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The Pennsylvania State University
The Graduate School
Department of Food Science
FACTORS AFFECTING BIOFILM FORMATION AND
TRANSITION OF LISTERIA MONOCYTOGENES TO THE LONG-
TERM-SURVIVAL PHASE AND THEIR POSSIBLE ROLES IN
PERSISTENCE IN FOOD PROCESSING PLANTS
A Dissertation in
Food Science
by
Jia Wen
2012 Jia Wen
Submitted in Partial Fulfillment of the Requirements
for the Degree of
Doctor of Philosophy
May 2012
ii
The dissertation of Jia Wen was reviewed and approved* by the following:
Stephen J. Knabel Professor of Food Science Dissertation Co-Advisor Co-Chair of the Committee
Ramaswamy C. Anantheswaran Professor of Food Science Dissertation Co-Advisor Co-Chair of the Committee
Edward G. Dudley Assistant Professor of Food Science
Allen T. Phillips Professor Emeritus of Biochemistry
Wei Zhang Associate Professor of Biology Associate Professor of Food Microbiology Illinois Institute of Technology Special Signatory
John D. Floros Professor of Food Science Head of the Department of Food Science
*Signatures are on file in the Graduate School
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ABSTRACT
Our earlier research showed that Listeria monocytogenes changes its cellular
morphology from bacilli to cocci and increases its resistance to heat and high pressure
during the transition to the long-term-survival (LTS) phase. The goal of this thesis
project was to understand factors affecting biofilm formation and transition to the LTS
phase and their relationships to persistence in the food processing environment. The
transition to the LTS phase was significantly affected by both initial cell density and pH
(P < 0.001) with initial cell density being the main determining factor. Control of
population growth/death kinetics appeared to be consistent with the Logistic Equation
and under the control of L. monocytogenes, not external environmental factors (e.g., loss
of nutrients). After 30-d incubation, the mean cell density was 4.3 ± 1.1 × 108 CFU/ml
and there was no significant difference between any of the initial cell density and pH
treatment combinations (P > 0.05).
To understand the transition of L. monocytogenes to the LTS phase on a gene
expression level, the transcriptomic profiles of L. monocytogenes at different growth
stages in tryptic soy broth with yeast extract (TSBYE) were compared using a whole-
genome DNA microarray. A total of 225 differentially expressed genes (≥ 4-fold, P <
0.05) were identified during the transition to the LTS phase. Genes related to cell
envelope structure, energy metabolism and transport were most significantly upregulated
in the LTS phase. The upregulation of compatible solute transporters may lead to
accumulation of cellular solutes, lowering intracellular water activity and thus increasing
iv
bacterial stress resistance in the LTS phase. The downregulation of genes associated with
protein synthesis may indicate a status of metabolic dormancy in the LTS phase. When
cells in the LTS phase were inoculated into fresh TSBYE the transcriptomic profiles
resembled those of log-phase cells (r = 0.94) and decreased baro- and thermotolerance
were observed.
The LTS phase may help L. monocytogenes persist over a long period of time
within harborage sites in food plants and subsequently transmit to food products during
processing. Specific strains of L. monocytogenes are known to persist in food processing
plants for years and cause contamination; however, there is a lack of understanding as to
why specific strains persist in different processing plants that process different foods.
Thus, we investigated the effects of different L. monocytogenes strains and different types
of food-conditioning films (FCFs) on cell attachment, growth, and biofilm and cocci
formation, which may help explain the persistence of specific strains in food processing
plants. Type of FCF, strain and their interaction significantly affected cell density after
2-d incubation (P < 0.001). Meat and poultry FCFs showed significantly higher cell
densities, as compared to the control without FCF (P < 0.05). All strains showed
medium to very high densities on the respective foods from which they were isolated,
except that the strain J1703 (isolated from turkey) showed very low cell density on
Wegman‘s Brand turkey deli but very high densities on all other brands of turkey deli
meat. Strains lacking the comK prophage showed lower cell densities than those
containing the prophage on all four meat and poultry FCFs (P < 0.05). Biofilms were
only formed by strains containing the comK prophage. Cocci were formed by all strains
v
on all FCFs after 2-weeks incubation. The ability of specific strains of L. monocytogenes
to form biofilms on specific FCFs and subsequently control their entry into the LTS
phase may explain why specific strains persist in different food processing plants and
cause contamination of foods manufactured in those plants.
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TABLE OF CONTENTS
LIST OF FIGURES……………………………………………………………….…….xi
LIST OF TABLES………………………………………………………………...…...xiv
LIST OF ABBREVIATIONS …………………………………………………….…...xv
ACKNOWLEDGEMENTS……………………………………………………….…..xvi
CHAPTER ONE. Statement of the problem……………...…………………………..…1
CHAPTER TWO. Literature review………………...…………………………………..4
2.1 Listeria monocytogenes………………………………………………….…....4
2.1.1 Taxonomy…………………………………………………….….….4
2.1.2 Morphology………………………………………………….….…..4
2.1.2.1 Colony morphology………………………………….……4
2.1.2.2 Cellular morphology………………………………….…...5
2.1.2.3 Morphology of cellular components………………….…...5
2.1.2.4 Morphology-related genes…………………………….…..6
2.1.3 Growth and survival conditions………………………………….....7
2.1.4 Presence in natural environments…………………………………...8
2.1.5 Persistence in food processing plants…………………………….....9
2.1.6 Presence in raw foods, retail environments, and RTE foods………11
vii
2.1.7 Listeriosis……………………………………………………….….12
2.1.7.1 Overview…………………………………………..……..12
2.1.7.2 Pathogenesis…………………………………………..….13
2.1.7.3 Sporadic cases, outbreaks and recalls……………………15
2.1.8 Dual lifestyle….……………………………………………………16
2.2. The long-term-survival (LTS) phase……………………………………..…18
2.2.1 The LTS phase in microorganisms……………………………...…18
2.2.2 Effect of the LTS phase on morphology and resistance to heat and
high pressure…………………………………….…………………19
2.2.3 LTS phase and dormancy…………………………………….……22
2.3 Transition between phases…………………………………………..……….22
2.4 Persisters……………………………………………………………..………25
2.4.1 Definition and presence……………………………………………25
2.4.2 Mechanism of antibiotic resistance………………………..……….26
2.4.3 Role of Toxin/Antitoxin (TA) systems in formation of persisters…27
2.4.4 Isolation of persisters………………………………………………29
2.5 Quorum sensing…………………………………………………………...…29
2.5.1 Overview…………………………………………………...………29
viii
2.5.2 QS in bacteria, archaea and eukaryotes……………………………30
2.5.3 Mechanisms of two major QS systems……………………………32
2.5.3.1 The AI-1 system………………………………….………32
2.5.3.2 The AIP system………………………………….……….32
2.6 Biofilms…………………………………………………………...…………34
2.6.1 Overview………………………………………………..….………34
2.6.2 Biofilms vs. planktonic cells…………………………………….…35
2.6.3 Biofilm formation and factors affecting it…………………………36
2.7 The viable but non-culturable state…………………………………….…….38
2.7.1 Overview………………………………………………………...…38
2.7.2 Enumeration of VBNC cells……………………………….………38
2.7.3 Criticism of the VBNC concept……………………………………39
2.8 Justification of my research………………………………………….………40
2.9 References……………………………………………………………………42
CHAPTER THREE. Listeria monocytogenes responds to its own viable cell density in
accordance with the logistic equation as it transitions to the long-term-survival
phase……………………………………………………………………………..65
3.1 Abstract………………………………………………………………………66
ix
3.2 Introduction…………………………………………………………….........67
3.3 Materials and methods……………………………………………………….69
3.4 Results………………………………………………………………………..73
3.5 Discussion……………………………………………………………………78
3.6 Acknowledgements…………………………………………………………..83
3.7 References……………………………………………………………………85
CHAPTER FOUR. Transcriptomic response of Listeria monocytogenes during
transition to the long-term-survival phase………………….…………………..106
4.1 Abstract……………………………………………………………………..107
4.2 Introduction……………………………………………………………...….108
4.3 Materials and methods ……………………………………………………..110
4.4 Results………………………………………………………………………114
4.5 Discussion…………………………………………………………………..119
4.6 Acknowledgements…………………………………………………………123
4.7 References…………………………………………………………………..125
CHAPTER FIVE. Effects of strain, type of food-conditioning film and their interaction
on cell density, biofilm formation and cocci formation and their possible roles in
persistence of Listeria monocytogenes in food processing plants……………...149
x
5.1 Abstract……………………………………………………………………..150
5.2 Introduction…………………………………………………………….......151
5.3 Materials and methods ……………………………………………………..153
5.4 Results………………………………………………………………………156
5.5 Discussion…………………………………………………………………..157
5.6 Acknowledgements…………………………………………………………163
5.7 References…………………………………………………………………..164
CHAPTER SIX. Summary and questions for future research………………………..183
6.1 Summary……………………………………………………………………183
6.2 Questions for future research……………………………………………….185
APPENDIX A. Preliminary data related to effects of population density, pH and
nutrients on the transition to death phase……………………………………....190
APPENDIX B. Response of long-term-survival-phase cultures of Listeria
monocytogenes to a decrease in population density……………………………198
APPENDIX C. Intracellular ATP levels at log, stationary, death and long-term-survival
phases…………………………………………………………………………...201
APPENDIX D. Sanitizer resistance of Listeria monocytogenes at different growth times
in the long-term-survival phase………………………………………………...205
xi
LIST OF FIGURES
Fig. 2.1. Pathogenesis of L. monocytogenes in human cells ……………………………14
Fig. 2.2. Growth and morphology of L. monocytogenes at different phases of the life
cycle, and subsequent survival after pressure (400 MPa for 180 s) or heat
(62.8°C for 30 s) treatment…………………………………………………….21
Fig. 2.3. Growth and morphology of long-term-survival phase cells of L. monocytogenes
after inoculation into fresh TSBYE at 35°C, and subsequent survival after
pressure (400 MPa for 180 s) or heat (62.8°C for 30 s) treatment…………….21
Fig. 2.4. Current model of biofilm formation ……………………………………..……37
Fig. 3.1. Schematic representation of the experimental design used in this study……...91
Fig. 3.2. Effects of cell density and pH on the transition of L. monocytogenes to the LTS
phase in filter-sterilized-stationary-phase TSBYE……………………..……..93
Fig. 3.3. After 720 h (30 d) in filter-sterilized-stationary-phase TSBYE cell densities of L.
monocytogenes ATCC 19115 in all 15 initial cell density/pH treatment
combinations converged to a narrow range of 4.3 ± 1.1 × 108 CFU/ml (mean ±
standard deviation)………………………………………….…………………96
Fig. 3.4. Transition of stationary-phase cells of L. monocytogenes ATCC 19115 at high
cell densities to the LTS phase in fresh TSBYE (■) and filter-sterilized-
stationary-phase TSBYE (□)…………………………………………………...98
Fig. 3.5. Observed growth of L. monocytogenes ATCC 19115 in fresh TSBYE (■) and
filter-sterilized-stationary-phase TSBYE (□) at initial pH 6.85 at 35°C and the
xii
predicted growth using the logistic equation (r = 0.8 h-1, K = 4 × 108 CFU/ml)
(▲)……………………………………………….…………………………100
Fig. 3.6. A schematic model of how L. monocytogenes responds to its own low or high
viable cell density as it transitions to the LTS phase, which results in cocci
formation and persistence……………………………………………………102
Fig. 4.1. Growth curves of L. monocytogenes F2365 in TSBYE at 35°C demonstrating
the transition from log to LTS phase (A) and the re-growth of LTS cells after
inoculation into fresh TSBYE (B) …………………………………………...131
Fig. 4.2. A circular map showing the global gene transcriptional profiles throughout the
life cycle of L. monocytogenes F2365………………………………………..134
Fig. 4.3. A hierarchical cluster plot showing the gene expression levels of selected
genes………………………………………………………………………….136
Fig. 4.4. A bar graph showing the fold changes of 5 upregulated and 5 downregulated
genes identified by DNA microarray and by RT-PCR experiments………....138
Fig. 5.1. Schematic of the eight-compartment CultureSlide experimental design for
assessing the cell densities of L. monocytogenes strains on different FCFs….173
Fig. 5.2. Fluorescence photomicrographs showing different cell densities of L.
monocytogenes on FCFs………………………………………………….…..175
Fig. 5.3. Fluorescence photomicrographs showing the degradation of FCFs and biofilm
formation by the ECV strain 08-5923………………………………………...177
Fig. 5.4. Examples of cocci formed after 2-weeks incubation at 30°C………………..179
xiii
Fig. 5.5. Proposed model for attachment, biofilm formation and cocci formation leading
to persistence of L. monocytogenes in food processing plants……………….181
Fig. A1. Effect of population density on the pattern of the death phase……………….192
Fig. A2. Effect of pH on the pattern of the death phase……………………………….194
Fig. A3. Effect of addition of nutrients on the pattern of the death phase…………….196
Fig. B1. LTS-phase cells of L. monocytogenes increased in cell density after a density
downshift in spent LTS-phase culture. ………………………………………199
Fig. C1. Intracellular ATP levels at log, stationary, death and LTS phases…………..203
Fig. D1. Inactivation of cultures of L. monocytogenes at 48 h and 2 weeks by 50 ppm
Ster Bac solution……………………………………………………………...206
Fig. D2. Inactivation of cultures of L. monocytogenes at 48 h and 2 weeks by 50 ppm
XY-12 solution………………………………………………………………..208
Fig. D3. Inactivation of cultures of L. monocytogenes at 48 h and 2 weeks by 25 ppm
Vortexx solution………………………………………………………………210
xiv
LIST OF TABLES
Table 3.1. Change in pH of stationary-phase cultures of L. monocytogenes ATCC 19115
during incubation in filter-sterilized-stationary-phase TSBYE at 35°C.…...104
Table 3.2. The estimated rate of maximum population growth (r) and carrying capacity
(K) for the 15 cell density/pH treatments. The values of r and K are derived
by fitting cell density data from 0 – 30 d to the Logistic Equation ………..105
Table 4.1. Primers used for qRT-PCR analysis………………………………..………140
Table 4.2. Genes that showed ≥ 4 fold change (P < 0.05) in at least one of the four
comparisons: 13-h log vs. 17-h stationary, 17-h stationary vs. 24-h death, 24-h
death vs. 168-h LTS, 168-h LTS vs. 336-h LTS………………...…………141
Table 5.1. Lineages, epidemic clones (ECs), sources, presence/absence of the comK
prophage and serotypes of the 7 strains analyzed in the present study…….170
Table 5.2. Effects of strain, type of FCF and their interaction on the cell density of L.
monocytogenes on glass slides after incubation at 30°C for 48 h…………..171
xv
LIST OF ABBREVIATIONS
ANOVA Analysis of Variance
ATCC American Type Culture Collection
ATP Adenosine triphosphate
cDNA Complementary DNA
CFU Colony Forming Unit
DNA Deoxyribonucleic acid
FCF Food-conditioning film
LTS Long-term-survival
ml milliliter
PCR Polymerase chain reaction
RIN RNA integrity number
RNA Ribonucleic acid
RTE Ready-to-Eat
SEM Scanning Electron Microscopy
TEM Transmission Electron Microscopy
TSAYE Tryptic Soy Agar with Yeast Extract
TSBYE Tryptic Soy Broth with Yeast Extract
µl microliter
µm micrometer
xvi
ACKNOWLEDGEMENTS
I would like to express my sincere gratitude to my advisors, Dr. Knabel and Dr.
Anantheswaran, for their invaluable guidance on my research and life. I learned so much
from them: asking deep and novel questions, raising multiple hypotheses, setting up
experiments, conducting statistical analyses and writing scientific papers. They also
taught me a lot about communication and critical thinking, and always encouraged me to
reach perfection and excellence in both research and life. Without their guidance I would
not have realized my dream to become a scientist.
I want to give my sincere thanks to my committee members, Dr. Dudley, Dr.
Phillips and Dr. Zhang, for their suggestions on my research directions, as well as on
specific projects. They encouraged me to ―think out of the box‖ and always gave me
support.
I would like to thank my project collaborators, Xiangyu Deng, Zengxin Li and
Valentina Alessandria, for all their input in the microarray and attachment projects.
Many special thanks will go to my previous and current lab mates, Yi Chen,
Melinda Hayman, Mei Lok, Fenyun Liu, Bindhu Verghese, Sneha Karthikeyan, Sara
Lomonaco and Rob Walker, for their support and friendship. Also I would like to thank
the faculty, staff and graduate students in the Food Science Department for all their help.
I want to thank my parents, Shiyan Wen and Lijun Zhang, and my girlfriend Jing
Guo for their love, encouragement and support.
Finally, I want to thank U.S. Department of Agriculture for the financial support
(Special Grant on Milk Safety to the Pennsylvania State University).
1
CHAPTER ONE
STATEMENT OF THE PROBLEM
Listeria monocytogenes is a gram-positive, non-spore-forming bacterium and the
causative agent of the life-threatening disease listeriosis. It is unique among all
foodborne pathogens, because it cycles between a pathogen in humans and animals and a
saprophyte in the environment. As a model intracellular pathogen, it has been
extensively studied to understand pathogen-host interactions and the adaptation of
bacteria to animal hosts. On the other hand, it may also be a model saprophytic pathogen,
because it is widespread in natural environments such as water, soil and vegetation and
exhibits long-term survival (LTS). Understanding the transition to and characteristics of
the LTS phase in L. monocytogenes may also help explain the LTS in various other
microorganisms.
Many studies have been conducted to investigate the mechanism(s) of growth-
phase transition of bacteria, with most of them focusing on the transition from log phase
to stationary phase at cellular and molecular levels. In contrast, transitions to death and
LTS phases have received much less attention. Although some reports presented
tentative explanations for the mechanism(s) causing the death phase, those hypotheses
were rarely tested and thus inconclusive. In fact, some proposed mechanisms regarding
cell death contradicted each other. Since the death phase is the transition phase leading to
2
the LTS phase, factors triggering the death phase should be identified and their effects on
the transition to the LTS phase should be studied. Preliminary studies showed that high
cell density might trigger the death phase and pH might affect the death rate, thus it
would be interesting to study the effects of cell density and pH on the transition to the
LTS phase.
There is a lack of general understanding of transition of bacteria to the LTS phase
at the molecular level. Therefore, a comprehensive study was also needed to investigate
the transcriptomic response of L. monocytogenes during transition to the LTS phase.
Since differential gene expression at log, stationary and death phases may all contribute
to the eventual transition to and/or characteristics of the LTS phase, transcription profiles
at those phases should also be analyzed. Expression microarrays are ideal for this
purpose because they allow transcriptional analysis at the whole-genome level.
L. monocytogenes at the LTS phase is highly resistant to heat and high pressure
and could also survive other environmental stresses. The LTS phase may also help L.
monocytogenes persist over a long period of time and facilitate its transmission from the
environment to harborage sites in food plants and finally to foods during production.
Specific strains of L. monocytogenes are known to persist in food processing plants for
years and cause contamination; however, there is a lack of understanding as to why
specific strains persist in different processing plants that process different foods. Thus, it
is critical to investigate the effects of different L. monocytogenes strains and different
types of food-conditioning films (FCFs) on biofilm formation and cocci formation, which
may help explain the persistence of specific strains in food processing plants.
3
Therefore, the objectives of this thesis research were:
Objective 1: Investigate the transition of L. monocytogenes to the LTS phase.
Specific aim 1: Study the effects of initial cell density and pH on the transition of
L. monocytogenes to the LTS phase.
Specific aim 2: Compare transcriptional profiles at select time points during the
log, stationary, death and LTS phases to understand the molecular mechanisms
underlying the transition to the LTS phase.
Objective 2: Study the mechanisms by which L. monocytogenes may persist in
food processing environments.
Specific aim 1: Study the specific adherence, growth and biofilm formation of
different strains on different FCFs.
Specific aim 2: Study cocci formation by different strains on different FCFs after
long-term incubation.
4
CHAPTER TWO
LITERATURE REVIEW
2.1 Listeria monocytogenes
2.1.1 Taxonomy
L. monocytogenes is a gram-positive, non-spore-forming, facultatively anaerobic,
saprophytic and pathogenic bacterium. Based on its phenotypic and genotypic
characteristics, it belongs to the genus Listeria which contains other species including L.
ivanovii, L. innocua, L. welshimeri, L. seelilgeri, L. grayi and L. marthii (Gray and
Killinger, 1966; Graves et al., 2010). Specifically, L. marthii is a recently reported new
species (Graves et al., 2010) based on various phenotypic studies, DNA homology and
16S rRNA analyses. So far, only L. monocytogenes and L. ivanovii were reported to be
pathogenic to humans, with L. monocytogenes causing the vast majority of human
illnesses (Kandler and Weiss, 1986; Guillet et al., 2010).
2.1.2 Morphology
2.1.2.1 Colony morphology
The colonies of L. monocytogenes on Modified Oxford Agar after 1–2 d
incubation are gray in color and 0.5–1.5 mm in diameter with sunken centers and dew-
5
drop appearance (Seelinger and Jones, 1986). V- or Y-shaped cell clusters were observed
in the young colonies (Gray and Killinger, 1966).
2.1.2.2 Cellular morphology
Cells of L. monocytogenes are usually small regular rods, with a diameter of ~0.5
μm and length of ~1 μm (Kandler and Weiss, 1986). Coccoid-shaped cells of L.
monocytogenes have been observed in smears from infected tissue samples (Seelinger
and Jones, 1986), broth cultures (Gray and Killinger, 1966; Wen et al., 2009) and 0.85%
salt solution after long-term incubation (Nannapaneni et al., 2008). Filamentous cells of
L. monocytogenes were formed at pH values of 5–6 or > 9 in tryptic soy broth (Isom et al.,
1995) and at 100% CO2 atmosphere in brain-heart infusion broth (Jydegaard-Axelsen et
al., 2005). Formation of septa was induced in filamentous cells when cells were removed
from the CO2 atmosphere and exposed to air (Jydegaard-Axelsen et al., 2005).
Filamentous cells were also formed at a high salt concentration of 1.5 M (Jørgensen et al.,
1995). The length of filamentous cells was significantly increased when salt
concentration increased (Isom et al., 1995), and cell division was induced in the
filamentous cells after a downshift of salt concentration (Jørgensen et al., 1995).
2.1.2.3 Morphology of cellular components
The cell wall is important for maintaining the regular shape of bacteria such as L.
monocytogenes, and the cell-wall-deficient cells (a.k.a., L-form cells) were reported to be
significantly larger in diameter compared to cells with walls. These cell-wall-deficient
6
cells were reported to be able to reproduce indefinitely (Dell‘Era et al., 2009). The
cytoplasmic membrane system in L. monocytogenes was reported to be more abundant
and variable compared to other bacteria, and these characteristics might help this
organism adapt to various environmental conditions (Edwards and Stevens, 1963). L.
monocytogenes produces peritrichous flagella and is motile at < 30°C but not at 37°C
(Farber and Peterkin, 1991). It can produce a continuous capsule layer around the cell
when growing on a glucose-enriched medium (Smith and Metzger, 1962).
2.1.2.4 Morphology-related genes
Based on the whole-genome analysis of L. monocytogenes, multiple genes
encoding morphology-related proteins were identified (Nelson et al., 2004). In the strain
F2365, LMOf2365_1647 encodes a surface polysaccharide biosynthesis protein.
LMOf2365_1088, LMOf2365_2398 and LMOf2365_2399 all encode a cell membrane
protein FtsW, which is required for peptidoglycan assembly of the cell wall during cell
elongation and division (Pastoret et al., 2004). LMOf2365_1738 encodes a cell-shape-
determining protein MreB (a bacterial actin homologue), which is essential for
maintaining the regular rod shape of cells (Wachi and Matsuhashi, 1989). Figge et al.
(2004) hypothesized that MreB might function as a bacterial cytoskeleton organizing the
peptidoglycan biosynthesis complex, and that MreB might coordinate the switch from
longitudinal to septal growth of cells.
7
2.1.3 Growth and survival conditions
L. monocytogenes can grow or survive at various conditions that normally inhibit
the growth or survival of many other non-spore-forming bacteria (Nolan et al., 1992). It
is psychrotrophic and can grow over a wide range of temperature from 1–45°C (Rocourt
and Buchrieser, 2007). It has also been reported that L. monocytogenes grew at -1.5°C on
sliced roast beef under vacuum, although its growth rate was very slow (Hudson et al.,
1994). It can grow under a wide pH range from 4.4–9.6 (Chaturongakul et al., 2008). L.
monocytogenes is a facultative anaerobe and thus can grow with or without the presence
of oxygen (Kandler and Weiss, 1986). Nolan et al. (1992) reported that L.
monocytogenes could grow at a salt concentration as high as 11% (w/w; Aw = 0.924), a
sucrose concentration up to 52% (w/w; Aw = 0.928) and a glycerol concentration up to
33% (w/w; Aw = 0.904) at 21°C.
L. monocytogenes can survive freezing (Faber and Peterkin, 1991). L.
monocytogenes can also survive starvation conditions. Liao and Shollenberger (2003)
reported that L. monocytogenes survived in pure water and phosphate-buffered saline
(PBS) at room temperature for at least 30 weeks, and they claimed that water and PBS
could be used for long-term preservation of bacterial cultures. Nannapaneni et al. (2008)
reported that L. monocytogenes survived in 0.85% NaCl solution for 2 years and
remained infective to Caco-2 cells. L. monocytogenes also survived in 0.3% bile and 5
mM bile-acid solutions, which simulate typical conditions in the human host
(Chaturongakul et al., 2008). However, L. monocytogenes was unable to survive for
8
more than 3 days in 42% and 55% high fructose corn syrup at 32°C (Niroomand et al.,
1998).
2.1.4 Presence in natural environments
L. monocytogenes is ubiquitous in the natural environment. The natural habitats
of L. monocytogenes include soil, water and decayed plant materials (Farber and Peterkin,
1991; Gray et al., 2006). Weis and Seeliger (1975) found significantly higher numbers of
L. monocytogenes in surface soil samples than in soil samples 10 cm below the surface,
and suggested that L. monocytogenes is a saprophyte living in plant-soil environments. L.
monocytogenes was also isolated from many bodies of surface water such as streams,
lakes and rivers, and it might be transmitted from the soil or contaminated sewage
effluents to these waterbodies. Although cell densities of L. monocytogenes in most of its
habitats are usually low, decayed plant materials can support the growth of this pathogen
to a high cell density, which could lead to its wide distribution and subsequent infection
of animals (Fenlon, 2007). It is present in animal feeds such as hay, oat, straw, silage,
which have been associated with listeriosis outbreaks in ruminants (Weidmann et al.,
1994; Fenlon, 2007). This organism has been isolated from feces of various mammals,
especially those feeding on grass and herbage (Grønstøl, 1979). The wide distribution of
L. monocytogenes in natural environments likely contributes to its transmission to and
within farms, food processing facilities and retail environments (Gray et al., 2006).
