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dissertation entitled

SURVIVAL OF ENTEROHEMORRHAGIC ESCHERICHIA COLI IN
FOOD AND THE GASTRIC ENVIRONMENT

presented by

Teresa Marie Bergholz

has been accepted towards fulfillment
of the requirements for the

Ph.D. degree in Food Science and Human
Nutrition

 

 

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Major Professor’s Signature

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Date

MSU is an affirmative-action, equal-opportunity employer

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SUR IVi

 

 

SURVIVAL OF ENTEROHEMORRHAGIC ESCHERICHIA COLI IN FOOD AND
THE GASTRIC ENVIRONMENT

By

Teresa Marie Bergholz

A DISSERTATION

Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of

DOCTOR OF PHILOSOPHY
Department of Food Science and Human Nutrition

2007

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ABSTRACT
SURVIVAL OF ENTEROHEMORRHAGIC ESCHERICHIA COLI IN FOOD AND
THE GASTRIC ENVIRONMENT
By
Teresa Marie Bergholz
Pathogenic strains of Escherichia coli, such as E. coli O157zH7, have

been shown to be unusually acid resistant, surviving acidic conditions typically
expected to inactivate bacteria. Adaptations for survival in low pH environments
may also contribute to risk of foodborne illness. These adaptations could
account for the low infectious dose (10 to 100 cells) and ability to contaminate
acidic foods, such as apple cider. Recently, Enterohemorrhagic E. coli (EHEC)
strains of serotypes other than O157:H7 have been implicated in outbreaks of
foodborne disease. Acid resistance (AR) studies in acidic foods and in defined
media have revealed considerable variation in survival rates among strains of E.
coli O157:H7. The basis of this research is to: 1) measure AR ability of strains
representing 3 major clonal groups of EHEC, 2) assess the effect of incubation in
food matrices on subsequent AR in a simulated gastric environment, and 3)
observe the genome-wide transcriptional response of E. coli O157:H7 to stressful
conditions. To address these goals, phenotypic AR assays were conducted in
two different environments: a minimal acidic environment designed to isolate one
of three known AR mechanisms, and a complex acidic environment that
simulates gastric conditions. Whole genome transcription profiling was

conducted to assess the response of O157zH7 to growth transitions in a minimal

mediL
owya
envro
ackfic
asses:
EHEC

ofEHE

medium. The extent to which the known AR mechanisms of E. coli contribute to
overall survival and the AR response in naturally acidic foods and the gastric
environments was then determined. Variation in survival rates under distinct
acidic conditions will provide data for the development of more accurate risk
assessment models. Observed differences in survival among the clonal groups of
EHEC in this study will allow further studies to characterize the genetic response

of EHEC induced by food matrices.

Copyright By

Teresa Marie Bergholz

| wish
suppo
compe

Linz, E

Many c
asasua
Abu-Ali
Mannin

Sankan

My fami

Large, 3

ACKNOWLEDGEMENTS

I wish to thank my mentor, Thomas S. Whittam, for his valuable advice and
support, and for providing the resources and opportunities for me to acquire
competence in a broad range of topics. I thank my committee members, John

Linz, Elliot Ryser, and Vincent Young for their suggestions and guidance.

Many of my fellow lab mates have provided suggestions, support, and technical
assistance that were critical to the completion of this research. I thank Galeb
Abu-Ali, Katie Hyma, Sivapriya Kailasan, Sara Kienzle, David Lacher, Shannon
Manning, Adam Nelson, Lindsey Ouellette, Megan Parsons, Weihong Qi, Janani

Sankaralingam, Cheryl Tarr, Seth Walk, and Lukas Wick.

My family has provided me with amazing support during my graduate career. I
wish to thank my parents, Charles and Johann Large, my sisters, Kim and Kellie

Large, and my husband, Peter Bergholz, for their love and encouragement.

LIST C
LIST C

Chapte
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Purpc

 

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TABLE OF CONTENTS

LIST OF TABLES .............................................................................................. viii
LIST OF FIGURES ............................................................................................ ix
Chapter 1. Literature Review ............................................................................ 1
Infectious dose and gastric acidity ................................................................. 9
Acid resistance of Escherichia coli O157:H7 .................................................. 10
Purpose .......................................................................................................... 13
Chapter 2. Variation in acid resistance among Shiga toxin-producing Escherichia
coli clones ......................................................................................................... 1 5
Summary ........................................................................................................ 16
Introduction .................................................................................................... 17
Materials and Methods ................................................................................... 19
RpoS status ............................................................................................ 19
AR mechanisms assays ......................................................................... 22
Results ........................................................................................................... 26
Variation in AR among E. coli O157:H7 ................................................. 30
Comparison of STEC clones .................................................................. 31
RpoS+ vs. RpoS- isolates ...................................................................... 35
Discussion ....................................................................................................... 39
Acknowledgements ......................................................................................... 45

Chapter 3. Past gene conversions between duplicated glutamate decarboxylase

genes (gadAB) in pathogenic Escherichia coli .................................................. 46
Summary ........................................................................................................ 47
Introduction .................................................................................................... 48
Materials and Methods ................................................................................... 52

GAD sequencing .................................................................................... 52
Results ........................................................................................................... 58
Paralogous gene conversions ................................................................ 60
Gene phylogenies .................................................................................. 64
Promoter region ...................................................................................... 68
Discussion ....................................................................................................... 71
Acknowledgements ......................................................................................... 74

Chapter 4. Variation in acid resistance among Enterohemorrhagic Escherichia

coli in a simulated gastric environment ............................................................. 75
Summary ........................................................................................................ 76
Introduction .................................................................................................... 77
Materials and Methods ................................................................................... 80

AR assays in the M88 ............................................................................ 82
Quantitative PCR .................................................................................... 85
Results ........................................................................................................... 86

vi

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Survival in the M88 ................................................................................ 90

Quantifying injured cells in the M88 ....................................................... 94
GAD transcription levels ......................................................................... 96
Discussion ...................................................................................................... 100
Acknowledgements ........................................................................................ 106

Chapter 5. Global gene expression patterns in Escherichia coli O157:H7 during

growth transitions in glucose minimal medium ................................................. 107
Summary ....................................................................................................... 108
Introduction ................................................................................................... 1 10
Materials and Methods .................................................................................. 114

RNA isolation ......................................................................................... 1 14
cDNA synthesis and hybridization ......................................................... 116

Data analysis ......................................................................................... 118
Results .......................................................................................................... 120
Significant changes in expression in exponential phase ....................... 122
Significant changes in expression in stationary phase .......................... 128
Expression of LEE island genes ............................................................ 136
Expression of AR fitness region genes .................................................. 145
Discussion ...................................................................................................... 151
Acknowledgements ........................................................................................ 160

Chapter 6. Summary and Synthesis ................................................................. 161
Future considerations .................................................................................... 165

References ....................................................................................................... 1 67

vii

LIST OF FIGURES
Figures in this dissertation are presented in color

Figure 1.1. Characteristics and relationship of EHEC and STEC ..................... 4
Figure 2.1. Diagram of assay conditions for acid resistance mechanisms ....... 21
Figure 2.2. Phylogenetic tree of STEC clonal groups ...................................... 29

Figure 2.3. Variation in survival rates of STEC for each acid resistance
mechanism ....................................................................................................... 32

Figure 2.4. Box plots of survival rates of STEC groups for each acid resistance
mechanism ....................................................................................................... 34

Figure 2.5. Box plots of survival rates for RpoS+ and RpoS- STEC ................ 37

Figure 3.1. Location of gadA and gadB on the K-12 MG1655 chromosome... 51

Figure 3.2. Putative recombination events in gadA and gadB .......................... 59
Figure 3.3. Alignment of variable nucleotide sites in gadA ............................... 62
Figure 3.4. Alignment of variable nucleotide sites in gadB ............................... 63

Figure 3.5. Neighbor-joining phylogenies for gadA and gadB codons 2-81 ..... 66
Figure 3.6. Neighbor-joining phylogeny for gadA and gadB codons 82-349 67
Figure 3.7. Promoter region consensus sequences for gadA and gadB .......... 70

Figure 4.1. Diagram of assay conditions for survival in the model stomach system

......................................................................................................................... 83
Figure 4.2. Box plots of survival rates in the model stomach system ............... 91
Figure 4.3. Box plots of survival rates in the model stomach before and after

incubation in apple juice ................................................................................... 93

Figure 4.4. Box plots of survival rates in the model stomach after incubation in
apple juice at different pH ................................................................................ 97

Figure 4.5. Box plots of relative transcript levels of gadA and gadB ................ 99

Figure 5.1. Average density of E. coli O157:H7 Sakai in MOPS minimal medium
over time .......................................................................................................... 1 15

viii

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Figure 5.2. Signal intensity plots ...................................................................... 121

Figure 5.3. QT clusters for significantly differentially expressed ORFs ............ 123
Figure 5.4. Proportion of backbone and O157-specific ORFs that change

significantly over specific time intervals ............................................................ 124
Figure 5.5. Residual dissolved oxygen tension in the MOPS media during growth
of E. coli O157:H7 Sakai .................................................................................. 129
Figure 5.6. Heatmap of expression of LEE genes over time ............................ 142
Figure 5.7. Heatmap of expression of TTS effector genes over time ............... 143

Figure 5.8. Heatmap of expression of Acid Fitness Region genes over time... 146
Figure 5.9. Expression plots of the genes encoding Shiga toxin over time ...... 148

Figure 5.10. Expression plots of genes from the TAI over time ........................ 149

ix

LIST OF TABLES

Table 1.1 EHEC outbreaks since 1982 ............................................................ 7
Table 2.1 STEC strains used in the acid resistance mechanisms study .......... 25
Table 2.2 Nucleotide diversity of STEC strains ................................................ 29
Table 3.1 E. coli strains used for gadA and gadB sequencing ......................... 53
Table 2.2 Primer sequences used for gadA and gadB sequencing ................. 55
Table 3.3 Significant fragments found using Sawyers test .............................. 61
Table 4.1 STEC strains used in the model stomach system study ................... 81

Table 4.2 QPCR primer and probe sequences for gadA, gadB and mdh ......... 86

Table 4.3 Log difference of injured and non-injured cells in the model stomach
system .............................................................................................................. 95

Table 5.1 ORFs with significant changes in expression exponential phase ..... 125

Table 5.2 ORFs with significant changes in expression during the transition to

stationary phase ............................................................................................... _ 130
Table 5.3 ORFs with significant changes in expression from transition to eariy

stationary phase ............................................................................................... 134
Table 5.4 ORFs with transient significant changes in expression .................... 138

Table 5.5 ORFs with significant changes in expression in early stationary phase
......................................................................................................................... 140

Table 5.6 Significant changes in expression of Type III secretion effectors ..... 144

Chapter 1

Literature Review

 

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Enteric bacteria, those that are adapted to and colonize the intestinal
habitat of humans and other animals, are typically transmitted via the fecal—oral
route from host to host. Although many enteric bacteria are harmless to the host,
some strains have acquired or evolved pathogenic mechanisms and can cause
disease in a wide range of hosts or be limited to a specific host species. Many
enteric bacteria that can be pathogenic to humans, such as Escherichia coli and
Salmonella enterica, are generally commensal organisms in animal hosts. Such
animal hosts serve as the major reservoir for these pathogen populations; direct
contact with these animals or their fecal material through food and water
represent the primary vehicles for transmission to humans. Contamination of
food with enteric pathogens can occur at many stages; during production on the
farm, food processing, and food handling (270)

It is estimated that foodborne disease accounts for 76 million illnesses and
5,000 deaths in the United States each year (173). Disease attributed to
foodborne pathogens ranges in severity from mild gastroenteritis to severe
complications, such as hemolytic uremic syndrome (HUS) and spontaneous
abortion, or death. Both disease severity and the risk of infection depend on the
virulence and the numbers of the pathogen ingested, susceptibility of the host,
and the type of food vehicle.

As a bacterial species, E. coli is normally a harmless organism comprising
part of the intestinal flora in warm-blooded animals. These bacteria are
ecologically diverse, and have evolved the ability to adapt to and survive in many

adverse secondary environments, such as the farm environment and in natural

 

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water sources. This ability to survive environmental stresses is critical for reentry
into a host and further propagation. Survival and persistence in external
environments and colonization of new hosts in the face of host defenses is also
an essential characteristic for transmission of pathogenic E. coli (89).

STEC and EHEC. There are a variety of diseases that are associated
with E. coli including many extra-intestinal infections such as urinary tract
infections and newborn meningitis (267). The primary focus of this research is
on the class of pathogenic E. coli that cause intestinal diseases, most notably
infectious diarrhea and its complications that have been linked to outbreaks of
foodborne illness (79, 267). These diarrheagenic E. coli strains have been
categorized into 5 distinct mechanisms or pathovars based on virulence factors,
clinical symptoms, mechanisms of pathogenicity, and serotype (79). One
pathovar is Shiga toxin-producing E. coli (STEC), which is classified by the ability
to produce Shiga toxin(s). Enterohemorrhagic E. coli (EHEC), the pathovar
capable of producing the most severe disease, has three characteristic traits; the
ability to produce Shiga toxin, to attach and efface epithelial cells, which is
encoded by the Locus of Enterocyte Effacement (LEE) pathogenicity island
(169), and the presence of the pO157 plasmid (149) (Fig. 1.1). EHEC strains
can be further divided into clonal groups based on genetic similarity. These
clonal groups contain strains that have similar genetic and phenotypic features,
but are not identical, and result from sharing a common ancestor. One feature,
for example, is the presence of specific surface antigens, which dictate the

different E. coli serotypes, such as O157:H7 and over 200 additional serotypes

 

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Figure 1.1. Characteristics and relationship of STEC and EHEC. More than 200
serotypes of E. coli that can produce Shiga toxin (STEC) have been isolated from
the bovine reservoir, but only a subset of these serotypes are associated from
cases of human disease. A subset of STEC is EHEC, which is classified by the
production of Shiga toxin, as well as the ability to attach and efface epithelial
cells encoded by the LEE island and the presence of the p0157 plasmid. The
most prevalent EHEC serotypes that cause cases of human illness, including
hemorrhagic colitis and HUS, are 0157, 0121, 0103, and the closely related

026 and 0111 serotypes.

 

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(non-0157) that also produce Shiga toxins. Many other serotypes of E. coli have
the ability to produce Shiga toxin and elicit disease similar to that of O157:H7
(18, 19, 32). Diarrheal disease caused by non-0157 EHEC has been increasing,
with recent outbreaks traced to strains of serotypes 026:H11, 0111:H8, and
0121 :H19 (31, 44, 167, 268). These serotypes mark genetically distinct clonal
groups, with EHEC 1 consisting of O157:H7 and its relatives and EHEC 2
consisting of serotypes 026:H11, 0111:H8, and relatives (208). EHEC of
serotype 0121:H19 belong to a lineage distinct from both 1 and 2 based on
Multi—locus sequence typing (MLST) (241 ), and is referred to as EHEC 3.

E. coli O157:H7 emerged as a cause of epidemic bacterial diarrhea after
several outbreaks of foobome illness were first linked to human disease in 1982
(211). In the US. alone, E. coli O157:H7 is estimated to account for 75,000
illnesses and 600 fatalities per year (173). Infection by E. coli O157:H7 can
result in hemorrhagic colitis and serious complications such as HUS (103), which
can result in kidney failure and death. In 1999, it was estimated that E. coli
serotype O157:H7 is most prevalent in the US, causing 75% of EHEC infections
in humans (173). Since then, studies on EHEC prevalence in sporadic cases of
human illness have found different rates of prevalence for different locations
around the US. A study of diarrheal cases in children in Seattle reported that
72% of STEC infections were caused by O157:H7 (139). In Nebraska, however,
non-0157 EHEC were as prevalent in diarrheal cases as O157:H7 (85). A two-
year study of diarrheal cases in Montana found 62% of sporadic EHEC infections

were due to non-0157 STEC (124). Whether the increasing prevalence of non-

 

 

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0157 EHEC is a result of the development of improved detection methods, or the
increasing frequency and geographic spread of these clones is unknown. While
an increase in sporadic cases of non-0157 EHEC has been observed, E. coli
O157:H7 is still the leading cause of outbreaks in North America.

Foodbome transmission. Outbreaks of E. coli O157:H7 have occurred as a
consequence of contamination of various foods, including ground beef (46, 56,
211), lettuce (114), alfalfa sprouts (28), raw milk (45), apple cider (16, 115), apple
juice (65), dry cured salami (47), and spinach (50). Because initial EHEC
outbreaks in the North America were attributed to contamination of ground beef
with E. coli O157:H7, most studies have only focused on prevalence of this
serotype in beef and dairy cattle. Early studies on prevalence of O157:H7 in
dairy and beef cattle estimated that less than 10% of cattle were positive (94).
The development of more sensitive culture and isolation techniques has led to
the finding that O157:H7 is more prevalent in cattle than previously thought;
around 10-50% of cattle are estimated to be positive for O157:H7 at any given
time (83, 120, 217). However, in the past decade, several outbreaks of human
illness have been attributed to non-0157 EHEC, which led researchers to begin
studying prevalence of non-0157 EHEC as well as O157:H7 in cattle. One
recent study found that 17/82 (21%) of cull cows were positive for EHEC, and of
the positives, 76% were non-0157 EHEC serotypes (121). A second study
reported that as many as 40% of beef carcasses were positive for EHEC
O157:H7 and 40-65% were positive for non-0157 EHEC, depending on the

season (1 1).

 

 

 

 

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Table 1.1. Confirmed outbreaks of EHEC since 1982.

 

 

Date Serotype Locale Vehicle Number Fatalities Ref.
affected
2006 O157:H7 Multi-state, U.S. Spinach 204 4 (50)
2005 O157:H7 Washington and Raw milk 18 - (45)
Oregon
2002 026:H- Austria Raw milk 2 - (5)
2002 O157:H7 Colorado Ground beef 28 - (49)
2002 O157:H7 Japan Beef 28 - (251)
2001 O157:H7 Ohio Airborne 23 - (257)
(county fair)
2000 01 571NM California Water (lake) 4 - (84)
2000 O157:H7 Ontario Water 2300 7 (1 19)
(drinking)
2000 026:H1 1 Germany Beef 1 1 - (268)
2000 O157:H7 Pennsylvania and Farm visits 56 - (55)
Washington
1999 O157:H7 California and Beef tacos 13 - (123)
Nevada
1999 O157:H7 British Columbia Dry fermented 135 - (161)
salami
1999 O157:H7 Scotland Water 6 - (153)
1999 0121:H19 Connecticut Water (lake) 1 1 - (167)
1999 O157:H7 England Pasteurized 88 - (98)
milk
1999 O1 1 1 :H8 Texas Ice 58 - (44)
(31 )
1999 O157:H7 New York Well water 128 - (52)
(27)
1998 O157:H7 Wyoming Water 71 - (192)
1998 O157:H7 Wisconsin Cheese curd 55 - (53)
1997 O157:H7 Finland Water (lake) 14 - (196)
1997 O157:H7 Connecticut Apple cider 66 (54)
1997 O157:H7 Michigan and Alfalfa sprouts 85 - (28)
Virginia
1997 026:H1 1 Japan unknown 32 - (116)
1997 O157:H7 Japan Radish sprouts 47 - (261)
1997 O157:H7 Colorado Hamburger 14 - (46)
1996 O157:H7 Japan School lunches 8004 5 (174)
1996 O157:H7 Western US Apple juice 28 - (65)
(unpasteurized)
1996 O157:H7 Connecticut Apple cider 14 - (1 15)
1996 O157:H7 Illinois and Lettuce 49 - (114)
Connecticut
1995 01 1 1 :H8 Australia Sausage 23 (43)
1994 01043H21 Montana Milk 18 - (51)
1994 O157:H7 California and Dry cured 23 - (47)
Washington salami
1992 O157:H7 Western US Hamburger 477 3 (56)
1992 O157:H7 Scotland Pool 5 - (29)
1992 01 1 1 Italy Unknown 9 1 (39)
1991 O157:H7 Massachusetts Apple cider 16 - (16)
1991 O157:H7 Oregon Lake 21 - (135)

 

 

Table

 

Date

1991
1990

 

1990

1985 C
1982 C

Table 1.1, continued

 

 

Date Serotype Locale Vehicle Number Fatalities Ref.
affected

1991 O157:H7 England Yogurt 16 - (176)
1990 O157:H7 North Dakota Roast beef 70 - (48)

O157:H7 Missouri Water supply 86 (237)
1990 (non-

chlorinated)
1985 O157:H7 Canada Unknown 73 19 (40)
1982 O157:H7 Michigan and Hamburger 47 - (211)
Oregon

 

 

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Infectious dose and gastric acidity. The infectious dose (i.e., the
minimum number of cells ingested to induce illness) is difficult to accurately
measure, but appears to vary widely across different pathogens and is important
for disease pathogenesis. For example, the infectious dose of Salmonella
serovars and Vibrio cholerae ranges from 105 to 1010 cells (24, 191 ), whereas the
infectious dose of Shigella strains'can be as low as 200 cells (143). Dose
response data have indicated that E. coli O157:H7 is highly infectious and a dose
of ~ 50 cells was estimated for the Sakai, Japan outbreak (246). One factor that
can contribute to such a low infectious dose is the ability of an organism to resist
inactivation by acid. The low pH of gastric secretions (pH 1.5 to 3.0) has long
been recognized as the first line of defense against foodborne enteric pathogens
(198). The ability of enteric bacteria to survive in stomach acid increases the
likelihood of colonizing the intestines and causing infection.

In addition to inherent acid resistance (AR) of the microbe, the presence
and nature of food can increase survival in the stomach. Proteins and fats from
foods are thought to provide more protection against acid stress than
carbohydrates (262). Food particles in human gastric fluid also have a protective
effect on enteric pathogens, as survival rates are greater in gastric fluid taken
after a meal compared to those taken before a meal when the pH is held
constant (198). Thus, there are at least two primary factors that influence
survival of enteric pathogens through the gastric barrier, presence and nature of

food particles in the stomach and the inherent AR ability of the pathogen.

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AR of E. coli O157:H7. Acid resistance refers to the ability of bacteria to
survive at normally lethal pH levels, (i.e., pH less than or equal to 2.5) (89). E.
coli O157:H7 is hypothesized to be highly resistant to acid because of
epidemiological data that indicate a low infectious dose. The number of viable
O157:H7 detected in foods implicated in disease outbreaks suggests that as few
as 10-100 viable cells can initiate disease (59). This characteristic of O157:H7
has led to the development of various in vitro assays to study different aspects of
pathogen survival in low pH environments. These assays have provided
additional evidence of AR in E. coli O157:H7 (8, 100, 155). For example,
variations of complex and minimal media and acidulants have been used to
demonstrate that E. coli O157:H7 can survive under various acidic conditions; at
least 2 h in Luria Broth (LB) pH 2.5 (100, 263) and over 24 h in LB at pH 3.0
(127). The observed high survival rate of several E. coli O157:H7 strains,
originally implicated in different outbreaks, led to the conclusion that all O157:H7
strains have an “unusual AR ability” (175). More recent AR studies on a larger
sample of O157:H7, however, have revealed substantial variability in survival
rates among O157:H7 strains (34, 35). The basis of this inter-strain variability is
presently unknown, and quantifying variation in survival rates among pathogenic
strains is important to develop accurate predictive models for risk assessment
purposes.

It is clear that many enteric bacteria have evolved multiple mechanisms
for surviving environmental stresses. Acid resistance in E. coli is achieved

through three different types of genetic systems; pH inducible AR systems (often

10

 

 

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based on amino acid decarboxylation), general stress response systems
associated with stationary phase, and systems involved with macromolecular
protection and repair (89). These systems function in distinct and overlapping
conditions. The glutamate system can be induced by low pH as well as
stationary phase, the arginine and oxidative systems are induced by low pH in
stationary phase (210). In addition, many AR responses are induced by the
alternate sigma factor RpoS in stationary phase (227, 252, 264). The addition of
acid to stationary phase cells will result in increased expression of genes already
induced by RpoS, as well as those induced by acid.

While not attributed to a specific AR mechanism, other proteins have been
identified as being involved with the AR of E. coli; these include RpoS (62), the
pH- and stationary phase induced chaperone, HdeA (93), the RNA-polymerase
associated protein, SspA (104), and the DNA binding protein, Dps (63).
Changes to the cell membrane are also important to successful AR; increases in
cyclopropane fatty acids are thought to decrease permeability of the membrane
to protons (58, 127). The production of colonic acid as part of exopolysaccharide
has also been shown to be critical to AR of E. coli by providing a negatively
charged mucoid barrier that repels protons (147, 163, 164). These functions
linked to AR have been elucidated in laboratory media conditions, and it is
assumed that these functions would also occur in naturally acidic environments,
such as encountered in agricultural settings, food items and processing, or the

stomach.

11

 

cond
icod
E.co

when

11-14

While the AR of E. coli O157:H7 can be measured under in vitro
conditions in the laboratory, it is also important to understand survival in different
food matrices, especially those implicated in prior outbreaks such as apple cider.
E. coli O157:H7 is capable of persisting in acidic foods for many days, especially
when stored at refrigeration temperatures (4°C). E coli O157:H7 can endure for
11-14 days in apple cider at 8°C (277), 5 days in pineapple juice at 22°C (178),
and 36 days in commercial mayonnaise at 7°C (265). E. coli O157:H7 can
survive for at least 8 days on lettuce stored at 4°C (20). Studies also have
indicated that E. coli O157:H7 exhibits increased survival in acidic foods after
exposure to mildly acidic conditions (150). Taken together, it is clear that E. coli
O157:H7 is well suited to survival in adverse environments. There is lack of
substantial comparisons, however, to survival rates of other EHEC groups to
assess whether O157:H7 strains are any different from other nonpathogenic E.
coli found in the environment.

AR of non-0157 EHEC. Although non-0157 serotypes of EHEC appear
to have a low infectious dose similar to O157:H7 strains (103), the AR capability
of these strains is not well investgated and has not been clearly characterized.
One study measuring survival of O157:H7 and other EHEC at pH 4.0 and pH 2.0
found that O157:H7 strains were more sensitive to acid than other EHEC, but a
firm conclusion could not be drawn since only a single strain of other EHEC
serotypes were tested (172). Because the focus of AR in E. coli has been limited
to O157:H7 strains, the potential of non-0157 EHEC to survive in foods and

cause disease has not been fully evaluated.

12

 

partic
to sup
or diff.
Based
EHEC
relatec
transm
EHEC,
surviva
product
DathOgg
1
environ
role in f
(Ii'seasE
human
ability 0
theabii
Comple.
glutamE
encOde

Purpose. To this point, the consensus view is that E. coli O157:H7 is
particularly well suited for adverse environments, but there is little direct evidence
to support the hypothesis that bacteria of this serotype have special adaptations
or different survival properties from typical E. coli found in the environment.
Based on the increasing prevalence of non-0157 EHEC, it is clear that non-0157
EHEC also should be assessed for stress resistance properties, especially those
related to survival in food matrices, which are key to both colonization and
transmission. By characterizing variation among and between clonal groups of
EHEC, the research conducted here is important to accurately determine EHEC
survival in acidic foods as well as develop effective means to validate food
products and processes that rely on acid to inactivate EHEC and other
pathogens.

The focus of this dissertation is to characterize EHEC survival in adverse
environments, with special emphasis on adaptations related to AR because of its
role in foodborne transmission. Survival in food alone is not sufficient to result in
disease; bacterial strains must also be able to survive passage through the
human stomach. The first part of the research described here investigates the
ability of specific EHEC clones to survive under low pH conditions; first observing
the ability to utilize specific AR mechanisms and second by quantifying survival in
complex acidic environments. One of the most important AR mechanisms is the
glutamate decarboxlase system, and the key isozymes of the system are
encoded by duplicated genes. Chapter three investigates the evolution of these

duplicated genes among distinct evolutionary lineages of pathogenic E. coli.

