UNDERSTANDING THE ROLES OF TRANSCRIPTIONAL REGULATORS FOR THE DEVELOPMENT OF NATURAL AND NOVEL INHIBITORS OF
LISTERIA MONOCYTOGENES.
A Dissertation Presented to the Faculty of the Graduate School
of Cornell University In Partial Fulfillment of the Requirements for the Degree of
Doctor of Philosophy
by Mary Elizabeth Palmer
May 2010

© 2010 Mary Elizabeth Palmer

UNDERSTANDING THE ROLE OF TRANSCRIPTIONAL REGULATORS FOR THE DEVELOPMENT OF NATURAL AND NOVEL INHIBITORS OF LISTERIA
MONOCYTOGENES
Mary Elizabeth Palmer, Ph. D. Cornell University 2010
Listeria monocytogenes is pathogenic intracellular foodborne bacterium that causes listeriosis, a rare, but serious disease in humans. Despite the use of antibiotics, the mortality rate remains at 20-30%. The ability of L. monocytogenes to survive transmission through food systems and to cause disease is attributed in part to the regulatory networks that control environmental stress adaptation and virulence functions. Therefore, a comprehensive understanding of the factors that are important to virulence, stress response and antimicrobial resistance will help us better develop novel inhibitors for therapeutics. With the information garnered from select regulators, it is possible to identify new drug targets and new drugs for treatment alternatives.
The purpose of this research in L. monocytogenes is to (i) determine the contributions of select transcriptional regulators to virulence functions, (ii) assess the contributions of two regulators to antimicrobial peptides response, and (iii) identify novel small molecule inhibitors of the regulator σB. In summary, we found that of central transcriptional regulators, σB, PrfA, HrcA, CtsR, σL, σH, and σC, σB contributes to invasion, PrfA contributes to cell-to-cell growth and CtsR, in addition to PrfA and σB, contributes to virulence in a guinea pig model of listeriosis. We determined that σB and σL are important to controlling expression of genes needed for resistance to the select antimicrobial peptides SdpC and Nisin, thus indicating that σB has a role in

virulence and stress survival as well as antimicrobial resistance. Therefore, we focused on σB as a promising novel drug target for the treatment of listeriosis. From a library of 57,000 small molecules, we identified a novel compound, sigmastatin, which inhibits the activity of σB and its regulon, inhibits Bacillus subtilis σB and severely impedes L. monocytogenes enterocyte invasion. With a solid understanding of the contributions and roles of various regulators in L. monocytogenes, novel inhibitors can be used to target those regulators, like σB, which are associated with survival, pathogenesis, and resistance. These novel agents can be used to treat listeriosis, extrapolated for use against other similar clinically relevant diseases, or used to gain insight into physiology of pathogenic bacteria and related gene regulation.

BIOGRAPHICAL SKETCH Mary Elizabeth (Liz) Palmer was born in Elmira, NY in December of 1981 to Mary and William R. Palmer. She attended the University of Virginia, Charlottesville, VA in 2000. There she discovered her passion for benchwork and scientific research while working for Dr. Tyvin Rich on circadian modulation of tumor produced growth factors in the radiation oncology department of the University of Virginia Hospital. After graduation in 2004 with a Bachelor’s of Science in Biological Sciences, she worked for a year at the Center for Infectious Diseases and Molecular Medicine at the State University of New York at Stony Brook. Here she dabbled in molecular microbiology, studying pathogens such as Yesinia pseudotuberculosis and Helicobacter pylori. With a love of pathogens and an interest in studying food safety she headed back to upstate NY (and its bitter winters) to work with Dr. Kathryn J. Boor in the Food Safety Lab in Food Science Department at Cornell University. To the delight of her 98 year old grandmother, she is a third generation Cornellian.
iii

This is dedicated to my family, Dev and Kara. iv

ACKNOWLEDGMENTS
I want to thank my major advisor Kathryn Boor for her support as well as all my committee members Martin Wiedmann, Marci Scidmore and Helene Marquis for their ideas, suggestions and contributions to not only my research but also to shaping my scientific mind. I want to thank Yvonne Chan, Sarita Raengpradub, Haley Oliver, Esther Fortes, Teresa Bergholz, Vania Ferreira, Yesim Soyer, Soraya Chaturongakul, Renato Orsi, Henk DenBakker, Sana Mujahid, Daina Ringus, Reid Ivy, Matt Ranieri Andrea Moreno Switt, Matt Staciewicz, Lorraine Rodriguez, Karin Hoelzer and Kitiya Vongkamjan for being wonderful labmates and friends. Your support and kindness will always be remembered. Thank you to Brad Njaa and Rachel Peters for their expertise in pathology. Thank you to the Den mothers Barbara, Sherry, Shelly and Maureen. Thank you to the FSL, I will miss you all so much. Thank you most of all to Dev, Mom, Dad, Sarah, Lexie, Warren, Grandma and little Kara.
The work described was supported National Institutes of Health Award No. 5R01AI052151-07 (to K. J. B.). The funding agency had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. This work has also been funded in part with Federal funds from the National Cancer Institute's Initiative for Chemical Genetics, National Institutes of Health, under Contract No. N01-CO-12400 and has been performed with the assistance of the Chemical Biology Platform of the Broad Institute of Harvard and MIT. The content of this work does not necessarily reflect the views or policies of the Department of Health and Human Service, nor does mention of trade names, commercial products or organizations imply endorsement by the U.S. Government.
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TABLE OF CONTENTS

Item Number Item Description
Biographical Sketch Dedication Acknowledgments Table of Contents List of Figures List of Tables List of Abbreviations List of Symbols

Page Number
iii iv v vi vii viii ix x

Chapter 1 Chapter 2
Chapter 3 Chapter 4
Chapter 5

Introduction Contributions of multiple transcriptional regulators to Listeria monocytogenes virulence and virulence associated functions. σB and σL Contribute to L. monocytogenes 10403S Response to the Antimicrobial Peptides SdpC and Nisin Identification of a small molecule that inhibits the L. monocytogenes σB regulon and its virulence-associated functions Conclusions and future work

1 12
38 66
141

vi

LIST OF FIGURES

Figure Number Figure Description

Page

Number

2.1 Invasion efficiency of L. monocytogenes regulator

21

mutants in Caco-2 intestinal epithelial cells

2.2

L. monocytogenes recovered from various guinea pig

25

organs at 72 hours post-infection

2.3 Weight development of guinea pigs infected with L. 26

monocytogenes strains over 72 hours post-infection

2.4

Guinea pig fecal shedding of L. monocytogenes at 24, 48,

27

72 hours post-infection

3.1

Normalized log transformed lmo2570 transcript levels

49

for L. monocytogenes strains

3.2

Viability of L. monocytogenes strains treated with nisin

52

4.1 Scatterplot of representative high-throughput screen 80

4.2 Structure of sigmastatin

82

4.3

Phenotypic agar assay for σB-dependent BSH activity in

84

the presence of sigmastatin at various concentrations.

4.4

qRT-PCR graphs of σB-dependent gene transcription

85

4.5 σB-dependent operons affected by sigmastatin

108

4.6

Invasion efficiency of L. monocytogenes in Caco-2 cells

113

following treatment with or without sigmastatin

4.7

The effect of sigmastatin on B. subtilis σB-dependent ctc

114

gene using β-galactosidase assay

AF.1

Scatterplot of representative SMM

149

vii

LIST OF TABLES

Table Number 2.1 2.2 3.1 3.2 4.1 4.2 4.3 4.4 4.5 AT.1

Table Description
L. monocytogenes strains used in Chapter 2. Intracellular growth of L. monocytogenes regulator mutants. L. monocytogenes and B. subtilis strains used in Chapter 3. Spot-on-lawn assay. L. monocytogenes and B. subtilis strains used in Chapter 4. Genes differentially expressed as a result of treatment with sigmastatin and correlation to σB regulation. Fold change of genes upregulated by sigmastatin in 10403S. Fold change of genes downregulated by sigmastatin in 10403S. Compilation of σB positive regulation across various conditions among all genes in L. monocytogenes. Appendix table of secondary screening candidates

Page Number
16 23
42
51 70
88
89-94
95
96-105
147-148

viii

LIST OF ABBREVIATIONS

Abbreviation RNAP CFU P.I. LAB ECF ZOI SM SMM HTS DOS

Abbreviated words RNA Polymerase
Colony forming unit Post-infection Lactic acid bacteria Extracytoplasmic function sigma factor Zone of inhibition Small molecule Small-molecule microarray High-throughput Screen Diversity Oriented Synthesis

ix

Abbreviation σ ∆

LIST OF SYMBOLS
Sigma factor Delta or delete, usually referring to a genetic deletion

x

CHAPTER 1 INTRODUCTION
Listeria monocytogenes and human listeriosis The foodborne pathogen L. monocytogenes is an environmentally ubiquitous
organism that can easily contaminate processing environments and, thus, food systems (32). While the disease caused by L. monocytogenes, called listeriosis, is rare, it can be fatal for susceptible individuals. L. monocytogenes infection is typically associated with underlying conditions. Specifically, it poses a serious threat to vulnerable populations, including pregnant women and their fetuses, neonates, the elderly and those with impaired immune systems (13). Among foodborne pathogens, it has one of the highest mortality rates (20%) and accounts for 10% of all foodborne deaths in the U.S. (29). It is capable of breaching three critical barriers intended to protect a human from pathogens: the gastrointestinal barrier, the blood-brain barrier, and the fetoplacental barrier. As a result, it can cause gastroenteritis, septicemia, meningitis, encephalitis, and abortion or stillbirth of a fetus.
Currently, antibiotics are employed for use against L. monocytogenes infection (42); however, because of the high case mortality rate caused by listeriosis, improved treatment is needed. Furthermore, various studies have identified single and multidrug resistant strains of L. monocytogenes isolated from foods and the environment (18). In one study, 10.9% of Listeria spp. and more specifically, 0.6% of L. monocytogenes isolated from retail foods displayed resistance to one or more antibiotics (46). In another study, 20 of 21 L. monocytogenes strains isolated from cabbage, environmental and water samples were resistant to two or more antibiotics (33). This increasing incidence of multidrug resistant pathogens (17) elicits the concern for lack of antibiotic recourse against the pathogenic bacteria. Thus, attempts to better
1

characterize the factors that contribute to virulence and antimicrobial resistance in L. monocytogenes will help provide a comprehensive understanding of the potential various factors have as viable targets for chemotherapeutic development. Transcription factors
Transcription factors promote differential expression of various genes depending upon the situational and temporal requirements of the bacterial cell (5). Pathogenic microorganisms require the ability to utilize a variety of proteins to adapt to stressful external conditions both inside and outside the host (47). One method to counter these rapidly changing environments involves employing a myriad of factors that control transcription in a complex multilayered network (2); these factors include alternative sigma factors as well as other activators (such as virulence regulators) and/or repressors (such as heat shock regulators) (21).
Alternative sigma factors dissociably interact with core RNA polymerase (RNAP), recognize certain promoter sequences and direct transcription of target gene sets critical to combating stress (21), resisting antimicrobials (3, 31) and maintaining viability and sustaining infection in the host (11, 30, 36). L. monocytogenes, for example, is exposed to adverse conditions in the environment, in food systems, during transmission and in the mammalian host. Therefore, in addition to general house keeping sigma factor σA, L. monocytogenes utilizes 4 alternative sigma factors, σB, σC, σH and σL to survive exposure. General stress response sigma factor σB in particular aids L. monocytogenes in withstanding external stressors including acidic pH encountered in the stomach (pH 2), bile salts in the duodenum, and elevated osmolarity throughout the intestinal tract (8, 14, 15, 35). In addition to conferring protection to adverse environmental elements, which can be a prerequisite to virulence (44), σB has also been shown to play a role in pathogenicity of L. monocytogenes. σB is important to regulating the transcription of a number of virulence genes including
2

inlAB (encoding two genes critical to attachment and invasion (26, 27)), bsh (encoding bile salt hydrolase needed for gastrointestinal passage (10, 12)) and prfA (encoding PrfA, which is the global virulence gene regulator critical to infection (45)) through upstream σB-dependent promoters (25). By regulating genes such as these, σB contributes to the establishment of infection in mammals (7, 19, 43). σB has also been shown to contribute to virulence in other gram positive human pathogens, including Bacillus anthracis (16) and Staphylococcus aureus (24, 28). While the other alternative sigma factors in L. monocytogenes are important for surviving select stresses, it is well established that σB has a prominent role in stress resistance and virulence, making it an excellent target of focus for developing and identifying novel anti-infective compounds. Targeting L. monocytogenes using chemical biology
Utilizing high-throughput chemical biology platforms for pharmaceutical development, researchers have the ability to extend drug discovery to identify novel small-molecule therapeutics from millions of compounds (1). Small molecules are simple organic chemical compounds, typically of low molecular weight, which can have useful biological effects (39). They are vital to biological functioning of living organisms (e.g. small molecules can function as hormones or neurotransmitters) and can bind macromolecules such as DNA, RNA or proteins to alter their activity (37, 40). Small molecules are frequently used for medicinal purposes (37) and have been for centuries (i.e. plant and fungal extracts). The first isolated natural small molecule was morphine, which was derived from an opium plant and subsequently sold for medicinal purposes by Heinrich Emanuel Merck (23). In addition to naturally occurring products made by living cells (9), chemists also create synthetic small molecules by combining chemical building blocks, such as ethanol or benzene (6). Using a combinatorial synthesis approach, chemists can realize vast combinations of
3

core functional groups to create complex and diverse small molecules (4). This process of synthesizing combinatorial libraries of diverse compounds is called diversity-oriented synthesis (DOS) (41). Many of these synthetic compounds are modeled after naturally-occurring bio-active small molecules (41) because natural compounds have proven to be very effective for use in treatment.
With their ability to cause phenotypic changes and modulate cell functions (37), small molecules can be used as probes for understanding biological systems (40), which then aids in the development of potential therapeutic drugs. This field of study is called chemical biology or chemical genetics because it employs chemical compounds to study genetics. For example, in the same way classical geneticists create gene mutations to alter the function of a single-gene product, chemical biologists use exogenous small molecules to alter the function of a single-gene product (39). Both approaches provide a more complete understanding of the biological consequences within a cellular context. When small-molecule screens are performed in a high-throughput format, chemical biologists expand current knowledge about biological processes and phenotypic consequences to identify novel and medicinallyhopeful perturbational agents (perturbagens) in a rapid and comprehensive manner. This approach can effectively be used to identify novel small molecules for targeting diseases caused by prokaryotes. The extensively studied intracellular pathogen, L. monocytogenes, is an ideal model for identifying small molecule agents for treating bacterial infections. By selecting specific biological targets in L. monocytogenes that are common to Gram-positive pathogens, knowledge garnered pertaining to drug discovery can be extrapolated for other bacteria. Also, attenuating the pathogen’s virulence and stress response attributes without killing it can eliminate selective pressure caused by disruption of essential gene functions (as done by classical antibiotics). Alleviating this pressure makes the pathogen susceptible to
4

pharmacological inhibitors ideally without eliciting resistance, reducing the likelihood of developing more bacteria impervious to the effects of antibiotics (34).
Utilizing this approach, ground-breaking work performed by Hung et al. in Vibrio cholerae, demonstrated that small molecules can inhibit essential molecular processes required for transcription of virulence genes in V. cholerae (22, 38). Based on that research, we hypothesize that certain synthesized and/or naturally-derived small molecules targeting a specific biological factor, such as a transcription factor, will hinder the infective process of L. monocytogenes. A target of particular interest in L. monocytogenes is general stress response sigma factor B, σB. As previously mentioned, it is an ideal target for inhibition by small molecules because it is important to both virulence and stress response and it is common to several significant human pathogens, such as those in the genera Bacillus and Staphyloccocus (44). Further impetus for targeting a factor, such as σB, which is specific to certain bacteria, is the aim of identifying well tolerated chemotherapy agents, which are not harmful to the mammalian host (20).
Therefore, focusing our understanding on transcriptional regulators (and their interactions), which contribute to the regulation of stress survival, antimicrobial resistance and virulence gene repertoire of L. monocytogenes, will ultimately provide a solid information base, which we can use to develop novel inhibitors of factors critical to pathogenesis. This will improve the search for new efficacious anti-infective drug candidates against pathogenic organisms, such as L. monocytogenes and may boost our understanding of the finer aspects of gene-regulation.
5

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CHAPTER 2 CONTRIBUTIONS OF MULTIPLE TRANSCRIPTIONAL REGULATORS TO
LISTERIA MONOCYTOGENES VIRULENCE FUNCTIONS
ABSTRACT The foodborne pathogenic bacterium, Listeria monocytogenes, causes listeriosis, a rare, but serious, invasive disease affecting both humans and animals. The ability of L. monocytogenes to survive transmission through food systems and to cause disease is attributed to both its environmental stress survival capabilities and its virulence gene repertoire. Stress response and virulence functions in L. monocytogenes have been ascribed to the pleiotropic transcriptional regulators σB and PrfA, however, little is known about the involvement of other transcriptional regulators in pathogenicity. To assess contributions of various regulatory proteins to virulence and virulenceassociated phenotypes, L. monocytogenes laboratory parent strain 10403S and a collection of otherwise isogenic strains each bearing in-frame deletions in genes encoding alternative sigma factors σL, σH, or σC or repressors CtsR or HrcA were tested in: (i) invasion assays with Caco-2 intestinal epithelial cells; (ii) intracellular growth assays in J774 mouse macrophage-like cells; and (iii) intragastric infections in the guinea pig model. We found that σB was essential for optimal invasion efficiency in intestinal epithelial cells and only PrfA was obligatory for wildtype cytosolic growth and spread. In addition to PrfA and σB, we found that CtsR also contributes to virulence during intragastric infection in the guinea pig. In summary, while no clear virulence-associated phenotypes were attributed to L. monocytogenes σL, σH, σC, or HrcA under the conditions tested, it is possible that regulatory networks exist among these regulators to enable at least partial functional compensation in the absence of a given factor.
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INTRODUCTION Rapid adaptation to stress and the external environment affords the
intracellular pathogen Listeria monocytogenes the ability to survive and persist in various niches, including mammalian hosts (6, 9). As a result L. monocytogenes causes a severe invasive disease, listeriosis, with a 20-30% fatality rate in the US (22). A network of regulatory proteins, such as alternative sigma factors (σB, σL, σH, σC), two-component regulators, and other transcriptional activators or repressors (i.e. PrfA, CtsR, HrcA), guides RNA polymerase to recognize certain promoter sequences. Utilization of this network permits coordination of signals and elicits responses by promoting differential expression of specific genes. The complex concerted effort of these regulatory proteins allows L. monocytogenes to sense and respond with specificity and fine tuning that allows survival and infection. Therefore, investigation into mechanisms through which L. monocytogenes controls virulence may provide insight and guidance for development of more effective disease intervention strategies.
Virulence roles for the global virulence regulator PrfA and the general stress sigma factor σB have been characterized and data indicate that PrfA and σB work in concert to coordinate the infectious process (3, 25, 31, 32). Virulence roles for others transcription factors, including alternative sigma factors σL, σH, σC and class I and III stress response repressors HrcA and CtsR, respectively, have been less extensively studied. While σL has been shown to contribute to carbohydrate metabolism and antimicrobial resistance (1, 26, 29), σH is important for growth in minimal or alkaline media (28) and σC, a L. monocytogenes lineage II-specific extracytoplasmic function (ECF) sigma factor, is important for response to heat stress (33). Currently, these alternative sigma factors have not been indicated in virulence regulation. However, CtsR and HrcA, which negatively regulate genes important for survival of stress, including heat shock (12, 23, 24), control genes upregulated in vivo and encode
13

proteins relevant to virulence (3), such as proteases and chaperonins. The repressor CtsR, regulates virulence-associated Clp proteases. Under stress conditions, CtsR repression is relieved and Clp proteases degrade damaged proteins, allowing the cell to tolerate stress (4) and promote escape from phagosomes (30). Furthermore, HrcA binds operators and represses transcription of genes encoding chaperonins, such as DnaK, which is up regulated intracellularly in macrophages (12) and GroE, which helps the bacterium survive phagocytosis (8) and the vacuolar compartment (4). Moreover, transcriptomic analysis of CtsR and HrcA in the L. monocytogenes strain 10403S (2), showed that CtsR and HrcA regulate genes encoding proteins important to acid and metabolic stress as well as virulence (14). Many of these genes are coregulated by σB, including hrcA itself (13) suggesting multiple layers of regulatory control amongst these regulatory proteins.
To build a more comprehensive understanding of the role each regulator plays in virulence-associated functions, we performed in vitro assays, using single and double in-frame deletions of the regulators. We know that σB is essential for attachment and infection of enterocytes (in Caco-2 human intestinal epithelial cells (17)) and that PrfA is important for L. monocytogenes replication in macrophages (7). It was also shown that, in addition to PrfA, σB is required for a successful L. monocytogenes gastrointestinal infection in guinea pigs (11). Therefore, with this knowledge we evaluated σB, σL, σH, σC, CtsR, HrcA and PrfA to pinpoint and/or reaffirm the contributions of each protein to invasion, intracellular growth, and in vivo infection. We also began assessing the interactions between select regulators. Phenotypic characterization of the regulators during critical aspects of the infectious cycle provides a foundation for understanding the contributions of each protein in addition to working towards unraveling and understanding the complex transcriptional regulatory networks coordinated by these proteins.
14

MATERIALS AND METHODS Bacterial strains
L. monocytogenes 10403S and eleven single and double isogenic mutant strains, ∆sigB(31), ∆sigC (Chaturongakul, unpub.), ∆sigH (Chaturongakul, unpub.), ∆sigL(26), ∆ctsR (14), ∆hrcA (13), ∆prfA (32), ∆sigB/∆sigH (Chaturongakul, unpub.), ∆sigB/∆ctsR (14), ∆ctsR/∆hrcA (13), and ∆sigB/∆hrcA (13) previously created in L. monocytogenes 10403S using splicing-by-overlap extension (SOE) PCR and allelic exchange mutagenesis used in this study (Table 2.1). Select single mutants including ∆sigC, ∆sigH, ∆sigL, ∆ctsR, ∆hrcA were also assessed in the animal model. Strains were grown to stationary phase, i.e., OD600=0.8 +1hr, allowing comparison to previous assessments of regulators sigB and PrfA using cells grown to stationary phase (11). Invasion assay
The human colorectal adenocarcinoma epithelial cell line Caco-2 (ATCC HTB-37) was cultured and invasion assays were performed as described by Garner et al. 2006 (11) with minor modifications. Briefly, 5.0 x 104 Caco-2 cells were seeded into 24-well plates (Costar, Corning, NY) 48 h prior to infection. For infection, the Caco-2 cells were inoculated with approximately 2.0x 107 L. monocytogenes cells (grown to stationary phase, i.e., OD600=0.8 +1hr); exact L. monocytogenes numbers used for infection were determined by plating on BHI agar. Intracellular L. monocytogenes numbers were determined 90 min post infection as previously described (11). Invasion efficiency was calculated as the number of bacteria recovered relative the number of bacteria used for inoculation (i.e., log (CFU/ml recovered/ CFU/ml inoculated). Data represent 4 independent experiments. Data were analyzed using one-way analysis of variance (ANOVA) and Dunnett’s t-test, performed in SAS® 9.0 (SAS Institute). Averages for three of four replicates for
15

Table 2.1: Strains used in this study

Strain

Genotype

Reference

L. monocytogenes FSL X1-001 L. monocytogenes FSL A1-254 L. monocytogenes FSL C3-126 L. monocytogenes FSL B2-124 L. monocytogenes FSL C3-113 L. monocytogenes FSL B2-046 L. monocytogenes FSL H6-190 L. monocytogenes FSL B2-101 L. monocytogenes FSL C3-123 L. monocytogenes FSL H6-193 L. monocytogenes FSL H6-194 L. monocytogenes FSL H6-198

parent strain 10403S 10403S ∆sigB 10403S ΔsigH 10403S ∆sigL 10403S ∆sigC 10403S ∆prfA 10403S ∆ctsR 10403S ∆hrcA
10403S ∆sigB/∆sigH 10403S ∆sigB/∆ctsR 10403S ∆sigB/∆hrcA 10403S ∆ctsR/∆hrcA

Bishop and Hinrichs, 1987 (2) Wiedmann et al., 1998 (31) Chaturongakul, unpublished Chaturongakul, unpublished Chaturongakul, unpublished Wong et al., 2004 (32) Hu et al., 2007 (14) Hu et al., 2007 (13) Chaturongakul, unpublished Hu et al., 2007 (14) Hu et al., 2007 (13) Hu et al., 2007 (13)