2.1.5 Persistence in food processing plants
9
L. monocytogenes has been detected in raw foods, on equipment and in final
products within food processing plants (Farber and Peterkin, 1991; Tompkin, 2002).
Different groups of strains have been found to dominate at different production steps in a
smoked-fish processing plant (Fonnesbech Vogel et al., 2001), and prevalent strains also
varied before and after sanitation (Chasseignaux et al., 2002). Various harborage sites
allowing establishment and reproduction of L. monocytogenes have been identified in
food processing plants, such as cracks in stainless steel covers, damaged rubber seals,
hard-to-clean areas inside slicers, wheel bearings of conveyor belts and switches of
equipment. Many of these sites can easily harbor food and cells of L. monocytogenes and
are hard to clean and sanitize, and thus might cause contamination of foods (Tompkin,
2002).
A wide variety of strains of L. monocytogenes have been isolated from food plants,
some of which have been shown to recur in multiple samples over long periods of time in
the same food processing plants and thus were referred to as persistent, resident or
recurrent strains (Norwood and Gilmour, 1999; Harvey and Gilmour, 2001). Persistent
strains have been shown to persist for months to years in plants manufacturing dairy,
seafood, meat and poultry products (Azadian et al., 1989; Lawrence and Gilmour, 1995;
Nesbakken et al., 1996; Boerlin et al., 1997; Loncarevic et al., 1998; Miettinen et al.,
1999). Some extremely persistent strains persisted for 7 and 12 years in food processing
plants manufacturing ice cream and deli poultry products, respectively (Miettnen et al.,
1999; Tompkin, 2002). Strains of L. monocytogenes in epidemic clone III (Olsen et al.,
2005) and V (Knabel et al., submitted) have been reported to persist in food processing
10
plants producing RTE deli meat and poultry products. However, there is a lack of
understanding as to why specific strains persist in different processing plants that process
different foods. Thus, it is critical to investigate the effects of different L. monocytogenes
strains and different types of food-conditioning films (FCFs) on biofilm formation and
cocci formation, which may help explain the persistence of specific strains in food
processing plants.
Compared to sporadic strains, persistent strains of L. monocytogenes exhibited
higher attachment to stainless steel, which is widely used in food processing facilities
(Norwood and Gilmour, 1999). Møretrø and Langsrud (2004) suggested that the
persistent strains of L. monocytogenes could readily attach to and form complex biofilms
on various surfaces in food processing plants, and thus these strains were hard to
eradicate by sanitation. A strain with high persistence coexisting with non-persistent
strains could eventually become predominant in the population and cause contamination
of food products (Schaffner, 2004). Compared to sporadic strains of L. monocytogenes,
persistent strains are likely to contaminate foods more frequently (Harvey and Gilmour,
2001). A mathematical model of cross-contamination by L. monocytogenes indicates that
the more persistent a strain is, the higher contamination level the finished products will
end up with (Schaffner, 2004). The presence of persistent strains of L. monocytogenes in
food processing facilities may not necessarily lead to listeriosis; however, the risk of
illnesses is high if highly virulent strains contaminate and grow in foods (Tompkin, 2002).
Tompkin (2002) suggested several strategies to control this pathogen in food processing
plants, including removing the pathogen from harborage sites, establishing a monitoring
11
program, rapid responses to positive results, verification, and short-term and long-term
assessment programs.
2.1.6 Presence in raw foods, retail environments, and RTE foods
The worldwide prevalence of L. monocytogenes in raw milk is ~2.2% (Farber
and Peterkin, 1991). This organism is usually found in the cheese curd and can grow to a
concentration of > 107 CFU/g in cheese (Michard and Jardy, 1989). L. monocytogenes
has also been isolated from various raw meat and poultry products, such as minced beef,
frozen beef patties, ground pork, seasoned sausage mix, dry and fresh sausages, lamb,
fresh turkey and frozen chicken (Breer and Schopfer, 1988; Lowry and Tiong, 1988; Pini
and Gilbert, 1988; Gilbert et al., 1989).
A survey of ready-to-eat (RTE) foods in retail markets in the United States
showed the overall prevalence of L. monocytogenes was 1.82%. Among the eight
product categories, fresh soft cheeses showed the lowest prevalence (0.17%) while
seafood salads showed the highest prevalence (4.7%). The contamination levels were <
10 CFU/g for most positive samples and > 100 CFU/g for some luncheon meats, seafood
salads and smoked seafood. This study also showed higher prevalence of L.
monocytogenes in some in-store-packaged foods such as luncheon meats, seafood salads
and deli salads, and higher contamination levels in manufacturer-packaged foods
(Gombas et al., 2003). Contamination levels for RTE foods associated with illnesses are
generally > 1000 CFU/g (International Commission on Microbiological Specifications
for Foods, 1996).
12
2.1.7 Listeriosis
2.1.7.1 Overview
L. monocytogenes can cause a life-threatening disease listeriosis (Gandhi and
Chikindas, 2007) and 99% of human listeriosis cases are due to the consumption of
contaminated foods (Mead et al., 1999; Scallan et al., 2011). There are thirteen serotypes
of L. monocytogenes based on their somatic and flagellar antigen types, namely 1/2a,
1/2b, 1/2c, 3a, 3b, 3c, 4a, 4ab, 4b, 4c, 4d, 4e and 7 (Farber and Peterkin, 1991), and
strains of serotype 1/2a, 1/2b and 4b cause most human listeriosis cases (Chaturongakul
et al., 2008).
The incubation time of listeriosis in the human body ranges from 3 - 70 d, and
the symptoms include gastroenteritis, abortion, meningitis and sepsis. L. monocytogenes
is a classic intracellular pathogen and thus an active cell-mediated immunity is required
for recovery from listeriosis (Farber and Peterkin, 1991; Scanllan et al., 2011). Therefore,
susceptible individuals include the elderly, infants, pregnant women and immuno-
compromised individuals including AIDS patients and those undergoing immuno-
suppressive drug therapy (Wemekamp-Kamphuis et al., 2004). This pathogen is a great
concern especially to pregnant women since it can cause fatal infections to the fetus
(Farber and Peterkin, 1991). The case-fatality rate of listeriosis was reported to be 20%
in 1999 (Mead et al., 1999) and 15.9% in 2011 (Scallan et al., 2011) in the U.S., 40% in
the 2008 Canada RTE meat outbreak (Gilmour et al., 2010) and 20% in the most recent
cantaloupe outbreak in the U.S. (http://www.cdc.gov/listeria/outbreaks/cantaloupes -
13
jensen-farms/101211/). Due to its high virulence and subsequent high fatality rate L.
monocytogenes is a leading cause of death associated with foodborne illnesses in the
United States (Mead et al., 1999; Scallan et al., 2011).
2.1.7.2 Pathogenesis
Once ingested by the human host, L. monocytogenes first attaches to the host cell
surface (Fig. 2.1A). The attachment process is mediated by two surface proteins,
internalin A (InlA) and internalin B (InlB), which bind to host surface proteins E-
cadherin and tyrosine kinase, respectively. The binding between the internalins and host
surface receptors eventually leads to pathogen uptake via phagocytosis (Fig. 2.1B)
(Dussurget et al., 2004; Stavru et al., 2011). Cells of L. monocytogenes internalized in
the vacuoles then express listeriolysin O (LLO) and two phospholipases, PC-PLC and PI-
PLC, to lyse the phagosomal membrane (Fig. 2.1C). After escaping into the cytosol of
host cells, L. monocytogenes adapts its metabolism to the cytoplasmic environment and
starts to reproduce (Fig. 2.1D). At the same time L. monocytogenes polymerizes actin
molecules into a network of branched filaments. This polymerization process propels L.
monocytogenes to move through the cytosol. When encountering the host cell membrane,
this pathogen continues pushing forward into the neighboring host cell (Fig. 2.1E), and
eventually it is engulfed in a double-membrane vacuole inside the adjacent cell (Fig.
2.1F). L. monocytogenes then starts a new cycle of vacuole lysis, replication and
intracellular movement (Dussurget et al., 2004; Cossart, 2007; Cossart and Toledo-Arana,
2008).
14
Fig. 2.1. Pathogenesis of L. monocytogenes in human cells (adapted from Dussurget et al.,
2004).
The intracellular nature of L. monocytogenes makes it able to break through three
critical barriers in the human body, namely the intestinal, placental and blood-brain
barriers (Hamon et al., 2006). After passing the intestinal barrier, this pathogen can move
through the bloodstream and lymph and infect the liver and the spleen, with the former as
the main site of infection. It can further reach the brain and the placenta via the
15
bloodstream (Cossart, 2007; Stavru et al., 2011). It has been reported that this pathogen
could infect a wide range of tissues (Cossart and Toledo-Arana, 2008).
2.1.7.3 Sporadic cases, outbreaks and recalls
There are approximately 1500–2500 cases of listeriosis annually in the United
States (Mead et al., 1999; Scallan et al., 2011), with most cases being sporadic (Farber
and Peterkin, 1991). Sporadic cases (i.e., illnesses occurring singly in scattered instances)
of listeriosis were associated with the consumption of various foods, such as cheeses,
cooked chicken, turkey frankfurters, sausages, mushrooms, raw milk, ice cream, cod roe
and alfalfa (Farber and Peterkin, 1991).
Despite efforts made by government agencies and the food industry to control this
pathogen in food processing and retail facilities (Tompkin, 2002), outbreaks of listeriosis
have occurred around the world. Outbreaks in developed countries have been well
documented since the 1980s (Farber and Peterkin, 1991). Large outbreaks of listeriosis
have occurred recently in multiple countries due to consumption of RTE meats (Gilmour
et al., 2010) and cheese (Fretz et al., 2010; Jackson et al., 2011) products. The outbreak
associated with RTE meats in Canada during 2008 caused 23 deaths and 57 confirmed
illnesses (http://www.phac-aspc.gc.ca/alert-alerte/listeria/listeria_20100413-eng.php;
Gilmour et al., 2010). In the United States, a multistate outbreak of listeriosis associated
with Mexican-style cheeses during 2008–2009 resulted in 8 illnesses, 7 of which were
pregnant women (Jackson et al., 2011). During January to June 2010, a listeriosis
outbreak due to consumption of head cheese caused 14 illnesses in Louisiana
16
(http://www.cdc.gov/mmwr/preview/mmwrhtml/mm6013a2.htm). Recently, a multistate
listeriosis outbreak linked to consumption of cantaloupe caused 146 illnesses and 30
deaths by December 8, 2011 (http://www.cdc.gov/listeria/outbreaks/cantaloupes-jensen-
farms/index.html).
To improve food safety, the U.S. Food and Drug Administration and the U.S.
Department of Agriculture currently impose a ―zero tolerance‖ standard for L.
monocytogenes in RTE foods (Gilbert, 1996). However, this pathogen is still detected in
various RTE foods, leading to expensive recalls in multiple states. Some recent examples
include the recalls of meat products (http://www.fsis.usda.gov/News_&_Events/
Recall_049_ 2010_Release/index.asp), dairy products (http://www.ktla.com/news/
landing/ktla-california-cheese-recall,0,129216.story) and vegetables and salads
(http://www.myhealthnewsdaily.com/dshs-orders-sangar-produce-to-close-recall-
products-in-texas-0618; http://www.listeriablog.com/listeria-watch/listeria-linked-to-
salad-in-rhode-island).
2.1.8 Dual lifestyle
L. monocytogenes is a unique foodborne pathogen, since it cycles between a
saprophyte in the environment and a pathogen in humans and animals (Toledo-Arana et
al., 2009). L. monocytogenes is a model saprophytic pathogen since it is ubiquitous in
water, soil and vegetation, and can survive various environmental stresses (Gray et al.,
2006). It has been extensively studied to understand pathogen-host interactions and the
adaptation of bacteria to animal hosts. It can also colonize and persist in various food
17
processing plants and retail environments (Farber and Peterkin, 1991; Tompkin, 2002;
Gombas et al., 2003). A comparative genomic study using L. monocytogenes strains
F2365, H7858 and F6854 showed that all three strains possess genes associated with
transport and utilization of a wide variety of sugars, such as glucose, mannose, chitin,
cellulose, trehalose and pullulan (Nelson et al., 2004). High conservation of these sugar-
metabolism genes across different strains may be critical for the growth and survival of L.
monocytogenes in the natural environment, since many of these sugars are common
carbon sources in its natural habitats like decayed vegetation and soil (Nelson et al., 2004;
Gray et al., 2006). L. monocytogenes is motile at temperatures below 30°C due to the
expression of flagella, which may allow it to access nutrient sources in natural
environments (Gray et al., 2006). None of the virulence genes of L. monocytogenes are
expressed at temperatures below 30°C. One main reason is that the positive regulatory
factor A (PrfA), the regulator controlling the expression of many virulence genes, is not
translated at these temperatures. Specifically, the transcript of prfA forms a hairpin
structure at temperatures below 30°C, which prevents the binding of ribosomal units onto
the transcript and thus inhibits the synthesis of PrfA (Hamon et al., 2006).
The switch of L. monocytogenes from a saprophyte to a pathogen has been well
studied (Chatterjee et al., 2006; Hamon et al., 2006; Toledo-Arana et al., 2009).
Virulence proteins are translated once L. monocytogenes enters the hosts. Specifically,
the hairpin structure of prfA transcript denatures at host body temperatures (37–40°C),
and the subsequent synthesis of PrfA leads to the coordinated expression of virulence
genes such as inlA and inlB (Chatterjee et al., 2006; Hamon et al., 2006). In the host, this
18
pathogen can quickly adjust its metabolism to adapt to an anaerobic and parasitic lifestyle
(Cossart and Toledo-Arana, 2008). A transcriptomic study demonstrated that ~17% of
the whole genome of L. monocytogenes was differentially expressed during intracellular
survival and growth (Chatterjee et al., 2006). A comparative genomics study showed that
L. monocytogenes possesses more transcriptional regulatory genes than the
nonpathogenic species L. innocua, and that these transcriptional regulators might be
essential for the quick transition from a saprophytic to pathogenic lifestyle (Glaser et al.,
2001). A more recent transcriptomic study showed that antisense RNAs played an
important role in regulating the transition of L. monocytogenes from saprophyte to
pathogen (Toledo-Arana et al., 2009). This special dual lifestyle of L. monocytogenes
makes it a multifaceted model for saprophytic pathogens (Hamon et al., 2006; Gray et al.,
2006).
2.2. The long-term-survival (LTS) phase
2.2.1 The LTS phase in microorganisms
In the life cycle of bacteria, cells often do not all die off during the death phase;
instead, a portion of the population can enter a dormant state and exhibit long-term
survival in the environment (Lappin-Scott and Costerton, 1990). Such a LTS state was
termed the fifth phase of the bacterial life cycle in Escherichia coli (Finkel, 2006) and
Micrococcus luteus (Steinhaus and Birkeland, 1939). Although there have been studies
regarding long-term starvation survival of L. monocytogenes in sterile distilled water
19
(Liao and Shollenberger, 2003) or nutrient-limited media (Herbert and Foster, 2001;
Lungu et al., 2010), there had been no report of a fifth phase in batch cultures of L.
monocytogenes in a nutrient dense medium until a recent study by Wen et al. (2009). In
that study, L. monocytogenes was shown to enter a LTS phase after the death phase in
tryptic soy broth with yeast extract (TSBYE). In the LTS phase cell density remained
stable at ~108 CFU/ml for at least 30 d at 35°C.
2.2.2 Effect of the LTS phase on morphology and resistance to heat and high
pressure
Changes in cell morphology as well as resistance to heat and high pressure of L.
monocytogenes during the transition to and out of the LTS phase have been investigated
(Wen et al., 2009). Rod-shaped cells transitioned to cocci and decreased in size as they
transitioned from log to the LTS phase (Fig. 2.2). Similar morphological changes during
long-term-starvation survival have been observed in both gram-positive and gram-
negative bacteria, including Staphylococcus aureus (Watson et al., 1998), Arthrobacter
globiformis (Demkina et al., 2000), E. coli (Kolter et al., 1993), a marine vibrio
(Novitsky and Morita, 1978) and other microorganisms (Thorne and Williams, 1997).
Thermo- and barotolerance increased as cells transitioned from log to the LTS phase. A
quantitative resistance study showed D400 MPa and D62.8°C increased 10 and 19 fold,
respectively (Fig. 2.2). After inoculation of cells at the LTS phase into fresh TSBYE,
cells rapidly reentered log phase, followed by stationary phase. During this transition
from the LTS to log phase, coccoid cells regained the rod shape and cells decreased in
20
thermotolerance and barotolerance (Fig. 2.3) (Wen et al., 2009).
21
Fig. 2.2. Growth and morphology of L. monocytogenes at different phases of the life cycle, and subsequent survival after pressure (400 MPa for 180 s) or heat (62.8°C for 30 s) treatment. (Gram stain) Bars = 10 μm. (SEM) Bars = 1 μm.
Fig. 2.3. Growth and morphology of long-term-survival phase cells of L. monocytogenes after inoculation into fresh TSBYE at 35°C, and subsequent survival after pressure (400 MPa for 180 s) or heat (62.8°C for 30 s) treatment. (SEM) Bars = 1 μm.
22
2.2.3 LTS phase and dormancy
During the LTS phase, there has been disagreement on whether cells are dormant.
One view is that these survivors are metabolically active and constantly reproducing. In
this scenario mutants with greater fitness take over the population, and such a process
could happen over and over during long-term survival (Finkel, 2006). Nevertheless,
dormancy during LTS has been reported in soil, rock and marine microorganisms
(Lappin-Scott and Costerton, 1990; Novitsky and Morita, 1977). Analogous to bacterial
endospores, coccoid-shaped LTS-phase cells of L. monocytogenes might also represent
dormant forms of bacteria (Wen et al., 2009).
2.3 Transition between phases
Many studies have been conducted to investigate the mechanism(s) of growth-
phase transition of bacteria, with most of them focusing on the transition from log phase
to stationary phase at cellular and molecular levels. Log-phase cells catabolize nutrients
and produce waste metabolites, which could alter the growth conditions such as nutrient
composition, oxygen content and pH. When one or more essential nutrient(s) is(are)
exhausted or when the waste products accumulate to an inhibitory level, the population
exhibits decelerated growth and then enters stationary phase without net growth
(Madigan, 2000). During transition to the stationary phase, more than half of the proteins
of L. monocytogenes ScottA showed significant changes in expression levels, e.g., DNA
polymerase was downregulated (Weeks et al., 2004). Another proteomics study on L.
23
monocytogenes EGDe suggested that overall protein synthesis decreased during the
transition from log phase to stationary phase based on the downregulation of some 30S
and 50S ribosomal proteins (Folio et al., 2004). Cells at stationary phase were reported to
be less physiologically active than at log phase (Kolter et al., 1993). A higher percentage
of dormant cells were formed when E. coli transitioned from log to stationary phase
(Keren et al., 2004). It is also known that rpoS encoding σs, a stationary phase sigma
factor, is upregulated when E. coli enters stationary phase, and σs subsequently induces
expression of a group of genes, which encode exonuclease, cell-shape-determination
protein and DNA protection protein (Kolter et al., 1993).
Stationary-phase cells in batch cultures eventually transition to the death phase;
however, little is understood about the mechanism(s) of this transition. Although some
reports presented tentative explanations for the mechanism(s) causing the death phase,
those hypotheses were rarely tested and thus inconclusive. In fact, some proposed
mechanisms regarding cell death contradicted each other. Cell death might be a passive
event because the culture environment can no longer support the high cell density at
stationary phase (Finkel, 2006). On the other hand, cell death might have been
programmed into the genome during evolution (Hochman, 1997; Finkel, 2006). Such
―programmed cell death (PCD)‖ was originally studied in eukaryotes (Saran, 2000), but it
has also been found in prokaryotes, e.g., the mazEF system in E. coli (Kolodkin-Gal et al.,
2007; Rice and Bayles, 2008), the pezAT system in Streptococcus pneumoniae (Khoo et
al., 2007) and the spoOA system in Bacillus subtilis (Hedrick et al., 2010). Bacteria
24
during PCD exhibited cell shrinkage, DNA fragmentation, RNA degradation and release
of cell contents (Hochman, 1997). At the end of stationary phase, bacteria may perceive
their high population density possibly via quorum sensing, and then the majority of the
population actively conducts programmed death and release nutrients for the survivors
(Finkel, 2006; Kolter et al., 1993). For example, degraded rRNA from dead cells might
provide nucleotides and energy for the surviving subpopulation at the end of death phase
and support their metabolism and future survival (Davis et al., 1986). The phenomenon
that surviving cells live on debris of dead cells has been termed cryptic growth (Kolter et
al., 1993). In this sense, cell death could be considered as a form of stress adaptation and
a fitness strategy to preserve the genome in survivors for future reproduction (Hochman,
1997).
The molecular mechanism which causes cells to enter the LTS phase is unknown.
Survivors of E. coli at the end of death phase might sense the signals released from
suicidal cells, which could terminate PCD and induce survivors to enter LTS phase when
90-99% of the population is dead (Finkel, 2006). Within the LTS phase, L.
monocytogenes was shown to maintain a stable density ranging from ~105 to ~106
CFU/ml in glucose-limited media (Herbert and Foster, 2001). Soil and rock
microorganisms have also been reported to maintain a constant density during the LTS
phase (Lappin-Scott and Costerton, 1990). However, based on a modeling analysis, it
has been reported that the bacterial population might not be able to maintain a true steady
state (Lavric and Graham, 2010). There is a lack of general understanding of transition
of bacteria to the LTS phase at the molecular level. Therefore, a comprehensive study
25
was needed to investigate the transcriptomic response of L. monocytogenes during
transition to the LTS phase. Understanding the transition to and characteristics of the
LTS phase in L. monocytogenes may also help explain the LTS in various other
microorganisms.
2.4 Persisters
2.4.1 Definition and presence
Persisters are a population of microorganisms that ―survive lethal concentrations
of antibiotics without any genetic resistance mechanisms‖ (Lewis et al., 2006). The
antibiotic-resistant persisters were first described by Bigger (1944), who found a
subpopulation of Staphylococcus spp. surviving lethal penicillin treatment did not
genetically acquire resistance to penicillin; instead, after regrowth of those survivors
(persisters) in fresh media the culture became sensitive to the antibiotic and a new
subpopulation of persisters survived. So far the presence of persisters has been found in
various bacteria such as S. aureus (Singh et al., 2009), E. coli (Shah et al., 2006),
Salmonella enterica serovar Typhimurium (Vazquez-Laslop et al., 2006), Pseudomonas
aeruginosa (Lewis, 2007), Gardnerella vaginalis and Lactobacillus acidophilus (Muli
and Struthers, 1998) as well as in fungi (Lewis, 2010).
Although persisters were defined based on their phenotypic resistance to
antibiotics, their presence is independent of antibiotic treatments. Instead, persisters
preexist in a bacterial population at all growth phases (Keren et al., 2004) and the
26
percentage of persisters in the population increases when the population transitions from
log phase to stationary phase (Lewis, 2005). Persisters have been shown to be present in
a microbial population at both planktonic (Keren et al., 2004) and biofilm states
(Costerton et al., 1999; Singh et al., 2009). The presence of persisters may be a general
fitness strategy against stresses including antibiotic treatments, and the persisters
surviving antibiotic treatments can preserve the collective genome of the population for
future reproduction (Kussell et al., 2005).
2.4.2 Mechanism of antibiotic resistance
The antibiotic resistance of persisters is not due to mutation, since the persister
population surviving the antibiotic treatment lost the resistance after cells were regrown
in fresh media (Singh et al., 2009). Instead, their resistance might be due to metabolic
dormancy (Lewis, 2007). Shah et al. (2006) studied the physiology of drug-resistant
persisters of E. coli by inserting a fluorescent reporter gene in the genome, and they
found that the fluorescence intensity of persisters was lower than that of drug-sensitive
non-persisters. They claimed that persisters might be dormant based on their low
translation level as well as downregulation of biosynthesis pathways (Shah et al., 2006).
Lewis (2007) further correlated the dormancy of persisters and their antibiotic resistance.
He hypothesized that dormant persisters might require a very low level of physiology to
maintain cell viability, and thus the binding between antibiotics and target cellular
components would not affect their survival. Moreover, persister cells might possess
multidrug tolerance (MDT) proteins that inhibit the cellular targets of antibiotics, and
27
such inhibition could protect these cellular targets from being corrupted by antibiotics
(Shah et al., 2006; Lewis, 2007). Therefore, the dormant persisters might serve as
specialized ―survivor cells‖ when the whole population faces antibiotic treatment (Lewis,
2007).
2.4.3 Role of Toxin/Antitoxin (TA) systems in formation of persisters
The formation of persisters may be due to the expression of toxin-antitoxin (TA)
modules in the bacterial genome (Gerdes et al., 2005; Shah et al., 2006; Lewis, 2007).
TA modules generally consist of two components; however, a rare three-component TA
module, ω-ε-ξ, has been found in gram-positive bacteria (Gerdes et al., 2005). There are
two genes in a typical TA system, with one encoding a stable toxin and the other
encoding an unstable antitoxin. The antitoxin can bind and neutralize the toxin but is
prone to degradation by proteases (Engelberg-Kulka et al., 2006). Some well-studied
toxin-antitoxin pairs include CcdB-CcdA, RelE-RelB, ParE-ParD, HigB-HigA, MazF-
MazE, Doc-Phd, VapC-VapB (Gerdes et al., 2005), YgiU-YgiT (Shah et al., 2006),
HipA-HipB (Schumacher et al., 2009), YafQ-DinJ (Harrison et al., 2009) and others
(Brown and Shaw, 2003). The known toxic effects of these toxins include inhibition of
the DNA gyrase to reduce transcription and replication (CcdB and ParE), inhibition of the
translation factor EF-Tu (HipA) and cleavage of mRNA to inhibit translation (MazE and
RelE) (Harrison et al., 2009; Schumacher et al., 2009). It has been hypothesized that the
toxins might cause programmed cell death (Kolodkin-Gal et al., 2007); however, some
28
evidence shows that toxins do not kill cells but inhibit the syntheses of macromolecules
and lead to reversible bacteriostasis (i.e., a no-growth state) (Pederson et al., 2002).
The current model of persister formation is that during cell growth increasing
environmental stresses lead to the activation of toxins, which decrease the overall
physiological activities and cause a dormant, drug-resistant state (Shah et al., 2006;
Schumacher et al., 2009; Singh et al., 2009). For example, stressful conditions can
inhibit the synthesis of an antitoxin MazE, which leads to the activation of the toxin
MazF and subsequent mRNA cleavage (Engelberg-Kulka et al., 2006). Similarly, amino
acid starvation can increase the transcription of relBE and cause enzymatic degradation
of the antitoxin RelB, which leads to the activation of toxin RelE (Pedersen et al., 2003).