13

 

Cha
prote
the b
four a
of EH
focus.
expre:
expres
such a

DUI knr

Characterization of EHEC survival in foods and identification of the potential
protective effects '(eg., acid habituation) stimulated by various food matrices is
the beginning step to understanding survival of EHEC the stomach. Chapter
four assesses the effects of storage in an acidic food on the subsequent survival
of EHEC in a simulated gastric environment. The final section of this dissertation
focuses specifically on E. coli O157:H7 to obtain a global view of gene
expression during growth transitions, with specific emphasis on changes in
expression of 0157-specific ORFs. Understanding how stressful conditions,
such as stationary phase, influence global gene expression further contributes to

our knowledge of EHEC ecology.

14

 

Lar

CHAPTER 2
Variation in Acid Resistance Among Shiga Toxin-producing Clones

of Pathogenic Escherichia coli

Large, T. M., S. T. Walk, and T. S. Whittam. 2005. Appl Environ Microbiol 71:2493-500.

15

 

Path
infec
evolv
amin<
gluco;
AR m.
toxin-p
and 6 .
were n
individt
a highl)
CPU/m1
I" SUl'Vi\
better p,
ComDare
hour. Th
less than
and SIQnit
that this or

mechanlSr

SUMMARY
Pathogenic strains of Escherichia coli, such as E. coli O157:H7, have a low
infectious dose and an ability to survive in acidic foods. These bacteria have
evolved at least 3 distinct mechanisms of acid resistance (AR) including two
amino acid decarboxylase dependent systems (arginine and glutamate) and a
glucose-catabolite repressed system. We quantified the survival rates for each
AR mechanism separately in clinical isolates representing three groups of Shiga
toxin-producing E. coli clones (O157:H7, 026:H11/0111zH8, and 0121:H19),
and 6 commensal strains from ECOR group A. Members of the STEC clones
were not significantly more acid resistant than the commensal strains using any
individual AR mechanism. The glutamate system provided the best protection in
a highly acidic environment for all groups of isolates (<0.1 log reduction in
CFU/mL per hour at pH 2.0). Under these conditions, there was notable variation
in survival rates among the 30 O157:H7 strains. The arginine system provided
better protection at pH 2.5, with a range of 0.03 to 0.41 log reduction per hour,
compared to the oxidative system, with a range of 0.13 to 0.64 log reduction per
hour. The average survival rate for the O157:H7 clonal group was significantly
less than that of the other STEC clones in the glutamate and arginine systems,
and significantly less than the 026/0111 clone in the oxidative system, indicating
that this clonal group is not exceptionally acid resistant using these specific

mechanisms.

16

 

vanet
eflen
sunfiv
pathog
infiecfii
277)
remsua
espech
(PH < 3
aCkficf.
determj
L
meChan
Denods
QIUtama.
Suggest
asthOSe
SYStems

which Dla

INTRODUCTION

Escherichia coli are ecologically versatile bacteria that have adapted to a
variety of ecological conditions encountered in both animal hosts and the
external environment. These organisms have evolved multiple mechanisms to
survive under low pH conditions. E. coli O157:H7, a food and waterborne
pathogen, has been considered highly acid resistant in nature because of its low
infectious dose (103) and ability to survive in acidic foods (175, 212, 265, 276,
277). In the laboratory, E. coli O157:H7 was shown to be exceptionally acid
resistant compared to other enteric bacteria, such as Salmonella enterica (100),
especially when exposed to mild acid (pH > 4) prior to exposure to strong acid
(pH < 3) (8, 150). Outbreaks of human disease caused by E. coli O157:H7 in
acidic foods such as apple juice (65) and salami (47) have stimulated interest in
determining the mechanisms behind the acid resistance of this organism.

Lin et al. developed assays to separate 3 different acid resistance (AR)
mechanisms by which E. coli can survive in low pH environments for extended
periods of time: two amino acid decarboxylase dependent systems (arginine and
glutamate) and a glucose-catabolite repressed system (154, 155). Their findings
suggest that these mechanisms promote survival in low pH environments such
as those encountered in the stomach and in acidic food products. The AR
systems are governed, in part, by the alternate sigma factor RpoS (42, 201 ),
which plays a central role in the regulation and expression of many proteins
involved in stationary phase and stress response (108, 144, 227). Mutant RpoS

alleles exist in natural populations of enterohemorrhagic E. coli (EHEC) and can

17

 

lead t

 

 

 

lead to acid sensitivity (263).

Studies of AR have focused on E. coli O157:H7 because it is one of the
most common E. coli types associated with outbreaks and sporadic cases of food
and waterborne disease. Diarrheal disease caused by Shiga toxin-producing E.
coli (STEC) of serotypes other than O157:H7 (non-0157 STEC) has increased
worldwide, with recent outbreaks attributable to serotypes 026:H11, 0111:H8,
and O121:H19 (44, 167, 268). These serotypes mark three genetically distinct
clonal groups of STEC; EHEC clonal group 1 consists of O157:H7 and its
nonmotile relatives, EHEC clonal group 2 consists of serotypes 026:H11,
0111:H8, and strains of serotype 0121:H19 represent a distinct clonal group of
STEC (208, 241 ). Non-0157 STEC are common in animal reservoirs, capable of
causing the same severe disease as E. coli O157:H7, and are thought to also
have a low infectious dose (103); however, these pathogens are associated with
disease outbreaks much less frequently than E. coli O157:H7. One reason for
this disparity in prevalence may result from differences in the inherent acid
resistance of the STEC clones and concomitant survival in acidic foods and low
infectious dose.

Here we address the question, “Do non-0157 STEC possess the same
ability to survive using the defined AR mechanisms as E. coli O157:H7?” To
address this question, we assembled a collection of clinical isolates representing
three clonal groups of STEC (0121:H19, 026/0111 and O157:H7) that have
been screened for RpoS expression and assessed strain-to strain variation in

survival at low pH using the three different AR mechanisms.

18

MATERIALS AND METHODS

Bacterial strains. A total of 66 pathogenic strains including 30 E. coli
O157:H7 strains, 18 026:H11 strains, 4 O111:H8 strains, and 14 0121:H19
strains were used in this study (Table 2.1). The strains were originally isolated
by different investigators from sporadic cases of diarrheal illness in the course of
surveillance studies of STEC in the USA (2, 85, 94, 124, 139). We also obtained
several additional STEC from Dr. James Rudrik of the Michigan Department of
Community Health, Dr. Lothar Beutin in Germany, and Dr. Roger Johnson in
Canada. The presence of the Shiga toxin 1 and 2 genes (stx1 and stx2) is given
in Table 1 as reported in the original publication or to us by the sender. In
addition, 6 non-toxigenic, commensal strains from the ECOR (189) group A
(ECOR strains 1, 2, 3, 4, 7, 10) were used for comparative purposes.

Sequence types and Shiga toxin production. Multilocus sequence
analysis of 7 housekeeping genes (aspC, prX, fadD, ich, IysP, mdh, and uidA)
was used to characterize the multilocus genotype of each strain. Alleles were
identified based on sequence comparisons for each gene and distinct allele
combinations were designated as sequence types (STs). The sequencing
methods and ST database are available at the STEC website
(http://www.shiqatox.net/mlst/index.html). The production of Shiga toxins was
determined by enzyme immunoassay (EIA) using the Premier EHEC test
(Meridian Diagnostics Inc., Cincinnati, Ohio) with the manufacturer’s instructions.

RpoS status. Stock cultures of each strain were screened indirectly for

the allelic state of rpoS with the hydrogen peroxidase ll (HPII) assay (26, 275).

19

 

Fore
indivi.
Bena
transfr
incuba
by dro
indicat
(214),
in the I-
tested f
HPll+ C.
was dro
by the rc
pick, Rp
Was Use.
Ar
Were "10.
day 0, an
and 9row

(L8G). LB

acid) (LBr,

NaNH4Hp‘

For each strain, we picked 96 colonies from an LB agar plate, which were
individually inoculated into wells of microtiter plates containing 100 pL of Luria-
Bertani (LB) broth. After 6 h of incubation at 37°C, cultures in the 96 wells were
transferred to replicate LB agar plates using a 96-pin replicator tool. Plates were
incubated for 24 h at 37°C, and were then tested for the expression of catalase
by dropping 30% (w/v) hydrogen peroxide on each colony. Vigorous bubbling
indicated positive HPII activity, which is strongly correlated to expression of RpoS
(214). Colonies were thus classified as RpoS+ if they exhibited vigorous bubbling
in the HPII assay. To confirm RpoS status, HPll+ colonies were subsequently
tested for glycogen production, which is also controlled by RpoS (109, 144).
HPll+ colonies were streaked onto LB agar and grown overnight at 37°C. Iodine
was dropped onto individual colonies and glycogen accumulation was detected
by the formation of a brown color on the colonies (109). For each strain, a single
pick, RpoS+ colony was grown in LB broth, frozen in 10% glycerol at -70°C, and
was used as stock for all subsequent AR assays.

Acid resistance (AR) mechanism assays. Methods for measuring AR
were modified from those of Lin et al. (154, 155) as diagrammed in Fig. 2.1. On
day 0, an RpoS+ isolate of each STEC strain was inoculated from freezer stocks
and grown overnight in LB medium (pH 7.0) at 37°C (Fig. 2.1). On day 1, 50 pL
of overnight culture was used to inoculate 10 mL of LB broth with 0.4% glucose
(LBG), LB broth buffered to pH 5.5 with 0.1M MES (morpholineethanesulfonic
acid) (LBMES), minimal E medium containing 73 mM K2HPO4, 17 mM

NaNH4HP04, 0.8 mM or 12mM MgSO4, 10 mM sodium citrate, and 0.4% glucose

20

 

 

meCIlar

 

Freezer stock of —> .'
RpoS+ colony

 

 

 

 

. EG + EG +
7‘ ‘ glutamate arginine
.‘ pH 2-0 ‘ _. _ pH 2.0
Control Test (2X) Control Test (2X) Control Test (2X)
Glutamate Oxidative Arginine
system system system

Figure 2.1. Diagram of assay conditions to discriminate the three AR

mechanisms.

21

 

(EG).
(BHIG
these
was m
used It

initial c

(EG), and Brain-Heart Infusion broth with a final concentration of 0.6% glucose
(BHIG). All media were prepared by filter sterilization. Strains were grown in
these media for 22 h at 37°C, with shaking at 120 rpm. Optical density (0Deoo)
was measured for each culture after 22 h of growth and this measurement was
used to determine the inoculum volume into the test environment to have an
initial density of ~ 106 CF U/mL.

The ability of the RpoS+ isolates to survive acidic conditions was
measured for three AR systems (154, 155): the glutamate-dependent (GLU)
system, the glucose-repressed oxidative (OXI) system, and the arginine-
dependent (ARG) system (Fig. 2.1). Cultures grown in LBG were tested in the
GLU system (EG supplemented with 5.7 mM sodium glutamate at pH 2.0),
cultures grown in LBMES were tested in the OXI system (EG at pH 2.5), and
cultures grown in BHIG were tested in the ARG system (EG supplemented with
0.6 mM arginine at pH 2.5). Cultures inoculated in EG at pH 2.0 and pH 2.5
without supplements served as controls for the GLU and ARG systems. The
control for the OXI system was cells grown overnight in EC pH 7.0 and tested in
EC pH 2.5. 4N HCI was used to adjust the pH of the test media, which was then
warmed to 37°C before use. All AR tests were conducted at 37°C. Two
replicates of each strain were tested per AR system. Samples were taken at 0, 2,
and 6 h and plated in duplicate on LB agar using the Autoplate 4000 (Spiral
Biotech, Bethesda, MD). Plates were incubated at 37°C for 24 h and then

enumerated using the 0 Count software (Spiral Biotech).

22

 

exan
were
the a

0.8ml

and lo
assay.
Iogw C
experir
time pc
NC). P
differen
ComDari

conduct

To determine if Mg+2 limitation was contributing to cell inactivation (for
example, through changing the permeability of the outer membrane), strains
were also tested in the EG test environments described as described above, with
the addition of 12mM MgSO4 (high Mg”) instead of the originally described
0.8mM MgSO4 (low Mg”).

Statistical Analysis. Plate counts were converted to log CFU/mL values,
and log decrease per hour for each assay was determined over the period of the
assay. We defined the survival rate, AV, as the change in viable cell counts (in
Iog1o CFU/mL) per hour and report the mean and standard deviation for each
experiment. Analysis of variance was conducted on the log CFU/mL for each
time point and the AV values for each assay using SAS (SAS Institute, Cary,
NC). Painrvise comparisons were made for each of the clonal groups; significant
differences were determined by the Tukey method, adjusted for multiple
comparisons. Phylogenetic analysis of the multilocus sequences was

conducted with MEGA 3 software (141 ).

23

 

1251
resoh
comrr
betwe
seroty
(Table
cluster:

Compri:

RESULTS

Clonal groups. The STEC strains were sequenced at 7 loci (3,753 bp,
1251 codons) and classified into 27 sequence types (STs) based on the alleles
resolved (Table 2.1). Most (24/30) of the O157:H7 strains belong to the most
common EHEC 1 sequence type (ST-66). The close genetic relationship
between the 026:H11 and 0111:H8 strains is reflected by the fact that both
serotypes are associated with the common EHEC 2 clone marked by ST-106
(Table 2.1). A neighbor-joining tree places the 27 STEC STs into 3 distinct
clusters (Fig. 2.2). The genetic diversity between strains within a cluster
comprises only a fraction of the nucleotide divergence between clusters (Table
2.2). The nucleotide diversity within clusters ranged from 0.7% for EHEC 1 to
0.23% for the EHEC 2 group whereas the divergence between clusters ranged
from 1.1% to 2.6% (Table 2.2).

RpoS status and Stx production. To determine the variability in RpoS
expression, we used the HP ll catalase assay to examine 96 colony picks from
each of 66 STEC strains (Table 2.1). For 32 of the strains, 100% of the 96
colonies tested were RpoS+. 0f the 30 E. coli O157:H7 strains tested, 15/30
(50%) had more than 90% RpoS+ colonies. In comparison, 19/22 (83%) and
12/14 (86%) strains of E. coli026/0111 and 0121:H19 had more than 90%
RpoS+ colonies, indicating significant variation between clonal groups in the
proportion of RpoS+ colonies (Gadj = 9.95, p < 0.01 with df = 2). A single RpoS+
colony was selected for each strain and used to create a stock for subsequent

experiments.

24

TABLE 2.1. Shiga toxin-producing E. coli strains used in this study.

 

 

 

Serotype Sequence
Strain Locale (Year) stwenes %Rp_oS+ type Stx EIA Source
E. coli O157:H7
TW07591 Mich. (1997) stx2 27 70 + 1
TW07695 Fla. (1997) stx1, stx2 74 60 + 1
TW07700 Calif. (1997) stx1, stx2 91 66 + 1
TWO7702 Ohio (1997) stx2 95 59 + 1
TWO7704 Ohio (1997) stx2 88 66 + 1
TWO7706 Utah (1997) stx1, stx2 88 66 + 1
TW07928 DC. (1998) stx2 99 66 + 1
TW07937 Mass. (1998) stx2 76 66 + 1
1W07938 Mass. (1998) stx1, stx2 100 66 + 1
TW07939 Mass. (1998) stx1, stx2 100 66 + 1
TW07941 Mass. (1998) stx1, stx2 45 66 + 1
TW07943 Mass. (1998) stx1, stx2 96 66 + 1
TW07945 Fla. (1998) stx2 94 66 + 1
TW07949 0.0. (1999) stx1, stx2 8O 66 + 1
TW07950 0.0. (1999) stx1, stx2 51 66 + 1
TW07952 DC (1999) stx1, stx2 82 66 + 1
TW07953 DC. (1999) stx1, stx2 38 66 + 1
1W07956 0.0. (1999) stx1, stx2 94 66 + 1
TW07957 0.0. (1999) stx1, stx2 63 66 + 1
TW07958 DC. (1999) stx1, stx2 53 66 + 1
TWO7961 Ohio (1998) stx1, stx2 96 66 + 1
TWO7962 Ohio (1998) stx1, stx2 82 66 + 1
TW08022 Mont. (2000) stx1, stx2 100 66 + 7
TW08026 Mont. (2000) stx1, stx2 100 71 + 7
TW08027 Mont. (2000) stx1, stx2 100 62 + 7
TW08030 Mont. (2000) stx2 45 72 + 7
TW08080 Mont. (2000) stx2 61 66 - 7
TW08609 Wash. (2000) stx2 100 66 + 7
TW08610 Wash. (2000) stx2 100 66 + 7
TW08612 Wash. (2000) stx1, stx2 100 66 + 7

25

TABLE 2.1, continued

 

 

 

26

Serotype Sequence
Strain Locale (Year) stx genes %Rms+ type Stx EIA Source
E. coli 026:H11
TW07594 Ariz. (1997) stx1 96 1 10 + 1
TW07595 Neb. (1998) stx1 100 106 + 3
TWO7600 Neb. (1998) stx1 100 106 + 3
TW07622 Mich. (2002) stx1, stx2 4 106 + 5
TW07814 Idaho (1997) stx1, stx2 100 104 + 6
TWO7936 Mass. (1998) stx1 100 10 + 1
TW07948 DC (1999) stx1 97 106 + 1
TW08024 Mont. (2000) stx1 100 1 14 + 7
TW08033 Mont. (2000) stx1 100 102 + 7
TW08038 Mont. (2000) stx1 98 101 + 7
TW08052 Mont. (2000) stx1 76 103 + 7
TW08060 Mont. (2000) stx1 100 1 12 + 7
TW08084 Mont. (2000) stx1 99 99 + 7
TW08569 Germany (1998) stx2 54 1 15 - 2
TW08570 Germany (1999) stx2 94 1 13 + 2
TW08571 Germany (2000) stx1, stx2 100 104 + 2
TW08637 Wash. (2000) stx1 100 106 + 7
TW09184 Mich. (2003) ND 100 106 + 5
E. coli 01 1 1:H8
TW07598 Neb. (1998) stx1, stx2 100 106 + 3
TW07601 Neb. (1998) stx1, stx2 100 106 + 3
TW08642 Wash. (1999) stx1 100 106 + 7
TW08643 Wash. (2001 ) stx1 100 106 + 7
E. coIiO121zH19
TW0761 5 Mich. (2002) stx2 82 182 + 5
TW07927 0.0. (1998) stx1, stx2 100 182 + 1
TW07931 Mass. (1 998) stx2 100 182 + 1
TW08036 Mont. (2000) stx2 1 00 1 82 + 7
TW08040 Mont. (2000) stx2 1 00 183 + 7
TW08042 Mont. (2000) stx2 1 00 1 80 + 7
TW08043 Mont. (2000) stx2 90 182 + 7
TW08055 Mont. (2000) stx2 88 187 + 7
TW08063 Mont. (2000) stx2 99 184 + 7

 

TABI

 

Seroty,

TVV08
TW08r
TVV08€
TVVOBE
TVVOBE

 

 

a Shiga t
t’Strains I
Gennany
Rudrik, ht

 

 

TABLE 2.1, continued

 

 

Serotype Sequence
Strain Locale (Year) stx genes %RpoS+ type Stx EIA Source
TW08091 Mont. (2000) stx2 100 179 + 7
TW08646 Wash. (2000) stx2 100 133 - 7
TW08649 Switzerland (2002) stx2 100 185 + 4
TW08650 Switzerland (2002) stx2 100 181 + 4
TW08653 Canada (2002) stx2 100 182 + 4

 

a Shiga toxin production tested by enzyme-linked immunoassay on single RpoS+ colony.
b Strains were obtained from 1, D. Acheson (2, 258); 2, L. Beutin, Robert Koch Institute,

Germany; 3, P. Fey (85); 4, R. Johnson, Laboratory of Foodbome Zoonoses, Canada; 5, J.
Rudrik, Michigan Department of Community Health; 6, A. O’Brien (95); and 7, P. Tarr (124, 139).

27

Table 2.2. Nucleotide diversity within and between 3 clonal groups of STEC.

 

Distance (%) between

 

 

groups
Clonal Number of Number of Diversity within

group isolates STs group (%) EHEC 1 EHEC 2
EHEC 1 30 7 0.07 :I: 0.03

EHEC 2 22 11 0.23 :I: 0.04 2.47 i 0.25

0121:H19 14 9 0.191004 2.601024 1.10:1:0.16

 

28

 

 

FIQUre 2.:

parameter

   
  

ECOR 1, 2, 3,10
ECOR4

 
 

103 99
102112

  
 

182(6) 184
179
181 135 180

    

EHEC 2
(026/0111) 0121:H19

0.20%

EHEC1
(O157:H7)

 

 

Figure 2.2. Neighbor joining tree showing the clonal groups of STEC based on
multilocus sequence types (STs). Distances were estimated by the Kimura 2-

parameter model. Number of isolates of each ST is given in parentheses.

29

 

The l
pl’OdL
positir

and to.

 

 

strain
STEC

had be

O157:H

cell den

 

(Anders(
0.32. A
Variance
total vari;
hfor the
not Show
Efl
bamerial ‘
StEibilizjng
Stability re
perceiVed
DreSent in

Dehheab'e

The RpoS+ derivatives of the 66 STEC strains were all tested for Shiga toxin
production using the Stx EIA; sixty-three of the 66 RpoS“ isolates were Stx
positive (Table 2.1). We further investigated the 3 Stx negative isolates by PCR
and found that one had lost the stx2 gene in comparison to the original STEC
strain and one had stx2 but did not express it. In the third case the original
STEC strain was also negative for the stx2 gene, suggesting that the toxin gene
had been lost in a previous transfer.

Variation in AR among E. coli O157:H7 strains. Strains of E. coli
O157:H7 exhibited variation in survival using each of the AR mechanisms. Final
cell densities after 6 h in the GLU system approximated a normal distribution
(Anderson-Darling Normality test, p = 0.121) with a mean log1o CFU/mL of 5.43 :I:
0.32. A nested ANOVA indicates that the among-strain component of the
variance is highly significant (F = 75.6, p < 0.001) and accounts for 92% of the
total variation in cell densities at 6 h. Similar distributions were observed after 6
h for the OXI system (3.09 :1: 0.71) and for the ARG system (4.79 :I: 0.46) (data
not shown).

Effect of magnesium on AR of E. coli O157:H7. The permeability of the
bacterial outer membrane (OM) can be disturbed by chelators that disrupt the
stabilizing interactions of Mg”, Ca”, and LPS molecules (255). Decreased OM
stability resulting from chelators could be amplifying cell inactivation that is
perceived to be a consequence of acid stress alone. For example, the citrate
present in the test environments could chelate Mg”, leading to a more

permeable OM.

30

 

(0.8rr
of low
AVGL;
distrib
had Sig
MgSO.
mean A
was inc
a signifi
System,
Me”. I

and low

 

CI

protectioi
0'01 (Star
0157147
Significan.
6 hot” tim
faster rate
a resUh, th
signifiCanU

Den'Od. Str

To test for the effects of Mg“2 limitation, we challenged strains in both low
(0.8mM M9804) and high (12mM MgSO4) Mg” concentration. Under conditions
of low Mg”, AVGLU values approximated a normal distribution, with a mean
AVGLU of -0.09 1: 0.05 (Fig. 2.3A). AVARG values also approximated a normal
distribution, with a mean AVARG of -0.22 :1: 0.08 (Fig. 2.30). The O157:H7 strains
had significantly greater survival rates in the GLU and ARG systems when the
M9804 levels were increased, and the variation in survival rates decreased. The
mean AVGLU with high Mg” was -0.05 :1: 0.02. The mean AVARG when MgSO4
was increased was -0.17 :1: 0.06. In the GLU system, 22/30 (74%) of strains had
a significantly greater survival rate with high Mg” than low Mg”. In the ARG
system, 14/30 (46%) of strains had a significantly higher survival rate with high
Mg”. In contrast, the survival rates were not significantly different between high
and low M9” for the OXI system.

Comparison of STEC clones. The glutamate system provided the most
protection in acidic conditions with average AVGLU values ranging from 0.004 :1:
0.01 (standard error of the mean) for the 026/0111 clone to -0.09 :I: 0.01 for the
O157:H7 group (Fig. 2.4A). In the first 2 h of the assay, the 0157 strains had a
significantly lower AVGLU compared to the 026/0111 strains. However, in the 2-
6 hour time period, the density of bacteria of the O157:H7 group decreased at a
faster rate (AVGLU_t2-t5 = -0.10 :I: 0.01) than strains of the other clonal groups. As
a result, the ability of E. coli O157:H7 strains to survive using the GLU system is
significantly less (p < 0.001) than the other clonal groups in the 6-hour assay

period. Strains of the non-0157 clonal groups were also tested under high Mg”

31

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saw... a

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8.0

32

condi
the Cir

values

acidic
026/O
assay,

significe

 

clonal g
donalg
ECOR s
Significa
System
Th
system, b
Values Far
strainS (Fi
group ind;
026/0111
arginine s)
GLU SYSte

variable ac

conditions, but there were no significant differences in survival rates for any of
the clonal groups with the exception of O157:H7 (data not shown), so all AV
values reported are from low Mg” test environments.

The oxidative system provided the least amount of protection against
acidic environments, with average AVox. values ranging from -0.28 :1: 0.01 for the
026/0111 strains to -0.44 :I: 0.01 for the O157:H7 strains (Fig. 2.43). In this
assay, strains in the O157:H7 clonal group decreased in numbers at a
significantly (p < 0.01) greater rate (AVox. = -0.44 :t 0.01) than the 026/0111
clonal group. The greatest variability in survival rate among strains of each
clonal group was seen in the OXI system (Fig 2.48). The E. coli 0121:H19 and
ECOR strains had a similar ability to survive acidic conditions, and were not
significantly different from the 026/0111 or O157:H7 groups using the oxidative
system as measured by average AVox1 (Fig. 2.4B).

The arginine system did not provide as much protection as the glutamate
system, but was more effective than the oxidative system at pH 2.5. AVARG
values range from -0.11 i 0.01 for 0121 strains to -0.22 1: 0.01 for the O157:H7
strains (Fig. 2.40). Pair wise comparisons of the average AVARG for each clonal
group indicate that the O157:H7 strains are significantly different from the
026/0111 and 0121 strains (p < 0.003) but not the ECOR A group in the
arginine system (Fig 2.4C). The majority of strains exhibit high survival with the
GLU system, whereas the ability to utilize the OXI and ARG systems are more

variable across strains. Strain TW08650 (0121:H19) has high survival rates in

33

 

Survival rate

 

Figure 2
hodzOnt;
SerOgrou
inmeox

System a

 

.0
re

A. B. C.
a 0.0. é T - . %
gs? . . . . . E
-o.e ( 0

 

 

 

 

 

 

 

0157 026/01110121 ECORA 0157 026/01110121 ECORA 0157 026/01110121 ECORA
Glutamate system Oxidative system Arginine system

Figure 2.4. Box plot of survival rates (AV) for strains in each clonal group. The
horizontal bar indicates the mean for each group. A. AV values for each
serogroup in the glutamate system at pH 2.0. B. AV values for each serogroup
in the oxidative system at pH 2.5. C. AV values for each group in the arginine

system at pH 2.0.

34

 

all thn
Strain
AVGLU

one mi
lrzctu Vt
donalg
42% of
and 52°;
62% is c
differenc
differenc
betIll/cen
the total \
the differ?

Rp
TW07695.
were SGIec
EaCh strajr

l'neigham-Sn

 

exhibits d 31'

SUWIVal raIE

I

all three mechanisms, with AVGLU = -0.01,AVox. = -0.13, and AVARG = -0.12.
Strain TWO7962 (O157:H7) exhibits low survival rates in two of the systems, with
AVGLU = -0.09,AVox1=-' -0.45, and AVARG = -0.35. Overall, the survival rate using
one mechanism is not significantly correlated with ability to survive using another
(Ichu 0x1 = 0.42. I'zox1 .. ARG = 0.19. r26... NR. = 0.15).

We partitioned the total phenotypic variance in survival rate into within-
clonal group and between-clonal group components. For the glutamate system,
42% of the variation is due to differences between strains within a clonal group
and 52% is due to differences between clonal groups. For the oxidative system,
62% is due to differences between strains within group, while 33% is due to
differences between clonal groups. For the arginine system, 67% is due to
differences between strains within a clonal group, and 30% is due to differences
between the clonal groups. These results indicate that a substantial fraction of
the total variation in acid resistance for a given mechanism is accounted for by
the differences between STEC lineages.

