=24)
8.33%
4.17%
4.17%
4.17%
The ANSR™ assay showed considerably higher levels of detection at each site, as seen in
Table 8. Salmonella was detected in 83.33% of samples collected from the River Raisin and
100% of the samples collected from the Grand River using this assay. The Red Cedar River
showed detectable levels of Salmonella from 50% of its samples, and Farm Canal 1 had 83.33%
of its samples also showing detectable levels of Salmonella. The specific samples and their
positive results for the naturally occurring Salmonella can be found in Table 9.

Table 8. Positive samples of naturally occurring Salmonella in surface water for the
ANSR ™ assay in Survey 1
River Raisin Grand River Red Cedar River Farm Canal 1
6
6
6
6
Samples
Positive Samples (N)
5
6
3
5
% Positive at site
83.33%
100.00%
50.00%
83.33%
Total % Positive (N=24)
20.83%
25.00%
12.50%
20.83%

39

Table 9. Naturally occurring and seeded samples found positive for Salmonella by
the USEPA 1682 method and the ANSR™ assay during Survey 1.
Naturally Occurring
Seeded**
™
USEPA 1682
USEPA 1682
ANSR
ANSR ™
River Raisin
1*
—
+
+
+
2
—
—
+
+
3
+
+
+
+
4
—
—
+
+
5
—
+
+
+
6
+
+
+
+
Grand River
1
+
+
+
+
2
—
+
+
+
3
—
+
+
+
4
—
+
+
+
5
—
+
+
+
6
—
+
+
+
Red Cedar River
1
+
+
+
+
2
—
—
+
+
3
—
+
+
+
4
—
—
+
+
5
—
+
+
+
6
—
—
+
+
Farm Canal 1
1
—
+
+
+
2
—
—
+
+
3
+
+
+
+
4
—
+
+
+
5
—
+
+
+
6
—
+
+
+
* Numbers correspond to sampling dates, 1 - 10/15/12, 2 - 10/22/12, 3 - 11/5/12, 4 11/12/12, 5 - 11/26/12, 6 - 12/17/12
** 100% of seeded samples were found positive by both methods. This was expected
as high concentrations of the bacteria were seeded into the surface water samples for
recovery analysis.

40

All samples found positive (N=5) for Salmonella using the USEPA 1682 assay were also
found to be positive with the ANSR™ assay, however there were 13 samples negative by the
USEPA 1682 method that were positive by the ANSR™ assay. Table 10 shows the five samples
found positive with both assays along with their corresponding MPN/100ml values. With the
exception of the River Raisin sample 6 (12/17/12), the concentrations were not noticeably
different.

Table 10. MPN/100ml levels for Salmonella in water samples found to be positive by both the
USEPA 1682 method and the ANSRTM assay during Survey 1.
™
EPA 1682 method
ANSR assay
(1)* Grand River (MPN/100ml)

0.67

0.67

(1)* Red Cedar (MPN/100ml)

2.55

2.34

(3)* River Raisin (MPN/100ml)

0.72

1.44

(3)* Farm Canal (MPN/100ml)

2.9

0.72

(6)* River Raisin (MPN/100ml)

0.67

8.22

* Numbers correspond to sampling dates, 1 - 10/15/12, 2 - 10/22/12, 3 - 11/5/12, 4 - 11/12/12, 5 11/26/12, 6 - 12/17/12

Specific MPN/100ml values for each sample can be found in Table 11. Samples not
found to be positive for Salmonella were listed as below the detection limit (DL) for both assays
(<0.65 MPN/100ml).

41

Table 11. Survey 1. MPN/100ml values for naturally occuring Salmonella in surface water samples
USEPA 1682 method
Sampling Site
River Raisin
Grand River
Red Cedar River
Farm Canal 1

1

a

<0.65*
0.670
2.550
<0.65

Sampling Date
3
4

2
<0.65
<0.65
<0.65
<0.65

0.720
<0.65
<0.65
2.900

<0.65
<0.65
<0.65
<0.65

5

6

<0.65
<0.65
<0.65
<0.65

0.670
<0.65
<0.65
<0.65

1
2
3
4
5
6
ANSR ™ assay
Sampling Site
River Raisin
0.720
<0.65
1.440
<0.65
10.860
8.220
Grand River
0.670
0.720
0.650
6.370
2.260
15.290
Red Cedar River
2.340
<0.65
0.720
<0.65
3.300
<0.65
Farm Canal 1
0.650
<0.65
0.720
2.020
6.930
3.930
a Numbers correspond to sampling dates, 1 - 10/15/12, 2 - 10/22/12, 3 - 11/5/12, 4 - 11/12/12, 5 - 11/26/12,
6 - 12/17/12
* The lower detection limit of <0.65 MPN/100ml

Log transformed Salmonella MPN/100ml values for each site obtained with the USEPA
1682 method and with the ANSR™ assay can be found in Figures 5 – 8 and Figures 9 – 12,
respectively. Average MPN/100ml values that have been log transformed for both detection
methods can be seen in Figures 13 and 14. Note that all data were log transformed after adding
0.325 (half the lower detection limit) to samples without detectable levels of Salmonella.

42

Figure 5. Log transformed MPN/100ml values for River Raisin that
were obtained from the USEPA 1682 method in Survey 1
2.5

Log MPN/100ml

2
1.5
1
0.5
Detection Limit
Log 0.65
0
1

2

3
4
Sample

5

6

Figure 6. Log transformed MPN/100ml values for Grand River that
were obtained from the USEPA 1682 method in Survey 1
2.5

Log MPN/100ml

2
1.5
1
0.5
Detection Limit
Log 0.65

0
1

2

3
4
Sample

5

43

6

Figure 7. Log transformed MPN/100ml values for Red Cedar River
that were obtained from the USEPA 1682 method in Survey 1

2.5

Log MPN/100ml

2
1.5
1
0.5
Detection Limit
Log 0.65

0
1

2

3
4
Sample

5

6

Figure 8. Log transformed MPN/100ml values for Farm Canal 1. that
were obtained from the USEPA 1682 method in Survey 1
2.5

Log MPN/100ml

2
1.5
1
0.5
Detection Limit
Log 0.65

0
1

2

3
4
Sample

5

44

6

Figure 9. Log transformed MPN/100ml values for River Raisin that
were obtained from the ANSRTM assay in Survey 1
2.5

Log MPN/100ml

2
1.5
1
0.5
Detection Limit
Log 0.65
0
1

2

3
4
Sample

5

6

Figure 10. Log transformed MPN/100ml values for Grand River that
were obtained from the ANSRTM assay in Survey 1
2.5

Log MPN/100ml

2

1.5

1

0.5
Detection Limit
Log 0.65
0
1

2

3
4
Sample

5

45

6

Figure 11. Log transformed MPN/100ml values for Red Cedar River
that were obtained from the ANSRTM assay in Survey 1
2.5