The toxin HipA may be activated via a similar mechanism (Schumacher et al., 2009). A
toxin gene ygiU was also found to be upregulated in persister cells compared to non-
persisters (Shah et al., 2006). The dormancy resulting from toxin activities may
subsequently cause the resistance of persisters (Shah et al., 2006).
Overexpression of toxins RelE, MazF and HipA increased the drug resistence of
persisters (Lewis, 2007). However, deletion of a single toxin gene, relE or mazF, did not
affect the resistance or formation of persisters in E. coli, possibly due to the high
redundancy of TA modules in the genome (Brown and Shaw, 2003; Lewis, 2007;
Schumacher et al., 2009).
Besides the TA modules, expression of other genes could also lead to persister
formation. Vázquez-Laslop et al. (2006) showed that more persisters were formed in E.
coli due to the overexpression of two proteins, DnaJ (from the chaperone system
29
DnaJ/DnaK/GrpE) and PmrC (a phosphoethanolamine-transferring enzyme). Both
proteins were toxic if overexpressed from plasmids, which could lead to dormancy and
persister formation (Vázquez-Laslop et al., 2006; http://ecoli.aist-nara.ac.jp/gb5/
Resources/archive/archive.html).
2.4.4 Isolation of persisters
Persister cells can be simply isolated by treating a wild-type culture with lethal
concentrations of antibiotics, and collecting surviving cells by sedimentation (Keren et al.,
2004). Another isolation method involves genetic engineering and cell sorting. First, a
reporter gene encoding a degradable green fluorescent protein is inserted downstream of
a promotor, whose acitvity correlates to the bacterial growth rate. Because persisters are
dormant with a low level of translation and no growth, they appear dimmer compared to
regular non-dormant cells. Thus persister cells can be isolated by sorting out the dimmer
cells (Shah et al., 2006).
2.5 Quorum sensing
2.5.1 Overview
The term quorum sensing (QS) was coined by Fuqua and Winans in a study on
cell commnunication in Agrobacterium (Fuqua and Winans, 1994). QS is a cell-to-cell
communication process to coordinate the behavior of single cells. A series of steps
involved in QS include the production and secretion of, and response to, the signaling
30
molecules termed autoinducers (Miller and Bassler, 2001). QS regulates biological
activities only when the population is at high cell density, and thus these behaviors can
help the population adapt to various environmental conditions and eventually facilitate
reproduction (Bassler, 2002). QS might be fine-tuned during evolution to maximize
fitness by regulating biological behaviors (Joelsson et al., 2006; Pai and You, 2009).
Biological behaviors regulated by QS include biofilm formation, sporulation, symbiosis,
peptide synthesis, virulence, bioluminescence and morphogenesis (Annous et al., 2009).
QS systems have been discovered in all three domains of life including bacteria, archaea
and eukaryotes (Miller and Bassler, 2001; Hornby et al., 2001; Paggi et al., 2003;
Williams, 2007).
2.5.2 QS in bacteria, archaea and eukaryotes
QS has been extensively studied in bacteria. Gram-negative bacteria mainly use
acylated homoserine lactones (AHLs; also termed autoinducer-1 or AI-1) and fatty acid
derivatives as QS signals, while gram-positive bacteria mainly use short, sometimes
modified autoinducing peptides (AIPs) as QS signals (Miller and Bassler, 2001; Bai and
Rai, 2011). The detection apparatus are highly specific for different autoinducers
(Bassler, 2002). For example, AHLs are detected by a cytosolic transcription factor in
Vibrio fischeri, and AIPs are perceived by a 2-component regulatory system in S. aureus
(Atkinson and Williams, 2009). QS has been found to regulate virulence in Vibrio,
Yersinia, Pseudomonas, Enterobacter and Agrobacteria (Williams, 2007), to induce
bioluminescence in V. fischeri (Bassler, 2002), to induce the production of antimicrobial
31
peptides or toxins in Bacillus, Staphylococcus and Streptococcus (Podbielski and
Kreikemeyer, 2004), to regulate biofilm formation in L. monocytogenes (Riedel et al.,
2009), to regulate nitrogen fixation in Rhizobium (Hoang et al., 2004), to regulate
programmed cell death (PCD) in E. coli by sensing the QS signal, the extracellular death
factor (EDF) (Kolodkin-Gal et al., 2007; Kolodkin-Gal and Engelberg-Kulka, 2008;
Belitsky et al., 2011), to induce the formation of persister cells of Pseudomonas
aeruginosa (Möker et al., 2010) and to regulate sporulation in Clostridium perfringens
(Li et al., 2011) and B. subtilis (Grossman and Losick, 1988; Solomon et al., 1996;
Shapiro, 1998). Specifically, B. subtilis was reported to produce an extracellular QS
signal to stimulate sporulation at high cell density (Grossman and Losick, 1988; Shapiro,
1998).
QS systems are not well understood in eukaryotes and archaea, although some
progress has been made in these areas. An autoinducing molecule, farnesol, was found to
affect the morphological change from a yeast-like to a mycelium-like form in the yeast
Candida albicans (Hornby et al., 2001). In a haloalkaliphilic archaeon Natronococcus
occultus, AHL molecules appeared to serve as QS signals regulating the production of
extracellular protease (Paggi et al., 2003).
32
2.5.3 Mechanisms of two major QS systems
2.5.3.1 The AI-1 system
In the autoinducer-1 (AI-1) QS system in gram-negative bacteria, AI-1 (a.k.a.,
AHL) molecules are produced by an AI-1 synthase (e.g., LuxI in V. fischeri). Short-
chain AI-1 molecules can freely diffuse across the cellular membrane, and long-chain AI-
1 molecules have to be actively transported out of cells. The concentration of AI-1
increases as the cell population increases. When the bacterial population reaches a
threshold density, the AI-1 will also reach a threshold concentration, leading to the
binding between AI-1 and a transcriptional factor (e.g., LuxR in V. fischeri). The
transcriptional factor (bound with AI-1) then binds to the promotor of the target gene and
activates the gene (Miller and Bassler, 2001). The AI-1 system coordinates bacterial
behaviors within a species. Currently this QS system has been found in more than fifty
gram-negative species such as P. aeruginosa and V. haveyi (Bassler, 2002).
2.5.3.2 The AIP system
Another typical intraspecies QS system is the AIP system usually found in gram-
positive bacteria. The AIP signaling molecules, which are usually modified peptides, are
synthesized in the cytoplasm and then actively transported out of the cells. Increased cell
density leads to accumulation of extracellular AIPs, which eventually causes the
detection of the AIP signal by two-component sensor kinases. The signal is then relayed
through a series of phosphorylation reactions from the two sensor proteins to the target
33
regulator. The regulator is activated once phosphorylated, and it subsequently affects the
transcription of genes under the control of the QS (Bassler, 2002; Novick, 2003).
A well studied AIP-type QS system is the accessory gene regulator (Agr) system,
which was first discovered in Staphylococcus species (Vuong et al., 2000) and then found
in many other gram-positive bacteria, including L. monocytogenes (Rieu et al., 2007) and
Clostridium species (Atkinson and Williams, 2009). There are four genes in the agr
operon: agrA encoding a response regulator AgrA, agrB encoding a membrane-bound
peptidase AgrB, agrC encoding a membrane-bound sensor AgrC, and agrD encoding an
AIP signal AgrD. AgrB modifies and exports AgrD out of cells, and when the
extracellular concentration of AgrD reaches a threshold level, the binding between AgrD
and AgrC will cause the phosphorylation of AgrC. Phosphorylated AgrC then
phosphorylates AgrA, which eventually causes up- or down-regulation of target genes
(Riedel et al., 2009). The Agr system in S. aureus regulates virulence, biofilm formation
and intracellular survival in host cells (Novick and Geisinger, 2008). In L.
monocytogenes, mutation of agrD decreased biofilm formation and virulence (Rieu et al.,
2007; Riedel et al., 2009) and mutation of agrA decreased biofilm formation (Rieu et al.,
2007). The Agr system is the only QS system that has been reported in L. monocytogenes
(Garmyn et al., 2009).
34
2.6 Biofilms
2.6.1 Overview
A biofilm is a microbial community enclosed in a self-secreted matrix on organic
or inorganic surfaces (Hunt et al., 2004; Costerton et al., 2009). Biofilms were first
described by Costerton et al. (1978). A biofilm can be formed by a wide variety of
microorganisms including bacteria, fungi, algae and protozoa. Biofilms can be formed
on many biotic or abiotic surfaces, such as living tissues, soil, marine sediments, medical
equipment (Donlan, 2002; Gandhi and Chikindas, 2007) and various surfaces in food
processing plants (Wong, 1998; Møretrø and Langsrud, 2004; Thévenot et al., 2006).
Microorganisms in a biofilm produce an adhesive matrix of polysaccharides,
proteins, nucleic acids and/or lipids, which are collectively termed Extracellular
Polymeric Substances (EPS) and account for 50–90% of the total organic matter in a
biofilm (Donlan, 2002; Vu et al., 2009). EPS facilitates the attachment of cells to
surfaces, forms the architecture of the biofilm and protects cells against environmental
stresses (Czaczyk and Myszka, 2007; Vu et al., 2009). There are usually microchannels
in the EPS structure, which may facilitate the transport of water, nutrients and metabolic
wastes into and out of biofilms (Annous et al., 2009). The formation of these
microchannels may be regulated by QS (Stanley and Lazazzera, 2004).
35
2.6.2 Biofilms vs. planktonic cells
Planktonic (freely suspended) cells can fix themselves on surfaces and form
biofilms. This transition leads to drastic changes in the transcription, translation and
phenotype of cells (Annous et al., 2009). Sauer (2003) and Jefferson (2004) reviewed
genes showing differential expression during biofilm formation, including those related
to adhesion, stress response, metabolism and quorum sensing. Biofilm cells also showed
different physiologies from those of planktonic cells (Gandhi and Chikindas, 2007).
Jefferson (2004) hypothesized that biofilms might be the normal ―default mode‖
for bacteria, and that the planktonic state might be an artifact in vitro. Compared to
planktonic cells, biofilm cells have several advantages which could enhance their survival
and growth and result in higher fitness (Jefferson, 2004). First, cells in the biofilm state
are protected against various stresses. Biofilm cells are more resistant to various stresses
than planktonic cells, including biological stresses (e.g., starvation, antibiotics and
immune response of the host), physical stresses (e.g., sheer forces, dehydration, heat,
freezing and pressure) and chemical stresses (e.g., sanitizers and pH shifts) (Møretrø and
Langsrud, 2004; Jayaraman, 2008). Second, cells can colonize nutrient-rich areas by
forming biofilms. When carbon sources were plentiful, biofilm formation was enhanced
in E. coli (O‘Toole, 2000) and S. aureus (Jefferson et al., 2004). On the other hand,
starvation can induce biofilm detachment, which allows cells to move freely to search for
a better habitat (Jefferson, 2004). For example, when nutrient supply was decreased,
Aeromonas hydrophila showed higher biofilm detachment rate (Sawyer and
36
Hermanowicz, 1998). A similar phenomenon was observed in P. aeruginosa (Hunt et al.,
2004). Third, cells in a biofilm benefit from their coordinated behaviors. One well-
studied phenomenon on this topic is enhanced gene transfer within biofilms, which has
been found in many bacteria such as S. mutans, V. cholera and E. coli (Licht et al., 1999;
Li et al., 2001; Blokesch and Schoolnik, 2007). Gene transfer could result in the
exchange of biological traits essential for survival or growth, such as antibiotic resistance
and enhanced biofilm formation (Verghese et al., 2011). Biofilm cells demonstrated a
higher frequency of gene transfer via transformation or conjugation than planktonic cells
(Annous et al., 2009).
2.6.3 Biofilm formation and factors affecting it
The current model of biofilm formation involves a series of steps. First, cells
reversibly attach to a biotic or abiotic surface, and then irreversibly attach to the surface
by producing adhesive EPS. Cells then reproduce and form microcolonies within the
EPS. It has been reported that bacterial populations in biofilms grows in accord with the
logistic equation (Indekeu and Sznajd-Weron, 2003). Finally, 3-dimentional structures
such as cell clusters and nutrient channels within the biofilm are formed, indicating the
formation of a mature biofilm (Stoodley et al., 2002) (Fig. 2.4).
37
Fig. 2.4. Current model of biofilm formation (adapted from Stoodley et al., 2002).
Many factors can affect biofilm formation, such as presence of conditioning films
(Barnes et al., 1999; Verghese et al., 2011), type of strain (Norwood and Gilmour, 1999),
temperature (Braindet et al., 1999), pH (Duffy and Sheridan, 1997) and nutrient level
(Hunt et al., 2004). Currently, biofilm formation is routinely measured using 96-well
plastic microtiter plates and artificial broths (Djordjevic et al., 2002). This method allows
rapid, quantitative and simultaneous analyses of multiple variables affecting biofilm
formation; however, this method does not accurately simulate the real-world conditions
such as the harborage sites in food processing plants where food-conditioning films are
present (Verghese et al., 2011).
38
2.7 The viable but non-culturable state
2.7.1 Overview
The viable but non-culturable (VBNC) state is a physiological state in which
bacteria exhibit some biological characteristics (such as metabolic activities and structure
integrity) but lose the ability to reproduce in vitro (McDougald et al., 1998; Weichart,
1999). The experimental evidence for the VBNC state was first shown in a study of E.
coli and V. cholerae incubated in artificial seawater (Xu et al., 1982), in which cell
densities during incubation remained stable as measured by acridine orange staining, but
declined as measured by the viable-cell plating method. Xu et al. (1982) suggested that
cells detectable by acridine orange staining but undetectable by plating were VBNC. It
has been reported that the VBNC state was induced by stressful conditions (such as
nutrient deprivation, low temperature or high osmolarity) in various microorganisms
including L. monocytogenes (Dreux et al., 2007; Besnard et al., 2000a & 2000b; Foong
and Dickson, 2004), S. enterica serovar Typhimurium (Turpin et al., 1993), E. coli
(Makino et al., 2000), Vibrio species (Colwell and Huq, 2005) and Yersinia pestis
(Pawlowski et al., 2011).
2.7.2 Enumeration of VBNC cells
VBNC cells are usually enumerated by calculating the difference between the
direct viable counts and the conventional plate counts. Direct viable counts are usually
conducted using an epifluorescence microscope or by flow cytometry after staining with
39
fluorescent dyes (Weichart, 1999). Acridine orange, which shows fluorescence after
binding to DNA and/or RNA, was traditionally used in direct counts (Frank et al., 1992;
Xu et al., 1982). In the last decade the LIVE/ DEAD BacLight kit has also been
commonly used in direct viable counts (Dreux et al., 2007). The BacLight staining
method evaluates viability based on cell membrane integrity. Specifically, a green
fluorescent dye (SYTO 9) stains all the cells, and then a red fluorescent dye (propidium
iodide) penetrates the cells with damaged membranes and inhibits the green fluorescence
of SYTO 9. After staining, cells showing green fluorescence are considered ―viable‖ and
those showing red fluorescence (due to their disrupted membranes) are considered dead
(Boulos et al., 1999). Another commonly used method for direct viable counts is CTC-
DAPI double staining, which evaluates viability based on the respiratory activity of live
cells (Besnard et al., 2000b). All cells are stained by a blue fluorescent dye DAPI (4,6-
diamidino-2-phenylindole) and then by a red redox dye CTC (5-cyano-2,3-ditotyl
tetrazolium chloride). Only the cells with active respiratory activity are able to reduce
CTC to an insoluble CTC salt that precipitates in the cytoplasm. Cells showing red
precipitate in a blue background are considered metabolically active and ―viable‖
(Besnard et al., 2000).
2.7.3 Criticism of the VBNC concept
The VBNC concept might be problematic for several reasons (Barer, 1997;
Bloomfield et al., 1998; Barer and Harwood, 1999; Weichart, 1999). First, the term
VBNC is self-contradictory and thus a misnomer (Barer and Harwood, 1999). Viability
40
is defined as the ability to reproduce (Roszak and Colwell, 1987), and thus if a cell does
not grow/reproduce (i.e., is non-culturable) it is considered non-viable (Weichart, 1999).
Second, the techniques used for ―direct viable counts‖ in VBNC studies may not
accurately enumerate viable cells. These techniques use some criteria (e.g., membrane
integrity, metabolic activity or presence of nucleic acids) to determine cell viability;
however, these standards are not sufficient or necessary to evaluate viability (Weichart,
1999). For instance, dead cells of L. monocytogenes inactivated by high pressure
maintained intact cell membranes and thus were scored as ―viable‖ by the LIVE/ DEAD
BacLight kit (Hayman, 2007); cells with lethal DNA damage could still exhibit metabolic
activities but they were no longer viable (Weichart, 1999). Therefore, it has been
suggested that the term VBNC should no long be used (Barer, 1997; Kell et al., 1998;
Weichart, 1999).
2.8 Justification of my research
Many studies have been conducted to investigate the growth-phase transition of
bacteria, with most of them focusing on the transition from log phase to stationary phase.
In contrast, there is a lack of understanding of transition to the LTS phase at both
population level and the molecular level. Therefore, a comprehensive study is needed to
investigate factors affecting the transition to the LTS phase as well as transcriptomic
responses during this transition. L. monocytogenes is a model saprophytic pathogen
demonstrating LTS, and thus this microorganism is ideal for this study. Understanding
41
the transition of L. monocytogenes to the LTS phase may also help explain the transition
to LTS phase in various other microorganisms.
L. monocytogenes at the LTS phase is highly resistant to heat and high pressure
and could also survive other environmental stresses. The LTS phase may help L.
monocytogenes persist over a long period of time and facilitate its transmission to
harborage sites in food plants and finally to foods. Specific strains of L. monocytogenes
are known to persist in food processing plants and cause contamination; however, there is
a lack of understanding as to why specific strains persist in different processing plants.
Thus, it is critical to investigate the mechanisms by which L. monocytogenes may persist
in food processing environments.
42
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65
CHAPTER THREE
LISTERIA MONOCYTOGENES RESPONDS TO ITS OWN VIABLE CELL
DENSITY IN ACCORDANCE WITH THE LOGISTIC EQUATION AS IT
TRANSITIONS TO THE LONG-TERM-SURVIVAL PHASE
Jia Wen*†, Sneha Karthikeyan, Jabari Hawkins‡, Ramaswamy C. Anantheswaran, and
Stephen J. Knabel.
Department of Food Science, the Pennsylvania State University, University Park, PA
16802.
* Corresponding author. Mailing addresses: Jia Wen, 5220 Hedgewood Drive, Apt 504,
Midland, MI 48640. Phone: (814) 321-2044. E-mail: [email protected].
Running title: Cell density controls L. monocytogenes transition to LTS
† Present address: 5220 Hedgewood Drive, Apt 504, Midland, MI 48640
‡ Present address: 66 Blair Road, Eighty Four, PA 15330
66
3.1 ABSTRACT
Listeria monocytogenes was recently found to enter a long-term-survival (LTS)
phase, which may help explain its persistence in natural environments and within food
processing plants. The purpose of this study was to investigate the effects of initial cell
density, initial pH and type of broth (fresh vs. filter-sterilized-stationary-phase) on the
transition of L. monocytogenes to the LTS phase and model the change in viable
population density with time. Initial cell density and initial pH both significantly affected
the transition of L. monocytogenes to the LTS phase (P < 0.001) with initial cell density
being the main determining factor. In contrast, type of broth did not significantly affect
the pattern of cell density change during the transition of stationary-phase cells to the
LTS phase (P > 0.05). No significant differences in cell densities were observed between
either type of broth or between any of the initial cell density/pH treatment combinations
after 30-d incubation, where the mean viable cell density was 4.3 ± 1.1 × 108 CFU/ml
(P > 0.05). L. monocytogenes responded to viable cell density in accordance with the
logistic equation during transition to the LTS phase. The Agr quorum-sensing system
does not appear to play a role in the transition to the LTS phase as an EGDe agrD
deletion mutant showed a similar transition pattern as the WT strain. Further research is
needed to better understand the control mechanisms utilized by L. monocytogenes as it
transitions to a coccoid, resistant and stable density state in the LTS phase.
67
3.2 INTRODUCTION
A long-term-survival (LTS) phase has been reported in Listeria monocytogenes
(Wen et al., 2009) as well as other microorganisms (Finkel, 2006; Lappin-Scott and
Costerton, 1990). All three strains of L. monocytogenes tested in a previous study
(ATCC19115, F5069 and Scott A) showed the same LTS pattern (Wen et al., 2009). L.
monocytogenes enters a coccoid, resistant and stable cell density state upon entry into the
LTS phase, which may help explain its long-term persistence in food processing plants
(Wen et al., 2009). Prior to the LTS phase in fresh Tryptic Soy Broth with Yeast Extract
(TSBYE; Becton Dickinson), cells of L. monocytogenes enter a death phase where 94%
of the population dies (Wen et al., 2009). According to conventional wisdom, the death
phase in bacteria is caused by depletion of nutrients and/or accumulation of metabolic
wastes in the stationary-phase cultures (Prescott et al., 2005; Navarro Llorens et al.,
2010). In contrast to this paradigm, preliminary data in the present study indicated that
the death phase of L. monocytogenes might be triggered by high viable cell density (~109
CFU/ml at the stationary phase) and the death rate might be affected by pH (data not
shown). During the LTS phase cells of L. monocytogenes become coccoid-shaped, more
resistant to heat and high pressure, and maintain a stable density at ~108 CFU/ml for at
least 30 d (Wen et al., 2009).
The logistic equation was developed by Pierre Verhulst in 1845 to model human
population growth when resources are abundant and also when they are limiting
(Verhulst, 1845). It is mathematically expressed as,
68
where,
N is the number of viable organisms in the population at any given time t;
dN/dt is the rate of population growth at any given time t;
r is the rate of maximum population growth; and
K is the carrying capacity of the environment.
The logistic equation assumes that the environment has a finite supply of
resources and the growth rate is proportional to the instantaneous population and the
amount of resources available at any given time (Peleg et al., 2007). The parameter K
(carrying capacity) is defined as the maximum population that can be sustained by the
environment (Fujikawa et al., 2004; Peleg et al., 2007). Initially, the logistic equation is
influenced to a large extent by the parameter r. When the population is low, the term rN
in equation 1 causes an exponential increase in the population (Vandermeer, 2010). The
rate of population growth increasingly slows down as N approaches K and finally reaches
zero. For microbial growth, the parameters r and K are affected by temperature, pH,
water activity and osmotic concentration of the environment (Peleg et al., 2007).
According to r- and K-selection theory (Pianka, 1972; Andrews & Harris 1986),
organisms need not produce offspring with specialized adaptations in environments
where resources are in abundance and competition is low. To maximize fitness in such
environments more effort is expended in producing a higher number of organisms in the
shortest time (referred to as r strategy), and these populations show a high r-value. In
69
contrast, when resources are depleted, organisms maximize fitness by producing fewer
offspring that are capable of surviving the competition (referred to as a K strategy), and
these populations are characterized by a lower r-value (Pianka, 1972).
Currently there is little understanding of how L. monocytogenes responds to its
own viable cell density, pH and type of broth (fresh vs filter-sterilized-stationary-phase)
during transition to the LTS phase. In addition, there are no published reports on
modeling the transition of L. monocytogenes to the LTS. Therefore, the purpose of the
present study was to investigate the effects of viable cell density, pH and type of broth on
the transition of L. monocytogenes to the LTS phase and also to model the influence of
viable cell density on this transition.
3.3 MATERIALS AND METHODS
Preparation of the stationary-phase culture. L. monocytogenes ATCC 19115,
a serotype-4b strain isolated from a patient with listeriosis (Begot et al., 1997) was used
in this study. The maintenance of the glycerol stock and the preparation of the bacterial
culture followed the protocol described by Wen et al. (2009). Briefly, a 24-h-old culture
of L. monocytogenes in TSBYE (Becton Dickinson, Sparks, MD) at 35°C was diluted
1:100 using 0.1% Bacto peptone (Becton Dickinson). One-tenth ml of the resulting
culture was inoculated into 100 ml of fresh TSBYE and incubated at 35°C. Viable cell
density during incubation was measured by plating on Tryptic Soy Agar with Yeast
Extract (TSAYE; Becton Dickinson). After incubation for 16 h, a stationary-phase
culture with a viable cell density of ~109 CFU/ml was obtained.
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Effect of initial viable cell density and initial pH on transition of stationary-
phase cells to the LTS phase in filter-sterilized-stationary-phase TSBYE. Aliquots of
0.2, 2, 20, 200 or 2000 ml of a stationary-phase culture of L. monocytogenes ATCC
19115 were pelleted at 13,000 × g for 15 min using a high-speed centrifuge (Model
Avanti-J-26 XPI; Beckman Coulter, Brea, CA) (Fig. 3.1). Sterile stationary-phase
TSBYE was prepared by filter-sterilizing the stationary-phase culture using a 0.2-ìm -
pore-size filter (Nalgene, Rochester, NY). Cell pellets (previously prepared from 0.2, 2,
20, 200 or 2000 ml of culture) were subsequently resuspended in 200 ml of the above
filter-sterilized-stationary-phase TSBYE to adjust viable cell density to ~106, ~107, ~108,
~109 or ~1010 CFU/ml, respectively. The resultant cultures were adjusted to pH 5.36
(natural pH of stationary-phase culture of L. monocytogenes ATCC 19115 in TSBYE),
pH 6.11 (midpoint between 5.36 and 6.85), or pH 6.85 (natural pH of fresh TSBYE)
using a sterile 1 M solution of NaOH. The 15 cell density/pH treatment combinations
(Fig. 3.1) were then incubated at 35°C and sampled regularly for up to 1 month. For each
of the treatment combinations viable cell density was measured by plating on TSAYE
and pH was measured using a Denver Instrument Model 250 pH meter (Denver
Instrument, Arvada, CO) equipped with a Symphony probe (VWR, Swedesboro, NJ).
The experiment was replicated 3 times.
Effect of type of broth on the transition of stationary-phase cells at high
initial densities to the LTS phase in fresh and filter-sterilized-stationary-phase
TSBYE. Stationary-phase cell pellets of L. monocytogenes ATCC 19115 and filter-
sterilized-stationary-phase TSBYE were prepared following the same protocol described
71
above. Cell pellets made from 1000 ml of stationary-phase cultures were resuspended in
100 ml of fresh TSBYE or filter-sterilized-stationary-phase TSBYE to reach a starting
concentration of 1010 CFU/ml. The resulting cultures were adjusted to pH 6.85 using a
sterile 1 M solution of NaOH. Cultures in fresh and stationary-phase TSBYE were then
incubated at 35°C and sampled regularly for up to 1 week by plating on TSAYE. The
experiment was replicated 3 times.