RpoS+ vs. RpoS' colonies. Eight strains of E. coli O157:H7 (TW07591,
TW07695, TWO7702, TWO7704, TW07928, TW07941, TWO7962, and TW08030)
were selected and the RpoS+ and RpoS' isolates derived from single colonies for
each strain were tested for their ability to survive using the three AR
mechanisms. In the glutamate system, the RpoS' isolates (colonies that
exhibited slow bubbling in the HP ll assay) all exhibited significantly lower mean

survival rates (tloos, so] = 35.97, p < 0.001) than the corresponding RpoS+ isolate

35

 

at bot.
2.5). l
agnMC
isolate
2.5). Ir

RpoS+

 

 

at both levels of Mg”, with the exception of strain TWO7962 in the low Mg” (Fig.
2.5). In the oxidative system, only strains TWO7962 and TW07928 did not exhibit
significantly lower (t[o_05, 60] = 24.39, p < 0.001) survival rates for the RpoS-
isolates compared to the corresponding RpoS+ isolate at both levels of Mg” (Fig
2.5). In contrast, the RpoS‘ isolates exhibited similar or greater survival than the

RpoS+ isolates in the arginine system (Fig. 2.5).

36

 

Survival rare
r5 6
A N

0
a)

Figure
Mm8m

Ullder loxl

 

 

0.2
A' +2 +2 8' +2 +2 C' +2 +2
0.8mm Mg 12mm Mg 0.8mm Mg 12mm Mg 0.8mm Mg 12mm Mg

2 0.0 . 1 E .
<0
k
a E a E
.E .0 2 T w ‘ a
a
co E Q Ei

0.4 ~ - -

0.6 - g B - i

 

 

 

 

 

 

 

RpoS- RpoS+ RpoS- RpoS+ RpoS- RpoS+ RpoS- RpoS+ RpoS- RpoS+ RpoS- RpoS+
Glutamate system Oxidative system Arginine system

Figure 2.5. Box plot of survival rates (AV) for paired RpoS+ and RpoS' isolates

from 8 strains of E. coli O157:H7 in each AR system. Tests were conducted

under low (0.8mM) Mg” and high (12mM) Mg” conditions.

37

 

and ot
survive
ability
of surv
Buchar
envnon
EHEC s
and wha-

strains v.

and Dati

mOGerate
0f acid acl
the aCid El
Vanabuny
0’5 Colic
PIOtect ag,|
|
s... cap!

I
rr

 

DISCUSSION

E. coli O157:H7 was first thought to have enhanced ability to resist acid
and other environmental stresses because of its low infective dose and ability to
survive in and be transmitted by acidic foods. Strain-to-strain variation in AR
ability has been reported (albeit with a small number of strains), with a continuum
of survival abilities classified from highly resistant to sensitive. For example,
Buchanan and Edelson observed that the ability to survive in acidic
environments varies substantially among isolates within a single serotype; AR of
EHEC strains grown to stationary phase was dependent on the type of acidulant
and whether the pH-dependent AR system had been induced (35). Benjamin
and Datta compared survival of 6 O157:H7 strains in LB at pH 2.5, two of the
strains were considered highly acid tolerant, while the other four strains were
moderately and slightly acid tolerant (12). Leyer et al. (1995) measured survival
of acid adapted and non-adapted cells of 5 strains of E. coli O157:H7, although
the acid adapted cells had a better survival rate than the non-adapted, there was
variability in survival rates among the strains (150). Lin et al. assayed 11 strains
of E. coli O157:H7 for their ability to use three different AR mechanisms to
protect against high acidity. Considerable variation existed in the ability of strains
to use a particular AR mechanism, indicating that not all O157:H7 strains had the
same capability to survive in a low pH environment using a specific AR
mechanism (155). Our analysis with 30 O157:H7 isolates collected
independently from sporadic cases of disease shows continuous variation in

ability to survive at low pH using these AR mechanisms and that survival rates fit

38

 

 

a non
phen

betwe
are ad

strains

examp

DUFDOSI

 

differen
growth ;
(growth.
strains \
a single
variatior
dUe to e
acid res
large 00

T
Stallone
eXDless
reSDOnS

required

a normal distribution. In addition, more than half (~50%-70%) of the total
phenotypic variation in acid resistance among STEC clones is a result of
between-strain variation within a clonal group. This finding suggests that there
are additional factors that contribute to variability in acid resistance of STEC
strains.

Understanding the extent of variation among pathogens of a group, for
example of E. coli O157:H7 strains, is of considerable importance, especially for
purposes of predictive modeling. Whiting and Golden have identified the
differences among strains of the same species as a major source of variation in
growth and survival studies; variations determined for four different parameters
(growth, survival, thermal inactivation, and toxin production) of E. coli O157:H7
strains were larger than the error calculated from experimental procedures using
a single strain or cocktail (269). We also observed that the strain-to-strain
variation within a clonal group was larger than variation between replicates or
due to experimental error. Thus, drawing conclusions about differences in the
acid resistance abilities of a variable group of bacteria will require examination of
large numbers of isolates.

The alternate sigma factor RpoS governs gene expression during
stationary phase and in response to stressful environments. Variation in the
expression of this global regulator can contribute to overall variability in stress
response. It is clear that expression of the alternate sigma factor rpoS is

required for stationary phase induction of acid resistance in E. coli and Shigella

39

(227)

static

becau
to star
expres
ability c

natural

Com pie
Sensitiv
STEC, \-
attempt,-
OUI' iSOIa
selecting
Li
tOUSethil
in reduce
resistancf'
elth E. C
not aQIEe
SUWived b
COMM-med

 

(227), and Arnold and Kaspar found that as the growth of E. coli O157:H7 neared
stationary phase, survival of cells in TSB at pH 2.0 increased dramatically (8).

Expression of rpoS can vary in natural populations of E. coli, possibly
because rpoS null mutants have a competitive advantage in prolonged exposure
to starvation conditions (275). Variation in rpoS, ranging from null to full
expression, could account for a component of the variability observed in AR
ability of E. coli. Waterman and Small found that mutant rpoS alleles exist in
natural populations of STEC during a survey of AR of 58 STEC strains.
Complementation with rpoS on a plasmid conferred AR to nine of 13 acid
sensitive strains (263). We also observed variation in allelic state of RpoS among
STEC, with E. coli O157:H7 strains having the most variability. In this study, we
attempted to correct for the potential confounding effects of rpoS null mutants in
our isolates by assaying RpoS activity indirectly through the HPII assay (26) and
selecting RpoS+ colonies to test AR.

Lin et al. determined that functional rpoS is required for a strain to be able
to use the oxidative mechanism of acid resistance. A mutant rpoS allele resulted
in reduced ability to utilize the arginine and glutamate mechanisms of acid
resistance (155). In a comparison of survival using a specific AR mechanism of
eight E. coli O157:H7 strains that had rpoS+ and rpoS isolates, our results did
not agree with those of Lin et al. In the arginine system, mutant rpoS isolates
survived better than the wild type isolates. The allelic state of RpoS was
confirmed in these strains by testing colonies for HPII activity with hydrogen

peroxide and for glycogen production with iodine. Arginine decarboxylase,

40

encoded by adiA, is positively regulated by CysB (42, 224) and by AdiY, a
transcriptional regulator encoded downstream of adiA (235). It is possible that
RpoS does not contribute to regulating expression of adiA. However, further
studies are needed to determine the role of RpoS and other regulators in
governing the arginine AR system.

Chelating compounds, as well as organic and inorganic acids, increase
the permeability of the outer membrane (3, 107), which has been linked to loss of
viability during acid stress in E. coli O157:H7 (127). Bacterial inactivation by acid
could be aided by the presence of chelators in the test environment. Citric acid
has been shown to be a potent OM permeabilizer in O157:H7, and the
perrneabilization effect can be abolished with the addition of 5mM Mg (107).
Mg” stabilizes cell membranes (30) by interacting with adjacent LPS molecules
(255). Our results indicate that high levels of magnesium in the low pH
environment significantly increase the survival rates of most E. coli O157:H7
strains in the glutamate system. The increase in survival is not as dramatic for
the arginine system, possibly because this system is tested at a higher pH.
Interestingly, bacteria of the other STEC clonal groups, as well as the ECOR
group, were not significantly affected by the increased levels of magnesium in
these two AR systems. No differences were observed in survival for the 0157
and 026/0111 clones in the OXI system at the two levels of magnesium, but the
0121 group showed a significantly lower survival rate at 12mM Mg in the OXI
system. Further work is needed to understand the mechanisms by which Mg”

concentration increases survival in acidic environments.

41

 

 

and
but
subs
adve
thnsp

MOSt l

 

seroty
ben~er
strains
Condni
the O1
PHise

seroty]

In vitro studies on AR of E. coli O157:H7 strains, other pathogenic E. coli,
and different enteric bacteria have indicated that E. coli O157:H7 can exhibit AR,
but whether O157:H7 strains have superior ability to survive in acid has not been
substantiated. The consensus seems to be that E. coli O157:H7 is well suited to
adverse conditions even though little direct evidence supports the hypothesis that
this pathogen is substantially different from other E. coli in AR abilities (168).
Most AR studies are based on only a few non-0157 STEC strains of the same
serotype, which reduces the statistical power to detect significant differences
between groups. For example, Miller and Kaspar observed survival of two
strains of E. coli O157:H7 and a single non-pathogenic strain under acidic
conditions (175). While a significant difference in survival was reported between
the O157:H7 and non-pathogenic strain, their suggestion that resistance to acidic
pH is an additional characteristic that distinguishes O157:H7 from other E. coli
serotypes (175) is not warranted because other pathogenic E. coli serotypes
were not tested. Benjamin and Datta compared survival of 14 EHEC strains in
LB at pH 2.5 and 3.0 and found that no correlation could be drawn between acid
tolerance and serotype (12), but again only a few non-0157 serotypes were
evaluated. McKellar and Knight surveyed a set of 19 EHEC strains for ability to
survive in TSB acidified with HCI (pH 2.0) and with acetic acid (pH 4.0). When
strains were grouped by source, they found that survival of outbreak strains in
acid was significantly greater than strains from human or animal sources. When
strains were grouped by serotype, however, strains of E. coli O157:H7 were

among the more acid sensitive, but clear conclusions could not be drawn since

42

 

 

 

 

notha

(D261-
sunfiv
eren

00mpa

 

 

pathog
SUDeno
COnditic
meChar

than be

other EHEC serotypes were only represented by a single strain each (172).
Berry et al. recently reported a comprehensive comparison of AR between 39
isolates of 0157 and 20 isolates of non-0157 biotype l E. coli, and found no
significant differences in the ability of strains from either group to survive in BHI
pH 2.5, and concluded that high acid tolerance is not unique to strains of
pathogenic E. coli (15). Our results indicate that strains of E. coli O157:H7 do
not have superior AR ability compared to strains of E. coli0121zH19 and
026:H11 using the three AR mechanisms. While E. coli O157:H7 clones can
survive in low pH using the three described AR mechanisms, there is no
evidence that O157:H7 has greater acid resistance in any single system
compared to other clones of STEC in these laboratory test environments.

In a comparison of three clonal groups of STEC and a clonal group of non-
pathogenic ECOR strains, we show that strains of E. coli O157:H7 do not have
superior AR ability using three specific AR mechanisms. Under natural
conditions, however, E. coli O157:H7 may have the ability to use multiple
mechanisms in combination, through some form of epitasis, to achieve higher AR
than bacteria of other clonal groups. It is also possible that E. coli O157:H7 has
evolved alternative mechanisms, yet to be identified, which contribute to acid
resistance in nature. This study suggests that there is no evidence for enhanced
AR of O157:H7 by a measurable superiority of one of the three major AR
mechanisms of E. coli. There is also compelling evidence for a substantial
strain-to-strain component of variation in survival rate; more than 60% of the total

variance in survival rates for the oxidative and arginine systems were attributed

43

 

to be

inter-

 

test a
STEC
under

patho

 

to between-strain differences. Our findings demonstrate that because of the high
inter-strain variability, a large number of isolates should be examined in order to
test and draw conclusions about survival of E. coli O157:H7 and non-0157
STEC. A narrow focus on a few isolates or a single serotype may seriously
underestimate the variability that underlies important phenotypic variation in

pathogens.

44

 

used
Koch
Unfio
VVash
Ek.Ja

resear

 

ACKNOWLEDGEMENTS
We would like to thank the following people for kindly providing the strains
used in this study: Dr. David W. Acheson, CFSAN; Dr. Lothar Beutin, Robert
Koch lnstitut; Dr. Paul D. Fey, University of Nebraska; Dr. Alison D. O’Brien,
Uniformed Services University of the Health Sciences; Dr. Phillip l. Tarr,
Washington University at St. Louis; Dr. Roger Johnson, University of Guelph; and
Dr. James Rudrik from the Michigan Department of Community Health. This

research was supported in part by the Michigan Agriculture Experimental Station.

45

CHAPTER 3
Past gene conversions between duplicated glutamate decarboxylase genes

(gadAB) in pathogenic Escherichia coli

Bergholz, T. M., C. L. Tarr, L. M. Christensen, D. J. Betting, and T. S. Whittam submitted to J.

Bacteriology

46

 

ESch
deca

invoh

vhdeh

Enkfl

seque

 

strains
genes
vanab
Eflelo.
Donut
COnve
SYHOr
allele.
Cone
SVen1

IESEN

SUMMARY
Escherichia coli produce two biochemically identical isofon’ns of glutamate
decarboxylase, which are encoded by the gadA and gadB genes and are
involved in acid resistance. These genes are unusual among the
Enterobacteriaceae in that they occur only in E. coli and Shigella. The loci are
widely spaced on the E. coli chromosome and the two genes are 97% similar in
sequence. Comparison of the nucleotide sequences of the gadA and gadB in 16
strains of pathogenic E. coli revealed 3.8% and 5.0% polymorphism in the two
genes, respectively. Alignment of the homologous genes identified a total of 103
variable sites including 21 fixed nucleotide differences between the loci, which
are located in the first 82 codons of the genes. Thirty-six variable sites were
polymorphic for the same nucleotides in both genes suggesting past gene
conversions or intergenic recombination. Phylogenetic analysis based on
synonymous substitutions indicated two cases in which specific gadA and gadB
alleles were more closely related to one another than to other alleles at the
corresponding locus. The results indicate that at least three gene conversion
events have occurred after the gad gene duplication in the evolution of this acid

resistance mechanism in pathogenic E. coli.

47

 

15
do
ba
int:
sys
061
E0
net
801

tor

INTRODUCTION

Escherichia coli can persist in extremely acidic environments (8, 100,
154). This ability to survive at low pH is thought to play a role in the low infective
dose of certain foodborne pathogens, such as E. coli O157:H7 (100, 103). Many
bacteria have evolved decarboxylation-antiporter systems for maintaining neutral
internal pH in acidic environments. The lysine and arginine decarboxlyase
systems are common in many enteric bacteria, whereas the glutamate
decarboxylase system is present only in E. coli and Shigella among the
Enterobacteriaceae. The glutamate decarboxylase system works to maintain
neutral cytoplasmic pH by decarboxylation of glutamate and export of y-
aminobuytric acid (89). This amino acid-based acid resistance system also acts
to repel incoming protons through the inversion of the membrane potential (209).

E. coli produce two isozymes of glutamate decarboxylase (GAD) that
protect cells in acidic environments. The expression of both isozymes is required
for survival at pH 2 (42). The two homologous genes that encode the GAD
isozymes are widely spaced on the chromosome (229). The gadA gene is
located at 78 minutes (3.67 Mb) on the K-12 chromosome (229), near two
transcriptional regulators of the GAD enzymes, gadX and gadW(118, 160), and
is part of the acid resistance fitness island (117) (Fig. 3.1). The gadB gene,
located at 33 minutes (1.57 Mb) on the K-12 chromosome (229), produces a
protein (GadB) that is nearly identical in amino acid sequence to GadA, and is
co-transcribed with the adjacent gene, gadC, an antiporter that exports y-

aminobutyrate (74, 112, 209).. GadW and GadX have been shown to bind the

48

 

sam
one
box,
esse
forth
same
9808

and g

 

 

 

found
dUplic

DI E.(

80d 9‘

same 44-bp promoter sequence, with two binding sites upstream of gadB and
one binding site upstream of gadA (248). A 20-bp sequence named the GAD
box, located between -52 and -73 bp in the gadA and gadBC promoters, is
essential for both acid and stationary phase induction (41 ), and is the binding site
for the essential transcriptional activator, GadE (158). The GAD system has the
same genomic arrangement in pathogenic E. coli O157:H7 where the gadA and
gadBC loci map to homologous locations in the chromosome (197). Both gadA
and gadB occur in virtually all E. coli (including Shigella strains) and are not
found in other enteric bacteria (102) suggesting that these genes arose by
duplication, rather than through horizonatal transfer events, early in the radiation
of E. coli and Shigella.

Duplicated genes can evolve in complex ways. They can rapidly diverge
and evolve new functions; alternatively, they can undergo paralogous gene
conversion because of their sequence similarity. This type of intergenic
recombination leads to genetic homogenization and has been observed in the
multicopy ribosomal genes (152). Here we use sequence comparisons in a well-
defined set of pathogenic E. coli strains to address the following questions: To
what extent have the duplicated gad structural genes diverged among
pathovars? Is there evidence of recent gene conversion between the paralogous
gad genes? Has there been divergence, conservation, or homogenization of
flanking sequences? To begin to address these questions, we sequenced the
gadA and gadB genes in representative strains of enteropathogenic and

enterohemorrhagic E. coli (78). These data were used to test hypotheses about

49

the role of mutation, recombination, and natural selection in the molecular

evolution of the duplicated genes.

50

 

1.566 Mb on K12

chromosome
glutamate

y-aminobutyric

acid antiporter
gadC

<—

1.573 Mb
GadE GadW/GadX

glutamate
decarboxylase l
isozyme G AD box
gadB pqu
<—

+—

 

 

[ITITIIJTIIII—E‘“

v - -"~1—.——{//////////////I//////J

 

gadB R1

gadA R1

 

 

 

V177 //ZT/ZZ ‘/ ”/1717 fl/ '/~"///.
gadX
<—
araC-Iike
3.662 Mb regulator

 

 
 

 

 
   

 

 

 

2050 bp gadB amplicon 2213 F1
2226 bp gadA amplicon 55A F1
‘« 1w :. . ,: \\XX\\\I
gadA I yth
+— <—
glutamate GAD box
decarboxylase 3.666 Mb
isozyme GadE GadW/GadX

 

 

 

Figure 3.1. Locations of gadA and gadB on the K-12 MG1655 chromosome.

The amplicons used for sequencing are marked for each loci. The GAD box is a

20 bp promoter sequence that is the binding site for the essential transcriptional

regulator GadE (158). The AraC-like regulatory proteins GadW and GadX bind

to two sequences upstream of gadB and a single sequence upstream of gadA

(248).

51

 

 

 

MATERIALS AND METHODS

Bacterial strains. Twelve strains of E. coli were used as sources of DNA
for cloning and sequencing of gadA and gadB (Table 3.1). The strains included
laboratory strain K-12 (ATCC 47076) and representative isolates of
enteropathogenic E. coli (EPEC) and enterohemorrhagic E. coli (EHEC) that
have been classified into clonal groups based on the sequence analysis of
conserved housekeeping genes (208). E. coli K-12 was included as a positive
control for the cloning and sequencing because the nucleotide sequences of
gadA and gadB are known (229) and the entire genome sequence is available
(25). For comparative purposes, we also obtained the homologous gad
sequences from four completely sequenced genomes including E. coli O157:H7
strains EDL-933 (197) and RIMD 0509952 (referred to here as Sakai) (105), both
of which were originally isolated from foodborne outbreaks of hemorrhagic colitis;
an 06:H1 uropathogenic E. coli isolate, CFT073 (267), originally recovered from
a patient with pyelonephritis (249); and type 2a Shigella flexneri strain 301
(abbreviated Sf301), which was originally isolated from a patient with shigellosis
in Beijing, China (126).

GAD cloning and sequencing. All strains listed in Table 3.1 were
inoculated from stocks stored in Luria-Bertani broth (LB) + 10% glycerol at -80°C
and were grown overnight in LB (Becton, Dickinson, Franklin Lakes, NJ) at 37°C.
Genomic DNA was isolated from 1 ml of culture using the Purgene DNA isolation
kit (Gentra Systems, Minneapolis, MN). The gadA gene was amplified by PCR

using locus-

52

Table 3.1. Serotypes and source of 12 E. coli strains sequenced for the
glutamate decarboxylase genes, gadA and gadB. Strains were classified into

clonal groups based on multilocus sequence comparisons (208).

 

 

No. Strain segflty'pe Source of original isolation Clonal grog)
Rough
1 K-12 (O-:H48) USA (1922) ECOR A
2 E2348/69 O127:H6 UK (1969) EPEC 1
3 DEC 1a 055:H6 USA (1956) EPEC 1
4 DEC 23 055:H6 Congo (1962) EPEC 1
5 93-111 O157:H7 USA (1993) EHEC 1
6 OK-1 O157:H7 Japan (1996) EHEC 1
7 DEC 5d 055:H7 Sri Lanka (1965) EHEC 1
8 DEC 8b O111:H8 USA (1986) EHEC 2
9 2666-74 O26:NM USA (1974) EHEC 2
10 DEC 11a 0128:H2 USA (1975) EPEC 2
11 DEC 12a O111:H2 UK (1950) EPEC 2
12 B170 0111:NM USA (1983) EPEC 2

 

53

 

 

spe
TAC
3.1)

CCT

 

specific primers (gadAF1 5’-TTAATGCCTCCTCCTTGAGC-3’, gadAR1 5’-
TACACGCCGTATATGCAGGA—3’) which produced a 2226 bp amplicon (Fig.
3.1). The gadB gene was amplified separately, (gadBF1 5’-
CCTGTCTGTACTGATGTAGCCA -3’, and gadBR1 5’-
GACTGAGCAGGAGCAATTGT-3’), and produced a 2050 bp amplicon (Fig. 3.1).
PCR reactions contained 1x Amplitaq Gold PCR buffer (Applied Biosystems,
Foster City, CA), 0.2mM each dNTP, 2mM magnesium chloride, 1mM each
primer, and 3U Amplitaq Gold Taq (Applied Biosystems, Foster City, CA). The
following PCR conditions were used: 94°C for 10 min., followed by 35 cycles of
92'C for 1 min., 57°C for 1 min. and 72°C for 2 min. The gadA and gadB
amplicons from each strain were purified using the QiaQuick PCR purification kit
(Qiagen, Valencia, CA) and then cloned into pCR2.1 using the TA Cloning Kit
(lnvitrogen, Carlsbad, CA). Since the known sequences are very similar, the
cloning step ensured that only gadA or gadB would be sequenced. Plasmids
were extracted using Ultra Clean Mini Plasmid Prep kit (Mobio, Solana Beach,
CA) and electrOphoresed on a 1% agarose gel to assure that the insert was
present. The amount of plasmid DNA was quantified spectrophotometrically and
90 - 100 ng used per sequencing reaction. Both gadA and gadB were
sequenced from the plasmids, using primers that were designed from E. coli K-
12 and EDL-933 gad sequences (GenBank Ascession NO 000913 and NC
002655) (Table 3.2). Cycle sequencing reactions contained 9.0 )ul CEQ DTCS
Quick Start premix (Beckman Coulter Inc., Fullerton, CA), 2.0 ,ul of 20 uM primer,

approximately 100 ng of

54

Table 3.2. Primer sequences used for gadA and gadB sequencing.

 

Primer

Sequence

 

gadB-56F
gadA/B204F
gadB666F
gadB806F
gadB1172F
gadB284R
gadB436R
gadA/B89OR
gadB1 166R
gadA305F
gadA635F
gadA719F
gadA1082F
gadA362R
gadA281 R
gadA1356R

5’ - GAGTCCTTTGCACTI'GCTTAC -3’
5’ -TTTCTGCCAGACCTGGGAC -3’

5’ — CTGCACGATGCGCTGGATAA — 3’
5’ - GATCAGTGCTTCAGGCCACA - 3’
5’ - CCTGTATGACCTCTCTGAAC — 3’
5’ -— GGCTGAGATTGCGGATATTC - 3’
5’ — TCCATACGCTTGCGCCAACG — 3’
5’ — AACGTTGAACACCAGTTCCT -3’
5’ — AGAGGTCATACAGGGTGTATCC -3’
5’ — ATATGGTTGCCGATCTGTGG — 3’
5’ — CCTACACCGGTAACTATGAG — 3’
5’ — ACCGGTATCGACATCGACAT -3’
5’ — GGCCGTATGAGTTCATCTGT — 3’
5’ - GTGCCAACGGCCTGACCATT - 3’
5’ — GCGGATTGCGGATA'ITCTTC — 3’
5’ — CGGGTGATCGCTGAGATATI' — 3’

 

55

 

 

Pfi‘

94°—
forZ
Sep
NJ).
SUSp

Coul

 

EXpo
(DNA
and a

EFsE

plasmid containing gadA or gadB and ddH20 to a final volume of 15 ,ul.
Prior to addition of DTCS premix and primer, the plasmid was nicked by
incubation at 96°C for 903. Amplification utilized an initial denaturing step at
94°C for 1 min, followed by 35 cycles of 96°C for 30 s, 48°C for 30 s, and 60°C
for 2 min. Upon completion of cycle sequencing, samples were purified with
Sephadex G—50 Fine columns (Amersham Pharrnacia Biotech Inc., Piscataway,
NJ), dried under vacuum centrifugation (Savant Instruments Inc., Holbrook, NY),
suspended in 40 ul of deionized formamide, and run on a CE02000XL (Beckman
Coulter lnc.). Samples were analyzed using the CE02000XL software and then
exported for further analysis with the SeqMan module of the Lasergene software
(DNASTAR Inc., Madison, WI). Sequences have been submitted to Genbank
and are accession numbers gadA (EF547378 —EF547388); gadB (EF551351-

EF551361)

Statistical analyses. We used two statistical approaches and computer
programs to detect recombination between gad sequences. The TOPAL
package uses a phylogenetic comparative approach to identify locations of
conflicting phylogenetic signal in aligned sequences. It is a graphical method
based on sliding a window along a DNA alignment, estimating a tree for the first
half of the data in the window using a Least Squares methods, and recording the
values of the sum of squares (171 ). The sequence data in the second half of the
window are then fit to the same tree topology and the sum of squares is

calculated. The difference in the sum of squares (D33) is tabulated for all

56

possible windows, in both fonrvard and reverse directions, and plotted against the
position in the sequence. Peaks in the D33 values mark the locations of putative
recombination events. We used TOPAL 2.0 (170), which implements a
standardization of the distances across windows to reduce the confounding
effects of regional rate variation. In our application, a window size of 500 bp was
used with a 10-bp increment between windows. Phylogenetic tree construction
was based on the Jukes-Cantor distance and the Least Squares method. One
hundred randomization tests were run to evaluate the significance of the
observed Dss values.

To detect paralogous gene conversion between the gad loci, we used
GENECONV (Version 1.81) (219), a permutation-based program that assigns
probability to matching fragments in aligned DNA sequences. The gadA and
gadB sequences from each strain were examined as pairs and calculations were
based on silent codon polymorphisms with 10,000 permutations.

The number of synonymous substitutions per synonymous site (ds) and
the number of nonsynonymous substitutions per nonsynonymous site (dN) were
estimated by the modified Nei-Gojobori method using MEGA3 (Version 3.1)
(141). Standard errors were estimated by a bootstrap method with 500
replications. Gene phylogenies were inferred by the neighbor-joining algorithm
and statistical confidence in the topologies was assessed by bootstrapping using

MEGA3.