Log MPN/100ml

2
1.5
1
0.5
Detection Limit
Log 0.65
0
1

2

3
4
Sample

5

6

Figure 12. Log transformed MPN/100ml values for Farm Canal 1 that
were obtained from the ANSRTM assay in Survey 1
2.5

Log MPN/100ml

2

1.5

1

0.5
Detection Limit
Log 0.65
0
1

2

3
4
Sample

5

46

6

Figure 13. Log transformed average MPN/100ml values for each
sampling site obtained from the USEPA 1682 method in Survey 1

2.5

Log MPN/100ml

2
1.5
1
0.5
Detection Limit
Log 0.65

0
River Raisin Grand River Red Cedar Farm Canal 1
Sampling Site

Figure 14. Log transformed average MPN/100ml values for each
sampling site obtained from the ANSRTM assay in Survey 1
2.5

Log MPN/100ml

2
1.5
1
0.5
Detection Limit
Log 0.65
0
River Raisin Grand River Red Cedar Farm Canal 1
Sampling Site

47

Figure 15 shows a comparison of the average log transformed MPN/100ml values for
Salmonella using both the USEPA 1682 method and the ANSR™ assay in Survey 1 where six
samples were collected from each of the four surface water site for a total of N = 24 samples.
Salmonella concentrations in MPN/100ml were log transformed in the same manner as
mentioned above. Figures 16 – 19 show a comparison of the log transformed concentrations of
Salmonella detected
ected by both methods at each site, with non-overlapping 95% confidence
intervals. The
he two methods were found to be significantly different by a Mann
Mann-Whitney
Whitney U test
(p-value 0.003, α=0.05).
Figure 15. Comparison of log transformed average MPN/100ml values
for Salmonella using the USEPA 1682 method and the ANSRTM assay
in Survey 1
3.5

Log MPN/100ml

3
2.5
2
1.5
1
0.5
Detection Limit
Log 0.65

0
EPA 1682

ANSR

48

Figure 16. Comparison of Log transformed MPN/100ml values of
Salmonella for River Raisin using the USEPA 1682 method and the
ANSRTM assay in Survey 1
2.5

Log MPN/100ml

2
1.5
1

Detection Limit
Log 0.65

0.5

EPA 1682

0

ANSR
1

2

3
4
Sample

5

6

Figure 17. Comparison of Log transformed MPN/100ml values of
Salmonella for Grand River using the USEPA 1682 method and the
ANSRTM assay in Survey 1
2.5

Log MPN/100ml

2
1.5
1
Detection Limit
Log 0.65
0.5

EPA 1682

0

ANSR
1

2

3

4

5

Sample

49

6

Figure 18. Comparison of Log transformed MPN/100ml values of
Salmonella for Red Cedar River using the USEPA 1682 method and
the ANSRTM assay in Survey 1
2.5

Log MPN/100ml

2
1.5
Detection
Limit Log 0.65

1

EPA 1682
0.5
ANSR
0
1

2

3

4

5

6

Sample
Figure 19. Comparison of Log transformed MPN/100ml values of
Salmonella for Farm Canal 1. using the USEPA 1682 method and the
ANSRTM assay in Survey 1
2.5

Log MPN/100ml

2
1.5
Detection Limit
Log 0.65

1

EPA 1682
0.5
ANSR
0
1

2

3
4
Samples

5

50

6

The combined results of all samples tested during Survey 1 can be seen in Figure 20,
which shows only four (River Raisin samples 5 and 6, Grand River sample 6, and Farm Canal
sample 5) out of the 24 samples taken having notable differences in the Salmonella
concentrations when comparing the USEPA 1682 method and the ANSR™ assays.

51

Figure 20. Log transformed MPN/100ml values for naturally occuring Salmonella for all
samples examined during Survey 1
2.5
Detection
Limit Log 0.65
EPA 1682
2

Log MPN/100ml

ANSR

1.5

1

0.5

0
1

2

3

4

River Raisin

5

6

1

2

3

4

5

6

1

Grand River

2

3

4

Red Cedar
Sample
52

5

6

1

2

3

4

5

Farm Canal 1

6

1.2 Recovery efficiency comparison
Samples, which were seeded with Salmonella Typhimurium (ATCC#14028) in order to
determine the recovery efficiency for each of the N = 24 samples collected during Survey 1, had
MPN/100ml values ranging from 4.13 to 160.90 MPN/100ml for both the USEPA 1682 method
and the ANSR™ assay (Table 12). Using a Mann-Whitney U test with seeded Salmonella sample
concentrations the USEPA 1682 method and the ANSR™ assay were found to be not
significantly different (p-value of 0.2467, α = 0.05). The USEPA 1682 method’s maximum and
minimum percent recoveries recorded during the Survey 1 study were 251.21% and 11.73%
respectively and for the ANSR™ assay they were 620.78% and 0% respectively. The average
percent recovery for the USEPA 1682 method for each sampling site ranged from 53.16% at the
Red Cedar River to 87.65% at the River Raisin during Survey 1 (Table 12). The average percent
recovery for the ANSR™ assay for each sampling site ranged from 47.57% at the Red Cedar
River to 288.88% at Farm Canal 1 (Table 12). Of the total N=24 samples taken during this
survey, only one sample from the USEPA 1682 method had a percent recovery outside of the
acceptable range (0 – 246%) as defined by the USEPA 1682 method, while the ANSR™ assay
had seven samples out of this range (Table 12). Since the Ongoing Precision Recovery samples
(OPR) (recovery efficiency samples using seeded laboratory reagent water) for both the USEPA
1682 method and the ANSR™ assay had percent recoveries all within the USEPA 1682 method’s
acceptable range (0 – 246%) it is suggested that the samples whose percent recoveries fall
outside of the acceptable range are yielding poor results due to a matrix interference effect (e.g.,
environmental contamination inhibiting the growth and recovery of Salmonella).

53

Table 12. Recovery efficiency results for the USEPA 1682 method and the ANSR

TM

assay during Survey 1.