16

10403S, ∆sigB, ∆ctsR, ∆brcA, ∆sigB/∆ctsR, ∆sigB/∆hrcA, ∆ctsR/∆hrcA were represented previously (13). To determine if there were statistically significant interaction effects between the sigB and the sigH, sigB and the ctsR, sigB and the hrcA, and the ctsR and the hrcA deletions, a two-way ANOVA (with Dunnett’s t-test) was performed. Interaction is a term in a statistics model in which the effect of one variable on an outcome is a function of another variable. In our model, the dependent variable was invasion efficiency; the independent variables included sigB + sigH + sigB*sigH + replicate. The factors “sigB” and “sigH” in the model, for example, indicate the presence or absence of that gene in the strains tested. This model was used to similarly assess the contributions of both genes in the other double mutant strains. Intracellular growth assay
The mouse macrophage-like cell line J774A.1 (ATCC TIB-67) was cultivated at 37°C with 5% CO2 in Dulbecco’s Modified Eagle Medium (DMEM) with Earle's salts and 1% Sodium Pyruvate (Gibco; Gaithersburg, MD) containing 10% fetal bovine serum (Gibco), 1.5 g/L Sodium Bicarbonate (Gibco), and 100µg/ml each Penicillin G and Streptomycin (J774 medium). At approximately 48 h before intracellular growth assays, J774 cells were seeded at a density of 2x105 cells/ml in each well of a 24 well plate using J774 medium without antibiotics. In order to activate macrophages, J774 cells were shifted to J774 media without antibiotics containing lipopolysaccharide (LPS; Sigma, St. Louis, MO) at final concentration of 100ng/ml 24 hours before infection. At 30 min prior to assay, fresh media without antibiotics was added. J774 cells were then inoculated with L. monocytogenes at a multiplicity of infection (MOI) of 1. L. monocytogenes used for infection were grown to early stationary phase (defined as OD600=0.8 +1hr) in BHI, flash frozen and stored in liquid nitrogen before the assays; bacterial numbers were determined (on BHI agar
17

plates) after freezing and immediately prior to the assays. At 30 min post inoculation, the J774 cells were washed with 1ml sterile PBS, followed by addition of 1ml of fresh media with 50µg/ml gentamicin. At 1.5, 3.5, 5.5, and 7.5 hr post-inoculation, inoculated J774 cells in different wells were washed three times with 1ml of sterile PBS and lysed with 500ul of ice cold sterile distilled water, followed by plating of the cell suspension on BHI agar to determine intracellular bacterial numbers at each time point. Intracellular growth was calculated as the number of bacteria recovered at each time point relative to the number of bacteria recovered at t=1.5 (i.e., [log CFU/ml t=x] - [log CFU/ml t=1.5]. Data represent 5 independent experiments. Data were analyzed using one-way ANOVA and Tukey’s studentized range (HSD) test, performed in SAS® 9.0 (SAS Institute). Animal care and housing condition
Animal protocols (# 2002-0060) were approved by the Institutional Animal Care and Use Committee prior to initiation of the experiments. Male Hartley guinea pigs (Elm Hill, Chelmsford, MA) weighing approximately 300g at about 3 weeks of age were housed individually allowing for collection of each animal’s fecal material. Animals were provided with feed and water ad libitum. Cages were changed daily, and animal health and weight were monitored and recorded daily. Animals were acclimated for 5 days prior to infection. Intragastric infection of guinea pigs
Intragastric infections of guinea pigs were performed as described previously (11). Briefly, animals were anesthetized with isoflurane administered via inhalation and L. monocytogenes (1 x1010 CFU) was inoculated intragastrically after stomach pH was buffered using by administrating 1.5 ml of PBS containing 125 mg calcium carbonate (pH 7.4). Data represent 4 independent experiments. Enumeration of L. monocytogenes from organs
18

Animals were euthanized for organ harvest at 72 h p.i. as was previously established by (20). The liver, mesenteric lymph nodes (MLN) and small intestine (a 20-cm portion, immediately proximal to the cecum) were aseptically removed, and processed according to Garner et al. 2006. Additionally, to confirm L. monocytogenes presence for each organ, 10ml of homogenate was added to 90ml of Listeria Enrichment Broth (Becton Dickenson, Sparks, MD) and incubated at 30°C for 48hrs, after which it was streaked onto Oxford agar (Oxoid, Ogdensburg, NY). After incubation at 30°C for 48 h, colonies exhibiting Listeria-like morphology were recorded as L. monocytogenes. Enumeration of L. monocytogenes from feces
Fecal pellets from each animal were collected daily and enumerated according to Garner et al., 2006. After incubation at 30°C for 48 h, colonies exhibiting Listerialike morphology were counted and recorded as L. monocytogenes. L. monocytogenes identification was confirmed on a representative subset of these colonies by plating on LMPM agar (Biosynth Biochemica & Synthetica, Naperville, Ill.). Statistical analyses
Data were analyzed using general linear model (GLM) with Tukey’s studentized range (HSD) test or Dunnett’s t-test. Analysis was performed with Statistical Analysis Software (SAS) 9.0 (SAS Institute, Inc., Cary, NC). Histopathology and immunohistochemistry
Histopathology and immunohistochemistry was performed by Brad Njaa and interpreted by Brad Njaa and Rachel Peters. Tissues from euthanized guinea pigs were fixed in 10% buffered formalin for a minimum of 48 h. Fixed tissues were processed using a Tissue Tek VIP E 300 (Sakura Finetek U.S.A., Inc. Torrance, CA) in preparation for paraffin embedding in a Tissue Tek embedding station (Sakura Finetek U.S.A., Inc.). Formalin-fixed, paraffin-embedded tissues were sectioned at a thickness
19

of 6 um, placed on glass slides, and stained with hematoxylin and eosin for microscopic evaluation. L. monocytogenes immunohistochemistry was performed as previously described (15) with minor modifications, as follows. Briefly, formalinfixed, paraffin-embedded tissues were sectioned to a thickness of 6 um and deposited on Probe-On glass slides. For each organ section investigated, one slide was stained using a polyclonal antibody to L. monocytogenes (Becton Dickinson, Sparks, MD), while the second slide was stained with a nonspecific antibody. The secondary antibody was an anti-immunoglobulin G antibody. All slides were stained using the avidin-biotin system, and the chromogen was diaminobenzidine. All slides were examined using an Olympus BX41 microscope. Photomicrographs were taken using a Q Imaging micropublisher 5.0 RTV (Burnaby, British Columbia, Canada) and a 50x or 100x oil objective lens.
RESULTS Caco-2 intestinal epithelial cell assay demonstrates the requirement of σB for L. monocytogenes attachment and invasion.
To assess the invasion capacity of various transcriptional regulator mutants in Caco-2 human enterocytes, we used laboratory parent strain 10403S and isogenic single mutants of regulators σB, σL, σH, σC, CtsR, HrcA and PrfA, as well as, strains containing double deletions of select regulators of interest, such as ∆sigB/∆sigH, ∆sigB/∆ctsR, ∆sigB/∆hrcA, and ∆ctsR/∆hrcA (Figure 2.1). We found that in line with previous assessments (10, 17), the ∆sigB strain showed significantly reduced invasion capacity (p<0.05). Two double mutant strains, ∆sigB/∆ctsR and ∆sigB/∆hrcA showed significantly lower invasion than 10403S (p<0.05). The ∆sigB/∆hrcA strain invasion capacity was similar to that of the single sigB knockout strain. Two-way ANOVA analyses of invasion data showed no significant “SigB*HrcA” interaction effect on
20

Figure 2.1: Invasion efficiency of L. monocytogenes transcriptional regulator mutant strains in the intestinal epithelial cell line Caco-2. Data shown represent the average of four independent experiments. “*” indicates mutant was significantly different (p<0.05) than the 10403S parent strain (GLM, Dunnett). Two-way ANOVA showed no interaction effect for double mutants. Portions of this data have been published previously (13), see materials and methods.
21

invasiveness (p>0.05). The ∆sigB/∆ctsR strain showed >0.5 log lower invasion than the ∆sigB strain. Initial analysis based on partial set of the data indicated an interaction of σB and CtsR on invasion (13), however, further analysis of complete set of data using two-way ANOVA analyses of “SigB*CtsR” indicated no significant interaction of σB and CtsR on invasion. This could mean that the lower invasion of ∆sigB/∆ctsR (lower than either of the respective single mutants) is probably a result of additive (rather than multiplicative) contributions of σB and CtsR to invasiveness. Though neither the ∆ctsR strain nor the ∆hrcA strain exhibited reduced invasiveness, the ∆ctsR/∆hrcA strain exhibited lower invasion than either of the single mutants, suggesting that the loss of both proteins produces a compounding effect on invasion capacity. However, statistical analyses of invasion data showed no significant “CtsR*HrcA” statistical interaction effect on invasiveness (p>0.05). The ∆sigH strain exhibited higher invasion than its parent (though not statistically significant). Interestingly, the effect of deleting sigH in the ∆sigB/∆sigH strain appeared to have moderated the effect on invasion produced by loss of intact sigB gene. Specifically, the ∆sigB/∆sigH strain did not exhibit reduced invasion like all other strains with a ∆sigB background (Figure 2.1). The statistical interaction effect of “SigB*SigH” was not significant, though (p>0.05).
Intracellular growth assay indicates none of the selected regulators, other than PrfA, contribute to intracellular growth and spread.
As L. monocytogenes is a facultative intracellular pathogen, we investigated a potential role for the transcriptional regulators in replication and spread in the intracellular microenvironment. In order to determine if the transcriptional regulator mutants were inhibited in their ability to adapt and grow once the bacteria were cytosolic, we performed intracellular growth assays using LPS-activated J774 mouse
22

Table 2.2. Intracellular growth assay results in J774 cells

Time Post inoculation
(hr)
1.5 hrs

10403S
0.0 (0.0)

Mean Log((CFU/ml t=x)/(CFU/ml t=1.5hr)) (SD) for L. monocytogenes strainsa

∆sigB
0.0 (0.0)

∆sigL
0.0 (0.0)

∆sigH
0.0 (0.0)

∆sigC
0.0 (0.0)

∆prfA
0.0 (0.0)

∆hrcA
0.0 (0.0)

∆ctsR
0.0 (0.0)

∆sigB/ ∆sigB/ ∆sigB/ ∆ctsR/∆h ∆sigH ∆ctsR ∆hrcA rcA

0.0 0.0 0.0 (0.0) (0.0) (0.0)

0.0 (0.0)

3.5 hrs

0.76 0.6 0.52 0.68 0.65 -0.14* 0.71 0.67 0.70 0.65 0.62 0.61 (0.21) (0.21) (0.19) (0.13) (0.17) (0.21) (0.20) (0.27) (0.14) (0.16) (0.20) (0.12)

5.5 hrs

1.27 1.14 1.07 1.16 1.11 -0.30* 1.03 1.13 1.13 1.09 1.18

1.11

(0.09) (0.18) (0.18) (0.31) (0.32) (0.12) (0.47) (0.20) (0.27) (0.28) (0.20) (0.15)

23

7.5 hrs

1.75 1.71 1.58 1.90 1.76 -0.21* 1.72 1.76 1.67 1.70 1.72

1.73

(0.22) (0.10) (0.21) (0.21) (0.07) (0.17) (0.13) (0.23) (0.21) (0.20) (0.29) (0.17)

a “*” indicates values that are significantly different (P<0.05; one way ANOVA with Tukey’s studentized range (HSD) test). Data represent 5 independent experiments. SD indicates standard deviation.

macrophage-like cells. The number of bacteria recovered at t=3.5, 5.5 and 7.5 hours post-infection (p.i.) were normalized to their respective number of bacteria recovered at initial invasion (t=1.5 h p.i.), in order to assess growth after and separate from invasion. This assay showed that σB does not directly contribute to intracellular growth in the macrophage, as its respective mutant and double mutants showed no inability to grow as compared to its parent strain. Likewise, σL, σH, σC, CtsR, and HrcA also did not contribute to intracellular growth, as their respective deletion mutants exhibited no hindrance of growth within the macrophages (Table 2.2). As seen previously (7), however, ∆prfA showed a complete loss in ability to multiply inside the cell (p<0.05; Table 2.2). Guinea pig model of listeriosis indicates CtsR contributes to L. monocytogenes virulence.
We used a guinea pig model of listeriosis to evaluate in vivo contributions of transcriptional regulators to virulence. It was previously discovered in our laboratory, that ∆prfA strain is avirulent and ∆sigB is virulence attenuated, thus they are both required for a successful L. monocytogenes gastrointestinal infection in guinea pigs (11). In order to gain a more comprehensive understanding of the roles of the other transcriptional regulators (σL, σH, σC, CtsR and HrcA) in comparison to PrfA and σB to virulence in vivo, we infected each guinea pig separately with 1x1010 CFU of each L. monocytogenes mutant in our panel of transcriptional regulators. Interestingly, we found that ∆ctsR was significantly less virulent than 10403S, as the animal infected with ∆ctsR had the lowest recovery (in log CFU/g organ) of L. monocytogenes in samples taken from the liver, spleen, mesenteric lymph nodes and the distal section of the ileum (p<0.05; Figure 2.2). ∆ctsR was recovered from all tissues ~ 1 to 1.5 Log CFU/g less than 10403S. The relative contributions of the other transcriptional
24

(a)
(b)
(c)
(d) Figure 2.2: Recovery of L. monocytogenes strains from organs of infected guinea pigs. Data shown represent the average of 4 independent experiments. “*” indicates that recovery of a mutant was significantly different (p<0.05) from 10403S (GLM, Tukey HSD). MLN indicates mesenteric lymph node.
25

Figure 2.3: Weight development for guinea pigs infected with various L. monocytogenes regulator mutants over 72 hours post-infection. Data shown represent the average of four independent experiments. Data were analyzed using general linear model (GLM) with Dunnett’s t-test.
26

(a)
(b)
(c) Figure 2.4: Recovery of L. monocytogenes strains from fecal specimens eliminated from infected guinea pigs. Data shown represent the average of four independent experiments. “*” indicates that recovery of a mutant was significantly different (p<0.05) than that of 10403S (GLM, Dunnett).
27

regulators were negligible as their respective mutants showed no detectable differences or attenuation in virulence as compared to 10403S. According to histopathology, differences in tissue samples were minimal among guinea pigs infected with the various strains. The infection proceeded over 72 hours, during which the animal infected with ∆ctsR exhibited no physiological signs of distress, whereas the other animals demonstrated some anorexia. In fact, as compared to 10403S, the animals infected with ∆ctsR strain had the greatest positive % weight change, approximately 16% higher than 10403S at 72 hrs post-infection (p<0.05; Figure 2.3), similar to the uninfected control. This ∆ctsR-infected animals’ average weight change was similar to that of ∆sigB-infected animals in Garner et al.(11). Animals infected with the ∆ctsR strain also eliminated the least bacteria in their fecal matter, approximately 4 Log CFU/g less than animals infected with 10403S at both 48 and 72 hours post-infection (p<0.05; Figure 2.4). Both animals infected with ∆sigL and ∆sigC eliminated significantly less bacteria than 10403S at 48 hours post-infection (p<0.05), however, not at any other time point.
DISCUSSION A bacterium’s cellular adaptation to external conditions is largely dependent
on differential expression of genes through the activity of multiple regulatory proteins including transcription factors. These complex and dynamic interactions between various regulatory proteins are critical to stress response (5) and the establishment of infection in a mammalian host for the intracellular pathogen L. monocytogenes (3, 25). Using phenotypic analysis to assess the contributions of seven transcriptional regulators, we determined whether and at what point each protein aids in the coordinated regulation of virulence and virulence-associated functions. Overall our data show that PrfA and σB are important to intracellular growth and entry,
28

respectively (3, 25), and that CtsR is an emerging factor that also promotes a successful infection in vivo. Simultaneously, we found that the other regulators examined (σL, σH, σC, and HrcA) do not seem to have apparent roles in virulence based on the conditions assessed here.
Using an in vitro model of interaction between L. monocytogenes and intestinal cells, we observed that σB is indeed necessary for optimal invasion efficiency, as there was an appreciable loss in invasion capacity of ∆sigB as compared to its isogenic parent. This is consistent with its known role regulating inlA and inlB (27), thereby mediating attachment and early invasion in intestinal epithelium and was previously seen (17, 18). Also in agreement with previous work (18), ∆prfA grown to early stationary phase at 37ºC, showed no significant impediment in ability to invade as compared to its parent strain, further exemplifying its relatively dispensable role in invasion. We did see that in a ∆sigB/∆sigH, the loss of σH moderates the lowered invasion capacity as a consequence of the loss of σB, resulting in no reduction in invasion. This may simply indicate that σH selectively regulates genes to reduce invasiveness and σB regulation increases invasiveness. Similar interplay has been seen between PrfA and σB, in which σB moderates the effects of PrfA activity conferring to the cell better control during infection (25). Previous transcriptional profiling assessments indicate σB and negative transcriptional regulators CtsR and HrcA share overlapping transcriptomes (13, 14). This commingling of gene regulation indicates these proteins interact in a coordinated fashion to promote stress survival (13) and possibly virulence. Most notably, while the single mutant of the gene encoding the repressor CtsR did not exhibit loss of invasion capacity, we found that the ∆sigB/∆ctsR strain exhibited invasion efficiency lower than a ∆sigB strain, consistent with previous reports (13, 14). This may indicate that regulator redundancy between
29

σB and CtsR causes an increased impairment in invasion; functional redundancy has been observed among alternative sigma factors in Bacillus subtilis (21).
After examining the roles of the transcriptional regulators in replication and cell-to-cell spread, we found that only PrfA has an evident role in this aspect of L. monocytogenes pathogenesis. We surmise that under these conditions in the chosen cell line, the transcriptional regulators are not involved in intracellular growth during the infection process and are likely more important to a niche outside the host. Alternatively, they may be able to compensate for one another such that defects could not be detected in this assay.
It has been shown that the guinea pig model of intragastric infection is the most representative of human listeriosis as transmitted through the ingestion of contaminated foods (11). The guinea pig was chosen for our in vivo assessments as it is a naturally permissive host for L. monocytogenes infection via the gastrointestinal route (19). While ∆prfA is avirulent and ∆sigB is virulence attenuated indicating both PrfA and σB are required for full scale infection in a guinea pig (11), our in vivo assay showed that CtsR is also important for L. monocytogenes pathogenesis. This correlates with known functions of CtsR. Surprisingly despite indications that, like CtsR, HrcA regulates genes important to virulence (dnaK, groESL) (4, 12), the ∆hrcA strain showed no attenuation of virulence in the guinea pig model.
CtsR, encoded by lmo0229, modulates virulence associated Clp proteases that degrade misfolded proteins. Under stress conditions, CtsR repression is relieved and Clp proteases degrade damaged proteins, allowing the cell to tolerate stress. This ability is particularly important in escaping the phagolysosome and intracytoplasmic compartments (4, 30). Specifically, a ∆ctsR in EGD-e background was impaired in intracellular growth in P388D1 murine macrophage cell line as well as plaque formation, according to Chatterjee et al.,(4). Though CtsR was not detected as a
30

requirement for virulence-associated functions in our in vitro experiments, it is clearly important in other models. In light of this, it could be that our chosen cell culture experiments were limited in their detection of CtsR as an important regulator in virulence. It is also possible that CtsR may not be as important in in vitro cell culture models in the strain 10403S as it is in EGD-e because of heterogeneity of function across different strains. It may also be that our animal model better represented the true role of CtsR as compared to our tissue culture model. Interestingly, the ctsR gene itself has been shown to be moderately up regulated specifically in the vacuolar compartment of the cell (4). Furthermore, of the 42 genes repressed by CtsR (14), 15 are upregulated during infection in the mouse spleen (3). In line with this, a strain with inactive CtsR, whose repressor function is lost due to a single amino acid deletion, exhibits virulence attenuation in a mouse (16). It is possible that the ctsR mutant tested in the guinea pig could not escape vacuolar compartments to the cytoplasm to replicate and continue the intracellular life cycle in the animal model. Because of the attenuation of the ∆ctsR strain seen in our studies, these data point to the necessity of the negative regulator CtsR in establishing a full-scale persistent L. monocytogenes infection in a mammal.
CtsR is likely an important determinant in L. monocytogenes pathogenesis because it confers ability to adapt to stresses during transit through the intestinal tract (14), to overcome metabolic and nutritional limitations (14) and to withstand cytosolic and vacuolar stresses encountered in host cells (4, 30). While the other transcription factors did not play a demonstrable role in virulence and virulence-associated functions, it remains to be seen whether they play role indirect roles in virulence under other circumstances, such as infection of the brain or placenta. The observed phenotype may also be a result of overlapping regulation with other factors. The evolutionary necessity for a compensatory mechanism to respond and neutralize any
31

deleterious effects resulting from the loss of certain factors is a possible explanation for lack of phenotype. While some factors such as PrfA, σB, and CtsR have roles in pathogenesis, it is plausible that the effects of the remaining regulators may be masked by the complex circuitry of the transcriptional regulators as a whole. The ability of L. monocytogenes to utilize this sophisticated network of factors advantageously permits this bacterium to swiftly modulate cell responses under a variety of adverse circumstances allowing the organism to sense, respond and survive.
32