57

 

pol.
am
nuc
ami
sites
non:
were
nons
PrOdi
agair
distin

used

 

RESULTS

DNA polymorphism. For the 16 gadA sequences, there were 53
polymorphic nucleotide sites (3.8% of 1401) and 11 (2.4% of 467) polymorphic
amino acid positions. The gadB sequences showed a slightly greater level of
nucleotide polymorphism (5.0%), which predicted only 9 (1.9%) polymorphic
amino acid positions. The rate of synonymous substitution per 100 synonymous
sites was 4.13 i 0.64 for gadA and 5.85 i 0.80 for gadB. The rates of
nonsynonymous substitution per 100 nonsynonymous sites for gadA and gadB
were 0.17 3: 0.06 and 0.14 i 0.05, respectively. The ratios of synonymous to
nonsynonymous substitution of 24:1 for gadA and 42:1 for gadB indicate that the
products of both genes are highly conserved with purifying selection acting
against point mutations that result in amino acid replacements. There are 14
distinct alleles for gadA and 12 distinct alleles for gadB. All sequences were
used for subsequent recombination analyses.

Putative recombinational breakpoints. As a first step to detecting
recombination within and between duplicated gad genes, we used a phylogenetic
approach as implemented by the TOPAL software package (170). In our
application, a 500-bp sliding window was used to plot the test statistic (Dss)
calculated at 10-bp intervals along the aligned 1401 bp of the gad coding
regions. At each point a tree was estimated based on the sequence data in the
first half of the window, and then Dss was calculated as the difference in the fit
(as measured by the sum of squares) between this tree and the data in the first

and the second half of the window. This analysis was repeated in both

58

 

OS

033

 

FIQUre
Statisu,
SeqUer
C)btain

gadA (

 

0012

 

 

 

 

 

 

gadA
00090
Dss 0.006)»
0.003” U M j
0 : — . - . : - : -
300 400 500 600 700 800 900 1000 1100
0012
gadB
00091
Dss
0.06

 

 

0 0 A
0.003 w
0 c . .

300 400 500 6

 

W
00 700 800 900 1000

1 100

 

 

 

 

 

 

 

statistic (difference in the sum of squares) is plotted over the length of the

sequence.

obtained by parametric bootstrapping. Recombination events were detected for

0.4
gadA and gadB
0.3 0
Dss 0.2 l.
0.1 " rm
0 : : : . -
300 400 500 600 700 800 900 1000 1100
Position in nucleotide sequence
Figure 3.2. Putative recombination events as detected by TOPAL. The Dss

The horizontal bar indicates the 95% significance level for Dss,

gadA (A), gadB (B), and gadA and gadB together (0).

59

 

 

 

 

directions for the 16 sequences of each locus separately and for the combined
32 sequences. Hypothetical breakpoints of past recombination are identified by
significantly large peaks in the running average Dss values. The peaks
designate positions where there are discrepancies in the phylogenies based on
sequence differences on either side of the peak. For gadA, two potential
recombinational breakpoints were identified: one between nucleotides 260-300
and a second between nucleotides 980-1120 (Fig. 3.2A). The secondary Dss
peaks near positions 400, 600, and 700 were not found to be significant. Two
potential recombinational breakpoints were also identified in gadB and are
located at positions 560-590 and 1110-1150 (Fig. 2.2B). The analysis of the
combined data for the gadA and gadB alleles reveals three significant Dss peaks
in the sliding window analysis (Fig. 3.2C). The peaks centered at positions 575
and 1060 are significant and reflect the primary breakpoints detected for the
individual locus comparisons. However, the broad peak at the 5’ ends of the
gadA and gadB comparisons is extreme (Dss > 0.35), remaining above the
critical value almost to position 500. This large, significant plateau of Dss
suggests that the 5' ends of gadA and gadB have a complex history of
divergence that cannot be explained by a single recombination event.
Paralogous gene conversions. We were primarily interested in
evidence of gene conversion or intergenic (paralogous) recombination between
gadA and gadB. To detect gene conversion between the loci, we analyzed all 32
gad sequences grouped as gadA and gadB pairs, one group for each of the 16

strains (16 groups with blocksize 2). Eighty-nine polymorphic synonymous sites

60

 

Ta

ger

 

wh
are

bas

Cor

 

 

Table 3.3. Significant fragments found by Sawyer's method (219) for detecting
gene conversion. Comparison 1 is a test for significant outer fragments,
whereas comparisons 2-4 are tests for inner fragments. The number differences
are the number of polymorphisms within the fragment. Simulated P values are

based on 10,000 permutations and are corrected for multiple comparisons.

 

 

Start End Simulated
Comparison position position Differences P value
1. 16 strains 1 111 or 114 9 or 10 0.0041
2. EPEC 2 244 639 24 0.0215
3. EPEC 1 604 1047 26 0.003
4. K-12 700 1176 28 0.008

 

61

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HHHHHHHHfiHHddHHHHHHHdHHHHHHHHanfiH

 

._oo_ 05 comics 9:05 5552.8 050 Le «.39

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. . .... ...‘.U..‘H ............. a .................... AOW\OVMNuv WInFNHO
...................... ‘............................................‘.............................nU...........-......... aflocflv ‘Nh

 

 

 

 

 

 

 

 

 

Eu ................ u ..................... :52 5.58
So ................ u ..................... 32-35 5.53
So ............. 0.0 ..................... :09 5.53
Eu ................ o ..................... 57:. 0.53
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l r ............ U ..................... A'F0uwouv Enuflo
. l. ----------- O ..................... “no Uflnv an” Hdflo
1 1.»... ........... U --------------------- A‘NH U-nv N“ u HHHO
......... 3 ..................... a: use 0.33
........... 0 3:53:33
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nhvohvmmovun0H0vNHmwovmnnohwomN0hunvnuohfiwndnnn. dn~ Ivan).thn~InIHN000honomhumvrw
nhomvomvnufloomhwmmflfldobunmvvnnnNHOQQFFMNNHFUNGv0nnnm>ouvvoorbunudl:. oonnwdddnovvuw NH
.FHHHHHHF -- 11111 11111 .HHHflHfi

HHHHHAHHHHHHHMHHHHdHHHHHHHHHHnHHHH

62

 

 

HHHaHfiAfifiaaHaHaHHHHHH

.1<<<<cccccccooddm¢cmq
HdHHHHHHHHHHH

 

earnersmememwmemmmmmmmvvvvv11v

itGiuncccocarwvrrcinH~QKVNN-
V'VMMMMMMNNNNNNNNNNNNNHHHHHHH

 

ommhwbwmmmmwfiawfi

0.0300 0

5.3 0050.6 020 0. :02? 05000 F ownm. 05 :_ 00000000 003 E0E00: .050 0:005:90 0:800 < .:o_0.0>:oo 0:00 .60
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0:200. <20 00.5 E0__:0_: 00x0: 0000:0 0: ._. .0_0>_0:0 26053 050.0 0 >0 00000000 3:69.005 6005:8000: 030:6
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..s..ss ...... s .......... s. s. ....... ssssss ...... s ss ... gas. ss.sss

- .U. o B ...... a .......... a g ....... @808 ...... U 8 A.“ onav wnnmmo
..s.. s ...... s .......... s..ss ....... ssssss ...... s.. ss .ssssssss .sscs.ssu. ss.ssss
s. .................. .s.ssss.sss..s...s.s.s ..... s.s..s s..ss .sssssssss .sssss. as.
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ss. s ......... s s..s ................ ss.ss s ..ss. s..ss.s so ................... sssssss. s .s.s..sssss.sssssssss .sss-sss. .s..s.s
as... ......... s s..s ................ ss.ss s .ss. s..ss.s ss ................... sssssss. s .s.ss.sssss.ss..sss.s ..-ss. .s..s.s
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as... ......... s. s..s ................ ss.ss.s ..ss.. s..ss.s ss ................... sssssss. s..s.s.ssssss.sssssssssw .ss sss. .s.sss
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63

we
ex<
witl
site
ma“
EHI
inch
sign
cor.
stra
mat:
3.4).
from
nUch

117g

 

that
groui
t0 ha

3' en

QadE
estim-

SYNC!

were identified in the 1,395 aligned bases (the start and stop codons were
excluded from the analysis). In the comparison of gadA and gadB sequences
within strains, GENECONV (219) detected 13 global outer fragments (runs of
sites that are unique to the group) and 10 significant inner fragments (runs of
matching sites between loci). The outer fragments were found in all but the five
EHEC 1 group strains (EDL-933, OK-1, Sakai, 93-111 and DEC 5d), and
included the first 111 or 114 nucleotides in gadA (Table 3.3, Comparison 1). The
significant run of synonymous polymorphic sites suggests that a recent
conversion event has homogenized the 5' end of the gadA sequences across
strains (see Fig. 3.3 and 3.4). The inner fragments include three distinct runs of
matching sites between the gad loci within a strain (highlighted in Fig. 3.3 and
3.4). EPEC 2 strains have a significant matching run between gadA and gadB
from nucleotide 244 to 629, EPEC 1 strains have a significant run from
nucleotide 603 to 1047, and K-12 has a significant run from nucleotide 700 to
1176 (Table 3.3). Each of these runs of matching synonymous sites suggests
that past gene conversion between gadA and gadB occurred within a clonal
group. It is noteworthy that only the EHEC 1 gadA and gadB sequences appear
to have diverged independently with no evidence of past gene conversions in the
3' end of the genes.

Gene phylogenies. To assess the level of divergence of the gadA and
gadB loci determined by past mutation and gene conversion events, we
estimated a gene phylogeny using the neighbor-joining algorithm and the

synonymous substitution rate. For this analysis the gad loci were each divided

64

 

St
()6
tre
sen
ark:
vver
cod l
Sing)
lflasn

forml

the g

Wher
grOUp

Segue

 

 

into two regions, based on the first detected significant inner fragments from
GENECONV (Table 3.3). The first region analyzed comprised 80 codons
(codons 2-81, ATG start codon excluded), which spanned the first 33
polymorphic nucleotide sites through position 243 (Fig. 3.5). The second region
comprised the next 385 codons 0' GA stop codon excluded) and included 70
polymorphic sites (Fig. 3.6).

The neighbor-joining tree produced by the analysis of synonymous
substitutions in the first 80 codons showed a deep split between the gadA and
gadB gene clusters (Fig. 3.5). For the 5' region of gadB, the topology is similar to
trees constructed from other gene sequences (38). The EPEC 1 cluster of
sequences branched off first then the EHEC 1 group splits off from the EPEC 2
and EHEC 2 lineage. The gadB sequences from EPEC 2 and EHEC 2 strains
were identical to the K-12 allele in the 5’ region. For the gadA tree, the first 80
codons were nearly identical with only 5 polymorphic sites, three of which are
singletons and are not informative. The EPEC 1 strains have substitutions at
position 123 and 162 (Fig. 3.3) so that their gadA sequences for the 5' region
formed a distinct cluster in the tree (Fig. 3.5).

The neighbor-joining tree for codons 82-465 was strikingly different from
the gene tree above (Fig. 3.6). In this case, there was no single divergence point
between gadA and gadB sequence clusters; instead, there were two cases
where the sequences of the two loci were more similar to each other within clonal
groups. The first case was for the K-12, EPEC 2 and EHEC 2 groups of

sequences. For

65

 

Codons 2-81

gadA

 

 

Jr gadB

 

 

5.0

__) .

 

O127:H6 (E2348/69)
055:H6 (DEC 13)
69 055:H6 (DEC 2a)

06:H1 (CFTO73)

‘55 K-12

—— 0128sz (DEC 11a)

0111:H2 (DEC 123)

D1112NM (B170)

0111:H8 (DEC 8b)

0261NM (2666-74)

FZA (Sf301)

O157:H7 (93-111)

O157:H7 (Sakai)

O157:H7 (EDL-933)

O157:H7 (OK-1)

 

 
   
  
  
   

8.50 :l: 3.49 T

055:H7 (DEC 5d)

01272H6 (E2348I69)
35-9 7 i 9'15 055:H6 (DEC 1a)
90 055:H6 (DEC 2a)
06:H1 (CFTO73)

K-12

01282H2 (DEC 11a)
0111:H2 (DEC 12a)
O111:NM (B170)
0111:H8 (DEC 8b)
026:NM (2666-74)
F2A (Sf301)

O157:H7 (93.111)
O157:H7 (Sakai)
O157:H7 (EDL-933)
O157:H7 (OK-1)
055:H7 (DEC 5d)

4.85 :l: 2.82

 

Fig. 3.5. Neighbor-joining phylogenies for gadA and gadB based on the the
number of synonymous substitutions per 100 synonymous sites (d. x 100).
Gene tree for the 5' region of the gad genes spanning codons 2-81. Bootstrap
values based on 1000 replicates are given at the internal nodes. Putative gene

conversion or duplication events are marked with solid circles. The average ds (

:L- SE) is given for each node of interest.

66

 

 

Codons 82-349
— FZA (Sf301)

— 0111:NM(B170)
0120112 (DEC 11a)
0111:H2 (DEC 12a)
gadA 0111:H8 (DEC 8b)

65 0262NM(2666-74)
K-12

99 gadB r K-12
0111:H8 (DEC 8b)
T 75» 0262NM (2666-74)
85 0120112 (DEC11a)
1.91;. 0.81 0111:H2 (DEC 12a)
O111:NM(B170)

 

 

 

O157:H7 (93-111)

 

O157:H7 (EDL-933)
59 10° O157:H7 (Sakai)
7. 7] :t: 1.9/ 0553H7 (DEC 5‘”

 

gadA '— 055:H7 (DEC 5d)

O157:H7 (ox-1)
0" O157:H7 (93-111)
01571-17 (Sakai)
5° 0157017 (EDL-933)

F2A ($1301)

 

gadB

 

    

055:H6 (DEC 2a)
O127:H6 (5234869)
055:H6 (DEC 1a)
06:H1 (CFTO73)

 

06:H1
(CFTO73)

 

 

 

80 D1271H6 (E2348l69)
055:H6(DEC1a)
' 10—4 59 055:H6(DEC 2a)

2.83 :l: 0.97

 

 

 

Fig. 3.6. Neighbor-joining phylogenies for gadA and gadB based on the the
number of synonymous substitutions per 100 synonymous Sites (ds x 100).
Gene tree for the 3' region of the gad genes including codons 82 to 465 (349 in
figure). Bootstrap values based on 1000 replicates are given at the internal
nodes. Putative gene conversion or duplication events are marked with solid

Circles. The average d5 ( 1: SE) is given for each node of interest.

67

these 6 strains, the gadA sequences were in one cluster and the gadB
sequences were in a second, closely related cluster. The gadA and gadB
sequences of these two clusters differed, on average, at 1.9% of the
synonymous sites (Fig. 3.6). The second case involved the three EPEC 1 strains
where again the gadA and gadB fell into two clusters that are more closely
related to each other (2.8 % different at synonymous sites) than to the other
gadA or gadB sequences. In contrast to these cases, the EHEC 1 gadA and
gadB sequences were highly divergent and Share a most recent common
ancestral sequence at the deepest part of the phylogeny (Fig. 3.6).

Promoter region. Nucleotide sequences were determined for the entire
region upstream of the coding sequence for both gadA and gadB (Fig. 3.7). The
promoter regions for gadA and gadB have diverged substantially upstream of -72
bp, but were similar to one another closer to the 5’ end of the genes. The
proportion of substitutions per site wasthree times higher for the gadB promoter
sequences (0.048 1 0.007) compared to the gadA promoter sequences (0.016 :1:
0.005). GadW and GadX have been Shown to bind the same 44-bp promoter
sequence, which is centered at 110.5 bp upstream of gadA and 220.5 bp
upstream of gadBC (248). Comparisons of the sequences here Showed that the
GadW/GadX binding site upstream of gadA was more conserved with only two
polymorphic Sites (Fig. 3.7A), whereas the GadW/GadX binding site upstream of
gadB (centered at 220.5 bp) contained 7 polymorphic sites (Fig. 3.76), and the
sequence identified as a weak binding Site (centered at 110.5 bp) contains 9

polymorphic Sites. The gadA and gadB promoter regions were conserved in the -

68

35 and -10 regions in our strains (Fig. 2.70). A comparison of the K-12 20-bp
GAD box sequence, located between -52 and -73 bp in the gadA and gadBC
promoters, to those determined here indicated that this region is well-conserved

in both promoters, with only two strains that contain a single base difference.

69

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70

DISCUSSION

In this work we present evidence for past gene conversion events
between the duplicated glutamate decarboxylase genes in E. coli. Gene
conversion is an intragenomic, nonreciprocal recombination event that results in
homogenized gene sequences (216). It is thought to be a common event in
bacterial pathogens where antigenic variation is important for survival in the host.
Comparisons of the gadA and gadB sequences in 16 pathogenic strains of E. coli
showed that at least 3 gene conversion events have occurred between these two
loci. Despite past homogenization of sequence regions between the two loci in
some clonal groups, the 5’ end of the genes remain distinctly different from one
another. The first 15 residues at the N - terminus of GadB plays a significant role
in the pH driven association of GadB to the membrane (38), and it is within these
15 residues where 4 of the 5 amino acid differences between GadA and GadB
occur (81 ). It is interesting to speculate that the 5’ amino acid differences
between gadA and gadB result in important functional differences, and that
natural selection has played a role in maintaining the different sequences in the
face of multiple gene conversion events that have occurred across the remainder
of the two loci. Alternatively, if natural selection is involved in maintaining the
differences between the two loci by suppressing gene conversion, it may be that

the important functional differences are found in the promoter regions.

The GAD system in E. coli has a complex regulatory network and is

subject to multiple levels of control. At least 11 regulatory proteins are known to

71

affect induction of the system, GadE being the essential regulator. The roles of
the other regulators are dependent on growth phase and media composition, and
focus on regulating expression of GAD via GadE (88, 158, 159, 166). This
complex system allows the GAD system to be activated under multiple
environmental conditions which may signal a transition to an acidic environment.
Bhagwat and Bhagwat found that while the promoter region for gadBC was
similar between E. coli O157:H7 and Shigella flexneri, the conditions for induction
of glutamate decarboxylase acid resistance were different for these two strains
(22). We found that the promoter regions of gadA were well conserved among
the strains sequenced, whereas the gadB upstream region is longer and is more
polymorphic among the strains. The gadA and gadB promoter regions diverge
upstream of -72bp, an observation consistent with a previous study of the gad
promoter regions in E. coli K-12 (41). The promoter region sequences for the -35
and -10 regions and the 20—bp region (the so-called GAD box) required for acid
and stationary phase induction are conserved among strains and are also the
same for each locus. RpoS is a known activator (41) of gadA and gadB, and and
the -10 sequences match the rpoS promoter consensus of Lee and Gralla (146).
The GadX binding sites are conserved among each locus, but the GadX binding
sites upstream of gadB contain multiple sequence differences. Recent evidence
based on activation of gadA and gadB by GadX and GadW suggests that the
molecular mechanism of activation for gadA and gadB are different (248). These
differences may be why the 5’ region of each locus has not been homogenized

by conversion events. One hypothesis is that differences in activation

72

mechanisms give E coli increased flexibility in controlling expression of GAD,
which is an important protective mechanism for survival in highly acidic
conditions. Two copies of GAD may be maintained because their function is key
to survival at low pH and need to be induced upon exposure to a variety of acidic
environments.

Among the enteric bacteria, glutamate decarboxylases are only found only
in the E. coli genospecies, which includes all but the most divergent Shigella
strains (229). Hersh et al. determined that gadC, along with gadA and gadB,
functions to protect cells in an acidic environment (112). It is clear that GAD
plays an important role in the acid resistance of pathogenic E. coli. The GAD
mechanism provides the most protection against strong acidity in pathogenic as
well as commensal strains (145, 154, 155). Here we find that EHEC 1 strains are
the only clonal group to not have undergone a gene conversion event in gadA
and gadB. Recent evidence of differential expression of gadA and gadB
between EHEC 0157 and EHEC 2 strains (14), and between EHEC 0157 and
Shigella flexnen' (22) suggest that regulation of GAD expression is distinct in
EHEC 0157 compared to other E. coli strains. Further investigations are
warranted to elucidate the functional implications of the absence of past gene

conversions between gadA and gadB in the evolution and emergence of E. coli

O157:H7.

73

ACKNOWLEDGMENTS
The authors thank David Lacher for critically reviewing the manuscript and Erica
Lehotzky for assistance with sequencing. This work was supported in part by
funds from the Michigan State University Agriculture Experimental Station and by
Food Safety NRI #2005-35201-16362 from the United States Department of
Agriculture. Part of the sequencing was conducted at the STEC Center which is
supported with Federal funds from the NIAID, NlH, DHHS, under NIH Research

contract NO1-Al-30058 (TSW).

74

Variation in Acid Resistance among Shiga Toxin-producing Escherichia

coli in a Simulated Gastric Environment

Bergholz, T. M., and T. S. Whittam. 2007. J Appl Microbiol 102:352-62.

75

SUMMARY
Aims: To compare survival of O157:H7 and non-0157 Shiga toxin-producing
Escherichia coli in a simulated gastric environment and to quantify the effect of
storage in an acidic food, apple juice, on subsequent survival of STEC in the
simulated gastric environment.
Methods and Results: To characterize acid resistance of STEC under conditions
mimicking the gastric environment, survival rates were measured in a model
stomach system (MSS) for two clonal groups of STEC: 14 strains of E. coli
O157:H7 and 12 strains of E. coli 026:H11 and 0111:H8. There were
significant differences between the two STEC groups, with the average survival
rate of 0157 strains in the M88 twice as great as the 026/0111 strains. The two
groups also differed in the percentage of injured cells in M88 and in the
expression levels of glutamate decarboxylase genes (measured by QPCR) in
stationary phase before cultures entered the M88.
Conclusions: The results indicate that E. coli O157:H7 has superior ability to
survive simulated gastric acidity compared to the non-0157 STEC, and becomes
acid resistant rapidly upon entry into stationary phase, which may underlie the

low infectious dose of this pathogen.

76

INTRODUCTION

Infections by E. coli O157:H7 are estimated to cause 75,000 illnesses
annually, and account for roughly two thirds of all disease associated with Shiga
toxin-producing E. coli (STEC) infections in the US (173). E. coli O157:H7 can
be transmitted via a variety of food vehicles, including ground beef (56), apple
juice and cider (16, 65), salami (47), lettuce (114), sprouts (28, 174), and cheese
curd (53). STEC of serotypes other than O157:H7 (non-0157 STEC) also
contribute to diarrheal disease, with recent outbreaks attributable to serotypes
026:H11, O111:H8, and 0121:H19 (44, 167, 268). Non-0157 STEC, however,
are associated with disease outbreaks much less frequently than E. coli
O157:H7, and the reason for the disparity in prevalence is currently unknown.
The non-0157 serotypes mark three genetically distinct clonal groups of STEC
(208, 241) all of which are of zoonotic origin (23, 120, 200). In addition, the non-
0157 STEC are capable of causing the same severe human disease as E. coli
O157:H7, are common in animal reservoirs (184), and also have a low infectious
dose (103).

Given the low infectious dose of all STEC, and the ability of E. coli
O157:H7 to persist in acidic foods, it is clear that characterizing and
understanding the acid resistance (AR) of STEC is critical, and may contribute to
better disease prevention strategies.

Bacteria are exposed to stresses in various stages of the human food
chain - from the production environment on the farm to conditions during food

processing and storage to the gastric acidity and bile salts encountered after

77

ingestion. In particular, bacteria must endure passage through the stomach,
where the low pH (pH 1.5 to 3.0) of gastric secretions has long been recognized
as a physical mechanism of defense against infection by foodborne pathogens
(198). The infectious dose of an enteric pathogen is related in part to the ability
of cells to survive gastric acidity (262). Dose response data have indicated that
E. coli O157:H7 is highly infectious (246) and in vitro assays have demonstrated
that E. coli O157:H7 is extremely acid resistant and can readily survive 3h in
synthetic gastric fluid at pH 1.5 (8, 213, 239). In addition to inherent AR of the
microbe, the presence and nature of food can influence survival in the stomach:
foods high in proteins and fats can protect against acid stress (262).

Incubation in certain foods can also influence the ability of E. coli to
survive gastric acidity. Exposure to mildly acidic environments, even for short
periods of time, can induce resistance mechanisms that protect cells from
extreme acidity (12, 33, 99, 154). Incubation in acidic foods has been shown to
increase the subsequent AR of E. coli O157:H7 (150). Storage of E. coli
O157:H7 in acidic foods, such as those implicated in outbreaks, has the potential
to induce genetic systems of AR and stress response (7, 89), resulting in
enhanced survival during passage through the stomach.

In many countries, disease outbreaks associated with non-0157 STEC
are not as common as those associated E. coli O157:H7: the reasons for this
disparity in prevalence are unknown. One possibility is that differences in
prevalence reflect differences in the inherent acid resistance of the STEC clones,

and concomitant survival in acidic foods and low infectious dose. Although AR

78

 

has been studied extensively in E. coli O157:H7, there is much less known about
AR in non-0157 STEC strains.

Previous experiments in our laboratory examined variation in the acid
resistance within and between STEC clones, and found that E. coli O157:H7
strains were no more acid resistant than non-0157 STEC strains in assays that
isolated each of three individual AR mechanisms (145). Under natural conditions
in more complex acidic environments, E. coli O157:H7 may have the ability to
utilize multiple mechanisms in combination to achieve higher AR. It is also
possible that E. coli O157:H7 has evolved alternative mechanisms, yet to be
identified, which contribute to acid resistance in nature. Here we address the
question of whether non-0157 STEC strains of serotypes 026:H11 and O111:H8
possess the same capacity as E. coli O157:H7 for surviving the gastric
environment. To do this, we have adapted the model stomach system described
by Just and Daeschel to simulate conditions in the human stomach containing
food (128). We also investigated the effects of mildly acidic apple juice and
storage temperature on subsequent survival of these pathogens in the simulated

gastric environment.

79

MATERIALS AND METHODS

Strain set and growth conditions. A total of 26 clinical STEC, including
fourteen O157:H7 strains, nine O26:H11, and three O111:H8 strains were used
in this study (Table 4.1). Twenty-three of the isolates were originally recovered
from patients with sporadic diarrheal illness and were shown to belong to two
clonal groups by multilocus sequence typing: 11 O157:H7 strains belonged to
enterohemorrhagic E. coli clonal group 1 (EHEC 1) and the 12 026/0111 strains
belonged to EHEC 2 (145). In addition, three E. coli O157:H7 strains from
foodborne outbreaks were included in the strain set: ATCC4895 (TW02302 =
EDL-933) isolated from a ground beef outbreak (21 1), RIMD0509952 (TW08264
= Sakai) involved in a radish sprout outbreak (174), and 13B88 (TW08263)
implicated in an outbreak associated with unpasteurized apple juice (65). All
strains were stored in Luria-Bertani (LB) broth with 10% glycerol at -70°C.
Strains were inoculated into 10mL of LB broth from freezer stocks and grown to
00600 = 0.1. This ~4h period of growth in LB allowed cells to recover before
transfer to MOPS minimal medium. MOPS minimal medium (10x) was prepared
as described in Neidhardt et al. (187) with the addition of 20mL of micronutrient
solution instead of 10mL. MOPS minimal medium contained 100mL of 10x
MOPS minimal medium, 10mL of 0.132M K2HPO4, and 5mL of 20% D-glucose
per liter. Cultures were inoculated into 50mL of MOPS minimal medium with
0.1% glucose at a ratio of 1:200 and grown to stationary phase at 37°C with
shaking. Cultures in MOPS were transferred at a ratio of 1:75 and grown for 9h

before transfer

80

TABLE 4.1. Shiga toxin-producing E. coli strains (STEC) used in the study of

acid resistance (AR) in the model stomach system (MSS). Survival rates per

hour (AV) in the M88 with the addition of apple juice (AJ) or after 24h storage in

AJ at cold (4°C) or room temperature (22°C) are given for three replicates of

 

 

 

 

 

 

 

each strain.
Survival rate per hour (mean i sd)
Serotype and
strain8 Locale (year) Isms.“ AVMMOC AVMSS “22°C Sourceb

E. coli O157:H7
TW02302a Mich. (1982) -0.17 i 0.10 —0.16 i 0.06 -0.39 i 0.10 6
TW07591 Mich. (1997) -O.60 i 0.05 -O.47 i 0.10 -O.55 i 0.22 1
TW07695 Fla. (1997) -0.08 i 0.05 -0.13 i 0.19 -0.43 :I: 0.22 1
TW07700 Calif. (1997) -0.11 00.05 -0.01 i 0.06 -0.39 3: 0.03 1
TWO7702 Ohio (1997) -0.08 i 0.01 -0.01 :t 0.02 -0.25 i 0.14 1
TWO7704 Ohio (1997) -0.05 :t 0.01 -0.06 i 0.03 -0.25 i 0.19 1
TWO7937 Mass. (1998) -0.07 i: 0.02 -0.08 i 0.04 -O.39 i 0.10 1
TW07945 Fla. (1998) -0.26 i 0.20 -0.07 i 0.06 -O.57 i 0.09 1
TWO7952 DC. (1999) -0.25 :I: 0.10 -0.15 i 0.09 -O.74 i 0.13 1
TW08022 Mont. (2000) -0.21 i 0.02 -0.12 d: 0.08 -0.49 i 0.09 7
TW08263a Calif. (1996) -0.12 i 0.11 -O.35 :l: 0.20 -0.40 i 0.10 3
TWO8264° Japan (1996) -0.27 i 0.06 -0.35 :t 0.20 -O.75 i 0.12 8
TW08609 Wash. (2000) -O.12 i 0.03 -0.06 :t 0.04 -0.52 :t 0.18 7
E. coli 026:H11
TW07594 Ariz. (1997) -O.12 i 0.07 -O.16 :t 0.03 -0.58 i 0.11 1
TW07595 Neb. (1998) -O.16 i 0.02 -O.32 i 0.04 -0.67 1: 0.12 4
TW07622 Mich. (2002) -0.37 i 0.03 -0.34 i 0.08 -0.76 i 0.08 5
TW07948 DC. (1999) -0.35 :t 0.07 -0.47 i: 0.10 -O.78 i 0.13 1
TW08024 Mont. (2000) -O.52 :t 0.2 -O.67 :I: 0.10 -0.98 :t 0.05 6
TW08033 Mont. (2000) -O.64 i 0.07 -O.52 i 0.18 -O.97 :I: 0.07 7
TW08060 Mont. (2000) -0.49 :t 0.19 -0.57 i 0.33 -0.63 i 0.49 7
TW08084 Mont. (2000) -0.39 i 0.19 -0.58 3: 0.08 -1.04 :t 0.17 7
TW08570 Germany (1999) -0.16 i 0.03 -0.33 :t 0.09 -O.79 i 0.05 2
E. coliO111:H8
TW07601 Neb. (1998) -O.41 :t 0.15 -0.40 i 0.08 -O.45 :t 0.32 3
TW08642 Wash. (1999) -O.37 i 0.07 -0.32 :t 0.08 -0.30 i 0.08 7
TW08643 Wash. (2001) -0.41 i 0.11 -0.49 i 0.17 -0.47 i: 0.32 7

 

a Denotes strains isolated from outbreaks. b Strains obtained from the following sources
1) D. Acheson, 2) L. Beutin, 3) P. Feng, 4) P. Fey, 5) J. Rudrik, 6) A. O’Brien, 7) P. Tarr,
and 8) T. Hayashi.