USEPA 1682 method
Site

Sample #*

ANSR

TM

assay

Seeded
Final
Average %
Final
Average %
%
%
Concentration Concentration
Recovery Concentration
Recovery
Recovery**
Recovery**
(CFU/100ml) (MPN/100ml)
at each site (MPN/100ml)
at each site

River Raisin
1
2
3
4
5
6

45.60
28.50
29.10
25.90
36.40
28.10

91.78
11.81
4.13
23.98
91.78
16.14

76.25
40.25
11.73
91.45
251.25
54.99

1
2
3
4
5
6

45.60
28.50
29.10
25.90
36.40
28.10

34.77
19.07
19.07
12.27
54.22
12.45

74.78
65.70
64.49
46.18
148.06
43.10

1
2
3
4
5
6

45.60
28.50
29.10
25.90
36.40
28.10

34.77
34.77
11.51
7.97
15.24
5.55

70.66
120.72
38.48
29.56
40.98
18.57

87.65

34.77
91.78
10.12
160.90
160.90
54.22

74.67
§
320.52
29.86
§
620.78
§
412.20
163.51

270.26

73.72

91.78
15.71
11.81
6.36
15.29
91.78

199.80
52.54
38.39
0.00
35.80
§
271.88

99.74

53.16

12.27
19.07
13.04
5.01
17.07
28.34

21.78
65.70
42.39
18.11
37.83
99.58

47.57

§

Grand River

Red Cedar River

54

Table 12. (cont’d)
Farm Canal 1.
1
2
3
4
5
6

45.60
28.50
29.10
25.90
36.40
28.10

34.77
12.68
34.77
54.22
23.98
5.55

75.54
43.30
109.64
208.36
64.99
18.57

86.73

91.78
91.78
10.12
160.90
9.98
160.90

199.85
320.52 §
32.34
§
614.23
8.38
557.95 §

288.88

OPR Samples ¶
1
45.6
91.78
201.27
34.77
76.25
2
28.5
34.77
121.86
23.19
81.27
3
29.10
6.81
23.43
34.77
119.62
4
25.90
9.98
38.58
54.22
209.61
5
36.40
34.77
95.52
9.98
27.42
6
28.10
28.34
100.73
16.14
57.37
* Numbers correspond to sampling dates, 1 - 10/15/12, 2 - 10/22/12, 3 - 11/5/12, 4 - 11/12/12, 5 - 11/26/12, 6 12/17/12
** Acceptable percent recovery range is from 0 to 246% as defined by the USEPA 1682 method. The ANSRTM assay acceptable percent
recovery range is currently undefined
§ Percent recoveries that are outside of the acceptable range as defined by the USEPA 1682 method
¶ Ongoing Precision Recovery samples

55

The average percent recovery for the USEPA 1682 method during Survey 1 was 75.32%,
with a maximum percent recovery of 251.5% and a minimum percent recovery of 11.73%, which
falls with the USEPA 1682 method’s defined acceptable percent recovery range of 0 to 246%
(Table 13). The average percent recovery for the ANSR™ assay during Survey 1 was 176.61%
with a maximum percent recovery of 620.78% and a minimum percent recovery of 0% (Table
13).

Table 13. Overal recovery efficiency comparison for N=24 samples in Survey 1.
USEPA 1682 method

ANSR ™ assay

Average % Recovery

75.32%

176.61%

Max % Recovery

251.25%

620.78%

Min % Recovery

11.73%

0%

Average OPR** % Recovery

96.90%

95.26%

Acceptable % Recovery

0 - 246%

UD*

* Undefined by the literature
** Ongoing Precision Recovery
Figures 21 – 24 compare the methods by showing the log transformed MPN/100ml
values of recovered seeded Salmonella from each sample from the various sites. Overlapping
95% confidence intervals are seen for most experiments. The different water samples from the
various locations did not influence any difference in recoveries between the two methods.

56

Figure 21. Comparison of Log transformed MPN/100ml values of
seeded Salmonella for River Raisin using the USEPA 1682 method
and the ANSRTM assay in Survey 1
3.5

Log MPN/100ml

3
2.5

Detection Limit
Log 0.65

2

EPA 1682
1.5
ANSR

1
0.5
0
1

2

3Sample4

5

6

Figure 22. Comparison of Log transformed MPN/100ml values of
seeded Salmonella for Grand River using the USEPA 1682 method
and the ANSRTM assay in Survey 1
3

Log MPN/100ml

2.5
2

Detection
Limit Log 0.65

1.5

EPA 1682

1

ANSR

0.5
0
1

2

3Sample4

5

57

6

Figure 23. Comparison of Log transformed MPN/100ml values of
seeded Salmonella for Red Cedar River using the USEPA 1682
method and the ANSRTM assay in Survey 1
3

Log MPN/100ml

2.5
2

Detection
Limit Log 0.65

1.5

EPA 1682

1
ANSR
0.5
0
1

2

3

4

5

6

Sample
Figure 24. Comparison of Log transformed MPN/100ml values of
seeded Salmonella for Farm Canal 1 using the USEPA 1682 method
and the ANSRTM assay in Survey 1
3.5

Log MPN/100ml

3
2.5
Detection
Limit Log 0.65

2

EPA 1682

1.5
1

ANSR

0.5
0
1

2

3

4

5

Sample

58

6

2. Survey 2 Results
2.1 Initial nonselective enrichment comparison
A comparison of the initial nonselective enrichment media (TSB used in USEPA 1682
method and Enrichment media #3 used in the ANSR™ assay) was undertaken to ascertain
whether this step influenced the growth of Salmonella and the subsequent lower detection by the
USEPA 1682 method. Both were compared for naturally occurring Salmonella with the USEPA
1682 method. Samples that were found positive for naturally occurring Salmonella using TSB
and/or Enrichment media #3 during this comparison can be seen in Table 14. Overall, 42.9% or 9
out of 21 samples were found positive for naturally occurring Salmonella when enriched with
TSB. With the use of the Enrichment Media #3, 28.6% or 6 out of 21 samples were found
positive for naturally occurring Salmonella (Table 14). Samples found positive with the use of
TSB and Enrichment media #3 had very similar average MPN/100ml values with 1.38 and 1.11
MPN/100ml respectively. Both initial nonselective enrichment media had very similar ranges
with samples found positive with TSB ranging from the lower limit of detection <0.65 to 2.09
MPN/100ml, and with samples found positive with Enrichment media #3 ranging from the lower
limit of detection <0.65 to 1.34 MPN/100ml (Table 14). The two media types were not
statistically different (p-values 0.0095, α=0.05) using a Fisher’s exact test for contingency. A
Mann-Whitney U test found no significant difference between the two media types results (pvalue 0.2821, α=0.05).

59

Table 14. MPN/100ml values for naturally occuring Salmonella using the
¶
USEPA 1682 method with two different initial enrichment medias (TSB
& Enrichment Media #3) during Survey 2.
MPN/100ml
¶
Enrichment Media #3
TSB
§
Farm Canal 2
<0.65
<0.65
Shiawasse River
<0.65
<0.65
Grand River 1
<0.65
<0.65
Raisin River 1
<0.65
<0.65
Saginaw River
<0.65
<0.65
Red Cedar River
1.30
<0.65
Detroit River
<0.65
<0.65
Rouge River
<0.65
<0.65
Grand River 2
1.39
<0.65
Muskegon River
2.09
1.34
Black River
0.65
1.30
St. Joseph River
<0.65
<0.65
Kalamazoo River
1.44
1.34
Ottawa River
2.90
1.34
Maumee River
0.67
0.67
River Raisin 2
<0.65
<0.65
Huron River
<0.65
<0.65
AS (A)*
<0.65
<0.65
AS (B)*
1.30
<0.65
HRC (A)*
0.67
<0.65
HRC (B)*
<0.65
0.65
Total % Positive (N =21)
42.9% (N=9)
28.6% (N=6)
a
1.38
1.11
Average (MPN/100ml)
MPN Range (MPN/100ml)

<0.65 - 2.09

<0.65 - 1.34

¶ Tryptic Soy Broth
§ <0.65 MPN/100ml Represents the lower detection limit for the MPN
calculations
* Wastewater Treatment samples are given individual codes for their location
in the treatment train AS (A) & HRC (A) - Influent, AS (B) & HRC (B) * Average MPN/100ml value calculated without the inclusion of values that
fell below the detection limit of <0.65 MPN/100ml

60

Figure 25 shows the log transformed MPN/100ml values for naturally occurring
Salmonella during the initial nonselective enrichment media comparison for all samples taken
during Survey 2. All of the 95% confidence intervals show an overlap. However, TSB appears to
improve the detection and more samples would need to be collected and tested to show statistical
significance.