Effect of agrD on the transition of L. monocytogenes to the LTS phase. agrD
is the gene encoding the signal peptide in the Agr quorum sensing system in L.
monocytogenes (Riedel et al., 2009). A wild-type strain L. monocytogenes EGDe and its
corresponding knockout mutant strain EGDeΔagrD, were used to study the effect of
agrD on the transition to the LTS phase. Specifically, 24-h-old cultures of the two strains
in TSBYE were diluted 1:100 using sterile 0.1% peptone water. One-tenth ml of each of
the resulting cultures was inoculated into 100 ml of fresh TSBYE followed by incubation
at 35°C for up to 240 h. Viable cell density changes during incubation of the two strains
were compared to study the effect of agrD on the transition of L. monocytogenes to the
LTS phase.
Changes in viable cell density of L. monocytogenes in fresh and filter-
sterilized-stationary-phase TSBYE from 0 - 25 h. A 24-h-old culture of L.
monocytogenes ATCC 19115 in TSBYE at 35°C was diluted 1:100 using 0.1% Bacto
peptone. One-tenth ml of the resulting culture was inoculated into 100 ml of fresh
TSBYE, and another 0.1 ml was inoculated into filter-sterilized-stationary-phase TSBYE
and both broths were incubated at 35°C. The viable cell density during incubation from 0
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- 25 h was determined by plating on TSAYE. This gave the observed values for
population (Nobs) at measured time-points.
Statistical analysis of viable cell density data. Viable cell density data were
analyzed using the general linear model of analysis of variance (ANOVA), Tukey‘s
pairwise comparison and linear regression (Minitab, Version 16.11; Minitab, State
College, PA) (α = 0.05).
Modeling of viable cell density data. Viable cell density data from fresh and
filter-sterilized-stationary-phase TSBYE were modeled using the logistic equation
(Equation 1). The dN/dt term was approximated as (N2-N1)/(t2-t1) and a second order
polynomial regression was used to fit the cell density data (Microsoft Excel 2007;
Microsoft, Redmond, WA).
Statistical analysis of model for change in viable cell density of L.
monocytogenes in fresh and filter-sterilized-stationary-phase TSBYE. A solution to
Equation 1 can be obtained using the variable-separation method and is shown as
equation 2.
where,
No = Initial number of viable organisms present in the population at time = 0
The change in viable cell density data from 0 - 25 hours in fresh and filter-
sterilized-stationary-phase TSBYE were modeled using equation 2. N0 was the
enumerated initial population. The predicted N(t) values were calculated using 0 ≤ r ≤ 1
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h-1 and 1 × 108 ≤ K ≤ 1 × 109 CFU/ml in equation 2. The optimal values for r and K in the
model were chosen as the ones resulting in minimum cumulative sum of squares of the
difference between the observed and the predicted values of the viable population at
different times. In other words, the strategy was to minimize Σ[(N(t)-Nobs]2. The F-
statistic as described below was used to determine the goodness of fit of the above model.
F-statistic = MSM/MSE
where,
MSM (Mean Squares due to Model) = Mean of the sum of squared differences
between Npre at each time point and the average of all Nobs
MSE (Mean Squares due to Error) = Mean of the sum of squared differences
between Npre and Nobs at each time point
A P-value was calculated for the above model, with P being the probability of
obtaining an F-test statistic as extreme as the observed value by chance using the F-
distribution. The change in viable cell density from 16 - 25 hours in fresh TSBYE was
also fitted to equation 2.
3.4 RESULTS
Preparation of the stationary-phase culture. L. monocytogenes grown in fresh
TSBYE (with an initial pH of 6.85) showed rapid growth during the log phase and
reached peak density of ~109 CFU/ml in stationary phase (15 - 17 h). The stationary
phase was followed by rapid die-off of the population as observed previously (data not
shown; Wen et al., 2009).
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Transition of stationary-phase cells at different initial densities and initial
pHs to the LTS phase in filter-sterilized-stationary-phase TSBYE. Cells responded
differently to different initial cell densities and initial pHs while they transitioned to the
LTS phase. The most dramatic changes in viable cell density took place within 0 - 24 h
(Fig. 3.2). After incubation for 24 h, viable cell densities in all cultures merged into a
narrow range of 4.2 ± 3.5 × 108 CFU/ml (mean ± standard deviation) (Fig. 3.2). Two-
way ANOVA revealed that both initial viable cell density and initial pH significantly
affected the change in viable population density from 0 - 24 h (P < 0.001); however, the
interaction between initial cell density and pH was not significant (P > 0.05). Linear
regression analysis revealed a strong relationship between initial viable cell density and
initial pH and viable cell density change from 0 - 24 h. The linear regression equation is
written as:
Change in viable cell density = 5.72 - 0.99 initial cell density + 0.45 initial pH
... Equation (3)
The linear coefficient of determination (R2) of the above equation is 92%,
indicating most of the changes in viable cell density could be explained by the two
independent variables of initial cell density and initial pH. Linear regression analysis
demonstrated that 88% of the change in viable cell density was explained by initial cell
density (in the range of 106 - 1010 CFU/ml), while only 4% was explained by initial pH
(in the range of 5.36 - 6.85).
Different initial cell densities had dramatic and contrasting effects on how cells
transitioned to the LTS phase in filter-sterilized-stationary-phase TSBYE. Specifically,
75
cultures at ~106, ~107 and ~108 CFU/ml showed growth from 0 - 24 h regardless of pH;
while cultures at ~109 and ~1010 CFU/ml showed death (Fig. 3.2). Tukey‘s pairwise
comparison showed that log changes in viable population density from 0 - 24 h varied
significantly due to initial cell density as follows: 1.83 (initial density 106 CFU/ml) =
1.85 (107) > 0.76 (108) > -0.63 (109) > -1.61 (1010) (P < 0.001; positive values indicate
growth and negative ones indicate death).
Compared to the dominant effect of initial cell density, initial pH demonstrated a
significant but minor effect on transition to the LTS phase. In general, cultures of L.
monocytogenes at pH 6.85 adjusted their viable population densities rapidly within 0 - 12
h and showed a smooth transition to the LTS phase (Fig. 3.2A). In contrast, cells at pHs
of 6.11 and 5.36 responded more slowly (Fig. 3.2B & C). Linear regression analysis
revealed that initial cell density could explain 88% of density change at pH 5.36, 93% at
pH 6.11 and 95% at pH 6.85. Initial pH also significantly affected viable cell density at
24 h (P < 0.05; Fig. 3.2). Tukey‘s pairwise comparison showed that at 24 h the mean
viable cell density at pH 6.85 (6.0 × 108 CFU/ml; Fig. 3.2A) was not significantly
different from that at pH 6.11 (4.5 × 108 CFU/ml; Fig. 3.2B), which were significantly
greater than that at pH 5.36 (1.3 × 108 CFU/ml; Fig. 3.2C) (P < 0.05).
Viable cell densities at 30 d in all 15 cell density/pH treatments converged to a
narrower range of 4.3 ± 1.1 × 108 CFU/ml (mean ± standard deviation) (Fig. 3.3). No
significant differences were observed between mean viable cell densities at 24 h and 30 d
(P > 0.05). ANOVA also revealed that neither initial density nor initial pH significantly
affected the final viable cell density at 30 d (P > 0.05). Tukey‘s pairwise comparison
76
revealed that there were no significant differences in viable cell density at 30 d between
treatments irrespective of their initial density and initial pH (P > 0.05). At 24 h the pHs
of the cultures at initial pHs of 5.36, 6.11 and 6.85 significantly decreased to 5.33, 5.77
and 6.32, respectively, and then significantly increased to 5.72, 6.34 and 6.66,
respectively, at 30 d (P < 0.05) (Table 3.1).
Effect of type of broth on the transition of stationary-phase cells at high
initial densities to the LTS phase in fresh and filter-sterilized-stationary-phase
TSBYE. To confirm that death at an initial high cell density (1010 CFU/ml) in filter-
sterilized-stationary-phase broth was due to high cell density and not the broth itself, the
above experiment at an initial cell density of 1010 CFU/ml was repeated with both fresh
and filter-sterilized broths. Results showed that stationary-phase cells initially at 1010
CFU/ml had the same death pattern during transition to the LTS phase in both fresh and
filter-sterilized broths (Fig. 3.4), and viable cell densities after 1 week in both broths were
not significantly different (P > 0.05) (Fig. 3.4).
Effect of agrD on the transition of L. monocytogenes to the LTS phase. L.
monocytogenes strains EGDe (wildtype) and EGDeΔagrD (mutant) showed the same
pattern of transition to the LTS phase (data not shown). The final densities of EGDe and
EGDeΔagrD at 240 h in the LTS phase were 1.68 × 108 and 1.59 × 108 CFU/ml,
respectively, and were not significantly different (P > 0.05).
Changes in viable cell density of L. monocytogenes in fresh and filter-
sterilized-stationary-phase TSBYE from 0 - 25 h. The population of L. monocytogenes
ATCC 19115 in filter-sterilized-stationary-phase TSBYE continued to steadily increase
77
until 15 h, after which it leveled off at 3.1 × 108 CFU/ml (Fig. 3.5). L. monocytogenes in
fresh TSBYE continued to increase until 16 h and then decreased for 3 h, after which the
population leveled off at 2.6 × 108 CFU/ml (Fig. 3.5).
Analysis of viable cell density data using the logistic equation during the
transition of stationary-phase cells to the LTS phase. Final stable cell densities for
the 15 treatment combinations of initial cell density/pH were estimated by fitting viable
cell density data from 0 - 30 d to the logistic equation (Table 3.2). The logistic equation
showed the highest goodness of fit for the data with initial density of 1010 CFU/ml (mean
R2 = 98.7%), lowest goodness of fit for the data with initial density of 108 CFU/ml (mean
R2 = 40.0%), and medium goodness of fit for data at other initial densities (P < 0.05).
Mean R2 did not vary due to different initial pHs (P > 0.05). The mean R2 for all 15
treatment combinations was 74.1% (Table 3.2).
Modeling the change in viable cell density in fresh and filter-sterilized-
stationary-phase TSBYE from 0 - 25 h using the logistic equation. Modeling using
the logistic equation revealed that the rate of maximum population growth/death (r) was
predicted to be 0.8 h-1 and the carrying capacity (K) was predicted to be 4.0 × 108
CFU/ml for both filter-sterilized-stationary-phase TSBYE and fresh TSBYE (Fig. 3.5).
The model for the change in cell density from 0 - 25 hours was not statistically significant
for fresh TSBYE (P > 0.05), but was significant for filter-sterilized-stationary-phase
TSBYE (P < 0.001).
Modeling the change of viable cell density of L. monocytogenes in fresh
TSBYE from 16 - 25 h using the logistic equation. The change in viable cell density in
78
fresh TSBYE from 16 - 25 h was modeled separately to better understand the overshoot
and leveling off of the population seen in fresh TSBYE (Fig. 3.5). For fresh TSBYE the
predicted value for r was 0.02 h-1 and that for K was 3.3 × 108 CFU/ml. Note, rather than
r being a parameter for the rate of cell growth in the logistic equation, from 16-25 h the
value of r corresponded to the rate of cell death. The model for the change in cell density
from 16 - 25 hours in fresh TSBYE was statistically significant (P < 0.05).
3.5 DISCUSSION
The results in Fig. 3.2 indicate that L. monocytogenes is able to control its own
viable population density during transition to the LTS phase. The observation that L.
monocytogenes responds differently to different initial densities was also reported in a
previous study, which involved starvation survival of L. monocytogenes strain EGD in a
glucose-limited medium (Herbert and Foster, 2001). It was also reported that marine
bacteria adjust their population densities during transition to the LTS phase (Morita, 1985;
Lappin-Scott and Costerton, 1990). In the present study, L. monocytogenes ATCC 19115
also reached a stable final cell density at the LTS phase in fresh and filter-sterilized-
stationary-phase TSBYE. Transition of bacteria to a stable final density may be due to
the ability of bacteria to sense and self-regulate their own cell densities consistent with
the carrying capacity (K) of the environment (Vandermeer, 2010).
During the transition to the LTS phase, L. monocytogenes may sense the value of
K based on levels of critical nutrients. Such ―K-sensing‖ may cause activation/inhibition
of specific gene(s) leading to cell growth/death in order to reach K. Another possibility is
79
that L. monocytogenes may sense its own cell density by direct cell-to-cell contact and
subsequently regulate growth and death. Such contact-dependent growth inhibition has
been reported in E. coli (Aoki et al., 2005). Quorum sensing (QS) may also play a role in
sensing and regulating cell density (Miller and Bassler, 2001; Annous et al., 2009;
Shapiro, 1998). When cell density is below the final stable density, cells may increase
cell density by activating reproduction-related genes; and when initial viable cell density
is above the final stable density, cells may express lethal toxin(s) and actively commit
suicide. Bacterial death induced by such genetically encoded toxins is a key
characteristic of programmed cell death (PCD), and QS-regulated PCD has been reported
to control the population density of E. coli (You et al., 2004; Kolodkin-Gal et al., 2007;
Kolodkin-Gal and Engelberg-Kulka, 2008). QS mediated by the Agr system has been
reported in L. monocytogenes (Riedel et al., 2009). However, deletion of agrD, the gene
encoding the signal peptide in the Agr system (Riedel et al., 2009), did not affect the
transition of L. monocytogenes to the LTS phase (data not shown). Further research is
needed to elucidate the mechanism(s) by which L. monocytogenes senses and regulates
its population density as it transitions to the LTS phase.
The decrease in pH of stationary-phase cultures during incubation from 0 - 24 h
(Table 3.1) is likely due to fermentation of carbohydrates in the broth. pH has been
reported to affect the survival and growth of L. monocytogenes (Tienungoon et al., 2000;
Abou-Zeid et al., 2007). It has also been reported that bacterial cytoplasmic pH could be
influenced by external pH (Booth, 1985), and that bacteria use pH homeostasis to
maintain an optimal cytoplasmic pH to support their physiological activities (Padan et al.,
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2005). In the present study, cultures at near neutral pH (6.85) would be expected to
maintain an optimal intracellular pH and thus might be primed to respond quickly to
changes in population density (Fig. 3.2A). In contrast, lower external pHs (6.11 and 5.36)
might disrupt optimal intracellular pH and make cells less physiologically active and thus
less responsive to cell density (Fig. 3.2B & C). However, after incubation for 30 d initial
pH did not significantly affect the final density (P > 0.05), possibly because cells initially
at different pHs adjusted/optimized their intracellular pHs and reached the same
intracellular pH at 30 d. A glutamate decarboxylase (GAD) system has been reported to
adjust intracellular and extracellular pHs in L. monocytogenes. The GAD system
increases cytoplasmic pH by consuming intracellular protons and producing alkaline α-
amino butyrate which is then exported from the cell (Hill et al., 2002). The export of
alkaline α-amino butyrate leads to an increase in extracellular pH (Hill et al., 2002),
which may explain the increase of extracellular pHs of cultures during incubation from
24 - 720 h (Table 3.1).
In the present study, viable cell densities at 30 d in all 15 cell density/pH
treatment combinations converged to a narrow range of 4.3 ± 1.1 × 108 CFU/ml in
TSBYE (Fig. 3.3). It has been previously reported that environmental microorganisms
maintain stable viable cell densities during long-term starvation survival (Lappin-Scott
and Costerton, 1990). The ability to enter a coccoid, resistant and stable cell density state
in the LTS phase may be a fitness strategy allowing L. monocytogenes to persist in
natural and food processing environments, making food contamination more likely
(Harvey and Gilmour, 2001). The ability to persist in food processing plants and cause
81
lethal invasive diseases makes L. monocytogenes a major concern for both public health
and the food industry (Harvey and Gilmour, 2001; Scallan et al., 2011).
The maintenance of a stable cell density in the LTS phase may be partly due to
LTS-phase bacteria consuming nutrients from lysed dead cells (Finkel, 2006). Such a
phenomenon was termed cryptic growth (Kolter et al., 1993). However, a combination of
cell lysis and the formation of dormant, coccoid cells by the remaining survivors (Wen et
al., 2009; Wen et al., 2011) may better explain the stable density of L. monocytogenes in
the LTS phase (Fig. 3.3). Dormancy during the LTS phase has been reported in marine,
soil and rock microorganisms (Lappin-Scott and Costerton, 1990; Novitsky and Morita,
1977; Boylen and Mulks, 1978) and recently in L. monocytogenes (Wen et al., 2011).
Dormancy may be a form of metabolic adaptation to preserve energy for long-term
maintenance of viability, and thus may represent a fitness strategy allowing delayed
reproduction of L. monocytogenes.
In the present study, L. monocytogenes grown in fresh TSBYE overshot the final
stable cell density in the LTS phase (Fig. 3.5). This overshoot was followed by rapid die-
off before the culture reached the final stable density (Fig. 3.5). In fresh broth, L.
monocytogenes may not turn off genes involved in cell growth until K is reached
(extended r strategy) and not fully activate PCD until after an additional one log increase
in the viable cell density (K strategy) (Pianka, 1972). It would likely take considerable
time to deactivate/activate these phenomena, which might explain why the rapidly
growing population in the highly nutritious fresh TSBYE increased one log above the
final stable density before death ensued (Fig. 3.5). In contrast, cells grown in filter-
82
sterilized-stationary-phase TSBYE did not overshoot the final stable density, but instead
cells slowly reduced their growth rate until they reached the final stable density (Fig. 3.5).
This may be due to a lower concentration of critical nutrients being available in filter-
sterilized-stationary-phase TSBYE.
During transition to and maintenance of the LTS phase, L. monocytogenes
controlled viable cell density in accordance with the logistic equation (Table 3.2). The
goodness of fit to this equation may be due to L. monocytogenes sensing its population
density and actively adjusting its growth/death rate to reach K, which is the basis of the
logistic equation (Vandermeer, 2010). The logistic equation showed the highest fit (R2 =
98.7%) at an initial density of 1010 CFU/ml (Table 3.2), possibly because PCD is under
tight regulatory control. Such tight control might be necessary to efficiently reduce the
population when viable cell density is above K, otherwise nutrients essential for
maintaining long-term cell viability might be quickly exhausted. The logistic equation
showed the lowest fit (R2 = 40.0%) at initial density of 108 CFU/ml (Table 3.2), possibly
because the plate count method is not accurate enough to reveal the true shape of the
density curves when the initial density is close to the final stable density. A previous
modeling study showed that bacterial populations in biofilms change according to the
logistic equation (Indekeu and Sznajd-Weron, 2003). Pai and You (2009) also proposed
that bacteria control population density in accordance with the logistic equation.
In conclusion, L. monocytogenes responded mainly to viable cell density as it
transitioned to a final stable density in the LTS phase. Stationary-phase cells at initial
densities of 106 - 108 CFU/ml showed growth, while cells at 109 - 1010 CFU/ml showed
83
death during this transition. The present study demonstrates that the rapid death of L.
monocytogenes was caused by high cell density (Figs. 3.2 & 3.4), which disagrees with
conventional wisdom that the death phase is due to loss of nutrients and/or production of
waste products (Prescott et al., 2005; Navarro Llorens et al., 2010). More importantly,
after 30 d all 15 cell density/pH treatment combinations yielded LTS-phase cells at
similar final densities (~4.3 × 108 CFU/ml). High levels of coccoid cells in the LTS
phase may help explain the persistence of L. monocytogenes in food processing plants
(Fig. 3.6). The transition of L. monocytogenes to the LTS phase in both fresh and filter-
sterilized-stationary-phase TSBYE was successfully modeled using the logistic equation.
The mechanism(s) allowing transition to and the maintenance of the LTS phase remain
unknown, but are likely fine-tuned by natural selection during long-term evolution of
bacteria. Further research is needed to better understand the control mechanisms utilized
by L. monocytogenes as it transitions to a coccoid, resistant and stable cell density state in
the LTS phase.
3.6 ACKNOWLEDGEMENTS
This study was supported by funds from a U.S. Department of Agriculture Special
Grant on Milk Safety to the Pennsylvania State University. We thank Colin Hill for
providing L. monocytogenes strains EGDe and EGDeΔagrD.
Jia measured and modeled the density change of L. monocytogenes for all the 15
initial density/pH treatment combinations. Sneha measured and modeled the density
change of L. monocytogenes in fresh and filter-sterilized-stationary-phase TSBYE. Jabari
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measured the density change of L. monocytogenes strains EGDe and EGDeΔagrD in
fresh TSBYE.
85
3.7 REFERENCES
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Fig. 3.1. Schematic representation of the experimental design used to study the effects of
initial cell density and initial pH on the transition of L. monocytogenes to the LTS phase
in filter-sterilized-stationary-phase TSBYE. The cell density was adjusted to ~106, ~107,
~108, ~109 or ~1010 CFU/ml by resuspending pellets of stationary-phase cells into filter-
sterilized-stationary-phase TSBYE. The pH of cultures was then adjusted to 5.36, 6.11 or
6.85 using 1 M sterile NaOH solution. This resulted in 15 cell density/pH treatment
combinations in total.
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Fig. 3.2. Effects of cell density and pH on the transition of L. monocytogenes to the LTS
phase in filter-sterilized-stationary-phase TSBYE. Cells of L. monocytogenes ATCC
19115 were incubated in TSBYE at 35°C for 16 h to reach the stationary phase. Cell
density was then adjusted to ~106 (○), ~107 (●), ~108 (▲), ~109 (♦) or ~1010 CFU/ml (■)
by adding stationary-phase cells into filter-sterilized-stationary-phase culture. The pH of
the cultures at different initial densities was adjusted to 6.85 (A), 6.11 (B) or 5.36 (C)
with subsequent incubation at 35°C. The data points and error bars represent means and
standard deviations based on three replications of the experiment.
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95
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Fig. 3.3. After 720 h (30 d) in filter-sterilized-stationary-phase TSBYE cell densities of
L. monocytogenes ATCC 19115 in all 15 initial cell density/pH treatment combinations
converged to a narrow range of 4.3 ± 1.1 × 108 CFU/ml (mean ± standard deviation).
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Fig. 3.4. Transition of stationary-phase cells of L. monocytogenes ATCC 19115 at high
cell densities to the LTS phase in fresh TSBYE (■) and filter-sterilized-stationary-phase
TSBYE (□). Pellets of stationary-phase cells were resuspended in fresh and filter-
sterilized-stationary-phase TSBYE to reach an initial density of 1010 CFU/ml, and then
the pHs of resulting cultures were adjusted to 6.85. Cultures were then incubated at 35°C
and sampled regularly for up to 1 week. Data points and error bars represent means and
standard deviations based on three replications of the experiment.
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100
Fig. 3.5. Observed growth of L. monocytogenes ATCC 19115 in fresh TSBYE (■) and
filter-sterilized-stationary-phase TSBYE (□) at initial pH 6.85 at 35°C and the predicted
growth using the logistic equation (r = 0.8 h-1, K = 4 × 108 CFU/ml) (▲).
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Fig. 3.6. A schematic model of how L. monocytogenes responds to its own low or high
viable cell density as it transitions to the LTS phase, which results in cocci formation and
persistence.
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Table 3.1. Change in pH of stationary-phase cultures of L. monocytogenes ATCC 19115
during incubation in filter-sterilized-stationary-phase TSBYE at 35°C (see Fig. 3.3). pH
data are means based on three replications of the experiment.
Incubation time (h) at 35°C Initial pH
5.36 6.11 6.85 0 5.36 6.11 6.85 12 5.35 5.85 6.37 24 5.33 5.77 6.32 720 5.72 6.34 6.66
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Table 3.2. The estimated rate of maximum population growth (r) and carrying capacity
(K) for each of the 15 cell density/pH treatments (see Fig. 3.2). The values of r and K are
derived by fitting cell density data from 0 – 30 d to the Logistic Equation (see Equation
1).
Initial cell density
(CFU/ml) Initial pH
r
(h-1
)
K
(CFU/ml) R²
106 5.36 0.1213 2.0 × 108 57.7% 106 6.11 0.2134 2.7 × 108 29.9% 106 6.85 0.463 4.6 × 108 78.5% 107 5.36 0.1697 3.4 × 108 71.5% 107 6.11 0.3612 4.5 × 108 98.3% 107 6.85 0.4949 6.2 × 108 90.7% 108 5.36 0.0585 2.9 × 108 14.3% 108 6.11 0.2144 5.4 × 108 70.3% 108 6.85 0.2589 8.6 × 108 35.5% 109 5.36 -0.2951 2.9 × 108 98.0% 109 6.11 -0.2908 4.2 × 108 97.4% 109 6.85 -0.4253 7.1 × 108 72.6% 1010 5.36 -0.1021 1.5 × 109 97.0% 1010 6.11 -0.0469 7.8 × 108 99.8% 1010 6.85 -0.0659 1.3 × 109 99.4%
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CHAPTER FOUR
TRANSCRIPTOMIC RESPONSE OF LISTERIA MONOCYTOGENES DURING
TRANSITION TO THE LONG-TERM-SURVIVAL PHASE
Jia Wen1†, Xiangyu Deng2†, Zengxin Li2, Edward G. Dudley1, Ramaswamy C.
Anantheswaran1, Stephen J. Knabel1, Wei Zhang2*
1Department of Food Science, Pennsylvania State University, University Park, PA 16802
2Institute for Food Safety and Health, Illinois Institute of Technology, Bedford Park, IL
60521
† Equal contributions.
* Corresponding author. Mailing addresses: Wei Zhang, Institute for Food Safety and
Health, Illinois Institute of Technology, Bedford Park, IL 60501. Phone: (708) 563-2980.
Fax: (708) 563-1873. E-mail: [email protected].
Running title: Transcriptomic response of L. monocytogenes
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4.1 ABSTRACT
Listeria monocytogenes can change its cellular morphology from bacilli to cocci
during the transition to the long-term-survival (LTS) phase. The LTS cells demonstrated
increased baro- and thermotolerance compared to their vegetative counterparts. So far,
the underlying mechanisms that trigger this morphological and physiological transition
remain largely unknown. In this study, we compared the transcriptomic profiles of a L.
monocytogenes serotype 4b strain F2365 at different growth stages in tryptic soy broth
with yeast extract (TSBYE) using a whole-genome DNA chip approach. We identified a
total of 225 differentially expressed genes (≥ 4-fold, P < 0.05) during the transition to the
LTS phase in TSBYE. Genes related to cell envelope structure, energy metabolism and
transport were most significantly upregulated in the LTS phase. The upregulation of
compatible solute transporters may lead to accumulation of cellular solutes, lowering
intracellular water activity and thus increasing bacterial stress resistance during the
transition to the LTS phase. The downregulation of genes associated with protein
synthesis may indicate a status of metabolic dormancy of the LTS cells. The
transcriptomic profiles of resuscitated LTS cells in fresh TSBYE resembled those of log
phase cells (r = 0.94) as the LTS cells rapidly resumed metabolic activities and
transitioned back to the log phase with decreased baro- and thermotolerance.
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4.2 INTRODUCTION
Listeria monocytogenes is the causative agent of a life-threatening disease,
listeriosis (Gandhi and Chikindas, 2007). This opportunistic pathogen can be found in a
wide variety of raw and ready-to-eat (RTE) foods including milk, cheese, produce, salads,
cooked sausage, deli meats, and so on (Farber and Peterkin, 1991; Tompkin, 2002;
http://www.listeriablog.com/listeria-watch/listeria-linked-to-salad-in-rhode-island/).