81

again at a ratio of 1:30 and grown for 6.5h to stationary phase, before using the
culture as inoculum for the model stomach system (MSS) and apple juice (Fig.
4.1). Culture density was monitored over time by measuring 00600 with a
Genesys 10 spectrophotometer (Spectronic Unicarn, Rochester, NY). Growth
curves for all strains indicated that cells enter stationary phase after 5h
(evidenced by no further increase in cell density) when transferred at a ratio of
1:30 with a final cell density ranging from 00500 0.85 to 0.95.

Acid resistance assays in the model stomach. The MSS was prepared
fresh daily as outlined by Just and Daeschel (128). Synthetic gastric fluid (SGF)
at pH 1.65 was prepared daily as described previously with the modification that
bovine bile replaced porcine bile (21). A total of 120mL of SGF was mixed with
309 of Gerber Turkey Dinner© baby food in sterile Stomacher bags (Seward, UK)
using a Stomacher 400C lab blender. Once mixed, the final pH of the M88 was
2.5 and bags and contents were warmed to 37°C before use.

Stationary phase cultures in MOPS medium (1 .5h after entry into
stationary phase, as determined by growth curves) were used to inoculate the
MSS bags with ~106 CFU/mL. The pH of the cultures in MOPS minimal medium
at this time point ranged from 6.8 to 7.0. The steps in the experimental
procedure are diagrammed in Figure 4.1. Two bags were inoculated per strain:
one with the addition of 15mL of apple juice pH 3.5 (M88 + AJ) and the other
with 15mL of phosphate buffered saline (MSS + PBS). The MSS + PBS sample

served as a control to determine if there were any survival effects resulting from

82

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

1| . 2
MOPS media MSS + AJ
.—> ——>-
MSS + PBS
8Ah 9h 6.5h pH 2.5
250j.lL — ‘
006,, = 0.1 i E _.
AJ 22°C MSS
L370 pH 3.5—’ ""'—‘ pH2.5
P - ,
4“ 24h
F
AJ 4°C MSS
freezer stock

 

 

 

 

 

 

Figure 4.1. Diagram of assay conditions for measuring survival in the model
stomach system (MSS) and apple juice (AJ). To begin an experiment, 0.25 ml of
bacterial culture grown in LB broth to an 00500 of 0.1 is transferred to MOPS
medium at pH 7.0 (Step 1), and then serially transferred twice to fresh medium.
At steps 2 and 3, aliquots of the 6.5 h MOPS culture are added to the M88 (Step
2) or into AJ (Step 3) for a final bacterial concentration of 106 CFU per ml. After a
24h incubation in AJ (Step 4), 15 ml of cultured juice is transferred into the M88

for a 1:10 dilution. The length of incubation time in hours is indicated.

83

the presence of the apple juice in the MSS. Immediately after inoculation and
addition of either apple juice or PBS, the contents of the stomacher bags were
homogenized in the Stomacher 400C lab blender at 230 rpm for 15s then placed
on a rocking platform at 37°C. Contents of the bags were mixed and sampled
every hour for three hours. Samples of the SGF-baby food mixture were diluted
in PBS and plated on LB agar using an Autoplate 4000 (Spiral Biotech, Norwood,
MA). The final samples were taken at 3h post inoculation and were plated on
both MacConkey and LB agar to determine the number of injured cells. Plates
were incubated at 37°C for 24h before enumerating survivors using the Q Count
(Spiral Biotech, NOf'WOOd, MA). The pH of the SGF-baby food mixture was
measured after the assay, and remained within 0.03 units of the target pH.
Three independent cultures (biological replicates) of each strain were assayed
for survival.

Survival in the M88 after 24h storage in apple juice. Pasteurized
apple juice with no added preservatives was purchased locally. Juice was
adjusted to pH 3.5 with the addition of 4N malic acid, sterilized through 0.22pm
filters, and stored frozen at -20°C until needed. Thawed juice was mixed
vigorously and divided into 100mL aliquots, which were stored at 22° and 4°C
one day prior to inoculation. Portions of the same stationary phase cultures in
MOPS medium were used to inoculate apple juice at 22°C (AJ 22°C) and apple
juice at 4°C (AJ 4°C) with ~106CFU/mL (Fig. 4.1, step 3). Inoculated juice was
vortexed briefly and then stored at 22 or 4°C. Samples were taken from each

inoculated juice after 24h and plated onto LB agar using an Autoplate 4000.

84

After 24h in apple juice, strains were inoculated into the MSS. prepared as
described above. A 15mL aliquot of inoculated juice was transferred to a MSS
bag (Fig. 4.1, step 4), homogenized and incubated at 37°C, and sampled as
described above. At least three independent biological replicates of each strain
were assayed for survival. Plates were incubated at 37°C for 24h before
enumerating survivors using the Q Count. Plate counts of each strain after
storage in AJ were used as the initial cell density for survival in the M88 after
24h in AJ.

Four strains from each clonal group (TW07622, TW07700, TWO7952,
TW08022, TW08024, TW08060, TW08264, and TW08642) were tested for
survival in the M88 after 24h storage in 4°C apple juice at either pH 3.5 or 7.0.
Apple juice was adjusted to pH 7.0 with 4M NaOH and then sterilized using
0.22pm Stericups (Millipore). Strains were inoculated into 100mL of AJ at either
pH and stored at 4°C for 24 h. The MSS assay was conducted as described
above.

Quantitative PCR. TaqMan primer and probe sets targeting gadA, gadB,
and mdh were developed by Applied Biosystems Assays-by-Design service
based on available genome sequences for these genes (Table 4.2). All TaqMan
probes were labeled with FAM fluorescent dye. Primer and probe sets were
evaluated for specificity by testing with either gadA or gadB located in the cloning
vector pCR 2.1 (Invitrogen). Primer and probe for gadA using gadB template and
gadB primer and probe with gadA template were not amplified, as no increase in

fluorescence during the Q-PCR run was observed. Six strains from the two

85

Table 4.2. Primers and probes for gadA and gadB detection using quantitative

 

 

PCR
Target Primer pair Probe
mdh F - TGGTACAGCAAGTTGCGAAAAC CTGCCCGAAAGCGTG
R - GTGGTGTTAACCGGGTI’AGTGATAA
gadA F - GGACCAGAAGCTGTTAACGGATTI' CCGCTCAGAACTACTCG
R - GCGATAGTAGAAATGGCC‘ITTGC
gadB F - GATTCACG'ITI'I'GGTGCGAAGT CTGCGATAGTGGAAATAG

R - ACGAGCGTTGCCATCAAGATATAAT

 

86

clonal groups (0157 strains TW02302, TW07591, TWO7704, TWO7937,
TW08022, and TW08264; 026 strains TW07594, TW07595, TW07622,
TW08024, TW08033, and TW08642) were selected based on their survival rates
in the M88 (two strains that represented the highest, median, and lowest survival
rates were selected from each group). Strains were grown in MOPS medium as
described above and cultures were sampled 1.5h after entry into stationary
phase, the same time point at which cultures were transferred to the M88 in the
original assays. Cultures were mixed with 2 volumes RNA Protect Bacterial
Reagent (Qiagen) and then centrifuged at 7,500 rpm for 10 min to pellet cells.
Cell pellets were stored at —70°C until further use.

Total RNA was extracted from the cell pellets using the hot acid phenol-
chloroforrn method (165). Two independently grown cultures were sampled for
each strain. Genomic DNA was removed from the RNA samples by on-column
DNase treatment using the RNeasy kit (Qiagen). Aliquots (2pg) of total RNA
were used for cDNA synthesis using the TaqMan Reverse Transcription kit
(Applied Biosystems). Reverse transcription reactions contained 1x TaqMan
buffer, 5.5 mM magnesium chloride, 500 pM each dNTP, 2.5 uM random
hexamers, 4U RNase inhibitor, and 12.5U MultiScribe Reverse Transcriptase
and were carried out under the following conditions: 10 min at 25°C, 30 min at
48°C and 5 min at 95°C. A total of 200ng of cDNA was used for quantitative
PCR (QPCR) on the 7900HT SDS (Applied Biosystems). QPCR reactions
contained 1x TaqMan Buffer A, 5.5 mM magnesium chloride, 200 uM each

dNTP, 0.15U AmpErase UNG, 0.3U AmpliTaq Gold, 900 nM forward and reverse

87

 

primers, and 250 nM TaqMan probe and were carried out under the following

conditions: 2 min at 50°C, 10 min at 95°C, followed by 40 cycles of 15s at 95°C
and 1 min at 60°C. TaqMan assays were run in triplicate for each culture
replicate. Increase in fluorescence over time was monitored and used to quantify
expression of gadA, gadB, and mdh in each strain.

All QPCR data were analyzed with SDS 2.1 (Applied Biosystems). A
standard curve was generated for each primer/probe set using a series of 10-fold
dilutions of the cloning vector pCR 2.1 (lnvitrogen) containing either gadA, gadB,

or mdh sequence. Relative quantities of each gene in each sample were
determined from the standard curve, which was included in each individual
QPCR run. The metabolic gene mdh was used as an endogenous control for
each sample, which was used to standardize the gadA and gadB expression
levels within each sample. To compare expression levels across strains,
expression levels of gadA and gadB were normalized to those of E. coli 026:H11
strain TW08033, which had the lowest survival rate in the MSS.
Statistical Analysis. Plate counts were converted to log CFU/ml values,
and log decrease per hour for each assay was determined over the period of the
assay. The survival kinetics were non-linear, so the difference between each
pair of time points was averaged to determine the survival rate for each
independent replicate of each strain. We defined the survival rate, AV, as the
change in viable cell counts (in logic CFU/ml) per hour and report the mean and
standard deviation for each experiment. The percentage of injured cells was

calculated as the difference between the 3h plate count samples from the MSS

88

(in log1o CFU/mL) on MacConkey agar and LB agar, divided by the total log1o

CFU/mL (on LB agar) and multiplied by 100.
Analysis of variance was conducted on the log CFU/ml for each time point

and the AV values for each assay using SAS 9.0 software (SAS Institute, Cary,

NC). Because the survival rates for each clonal group were not normally
distributed, painivise comparisons were made between the clonal groups using
nonparametric statistical analyses. The Kruskal-Wallis test uses the H test

statistic, which approximates the theoretical 38 distribution, and was used to test

for significant differences between clonal groups and treatments (193).

Distribution of relative expression levels determined by QPCR were found

to be non-normal and pair-wise comparisons were made between the clonal

groups using the Kruskal-Wallis test to test for significant differences between

clonal groups.

89

RESULTS

Survival in the model stomach system. To quantify survival in a
simulated gastric environment, strains were grown to stationary phase and
transferred to the MSS pH 2.5, where the numbers of viable cells were assessed
hourly for 3h. AVMSS (log decrease per hour in the MSS) values for each clonal
group did not fit a normal distribution (Anderson-Darling test for normality p <
0.005), thus the Kruskal-Wallis rank sum method was used to compare AVMSS
between clonal groups. Stationary phase cultures were inoculated directly into
MSS pH 2.5 with the addition of either PBS or apple juice (AJ) to determine if the
presence of AJ conferred a protective effect in the MSS. The survival rates
(AVMSS+sz and AVMSSW) were similar for the 0157 strains (H = 0.40, df = 1, P =
0.52) and the 026/0111 strains (H = 0.38, df = 1, P = 0.53) and thus only
AVMSSW was used for further comparisons (Fig. 4.2). E. coli O157:H7 strains
had a mean AVMSSW of -0.17 i 0.15 which was significantly greater (H = 6.61, df

= 1, P = 0.01) than that of the 026/0111 strains, with a mean AVMSSW of 037 1r

0.18 (Fig. 4.3 columns 1 and 2).

Survival in the MSS after incubation in apple juice. The effect of
storage in AJ on subsequent survival in gastric acid was tested by using
stationary phase cultures to inoculate AJ held at either 22°C or 4°C. After 24h in
AJ (Fig. 4.1, step 3), samples were transferred to the MSS where the number of

surviving cells were assessed at hourly intervals for 3h. Comparison of AVMSS

“4°C (log decrease per hour in the MSS after 24h storage in AJ at 4°C) revealed

90

 

 

MSS + PBS

 

 

0.0 ~-

 

 

l
.0
N

J

r

 

 

 

 

 

 

 

 

 

 

 

 

Survrval rate
(log decrease per hour)
6
j

 

J-
1 * a
-0 6 “r o O
O
0 e
O
-0-8 4 m 0157 (n = 14)

 

 

|:| 026/0111 (n= 12)

 

 

 

 

Figure 4.2. Box plots of survival rates (AV) in the model stomach system (MSS)
with the addition of either apple juice (AJ) or phosphate buffered saline (PBS) for
strains in each clonal group. The horizontal bar indicates the median for each

group. Survival rates were not significantly different between treatments.

91

that O157:H7 strains had a significantly different (H = 12.96, df=1, P = 0.0002)
mean survival rate (AVMSS Mac = -0.13 i 0.15) that was more than three times
greater than that of the 026/0111 strains (AVMSS AJ4oc = -0.41 i 0.19) (Fig. 4.3,
columns 3 and 4). The 0157 strains as a group have a similar survival rate in

the MSS before and after storage in AJ at 4°C (Fig. 4.3 columns 1 and 3). When
comparing AVMSSW and AVMSS Mac for individual strains within the 0157 group,
only strain TWO7702 had significantly higher AVMSS “4°C (P = 0.005). Although 9
of the 14 (64%) 0157 strains had a measurable increase in AVMSS Mac compared
to AVMSSW, the mean increase was not significant. The O26/O111 strains also
had a similar survival rate in the MSS before and after storage in AJ 4°C (Fig. 4.3
columns 2 and 4). In this case, 7 out of 12 (58%) 026/0111 strains had a
measurable decrease in survival rate after storage in AJ 4°C, and two of these

strains (TW07595 and TW08570) had a significantly lower AVMSS AJ4oC compared

to AVMSS-
Storage in AJ held at 22°C decreased survival in the MSS for strains of

both clonal groups compared to AVMSSW and AVMSS Mac (Fig. 4.3). E. coli

O157:H7 strains had a mean AVMSS “22°C of -0.47 i 0.19, which was significantly

greater (H = 5.6, df=1, P = 0.01) than that of the O26/O111 strains, with a mean
AVMSS AJ22°C of -0.68 i 0.28 (Fig 4.3, columns 5 and 6). By comparing survival
within clonal groups, we found that both 0157 and O26/O111 strains have

significantly lower AVMSS Amoc compared to either AVMSS or AVMSS AJ4°C-

92

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

MSS after MSS after
0 2 q MSS AJ 4°C AJ 22°C
0.0 .
g .-
O
.8 -0.2 -- I 3
~93 “ Ti
9 8- -o.4 -~ 4
a 0 . i
> (O O '
~ ‘3 C
E 9 -0.6 " o
a o 0 .
(0.8 e —
o) -0.8 fl .
g 0
-10 .. [:j O157(n=14)
[j 026/0111 (n =12)
-1.2 -» I

 

Figure 4.3. Box plots of survival rates (AV) in the model stomach system (MSS)
before and after incubation in either apple juice at 4°C (AJ 4) or apple juice at
22°C (AJ 22) for strains in each clonal group. The horizontal bar indicates the
median for each group. 0157 strains have a significantly higher (H = 12.96, df =
1, P = 0.0003) survival rate compared to the 026 strains in the MSS and

MSS-I-AJ 4.

93

Quantifying injured cells in the MSS. Injured cells were quantified by
plating on LB and MacConkey agar after 3h in the MSS, and calculating the log
difference in CFU per ml after 24h on the two media. The mean log difference
between the injured and uninjured cells after 3h in the MSS was 0.49 i 0.31 for
the 0157 strains, which was significantly less than 1.37 i 0.88 for the 026/0111

strains (Table 4.3, H = 5.12, df = 1, P = 0.02). The log difference between the

El ‘ —‘__5L.».

injured and uninjured population after 3h in the MSS was similar for the 0157
strains before and after storage in AJ 22°C, but there was significantly lower
injury (0.32 i 0.27, H = 10.58, df = 1, P = 0.001) in the MSS after AJ 4°C (Table
4.3). The comparison of the log difference injury for the 026/0111 strains in the
MSS indicates a significant difference between MSS and MSS AJ 4°C (H = 9.55,
df=1, P = 0.002), and the log difference of injured and uninjured cells is
significantly lower for the 026 strains (H = 14.5, df = 1, P = 0.001) in MSS AJ at

22°C compared to the MSS without pre-exposure to AJ (Table 4.3).

Survival in the MSS after storage in AJ 4°C at pH 3.5 and 7.0. After
observing that STEC strains, regardless of clonal group, survived better after
cold storage (i.e., AVMss N400 > AVMSS mm), we tested whether low temperature
or increased acidity led to the protective effect on subsequent survival in the

MSS. Four strains from each clonal group were selected and incubated in AJ
4°C at either pH 3.5 or pH 7.0. After 24h, part of the inoculated juice was

transferred to the MSS to assay survival. Incubation in AJ 4°C pH 3.5 did not

94

Table 4.3. Log difference of noninjured and injured cells (mean :I: sd) measured

after 3h in the model stomach system (MSS) following incubation in apple juice

 

 

 

(AJ) at 4°C or 22°C.
Log difference of noninjured and injured cells
MSS after MSS after
Clonal group No. strains MSS alone AJ 4°C AJ 22°C
O157:H7 14 0.49 :l: 0.31 0.32 :l: 0.27 0.63 i: 0.34
026/0111 12 1.37 i 0.88 0.77 :l: 0.48 0.58 :t 0.41

 

95

affect survival rates for the O157:H7 or 026/0111 strains compared to survival in
the MSS before incubation in AJ, as seen previously (Fig. 4.4). The O157:H7

strains had a mean AVMSS AJ4°C pH 7,0 of -0.71 i 0.34, which was similar compared

to the 026/0111 strains with a mean AVMSS Mac pH 7,0 of -0.85 i 0.34. Storage
in AJ 4°C pH 7.0 significantly (1‘ = -7.29, df = 57, P < 0.0001) decreased survival
in the MSS for both the 0157 and 026/0111 strains compared to their original 1 i
AVMss, which indicates that the acidity of the apple juice contributed more to l
subsequent acid challenge and survival than cold temperature, although the
malate ion concentration would be different in the AJ pH3.5 compared to the AJ
pH 7.0.
Transcription of glutamate decarboxylase in stationary phase. The
glutamate decarboxylase system has been shown to provide the greatest acid
resistance in E. coli (77, 145, 155) and is critical to survival of O157:H7 during
passage through the gastrointestinal tract of calves (202). We hypothesized that
the difference in MSS survival between 0157 and 026/0111 clonal groups
results from differences in the level of transcription of glutamate decarboxylase
genes, gadA and gadB. To test this, TaqMan assays were developed targeting
gadA and gadB, which encode the glutamate decarboxylase isozymes. Primer
and probe sets were found to be specific to gadA or gadB when each set was
tested with either gadA or gadB as a template (see Materials and Methods).
Transcript levels of gadA and gadB were measured for six strains of each group

that represented the range of survival rates for the group (two strains with the

highest, median, and lowest survival rates).

96

 

 

MSS after MSS after
MSS AJ 4°C pH 3.5 AJ 22°C pH 7

0.0 'i' '

 

 

 

-0.2 1~
-o.4 t l; I
-0.6 -— I ~' .

 

 

 

 

 

 

 

 

 

 

Sun/[val rate
(log decrease per hour)

 

 

 

 

 

-0.8 -- .4 :'
Iii...“ —‘
-1.0 --
0157 (n = 4)
42 'r [J 026/0111 (n= 4) J I

 

 

 

 

Figure 4.4. Box plots of survival rates (AV) in the model stomach system (MSS)
before and after incubation in either apple juice pH 3.5 at 4°C (AJ 4 pH 3.5) or

apple juice at pH 7.0 at 4°C (AJ 4 pH 7.0) for 4 strains from each clonal group.

The horizontal bar indicates the median for each group.

97

 

0157 strains had significantly greater relative levels of transcription of both gadA
(H = 10.46, df = 1, P = 0.0012) and gadB (H = 15.42, df = 1, p <0.001) compared
to the 026 strains (Fig. 4.5). Relative transcription levels of gadA and gadB were
highly correlated (:2 = 0.89). Transcription of gadA was more variable than gadB
among strains in both clonal groups. Relative transcription levels of either gadA
or gadB were not positively correlated with survival rates in the MSS (r2980... =

0.21, 1293,13 = 0.15), indicating that other factors play a role in enduring the MSS

environment.

98

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

gadA gadB
O
10 -- __
S
'17) 8 a-
8
a 6 4_ 7"1‘ 0 e
35
3:3 4 .-
16
Q) , e
m 2 -- J.- é
+
0 -I-
E 0157 (n = 6)
[j 026/0111 (n = 6)

 

 

 

 

 

 

Figure 4.5. Box plots of relative transcription values for gadA and gadB for 6
strains of 0157 and 6 strains of 026. Correlating the increase in fluorescence
over time to a standard curve for each target gene and then normalizing across
strains determined the relative transcription for each strain. The horizontal bar
indicates the median for each group. The 0157 strains have significantly higher

transcription of gadA (P = 0.0012) and gadB (p <0.0001) compared to the 026

strains.

99

DISCUSSION

Gastric acidity has long been seen as one of the first lines of defense
against foodborne pathogens, and the ability to survive gastric acidity is thought
to be associated with the infectious dose of a pathogen. Epidemiological data
from outbreaks suggest that E. coli O157:H7 has a low infectious dose,
estimated to be approximately 100 cells (59). Human volunteer studies have
been utilized in the past to assess the infectious dose of an organism; this
approach is not feasible for E. coli O157:H7 or other STEC because of the
potential of severe disease complications resulting from the effects of Shiga
toxin. Instead, inferences about infectious dose have been drawn from the
investigation of the survival of STEC strains in experiments designed to simulate
the conditions in the human digestive tract. Previous studies that have compared
survival of pathogenic E. coli in simulated gastric environments have focused
mainly on E. coli O157:H7. Arnold and Kaspar, for example, measured survival
of strains of several pathotypes of E. coli in SGF pH 1.5 and found that EHEC
O157:H7 strains persisted longer compared to strains of enteroinvasive E. coli,
enteropathogenic E. coli, and Shigella dysenten'ae (8). Roering et al. compared

survival among strains of Salmonella enterica serovar Typhimurium DT104,
Listeria monocytogenes, and E. coli O157:H7 and found that O157:H7 strains
endured for 3h in SGF pH 1.5 compared to survival of 20 min or less for
Salmonella and Listeria strains (213). In addition, Tamplin observed that
01 57:H7 strains had a 4-fold higher survival rate when mixed with cooked

ground beef and inoculated into SGF compared to Shigella strains (239). In his

100

 

study, the average survival for 10 O157:H7 strains in SGF plus cooked ground
beef (—0.08 i- 0.07) was greater than the average survival rate observed here in
the MSS (—0.17 i 0.15), which comprised SGF plus homogenized turkey.

The MSS is a valuable tool for evaluating survival of bacterial cells in a
gastric environment after ingestion of a meal and was devised to address the

criticism that survival studies in SGF alone do not accurately the complexity of

 

the gastric environment after eating (128). Food remains the predominant
transmission route for E. coli O157:H7 (206), so creating a gastric environment
that accounts for the impact and quality of food is important. The gastric
environment is dynamic, and pH fluctuates considerably after ingestion of food
(80). De Jonge and colleagues recently modeled survival of E. coli O157:H7
under dynamic pH profiles designed to mimic those of the human gastric
environment after eating, and found that acid adaptation can occur during
passage through the gastric barrier (75). While the MSS utilized in this study
does not account for variable conditions, such as changes in pH and rate of

gastric emptying, it does contain food particles and digestive enzymes, and

yielded highly reproducible results with STEC strains.

Environmental stresses may damage cellular components and impair the
ability of microorganisms to survive subsequent stress. Many studies examining

survival in gastric environments have not quantified the percentage of cells

injured. In an extensive survey of AR of 0157 and non-0157 commensal bovine

isolates, Berry et al. found that O157:H7 strains had a significantly higher

percentage of injured cells compared to the non-0157 commensal isolates when

101

 

exposed to Brain Heart Infusion broth at pH 2.5 for 6h (15). In these cases, AR
was tested in a variety of media at different lengths of time, and isolated from
different sources (bovine vs. human), and 0157 isolates were compared to
commensal isolates, which could account for differences observed in the
percentage of injured cells. Murinda and colleagues found that O157:H7 strains
were more acid resistant compared to non-0157 STEC when testing at pH 2.5
for 30s (177), although cells were recovered from acid stress on selective media,
which also indicates the effect of injury on non-0157 STEC. This observation is
consistent with our finding that the amount of injured cells after 3h in the MSS
was significantly greater for the O26/O111 strains compared to the 0157 strains.
These results indicate that differences among strains in the AR phenotype may

also be evident in the injury status of cells.