61

2.5

Figure 25. Log transformed MPN/100ml values for naturally occuring Salmonella during initial
enrichment media comparison in Survey 2

Detection Limit
Log 0.65

2

Log MPN/100ml

TSB
Enrichment Media
#3

1.5

1

0.5

0

Sampling Sites

62

2.2 Survey 2 detection method comparison
During Survey 2, 21 samples were tested with 42.9% (N = 9) and 95.2% (N = 20) found
positive by the USEPA 1682 method and the ANSR™ assay, respectively (Table 15). Samples
found positive by the USEPA 1682 method had concentrations ranging from the lower detection
limit of the MPN method of <0.65 MPN/100ml to 2.09 MPN/100ml (Table 15). Samples found
positive by the ANSR™ assay had concentrations ranging from the lower detection limit (<0.65
MPN/100ml) of the MPN method to the upper detection limit (>160.90 MPN/100ml) of the
MPN method (Table 15). The average concentration of Salmonella detected by the USEPA 1682
method and the ANSR™ assay were 1.38 and 24.37 MPN/100ml, respectively. The two methods’
results from Survey 2 were found to be significantly different (p-value 0.000004, α = 0.05) by a
Mann-Whitney U test.

63

Table 15. MPN/100ml values and total percent of samples found positive
for naturally occuring Salmonella by the USEPA 1682 method and the
ANSR™ assay during Survey 2
USEPA 1682 method
ANSR ™ assay
Farm Canal 2
2.62
<0.65§
Shiawasse River
<0.65
12.99
Grand River 1
<0.65
17.67
Raisin River 1
<0.65
17.07
Saginaw River
<0.65
5.17
Red Cedar River
1.3
6.57
Detroit River
<0.65
15.63
Rouge River
<0.65
1.95
Grand River 2
1.39
2.62
Muskegon River
2.09
27.08
Black River
0.65
3.28
St. Joseph River
<0.65
11.73
Kalamazoo River
1.44
17.07
Ottawa River
2.9
160.9
Maumee River
0.67
8.55
River Raisin 2
<0.65
12.68
Huron River
<0.65
160.9
AS (A)*
<0.65
0.72
AS (B)*
1.3
0.65
HRC (A)*
0.67
1.55
HRC (B)*
<0.65
<0.65
Total % Positive (N =21)
42.9% (N=9)
95.2% (N=20)
Average (MPN/100ml)**
1.38
24.37
MPN Range (MPN/100ml)

<0.65 – 2.09

<0.65 – >160.90 ¶

§ <0.65 MPN/100ml Represents the lower detection limit for the MPN
* Wastewater Treatment samples are given individual codes for their location
in the treatment train AS (A) & HRC (A) - Influent, AS (B) & HRC (B) * Average MPN/100ml value calculated without the inclusion of values below
the detection limit of <0.65 MPN/100ml
¶ >160.90 MPN/100ml represents the upper detection limit for MPN

64

Although there were considerably more samples found positive by the ANSR™ assay (N
= 20) than with the USEPA 1682 method (N = 9), all of the samples that were found positive by
the USEPA 1682 method were also found positive by the ANSR™ assay. These samples along
with their MPN/100ml values can be seen in Table 16.
Table 16. MPN/100ml values for naturally occuring Salmonella in water samples found to be
™
positive by both the USEPA 1682 method and ANSR assay during Survey 2.
™
EPA 1682 assay
ANSR assay
Red Cedar River

1.30

6.57

Grand River 2

1.39

2.62

Muskegon River

2.09

27.08

Black River

0.65

3.28

Kalamazoo River

1.44

17.07

Ottawa River

2.90

160.90

Maumee River

0.67

8.55

AS (B)*

1.30

0.65

HRC (A)*

0.67

1.55

* Wastewater Treatment samples are given individual codes for their location in the treatment train AS
(A) & HRC (A) - Influent, AS (B) & HRC (B) - Nondisinfected effluent

As seen in Figure 26, Survey 2 had considerably more samples (N = 11) with different
observable concentrations between the two detection methods than were seen in Survey 1 (see
Figure 20). This included the samples from the Shiawassee River, the Grand River, River Raisin,
the Detroit River, the Muskegon River, the St. Joseph River, the Kalamazoo River, the Ottawa
River, the Maumee River, and the Huron River.

65

Figure 26. Survey 2 Log transformed MPN/100ml values for naturally occuring Salmonella
using the USEPA 1682 method and the ANSRTM assay

3

2.5

Lower Detection
Limit Log 0.65
Upper Detection
Limit Log 160.9
EPA 1682

Log MPN/100ml

2

ANSR

1.5

1

0.5

0

Sampling Site
66

Figure 27, seen below, shows the average log transformed MPN/100ml values for both
the USEPA 1682 method and the ANSRTM assay with the 95% confidence intervals for both
overlapping.
Figure 27. Survey 2 average Log MPN/100ml values for the USEPA
1682 method and the ANSRTM assay
3.5
3

Log MPN/100ml

2.5
2
1.5
1
0.5
Detection Limit Log
0.65

0
EPA 1682

ANSR

67

3. Independent Genetic Confirmation and Combined Survey Results
All 21 samples tested during Survey 2 by the USEPA 1682 method and ANSR™ assay
were also analyzed by qPCR as an independent test in order to determine if the discrepancies
between the two were false positives or negatives. Table 17 shows the results of the qPCR
analysis for both the USEPA 1682 method samples and the ANSR™ assay. Of the 21 samples
42.9% (N = 9) were positive by USEPA 1682 method whereas 47.62% (N = 10) were found
positive by qPCR with a 90.48% (N = 19) overall agreement between the two (Table 17). Of the
21 samples tested with the ANSR™ assay (with 95.2% positive, N=20), 71.43% (N = 15) were
found positive by qPCR with a 76.19% (N = 16) overall agreement between the two (Table 17).