Consumption of contaminated foods by L. monocytogenes may cause severe disease
symptoms among high risk populations, particularly for newborns, pregnant women, the
elderly, and other immuno-compromised population (Wemekamp-Kamphuis et al., 2004).
L. monocytogenes infections have led to an approximate 15.9% case-fatality rate, making
it a leading cause of deaths associated with foodborne infections in the United States
(Scallan et al., 2011). The intracellular life cycle of L. monocytogenes has triggered
extensive studies on the pathogen-host interactions and bacterial adaptation (Hamon et al.,
2006; Toledo-Arana et al., 2009). However, the saprophytic part of its life cycle outside
the host has received much less attention, despite the fact that this bacterium is
widespread in natural as well as food processing environments (Gray et al., 2006) and is
capable of surviving various environmental stresses such as starvation and low
temperature (Herbert and Foster, 2001; Lungu et al., 2010).
It is generally accepted that, in confined broth systems, bacterial stationary phase
is followed by the death phase, in response to environmental changes such as the
depletion of available nutrients and/or accumulation of toxic metabolic wastes (Finkel,
2006). It was also suggested that cell death may have been programmed into the bacterial
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genome during evolution (Hochman, 1997; Finkel, 2006). Programmed cell death (PCD)
was originally proposed in eukaryotes, but was also found in prokaryotes, such as the
PCD system encoded by mazEF in Escherichia coli (Kolodkin-Gal et al., 2007). During
PCD bacteria exhibit cell shrinkage, RNA degradation and release of cell contents
(Hochman, 1997). Toward the end of stationary phase, bacteria may perceive high
populations through quorum sensing mechanisms, which consequently trigger the
majority of the population to actively conduct programmed death and release nutrients to
allow a smaller population of the species to survive (Finkel, 2006; Kolter et al., 1993).
Previous studies have shown that saprotrophic bacteria do not all die in the death
phase. Instead, a small portion of the population may enter a dormant state and exhibit
long-term survival (LTS) (Lappin-Scott and Costerton, 1990). Various forms of LTS
cells were reported in saprotrophic bacterial species such as Micrococcus luteus
(Steinhaus and Birkeland, 1939) as well as some enteric bacterial species such as E. coli
(Finkel, 2006). The LTS phase was also observed in L. monocytogenes by Wen et al.,
during which the cell density was found to remain at ~108 CFU/ml in tryptic soy broth
with yeast extract (TSBYE) for over 30 days (Wen et al., 2009). These LTS cells were
found to be predominantly cocci and highly resistant to both heat and high pressure
stresses (Wen et al. 2009). The mechanisms that trigger listerial cells to transit from
bacilli to cocci during the LTS phase remain unclear yet intriguing. In this study, we
compared the global gene expression profiles at select time points during the log,
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stationary, death and LTS phases of L. monocytogenes in TSBYE to help us better
understand the molecular mechanisms underlying this transition process.
4.3 MATERIALS AND METHODS
Bacterial strain and growth conditions. L. monocytogenes strain F2365
(serotype 4b, genetic lineage I) implicated in an outbreak of listeriosis in California in
1985 associated with the consumption of a Mexican-style cheese (Linnan et al., 1988)
was used in this study. The genome of this strain has been fully sequenced and annotated
(Nelson et al., 2004). To prepare the bacterial inoculum, F2365 was streaked onto tryptic
soy agar with yeast extract (TSAYE) (Becton Dickinson, MD) from a glycerol stock
culture at -80°C followed by incubation at 35°C for 2 d. One colony was picked from the
plate, inoculated into 10 ml of TSBYE (Becton Dickinson, MD) and incubated at 35°C
for 1 d. The resulting culture at ~109 CFU/ml was diluted 1:100 using 0.1% peptone
water (Becton Dickinson, MD), and 0.1 ml of the diluted culture was inoculated into 100
ml of TSBYE at 35°C.
Cells of L. monocytogenes strain F2365 at log, stationary, death and LTS phases
were collected at 13 h, 17 h, 24 h, 168 h and 336 h, respectively. To ―germinate‖ L.
monocytogenes from the LTS phase to the log phase, 1 ml of the LTS-phase culture at
336 h was inoculated into 100 ml of fresh TSBYE and incubated at 35°C for 8 h. Cell
concentrations at different time points were determined by serial dilutions and plate
counting on TSAYE plates at 35°C for 2 d. Growth curves were replicated at least three
times.
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Light microscopy. One-tenth ml of cell cultures at different phases were Gram
stained and examined at a magnification of 1,000 × using a BX51 light microscope
equipped with a DP20 camera (Olympus Optical, Tokyo, Japan) as previously described
(Wen et al 2009). At each phase coccoid- and rod-shaped cells were enumerated in three
fields. The percentages of coccoid-shaped cells at different phases were recorded and
results were analyzed using analysis of variance (ANOVA) and Tukey‘s pairwise
comparison (α = 0.05) using Minitab version 15.0 (Minitab, PA).
RNA extraction. Bacterial total RNA was isolated using the TRIzol method as
previously described by Toledo-Arana et al. (2009) with minor modifications. Briefly,
100 ml of the culture at each incubation time point (13, 17, 24, 168 and 336 h, as well as
8-h re-growth of LTS cells in fresh TSBYE) was centrifuged at 13,000 × g for 3 min and
the resulting pellet was resuspended in 400 µl of a solution containing 10% glucose, Tris
(pH 7.6) at 12.5mM and EDTA at 10 mM. Sixty µl of 500 mM EDTA and 500 µl of acid
phenol (Applied Biosystems/Ambion, TX) were added to resuspended cells, and the
mixture was transferred to a Lysing Matrix B tube (MP Biomedicals, Solon, OH)
containing 0.1-mm silica beads. Cells were then lysed using a FastPrep-24 cell
homogenizer (MP Biomedicals) at a speed of 5.0 m/s for 45 s. The tube containing lysate
was then cooled in ice for 1 min followed by centrifugation at 14,000 rpm for 10 min.
The upper layer (aqueous phase) of the lysate was mixed with 1 ml of TRIzol (Invitrogen,
Carlsbad, CA) at room temperature for 5 min, and then mixed with 100 µl of chloroform
(Sigma-Aldrich, Allentown, PA) for 3 min, followed by centrifugation at 14,000 rpm at
4°C for 10 min. The colorless upper layer was mixed with 200 µl of chloroform,
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incubated for 5 min at room temperature, and centrifuged at 14,000 rpm at 4°C for 5 min.
The aqueous phase was then transferred to a new tube containing 500 µl of 2-propanol
(Sigma-Aldrich), incubated at room temperature for 15 min, and centrifuged at 14,000
rpm at 4°C for 15 min to precipitate RNA. Pelleted RNA was washed using 1 ml of 75%
ethanol (Sigma-Aldrich) and centrifuged at 14,000 rpm at 4°C for 5 min. After decanting
the ethanol the RNA pellet was vacuum dried, dissolved in RNase-free water, and stored
at -80°C. Two biological replicates for each sampling time point were performed. The
integrity of all RNA samples was evaluated using an Agilant 2100 bioanalyzer (Agilant
Technologies, Santa Rosa, CA). Absorbance ratios of 260 nm to 280 nm as well as 260
nm to 230 nm were measured using a NanoDrop ND-1000 spectrophotometer (NanoDrop
Technologies, Wilmington, DE).
DNA chip design and hybridization. Based on the annotated genome of L.
monocytogenes F2365 (GenBank accession# NC_002973) (Nelson et al., 2004), a whole
genome expression array was designed by the Roche NimbleGen Company (Roche
NimbleGen, Madison, WI) to target a total of 2821 protein-coding genes (including
putative protein-coding genes) on a single chip. Each of the 2821 genes was targeted by
an average of 12 randomly printed 60-mer oligonucleotide probes in duplicate. The DNA
chips were synthesized by Roche NimbleGen (Roche NimbleGen, Madison, WI) in a
format of 4 × 72 K (4 identical chips per slide; 72,000 probes per chip). cDNA synthesis,
labeling, hybridization and scanning were performed at Roche NimbleGen according to
the NimbleGen Array User‘s Guide (http://www.nimblegen.com/products/lit/expression_
userguide_v5p0.pdf). Briefly, 10 µg of total RNA from each RNA sample was reverse
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transcribed to cDNA using a Superscript Double Stranded cDNA Synthesis Kit
(Invitrogen). cDNA samples were then labeled with Cyanine 3 (Cy3) using Cy3-Random
Nonamers (Invitrogen). Hybridizations of labeled cDNA were performed at 42°C for
16–20 h in the Precision Mixer Alignment Tool (PMAT) (Roche NimbleGen), followed
by washing and scanning at a pixel size of 5 µm using a GenePix 4000B Scanner (Axon
Instruments, Union City, CA). Raw chip images were collected and analyzed using the
GenePix software (Molecular Devices, Sunnyvale, CA) by Roche NimbleGen. The
hybridization experiment was replicated at least two times for each sample and each time
point.
Statistical data analysis. Scanned images were used to extract raw probe
intensities using the Robust Multichip Average (RMA) algorithm (Irizarry et al., 2003)
by Roche NimbleGen. Transcription data were normalized using quantile normalization
(Bolstad et al., 2003) by Roche NimbleGen. To evaluate experiment reproducibility,
ArrayStar 3 (DNAStar, Madison, WI) was used to measure linear correlation coefficient
(r) between the transcription data of two biological replications by Jia Wen. ArrayStar 3
was also used in this study to compare the transcriptional profiles at adjacent time points
(i.e., 13 h vs. 17 h, 17 h vs. 24 h, 24 h vs. 168 h and 168 h vs. 336 h) to indentify genes
with significant transcriptional changes (≥ 4-fold, P < 0.05) using student‘s t-test (Wang
et al., 2010) by Jia Wen. Gene Set Enrichment Analysis (GSEA) software (Broad
Institute; http://www.broadinstitute.org/ gsea/index.jsp) was used to compare and identify
differentially transcribed gene categories in the LTS phase (168 h and 336 h) versus the
log phase (13-h or 8-h log phase resuscitated from LTS phase) with a cutoff False
114
Discovery Rate (FDR) of 0.25, and this GSEA analysis was done by Xiangyu Deng.
Gene categories and annotations were based on the Comprehensive Microbial Resource
at J. Craig Venter Institute (JCVI) (http: //cmr.jcvi.org/cgi-bin/CMR/shared/RoleList.cgi).
A circular map was constructed using GenomeViz 1.2 software (Ghai et al., 2004) by
Zengxin Li.
Validation of microarray data by quantitative reverse transcription PCR
(qRT-PCR). qRT-PCR was performed by Zengxin Li to validate DNA chip results. Ten
genes that showed significant upregulation or downregulation (P < 0.05) were selected
for qRT-PCR (Table 4.1). 16S rRNA (LMOf2365_16SA) was used as the reference.
Forward and reverse primers were designed (Table 4.1) using Primer 3
(http://frodo.wi.mit.edu/primer3/) to produce an amplicon size of ~150–200 bp (Rozen
and Skaletsky, 2000). RNA samples prepared from 13-h and 24-h bacterial cultures were
used for qRT-PCR. Transcriptor First Strand cDNA Synthesis Kit (Roche Disgnostics,
Mannheim, Germany) was used to generate cDNA from 1 ug of purified total RNA.
After cDNA synthesis, PCR reactions were performed using a LightCycler 480 (Roche
Applied Science, Oswego, IL) as previously described (Wang et al., 2010).
Data accession number. The DNA chip data from this study have been
deposited in the NCBI Gene Expression Omnibus under accession number GSE 26690.
4.4 RESULTS
Growth patterns and morphological changes of L. monocytogenes in TSBYE.
Exponential growth of F2365 (Fig. 4.1A-I) lasted until the onset of stationary phase at 16
115
h. After maintaining the peak density of 1.2–1.8 × 109 CFU/ml at 2-h-long stationary
phase (Fig. 4.1A-II), the cell density rapidly declined (death phase, Fig. 4.1A-III) from
1.4 × 109 CFU/ml at 18-h to 5.4 × 107 CFU/ml at 40 h. Following the death phase, the
bacterial population increased slightly and then maintained at ca. 1–2 × 108 CFU/ml at
the LTS phase (Fig. 4.1A-IV & V) for at least 16 d. After re-inoculation of 336-h LTS-
phase cells into fresh TSBYE, cells entered a 2-h lag phase and then resumed exponential
growth in log phase (Fig. 4.1B-VI). We also observed that the size of bacterial cells
decreased throughout the transition from log to LTS phase and coccoid-shaped LTS cells
started to appear at 24-h death phase. Tukey‘s pairwise comparison showed that
percentage of cocci significantly (P < 0.05) increased from 2.67% at 24-h death phase to
72.65% at 168-h LTS phase and to 92.60% at 336-h LTS phase.
Array data reproducibility. We compared the array data reproducibility
between all duplicate transcriptional profiles at each time point. All pairwise
comparisons indicated high data reproducibility with linear correlation coefficient (r)
values above 0.95. It is worth mentioning that integrity measurements of the RNA
samples suggested significant degradation of 16S and 23S ribosomal RNA in the LTS
phase (RNA Integrity Number or RIN = 3.6) compared to those at log phase (RIN = 9.8),
stationary phase (RIN = 9.3), and death phase (RIN = 8.5).
Differentially expressed genes during the transition from log to LTS phase.
We compared transcriptional profiles of L. monocytogenes F2365 at each of the two
adjacent time points throughout transition from log phase to LTS phase (i.e., 13 h vs. 17 h,
17 h vs. 24 h, 24 h vs. 168 h, 168 h vs. 336 h). We identified a total of 225 genes with ≥
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4 fold up- or downregulation (P < 0.05) in at least one of the four comparisons. The
functional categories, annotations and transcription values at each time point of the 225
genes (representing 8.0% of all the 2821 protein-coding genes in F2365) are provided in
Table 4.2. The majority of these 225 differentially expressed genes were associated with
hypothetical proteins or proteins with unknown functions (n = 64), transport and binding
proteins (n = 41), protein synthesis (n = 25), cell envelope (n = 21) and energy
metabolism (n = 19). Global transcriptional profiles at all 5 times points from 13-h log
phase to 336-h LTS phase are shown in a circular map (Fig. 4.2).
When cells transitioned from 17-h stationary to 24-h death phase, 39 genes
showed ≥ 4 fold upregulation and 64 genes showed ≥ 4 fold downregulation (P < 0.05).
Fourteen of these upregulated genes were related to protein synthesis, including genes
encoding 50 S ribosomal proteins, 30 S ribosomal proteins, translation initiation factor
IF-2 and prolyl-tRNA synthetase. When cells exhibited rapid death at 24 h, transcription
of dnaK increased 9.0 fold (Fig. 4.3). Downregulated genes during the transition from
stationary phase to death phase included genes associated with the cell envelope
including nine putative membrane protein genes, LMOf2365_1088 encoding a membrane
protein FtsW and LMOf2365_1738 encoding a cell-shape-determining protein MreB (Fig.
4.3). Two energy-metabolism-associated genes, qoxB and atpI were also significantly
downregulated. Fifteen transporter protein genes were downregulated 4–12.2 fold, the
products of which transport amino acids and peptides, carbohydrates, drug molecules,
nucleosides, anions and cations (Table 4.2).
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When L. monocytogenes transitioned from rapid death at 24 h to the LTS phase at
168 h, dramatic changes in the transcription profiles were observed, with 69 upregulated
and 31 downregulated genes showing ≥ 4 fold change in transcription (P < 0.05).
Transcription levels of seven energy-metabolism-associated genes including atpI were
significantly increased at the LTS phase. Among the upregulated genes related to cell
envelope structures, there were seven putative membrane protein, one surface
polysaccharide synthesis gene LMOf2365_1647, LMOf2365_1738 coding for cell-shape-
determining protein MreB (Fig. 4.3), and a cell-wall-synthesis related gene mraY.
LMOf2365_1088 and LMOf2365_2399 both encoding a membrane protein FtsW showed
5.3- and 4.4-fold upregulation, respectively (Fig. 4.3). Several upregulated genes
encoding compatible solute transporters included a glycine-betaine-transporter gene
LMOf2365_2124 (5.0 fold up), a glycerol uptake facilitator protein gene glpF-2
(LMOf2365_1558; 5.9 fold up) and a trehalose-specific transporter (IIBC component)
gene treB (LMOf2365_1272; 20.6 fold up) (Fig. 4.3). A relatively large group of cation-
transporter genes were significantly induced 4.2–50.9 fold, including two zinc transporter
genes (zurA-1 and zurM-1) and ten other genes (Table 4.2).
During transition from 24-h death phase to 168-h LTS phase, nine genes coding
for ribosomal proteins were downregulated. The 4.1-fold downregulation of the RNA
polymerase gene rpoA coincided with the downregulation of ribosomal protein genes
(Fig. 4.3). Two universal stress protein genes and a chaperone gene groES were
downregulated (Table 4.2).
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Transcriptomic profiles between 168 h and 336 h in the LTS phase showed
minimum variations; the linear correlation coefficient (r) between these two
transcriptional profiles was 0.93. Eighteen genes with ≥ 4 fold changes (P < 0.05) were
observed, which accounted for only 0.6% of the 2821 protein-coding genes. These
differentially expressed genes included seven transporter protein genes, groES, five
hypothetical protein genes and two genes encoding proteins with unknown functions. All
the transporter genes were downregulated whereas groES was upregulated (Table 4.2).
After inoculation of LTS-phase cells into fresh TSBYE with incubation at 35°C,
cells rapidly resumed growth and entered log phase. The linear correlation coefficient (r)
between the transcriptional profiles of 8-h log phase after re-inoculation of LTS cells and
the original log phase at 13 h was 0.94. Pair-wise comparisons between the two LTS
time points (168 and 336 h) and the two log phase time points (8-h and 13-h after re-
inoculation) were conducted using GSEA to identify gene functional categories that were
differentially regulated during the transition from LTS to log phase. Compared to LTS
phase, log phase was characterized by upregulation of genes mainly associated with
amino acid synthesis, protein synthesis, fatty acid and phospholipid synthesis, cell
envelope synthesis, ribonucleotide synthesis, transcription, detoxification, transport
proteins and cell division. Downregulated gene sets in log phase were mainly related to
protein folding and stabilization, energy metabolism and cellular motility.
Validation by qRT-PCR. Ten genes, including 2 stress response genes and 2
cell division and reshaping genes, were analyzed using qRT-PCR to validate the results
from the DNA array experiments. Fold changes of all 10 genes based on qRT-PCR were
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highly consistent with those based on DNA chip hybridization (Fig. 4.4) (r =0.977). This
confirmed that the DNA chip data reflected the true level of gene transcription.
4.5 DISCUSSION
As mentioned above, bacterial cell death is likely triggered by PCD, an altruistic
behavior to preserve survivors in the population (Finkel, 2006). One characteristic of
PCD is intracellular acidification (Saran, 2000), which can be counteracted by exporting
protons at the cost of ATP hydrolysis by ATP synthase (Hill et al., 2002; Zheng and
Ramirez, 2000). In the present study, L. monocytogenes at death phase showed 13.2 fold
downregulation of atpI, which encodes a protein component of ATP synthase (Fig. 4.3),
compared to the stationary phase. Such downregulation could result in decreased ATP
synthase activity and thus insufficient proton export, leading to aggravated acidification
in the cytoplasm and subsequent cell death. Downregulation of genes encoding FtsW
required for peptidoglycan assembly of the cell wall (Pastoret et al., 2004) and MreB may
collectively contribute to the morphological change from rods to cocci in death and LTS
phases. Upregulation of dnaK (Fig. 4.3) in the death phase may increase the general
resistance of L. monocytogenes during and after the death phase, as DnaK stabilizes
proteins under various types of stresses (Hill et al., 2002). This may partly explain why
LTS-phase cells of L. monocytogenes were significantly more resistant to heat and high
pressure than cells at stationary phase (Wen et al., 2009).
When listerial cells entered the death phase, the majority of cells died and ~10%
of the population survived (Fig. 4.1A-III). We found 14 genes related to protein
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synthesis were upregulated in the death phase, including ribosomal protein genes and a
gene encoding a translation initiation factor. It was reported that newly synthesized
proteins at the early stage of starvation were critical for maintaining long-term survival of
L. monocytogenes (Herbert and Foster, 2001; Lungu et al., 2010) and E. coli (Reeve et al.,
1984). Surviving cells may live or even grow on the debris of dead cells; such a
phenomenon was termed ―cryptic growth‖ (Kolter et al., 1993). The death of the
majority of the population may be a fitness strategy to preserve survivors for future
reproduction (Hochman, 1997). Degradation of 16S and 23S rRNA was also observed in
the death phase. RNA degradation is one of the characteristics of PCD (Hochman, 1997).
Degraded rRNA from dead cells may provide additional nucleotides and energy for the
surviving population (Davis et al., 1986) to support their metabolism during the
subsequent LTS phase (Fig. 4.1A-IV & V).
The specific mechanism of how L. monocytogenes transits from the death phase
to the LTS phase requires more in-depth investigation. Survivors at the end of death
phase may perceive signals released from lysed dead cells, exit PCD and then enter the
LTS phase (Finkel, 2006). Upregulation of atpI (encoding ATP synthase protein I; Fig.
4.3) observed during the LTS phase is consistent with this hypothesis. We speculate that
viable cells at the end of death phase may synthesize higher levels of ATP synthase to
stimulate proton export, which may alleviate intracellular acidification and terminate
PCD. The ability of ATP synthase to regulate cytoplasmic pH by proton extrusion has
been well documented (Hill et al., 2002). ATP synthase might be expressed at a constant
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high level during the LTS phase to maintain pH homeostasis, which may prevent PCD
and keep the remaining cells viable.
The transcriptional profiles at the LTS phase revealed upregulation of three genes
encoding transporters for compatible solutes such as glycine betaine and trehalose (Fig.
4.3). Compatible solutes are small molecules that can be accumulated in the cytoplasm to
high concentrations without adversely perturbing physiological functions (Yancey et al.,
1982; Burg and Ferraris, 2008). Transcriptional regulation of compatible-solute-
transporter genes has been well studied in L. monocytogenes (Cetin et al., 2004; Fraser et
al., 2003; Sue et al., 2003). During the LTS phase, high levels of compatible solutes may
be taken up from the growth medium and accumulated in the cytoplasm, resulting in
increased thermo- and barotolerance (Wen et al., 2009). Glycine betaine was reported to
be accumulated in cells of L. monocytogenes under osmotic stress and thus enhance
osmotolerance and cryotolerance (Ko et al., 1994; Bayles and Wilkinson, 2000).
Similarly, accumulation of trehalose in bacteria could be induced by a variety of stress
conditions and thus may protect cells against stresses including heat, cold, desiccation
and oxidation (Elbein et al., 2003). It was reported that high concentrations of trehalose
lead to lowered water activity (Galmarini et al., 2008), and that lowered water activity
enhances barotolerance in L. monocytogenes possibly through protein stabilization
(Hayman et al., 2008).
Upregulation of other transporter genes (associated with transport of
carbohydrates, Fe2+ and Zn2+) coincided with the upregulation of a few energy
metabolism genes associated with glycolysis, pentose phosphate pathway and glycerol
122
utilization at the LTS phase. Upregulation of these energy-metabolism-related genes may
benefit transport of cellular materials. Carbohydrate uptake is likely to be necessary to
meet the need for a carbon source during the LTS phase. Uptake of Fe2+ and Zn2+ might
be vital to maintain the functions of metalloenzymes during the LTS phase (Thöny-
Meyer, 1997).
Compared to the log phase, LTS-phase cells had much lower transcription
activities which indicate metabolic dormancy. For instance, downregulation of rpoA was
observed during the LTS phase (Fig. 4.3), indicating reduced transcription. Furthermore,
significant degradation of 16S and 23S rRNA was observed in LTS phase cells, which
was consistent with some previous reports (Lappin-Scott and Costerton, 1990)
(Deutscher, 2003). Loss of functional ribosomal RNA and downregulation of ribosomal
protein genes during the LTS phase may result in lower protein translation and
subsequent dormancy. Protein synthesis was reported to be significantly lower in
dormant cultures of Mycobacterium tuberculosis (Hu et al., 1998). Dormancy is
therefore an adaptive strategy under suboptimal growth conditions to enhance the long-
term survival of bacteria including L. monocytogenes. Within LTS phase, cells may stay
dormant and thus their transcriptional profile may remain largely unchanged. This
hypothesis is supported by the similarity (r = 0.93) between the gene transcriptional
profiles at 168 h and 336 h within the LTS phase. The present study also showed LTS-
phase cells rapidly resumed exponential growth and entered log phase after exposure to
fresh TSBYE (Fig. 4.1B). LTS-phase cells appeared to rapidly exit dormancy and utilize
fresh nutrients to resume replication, as evidenced by upregulation of gene sets related to
123
transport and cell division. To meet the metabolic needs for rapid growth, it is necessary
to boost the synthesis of cellular components, which is supported by the observed
upregulation of gene sets associated with syntheses of ribonucleotides, amino acids,
proteins and cell envelope components.
In conclusion, we found dramatic transcriptional changes as L. monocytogenes
transitioned from the log phase to the LTS phase. We speculate that viable cells at the
end of the death phase might synthesize high levels of ATP synthase to stimulate proton
export, alleviate intracellular acidification, terminate PCD and then transit to the LTS
phase. The upregulation of compatible solute transporter genes during the LTS phase
may enhance resistance of L. monocytogenes to various stresses, resulting in long-term
survival. LTS-phase cells may be metabolically dormant, as indicated by the
downregulation of genes related to transcription and translation. Understanding the
transition to and characteristics of the LTS phase in L. monocytogenes may also shed new
insights to the long-term survival strategies utilized by other bacterial species.
4.6 ACKNOWLEDGEMENTS
This study was supported by the U. S. Food and Drug Administration research
fund to the Institute for Food Safety and Health (formerly the National Center for Food
Safety and Technology) and by funds from a USDA Special Grant on Milk Safety to the
Pennsylvania State University. Xiangyu Deng is a recipient of a Fieldhouse research
fellowship at the Illinois Institute of Technology. The funders had no role in study design,
data collection and analysis, decision to publish, or preparation of the manuscript.
124
This manuscript has been published on Appl. Environ. Microbiol. 77:5966–5972.
All coauthors collarborated on the design of the experiment. RNA extraction and quality
measurement were conducted by Jia Wen. Microarray manufacturing, cDNA synthesis,
labeling and hybridization and microarray scanning were conducted by the Roche
NimbleGen Company. Microarray data were analyzed by Jia Wen using the ArrayStar
software and Xiangyu Deng using the GSEA software. Microarray results were
confirmed using qRT-PCR by Zengxin Li. Jia Wen wrote this manuscript, except that
Zengxin Li wrote the two paragraphs about methods and results of qRT-PCR and made a
gene-expression circular map.
