Protein Expression in nucleatum ATCC 10953 Biofilm Cells Induced by an Alkaline Environment

A thesis submitted in fulfilment of the requirements for admission to the degree of Doctor of Philosophy

By

Jactty Chew

B.Sc (Biotech) (Monash University, Malaysia), B.Sc (Biotech) (Hons) (Monash University, Malaysia)

School of Dentistry The University of Adelaide South Australia December 2010

Signed Statement

This work contains no material which has been accepted for the award of any other degree or diploma in any university or other tertiary institution and, to the best of my knowledge and belief, contains no material previously published or written by another person except where due reference has been made in the text.

I give my consent to this copy of my thesis, when deposited in the

University Library, being available for loan and photocopying.

Signed: ...... Date: ......

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Acknowledgements

I would like to thank a number of people who provided assistance during the course of this work.

Dr. Neville Gully, Dr. Peter Zilm and Dr. Janet Fuss, who acted as my supervisors. This study would not have been possible without their guidance, advice and encouragement.

Dr. Tracy Fitzsimmons, Mr. Krzyztof Mrozik, Mr.Victor Marino, Ms. Lyn Waterhouse (Adelaide Microscopy Centre) and the staff at the Adelaide Proteomic Centre for their advice and technical assistance on many occasions.

My friends, for their support and encouragement.

My parents, Da-Jie, Thien, Han and Yen, for their understanding, continual support and faith.

The University of Adelaide, for awarding me a scholarship (ASI).

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Abstract

Fusobacterium nucleatum is a Gram negative anaerobic bacterium, frequently isolated from both healthy and diseased and has been implicated in the aetiology of periodontal diseases. Studies have shown that this organism increases in number and proportion in diseased periodontal pockets suggesting that changes in the environment favour the growth of the organism. One of the physico-chemical changes that occurs in the diseased periodontal sulcus is an increased environmental pH, which may be as high as 8.5. In our laboratory, F. nucleatum subspecies polymorphum ATCC

10953 formed mono-culture biofilms at pH 8.2. The formation of biofilms in nature is a survival strategy for , often associated with altered physiology and increased virulence. The aim of this study was to investigate changes in F. nucleatum protein expression associated with these alkaline induced biofilms.

A chemostat was used to produce F. nucleatum planktonic and biofilm cells grown at pH 7.4 and 8.2, respectively. The bacterial cells were separated into cell envelope and cytoplasmic fractions. The soluble proteins were extracted from each fraction and resolved by two-dimensional gel electrophoresis. Fifty five differentially expressed proteins were identified using mass spectrometry and database searching.

These proteins were classified according to functional class, including metabolic, transport and stress proteins.

One of the most interesting findings was the significant up-regulation of

Fusobacterial Outer Membrane Protein A (FomA), a porin that is responsible for the coaggregation between F. nucleatum and periodontopathogens. It has been suggested that this protein may be a target for future vaccination against periodontal diseas es.

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Other proteomic findings showed that transport proteins such as ATP binding cassette (ABC) transporter binding protein was down-regulated while tripartite ATP- independent (TRAP) transporter binding protein was enhanced in the alkaline environment. ABC transporters require ATP hydrolysis for solute transport. In contrast,

TRAP transporter is ATP independent and co-transports protons into the cytoplasm, which may help maintain a neutral intracellular pH under alkaline conditions. The regulation of these transport proteins reflected the conservation of energy and maintenance of pH homeostasis in biofilm cells.

In addition, F. nucleatum regulated key in energy-producing pathways.

A combination of proteomic and other methods confirmed that biofilm cells increased their glucose uptake and storage as intracellular polyglucose in comparison to their planktonic counterparts. Furthermore, the increased energy requirement of these cells was associated with changes in metabolism resulting in both qualitative and quantitative shifts in acidic end-products.

Bacterial biofilm formation can be triggered by adverse environmental conditions and the expression of stress proteins help to protect cellular functions. The stress proteins, GroEL and RecA, were up-regulated in biofilm cells and the expression of these proteins was verified using Western-blotting and quantitative real-time polymerase chain reaction techniques.

In conclusion, this investigation successfully identified F. nucleatum proteins that were regulated in response to alkaline conditions, similar to those that are thought to exist in diseased periodontal pockets. The physiological adaptations observed, to the

iv changed ecology, provide evidence at the molecular level supporting the key role of F. nucleatum in the establishment of mature dental plaque.

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Table of Contents Signed statement…………………………………………………………………………i

Acknowledgements .……………………………………………………………………ii

Abstract .……………………………………………………………………………….iii

List of figures …………………………………………………………………………..xi

List of tables ……………………………………………..……………………………xiii

List of abbreviations …………………………………………………………………..xiv

1 Introduction ...... 1

1.1 Periodontal Diseases ...... 2 1.1.1 Aetiology of periodontal diseases ...... 3 1.1.2 Dental plaque and periodontal diseases ...... 4 1.1.2.1 Microbial complexes in subgingival plaque ...... 4 1.1.2.2 Microbial homeostasis and the ecological plaque hypothesis ...... 5 1.1.3 Treatments and preventions of periodontal diseases ...... 9 1.2 Fusobacterium nucleatum ...... 11 1.2.1 F. nucleatum and periodontal diseases ...... 13 1.2.1.1 Interaction with bacterial species ...... 14 1.2.1.2 Interaction with host cells ...... 16 1.2.1.3 Other potential virulence factors ...... 18 1.2.2 Growth of F. nucleatum in vitro ...... 19 1.2.3 Biofilm formation of F. nucleatum is induced by an alkaline growth pH 22 1.3 Bacterial biofilms ...... 23 1.3.1 Development of biofilms ...... 24 1.3.2 Environmental conditions influence biofilm formation ...... 26 1.3.2.1 Survival strategy: Bacterial stress response ...... 26 1.3.2.2 The pH stress response ...... 28 1.3.3 Altered phenotypes and physiology in bacterial biofilm cells ...... 30 1.4 Proteomics ...... 32 1.4.1 Proteomic techniques ...... 33 1.4.1.1 Protein preparation and separation using 2DE ...... 33 1.4.1.2 Protein identification using mass spectrometry ...... 35 1.4.2 Validation of proteomic results ...... 37 1.5 Overall aims ...... 39

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2 Growth of F. nucleatum and biofilm production in vitro in response to environmental pH ...... 40

2.1 Introduction ...... 41 2.2 Aim ...... 44 2.3 Materials and Methods ...... 45 2.3.1 Effects of environmental pH on the F. nucleatum growth ...... 45 2.3.1.1 Bacterial culture ...... 45 2.3.1.2 Growth media ...... 45 2.3.1.3 Continuous culture conditions and sampling ...... 45 2.3.1.4 Auto-aggregation assay ...... 47 2.3.2 Mono-culture biofilm production ...... 48 2.3.2.1 96-well microtitre plate ...... 48 2.3.2.2 Modified Robbins Device ...... 49 2.3.2.3 Scanning electron microscopy ...... 49 2.4 Results ...... 50 2.5 Discussion ...... 57 2.6 Summary ...... 58

3 A proteomic assessment of outer membrane protein expression in F. nucleatum biofilm cells ...... 59

3.1 Introduction ...... 60 3.2 Aim ...... 61 3.3 Outer membrane protein expression in F. nucleatum biofilm cells ...... 62 3.3.1 Materials and Methods ...... 62 3.3.1.1 Growth and harvesting of F. nucleatum ...... 62 3.3.1.2 Preparation of bacterial cell lysate ...... 63 3.3.1.3 Preparation of outer membrane proteins ...... 63 3.3.1.4 Protein separation by two-dimensional gel electrophoresis ...... 64 3.3.1.5 Protein identification by tandem mass spectrometry ...... 66 3.3.1.6 Protein characterisation using web-based bioinformatics tools ...... 67 3.3.2 Results ...... 68 3.3.2.1 Outer membrane protein extraction and separation ...... 68 3.3.2.2 Outer membrane protein expression in F. nucleatum biofilm cells ...... 72 3.4 Investigation of possible post-translational modifications of Fusobacterial Outer Membrane Protein A ...... 77

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3.4.1 Materials and Methods ...... 77 3.4.1.1 Prediction of FomA post-translational modifications using ProMost web-tool ...... 77 3.4.1.2 Detection of phosphorylated FomA using ProQ Diamond Phosphoprotein Stain ...... 77 3.4.2 Results ...... 79 3.4.2.1 Prediction of FomA post-translational modifications using ProMost web-tool ...... 79 3.4.2.2 Detection of phosphorylated FomA using ProQ Diamond Phosphoprotein Stain ...... 81 3.5 Discussion ...... 83 3.5.1 Outer membrane protein enrichment by carbonate treatment ...... 83 3.5.2 Altered cell envelope protein expression in biofilm cells ...... 84 3.5.2.1 Proteins down-regulated in F. nucleatum biofilm cells ...... 85 3.5.2.2 Proteins up-regulated in F. nucleatum biofilm cells ...... 87 3.5.3 Regulation of protein expression and pH homeostasis ...... 88 3.5.4 Regulation of FomA and its possible post-translational modifications .... 90 3.6 Summary ...... 93

4 A proteomic analysis of cytoplasmic protein expression in F. nucleatum biofilm cells ...... 94

4.1 Introduction ...... 95 4.2 Aim ...... 96 4.3 Materials and Methods ...... 97 4.3.1 Growth and harvesting of F. nucleatum ...... 97 4.3.2 Sample preparation for proteomic analysis ...... 98 4.3.3 Protein separation by two-dimensional gel electrophoresis ...... 99 4.3.3.1 First dimension protein separation: Isoelectric focusing ...... 99 4.3.3.2 Second dimension electrophoresis ...... 101 4.3.4 Protein expression analysis ...... 101 4.3.5 Protein identification by mass spectrometry ...... 102 4.3.5.1 Mass spectrometry sample preparation ...... 102 4.3.5.2 MALDI mass spectrometry (MS and MS/MS) ...... 102 4.3.5.3 Liquid chromatography-ESI mass spectrometry (MS and MS/MS) ...... 103 4.3.5.4 Protein identification ...... 103 4.4 Results ...... 104 4.4.1 Cytoplasmic protein profiles ...... 104

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4.4.1.1 Separation of proteins with a pI between 4 and 7 ...... 104 4.4.1.2 Separation of proteins with a pI between 7 and 10 ...... 104 4.4.2 Cytoplasmic protein regulation in F. nucleatum biofilm cells ...... 108 4.5 Discussion ...... 114 4.5.1 Cytoplasmic protein profiles ...... 114 4.5.2 Biofilm growth and protein expression ...... 115 4.5.2.1 Energy producing pathway proteins ...... 116 4.5.2.2 Stress proteins ...... 118 4.6 Summary ...... 122

5 Validation of key regulated proteins identified by proteomic analysis in biofilm cells ...... 123

5.1 Introduction ...... 124 5.2 Aims ...... 126 5.3 Validation studies of stress proteins ...... 127 5.3.1 Materials and Methods ...... 127 5.3.1.1 Western blotting analysis of GroEL, DnaK and RecA ...... 127 5.3.1.2 Quantitative real time PCR analysis of groEL, dnaK and recA ...... 128 5.3.2 Results ...... 132 5.3.2.1 Western blotting analysis of GroEL, DnaK and RecA ...... 132 5.3.2.2 Quantitative real-time PCR analysis of groEL, dnaK and recA ...... 135 5.4 Confirmatory studies of metabolic activity ...... 137 5.4.1 Materials and Methods ...... 137 5.4.1.1 assays ...... 137 5.4.1.2 End-product and glucose estimation using high performance liquid chromatography...... 141 5.4.1.3 Intracellular polyglucose assay ...... 141 5.5 Results ...... 143 5.5.1 Butanoate:acetoacetate coenzyme A assay ...... 143 5.5.2 Lactate dehydrogenase assay ...... 144 5.5.3 NAD-specific glutamate dehydrogenase assay ...... 144 5.5.4 Intracellular polyglucose determination and acidic end-product profiles ...... 146 5.6 Discussion ...... 148 5.6.1 Expression of key stress proteins ...... 148 5.6.2 Confirmatory studies of bacterial metabolic activity ...... 151

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5.7 Summary ...... 156

6 Summary discussion ...... 157

6.1 Biofilm growth and physiology in alkaline environments ...... 158 6.2 Biofilm formation of F. nucleatum subspecies nucleatum ...... 164 6.3 Limitations and future studies ...... 164

7 Appendices ...... 167

7.1 Composition of Chemically Defined Medium (CDM) ...... 168 7.2 Matchset of 2DE gels used for outer membrane protein (pI 4-7) expression analysis ...... 170 7.3 Matchset of 2DE gels used for outer membrane protein (pI 7-10) expression analysis ...... 171 7.4 Optimisation of cytoplasmic protein (pI 7-10) separation ...... 172 7.5 Matchset of 2DE gels used for cytoplasmic protein (pI 4-7) expression analysis ...... 177 7.6 Matchset of 2DE gels used for cytoplasmic protein (pI 7-10) expression analysis ...... 178 7.7 Optimisation of protein load for Western blotting ...... 179 7.8 Determination of end products and glucose using HPLC ...... 180 7.9 Butanoate/acetoacetate CoA transferase assay ...... 181 7.10 Optimisation of lactate dehydrogenase assay ...... 182 7.11 Optimisation of NAD-specific glutamate dehydrogenase assay ...... 184 7.12 Biofilm formation by F. nucleatum subspecies nucleatum ATCC 25586 ..... 186

8 Bibliography ...... 187

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List of Figures Figure 2-1. Appearance of bacterial growth at pH 7.4 and pH 8.2 in chemostat culture vessels ...... 52

Figure 2-2. Auto-aggregation rate of cells grown at differing environmental pH ...... 52

Figure 2-3. Gram stained images of bacterial cells grown at pH 7.4 and 8.2 under 1000x magnification ...... 53

Figure 2-4. Effect of environmental pH on the F. nucleatum cell protein content ...... 54

Figure 2-5. Effect of environmental pH on the growth of F. nucleatum in a chemostat 54

Figure 2-6. Bound F. nucleatum cells grown at pH 7.4 and 8.2 using the microtitre plate method ...... 55

Figure 2-7. Scanning electron micrographs of F. nucleatum cells grown at pH 7.4 and 8.2 ...... 56

Figure 3-1. 2DE gels of outer membrane proteins between pI 4-7 separated from planktonic and biofilm cells ...... 70

Figure 3-2. 2DE gels of outer membrane proteins between pI 7-10 separated from planktonic and biofilm cells ...... 71

Figure 3-3. Gaussian 2D gel images of outer membrane proteins between pI 4-10 ...... 74

Figure 3-4. Predicted migration of FomA following N-deamination and phosphorylation on the theoretical gel image generated by ProMost web-tool ...... 80

Figure 3-5. Separation by 2DE of outer membrane proteins from biofilm cells and stained with ProQ Diamond Stain or Flamingo Fluorescent Stain ...... 82

Figure 4-1. 2DE gels of cytoplasmic proteins between pI 4-7 separated from planktonic and biofilm cells ...... 105

Figure 4-2. 2DE gels of cytoplasmic proteins between pI 7-10 separated from planktonic and biofilm cells ...... 106

Figure 4-3. Gaussian 2D gel images of cytoplasmic proteins between pI 4-10 ...... 107

Figure 5-1. Western blotting analysis of DnaK……………………………………….131

Figure 5-2. Western blotting analysis of GroEL……………………………………...132

Figure 5-3. Western blotting analysis of RecA……………………………………….132

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Figure 5-4. Differential expression of target genes in planktonic and biofilm cells following normalisation……………………………………………………………….135

Figure 5-5. NAD-specific glutamate dehydrogenase activity measured in cells grown at different environmental pH…………………………………………………………...144

Figure 5-6. Possible metabolic pathways of planktonic cells grown at pH 7.4....…….154

Figure 5-7. Possible metabolic pathways of biofilm cells grown at pH 8.2…………..154

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List of Tables Table 2-1. The pH of bacterial cultures before and after incubation overnight at 37ºC . 55

Table 3-1. Outer membrane protein recovery using sodium carbonate treatment ...... 69

Table 3-2. The number of replicate gels used in outer membrane protein analysis ...... 69

Table 3-3. Up- or down- regulated outer membrane proteins of F. nucleatum ATCC 10953 grown as a biofilm under alkaline conditions ...... 75

Table 3-4. Potential FomA post-translational modifications predicted using ProMost web-tool ...... 80

Table 4-1. Proteins involved in energy-producing pathways that were regulated in both biofilm and planktonic cells of F. nucleatum ...... 110

Table 4-2. Stress proteins that were regulated in both biofilm and planktonic cells of F. nucleatum ...... 112

Table 5-1. List of commercially available antibodies...... 130

Table 5-2. Designed primers used for qRT-PC ……………………………………..130

Table 5-3. Expression of RecA in planktonic and biofilm cell lysates determined by Western blotting ……………………………………………………………………...133

Table 5-4. Expression of GroEL in planktonic and biofilm cell lysates determined by Western blotting ……………………………………………………………………...133

Table 5-5. Transcript levels of housekeeping gene 16S rRNA and target genes dnaK, groEL and recA in planktonic and biofilm cells……………………...... 135

Table 5-6. Butanoate:acetoacetate coA transferase present in biofilm and planktonic cells.....………………………………………………………………………………...142

Table 5-7. Lactate dehydrogenase activity in biofilm and planktonic cells...………...144

Table 5-8. Glucose consumption and metabolic end-products by planktonic and biofilm cells .....………………………………………………………………………………..146

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List of abbreviations

2DE – Two-dimensional gel electrophoresis ABC transporter – ATP-binding cassette transporter BAT – Butanoate: acetoacetate Coenzyme-A transferase BHI – Brain Heart Infusion CDFF – Constant depth film fermentor CDM – Chemically Defined Media CID – Collision-induced dissociation DTT – Dithiothreitol EC – Enzyme Commission EF – Elongation factor Eh – Redox potential EPS – Extracellular polymeric substance ExPASy – Expert Protein Assay Systems FomA – Fusobacterial outer membrane protein A GCF – Gingival crevicular fluid HGEC – Human gingival epithelial cell HGT – Horizontal gene transfer HPLC – High Performance Liquid Chromatography HSR – Heat shock response IEF – Isoelectric focusing IP – Intracellular polyglucose IPG – Immobilised pH gradient LC-ESI – Liquid Chromatography – Electrospray Ionisation LDH – Lactate dehydrogenase LPS – Lipopolysaccharides MALDI-TOF – Matrix-Assisted Laser Desorption/Ionization-Time-of-Flight MMP – Matrix metalloproteinase MRD – Modified Robbins Device MS – Mass spectrometry MW – Molecular weight NAD+-GDH – NAD+-specific glutamate dehydrogenase NCBI – National Center for Biotechnology Information OMP – Outer membrane protein OmpIP – Outer membrane protein Insertional Porin pI – Isoelectric point PSORTb – Subcellular localisation prediction tool for bacterial cells PTM – Post-translational modifications RCDC – ReduCing agent and Detergent Compatible RND antiporter – Resistance-Nodulation-Cell Division antiporter RT-PCR – Real Time-Polymerase Chain Reaction SDS – Sodium dodecyl sulphate SEM – Scanning Electron Microscopy TBP – Tributyl phosphine TRAP-transporter – Tripartite ATP-independent transporter TTT transporter – Tripartite tricarboxylate transporter

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1 Introduction

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1.1 Periodontal Diseases

Periodontal diseases are a group of inflammatory diseases of the soft and hard tissue supporting structures of the teeth and include the gingivae, periodontal ligament, and supporting alveolar bone. Periodontal diseases are highly prevalent. According to a report by the Australian Research Centre for Population Oral Health (ARCPOH), periodontal diseases are the fifth most common health problem in the Australian population (Ellershaw et al., 2005).

Gingivitis is an inflammatory condition of the soft tissues supporting the teeth and arises from the host immune response to the accumulation of dental plaque on teeth.

This inflammatory condition is highly prevalent; population studies have shown that it affects up to 56% and 82% of adolescents in Sweden (Ericsson et al., 2009) and the US

(Albandar & Rams, 2002), respectively. Individuals with show clinical signs including red and swollen gingivae. In addition, bleeding may occur on gentle probing.

Gingivitis is readily reversible by the application of adequate tooth cleaning methods

(Kinane, 2001; Samaranayake, 2002).

It is now accepted that gingivitis usually proceeds periodontitis; however, not all gingivitis progresses to periodontitis. Periodontitis occurs only in a subset of the population (Kinane et al., 2007). The prevalence of periodontitis varies between countries; for example, 35% of US adults (Albandar et al., 1999) and 87% of German adults suffer from periodontitis (Holtfreter et al., 2010). In periodontitis, the release of inflammatory molecules by host causes the breakdown and loss of connective tissue fibers anchoring the root to gingival connective tissues and alveolar bone. As a result, epithelial cells proliferate apically along the root surface forming periodontal pocket

2

(Kinane, 2001; Pihlstrom et al., 2005). The assessment of periodontitis clinically is by the measurement of pocket depth, a measurement greater than 3 mm indicates disease.

Severe periodontitis can eventually lead to tooth loss if left untreated (Samaranayake,

2002).

1.1.1 Aetiology of periodontal diseases

Periodontal diseases result from interactions between in dental plaque and the host response, which may be modulated by genetic, environmental and acquired risk factors (Pihlstrom et al., 2005; Wolff et al., 1994).