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18. Kim, H., H. Marquis, and K. J. Boor. 2005. σB contributes to Listeria monocytogenes invasion by controlling expression of inlA and inlB. Microbiology 151:3215-3222.
19. Lecuit, M., S. Dramsi, C. Gottardi, M. Fedor-Chaiken, B. Gumbiner, and P. Cossart. 1999. A single amino acid in E-cadherin responsible for host specificity towards the human pathogen Listeria monocytogenes. EMBO J 18:3956-63.
20. Lecuit, M., S. Vandormael-Pournin, J. Lefort, M. Huerre, P. Gounon, C. Dupuy, C. Babinet, and P. Cossart. 2001. A transgenic model for listeriosis: Role of internalin in crossing the intestinal barrier. Science 292:1722-1725.
21. Mascher, T., A.-B. Hachmann, and J. D. Helmann. 2007. Regulatory overlap and functional redundancy among Bacillus subtilis extracytoplasmic function σ factors. J Bacteriol 189:6919-6927.
22. Mead, P. S., L. Slutsker, V. Dietz, L. F. McCaig, J. S. Bresee, C. Shapiro, P. M. Griffin, and R. V. Tauxe. 1999. Food-related illness and death in the United States. Emerg Infect Dis 5:607-25.
23. Nair, S., I. Derre, T. Msadek, O. Gaillot, and P. Berche. 2000. CtsR controls class III heat shock gene expression in the human pathogen Listeria monocytogenes. Mol Microbiol 35:800-811.
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24. Narberhaus, F. 1999. Negative regulation of bacterial heat shock genes. Mol Microbiol 31:1-8.
25. Ollinger, J., B. Bowen, M. Wiedmann, K. J. Boor, and T. M. Bergholz. 2009. Listeria monocytogenes σB modulates PrfA-mediated virulence factor expression. Infect Immun 77:2113-24.
26. Palmer, M. E., M. Wiedmann, and K. J. Boor. 2009. σB and σL contribute to Listeria monocytogenes 10403S response to the antimicrobial peptides SdpC and nisin. Foodborne Pathog Dis 6:1057-1065.
27. Raengpradub, S., M. Wiedmann, and K. J. Boor. 2008. Comparative analysis of the σB-dependent stress responses in Listeria monocytogenes and Listeria innocua strains exposed to selected stress conditions. Appl Environ Microbiol 74:158-171.
28. Rea, R. B., C. G. M. Gahan, and C. Hill. 2004. Disruption of putative regulatory loci in Listeria monocytogenes demonstrates a significant role for Fur and PerR in virulence. Infect Immun 72:717-727.
29. Robichon, D., E. Gouin, M. Debarbouille, P. Cossart, Y. Cenatiempo, and Y. Hechard. 1997. The rpoN (sigma54) gene from Listeria monocytogenes is involved in resistance to mesentericin Y105, an antibacterial peptide from Leuconostoc mesenteroides. J Bacteriol 179:7591-7594.
30. Rouquette, C., C. d. Chastellier, S. Nair, and P. Berche. 1998. The ClpC ATPase of Listeria monocytogenes is a general stress protein required for virulence and promoting early bacterial escape from the phagosome of macrophages. Mol Microbiol 27:1235-1245.
31. Wiedmann, M., T. J. Arvik, R. J. Hurley, and K. J. Boor. 1998. General stress transcription factor σB and its role in acid tolerance and virulence of Listeria monocytogenes. J Bacteriol 180:3650-3656.
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32. Wong, K. K. Y., and N. E. Freitag. 2004. A novel mutation within the central Listeria monocytogenes regulator PrfA that results in constitutive expression of virulence gene products. J Bacteriol 186:6265-6276.
33. Zhang, C., J. Nietfeldt, M. Zhang, and A. K. Benson. 2005. Functional consequences of genome evolution in Listeria monocytogenes: the lmo0423 and lmo0422 genes encode σC and LstR, a lineage II-specific heat shock system. J Bacteriol 187:7243-7253.
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CHAPTER 3 σB AND σL CONTRIBUTE TO L. MONOCYTOGENES 10403S RESPONSE TO
THE ANTIMICROBIAL PEPTIDES SDPC AND NISIN1
ABSTRACT The ability of the foodborne pathogen Listeria monocytogenes to survive antimicrobial treatments is a public health concern, therefore, this study was designed to investigate genetic mechanisms contributing to antimicrobial response in L. monocytogenes. In previous studies, the putative bacteriocin immunity gene lmo2570 was predicted to be regulated by the stress responsive alternative sigma factor, σB. As the alternative sigma factor σL controls expression of genes important for resistance to some antimicrobial peptides, we hypothesized roles for lmo2570, σB, and σL in L. monocytogenes antimicrobial response. Results from phenotypic characterization of a L. monocytogenes lmo2570 null mutant suggested that this gene does not contribute to resistance to nisin or to SdpC, an antimicrobial peptide produced by some strains of Bacillus subtilis. While lmo2570 transcript levels were confirmed to be σB-dependent, they were σL-independent and were not affected by the presence of nisin under the conditions used in this study. In spot-on-lawn assays with the SdpC-producing B. subtilis EG351, the L. monocytogenes ΔsigB, ΔsigL and ΔsigB/ΔsigL strains all showed increased sensitivity to SdpC, indicating that both σB and σL regulate genes contributing to SdpC resistance. Nisin survival assays showed that σB and σL both affect L. monocytogenes sensitivity to nisin in broth survival assays, i.e., a sigB null mutant is more resistant than the parent strain to nisin, while a sigB null mutation in ∆sigL background leads to reduced nisin resistance. In summary, while the σB-
1 Published as Palmer, M. E., M. Wiedmann, and K. J. Boor. 2009. σB and σL contribute to Listeria monocytogenes 10403S response to the antimicrobial peptides SdpC and nisin. Foodborne Pathog Dis 6:1057-1065.
38

dependent lmo2570 does not contribute to resistance of L. monocytogenes to nisin or SdpC, both σB and σL contribute to the L. monocytogenes antimicrobial response.
INTRODUCTION The Gram-positive, facultative intracellular foodborne pathogen Listeria
monocytogenes is the causative agent of listeriosis, which has a human case-fatality rate > 20% in the U.S. (36). The vast majority of human listeriosis cases have been reported to occur via consumption of contaminated foods (36), therefore, development of more effective methods for controlling the presence of L. monocytogenes in foods is a desirable goal. To that end, various antimicrobial peptides have been investigated as a potential means for inhibiting growth of L. monocytogenes in foods (13, 39).
Bacteriocins are bacterially produced antimicrobial peptides that are generally most effective against other bacteria that are genetically similar and present in similar ecological niches. To enhance producer strain self-preservation, bacteriocin production is frequently coupled with production of cognate bacteriocin immunity proteins (13, 17, 45). For example, NisI, which provides immunity to nisin, is encoded downstream of the nisin biosynthesis genes in Lactococcus lactis (19). Previous studies have reported that bacteriocin production can be influenced by bacterial environmental stress response pathways such as the RecA-dependent SOS response and the ppGpp-dependent stringent response (16). Bacteriocin production and immunity to antimicrobials are hypothesized to enhance the ability of producer bacteria to vie for limited nutrients in the presence of competitors (40).
Lactic acid bacteria (LAB) are recognized as producers of various bacteriocins (27, 30). LAB are commonly present in human foods, and therefore, the bacteriocins that they produce, such as pediocin PA-1/AcH, enterocins, and/or sakacins also may be present in foods (13). Currently, only nisin, a class I lantibiotic bacteriocin
39

produced by the lactic acid bacterium Lactococcus lactis, has Generally Recognized as Safe (GRAS) status for intentional application as an antimicrobial in the U.S. food industry (27). Nisin creates membrane-spanning pores in the bacterial cell wall, which enable dissipation of the cell’s proton motive force (7, 8). Although, in general, nisin has been demonstrated as an effective antilisterial peptide (5), some strains of L. monocytogenes have developed resistance to both nisin and pediocin PA-1 (22-24). L. monocytogenes resistance to nisin is a concern to the segments of the food industry (e.g., dairy, poultry) that currently use this peptide to control pathogen growth (22). A better understanding of the molecular mechanisms contributing to antimicrobial resistance in foodborne pathogens could lead to development of improved food safety intervention strategies. One means to that end is to identify and examine putative bacteriocin immunity genes and their physiological roles in protecting the producer strain against either endogenously or exogenously produced antimicrobial peptides.
In a previous study, Kazmierczak et al. (28) identified lmo2570 as a putative σB-dependent gene with 45% similarity to the B. subtilis bacteriocin immunity gene sdpI (yvaZ) (cmr.jcvi.org), which encodes SdpI. SdpI is a membrane protein conferring resistance to the endogenously-produced antimicrobial peptide SdpC (9, 18). Butcher and Helmann (2006) found that while SdpI has a predominant role in conferring resistance to SdpC, the B. subtilis regulon contolled by σW, an extracytoplasmic function sigma factor (ECF), provides secondary immunity to this antimicrobial peptide. Taken together, these data indicate the importance in antimicrobial resistance of both immunity genes and transcriptional level regulatory mechanisms as mediated by alternative sigma factors.
We hypothesized that σB and σL contribute to antimicrobial response in L. monocytogenes. σB has been shown to regulate response to antimicrobial peptides in other Gram-positive bacteria. To illustrate, the B. subtilis σB regulon is up-regulated
40

following treatment with either bacitracin or vancomycin (33). In a collection of teicoplanin-resistant Staphylococcus aureus mutants, the majority of the mutations responsible for antimicrobial resistance mapped to rsbW, which encodes the RsbW anti-sigma factor that sequesters σB to prevent it from interacting with RNA polymerase. The teicoplanin-resistant strains with mutations in rsbW showed increased σB activity relative to their parent strain (the MB33 rsbU mutant strain) or to other strains carrying the rsbU wild-type allele (6), providing evidence of a link between σB activity and antimicrobial resistance. σB also has been shown to contribute to bacterial stress response regulation in Staphylococcus aureus (10). L. monocytogenes alternative sigma factor σL regulates expression of genes that mediate sensitivity to antimicrobials such as the class IIa bacteriocin, mesentericin Y105 (42), hence σL also has been associated with antimicrobial response. Therefore, in the studies described below, phenotypic and genotypic assessments were used to determine the contributions of σB, σL and Lmo2570 to the L. moonocytgenes response to SdpC and nisin.
MATERIALS AND METHODS Bacterial strains and growth conditions
L. monocytogenes parent strain 10403S (serotype 1/2a), and otherwise isogenic sigB and sigL single and double null mutants (∆sigB; FSL A1-254, ∆sigL; FSL B2124, ∆sigB/∆sigL; FSL B2-127) were used in this study. L. innocua FSL C2-008 (47), L. ivanovii FSL C2-010, L. welshimeri FSL N1-064, and L. seeligeri FSL N1-067 (Table 3.1) were used to assess intragenus competition with the L. monocytogenes parent and mutant strains. To examine the susceptibility of the L. monocytogenes parent and mutant strains to a closely related bacterium that produces an antimicrobial peptide, we used strains of B. subtilis that produce SdpC, the bacteriocin whose
41

Table 3.1: Strains used in this study

Strain

Characteristics

Reference or source

L. monocytogenes 10403S

laboratory parent strain

Bishop and Hinrichs, 1987

L. monocytogenes FSL A1-254

10403S ∆sigB

Wiedmann et al., 1998

L. monocytogenes FSL P1-002

10403S ∆lmo2570

This study

L. monocytogenes FSL B2-124

10403S ∆sigL

Chaturongakul, unpublished

L. monocytogenes FSL B2-127

10403S ∆sigB/∆sigL

Chaturongakul, unpublished

L. innocua FSL C2-008 L. ivanovii FSL C2-010

Woodling and Moraru, 2005 Wiedmann, unpublisheda

L. welshimerii FSL N1-064

Fish processing plant

environment

L. seeligeri FSL N1-067

Fish processing plant

environment

B. subtilis PY79

prototroph, parent strain

Youngman et al., 1984

B. subtilis EG351

PY79 Pspac-hy-sdpABC

aIsolate kindly provided (as USDA 2717) by I. Wesley, USDA-ARS

Butcher and Helmann, 2006

42

cognate immunity gene is predicted by sequence similarity to be homologous to L. monocytogenes lmo2570. These strains included B. subtilis prototroph (PY49) (48) and its mutant EG351 (PY79 Pspac-hy-sdpABC) (gift of Dr. J. Helmann, Dept of Microbiology, Cornell University), which expresses SdpC under control of an inducible promoter.
L. monocytogenes strains were grown in brain heart infusion broth (BHI; Difco, Sparks, MD) at 37ºC with shaking (250 rpm) overnight (16-18 h), then were subcultured (1:100) and grown as described below for each experiment. B. subtilis strains were grown in Luria-Bertani (LB) broth as described for L. monocytogenes, unless otherwise stated. The ∆sigB/∆sigL strain grew more slowly than the 10403S, ∆sigB; and ∆sigL strains, requiring an additional incubation time of ~30 minutes to reach the same OD600. Mutant construction
An in-frame 543 base pair deletion within lmo2570 was created in L. monocytogenes 10403S using splicing-by-overlap extension (SOE) PCR and allelic exchange mutagenesis (25). Primers used were 5’-GGA AGC TTT AAG GCA CTG TGA GCC TGG-3’ (lmo2570 SOEA), 5’-TCA TAC TAG GAA ATA TAC CAA C3’(lmo2570 SOEB), 5’-GTT GGT ATA TTT CCT AGT ATG ATT ATT GTT GTT G-3’(lmo2570 SOEC), 5’-GGG GTA CCT CAG GTT CAC TGG CAG CTA G-3’ (lmo2570 SOED). Primers were synthesized by IDT Technologies (Coralville, IA). Allelic exchange mutagenesis was confirmed through PCR and subsequent DNA sequencing, the latter of which was performed by the Cornell BioResource Center (Ithaca, NY). The Δlmo2570 mutation did not affect growth rate of the mutant strain relative to the 10403S parent strain when both were grown in BHI at 37ºC with shaking at 250 rpm (data not shown).
43

Spot-on-lawn Assays Spot-on-lawn assays were performed in triplicate as described by (9). Briefly,
to create lawns, 100µl of a given strain, (i.e., 10403S, Δlmo2570, ΔsigB, ∆sigL, or ∆sigB/∆sigL) that had been grown to an optical density of OD600= 0.4 was inoculated into 2 ml of 0.7% LB soft agar that had been tempered at 50ºC. To induce Pspacregulated transcription of sdpABC when EG351 was used as the spotting strain, 1mM isopropyl-β-D-thiogalactopyranoside (IPTG) also was added to the tempered agar that had been inoculated with bacteria. Each mixture was then poured into one well in an 8-well rectangular multidish (26mm x 33mm; Nunc, Rochester, NY). The plates were then dried in a laminar hood for 30 minutes. Subsequently, 4µl of the strain being assessed for bacteriocin production (e.g., 10403S, PY79 or EG351), which had been grown to an OD600= 0.6, was spotted on the agar in the middle of each well. The plates were covered with lids and incubated in a moist container at 37ºC for 22-24 hours. In addition to the 10403S, PY79 or EG351 test strains, isolates representing 5 Listeria species also were used as spotting strains to determine if the lawn strains would demonstrate sensitivity to bacteria representing different species within the same genus (Table 3.1). Sensitivity of lawns to potential bacteriocin producer strains was assessed by measuring the zone of inhibition (zoi) around the growth of the spotted strain. Radii of zoi were determined by measuring the diameters of both the spotted colony and the surrounding zoi in pixels (px). The diameter of the spotted colony was then subtracted from the diameter of the zoi and the resulting product was divided by 2 (to yield a radius). Measurements were performed using Adobe® Photoshop® CS (Adobe Systems Incorporated, Mountain View, CA.).
Radii of zoi produced on the various lawn strains were initially compared to zoi produced on the 10403S reference lawn using one-way ANOVA with Dunnett’s ttest, using SAS® 9.0 (SAS Institute, Inc., Cary, NC). To determine if there were
44

statistically significant interaction effects between the sigB and the sigL deletions, a two-way ANOVA (with Tukey’s adjustment for multiple comparisons) was performed. In this model, the dependent variable was zoi radius, the independent variables included sigB + sigL + sigB*sigL + replicate. The factors “sigB” and “sigL” in the model indicate the presence or absence of that gene in the strains tested. An adjusted p<0.05 was considered significant in this and all other statistical analyses. Nisin MIC determination
The objective of this experiment was to determine a minimal inhibitory concentration of nisin for log phase L. monocytogenes to enable selection of an appropriate sub-lethal concentration for subsequent qRT-PCR experiments. Nisin’s solubility and activity are optimal at pH 3 and 3.5, respectively (1), therefore, nisin is typically dissolved in an acidified solution prior to use (26, 31). As σB expression and activity are induced at low pH (3, 44), we predicted that addition of nisin in an acidified solution to the various cultures would up-regulate expression of the σB regulon, thus conferring a survival advantage to the wildtype over the ∆sigB strain (20, 21, 46) and, hence, confounding interpretation of our experimental results. Therefore, to avoid induction of σB activity, nisin was dissolved in sterile distilled water (1000AU/ml) and the pH of the final solution was adjusted to 7.0 using 0.01N sodium hydroxide. Nisin solutions at pH 7.0 were used throughout these experiments. The nisin solutions were filter sterilized with a 0.2µm, 25mm syringe filter (NALGENE®, Thermo Fisher Scientific, Waltham, MA) and diluted to the test concentrations. The minimum inhibitory concentration (MIC) of nisin (Sigma; St. Louis, MO) was determined for all strains of L. monocytogenes by measuring absorbance at OD600 using a FusionTM Universal Microplate Analyzer (PerkinElmer; Shelton, CT). Strains were grown overnight, subcultured 1:100, and grown to OD600=0.4. Strains were inoculated to a final concentration of 1x104 CFU/well into 96-well round bottom
45

microplates (Costar, Corning, NY) and the edges were sealed with Parafilm® (Alcan Packaging; Neenah, WI) to prevent evaporation. OD600 measurements were taken following 24 h incubation at 37ºC with shaking. The lowest concentration that inhibited growth for all strains after a 24 hr incubation in a 96 well plate format was 100AU nisin/ml, as determined in three replicate trials. Therefore, a sub-lethal concentration of 75AU nisin/ml was used for TaqMan qRT-PCR assays. Total RNA isolation
L. monocytogenes 10403S and ΔsigB were grown to logarithmic phase (OD600=0.4) and collected after (i) incubation for 10 min following addition of nisin in sterile distilled water to yield a final concentration of 75AU/ml nisin; (ii) incubation for 10 min following addition of an equivalent volume of sterile distilled water without nisin; (iii) incubation for 10 min without any addition. RNA isolation and purification was performed as previously described (41, 44), except that DNase treatments were performed using TURBOTM DNase (Ambion, Austin, TX) following the manufacturer’s instructions. Total nucleic acid concentrations and purity were estimated using absorbance readings (260 nm/280 nm) on a NanoDropTM ND-1000 spectrophotometer (NanoDrop Technologies, Rockland, DE). Quantitative Reverse-Transcriptase PCR (qRT-PCR)
Transcript levels of lmo2570, as well as of two housekeeping genes, rpoB and gap, were quantified using TaqMan primers and probes and the ABI Prism® 7000 Sequence Detection System (Applied Biosystems, Foster City, CA) as previously described (29). Data were analyzed using the ABI Prism® 7000 Sequence Detection System (SDS) software (Applied Biosystems) as previously described by Sue et al. (44). Primer Express® 1.0 software (Applied Biosystems) was used to design oligonucleotide primers and TaqMan probes for lmo2570: forward primer (5’- AAG TGG CGG TGC ATT TCG-3’), reverse primer (5’-TAA GCC AAG CCA CTT TTG
46

CAT-3’), probe (6FAM 5’-ACG GAC TTC TCC CCA GAT-3’ MGB-NFQ). Primers and probes for gap and rpoB were previously described (43, 44), respectively.
Transcript levels of lmo2570, as determined by qRT-PCR, were log10 transformed and then normalized to the geometric mean of transcript levels from the housekeeping genes rpoB and gap as previously described (29). Statistical analyses of normalized lmo2570 transcript levels were performed using one-way ANOVA and Tukey’s studentized range (HSD) test, performed in SAS® 9.0 (SAS Institute). Nisin survival assay
The objective of this assay was to measure relative survival characteristics of stationary phase L. monocytogenes 10403S, ΔsigB, ΔsigL, ΔsigB/ΔsigL,and Δlmo2570 strains in the presence of an initially lethal concentration of nisin (150AU/ml nisin). Strains were grown in BHI at 37oC with shaking overnight, followed by a 1% subculture and growth to logarithmic phase (OD600=0.4), followed by a second subculture and growth to stationary phase (OD600=1.0 +3 hours), followed by a third 1% subculture (0.5ml into 50ml, final concentration of ~2x107 CFU/ml) in a 300 ml flask (Bellco, Vineland, NJ). Nisin (150 AU/ml, pH 7.0) was added to the BHI and cultures were incubated at 37oC with shaking for an additional 9 h. Bacterial numbers were determined prior to and after the addition of nisin. Specifically, samples were taken at 0, 0.5, 1, 2, 3, 4, 5, 6, and 9 h post-addition and spiral plated on brain heart infusion (BHI) agar using a Spiral Biotech Autoplate® 4000 (Spiral Biotech; Norwood, MA). Colonies were enumerated with a QCountTM (Spiral Biotech) after 24 h incubation at 37°C. Colony counts were transformed to log10 CFU/ml.
Data from the nisin survival assay were used to calculate two parameters: (i) bacterial reduction after 0.5 h of nisin exposure; and (ii) growth rate during recovery and re-growth (between 1 and 9 h after nisin exposure). Linear regression was used to determine the slope representing the change in bacterial numbers for each strain from
47

1 h to 9 h (i.e., the period when viable cell numbers were increasing [re-growth]); this value represents the bacterial growth rate in log10 growth/h. Statistical analyses were then performed on both parameters. First, a one way ANOVA (with Dunett’s t-test or Tukey’s studentized range [HSD] test) was performed to determine if (i) bacterial reduction or (ii) growth rate differed between the mutant strains and the parent strain. To determine if there were statistically significant interaction effects between the sigB and the sigL deletions, a two-way ANOVA (with Tukey’s adjustment for multiple comparisons) was performed. In this model, the dependent variable was either (i) bacterial reduction after nisin exposure or (ii) growth rate during re-growth; the independent variables included sigB + sigL + sigB*sigL + replicate. The factors “sigB” and “sigL” in the model indicate the presence or absence of that gene in the strains tested.
RESULTS No intragenus competition was evident between L. monocytogenes and the
other Listeria strains tested (Table 3.1). Specifically, no zones of inhibition occurred between any of the listerial species that were used as spotting strains (L. innocua, L. ivanovii, L. welshimeri, or L. seeligeri) and any of the L. monocytogenes lawn strains (10403S, ∆sigB, ∆sigL or ∆sigB/∆sigL; data not shown).
lmo2570 is σB, but not σL dependent and does not contribute to resistance to nisin or SdpC
qRT-PCR was initially used to determine whether either σB or σL contributes to transcription of lmo2570, a putative bacteriocin immunity gene (Figure 3.1). lmo2570 transcript levels were consistently and significantly lower in the ΔsigB strain as compared to the 10403S parent strain (p<0.05; Figure 3.1), indicating σB-dependent
48

Figure 3.1: Normalized log transformed lmo2570 transcript levels for L. monocytogenes 10403S (grey bars), ΔsigB (black bars) and ΔsigL (hatched bars). Transcript levels were determined by qRT-PCR using RNA isolated from logarithmic phase (OD600= 0.4) L. monocytogenes that had been: (i) incubated for 10 min following addition of nisin in sterile distilled water to yield a final concentration of 75AU/ml nisin; (ii) incubated for 10 min following addition of an equivalent volume of sterile distilled water without nisin; or (iii) incubated for 10 min without any addition. Transcript levels were log transformed and normalized to the geometric mean of the transcript levels for the housekeeping genes rpoB and gap. Values represent mean transcript levels from three independent RNA collections; error bars indicate one standard deviation from each mean. Overall ANOVA (GLM) showed a significant effect of the factor “strain”, but no effect of the factor condition (i.e., “no addition”, “addition of water”, or “addition of nisin”). Tukey’s test showed significantly lower transcript levels for lmo2570 in the sigB strain as compared to the parent strain; transcript levels did not differ significantly between the sigL strain and the parent strain.
49

transcription of lmo2570. While lmo2570 transcript levels were consistently higher in the ΔsigL strain as compared to the parent strain, this difference was not significant (p>0.05; Figure 3.1). The presence of nisin at a sub-lethal concentration (75 AU/ml) did not affect lmo2570 transcript levels (Figure 3.1). Normalized lmo2570 transcript levels in 10403S were low, ranging from 0.007 to 0.034. To put these low lmo2570 transcript levels into biological context, in 10403S, ~0.02 transcripts of lmo2570 were present relative to the mean transcript levels of the highly expressed housekeeping genes, rpoB and gap, as indicated in Figure 3.1. The lmo2570 transcript levels observed in the present study are on the same order of magnitude as those reported previously for other σB-dependent genes (e.g., opuCA and bsh (11)).
Spot-on-lawn assays were used to compare the sensitivities of L. monocytogenes 10403S and Δlmo2570 to SdpC, the antimicrobial peptide whose cognate immunity gene shares amino acid similarity with Lmo2570. Specifically, B. subtilis PY79 (which naturally produces SdpC) and B. subtilis EG351 (which overexpresses SdpC in the presence of IPTG) were spotted on lawns of either 10403S or Δlmo2570. Zones of inhibition for 10403S and Δlmo2570 (Table 3.2) did not differ significantly (p>0.05), indicating that lmo2570 does not contribute to SdpC resistance. Neither 10403S nor Δlmo2570 showed inhibition by 10403S (Table 3.2) or by any other Listeria species (data not shown), indicating absence of intragenus inhibition, at least among the strains tested.
To characterize the responses of stationary phase L. monocytogenes 10403S and Δlmo2570 to nisin, we evaluated survival of ~ 2 X 107 CFU/mL 10403S or Δlmo2570 in BHI in the presence of 150 AU nisin /ml (Figure 3.2). Exposure to nisin for 30 min led to 4.0 and 3.9 log reductions in bacterial numbers for 10403S and Δlmo2570, respectively (Figure 3.2), indicating no difference in nisin susceptibility between these strains. After the initial killing by nisin, bacterial numbers increased
50

Strain spotteda

Table 3.2: Spot-on-lawn assay results

Mean zone of inhibition radii (SD) for L. monocytogenes strainsb

10403S ∆lmo2570 ∆sigB

∆sigL ∆sigB/∆sigL

L. monocytogenes 10403S

0.0 (0.0) 0.0 (0.0)

0.0 (0.0)

0.0 (0.0)

0.0 (0.0)

B. subtilis PY79 11.8 (1.6) 10.0 (2.2) 11.5 (4.9) 16.7 (0.6) 20.5* (2.3)

B. subtilis EG351 (IPTG)

13.5 (3.9) 8.3 (2.9) 28.2* (1.6) 20.7* (5.3) 32.0* (2.6)

a Strains spotted on the lawns are listed in left column; the average zoi radius around each spot

is shown for each lawn, with standard deviations (SD) in parentheses.