125
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Fig. 4.1. Growth curves of L. monocytogenes F2365 in TSBYE at 35°C demonstrating
the transition from log to LTS phase (A) and the re-growth of LTS cells after inoculation
into fresh TSBYE (B). Different background colors indicate different growth phases.
Cultures at 13-h log (I), 17-h stationary (II), 24-h death (III), 168- and 336-h LTS (IV &
V) phases, as well as 8-h log phase (VI) after inoculating LTS-phase cells into fresh
TSBYE, were used for DNA chip analysis. Means and standard deviations based on
three replications were plotted as data points and error bars.
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133
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Fig. 4.2. A circular map showing the global gene transcriptional profiles throughout the
life cycle of L. monocytogenes F2365. The map compares the gene expression profiles
between 13-h log phase and five other RNA sampling time points. The innermost scale
indicates nucleotide coordinates on the genome. From inside out, the second circle
shows the color-coded gene categories based on protein function (see the bottom for
color-coded categories). The next five circles represent the transcription patterns at 8-h
regrowth, 17-h stationary, 24-h death, 168-h LTS and 336-h LTS phases, respectively.
The blue and red colors in each circle indicate the up- and downregulated genes,
respectively. The fold changes of differentially expressed genes are color-coded relative
to those of the 13-h log phase (see the upper-right side for color-coded fold changes).
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Fig. 4.3. A hierarchical cluster plot showing the gene expression levels of selected genes
related to resistance to stresses (dnaK), morphology (LMOf2365_1088 and
LMOf2365_2399, both encoding FtsW, and LMOf2365_1738 encoding MreB),
transportation of compatible solutes (LMOf2365_2124, glpF-2 and treB), RNA synthesis
(rpoA) and pH regulation (atpI) at stationary, death and LTS phases. The color scale
indicates log2 gene expression values.
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138
Fig. 4.4. A bar graph showing the fold changes of 5 upregulated and 5 downregulated
genes identified by DNA microarray and by RT-PCR experiments. The fold changes
were converted into log2 values. Error bars represent the standard deviations.
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Table 4.1. Primers used for qRT-PCR analysis.
Gene IDa Gene name Annotation Primersb Sequence (5'-3') LMOf2365_2121 lipase F acgctatctcctgcaacgat R gctcgcgttgttactgttga LMOf2365_2099 groEL chaperonin GroEL F gcctgctccttctacgattg R ctcctggttttggtgatcgt LMOf2365_1602 universal stress protein family F ggcagacaaagctaccgaat R aagtccagttgcaccacaca LMOf2365_1492 dnaK chaperone protein DnaK F gtcttttgccattggacgtt R gcaattcaaggtggcgtaat LMOf2365_2238 gpm phosphoglycerate mutase F ataatgccgttcgttcaagc R acaggttggcatgatgttga LMOf2365_1701 menA 1,4-dihydroxy-2-naphthoate F gtggtggacgcttctgattt octaprenyltransferase R aaaggtaagcaagggccaat LMOf2365_1220 hypothetical protein F gccgccgaacattaagataa R gggttggtggtggaacatta LMOf2365_1834 acpP acyl carrier protein F tcactgcatcaccaactgtg R atcatcgtcgaccgtttagg LMOf2365_1088 cell division protein, F gattggctcaggtggtgttt FtsW/RodA/SpoVE family R tgcttgaatcgcaatcagac LMOf2365_1738 cell shape-determining protein F ggtgtgagaaagcccaatgt R cagcgacaggtaaatcagca LMOf2365_16SA 16S ribosomal RNA F cccttatgacctgggctaca R cctaccgacttcgggtgtta
a Gene information for each gene was retrieved from National Center for Biotechnology
Information (NCBI).
b F, Forward; R, Reverse.
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Table 4.2. Genes that showed ≥ 4 fold change (P < 0.05) in at least one of the four
comparisons: 13-h log vs. 17-h stationary, 17-h stationary vs. 24-h death, 24-h death vs.
168-h LTS, 168-h LTS vs. 336-h LTS. A total of 225 differentially expressed genes were
identified throughout the transition from 13-h log to 336-h LTS phase. Gene
transcription values are means of normalized signal intensities of the genes based on two
replications. The star sign beside a log2 gene transcription value at a specific time point
indicates there is ≥ 4 fold change (P < 0.05) in gene transcription at that time point
compared with its adjacent previous time point.
Category a Gene ID Gene
symbol Annotation Log2 gene transcription values
13 h 17 h 24 h 168 h 336 h Amino acid biosynthesis
LMOf2365_0623 metX homoserine O-acetyltransferase
10.9 13.3* 11.5 10.4 9.8
LMOf2365_0999 ilvE branched-chain amino acid aminotransferase
13.3 12.2 9.3* 11.9* 11.1
LMOf2365_1555 pheA prephenate dehydratase 11.0 8.8* 8.5 9.3 9.2 LMOf2365_2011 leuB 3-isopropylmalate
dehydrogenase 9.3 9.4 11.4 9.2* 9.5
LMOf2365_2012 leuC 3-isopropylmalate dehydratase, large subunit
9.3 9.8 11.7 9.7* 9.3
LMOf2365_2013 leuD 3-isopropylmalate dehydratase, small subunit
10.0 10.6 12.1 10.1* 10.0
LMOf2365_2014 ilvA threonine dehydratase 9.7 9.9 12.1* 10.1* 10.2 LMOf2365_2134 glutamine
amidotransferase, SNO family
14.1 15.3 12.4* 11.9 12.1
Biosynthesis of cofactors, prosthetic groups & carriers
LMOf2365_1608 putative inorganic polyphosphate/ATP-NAD kinase
9.1 9.0 10.8 8.6* 9.0
LMOf2365_1701 menA 1,4-dihydroxy-2-naphthoate octaprenyltransferase
14.2 11.9* 8.2* 12.2* 10.5
LMOf2365_1709 gsaB glutamate-1-semialdehyde-2,1-aminomutase 2
11.8 10.5 9.3 11.4* 10.1
LMOf2365_2088 ctaB protoheme IX farnesyltransferase
11.8 11.5 9.9 12.0* 10.7
Cell envelope LMOf2365_0317 putative membrane protein 10.3 11.4 9.1* 9.7 9.8 LMOf2365_0482 putative membrane protein 11.7 9.1* 7.7 9.5 9.0 LMOf2365_0606 putative membrane protein 12.5 14.6* 12.9 12.8 12.5
142 LMOf2365_0634 putative membrane protein 13.6 10.9* 9.1 11.2* 10.2 LMOf2365_0761 putative membrane protein 11.6 12.4 9.7* 10.3 9.5 LMOf2365_0810 putative membrane protein 12.6 10.9 8.7* 10.6 9.4 LMOf2365_0848 putative membrane protein 11.3 9.1* 7.8 9.2 8.3 LMOf2365_0930 putative membrane protein 11.2 9.2* 7.9 8.8 8.4 LMOf2365_0994 dltA D-alanine-D-alanyl carrier
protein ligase 10.3 10.7 13.4* 11.0* 11.3
LMOf2365_1219 putative membrane protein 13.0 9.8* 7.6* 10.1* 9.9 LMOf2365_1220 putative membrane protein 13.8 10.1* 8.2 10.8* 9.3 LMOf2365_1404 putative membrane protein 11.3 10.8 8.8 11.0* 10.2 LMOf2365_1647 polysaccharide
biosynthesis family protein
12.5 12.0 9.5* 11.9* 10.2
LMOf2365_1695 putative laminin-binding surface protein
10.4 8.4* 8.6 10.2 9.3
LMOf2365_1738 cell shape-determining protein
14.4 12.5 10.4* 12.6* 11.6
LMOf2365_1873 putative membrane protein 15.6 14.7 12.6* 15.2* 14.2 LMOf2365_2069 mraY phospho-N-
acetylmuramoyl-pentapeptide-transferase
12.9 10.8 8.6 11.1* 9.6
LMOf2365_2177 putative membrane protein 13.8 12.7 10.6* 12.3 11.5 LMOf2365_2179 putative membrane protein 12.9 12.2 9.7* 11.8* 9.8 LMOf2365_2180 putative membrane protein 11.8 10.2 8.1* 10.6* 9.2 LMOf2365_2360 putative membrane protein 11.6 11.7 9.1* 10.1 9.0
Cellular processes: adaptation to atypical condition
LMOf2365_1602 universal stress protein family
9.3 12.0* 13.5 11.2* 12.9
LMOf2365_2653 universal stress protein family
10.1 12.3 14.1 11.4* 11.7
Cellular processes: cell division
LMOf2365_1088 cell division protein, FtsW/RodA/SpoVE family
14.6 12.6 10.3* 12.7* 11.0
LMOf2365_2398 cell division protein, FtsW/RodA/SpoVE family
12.7 10.6* 9.3 10.9 9.9
LMOf2365_2399 cell division protein, FtsW/RodA/SpoVE family
12.5 10.2 8.7 10.9* 10.5
Cellular processes: detoxification
LMOf2365_1458 sod superoxide dismutase, Mn 13.6 14.7 11.5* 12.7 12.3 LMOf2365_1605 thiol peroxidase 10.5 10.9 13.2* 11.7 11.6 LMOf2365_1625 putative peroxiredoxin 12.7 12.5 10.4* 11.3 10.8 LMOf2365_2263 putative arsenate reductase 9.9 11.5 12.4 10.2* 10.7
Cellular processes: pathogenesis
LMOf2365_0057 putative accessory gene regulator protein B
12.2 12.6 14.3 12.0* 13.3
LMOf2365_1893 hlY-III hemolysin III 12.8 11.8 9.6* 11.0 10.3 Cellular processes:
LMOf2365_0208 spoVG
-2
stage V sporulation protein G
11.5 10.7 12.9* 11.2 11.7
143
sporulation & germination
Cellular processes: toxin production & resistance
LMOf2365_0548 drug resistance transporter, EmrB/QacA family
14.9 11.6* 10.0 12.5* 11.4
LMOf2365_0856 tetA tetracycline resistance protein
9.5 8.9 8.3 10.9* 9.2
LMOf2365_2098 cbH choloylglycine hydrolase 8.3 9.8 12.2* 9.2* 8.7 DNA metabolism
LMOf2365_0054 ssb-1 single-strand binding protein
12.5 11.6 13.7* 12.9 13.5
LMOf2365_0517 MutT/nudix family protein 12.2 13.6 12.7 14.9* 15.3 LMOf2365_1243 uvrC excinuclease ABC, C
subunit 12.3 12.1 10.0* 11.7 10.6
LMOf2365_1667 putative exonuclease SbcD
10.6 11.3 8.7* 9.1 9.0
LMOf2365_1910 5-3 exonuclease family protein
11.9 10.8 11.2 13.8* 13.7
Energy metabolism
LMOf2365_0017 qoxB quinol oxidase AA3, subunit I
13.1 13.4 11.4* 15.0* 13.7
LMOf2365_0018 qoxC cytochrome aa3 quinol oxidase, subunit III
13.4 12.7 12.1 15.4* 14.0
LMOf2365_0362 tkt-1 transketolase 11.0 12.2 12.2 15.2* 14.4 LMOf2365_0363 transaldolase 9.8 10.5 10.9 13.3* 12.7 LMOf2365_0550 glycosyl hydrolase, family
4 11.3 13.4* 14.0 13.2 12.9
LMOf2365_0568 putative tagatose 1,6-diphosphate aldolase
10.7 13.1* 12.7 11.8 11.4
LMOf2365_0829 scrK fructokinase 11.6 13.9* 12.6 12.5 12.5 LMOf2365_0935 gabD succinate-semialdehyde
dehydrogenase 9.3 10.9 12.5 10.1* 10.1
LMOf2365_1242 trx-1 thioredoxin 9.8 9.4 11.2 10.2 12.2* LMOf2365_1271 treC-2 trehalose-6-phosphate
hydrolase 11.2 9.9 9.9 12.6* 12.4
LMOf2365_1557 glpK-2 glycerol kinase 9.1 9.1 9.1 11.3* 10.2 LMOf2365_1601 ald alanine dehydrogenase 11.1 14.0* 14.4 14.1 14.5 LMOf2365_1656 adhE aldehyde-alcohol
dehydrogenase 12.0 14.1* 15.6 15.2 15.4
LMOf2365_1734 flavodoxin 12.1 9.7* 8.6 9.6 9.2 LMOf2365_2238 gpm phosphoglycerate mutase 10.0 12.5* 15.4* 14.6 15.3 LMOf2365_2429 gpmA phosphoglycerate mutase 10.8 11.6 14.7* 13.1 14.1 LMOf2365_2430 tpiA-2 triosephosphate isomerase 11.2 12.1 14.9* 14.4 15.2 LMOf2365_2444 NADH:flavin
oxidoreductase 11.5 14.2* 14.9 14.5 14.5
LMOf2365_2509 atpI ATP synthase protein I 15.2 14.4 10.7* 14.0* 12.1 Fatty acid & phospholipid metabolism
LMOf2365_0367 dihydroxyacetone kinase 8.9 9.9 10.5 12.6* 11.3 LMOf2365_0368 dihydroxyacetone kinase 9.1 10.5 10.6 13.4* 11.9 LMOf2365_1834 acpP acyl carrier protein 15.2 11.8* 9.7 11.8* 10.8 LMOf2365_2121 Lipase 10.6 11.4 13.9* 12.3 13.5
144 LMOf2365_2404 estA tributyrin esterase 12.2 12.3 10.2* 11.0 10.4 LMOf2365_2674 putative dihydroxyacetone
kinase, Dak1 subunit 10.5 12.9* 13.7 12.3 13.1
Hypothetical proteins
LMOf2365_0038 Hypothetical protein 12.2 12.0 9.4* 10.5 9.7 LMOf2365_0139 Hypothetical protein 10.2 11.7 12.4 10.1* 10.3 LMOf2365_0144 Hypothetical protein 9.6 11.9* 12.3 12.7 14.3 LMOf2365_0220 Hypothetical protein 9.1 11.2* 12.6 9.0* 9.9 LMOf2365_0290 Hypothetical protein 11.1 13.1* 13.8 12.8 13.2 LMOf2365_0403 Hypothetical protein 9.1 9.2 11.2* 9.8 10.3 LMOf2365_0404 Hypothetical protein 9.8 10.3 12.4* 11.2 12.0 LMOf2365_0432 Hypothetical protein 12.0 11.7 8.9* 11.6* 10.0 LMOf2365_0607 Hypothetical protein 13.1 13.0 10.2* 12.4* 10.4* LMOf2365_0632 Hypothetical protein 12.9 12.4 9.8* 10.5 9.9 LMOf2365_0633 Hypothetical protein 12.5 13.3 10.0* 11.3 10.0 LMOf2365_0797 Hypothetical protein 11.5 11.1 8.6* 9.8 9.2 LMOf2365_0817 Hypothetical protein 10.3 12.0 14.8* 12.9 13.7 LMOf2365_0836 Hypothetical protein 11.7 13.4 10.1* 10.5 10.1 LMOf2365_0958 Hypothetical protein 9.8 10.9 11.0 8.4* 9.4 LMOf2365_1179 Hypothetical protein 12.7 12.5 10.1* 12.1 11.0 LMOf2365_1180 Hypothetical protein 11.5 11.8 8.8* 10.7 9.3 LMOf2365_1302 Hypothetical protein 12.5 11.9 8.7* 11.1 9.2 LMOf2365_1321 Hypothetical protein 9.0 9.1 10.0 9.5 11.8* LMOf2365_1349 Hypothetical protein 11.6 11.9 14.0* 12.2 12.9 LMOf2365_1462 Hypothetical protein 10.7 8.7* 7.0 8.6 7.8 LMOf2365_1591 Hypothetical protein 11.4 11.6 9.6* 11.3 10.5 LMOf2365_1669 Lipoprotein, putative 12.4 9.2* 9.7 9.6 9.0 LMOf2365_1686 Hypothetical protein 13.8 12.2 10.1* 12.9* 10.7* LMOf2365_1694 Hypothetical protein 12.2 12.0 9.2* 12.2* 10.0* LMOf2365_1711 Hypothetical protein 12.6 13.2 10.9* 10.8 10.3 LMOf2365_1823 Hypothetical protein 12.0 9.6* 9.7 10.9 10.1 LMOf2365_1948 Hypothetical protein 13.6 14.3 11.7* 12.2 11.1 LMOf2365_2094 Hypothetical protein 10.3 7.8* 8.2 8.2 8.3 LMOf2365_2103 Hypothetical protein 10.7 8.6* 8.5 8.2 8.2 LMOf2365_2110 Hypothetical protein 12.8 11.8 9.9 12.5* 11.5 LMOf2365_2146 Hypothetical protein 15.0 15.3 12.8* 13.3 12.1 LMOf2365_2288 Hypothetical protein 11.4 11.1 9.0* 9.4 8.6 LMOf2365_2294 Hypothetical protein 11.4 11.9 8.7* 10.9* 9.5 LMOf2365_2359 Hypothetical protein 13.1 13.2 10.8* 12.2 11.0 LMOf2365_2366 Hypothetical protein 11.3 13.9* 12.4 11.7 12.4 LMOf2365_2367 Hypothetical protein 12.4 15.3* 14.9 14.4 14.5
145 LMOf2365_2409 Hypothetical protein 12.1 14.4* 15.0 15.2 14.9 LMOf2365_2427 Hypothetical protein 9.5 10.7 13.6* 11.9 13.2 LMOf2365_2547 Hypothetical protein 11.0 13.1* 14.0 12.6 14.0 LMOf2365_2559 Hypothetical protein 11.9 11.4 9.2* 10.7 9.1 LMOf2365_2562 Hypothetical protein 11.0 11.8 10.6 11.9 9.9* LMOf2365_2613 Hypothetical protein 12.6 12.2 9.7* 12.3* 11.6 LMOf2365_2651 Hypothetical protein 10.7 11.2 13.4* 12.5 13.3 LMOf2365_2676 PTS system IIA
component family protein 10.4 12.5* 14.1 13.0 14.5
LMOf2365_2691 Hypothetical protein 12.8 10.5* 8.7 9.5 8.6 LMOf2365_2716 Hypothetical protein 10.9 13.4* 12.1 11.7 12.1 LMOf2365_2819 Hypothetical protein 11.9 14.9* 15.5 15.6 15.8
Mobile & extrachromosomal element functions
LMOf2365_0146 putative prophage LambdaLm01, holin
9.4 10.0 12.2* 10.6 11.5
Protein degradation
LMOf2365_1226 putative peptidase 10.9 11.1 12.8 10.8* 11.8 LMOf2365_1600 pepQ proline dipeptidase 11.9 14.1* 15.3 14.1 14.9 LMOf2365_2289 intracellular protease, PfpI
family 13.4 15.4* 15.3 15.1 14.9
Protein modification & repair
LMOf2365_1072 def peptide deformylase 11.7 11.4 10.3 12.4* 11.2
Protein folding & stabilization
LMOf2365_1492 dnaK chaperone protein DnaK 8.7 10.5 13.7* 12.9 14.3 LMOf2365_2099 groEL chaperone protein GroEL 10.6 13.3* 14.4 12.8 13.6 LMOf2365_2100 groES chaperone protein GroES 9.5 12.2 11.9 9.1* 11.3*
Protein synthesis
LMOf2365_0222 ribosomal 5S rRNA E-loop binding protein
10.7 13.1* 13.5 11.2* 10.7
LMOf2365_0260 rplK ribosomal protein L11 14.3 12.1* 12.8 13.1 12.4 LMOf2365_1009 prfC peptide chain release
factor 3 12.4 10.0* 9.6 10.9 10.8
LMOf2365_1336 proS prolyl-tRNA synthetase 10.5 9.1 11.1* 11.2 11.6 LMOf2365_1342 infB translation initiation factor
IF-2 10.1 9.5 11.6* 11.1 11.3
LMOf2365_1347 rpsO ribosomal protein S15 12.0 10.1 13.0* 10.2* 9.9 LMOf2365_1561 rplU ribosomal protein L21 12.8 11.4 11.9 11.0 13.1* LMOf2365_1618 rpsD ribosomal protein S4 15.1 13.0* 14.2 13.8 12.6 LMOf2365_1814 rplS ribosomal protein L19 15.4 14.4 11.5* 14.3* 12.8 LMOf2365_1824 rpsP ribosomal protein S16 14.8 11.3* 12.0 13.9 12.6 LMOf2365_1911 rpsN-1 ribosomal protein S14 11.7 10.8 10.5 13.6* 12.8 LMOf2365_2587 rpmD ribosomal protein L30 11.2 10.5 12.9* 10.6* 11.4 LMOf2365_2589 rplR ribosomal protein L18 10.9 9.9 11.7 9.3* 9.5 LMOf2365_2590 rplF ribosomal protein L6 10.5 9.8 13.0* 11.2 11.3 LMOf2365_2591 rpsH ribosomal protein S8 10.5 9.6 11.8* 9.2* 9.1
146 LMOf2365_2592 rpsN-2 ribosomal protein S14 9.9 8.5 11.2* 9.3 10.2 LMOf2365_2593 rplE ribosomal protein L5 10.1 9.2 11.4* 9.5 9.8 LMOf2365_2595 rplN ribosomal protein L14 10.8 10.3 13.1* 11.0* 11.1 LMOf2365_2596 rpsQ ribosomal protein S17 9.7 9.0 10.9 8.5* 8.9 LMOf2365_2597 rpmC ribosomal protein L29 9.8 9.0 11.6* 10.0 10.7 LMOf2365_2598 rplP ribosomal protein L16 11.2 10.7 13.9* 11.8* 12.9 LMOf2365_2599 rpsC ribosomal protein S3 11.5 10.7 13.5* 11.1* 10.8 LMOf2365_2600 rplV ribosomal protein L22 10.7 10.2 12.5* 12.3 13.4 LMOf2365_2601 rpsS ribosomal protein S19 11.8 10.6 12.6* 12.5 12.6 LMOf2365_2846 rpmH ribosomal protein L34 10.3 8.0* 8.7 7.7 8.5
Purines, pyrimidines, nucleosides & nucleotides
LMOf2365_1110 guaA GMP synthase 13.4 11.2* 11.2 12.3 11.8 LMOf2365_1800 purE phosphoribosylaminoimid
azole carboxylase 9.8 10.1 9.6 11.9* 11.7
LMOf2365_2584 adenylate kinase 10.2 9.5 11.8* 10.3 10.1 Regulatory functions
LMOf2365_0978 transcriptional regulator, GntR family
10.1 10.2 12.3* 11.4 11.9
LMOf2365_1047 putative transcriptional regulator
11.0 8.9* 8.3 8.9 8.4
LMOf2365_1536 nitrogen regulatory protein P-II
12.2 9.8* 11.0 9.6 9.7
LMOf2365_1907 iron-dependent repressor family protein
11.4 9.9 9.5 11.2 9.2*
LMOf2365_2715 transcriptional regulator, MerR family
11.0 13.4* 12.5 12.1 12.4
Transcription LMOf2365_2579 rpoA DNA-directed RNA polymerase, alpha subunit
10.6 9.3 11.3 9.2* 9.7
Transport & binding proteins
LMOf2365_0114 PTS system, mannose/fructose/sorbose family, IIC component
13.2 14.5 11.5* 11.2 11.3
LMOf2365_0169 zurA-1 zinc ABC transporter, ATP-binding protein
9.6 8.9 9.0 11.5* 9.9
LMOf2365_0170 zurM-1 zinc ABC transporter, permease protein
11.4 10.1 8.8 14.5* 11.7*
LMOf2365_0389 PTS system, beta-glucoside-specific, IIC component
8.6 9.1 9.6 11.8* 10.9
LMOf2365_0584 proton-dependent oligopeptide transporter
11.8 11.0 8.9* 10.2 9.3
LMOf2365_0676 amino acid permease family protein
13.5 11.0* 9.2 11.1 10.1
LMOf2365_0803 amino acid permease family protein
12.4 10.3* 9.1 11.3* 10.4
LMOf2365_0819 Na+/H+ antiporter 11.3 11.4 9.4* 11.0 9.7 LMOf2365_0865 glutamine ABC
transporter, ATP-binding protein
11.5 9.4* 9.2 9.5 9.6
LMOf2365_0934 formate/nitrite transporter family protein
15.1 13.3 9.9* 11.5 9.9
LMOf2365_0967 putative transporter 11.9 9.9* 8.2 9.6 9.4 LMOf2365_1002 drug resistance
transporter, EmrB/QacA 13.6 11.5* 9.2* 11.4* 10.0
147 family
LMOf2365_1014 cation transport protein 13.2 11.3 10.0 12.3* 11.4 LMOf2365_1264 putative transporter 10.6 12.2 10.6 13.6* 13.1 LMOf2365_1272 treB PTS system, trehalose-
specific, IIBC component 12.2 11.1 10.3 14.7* 13.2
LMOf2365_1409 putative ABC transporter, permease protein
13.7 12.2 10.0 14.2* 12.1*
LMOf2365_1410 putative ABC transporter, permease protein
14.0 13.3 10.6 14.1* 11.8*
LMOf2365_1450 ABC transporter, ATP-binding protein
10.3 8.3* 8.5 9.3 8.7
LMOf2365_1466 zurA-2 zinc ABC transporter, ATP-binding protein
11.2 8.8* 8.7 9.8 10.2
LMOf2365_1558 glpF-2 glycerol uptake facilitator protein
10.9 12.2 11.9 14.5* 13.0
LMOf2365_1659 putative ABC transporter, permease protein
11.8 11.6 10.4 11.9 9.7*
LMOf2365_1721 cation efflux family protein
13.7 11.9 8.3* 11.4* 10.3
LMOf2365_1786 sodium:neurotransmitter symporter family protein
13.3 10.9* 8.9* 11.3* 10.3
LMOf2365_1988 iron compound ABC transporter, permease protein
11.0 10.5 9.1 11.2* 10.5
LMOf2365_2124 glycine betaine transporter 14.3 13.1 10.2* 12.5* 11.0 LMOf2365_2137 feoB ferrous iron transport
protein B 10.8 10.9 8.3* 10.6* 9.1
LMOf2365_2283 amino acid ABC transporter, permease protein, His/Glu/Gln/Arg/opine family
11.2 9.1* 9.3 9.1 8.9
LMOf2365_2284 amino acid ABC transporter, ATP-binding protein
12.3 10.1* 9.8 9.8 10.2
LMOf2365_2287 xanthine/uracil permease family protein
13.9 11.7 9.2* 11.4 12.0
LMOf2365_2344 PTS system, cellobiose-specific, IIB component
9.6 10.2 11.9 9.3* 10.4
LMOf2365_2350 major facilitator family transporter
14.4 13.1 11.0* 12.3 10.9
LMOf2365_2351 putative Na+/H+ antiporter component A
13.7 12.8 10.3* 13.3* 11.8
LMOf2365_2352 putative Na+/H+ antiporter component B
13.6 12.5 10.6 13.0* 11.0*
LMOf2365_2353 putative Na+/H+ antiporter component C
14.1 13.2 11.1* 13.7* 11.3*
LMOf2365_2394 cation efflux family protein
9.0 8.8 9.0 11.6* 12.1
LMOf2365_2401 iron compound ABC transporter, permease protein
12.4 11.1 8.7* 11.5* 10.9
LMOf2365_2442 amino acid permease family protein
13.6 11.5 10.0 12.0* 10.8
LMOf2365_2560 lmrB-2 lincomycin resistance protein LmrB
10.9 10.5 11.1 13.6* 12.6
148 LMOf2365_2664 PTS system, beta-
glucoside-specific, IIC component
7.7 7.6 7.7 10.0* 8.1
LMOf2365_2732 ABC transporter, permease/ATP-binding protein
13.2 13.9 12.9 14.0 11.4*
LMOf2365_2835 major facilitator family transporter
11.7 11.4 8.5* 10.3 8.5
Unknown function
LMOf2365_0095 putative transferase 12.6 14.6* 13.9 14.3 14.0 LMOf2365_0242 UVR domain protein 8.2 9.0 11.3* 11.1 9.5 LMOf2365_0287 phosphoglycerate mutase
family protein 10.2 10.3 12.0 10.6 12.7*
LMOf2365_0582 CBS domain protein 9.6 10.2 13.2* 10.7* 11.9 LMOf2365_0653 acetyltransferase, GNAT
family 9.7 11.0 11.0 8.5* 9.1
LMOf2365_1019 CAAX amino terminal protease family protein
13.5 12.8 8.9* 11.7 9.9
LMOf2365_1239 CvpA family protein 12.0 10.7 9.1 11.6* 10.4 LMOf2365_1487 GatB/Yqey domain
protein 10.4 10.4 12.1 11.1 13.2*
LMOf2365_1519 DedA family protein 12.9 10.8* 8.5* 10.1 9.1 LMOf2365_1534 Rrf2 family protein 12.0 10.8 8.6* 10.2 8.8 LMOf2365_1556 GTP-binding protein,
GTP1/OBG family 13.0 10.2* 9.9 12.2* 12.0
LMOf2365_1899 DedA family protein 11.2 9.7 8.8 11.2* 9.3 LMOf2365_1938 YitT family protein 12.0 11.2 8.5* 10.6* 9.6 LMOf2365_2223 MecA family protein 9.5 10.0 12.4* 10.7 12.5 LMOf2365_2544 putative amidase 9.1 9.9 11.9* 10.6 10.4 LMOf2365_2780 DNA-binding protein 9.5 10.8 13.3* 11.3* 12.9
a Gene category information of L. monocytogenes F2365 was obtained from the
Comprehensive Microbial Resource at J. Craig Venter Institute (JCVI).