To date, a number of risk factors have been identified to be responsible for the progression of the diseases (Kinane, 2001). For example, an association between the consumption of alcohol (Amaral et al., 2009; Tezal et al., 2001) and high levels of emotional and psychosocial stress (LeResche & Dworkin, 2002) are thought to be risk factors. An impaired host response resulting from genetic disorders such as leukaemia, thrombocytopenia and leucocyte disorders also cause certain individuals to be more susceptible to periodontal diseases (Pihlstrom et al., 2005). Increasing evidence has shown that smoking confers a considerably increased risk of and modifies the host response against periodontal pathogens. Previous studies have shown that smoking affects the humoral, cellular immune and inflammatory systems, and has effects throughout the cytokine and adhesion molecule network (Rivera-Hidalgo, 2003;

Zee, 2009).

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1.1.2 Dental plaque and periodontal diseases

Despite their multifactorial nature, microbial consortia present in dental plaque are a primary initiating factor of inflammatory periodontal diseases. The bacterial microflora of the oral cavity is diverse and more than 700 bacterial species have been detected using culture-independent methods (Aas et al., 2005; Faveri et al., 2008; Paster et al., 2006). Of those identified, over 400 bacterial species have been isolated from subgingival plaque and the remainder were found in other oral sites (Paster et al., 2001;

Paster et al., 2006). These bacterial species are thought to play important roles in the maintenance of oral health and in the aetiology of oral diseases in humans (Kumar &

Schweizer, 2005; Socransky et al., 2002).

1.1.2.1 Microbial complexes in subgingival plaque

In 1998, Socransky and co-workers analysed over 13,000 subgingival plaque samples from healthy and periodontally diseased subjects and grouped the identified bacterial species present into colour-coded complexes. The authors suggested that the associations among bacterial species in dental plaque are not random (Socransky et al.,

1998).

There were five major complexes identified. The yellow complex consisted of

Streptococcus species such as S. mitis, S. sanguinis and S. oralis; the purple complex comprised of species Actinomyces odontolyticus and Veillonella parvula; Eikenella corrodens, sputigena, Capnocytophaga ochracea, Capnocytophaga gingivalis and Aggregatibacter actinomycetemcomitans (previously known as

Actinobacillus actinomycetemcomitans) were assigned to the green complex; a tightly related orange complex, which consisted of Fusobacterium nucleatum/,

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Prevotella intermedia, Peptostreptococcus micros, Campylobacter rectus and

Campylobacter gracilis and a that included ,

Bacteroides forsythus (now classified as ) and denticola

(Socransky et al., 1998). The yellow and purple complexes comprise the predominant bacterial species isolated from human dental plaque. They are recognised as early colonisers on the tooth surface (Aas et al., 2005; Diaz et al., 2006; Kolenbrander, 2000;

Kolenbrander et al., 2002; Kolenbrander et al., 2006; Ximénez-Fyvie et al., 2000).

The same study by Socransky et al. (1998) reported that increased numbers of the bacterial species assigned to the red complex showed a correlation with clinical signs of periodontal disease, particularly increased pocket depth and . The orange complex species showed a relationship with pocket depth; however, did not show a significant association with bleeding on probing. Bacteria from these two complexes are closely associated as the orange complex bacteria appear to precede and facilitate colonisation by red complex species (Socransky et al., 1998).

Therefore, it is thought that the orange complex bacteria, particularly F. nucleatum, play a crucial role in the establishment of mature dental plaque by bridging the early and late colonisers during the establishment of subgingival plaque (Kolenbrander, 2000;

Kolenbrander et al., 2002; Kolenbrander et al., 2006).

1.1.2.2 Microbial homeostasis and the ecological plaque hypothesis

As shown by Socransky et al. (1998) and Ximénez-Fyvie et al. (2000), the non- periodontopathogenic early colonisers are mainly Gram positive cocci (Streptococcus species, Veillonella species and Actinomyces species) and periodontopathogenic Gram negative bacteria (P. gingivalis, T. denticola and T. forsythia) are present in the plaque

5 from both healthy and diseased individuals. The maturation of dental plaque involves changes in the composition of bacterial species over time. A shift from a dental plaque dominated by Gram positive bacteria to a mature dental plaque dominated by Gram negative anaerobes has been observed in the progression of periodontal disease. Dental plaque development involves changes in physiological conditions, particularly environmental pH, nutrient supply and redox potential (Eh) (Marsh and Bradshaw,

1997). Microbial homeostasis is a dynamic process that is determined by synergistic and antagonistic interactions among oral bacteria (Marsh, 1994). The ecological plaque hypothesis suggests that ecological shifts in dental plaque are associated with the shifts in microbial homeostasis and explains the development of caries and periodontal disease (Marsh, 1994).

The average redox potential of the healthy has been reported to be high, in the order of +75 mV, compared to that for periodontal pockets which may decrease to values as low as -157 mV (Diaz & Rogers, 2004; Kenney & Ash Jr, 1969).

In 2006, Diaz and co-workers examined plaque samples that accumulated on removable enamel chips that were worn intraorally by human subjects. Plaque samples produced after four and eight hours were analysed and the authors concluded that Streptococcus,

Neisseria and Rothia species were the most dominant. These bacterial species, frequently identified as members of the normal oral microflora, are either aerobic or facultative anaerobes and thus tolerant of elevated redox potential. In contrast, obligate anaerobes such as P. gingivalis were not detected (Diaz et al., 2006).

The metabolic activity of the plaque community is closely associated with the pH of its microenvironment. While the fluids of the oral cavity are relatively pH neutral,

6 saliva having a pH 6.25-7.25 (Olsen, 2006), gingival crevicular fluid (GCF) samples from healthy subjects have pH of 6.9 (Bickel & Cimasoni, 1985). Most supragingival plaque species especially those that colonise the enamel surface, ferment carbohydrates to produce energy. The fermentation of carbohydrates produces acidic end products.

Therefore, residing bacteria must be able to withstand pH change as plaque pH may fall to 5.3 in response to carbohydrate challenge (Zaura & ten Cate, 2004). Early colonising

Streptococcus species, such as S. mitis, S. sanguinis and S. gordonii, have an optimal growth pH of 5.5 and are aciduric as they are able to tolerate a pH 4 environment for an hour (Castillo et al., 2000). In summary, a healthy gingival sulcus is dominated by bacterial species that are able to proliferate in an environment with a relatively high redox potential, acidic to neutral pH with carbohydrates as the primary nutrient source.

The bacterial species present in dental plaque interact both synergistically and antagonistically to maintain homeostasis. For instance, Veillonellae species, members of the purple complex, are frequently found in the presence of streptococci (Kolenbrander et al., 2002). Veillonella atypica is unable to ferment sugars but utilises the lactic acid resulting from carbohydrate fermentation by S. gordonii to produce energy (Egland et al., 2004). V. atypica also coaggregates with S. gordonii through a 45 kDa cell surface adhesin protein (Hughes et al., 1992). The environmental conditions at the early stage of plaque formation may not favour the growth of late colonisers. The low number of

Gram negative bacteria present in the plaque at this stage is due, in part, to antagonistic interactions with the resident bacteria. For example, early studies have shown that certain viridans streptococci produce hydrogen peroxide and inhibit the growth of periodontopathogenic bacteria (Hillman et al., 1985; Marsh & Bradshaw, 1995).

Pathogens such as P. gingivalis which prefer a mild alkaline growth pH are not

7 favoured in an environment below neutrality (Nelson et al., 2003; Takahashi &

Schachtele, 1990).

Generally the composition of bacteria in plaque remains stable over time; however, microbial homeostasis is breached when oral hygiene is neglected. As a consequence, plaque can accumulate at the and induce a host immune response that leads to an increase in the flow of GCF (Tsalikis, 2010). GCF is a serum- like fluid, composed of host defence molecules (including interleukins and immunoglobulins), proteins, peptides and free amino acids that acts as a nutrient supply for the bacteria residing in plaque (Marsh, 1994). An increased flow of GCF arising from the host inflammatory response is thought to support the growth of proteolytic bacteria. This is demonstrated by the enrichment of proteolytic Gram negative species observed in diseased plaque. The metabolism of proteins, peptides and amino acids produces ammonium ions, leading to an increase in the pH of the subgingival environment. GCF samples collected from periodontally diseased subjects are more alkaline than those from healthy patients, with a pH value up to 8.5 observed, depending on the type of electrode employed, (Bickel & Cimasoni, 1985; Bickel et al., 1985a;

Eggert et al., 1991).

Within the biofilm structure, extensive pH gradients can establish. Using a constant depth film fermentor, Vroom et al. (1999) developed a 10-species biofilm of

100 µm thickness and showed that, after the exposure to sucrose, a pH gradient ranging between 3 and 8 developed within the biofilm (Vroom et al., 1999). Alkalinisation of the diseased periodontal pocket and within the biofilms present is thought to facilitate the proliferation of the “so-called” red complex bacteria.

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In addition, as periodontal disease progresses, the depth of the periodontal pocket increases due in part to the release of bacterial (Bachrach et al., 2004;

Lamont & Jenkinson, 1998). Also, the redox potential was found to decrease with increasing pocket depth and was found to range from +14 mV to -157 mV (Diaz et al.,

2006; Kenney & Ash Jr, 1969). The changes in environmental pH and redox potential are proposed to suppress the growth of early-colonising bacteria and result in a shift in the microbial community towards the predominantly Gram negative anaerobic species, ie. favouring the presence of the key periodontopathogenic bacteria P. gingivalis, T. denticola and T. forsythia.

1.1.3 Treatments and preventions of periodontal diseases

Preventive dental therapy has primarily been focused on removing dental plaque. To date, mechanical remains a key aspect of treatment. It disrupts the biofilms that induce inflammation in the gingival tissues. However, the use of mechanical debridement alone in periodontal treatment in many cases is insufficient and has a relatively short-term effect. The use of adjunctive systemic antibiotics in periodontal treatment has proven to offer an additional benefit over alone (Heitz-Mayfield, 2009). However, the action of the antibiotic may be ineffective as periodontal disease is the result of a mixed microbial infection. To overcome this problem, the most commonly used systemic antibiotic therapy includes a combination of amoxicillin and metronidazole targeting Gram negative anaerobes

(Heitz-Mayfield, 2009).

Despite the use of adjunctive antibiotic therapy and mechanical debridement offering positive results in the treatment of periodontal diseases, an increasing number

9 of reports indicates rise in the development of antibiotic resistance by periodontal pathogens (Ardila et al., 2010; Kulik et al., 2008; Mosca et al., 2007). Antibiotic resistance between bacterial species develops through horizontal gene transfer (HGT) and it is particularly relevant in dental plaque as hundreds of different bacterial species have been identified in human dental plaque and close proximity facilitates HGT

(Sorensen et al., 2005). The transfer of antibiotic resistance genes between S. gordonii and Enterococcus faecalis, a species that is frequently isolated from infected root canals, was investigated by Sedgley and colleagues in 2008. The results showed that bidirectional transfer of an erythromycin resistance determinant on the conjugative plasmid pAM81 occurred between the two bacterial species (Sedgley et al., 2008).

Therefore, HGT of antibiotic resistance in dental biofilms could enhance bacterial virulence.

Alternatives to antibiotics have been extensively researched over the years. For example, P. gingivalis has been identified as a potential target for vaccine development by Persson (2005) while others have investigated the use of natural products and peptides as alternative antimicrobial agents. Promising results have been demonstrated using a number of phyto-chemicals such as tea tree oil, manuka honey, green tea extract, eucalyptol and thymol (Allaker & Douglas, 2009; Haffajee et al., 2009; Wu,

2009) that have been incorporated into a variety of oral care products. The accepted polymicrobial nature of oral disease (caries and periodontal diseases) has also led to the investigation of probiotic therapy (Meurman & Stamatova, 2007) and several studies suggest this method may be useful in maintaining oral health. One study showed a reduction in initial caries in kindergarten children associated with long-term consumption of milk containing the probiotic Lactobacillus rhamuosus GG strain

10

(Hatakkab et al., 2001). To date, however, no studies have investigated the effect of probiotic therapy on periodontal disease.

A better understanding of the interactions between bacteria and the host response that leads to the transition from health to disease requires further investigation of oral microbes and their interaction with host tissues. The increased knowledge acquired may be useful in the development of new antimicrobial agents and therapy in order to control these highly prevalent human diseases.

1.2 Fusobacterium nucleatum

The are anaerobic Ghe ram negative bacteria which are part of the normal flora in humans. Colonising the oropharyngeal and gastrointestinal tract,

Fusobacterium species are amongst the most common bacteria associated with human anaerobic infections (Mira et al., 2004). There are 13 species in the genus

Fusobacterium and of these, F. necrophorum and F. nucleatum are considered to be the most pathogenic (Kapatral et al., 2002; Sigge et al., 2007).

F. nucleatum is fusiform in shape, with a cell length normally varying between 5 to 10 µm (Bolstad et al., 1996). It is a putative isolated from dental plaque of both healthy and diseased periodontal sites. However, its proportion in the plaque biofilm increases significantly in sites with periodontitis, refractory periodontitis and acute necrotising ulcerative gingivitis (ANUG) (Paster et al., 2001).

There is increasing evidence that F. nucleatum is implicated in secondary root canal infections (De Paz et al., 2003; Fabricius et al., 2006; Siqueira Jr & de Uzeda, 1996)

11 and halitosis (Liu et al., 2010). It is also one of the most common species isolated from extraoral infections in a variety of fluids and tissues including blood, brain, lung, liver, joint, abdominal and obstetrical and gynaecological infections and abscesses. Due to its frequent isolation from intrauterine infections, F. nucleatum has also been suggested to be associated with an increased incidence of (Cahill et al., 2005; Han et al., 2004; Karpathy et al., 2007; Mikamo et al., 1998).

There are five subspecies of F. nucleatum: polymorphum, nucleatum, vincentii, fusiforme and animalis (Bolstad et al., 1996; Dzink et al., 1990; Gharbia & Shah, 1990;

Karpathy et al., 2007). The complete genomes of F. nucleatum subspecies nucleatum

ATCC 25586 (Kapatral et al., 2002) and polymorphum ATCC 10953 (Karpathy et al.,

2007) are available (Oralgen Databases http://www.oralgen.lanl.gov/). A partially sequenced genome for subspecies vincentii ATCC 49256 has been published (Kapatral et al., 2003).

Despite the similar fermentation profiles and acidic end-products of F. nucleatum subspecies, the heterogeneity of the species has been demonstrated by the analysis of cellular fatty acids, peptidoglycan and DNA base composition (Gharbia &

Shah, 1990). The heterogeneity is also validated by genomic sequencing as 17% of the genes of subspecies nucleatum ATCC 25586 does not appear in the subspecies vincentii

ATCC 49256 genome. Conversely, 20% of the genes of subspecies vincentii ATCC

49256 are absent from subspecies nucleatum ATCC 25586 (Karpathy et al., 2007). In

1991, Gharbia and Shah examined F. nucleatum type strains and clinical isolates and proposed that F. nucleatum subspecies nucleatum was more frequently isolated from

12 periodontally diseased sites, while subspecies polymorphum and fusiforme were more associated with healthy sites (Gharbia and Shah, 1991).

F. nucleatum subspecies polymorphum ATCC 10953 was used in the present study. Its genome is 2.43 Mb and the organism contains three plasmids, pFN1, pFN2 and pFN3 that are absent in the other two subspecies. The GC content of all three F. nucleatum subspecies is 27%, a value lower than that found in most bacterial genomes

(Kapatral et al., 2002). Mira and colleagues (2004) analysed the evolutionary relationships of F. nucleatum genes by phylogenetic analysis and comparative genomic tools and reported that Fusobacterium has a core genome different to other bacterial lineages, and branches out at the base of the Firmicutes (low G+C Gram positive bacteria). Depending on the method used, 35-56% of Fusobacterium genes were reported to be acquired through HGT and most likely are from , , and the Firmicutes themselves (Mira et al., 2004).

1.2.1 F. nucleatum and periodontal diseases

Bacterial virulence factors enable cell colonisation on tooth and root surfaces and suppress host cell functions. Known periodontopathogenic virulence factors include bacterial adhesins, proteases and LPS (Furuta et al., 2009). The unusual adhesive properties of F. nucleatum enabling the cell to bind to a variety of bacterial strains and host cells make this bacterium important in oral biofilm formation and periodontal disease pathogenesis (Kolenbrander et al., 2006).

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1.2.1.1 Interaction with bacterial species

It has been reported that F. nucleatum coaggregates with early colonisers such as

Streptococcus, Actinomyces, Veillonella and Selenomonas species (Andersen et al.,

1998; Bradshaw et al., 1998; Edwards et al., 2007; Kolenbrander et al., 1989) and periodontopathogens P. gingivalis, A. actinomycetemcomitans and T. forsythia

(Metzger et al., 2001; Rosen et al., 2003; Rosen & Sela, 2006; Rupani et al., 2008;

Sharma et al., 2005). It is also found to coaggregate with bacteria of non-oral origin such as Weissella cibaria (Kang et al., 2005) and Helicobacter pylori (Andersen et al.,

1998).

The characterisation of F. nucleatum‟s ability to coaggregate is based on its inhibition by either D-galactose or D-arginine. The interaction between F. nucleatum and Gram-negative „late‟ colonising bacteria is associated with a galactose-binding protein while its coaggregation with Gram positive early colonisers is attributed to an arginine-inhibitable interaction (Kaplan et al., 2009).

An early study reported that the coaggregation between F. nucleatum and

Streptococcus cristatus was mediated by a S. cristatus serine-rich repeat protein

(Kolenbrander et al., 1989). A subsequent study showed that this protein interacts with a 360 kDa arginine-binding protein present on the F. nucleatum cell surface (Edwards et al., 2007). More recently, Kaplan reported that the arginine-inhibitable protein of F. nucleatum is the outer membrane protein RadD and is also involved in its coaggregation with Streptococcus sanguinis, S. gordonii, S. oralis and Actinomyces naeslundii. As expected, mutant strains lacking this adhesin demonstrated a reduced coaggregation activity with S. cristatus which did not affect coaggregation between P. gingivalis and

14

F. nucleatum, suggesting that RadD is recognised only by early colonisers (Kaplan et al., 2009). RadD has also been shown to adhere to secretory IgA and the authors suggested that this property may facilitate the F. nucleatum invasion of gingival soft tissues (Edwards et al., 2007).

Another interaction mediating the coaggregation between F. nucleatum and oral bacteria is inhibited by simple sugars such as galactose and lactose (Kolenbrander et al.,

1989; Kolenbrander & Andersen, 1989). In contrast to the arginine-binding protein, this carbohydrate-lectin interaction has been demonstrated to be involved in the coaggregation between F. nucleatum and the late colonising bacteria including A. actinomycetemcomintans, P. gingivalis and various Bacteroides species (Kolenbrander et al., 1989; Rosen et al., 2003). Independent research groups examined the coaggregation between F. nucleatum and P. gingivalis and reported that the adhesin responsible for this interaction was localised in the outer membrane of F. nucleatum and has a molecular weight between 30 and 40 kDa (Kinder & Holt, 1993; Shaniztki et al.,

1997). Antiserum (Kinder & Holt, 1993), and monoclonal antibody (Shaniztki et al.,

1997) raised against this adhesin blocked the coaggregation with P. gingivalis. A recent investigation reported that this 40 kDa adhesin is the Fusobacterial outer membrane protein A (FomA) and neutralisation of FomA inhibited the coaggregation with P. gingivalis and subsequent biofilm formation (Liu et al., 2010). The P. gingivalis carbohydrate receptors that interact with FomA were identified as capsular polysaccharide and LPS. It has also been claimed that the LPS of A. actinomycetemcomitans is responsible for FomA mediated coaggregation (Rosen et al.,

2003; Rosen & Sela, 2006; Rupani et al., 2008).

15

The importance of F. nucleatum in the development of multi-species biofilms has been demonstrated in several studies. Bradshaw and colleagues (1998) investigated the coaggregation patterns between obligately anaerobic and oxygen-tolerant bacterial species and showed that F. nucleatum was essential for the coaggregation of the otherwise non-coaggregating obligate anaerobes, such as P. gingivalis, to oxygen tolerant species such as S. oralis (Bradshaw et al., 1998). These findings emphasize the important bridging role that F. nucleatum is thought to play in the establishment of mature dental plaque.

As well as facilitating coaggregation, F. nucleatum interacts synergistically with late colonisers T. forsythia and P. gingivalis. Sharma and colleagues (2005) showed that the abundance of T. forsythia increased six-fold while F. nucleatum cells increased two- fold when grown as a co-culture compared to their respective mono-cultures (Sharma et al., 2005). A similar symbiotic effect was also reported recently that F. nucleatum and

P. gingivalis co-culture increased biofilm yields three-fold higher than those of their mono-species biofilms (Saito et al., 2008).

1.2.1.2 Interaction with host cells

The adhesive properties of F. nucleatum are not restricted to interactions with oral bacteria. F. nucleatum has also been reported to attach to human epithelial cells

(Edwards et al., 2006; Han et al., 2000; Ozaki et al., 1990; Weiss et al., 2000). The adherence to epithelial cells is important for colonisation of bacteria as it is essential in the initiation of an infection. The binding to epithelial cells may trigger the expression of immune response mediators. As demonstrated in a study by Han et al. (2000), F. nucleatum attached to cell cultures of human gingival epithelial cells (HGEC) and KB

16 oral epithelial cell lines. The binding to epithelial cells was associated with an increased production of the pro-inflammatory chemokine, interleukin-8 (IL-8) from the HGEC. It is thought that IL-8 regulates neutrophil migration into the gingival sulcus (Han et al.,

2000). The binding of F. nucleatum to epithelial cells was shown to be inhibited by sugars (including galactose and lactose) (Edwards et al., 2006; Han et al., 2000; Weiss et al., 2000) and it was suggested that this binding involves the same adhesin responsible for the coaggregation between F. nucleatum and late colonisers, P. gingivalis and A. actinomycetemcomitans (Weiss et al., 2000).