Prior exposure to mild acidity can increase the subsequent AR of E. coli
upon exposure to extreme acidity. As E. coli O157:H7 has been transmitted in
acidic foods, it has the potential to become more acid resistant and thus have
improved survival in passage through the stomach. However, severe stress
imposed by food matrices could also lead to increased cell injury and decreased
bacterial survival. Relatively few studies have explored the impact of incubation
in particular food matrices on the ability of bacterial cells to survive subsequent
acid stress. The results of the experiments presented here indicate that there is
no significant increase in survival rates of the 0157 or 026/0111 populations in
the MSS after storage in AJ for 24h. We found that the storage temperature of

inoculated AJ significantly influenced subsequent survival of STEC in the MSS,

102

 

with incubation at 22°C leading to decreased survival in the MSS. A similar
temperature effect was found for E. coli O157:H7 inoculated into apple juice, with
survival in SGF at pH 2.5 greater after cold storage (at 4°C) versus storage at

room temperature (21°C) (254). Naim and colleagues (179) found a strong
protective effect of sausage on the ability of E. coli O157:H7 to survive gastric
acid. They inoculated three O157:H7 strains into sausage batter and measured
survival in an in vitro digestive challenge after fermentation and drying, and found
that digestive juices offered no real challenge to the bacterial cells ingested
within dry fermented sausage (179).

Three strategies have been identified for acid tolerance in bacteria — those
involved with changes in membrane composition, the use of enzymatic or
physiological mechanisms to maintain internal pH, and systems to repair and
prevent damage to essential cellular components by pH (63, 117). It is clear that
E. coli O157:H7 alters its membrane fatty acid profiles (273, 274), decreases
membrane permeability in response to acidic conditions (127), and induces
several mechanisms that maintain internal pH (155, 209). In addition, expression
of the DNA binding protein, Dps, protects DNA from damage resulting from acid
stress (63, 180). Differences in any of these systems could account for variation
in AR within and between STEC clonal groups. Our experiments indicate that
there is a significant between-group component of variation resulting from
differences in the transcription of glutamate decarboxylase genes, gadA and
gadB. Glutamate decarboxylase has been shown to be critical to survival of

O157:H7 during passage through calves (202) and important to survival under

103

extremely acidic conditions such as the stomach. Here we observed difference
among clonal groups in the transcription levels of gadA and gadB, however,
these levels were not highly correlated with survival rates among strains within
groups, suggesting that other factors are also contributing to the AR and survival
rates in the model stomach.

In a comparison of two clonal groups of STEC, we have shown that E. coli
O157:H7 strains have a superior ability to survive in a simulated gastric
environment compared to the non-0157 STEC serotypes 026:H11 and O111:H8
strains. This difference, however, is alleviated when cultures are held at
stationary phase for longer periods of time. The findings suggest that E. coli
O157:H7 cells rapidly achieve an enhanced state of AR in early stationary phase,

an ability that may underlie the low infectious dose of this pathogen.

104

ACKNOWLEDGEMENTS

We thank the following people for kindly providing the strains used in this
study: David W. Acheson, CFSAN; Lothar Beutin, Robert Koch Institute; Peter
Feng, CFSAN; Paul D. Fey, University of Nebraska; James Rudrik, Michigan
Department of Community Health; Alison D. O’Brien, Uniformed Services
University of Health Sciences; Philip I. Tarr, Washington University at St. Louis;
and Tetsuya Hayashi. Genome Information Research Center, Osaka University.
We also thank Shannon Manning for helpful discussion. We thank Megan
Parsons for assistance with the model stomach assays and Annette Thelen for
excellent technical assistance with the Q-PCR assays.

This work was supported by USDA NRI Competitive Grant number 2005-
35201-16362 and in part by the Michigan Agricultural Experiment Station. TMB
was supported by a DHS graduate fellowship from The National Center for Food

Protection and Defense.

105

CHAPTER 5
Global transcriptional response of Escherichia coli 01 57:H7 to growth

transitions in glucose minimal medium

106

 

SUMMARY

Global patterns of gene expression of Escherichia coli K-12 during growth
transitions have been extensively investigated, however, comparable studies of
E. coli O157:H7 have not been explored, particularly with respect to factors
regulating virulence genes and genomic islands specific to this pathogen. To
examine the impact of growth phase on the dynamics of the transcriptome,
O157:H7 Sakai strain was cultured in MOPS minimal media (0.1% glucose),
RNA harvested at 10 time points from early exponential to full stationary phase,
and relative gene expression was measured by co-hybridization on high—density
DNA microarrays. Analysis of variance (R/MAANOVA, Fs test) identified 442
(36%) of 1239 0157-specific ORFs and 2110 (59%) of 3647 backbone ORFs
that changed in expression significantly over time. QT cluster analysis placed
2468 of the 2552 significant ORFs into 12 groups; each group representing a
distinct expression pattern. ORFs from the largest cluster (n=1078) decreased in
expression from late exponential to early stationary phase: most of these ORFs
are involved in functions associated with steady state growth. Also represented
in this cluster are ORFs of the TAI island, encoding tellurite resistance and
urease activity, which decreased ~4-fold. Most ORFs of the LEE island, with the
exception of espZ, decreased ~2-fold by early stationary phase. ORFs encoding
proteins secreted via the LEE encoded type III secretion system, such as tccP
and espJ, also decreased in expression from exponential to stationary phase.
Three of the clusters (n=154) comprised genes that are transiently upregulated at

the transition into stationary phase and included genes involved in nutrient

107

scavenging. Upregulated genes with an increase in mRNA levels from late
exponential to early stationary phase belonged to one cluster (n=923) which
includes genes involved in stress responses (eg. gadAB, osmBC, and dps).
These transcript levels remained relatively high for >3h in stationary phase. The
Shiga toxin genes (stx1 AB and stx/28) were significantly induced after transition
into stationary phase. Expression of 307 0157-specific ORFs was modulated in
a growth dependent manner. These results provide a baseline transcriptional
profile that can be compared to patterns of gene expression of this important

foodborne pathogen under adverse environmental conditions.

108

 

INTRODUCTION

Enterohemorrhagic Escherichia coli (EHEC), a food and water- borne
pathogen of zoonotic origin, are an important cause of acute gastroenteritis in
humans. O157:H7 is the predominant serotype of enterohemorrhagic E. coli
causing illness in the United States (173). One of the hallmarks of EHEC
pathogenesis is the formation of attaching and effacing lesions on intestinal
epithelial cells (90, 256) , resulting in intimate adherence of the bacterial cell to
the intestinal mucosa. The ability to form AlE lesions is encoded by the Locus of
Enterocyte Effacement (LEE), a 35 kb pathogenicity island (169). Another
virulence characteristic of EHEC is the production of Shiga toxin, a two
component toxin that inhibits protein synthesis in eukaryotic cells (194). The
genes for production of Shiga toxin are located in intact or partial genomes of
lambda prophages that are inserted into the chromosome (110, 220). Together,
the LEE and Shiga toxins are considered to be two of the principal virulence
determinants that mediate development of hemorrhagic colitis and the life-
threatening hemolytic uremic syndrome (133).

Regulation of these virulence factors is influenced by global regulators as
well as regulators specific to the virulence factor. Many of the virulence factors in
EHEC are carried on ‘foreign’ DNA, which is not present in E. coli K-12, and their
expression is typically regulated by transcriptional regulators also carried on
these mobile elements. EHEC can utilize global regulators that are common to
all E. coli as well as 0157-specific regulators to control expression of virulence

factors (133, 230). For example, the expression of LEE is dependent on growth

109

phase and responds to nutrient downshifts via ppGpp signaling through DksA to
Ler (_I=EE encoded regulator) and PchA (181). LEE expression can also be
upregulated by activation of Ler in the presence of bicarbonate ions (1 ), and also
through quorum sensing, where autoinducer 3 activates LEE expression through
Ler (232). Shiga toxin expression is linked to expression of the phage lysis
genes (259, 260) and can be controlled by the iron responsive regulator Fur (36).
In addition to the known virulence determinants, E. coli O157:H7 Sakai and EDL-
933 contain ~ 1.3 Mb of sequence not found in E. coli K-12; these sequences
occur as part of the genetic material of 18 Iysogenic phages and 6 phage-like
elements that have integrated into the Sakai genome (105) and as 177 0157
strain-specific islands (OI number) in the EDL-933 strain (197). Recently it was
identified that many effector proteins that are secreted via the LEE-encoded type
three secretion system are specified by genes distributed throughout the genome
on Iambdoid phages (247).

E. coli is metabolically versatile and can adapt to growth under a wide
range of conditions, adaptations essential to a bimodal lifestyle either in the
primary habitat within animal hosts or in secondary habitats as free living cells in
the natural environment (101). Slowing metabolic activity during stationary
phase reflects a survival mechanism in nutrient poor environments in which
bacteria undergo a variety of morphological and physiological changes. In
stationary phase, E. coli are more resistant to a number of stresses, including pH
stress (100, 227) and osmotic stress (125), both of which occur during transit

through the colonization of the host intestinal tract. Important to the transmission

110

route of E. coli O157:H7 is the ability to persist in adverse environments until
entering a new host. Hence studying growth transitions and how they relate to
the lifestyle of E. coli O157:H7 is essential to understanding the persistence and
spread of this pathogen from the bovine reservoir to foods and to humans.

Microarrays have been utilized to observe changes induced in the
transcriptome of E. coli during growth transitions and in response to different
environmental stresses. Global transcription profiling of E. coli K-12 indicates a
coordinated response to growth arrest (57). The sigma factor RpoS plays a
crucial role in transcription of stationary phase and stress response genes (142,
195, 266). Microarray studies have been conducted to understand how E. coli K-
12 responds to growth at low pH (106, 252), on rich and minimal media (240),
during anaerobic growth (66, 132, 215), and growth transitions in rich medium
(10). Transcriptional responses of O157:H7 during growth on plasma
membranes (72) and upon exposure to norfloxacin (111), have been
investigated, and microarrays have been used to identify genes regulated by
quS in E. coli O157:H7 (233), but a full picture of global gene expression during
growth transitions, while studied in E. coli K-12, has not been investigated in the
food borne pathogen E. coli O157:H7.

A key component to understanding the adaptations and ecology of E. coli
O157:H7 is to determine how the 0157-specific genomic elements respond to
one of the critical stages of growth, stationary phase. It has been well-
documented that some virulence factors are regulated by environmental cues

that signal the presence of the host environment, but what is less understood is

111

how these virulence factors are expressed in a non-host environment. Here we
address the question of how O157:H7 responds to growth transitions by
determining the changes in global gene expression patterns in O157:H7 during
the transition from exponential to stationary phase in minimal medium, with
particular interest in expression patterns of 0157-specific genes. The
transcriptome of E. coli O157:H7 Sakai was examined by comparative microarray
hybridizations at 10 different time points during exponential and stationary phase

and compared to obtain a temporal pattern of gene expression.

112

MATERIALS AND METHODS
Growth conditions. E. coli O157:H7 RIMD0509952 (Sakai) involved in a radish
sprout outbreak (174) was stored at —70°C in LB broth and 10% glycerol. The
Sakai strain was inoculated into 10mL of LB broth from freezer stocks and grown
to ODsoo = 0.1. This ~4h period of growth in LB allowed cells to recover before
transfer to MOPS minimal media. MOPS minimal media (10x) was prepared as
described in Neidhardt et al. (187) with the addition of 20mL of micronutrient
solution instead of 10mL. MOPS minimal media contained 100mL of 10x MOPS
minimal media, 10mL of 0.132M K2HPO4, and 5mL of 20% D—glucose per liter.
Cultures were inoculated into 50mL of MOPS minimal media with 0.1% glucose
at a ratio of 1:200 and grown to stationary phase at 37°C with shaking. Cultures
in MOPS were transferred to 100mL MOPS in 250mL plastic culture flasks
(Nalgene, Rochester, NY.) at a ratio of 1:75 and grown for 9h before transfer
again to 100mL MOPS in 250mL plastic culture flasks at a ratio of 1:30 and this
culture was used to sample RNA during growth (Fig. 5.1). Cultures were
sampled at 3h (ODeoo :02), 4h (ODeoo z0.45), 4.33h (ODeoo z0.73), 4.66h
(ODeoo zO.93), 5h (00600 z1), 5.5h, 6h, 7h, 7.5h, and 8h. After 5h, the culture
density remained at ODeoo z1. Residual dissolved oxygen tension was
monitored using a dOz probe and Consort C535 multimeter (Topac
Instrumentation, Hingham, Mass.) for three independent cultures in MOPS
minimal medium.
RNA isolation. At each time point, 4mL of culture was mixed with 8mL of

RNAProtect (Qiagen, Valencia, Calif), vortexed, and centrifuged at 4°C,

113

 

 

ODeoo

 

Exp. iL.Exp§Trn.§ E. St. Stationary

 

 

.1).--
-l-

 

0.1

-u-

l
l

3 4 5 6 7 8
Time (h)

 

 

 

Figure 5.1. Average growth of E. coli O157:H7 Sakai in MOPS minimal medium
plotted as the increase in cell density, measured at ODsoo, over time. Error bars
represent the standard deviation of four culture replicates. Samples were taken
from the culture at the time points plotted and RNA was extracted. The growth
phases are marked; Exp.= exponential growth, L. Exp.= late exponential growth,
Trn = transition to stationary phase, E. St = early stationary phase, and

Stationary = stationary phase.

114

7500 rpm for 10 min to pellet cells. The supernatant was removed and cell
pellets stored at —70°C for 1 week or less before RNA extraction. Cell pellets
were suspended in 700pL of 95°C lysis buffer (20mM sodium acetate pH 5.2,
2mM EDTA pH 8.0, and 0.5% SDS), held at 95°C for 303 then mixed with 700uL
of 65°C Acid-Phenol: Chloroform, pH 4.5 (with IAA, 125:2521) (Ambion, Austin,
Tex.). Samples were held at 65°C with periodic shaking for at least 6 minutes
before centrifuging at 12000 rpm for 10 min. Supernatant was extracted again
with acid-phenol:chloroform and then with chloroformzisoamyl alcohol (24.1 ).
RNA was precipitated for at least 1 hour at —20°C in 2.5V 100% ethanol and
1/10V 3M sodium acetate pH 5.2. RNA was suspended in 1mM sodium citrate
pH 6.5 and stored at —70°C. RNA samples were purified and treated with Dnase
using the Rneasy kit (Qiagen). RNA quality was assessed by observing the 23s
and16s rRNA bands by electrophoresis on a formaldehyde-agarose gel. RNA
was checked for complete DNA digestion by PCR.

cDNA synthesis and hybridizations. Reverse transcription reactions contained
6pg RNA, 2ug random primers (lnvitrogen, Carlsbad, Calif.), 1x first strand buffer
(lnvitrogen), 10mM D'I'I', 400U Superscript II (lnvitrogen), 0.5mM each dATP,
dCTP, and dGTP, 0.3mM dTTP, and 0.2mM amino-allyl dUTP. 30pL reactions
were incubated overnight at 42°C. cDNA was purified using PCR cleanup
columns (Qiagen) with Phosphate wash buffer (5mM K2HPO4, pH 8.0,
80%EtOH) and phosphate elution buffer (4mM K2HPO4, pH 8.5). Amino-allyl
labeled cDNA was dried and resuspended in 0.1 M sodium carbonate pH 9.3 and

coupled with either Cy3 or Cy5 (Amersham Biosciences, Piscataway, NJ.)

115

Uncoupled dye was removed by another purfication using the PCR cleanup kit.
Concentration of cDNA and amount of incoporated dye was measured for each
sample using a Nanodrop spectrophotometer (Ambion).

Oligo microarray. The E. coli oligo set version 1.0.1 (Operon) was printed onto
Corning UltraGaps (Corning Incorporated, Acton, Mass.) coated slides at the
Research Technology Support Facility at Michigan State University. The Qiagen
oligo set contained 5,978 probes specific for three E. coli strains,K—12 (MG1655)
(25), O157:H7 Sakai (105), and O157:H7 EDL-933 (197), in which 5,943 probes
were 70-mer oligonucleotides, and 35 probes had lengths that ranged from 41-
69 bp. The oligo set also contained 12 randomized negative control 70-mer
oligonucleotides. There are a common set of 3807 probes that target 3807
ORFs present based on the genome sequences of E. coli K-12 MG1655 , E. coli
O157:H7 EDL-933 , and O157:H7 Sakai , which we refer to as the backbone
ORFs. There are also 1741 probes targeting ORFs that are specific to one or
two of the genomes, typically to O157:H7 EDL-933 and Sakai. These probes are
referred to as the 0157-specific ORFs. Probes designed to target K-12-specific
or EDL-933 specific ORFs were not considered after the data analysis stage
because these genes are not present in the Sakai genome. We used the Qiagen
annotation for the probe set, and analyzed only the 4886 probes that were
assigned a ECs number.

Hybridization conditions. Arrays were cross-linked by exposure to 600 mJ UV
before blocking in 1% SDS, 5x SSC, and 1mg/mL BSA at 42°C for 1 hour. After

blocking, arrays were washed 2x 5 min in 0.1x SSC and 2x 303 in H20. Dried

116

arrays were placed into hybridization cassettes (TeIeChem lntemational,
Sunnyvale, Calif.) and the cDNA samples were suspended in 10 mM EDTA,
denatured at 95°C for 5 min and then mixed with 40uL of SlideHyb buffer 1
(Ambion) and loaded under a coverslip onto the array. Hybridizations were
canted out at 47°C for 16-18h. After hybridization, arrays were washed in 2x
SSC, 0.5% SDS 37C for 5 min, followed by 2x 5 min in 0.1x SSC, 0.1% SDS
37°C, and then 2x 2.5min in room temperature 0.1x SSC. Arrays were scanned
using an Axon 4000b scanner (Molecular Devices, Sunnyvale, Calif) and images
were analyzed using GenePix 6.0 (Molecular Devices). The 3h sample served
as a common reference and was hybridized with all subsequent samples. Array
data have been deposited at the NCBI Gene Expression Omnibus (Accession
xxxx).

Data analysis. Raw intensity values for all probes on each array were
normalized using pin-tip LOWESS (204) in R v.2.2.1 (245) with the MAANOVA
(v. 0.98-8) package (205). Signals from two replicate probes on each array were
averaged and '092 transformation applied. Pairwise differences in transcription
levels over time were determined using a mixed model ANOVA in R/MAANOVA.
The ANOVA modeling allows for consideration of appropriate error structures for
experiments with multiple sources of variation in microarray measurements
(136). The random effects of the model were biological replicate and array
effects, whereas the fixed effects were time point and dye effects (70).
Significant changes in expression over time were determined using the Fs test

(71) with the False Discovery Rate (FDR) correction applied and a cutoff p-value

117

of 0.00001. Pair wise contrasts of time points were estimated by the t—test in
MAANOVA. Contrast p-values were corrected for multiple testing by using the
FDR correction. Probes with significant changes in expression over time were
grouped by the time points at which the significant change occurred, as
determined by the contrast analysis (FDR corrected p value < 0.05). QT
clustering of significant ORFs was conducted in MeV v. 3.1 (www.tigr.org) with a

diameter of 0.4 and a minimum cluster size of 15.

118

RESULTS
Analysis of expression ratios. The results are based on O157:H7 Sakai
cultures collected from 10 time points of growth in minimal media with 4
replicates at each point for a total of 40 samples. The samples collected from the
mid-exponential phase 3-hour time point (Fig. 5.1) were used as a common
reference for hybridization and analysis for all subsequent time points in a
blocked reference design (234). This design allowed for indirect comparison
between all time points via the 3-hour samples (64, 271 ). We incorporated the
dye-swaps data among the four biological replicates which can confound dye
effects and biological replicate effects. However, if there is significant variation in
the rate of dye incorporation from one labeling reaction to another, this would
result in large dye-effects compared to biological replicate effect (137), an effect
not found in our experiments. In general, the variance estimates for the
biological replicate effects were small (median 0.0008), compared to the variance
estimates (<0.5%) for the array effects (median 0.16). Most ORFs (4362/5886,
90%) had similar signal intensities for the mid- and late- exponential phase
samples, indicating that there were few overall differences in expression between
these time points (Fig. 5.2A and 5.2B). In contrast, there were many differences
in expression found between mid-exponential phase samples and samples from

the transition point (Fig. 5.20) and early stationary phase (Fig. 5.2D).

119

 

 

 

Average signal intensity for 4h samples
Average signal intensity for 4.6h samples

 

 

 

 

 

 

 

 

 

00° 0 swearing:
,gois rifled: ii-

_;
O

0)
l

J

 

Average signal intensity for 6h samples

3:

 

Average signal intensity for 5h samples

 

 

 

 

 

 

I I I I I l I I I I I I I
102 1o3 104 1o5 102 104 105
Average signal intensity for mid-exponential phase samples (3h) Average signal intensity for mid-exponential phase samples (3h)

Figure 5.2. Average signal intensity plots for four of the time points sampled.
The normalized signals of 4,886 ORFs for E. coli O157:H7 Sakai were averaged
from four hybridizations, representing four biological replicates for the 3h vs 4h
comparison (A.), the 3h vs 4.6h comparison (3.), the 3h vs 5h comparison (C.),
and the 3h vs. 6h comparison (0.). The Lowess line is plotted for each graph in

white.

120

Summary of significant changes in gene expression over time. A total of
4,886 O157:H7 Sakai ORFs were targeted on the microarray, and a significant (p
< 0.00001) change in expression for at least one time point was detected for
3,050 ORFs. The false discovery rate (FDR) correction for multiple testing
applies a step-wise correction to the p values, and this was used to narrow the
dataset by increasing the stringency. The 2,552 ORFs with the lowest p values

after correction (p-value < 1 x 10”) were considered for further analysis.

Trends in gene expression identified by significant contrasts between
subsequent time points and QT clustering. Expression profiles of the 2,552
ORFs with significant differences were clustered into 12 groups using QT
clustering (Fig. 5.3), which groups similar expression profiles based on jackknife
correlations (113). The majority of ORFs were classified into 12 groups; only 84
were not placed into a cluster. ORFs were also classified based on the time
intervals at which a significant change in gene expression occurred. A subset of
524 ORFs had differences in expression from mid- to late-exponential phase (3h
to 4h) (Fig. 5.4). The majority of ORFs, 1,668 of 4,886 (34%), had significantly
changed in transcript level at the stationary phase transition point (4.6 to 5h).
Additionally, 1,045 of 4,886 (20%) ORFs significantly changed during the

transition point to early stationary phase (5 to 5.5h).

Significant changes in exponential phase — response to limiting oxygen.

There are 524 ORFs with significant differences in transcript level during

121

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

3 3 3
QTC 1 - 1078 genes QTC 2 - 923 genes QTC 3 - 114 genes
2 -i- 2 -l- 2 --
i 1 a 1 -~ 1 +-
0 o «r o -- o --
3.1 -- .1 - .11
2 -~ -2 -- 2 --
-3 i i i l t s a t i t i i i e : i 1 i : s
3 3 3
QTC 4 - 96 genes QTC 5 - 54 genes QTC 6 - 47 genes
2 e- 2 -- 2 --
.5 1 "" 1 - 1 -r-
is?
go -' 0 -u- 0 —n—
N
3-1 “F .1 -I— -1 -1—
-2 -- -2 ~- -2 -—
3 : i i I i 4. .3 i i i I i . -3 + r i i 1 1
3 QTC 7 - 43 genes 3 QTC 8 - 32 genes 3 QTC 9 - 22 genes
2 " 2 -- 2 .—
c 1 —L 1-- 1 -~
.9
g o a o -— 0 -~
, i
N -1 -- 1 -- 1-r
3
-2 -1l— .2 -i- 2 di—
-3 i t 1‘ t 4. t -3 ‘ ‘r t i i i 3 I t i I l t
3 QTC 10 - 21 genes 3 QTC 11 - 19 genes 3 QTC 12 - 17 genes
2 —- 2 -- 2 ._
g 1 -- 1 -- 1 --
a
5 o ~~ o -- o --
N
3-1 -- 1 -*- 1 ~1-
2 A— 2 + 2 ->—
-3#ll#ti-3.tiiI%-3trtttr
3 4 5 6 7 8 3 4 5 6 7 8 3 4 5 6 7 8
Tune (h) Time (h) Time (h)

 

 

 

Figure 5.3. QT clusters of the 2,552 significant ORFs. Average expression
profiles were determined for the ORFs in each cluster and plotted with the

standard deviation for each time point.

122

 

 

3-4h 4-4.3h 4.3-4.6h 4.6-5h 5-5.5h 5.5-6h

Percent of ORFs with significant
increase in expression

 

Percent of ORFs with significant
decrease in expression
a

20-

 

— backbone genes
—0157-specific genes

 

 

 

25

 

 

 

 

Figure 5.4. Proportion of backbone genes (those common to E. coli K12 and
O157:H7) and O157:H7 specific genes that are significantly differentially
expressed, either increasing or decreasing, at specific time intervals. The ORFs
with significant changes in expression between two time points were determined

from the contrast analysis.

123

Table 5.1. Significant ORFs (p value < 1 x 107) with greater than 4-fold
significant change between 3h and 4h.

 

 

I092
Ecs change in OT
number Genea function expression clusterb
ECsO236 0157 Unknown function -2.89 10
ECsO482 cyoE protoheme IX farnesyltransferase -2.08 10
ECsO483 cyoD cytochrome c ubiquinol oxidase subunit IV -3.18 10
ECsO484 cyoC cytochrome c ubiquinol oxidase subunit lIl -3.32 10
ECsO485 cyoB cytochrome c ubiquinol oxidase subunit l -3.87 10
ECsO486 cyoA cytochrome c ubiquinol oxidase subunit II -3.89 10
ECsOGGO dcuC transport of dicarboxylates 2.20 6
ECsO746 ' M succinate dehydrogenase, cytochrome b556 -3.15 10
ECsO747 sth succinate dehydrogenase, hydrophobic subunit -2.66 10
ECsO748 MA succinate dehydrogenase -3.06 10
ECsO749 s_dޤ succinate dehydrogenase -2.79 10
ECsO750 - orf, hypothetical protein -2.24 10
ECs0751 sucA 2-oxoglutarate dehydrogenase -2.20 10
ECsO768 gm cytochrome d terminal oxidase 2.56 4
ECsO769 9mg cytochrome d terminal oxidase 2.58 4
ECsO916 yliH putative receptor 3.84 2
ECsOQ79 dmsA anaerobic dimethyl sulfoxide reductase 3.71 4
ECsOQ81 dmsC anaerobic dimethyl sulfoxide reductase 2.08 4
ECs0986 pflB formate acetyltransferase 1 2.04 6
ECs1572 pepT putative peptidase T 2.64 2
ECs1728 narK nitrite extrusion protein 2.98 6
ECs1729 narG nitrate reductase 1, alpha subunit 2.25 4
ECs1741 adhE CoA-linked acetaldehyde dehydrogenase 2.50 6
ECs1756 ych putative outer membrane protein 4.15 4
ECs1875 ych probable amidotransferase subunit -2.28 2
ECs2027 ydcl putative transcriptional regulator LYSR-type -2.41 2
ECsZO78 fdnG formate dehydrogenase-N, nitrate-inducible 2.30 6
ECsZ150 yde orf, hypothetical protein 4.70 4
ECsZZ93 ynfE putative oxidoreductase, major subunit 2.83 6
ECsZ457 yde orf, hypothetical protein 2.03 6
ECs2463 ynjE putative thiosulfate sulfur transferase 3.47 1
E032614 yecH orf, hypothetical protein 3.09 6
ECs3061 fruB PTS system, fructose-specific Ilepr component 2.02 1
ECs3084 dst disulfide oxidoreductase 2.04 1
ECs3088 ccmC heme exporter protein C 2.19 1
ECs3090 ccmA ATP binding protein of heme exporter A 2.64 1
EC33091 napC cytochrome c-type protein 3.55 1
ECs3092 napB cytochrome c-type protein 3.30 1
ECs3093 napH ferredoxin-type protein: electron transfer 3.04 un
ECs3180 ackA acetate kinase 2.28 6
ECs3226 ych orf, hypothetical protein 3.02 6
ECs3445 yflD putative formate acetyltransferase 4.48 6
ECs3460 yfiA putative yhbH sigma 54 modulator 2.12 2
ECs3582 hypA pleiotrophic effects on 3 hydrogenase isozymes 2.92 6

 

124

Table 5.1, continued.