68

Table 17. Samples found positive for naturally occuring Salmonella by the USEPA 1682
method and the ANSR™ assay and confirmed with qPCR during Survey 2.
USEPA 1682
qPCR
ANSR ™ assay
qPCR
Farm Canal 2
—
—
+
+
Shiawasse River
—
—
+
+
Grand River 1
—
—
+
+
Raisin River 1
—
—
+
+
Saginaw River
—
+
+
—
Red Cedar River
+
+
+
+
Detroit River
—
—
+
—
Rouge River
—
—
+
+
Grand River 2
+
+
+
+
Muskegon River
+
+
+
+
Black River
+
+
+
+
St. Joseph River
—
—
+
+
Kalamazoo River
+
+
+
+
Ottawa River
+
+
+
+
Maumee River
+
—
+
+
River Raisin 2
—
—
+
+
Huron River
—
—
+
+
AS (A)*
—
—
+
—
AS (B)*
+
+
+
—
HRC (A)*
+
+
+
—
HRC (B)*
—
+
—
—
Total % Positive (N =21) 42.9% (N=9) 47.62% (N=10) 95.2% (N=20) 71.43% (N=15)
% Agreement with qPCR
90.48% (N=19)
76.19% (N=16)
* Wastewater Treatment samples are given individual codes for their location in the treatment
train AS (A) & HRC (A) - Influent, AS (B) & HRC (B) - Nondisinfected effluent
Twelve ANSR™ assay samples from Survey 1 were also analyzed by qPCR in order to
increase the confidence of the statistical analysis. The results for these samples can be seen in
Table 18. Using the statistical software, R, Fisher Exact Test’s for contingency was run for each
combination of sample results from the USEPA 1682 method, the ANSR™ assay, and the qPCR
assay. The null hypothesis with this test is that the positive and negative results obtained by each
of the two methods are independent of the results obtained by the other, thus should reveal
different results. The alternative hypothesis is that the results of one method are dependent (or

69

congruent) on the results obtained by the other. The comparison of the USEPA 1682 method
against the ANSR™ assay at 95% confidence was not significant (p-value 0.08059) and thus does
not allow for the rejection of the null hypothesis that these two method’s results are independent
of each other. The comparison of the USEPA 1682 method against the qPCR assay at 95%
confidence was found to be significant (p-value 0.001905) allowing for the rejection of the null
hypothesis that these two tests are independent. The comparison of the ANSR™ assay and the
qPCR assay at 95% confidence was significant (p-value 0.04448) and thus allows for the
rejection of the null hypothesis that these two tests are independent.

Table 18. Survey 1. ANSR
Site
River Raisin

TM

assay samples analyzed by qPCR

Sample*

ANSR ™ assay

qPCR**

4
5
6

—
+
+

—
—
+

4
5
6

+
+
+

—
—
—

4
5
6

—
+
—

—
—
—

Grand River

Red Cedar River

Farm Canal 1.
4
+
—
5
+
—
6
+
—
* Numbers correspond to sampling dates, 1 - 10/15/12, 2 - 10/22/12,
3 - 11/5/12, 4 - 11/12/12, 5 - 11/26/12, 6 - 12/17/12
** Samples from Survey 1. were not concentrated as previously
described for samples saved for genetic testing during Survey 2.

70

When Survey 1 and Survey 2’s results are combined, the total percent positive for the
USEPA 1682 method and ANSR™ assay were 31.1% (N = 14) and 84.4% (N = 38), respectively
(Table 19). The average MPN/100ml for the USEPA 1682 method for both surveys was 0.81
MPN/100ml (Table 19). The average MPN/100ml for the ANSR™ assay for both surveys was
12.34 MPN/100ml. The USEPA 1682 method showed a range of MPN/100ml values from <0.65
to 2.90 over the course of both assays, while the ANSR™ assay showed a much wider range of
values from <0.65 to >160.90 MPN/100ml (Table 19).
Table 19. Survey 1 and Survey 2 combined results for method comparison
USEPA 1682 method

ANSR TM assay

Survey 1. (N = 24)
Total % Positive
Average (MPN/100ml)*
MPN Range (MPN/100ml)

20.8% (N = 5)
0.31
<0.65 - 2.90

75.0% (N = 18)
2.83
<0.65 - 15.29

Survey 2. (N = 21)
Total % Positive
Average (MPN/100ml)*
MPN Range (MPN/100ml)

42.9% (N = 9)
1.38
<0.65 - 2.90

95.2% (N = 20)
23.21
<0.65 - >160.90**

Survey 1 and 2 Combined
Total % Positive
31.1% (N = 14)
84.4% (N = 38)
Average (MPN/100ml)*
0.81
12.34
MPN Range (MPN/100ml)
<0.65 - 2.90
<0.65 - >160.90
* Average MPN/100ml calculated with only the inclusion of samples found to be
positive
** <0.65 MPN/100ml and >160.90 MPN/100ml represent the lower and upper limits
of detection for the MPN method, repectively

The ANSR™ assay had a calculated sensitivity of 100%, specificity of 22.58%, a false
positive rate of 77.4%, and a false negative rate of 0% based on the USEPA 1682 method. When
these values were calculated based on the results obtained from qPCR assay for samples positive
by the ANSR™ assay in part of Survey 1 (Table 18) and Survey 2, the ANSR™ assay’s sensitivity
and false negative rate were identical and only small variations were seen in its calculated

71

specificity and false positive rate (25% and 75%, respectively). The ANSR™ assay also had a
precision and accuracy of 36.8% and 46.67%, respectively. Using the qPCR as a reference, the
USEPA 1682 method had a sensitivity and specificity of 80% and 90.9%, respectively. The
USEPA 1682 method also had a false positive and negative rate of 9.1% and 20% respectively.
The USEPA 1682 method’s precision and accuracy were higher than the ANSR™ assay as well
with 80% and 85.7%, respectively.

72

Chapter 4. Discussion
The 54% (N = 13/24) discrepancy of positive samples between the methods seen in
Survey 1 (USEPA 1682 method 5/24 positive, ANSR™ assay 18/24 positive) was initially
concerning. Due to the USEPA 1682 method’s recognition as the “gold standard” for Salmonella
detection this discrepancy was hypothesized to have been caused by false positive results from
the ANSR™ assay and possibly some false negatives from the USEPA 1682 method. While the
ANSR™ assay provided similar MPN/100ml values (Table 10) the high percentage of samples
could not be confirmed as “true positives” using the qPCR assay. These initial results show a
difference in the sensitivity and specificity of the two methods even while each method’s
recovery efficiency with seeded surface water samples, when using a Mann-Whitney U test,
were found to not significantly differ (p-value 0.2467, α = 0.05) in their ability to detect
Salmonella when present in concentrations above both methods’ limits of detection.
Due to the discrepancies seen between the two methods in Survey 1, a second survey was
performed, but instead of only examining water samples from four different sites (three of which
were within relatively close proximity as seen in Figure 3), Survey 2 looked at 17 different
surface water sites (Figure 4) from a variety of different areas around Michigan and Northern
Ohio, along with four wastewater samples. This was done in order to analyze the performance of
the two methods in a more diverse set of water samples with varying drainage areas, major
watersheds, flows, and wastewater inputs (Table 4). Sampling was limited to the lower half of
Michigan’s Lower Peninsula and Northern Ohio, in order to increase the chances of finding
Salmonella in surface water samples due to the known input from wastewater treatment plants
and nonpoint source pollution which are more abundant than in Northern Michigan.