b Radii were determined from three independent experiments by measuring diameters of the zones of inhibition (zoi) in pixels using Adobe® Photoshop® CS; “*” indicates values that are

significantly different (P<0.05; one way ANOVA with Dunnett’s t-test) from the zoi produced

on the L. monocytogenes 10403S lawn

51

Figure 3.2: Viable numbers of stationary phase L. monocytogenes 10403S, Δlmo2570, ΔsigB, ΔsigL and ΔsigB/ΔsigL at various time points following exposure to 150 AU/ml nisin. Values are reported as log CFU/ml. Data shown represent the average of four independent experiments; error bars represent one standard deviation from each mean.
52

between 1 and 9 h post-nisin exposure, reflecting growth of cells that had survived nisin exposure. Specifically, after 9 h, bacterial numbers were 6.9 and 6.6 log for the parent strain and Δlmo2570, respectively, further supporting that Δlmo2570 and 10403S susceptibilities to nisin do not differ.
σB and σL both contribute to resistance to the SdpC antimicrobial peptide produced by B. subtilis
Spot-on-lawn assays were used to compare the sensitivities of L. monocytogenes 10403S, ∆sigB, ∆sigL, and ∆sigB/∆sigL to the antimicrobial peptide SdpC. The zones of inhibition (zoi) produced by B. subtilis PY79 did not differ between L. monocytogenes 10403S and ∆sigB or ∆sigL; the zoi produced by PY79 on the ∆sigB/∆sigL lawn was significantly larger than the zoi produced on 10403S (p<0.05; Table 3.2), suggesting the possibility that σB and σL contribute to SdpC resistance in an additive fashion. B. subtilis EG351 produced significantly larger zoi on ∆sigB (28.2 ± 1.6 px), ∆sigL (20.7 ± 5.3 px) and ∆sigB/∆sigL lawns (avg 32.0 ± 2.6 px), as compared to the zoi produced on 10403S (13.5 ± 3.9; Table 3.2). Two-way ANOVA analyses of zoi data showed no significant “sigB*sigL” interaction effect on SdpC sensitivity (p>0.05), further supporting the notion of additive (as compared to multiplicative) contributions of σB and σL to SdpC resistance. Overall, results from this assay indicate that alternative sigma factors σB and σL both contribute to resistance to SdpC.
σB and σL both contribute to response to the bacteriocin nisin A 30 min exposure to nisin (150AU/ml) resulted in a 4.0 log reduction in
bacterial numbers for stationary phase 10403S. By comparison, reduction of ∆ sigB bacterial numbers was significantly less (3.0 log reduction; p<0.05; Figure 3.2),
53

indicating increased nisin resistance of this strain relative to that of 10403S. Bacterial numbers for the ∆sigL and ∆sigB/∆sigL strains were reduced by 3.9 and 4.5 log; these reductions were not significantly different from that of 10403S (p>0.05; Dunnett’s ttest). Interestingly, log reduction for the ∆sigB/∆sigL strain was significantly (p>0.05; Tukey’s HSD) greater (4.5 log) as compared to the ∆sigB strain (3.0 log reduction), indicating a significant effect of the sigL deletion on nisin killing in a ∆sigB background. Based on the reductions in bacterial numbers between 0 and 0.5 h, twoway ANOVA analyses found a significant “sigL*sigB” interaction effect on survival following nisin exposure, indicating that the effect of one sigma factor on survival differs depending on the presence or absence of the other sigma factor.
After initial killing by nisin, all strains showed re-growth between 1 and 9 h post nisin exposure. Rates of re-growth, as represented by the slopes of the graphs between 1 and 9 h post-exposure, were compared among the strains. While the growth rate for the ∆sigL strain (0.39 log CFU/h) was not significantly different from that of the parent strain (0.41 log CFU/h), both the ∆sigB and the ∆sigB/∆sigL strains showed significantly slower growth rates (0.36 log CFU/h for both; p<0.05, Dunett’s t-test) as compared to the parent strain. Although no significant “sigB*sigL” interaction effect on nisin survival was identified by two-way ANOVA analysis, the sigB deletion had a significant effect on growth rate, with sigB deletion strains (i.e., ∆sigB, ∆sigB∆sigL) showing slower growth rates as compared to the corresponding strains with intact sigB genes.
DISCUSSION We hypothesized that alternative sigma factors σB and σL and the hypothetical
bacteriocin immunity gene, lmo2570, contribute to L. monocytogenes antimicrobial response. This hypothesis was based on previous observations including: (i) σB and its
54

homolog σF contribute to antimicrobial response in other Gram-positive organisms (6, 33, 37), (ii) L. monocytogenes σL controls sensitivity to class IIa bacteriocins, mesentericin Y105, pediocin PA-1, and enterocin A, and (iii) the putative σBdependent lmo2570 has sequence homology to the B. subtilis bacteriocin immunity gene, sdpI. To test our hypothesis, we assessed the sensitivities of strains with null mutations in sigB, sigL, sigB/sigL, or lmo2570 to the antimicrobial peptides SdpC and nisin. We also characterized transcription of lmo2570 in 10403S, ∆sigB, and ∆sigL strains exposed to a sub-minimal inhibitory concentration of nisin. Our results show that (i) while lmo2570 is σB-dependent, it does not contribute to resistance to SdpC or nisin; and (ii) both σB and σL contribute to resistance to the antimicrobial peptide SdpC, as shown by results from spot-on-lawn assays. In addition, both σB and σL affect L. monocytogenes sensitivity to nisin in broth survival assays. Specifically, while loss of only sigB renders the resulting strain more resistant to nisin than the parent strain, loss of sigB in a ∆sigL background leads to reduced nisin resistance relative to the original parent strain.
The effects of antimicrobial peptides on L. monocytogenes appear to differ depending on the class of peptide, the strain, initial number of bacteria, growth phase, and the assay used for evaluation. To illustrate, σB was reported previously to contribute to L. monocytogenes tolerance to nisin or lacticin 3147 in broth assays (4), but not to nisin, lacticin 3147, or sakacin A resistance in agar overlay assays (4, 38). Moorhead and Dykes (2003) showed that a L. monocytogenes serotype 1/2a wildtype strain was less resistant to nisin than a serotype 4c wildtype strain, suggesting differences in antimicrobial sensitivities among strains. It is also likely that other environmental stresses (in addition to the presence of the antimicrobial peptide) imposed upon the cells also evoke differential phenotypic responses from the cells
55

(e.g., exposure to low pH induces σB activity in L. monocytogenes), which may provide cross-resistance to multiple stresses (21).
lmo2570 is σB-dependent, but does not contribute to antimicrobial resistance L. monocytogenes Lmo2570 is 45% similar at the amino acid level to the B.
subtilis immunity protein SdpI, which confers immunity against SdpC (9, 18), therefore, we hypothesized that lmo2570 may play a role in antimicrobial immunity in L. monocytogenes. lmo2570 was predicted as σB-dependent in previous microarray experiments (28). As previous reports have shown bacteriocin immunity genes can contribute to resistance to multiple antimicrobials (35), in addition to examining its role in SdpC resistance, we also tested the contributions of lmo2570 to resistance to the commercially available bacteriocin, nisin. The ∆lmo2570 strain did not show reduced sensitivity to either SdpC or nisin. Exposure to nisin did not induce transcription of lmo2570 in either the wildtype or any of the mutant strains. lmo2570 thus does not appear to be important for SdpC or nisin resistance in L. monocytogenes. A role for this gene in resistance to other bacteriocins or in contributing to nisin and SdpC resistance under environmental conditions not tested here cannot be excluded by our data, however. Our confirmation of lmo2570 as σB-dependent suggests a role for lmo2570 in L. monocytogenes survival or growth under conditions that remain to be defined.
σB and σL both contribute to L. monocytogenes response to SdpC and nisin We found clear evidence that alternative sigma factors σL and σB both
contribute to SdpC resistance. Specifically, as determined in a spot-on-lawn assay, we showed that both the ∆sigB and the ∆sigL strains were significantly more susceptible to the bactericidal effect of the antimicrobial peptide SdpC produced by B. subtilis
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EG351 than the otherwise isogenic 10403S parent strain. Characterization of a ∆sigB/∆sigL double mutant strain suggested that deletion of both genes had an additive, but not an interactive, effect on SdpC resistance. However, deletions of both ∆sigB and ∆sigL had an interactive effect on L. monocytogenes resistance to nisin. Specifically, while the ∆sigB/∆sigL strain showed decreased resistance to nisin as compared to the ∆sigB strain, the ∆sigB strain showed increased resistance to nisin as compared to the parent strain, which has both sigB and sigL intact. The interactive effect observed following the loss of both sigB and sigL may indicate that at least some genes important for recovery and re-growth following nisin exposure are coregulated, either directly or indirectly, by these alternative sigma factors. We also found that re-growth of both the ∆sigB and the ∆sigB/∆sigL strains following nisin exposure was slower than that of the parent strain, consistent with previous observation that σB is important for B. subtilis recovery following rifampin treatment (2). The overall observation that a deletion of the gene encoding σL (i.e., a single deletion) does not affect L. monocytogenes resistance to nisin is consistent with observations by (15), who reported that σL (which has also been designated as RpoN) is not involved in L. monocytogenes nisin resistance. Relative to its otherwise isogenic parent, a ∆rpoN L. monocytogenes strain (i.e., a strain lacking σL) has previously shown increased resistance to the class IIa nonlantibiotic bacteriocins mesentericin Y105, pediocin PA-1, and enterocin A (15, 42), consistent with our findings that σL contributes to resistance to some bacteriocins (i.e., SdpC). Our findings, as well as previous findings by others (15), thus support that different regulatory elements are critical for the ability of L. monocytogenes, and other bacteria, to respond to different bacteriocins, a notion consistent with the diverse nature of this group of antibacterial compounds.
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Overall, our data indicate that σB and σL both contribute to the ability of L. monocytogenes to respond to antimicrobials. Regulatory interactions among multiple alternative sigma factors also have been shown to contribute to antibiotic resistance in B. subtilis. Specifically, three (σM, σW, and σX) of the seven B. subtilis ECF alternative sigma factors have overlapping regulons that contribute to antibiotic resistance, as demonstrated by the greatly enhanced sensitivity of a triple MWX mutant to various antimicrobials, including nisin (32). Strains bearing single or double mutations in the genes encoding these alternative sigma factors displayed considerably less antimicrobial sensitivity than the strain with the triple mutation (32). Thus, in combination with previous studies, our data support a model in which multiple alternative sigma factors contribute to regulatory networks important for finetuning transcriptional regulation of gene expression to help optimize bacterial cell resistance to antimicrobial peptides.
CONCLUSIONS Alternative sigma factors have been shown to regulate genes and operons
critical for resistance to antimicrobials in various bacteria, including B. subtilis, L. monocytogenes, Salmonella enterica serovar Typhimurium, S. aureus, and Vibrio cholerae (9, 14, 34, 42, 49). Our data indicate that σB and σL, as well as the simultaneous presence of both σB and σL, contribute to antimicrobial response in L. monocytogenes in a manner that is dependent on the antimicrobial that is present. The results reported in this study provide further evidence of the importance of regulatory networks for fine-tuning L. monocytogenes responses to changing environmental conditions (12).
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49. Zhang, H., K. Morikawa, T. Ohta, and Y. Kato. 2005. In vitro resistance to the CSαβ-type antimicrobial peptide ASABF-α is conferred by overexpression of sigma factor sigB in Staphylococcus aureus. J Antimicrob Chemother 55:686-691.
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CHAPTER 4 IDENTIFICATION OF A SMALL MOLECULE THAT INHIBITS THE L. MONOCYTOGENES σB REGULON AND ITS VIRULENCE ASSOCIATED
FUNCTIONS
ABSTRACT For some bacteria, current treatment options are minimally effective. In fact, for Listeria monocytogenes, which causes the rare but potentially deadly disease listeriosis, the mortality rate remains at 20-30% despite the use of antibiotics. This indicates the need for the identification of new drug targets for increasing efficacious treatment alternatives. The L. monocytogenes alternative stress response sigma factor σB represents a conserved biological target, highly relevant to several important Grampositive human pathogens. In these pathogens, σB regulates virulence genes and contributes to survival under host-associated stress conditions, such as those encountered in the gastrointestinal lumen. Using a high-throughput cell-based format to identify inhibitors of L. monocytogenes σB, approximately 57,000 compounds were screened from a compilation of natural and synthesized small molecules. Subsequent screening identified a compound on which transcriptional and phenotypic profiling were performed. The compound, sigmastatin (IC50=3.5µM), showed targeted down regulation of the majority of the σB regulon yielding a transcriptional profile similar to a genetic knockout of sigB. From the genes downregulated by sigmastatin, 75% were σB dependent. Specifically, according to microarray analysis, of the 208 genes that were downregulated as a result of treatment with this compound, 156 were positively regulated by σB, including key virulence and stress response genes such as inlA, inlB, bsh, hfq, opuC, bilE. This compound also hinders L. monocytogenes invasion in human intestinal epithelial cells. Interestingly, this small molecule was also capable of
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inhibiting σB activity in Bacillus subtilis. The ability of sigmastatin to produce a chemical knockout phenotype comparable to a genetic knockout supports its usefulness as a biological probe. Not only does this allow us to explore the molecular underpinnings of L. monocytogenes that drive virulence and stress response, but it also helps to elucidate complex regulatory networks in order to develop better methods to control this pathogen.
INTRODUCTION Listeria monocytogenes is the causative agent of a rare, but potentially fatal,
foodborne disease called listeriosis. Listeriosis has a high case fatality rate, accounting for ~10% of all deaths from foodborne diseases in the US (61). According to the Centers for Disease Control, in 2008 the incidence of listeriosis infections had not declined in the US in the preceding three years (1). Furthermore, there has been an increasing incidence of listeriosis in Europe since 2004 (33). These data suggest the need for development of more effective preventive strategies and interventions.
L. monocytogenes can transition from a saprotrophic existence under a wide range of environmental conditions (70) to intracellular infection in a diverse array of hosts (101). The ability of L. monocytogenes to transform from saprotroph to intracellular pathogen is influenced by regulatory networks that control virulence factor expression in response to environmental signals (18). σB is one important component of a network that links environmental stress survival and virulence in L. monocytogenes (72, 98). σB networks contribute to transmission of L. monocytogenes, including during the gastrointestinal and systemic stages of infection (14, 30).
Sigma (σ) factors are dissociable subunits of prokaryotic RNA polymerase. The association of a specific alternative sigma factor (such as σB) with core RNA polymerase under appropriate environmental conditions reflects a transcriptional
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regulatory mechanism that can rapidly reprogram global gene expression patterns in response to environmental signals. Through microarray analyses, the σB regulon in L. monocytogenes has been reported to include >150 genes (45, 77). Because of its key role in L. monocytogenes stress resistance and virulence, and hence, in transmission of this pathogen, σB is a promising target for investigation and development of novel therapeutic intervention strategies.
Identification of novel anti-infective agents by screening small-molecule libraries for inhibitors or perturbational agents of specific targets is one promising approach for development of new therapeutics. Such strategies have been used to identify inhibitors of virulence-related two-component regulators and quorum sensing in Pseudomonas aeruginosa (36, 37, 83), inhibitors of the Type 3 Secretion System in multiple Gram-negative bacteria (3, 44, 69), inhibitors of anthrax lethal factor in Bacillus anthracis (41, 75, 84), and inhibitors of the virulence regulator ToxT in Vibrio cholerae (40), among others. To test the hypothesis that a small molecule can inhibit σB activity, a high-throughput assay was used to screen multiple smallmolecule libraries. The ability of the most promising compound to inhibit σB activity was further assessed by small molecule binding microarray analysis, whole genome microarray, qRT-PCR, and phenotypic profiling, including bile salt hydrolase activity and Caco-2 cell invasion assays. Further, the compound was assessed for its ability to inhibit σB activity across genera.
MATERIALS AND METHODS Strain and media selection
As L. monocytogenes opuCA transcription has been clearly established as σBdependent (45, 97), an opuCA-gus reporter fusion was selected for monitoring σB activity. The strains used in this study included the L. monocytogenes parent strain
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10403S (serotype 1/2a)(7), its otherwise isogenic sigB mutant derivative (∆sigB; FSL A1-254 (102)), a reporter strain for σB activity (10403S opuCA-gus; FSL S1-063) and a negative control reporter strain for σB activity (∆sigB opuCA-gus; FSL S1-059) (Table 4.1). To achieve low background fluorescence, a chemically defined minimal medium (76) with 25mM glucose (DMG) (26) was used for the high-throughput screen. Cells were grown in brain heart infusion broth (BHI; Difco, Sparks, MD) for phenotypic and transcriptional profiling assays. High-throughput Cell-Based Small Molecule Screen Primary Cell Based Screen
The L. monocytogenes opuCA-gus fusion strain FSL S1-063 was used in a cellbased high-throughput screen (HTS) against ~ 57,000 compounds. As reported at ChemBank.Broad.Harvard.edu, compounds came from a multitude of libraries including libraries of (i) known bioactive compounds (i.e., SPBio, SMP libraries); (ii) synthetic compounds from diversity oriented synthesis (i.e., CMLD, ICCB, PK04, Ald1.1-H, Sulf1.1-A libraries); (iii) natural products (i.e., PhilEx, ICBGEx libraries), and (iv) pharmaceuticals.
Multidrop liquid handling robots (Matrix, ThermoFisher) were used to dispense 27µl of DMG into black walled clear bottom 384-well plates (Nunc; Rochester, NY), then 100nl of ~10mM stock of each small molecule was transferred from library stock or source plate to assay plates in a total volume of 30µl (e.g., 80092163 had a stock concentration of 19.3mM, producing a 64.3µM final concentration in the well) by the CyBi®-Well Vario pin transfer robot (CyBio AG; Jena, Germany). Each source plate contained ~15 dimethyl sulfoxide (DMSO)-only wells as negative internal control wells. All source plates were pinned in duplicate to provide experimental replicates (i.e., plates A and B). Two DMSO base plates were also included as external plate controls. A custom assay plate containing 192 wells of
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Table 4.1: Strains used in this study

Strain 10403S FSL A1-254 (LM ∆sigB)
FSL S1-063 (LM opuCA-gus)

Characteristics
Laboratory Parent Strain Control strain, complete inhibition of σB activity
Reporter strain for σB activity in L. monocytogenes

Reference Bishop and Hinrichs, 1987
Wiedmann et al., 1998
Ferreira et al., 2003

FSL S1-059 (LM ∆sigB opuCAgus)
FSL P1-015 (BS PB198 amyE::pDH32-ctc trpC2)

Negative control reporter strain for σB activity in L.
monocytogenes
Reporter strain for σB activity in B. subtilis

Ferreira et al., 2003 Boylan et al., 1992

FSL P1-017 (BS PB345 amyE::pDH32-ctc sigB∆3::spc
trpC2)

Negative control reporter strain for σB activity B.
subtilis

Boylan et al., 1993

FSL P1-019 (BS PB252 amyE::PA-lacZ trpC2)

Reporter strain for σA activity B. subtilis

Wise and Price, 1995

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10403S opuCA-gus strain FSL S1-063 and 192 wells of the otherwise isogenic ΔsigB opuCA-gus strain FSL S1-059 was treated with only DMSO, was used as a control. After 3µl of L. monocytogenes grown to OD600=0.4 and diluted 1:50, was added to the plate containing the compounds, the plates were sealed and incubated for 18 h at 37°C. To determine bacterial numbers after the incubation period, absorbance (OD600) was measured using a Synergy™ HT Multi-Mode Microplate Reader (BioTek Instruments; Winooski, VT) at approximately 18 h. To prepare the plates for fluorescence measurements, black seals (Perkin Elmer; Waltham, MA) were affixed to the back of the plates after the T=18 h absorbance readings. For the GUS assays, cells were lysed using 5µl of 2x CelLyticB (Sigma; St. Louis, MO and Protease Inhibitor Cocktail (Sigma) mixture (1ml 2x CelLyticB and 0.05ml Protease Inhibitor Cocktail), immediately prior to addition of 4µl of 1.6mg/ml 4-methylumbelliferyl β-D glucuronide hydrate (4-MUG; Sigma) in DMSO. Reactions were incubated in the dark for 1 h at room temperature (~23oC) then reactions were stopped by addition of 0.2M Na2CO3 (Sigma). Fluorescence was read using a Wallac 2102 EnVision™ Multilabel Reader (Perkin Elmer) with an excitation wavelength of 355nm and an emission of 460nm. Statistical analysis of primary screen data
To identify compounds that inhibited σB activity without affecting L. monocytogenes growth, opuCA-directed GUS activity in the presence of each compound was calculated by dividing relative fluorescence units (RFU) by cell density (in OD600 units) (RFU/OD, (88)) Statistical analyses were conducted in collaboration with the Broad Institute and performed as previously described (47, 50). Briefly, the median value from the internal DMSO-only wells in a given plate were subtracted from raw values of all 12 to 20 internal DMSO-only wells within the same assay plate. The resulting scores were used to build a distribution across assay plates
71

between replicates (i.e., replicate plates A and B), which was used to identify outliers within the internal DMSO-only wells (i.e., values that differ from the median by more than 2.57 standard deviations). Values for the DMSO-only wells (after removal of outliers) were used to calculate an average baseline for each plate. Raw values from duplicate plates containing only DMSO inoculated with the wildtype gus fusion strain were used to calculate the standard deviation of the baseline value per replicate. The average baseline per plate and standard deviation per replicate, together with raw values from wells with small molecules, were used to calculate Z scores for each small molecule; the Z score represents the deviation of a well with a small molecule from the baseline mean (in standard deviation units). The Z score allows interpretation of the effect of a small molecule on the activity of σB. Z scores <-3 standard deviations were considered significant, indicating that a given small molecule inhibits the σB. Raw and analyzed data were deposited in ChemBank (86, 94). Z-scores were analyzed using the commercial software package Spotfire DecisionSite Analytics (TIBCO Spotfire; Somerville, MA) to enable 2 dimensional data visualization and identification of positive candidates with high reproducibility. Compounds that generated Z-scores below -3 (based on adjusted RFU/OD data values) were considered to inhibit σB activity. Secondary screen and dose response curve
Forty-one compounds that appeared to inhibit σB activity (Z-score ≤-3 in both replicates) were selected for secondary cell-based screening using the same format and reporter fusion as described above. These compounds were assessed for their abilities to interfere with σB activity and to calculate initial IC50 values (i.e., concentration that inhibits 50% of σB activity). Each compound was diluted in a series of six 1/5 dilutions of the stock concentration. For example, starting from 19.3mM stock, 80092163 was diluted in a series of six 1 to 5 dilutions and pinned into an assay plate. A
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total of 14 compounds were assessed for follow-up analyses by “drug developability criteria” including size/molecular weight, hydrogen bonds, structure-based potential toxicity, etc (58). Small-Molecule Microarray Screens
Small-molecule microarrays (SMMs) were printed on glass slides at the Broad Institute as described previously (12, 13, 22). Two different arrays (each slide printed with 8,500 small-molecule [SM] spots and 1,500 DMSO control spots) were used for our screens. The immobilized SMs included 8,500 compounds from diversity oriented synthesis (DIV06) and 8,500 compounds representing natural products, FDAapproved drug-like compounds, and known bioactive compounds (NPC1) ((22) Chembank.broadinstitute.org). SMM screening (three replicates) was performed as described by Bradner et al. (12). Briefly, His-tagged σB was purified from E. coli M15 (kindly provided by W. Goebel (8)). Slides were incubated at 4°C with PBST (PBS plus 0.1% Tween 20) containing 1µg/µl of His-tagged σB and 1 mg/ml BSA. Slides were washed with PBST buffer 3 times. Slides were incubated in Alexa Fluor 647 labeled anti-His antibody (1:2000 in PBST) for 1 hour at 4°C. Slides were washed 3 times in PBST and 1 time with PBS. Slides were rinsed with deionized water to remove buffer salts and dried by centrifugation at <1000 rpm. Slides were then scanned with an Axon 4000B and analyzed using GenePix® Pro 6 image analysis software. Data analyses included (i) assessment of signal-to-noise ratio (SNR) of the spot feature; (ii) Z-score calculations based on comparison of signals from compound spots compared to signals from DMSO control spots within a slide; and (iii) composite Z-score calculations for data from the three replicates. Spotfire Analytics software was used for 3-dimensional data visualization. Compounds from the DIV 06 library with Z-scores ≥ 0.925 or from the NPC1 library with Z-scores ≥ 1.2 were considered
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potential binders. Compounds considered for further analysis were identified by both SMM and HTS. Phenotypic Profiling and qRT-PCR
The most promising lead compound identified by SMM and HTS (8009-2163) was not commercially available. Therefore, an analog (2-Phenyl-ethenesulfonic acid (4-fluoro-phenyl) amide or T0513-8332, FW 277) was obtained from Enamine Ltd. (Kiev, Ukraine). This compound, T0513-8332 (2-Phenyl-ethenesulfonic acid (4fluoro-phenyl) amide), designated sigmastatin, has a fluorine substituted for a hydrogen in the original compound. Sigmastatin was dissolved in DMSO to a final concentration of 10mM. The solution was filter sterilized using with a 0.1µm filter compatible with DMSO (OMNIPORETM Membrane filter, Millipore Corporation, Billerica, MA) which was fitted in a Swinney Stainless 13mm holder for syringe filtration (Millipore Corporation). Bile Salt Hydrolase (BSH) activity assay
As the L. monocytogenes bsh gene, which encodes bile salt hydrolase, is σB dependent (23, 45, 72, 96), a BSH activity assay was used to determine the optimal concentration of sigmastatin needed for σB inhibition. Four- well multidish plates (26mm x 33mm; Nunc) containing 6ml of either BHI agar or de Man, Rogosa and Sharp (MRS) agar medium (BD Biosciences; San Jose, CA) containing 0.5% (w/v) glycodeoxycholic acid sodium (GDCA) salt (Calbiochem®; San Diego, CA) (21) with either (i) no sigmastatin; or (ii) 96, 193 or 290 µM (5, 10, 15x the 19.3mM stock concentration) of sigmastatin were prepared and allowed to dry overnight. L. monocytogenes 10403S and ∆sigB were grown in BHI broth to exponential phase, defined as OD600= 0.4, then 4µl of culture was spotted in parallel on the MRS and BHI agars (BHI; Difco, Sparks, MD). The MRS agar plates were incubated in a BDBBL™ GasPak™ Anaerobic system (BD; Franklin Lakes, NJ) containing an activated
74