 

 

log2
Ecs change in QT

number Genea function expression clusterb
ECs3583 hypB guanine-nucleotide binding protein 2.10 6

plays structural role in maturation of all 3
EC53586 hypE hydrogenases 2.10 6
ECs3799 O157 orf; hypothetical protein 2.92 6
ECs3800 O157 orf; hypothetical protein 2.33 6
ECs3802 O157 putative ATP-binding protein of ABC transport system 2.40 6
ECs3833 ansB periplasmic L-asparaginase II 3.41 4

probable cytochrome NilFe component of
ECs3880 hbe hydrogenase-2 2.05 4
ECs4040 yth orf, hypothetical protein 3.11 1
ECs4216 nirB nitrite reductase (NAD(P)H) subunit 3.55 1
ECs4343 £154 periplasmic binding protein for nickel 3.46 1
ECs4344 yi_kB_ transport of nickel, membrane protein 2.10 6
ECs4347 LlrE ATP-binding protein of nickel transport system 2.85 1
ECs4398 yth putative cytochrome C peroxidase 2.86 6
ECs4456 yial orf, hypothetical protein 2.40 4
ECs4750 yigl orf, hypothetical protein -2.55 10
ECs4834 sodA superoxide dismutase, manganese -2.40 7
ECs4874 gldA glycerol dehydrogenase, (NAD) 2.86 6
ECS5052 nrfA periplasmic cytochrome c(552) 3.08 un
ECsSO53 nrfB formate-dependent nitrite reductase 2.86 6
ECs5054 nrfC formate-dependent nitrite reductase; Fe-S centers 2.96 un

fumarate reductase, anaerobic, iron-sulfur protein
ECsS134 frdB subunit 2.50 8
EC55135 frdA fumarate reductase, anaerobic, flavoprotein subunit 2.11 8
ECS5214 nrdG anaerobic ribonucleotide reductase activating protein 2.02 6
ECs5215 nrdD anaerobic ribonucleoside-triphosphate reductase 3.54 6
ECs5298 yjiM orf, hypothetical protein 2.76 8

 

“ Genes in bold have been identified as being under the control of the transcriptional regulator

FNR (66, 132). Underlined genes have been identified as being controlled by the transcriptional
regulator ArcA (156). Genes with “0157" indicate that these are 0157-specific ORFs.
b QT cluster un. means unassigned

125

exponential phase - a total of 113 genes changed only in exponential phase,
whereas the remainder had altered levels of expression in exponential as well as
stationary phase. ORFs that decreased in expression included those encoding
enzymes of the TCA cycle, such as sucA, and sdhABCD (Table 5.1, Fig. 5.3,
QTC 10). Many of the ORFs (94/524, 18%) that changed in transcript level are
regulated by the transcription factor FNR (66, 132), whereas a smaller set of
ORFs (29/524, 5%) are known to be regulated by ArcA (156). Genes that
increased included those encoding anaerobic electron acceptors, fumarate
reductase, frdAB, and DMSO reductase, dmsA and dmsC, as well as a DMSO
reductase paralog, ynfE (Table 5.1, Fig. 5.3, QTC 6). Genes encoding the
cytochrome c biogenesis system (ccmABCDEFG) and the cytochrome c-like
protein (napB and napC) also had a significant increase from 3h to 4h. The
operon encoding the low affinity cytochrome oxidase bo (cyoABCD) decreased 9
to15- fold in expression from 3h to 4h (Table 5.1). Concomitantly, expression of
the high affinity cytochrome oxidase bd, cydAB, increased 4-5 fold. Expression
of the high and low affinity cytochrome oxidases is directly influenced by the level
of available oxygen in the media (4, 250). The altered expression of the
cytochrome oxidases as well as the increase in F NR-controlled, anaerobic
associated genes and decrease in TCA cycle enzymes is consistent with the
hypothesis that oxygen became limiting under the culture conditions that were
used. Subsequent measurement of the residual dissolved oxygen tension in the
culture during growth indicates that residual dissolved 02 levels decreased

almost to zero (95% decrease in 02) during exponential phase (Fig. 5.5). As

126

cultures entered stationary phase, the O2 levels began to increase, as the

demand for 02 by the culture decreased.

Significant change in 0157-specific genes during exponential phase.

In contrast to the backbone genes, only a small proportion (6%) of the 0157-
specific ORFs changed significantly in expression measured during exponential
phase (Fig. 5.4). While statistically significant, most of the differences in
expression level for these 61 genes were not substantial ranging from 1.2- to 2-
fold. Many of these ORFs are of unknown function, but of the 45 0157-specific
ORFs that increased from 3-4 h, 9 of them were from a single Sakai phage (Sp)
15 region which encodes the Shiga toxin 1 phage, and contains 5 ORFs from Sp
5 which encodes the Shiga toxin 2 phage. Interestingly, ECs2381 a locus whose
expression has been shown to contribute to colonization in the bovine
gastrointestinal tract (82), was also significantly induced 1.8 fold from 3 to 4 h.
There were 4 additional 0157-specific ORFs that had a greater than 4-fold
change in expression from 3-4 h (Table 5.1). These 4 ORFs were not associated

with any of the Sakai phages, and have unknown or putative functions.
Genes with significant changes in expression at the stationary phase

transition point. The majority of significant changes in transcript level occurred

at the stationary phase transition point (Fig. 5.4). There were 176 ORFs with a 4-

127

 

 

1O
8..
Q 6'
U)
a
5 4-
9
2-
0—

 

 

 

 

Time (h)

 

 

Figure 5.5. Residual dissolved oxygen in the MOPS minimal medium during
growth of E. coli O157:H7 Sakai. Mean rDOT from three independent cultures is

plotted with the error bars indicating the standard deviation.

128

Table 5.2. Significant ORFs (p value < 1 x 107) with greater than 4-fold significant
changes in expression at the stationary phase transition point (4.6 to 5h).

 

 

logZ , logZ

change in OT change in QT
Ecs no. Gene8 expression cluster Ecs no. Genea expression cluster
ECsOO34 dapB -2.63 1 ECs1286 0157 -2.09 1
ECsOO35 fllfi -4.23 1 ECs1426 mdoG -2.46 5
EC30068 araC 2.57 2 ECs1440 gy_r_C -2.65 1
ECs0072 tpr -2.25 1 ECs1468 plsX -2.16 1
ECsOO83 fruL -2.06 1 ECs1479 ptsG -3.09 1
EC50124 speD -2.74 1 ECs1492 mfd -2.33 1
ECsO149 dksA 2.12 2 ECs1604 ych -2.19 1
E0501 50 still 2.83 2 ECs1662 0157 -2.53 1
ECsO191 yan 2.42 2 ECs1684 dadA 2.34 2
ECsOZO7 dniR -2.11 1 ECs1710 ychH 4.61 2
ECs0334 0157 -2.07 1 ECs1712 m; -2.68 1
ECsO374 0157 2.42 2 ECs1859 mb -2.70 1
ECsO401 mhpR 4.33 3 ECs1954 0157 -2.61 1
ECsO416 0157 3.55 2 ECs1955 0157 -3.22 1
ECsO468 rin -2.27 1 ECs2011 yan 2.34 2
ECsO483 cyoD 2.06 10 EC32021 all/3 3.60 2
ECsO485 cyoB 2.01 1 0 E052027 ydcl 3.35 2
ECsO489 LOLA 2.10 2 ECs2031 ych 2.99 2
EC30584 purK -2.27 1 ECsZO37 ych 2.71 2
ECsOS85 purE -2.66 1 ECs2083 sch -2.18 1
EC50610 cusC -2.31 1 ECle82 0157 3.21 2
ECsO61 1 cusF -2.82 1 EC52293 ynfE -2.97 6
ECsO61 2 cusD -2.62 1 ECs2296 yan —2.03 6
ECsO61 3 yde -2.70 1 ECsZ362 lhr -2.16 1
ECs0670 dacA -2.32 1 ECs2367 purR -2.51 1
ECsOB81 mg 2.78 2 EC32431 yniA 3.52 2
ECsO773 tolR -2.29 1 ECsZ433 yniC 2.44 3
ECsO782 aroG -3.09 1 ECs2445 osmE 2.33 2
ECsO800 yth -2.13 1 ECs2510 yeaV 2.06 3
ECs0814 O1 57 -2.1 5 1 ECs2692 yodD 2.96 2
ECsO869 yth 3.14 2 ECs2714 espJ -2.01 1
ECs0890 gas 2.64 2 ECsZ737 pchC 3.43 2
ECs0943 artJ -2.81 1 ECs2814 yeeD -2.09 1
ECsO966 M 2.75 2 ECsZ816 yeeF -2.55 1
EC30968 clpA 2.89 3 ECs2839 0157 -2.23 1
ECs0986 pflB -2.61 6 ECs2840 wde -2.14 1
ECs1008 MB 2.15 2 ECs2847 0157 -2.25 1
ECs1029 Mg -2.88 1 ECs3038 yeiT 2.14 3
ECs1037 rmf 2 .24 2 ECs3060 fruK -2.1 3 1
ECs1072 0157 -2.13 5 ECs3061 fruB -2.38 1
ECs1091 0157 3.24 2 ECs3136 yfaX -2.04 1
E031 137 yccC -2.27 1 ECs3149 menC -2.06 1
ECs1138 ych -2.78 1 ECs3180 ackA -2.33 6
E051 139 ych -2.74 1 ECs3194 argT 4.08 2

129

Table 5.2. continued.

 

 

Iog2 logZ

change in OT change in QT
Ecs no. Gene“ expression cluster Ecs no. Gene“ expression cluster
ECs3196 purF -2.94 1 E034188 hopD 2.38 2
E033197 cva -3.35 1 E034192 M -2.57 1
E03321 2 mepA -2.29 1 E034193 [ps_L -2.67 1
E033287 ptsH -2.66 1 E034204 yheT -2.00 1
E033296 cysP -2.17 1 E034249 yth -2.53 1
E033361 purM -4.24 1 ECs4294 M 2.48 2
E033387 sseA 2.18 2 ECs4343 nikA -3.22 1
E033403 hcaR 2.50 3 ECs4347 nikE -2.38 1
ECs3423 purL -2.45 1 E034366 )thg 4.26 2
ECs3448 tGC 2.24 2 E034481 lIdP 2.39 2
ECs3463 tyrA 3.67 3 E034490 gpml -3.35 1
ECs3464 aroF 3.30 3 E034512 [mn_B -2.38 1
ECs3471 yle 358 1 E034553 0157 -2.10 1
E033546 eer -3.47 1 E034575 9300 -2.00 1
E033559 sr1A_1 2.08 3 E034632 yidA -2.02 1
E033595 rpoS 2.80 2 E034638 rpmH -2.47 1
E033606 M -2.59 1 ECs4639 mpA -2.92 1
E033640 pyrG -2.98 1 E034679 gtLE -2.33 1
E033659 fucO 2.17 3 ECs4716 rho -2.56 5
E033675 m -4.09 1 E034720 wecC -2.34 1
E033677 r908 21 7 1 E034741 xerC -2.08 1
E033701 yqu 2.50 3 E034756 yigL -2.18 1
E033743 ygeW 2.08 2 ECs4759 metE -2.76 1
E033749 yqu 2.45 2 E034791 glnL -2.80 1
ECs3799 O1 57 -2.69 6 ECs4870 m_etE -3.65 1
E033800 O1 57 -2.43 6 ECs4885 gag -3.62 1
ECs3802 O1 57 -2.43 6 ECs4887 argC -3.99 1
E03381 1 yggG 2.28 2 ECs4888 argB -3.62 1
E03381 8 metK -3.03 1 E034893 O1 57 2.09 2
ECs3840 nqu 2.44 2 E03490? M -3.02 1
E033931 g_lg§ 2.94 2 5034908 LU -2.64 1
E034052 argG -2.68 1 ECs4909 M -2.60 1
E034103 rpsl -2.28 1 ECs4929 purH -4.14 1
E0341 32 yth -2.54 1 E034931 LnflA; -3.78 1
E0341 50 3mg 2.24 2 E034932 aceB 5.35 2
ECs4156 mscL 2.01 2 E035004 0157 2.14 2
E034161 M -2.17 1 E035046 ych -3.81 1
ECs4162 IpsK -2.36 1 E035051 acs 5.55 2
E034164 rpmJ -2.03 1 E035067 O1 57 2.21 2
E034165 prlA -2.53 1 E035153 M -2.06 1

130

Table 5.2, continued.

 

 

logz logZ
change in OT change in OT
Ecs no. Gene“ expression cluster Ecs no. Gene‘3 expression cluster
E035164 yij 4.54 2 E035240 ngR 2.24 2
E035192 cysQ 2.01 2 E035259 O1 57 -2.09 1
E035231 argl -3.29 1 E035354 rob 2.15 2
E035235 vaIS -2.58 1 ECs5360 yti 2.03 2

 

a Gene names in bold have been found to be regulated by RpoS (142, 195, 266) and those
underlined are genes that increase or decrease in stationary phase in E. coli K-12 (57). Genes
with “0157” indicate that these are 0157-specific ORFs.

131

fold or greater change in expression at the stationary phase transition point
(Table 5.2). 154 ORFs that significantly increased or decreased during the
transition to stationary phase (4.6-5h) also increased or decreased in early
stationary phase (5-5.5h) (Table 5.3). A decrease in expression of ribosomal
genes and genes involved in nucleotide and amino acid synthesis was observed
from 4.6-5h and 5-5.5h (Tables 5.2 and 5.3, Fig. 5.3 QTC 1). Many of the genes
that were significantly down-regulated upon entry into stationary phase have

been also observed in E. coli K-12 (57) (Tables 4.2 and 4.3).

Expression of rpoS, encoding the stationary phase sigma factor, increased 7 fold
during the transition point and the rpoS transcriptional activator, dksA, increased
4.3 fold at the same interval (Table 4.2). Increased expression of genes known
to be regulated by RpoS, such as bolA, dps, osmE, ath and ung occured at
the stationary phase transition point (Tables 5.2 and 5.3, Fig. 5.3 QTC 2). The
greatest increase in transcriptional level for an upregulated gene (45-fold) during
the stationary phase transition was acs, encoding acetyl CoA synthetase (Table
4.2). Acs expression is controlled in part by RpoS (226). Also induced at a very
high level during the transition point into stationary phase were 8098 (40 fold)
and 309A (32 fold), which encode components of the glyoxalate shunt.
Increased expression of acetyl CoA synthetase as well as genes of the
glyoxalate shunt is consistent with the hypothesis that cells are scavenging

acetate produced during rapid growth on glucose in exponential phase.

132

Table 5.3. Significant ORFs (p value < 1 x 107) with greater than 4-fold
significant changes in expression from late exponential to early stationary phase
(4.6 to 5.5h)

 

 

Log2 Log2
change in OT change in OT
Ecs no. Gene3 expression cluster Ecs no. Gene8 expression cluster
E030026 rpsT -4.14 1 E032450 ydjS 3.94 2
E0301 18 806E -3.27 1 E032451 - 4.1 1 2
5030123 yacL 3.1 7 2 E032452 - 5.88 2
E030172 {if -4.58 1 E032453 - 4.62 2
E030173 p yrH -3.15 1 E032454 a_rgQ 6.97 2
E030199 yaeC -3.14 1 E032463 - -4.04 1
E030201 abc -3.32 1 E032467 gdhA -5.15 1
E030248 fadE 4.57 2 E032492 mg 4.08 2
E030383 1ah_0 4.53 2 E032527 manX -2.95 1
E030384 prpR 3.63 2 E032528 manY -3.21 1
E03041 5 aqu 5.21 2 E032546 - 4.09 2
E030502 deA -3.80 1 E032609 araF 3.61 2
E030540 0157 3.52 2 E032613 ltn -3.88 1
E030541 0157 4.1 1 2 E032888 yegP 4.47 2
E030990 serC -3.29 1 E032892 yegS 3.38 2
E030991 aroA -4.24 1 E033022 yohc 6.12 2
ECs1050 yccV 3.88 2 ECs3029 0157 4.04 2
ECs1266 phoH 7.69 2 E033030 0157 3.38 2
ECs1438 yceP 4.78 2 E033077 M -3.64 1
E031466 yceD -2.86 1 E0331 14 gyrA -3.38 1
E031603 QLLB -3.58 1 E033181 pta -3.50 1
ECs1683 y_cg§ 5.36 2 E033224 fadJ 4.26 2
ECs1831 yciG 2.91 2 ECs3225 fadl 4.50 2
ECs1849 aan 4.07 2 E033227 [Q 5.39 2
E031874 ych 4.73 2 ECs3259 - 3.57 2
ECs1875 ych 5.14 2 E033271 - 3.06 2
E031997 ynaF 3.42 2 ECs3286 gals -3.68 1
E032028 ych 3.64 2 E033338 purC -5.04 1
E032036 ych 3.78 2 E033401 csiE 3.77 2
E032044 ydcs 5.22 2 ECs3445 yfiD -6.36 6
E032045 ydcT 5.23 2 ECs3465 yflL 3.81 2
E032082 ath 3.62 2 E033469 lplS -4.28 1
E032092 - 3.70 2 E033470 gln_D -5.46 1
E0321 1 3 O1 57 -3.24 1 E033472 rpsP -5.57 1
E032120 - 4.17 2 E033520 ygaT 4.88 2
E032145 ydel 3.74 2 E033523 gabT 3.14 2
E032150 - -5.10 4 E033619 913;! -4.11 1
E032295 - -2.81 6 ECs3742 ygeV 3.64 2
E032346 tyrS -2.97 1 E033750 191.1 4.14 2
E032385 ynhG 3.80 2 E033771 - 3.52 2
E032389 ynhD 3.58 2 ECs3784 serA -3.66 1

133

Table 5.3, continued

 

 

L092 L092

change in OT change in QT
Ecs no. Gene3 expression cluster Ecs no. Genea expression cluster
E033810 tktA -4.16 1 E034433 yij 3.88 2
E033896 thE 2.86 2 E034464 aldB 3.02 2
E033948 rpsU -3.14 1 ECs4473 yin 3.78 2
E033955 yng 5.55 2 E034475 mtlA 2.84 2
E033963 ygjL 4.06 2 E03461 1 iIvN 4.84 2
ECs4034 yth 3.67 2 ECs4612 ilvB 4.41 2
ECs4040 yth -3.40 1 E034633 yidB 3.85 2
E034091 gig -3.19 1 ECs4675 QILG -3.77 1
E034104 M! -4.05 1 ECs4676 _a_tLA -4.02 1
E0341 12 yth 3.56 2 E034677 gal-l -4.50 1
E034128 accC —3.51 1 ECs4678 QILF -4.08 1
E034133 173 -3.76 1 E034680 '21th -3.27 1
E0341 59 m -3.99 1 E034686 asnA -3.68 1
ECs4160 [2% -4.1 1 1 ECs4758 metR -3.26 1
E034166 rpIO -3.63 1 ECs4774 fadB 6.58 2
E034167 rme -3 .70 1 E034792 glnA -5.34 1
E034168 rpsE -3.75 1 ECs4884 yijP -3.62 1
E034171 rpsH -4.00 1 E034889 argH -4.04 1
E034173 LIE -4.26 1 E034906 rle -4.80 1
E034174 M -4.14 1 E034928 purD -4.58 1
ECs4175 m_N -4.46 1 E034933 aceA 4.98 2
E0341 76 4:30 -4.30 1 E034934 aceK 2.90 2
ECs4177 rme -4.12 1 E035007 lysC -3.43 1
E034179 4330 -5.07 1 ECs5049 actP 4.17 2
ECs4180 [M_\/ -4.74 1 E035050 yjc_H 3.78 2
E034181 113§ -4.14 1 E035089 ghnB 4.22 2
E034182 M -4.83 1 E035109 yde 3.07 2
E034183 mm -5.75 1 ECs5123 groE§ -3.66 . 1
E034184 rpID -6.50 1 E035176 IpsF -4.84 1
E034185 rplC -4.69 1 E035177 mg -5.00 1
E034186 rsz -5.93 1 E035178 M -4.46 1
E034189 0157 3.24 2 E035179 _rb_ll -3.90 1
E034191 rug -3.27 1 E035194 yth 4.59 2
E034212 fic 4.44 2 E035205 m 6.29 2
E034213 yth 4.1 3 2 E035222 MB. -2.95 1
E034299 uggB 6.46 2 E035298 yjiM -1 .43 8

 

 

a Gene names in bold have been found to be regulated by RpoS (142, 195, 266) and those
underlined are genes that increase or decrease in stationary phase in E. coli K-12 (57). Genes
with “0157” indicate that these are 0157-specific ORFs.

134

0157-specific genes that decreased in the transition to stationary phase include
those encoding the 0157 LPS antigen (E032839, E032840, and E032847) with
a greater than 4 fold decrease in expression (Table 5.3, Fig. 5.3 QTC 1) as well
as E032835, ECs2836, E032838, E032841, E032844, and E032845 (1.6 to 3
fold decrease) from that genomic region. E032113, part of the F9 fimbrial
operon (157), decreased in expression from exponential to stationary phase
(Table 5.3). Other ORFs in this operon, E032107-2112, also decreased
significantly (~1.5 fold) upon entry into stationary phase. 0157-specific ORFs
that increased in stationary phase included E032182, E032737, and E035067,
which encode putative transcriptional regulators. E032737 (pchC) encodes a
perC-Iike homolog reported to regulate LEE transcription (122). E030415,
encoding a perimplasmic ferric iron transporter, had the greatest increase in
expression of the 0157-specific ORFs, with a 37-fold increase during the

transition point (Table 4.2).

Genes with transient expression at the stationary phase transition point.

A subset of 120 ORFs were expressed transiently from late exponential to early
stationary phase. These ORFs changed significantly during the 4.6 to 5h time
interval, and then again during the 5 to 5.5h interval, but in the opposite direction
(Fig. 5.3 QTC 3, 11, and 12). Genes with this pattern of transcription included
many involved in nutrient scavenging and turnover. Genes encoding both the
anaerobic (glpABC) and aerobic (glpD) sn-glycerol 3 phosphate dehydrogenase

increased >4 fold during the transition into stationary phase, and then decreased

135

significantly in early stationary phase (Table 5.4). This pattern is consistent with
the understanding that the glp metabolic system serves as a salvage pathway for
glycerol derived from degradation of phospholipids and triacylglycerol. The
murPQ operon was expressed transiently, with a 5.3 fold increase followed by a
2 fold decrease in early stationary phase. The products of murPQ are involved in
peptidoglycan turnover (253) suggesting that membrane components are being
recycled and utilized for energy. A dicarboxylate transporter, dctA, was induced
24-fold upon entry into stationary phase, then decreased 4-fold in early stationary
phase (Table 5.4).

Only 12 O1 57-specifc ORFs were expressed transiently upon entry into
stationary phase. ECs1772, a putative intestinal colonization factor, decreased
upon entry into stationary phase, then significantly increased in expression (>4

fold increase from 5 to 5.5h) (Table 5.4).

Genes with a significant change in expression in early stationary phase.
The interval with the second largest number of significantly modulated ORFs was
in early stationary phase, during the 5 to 5.5h interval (Fig. 5.4). A number of
these ORFs that had significant increases in expression levels during this interval
are transcriptional regulators, such as the AraC-like regulators gadW and gadX,
involved in regulation of the glutamate decarboxylase system of acid resistance
(Table 4.5). Genes encoding DNA binding proteins, such as hha and cpr, also
increased in transcription in early stationary phase. Components of the glycerol

metabolism pathway were activated in early stationary phase, including ung

136

Table 5.4. Significant ORFs (p value < 1 x 107) with transient expression at the
stationary phase transition point with expression changes greater than 4-fold.

 

 

log2 logz
change in change in
expression expression QT
Ecs no. Genea 4.6-5h 5-5.5h cluster

E030057 deA -2.43 0.66 1
E030287 0157 -1.09 2.91 2
E030358 betB 3.07 -1 .25 3
E030359 betl 2.99 -1 .48 3
E030418 0157 2.19 -0.64 3
ECsO486 cyoA 2.77 -1 .08 10
ECsO746 sdhC 2.93 -1 .74 10
E030747 sth 2.55 -1 .59 10
ECsO748 sdhA 2.76 -1 .38 10
E030778 nadA -2.57 0.76 1
E030946 artl -2.66 0.40 5
E030987 focA -3.38 1 .1 1 6
E030994 rpsA -3.78 0.66 1
E031261 putP 2.80 -0.92 10
E031741 adhE -4.01 1.17 6
ECs1744 oppB -2.05 1 .36 7
E031772 0157 -0.68 2.22 2
E032383 pku -2.42 1 .49 un
E032387 sufS 0.71 2.17 2
E032481 yde -2 .44 0.84 6
E032482 ydjl -2.18 0.81 6
E032486 yeaC 3.00 -0.57 2
E032487 yeaA 3.08 -0.60 3
ECs3042 mng 3.96 -1 .13 3
ECs3043 galS 2.14 -0.87 3
E033117 nrdA 1.83 -3.64 1 1
E033125 ngT 1 .65 -2.22 4
E033126 ngA 3.61 -2.78 3
E03312? glpB 2.59 -2.15 3
ECs3128 glpC 2.92 -2.94 un
ECs3142 ytbF -2.54 1.59 12
ECs3143 mm -2.50 1 .03 5
E033144 yfbH -2.66 0.98 5
ECs3299 yer 2.39 -0.89 3
E033300 yfeV 2.28 -0.99 3

137

Table 5.4 continued.

 

 

log2 logZ
change in change in
expression expression QT
Ecs no. Genea 4.6-5h 5—5.5h cluster

E033305 ypeA 2.05 -0.81 3
E033393 hscB -1 .37 2.51 7
E033748 yqu 2.23 -0.78 3
ECs4109 mdh 2.05 -1.40 un
ECs4269 glpD 4.17 -1 .82 3
ECs4408 dctA 4.59 -1 .93 3
ECs4498 rfaF -2.10 0.85 12
ECs4705 ilvD -2.03 1 .03 12
ECs4726 yifM 3.51 -3.62 un
ECs4750 yigl 2.77 -1.14 10
E034840 fieF 3.76 -3.29 3
E034851 ngK 3.54 -3.73 4
E034852 glpF 5.22 -4.1 0 3
E034918 yjaE 4.21 -1.58 3
E035298 yjiM 0.71 -2.14 8
E035313 yti 1.38 -3.28 un

 

138

3 Genes with “0157” indicate that these are 0157-specific ORFs.

Table 5.5. Significant ORFs (p value < 1 x 107) with greater than 4-fold
expression changes in early stationary phase.

 

 

logz log2

changein changein

expression QT expression QT
Ecs no. Jene 5-5.5h cluster Ecs no. gene 5-6h cluster
E030342 ykgC 2.30 2 E030568 90! 4.04 2
E030513 hha 2.55 2 E030569 gip 3.56 2
E030514 ybaJ 2.16 2 E030570 ybe 4.89 2
E030732 ybgA 2.27 2 E030571 ybe 3.27 2
E030733 phrB 2.15 2 E032095 yddV 4.27 2
ECsO769 cydB -2.03 4 E032122 ydeZ 4.88 2
E030957 poxB 2.47 2 E032123 yneA 5.14 2
E030973 tpr -2.25 1 E032124 yneB 4.46 2
ECs1155 cpr 2.26 2 E032705 yedU 4.54 2
E031236 IomW 2.21 2
E031 756 yciD -3.21 4
E032047 ych 3.59 2
E032048 ych 2.35 2
E032386 ynhA 2.75 2
E032388 ynhC 2.89 2
E032493 yeaH 4.26 2
E032529 manZ -2.65 1
E033395 ich 2.61 2
ECs3396 yfllO 2.04 2
ECs4046 rpsO -2.20 1
E034295 ugpQ 2.20 2
ECs4296 ung 2.60 2
E034297 ung 2.12 2
ECs4305 livK -2.23 1
ECs4395 gadW 2.10 2
ECs4396 gadX 2.76 2
E034534 intL 2.50 2
E034673 atpC -2.24 1
E035108 yjdl 2.20 2
E035269 yth 3.09 2

139

and ung, components of the glycerol 3 phosphate transporter, and ugpQ, the

cytosolic glycerophosphoryl diester phosphodiesterase (Table 5.5).