73

Survey 2 showed a similar pattern of detection as in Survey 1, with a 52.3% (N=11/21)
discrepancy between the two methods (USEPA 1682 method 9/21 positive and the ANSR™
assay 20/21 positive). Two (Ottawa River and Farm Canal 2) out of the 21 surface waters chosen
for this survey did not receive discharges from wastewater treatment plants (Table 4). While the
Ottawa River sample was found to be positive by both methods, only the ANSR™ assay found
Farm Canal 2 positive. The majority of surface waters where sampling took place also had large
proportions of rural land use cover contributing to each respective watershed (Table 4).
The exclusion of the use of different enrichment media between the two methods as a
factor in the difference in detection seen in this study, was achieved after comparing the samples
(N = 21) enriched by TSB (42.9% positive) and Enrichment media #3 (28.6% positive) using a
Fisher Exact test (p-value 0.0095, α = 0.05). This suggests that the initial enrichment media does
not contribute to any significant differences in detection seen using the USEPA 1682 method. In
order to attempt to confirm these results, the two media would have to be tested using the
USEPA 1682 method, the ANSR™ assay, and qPCR. It is still not clear whether the amplification
reaction is responsible for the increased detection seen in the ANSR™ assay. That being said, the
use of TSB with the ANSR™ assay was not advisable due to the assay’s sensitivity to matrix
effects caused by variability of composition of other enrichment media. Thus more work in this
area is needed in order to determine if any significant differences would occur if alternative
enrichment or selective media were used prior to sample analysis with the ANSR™ assay. For
example, sodium pyruvate, which has been recognized as a key molecule for the resuscitation of
Salmonella from a viable-but-not-culturable (VBNC) or sub-lethally injured state back to a
growing and colony forming state, is present in the ANSR™ assay enrichment media but not in
the TSB (Gurtler and Kornacki, 2009, Morishige et al., 2013). This may have contributed to

74

considerable disparity between the two methods if high enough levels of possibly VBNC
Salmonella cells were present in the sample. The ANSR™ assay as well as the qPCR should be
able to detect those populations along with other healthy organisms but the qPCR did not
confirm this hypothesis.
Although 84.4% of samples positive for all surface water samples (N = 45) during this
study using the ANSR™ assay is high, other studies have reported similar rates in surface waters
(e.g., rivers and streams) (e.g., Haley et al., 2009) (Table A1). In surface waters that regularly
receive fecal pollution through agricultural runoff and wastewater effluent discharges, Haley et
al. (2009) reported that 79.2% (N = 57) of their 72 samples collected from surface waters in a
rural watershed were found positive for Salmonella, while Thomas et al. (2012) found 78.4% of
samples were also positive (Table A1). The majority of land use (>75%) within each of the
watersheds where surface water sampling took place for this study was rural, with the exception
of the Detroit watershed, which had approximately 50% rural and 50% urban land use, but has
one of the largest areas of treated and untreated sewage input in the state (i.e. the city of Detroit)
(Table 4).
As seen in Table A1, Salmonella detection in surface water varies widely in both percent
detected and concentration. Salmonella prevalence can range anywhere from 2.55% (MartinezUrtaza et al. 2010) to 100% (Lemarchand and Lebaron, 2003, Bonadonna et al. 2006) with
concentrations of 0.006 CFU/100ml to 106 CFU/100ml (Joyti et al., 2010, Lemarchand and
Lebaron, 2003). One of the most important factors in the relative presence and concentration of
Salmonella in any given surface water is the pollution input being received, whether it be raw
human sewage as in the case of Jyoti et al. (2010) with Salmonella concentrations reaching up to

75

106 CFU/100ml or in the case of Patchanee et al. 2010 where Salmonella prevalence was high in
a variety of watersheds with different nonpoint sources of pollution.
All methods have advantages and disadvantages, however PCR methods are now gaining
acceptance in regard to both their specificity and sensitivity. The samples from Survey 2 were
tested using a proprietary qPCR 16s rDNA gene target array, which has a detection limit of 100
gene copies for S. enterica in metagenomic samples (Fosbrink et al., 2013, Qiagen, 2014). This
qPCR array allowed for the confirmation of 90.48% (N = 19) and 76.19% (N = 16) of the
samples from the USEPA 1682 method and ANSR™ assay, respectively (Table 17).
This use of an independent genetic confirmation of our results, allowed us to determine
that while the ANSR™ assay has 100% sensitivity it also has a high false positive rate (77.4%),
and low specificity (22.58%). In contrast, the USEPA 1682 method had a lower sensitivity
(80%), but had a higher specificity (90.9%), and a considerably lower false positive rate (9.1%).
That being said, use of the ANSR™ assay may still be applicable to water quality testing as a
screening method, but currently cannot be used as a replacement for standard culture-based
methods. Perhaps it is suited more as a tool for rapid screening of sites suspected of receiving
fecal pollution that may pose a human health risk or used in conjunction with multiple pathogen
detection studies. One would need to understand which microbes are causing the false positives
and perhaps redesign the primers.
While most water quality is still monitored through the use of indicator organisms (e.g.,
E. coli), a growing interest in pathogen detection and monitoring has arisen. This is in part due to
molecular methods, which have been undergoing a general increase in specificity, sensitivity,
and decrease in assay time and cost, becoming more readily available for widespread use.
Interest in the detection and monitoring of individual pathogens has also arisen from the

76

recognition that current fecal indicators may be unable to accurately predict the presence and
levels of enteric pathogens, but this is still debated within the scientific community (Lemarchand
and Lebaron, 2003, Arvanitidou et al., 2005, Harwood et al., 2005, Savichtcheva et al., 2007, Wu
et al., 2011, Payment and Locas, 2011). However, without the incorporation of a growth and
possibly selection step, molecular techniques for pathogen detection cannot differentiate between
live and dead organisms, which is crucial if an accurate assessment of potential risk to human
health is to be determined. The ANSR™ assay represents a potentially valuable resource for
water quality testing due to its inclusion of both molecular detection and culture techniques.
While one can also run culture and PCR together, currently setting up an MPN would be more
time consuming. In addition, results are available within minutes with ANSR™ while PCR
would take 2 to 3 more hours.
Since the Nicking Enzyme Amplification Reaction (NEARTM) technology developed by
Van Ness et al. (2003), was only recently licensed by Neogen for use in the development of the
ANSR™ assay for the detection of Salmonella in food matrices, there are currently no previous
publications on its potential application and efficacy in water matrices. The only current
publication available is Neogen’s own validation study for approval by the association of
analytical communities (AOAC) in 2013 (Mozola et al., 2013). This technology, and specific
assay require more study in order to determine whether they have widespread applicability in
water quality testing. It may be that new primers are needed for use with water samples, or
additional purification or dilution steps may need to be taken in order to prevent any matrix
interference effects from influencing detection results.
Because this assay incorporates both molecular detection and growth in a non-selective
enrichment media, this assay should be able to incorporate the speed and accuracy of this