149
CHAPTER FIVE
EFFECTS OF STRAIN, TYPE OF FOOD-CONDITIONING FILM AND THEIR
INTERACTION ON CELL DENSITY, BIOFILM FORMATION AND COCCI
FORMATION AND THEIR POSSIBLE ROLES IN PERSISTENCE OF
LISTERIA MONOCYTOGENES IN FOOD PROCESSING PLANTS
Jia Wen1*, Valentina Alessandria2, Rob Walker1, Ramaswamy C. Anantheswaran1, and
Stephen J. Knabel1
1The Pennsylvania State University, University Park, PA 16802, USA
2Department of Exploitation and Protection of Agricultural and Forest Resources,
Agricultural Microbiology and Food Technology Sector, Faculty of Agriculture,
University of Turin, Italy
150
5.1 ABSTRACT
Specific strains of Listeria monocytogenes are known to persist in food processing
plants for years and cause contamination; however, there is a lack of understanding as to
why specific strains persist in different food processing plants. Thus, we investigated the
effects of different L. monocytogenes strains and different types of food-conditioning
films (FCFs) on cell attachment, growth, and biofilm and cocci formation, which may
help explain the persistence of specific strains in food processing plants. Type of FCF,
strain and their interaction significantly affected cell density after 2-d incubation at 30°C
(P < 0.001). Meat and poultry FCFs showed significantly higher cell densities, as
compared to the control without FCF (P < 0.05). All strains showed medium to very
high densities on the respective foods from which they were isolated, except that the
strain J1703 (isolated from turkey) showed very low cell density on Wegman‘s Brand
turkey deli but very high densities on other brands of turkey deli meat. Strains lacking
the comK prophage showed lower cell densities than those containing the comK prophage
on all four meat and poultry FCFs (P < 0.05). Biofilms were only formed by strains
containing the comK prophage. Cocci were formed by all strains on all FCFs after 2-
weeks incubation. The ability of specific strains of L. monocytogenes to form biofilms on
specific FCFs and subsequently control their entry to the long-term-survival phase may
explain why specific strains persist in different food processing plants and cause
contamination of foods manufactured in those plants.
151
5.2 INTRODUCTION
Listeria monocytogenes is a pathogenic bacterium causing 1500–2500 cases of
listeriosis annually in the United States (U.S.) (Mead et al., 1999; Scallan et al., 2011). It
has one of the highest mortality rates of all foodborne pathogens (Scallan et al., 2011).
Currently the U.S. Food and Drug Administration and the U.S. Department of
Agriculture have a ―zero tolerance‖ policy for L. monocytogenes in RTE foods (Gilbert,
1996). Despite the efforts made by government agencies and the food industry around
the world to control this pathogen in food processing and retail facilities (Tompkin, 2002),
large outbreaks of listeriosis still occurred in multiple countries due to consumption of
contaminated RTE foods, such as RTE meats (Gilmour et al., 2010;
http://www.cdc.gov/mmwr/preview/mmwrhtml/mm6013a2. htm), cheeses (Fretz et al.,
2010; Jackson et al., 2011) and cantaloupes (http://www.cdc.gov/listeria/outbreaks/
cantaloupes-jensen-farms/101211/).
Specific strains of L. monocytogenes are known to persist for months to years in
food plants manufacturing meat and poultry products, dairy foods and seafood (Azadian
et al., 1989; Lawrence and Gilmour, 1995; Nesbakken et al., 1996; Boerlin et al., 1997;
Loncarevic et al., 1998; Miettinen et al., 1999; Tompkin, 2002). L. monocytogenes
strains in epidemic clone II (ECII) (Eifert et al., 2005), ECIII (Olsen et al., 2005) and
ECV (Knabel et al., submitted) were reported to be persistent in food processing plants
producing RTE meats and poultry products. These persistent strains may cause most
contamination of finished products in processing facilities (Norton et al., 2001; Kabuki et
al., 2004). Many possible reasons have been proposed to explain the persistence of
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specific strains in food processing plants, such as reintroduction of the same strain in the
same plant, physiological adaptation to starvation, resistance to sanitizers, and interaction
between persistent strains of L. monocytogenes and other microorganisms to form a
stable ecosystem (Orsi et al., 2008; Renier et al., 2010; Williams, 2011). Verghese et al.
(2011) recently showed that specific persistence of different strains in different food
plants might be due to rapid niche-specific adaptation driven by repeated cycles of
recombination between comK prophages of different strains. Many authors have also
speculated that persistence of L. monocytogenes might be due to their ability to form
biofilms (Møretrø and langsrud, 2004; Van Houdt and Michiels, 2010; Verghese et al.,
2011). Conditioning films may enhance the adherence of microorganisms to surfaces, act
as important nutrient sources and thus promote biofilm formation (Bowden and Li, 1997).
A long-term-survival (LTS) phase was recently reported in L. monocytogenes
(Wen et al., 2009). L. monocytogenes has been found to persist for a long period of time
during the LTS phase, where cells become coccoid-shaped and more resistant to heat and
high pressure compared to stationary-phase cells (Wen et al., 2009). It has been
speculated that persistence of L. monocytogenes in food processing plants might be due to
transition to and maintenance of this resistant LTS state in hard-to-clean harborage sites
(Wen et al., 2009).
Currently there is a lack of knowledge on the phenotypic mechanism(s)
responsible for the persistence of L. monocytogenes. Thus the purpose of the study was
to investigate the effects of strain, type of food-conditioning film (FCF) and their
interaction on cell density on FCFs, biofilm formation and cocci formation by L.
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monocytogenes, which may play important roles in the persistence of L. monocytogenes
in food processing plants.
5.3 MATERIALS AND METHODS
Preparation of FCFs. For all the seven strains, five foods were used to make
FCFs to study cell densities, biofilm formation and cocci formation. These five foods
included soft cheese (Brie; Président, New York, NY), hot dog (containing salt,
potassium lactate, sodium phosphate, sodium diacetate, sodium erythorbate and sodium
nitrite) (Nittany Lion king franks; Kessler‘s, Lemoyne, PA), turkey (containing salt,
sodium phosphate and cottonseed oil) (fully cooked turkey breast; Wegmans Food
Markets, Rochester, NY), ham (containing salt, sodium phosphate and sodium nitrite)
(Black Label; Hormel Foods Sales, Austin, MN) and chicken (containing salt, potassium
lactate, sodium phosphate and sodium diacetate) (Oven Stuffer chicken breast with rib
meat; Perdue, Salisbury, MD). Sterile water containing no food served as the negative
control for foods. To eliminate confounding background flora, all foods were steamed
for 60 min and then cooled down in ice slurry for 10 min until they reached 22°C. Ten
grams of each food was then sampled aseptically from the center of each product and
blended with 40 g of sterile water using an Osterizer blender (Oster, Shelton, CT). Sixty
microliters of the homogeneous food slurry of each food or sterile water (negative control)
was then transferred into each compartment on an eight-compartment CultureSlide
(Falcon, Becton Dickinson, Franklin Lakes, NJ). Prior to inoculation, food slurries or
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sterile water (negative control) in compartments were air dried at 35°C for 3 h to form
FCFs.
In addition, to study cell densities of the strain J1703 at 48 h on other brands of
turkey deli meat, the Northewestern brand (Oven roasted turkey breast; Jennie-O Turkey
Store Sales, Willmar, MN), Dilusso brand (skinless turkey breast; Melting Pot Foods,
Austin, MN), Prima Della brand (Prima Della, Bentonville, AR) and Market Street brand
(Market Street, Sunbury, PA) turkey deli samples were used to make FCFs for the
inoculation of J1703 only. (This part was conducted by Rob Walker, an undergraduate
student I supervised).
Preparation of bacterial cultures. Seven strains of L. monocytogenes were used
in this study, including two strains lacking the comK prophage [a Lineage III strain (W1-
111) and an ECI strain (F2365)] and five strains containing the comK prophage [three
ECII strains (J1703, H7858 and OB020790), an ECIII strain (N3-031) and an ECV strain
(08-5923)] (Table 5.1). Sterile Tryptic Soy Broth with Yeast Extract (TSBYE)
containing no cells served as the negative control for strains. The protocol for
preparation of inoculates was adapted from the report by Kushwaha and Muriana (2009).
Prior to inoculation of FCFs, all strains of L. monocytogenes were incubated in TSBYE at
35°C for 17 h and then diluted for 10-5 fold with sterile TSBYE. Two-hundred
microliters of diluted culture of each strain was then added to each FCF with subsequent
incubation at 30°C. Fig. 5.1 shows the schematic of the CultureSlide assay for assessing
the densities of L. monocytogenes strains on different FCFs.
155
Fluorescence microscopy. Fluorescence microscopy was used to study the
effects of strains of L. monocytogenes, FCF and their interaction on the cell density of L.
monocytogenes on FCFs after incubation in TSBYE. The protocol for fluorescent
microscopy was adapted from the report by Kushwaha and Muriana (2009). After
incubation of different strains on FCFs on CultureSlides for 48 h or 14 d, each
compartment was rinsed using 200 µL of Tris buffer (pH 7.4; 0.05 M) three times, and
then stained using 200 µL of 5,6-carboxy-fluorescein diacetate (5,6-CFDA, Invitrogen,
Carlsbad, CA) solution with incubation at 25°C for 15 min. After fluorescent staining
each compartment was rinsed with Tris Buffer (pH 7.4; 0.05 M) three times. The
chambers of the CultureSlides were removed and the remaining slides were examined
using a BX51 fluorescence microscope (excitation wavelength at ~490 nm; detection
wavelength at ~510 nm) equipped with a DP20 camera (Olympus Optical, Tokyo, Japan).
Pictures of each slide were taken at magnifications of 100× and 400×. Cell density,
biofilm formation and cocci formation were evaluated by visual examimation of
photomicrographs. Cell densities of different strains present on different FCFs were
indicated by numbers ranging from 0 - 5, with 0 indicating absence of cells, 1 indicating
very low, 2 indicating low, 3 indicating moderate, 4 indicating high and 5 indicating very
high amount of cells observed on the slides. The experiment was replicated twice.
Statistical data analysis. Mean density scores were analyzed statistically using
Analysis of Variance (ANOVA) with Minitab software (version 16.0; Minitab, State
College, PA). Pairwise comparisons were made by using Tukey‘s least significance
difference test (α = 0.05).
156
5.4 RESULTS
Cell density at 48 h. ANOVA revealed that type of strain, type of CF and the
interaction between them all significantly affected cell density at 48 h on CultureSlides
(P < 0.001) (Table 5.2). Tukey‘s multiple comparison revealed that strains lacking the
comK prophage (Lineage III and ECI) showed significantly lower cell densities across all
five FCFs, compared to the two strains containing the comK prophage (ECIII and ECV,
both are serotype 1/2a) (P < 0.05). On all four meat and poultry FCFs, the two strains
lacking the comK prophage showed lower cell densities than all strains containing the
prophage (except the ECII strain J1703) (P < 0.05). Interestingly, the ECV strain
produced very high cell densities when growing on all four RTE meat and poultry FCFs
(hot dog, turkey, ham and chicken); however, very low cell density was observed on soft
cheese for this ECV strain. All strains showed medium to very high densities on the
respective types of foods from which they were isolated (Table 5.2), except that J1703
(isolated from turkey deli) showed very low cell density on Wegman‘s Brand turkey deli
(Table 5.2) but very high densities on other brands of turkey deli (including Northwestern,
Market Street, Prima Della and Dilusso brands; data not shown). Meat and poultry FCFs
showed significantly higher cell densities compared to the control without FCF (P <
0.05). Among all the FCFs, chicken produced the highest average cell density across all
the strains (P < 0.05), followed by ham, turkey, hot dog and soft cheese (Table 5.2).
Biofilm formation at 48 h. No biofilms were formed by the ECI strain F2365
and Lineage III strain W1-111, which both lack the comK prophage. In contrast, biofilms
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were formed on multiple meat and poultry FCFs by the ECII strain H7858, ECIII strain
N3-031 and ECV strain 08-5923, which all contain the comK prophage. The ECV strain
08-5923 produced mature biofilms on all four RTE muscle foods (hot dog, turkey, ham
and chicken), and the RTE muscle foods appeared degraded in the presence of biofilms.
The ECV strain formed biofilms containing large numbers of cells embedded in web-like
Extracellular Polymeric Substances (EPS) with empty areas in the network (Fig. 5.2 &
5.3). However, no biofilms were observed on soft cheese for this ECV strain. FCFs were
visible when cell densities were low or moderate; no FCFs were left when heavy biofilms
were formed by some strains. In the absence of FCFs, no biofilms were observed (Fig.
5.3), even though some cells attached to the glass slide and nutrients were available in the
form of TSBYE.
Cocci formation at 2 weeks. Coccoid-shaped cells were formed by all seven
strains on all five FCFs after 2-weeks incubation. Cell densities of all strains on all FCFs
ranged from medium to very high, while all strains showed very low cell density in the
absence of FCFs. Fig. 5.4 shows examples of cocci formed after 2-weeks incubation.
5.5 DISCUSSION
Type of FCF significantly affected cell density at 48 h on CultureSlides in the
present study (P < 0.001) (Table 5.2), which agreed with a previous report that
conditioning films can affect the physical and/or chemical characteristics of the surface,
such as hydrophobicity and surface free energy, which may affect bacterial attachment
(Dickson and Koohmaraie, 1989). Specifically, meat and poultry FCFs significantly
158
enhanced cell density at 48 h, as compared to the control without FCF (P < 0.05) (Table
5.2). Somers and Wong (2004) previously demonstrated that RTE meat residues
facilitated attachment of L. monocytogenes and subsequent formation of biofilms. FCFs
may serve as nutrient sources supporting growth of attached bacteria and subsequent
biofilm formation (Bowden and Li, 1997; Donlan, 2002), which is consistent with the
observation that no FCFs were observed when heavy biofilms were formed (Fig. 5.3). In
contrast, the presence of a soft cheese FCF did not increase cell density at 48 h, compared
to the non-FCF control (Table 5.2). This is consistent with reports that milk proteins,
such as casein and lactoglobulin, inhibited the attachment of L. monocytogenes to
surfaces (Helke et al., 1993; Wong, 1998). Future research is needed to study the
digestion of FCFs during biofilm formation using time-lapse microscopy.
All strains in ECI, ECII and ECIII showed medium to very high cell densities on
the respective foods from which they were isolated (Table 5.2) [except that the ECII
strain J1703 (isolated from turkey deli) showed very low cell density on Wegman‘s
Brand turkey deli (Table 5.2) but very high densities on other brands of turkey deli]. It is
possible that these strains have evolved and subsequently adapted to the types of foods
produced in food processing plants where they reside. The same food(s) constantly being
manufactured in a given plant may present a strong selective pressure on L.
monocytogenes strains, perhaps only the strain that attaches and grows best can be
selected during interspecies competition to eventually dominate the plant environment
(Verghese et al., 2011). This could also well explain why different food processing
plants usually have plant-specific subtypes of L. monocytogenes (Miettinen et al., 2001;
159
Verghese et al., 2011). The present study showed that strain J1703 (isolated from turkey
deli) showed very low cell density on Wegman‘s brand turkey (Table 5.2) but high
densities on the other brands of turkey. It is possible that the difference in cell density is
due to different types of sodium phosphates present in the three turkey products, since
different kinds of phosphates have different antimicrobial properties against gram
positive bacteria like L. monocytogenes (Knabel et al., 1991). Another possibility is that
the presence of cottonseed oil in Wegman‘s brand turkey inhibited cell growth. The high
cell densities of J1703 on Northwestern, Market Street, Prima Della and Dilusso brands
of turkey deli meat are consistent with the fact that turkey deli meat caused more
listeriosis outbreaks than any other RTE meat and poultry products, which is possibly
because the pH of turkey deli is higher than other RTE meat and poultry products (Glass
and Doyle, 1989).
In the present study, strains containing the comK prophage (except J1703) showed
significantly higher densities across all 4 meat and poultry FCFs, compared to those
lacking the prophage (P < 0.05). Strains containing the comK prophage belong to ECII,
ECIII and ECV, and these ECs all have been reported to be persistent in meat and poultry
processing plants (Eifert et al., 2005; Olsen et al., 2005; Knabel et al., submitted).
Persistent strains have been shown to adhere better than nonpersistent ones (Norwood
and Gilmour, 1999; Lundén et al., 2000), and better adherence and subsequent growth
and biofilm formation of L. monocytogenes strains may in turn contribute to their
persistence and transmission to foods (Kushwaha and Muriana, 2009). It is possible that
the comK prophage contains genes essential for attachment and growth on RTE meat and
160
poultry foods, and these genes have been characterized as ―adaptons‖ by Verghese et al.
(2011) since they may allow specific strains to adapt to different foods and subsequently
persist in processing plants producing these foods. In the present study, strains lacking
the comK prophage (i.e., ECI strain J1703 and Lineage III strain W1-111) showed
significantly lower cell densities on meat and poultry FCFs compared to those containing
this prophage, which is consistent with reports that ECI and Lineage III strains appeared
to be adapted to animals, rather than foods or food processing plants (Boerlin and
Piffaretti, 1991; Orsi et al., 2010; Xiangyu Deng, personal communication).
After incubation on multiple meat and poultry FCFs at 48 h, biofilms were only
formed by the ECII strain H7858, ECIII strain N3-031 and ECV strain 08-5923, which all
contain the comK prophage and belong to the persistent ECs (Eifert et al., 2005; Olsen et
al., 2005; Knabel et al., submitted); however, no biofilm was formed by strains F2365
and W1-111, which lack this prophage. These results agreed with previous reports that
persistent strains are better biofilm formers than sporadic strains (Borucki et al., 2003)
and that F2365 (serotype 4b) is less capable of forming biofilms, compared to serotype
1/2a strains (Marsh et al., 2003). Biofilms formed by strains H7858, N3-031 and 08-
5923 may not only help cells colonize nutrient-rich surfaces, but also protect cells against
various stresses such as dehydration and sanitation in food processing plants (Jefferson,
2004). Therefore, formation of biofilms may enhance the growth and survival of specific
strains in food processing plants and contribute to their persistence (Takahashi et al.,
2009). It is possible that the comK prophage contains genes essential for biofilm
formation; however, it is also possible that other genetic variations between the strains
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containing or lacking this prophage caused the observed differences in biofilm formation.
Therefore, further research is needed to test the hypothesis that the comK prophage is
essential for biofilm formation.
In the present study, coccoid-shaped cells were formed by all seven strains on all
five FCFs after long-term incubation (Fig. 5.4). Formation of cocci indicates that cells of
L. monocytogenes are in the LTS phase, where they are more resistant to heat and high
pressure (Wen et al., 2009) and possibly also resistant to environmental stresses such as
dehydration and starvation. It has been suggested that these cocci are dormant, based on
the observation that genes associated with protein synthesis were downregulated in the
LTS phase (Wen et al., 2011). Perhaps high resistance and dormancy of LTS-phase cocci
are physiological adaptations that help L. monocytogenes persist in harborage sites in
food processing plants. Once the LTS-phase cocci are transmitted to foods, the cocci
might quickly ―germinate‖ to rod-shaped cells (Wen et al., 2009) and subsequently grow
to dangerous levels and contaminate large masses of foods.
A model for the persistence of specific strains of L. monocytogenes in food
processing plants has been developed (Fig. 5.5). In this model persistent strains of L.
monocytogenes specifically attach to and grow on the foods they adapt to, then digest
foods to form biofilms, and finally form resistant cocci to achieve long-term persistence
and cause food contamination (Fig. 5.5). This model is consistent with previous reports
that persistent strains demonstrated stronger attachment (Lundén et al., 2000) and biofilm
formation (Borucki et al., 2003) compared with nonpersistent strains, and that coccoid-
162
shaped cells of L. monocytogenes survived for at least 1 month and rapidly germinated
and grew upon inoculation into fresh TSBYE (Wen et al., 2009).
In conclusion, type of FCF, strain and their interaction significantly affected cell
density after 2-d incubation (P < 0.001). Meat and poultry FCFs showed significantly
higher cell densities, as compared to the control without FCF (P < 0.05). All strains
showed medium to very high densities on the respective foods from which they were
isolated, except that the strain J1703 (isolated from turkey) showed very low cell density
on Wegman‘s Brand turkey deli but very high densities on all other four brands of turkey
deli meat. Strains lacking the comK prophage showed lower cell densities than those
containing the prophage on all four meat and poultry FCFs (P < 0.05). Biofilms were
only formed by strains containing the comK prophage. Cocci were formed by all strains
on all FCFs after 2-weeks incubation. The ability of specific strains of L. monocytogenes
to form biofilms on specific FCFs and subsequently control their entry to the LTS phase
may explain why specific strains persist in different food processing plants and cause
contamination of foods manufactured in those plants. Effective strategies should be
developed to control the persistence of L. monocytogenes in food processing plants, such
as regular and deep cleaning of equipment to remove FCFs and biofilms before sanitizing,
or using chlorinated alkaline foaming detergents to completely remove FCFs to prevent
cell attachment and biofilm formation. Also, redesigning of equipment to eliminate
harborage sites may prevent FCFs from building up and thus prevent biofilm and cocci
formation. The results indicate that the comK prophage may contain genes essential for
163
attachment, growth and biofilm formation of L. monocytogenes on meat and poultry
FCFs. Further research is needed to test this hypothesis.
5.6 ACKNOWLEDGEMENTS
This research was funded by a U.S. Department of Agriculture Special Grant on
Milk Safety to the Pennsylvania State University.
Data in this manuscript have been published as part of the publication on Appl.
Environ. Microbiol. 77:3279–3292. Jia Wen studied cell densities of all 7 strains on all 5
food-conditioning films (including the Wegman‘s brand turkey deli), biofilm formation
and cocci formation. Jia Wen and Dr Knabel analyzed the data. Jia Wen wrote this
entire chapter. Valentina Alessandria collaborated with Jia Wen to develop the protocol
of the CultureSlide technique. Rob Walker (an undergraduate student supervised by Jia
Wen) studied the cell densities of the strain J1703 on other brands of turkey deli meat.
164
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Verghese, B., M. Lok, J. Wen, V. Alessandria, Y. Chen, S. Kathariou, and S. Knabel.
2011. comK prophage junction fragments as markers for Listeria monocytogenes
genotypes unique to individual meat and poultry processing plants and a model for
169
rapid niche-specific adaptation, biofilm formation, and persistence. Appl. Environ.
Microbiol. 77:3279–3292.
Wen, J., R. C. Anantheswaran, and S. J. Knabel. 2009. Changes in barotolerance,
thermotolerance and cellular morphology throughout the life cycle of Listeria
monocytogenes. Appl. Environ. Microbiol. 75:1581–1588.
Wen, J., X. Deng, Z. Li, E. G. Dudley, R. C. Anantheswaran, S. J. Knabel, and W.
Zhang. 2011. Transcriptomic response of Listeria monocytogenes during transition to
the long-term-survival phase. Appl. Environ. Microbiol. 77:5966–5972.
Williams, S. K. 2011. Molecular ecology of Listeria monocytogenes and other Listeria
species in small and very small ready-to-eat meat processing plants. J. Food Prot.
74:63–77.
Wong, A. C. 1998. Biofilms in food processing environments. J. Dairy Sci. 81:2765–
2770.
170
Table 5.1. Lineages, epidemic clones (ECs), sources, presence/absence of the comK
prophage and serotypes of the 7 strains analyzed in the present study. a
Strain Lineage/
EC
Food where the
strain was isolated
Presence of the
comK prophage Serotype
Year of
isolation
W1-111 Lineage III Animal No 4c Unknown
F2365 LineageI/
ECI Cheese No 4b 1985
J1703 LineageI/
ECII Turkey deli Yes 4b 2002
H7858 LineageI/
ECII Hot dog Yes 4b 1998
OB020790 LineageI/
ECII Chicken Yes 4b 2002
N3-031 LineageII/
ECIII Turkey deli Yes 1/2a 1988
08-5923 LineageII/
ECV RTE meat Yes 1/2a 2008 a Source of strain information: Verghese et al., 2011.