Expression of LEE island genes upon entry to stationary phase. Of the 41
LEE island ORFs, 23 had a significant (p < 0.00001) differences in expression in
growth into stationary phase under minimal media (Fig. 5.6). Most of these
ORFs were downregulated in transition to stationary phase (between 4.6 and
5h). Three of these ORFs, espB, espD, and espZ, also had a significant change
in expression in early stationary phase (5h to 5.5h). The greatest reduction in
expression was seen in espB and espD, which both decreased 5.5- fold from late
log to early stationary phase. Expression of espA also decreased > 5- fold over
this interval, but the error variance for the espA probe was large and thus was
not deemed significant. While the overall trend for the LEE island genes was a
decrease in expression during the transition to stationary phase, the exception
was espZ which had a 3.2 fold increase in expression from late log to early
stationary phase, indicating that this ORF may be activated by stationary phase

associated regulators.

Expression changes of LEE effectors. More than 60 putative effectors that are
translocated through the LEE-encoded TTSS were recently identified (247) and
of these TTSS effectors, 33 were found here to have significant (p < 0.00001)

changes in expression over time (Fig. 5.7). A total of 7 TTSS effector genes

140

 

 

 

 

LEE3

 

 

 

 

Int: : -

 

 

Figure 5.6. Heatmap of gene expression over time for the ORFs of the LEE

island. An asterisk next to an ORF name denotes that expression levels for at

least one time point was statistically significantly different from expression levels

for the other time points for that ORF.

141

 

 

UEE‘J‘. : ,-
1092 1.8 0 1092 -1.8

 

 

 

Figure 5.7. Expression heatmap of TTSS effector ORFs that had a significant

change in expression over time.

142

Table 5.6. Significant changes in transcript levels of type III secretion system
effectors.

 

 

E03 number Gene name Logz change in
expression from 4.6h to
5.5h

E030061 espY1 3.03

E030073 esp Y2 1 .24

ECs1825 espM1 1 .47

E031994 nleGZ-Z 1 .41

E0321 56 nleGZ-3 1 .63

E032229 nleGZ-4 -1 .1 5

E032714 espJ -2.18

E032715 tccP -1 .39

ECs3488 nle66-3 1 .1 1

ECSs4554 espB -2.41

E034561 tir -1.72

E034571 espZ 1 .69

E034643 espL3 1 .77

143

(nleA, nleG, espM1, espM2, nleGZ-2, eSpZ, and nleGZ-3) increased in
expression between mid- and late- exponential phase (3h to 4h). At the
stationary phase transition point, 11 ORFs increased in expression and 7 ORFs
had a significant decrease in expression (espJ, tccP, tir, espF, espB, espM2, and
nleD) (Fig. 5.7). In early stationary phase, eleven ORFs were upregulated, with
the exception of espB (2.5 fold decrease). From late exponential to early
stationary phase (4.6h to 5.5h), 8 ORFs had an increase in expression > 2- fold,
with espY1 increasing 9 fold. Over this same time interval, 5 'I'I'SS effector
ORFs had a 2 fold or greater decrease in expression, with espB decreasing 5.3

fold (Table 5.6).

Significant increase of genes from the acid fitness region in stationary
phase. A cluster of 12 genes located in the E. coli K-12 genome at position
3652706 to 3665603 bp has recently been termed the acid fitness island (AFI)
(117). In E. coli O157:H7 Sakai, the homologous region contains a 9 kb insertion
between yhiF (ECs4378) and yhiD (E034388). This AFI genomic region
contains multiple transcriptional regulators that control expression of the
glutamate dependent acid resistance (GDAR) system, such as GadE, GadX, and
GadW, that are also known to influence LEE expression (225, 243). The
microarray comparisons demonstrate that the expression of the AFI genes
increased significantly from exponential to stationary phase, and included some

of the greatest increases in transcript level among the whole genome (Fig. 5.8).

144

 

3h
4h
4 3h

ECs4377 slp
ECs4378 yhiF
ECs4388 yhiD
E034389 hdeB

ECs4390 hdeA I " I

ECs4391 hdeD
E034392 gadE m
E034393 yhiU

ECs4394 yhiV

E034395 gadW

ECs4396 gadX

ECs4397 gadA u

 

IE; 1;- .-
|092 3 0 km 4

 

 

 

 

Figure 5.8. Expression heat map of 12 genes from the acid fitness region
(AFI).

Expression values determined from the ANOVA analysis are represented
colorimetrically, with dark red representing expression = 3 and dark green
representing expression = -4 on a log2 scale. Over half of the AFI genes (7/12)

had a significant increase in expression from mid- to late- exponential phase

145

Transcript levels of gadA increased 111 fold and levels of gadE increased 85 fold
from mid-exponential (3h) to stationary phase (6h). Over half of the AFI genes
(7/12) had a significant increase in expression from mid- to late- exponential
phase (Fig. 5.8). Transcript levels of gadA, gadE, and slp also increased
significantly from 4.3 to 4.6h, which is different from the expression profiles of the

other known stationary phase activated genes that increase from 4.6-5h.

Increased expression of Shiga toxin genes upon entry into stationary
phase. Over 50% of Sp 15 ORFs and 60% of Sp 5 ORFs had a significant (p <
0.00001) change in expression over time. The genes encoding Shiga toxin 1,
stx1A and stx1B, as well as those encoding Shiga toxin 2, stx2A and stXZB, had
a significant difference in expression between mid- and late- log phase (3 to 4h,
Fig.5.9). Stx1 genes decreased ~ 1.8 fold over this interval, while Sb<2 genes
increased ~ 1.7 fold. Stx1 genes increased significantly in early stationary
phase, with a ~ 4- fold increase from 5 to 6h. A significant 25 fold increase for
stsz occurred at the transition point into stationary phase, but a concomitant
increase was not observed for the stx2A ORF on the microarray. The ORFs
encoding the phage replication factors 0 and P, as well as the CH regulatory
protein and the Q antiterminator protein, increased in expression upon entry into
stationary phase, but it is difficult to attribute these ORFs to a specific phage, as

there are multiple copies of these ORFs throughout the genome.

146

 

2.0

 

0.5 a- \\ s

 

 

 

 

 

 

 

 

:
.9
to e
g r
a 0.0 --
x
a)
(g, -0.5 ~-
9.
-1.0 ~-
+ stx1A
. + stx1B
-1,5 -- —I— stx2A
—0— stx28
-2.0 I I I I I I
3 4 5 6 7 8

Time (h)

 

 

 

Figure 5.9. Expression profiles of stx1 and stx2 over time.

147

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

A.
2.0
+ ureD
1 5 _ -'v— ureA
' -I- ureC
—<>— ureE
c _ —.
2 1 0
2
g 0.5 -
X
e
g: 0.0 —
...l
-0.5 —
-1.0 -
.105 I I I I I
3 4 6 7 8
Time (h)
B.
2.0
+ terZ
c
.2
m
to
2
G
x
o
N
D)
O
.l
Time (h)

 

 

Figure 5.10. Expression profiles of the urease genes (A.) and the genes

encoding tellurite resistance (B.).

148

x!

Expression changes in tellurite resistance genes and urease genes.
Proteins involved in tellurite resistance are encoded on a pathogenicity island
(TAI) along with genes encoding urease and are present in a single copy in E.
coli O157:H7 Sakai (105). Significant changes in expression over time were
detected only for terZ, terD, and terE (Fig. 4.10A). Probes targeting terB and
terC are not present on the array. Expression of these three genes increased
significantly between mid- and late- exponential phase (~ 1.5 fold) and then
decreased 5.9- to 7.4- fold during the transition into stationary phase and in early
stationary phase (4.6h to 5.5h interval). These three ORFs were grouped into
QT cluster 1. The ureD, ureA, ureC, ureE, and ureG ORFs had a significant
decrease in expression from late exponential to early stationary phase (1.5 to 3
fold) and were grouped into QT cluster 1 (Fig. 5108). Also located on this
genomic island is the adherence factor iha (242), but there is no significant

change in iha mRNA over time.

149

DISCUSSION

By monitoring global transcript levels over time, we have measured the
numerous alterations in gene expression patterns of E. coli O157:H7 when
growing exponentially and then entering stationary phase in minimal medium. E.
coli O157:H7 responded to low 02 by increasing expression of genes encoding
anaerobic electron acceptors and decreasing expression of genes encoding the
TCA cycle. 0157-specific ORFs, including nleA, nleG, and espZ, which encode
TTS effectors, were upregulated during this interval. As cells entered stationary
phase, ~ 50% of the ORFs in the genome responded with significant changes in
transcript level. The global trends include downregulation some of virulence
factors, such as the components of the TTSS and some of the TTSS effectors, in
stationary phase. Other TTS effectors and the genes encoding Shiga toxin
increased in transcript level upon entry to stationary phase. Entry into stationary
phase was characterized by the increase in expression of known stress and
survival related genes, such as the osmotic stress proteins encoded by osmB
and osmC, and the glutamate dependent acid resistance system encoded by
gadA and gadBC.

As E. coli O157:H7 cells grew in the minimal medium, 02 levels in the
medium decreased and were not replenished from the atmosphere (Fig. 5.5).
The decrease in available oxygen was reflected in the transcriptome, with
significant increases in genes encoding anaerobic electron acceptors, decrease
in transcript levels of TCA cycle genes, and increase in expression of pyruvate

formate Iyase and concomitant decrease in expression of the pyruvate

150

dehydrogenase complex. 5 % (73/1239) of 0157-specific ORFs also had
significant changes in expression over the same time interval, which leads one to
speculate that these 0157-specific ORFs may be regulated by transcription
factors that influence expression of respiratory genes, such as FNR and ArcA.
Virulence factor expression in enteric pathogens, such as Salmonella
Typhimurium and Vibrio cholera, can be controlled by the redox state regulators
FNR and ArcA (86, 140, 222). A search for the FNR and ArcA binding
sequences 500bp upstream of all ORFs in the Sakai genome (using Pattern
Search on coliBASE (61 )) did not identify any of the ORFs that were significantly
expressed or repressed in exponential phase. It may be that these 0157-specific
ORFs are regulated indirectly through FNR or ArcA, or are modulated in a
completely different manner. Recently Ando et al. (6) demonstrated that
anaerobic growth in the presence of the electron acceptors nitrate or TMAO
accelerates maturation of the TTSS, independently of new protein synthesis .
They found that the narGHlJ operon must be present for the maturation of the
'I‘I'SS. Our findings indicate that the expression of this operon increases
significantly in late exponential phase, which is consistent with the narGHIJ
operon contribution to the maturation of the TTSS in this environment.

Much is known about regulation of LEE expression; multiple circuits of
activation and repression have been identified. LEE expression can be induced
in response to a number of environmental stimuli, and while common
transcriptional regulators and signaling systems can activate LEE expression,

they all typically act on Ler or the recently described GrlA and GrlR regulators

151

located on the LEE island (76), which then alter expression of other LEE genes.
LEE expression is known to be maximal in late exponential phase (232, 272).
Nakanishi et al. determined that LEE can be stimulated during nutrient
downshifts through activation of ppGpp and DksA (181). The pattern of espB
expression observed during induction of LEE with ppGpp is similar to our
observation, in that espB expression increases from mid- to late-exponential
phase, and then decreases in stationary phase. LEE transcription can also be
stimulated by quorum sensing, via the auto-inducer 3 signaling system that
activates ler (232). The quorum sensing activated regulator QseA which activates
LEE transcription (231 ), had a decrease in transcript levels from exponential to
stationary phase (Fig. 5.3 QTC 1). SdiA is a longer-term stationary phase-
related quorum sensing system and represses expression of virulence factors,
including LEE (131). Many of the common E. coli regulators that have been
shown to repress LEE expression, such as Hha (223), SdiA, GadE (243), YhiF
(243), and IHF (91, 151), all increased in mRNA levels from exponential to
stationary phase (Table 4.2, Fig. 5.3 QTC 2). Other LEE activators, such as

PchC (122), had a significant increase in mRNA in stationary phase.

The decrease in expression of LEE genes from exponential to stationary phase
that we observed is similar to the expression changes of LEE genes when
O157:H7 is attached to red blood cells (72). In comparing our dataset to the
genes that were found to be significantly up- or down-regulated in O157:H7

attached to red blood cells, we found that of the 299 ORFs reported to be

152

downregulated in attached cells, 185 had significant changes in expression over
time, and 73% of these ORFs were classified in OT cluster 1 (Fig. 5.3), which
includes the LEE genes. Of the 105 ORFs reported to be upregulated in
attached cells, 89 had significant changes in expression over time, and 56% of
these significant ORFs were placed in OT cluster 2 (Fig. 5.3), suggesting that
many of the ORFs expressed or repressed during attachment to plasma
membranes are also regulated by growth phase. EspZ is known to be
translocated through the TTSS, but its function is unknown’ (130). One of the
main differences among the LEE genes is the increase in espZ mRNA upon
entry into stationary phase, which at first glance, appears to be an anomaly, but
is part of a larger pattern when examined in the context of the LEE and non-LEE
encoded type III secretion system effectors.

Not as much known about regulation of non-LEE encoded TTSS effector
genes. The presence of some of the 'I'I'SS effectors in the genome is variable
among 0157 strains (190). The TTSS effectors tccP and espJ are located on
Sakai phage 14 (EDL-933 OI#79); tccP (espFu) is necessary for actin
recruitment and NE lesion formation in E. coli O157:H7, while espJ influences
colonization dynamics (37, 73, 97). Reading et al. recently determined that the
Al-3, epinephrine, and norepinephrine response regulator QseF (yflrA) is an
indirect transcriptional activator of tccP (207). Our data indicate that as Sakai
0157:H7 entered stationary phase, qseF mRNA levels decrease significantly (2
fold) (QTC 1) and tccP-espJ levels decreased significantly as well. This

decrease in tccP and espJ transcript levels in stationary phase is in contrast to a

153

previous study, where the activity of the tccP-espJ promoter was found to be the
same in exponential vs. stationary phase cultures of EDL-933 in Dulbecco’s
Modified Eagle Medium as monitored by a gfp fusion (96). Some of the TTSS
effectors appear to respond to low 02 conditions, as 7 effectors increased
significantly from mid- to late-exponential phase when oxygen levels decreased.
Of the TTSS effectors with significant changes in expression over time, ~50%

have decreased expression from exponential to stationary phase, while the other

half have increased expression in stationary phase. Some of these TTSS
effectors are predicted to be pseudogenes; one example is espL3, an ORF that
was found to have a significant increase in expression at the stationary phase
transition point. Many of the TTSS effectors with increased expression levels in
stationary phase are members of the NleG family (Table 5.7), one of the largest
families of ITS effectors that contain many duplications (247). The utility of the
increased expression of some of the TTS effectors in stationary phase is unclear
and warrants further investigation.

The ability to resist low pH is a crucial adaptation and critical component
of the low infectious dose of E. coli O157:H7. The GDAR system provides the
greatest protection against low pH, allowing survival at levels of gastric acidify
(e.g. pH 2.0) as long as glutamate is present in the environment (155). Genes
encoding the glutamate decarboxylase isozymes and the glutamate-gamma-
aminobutyric acid antiporter are under tight control and can be activated via a
number of environmental cues and transcriptional regulators (88), and many

components of the system are located in the AF I region (117). Transcription

154

factors located on the AF I, such as GadE and GadX, regulate a large number
(>40) of ORFs outside of the GAD system in E. coli K-12 (117, 159), and it is
known that these factors serve as repressors of LEE transcription (225, 243), but
it is unknown what the effect of these regulators are on a genome-wide scale in
E. coli O157:H7.

Important questions remain about the factors regulating the Shiga toxins
which are encoded by lambda-like bacteriophages (220). These phages can be
transferred between many E. coli strains in nature and were the principal
virulence factors acquired as a key step in the evolution of E. coli O157:H7.
(208). How they become influenced in expression and regulated when acquired
intoanew genome is not fully understood. Early work described two types of
Shiga toxin (188) encoded by Stx1 and Stx2, both located within the late operons
of the Stx-encoding phages (185, 199). Stx1 expression is regulated in part by
the iron-dependent transcriptional repressor, Fur (36). Exposure of E. coli
O157:H7 to low iron conditions result in an increase of stx1 transcription (259).
Our growth experiments indicate that the stx1 genes decrease in expression from
mid- to late-exponential phase, at the same interval when 02 levels decrease
(Fig. 4.9). Iron becomes more soluble as 02 concentration decreases, which
could lead to the decreased stx1 expression. Stx1 expression can be induced at
2 other promoters in addition to the Fur binding site, those that require the phage
anti-terminator Q (186) and the phage anti-terminator N (259). Stx2 transcription
is initiated at the late phage promoter and is dependent on the anti-repressor Q

protein (260). The production of Stx2 is part of the lytic cycle of the

155

. - .1.

 

bacteriophage. Prophages can be induced by antimicrobials, and antimicrobials
have been shown to induce stx2 expression (9, 138). Herold et al. found that
phage genes were induced upon exposure of E. coli O157:H7 EDL-933 to
norfloxacin; phage late genes and stx2 were induced 150-fold (111). Levels of
stx2 mRNA are higher in starved O157:H7 compared to exponentially growing
cells (148), indicating an overall trend of stx2 expression induction by stress.
Here we observed that stx1 and stx2 expression increased in stationary phase. ‘
Herold et al. also reported that stx2A is transcribed more efficiently than stXZB,
and may be regulated at the posttranscriptional level (111). This difference in
transcription efficiency between stx2A and stXZB was also reflected in our data,
as stXZB levels did not appear to increase as the stx2A levels did in stationary
phase (Fig. 4.9).

The genomic island termed the Tellurite and Adherence Island (TAI)
contains genes encoding tellurite resistance, urease, and an adherence factor,
iha (242). The urease operon and the tellurite genes are thought to be
ubiquitously distributed in EHEC strains (183, 244) but is not present in O157:NM
or 055:H7 strains (92). Urease activity has been detected in relatively few EHEC
strains. In O157:H7 Sakai, the urease genes have been detected at the transcript
level, but a functional protein is not produced due to a stop codon present in
ureD (182). Saridakis et al. surveyed 25 O157:H7 strains that were isolated from
humans, cattle, and pigs for ureC and the ability to resist low pH using the urease
enzyme. UreC was detected in all of the isolates but there was no evidence for

urease-mediated acid resistance (218). Here we see that the genes encoding

156

 

urease are expressed during exponential growth and transcript levels decrease
significantly as cells enter stationary phase. The genes encoding tellurite
resistance are functional in EHEC, and can be separated into two classes -
those that are expressed constitutively during growth (terDEZ) and those that are
expressed only during growth in the presence of tellurite (terBCF) (244). Our
data show this as well, with terD, E, and Z all expressed during exponential
growth, with a significant decrease in transcript levels upon entry to stationary
phase, and no significant changes in transcript levels for terA and F.

Transition to stationary phase is characterized by complex physiological
changes to the bacterial cell. Many of the changes in expression patterns
observed for O157:H7 in this study have also been observed for E. coli K-12
entering stationary phase (57). The sigma factor RpoS plays a critical role in
transcribing genes associated with stationary phase and stress response (142,
195, 266), many of these genes, such as bolA, dksA, osmC, and osmB, have
been identified in this study as well. We still do not know about most of the
0157-specific ORFs, and what regulators control their expression. Here we have
classified some of the well-known O157 genes, as well as 0157 genes of
unknown function, as being modulated during growth transitions. Global
transcriptional profiling of isogenic mutants for a specific regulator have been
conducted for a number of regulators in E. coli K-12. As we know more about
the regulation of virulence loci, such as the LEE, by global regulators, it would be
very useful to perform these profiling experiments in E. coli O157:H7 and other

pathogenic E. coli.

157

t 374'"

 

Here we provide the first description of global transcriptome profiling of O157:H7
during growth transitions. Many 0157-specific ORFs responded to the growth
transition with significant changes in gene expression. As a large proportion of
0157-specific ORFs are of unknown function, these data are important for
comparison to other growth conditions and elucidating the function and factors
regulating these unknown ORFs. This provides the opportunity to identify ORFs
of interest, based on expression profiles, and compare expression within 0157
population or with other EHEC populations. It is useful to understand what
transcriptional changes occur from exponential to stationary phase, as these

transitions are important to the lifestyle of O157:H7.

158

 

ACKNOWLEDGEMENTS
The authors thank Katherine Schaeffer for assistance with the dog
measurements and Galeb Abu-Ali, Peter Bergholz, and Sivapriya Kailasan
Vanaja for helpful scientific discussions and critical review of the manuscript. This
work was supported by Food Safety NRI #2005-35201-16362 from the United
States Department of Agriculture. This work will be submitted to BMC

Microbiology.

159

Chapter 6

Summary and Synthesis

160

.2'. A. _. .1"

.F'.

 

The purpose of the work presented here was to characterize the
phenotypic and genetic basis of survival that contributes to the transmission of
EHEC. One advantage of these studies is the use of multiple strains
representing specific clonal groups of E. coli for comparison. This work has
contributed to the greater body of knowledge about EHEC acid resistance by
showing that E. coli O157:H7 can be more acid resistant compared to non-0157
strains (i.e. members of the EHEC 2 group) in complex acidic environments, and
this may be due, in part, to differences in expression of the glutamate
decarboxylase acid resistance system.

For comparing survival between O157:H7 and non-0157 EHEC, multi-
locus sequence typing was used to define groups of EHEC based on genetic
similarity. Under these growth conditions and test environments, 0157 strains
were no more AR than non-0157 EHEC. Results indicate that the ability to
utilize these mechanisms when induced both by acid and stationary phase are
similar among all EHEC strains. Under natural conditions in complex acidic
environments, however, E. coli O157:H7 may have the ability to use multiple
mechanisms in combination, through some form of epitasis, to achieve higher AR
than E. coli of other clonal groups.

The glutamate decarboxylase acid resistance system was found to
provide the greatest protection against low pH in the comparison of AR
mechanisms. Of the specific amino acid dependent AR mechanisms, the lysine
and arginine decarboxlyase systems are common in many enteric bacteria,

whereas the GAD system is present only in E. coli and Shigella among the

161

Enterobacteriaceae. A similar GAD system is present in some Gram-positive
bacteria, including the food borne pathogens Listeria monocytogenes and
Clostridium perfringens (228). The GAD system in Listeria is capable of
protecting L. monocytogenes in low pH environments, such as gastric fluid (67)
and acidic foods (68), and also is involved in growth at mildly acidic pH (69).
While the GAD system is not involved in survival of E. coli O157:H7 in low pH
foods (202), it is essential for survival of O157:H7 Sakai in the MSS (129) and is
expressed in E. coli K-12 during growth at pH 5.5 (252). Variations in levels of
GAD activity between different strains of L. monocytogenes was found to
correlate significantly with levels of tolerance to gastric fluid (67). In the MSS
study reported here, we found that transcript levels of gadA and gadB were
significantly higher in the 0157 strains compared to the 026 strains, but this was
not significantly correlated with survival rate in the MSS.

The glutamate decarboxylase enzyme is encoded by two duplicated
genes, gadA and gadB, and strains were selected from diverse groups of
pathogenic E. coli, based on genetic similarity, to determine evolutionary history
of the glutamate decarboxylase enzymes. A gene conversion event had
occurred in all pathogenic lineages studied, with the exception of EHEC 1.
Recent evidence of differential expression of gadA and gadB between EHEC
0157 and EHEC 2 strains (14), and between EHEC 0157 and Shigella flexneri
(22) suggest that regulation of GAD expression is distinct in EHEC 0157

compared to other E. coli strains.

162

 

Survival in a complex acidic environment was assessed for the same
populations of EHEC. O157 strains survived better than 026 strains in this
simulated gastric environment and the 026 strains had a higher proportion of
injured cells after 3h in the gastric environment. Previous studies that have
compared survival of pathogenic E. coli in simulated gastric environments have
focused mainly on E. coli O157:H7. In SGF pH 1.5, EHEC O157:H7 strains
persisted longer compared to strains of enteroinvasive E. coli, enteropathogenic
E. coli, and Shigella dysenteriae (8). O157:H7 strains endured for 3h in SGF pH
1.5 compared to survival of 20 min or less for Salmonella and Listeria strains
(213). In addition, O157:H7 strains had a 4-fold higher survival rate when mixed
with cooked ground beef and inoculated into SGF compared to Shigella strains
(239). In his study, the average survival for 10 O157:H7 strains in SGF plus
cooked ground beef (—0.08 i 0.07) was greater than the average survival rate
observed here in the MSS (—0.17 i 0.15), which comprised SGF plus
homogenized turkey.

Incubation in particular food matrices can influence the ability of bacterial
cells to survive subsequent acid stress. For E. coli O157:H7 and O26:H11,
storage in apple juice at 4°C did not influence subsequent survival for both
groups of strains, and storage in apple juice at 22°C resulted in decreased
survival for both groups. A similar temperature effect was found for E. coli
O157:H7 inoculated into apple juice, with survival in SGF at pH 2.5 greater after
cold storage (at 4°C) versus storage at room temperature (21°C) (254). For

foodborne pathogens that can multiply at 4°C, such as L. monocytogenes,

163

 

storage of an inoculated food product at refrigeration temperatures results in
increased acid resistance as the bacteria can multiply during storage (87, 236).
Bacterial growth conditions and the intrinsic and extrinsic properties of the food
product clearly play a role in survival of food borne pathogens. Determining how
foodborne pathogens respond and adapt to these stressful conditions will lead to
development of new methods to decrease and eliminate survival of these
pathogens in foods.

Global patterns of gene expression. This work provided the first
description of global transcriptome profiling of O157:H7 during growth transitions.
Many 0157-specific ORFs responded to the growth transition with significant
changes in gene expression. As a large proportion of 0157-specific ORFs are of
unknown function, these data are important for comparison to other growth
conditions and elucidating the function and factors regulating these unknown
ORFs. This provides the opportunity to identify ORFs of interest, based on
expression profiles, and compare expression within 0157 population or with
other EHEC populations. It is useful to understand what transcriptional changes
occur from exponential to stationary phase, as these transitions are important to
the lifestyle of O157:H7.

Global transcriptional profiling has been utilized to characterize stress
responses and pathogenesis in other foodborne pathogens, such as Salmonella
enterica Typhimurium, L. monocytogenes, and Vibrio cholera. These studies
have identified genes involved in bile (203) and antimicrobial (238) resistance as

well as the role of IHF in stationary phase gene expression (162) in Salmonella

164 .

Typhimurium. Transcription profiling of L. monocytogenes indicates that 17% of
the genome is activated during intracellular growth (60) and that virulence genes
as well as stress response genes are activated by the sigma factor SigB (134).
Gene expression duing biofilm development has been assessed for Vibrio
cholera (221 ). As more data are collected from transcription profiling of
foodborne pathogens, this will allow researchers to highlight common and unique
strategies used by these organisms to survive under stresses that are
representative of those found in foods, processing environments, and the human
host

Future investigations. Numerous questions still remain about survival of
EHEC 1 and EHEC 2 in adverse environments. The GAD sequencing results
indicated that a gene conversion event has occurred in all clonal groups tested,
with the exception of EHEC 1. A natural extension of that work would be to then
investigate how the conversion event may influence expression of these genes.
Many of the strains that were sequenced were RpoS deficient isolates, and
would not serve as an appropriate comparison of GAD expression, as RpoS
influences GAD expression. A new set of strains has been selected to represent
EPEC 1 and 2, EHEC 1 and 2, and include Shigella strains (129) and only RpoS
positive isolates will be used to elucidate differences in GAD expression across
these strains.

Global transcriptional response of O157:H7 exposed to apple juice is
currently being investigated (13). The main transcriptional responses to the

combined osmotic and acidic stress of the apple juice were increased production

165

of genes involved in membrane stress and protein mis-folding in the periplasm.
Inactivation of the membrane stress transcriptional activator cpxP significantly
decreased in survival of O157:H7 in apple juice. By comparing genes induced by
apple juice in O157:H7 to those induced in 055:H7, an 0157-specific response
has been identified. These data lead to hypotheses about the evolution of
regulation of these stress response genes in O157:H7 from 055:H7, and remain
to be tested.

The studies described here have only used EHEC strains isolated from
cases of human disease. Recent work suggests that the population of EHEC in
the bovine reservoir is more diverse than the population isolated from humans
(17). It is unclear why only a sub-population of bovine EHEC cause disease in
humans, but there are many hypotheses that could be tested, such as that the
bovine EHEC are not as resistant to stresses that may be encountered during

food processing and storage compared to the human EHEC.

166

10.

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