BD-BBL™ GasPak™ Plus anaerobic system envelope with Palladium Catalyst. BHI plates were incubated aerobically. Both sets of plates were incubated for 48 hours at 37°C. The assay was replicated three times. Cell collection and RNA isolation for qRT-PCR and microarray analyses
L. monocytogenes 10403S and ΔsigB strains were grown overnight in 5 ml of BHI broth at 37ºC with shaking (230 rpm), then were sub-cultured twice using a 1% (vol/vol) transfer into 5 ml of pre-warmed BHI. Each time, cells were grown to OD600 = 0.4. When the second sub-culture reached OD600 = 0.4, cells were treated with a total volume of 76 µl comprised of (i) sigmastatin (to yield final concentrations ranging from 1µuM to 128µM) and/or (ii) DMSO, followed immediately by addition of either 324µl of (i) 5M NaCl (to yield a final concentration of 0.3M NaCl, an osmotic stress that induces σB (77)) or (ii) sterile distilled water. Treated cultures were then incubated at 37ºC with shaking (230 rpm) for 10 min. Following incubation, a 2X volume of RNAprotectTM (Qiagen Inc, Valencia, CA.) was added to the treated cultures, mixed and held at room temperature for 10 min. The cells were harvested following centrifugation for 10 min at 5000 X g, supernatant was discarded and tubes containing cell pellets were stored at -80 ºC. RNA was extracted and DNase treated using Ambion RiboPureTM-Bacteria Kit (Ambion, Austin, TX) according to the manufacturer’s instructions. Total nucleic acid concentrations and purity were assessed using the NanoDrop ND-1000 spectrophotometer (NanoDrop Technologies, Rockland, DE). RNA quality was analyzed using the 2100 Bioanalyzer (Agilent Technologies; Santa Clara, CA) and only RNA with an integrity number of ≥8 was used. Each treatment was replicated at least 3 times. TaqMan qRT-PCR
Transcript levels from the σB-dependent genes opuCA and gadA, as well as from two housekeeping genes, rpoB and gap, were quantified from the harvested RNA
75

using TaqMan primers and probes and the ABI Prism® 7000 Sequence Detection System (Applied Biosystems, Foster City, CA) as previously described (46). Data were analyzed using the ABI Prism® 7000 Sequence Detection System (SDS) software (Applied Biosystems) as previously described by Sue et al. 2004. Primers and probes for gap and rpoB were reported previously (85, 97), respectively. Transcript levels from opuCA and gadA were log10 transformed and then normalized to the geometric mean of transcript levels from the housekeeping genes rpoB and gap as previously described (46). Statistical analyses of normalized opuCA and gadA transcript levels were performed using one-way ANOVA and Tukey’s Studentized Range HSD multiple comparison test using SAS® 9.0 (SAS Institute).
Starting with the primary screen concentration (64µM) of sigmastatin, the concentration needed to achieve 50% of maximal inhibition (IC50) was determined by measuring log-normalized transcript levels from opuCA collected from cells treated with a series of 1:2 dilutions. These data were analyzed using SigmaPlot® 10.0 (Systat Software Inc.; Evanston, IL) standard curve analysis under the pharmacology function. Invasion Assay
L. monocytogenes invasion assays using the human colorectal adenocarcinoma epithelial cell line Caco-2 (ATCC HTB-37) were performed as described by Garner et al. (2006) with minor modifications. Briefly, 5.0 x 104 Caco-2 cells were seeded into 24-well plates (Costar, Corning, NY) 48 h prior to infection. L. monocytogenes 10403S and ΔsigB strains were grown overnight in 5 ml of BHI broth at 37ºC with shaking (230 rpm), then were sub-cultured twice using a 1% (vol/vol) transfer into 5 ml of pre-warmed BHI. Each time, cells were grown to OD600 = 0.4. When the second sub-culture reached OD600 = 0.4, cells were treated with a total volume of 76 µl comprised of (i) sigmastatin to yield final concentrations of 64µM (primary screen concentration) or 8µM (lowest concentration with full efficacy according to qRT-
76

PCR) and/or (ii) DMSO, followed immediately by addition of either 324ul of (i) 5M NaCl or (ii) sterile distilled water. Treated cultures were then incubated at 37ºC with shaking (230 rpm) for 30 min. For infection, the Caco-2 cells were inoculated with approximately 2.0x 107 L. monocytogenes cells; L. monocytogenes numbers used for infection were confirmed by plating on BHI agar. Intracellular L. monocytogenes numbers were determined 90 min post infection as previously described (30). Invasion efficiency was calculated as the number of bacteria recovered relative to the lnumber of bacteria used for inoculation (i.e., Log ([CFU/ml recovered] /[CFU/ml inoculated]). Four biological replicates were each performed in triplicate wells. Statistical analysis was performed using one-way ANOVA and Tukey’s studentized range (HSD) test, performed in SAS® 9.0 (SAS Institute). Whole Genome Microarray cDNA labeling and hydbridization
cDNA labeling was performed as previously described (72) with minor modifications. For cDNA synthesis, 6 μg total RNA was mixed with 3 μg random hexamers and incubated for 10 min at 70°C, then held on ice for 5 min. Superscript III RT, Aminoallyl 2'-Deoxyribonucleoside Triphosphates, dithiothreitol, RNaseOUT, and buffer were added and the reaction was incubated at 42°C for approximately 17 h. The reaction was stopped and purified according to Ollinger et al., 2009 (72). cDNA coupling reactions with CyTM3 or CyTM5 monofunctional fluorescent dyes (GE Healthcare UK Ltd; UK) were performed for 1 h at room temperature. L. monocytogenes whole genome microarrays were constructed as previously described (15, 77). Microarray hybridization was performed as previously described (72). Array data were deposited at the Gene Expression Omnibus (GSE16887).
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Statistical analysis of microarray data Raw intensity values for all probes on each array were normalized using pin-
tip LOWESS (77) in R v.2.2.1 with the LIMMA package. Signals from two replicate probes on each array were averaged and log2 transformations were performed after normalization. Differences in transcript levels between strains were determined using a linear model and p values were determined using eBayes. Differences in transcript levels were considered meaningful if they met both adjusted p-values <0.05 (indicating significant expression), fold changes of ≥2 (indicating differential expression) and probe cross-hybridization index (CHI) of >90%. Although lmo0263 was inhibited by sigmastatin, the CHI was 80% and was not included in our assessments. Gene Set Enrichment Analysis (GSEA) (95) was used to identify gene sets that were significantly enriched among genes up or down-regulated in a given mutant strain. GSEA was run on the ranked list of Log Fold Change values obtained from the fitted normalized data in LIMMA with 1000 permutations and exclusion of gene sets with less than 5 or greater than 2000 members. Genes were classified into sets based on the TIGR Comprehensive Microbial Resource (http://cmr.tigr.org) subrole categories for L. monocytogenes EGD-e. False discovery rate q-values less than 0.25 were considered significant (95). β-galactosidase enzyme assays in B. subtilis
B. subtilis strains P1-015 (PB198 amyE::pDH32-ctc trpC2 (11)) and an otherwise isogenic sigB mutant P1-017 (PB345 amyE::pDH32-ctc sigB∆3::spc trpC2 (10)) were used as reporter strains for measuring σB activity (Table 4.1). The Pctc-lacZ reporter fusion was chosen for monitoring σB activity as ctc transcription has been clearly established as σB –dependent (65). A B. subtilis strain P1-019 (PB252 amyE::PA-lacZ trpC2 (104)) was used to assess whether treatment with 64µM of the compound affected the housekeeping sigma factor σA in B. subtilis. Strains were
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grown overnight in 5 ml of buffered Luria Bertani (LB) broth at 37ºC with shaking (230 rpm), then were sub-cultured twice using a 1% (vol/vol) transfer into 5 ml of prewarmed LB. Each time, cells were grown to OD600 = 0.4. When the second subculture reached OD600 = 0.4, cells were treated with 76µl of (i) sigmastatin and DMSO (final 8 or 64µM) or (ii) DMSO only, followed immediately by addition of 324µl of (i) 5M NaCl (final 0.3M) or (ii) sterile distilled water. Treated cultures were then incubated in the 37ºC shaking incubator for 30 min. OD600 was recorded and 0.2ml of the culture was added to a tube containing 2.8ml Z-buffer. 0.02ml toluene was added to permeabilize the cells. Pre-warmed 0.4ml of 4mg/ml ortho-nitrophenyl-βgalactoside (ONPG) was added and the time of addition was noted. The reaction proceeded for 85 minutes, after which, 1ml of 1M sodium carbonate was added to stop the reaction. OD420 was read and Miller Units were calculated (106). Statistical anlaysis of β-galactosidase activity was assessed using one-way ANOVA and Tukey’s studentized range (HSD) test, performed in SAS® 9.0 (SAS Institute).
RESULTS A high-throughput cell-based screen identifies promising small molecules that interfere with σB activity
A high-throughput cell-based screening assay (HTS) was developed, validated, and used to identify small molecules that inhibit σB activity. The premise of the 384well plate-formatted assay was that a compound that reduced β-glucuronidase activity generated by a reporter fusion between the σB–dependent opuCA promoter and gus (encoding GUS) (96) without affecting L. monocytogenes growth (according to plate assay) would be a candidate for further consideration (Chembank Screening Project: SigBInhibition). Based on this primary screen (Figure 4.1), 41 compounds were identified that inhibited σB activity (Z score ≤ -3 in both replicates for RFU and
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Figure 4.1: Scatterplot of high-throughput screen. Scatterplot of Z-scores calculated from GUS activities (in relative fluorescent units [RFU]) normalized to cell density (in OD600 units) for small molecules tested in duplicate in an initial screen. Red dots represent DMSO controls; blue dots are small molecules tested.
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RFU/OD and Z score ~0 for OD) and were selected for secondary cell-based screening. Compounds that produced induction were not pursued further.
The secondary screening data obtained from the opuCA-gus reporter fusion strain (as described above) in the presence of 6 five-fold dilutions of the initial concentration (e.g. starting from a 64uM final concentration, 8009-2163 was diluted 1 to 5 six times) of each of the 41 compounds, was used to calculate preliminary IC50 values (i.e., concentration that inhibits 50% of σB activity). 14 compounds inhibited σB activity at concentrations lower than initially tested in the primary screen. Based on drug developability criteria (58) and cytotoxicity information in ChemBank (chembank.broad.harvard.edu), poor drug candidates were eliminated, leaving three promising compounds that inhibited L. monocytogenes σB. One particularly effective lead compound, 8009-2163 (IC50= ~15µM), was not commercially available, thus an analog of this compound (T0513-8332) was utilized (Figure 4.2). T0513-8332 (2Phenyl-ethenesulfonic acid (4-fluoro-phenyl) amide), hereafter designated sigmastatin, has fluorine substituted for the hydrogen in the original compound. While 8009-2163 shows minimal evidence for cytotoxicity, based on cytotoxicity data in ChemBank, sigmastatin shows no evidence for cytotoxicity (chembank.broad.harvard.edu).
Identification of σB binders using small-molecule microarrays (SMM) The ability of various small molecules to bind to σB was assessed using a SMM
screen with His-tagged σB. Using replicate Z-scores in Spotfire analytics, 19 high scoring σB ligands were identified (see Appendix Figure AF.1 for representative scatterplot). This included 10 compounds from the diversity oriented (DIV06) library and 9 from the natural products and commercial compound (NPC1) library. Several compounds were eliminated because of promiscuity (n=5), non-specific binding (n=5),
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Figure 4.2: Structure of sigmastatin. Chemical structure of σB inhibitor sigmastatin (2-Phenyl-ethenesulfonic acid (4-fluoro-phenyl) amide).
82

or because they were not commercially available (n=6). Three purchasable ligands fit our criteria of lacking promiscuity and non-specificity, but subsequent assays showed they were not effective at inhibiting σB activity. Several other compounds were tested but these compounds did not affect σB -dependent transcription in phenotypic and genotypic assays. Therefore, while these compounds may bind σB, they do not inhibit σB activity. Though not among the top 19 binding compounds, sigmastatin was within the top 10% of the strongest σB ligands, suggesting the possibility of a direct interaction. These SMM data provide initial evidence that sigmastatin may inhibit σB activity by interacting with σB
Multiple lines of evidence support σB activity inhibition by sigmastatin. To determine the phenotypic effects of sigmastatin at various concentrations, we
qualitatively assessed its effect on the activity of bile salt hydrolase, which is the product of the σB-dependent bsh and required for survival in vivo (98). L. monocytogenes treated with sigmastatin at concentrations of 96µM and 193 µM showed no BSH activity (Figure 4.3), although L. monocytogenes growth on BHI did not appear to be affected. At 290µM of sigmastatin, L. monocytogenes produced no BSH activity and showed complete growth inhibition on BHI (Figure 4.3).
To quantitatively assess the affects of σB –driven transcription, quantitative reverse transcriptase PCR (qRT-PCR) was used on mRNA from L. monocytogenes that had been exposed to σB inducing conditions (i.e., 0.3M NaCl for 10 min) in the presence or absence of various concentrations of sigmastatin. Based on qRT-PCR experiments, there was a ~40-fold reduction in transcript levels for both σB -dependent genes opuCA and gadA following treatment with 64 µM of sigmastatin relative to transcript levels in cells that were not treated with sigmastatin (Figure 4.4a & 4.4b; p<0.05, GLM Tukey). Concentration-dependent assessment of the effect of sigmastatin on
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Figure 4.3: Phenotypic agar assay for σB-dependent BSH activity in the presence of sigmastatin at various concentrations. Left panel shows L. monocytogenes spotted on BHI agar, indicating growth, and the right panel shows L. monocytogenes spotted on MRS agar, indicating Bile Salt Hydrolase activity. Each well for both agars contains various concentrations of sigmastatin, from bottom to top: no treatment, 96µM, 193µM, 290µM. L. monocytogenes grew on wells of BHI agar containing no treatment, 96µM and 193µM of sigmastatin, while little growth occurred at 290µM. L. monocytogenes deconjugated bile salts on MRS containing no sigmastatin, however, BSH was inhibited in all wells containing sigmastatin.
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(a)
(b) Figure 4.4. qRT-PCR graphs illustrating σB-dependent opuCA and gadA transcription. qRT-PCR graphs illustrating normalized log transformed (a) opuCA and (b) gadA transcription in L. monocytogenes 10403S and ∆sigB strains treated with 0.3M NaCl, with or without sigmastatin at various concentrations for 10 min. Values represent mean log normalized transcript levels from at least three independent RNA collections.
85

transcription showed that transcript levels of opuCA in salt-treated 10403S cells, concurrently treated with sigmastatin (ranging from 8µM to 128µM), produced transcript levels which were equivalent to those seen in the ∆sigB strain (p>0.05). At 4µM, sigmastatin significantly reduced transcription of opuCA (p<0.05), however, not to levels equivalent to ∆sigB strain (data not shown). Using Sigmaplot and lognormalized transcript levels of opuCA, the concentration at which half the maximal inhibition (IC50) occurred was determined to be 3.5µM. Importantly, absolute transcript levels for the housekeeping genes rpoB and gap were similar for 10403S with and without exposure to sigmastatin, indicating that this small molecule specifically inhibits transcription of σB-dependent genes and does not affect housekeeping functions. As a consequence of treatment with sigmastatin, there was no further σB directed activity in a treated strain as compared to a ∆sigB strain. Sigmastatin is effective in that there is not a reduction but elimination of σB-dependent activity at levels as low as 8µM.
L. monocytogenes whole genome microarray identified 208 genes downregulated by treatment with sigmastatin; 156 of these genes are regulated by σB. To further characterize the ability of sigmastatin to specifically inhibit σB, transcriptional profiling was performed using a L. monocytogenes whole-genome microarray. Microarray profiling was performed on four independent RNA collections from log-phase salt stressed L. monocytogenes exposed to either 64 µM sigmastatin or to DMSO alone (the solvent used for sigmastatin). Analysis of microarray data (using LIMMA package for R) showed that 208 genes were downregulated and 32 genes were upregulated as a result of treatment with sigmastatin (adj. p<0.05 and FC≥2). 156 of the 208 sigmastatin-downregulated genes are positively regulated by σB and 7 of the 32 sigmastatin-upregulated genes are negatively regulated by σB, based on
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previous microarray analysis in 10403S and EGD-e, RNA seq and HMM (Table 4.2, Table 4.3 and 4.4) (35, 45, 71, 72, 77, 98). There are 115 sigmastatin-downregulated genes positively regulated by σB in both the L. monocytogenes strains 10403S and EGD-e (35, 45, 71, 72, 77, 98). An additional 21 genes are positively regulated by σB specifically in the L. monocytogenes strain 10403S (45, 71, 72, 77) and another 20 are positively regulated by σB specifically in the L. monocytogenes strain EGD-e (35, 98). Interestingly, sigmastatin inhibited >90% of genes with an HMM identified σBdependent promoter.
Of the 208 sigmastatin-downregulated genes, 126 were found to be positively regulated by σB during infection in the murine intestine (98), including 17 σBdependent genes specific to the intestine (Table 4.4 and 4.5). Among these 17 genes, 9 genes were of unknown or hypothetical function. Three genes lmo0584, lmo0648, lmo0650 were similar to membrane proteins, while lmo0649 and lmo0651 were similar to transcriptional regulators. One gene, lmo1992 was similar to alphaacetolactate decarboxylase, another, lmo1789, was similar to Nad(P)h Oxidoreductase chain B and a third, lmo0406, was similar to B. subtilis YyaH protein.
Results from Gene Set Enrichment Analysis (GSEA, Broad Institute, Cambridge, MA) further supported that sigmastatin extensively inhibited the σB regulon, as the known σB -regulated genes were significantly enriched among sigmastatin-downregulated genes (False discovery rate (FDR) q<0.0001). Note that in GSEA gene sets are considered significant at an FDR q<0.25. To assess the effects of sigmastatin on the function of other alternative sigma factors, transcript levels for genes in the σH and σL regulons were assessed. 14 of the 30 genes identified as σH– dependent (p<0.05, FC≥2) were significantly and differentially downregulated by sigmastatin (adj. p<0.05, FC≥2), however, 12 of those 14 genes are also σB dependent. GSEA showed that the σH-only regulon (comprised of genes that are only
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Table 4.2: Number of genes differentially expression as a result of treatment with

sigmastatin and correlation to σB regulation

Positively σB regulated

208 genes downregulated by
sigmastatin 156 a

32 genes upregulated by sigmastatin
2

Negatively σB regulated

6b

7

σB-dependent promoters

86 c

0

a 156 genes includes 4 genes positively regulated by σB under some conditions, but negatively regulated by σB under other conditions. See supplemental tables 1 and 3. b 6 genes includes 4 genes negatively regulated by σB under some conditions, but positively regulated by σB under other conditions. See supplemental tables 2 and 3. c σB-dependent promoters as determined by in silico analysis using Hidden Markov Model (71).

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Table 4.3. Comparison of genes downregulated by T0513 and σB dependent genes previously identified in 10403S and EGD-e
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Gene name
lmo0019 lmo0025 lmo0026 lmo0036 lmo0038 lmo0039 lmo0043 lmo0090 lmo0105 lmo0129 lmo0133 lmo0134 lmo0169 lmo0170 lmo0232 lmo0274 lmo0291 lmo0321 lmo0326 lmo0335 lmo0336 lmo0337 lmo0339 lmo0405 lmo0406 lmo0407 lmo0408 lmo0433 lmo0434 lmo0439 lmo0445 lmo0479 lmo0515 lmo0524 lmo0528 lmo0529 lmo0530 lmo0531 lmo0539 lmo0554 lmo0555 lmo0579 lmo0580 lmo0584 lmo0589 lmo0590 lmo0591 lmo0593 lmo0596 lmo0598

Sigmastatin FCa
-21.3 -10.3 -4.4 -4.9 -3.8 -5.1 -26.0 -2.4 -2.2 -2.4 -4.5 -29.6 -11.7 -12.5 -2.1 -3.5 -5.2 -29.0 -2.1 -3.2 -3.6 -3.0 -3.9 -11.3 -5.0 -2.2 -2.6 -31.3 -24.6 -14.4 -16.1 -7.7 -19.9 -4.5 -2.0 -2.4 -2.3 -2.4 -21.6 -28.5 -11.2 -3.5 -3.2 -3.0 -7.6 -8.2 -10.2 -3.0 -86.4 -2.8

Sigmastatin adj. p value
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0023 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0019 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0005

σB salt FCb
5.1 1.2 1.1 1.6 1.5 1.6 4.0 1.3 1.0 1.1 2.4 6.7 2.8 4.0 2.5 1.1 2.0 3.0 1.0 1.3 1.2 1.3 1.4 2.0 1.5 1.3 1.2 2.0 2.0 1.7 2.5 1.3 2.6 1.5 1.0 1.1 0.9 1.1 18.8 4.0 2.8 1.5 2.2 1.2 3.9 4.1 5.4 1.5 17.7 1.0

σB salt adj. p valueb
0.0004 0.0354 0.4304 0.0062 0.0031 0.0103 0.0001 0.0262 0.7338 0.6574 0.0070
0.0001
0.0044
0.0003
0.0010 0.8128 0.0004
0.0006 0.9104 0.0081 0.3953 0.0252 0.0269 0.0012 0.0028 0.1307 0.1957 0.0135
0.0124 0.0073 0.0006 0.0362 0.0020 0.4178 0.9412 0.3424 0.8041 0.7082 0.0001
0.0004
0.0032 0.0225 0.0002 0.1896 0.0002
0.0002
0.0008 0.0092 0.0001 0.8496

σB stationary FCb
3.5 1.2 1.0 1.1 1.1 1.1 3.5 1.0 1.0 1.1 1.2 6.2
2.1 1.9 0.8 1.2 2.1
4.3 1.0 1.0 1.1 1.1 1.1 2.1 1.6 1.5 1.5 1.3 1.4 1.4 3.0 1.4 2.1 1.9 1.2 1.1 1.1 1.1 13.7
4.4
4.1 1.5 1.6 1.4 1.8 1.9 1.7 4.3
12.3 1.1

σB stationary ad. p valueb
0.0010 0.1720 0.8397 0.7999 0.3917 0.7672
0.0003 0.8873 0.7668 0.8473 0.4341
0.0001
0.0025 0.1905 0.4050 0.2321
0.0006
0.0000 0.9343 0.9526 0.8672 0.7318 0.6423
0.0010 0.0158 0.0029 0.0074 0.0608 0.0085 0.1021
0.0004 0.0852
0.0116 0.0007 0.1568 0.3850 0.6027 0.8592
0.0000
0.0001
0.0003 0.0035 0.0219 0.0342 0.0018 0.0047 0.0006
0.0001
0.0002 0.7203