77

molecular based detection, and the increased confidence of having potentially live infectious
organisms present through the use of an enrichment step. However, since an enrichment step is
used by this assay and the results are presence/absence, a MPN enumeration method must also be
incorporated in order to determine concentrations of the target organism. The ANSR™ assay is
reported to have a detection limit of 104 CFU/ml after enrichment with 99.1% inclusivity for all
Salmonella genetic subgroups and 100% exclusivity based on the 38 non-salmonellae tested
(Mozola et al., 2013). However, these values are based on detection in specific food matrices
(e.g., ground beef, milk, etc.), and as we have seen here water matrices can produce considerably
different results.
In comparison to more traditional culture-based methods (USEPA 1682, ISO 6579) for
Salmonella detection, the ANSR™ assay is considerably faster, less labor intensive, and has been
shown to be inclusive for all genetic subgroups of S. enterica and S. bongori, unlike the USEPA
1682 method which has the potential for excluding less common Salmonella serovars at several
different steps. For example, in the USEPA 1682 method, non-motile or monophasic
(Salmonella which can only express one “H” flagular antigen) would fail to show motility on
MSRV (Bugarel et al., 2012). Also, almost 300 environmental serovars, which are known to
have variable lactose fermentation capacity, would cause false negative results on XLD and TSI
media (Table 1), which are critical in the USEPA 1682 method for distinguishing potential
positive Salmonella samples from non-target organisms, with Park et al. (2012) showing a
specificity of only 73.0% for Salmonella on XLD media. Marita et al. (2006) reported the
isolation of 10 strains of S. Enteritidis, which is one of the most common serovars associated
with human illness (Figures 1 and 2), produced negative results for lysine-decarboxylase activity
(i.e. the lysine iron agar slants used in the USEPA 1682 method). There is also the limitations of

78

the use of the polyvalent O antiserum (a-I & vi), used in the USEPA 1682 method, which while
targeting the Salmonella serovars most commonly associated with human illness only covers
about 23.73% (N=612) of all of the 2,579 recognized Salmonella serovars, without the inclusion
of testing with additional antiserums (Grimont and Weill, 2007). These deficiencies in ability of
the current USEPA 1682 method in detecting less common Salmonella serovars are often
addressed through modification of method procedures through the inclusion of additional
selective media and biochemical tests, along with the possible addition of molecular methods,
and confirmatory steps (e.g., pulsed field gel electrophoresis or additional serotyping).
The USEPA 1682 method is considered the “gold standard” for Salmonella detection in
water, but it is limited by the ability of each of its tests to include or exclude potential Salmonella
isolates based on selection criteria that are not consistently shared among serovars of Salmonella
(e.g., lactose fermenters, non-motile strains, and serovars without O antigens recognized by the
polyvalent O antisera used by the method). The ANSR™ assay is also limited by its own
detection limit and its sensitivity to matrix effects, which can inhibit its PCR reaction. While the
limitations of traditional culture-based methods seem to be significant, culture-based detection is
still used more often than molecular methods for Salmonella detection (Table A1). Looking at
the studies found in Table A1, 75% of Salmonella detection studies were conducted using
culture-based methods. While molecular methods were used less often, they had almost
equivalent average detection rates with culture-based methods having an average of 33.37%
samples positive for Salmonella, and molecular methods having 31.74% positive. Both detection
methods showed a very wide range of detection with 4 – 79.17% positive and 3.13 – 87.5%
positive for culture-based and molecular methods, respectively (Table A1).

79

It is also important to consider that if the Salmonella were clumped together or attached
to suspended particles in the water samples they may not have been distributed evenly
throughout the samples, thus allowing for some samples to receive greater concentrations of
Salmonella than others as seen in Droppo et al. (2009) where the resuspension of sediment
particles greatly affected Salmonella detection. The even distribution of organisms throughout
the sample is one of the basic assumptions made in order to perform MPN enumeration. If this
assumption is violated then the estimated concentration of samples may be incorrect. Although
this is a potential flaw with MPN calculations, the use of sample homogenization and filtration
may allow for the reduction of any influence this may have on samples. This area requires more
research to determine what significance suspended solids have in differences between paired
samples. However, only five out the 20 studies examined in Table A1 since 2009 reported on
Salmonella concentrations.
In order to determine any further applicability of the ANSR™ assay for water quality
testing, a series of experiments should look at the detection limit for various Salmonella serovars,
the effects of sodium pyruvate on the regrowth of VBNC cells and the ability of the ANSR™
assay to detect those cells. Also, determining if reducing the non-selective enrichment time for
the ANSR™ assay and incorporating a selective media before performing the assay, will help to
increase the confidence that Salmonella is being detected rather than a false positive caused by
an interference effect from the water matrix. It is also crucial to determine the effects, if any, that
different water types and quality (e.g., fresh, marine, brackish) have on the ability of the assay to
accurately detect Salmonella. In addition, more study is needed in determining the cause of false
positives between the PCR and ANSR™ assay. This could be accomplished by enriching samples
initially in TSB media, testing them with PCR, and then transferring them to Enrichment media

80

#3 and retesting by PCR and also by the ANSR™ assay. This could also be followed by isolation
and identification through the use of selective media and serotyping, allowing us to determine if
any specific serovars are not detected by specific methods.

81

APPENDIX

82

Table A1. Studies on Salmonella occurance in surface waters
Frequency positive samples
%( or # of isolates*)

Total
samples

Values

Surface water
matrix

Survey extent

Country

Reference

62.7% (N=241)

384

ND**

Freshwater

8 sampling sites, 4 years

Brazil

Palhares et al. (2014)

7.20% (N=19)

264

ND

Freshwater

12 sampling sites, 22
months

USA

Liang et al. (2013)

31.11% (N=42)

135

ND

Freshwater

12 sampling site, 4 months

USA

Haack et al. (2013)

29.84%(N=57)

191

<0.03 - 0.418
MPN/100ml

Freshwater

10 sampling sites, 2 years

China

Xiao et al. (2013)

78.4% (N=91)

116

ND

Freshwater

3 sampling sites, 2 years

Canada

Thomas et al. (2012)

30.71% (N=74)

241

<0.075 - 0.725
MPN/100ml

Freshwater

14 sampling sites, 2 years

USA

Walters et al. (2013, 2011)

49.4%(N=154)

312

ND

Freshwater

Cameroon

Henriette et al. (2012)

7.14% (N=18)

252

ND

Freshwater

USA

Gorski et al. (2011)

8.47% (N=29)

342

ND

Freshwater

9 sampling sites, 2 years

Canada

Jokinen et al. (2011)