Matrix metalloproteinases (MMPs) are responsible for the degradation of the collagenous structure of the periodontium. In 2005, Uitto and co-workers demonstrated that F. nucleatum cells stimulated the expression of collagenase 3, a potent MMP, in human epithelial cells (HaCaT keratinocyte line). The expression of collagenase 3 is associated with tissue destruction and migration of epithelial cells during the pathogenesis of periodontal disease (Uitto et al., 2005). A separate study reported that exposure to F. nucleatum LPS stimulated the production of MMP-9 (Grenier &

Grignon, 2006). The enhanced production of MMP-9 by F. nucleatum may also contribute to the pathogenesis of periodontal disease.

Bleeding on probing is associated with the formation of periodontal pockets. F. nucleatum was also reported to agglutinate and haemolyse erythrocytes (Gaetti-Jardim

& Avila-Campos, 1999). The haemagglutination and haemolysis of erythrocytes provides a source of iron which is essential for bacterial growth; in particular, growth of recognised periodontal pathogens such as P. gingivalis (Gaetti-Jardim & Avila-Campos,

1999).

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The LPS of F. nucleatum was also found to bind to macrophage-like cells in vitro and induce the secretion of pro-inflammatory cytokines interleukin-1β (IL-1β), interleukin-6 (IL-6), chemokine interleukin-8 (IL-8) and tumour necrosis factor-α

(Grenier & Grignon, 2006). The increased secretion of IL-1β and tumour necrosis factor-α is known to amplify the inflammatory response, the induction of connective tissue-degrading enzymes and osteoclastic bone resorption, all of which are commonly observed in the progression of periodontal disease.

F. nucleatum has also been shown to adhere to lymphocytes and induce apoptosis (Kaplan et al., 2005; Weiss et al., 2000). The apoptosis of lymphocytes appears to be linked to an outer membrane protein, Fap2 (Kaplan et al., 2005). It is thought that this may help the bacteria to neutralise the host immune response once infection is initiated. In addition, F. nucleatum also binds to polymorphonuclear leukocytes (Ozaki et al., 1990; Weiss et al., 2000). As this binding can be inhibited by galactose, it is likely that the binding to polymorphonuclear leukocytes involves the same adhesin involved in the coaggregation between F. nucleatum and the late colonisers mentioned previously (Section 1.2.1.1) (Weiss et al., 2000).

1.2.1.3 Other potential virulence factors

The genome sequence of F. nucleatum subspecies polymorphum ATCC 10953 reveals 132 potential virulence genes that may play a role in its pathogenicity (Karpathy et al., 2007). The identified genes encode for butanoate fermentation, iron acquisition, drug transporters, β-lactamases, outer membrane proteins, type IV and V secretion proteins, proteases and LPS synthesis. Of these, the roles of LPS and a few outer membrane proteins that encode for adhesins (FomA, RadD, Fap2) were investigated (as

18 discussed in the previous section). A discussion of antibiotic resistance and drug transporters follows and butanoate synthesis will be discussed in Section 1.2.2.

In 2008, F. nucleatum clinical isolates were demonstrated to be resistant to ampicillin (Al-Haroni et al., 2008). Using a proteomic approach, the authors suggested that ampicillin resistance was due to a class-D β-lactamase and ATP-binding cassette

(ABC) transporter. The genome sequences of all three F. nucleatum subspecies reveal the presence of a variety of genes involved with increased antibiotic resistance. These include genes coding for β-lactamases and drug transporters such as a macrolide symporter, Resistance-Nodulation-cell Division (RND) antiporters and ABC antimicrobial peptide transporters (Karpathy et al., 2007), thus, conferring antibiotic resistance to penicillins (phenoxymethylpenicillin, benzylpenicillin, ampicillin, piperacillin and cloxacillin) (Tuner et al., 1985) and reduced susceptibility to amoxicillin and clarithromycin (Mosca et al., 2007).

Other recognised virulence factor genes found in the F. nucleatum subspecies polymorphum genome code for proteins including a VacJ homolog, a protein involved in the intracellular spread of Shigella flexneri, and TraT, a complement resistance protein (Karpathy et al., 2007).

1.2.2 Growth of F. nucleatum in vitro

The growth of F. nucleatum in vitro has been investigated and characterised under a variety of environmental conditions. These include the effects of oxidative stress (da Silva et al., 2006; Diaz et al., 2000; Diaz et al., 2002; Diaz et al., 2006; Silva et al., 2005; Silva et al., 2010), amino acid and sugar metabolism (Gharbia et al., 1989;

19

Gharbia & Shah, 1991; Robrish et al., 1987; Robrish & Thompson, 1988; Robrish &

Thompson, 1990; Rogers & Zilm, 1995; Rogers et al., 1998a; Rogers et al., 1991;

Rogers et al., 1992; Rogers et al., 1998b; Takahashi & Sato, 2002; Zilm et al., 2002;

Zilm et al., 2003) and growth pH (Rogers et al., 1991; Zilm et al., 2002; Zilm et al.,

2003; Zilm et al., 2007).

As mentioned in Section 1.1.3, the subgingival plaque is relatively anoxic with a residual oxygen level of about 10% of air-saturated water (Diaz et al., 2000). Despite the fact that F. nucleatum is an anaerobe, it is capable of growth in an environment containing up to 20% oxygen (Diaz et al., 2000) by increasing the activity of NADH oxidase (Diaz et al., 2002). Unlike F. nucleatum, P. gingivalis is a capnophile and mono-cultures cannot survive in the absence of environmental carbon dioxide (Diaz et al., 2002). However, when co-cultured with F. nucleatum without the CO2 supplementation, P. gingivalis survived in an environment containing 20% oxygen

(Diaz et al., 2002). These results indicate that F. nucleatum metabolism lowers the redox potential and supplies CO2, supporting the growth of P. gingivalis.

F. nucleatum has been called metabolically versatile as it is able to utilise both sugars and amino acids as energy sources (Robrish & Thompson, 1990; Rogers et al.,

1991; Takahashi & Sato, 2002). The bacterium is weakly saccharolytic and ferments sugars (glucose, galactose and fructose) to produce a mixture of formate and lactate as major end products. It appears that the uptake of glucose is amino acid-dependent

(Robrish et al., 1987; Robrish & Thompson, 1990). Growth in the presence of glucose and fructose, resulted in the cells forming intracellular polyglucose (IP) (Robrish &

Thompson, 1990; Rogers et al., 1998b; Zilm et al., 2003). The degradation of IP is

20 suppressed by the addition of key amino acids and it is believed that the formation of IP plays a role in cell survival as its catabolism may use to generate ATP during periods of nutrient limitation (Robrish & Thompson, 1988).

F. nucleatum can grow using amino acids as energy sources in the absence of sugars. Histidine, glutamic acid, lysine and serine are among the amino acids that are readily taken up and metabolised to generate energy (Gharbia et al., 1989; Robrish &

Thompson, 1988; Rogers et al., 1992). The bacterium has been shown to preferentially take up peptides contained in growth medium supplements such as yeast extract rather than energy-yielding free amino acids histidine and glutamate (Gharbia et al., 1989).

More recently, Takahashi and Sato demonstrated that F. nucleatum uses di-peptides in preference to larger peptides (Takahashi & Sato, 2002). Despite the preference for peptide consumption, the lack of significant activity is thought to restrict metabolism of proteins such as casein or albumin (Rogers et al., 1991). As discussed previously, synergistic interactions exist between F. nucleatum and P. gingivalis. P. gingivalis possesses that may cleave proteins to release small peptides that can be utilised by F. nucleatum.

In the oral cavity, bacterial metabolism has a strong influence on the environmental pH. The fermentation of carbohydrate decreases environmental pH as a result of the production of acidic end products; conversely, the catabolism of amino acids and peptides releases ammonium ions (and weak acids) and results in an alkalisation of the environment. F. nucleatum is able to tolerate a wide pH range.

Nakajo and colleagues (2004) reported that F. nucleatum isolated from dentine of infected root canals was able to survive at pH 9 over 7 days of incubation (Nakajo et al.,

21

2004). In the study of Takahashi (2003) on the growth of F. nucleatum, the organism was found to grow at a pH as low as 5.5 and this pH is lower than that tolerated by P. gingivalis. Also, several researchers have suggested that F. nucleatum has a higher acid- neutralising activity than either P. intermedia and P. gingivalis (Rogers et al., 1991;

Takahashi & Sato, 2002; Takahashi, 2003). The continued supply and metabolism of peptides and free amino acids through increased GCF flow to the subgingival plaque bacteria during disease elevates the surrounding pH above neutrality (Takahashi, 2003), as mentioned in Section 1.1.2.2. It is likely that this ability facilitates the succession of more acid-sensitive bacteria in mature dental plaque.

1.2.3 Biofilm formation of F. nucleatum is induced by an alkaline growth pH

Previously, our laboratory investigated the effect of environmental pH on the growth of F. nucleatum subspecies polymorphum in continuous culture and showed that this organism grows as a planktonic culture between pH 5.8 and 8.1 (Rogers et al.,

1991; Zilm et al., 2007; Zilm & Rogers, 2007). However, after three days exposure to pH 8.2, F. nucleatum formed a mono-culture biofilm (Zilm and Rogers, 2007). As mentioned previously, a number of studies have shown that the environment of the periodontal pocket is alkaline and the pH may be as high as 8.5 (Bickel & Cimasoni,

1985; Bickel et al., 1985a; Eggert et al., 1991).

The development of biofilms was associated with increased cell surface hydrophobicity (Zilm & Rogers, 2007). The change is thought to facilitate attachment of the cell to surfaces and contribute to the mono-culture biofilm development investigated in this study. Furthermore, F. nucleatum biofilm cells exhibited a change in cell morphology resulting in a significant increase in length. Interestingly, this

22 filamentous cell morphology has also been associated with oxidative stress in this organism (da Silva et al., 2006; Diaz et al., 2002). Although morphological changes appear to be a typical stress response in F. nucleatum, little is known of the changes at the molecular level associated with biofilm formation under stress conditions.

1.3 Bacterial biofilms

Biofilms are bacterial communities that attach to a surface or to each other and encased in a matrix of extracellular polymeric substance (EPS). Bacterial biofilms are ubiquitous in nature and can be found in various environments including hydrothermal hot springs, freshwater rivers, industrial water systems, medical-implants and the human body (Davey & O'toole, 2000).

Microbial biofilms present in the human body have long been implicated in infectious diseases and are thought to be involved in more than 80% of bacterial chronic inflammatory and infectious diseases (Sakakibara et al., 2007). Chronic bacterial prostatitis, otitis media, cystic fibrosis and periodontal diseases are among the biofilm- associated infections in humans (Donlan, 2001). Increasing evidence shows that bacteria growing in biofilms exhibit an altered phenotype in relation to their growth rate and gene transcription (Hall-Stoodley and Costerton, 2004). In clinical settings, the primary concern with biofilm diseases is the protection afforded to their member species against antimicrobial agents and host immune response. It has been show that bacteria growing in biofilms can be up to 1,500 times more resistant to antimicrobial agents when compared to their planktonic (non-adherent) counterparts (Sauer et al.,

2007).

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1.3.1 Development of biofilms

The formation of a bacterial biofilm is attributed to both genetic and environmental factors (Hall-Stoodley et al., 2004). Genetic and molecular studies have revealed that the development of a biofilm is a dynamic process that involves distinct stages which include surface attachment, microcolony formation, maturation and dispersal (Sauer et al., 2002; Sauer, 2003).

The establishment of biofilms is not a random event and is likely influenced by quorum sensing (Parsek & Greenberg, 2005). Bacterial cells produce extracellular signals (known as autoinducers, AI) and as the cell population increases the autoinducers accumulate in the localised environment. Once autoinducers reach a concentration threshold they bind to receptors and together activate the transcription of certain genes that may affect biofilm formation. Three different quorum sensing signalling systems have been described; acyl-homoserine lactone (acyl-HSL) associated in Gram negative species, a peptide-based signalling system in Gram positive species; and the AI-2 system that is found in several Gram positive and Gram negative species

(Parsek & Greenberg, 2005).

Biofilm formation is initiated when planktonic cells attach to a surface. The adhesion of cells to a surface is largely dependent on cell-surface interactions. It has been suggested that rough and hydrophobic surfaces favour the initial attachment of bacterial cells. Rough surfaces provide increased surface area and decreased shear force while hydrophobic interactions help to overcome repulsive forces between bacterial cells and the substratum surface (Donlan, 2002; Prakash et al., 2003a). Flagella and pili have been identified as two of the key structures during the early stage of Pseudomonas

24 aeruginosa biofilm formation (Klausen et al., 2003; O'Toole & Kolter, 1998). Flagella mediate bacterial motility and aid in movement in liquid and attachment to a surface.

Type IV pili allow bacteria to spread or migrate along surfaces and are thought to play a role in micro-colony formation (Klausen et al., 2003). In Gram positive bacteria, the biofilm-associated protein (bap) of Staphylococus aureus (Cucarella et al., 2001) and polysaccharide intercellular adhesin/hemagglutinin of Staphylococcus epidermidis

(Rupp et al., 1999) play roles in the adhesion of Staphylococcus species onto substratum surface.

Following the attachment of bacteria and formation of a microcolony, maturation of the bacterial biofilm occurs. Mature biofilms are complex structures which are not simply structurally homogeneous monolayers of bacterial cells. Advances in microscopy such as confocal laser scanning microscopy, have facilitated a more detailed study of the architecture of mature biofilms. The key to mature biofilm development has been linked to the production of EPS which is comprised of polysaccharides, nucleic acids and proteins (Nielsen et al., 2005). The mushroom and mound structures observed in wild type P. aeruginosa were suppressed in non-EPS forming mutant strains. The mutant strain formed biofilms that were flat and patchy, in contrast to mucoid wild type P. aeruginosa (Nivens et al., 2001). A similar observation has been reported for Vibrio cholerae in which EPS is required for complex structural biofilm development (Hall-Stoodley & Stoodley, 2002).

In the last stage of biofilm formation, aggregates of biofilm cells may detach and disperse. Interestingly, dispersed biofilm cells maintain characteristics of biofilms and are able to re-colonise a new surface (Kirov et al., 2007; Sauer et al., 2007).

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1.3.2 Environmental conditions influence biofilm formation

Bacterial biofilm formation in nature is an important mechanism for cell survival in hostile conditions. Biofilm formation is thought to be initiated by interactions between planktonic bacteria and a surface in response to appropriate environmental signals (Loo et al., 2000).

Loo and colleagues (2000) demonstrated that the facultative anaerobe S. gordonii formed biofilms of greater biomass when cultured under anaerobic conditions than under aerobic conditions. Also, a nutritionally rich environment restricted biofilm formation of the same organism when compared to growth in a limited nutritional environment (Loo et al., 2000). Nutrient-limiting conditions have previously been reported to initiate biofilm formation by facilitating the adhesion step. Korber et al.

(1995) suggested that the attachment to a surface is an attempt by bacteria to exploit a source of essential nutrients that are in short supply. A separate study examined the effects of osmolarity on A. actinomycetemcomitans biofilm formation and claimed that biofilm growth was enhanced in a high osmolarity environment (0.4 M NaCl) (Haase et al., 2006). The tested osmolarity was double the salt concentration found in saliva and

GCF (0.15 M NaCl). This observation suggests that biofilm formation is stimulated by adverse growth conditions and may represent a survival strategy of microorganisms.

1.3.2.1 Survival strategy: Bacterial stress response

Bacteria in nature sense environmental signals and process this information into specific transcriptional responses. Under adverse conditions, the regulation of genes involved with stress responses are thought to enable cells to better cope with these

26 environments. In bacterial cells, two stress response systems have been identified, the heat shock response (HSR) and the general stress response (Ron, 2006).

HSR was the first global regulatory system identified in bacterial cells in response to high temperature. HSR is found in all living cells (Feder & Hofmann, 1999) and it protects the homeostasis of cellular processes and aids in thermo-tolerance. HSR induces synthesis of a large number of proteins that are known as heat shock proteins

(HSPs). Many of the HSPs identified are either molecular chaperones (e.g, GroEL,

GroES, DnaK and DnaJ) or ATP-dependent proteases (e.g., ClpP, Lon (La) and HslVU) that assist protein folding and protein degradation under stress conditions. HSPs are now known to be induced by exposure to high salt, heavy metals and environmental pH

(Bannantine et al., 2008; Feder & Hofmann, 1999; Gao et al., 2004; Goulhen et al.,

1998; Roy et al., 2006; Taormina & Beuchat, 2001). The control of HSR in Gram negative bacteria is thought to be regulated either by transcriptional activators such as alternative sigma factors (σ32 or σE primarily) or repressors such as HrcA protein (Ron,

2006).

In E. coli, the general stress response is induced during carbon starvation or entry into the stationary phase (Hengge-Aronis, 2000). Alterations in cellular physiology and morphology enhance survival by increasing stress resistance and preventing cellular damage rather than by repairing it (Hengge-Aronis, 2000). The general stress response is now thought to regulate almost 500 genes. The genes that are involved in the general stress response are transcribed by an alternative sigma factor, σs

(RpoS) (Ron, 2006). An early study investigated the acid (pH 5.0) and base (pH 10.5) resistance in E. coli and S. flexneri and the role of sigma factor RpoS on cell growth

27

(Small et al., 1994). The results showed that sigma factor encoded by rpoS is required for extreme acid resistance in aerobic cultures of E. coli and S. flexneri and for base resistance in E. coli.

1.3.2.2 The pH stress response

A previous study in our laboratory investigated the protein expression of F. nucleatum planktonic cells growing at pH ranging from 6.4 to 7.8 and reported that certain proteins were differentially expressed in response to external pH. Of the regulated proteins, it was concluded that bacterial cells altered their metabolic activities to increase energy production under sub-optimal growth pH. Moreover, F. nucleatum regulated GroEL and DnaK at differing growth pH (Zilm et al., 2007).

The effects of extreme growth pH on gene and protein expression of bacterial cells grown as batch culture have been extensively studied over the years. The findings of these studies showed that bacterial cells growing under different acidic and alkaline environments regulate intracellular pH homeostasis using different mechanisms. The increased expression of proton-translocating F1F0-ATPase that extrudes H+ from S. mutans was claimed to help the cell to maintain a neutral intracellular pH (Belli &

Marquis, 1991; Kuhnert et al., 2004; Len et al., 2004a). The F1F0 proton-translocating

ATPase is a major determinant in the maintenance of a pH gradient across the cell membrane (Belli & Marquis, 1991). In contrast, bacterial cells growing in basic pH conditions maintain internal pH via the promotion of proton capture and retention (e.g. the ATP synthase and monovalent cation/proton antiporters) (Padan et al., 2005). The increased up-regulation of Na+:H+ antiporters is frequently found in bacteria cultured

28 under alkaline environments (Appelbe & Sedgley, 2007; Blankenhorn et al., 1999;

Karpel et al., 1991; Padan et al., 2005; Stolyar et al., 2007).

In order to survive in suboptimal environmental pH, in addition to the maintenance of pH homeostasis, bacteria also modulate other cellular physiological processes. For example, E. coli motility, nutrient catabolism, oxidative stress response and transporter activity were all found to be pH-dependent (Maurer et al., 2004; Stancik et al., 2002). Mauer and colleagues (2004) grew E. coli K-12 in potassium-modified

Luria-Bertani medium buffered at pH 5.0, 7.0 and 8.7 and analysed the gene expression profiles using DNA microarray. At pH 8.7, the transcription of genes associated with flagellar motility and chemotaxis were repressed. Flagellar rotation is driven by the proton motive force. At high pH, the cell‟s transmembrane proton potential is diminished and led to suppressed flagellar motility and chemotaxis (Maurer et al.,

2004).

Stancik et al. (2002) examined protein expression of E. coli cells cultured at pH between 4.9 and 9.0 and reported that cells grown at high pH regulated enzymes in the catabolism of arginine and glutamate in order to increase acidic end-products in preference to amines. For instance, the expression of tryptophanase (tnaA) was induced at alkaline pH (Stancik et al., 2002). This enzyme deaminates serine and cysteine to generate acids and may help maintain internal pH. In addition, periplasmic proteins

YceI as well as the porins MalE and OmpA were induced by the high pH (Stancik et al.,

2002). Growing in an acidic condition, E. coli induced the lysine decarboxylase operon which generates amines that are exported to the environment and help to alkalinise the growth medium (Blankenhorn et al., 1999). The studies of adaptation of bacterial cells

29 to sub-optimal growth pH were also conducted on S. mutans (Len et al., 2004b; Wilkins et al., 2002), S. oralis (Wilkins et al., 2001), S. flexneri (Small et al., 1994) and

Desulfovibrio vulgaris (Stolyar et al., 2007). As with E. coli, the bacterial physiology of these species was suggested to be pH-dependent also.

As discussed earlier, F. nucleatum ATCC 10953 grew in a chemostat as a planktonic culture at a pH environment below pH 8.1 (Rogers et al., 1991; Zilm et al.,

2007; Zilm & Rogers, 2007). The transition from planktonic culture to biofilm growth at pH 8.2 was suggested to be a stress response (Zilm & Rogers, 2007). The dramatic macroscopic changes observed are thought to be associated with cellular physiological modifications that enable cells to switch from planktonic to a biofilm growth. This change may confer increased alkaline-tolerance to F. nucleatum.