σB dependent promoterc
+ . . . . . + . . . + + + + . + . + . . . . . + . . . + + + . . + . . . . . + + + . . . . . . + +

σB dependent in other studiesd +
+
+ + + +
+ + +
+ + + + + + + +
+ +
+
+ + + + + +
+ + + +

90

Gene name
lmo0602 lmo0606 lmo0607 lmo0608 lmo0610 lmo0626 lmo0628 lmo0629 lmo0642 lmo0647 lmo0648 lmo0649 lmo0650 lmo0651 lmo0653 lmo0654 lmo0655 lmo0669 lmo0670 lmo0671 lmo0722 lmo0759 lmo0760 lmo0761 lmo0781 lmo0782 lmo0783 lmo0784 lmo0794 lmo0796 lmo0819 lmo0820 lmo0821 lmo0869 lmo0880 lmo0893 lmo0894 lmo0895 lmo0896 lmo0904 lmo0911 lmo0913 lmo0915 lmo0937 lmo0953 lmo0956 lmo0957 lmo0958 lmo0994 lmo0995

Sigmastatin FCa
-21.9 -8.9 -12.6 -13.5 -20.6 -2.3 -27.1 -6.2 -3.7 -7.8 -4.2 -3.8 -3.4 -2.1 -2.2 -16.3 -12.7 -71.3 -51.4 -2.0 -22.8 -3.6 -3.6 -3.3 -14.8 -22.9 -26.9 -24.0 -28.1 -16.1 -4.7 -2.7 -2.2 -3.0 -70.7 -6.6 -6.9 -6.7 -6.9 -2.7 -5.7 -80.0 -6.6 -4.7 -20.8 -5.0 -3.2 -2.4 -39.8 -35.6

Sigmastatin adj. p value
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

Table 4.3 (Continued)

σB salt FCb

σB salt adj. p valueb

σB stationary FCb

σB stationary ad. p valueb

5.5 0.0003

3.0 0.0007

1.5 0.0045

1.2 0.1320

2.1 0.0011

1.1 0.4354

3.3 0.0001

0.8 0.1862

1.6 0.0015

2.3 0.0006

1.3 0.0421

1.2 0.2172

2.2 0.0018

3.8 0.0002

1.7 0.0126

2.3 0.0002

1.1 0.5652

1.5 0.4353

4.8 0.0005

3.6 0.0029

1.6 0.1804

1.6 0.0031

1.3 0.1622

1.4 0.0076

1.3 0.0413

1.2 0.1961

1.4 0.0385

1.0 0.9298

2.3 0.0025

1.2 0.5446

2.2 0.0004

2.1 0.0027

2.5 0.0006

2.0 0.0004

34.4 0.0001

34.4 0.0000

12.8 0.0001

8.3 0.0001

1.1 0.7433

1.0 0.9100

4.5 0.0005

2.3 0.0074

1.3 0.0383

1.3 0.0138

1.3 0.0322

1.4 0.0076

1.2 0.6426

1.0 0.9955

2.9 0.3955

3.3 0.0000

10.3 0.0001

12.6 0.0000

8.9 0.0001

10.8 0.0000

8.5 0.0001

10.2 0.0000

2.3 0.0016

5.5 0.0002

6.7 0.0001

3.7 0.0000

1.6 0.0062

1.4 0.0267

1.0 0.8773

1.1 0.8282

1.3 0.0085

1.4 0.0105

1.2 0.0386

1.5 0.0282

12.3 0.0001

13.0 0.0002

2.2 0.0005

1.5 0.0071

2.4 0.0011

1.3 0.0473

5.1 0.0001

2.1 0.0004

4.2 0.0003

2.2 0.0003

1.2 0.0835

1.5 0.0065

3.8 0.0004

2.7 0.0006

9.7 0.0002

5.8 0.0001

1.2 0.3224

0.8 0.1093

3.3 0.0090

3.8 0.0006

3.0 0.0007

3.0 0.0001

2.7 0.0001

2.3 0.0000

2.2 0.0003

1.7 0.0010

2.0 0.0002

1.5 0.0634

7.6 0.0003

7.3 0.0000

3.9 0.0003

2.8 0.0005

σB dependent promoterc
+ . . . + . + + . . . . . . . + + + + . + . . . + + + + + + . . . . + + + + + . + + . + + . . . + .

σB dependent in other studiesd +
+ + + +
+ + + + +
+ + + +
+ + + + + + + + + + +
+ + + + + +
+ +
+ + + + + + +

91

Gene name
lmo1018 lmo1026 lmo1027 lmo1139 lmo1140 lmo1241 lmo1251 lmo1261 lmo1266 lmo1293 lmo1295 lmo1340 lmo1375 lmo1421 lmo1422 lmo1425 lmo1426 lmo1427 lmo1428 lmo1432 lmo1433 lmo1452 lmo1453 lmo1454 lmo1526 lmo1538 lmo1539 lmo1580 lmo1601 lmo1602 lmo1605 lmo1606 lmo1666 lmo1694 lmo1698 lmo1704 lmo1789 lmo1790 lmo1830 lmo1883 lmo1912 lmo1913 lmo1929 lmo1930 lmo1992 lmo2006 lmo2066 lmo2067 lmo2085 lmo2092

Sigmastatin FCa
-2.1 -2.0 -2.0 -2.1 -10.8 -22.7 -3.2 -12.0 -2.0 -4.1 -4.0 -2.2 -16.5 -9.0 -8.2 -26.7 -15.8 -27.1 -49.9 -2.3 -35.6 -3.2 -3.2 -3.7 -9.1 -3.2 -3.7 -4.1 -3.3 -2.7 -2.0 -2.9 -6.2 -28.9 -2.9 -2.3 -3.7 -2.0 -8.8 -5.4 -2.4 -2.2 -2.0 -2.1 -3.0 -2.1 -2.2 -37.5 -21.1 -2.4

Sigmastatin adj. p value
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0001 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

Table 4.3 (Continued)

σB salt FCb

σB salt adj. p valueb

σB stationary FCb

σB stationary ad. p valueb

0.9 0.1656

1.3 0.0631

1.8 0.2523

0.8 0.0637

2.5 0.0011

0.8 0.1705

0.8 0.3268

1.2 0.1902

4.5 0.0001

5.9 0.0000

2.3 0.0017

2.6 0.0001

1.1 0.7622

1.0 0.8895

1.9 0.0003

1.8 0.0195

0.5 0.0011

1.3 0.2658

2.2 0.0003

0.3 0.0018

2.5 0.0003

3.9 0.0000

1.0 0.8446

1.1 0.6705

2.9 0.0006

3.4 0.0003

3.1 0.0002

1.5 0.0410

2.9 0.0012

1.3 0.2771

7.0 0.0003

1.8 0.0027

2.7 0.0028

1.0 0.9582

3.0 0.0004

2.0 0.0011

10.1 0.0004

2.9 0.0000

1.8 0.0019

2.1 0.0002

2.7 0.0014

2.6 0.0004

1.0 0.8390

1.0 0.8428

1.1 0.4123

1.2 0.1549

1.1 0.6346

1.0 0.8592

2.6 0.0003

1.9 0.0459

1.4 0.2131

0.3 0.0005

1.8 0.0453

0.2 0.0001

1.9 0.0039

2.6 0.0000

2.5 0.0004

4.2 0.0000

3.6 0.0001

5.1 0.0000

1.3 0.0478

1.7 0.0458

1.2 0.1152

2.8 0.0005

1.8 0.0125

1.5 0.0152

3.4 0.0002

2.6 0.0002

1.5 0.0862

2.0 0.0004

1.4 0.0110

1.8 0.0005

1.3 0.0341

1.4 0.0096

1.8 0.0315

1.5 0.0199

2.0 0.0078

2.4 0.0007

2.5 0.0013

12.0 0.0000

1.0 0.9728

1.1 0.4354

1.0 0.8086

0.9 0.6772

0.9 0.5359

1.5 0.0495

1.2 0.1519

1.5 0.0341

1.9 0.0013

0.6 0.2004

0.7 0.0171

0.5 0.0036

1.2 0.1757

0.9 0.6796

3.2 0.0004

4.4 0.0004

5.0 0.0002

11.1 0.0000

1.2 0.1013

1.0 0.8592

σB dependent promoterc
. . . . . + . . . . + . . + . + + + + . + . . . + . . . + + . . . + + . . . + + . . . . . . . + + .

σB dependent in other studiesd
+ + +
+
+/+ + + + + + + + + + +
+ + + +/+/+ + + + +
+ + + + + + +
+
+/+ +

92

Gene name
lmo2130 lmo2131 lmo2132 lmo2157 lmo2158 lmo2173 lmo2174 lmo2191 lmo2205 lmo2213 lmo2230 lmo2231 lmo2232 lmo2269 lmo2281 lmo2283 lmo2356 lmo2357 lmo2358 lmo2375 lmo2386 lmo2387 lmo2391 lmo2398 lmo2399 lmo2400 lmo2434 lmo2436 lmo2454 lmo2463 lmo2471 lmo2484 lmo2485 lmo2494 lmo2570 lmo2571 lmo2572 lmo2573 lmo2602 lmo2603 lmo2670 lmo2671 lmo2672 lmo2673 lmo2674 lmo2685 lmo2695 lmo2696 lmo2697 lmo2724

Sigmastatin FCa
-3.8 -3.0 -9.4 -40.4 -22.9 -2.6 -6.7 -2.5 -3.8 -55.2 -54.7 -21.0 -2.6 -11.9 -2.5 -2.2 -3.8 -2.3 -3.0 -2.1 -4.4 -30.1 -26.4 -8.7 -4.3 -4.4 -21.7 -2.0 -3.3 -13.0 -2.1 -2.2 -2.2 -12.4 -3.5 -32.9 -27.3 -36.1 -10.8 -5.6 -6.0 -7.9 -9.5 -46.3 -3.5 -2.2 -20.8 -20.6 -6.6 -12.6

Sigmastatin adj. p value
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0008 0.0004 0.0000 0.0010 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

Table 4.3 (Continued)

σB salt FCb

σB salt adj. p valueb

σB stationary FCb

σB stationary ad. p valueb

1.3 0.0643

2.5 0.0892

1.1 0.2793

1.0 0.9253

1.8 0.0133

3.8 0.0005

13.9 0.0001

11.6 0.0000

17.8 0.0429

15.6 0.0000

1.2 0.0597

0.9 0.3349

1.2 0.0785

1.2 0.1434

3.5 0.0001

2.7 0.0001

4.3 0.0001

2.2 0.0020

14.1 0.0001

11.6 0.0000

33.9 0.0001

21.8 0.0001

2.6 0.0106

2.3 0.0004

4.9 0.0025

1.6 0.0104

2.5 0.0092

5.7 0.0000

1.1 0.1657

1.2 0.1188

1.1 0.3049

1.0 0.8299

1.2 0.0938

1.1 0.8214

1.3 0.1550

1.3 0.0589

1.2 0.0700

1.1 0.5653

0.8 0.1706

1.1 0.2510

2.3 0.0017

1.6 0.0012

1.2 0.2264

2.1 0.0019

5.7 0.0003

7.5 0.0000

7.0 0.0001

2.3 0.0006

1.9 0.0136

1.3 0.1394

1.4 0.0179

1.3 0.1273

4.1 0.0023

3.4 0.0001

1.1 0.4100

1.3 0.0918

1.6 0.0377

4.4 0.0000

1.7 0.0029

2.3 0.0001

2.2 0.0001

1.0 0.9515

6.2 0.0002

5.2 0.0000

4.4 0.0002

3.7 0.0000

2.4 0.0006

2.7 0.0001

3.1 0.0016

1.9 0.0019

6.2 0.0001

4.0 0.0000

4.4 0.0005

3.5 0.0000

4.9 0.0004

4.0 0.0000

1.4 0.0393

2.2 0.0004

2.9 0.0026

3.8 0.0004

2.3 0.0031

1.9 0.0015

2.8 0.0002

2.8 0.0000

1.9 0.0137

1.7 0.0738

7.4 0.0003

13.2 0.0000

1.8 0.0033

2.7 0.0000

0.9 0.4081

1.2 0.1862

7.0 0.0001

1.3 0.2896

6.5 0.0001

1.5 0.0469

1.9 0.0146

1.9 0.0147

3.7 0.0010

1.8 0.0033

σB dependent promoterc
. . + + + . . . . + + . . + . . . . . . . + + . . . + . + + . . . . + + + + + + + + + + + . . . . +

σB dependent in other studiesd +
+ + +
+ + + + + +
+
+ + + + +
+
+ +
+ + + + + + + + + + + + + +
+ + + +

93

Table 4.3 (Continued)

Gene name

Sigmastatin FCa

Sigmastatin adj. p value

σB salt FCb

σB salt adj. p valueb

σB stationary FCb

σB stationary ad. p valueb

σB dependent promoterc

σB dependent in other studiesd

lmo2733

-3.2

0.0000

1.0 0.8786

1.1 0.2455 +

+

lmo2734

-2.1

0.0007

1.2 0.0718

1.2 0.2295 .

lmo2735

-2.0

0.0023

1.1 0.2246

1.2 0.1684 .

lmo2739

-5.0

0.0000

2.2 0.0007

1.3 0.0715 .

lmo2740

-4.6

0.0000

1.9 0.0008

1.2 0.1449 .

lmo2741

-4.6

0.0000

1.8 0.0057

1.2 0.0869 .

lmo2748 -67.5

0.0000 15.6 0.0002

8.9 0.0000 +

+

LMOinlD

-7.4

0.0000

1.2 0.2104

1.1 0.6806 +

+

a FC indicates fold change

b This work was reported in Raengpradub et al., 2008.

c σB -dependent promoter was determined by HMM in Oliver et al., 2009.

d Other studies: Hain et al., 2008, Kazmierczak et al., 2003, Oliver et al., 2009, Ollinger et al., 2009,

Toledo-Arana et al., 2009.

Items listed in bold are significantly and differentially expressed; “+” indicates positively regulated in

other studies and “-” indicates negatively regulated in other studies. “+/-” indicates positively and

negatively regulated in other studies

94

Table 4.4. Comparison of genes upregulated by sigmastatin and σB dependent

genes previously identified in 10403S and EGD-e

Gene

Sigma Sigma- σB

σB salt σB

σB σB

σB

name

-statin FCa

statin adj. p value

salt adj. p stationary FCb valueb FCb

stationary adj. p valueb

dependent promoterc

dependent in other studiesd

lmo0194

2.1 0.0000 1.0 0.7701

0.9 0.5751 .

lmo0195

2.5 0.0001 0.9 0.1975

0.8 0.1647 .

lmo0560

2.4 0.0000 0.7 0.0872

0.4 0.0000 .

lmo0604

2.6 0.0000 1.0 0.8953

0.9 0.1543 .

lmo0678

2.3 0.0008 0.9 0.5684

0.9 0.5568 .

lmo0679

4.0 0.0000 0.5 0.0008

0.7 0.0897 .

lmo0680

4.7 0.0000 0.8 0.0399

0.7 0.0432 .

lmo0681

3.3 0.0000 0.6 0.0019

0.9 0.4655 .

lmo0685

2.1 0.0002 0.6 0.0315

0.7 0.0179 .

lmo0686

2.0 0.0000 0.7 0.3102

1.0 0.9515 .

lmo0955

2.0 0.0001 0.9 0.6439

1.2 0.1147 .

lmo0971

2.1 0.0000 0.5 0.0015

0.8 0.1608 .

lmo0973

2.2 0.0000 0.7 0.0121

0.9 0.8428 .

lmo0974

2.6 0.0000 0.4 0.0010

1.0 0.9231 .

lmo1440

2.0 0.0000 0.8 0.1900

1.3 0.2283 .

-

lmo1518

2.7 0.0000 0.6 0.0004

1.0 0.7859 .

lmo1637

2.2 0.0000 1.1 0.7880

1.3 0.1219 .

+

lmo1699

4.7 0.0000 0.2 0.0001

0.8 0.3118 .

lmo1700

5.7 0.0000 0.2 0.0001

0.9 0.2974 .

lmo1919

2.7 0.0000 1.6 0.0683

1.0 0.9092 .

lmo2114

3.7 0.0000 0.3 0.0001

1.0 0.9829 .

lmo2115

3.7 0.0000 0.7 0.0419

0.9 0.7332 .

lmo2150

2.9 0.0000 0.5 0.0011

0.8 0.1888 .

lmo2156

3.0 0.0000 0.8 0.1686

1.0 0.9706 .

lmo2177

3.0 0.0000 1.2 0.0860

0.9 0.2393 .

lmo2219

2.3 0.0000 0.5 0.0007

1.0 0.7667 .

lmo2439

2.7 0.0000 0.7 0.0465

1.2 0.5422 .

lmo2567

2.4 0.0000 1.0 0.9800

1.1 0.3660 .

lmo2568

2.0 0.0000 1.0 0.9544

1.1 0.8473 .

+

lmo2687

3.5 0.0000 1.1 0.6130

0.9 0.4532 .

lmo2688

2.7 0.0000 1.4 0.1067

1.0 0.8594 .

lmo2689

3.0 0.0000 0.9 0.7075

1.0 0.8351 .

a FC indicates fold change

b This work was reported in Raengpradub et al., 2008.

c σB -dependent promoter was determined by HMM in Oliver et al., 2009.

d Other studies: Hain et al., 2008, Kazmierczak et al., 2003, Ollinger et al., 2009, Toledo-Arana et al.,

2009.

Items listed in bold are significantly and differentially expressed; “+” indicates positively regulated in

other studies and “-” indicates negatively regulated in other studies. “+/-” indicates positively and

negatively regulated in other studies

95

Table 4.5: Positively regulated σB dependent genes previously identified in L. monocytogenes 10403S and EGD-e
96

97

Gene name
inlD lmo0013 lmo0019 lmo0043 lmo0045 lmo0100 lmo0122 lmo0133 lmo0134 lmo0137 lmo0169 lmo0170 lmo0200 lmo0202 lmo0205 lmo0210 lmo0211 lmo0231 lmo0232 lmo0263c lmo0265 lmo0274 lmo0291 lmo0292 lmo0293 lmo0314 lmo0321 lmo0341 lmo0342 lmo0343 lmo0344 lmo0345 lmo0346 lmo0347 lmo0348

Gene symbol
inlD qoxA lmo0019 lmo0043 ssb lmo0100 lmo0122 lmo0133 lmo0134 lmo0137 lmo0169 lmo0170 prfA hly plcB ldh ctc lmo0231 clpC lmo0263 lmo0265 lmo0274 lmo0291 lmo0292 lmo0293 lmo0314 lmo0321 lmo0341 lmo0342 lmo0343 lmo0344 lmo0345 lmo0346 lmo0347 lmo0348

sigmastatin -
-
-
-
-

Raengpradub et al. 2008
+
+ +
+ + + + +
-
+ + + + +
+
+ + + + + + + + + + +

Kazmierczak et al. 2003
+ +

Ollinger et al. 2009
+ +

+ +
+ + -
+ +
+
+ +
+ +
+

Oliver et al. 2009
+ +
+ + +

Toledo- Hain Arana et et al. al. 2009 2008
+ ++ ++
+ +
+ ++

σB dependent promotera +
+ +
+ +

++ ++
+ + +

++ ++

++ ++ ++
+
++

++ ++
+
+

σB OVERALLb
+ + + + + + + + + + + + +/+/+ + + + + + + + + + + + + + + + + + + + +

98

Gene name
lmo0351 lmo0353 lmo0372 lmo0373 lmo0374 lmo0386 lmo0398 lmo0399 lmo0400 lmo0401 lmo0402 lmo0403 lmo0405 lmo0406 lmo0407 lmo0408 lmo0433 lmo0434 lmo0438 lmo0439 lmo0445 lmo0495 lmo0514 lmo0515 lmo0524 lmo0529 lmo0539 lmo0554 lmo0555 lmo0579 lmo0580 lmo0582 lmo0584 lmo0589

Gene symbol
lmo0351 lmo0353 lmo0372 lmo0373 lmo0374 lmo0386 lmo0398 lmo0399 lmo0400 lmo0401 lmo0402 lmo0403 lmo0405 lmo0406 lmo0407 lmo0408 inlA inlB lmo0438 lmo0439 lmo0445 lmo0495 lmo0514 lmo0515 lmo0524 lmo0529 lmo0539 lmo0554 lmo0555 lmo0579 lmo0580 iap lmo0584 lmo0589

sigmastatin
-
-

Raengpradub et al. 2008
+
+ + + + + + +
+ +
+ + +
+ + + +
+

Table 4.5 (continued)

Kazmierczak et al. 2003

Ollinger et al. 2009

Oliver et al. 2009

+

+

+ ++
+ ++ ++ +
++ ++
++ ++
++ ++ ++ + + +

ToledoArana et al. 2009
+ + + + + + +
+ +
+ + +
+ + + + +
+

Hain et al. 2008
+
+
+ + + + + + -

σB dependent promotera
+ + +
+
+ + +
+
+ + +

σB OVERALLb
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +/+ +

99

Gene name
lmo0590 lmo0591 lmo0592 lmo0593 lmo0596 lmo0602 lmo0607 lmo0608 lmo0610 lmo0626 lmo0628 lmo0629 lmo0647 lmo0648 lmo0649 lmo0650 lmo0651 lmo0653 lmo0654 lmo0655 lmo0669 lmo0670 lmo0676 lmo0722 lmo0723 lmo0724 lmo0735 lmo0736 lmo0737 lmo0738 lmo0739 lmo0759 lmo0760 lmo0761

Gene symbol
lmo0590 lmo0591 lmo0592 lmo0593 lmo0596 lmo0602 lmo0607 lmo0608 lmo0610 lmo0626 lmo0628 lmo0629 lmo0647 lmo0648 lmo0649 lmo0650 lmo0651 lmo0653 lmo0654 lmo0655 lmo0669 lmo0670 lmo0676 lmo0722 lmo0723 lmo0724 lmo0735 lmo0736 lmo0737 lmo0738 lmo0739 lmo0759 lmo0760 lmo0761

sigmastatin
-
-
-
-

Raengpradub et al. 2008
+ + + + + + + + +
+ + +
+ + + + +
+

Table 4.5 (continued)

Kazmierczak et al. 2003

Ollinger et al. 2009

Oliver et al. 2009

+ ++ ++ ++
++
++ ++

++ ++ + ++ ++
++

ToledoArana et al. 2009
+ + +
+ + + + + + + + +
+ + + +
+ + +
+ + +

Hain et al. 2008 + + + + +
+
+
+ + + +
+ + + + +

σB dependent promotera
+ + +
+ + +
+ + + + +

σB OVERALLb
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

100

Gene name
lmo0781 lmo0782 lmo0783 lmo0784 lmo0788 lmo0794 lmo0796 lmo0819 lmo0830 lmo0869 lmo0870 lmo0880 lmo0893 lmo0894 lmo0895 lmo0896 lmo0900 lmo0911 lmo0913 lmo0929 lmo0937 lmo0943 lmo0944 lmo0953 lmo0956 lmo0957 lmo0958 lmo0994 lmo0995 lmo0996 lmo0997 lmo1001 lmo1014 lmo1027

Gene symbol
lmo0781 lmo0782 lmo0783 lmo0784 lmo0788 lmo0794 lmo0796 lmo0819 fbp lmo0869 lmo0870 lmo0880 rsbV rsbW sigB rsbX lmo0900 lmo0911 lmo0913 lmo0929 lmo0937 fri lmo0944 lmo0953 lmo0956 lmo0957 lmo0958 lmo0994 lmo0995 lmo0996 clpE lmo1001 gbuA lmo1027

sigmastatin
-
-
-
-
-
-
-
-

Raengpradub et al. 2008
+ + + + + + +
+
+ + + + +
+ +
+
+ + + + + + +
+ +

Table 4.5 (continued)

Kazmierczak et al. 2003

Ollinger et al. 2009

Oliver et al. 2009

++

++

++

+ ++

+ ++ ++ +

+ ++ ++ ++ ++ ++
+ ++ ++
++
++ ++
+ + + ++ +
-

ToledoArana et al. 2009 + + + +
+ +
+ +
+
+ + + + + + + + + + + + +
+

Hain et al. 2008 + + + +
+ + +
+
+
+
+ +
+ + +
+
+

σB dependent promotera + + + +
+ +
+ + + + +
+ +
+
+
+

σB OVERALLb
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +/+ + +

101

Gene name
lmo1052 lmo1053 lmo1055 lmo1068 lmo1113 lmo1114 lmo1119 lmo1134 lmo1139 lmo1140 lmo1154 lmo1159 lmo1168 lmo1241 lmo1245 lmo1256 lmo1261 lmo1291 lmo1293 lmo1295 lmo1340 lmo1348 lmo1375 lmo1376 lmo1387 lmo1397 lmo1421 lmo1422 lmo1425 lmo1426 lmo1427 lmo1428 lmo1432 lmo1433