12.90% (N=24)

186

ND

Freshwater

4 sampling sites, 2 years

Canada

Jokinen et al. (2010)

54.65% (N=47)

86

ND

Freshwater

$ sampling sites, 2 years

USA

Patchanee et al. (2010)

87.5% (N=7)

8

104 - 10 6
CFU/100ml

Freshwater

8 sampling sites, 1 day

India

Jyoti et al. (2010)

6.99% (N=10)

143

ND

Freshwater

10 sampling sites, 10
months

USA

Schriewer et al. (2010)

3.13% (N=1)

32

ND

Freshwater

8 sampling sites, 14 months

Australia

Ahmed et al. (2009)

62.5% (N=20)

32

ND

Freshwater

1 sampling site

Canada

Droppo et al. (2009)

83

13 sampling sites, 12
months
20 sampling sites,
14months

Table A1. (cont’d)
4.12% (N=10)

243

79.17% (N=57)

72

42.10% (N=8)

19

15.66% (N=13)

87

4-15%a

Seawater

6 sampling sites, 4 years

Morocco

Setti et al. (2009)

Freshwater

6 sampling sites 1 year

USA

Haley et al. (2009)

Freshwater

Numerous sampling sites, 2
years

USA

Byappanahalli et al. (2009)

ND

Freshwater

2 sampling sites, 1 year

Czech
Republic

Dolejská et al. (2009)

1600

ND

Freshwater

24 sampling sites, 3 years

Canada

Wilkes et al. (2009)

14.3% (N=7)

49

ND

Freshwater

10 sampling sites, 5 days

Netherlands

Heuvelink et al. (2008)

20.0% (N=7)

35

ND

Wetland water

25 sampling sites, 2 months

Canada

Shellenbarger et al. (2008)

74.70% (N=62)

83

ND

Freshwater

82 sampling sites, 1 day

USA

Meinersmann et al. (2008)

11.16% (N=26)

233

ND

Freshwater

Random samples, 2 years

Mexico

Simental and Martinez-Urtaza
(2008)

3.80% (N=3)

79

ND

Freshwater

8 sampling sites, 1 year

Netherlands

Schets et al. (2008)

9.93% (N=72)

725

ND

Freshwater

25 sampling sites, 15
months

New
Zealand

Till et al. (2008)

53.33% (N=30)

30

ND

Freshwater

5 sampling sites, 3 months

Japan

Svichtcheva et al. (2007)

92.59% (N=25)

27

1.51 x 10 3 - 4.15
x 10 3 CFU/100ml

Seawater

Random samples, 6 months

Lebanon

Harakeh et al. (2006)

Freshwater

Random samples, 6 months

Lebanon

Harakeh et al. (2006)

Freshwater b

8 sampling sites, 2 years

Italy

Bonadonna et al. (2006)

Seawater

4 sampling sites, 4 years

Spain

Martinez-Urtaza et al. (2004)

94.44% (N=17)

18

100% b

>300

2.55% (N=18)

707

ND
0.25 - 3.63
MPN/100ml
0.2 - 1.7
MPN/100ml

3.73x10 2 1.10x10 5
CFU/100ml
10 - 10 4
MPN/100ml
ND

84

Table A1. (cont’d)
5.36% (N=43)

802

ND

Freshwater

16-21 sampling sites, 2
years

Canada

Gannon et al. (2004)

74.93% (N=281)

375

ND

Freshwater

8 sampling sites

South Africa

Obi et al. (2004)

100% (N=10)

10

10 - 10
MPN/100ml

Freshwater

1 sampling site, 1 year

Italy

Bonadonna et al. (2003)

6.23% (N=89)

1429

ND

Freshwater

135 sampling sites, 2 years

Canada

Johnson et al. (2003)

57.14% (N=8)

14

ND

Freshwater

Weekly sampling, 5 months

Africa

Obi et al. (2003)

100% (N=8)

8

0.006 - 4.24
CFU/100ml

Freshwater

France

Lemarchand and Lebaron
(2003)

230 isolates + 183 isolates
from flood events

76

ND

Freshwater

4 sampling sites, 19 months

France

Baudart et al. (2000)

48.39% (N=135)

279

1.3 - 74
CFU/100ml

6 sampling sites, 2 years

Portugal

Catalo Dionisio et al. (2000)

7.02 (N = 222)

3164

N/A

Seawater and
estuarine water
Seawater and
freshwater

236 sampling sites, 5 years

USA

Polo et al. (1999)

2 sampling sites, 2 years

India

Sharma and Rajput (1995)

21 sampling sites, 2 years

USA

This thesis

21 sampling sites, 2 years

USA

This thesis

2

5

18.8 - 204.3
Freshwater
MPN/100ml
<0.65 - 2.90
C
45
Freshwater
31.1% (N=14)
c
MPN/100ml
<0.65 - >160.90
d
45
Freshwater
84.4% (N=38)
MPN/100mld
* Number of isolates used when frequency of positive samples not reported
** Concentration of Salmonella was Not determined (ND)
a different percentages in different seasons
b Highly contaminated area with direct sewage discharge occuring
c Salmonella detected using the USEPA 1682 method
TM
d Salmonella detected using the ANSR assay
136 isolates

48

85

Figure A1. River Raisin MPN/100ml values for Survey 1 obtained
from the USEPA 1682 method
40
35

MPN/100ml

30
25
20
15
10
5
0
1

2

3

4

5


6

Sample

Figure A2. Grand River MPN/100ml values for Survey 1 obtained
from the USEPA 1682 method
40
35

MPN/100ml

30
25
20
15
10
5
0
1

2

3

4
Sample

86

5


6

Figure A3. Red Cedar River MPN/100ml values for Survey 1
obtained from the USEPA 1682 method
40
35

MPN/100ml

30
25
20
15
10
5
0
1

2

3

4

5


6

Sample
Figure A4. Farm Canal 1 MPN/100ml values for Survey 1
obtained from the USEPA 1682 method
40
35

MPN/100ml

30
25
20
15
10
5
0
1

2

3

4
Sample

87

5


6

Figure A5. River Raisin MPN/100ml values for Survey 1 obtained
from the ANSRTM assay
40
35

MPN/100ml

30
25
20
15
10
5
0
1

2

3

4

5


6

Sample
Figure A6. Grand River MPN/100ml values for Survey 1 obtained
from the ANSRTM assay
40
35

MPN/100ml

30
25
20
15
10
5
0
1

2

3

4
Sample

88

5


6

Figure A7. Red Cedar River MPN/100ml values for Survey 1
obtained from the ANSRTM assay
40
35

MPN/100ml

30
25
20
15
10
5
0
1

2

3

4

5


6

Sample
Figure A8. Farm Canal 1. MPN/100ml values for Survey 1
obtained from the ANSRTM assay
40
35

MPN/100ml

30
25
20
15
10
5
0
1

2

3

4
Sample

89

5


6

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90

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