171
Table 5.2. Effects of strain, type of FCF and their interaction on the cell density of L.
monocytogenes on glass slides after incubation at 30°C for 48 h in TSBYEa. Data in the
table are based on two replications of the experiment.
Strain
FCF Strain
means for
all FCFs d
No FCF
control
Soft
cheese Hot dog Turkey
e Ham Chicken
No L. mono. control b 0 0 0 0 0 0 0 A
Lineage III (W1-111) 1.0 1.0 1.0 1.5 2.5 1.0 1.4 B
ECI (F2365) 1.0 3.0 1.5 1.5 2.0 2.0 2.0 BC
ECII (J1703) 1.0 1.5 2.5 1.0 3.0 4.0 2.4 BC
ECII (H7858) 1.0 1.0 2.5 4.0 3.5 3.5 2.9 CD
ECII (OB020790) 1.0 3.0 3.0 3.0 2.5 4.0 3.1 CDE
ECIII (N3-031) 1.0 3.5 3.5 4.5 4.0 3.5 3.8 DE
ECV (08-5923) 1.0 1.0 5.0 5.0 4.5 5.0 4.1 E
FCF means for all strains c 1.0 a 2.0 ab 2.7 bc 2.9 bc 3.1 bc 3.3 c
a. The numbers in the table indicate cell densities of different strains present on different
FCFs, with 0 indicating absence of cells, 1 indicating very low, 2 indicating low, 3
indicating moderate, 4 indicating high and 5 indicating very high amount of cells
observed on the slides. Fractional numbers are calculated based on the results of two
replications.
b. L. mono., L. monocytogenes.
c. Means of one FCF for all strains are calculated by averaging all the values in the
column (except that for the no L. monocytogenes control) corresponding to that FCF;
means in this row that do not share a lowercase letter are significantly different (P < 0.05).
172 d. Means of one strain for all FCFs are calculated by averaging all the values in the row
(except that for the no FCF control) corresponding to that strain; means in the column
that do not share an uppercase letter are significantly different (P < 0.05).
e. Wegman‘s brand turkey deli was used to make this FCF.
173
Fig. 5.1. Schematic of the eight-compartment CultureSlide experimental design for
assessing the cell densities of L. monocytogenes strains on different FCFs. Prior to
inoculation each CultureSlide was coated with a FCF (soft cheese, all meat hot dog,
turkey, chicken or ham) or just sterile water (negative control for FCF). After the FCFs
were dried, each compartment was inoculated with a specific strain of L. monocytogenes,
or in the case of the upper left compartment, just broth (no L. monocytogenes - negative
control for strain). CultureSlides were then incubated at 30°C for 48 h or 14 d to allow
cells to adhere and grow prior to fluorescent staining and microscopic examination.
174
175
Fig. 5.2. Fluorescence photomicrographs showing different cell densities of L.
monocytogenes on FCFs. The number in the upper right corner of each picture indicates
the cell density score, with 0 indicating absence of cells, 1 indicating very low, 2
indicating low (hot dog with strain J1703), 3 indicating moderate (hot dog with strain
OB020790), 4 indicating high (chicken with strain J1703) and 5 indicating very high
amount of cells observed on the slides (turkey with strain 08-5923). Lm, L.
monocytogenes. Bar, 20 µm.
176
5 4
3 2
No FCF with Lm control 1 Turkey FCF with no Lm control 0
177
Fig. 5.3. Fluorescence photomicrographs showing the degradation of FCFs and biofilm
formation by the ECV strain 08-5923. Red arrows indicate undegraded FCFs and yellow
ones indicate biofilm formation. Bar, 40 µm.
178
Magnification 100 × Magnification 400 ×
Ham
Hot dog
Chicken
179
Fig. 5.4. Examples of cocci formed after 2-weeks incubation at 30°C. Bar, 20 µm.
180
181
Fig. 5.5. Proposed model for attachment, biofilm formation and cocci formation leading
to persistence of L. monocytogenes in food processing plants.
182
183
CHAPTER SIX
SUMMARY AND QUESTIONS FOR FUTURE RESEARCH
6.1 SUMMARY
Our earlier research showed that Listeria monocytogenes changes its cellular
morphology from bacilli to cocci and increases its resistance to heat and high pressure
during the transition to the long-term-survival (LTS) phase. In the present study, the
transition of L. monocytogenes to the LTS phase was investigated on both a population
level and a gene expression level. On the population level, the transition to the LTS
phase was significantly affected by both initial cell density and pH (P < 0.001).
Stationary-phase cells at initial densities of 106 - 108 CFU/ml showed growth, while cells
at 109 - 1010 CFU/ml showed death during this transition. Population growth/death
kinetics appeared to be consistent with the Logistic Equation. After long-term incubation,
the mean cell density was 4.3 × 108 CFU/ml and there was no significant difference
between any of the initial cell density and pH treatment combinations (P > 0.05). To
understand the transition to the LTS phase on a gene expression level, the transcriptomic
profiles of L. monocytogenes at different growth stages were compared. Genes related to
cell envelope structure, energy metabolism and transport were upregulated in the LTS
phase. The upregulation of compatible-solute transporters may lead to accumulation of
184
these solutes, lowering intracellular water activity and thus increasing bacterial stress
resistance in the LTS phase. The downregulation of genes associated with protein
synthesis may indicate dormancy in the LTS phase. The mechanisms of the transition to
the LTS phase may be fine-tuned by natural selection during evolution. Understanding
the transition to the LTS phase in L. monocytogenes may shed new insights to the LTS
strategies utilized by other bacteria.
The LTS phase may help L. monocytogenes persist over a long period of time
within harborage sites in food plants and subsequently transmit to food products. Thus,
we investigated the effects of different L. monocytogenes strains and different types of
food-conditioning films (FCFs) on cell density, biofilm formation and LTS-phase cocci
formation, which may help explain the persistence of specific strains in food processing
plants. Type of FCF, strain and their interaction significantly affected cell density after
2-d incubation (P < 0.001). Meat and poultry FCFs showed significantly higher cell
densities, as compared to the control without FCF (P < 0.05). All strains showed
medium to very high cell densities on the respective foods from which they were isolated.
Biofilms were only formed by strains containing the comK prophage. LTS-phase cocci
were formed by all strains on all FCFs after 2-weeks incubation. The ability of specific
strains of L. monocytogenes to attach to, grow on and form biofilms on specific FCFs and
subsequently enter the LTS phase may explain why specific strains persist in different
food processing plants.
185
6.2 QUESTIONS FOR FUTURE RESEARCH
1. How do cells of L. monocytogenes sense the population density?
The results in Chapter 3 indicate that L. monocytogenes may be able to sense the
population density during the transition to the LTS phase. Perhaps cells can sense their
density via quorum sensing. The Agr system is the only quorum sensing system that has
been reported in L. monocytogenes; however, deletion of of agrD did not affect the
transition to the LTS phase. Maybe L. monocytogenes senses its population density via
other quorum sensing system(s) and further research is needed to identify these quorum
sensing system(s). We may get clues by conducting a gene expression study comparing
the expression profiles of cells dying at high density (e.g., 1010 CFU/ml) and those
growing at low density (e.g., 106 CFU/ml). Maybe genes involved in the sensing of
population density are among upregulated and/or downregulated genes.
2. How do cells of L. monocytogenes sense the carrying capacity (K)?
The results in Chapter 3 indicate that L. monocytogenes may be able to sense K
(which may equate to the stable density at the LTS phase) and finally reach K regardless
of initial cell density. The question is how do cells sense K? Maybe cells can sense the
value of K in a given environment based on levels of some critical nutrients. Further
research is needed to answer this important and interesting question.
186
3. How do cells of L. monocytogenes regulate the population density to reach K?
During the transition of L. monocytogenes to the LTS phase, cells regulated the
population density to reach the stable density at the LTS phase (which may equate to K).
Maybe cells can activate growth-related genes and inhibit the expression of death-related
genes when the population density is below K, which may lead to population growth; on
the other hand, cells may deactivate growth-related genes and activate death-related
genes when the population density is above K, which may cause population death.
Maybe growth- and death-related genes are under the control of quorum sensing. We
may get clues by comparing the expression profiles of cells dying at high density and
those growing at low density. Maybe genes involved in the regulation of growth/death
are among up- or down-regulated genes.
4. Is K affected by the nutrient level of the broth?
Results of Chapter 3 show that cells of all the treatment combinations maintained
at ~4 × 108 CFU/ml which may equate to K of the spent TSBYE. It would be interesting
to determine whether the level of K is affected by the nutrient level of the broth. The
effect of nutrient level on K could be studied by comparing the K values supported by
broths at different nutrient concentrations (e.g., we could compare K values in water, 2/3
water + 1/3 TSBYE, 1/3 water + 2/3 TSBYE, and TSBYE).
187
5. Do LTS-phase cells of L. monocytogenes accumulate compatible solutes?
The expression profile at LTS phase revealed upregulation of genes encoding
transporters for compatible solutes such as glycine betaine, glycerol and trehalose.
Accumulation of compatible solutes has been shown to protect cells against various
stresses including heat, cold, desiccation and oxidation. Therefore it is interesting to
study whether LTS-phase cells of L. monocytogenes accumulate any of those compatible
solutes. If cells do accumulate compatible solutes, then it is worthwhile to study the
correlation between the compatible solute concentrations and the high resistances to heat
and high pressure observed at the LTS phase.
6. Are LTS-phase cells of L. monocytogenes dormant?
LTS-phase cells of L. monocytogenes are analogous to spores in terms of
longevity, pressure and heat resistance and their coccoid shape. Since spores are dormant,
it is possible that LTS-phase cells are also dormant. Dormancy levels of cells could be
evaluated by measuring cell respiration, RNA/protein synthesis and ATP level (see
Appendix C for preliminary data of ATP levels at different phases).
7. Are LTS-phase cells of L. monocytogenes at a low intracellular water activity (Aw)?
Spores are known to be resistant to stresses due to lowered Aw, which may also
be the case in LTS-phase cells. The microarray study indicates L. monocytogenes may
accumulate compatible solutes at the LTS phase and thus lower its cellular Aw. If the
Aw of LTS-phase cells is low, then physiological levels of cells may also be low, and
188
cellular proteins at low Aw may be stabilized under various stresses. Thus low Aw may
lead to dormancy and high resistance to stresses.
8. Can LTS-phase cells survive sanitizer treatments currently used in the food industry?
L. monocytogenes at the LTS phase was shown to be more resistant to heat and
high pressure, compared to cells at log, stationary and death phases. Therefore, it is
possible that LTS-phase cells are also more resistant to sanitizers than cells at earlier
growth phases. However, current sanitizer efficacy tests are usually based on the
inactivation of overnight bacterial cultures rather than LTS-phase cells, and thus we may
have overestimated sanitizer efficacies. From a food safety standpoint, it is critical to
study the resistance of LTS-phase cells to various sanitizers currently used in the food
industry (see Appendix D for preliminary data of resistance of LTS-phase cells to
multiple sanitizers).
9. What is the proteomic profile of LTS-phase cells of L. monocytogenes?
Our microarray study revealed the transcriptional profiles at various phases.
However, transcription and translation levels do not always agree. For some genes of
Saccharomyces cerevisiae, a 10–25 fold increase in protein levels was observed when
carbon source was altered from galactose to ethanol, while their mRNA levels remained
unchanged (Griffen et al., 2002). Considering such discrepancies, a proteomic study
could be conducted for a better understanding of the transition of L. monocytogenes to the
LTS phase on the translation level.
189
10. Is the intracellular pH of death-phase cells lower than stationary-phase cells?
Bacterial death during death phase may be due to programmed cell death (PCD).
One critical characteristic of PCD is intracellular acidification, which could be
counteracted by exporting protons at the cost of ATP hydrolysis by ATP synthase. The
microarray study showed downregulation of atpI at death phase, which encodes a protein
component of ATP synthase. Such downregulation could result in decreased ATP
synthase activity and thus insufficient proton export, which may lead to aggravated
acidification in the cytoplasm and subsequent PCD. Thus further investigation is needed
to determine whether the intracellular pH of death-phase cells is in fact lower than
stationary-phase cells, and if this is responsible for the cell death.
11. Can the colonization of L. monocytogenes on food-conditioning films (FCFs) be
prevented by using the chlorinated alkaline foaming detergent?
The chlorinated alkaline foaming detergent may completely remove food residues
on equipment surfaces, and thus prevent L. monocytogenes from colonizing food
processing facilities (harboring food residues in hard-to-clean parts). The efficacy of this
detergent can be tested by applying this detergent onto CultureSlides coated with various
FCFs, and then observing the attachment, growth and biofilm formation by L.
monocytogenes.
190
APPENDIX A
PRELIMINARY DATA RELATED TO EFFECTS OF POPULATION DENSITY,
PH AND NUTRIENTS ON THE TRANSITION TO DEATH PHASE
Purpose: To study effects of population density, pH and nutrients on the
transition of Listeria monocytogenes from stationary to death phase.
Ho: (a) Decrease of cell density has no effect on the transition of the stationary-
phase culture of L. monocytogenes to death phase. (b) Increase of pH has no effect on the
transition of the stationary-phase culture of L. monocytogenes to death phase. (c)
Addition of nutrients has no effect on the transition of the stationary-phase cultures of L.
monocytogenes to the death phase.
Methods: Effects of population density, pH and nutrients on the transition from
stationary to death phase were examined individually. For all three experiments, cells of
L. monocytogenes ATCC 19115 were incubated in TSBYE at 35°C for 16 - 17 h to reach
stationary phase before applying treatments. To study the effect of cell density, the
culture was diluted to 3.4 × 107 CFU/ml by adding 5 ml of the stationary-phase culture
into 45 ml of filter-sterilized stationary-phase culture; in control 5 ml of the stationary-
phase culture was added into 45 ml of the same stationary-phase culture (not filter-
sterilized), and the cell concentration was 5.7 × 108 CFU/ml. To study the effect of pH,
pH of the stationary-phase culture was adjusted from 5.36 (the natural pH of stationary-
phase cultures) to 6.85 (the natural pH of fresh TSBYE) by adding NaOH; in control the
191
pH remained at 5.36. To study the effect of nutrients, 5 ml of 10× TSBYE (without salts)
was added into 45 ml of culture; in control 5 ml of sterile water was added into 45 ml of
culture. Then cell concentrations were monitored for up to 3 d by plate counting on
TSAYE at 35°C.
Results: After population density of the stationary-phase culture decreased from
5.7 × 108 to 3.4 × 107 CFU/ml, there was no death phase during incubation; in contrast,
the control showed rapid death (Fig. A1). After pH was increased from 5.36 to 6.85, the
culture still showed death during incubation, but the death rate was significantly lower
than that of the control at pH 5.36 (P < 0.05) (Fig. A2). The addition of nutrients to the
stationary-phase culture did not prevent the population from entering death phase (Fig.
A3).
Conclusion: The death phase is triggered by high cell density at the stationary
phase. The death phase is not triggered by the low pH at stationary phase, but pH can
affect the death rate. The death phase is not triggered by lack of nutrients in the
stationary-phase culture.
192
Fig. A1. Effect of population density on the pattern of the death phase. Cells of L.
monocytogenes ATCC 19115 were incubated in TSBYE at 35°C for 17 h to reach the
stationary phase. The population density of the culture was adjusted to 3.4×107 CFU/ml
by adding 5 ml of the culture at stationary phase into 45 ml of filter-sterilized culture at
stationary phase. In control 5 ml of the culture at stationary phase was added into 45 ml
of culture at stationary phase and the cell concentration was 5.7×108 CFU/ml. Data
points and error bars represent means and standard deviations based on 3 replications of
the experiment.
193
1.00E+00
1.00E+01
1.00E+02
1.00E+03
1.00E+04
1.00E+05
1.00E+06
1.00E+07
1.00E+08
1.00E+09
0 20 40 60 80
Growth time (h) at 35°C
CFU
/ml
Initial concentration at 3.4E7 CFU/ml
Initial concentration at 5.7E8 CFU/ml
194
Fig. A2. Effect of pH on the pattern of the death phase. Cells of L. monocytogenes
ATCC 19115 were incubated in 50 ml of TSBYE at 35°C for 17 h to reach the stationary
phase. The pH of the culture at stationary phase was adjusted to 6.85 (the natural pH in
fresh TSBYE) by adding NaOH. Data points and error bars represent means and
standard deviations based on 3 replications of the experiment.
195
1.00E+00
1.00E+01
1.00E+02
1.00E+03
1.00E+04
1.00E+05
1.00E+06
1.00E+07
1.00E+08
1.00E+09
1.00E+10
0 20 40 60 80
Growth time (h) at 35°C
CFU
/ml
At pH of 6.85
At pH of 5.36
196
Fig. A3. Effect of addition of nutrients on the pattern of the death phase. Cells of L.
monocytogenes ATCC 19115 were incubated in TSBYE at 35°C for 17 h to reach the
stationary phase. 5 ml of 10× TSBYE (without salts) was added into 45 ml of stationary-
phase culture. In control 5 ml of sterile water was added into 45 ml of stationary-phase
culture. Data points and error bars represent means and standard deviations based on 3
replications of the experiment.
197
1.00E+00
1.00E+01
1.00E+02
1.00E+03
1.00E+04
1.00E+05
1.00E+06
1.00E+07
1.00E+08
1.00E+09
1.00E+10
0 20 40 60 80
Growth time (h) at 35°C
CFU
/ml
Add nutrients
Add water
198
APPENDIX B
RESPONSE OF LONG-TERM-SURVIVAL-PHASE CULTURES OF LISTERIA
MONOCYTOGENES TO A DECREASE IN POPULATION DENSITY
Purpose: To study the response of the long-term-survival-phase (LTS-phase)
culture of L. monocytogenes to the decrease of population density in spent TSBYE.
Ho: Decrease of the density of LTS-phase culture from ~108 to ~106
CFU/ml in
spent LTS-phase TSBYE will not cause any changes in population density during
incubation.
Methods: To study whether LTS-phase cells of L. monocytogenes could respond
to density change in spent LTS-phase culture, cells of L. monocytogenes ATCC 19115
were grown in TSBYE at 35°C for 26 d to yield the LTS-phase culture at ~108 CFU/ml.
Cell density was then adjusted to ~106 CFU/ml by resuspending LTS-phase cells in filter-
sterilized LTS-phase culture. The resulting culture was incubated at 35°C with cell
density monitored by plating on TSAYE. The experiment was replicated three times.
Results: After the density of LTS-phase culture was decreased from ~108 to ~106
CFU/ml in spent LTS-phase broth, the population increased to and then maintained at the
original cell density of ~108 CFU/ml after 72-h incubation (Fig. B1).
Conclusion: The culture of L. monocytogenes at the LTS phase can respond to
the decrease of population density by growing to reach the original density (~108
CFU/ml).
199
Fig. B1. LTS-phase cells of L. monocytogenes increased in cell density after a density
downshift in spent LTS-phase culture. Cells of L. monocytogenes ATCC 19115 were
grown in TSBYE at 35°C for 26 d to yield the LTS-phase culture with a cell density of
~108 CFU/ml. Cell density was then adjusted to ~106
CFU/ml by diluting LTS-phase
cells using filter-sterilized LTS-phase culture. The resulting culture was incubated at
35°C with cell density monitored by plating on TSAYE. Data points and error bars
represent means and standard deviations based on three replications of the experiment.
200
201
APPENDIX C
INTRACELLULAR ATP LEVELS AT LOG, STATIONARY, DEATH AND
LONG-TERM-SURVIVAL PHASES
Purpose: To compare the intracellular ATP level of cells of L. monocytogenes at
the long-term-survival (LTS) phase with other phases.
Ho: There is no difference between the intracellular ATP level at the LTS phase
and that at log, stationary or death phase.
Methods: Cells of L. monocytogenes F2365 were grown in TSBYE to reach 13-h
log, 17-h stationary, 24-h death and 168-h, 336-h and 718-h LTS phases. Cells were
lysed and cellular contents were analyzed for ATP contents using BacTiter-Glo reagents.
This method is based on the luciferase reaction, in which ATP molecules from the
samples were converted into 560 nm green light when luciferase catalyzes luciferin into
oxyluciferin. The light intensity from each sample was measured by a luminometer, and
the intensity scores were converted into ATP concentration values based on a standard
curve showing the correlation between light intensity and ATP concentration.
Extracellular ATP in the medium would affect the results; therefore, the ATP values of
filter-sterilized cultures were subtracted from the whole-culture ATP values to calculate
intracellular ATP values. The intracellular ATP values (mol/ml) were divided by cell
density (CFU/ml) to calculate ATP concentration per cell (mol/CFU).
202
Results: Intracellular ATP per viable cell at each growth phase was shown in Fig.
C1. Tukey‘s comparison of means showed that there was no significant difference
between the ATP levels at log, stationary and death phases (P > 0.05), and that there was
no significant difference between the ATP concentrations within the LTS phase (P > 0.05)
(Fig. C1). However, ATP levels at log, stationary and death phases were significantly
higher than 168-h and 718-h LTS phase (P < 0.05) (Fig. C1).
Conclusion: The intracellular ATP level at the LTS phase is significantly lower
than those at log, stationary and death phases.
203
Fig. C1. Intracellular ATP per viable cell at log, stationary, death and LTS phases.
204
1.0E-19
1.0E-18
1.0E-17
1.0E-16
0 200 400 600 800
Growth time (h) in TSBYE at 35°C
Intr
ac
ellu
lar
AT
P p
er
via
ble
cell
(mo
l/C
FU
)
205
APPENDIX D
SANITIZER RESISTANCE OF LISTERIA MONOCYTOGENES AT DIFFERENT
GROWTH TIMES IN THE LONG-TERM-SURVIVAL PHASE
Purpose: To compare the sanitizer resistances of 48-h-old and 2-weeks-old
cultures of Listeria monocytogenes at the long-term-survival (LTS) phase.
Ho: There is no difference between the resistances of cultures of L.
monocytogenes at 48 h and 2 weeks to various sanitizers.
Methods: Cultures of L. monocytogenes F2365 were grown in TSBYE at 35°C
for 48 h and 2 weeks. Cell densities before sanitizer treatments were adjusted to 108–109
CFU/ml. One ml of each culture was added into 99 ml of the sanitizer solution of Ster
Bac (quaternary ammonium compound) at 50 ppm, XY-12 (sodium hypochlorite) at 50
ppm, or Vortexx (peracetic acid) at 25 ppm. After sanitizer treatments for 15 s and 30 s,
cells were enumerated by plating on TSAYE with subsequent incubation at 35°C for 48 h.
The experiment was replicated once.
Results: Compared to 48-h-old culture, 2-weeks-old culture seemed more
resistant to Ster Bac at 50 ppm (Fig. D1), less resistant to XY-12 at 50 ppm (Fig. D2),
and equally resistant to Vortexx at 25 ppm (Fig. D3). The cell concentrations of both
cultures after the Vortexx treatment for 30 s were below the detection limit (10 CFU/ml)
(Fig. D3).
206
Fig. D1. Inactivation of cultures of L. monocytogenes at 48 h and 2 weeks by 50 ppm Ster
Bac solution.
207
1.0E+00
1.0E+01
1.0E+02
1.0E+03
1.0E+04
1.0E+05
1.0E+06
0 5 10 15 20 25 30 35
Time (s) in 50 ppm Ster Bac
CF
U/m
l s
an
itiz
er
so
luti
on
48-h-old cells
2-week-old cells
208
Fig. D2. Inactivation of cultures of L. monocytogenes at 48 h and 2 weeks by 50 ppm
XY-12 solution.
209
1.0E+00
1.0E+01
1.0E+02
1.0E+03
1.0E+04
1.0E+05
1.0E+06
0 5 10 15 20 25 30 35
Time (s) at 50 ppm XY-12
CF
U/m
l sa
nit
izer
so
luti
on
48-h-old cells
2-week-old cells
210
Fig. D3. Inactivation of cultures of L. monocytogenes at 48 h and 2 weeks by 25 ppm
Vortexx solution.
211
1.0E+00
1.0E+01
1.0E+02
1.0E+03
1.0E+04
1.0E+05
1.0E+06
0 5 10 15 20 25 30 35
Time(s) at 25 ppm Vortexx
CF
U/m
l sa
nit
izer
so
luti
on
48-h-old cells
2-week-old cells
JIA WEN (814) 321-2044 [email protected] Food microbiologist with 5 years of experience in food safety & food processing. HACCP & ServSafe Manager certified. Hands-on skills on various food microbiology studies, thermal/ high pressure processing & product development. Proficiency in food microbiology, dairy science, food engineering, molecular biology, computer & statistics. Independent critical thinker as well as effective team player. Multitasking & quick learning capability with attention to detail.
EXPERIENCE
Research assistant, the Pennsylvania State University, PA Thesis research [2006-2012]
Developed a model system to study biofilm formation of Listeria monocytogenes. - Investigated biofilm formation of different strains on cheese, turkey, chicken, hot dog & ham Investigated inactivation mechanisms of L. monocytogenes by high pressure processing. Discovered the ―long-term-survival phase‖ in L. monocytogenes & proved that cells at this phase
were the most resistant to heat & pressure in bacterial life cycle. - Challenged the current paradigm of microbial growth curve which lacks the long-term-survival phase - Revealed potential inadequacy of current food processes on microbial inactivation - Unravelled mechanisms of long-term survival at both physiological & genetic levels
Industrial project [2009-2011]
Evaluated the efficacy of pasteurization on inactivation of pathogens & spoilage microbes in liquid sweeteners.
- Collaborated with a food microbiologist, three engineers & industry representatives in experimental design
- Independently conducted pasteurization experiments in a batch system - Collaborated with a food engineer in assembling & operating a continuous pasteurizer - Applied GMP & HACCP to the continuous pasteurization treatment simulating the industrial process - Validated results of batch pasteurization by continuous pasteurization
Teaching assistant, the Pennsylvania State University, PA
Supervised 160+ students for Food Facts and Fads (FD SC 105). [2011]
Supervised 50+ students & gave lectures for Applied Food Microbiology Lab. [2007 & 2010] Member of product development team, the Pennsylvania State University, PA [2010-present]
Responsible for product safety & shelf life testing during the development of "Par-Fections" (a portable parfait snack) in the 2011 Product Development Competition sponsored by Institute of Food Technologists (IFT).
Member of product development team, the Pennsylvania State University, PA [2007]
Formulated & manufactured a protein-fortified yogurt in the course Science & Technology of Dairy Foods.
EDUCATION
Ph.D. in Food Science, the Pennsylvania State University, PA. [05/2012]
Thesis: Factors affecting biofilm formation and transition of Listeria monocytogenes into the long-term-survival phase and their possible roles in persistence in food-processing plants. M.S. in Food Science, the Pennsylvania State University, PA. [12/2008]
Thesis: Changes in barotolerance, thermotolerance & morphology of L. monocytogenes throughout life cycle. B.S. in Biological Sciences, China Agricultural University, China. [07/2006]