1.3.3 Altered phenotypes and physiology in bacterial biofilm cells

The characterisation of bacterial cells growing as biofilms using genomic, transcriptomic and proteomic techniques leads to an increased understanding of their physiology. The alteration in expression of genes and proteins in E. coli biofilm and planktonic cultures has been investigated by a number of research groups. Schembri and co-workers employed DNA microarrays and identified seventy-nine genes, representing

1.8% of the E. coli genome, whose expression was significantly altered during biofilm growth (Schembri et al., 2003). Similar results were reported by Beloin et al. (2004) who found that 1.9% of the genome was up- or down-regulated, by at least two-fold, in biofilm cells. Bacterial adhesion, stress responses, cell envelope biogenesis, transport proteins, energy and metabolic functions were among the greatly influenced bacterial genes. Using a proteomic approach, Trémoulet and co-workers (2002) noted that the

30 expression of metabolic proteins, sugar and amino acid transporters and regulator proteins were altered in biofilm cells. An adhesin, outer membrane protein A (OmpA) was found to be over-expressed in biofilm cells (Orme et al., 2006). The observed elevated expression of porin OmpA was thought to be necessary for the transport of polymeric substances required during biofilm formation (Orme et al., 2006).

Using transcriptomic and proteomic analysis, the gene and protein expression of mono-culture biofilms of S. aureus were investigated by Resch and co-workers in 2005 and 2006 (Resch et al., 2005; Resch et al., 2006). The transcriptomic results showed that the cell envelope is a very active compartment as genes encoding binding proteins, proteins involved in the synthesis of murein and glucosaminoglycan polysaccharide intracellular adhesin were significantly up-regulated. Increased metabolic activities were observed with elevated formate fermentation and urease activity (Resch et al.,

2005). These results were supported by the proteomic findings in which biofilm cells expressed higher levels of proteins associated with cell attachment and peptidoglycan synthesis and showed increased pyruvate and formate metabolism (Resch et al., 2006).

Investigations on Gram positive and negative bacterial biofilms demonstrated key differences in bacterial physiology. In spite of the roles of flagellae and pili discussed in Section 1.3.1, the cell envelope, containing adhesins, transport proteins, and peptidoglycan layers, is also thought to be essential in biofilm development. The expression of the envelope components may also be regulated in the transition from planktonic culture to biofilm growth. The adaptation of bacterial cells during the establishment of biofilms is also characterised by altered metabolic activities as bacteria may encounter higher cell density and thus limited nutrient availability (Sauer, 2003).

31

A review of the literature shows that proteomic studies have revealed a larger physiological difference (up to 50% of the proteome) between planktonic and biofilm bacteria compared to transcriptomic analyses (1 to 38%) (Oosthuizen et al., 2002; Sauer et al., 2002; Svensater et al., 2001). One apparent explanation is that these analyses provide sensitive but transient snapshots of gene expression that they do not necessarily correlate with phenotype (Vilain et al., 2003). Therefore, the discovery of regulated proteins could be considered to be more reliable when explaining the phenotypic changes in cells growing as a biofilm. For this reason, a comparison of protein expression in F. nucleatum biofilm and planktonic cultures was undertaken using proteomic techniques.

1.4 Proteomics

The proteome is defined as the set of all expressed proteins in a cell, tissue or organism. In contrast to a cell‟s static genome, the proteome is dynamic as the estimated number of proteins encoded by the genome is two to three orders of magnitude higher than the number of genes (Peng & Gygi, 2001). For example, current estimates indicate that just over 30,000 genes in the human genome leads to the synthesis of more than 1 million proteins (Cash, 2008). The difference between gene and protein expression is largely due to the presence of protein isoforms and post-translational modifications

(PTM). The latter accounts for a major proportion of the complexity of the proteome and is thought to affect the activity of proteins (Rappsilber & Mann, 2002). The identification of potentially all PTMs in a single polyacrylamide gel using two- dimensional gel electrophoresis (2DE) is one of the best characteristics of 2DE.

32

The study of the proteome, proteomics, defines the global protein complement expressed by a cell at both qualitative and quantitative levels (Cash, 2008). Proteomics is commonly used to study of protein expression mapping that involves the quantitative study of global changes in protein expression in cells or tissues is particularly important

(Palzkill, 2002). It is commonly employed in biological and clinical studies and allows for the comparison of protein profiles expressed in cells or tissues under different environmental conditions which may reflect different disease states. Proteins differentially expressed under different growth conditions may help to identify key proteins that may be important in clinical treatment or that may have a role as biomarker for diseases (Palzkill, 2002).

1.4.1 Proteomic techniques

The ability to identify proteins accurately on a large scale is critical to global proteomic studies. Typically, the two major steps in proteomics are protein separation using 2DE followed by protein identification by mass spectrometry (MS). While other non-gel based technologies are rapidly emerging, protein separation using 2DE remains a widely used robust technique (Cordwell, 2006).

1.4.1.1 Protein preparation and separation using 2DE

In order to identify proteins that are modulated under certain conditions, the protein mixture must first be resolved into individual proteins or „spots‟ before levels of expression can be analysed. Two dimensional gel electrophoresis (2DE) resolves proteins using two steps, the first of which is to separate a protein mixture across an immobilised pH gradient (IPG) strip using isoelectric focusing (IEF). During IEF, an

33 electric field is applied and denatured proteins migrate to their isoelectric point (pI) as they travel through the pH gradient of the IPG strips. The pI is reached when the protein carries no net charge. In the second dimension, the isoelectric focused proteins are separated on their molecular weight by electrophoresis in the presence of sodium dodecyl sulphate (SDS). Individual proteins in the gel are then visualised by use of a protein binding stain. The expression of each protein can then be analysed using commercially available software (Garfin, 2003).

Despite the fact that 2DE remains the preferred technique for protein separation, limitations do exist. Very often, proteins of low abundance and alkaline (pI>7) proteins are under-represented in 2DE gels (Lubec et al., 2003). Therefore, optimisation of the running conditions, in particularly sample preparation and IEF running conditions are required to yield good resolution.

Investigations that target low abundance proteins rely on protein sample preparation. A mere increase in the protein load to the 2DE gel may not be ideal because the presence of high abundance proteins may dominate and mask the presence of low abundance proteins. Moreover, high protein load affects protein resolution on gels (Garfin, 2003). Very often, pre-fractionation or enrichment step of proteins prior to

2DE reduces the sample complexity and increases the “visibility” of low abundance proteins (Lubec et al., 2003). Techniques such as differential extraction (Molloy et al.,

1998) and subcellular fractionation (Babujee et al., 2006; Molloy et al., 2000) have been proven to reduce the complexity of samples and allows for visualisation of low abundance proteins on 2DE gels.

34

The difficulty in resolving alkaline proteins is attributed to reverse electro-endo- osmotic flow during the IEF in the basic pH range (pH 8-10) of the IPG strips. During

IEF, charged water molecules or reducing agents such as DTT migrate from the cathode to the anode. As results, horizontal streaking forms after staining of the gel. The loss of water from the basic pH end of the IPG strip leads to drying of the strip while depletion of reducing agent causes the reformation of disulphide bonds that interfere with the migration of proteins (Li et al., 2004; Pennington et al., 2004). In order to overcome these problems, anodic cup-loading and the use of reducing agents such as hydroxyethyl disulphide (commonly known as Destreak Reagent by GE Lifescience) have been demonstrated to improve unwanted horizontal streaking of basic proteins on 2DE gels

(Olsson et al., 2002). It was explained that the cup-loading method reduced the occurrence of protein precipitation that occurs during sample entry and thus gave better protein resolution (Pennington et al., 2004) while HED oxidises thiols to mixed disulphides and prevents horizontal streaking at the basic pH range of the IPG strips

(Olsson et al., 2002).

1.4.1.2 Protein identification using mass spectrometry

The development of MS marks a major advance in proteomics studies.

Following protein separation and analysis, protein spots are excised and digested into peptides by the addition of a sequence-specific such as trypsin. The resultant peptides are then subjected to MS analysis (Palzkill, 2002). A mass spectrometer consists of an ion source that converts molecules into gas-phase ions, a mass analyser that separates ions by their mass-to-charge (m/z) and an ion detector.

35

Matrix-assisted laser desorption/ionization (MALDI) and electrospray ionisation

(ESI) are the two techniques most commonly used to ionise peptides for MS analysis

(Aebersold & Mann, 2003). MALDI is an ionisation technique that creates ions from a dry sample and releases them into the gas phase through the presence of a laser energy absorbing matrix. MALDI-MS is normally used to analyse relatively simple peptide mixtures (Aebersold & Mann, 2003). It is usually used in conjunction with a time-of- flight (TOF) mass analyser that measures the time taken for ions to travel from the ionising source to the detector. This can then be converted to the m/z values to obtain a mass spectrum for the protein under investigation. In contrast to MALDI, ESI ionises peptides in a solution usually that are separated by “in-line” liquid-based separation (eg.

High Performance Liquid Chromatography, HPLC). ESI is commonly coupled with an ion-trap mass analyser. The ion-trap mass analyser selects stored ions to be released to a detector (Lahm & Langen, 2000).

The initial protein identification relies on the peptides undergoing ionisation to produce a set of peptide masses (peptide mass fingerprint) characteristic of the parent protein. The peptide masses are then searched against a database to identify the protein from which they are derived (Aebersold & Mann, 2003; Lahm & Langen, 2000).

Tandem MS (MS/MS) is the next step in accurate protein identification. A tandem MS is a mass spectrometer that has more than one, usually two, mass analysers.

It is now widely used to obtain the peptide sequence and to match the data derived from peptide sequences in protein databases. The two mass analysers are separated by a collision cell into which an inert gas (e.g. argon, xenon) is admitted to collide with the selected sample ions to generate collision-induced-dissociation (CID) spectra.

36

Identification of each peptide, and therefore the protein of origin, may be performed by protein sequence database analysis of the CID spectra through database searching of a limited stretch of amino acid sequence. Protein identification by MS/MS analysis is more accurate analysis than peptide mass fingerprinting (Aebersold & Mann, 2003;

Banks et al., 2000; Lubec et al., 2003).

1.4.2 Validation of proteomic results

Over the past 30 years, the field of proteomics has expanded at a rapid rate. A large variety of new instruments, techniques and methodologies has become available.

The variation in methods used is thought to be associated with the difficulty in assessing the reliability of the obtained protein identifications (Martens & Hermjakob, 2007). The major concerns include experimental design, details of bioinformatic tools and databases employed, and the lack of proteomic data sharing among users (Carr et al.,

2004; Martens & Hermjakob, 2007; Wilkins et al., 2006).

A few reviews and guidelines were published in the last few years in order to address these issues. One set of guidelines has been published by the Journal of

Molecular and Cellular Proteomics in 2004. The guidelines encouraged researchers to provide sufficient information about the software used for peptide and protein identification. This set of guidelines aimed to improve as well as control the quality of the proteomics data used in publications (Carr et al., 2004). In contrast, the journal

Nature Biotechnology (Nature-Biotechnology, 2007), recommends data sharing and encourages free access to published data by users. The guidelines by the Journal of

Proteomics emphasises disclosure on experimental design, information on protein and peptide identifications and submission of proteomic data. Additionally, they request that

37 researchers perform confirmatory studies on biologically important differences in protein expression (Wilkins et al., 2006). This is because the generation of proteomic data involves multi-step procedures and experiments that usually produce a modest number of samples to reveal proteins that are differentially expressed and may not be significant when subjected to rigorous statistical analysis (Hunt et al., 2005).

38

1.5 Overall aims

F. nucleatum plays a central role in the formation of mature subgingival dental plaque that is closely linked to the onset of periodontal diseases and has therefore been extensively studied using planktonic culture. However, dental plaque is a biofilm. The formation of biofilms of F. nucleatum induced by an elevated environmental growth pH in vitro, similar to that in diseased subgingival pockets, provides an insight into how this organism adapts to the allogenic changes associated with disease.

Therefore, the overall aim of this study was to examine changes in the F. nucleatum bacterial protein expression associated with the biofilm formation in an environment that mimics the disease periodontal site.

Selective enrichment of cell envelope and cytoplasmic protein was used to increase proteome coverage and has been dealt with in separate chapters. The expression of identified regulated proteins was then validated using various methods including enzyme assays, acidic end products analysis, Western blotting and qualitative real-time PCR.

39

2 Growth of F. nucleatum and biofilm production in vitro in response to environmental pH

40

2.1 Introduction

As noted in Section 1.2.3, the chemostat growth of F. nucleatum under differing growth pH (5.8-8.2) has been investigated in our laboratory. When grown under controlled conditions in continuous culture at a pH below 8.2, the bacterium grows as planktonic cells. However, an extended exposure (greater than three days) to an increase in pH to 8.2 induces the formation of biofilms.

The observation of biofilm formation in the chemostat was associated with an altered cell morphology, with cells becoming more elongated than those grown at pH

7.8 (Zilm & Rogers, 2007). Preliminary studies indicated that increased cell surface hydrophobicity may contribute to biofilm formation by F. nucleatum in vitro (Zilm &

Rogers, 2007). The development of F. nucleatum mono-culture biofilms under alkaline conditions (pH 8.2 and above) in vitro aligns with the observation of increased pH in vivo in which the pH of GCF samples of subjects with periodontal disease are usually alkaline and may be up to pH 8.6 depending on the pH measurement method used

(Bickel & Cimasoni, 1985; Bickel et al., 1985b; Eggert et al., 1991).

As stated in Section 1.5, the overall aim of this study was to examine changes in the protein expression of F. nucleatum associated with the biofilm formation. Thus, a biofilm-producing model was required. A few considerations were taken into account when developing a reproducible biofilm-producing model in the laboratory. The method employed must be able to produce a sufficient quantity of biofilm for analysis by two- dimensional gel electrophoresis (2DE). Furthermore, the growth parameters must be able to be controlled and manipulated.

41

Currently, there are a few commonly used systems for producing biofilms in the laboratory and each method has advantages and limitations. Most biofilm systems are either static-growth condition systems, such as 96-well microtitre plate, or flow condition systems, such as constant depth film fermentor and Modified Robbins Device

(MRD) (Azevedo et al., 2009; Sauer et al., 2002; Sissons, 1997; ten Cate, 2006).

The static biofilm system using a 96-well microtire plate method is commonly used to produce bacterial biofilms. It involves the inoculation and incubation of a bacterial culture in a 96-well microtitre plate. The rate of biofilm development is quantitated based on the surface attached cells stained with crystal violet and is measured spectrophotometrically. This model is relatively simple but may not adequately control growth conditions. The microtitre plate assay has been used to study biofilm architecture and the factors that affect the rate of mono-culture biofilm formation of numerous bacterial species. These include, Listeria monocytogenes

(Harvey et al., 2007), E. coli (Rivas et al., 2007), A. actinomycetemcomitans (Haase et al., 2006) and P. aeruginosa (Abdi-Ali et al., 2006).

Flow-condition biofilm models are designed so that the stages of biofilm development can be monitored. In addition, flow condition systems enable the control of growth parameters of the culture (ten Cate, 2006). The constant depth film fermentor

(CDFF) is commonly used in dental plaque studies (Azevedo et al., 2009; Kinniment et al., 1996; Kinniment et al., 2008; Wilson et al., 1998). This system consists of recesses in which biofilms are grown to a pre-determined thickness using cells that are grown to steady state. A biofilm sample can be removed separately without disturbing the structure of biofilm (Kinniment et al., 1996). The MRD is another model that is

42 considered as one of the most widely used biofilm devices (Wilson, 1996). The MRD was designed to be used in conjunction with continuous culture. In recent years, MRD has been adapted for the study of oral microbial biofilms. It contains a number of sample coupons and each can be harvested separately (Johnston & Jones, 1995). It was used in the investigations of the antimicrobial susceptibility of P. gingivalis biofilms

(Bercy & Lasserre, 2007; Noiri et al., 2003) and the effect of plant extracts on S. mutans biofilm formation (Honraet & Nelis, 2006).

43

2.2 Aim

The first aim of this investigation was to examine the growth of F. nucleatum at different pH conditions, ranging from 7.2 to 8.4, attempting to reproduce the biofilm formation that was observed at pH 8.2 and above as reported in previous studies. The second aim was to develop a model that produced biofilms consistently so cells could be used for subsequent analyses. The Modified Robbins Device and microtitre plate methods were investigated to determine their potential for production and analyses of F. nucleatum biofilms.

44

2.3 Materials and Methods 2.3.1 Effects of environmental pH on the F. nucleatum growth

2.3.1.1 Bacterial culture

Fusobacterium nucleatum subspecies polymorphum ATCC 10953 was maintained on anaerobic blood agar plates (Oxoid, Australia) in an atmosphere containing N2/CO2/H2 (90:5:5) at 37°C. Starter cultures were grown in Brain Heart

Infusion (BHI) broth (Oxoid), supplemented with 0.2 % (w/v) yeast extract (Oxoid), for

48 hours prior to chemostat inoculation.

2.3.1.2 Growth media

A chemically defined medium (CDM) containing amino acids, vitamins, salts, nucleotides and buffers (Appendix 7.1) was used to grow F. nucleatum in a chemostat

(Van der Hoeven et al., 1985). The energy yielding amino acids, glutamic acid (20 mM), histidine (10 mM) and lysine (10 mM) were included at elevated concentrations while the remaining amino acids were included at a concentration of 1 mM (Gharbia et al., 1989; Rogers et al., 1991). Glucose and thioglycollic acid were added to a final concentration of 10 mM and 0.5 g/L, respectively. The medium pH was adjusted to 7.2 with potassium hydroxide. The CDM was sterilised by membrane filtration using a 0.22

µm membrane (Millipore, Billerica, MA, USA).

2.3.1.3 Continuous culture conditions and sampling

Growth in a 365 mL working volume chemostat (model C30 BioFlo, New

Brunswick Scientific, Edison, NJ, USA) was initiated by inoculation with 100 mL of starter culture (OD560nm 1.0). The organism was grown under batch conditions for 18-24 hours to allow the culture to reach mid exponential phase (Zilm et al., 2007). The

45 medium was pumped at a flow rate of 27 mL/hour to give an imposed dilution rate of D

= 0.069 per hour. Using the relationship, Tg (generation time) = ln 2/D, this gave a bacterial generation time of 10 hours. This growth rate was chosen because bacteria growing in plaque are reported to have a generation of 7-12 hours (Socransky et al.,

1977).

The temperature of the continuous culture was controlled at 37 ± 0.5°C and anaerobic conditions were maintained by gassing the culture vessel with nitrogen. The pH of the culture was maintained at 7.2 ± 0.1 by the automatic addition of either 2 M

KOH or 2 M HCl using a Fermac 260 pH controller (Electrolab, Cupertino, CA, USA).

Once steady state (10 generations) was achieved, 100 mL of cells were harvested over five consecutive days and stored at -70oC. The growth pH was then increased incrementally by 0.2 units to pH 7.4 ± 0.1 by the automatic addition of 2 M KOH using the pH controller. Culture sampling was performed as described above. The same protocol was repeated until pH 8.4 ± 0.1. Biofilm formation was found to develop three days after the pH change to 8.2 ± 0.1. Culture agitation was increased during sampling in order to dislodge biofilms from the culture vessel surfaces. Samples were withdrawn from the vessel and cell aggregates were allowed to settle while planktonic cells were carefully decanted and the resultant biofilm cells were used for further analyses.

The purity of bacterial culture was checked daily by Gram staining. Culture yields from growth at differing pH conditions were measured by optical density

(OD560nm) and total cellular protein determination using Coomassie Plus Protein Assay

Kit (Thermo Scientific, Rockford, IL, USA). At growth pH of 8.2, only total cellular protein was used as a measure of cell yield.

46

2.3.1.4 Auto-aggregation assay

The method used to compare the ability of cells grown under different conditions to auto-aggregate was based on the protocols by Shen and co-workers with slight modifications (Shen et al., 2005).

F. nucleatum was grown at either pH 7.4 and 8.2 were used in this investigation.

Only planktonic cells were harvested and washed twice in phosphate buffered saline

(PBS) (0.1 M, pH 7.5). Bacterial cells were resuspended in coaggregation buffer consisting of CaCl2 (1 mM), MgCl2 (1 mM), NaN3 (0.02%) and NaCl (0.15 M)

9 (dissolved in 0.001 M Tris adjusted to pH 8.0) to an OD560nm of 0.9, equivalent to 5x10 microorganisms per mL.

The culture was vortexed and 1 mL of the bacterial suspension was transferred to a UV-Vis cuvette (Kartell Labware, Noviglio, MI, Italy). The OD at time zero was measured using a spectrophotometer at a wavelength of 560 nm. A change in the

OD560nm was determined after 60 minutes. The degree of cell aggregation was calculated from triplicate samples as the precent decrease of OD560nm after 60 minutes using the equation below:

*ODt0 – optical density of bacterial culture at time 0; ODt60 – optical density of culture after 60 minutes

47

2.3.2 Mono-culture biofilm production

2.3.2.1 96-well microtitre plate

The effect of growth pH on the formation of F. nucleatum biofilm was tested using a standard microtitre plate biofilm assay based on that by Haase and colleagues

(2006) with slight modifications (Haase et al., 2006). F. nucleatum ATCC 10953 was cultured under anaerobic conditions in BHI supplemented with yeast extract at 37°C for

18 hours. The OD of the culture was determined at 560 nm and cells were harvested by centrifugation at 8,000 x g for 15 minutes at 4ºC. The cells were then standardized to an

OD560nm of 0.050 in BHI broth adjusted to either pH 7.4 or 8.2. This equated to approximately 107 colony forming units per mL on anaerobic blood agar.