Gene symbol
pdhA pdhB pdhD lmo1068 lmo1113 lmo1114 lmo1119 lmo1134 lmo1139 lmo1140 lmo1154 lmo1159 ackA2 lmo1241 lmo1245 lmo1256 lmo1261 lmo1291 glpD lmo1295 lmo1340 lmo1348 lmo1375 lmo1376 lmo1387 cinA lmo1421 lmo1422 opuCD opuCC opuCB opuCA lmo1432 lmo1433

sigmastatin
-
-
-
-

Raengpradub et al. 2008
+
+
+
+ +/+ + + + + + + + + + + +

Table 4.5 (continued)

Kazmierczak et al. 2003

Ollinger et al. 2009

Oliver et al. 2009

+

-

++
++
+
+ ++ +
++ +
+ ++
++ ++ + +
+ + ++

ToledoArana et al. 2009
+ + + + + + + + +
+ + + +
+
+
+
+ + + + + + + +

Hain et al. 2008
+ +
+
+ +
+
+
+
+ + + + + + +

σB dependent promotera
+
+
+ + + + + +

σB OVERALLb
+ +/+ + + + + + + + + + + + + + + + +/+ + + + + + + + + + + + + + +

102

Gene name
lmo1434 lmo1435 lmo1453 lmo1454 lmo1475 lmo1526 lmo1538 lmo1539 lmo1580 lmo1601 lmo1602 lmo1605 lmo1606 lmo1637 lmo1672 lmo1684 lmo1694 lmo1698 lmo1704 lmo1788 lmo1789 lmo1790 lmo1830 lmo1848 lmo1849 lmo1866 lmo1873 lmo1883 lmo1929 lmo1932 lmo1933 lmo1934 lmo1966 lmo1992

Gene symbol
lmo1434 lmo1435 lmo1453 rpoD hrcA lmo1526 lmo1538 lmo1539 lmo1580 lmo1601 lmo1602 murC lmo1606 lmo1637 menE lmo1684 lmo1694 lmo1698 lmo1704 lmo1788 lmo1789 lmo1790 lmo1830 lmo1848 lmo1849 lmo1866 lmo1873 lmo1883 ndk lmo1932 lmo1933 hup lmo1966 lmo1992

sigmastatin
-
+
-
-
-
-

Raengpradub et al. 2008
+ + + + +
+
+ + + +
+ + +/-
+ +
+

Table 4.5 (continued)

Kazmierczak et al. 2003

Ollinger et al. 2009

Oliver et al. 2010

+

+

+

+

++ + + + + + ++ + + ++

+ ++ + ++

++
+ + ++
+ ++
+ -

ToledoArana et al. 2009
+ + + + + +
+ + + + + +
+ + +
+ +

Hain et al. 2008
+
+ + + + +
+
+ -
+

σB dependent promotera
+ + +
+ +
+
+

σB OVERALLb
+ + + + + + +/+/+ + + + + + + +/+ + + + + + + +/+/+ + + + + + + + +/-

103

Gene name
lmo1998 lmo1999 lmo2000 lmo2001 lmo2002 lmo2003 lmo2004 lmo2030 lmo2031 lmo2041 lmo2057 lmo2064 lmo2067 lmo2085 lmo2130 lmo2132 lmo2153 lmo2157 lmo2158 lmo2159 lmo2160 lmo2161 lmo2163 lmo2169 lmo2174 lmo2175 lmo2191 lmo2205 lmo2213 lmo2230 lmo2231 lmo2232 lmo2269 lmo2271

Gene symbol
lmo1998 lmo1999 lmo2000 lmo2001 lmo2002 lmo2003 lmo2004 lmo2030 lmo2031 lmo2041 ctaB lmo2064 lmo2067 lmo2085 lmo2130 lmo2132 lmo2153 sepA lmo2158 lmo2159 lmo2160 lmo2161 lmo2163 lmo2169 lmo2174 lmo2175 lmo2191 lmo2205 lmo2213 lmo2230 lmo2231 lmo2232 lmo2269 lmo2271

sigmastatin
-
-

Raengpradub et al. 2008
+ + + +
+
+ + + + +/+ +
+ + + + + + +

Table 4.5 (continued)

Kazmierczak et al. 2003

Ollinger et al. 2009

Oliver et al. 2010

+
+ + +
+ ++ + ++
++ ++
+ ++ ++

+ ++ ++ + ++ ++
+ ++

ToledoArana et al. 2009 + + + + + + +
+ +
+ + +
+
+
+ + +
+ +

Hain et al. 2008
+ +
+ +
+ + + +

σB dependent promotera
+ + + + +
+ + +

σB OVERALLb
+ + + + + + + + + + + + + + + + + + + + + +/+ + + +/+ + + + + + + +

104

Gene name
lmo2289 lmo2290 lmo2352 lmo2386 lmo2387 lmo2389 lmo2391 lmo2398 lmo2399 lmo2434 lmo2437 lmo2454 lmo2456 lmo2463 lmo2471 lmo2484 lmo2485 lmo2494 lmo2511 lmo2522 lmo2527 lmo2568 lmo2570 lmo2571 lmo2572 lmo2573 lmo2602 lmo2603 lmo2670 lmo2671 lmo2672 lmo2673 lmo2674 lmo2686

Gene symbol
lmo2289 lmo2290 lmo2352 lmo2386 lmo2387 lmo2389 lmo2391 ltrC lmo2399 lmo2434 lmo2437 lmo2454 pgm lmo2463 lmo2471 lmo2484 lmo2485 lmo2494 lmo2511 lmo2522 lmo2527 lmo2568 lmo2570 lmo2571 lmo2572 lmo2573 lmo2602 lmo2603 lmo2670 lmo2671 lmo2672 lmo2673 lmo2674 lmo2686

sigmastatin
-
-
-
-
+ -

Raengpradub et al. 2008
+ + + +
+ +
+ + + + + + + + + + -
+ + + + + + + +
+ +

Table 4.5 (continued)

Kazmierczak et al. 2003

Ollinger et al. 2009

Oliver et al. 2010

++ ++
+ ++
+ ++ + ++
++
+ ++
++ + ++
++ +

+ ++ ++ ++ ++
+ ++ ++ ++ ++ ++
+ ++ ++

ToledoArana et al. 2009 +
+ +
+ +
+
+
+
+ + + + + + + + + + + + + + + + + + +

Hain et al. 2008
+ +
+ +
+
+ + + +
+ + + + + +
+ + + +

σB dependent promotera
+ +
+ + +
+ + + + + + + + + + +

σB OVERALLb
+ + + + + + + + + + + + +/+ + + + + + +/+ + + + + + + + + + + + + +

Table 4.5 (continued)

Gene name

Gene symbol

sigmastatin

Raengpradub et al. 2008

Kazmierczak et al. 2003

Ollinger et al. 2009

Oliver Toledo- Hain et al. Arana et et al. 2010 al. 2009 2008

σB dependent promotera

σB OVERALLb

lmo2695 lmo2695 -

+

+

++

+

lmo2696 lmo2696 -

+

++

+

lmo2697 lmo2697 -

++

+

lmo2707 lmo2707

++

lmo2724 lmo2724 -

+

+

++

++

+

lmo2733 lmo2733 -

+ ++

lmo2739 lmo2739 -

+

+

lmo2748 lmo2748 -

+

+

++

++

+

lmo2785 kat

+

- +/-

a σB -dependent promoter was determined by HMM in Oliver et al., 2009. b σB –dependence over all studies: Hain et al., 2008, Kazmierczak et al., 2003, Oliver et al., 2009, Ollinger et al., 2009, Raengpradub et al., 2008, Toledo-

Arana et al., 2009. c lmo0263 was identified as inhibited by sigmastatin, however, the percent identity for the oligonulceotide probe to sequence match was 80, the cut off for an

acceptable probe is ≥90%. Therefore this gene was not considered further.

105

“+” indicates positively regulated in other studies and “-” indicates negatively regulated in other studies. “+/-” indicates positively and negatively regulated in

other studies

regulated by σH and not co-regulated by σB) was not significantly enriched as a result of sigmastatin treatment (FDR q=0.472). Furthermore, GSEA of the σL regulon showed that it was not significantly enriched as a result of treatment with sigmastatin (FDR q=0.836). GSEA was used to determine the biological role category distribution of genes that were differentially affected by sigmastatin. Gene sets representing biological functions including Cellular Processes: Adaptations to Atypical Conditions and Energy Metabolism (other) were enriched amongst sigmastatin-downregulated genes (FDR q=0.060 and q=0.201, respectively). The Cellular Processes: Pathogenesis gene set was also overrepresented amongst sigmastatin-downregulated genes, although just short of significant (FDR q=0.251). Conversely, biological functions such as Cellular Processes: Chemotaxis and Motility, Protein Fate: Protein Folding and Stabilization, and Amino Acid Biosynthesis: Histidine Family were enriched among sigmastatin-upregulated genes (FDR q<0.0001, q=0.008, q=0.031, respectively). GSEA was also performed on the subset of genes that were downregulated by sigmastatin but not σB regulated, and it was determined that no role category or regulator, amongst those tested, was enriched in this group of genes (FDR q>0.25).
Genes that showed significantly reduced transcript levels in sigmastatin treated cells include the virulence and in vivo viability-associated genes inlD, bilEAB, bsh, hfq, clpC, opuC, and gadA as well as known virulence genes inlA, inlB. In fact, the regulon of PrfA, the pleiotropic virulence gene regulator, was significantly enriched among the data set, as three genes inlA, inlB, plcA, of its small regulon were enriched among sigmastatin downregulated genes (FDR q=0.095). Both inlA and inlB were significantly downregulated by sigmastatin, however, plcA, encoding phospholipase C, was downregulated (adj. p<0.05), but with a fold change of 1.57 it did not meet our cut off criteria of ≥ 2 fold differential transcription. Interestingly, nineteen genes of the Group III PrfA-regulated genes co-controlled by σB are both upregulated in the mouse
106

spleen (14) and inhibited by sigmastatin. This data set includes three genes differentially regulated in the host and identified as potential virulence factors (14), such as lmo1601 (general stress protein), lmo1602 (unknown protein), and lmo2157 (SepA, a metalloprotease in S. epidermis (53) and upregulated in L. monocytogenes during intracellular infection (16)). A fourth gene inhibited by sigmastatin, also identified as a potential virulence factor by Camejo et al., 2009 (14), is lmo0915, a component of a phosphotransferase system whose regulation has yet to be attributed to any factor. Additionally, sigmastatin also downregulated lmo0937, a gene upregulated in the mouse spleen at 48 hr post-infection and found in the Group III PrfA regulated genes not thought to be controlled by σB (14).
Operons identified by Raengpradub et al., 2008 (77) as σB –regulated were also significantly differentially transcribed after treatment with sigmastatin, these include inlAB (mediates entry into non-professional phagocytes(17)), opuCABCD (involved in compatible solute transport), and the 2 gene operon, lmo1699 and lmo1700 (involved in methyl accepting chemotaxis) (Figure 4.5). The autoregulated sigB operon consisting of lmo0893-0896 (rsbV, rsbW, sigB, rsbX) was downregulated, which likely contributed to further downregulation of the entire regulon. Of note, regulators of σB, rsbX and upstream gene rsbU were recently found to be up regulated during infection in a mouse spleen (14).
Several additional σB-dependent genes downregulated by sigmastatin were previously shown to be upregulated during intracellular infection (16) including lmo0232 (clpC), lmo0445 (transcriptional regulator), lmo0783 (part of an operon encoding manose phosphotransferase system components, each gene of which is downregulated by sigmastatin) and lmo2672 (also similar to a transcriptional regulator). Furthermore, sigmastatin inhibited cell wall-associated genes, which are
107

Figure 4.5: σB-dependent operons affected by sigmastatin. According to transcriptional profiling of cells treated with sigmastatin, various genes in operons were significantly differentially expressed, including downregulated operons comprised of virulence genes and genes encoding compatible solute transporters. Expression of genes in a motility and chemotaxis operon was upregulated as a result of treatment. As our compound mimics a σB null status in the cell, genes that are positively regulated by σB will be downregulated by sigmastatin.
108

upregulated under intracellular conditions (16) and in the murine intestine (98), including inlA, inlD, lmo0610, lmo0880, lmo2085 (all of which contain LPXTG sorting motif for cell-wall anchoring) and inlB (with a GW domain for mediating binding to host ligands (60)).
Three genes important to glycerol utilization and also required for intracellular listerial growth (43), which were downregulated by sigmastatin, are also regulated by σB either positively or negatively under various conditions. Utilization of glycerol as a carbon source in intracellular environments (16) is required for intracellular survival (43). Two of these glycerol utilization genes, lmo1538 (glycerol kinase) and lmo1539 (glycerol uptake facilitator) are downregulated by sigmastatin and are negatively regulated by σB in stationary phase and salt stress conditions (77). Interestingly, however, both genes were upregulated by σB in the intestine (98) and during intracellular replication (16). The third gene downregulated by sigmastatin, lmo1293, (glpD) a glycerol-3-phosphate dehydrogenase, was positively regulated by σB in salt (77), intracellularly (43) and in the gastrointestinal tract (98) but downregulated by σB in stationary phase (77). The σB regulon clearly differs depending on the condition and needs of the bacterial cell (45). In addition to our data, this is further supported by Camejo et al., who found that 40 σB dependent genes downregulated in stationary phase (35), were activated in vivo in the mouse spleen (14). Furthermore, transcriptional profiling of L. monocytogenes in the murine intestine indicates three genes (lmo0642, lmo1251, and lmo1930) exhibited higher expression in the intestine in a WT strain but not ∆sigB strain (98). These three genes were also downregulated by sigmastatin. It is likely that several of the genes inhibited by sigmastatin and not previously attributed to σB regulation under in vitro conditions, are σB-dependent but only under very specific conditions, such as those in the mouse intestine. To support this, Camejo et al. demonstrated that expression of genes in vitro is indeed lower than
109

expression of genes in vivo (14). Most importantly, however, it is clear that sigmastatin downregulates many genes that enable the bacterium to cause infection.
A smaller proportion of genes upregulated by sigmastatin, as compared to genes downregulated by sigmastatin, are σB-dependent
Of the 32 genes upregulated by sigmastatin, only 7 genes were negatively regulated by σB in both EGD-e and 10403S strains (35, 77). The σB-dependent genes with known functions included an ABC transporter (lmo2114), D-alanine-activating enzyme (dltA), post-translocation chaperone (prsA), methyl-accepting chemotaxis protein (lmo1699-1700), and NADP glutamate dehydrogenase (lmo0560). Though two genes, lmo2568 (of unknown function) and lmo1637 (similar to membrane protein), were upregulated by sigmastatin, they were also positively regulated by σB in the intestine (98) and during various growth phases (35), respectively. Among the non σB-dependent genes upregulated by sigmastatin, some were involved in ABC transport, motility and cell-division, but most had unknown functions (Table 4.4).
As previously mentioned, genes ascribed to the motility and chemotaxis role category, were enriched among sigmastatin-upregulated genes according to GSEA. The large flagellar biosynthesis and motility operon (lmo0673-0718) contains 13 recognized σB dependent genes (77). Interspersed among these, are six sigmastatinupregulated genes, which have not been described previously as σB-dependent, including lmo0678, lmo0679, lmo0680, lmo0681, lmo0685, lmo0686. Many additional motility genes in this operon were enriched among sigmastatin-upregulated genes (FDR q<0.0001), although they were not significantly and differentially expressed. Because of the high number of motility genes affected by sigmastatin, GSEA was performed on the regulons of known chemotaxis and motility related regulators DegU, MogR and CodY. This analysis also showed that the DegU operon
110

(as defined by Williams et al., 2005 (103)) was enriched in our gene set, among upregulated genes (FDR q<0.0001). Although MogR, the transcriptional repressor of flagellum genes (34, 89), was shown to be σB-dependent (98), its regulon (89) was not significantly enriched among our microarray dataset (FDR q=0.257). CodY, the transcriptional repressor of motility and chemotaxis in B. subtilis (64), also negatively regulates flagellar components in L. monocytogenes (5). Therefore, the genes in the CodY regulon were examined and were found to be significantly enriched among upregulated genes as a result of treatment with sigmastatin (FDR q<0.0001).
We surmise that several new genes, which were significantly and differentially expressed resulting from treatment with sigmastatin, may be σB dependent if they are part of an operon containing at least one σB dependent gene also affected by sigmastatin. For example, previously only lmo0974 or dltA (the first gene in the operon important for modifying lipotechoic and wall techioc acid) was shown to be negatively regulated by σB. However, in addition to dltA, other genes in this operon (lmo0973 (dltB) and lmo0971 (dltD)) were also significantly upregulated as a result of treatment of sigmastatin. Therefore, it is possible that dltB and dltD are also σB dependent, yet have not been discovered as such because of less consistent negative regulation.
Noticeably more σB-dependent genes were inhibited by sigmastatin than were induced, therefore, σB-dependent genes from multiple conditions previously tested (35, 45, 72, 77, 98) were compared to identify trends in σB regulation. Only a very small core group of σB negatively regulated genes (14 of 264 total) were recurrent in multiple assays (i.e. genes that were identified as negatively regulated by σB in two or more assessments of σB dependence). Conversely, there was a large core group of σB positively regulated genes (137 of 282) (i.e. genes that were identified as positively regulated by σB in two or more assessments of σB dependence) (Table 4.5). Therefore,
111

it seems that the accuracy of predicting a “regulon” of σB repressed genes based on one assay condition is lower than for positively regulated genes, as seen by increased variability among σB repressed genes from different assays. This variability is likely a consequence of the differences introduced at each level of regulation by other transcription factors that overlap in regulation of so-called σB-repressed genes (Table 4.5). It is evident however, that sigmastatin truly targets the core σB regulon, as it inhibited transcription of 125 of the 137 genes that were positively regulated by σB in two or more assays.
Sigmastatin reduces L. monocytogenes invasion of human enterocytes To quantify the effect of sigmastatin on L. monocytogenes invasion in Caco-2
human enterocytes, the invasion capacity of exponential phase L. monocytogenes cells was assessed after exposure to a low concentration of NaCl (0.3M) known to induce σB-activity and treatment with or without sigmastatin. Treatment with sigmastatin, at both 64µM and 8µM, significantly reduced L. monocytogenes invasion capacity by approximately 1.4 and 1.5 logs, respectively, producing a 25 and 32 fold reduction in invasion capacity as compared to WT invasion (Figure 4.6; p<0.05). ]This provides clear phenotypic evidence that sigmastatin hinders virulence functions regulated by σB, which are critical to the establishment of orally acquired listeriosis (30).
Inhibition of σB in B. subtilis indicates specificity across genera In order to determine if sigmastatin could inhibit σB activity in the closely
related Gram-positive microbe B. subtilis, a β-galactosidase enzymatic assay monitoring a σB-dependent ctc-lacZ reporter fusion in B. subtilis was utilized. This assay showed that treatment with sigmastatin at 64µM significantly inhibited σB-
112

Figure 4.6: Invasion assay. Bar graph of invasion efficiency of L. monocytogenes in the human intestinal epithelial cell line Caco-2. Strains and corresponding treatments are indicated on the x-axis. These include wildtype treated with 0.3M NaCl (wt salt), wildtype treated with 0.3M NaCl & 64µM sigmastatin (wt salt & 64µM sigmastatin), wildtype treated with 0.3M NaCl & 8µM sigmastatin (wt salt & 8µM sigmastatin), and the isogenic sigB null strain treated with 0.3M NaCl (∆sigB salt). Invasion efficiency was calculated as the number of bacteria recovered relative to the number of bacteria used for inoculation (i.e., Log ([CFU/ml recovered] /[CFU/ml inoculated]). Data represent four biological replicates, each performed in triplicate. Bars with different letters indicate strain/treatments that differed significantly (p<0.05; GLM Tukey). These experiments demonstrate the utility of the identified compound at inhibiting attachment and invasion of human enterocytes.
113

Figure 4.7: B. subtilis β-galactosidase assay. Bar graph of β-galactosidase activity in Miller Units (MU) from B. subtilis strains with (a) σB-dependent Pctc-lacZ reporter fusions exposed to various conditions. These include wildtype treated with 0.3M NaCl and DMSO (wt salt), wildtype treated with 0.3M NaCl & 64µM sigmastatin (wt salt & 64µM sigmastatin), wildtype treated with 0.3M NaCl & 8µM sigmastatin (wt salt & 8µM sigmastatin), and the isogenic sigB null strain treated with 0.3M NaCl (∆sigB salt) and DMSO. (b) shows σA-dependent PrsbRSTU-lacZ fusion treated with either 0.3M NaCl & 64uM sigmastatin (PsigA-lacZ salt & 64µM sigmastatin) or 0.3M NaCl & DMSO (PsigA-lacZ salt & DMSO). Data represent at least three biological replicates. Bars with different letters indicate strain/treatments that differed significantly (p<0.05; GLM Tukey). Sigmastatin (64µM) inhibits σB activity in B. subtilis, producing lacZ levels similar to those in a B. subtilis ∆sigB strain. Some inhibition also occurs at 8µM concentration of sigmastatin. Sigmastatin treatment does not affect σA activity. This data shows the identification identified an effective and specific inhibitor of σB-dependent gene expression, which is effective in different Gram-positive bacteria (i.e., the genera Listeria and Bacillus).
114

dependent ctc lacZ enzyme activity (p<0.05; GLM Tukey) almost 2 fold, to levels equivalent to a ∆sigB strain (Figure 4.7a ; p>0.05). Treatment with 8µM sigmastatin also reduced σB-dependent enzyme activity, however, not significantly from WT salt treated cells (p>0.05; GLM Tukey). Notably, a σA-dependent lacZ fusion (104) showed no difference in β-galactosidase activity when treated with 64µM sigmastatin or DMSO (Figure 4.7 b), further pointing to the specificity of sigmastatin for inhibiting σB.
DISCUSSION Using a high-throughput screen of 57,000 small molecules, 41 candidate
compounds were identified as potential inhibitors of the L. monocytogenes σB alternative sigma factor; subsequent screens produced one compound that specifically interfered with σB activity. Sigmastatin selectively inhibited σB-mediated transcription as shown by qRT-PCR of σB-dependent genes and whole-genome microarray analysis of cells treated with the compound relative to untreated cells. This compound also prevented L. monocytogenes invasion into human intestinal epithelial cells and inhibited σB-directed activity in the Gram-positive bacterium B. subtilis. Overall our data show (i) novel small molecules can inhibit the σB regulon with high specificity and yield transcriptional profiles similar to a genetic null mutation of the sigB gene and (ii) one such compound can be used to prevent expression of L. monocytogenes virulence factors important to disease etiology, also inhibiting σB activity across genera. Thus, these results provide further evidence that chemical genetics is a valuable approach for identifying potential novel anti-infective therapeutics via targeting transcription factors.
115