A bacterial suspension (200 µL) was inoculated into wells of a flat-bottomed polystyrene 96-well microtitre plate (Becton Dickson Falcon, Franklin Lakes, NJ,

USA). Wells containing 200 µL of growth medium alone were used as negative controls. Plates were incubated under anaerobic conditions at 37ºC for 21 hours.

Following incubation, any unattached cells were decanted and pooled and the pH was measured. The microtitre plate was then gently rinsed with PBS at pH 7.2 and the attached cells were fixed with methanol for 15 minutes and air dried. The fixed cells were then stained with 0.1% (w/v) crystal violet (Oxoid) for 5 minutes before removal by rinsing with distilled water. The plates were air dried and the stain was solubilised with 95% (v/v) ethanol. The amount of crystal violet released was quantified spectrophotometrically at 595 nm using a BIO-TEK Powerwave XS microtitre plate reader (Winooski, VT, USA).

48

2.3.2.2 Modified Robbins Device

F. nucleatum was grown in a chemostat as described in Section 2.2.1.3. In this study, human enamel slices (5 mm x 5 mm) were silicone-bonded onto stainless steel

MRD coupons. Growth pH in the chemostat was controlled at either pH 7.4 or 8.2. The

MRD was connected to the outflow of the chemostat once steady-state growth was achieved. A constant temperature in the MRD chamber was maintained at 37ºC using a heating block (Ratek Instruments, Boronia, Vic, Australia). After 10 days, coupons were removed and examined using scanning electron microscopy (SEM).

2.3.2.3 Scanning electron microscopy

The morphology of F. nucleatum cells, cultured in different pH environments was examined using SEM. Coupons were fixed overnight in a solution containing 4%

(v/v) paraformaldehyde, 1.25% (v/v) glutaraldehyde and 4% (w/v) sucrose in PBS.

Fixed biofilms were washed twice in PBS for 5 minutes and post-fixed in 1% (v/v) osmium tetroxides (OsO4) and dehydrated in increasingly concentrated ethanol solutions. Biofilms on coupons were dried using critical point drying in 100% ethanol.

The dehydrated biofilm specimens were coated with platinum and examined using a

Phillips XL 30 Field Emission Scanning Electron Microscope (Amsterdam, The

Netherlands).

49

2.4 Results

The growth of F. nucleatum was influenced by environmental pH. Results showed that F. nucleatum ATCC 10953 grew as a planktonic culture between pH 7.2 and 8.1 (Figure 2-1a). The bacterium adhered to surfaces and formed biofilms at a growth pH above 8.2 (Figure 2-1b). Under alkaline conditions, a mixture of planktonic cells and biofilms were present in the culture vessel. The auto-aggregation assay showed that there was a significant (p<0.01, Student‟s t-test) higher auto-aggregation rate, 34%, in cells cultured at pH 8.2 compared to those grown at pH 7.4, 22%, over a period of 60 minutes (Figure 2-2). Associated with the formation of biofilm, a change in cell morphology was observed microscopically (Figure 2-3). Biofilm cells were highly filamentous when compared to the planktonic cells grown at pH 7.4.

The formation of biofilms was found to be associated with a decrease in bacterial cell yield. The optimal growth pH of F. nucleatum ATCC 10953 is approximately 7.4 as indicated by Figures 2-4 and 2-5. This confirms our previous finding in our laboratory for this organism (Rogers et al., 1991). Bacterial cell yield declined at culture pH greater than 7.5 and a sharp decrease in OD560nm was observed at pH 8.2 due to biofilm growth (Figure 2-5). The decrease in OD560nm was consistent with a reduction in total cellular protein content (Figure 2-4) with the biomass at culture pH

8.2 containing one third the protein (0.135 ± 0.040 g protein/L) compared to cultures grown at pH 7.4 (0.433 ± 0.037 g protein/L culture) due to biofilm development. As 7.4 was the optimal growth pH for F. nucleatum ATCC 10953, it was decided to use planktonic cells grown at this condition to compare to biofilm grown at pH 8.2 for subsequent studies.

50

The microtitre plate method measured the adherent F. nucleatum cells grown in

BHI broth at two pH conditions. The difference in the attached biofilm cells under two pH conditions was not significant (Figure 2-6) ( p = 0.10, Student‟s t-test). Further investigation showed that the pH of the culture in wells changed during the 21 hours of incubation (Table 2-1) indicating pH control was a major issue. The fall in culture pH over the incubation period would have affected the pH-dependent nature of F. nucelatum biofilm formation.

Using a MRD, a few clusters of cells were found on the surface of enamel slices after a 10-day incubation period under both pH conditions (Figure 2-7). When compared, pH 7.4 biofilms contained few cells that were approximately 10 µm in length. However, increased cell length was more evident in biofilms grown at elevated pH where cells regularly exceeded 20 µm in length.

Although a combination of continuous culture and MRD provided strict control over growth pH during biofilm production, only small biofilm yields were recovered

(Figure 2-7). Therefore, this method was not able to satisfy the requirements of this study to produce sufficient cell yield for proteomic analysis.

In contrast, much higher amount of biofilms formed on surfaces in the chemostat culture vessel at pH 8.2 (Figure 2-1). For this reason, it was decided that biofilms harvested from the chemostat culture vessel would be suitable for subsequent analyses.

51

Figure 2-1. Appearance of bacterial growth at pH 7.4 (a) and pH 8.2 (b) in chemostat culture vessels

40 34.3 35

30 22.1 25

20 aggregation - 15

% Auto % 10

5

0 pH 7.4 pH 8.2

Figure 2-2. Auto-aggregation rate of cells grown at differing environmental pH

52

a

b

Figure 2-3. Gram stained images of bacterial cells grown at pH 7.4 (a) and 8.2 (b) under 1000x magnification

53

Figure 2-4. Effect of environmental pH on the F. nucleatum cell protein content

*For bacterial culture growing at pH 8.2, samples containing a mixture of planktonic cells and biofilms were used.

Figure 2-5. Effect of environmental pH on the growth of F. nucleatum in a chemostat

54

0.18 0.148 0.125 0.16 0.14 0.12 0.1 595nm 0.08 OD 0.06 0.04 0.02 0 pH 7.4 pH 8.2 Growth pH

Figure 2-6. Bound F. nucleatum cells grown at pH 7.4 and 8.2 using the microtitre plate method

Table 2-1. The pH of bacterial cultures before and after incubation overnight at 37ºC

Medium pH before Medium pH after

incubation* incubation^

7.42 6.96

8.22 7.24

*pH of bacterial suspension in BHI broth before inoculation

^pH of growth medium following overnight incubation

(36 replicates for each growth pH)

55

b a

c d

e f c c

Figure 2-7. Scanning electron micrographs of F. nucleatum cells grown on enamel slices bonded to Modified Robbins Device coupons at pH 7.4 (a,c,e) and 8.2 (b,d,f)

56

2.5 Discussion

In this study, F. nucleatum formed biofilms in vitro when exposed to elevated environmental pH. The auto-aggregation assay showed that chemostat cells grown at pH 8.2 exhibited a 10% increase in cell aggregation as compared with planktonic culture (Figure 2-2). The absence of capsule in F. nucleatum biofilms reported in previous publication (Zilm & Rogers, 2007) and the increased auto-aggregation in cells cultured at pH 8.2 led to the conclusion that the biofilm formation may be associated with altered cell surface protein expression.

Different biofilm producing methods were investigated for their suitability for proteomic analysis. As a static biofilm system, the microtitre plate biofilm method did not meet the requirements. The decline in culture pH over the incubation was presumably due to the metabolic activity that produced acidic end products causing the fall in culture pH. Although this method is simple, others have recently reported its lack of reproducibility making it unreliable for the study of bacterial biofilms (Azevedo et al., 2009). The MRD confirmed the effect of growth pH on the formation of biofilms in vitro as bacterial cells cultured at elevated pH showed a more extensive biofilm formation on the enamel surfaces. Unfortunately, the biofilm yield using the MRD was not sufficient after 10 days and the deficiency of this system has been highlighted in a recent review by Azevedo and colleagues (2009).

Both the MRD and 96-well microtitre plate biofilm systems tested were unsuitable for the purpose of this study. It was decided therefore to use F. nucleatum grown within the chemostat as the use of chemostat had many advantages in term of cell yield and pH control.

57

2.6 Summary

Environmental growth conditions influenced the growth of F. nucleatum in vitro. The bacterium grows optimally at pH 7.4, as a planktonic culture and a change to a pH of 8.2 initiates the formation of biofilms which is associated with a change in cell morphology. Two commonly used biofilm producing systems, microtitre plate and

MRD, were found to be inadequate for the aim of this study.

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3 A proteomic assessment of outer membrane protein expression in F. nucleatum biofilm cells

59

3.1 Introduction

In contrast to the notion that bacterial biofilms are usually associated with the production of EPS, mono-culture biofilms of F. nucleatum grown in a chemostat at pH

8.2 did not produce any detectable EPS (Zilm & Rogers, 2007). Their formation in an elevated pH environment was reported to be associated with an increase in cell surface hydrophobicity (Zilm & Rogers, 2007) and increased auto-aggregation rate (Figure 2-

6).

It was speculated that the formation of F. nucleatum mono-culture biofilms may be associated with a change in the bacterial protein expression, as numerous genes and proteins have been identified as determinants of bacterial biofilm formation using genetic and proteomic analyses (Section 1.3). These include cell envelope biogenesis-, stress response-, energy- and metabolism-, adhesion- and transport-related genes and proteins (Klausen et al., 2003; O'Toole & Kolter, 1998; Resch et al., 2005; Resch et al.,

2006; Schembri et al., 2003; Trémoulet et al., 2002).

The pathogenicity of F. nucleatum is largely due to the adhesive properties that allow the bacterium to interact with periodontopathogens and host cells during the pathogenesis of periodontal disease (Section 1.2.1). The identified adhesins, RadD,

FomA and FapA are among the outer membrane proteins (OMPs) that are responsible for interspecies and host cell interactions. As the expression of bacterial OMPs has been recognised to be influenced greatly by the environment (Lin et al., 2002), it was hypothesised that the switch from planktonic to biofilm growth at pH 8.2 in the chemostat was linked to changes in OMP expression.

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3.2 Aim

The aim of this study was to identify OMPs that are differentially expressed by F. nucleatum biofilm cells grown in an alkaline environment. The altered expression of

OMPs of this key organism may play a significant role in the development of dental plaque isolated from diseased pockets.

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3.3 Outer membrane protein expression in F. nucleatum biofilm cells

3.3.1 Materials and Methods

All amino acids, endonucleases, the bacterial protease inhibitor cocktail and

Coommassie Blue R-250 Stain were purchased from Sigma Aldrich (St Louis, MO,

USA). All 2DE chemicals, immobilised pH gradient (IPG) strips, isoelectric focusing

(IEF) equipment, GS-800 Calibrated Densitometer and PD-Quest-2D Analysis Software used for proteomic analysis were purchased from Bio-Rad Laboratories (Hercules, CA,

USA). Brain Heart Infusion, yeast extract powder and anaerobic blood agar plates were purchased from Oxoid (Australia).

3.3.1.1 Growth and harvesting of F. nucleatum

A planktonic culture of F. nucleatum subspecies polymorphum ATCC 10953 was grown anaerobically using a model C30 BioFlo Chemostat (New Brunswick

Scientific, Edison, NJ, USA) as described in Section 2.2.1.3. A chemically defined medium (CDM) (Section 2.2.1.2) containing glucose, amino acids, vitamins, salts, nucleotides, and buffers was used to grow F. nucleatum in the chemostat. The energy yielding amino acids, glutamic acid, histidine and lysine, were elevated to 20 mM, 10 mM and 10 mM respectively, while the remaining amino acids were included at a concentration of 1 mM. The generation time of culture was maintained at 10 hours by controlling the rate of incoming medium. The culture pH was maintained at pH 7.4 ±

0.1 by the automatic addition of 2 M KOH. Once steady state (10 generations) was achieved, the planktonic culture was harvested over five consecutive days and kept at -

70°C until further analysis. Following the final harvesting, the growth pH was increased by 0.2 unit increments to 8.2 ± 0.1 over an 8 hour period. The bacterial cells co-adhered

62 and formed biofilms in the culture vessel after a period of three days and the biofilm culture was collected as described previously (Section 2.2.1.3).

3.3.1.2 Preparation of bacterial cell lysate

Samples collected from either planktonic or biofilm cultures over five consecutive days were pooled. Cells were collected by centrifugation at 8,000 x g at

4°C for 10 minutes and washed twice in 50 mM Tris/HCl buffer, pH 7.5. Nucleic acids were degraded by incubation on ice following the addition of 550 units of DNase I

(Sigma Aldrich), 1000 units of Ribonuclease A (Sigma Aldrich) and 50 mM of magnesium chloride for 1 hour. Endogenous proteinase activity was controlled during cell lysis by the addition of a protease inhibitor cocktail (Sigma Aldrich). Samples were lysed by five sonication cycles of 10 seconds on ice. Any unbroken cells were removed by centrifugation at 2,500 x g at 4°C for 15 minutes. The protein concentration of cell lysates was determined using Coomassie Plus Protein Assay kit (Thermo Scientific).

3.3.1.3 Preparation of outer membrane proteins

Bacterial OMPs were extracted using the method employed by Molloy et al.

(2001) with modifications. A cell lysate equivalent to 40 mg of cellular protein was diluted ten-fold with ice cold 0.1 M sodium carbonate (pH 11) and stirred slowly on ice for 1 hour. The carbonate-treated membranes were then collected by ultra-centrifugation at 115,000 x g for 1 hour at 4°C in a Beckman-Coulter 55.2 Ti Rotor (Brea, CA, USA).

The resulting pellet was washed with 50 mM Tris/HCl buffer and collected using ultra- centrifugation at 115,000 x g for 1 hour at 4°C. Extracted OMPs were solubilised with

ReadyPrep Reagent 3 buffer (Bio-Rad Laboratories) containing 5 M urea, 2 M thiourea,

2% (w/v) CHAPS, 2% (w/v) detergent sulfobetaine (SB) 3-10, 40 mM Tris, 0.2% Bio-

63 lyte 3/10 and 2 mM tributyl phosphine (TBP). Purified samples were clarified by centrifugation at 15,000 x g for 30 minutes at 4°C. The concentration of membrane proteins extracted and solubilised in buffer containing reducing agents and detergents was determined using ReduCing agent and Detergent Compatible (RCDC) Protein

Assay Kit (Bio-Rad Laboratories).

3.3.1.4 Protein separation by two-dimensional gel electrophoresis

The first dimension of protein separation was based on protein isoelectric point

(pI) through isoelectric focusing (IEF). IEF was performed on 11 cm pre-cast immobilised pH gradient (IPG) strips with a pH range of 4-7 and 7-10, using a Protean

IEF cell (Bio-Rad Laboratories). Briefly, either 250 µg or 400 µg of soluble OMP preparations was cup-loaded on the anode end of IPG pH 4-7 and pH 7-10 strips, respectively. The IEF cycle consisted of a conditioning step at 150 volts for 1 hour to remove salt ions and other charged contaminants; a protein entry step at 300 volts for 2 hours; a linear voltage increasing to 8,000 volts over 5 hours; a protein focusing step occurred for 30,000 volt-hour with a 50 µA/strip current limit and the temperature was maintained at 20°C. Between three and six IPG strips from planktonic or biofilm cells were run concurrently.

After IEF, the IPG strips were subjected to a two-step equilibration as described by Görg et al. (1998). The first equilibration was to maintain the reduced disulphide bonds by incubating IPG strips in 2% (w/v) dithiothreitol (DTT) in equilibration buffer

(6 M urea, 2% (w/v) SDS, 0.05 M Tris/HCl buffer (pH 8.8), 20% (v/v) glycerol). The second equilibration buffer included 2.5% (w/v) iodoacetamide to prevent free thiol groups forming disulphide bonds. Polyacrylamide gels (12%T 3.3%C) were cast using a

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Protean II XL casting chamber (Bio-Rad Laboratories). Isoelectric focused proteins were separated in the second dimension using a Protean II XL Multi-cell (Bio-Rad

Laboratories) which allowed six gels to be run simultaneously in tris-glycine tank buffer

(25 mM Tris, 192 mM glycine, 0.1% (w/v) SDS). Molten agarose (1% w/v) was used to seal the IPG strips and the agarose contained 1% (w/v) bromophenol blue as a tracking dye. Proteins were resolved at 7 mA/gel until the dye front reached the bottom of the gels. Gels were stained in a solution containing 0.025% (w/v) Coomassie Brilliant Blue

R-250 (Sigma Aldrich), 40% (v/v) methanol and 10% (v/v) acetic acid overnight. Gel destaining was performed in two steps. First destaining was performed by incubating gels in destaining solution I containing 50% (v/v) methanol and 10% (v/v) acetic acid for 1 hour. The second destaining was performed overnight with a destaining solution containing 10% (v/v) acetic acid and 5% (v/v) methanol.

After the staining steps, gels were scanned using a GS-800 Calibrated

Densitometer (Bio-Rad Laboratories) and image analysis was performed using PD-

Quest software (Bio-Rad Laboratories). Replicate groups containing four to five gels from either planktonic or biofilm cells were used for analysis. Proteins were detected, matched, manually edited and normalised using total density in the gel. A matchset was created to identify significantly regulated proteins (Appendices 7.2 and 7.3).

Analysis sets containing proteins showing significant quantitative changes were created and these proteins were excised from the gels for MS analysis and protein identification. Differences between the means of protein quantity of replicate groups were analysed for statistical significance using the Student t-test in PD-Quest software.

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3.3.1.5 Protein identification by tandem mass spectrometry

This component of the study was performed at the Adelaide Proteomics Centre,

University of Adelaide.

Significantly regulated (p<0.05) protein spots were destained and digested with

100 ng of trypsin per sample according to the „low salt‟ protocol (Zhang et al., 2007).

One microlitre of each sample was applied to a 600 μm AnchorChip (Bruker Daltonics

GmbH, Bremen, Germany). MALDI-TOF mass spectra were acquired using a Bruker

Ultraflex III MALDI-TOF/TOF mass spectrometer (Bruker Daltonics GmbH) operating in reflectron mode under the control of the flexControl software (Version 3.0, Bruker

Daltonics GmbH). Peptide standards (Bruker Daltonics GmbH) were used to perform external calibration under the same conditions. Spectra were obtained at random locations over the surface of the matrix spot at a density determined by the operator.

Between three and six of the most highly abundant sample ions (i.e. non-trypsin and non-keratin) were selected as precursors for MS/MS analysis. MALDI-TOF/TOF was performed in the LIFT mode using the same spot on the target.

MS and MS/MS spectra were subjected to smoothing, background subtraction and peak detection using flexAnalysis (version 3.0, Bruker Daltonics GmbH). The spectra and mass lists were exported to BioTools (version 3.1, Bruker Daltonics

GmbH). The MS and corresponding MS/MS spectra were combined and submitted to the in-house Mascot database-searching engine (version 2.2, Matrix Science: htt://www.matrixscience.com).

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3.3.1.6 Protein characterisation using web-based bioinformatics tools

The protein sequences for each significantly regulated protein were retrieved from the National Centre for Biotechnology Information (NCBI) website. The gene ID was determined for each protein entered on the Oralgen Databases

(http://www.oralgen.lanl.gov) by blasting the protein sequence on the ExPASy (Expert

Protein Assay System) website (http://www.expasy.ch/tools/blast/). The gene name, gene functional class, predicted molecular weight, predicted pI, gene locus, gene nucleotide sequence and protein sequence of a protein are amongst the information available on Oralgen Databases.

The subcellular location of bacterial proteins was predicted using PSORTb v3.0

(http://www.psort.org/psortb/). PSROTb predicts the subcellular localisation of a protein based on the protein motif, the presence of signal peptide and trans-membrane alpha helices, and peptide sequence homology of proteins of known subcellular localisation. The ß-barrel-containing integral outer membrane proteins were identified using the β-barrel Outer Membrane Protein Predictor (BOMP)

(http://services.cbu.uib.no/tools/bomp).

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3.3.2 Results

3.3.2.1 Outer membrane protein extraction and separation

Approximately 40 mg of cellular proteins were used for OMP extraction; 4.80 mg and 4.45 mg of OMP was extracted from the total cellular protein of planktonic and biofilm cultures, respectively (Table 3-1). The isolated OMPs represented approximately 11-12% of the total cellular protein in planktonic and biofilm cells.

Twelve replicate 2DE gels from either the planktonic or biofilm OMP preparation were produced. Six of the twelve gels had a pH range of 4-7 and the remaining had a pH range of 7-10. Of the 24 gels, a total of 17 gels showing highly resolved and reproducible protein separation patterns and were selected for analysis

(Table 3-2). The matchsets containing proteins separated within the pH range 4-7 and 7-

10 are shown in Appendices 7.2 and 7.3, respectively. Using the carbonate treatment and Coomassie Brilliant Blue R-250 staining, 91 proteins were successfully resolved on

2DE gels. Sixty nine proteins had a pI between 4 and 7 (Figure 3-1) while 22 proteins had a pI between 7 and 10 (Figure 3-2).

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Table 3-1. Outer membrane protein recovery using sodium carbonate treatment

Bacterial cells Total cellular protein OMP OMP (mg)1 (mg)2 extraction (%) Planktonic 40.1 ± 2.3 4.8 ± 0.4 12.0 Biofilms 42.0 ± 2.3 4.5± 0.2 10.8 1Total cellular protein in cell lysates 2 Outer membrane protein concentrations extracted from cell lysates Results were based on six replicates.

Table 3-2. The number of replicate gels used in outer membrane protein analysis

Bacterial cells pH 4-7 IPG strip pH 7-10 IPG strip Planktonic 4 5 Biofilm 4 4 Total of gels in group 8 9

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Figure 3-1. 2DE gels of outer membrane proteins between pI 4-7 separated from planktonic (a) and biofilm (b) cells

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Figure 3-2. 2DE gels of outer membrane proteins between pI 7-10 separated from planktonic (a) and biofilm (b) cells

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3.3.2.2 Outer membrane protein expression in F. nucleatum biofilm cells

Of the 91 proteins successfully resolved on 2DE gels, 24 were significantly regulated (at least two-fold changes in expression, Student‟s t-test p < 0.05) (Figure 3-

3). Of the 24 proteins identified, eight regulated proteins were found to be either up- regulated or only detected in biofilm cells. Six proteins were down-regulated in biofilm cells while ten were only detected in planktonic cells. Significantly expressed proteins were analysed and identified using MS. Although 24 proteins were found to be regulated in biofilm cells, 15 were identified as unique proteins with some containing multiple isoforms (Table 3-3).

The subcellular localisation of these proteins was determined using PSORTb web-tool. Of the identified proteins, 12 proteins were predicted to be membrane associated and the remaining proteins being either cytoplasmic protein or of unknown subcellular localisation (Table 3-3). Six of the membrane-associated proteins were

OMPs and four of these were identified as Fusobacterial Outer Membrane Protein A

(FomA) isoforms. Five were identified as periplasmic proteins and included two isoforms of Dicarboxylate: Proton (H+) Tripartite ATP-independent Periplasmic

(TRAP) transporter binding protein, two isoforms of ABC superfamily binding protein and a Tripartite Tricarboxylate Transporter TTT family receptor protein. Contamination of OMP samples by non-outer membrane proteins was observed in this study. Nine proteins were predicted to be localised in the cytoplasm; these included two ribosomal proteins S2, and three elongation factors (EF).

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The regulated proteins were sorted into groups based on their function. These groups include proteins associated with transport, translation and unknown function

(Table 3-3).

Eight proteins were categorised as transport related proteins. Three of these proteins were up-regulated (TRAP transporter binding protein, NADH dehydrogenase and FomA) while three were down-regulated in biofilm cells (resistance nodulation cell- division (RND) superfamily antiporter, TTT receptor protein and OmpIP family outer membrane protein) or only detected in planktonic cells (ATP Binding Cassette (ABC) transporter binding proteins and electron transfer flavoprotein subunit B).

Three hypothetical proteins were found to be regulated by biofilm cells. Two of these were down-regulated (hypothetical protein FNP 0594 and 0283) in biofilm cells while FNP 1008 was up-regulated.

Using Coomassie Brilliant Blue-250 staining, three isoforms of FomA were resolved in planktonic culture compared to four in biofilm cells (Figure 3-3). FomA is a major OMP of F. nucleatum and it was predicted to be a β-barrel integral protein using

BOMP. The expression of all FomA isoforms was elevated in biofilm cells. The theoretical pI of FomA is 8.4 and the pI of the isoforms ranged of from 7.5 to 9.5.

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Figure 3-3. Gaussian 2D gel images of outer membrane proteins between pI 4-10 This is a synthetic image containing protein spots from both planktonic and biofilm cells. Refer to Table 3-3 for protein identity.

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Table 3-3. Up- or down-regulated outer membrane proteins of F. nucleatum ATCC 10953 grown as a biofilm under alkaline conditions

Function Protein name1 Spot Accession Gene % Seq Pred. Subcel. BOMP8 Ratio9 2 number3 ID4 MS/MS5 MW/pI6 Local.7 Transport Dicarboxylate: Proton (H+) TRAP-T family 2a 197736001 0524 9.8 38.9/5.0 P 0 6 tripartite ATP-independent transporter 2b 197736001 12.7 10 binding protein NADH dehydrogenase (ubiquinone), RnfG 3 197735248 2186 10.2 19.0/4.6 U 0 + subunit RND superfamily resistance-nodulation-cell 5a 197735985 0508 10.1 40.8/5.2 C 0 0.3 division antiporter 5b 197735985 6.5 0.4 Possible TTT family tricarboxylate 7 197735469 2414 21.2 35.2/5.5 P 0 0.1 transporter receptor protein ABC superfamily ATP binding cassette 8a 197735789 0306 23.6 32/4.7 P 0 - transporter binding protein 8b 197735789 23.6 - Electron transfer flavoprotein subunit B 9 197736919 1468 14.5 28.6/4.7 U 0 - OmpIP family outer membrane 14 197735257 2196 7.5 78.1/8.8 OM 0 0.4 porin/bacterial surface antigen (D15) Fusobacterial outer membrane protein A 15a 404160 0972 11.7 42.3/8.4 OM 1 4.4 (FomA) 15b 404160 11.7 25.8 15c 404160 13.6 + 15d 404160 5.4 7.7 Metabolism Possible Methenyl tetrahydrofolate 10a 197736852 1401 27.8 22.9/4.9 C 0 - cyclohydrolase 10b 197736852 11.8 - Hypothetical Hypothetical protein FNP_1008 1 197736473 1008 6.2 45.5/4.9 CE 0 3.0 protein Hypothetical protein FNP_0594 4 197736070 0594 12.1 9.9/4.7 U 0 0.3 Hypothetical protein FNP_0283 6 197735767 0283 6.4 18.0/5.0 OM 0 0.4 Protein Ribosomal protein S2 11a 197736503 1042 8.5 27.9/5.3 C 0 - synthesis 11b 197736503 12.6 - Elongation factor EF-Ts 12 197736504 1043 28.9 17.9/5.1 C 0 - Elongation factor EF-Tu 13a 197735216 2153 9.1 43.4/5.1 C 0 - 13b 197735216 8.9 - 75

1 Protein identity retrieved from the Mascot database search engine 2 Protein spot number as shown in Figure 3-3 3 Protein accession number on National Centre for Biotechnology Information (NCBI) 4 Annotated gene ID on Oralgen Databases 5 Percentage of sequenced peptides from MS/MS analysis found to match the identified protein 6 Predicted molecular weight (MW) and isoelectric point (pI) of protein determined from Oralgen Databases 7 Subcellular localisation of a protein predicted using PSORTb web-tool (http://www.psort.org/psortb/) C: cytoplasm; CE: cell envelope; P: periplasm; OM: outer membrane; U: unknown 8 Prediction of β-barrel integral outer membrane protein by BOMP (http://services.cbu.uib.no/tools/bomp) 9 Mean ratio of protein quantity in biofilm cells against protein quantity in planktonic cells, calculation based on 4-5 replicate gels (p < 0.05 using Student‟s t-test) + Proteins that were only resolved in biofilm cells - Proteins that were only resolved in planktonic cells

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3.4 Investigation of possible post-translational modifications of Fusobacterial Outer Membrane Protein A

Results showed that four isoforms of FomA were identified in biofilm cells

(Spot 15, Figure 3-3) suggesting possible PTMs of FomA. PTMs of proteins are common in bacterial cells and PTM of FomA has not been previously published in the literature. The aim of this investigation was to determine if any of the FomA isoforms arise from single or multiple PTMs.

3.4.1 Materials and Methods

3.4.1.1 Prediction of FomA post-translational modifications using ProMost web- tool

ProMost (Protein Modification Screening Tool) web-based tool

(http://proteomics.mcw.edu/promost) predicts various possible PTMs on a protein and the effect of PTM on protein pI and molecular weight. The FASTA peptide sequence of

FomA was retrieved from ExPASy BLAST Form website

(http://www.expasy.ch/tools/blast/) and the possible PTMs were calculated and predicted using ProMost.

3.4.1.2 Detection of phosphorylated FomA using ProQ Diamond Phosphoprotein Stain

Using the same OMP preparation (Section 3.3.1.3), 200 µg of cellular protein were separated by 2DE as described in Section 3.3.1.4. Due to the high sensitivity of the fluorescent stains used in this investigation, protein concentration used for 2DE was halved.

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Visualisation of phosphorylated proteins was performed using ProQ Diamond

Phosphoprotein Stain (Invitrogen, Carlsbad, CA, USA) according to the protocols recommended by the manufacturer. The gel was fixed in 50% (v/v) methanol and 10%

(v/v) acetic acid overnight and washed with MiliQ water and stained using ProQ

Diamond Phosphoprotein Stain for 2 hours. Destaining was performed using ProQ

Diamond Destaining Solution (Invitrogen) for 1.5 hours. Phosphorylated proteins were then visualised at 580 nm using a Typhoon scanner (GE Healthcare Life Sciences,

Buckinghamshire, UK).

Total protein staining was performed using Flamingo Fluorescent Stain (Bio-

Rad Laboratories). The washed Phosphoprotein Stained gel was counterstained using

Flamingo Staining Solution overnight. Destaining was performed using 0.1% (w/v)

Tween 20 for 20 minutes. The gel was washed in MiliQ water and scanned at absorbance 535 nm. The identification of phosphorylated FomA was performed by overlaying ProQ Diamond stained gel images and Flamingo fluorescent stained gel images.

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3.4.2 Results

3.4.2.1 Prediction of FomA post-translational modifications using ProMost web-tool

Using the ProMost web-tool, it was predicted that FomA does not undergo post- translational acetylation nor glycosylation (Table 3-4). However, FomA may be N- deaminated and phosphorylated (Table 3-4 and Figure 3-4). FomA N-deamination and phosphorylation may alter the pI of the protein. These PTMs may shift the pI of FomA to the acidic end but do not significantly affect the molecular weight. Phosphorylation involves the addition of a phosphate group by replacing neutral hydroxyl group on serines (S), threonines (T) or tyrosines (Y). Based on the ProMost prediction, FomA may be phosphorylated multiple times. However, only three possible S/T and Y phosphorylations are listed in Table 3-4. This is because the IPG strips used to separate

OMPs in this study ranged between pH 7 and 10. Two phosphorylations would decrease the pI of FomA to 6.79, which was out of the pH range coverage of IPG strips used.

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Table 3-4. Potential FomA post-translational modifications predicted using ProMost web-tool

Post translational modification Number of possible Isoelectric point (pI) PTM Acetylation Not identified 8.12

Glycosylation Not identified 8.12

N-deamination 1 7.64

Phosphorylation1 i. PhosST2 0 8.12 1 7.26 2 6.79 3 6.52

3 0 8.12 ii. PhosY 1 7.25 2 6.79 3 6.52

1ProMost predicts a maximum of 10 phosphorylation sites on FomA with predicted pI ranging from 8.12 (no-phosphorylation) to 5.53 (10 phosphorylations). Potential phosphorylation that occurs more than three times on FomA is not listed in the table. 2Phosphorylation of serine (S) or threonine (T) 3Phosphorylation of tyrosine (Y)

Figure 3-4. Predicted migration of FomA following N-deamination and phosphorylation on the theoretical gel image generated by ProMost web-tool

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3.4.2.2 Detection of phosphorylated FomA using ProQ Diamond Phosphoprotein Stain

Following the ProMost prediction, it was believed that the FomA isoforms present in biofilm cells (Figure 3-2) were most likely phosphorylated. Using a commercially available phosphoprotein stain, ProQ Diamond Phosphoprotein Stain

(Invitrogen), multiple phosphorylations on FomA were detected (Figure 3-5a). The

OMP separation pattern (Figure 3-5b) showed a reproducible OMP profile compared to the previous 2DE gel images (Figure 3-2b). The staining revealed that, of the four isoforms of FomA, two were phosphorylated (Spots 15a and 15b, Figure 3-5). These findings agree with the decrease in the pI of FomA isoforms predicted by ProMost.

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Figure 3-5. Separation by 2DE of outer membrane proteins from biofilm cells and stained with ProQ Diamond Stain (a) or Flamingo Fluorescent Stain (b) Refer to Table 3-3 for protein identity.

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3.5 Discussion

Stressful environmental conditions are known to influence the growth of bacteria. In dealing with stress, bacteria form biofilms. The present study deals with the formation of biofilms in response to elevated environmental pH. This may be an adaptation mechanism of F. nucleatum in response to increasing alkalinity that occurs in diseased periodontal pockets. In this study, cell envelope membrane proteins that were differentially expressed by biofilm cells were identified.

3.5.1 Outer membrane protein enrichment by carbonate treatment

The OMP enrichment method used in this study was based on the carbonate treatment proposed by Molloy and co-workers in 2000. This method claimed to be a rapid method for isolating OMPs with only minor contamination by non-membrane proteins (Molloy et al., 2000). In their study, the authors reported that the carbonate treatment method successfully extracted OMPs of E. coli and the OMP represented 6% of the total cellular protein. The percentage of extracted F. nucleatum OMP was about twice that reported for E. coli and represents a difference between the bacterial species.

As reported in Section 3.3.2.2, contamination of OMP preparation by non-

OMPs was observed. In the present study, of the 24 of proteins identified, 12 were

OMPs. Periplasmic and cytoplasmic membrane proteins were also identified and the presence of these non-OMPs in the protein preparation following carbonate treatment has been previously reported (Babujee et al., 2006; Molloy et al., 2000).

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In this study, the ribosomal protein S2 and elongation factors EF-Ts and EF-Tu were isolated from the carbonate-insoluble pellet. Using PSROTb, ribosomal protein S2 were predicted to be localised in the cytoplasm. The isolation of ribosomal proteins in

Gram negative bacterial cell envelopes has been frequently reported (Ang et al., 2008;

Chung et al., 2007; Seyer et al., 2005; Veith et al., 2009). Two other cytoplasmic proteins, both elongation factors, have also been reported to be present in the outer membrane proteome of Actinobacillus pleuropneumoniae (Chung et al., 2007). The reasons for the presence of these proteins in the membrane fraction were not addressed in those studies and remain unclear.

As more than half of the identified proteins were cell envelope-associated, we refer to these proteins as cell envelope proteins in the following discussion.

3.5.2 Altered cell envelope protein expression in biofilm cells

As shown in Table 3-3, proteins associated with transport, translation and unknown function were significantly regulated in biofilm cells. The down-regulation of ribosomal protein S2, elongation factor EF-Ts and EF-Tu that are involved in protein synthesis may be associated with the decrease in cell numbers at pH 8.2 as observed in

Chapter 2 (Figure 2-5). However, the regulation of three hypothetical proteins remains unclear due to their unknown functions. The majority of these regulated proteins (14 of

24) were involved in solute transport and the following discussion focuses on their expression at pH 8.2.

In bacterial cells, approximately 10% of genes encode for transport proteins.

The majority of which are localised in the bacterial membrane (Driessen et al., 2000).

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Transport proteins may be sorted into groups based on their structure and transport mechanisms. There are two major structural classes of transporter membrane domains: the alpha helices bundles and β-barrels. The latter are found exclusively in the outer membrane of Gram negative bacteria and allow protein to form pores in the outer membrane (Gelfand & Rodionov, 2008). A second classification of transport proteins is based on their transport mechanism and there are five categories; namely channels and pores, primary active transporter, secondary transporters (driven by electrochemical gradient), group translocators and transmembrane electron flow carriers (Gelfand &

Rodionov, 2008). The differentially expressed transport-proteins were from different categories and are discussed below.

3.5.2.1 Proteins down-regulated in F. nucleatum biofilm cells

Using Coomassie Brilliant Blue R-250 stain, two isoforms of the ABC superfamily ATP binding cassette transporter binding proteins were detected only in planktonic cells indicating that the expression of these proteins was down-regulated or negligible in the biofilm cells. ABC transporters are primary transporters as they hydrolyse ATP to drive substrate uptake against high concentration gradients in a unidirectional manner (Davidson et al., 2008). A decrease in the expression of this protein may be due to an increased energy burden associated with biofilm conditions

(and will be discussed in detail in the following chapter) and by down-regulating its expression, the cells may conserve energy as ABC transporters utilises ATP.

The expression of a TTT family (tripartite tricarboxylate transporter) receptor protein, a secondary transporter, was down-regulated seven-fold in biofilm cells. The substrates of this transporter include citrate, isocitrate, and cis-aconitate (Antoine et al.,

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2005; Winnen et al., 2003). This transporter works in a protein complex and is encoded by the TctABC gene cluster in Salmonellae typhimurium. The homologues of TctABC were identified through data mining on the genome sequence of F. nucleatum subspecies polymorphum (gene ID 2414, 2415 and 2416, Oralgen Databases). However, the two membrane proteins of the TTT transporter (gene ID 2415 and 2416 as, Oralgen

Databases) were not found to be differentially expressed by biofilm cells. The altered expression of the TTT binding protein may be associated with a change in metabolic pattern of fusobacterial biofilm cells.

In biofilm cells, two isoforms of a secondary transporter, RND superfamily resistance-nodulation-cell division antiporter, were down-regulated three- to four-fold in the biofilm cells. The principal role of RND antiporters is the efflux of antibiotics and RND superfamily transporters are now recognised as the major contributor to both intrinsic and acquired multidrug resistance in Gram negative bacteria (Kumar &

Schweizer, 2005; Murakami & Yamaguchi, 2003). The gene that encodes for RND antiporter was identified as one of the potential virulence factors of F. nucleatum subspecies polymorphum (Karpathy et al., 2007). The suppressed expression of this protein in the antibiotic-free chemostat remains unclear. The down-regulation of RND antiporters in this study was speculated to be related to a change in metabolic activity by biofilm cells as RND antiporters are also reported to be involved in the efflux of metabolites such as citrate and iso-citrate in E. coli (Helling et al., 2002).

Another protein that was down-regulated in biofilm cells was OmpIP (Outer membrane protein Insertional Porin). This protein was down-regulated two-fold in biofilm cells. It should be noted that this protein is described as a porin on the NCBI

86 database and it is annotated as the bacterial surface antigen D15 on Oralgen Databases.

Antigen D15 is a known antigen present in Haemophilus influenza (Thomas et al.,

1990), Pasteurella multocida, Neisseria gonorrhoeae and Neisseria meningitidis

(Manning et al., 1998), and is highly immunogenic in animal models (Thomas et al.,

1990). OmpIP porin was also reported to play an important role in outer membrane protein assembly (Saier, 2006). While the observed suppression of this protein under alkaline conditions is unclear, the changes may decrease diffusion into the periplasmic space and in changes in the membrane composition. This porin was reported to increase at pH 7.8 by Zilm et al. (2010). However, it should be noted that the comparison growth pH for that study was 7.2 and the expression of the porin may be down-regulated at that pH.

3.5.2.2 Proteins up-regulated in F. nucleatum biofilm cells

In contrast to the down-regulation of two other secondary transporters, RND receptor protein and TTT binding proteins, TRAP transporter binding proteins were up- regulated in biofilm cells. TRAP transporters have been shown to transport a wide range of substrates including dicarboxylate malate, fumarate, succinate (Prakash et al.,

2003b), pyruvate and other 2-oxoacids, sialic acid and glutamate (Mulligan et al.,

2007). This secondary transporter requires proton motive force to drive substrates across the membrane and is a symporter that co-transports a proton into cells (Linton &

Higgins, 1998). Therefore, the combined effect of an increase in expression of TRAP transporters with a decrease in the expression of ABC transporters may lead to a reduction in ATP consumption. Furthermore, the increased expression of TRAP transporter may play a role in maintaining pH homeostasis in an alkaline environment by increasing the influx of protons. NADH dehydrogenase is an enzyme that catalyses

87 the oxidation of NADH by transferring electrons to ubiquinones and establishes a proton motive force across the bacterial cell membrane (Gyan et al., 2006). The up- regulation of NADH dehydrogenase was in accordance with the increased abundance of

TRAP-T binding proteins in the biofilm cells.

3.5.3 Regulation of protein expression and pH homeostasis

Biofilm development in F. nucleatum observed in the present study was induced by an increasing growth pH. The regulation of transport proteins in biofilm cells was likely to be associated with a change in the bacterial metabolic patterns as these transporters are substrate-specific. Nonetheless, the regulation of transporters by biofilm cells may be associated with pH homeostasis in cells due to the involvement of the different transport mechanism with proton movement.

As noted in Section 1.3.2.2, bacterial cells maintain an intracellular pH between

7.4 and 7.6 under alkaline conditions. In order to achieve neutral pH, bacteria maximise proton retention and capture (Booth, 1985; Padan et al., 2005). The pH homeostasis of bacterial cells exposed to alkaline stress is greatly influenced by the activity of Na+:H+ antiporters. Four predicted Na+:H+ antiporter genes were found in the genome of F. nucleatum subspecies polymorphum ATCC 10953 (Oralgen Databases). However, these proteins were not differentially expressed in the present study. These antiporters were predicted to be cytoplasmic proteins using PSROTb web-tool and thus may not have been isolated in the cell envelope fraction. The Na+:H+ antiporter imports a proton into the cytoplasm in exchange for sodium (Padan et al., 2005). The H+ and Na+ levels in bacterial cells are thus influenced by environmental pH (Padan et al., 2005).

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Growing at pH 8.2, the synthesis of H+:Na+ antiporters are thought to be induced, in order to maintain a lower pH inside the cell. Under such situation, the bacterial cells will control the efflux of Na+ so that the H+:Na+ antiporters are kept active. The identified TTT transporters are Na+ dependent secondary transporters

(Winnen et al., 2003). The decreased TTT receptor protein prevents the entry of Na+ via the TTT receptor, so that the Na+ ions are channelled into the H+:Na+ antiporters to cope with elevated environmental pH.

In addition, bacteria growing under suboptimal environmental pH regulate metabolic activity to control the production of acids and amines (Section 1.3.2.2). ABC transporters have been reported to mediate the uptake of nutrients including mono- and oligosaccharides, amino acids, peptides and vitamins and they are involved in the efflux of peptides, hydrophobic drugs, polysaccharides, and proteins to the environment

(Davidson et al., 2008).