The identified small molecule inhibits σB regulon with high specificity and yields transcriptional profiles similar to a genetic null mutation.
A conserved protein is a valuable target against which novel anti-infectives can be developed because of their potential to provide broad-spectrum anti-virulence drugs. In fact, screens have identified a class of inhibitors effective against the conserved type 3 secretion system (TTS) in pathogens Yersinia spp., Salmonella spp., Shigella flexneri, P. aeruginosa, Escherichia coli, and Chlamydia spp. (3, 39, 44, 66, 68, 69, 105). Similarly, from a collection of 150,000 compounds, a highly effective inhibitor of the virulence-associated membrane histidine sensor kinase QseC in enterohemorrhagic E. coli (78) was also found to inhibit QseC homologs in other pathogens (i.e., S. Typhimurium, Francisella tularensis). In addition, Lieberman et al. used a small molecule screen to identify neuroleptic drugs that have potential as therapeutics for various intracellular bacterial pathogens, including L. monocytogenes (55, 56). These studies indicate that compounds identified as interfering with virulence-associated characteristics in one pathogen may be effective in a broad range of bacterial pathogens.
Limited work, however, has been performed to identify novel inhibitors of transcription factors for therapeutic use. In a eukaryotic system, Koehler et al., 2003 (52) used a small molecule microarray and transcriptional profiling to identify a smallmolecule inhibitor of the Hap3p subunit of Hap 2/3/4/5p yeast transcription factor, whose regulation of mitochondrial function is relevant as a model system for identifying inhibitors of human diseases such as diabetes and cancer. The identified compound produced a transcriptional profile equivalent to a chemical genetic knockdown of Hap2/3/4/5p. In prokaryotes, there have been some successes in identifying small molecules that inhibit members of the AraC family of transcriptional regulators. This family of regulators, like σB, contributes to the transcription of
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multiple stress response (54) and virulence factors (6, 27), providing a target whose activity is broad in scope, rather than a target whose discrete virulence-associated gene product is unique to a single organism (9). In fact, Hung et al. identified a small molecule inhibitor of V. cholerae virulence transcriptional regulator ToxT (an AraC/XylS transcriptional regulator) that inhibited the transcription of critical virulence components: cholera toxin and toxin co-regulated pilus (40). Small molecules have also been used to target other proteins from the AraC family of bacterial transcription factors, including MAR proteins MarA, SoxS and Rob in E. coli (9) and LcrF in Yersinia spp (49), preventing regulator-DNA binding. These compounds were successful at inhibiting virulence in vitro and in vivo, suggesting that targeting a pathogen at the transcriptional level is very effective method for inhibition. Another screen identified compounds that inhibit binding of β1 of core RNAP to σ70 in E. coli (31). To our knowledge, ours is the first evidence for the identification of a small-molecule inhibitor of a bacterial alternative sigma factor that targets a large majority of its regulon, producing a transcription profile similar to that of a sigma factor null strain thus mimicking the loss of functional σB in a cell.
The small molecule identified here, sigmastatin, targets σB at an IC50 of 3.5uM. The observed activity levels in this low micromolar range are promising because the minimal bacteriocidal concentrations of gentamycin, ampicillin, and streptomycin (against L. monocytogenes) are in the range of 2 - 46 µM (63) and the ToxT-inhibiting virstatin (40) showed an MIC between 3 and 40 µM (depending on the target strain). According to whole-genome microarray analysis, 64µM sigmastatin treatment produced inhibition of 55% (156/282) of all genes shown to be positively regulated by σB under at least one condition (of 7 assessed) and inhibition of >91% (125/137) of genes positively regulated by σB under two or more assay conditions (35, 45, 71, 72, 77, 98). Observation of multiple assays indicates that while 264 genes were shown to
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be repressed by σB under at least one condition in multiple assays (35, 45, 72, 77, 98), only 14 were differentially expressed under two or more conditions. Interestingly, sigmatatin upregulated only 7 of these 264 σB repressed genes, none of which are among the 14 σB-dependent genes identified under two or more conditions, suggesting limited utility in targeting negatively regulated genes. The results of treatment with this compound may highlight the more central role of σB as a positive regulator with a strong core of positively regulated genes and an indirect role as a negative regulator with very limited set of genes directed under multiple conditions.
Toledo-Arana et al. (98) identified 172 genes in L. monocytogenes EGDe, which were up regulated by σB in the mouse intestinal lumen. Sigmastatin significantly downregulated 126 of these genes and significantly upregulated 1 of these genes. Of these genes, 17 were σB-dependent specifically in the intestinal infection (98). Not only is σB is critical to L. monocytogenes adaptation and virulence in the intestine (98) and throughout the gastrointestinal tract in its entirety (29), but it also regulates genes involved in intracellular survival and proliferation (16). The effectiveness of this compound at producing a σB null status in the cell, inhibiting genes which are important during intestinal infection and preparation for systemic infection as well as adaptation for the intracellular environment, strongly suggests that this compound holds promise as an excellent therapeutic or prophylactic for the treatment of listeriosis.
Small molecules targeting σB can be used to probe stress response and regulatory networks in L. monocytogenes
To also ascertain whether sigmastatin activity was specific to σB, the effect of our anti-infective compound on other alternative sigma factors, including σH and σL, was assessed. σH is important to growth in minimal and alkaline media (80) and
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previous microarray analysis showed that much of the σH regulon overlaps with that of σB. Therefore, the effect of sigmastatin on the σH regulon was evaluated. It was determined that the majority of the σH-regulated genes that were affected by the compound were also regulated by σB and that the σH-only regulon (the set of genes in which σB co-regulated genes were removed) was not enriched. σL, also known as RpoN, contributes to carbohydrate metabolism and antimicrobial resistance (2, 81) and is associated with σB in so doing (74). The σL regulon, however, was not enriched in our microarray dataset. Moreover, this compound exhibited specificity for this alternative sigma factor in that it not only inhibited L. monocytogenes σB, but it also inhibited σB activity (but had no effect on σA activity) in the Gram-positive model organism B. subtilis, which is closely related to other low G+C content pathogens. These assessments support the notion that σB is a preferential alternative sigma factor target.
Multiple lines of evidence demonstrate a central network between σB and PrfA (14, 72), which coordinates in vivo expression of genes required for the infectious process (14). The direct regulatory control σB has over PrfA via the P2prfA promoter is well established (67, 79, 85), however, it is increasingly clear that there are additional layers of indirect σB regulation. As suggested by Ollinger et al., σB may act as a posttranscriptional switch that downregulates excessive PrfA activity (72). Those data indicated that multiple virulence genes were differentially expressed in the presence of PrfA* depending on the presence or absence of σB. Specifically, σB moderated the PrfA regulon in a PrfA* background helping to mediate host-cell damages effects of PrfA-dependent virulence genes (72). In our dataset, the PrfA regulon was significantly enriched among sigmastatin-downregulated genes. The fact that two PrfA-dependent genes, lmo0937 and plcA, with no known association with σB regulation, were both downregulated by an otherwise σB-specific inhibitor
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(sigmastatin), suggests there are additional and complex layers of regulation and fine tuning that occur between PrfA and σB. However, it is evident that the interplay between these regulators is fundamental in order to co-regulate these subsets of virulence genes to survive and promote infection.
Mounting evidence points to a fundamental role for σB in chemotaxis and motility. Specifically, several genes in a large operon of flagellar structural components were identified to be negatively regulated by σB (77). Additionally, sigB null mutants exhibit increased swarming on agar (77, 98). We surmise that the complex relationship of σB to motility could be explained by direct or indirect coregulation with one or more of the multiple regulators (DegU, MogR and CodY) involved in motility and chemotaxis, allowing for fine-tuning of L. monocytogenes response to varied environments. As a result of sigmastatin treatment, several genes important to motility and chemotaxis were upregulated. Specifically, 6 genes on the flagellum biosynthesis operon (lmo0673-0718) were significantly and differentially upregulated. Although they were not among the 13 previously identified σB-dependent genes found in this operon (77), they were interspersed among them on the operon. The majority of this flagellar operon is regulated by DegU, a positive activator of flagellum biosynthesis (51), including those 6 sigmastatin upregulated genes (103). Furthermore, 2 genes upregulated by sigmastatin, which comprise the methylaccepting chemotaxis operon, lmo1699 and lmo1700, are negatively regulated by σB (77) and are also regulated by DegU (103). In fact, the DegU and CodY regulons, but not MogR, were significantly enriched among upregulated genes, suggesting an overlap with the negative regulatory function that σB has on motility (77, 98). Furthermore, while Listeria is known to downregulate flagellar genes during infection (14) to evade the immune system, increased expression of flagellar components can induce potent proinflammatory affects via TLR5-mediated immunogenicity (100).
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Therefore, the ability of sigmastatin to upregulate flagellar components validates its potential as an anti-listeriosis drug.
These data regarding the expression of various regulons after treatment with a target-specific compound, such as sigmastatin, support the assertion of complex networks among transcription factors in L. monocytogenes (18, 38).
Small molecules targeting transcriptional regulators, including alternative sigma factors, show promise as therapeutic and environmental control agents
Chemical genetics entails the use of small molecules to probe and/or alter biological targets, allowing for a better understanding of certain processes and for identification of novel therapeutics. Identifying compounds that are active against biological targets such as disease-causing mechanisms of pathogenesis in microorganisms has proven to be successful approach for developing new classes of “antibiotics”. Specifically, previous work using high-throughput screens to identify small-molecule inhibitors of virulence in V. cholerae were successful, allowing for the identification of the small molecule that disrupted protein-protein interactions of transcription factor ToxT, resulting in the prevention and treatment of cholera postinfection. In the high-throughput assay, we found that of the genes inhibited by sigmastatin, 75% were previously reported as σB dependent (35, 45, 72, 77, 98). This shows genome wide evidence for inhibition by a highly selective small molecule capable of modulating transcriptional regulation of genes critical to stress response and virulence in the Gram-positive pathogen L. monocytogenes.
The alternative sigma factor σB is important for responding to stimuli from specific environments including those triggered by transit through the host gastrointestinal tract (4, 29). σB modulates gene expression, including expression of stress response and virulence factors, and is therefore important to establishing an
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infection in the mammalian host. In addition to σB’s role in transcription of virulence and in vivo viability associated genes, there is burgeoning evidence that σB contributes to infection in animal models. In B. anthracis, Fouet et al. (28), showed that a deletion mutant of sigB was less virulent than the isogenic parental strain. Specifically, there was a one log-unit lower LD50 in a B. anthracis sigB mutant as compared to the parent strain. The authors suggest that sigB may contribute to virulence by allowing B. anthracis to persist in the bloodstream of the mammalian host during septicemia, the final stage of anthrax (28). In Staphylococcus aureus, σB controls a sarA promoter and SarA activates agr, which in turn encodes a protein that regulates virulence. According to Jonsson et al (42), a S. aureus strain, which was defective in σB activity because of an impaired posttranslational activator of σB (rsbU), showed increased arthritogenicity and sepsis compared to a strain with a repaired rsbU. Similar to B. anthracis, Jonsson et al. suggested that either σB itself or regulation of it by RsbU promotes S. aureus survival in the bloodstream, preventing clearance and allowing establishment of infection (42). Furthermore, Lorenz et al. also showed that functional loss of σB results in a decrease of S. aureus virulence in central venous catheter-related diseases manifested by significantly reduced multiorgan infection caused by σB deficient strains (59). In L. monocytogenes, Garner et al. showed that as compared to a wildtype strain, a sigB null strain of L. monocytogenes shows reduced infection in a guinea pig model via an intragastric route (30). Because of its role in virulence and viability of multiple human pathogens in the host, σB represents a suitable target for inhibition by novel small molecules.
In our phenotypic and transcriptional profiling experiments, σB-dependent virulence genes, such as inlAB, bsh, bilE, clpC and hfq were significantly downregulated as a result of treatment with sigmastatin. While each of these genes has been shown to contribute to virulence individually (14, 19, 23, 57, 82, 91), a
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compound that can inhibit transcription of all of these genes provides an increased advantage over a compound that targets only one virulence factor (9). Furthermore, stress response and virulence-associated genes opuC (92) and gadA (20), which are important to survival during passage through the host, were also significantly downregulated by the compound. The ability of this compound to target a wide array of genes required for virulence and in vivo viability suggests it has great promise as a therapeutic, as the targeted genes directly contribute to pathogenesis in an animal model.
The Caco-2 human intestinal epithelial cell line provides insight into the interaction between the intracellular pathogen L. monocytogenes and intestinal epithelial cells and correlates well to the animal model (30). σB is essential for attachment and infection of enterocytes, as demonstrated in Caco-2 human intestinal epithelial cells (30, 48) and is also a requirement for invasion and establishment of infection in the guinea pig model of listeriosis. We used this in vitro system to determine the effect sigmastatin has on the ability of L. monocytogenes to invade human enterocytes. Sigmastatin severely impedes L. monocytogenes attachment and invasion of human intestinal epithelial cells, likely because of the drastically reduced expression of σB-directed virulence genes inlA and inlB. In fact, sigmastatin inhibited σB activity to such a degree that it reduced L. monocytogenes invasion capacity to that of a ∆sigB strain. Most notably, sigmastatin worked rapidly; affecting σB directed transcription in less than 10 minutes and subsequent translation in less than 30 minutes of treatment (according to qRT-PCR and invasion assays). This model provides strong phenotypic substantiation that this inhibitory small molecule is able to hinder the virulence functions of σB, which are critical to the establishment of orally acquired listeriosis.
Emerging evidence supports that transcription factors in microorganisms are
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also promising targets for anti-virulence inhibitors (9, 40, 49, 87). These conserved proteins control multiple genes important to virulence, regulating mechanisms of pathogenesis across multiple microorganisms. This work demonstrates that the regulator critical for L. monocytogenes gene expression during infection and stress survival is an excellent target for broad range novel therapeutics. Indeed, from our screen of 57,000 compounds we found that sigmastatin inhibition was specific across genera, substantiating the assertion that such a compound can be used to target homologues of this protein regulator in Gram-positive pathogens. This type of chemical modulator of virulence and in vivo viability, which impairs bacterial invasion and persistence in the host via inhibition of genes also increases the microbe’s susceptibility to mammalian host defenses. Such a compound can render the organism innocuous and easily cleared by the immune system. This is particularly beneficial for immunocompromised hosts, for whom listeriosis causes the highest morbidity and mortality (25-45%) (32).
Targeting an alternative sigma factor for the development of anti-virulence therapeutics may be beneficial for other diseases for which emerging drug-resistance is thwarting treatment. In fact, this approach might also be applied to alternative sigma factor σF (closely related to σB) (25, 73) in Mycobacterium tuberculosis, which regulates virulence-associated genes important to pathogenesis (90, 93) and antimicrobial resistance (62). This work also establishes a foundation for developing small molecules that can be used to interfere with the ability to survive environmental stress conditions, proving beneficial for controlling transmission and reservoirs of pathogens that persist in the environment. For example, because certain families of alternative sigma factors are conserved, it is possible to use an inhibitor similar to the one discovered in this work, to control sporeformers. Not only are σB and σF important for many Gram-positive pathogens’ survival in the host, they are also important for
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enabling survival outside the host prior to infection (99). In fact, a sigB deletion strain of B. cereus, a foodborne pathogen and close relative of B. anthracis, exhibits delayed onset of sporulation and subsequently, less efficient germination (99). Furthermore, development of compounds targeting σF, which is important to sporulation in B. anthracis (24), may provide a form of environmental control of anthrax. The application of a compound that produces a more susceptible cell is ideal for sensitizing the cell to control agents and other preventative measures. As our data demonstrates, targeting alternative sigma factors is a worthwhile approach to consider for future drug development or environmental control agents for inhibition of microbial transmission.
By utilizing chemical-genetics strategies, identification of anti-virulence agents active against L. monocytogenes transcriptional regulators, such as alternative sigma factors, could produce human chemotherapeutics active against a number of similar Gram-positive pathogens. Such strategies would potentially help avoid the misuse of classical antibiotics, preserving their efficacy for situations warranting their application. This approach affords us the opportunity to develop novel agents which abrogate or reduce L. monocytogenes pathogenicity and possibly other Gram-positive clinically relevant pathogens, which will help to mitigate the burdens currently beleaguering public health and safety. This research provides a better understanding of the benefits derived from employing chemical genetics in concert with microbiology for drug development for human pathogens. By extrapolating what we learn from one organism and harnessing this knowledge, we can rationally develop drugs intended to target the very factors which are essential to microbial survival in the host and pathogenicity in a number of similar disease-causing microorganisms.
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CHAPTER 5 CONCLUSIONS
Listeria monocytogenes has the highest fatality rate among foodborne pathogens and disproportionately affects infants, the elderly and immunocompromised individuals. The loss of life and financial burdens caused by L. monocytogenes warrant investigation into new methods for control and alleviation of this public health threat. Increasing interest in employing chemical genetics to answer questions in the realms of both eukaryotic and prokaryotic biology has permitted a better understanding of biological functioning (e.g. pathways and proteins) (12). Chemical genetics has also lent itself to the identification of valuable compounds with potential for treating human diseases, such as those caused by harmful viruses, parasites, and bacteria (4-6, 8, 11). In particular, this approach has been successfully used to inhibit transcription factors from the AraC family of regulators which contribute to virulence in several bacterial pathogens (1, 2).
Building upon this foundation, we set out to identify new bacterial targets in L. monocytogenes for anti-infective development. First, our research aimed to determine the contributions of select transcriptional regulators to virulence and to antimicrobial resistance to select peptides. Results from the research supported that σB promotes invasion, PrfA is critical to cell-to-cell growth and showed that CtsR, in addition to PrfA and σB, are important to virulence in a guinea pig model of listeriosis. Moreover, σB and σL both contribute to antimicrobial peptide resistance, coordinating response to both SdpC and nisin. Therefore, we chose the alternative sigma factor σB as our target of choice for anti-infective development because of its role in regulating stress response and antimicrobial resistance, promoting in vivo viability and contributing to virulence in L. monocytogenes. In order to discover novel small molecules capable of
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attenuating human listeriosis via inhibition of σB, we used a high-throughput format to screen ~57,000 small molecules to identify selective inhibitors of σB. The resulting promising compounds were reassessed using a secondary cell-based HTS format as well as qRT-PCR; one select compound was then comprehensively validated through a variety of methods such as transcriptional profiling, phenotypic assessments, and mammalian tissue culture infection models. We identified a potential anti-virulence agent, sigmastatin, with high specificity for σB in both Listeria and Bacillus, producing a chemically induced σB null status equivalent to a genetic knockout of σB in the cell. This novel agent inhibited invasion thus reducing L. monocytogenes pathogenicity pointing to the possibility of application against other Gram-positive clinically relevant pathogens.
We anticipate that this work will further the development of novel therapeutic agents that are detrimental to the pathogenic potential of prokaryotes but entirely benign to eukaryotes. In using the approach described in the work presented here, the ultimate goal is to help reduce public health and safety issues caused by the foodborne pathogen L. monocytogenes, while avoiding the misuse of classical antibiotics and preserving them for warranted situations. For the future of this work, the next steps to pursue would involve forming a complete picture of sigmastatin’s mode of action. In order to do this: (i) numerous derivatives of this compound will need to be tested in order to determine the most important sub-structures on the molecule that contribute to optimal inhibition and (ii) sigmastatin target-identification will need to be performed. As data from our small-molecule microarray suggested the possibility that σB and sigmastatin bind, we hope to elucidate how sigmastatin is inhibiting σB activity via identification of true targets. Previous work has shown that small molecules can inhibit protein-protein interactions (9) or protein-DNA interactions (3), thus, we surmise that sigmastatin may act in a number of ways. For instance, it may prevent
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effective core RNAP- σB association and/or subsequent promoter binding or it may act upstream on regulators of σB (Rsb). We suggest using a general target identification method such as SILAC (stable isotope labeling with amino acids in cell culture) (7) or a more specific method such as SPR (surface plasmon resonance). SILAC, which is used in quantitative proteomics, involves a combination of isotope labeling, affinity chromatography and mass spectrometry to identify any potential protein binders to a bead-conjugated small molecule (SM). This method entails the use of one lysate population labeled with a light isotope and exposed to both a bead-affixed SM and a soluble SM (that acts as competitor bait reducing the number of target proteins that bind the SM-bead) and one lysate population labeled with heavy isotope exposed only to the SM-bead. The resulting heavy to light peptide fragment ratio allows for identification of true protein “interactors” using mass spectrometry (7). We might also use SPR to immobilize a variety of potential targets to determine affinity and specificity of protein-ligands pairs or even to determine the effects of a small molecule ligand on a protein complexed with other molecules, such as DNA. Alternatively, a genetic approach can be used to identify the protein target of sigmastatin. This would involve screening for bacteria exhibiting a resistance phenotype, such as mutant colonies able to deconjugate bile salts on selective agar containing inhibitory levels of sigmastatin. Subsequently, the mutant colonies would be assessed to determine a genetic explanation for resistance using total genome sequencing approaches, such as Solexa (Illumina) or 454 sequencing, pinpointing mutations in the target that elicited resistance (10). Ultimately, this information may allow for the realization of a highly effective listeriosis treatment option.
Identification of sigmastatin and a subsequent understanding of its mode of action serve as a stepping stone for the identification of similar compounds targeted against other sigma factors of interest. We anticipate that this may provide an avenue
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for the generation of compounds aimed at an expansive array of applications (beyond anti-virulence agents), such as the identification of environmental control agents or simply for improving our current understanding of gene regulation and regulatory networks.
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APPENDIX

Appendix Table AT.1: Secondary Screening candidates

Virtual ID

Vender ID

Decision

SPBio_000086 SPBio_000596 SPBio_001858

Spectrum01500167 Spectrum00210477 Spectrum00200034

Not available commercially Effective in secondary, Not available commercially at the time, actinonin Effective in secondary, Not available commercially at the time, atranorin

SPBio_002673 Ald1.1-H_000308

Prestwick_000747 Not pursued after secondary, antibiotic Would need resynthesis

ACon1_001486

NP-005144

Not available

ChemDiv3_002815 4151-0291 ChemDiv3_002999 4237-0075a

Not pursued after secondary Pursued after secondary

ChemDiv3_005866 6145-0438 ChemDiv3_005911 6079-1959

ChemDiv3_006137 ChemDiv3_007160
ChemDiv3_007374

6228-2502 8012-2663
8009-2163 a

ChemDiv3_010387 ChemDiv3_010413 Maybridge4_001879 Maybridge4_001886

C614-5726 C660-0131 JFD00174 JFD02846

Not pursued after secondary Not pursued after secondary Not pursued after secondary, aryl hydrazone Not pursued after secondary Pursued after secondary, minimal cytotoxicity Not pursued after secondary, aryl hydrazone Not pursued after secondary, cytotoxic Not pursued after secondary Not pursued after secondary

Maybridge4_001932 JFD03061 a

Pursued after secondary

Maybridge4_001966 JFD02331

Not pursued after secondary

Maybridge4_001967 JFD00263
Maybridge4_002150 KM06170
Maybridge4_002415 KM04727
Maybridge4_003857 S13598 Maybridge4_004192 SEW02081

Not pursued after secondary Not pursued after secondary, aryl hydrazone Not pursued after secondary, aryl hydrazone Pursued as growth inhibitor, not effective as sigB or growth inhibitor Not pursued after secondary

Maybridge4_004329 Maybridge4_004330 Maybridge4_004364 Maybridge4_004503

SP01461 SPB02585 SPB01534 SP01411

Maybridge4_004548 SPB02493 Maybridge4_004591 SPB06794

Maybridge4_004694 SPB06723

Enamine_001246 T0504-0705

Enamine_001250 T0500-0388

Not pursued after secondary Not pursued after secondary Not pursued after secondary Not pursued after secondary Not pursued after secondary, aryl hydrazone Not pursued after secondary Not pursued after secondary, aryl hydrazone Not pursued after secondary, problems with dilution Not pursued after secondary, problems with dilution

Result of σB inhibition Possible growth inhibitor
Might work
Might work Possible growth inhibitor Might work Possible growth inhibitor Possible growth inhibitor Not effective Possible growth inhibitor Unclear
Might work Unclear Works-used derivative T0513
Might work Might work Unclear Unclear Works- needs optimization Possible growth inhibitor Possible growth inhibitor
Might work
Might work
Not effective Might work Possible growth inhibitor Might work Might work Unclear
Might work Might work
Might work
Unclear
Unclear

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Appendix Table AT.1 (Continued)

Not pursued after secondary, aryl

hydrazone, problems with dilution,

Enamine_001604 T0505-1745

structure similar to 4237-0075

Unclear

TimTec1_003523 ST047887

Not pursued after secondary

Possible growth inhibitor

TimTec1_003545 ST050178

Not pursued after secondary, cytotoxic Might work-ytotoxic

TimTec1_003817 ST050863

Not pursued after secondary

Possible growth inhibitor

TimTec1_003987 ST057360

Not pursued after secondary

Unclear

TimTec1_005049 ST212074

Not pursued after secondary

Might work

TimTec1_005093 ST211458

Not pursued after secondary

Unclear

TimTec1_007542 ST5024987

Not pursued after secondary

Unclear

a Compounds or derivatives of compounds in bold were assessed further using qRT-PCR, etc.

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control spot compound spot
Appendix Figure AF.1: Scatterplot of small-molecule microarray screen. Threedimensional scatterplot of Z-scores calculated from normalized fluorescence intensity resulting from interaction between σB and printed small-molecule ligands. Arrays were tested in triplicate and bound His-tagged σB was detected using Alexa Fluor 647 labeled anti-His antibody. Red dots represent DMSO controls; blue dots are small molecules ligands tested.
149