The ABC domain of this protein reveals that it is most likely to be a glutamine- binding ABC transporter in F. nucleatum (Oralgen Databases). Glutamine was found to be one of the most abundant free amino acids in human serum and GCF (Syrjänen et al., 1990; Wuu et al., 1988). Glutamine can be taken up by F. nucleatum and converted to glutamate by glutamate-ammonia (gene ID 0503, Oralgen Databases) in the cytoplasm. The reaction produces ammonia and leads to increased intracellular pH within cell cytoplasm. The down-regulation of ABC transporter binding protein in biofilm cells limits the uptake of glutamine and the subsequent ammonia release in cytoplasm.

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3.5.4 Regulation of FomA and its possible post-translational modifications

In biofilm cells, FomA was significantly up-regulated. This protein is a porin and its sequence was determined well before the genome sequences of F. nucleatum subspecies nucleatum ATCC 25586, subspecies vincentii ATCC 49256 and subspecies polymorphum ATCC 10953 were available (Bolstad & Jensen, 1993; Bolstad et al.,

1994; Kleivdal et al., 1999). This protein is a non specific and voltage-dependent general diffusion porin (Kleivdal et al., 1995; Kleivdal et al., 1999). FomA is a major immunogen of F. nucleatum (Takada et al., 1988) is now recognised as a virulence factor of F. nucleatum subspecies polymorphum (Karpathy et al., 2007).

Although FomA is known as a porin, its role as an adhesin has been extensively investigated. A recent study showed that FomA is responsible for the binding of F. nucleatum to human salivary protein statherin-derived peptide (Nakagaki et al., 2010).

The ability to bind to statherin underlines the role of F. nucleatum as a bridge organism that is frequently isolated from healthy and diseased plaque as the binding of oral bacteria to statherin occurs in the initial development of dental plaque. The up- regulation of FomA by F. nucleatum biofilm cells in an alkaline environment suggests a possible role of FomA as a non-specific adhesin as the bacterium adhered to stainless steel baffles and glass surfaces in the chemostat vessel.

Coaggregation between F. nucleatum and a wide range of oral bacteria has been well documented (Kaufman & DiRienzo, 1989; Kolenbrander et al., 1989). FomA is responsible for coaggregation with late colonisers such as A. actinomycetemcomintans,

P. gingivalis and Bacteroides species (Kinder & Holt, 1993; Kolenbrander et al., 1989;

Rosen et al., 2003). A recent study also supported the role of FomA in coaggregation

90 with P. gingivalis (Liu et al., 2010). The binding capacity of F. nucleatum ATCC

10953 that was reduced by inactivated anti-FomA serum exhibited a decreased degree of coaggregation and biofilm formation with pathogenic P. gingivalis. Auto- aggregation, coaggregation and biofilm formation are the key events in the development of oral dental plaque that is associated with the periodontal disease. The increased expression of FomA in a pH environment similar to that found in diseased periodontal pockets may be responsible for increased coaggregation with P. gingivalis and may contribute to the increased numbers of this periodontal pathogen in the transition from health to disease (Kolenbrander et al., 2006).

The importance of FomA in the coaggregation between F. nucleatum and P. gingivalis and its role in the progression of periodontal disease was further investigated by Liu and colleagues (2010). Mice that were injected with a bacterial suspension of P. gingivalis and F. nucleatum neutralised with anti-FomA showed a significant suppression of abscess formation and gingival swelling. Furthermore, the production of volatile sulphur compounds by the co-culture was also found to decrease (Liu et al.,

2010).

As discussed earlier, a different number of FomA isoforms was detected in F. nucleatum biofilm cells while there were only three observed in planktonic cells

(Figures 3-2 and 3-3). This finding demonstrated that the high pH environment affected the PTM on FomA.

The identification of FomA isoforms has not previously been described. Four isoforms of FomA were identified in biofilm cells and two of these (Spots 15a and 15b,

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Figure 3-5) were post-translationally modified by phosphorylation. It has been suggested that most proteins go through some form of PTM in order to perform their molecular function (Blom et al., 2004). Phosphorylation is the most common form of

PTM as more than 30% of proteins are modified by reversible covalent modification

(Hubbard & Cohen, 1993). The primary role of protein phosphorylation is regulatory activity through signal transduction pathways. Reviews on protein phosphorylation suggest that this PTM is involved in the metabolism, proliferation, differentiation of apoptosis in eukaryotes and prokaryotes (Blom et al., 2004).

Phosphorylation occurs on serine, threonine and tyrosine residues. A number of web-tools allow for the prediction of phosphorylation sites based on the protein sequence and structure. Phosphorylation of tyrosine occurs in the acidic motif of a peptide sequence in which acidic residues are usually found several residues away from a tyrosine. In contrast, a serine/threonine phosphorylation site is usually identified in the basic motifs of protein (Zhu et al., 2005).

The role of phosphorylated FomA identified in this study was not further investigated. However, it is interesting to note that other researchers have reported the existence of porin isoforms in E. coli, (Molloy et al., 2000; Xia et al., 2008), Dickeya dadantii (Babujee et al., 2006) and of P. aeruginosa biofilms (Seyer et al., 2005) in various environmental conditions.

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3.6 Summary

F. nucleatum is a key organism in the development of dental plaque which has increased number and proportion increase in diseased pockets in an environment characterized by alkaline pH. The formation of F. nucleatum biofilms in continuous culture in response to such an increased pH was associated with a change in cell surface properties and the expression of cell envelope proteins.

An increased expression of FomA was observed in biofilm cells. The increased levels of this protein may be involved in the formation of biofilms and it may relate to its role as „bridge organism‟ in the development of mature dental plaque in vivo.

A number of transport proteins were significantly regulated by F. nucleatum.

Decreased expression of ABC binding proteins and increased expression of secondary transporter TRAP are consistent with ATP conservation by biofilms and may be associated with a change in the changed metabolic pattern observed in biofilm cells under stress conditions. In addition, regulation of specific transport proteins by biofilm cells may be closely associated with maintenance of intracellular pH.

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4 A proteomic analysis of cytoplasmic protein expression in F. nucleatum biofilm cells

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4.1 Introduction

The formation of a microbial biofilm under a particular condition is governed by a complex interplay between physico-chemical factors and the physiological and genetic properties of the inhabiting microorganisms (Hall-Stoodley et al., 2004;

Spormann, 2008). As mentioned in Section 1.3.1-1.3.2, the switch from free-floating cells to biofilm growth mode involves an altered gene and protein regulation. Flagella, pili, adhesins and EPS production by bacteria are essential in the formation of bacterial biofilms as determined by genetic studies. The majority of these proteins are present in the bacterial cell envelope and the changes in cell envelope protein expression by F. nucleatum biofilms have been addressed in the previous chapter.

A key finding from the previous chapter was an increased amount of the porin

FomA in biofilm cells. It is believed that the elevated expression of FomA may contribute to the auto-aggregation and biofilm formation observed as FomA is responsible for the coaggregation of F. nucleatum with oral bacteria (Kaufman &

DiRienzo, 1989; Kinder & Holt, 1993; Kolenbrander et al., 1989; Liu et al., 2010). In addition, a number of transporters were differentially regulated, indicating a change in the cellular physiology under imposed stress conditions. These findings led to the further investigation of the F. nucleatum biofilm physiology with the focus on the cytoplasmic protein expression.

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4.2 Aim

The aim of this study was to indentify cytoplasmic proteins (pI 4 - 10) that are differentially expressed by F. nucleatum grown at pH 8.2. To date, proteomic studies that have been conducted on the cytoplasmic proteins of F. nucleatum focused on proteins within the pI range of 4-8 and this study was the first to investigate cytoplasmic proteins with a pI up to 10.

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4.3 Materials and Methods

All amino acids, endonucleases and bacterial protease inhibitor cocktail were purchased from Sigma Aldrich (St Louis, MO, USA). All 2D-PAGE chemicals, IPG strips, IEF equipments, MicroRotofor Liquid Phase IEF Cell, GS-800 Calibrated

Densitometer and PD-Quest 2D Image Analysis software used for proteomic analysis were purchased from Bio-Rad Laboratories (Hercules, CA, USA). Brain Heart Infusion, yeast extract and anaerobic blood agar plates were purchased from Oxoid (Australia).

4.3.1 Growth and harvesting of F. nucleatum

Planktonic (pH 7.4) and biofilm (pH 8.2) cells were cultured and harvested as described in Section 3.3.1.1.

Briefly, a planktonic culture of F. nucleatum ATCC 10953 was grown anaerobically using a model C30 BioFlo Chemostat (New Brunswick Scientific) as described in Section 2.2.1.3. A chemically defined medium (CDM) (Section 2.2.1.2) containing glucose, amino acids, vitamins, salts, nucleotides, and buffers was used to grow F. nucleatum in a chemostat. The energy yielding amino acids, glutamic acid, histidine and lysine were elevated to 20 mM, 10 mM and 10 mM respectively, while the remaining amino acids were included at a concentration of 1 mM. The generation time of culture was maintained at 10 hours by controlling the rate of incoming medium. The culture pH was maintained at pH 7.4 ± 0.1 by the automatic addition of 2 M KOH.

Once steady state (10 generations) was achieved, the planktonic culture was harvested over five consecutive days and kept at -70°C until further analysis. Following the final harvesting, the growth pH was increased by 0.2 unit increments to 8.2 ± 0.1 over an 8 hour period. The bacterial cells co-adhered and formed biofilms in the culture vessel

97 after a period of three days and the biofilm culture was collected as described previously (Section 2.2.1.3).

4.3.2 Sample preparation for proteomic analysis

The cytoplasmic proteins of F. nucleatum from planktonic and biofilms cultures were prepared using the method described by Zilm et al. (2007).

Bacterial cell lysates were prepared as described in Section 3.3.1.2. Briefly, planktonic (pH 7.4) and biofilm cultures (pH 8.2) were collected by centrifugation at

8,000 x g at 4°C for 10 minutes and washed twice in 50 mM Tris/HCl, pH 7.5. Cells were lysed by sonication on ice and any unbroken cells were removed by centrifugation at 2500 x g for 15 minutes at 4°C. Protease inhibitor cocktail (500 μL) was added during cell lysis to control endogenous proteinase activity. Nucleic acids were degraded by a solution containing 550 units of DNase I, 1000 units of Ribonuclease A and 50 mM of magnesium chloride following incubation on ice for 1 hour. Cell free lysates were then centrifuged at 20,000 x g for 30 min at 4°C to separate the cell envelope

(inner and outer membranes) from the cell cytoplasm. The supernatant, containing the cytoplasmic proteins, was further purified by protein precipitation following the addition of 4 volumes ice cold acetone. The reducing agent dithiothreitol (DTT) was added to a final concentration of 50 mM and the suspension was incubated at -20°C overnight. The protein precipitate was then collected by centrifugation at 10,000 x g for

30 minutes at 2°C. The residual acetone was removed by evaporation. Purified samples were then solubilised in appropriate sample solubilisation buffers (See Section 4.3.3) and clarified by centrifugation at 15,000 x g for 30 minutes at 15 °C. Aliquots of supernatant were then stored at -80°C until required for 2DE analysis.

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4.3.3 Protein separation by two-dimensional gel electrophoresis 4.3.3.1 First dimension protein separation: Isoelectric focusing

The concentration of cytoplasmic proteins extracted and solubilised in buffer containing reducing agents and detergents was determined using RCDC Protein Assay

Kit (Bio-Rad Laboratories). IEF was performed on pre-cast Immobilised pH Gradient

(IPG) strips with pH ranging from 4-7 and 7-10. Soluble cytoplasmic proteins, within pI

4 and 7 were separated using protocols based on that of Zilm et al. (2007). However, when employing the same protocols for proteins within pI gradient 7 to 10, poor protein resolution was observed (Appendix 7.4). Thus, optimization of 2DE running conditions was required for protein separation within this pH range.

4.3.3.1.1 Separation of proteins with a pI between 4 and 7

Cytoplasmic proteins that were separated using pH 4-7 IPG strips were solubilised using ReadyPrep Reagent 2 (Bio-Rad Laboratories) containing 8 M urea,

4% (w/v) CHAPS and 0.2% (w/v) ampholytes pH 4-7. The reducing agent tributyl- phosphine (TBP) (2 mM) was also included in the solubilisation buffer. IPG strips were rehydrated using ReadyPrep Reagent 3 (Bio-Rad Laboratories) containing 5 M urea, 2

M thiourea, 2% (w/v) CHAPS, 2% (v/v) detergent sulfobetaine (SB) 3-10, 2 mM TBP,

40 mM Tris and 0.2% (w/v) ampholytes with the addition of a reducing agent, DeStreak

Reagent (GE Healthcare Life Sciences), at a concentration of 12 µL/mL. A total of 1.2 mg of cytoplasmic proteins was applied by cup-loading to the anode end. IEF was conducted overnight on 17 cm pre-cast IPG strips using a Protean IEF Cell (Bio-Rad

Laboratories). An IEF cycle consisted of a conditioning step at 150 volts for 1 hour to remove salt ions and charged contaminants; a protein entry step at 300 volts for 1.5 hours; a linear voltage increasing to 10,000 volts over 5 hours; a protein focusing step

99 occurred for 50,000 volt-hour with a 50 µA/strip current limit and the temperature was maintained at 20°C. Six IPG strips/sample were run concurrently. The IPG strips containing isoelectric focused proteins were stored at -20ºC until required for 2DE.

4.3.3.1.2 Separation of proteins with a pI between 7 and 10

Cytoplasmic proteins were solubilised in Bio-Rad ReadyPrep Extraction

Reagent 3. The solubilising agent uses the combination of two chaotropic agents, 2 M thiourea and 5 M urea, and a sulfobetaine detergent which improves solubilisation of relatively hydrophobic proteins.

Prior to gel-based IEF, solubilised proteins were fractionated using

MicroRotofor Liquid-Phase IEF Cell (Bio-Rad Laboratories). Cytoplasmic proteins (10 mg) were loaded into a focusing chamber. Liquid IEF was performed at 20ºC at 1 watt for 2 hours until the completion of protein focusing. Isoelectric focused proteins were separated into 10 fractions between pI 3 and 10. The fractions between pI 7 and 10 were pooled, and following protein determination, separated by 2DE.

Rehydration of 11 cm pH 7-10 IPG strips was performed using ReadyPrep

Extraction Reagent 3 with the addition of DeStreak Reagent (12 µL/ml rehydration buffer) overnight. Proteins present in the pH 7-10 fraction (100 µg) were cup-loaded at the anodic end. A paper wick wetted with 8 µL of DeStreak Reagent (GE Healthcare

Life Sciences) was placed at the cathode end of the IPG strip. An IEF cycle consisted of a conditioning step at 150 volts for 1 hour to remove salt ions and charged contaminants; a protein entry step at 300 volts for 1.5 hours; a linear voltage increasing to 8,000 volts over 5 hours; a protein focusing step occurred for 30,000 volt-hour with a

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50 µA/strip current limit and the temperature was maintained at 20°C. Six IPG strips were run concurrently. The IPG strips were stored at -20ºC after the completion of IEF until required for 2DE.

4.3.3.2 Second dimension electrophoresis

Separation of proteins based on their molecular weight was performed as described in Section 3.3.1.4. Gels with separated proteins between pI 4 and 7 were stained with Coomassie R-250 (Section 3.3.1.4) while the gels with proteins between pI between 7 and 10 proteins were stained using the more sensitive Flamingo Fluorescent

Stain (Bio-Rad Laboratories) (Section 3.4.1.2) to allow for the smaller protein load.

4.3.4 Protein expression analysis

After the staining steps, gels were scanned and image analysis was performed using PD-Quest software (Bio-Rad Laboratories). Replicate groups containing between three and five gels from either planktonic or biofilm cells were used for analysis.

Proteins were detected, matched, manually edited and normalised using total density in the gel. Matchsets were created to identify significantly regulated proteins (Appendices

7.5 and 7.6).

Analysis sets containing proteins showing significant quantitative changes were created and these proteins were excised from the gels for MS analysis and protein identification. Differences between the means of protein quantity of replicate groups were analysed for statistical significance using the Student‟s t-test in PD-Quest software.

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4.3.5 Protein identification by mass spectrometry

This component of the study was performed at the Adelaide Proteomics Centre,

The University of Adelaide.

4.3.5.1 Mass spectrometry sample preparation

The excised protein spots were destained and digested according to the „low salt‟ protocol (Zhang et al., 2007). One microlitre of each sample was applied to a 600

μm AnchorChip (Bruker Daltonics GmbH, Bremen, Germany) according to the α- cyano-4-hydroxycinnamic acid (HCCA) method (Zhang et al., 2007).

4.3.5.2 MALDI mass spectrometry (MS and MS/MS)

MALDI-TOF mass spectra were acquired using a Bruker Ultraflex III MALDI-

TOF/TOF mass spectrometer (Bruker Daltonics GmbH) operating in reflectron mode under the control of the flexControl software (Version 3.0, Bruker Daltonics GmbH).

Peptide standards (Bruker Daltonics GmbH) were used to perform external calibration under the same conditions. Spectra were obtained at random locations over the surface of the matrix spot. Between three and six of the most highly abundant sample ions (i.e. non-trypsin and non-keratin) were selected as precursors for MS/MS analysis. MALDI-

TOF/TOF was performed in the LIFT mode using the same spot on the target. MS and

MS/MS spectra were subjected to smoothing, background subtraction and peak detection using flexAnalysis (version 3.0, Bruker Daltonics GmbH).

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4.3.5.3 Liquid chromatography-ESI mass spectrometry (MS and MS/MS)

Peptides were separated by chromatography using an Agilent Protein ID Chip column assembly (40 nL trap column with 0.075 x 43 mm C-18 analytical column) housed in an Agilent HPLC-Chip Cube Interface connected to an a HCT ultra 3D-Ion-

Trap mass spectrometer (Bruker Daltonics GmbH). The column was equilibrated with

4% acetonitrile (CAN)/0.1% formic acid (FA) at 0.5 µL/minute and the samples eluted with an ACN gradient (4%-31% in 32 minutes). Ionisable species (300 < m/z < 1200) were trapped and one or two of the most intense ions eluting at the time were fragmented by collision-induced dissociation (CID). MS and MS/MS spectra were subjected to peak detection using DataAnalysis (version 3.4, Bruker Daltonics GmbH).

4.3.5.4 Protein identification

The spectra and mass lists were exported to BioTools (version 3.1, Bruker

Daltonics GmbH). The MS and corresponding MS/MS spectra were combined and submitted to the in-house Mascot database-searching engine (version 2.2, Matrix

Science: http://www.matrixscience.com). The specifications used were as follows:

Taxonomy : All entries Database : NCBI non-redundant 20080622 Enzyme : Trypsin Fixed modification : Carbamidomethyl (C) Variable modifications : Oxidation (M) Mass tol MS : 0.3 Da MS/MS tol : 0.4 Da Peptide charge : 1+, 2+ and 3+ Missed cleavages : 1

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4.4 Results 4.4.1 Cytoplasmic protein profiles

In this study, different 2DE running conditions were used for the separation of proteins within pI 4-7 and 7-10. Cytoplasmic proteins in the latter required optimisation of the 2DE to allow for good protein resolution.

4.4.1.1 Separation of proteins with a pI between 4 and 7

Good protein separation was observed using 17 cm IPG strips and Coomassie

Blue R-250 staining. The representative gels from each group are shown in Figure 4-1.

Two hundred and forty five proteins with molecular weights between 10,000 to 80,000 kDa were resolved successfully (Figure 4-1). Of the 245 resolved proteins, the expression of 24 proteins was significantly modulated (p<0.05, Student‟s t-test) by changing the external pH from 7.4 to 8.2 (Figure 4-3 and Tables 4-1, 4-2). When cells were grown at pH 8.2, six proteins were up-regulated and eight were detected in biofilm cells only. Conversely, the expression of seven proteins was down-regulated at pH 8.2 and three proteins were found only in planktonic cells.

4.4.1.2 Separation of proteins with a pI between 7 and 10

Representative gels from planktonic and biofilm cells are shown in Figure 4-2.

Eighty five proteins were resolved. The number of cytoplasmic proteins resolved was approximately one third of that separated between pI 4 and 7. Of the 85 proteins, 14 were significantly regulated (p<0.05). Three proteins were found to be up-regulated and one protein was detected only in biofilm cells. In contrast, five proteins were up- regulated and six were detected only in the planktonic cells.

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Figure 4-1. 2DE gels of cytoplasmic proteins between pI 4-7 separated from planktonic (a) and biofilm (b) cells

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Figure 4-2. 2DE gels of cytoplasmic proteins between pI 7-10 separated from planktonic (a) and biofilm (b) cells

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Figure 4-3. Gaussian 2D gel images of cytoplasmic proteins between pI 4-10 Refer to Tables 4-1 and 4-2 for protein identity.

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4.4.2 Cytoplasmic protein regulation in F. nucleatum biofilm cells

Of the 330 resolved cytoplasmic proteins, the expression of 31 proteins was significantly regulated (p<0.05, Student‟s t-test) by environmental pH (Figure 4-3) and successfully identified by either MALDI-MS or LC-ESI-MS.

The identities of these proteins are presented in Table 4-1 and Table 4-2. A number of proteins were identified as isoforms and are indicated in Figure 4-3 and

Tables 4-1 and 4-2 by the same number with differing letters. Following identification, proteins were sorted into two functional classes, proteins involved in energy production pathways (Table 4-1) and stress proteins (Table 4-2). When grown at pH 8.2, F. nucleatum biofilm cells increased the expression of glutaconyl-CoA decarboxylase A subunit, D-lactate dehydrogenase (LDH), di-peptide binding proteins, 60 kDa chaperonin (GroEL), peptidyl-prolyl cis-trans and recombination protein

RecA. The expression of glutamate formiminotransferase, pyruvate synthase, butanoate/acetoacetate coA transferase (BAT) α-subunit, 70 kDa chaperone protein

(DnaK) and alkyl hydroperoxide reductase C22 and elongation factor was down- regulated by alkaline pH.

The proteomic data showed that six isoforms of NAD-specific glutamate dehydrogenase (NAD-GDH) (Figure 4-3) were significantly modulated by growth pH; however, two isoforms (Spots 2a and 2b, Figure 4-3) were up-regulated while four were down-regulated (Spots 2c-2f, Figure 4-3). Interestingly, the up-regulated NAD-GDH were found within pI 4-7 while the other four NAD-GDH were found within pI 7-10 with significant reduction in molecular weight and increase in pI value. Similar

108 observations were made in butyryl-CoA dehydrogenase and phosphate acetyltransferase which showed varying levels of regulation between isoforms.

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Table 4-1. Proteins involved in energy-producing pathways that were regulated in both biofilm and planktonic cells of F. nucleatum Function Protein identification E.C no1 Gene ID Protein NCBI Density5 Mean % seq Pred. Obs. on no3 Accession no4 (x103) ratio6 MS7 MW/pI8 MW/pI9 Oralgen2

Peptide Di-peptide binding protein n/a 0440 1a 167008554 1.6 + 25 56.9/5.3 55/4.6 transport DppA 1b 167008554 5.9:0.7 8.6 21 55/4.8 1c 167008554 4.1 + 25 55/4.9 1d 167008554 1.8 + 24 55/5.0 Oxo- NAD-specific glutamate 1.4.1.2 1750 2a 167007270 18.5:3.9 4.8 29 46.6/6.1 48/6.2 glutarate dehydrogenase 2b 167007270 18.8:6.0 3.1 52 48/6.6 pathway 2c 167007270 1.6:7.5 0.2 10 35/7.9 2d 167007270 5.9:49.3 0.1 31 23/9.5 2e 167007270 2.7:16.6 0.2 32 24/8.0 2f 167007270 24.4 - 30 28/9.0 Glutaconyl-CoA 4.1.1.70 0224 3a 167007008 2.5:1.1 2.3 40 64.1/5.1 62/5.3 decarboxylase A subunit 3b 167007008 1.7 + 34 62/5.4 Histidine Glutamate 2.1.2.5 1404 4 167007602 7.2 0.1 47 36.0/5.5 38/5.6 catabolism formiminotransferase Butanoate Butanoate: acetoacetate 2.8.3.9 0970 5a 167008030 3.7 - 36 23.3/6.1 23/5.8 synthesis Coenzyme A transferase α 5b* 167008030 2.9 - 50 23/6.1 subunit Butyryl-CoA 1.3.99.2 1467 6a* 197736918 6.7 - 31 41.8/7.8 39/8.1 dehydrogenase 6b* 197736918 13.0:8.8 1.5 42 40/8.2 6c* 197736918 11.7:2.7 4.3 41 42/7.1 Acetate Phosphate acetyltransferase 2.3.1.8 0618 7a* 197736093 12.1:24.2 0.5 28 36.0/7.6 38/7.8 synthesis 7b* 197736093 3.8 + 7 39/7.6 Pyruvate D-lactate dehydrogenase 1.1.1.28 1749 8 167007271 1.2 + 41 37.8/6.1 36/6.1 metabolism Pyruvate synthase 1.2.7.1 2072 9* 34763801 1.3 - 1 132.1/6.7 58/7.7

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All proteins were identified using MALDI MS except those marked with „*‟ were identified using LC-ESI MS 1 Enzyme Commission (EC) number, refer to Figures 5.6 and 5.7 2 Annotated gene ID on Oralgen Databases 3 Protein number as shown in Figure 4-3 4 Protein accession number on National Centre for Biotechnology Information (NCBI) 5 The average of protein density of biofilm cells compared to planktonic cells on gel images determined by PD-Quest software 6 Mean ratio of biofilm protein quantity against planktonic cell protein quantity; calculation based on 3-5 replicate gels (p<0.05 using Student‟s t-test) 7 Percentage of peptide sequence coverage 8 Predicted molecular weight (MW) and isoelectric point (pI) of protein determined from Oralgen Databases 9 Observed MW and pI of protein determined from 2DE gels + Proteins that were only resolved in biofilm cells - Proteins that were only resolved in planktonic cells

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Table 4-2. Stress proteins that were regulated in both biofilm and planktonic cells of F. nucleatum Function Protein identification Gene ID on Protein NCBI Density Mean % seq Pred. Obs. Oralgen1 no2 Accession (x103)4 ratio5 MS6 MW/pI7 MW/pI8 no3

Heat shock protein 60 kDa chaperonin (GroEL) 1329 10a 19714198 0.9:0.3 3.2 # 57.5/5.0 57/4.7 10b 3.9:0.8 4.9 # 57/4.7 10c 3.8 + # 57/4.9

70 kDa chaperone protein 1258 11a 19713544 0.7:3.2 0.2 # 65.3/5.0 65/4.7 (DnaK) 11b 0.2:2.5 0.1 # 65/4.7

Protein modification Peptidyl-prolyl cis-trans 0293 12 167006939 0.8 + 55 26.7/5.0 27/4.6 isomerase

DNA repair Recombination protein RecA 1811 13 167006826 3.4 + 59 35.2/5.6 35/5.5

Oxidoreductase Alkyl hydroperoxide 2288 14 166243452 0.3:2.8 0.1 51 21.1/5.0 22/5.0 activity reductase C22

Protein synthesis Elongation factor Ts 1043 15 60416365 0.2:2.0 0.1 # 33.0/5.0 35/5.1 0.7:2.8 0.1 # 35/5.3

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All proteins were identified using MALDI MS 1 The protein identity on Oralgen Databases (http://www.oralgen.lanl.gov/) 2 Protein number as shown in Figure 4-3 3 Protein accession number on NCBI 4 The average of protein spot density of biofilm cells compared to planktonic cells on gel images determined by PD-Quest software 5 Mean ratio of biofilm protein quantity against planktonic cell protein quantity; calculated based on 3-5 replicate gels (p<0.05 using Student‟s t- test) 6 Percentage of peptide sequence coverage calculated from MS analysis found to match the identified protein 7 Predicted molecular weight (MW) and isoelectric point (pI) of protein determined from Oralgen Databases 8 Observed MW and pI of protein determined from 2DE gels + Spots that were only resolved in biofilm cells - Spots that were only resolved in planktonic cells # Previously identified and published (Zilm et al., 2007)

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4.5 Discussion 4.5.1 Cytoplasmic protein profiles

2DE is recognized as one of the most effective separation methodologies for global analysis of proteins (Cordwell, 2006). The use of a proteomic approach on the whole cell or cytoplasmic protein expression of F. nucleatum has been reported but these studies focused on those proteins with pI within the 4-8 range (Al-Haroni et al.,

2008; Zilm et al., 2007). To date, only one study has explored the expression of proteins with a pI between 7 and 10 on F. nucleatum cells (Zilm et al., 2010). The reason for the lower than expected number of proteins between pI 7 and 10 reflects the limitations of gel-based separation of basic hydrophobic proteins. Significant optimization of 2DE protocols was essential as in silico representation of F. nucleatum subspecies nucleatum proteome 2-D map demonstrated that approximately half of the cell proteins have a pI

>7 (Zilm et al., 2007).

In this study, the protocols used for the separation of proteins within pI 4-7 yielded good protein resolution (Figure 4-1). However, very few proteins within pI 7-10 were resolved on gels using the same protocol (Appendix 7.4I). The observation of a small number of proteins between pI 7 and 10 appearing on the gels was likely due to the comparatively high abundance of soluble proteins in the pI 4-7 range in the applied sample. This phenomenon was observed as excessive staining occurred in the pI 7 region of the gel (Appendix 7.4II). Therefore, prior to 2DE, proteins were fractionated in liquid-based IEF (MicroRotofor Liquid-IEF Cell) to enrich for proteins pI >7. The unequal distribution of protein abundance across the pI 3-10 has been reported and the overcrowding of acidic proteins (pI 4-7) was shown for the parasitic protozoa Leishenia amazonensis (Brobey & Soong, 2007).

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Horizontal streaking is commonly observed in proteins with pI 7-10 using 2DE and optimization of IEF is crucial for good protein separation. The appearance of horizontal streaking is mainly due to the intra- and inter-molecular disulphide bridges resulting from the oxidation of the sulphydryl group on the amino acid cysteine

(Pennington et al., 2004). In the past, horizontal streaking was usually caused by the migration of the reducing agent DTT, as the sulphydryl group of DTT has a pKa close to 9, out of the pH 9-10 region of the IPG strip during IEF. This migration leaves the basic-range proteins (pI >7) without the protection of the reducing agent and thus the proteins may form mixed disulphides that give rise to horizontal streaking (Luche et al.,

2004). In the present study, DTT was replaced by TBP (in the sample solubilisation step) and DeStreak Reagent (in the IPG strip rehydration step). These reducing agents are stable during IEF and have been shown to give a better protein resolution (Luche et al., 2004). However, horizontal streaking was still observed. The use of a wick containing the DesStreak Reagent on the cathode end of the IPG strip was found to improve protein separation further (Appendix 7.4V). It was suggested that water in the cathode wick may migrate into the IPG strip through electro-osmosis during IEF leading to hydrolysis of the polyacrylamide and cause a change in the pH gradient of the

IPG strip close to the pI 10 region (Kask et al., 2009). The use of a DeStreak Reagent containing wick may not only prevent the shift of water from wick into the IPG strip but may also act as a reservoir of reducing agent and helping separation of proteins between pI 7 and 10.

4.5.2 Biofilm growth and protein expression

Protein synthesis is an energetically expensive process and so it is expected that bacterial cells regulate protein expression under different environmental conditions.

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Under the imposed alkaline stress condition, F. nucleatum formed mono-culture biofilms in vitro and this change in phenotype was associated with the modulation of energy producing and stress proteins.

4.5.2.1 Energy producing pathway proteins

Oral plaque bacteria constantly experience ecological changes including nutrient availability and environmental pH (Marsh, 1994). In this study, the neutral (pH 7.4) and alkaline (pH 8.2) growth pH used mimic the periodontally healthy and diseased environments in the oral cavity (Bickel & Cimasoni, 1985; Bickel et al., 1985a; Eggert et al., 1991). The proteomic findings demonstrated a change in cell metabolism associated with a change in growth pH and therefore potentially in disease.

As shown in Table 4-1, two NAD-GDH proteins were up-regulated while four were down-regulated in the alkaline environment. Spots 2a and 2b (Figure 4-3) appear to match the theoretical pI and MW but the pI and MW of spots 2c-f varied considerably from the predicted values. Spots 2c-f (Figure 4-3) may arise from premature termination or proteolytic cleavage that resulted in smaller fragments with a potential loss of charge that changed the protein migration. Due to the loss of more than

50% of the expected molecular weight it is likely that NAD-GDH fragment will not be biologically functional as an enzyme and resulted in the discrepancy in expression levels detected.

Previous studies have shown that F. nucleatum metabolises free amino acids and small peptides and is able to grow in the absence of glucose (Rogers et al., 1991a).

Glutamate, one of the energy yielding amino acids, is metabolised to ammonia, acetate

116 and butanoate through the 2-oxoglutarate pathway (Gharbia & Shah, 1991; Takahashi &

Sato, 2002). NAD-GDH catalyses the first step in the glutamate catabolism by converting glutamate to α-ketoglutarate, an intermediate that is later fed into the butanoate pathway. This enzyme is allosterically inhibited by ATP and activated by

ADP (Elliot & Elliot, 1997). The up-regulation of NAD-GDH indicated an increased

ATP requirement in biofilm cells. It was thought that the glutamate metabolism facilitates glucose uptake and metabolism (Robrish et al., 1987). As shown by the increased expression of LDH in biofilm cells. The LDH catalyses the reduction of pyruvate to lactate and results in the production NAD+. This step would allow anaerobic glycolysis to be completed and provide increased ATP for biofilm cells.

The increased activity of the 2-oxoglutarate pathway in biofilm cells may also result from the up-regulation of glutaconyl-CoA decarboxylase subunit A, a key enzyme of the 2-oxoglutarate pathway. This enzyme converts glutaconyl-CoA to crotonyl-CoA which can be then converted into either butanoyl-CoA or acetoacetyl-

CoA that leads to the production of butanoate and acetate, respectively (Bendrat &

Buckel, 1993). Accordingly, this enzyme may act as a control point in the 2- oxoglutarate pathway.

In biofilm cells, the enzyme butanoate: acetoacetate CoA transferase α subunit was down-regulated. This enzyme converts butanoyl-CoA to butanoate. The reduced level of this enzyme may lead to the diversion of the key intermediate crotonyl-CoA towards acetate production yielding more ATP than butanoate synthesis (Zilm et al.,

2002).

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It was noted that even though the conversion of glutamate to α-ketoglutarate was enhanced in the biofilm cells, glutamate formiminotransferase, an enzyme involved in the conversion of histidine to glutamate, was down-regulated. This could be explained by the limited availability of free histidine in the saliva and GCF of periodontal patients

(Syrjänen et al., 1990). Glutamate is the most abundant free amino acid found in human

GCF (Syrjänen et al., 1990). The planktonic culture used in the current study was grown in a pH environment thought to be associated with a healthy gingival sulcus. As the environmental pH changes, F. nucleatum adapts by adjusting its metabolic activity.

4.5.2.2 Stress proteins

The development of F. nucleatum biofilms is stimulated by growth at pH 8.2. It is now recognized that the formation of biofilms is one of the responses of microorganisms to environmental stress (Fux et al., 2005; Stoodley et al., 2003).

Therefore, it is not surprising that a set of stress proteins were altered at elevated growth pH.

At pH 8.2, biofilm cells increased the expression of recombination protein A

(RecA). Acidic and alkaline pH environments have been shown to denature DNA by depurination (Lindahl & Nyberg, 1972; Studier, 1965) and the loss of purines results in the separation of the double-stranded DNA (Lindahl & Nyberg, 1972). The un-repaired

DNA damage is detrimental to bacterial cells as it prevents DNA replication and cell growth. The repair of the DNA gap relies on the recombinational DNA repair proteins including RecA (Webb et al., 1997). RecA binds to DNA at the replication forks in stalled replication and forms a filament in the exposed DNA gap. RecA pairs the ssDNA with homologous dsDNA from the opposite site of the replication forks and

118 promotes DNA strand exchange between them. The gap containing ssDNA is then converted into duplex DNA (Webb et al., 1997).

The regulation of RecA under acidic environments has been reported. Quivey and colleagues (1995) showed that RecA was required for the survival of S. mutans cells grown at neutral pH when subjected to a rapid drop in pH and a RecA-deficient strain was more susceptible to acid killing (Quivey et al., 1995). The proteomic finding of Len and colleagues (2004) supported the role of RecA under an acidic environment

(pH 5) with an observed increased RecA expression. The up-regulation of RecA at pH

8.2 in the present study and the report of the RecA regulation under acidic conditions showed that RecA is a stress-related protein that is employed to protect the DNA integrity in suboptimal pH environments.

Under alkaline conditions, the expression of two isoforms of elongation factor,

EF-Ts was down-regulated. During protein translation, three elongation factors, EF-Tu,

EF-Ts and EF-G, escort aminoacyl tRNAs to the ribosome and translocate the ribosome along the mRNA (Len et al., 2004b). In S. mutans, EF-Ts and EF-G were up-regulated in cells grown at pH 5.0 and the authors speculated that the increased expression of these chaperone-like proteins towards unfolded and denatured proteins may help to protect the cells under acidic stress conditions (Len et al., 2004b). In the previous chapter, EF-Ts and EF-Tu were found to be up-regulated in F. nucleatum cells grown at pH 7.4 (Table 3-3) compared to biofilm cells. The down-regulation of EF-Tu and EF-Ts may be associated with the decrease in cell numbers seen at pH 8.2 (Figure 2-5).

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Molecular chaperones, including heat shock proteins (HSPs), are a group of proteins that play important roles in cellular processes including protein folding, protein translocation, and assembly and disassembly of protein complexes (Feder & Hofmann,

1999; Lemos et al., 2007; Parsell & Lindquist, 1993). Growing as biofilms, the expression of Hsp70 chaperone protein (E.coli homolog DnaK), Hsp60 chaperonin

(E.coli homolog GroEL) and peptidyl-prolyl cis-trans isomerase were altered.

In biofilm cells, two isoforms of DnaK were down-regulated while three isoforms of GroEL were up-regulated. These ATP-dependent HSPs have been found to cooperate in vitro. DnaK is a chaperone protein that binds to the hydrophobic regions of nascent proteins and either maintains an unfolded polypeptide in which it can then be transferred to chaperonin GroEL for folding to its native state or assists folding of polypeptides (Hartl, 1996; Young et al., 2004). Previous work in our laboratory showed that DnaK was down-regulated five-fold at pH 7.8 when compared to pH 7.4 (Zilm et al., 2007). The current study demonstrated up to a 12-fold down-regulation of DnaK in cells grown at pH 8.2. The down-regulation of DnaK in both studies indicated a correlation between alkaline pH and the expression of DnaK.

Chaperonin GroEL is characterised by its two-stacked-rings structure and a cavity in which an unfolded peptide binds. The cavity is sealed with GroES, a 10 kDa co-chaperone (Masters et al., 2009). GroEL encapsulates a substrate polypeptide in the cavity which allows the completion of folding and prevents non-productive interactions with other unfolded polypeptides. This chaperonin functions at later stages of protein folding and is required for post-translational folding of unfolded or partially folded peptides (Masters et al., 2009) and protein degradation (Kandror et al., 1994; Kandror et

120 al., 1999). Interestingly, the same study by Zilm et al. (2007) reported a slight decrease in GroEL expression at pH 7.8 as opposed to the current investigation. As GroEL is involved in the later stages of protein folding, it is thought that an increasing intracellular pH may lead to aggregation of proteins or formation of misfolded proteins in cells which is required with the increase in GroEL expression.

Peptidyl-prolyl cis-trans isomerase (PPIase) is another HSP that was up- regulated at pH 8.2. This protein was only detected in biofilm cells at low abundance

(Table 4-1). PPIase assists the isomerization of trans configuration to cis configuration of prolyl bonds (Xamino acid-proline bonds) in peptides (Parsell & Lindquist, 1993). Three known unrelated-families of PPIase have been identified in the cell periplasm (Miot &

Betton, 2004) and evidence shows that PPIase such as SurA, PpiA, PpiD and FkpA are involved in outer membrane protein folding (Bos et al., 2007). The role of PPIase is not well characterised. But, as it is suggested to play a role in the folding of outer membrane proteins, the increased expression of PPIase in the present study may be associated with the increased expression of FomA as described in Chapter 3.

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4.6 Summary

The present study investigated changes in protein expression that occurred during F. nucleatum biofilm development using proteomic techniques. When growing as biofilms, F. nucleatum regulated certain cytoplasmic proteins in response to the changing growth environment. Two major groups of proteins that were regulated by the alkaline pH were those involved in major energy producing pathways and stress proteins.

At pH 8.2, the cell yield of F. nucleatum reduced (Figure 2.4); however, regulation of metabolic enzymes demonstrated a potentially increased energy requirement. Extra energy generated is required in order to maintain cellular functions in an increasing intracellular pH. For instance, protein folding by stress proteins GroEL and DnaK activities are ATP-dependent and thus require energy.

Despite the success of the proteomic techniques used to identify significantly regulated proteins, validation by alternative methods is desirable and has now become a component of standard proteomic protocols.

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5 Validation of key regulated proteins identified by proteomic analysis in biofilm cells

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5.1 Introduction

As discussed in Section 1.4.2, the use of high quality data is essential in proteomic studies. To give meaningful results in the present study, experiments were carefully designed following the guidelines published for proteomic research (Carr et al., 2004; Martens & Hermjakob, 2007; Wilkins et al., 2006). It is highly recommended that proteins that are differentially expressed and identified by proteomic techniques are validated using alternate methods in order to confirm the findings (Wilkins et al., 2006).

The expression of cell envelope and cytoplasmic proteins was examined in

Chapters 3 and 4. The proteomic study showed that stress proteins, transport proteins and metabolic proteins were among the functional groups of proteins that were significantly regulated by F. nucleatum cells when growing in a biofilm at alkaline pH.

The analysis revealed an altered expression of two heat shock proteins (HSPs),

GroEL and DnaK (Table 4-2). The differential expression of these proteins by biofilm cells was of interest as the expression of bacterial HSPs has been reported to be implicated in the development of human periodontal diseases (Ando et al., 1995). A key protein in DNA repair, RecA, was also found to be up-regulated in biofilm cells.

The regulation of these stress proteins by the bacterial cells reflected the potential damage imposed by growth in alkaline pH.

When growing as mono-culture biofilms, F. nucleatum cells also differentially expressed transport proteins and metabolic enzymes. The modulated expression of substrate-specific transporters including tripartite ATP-independent transporter (TRAP-

T), tricarboxylate tripartite transporter (TTT) and di-peptide binding proteins indicated a

124 switch in metabolic patterns (Tables 3-3 and 4-1). The intracellular regulation of enzymes such as lactate dehydrogenase (LDH), NAD-specific glutamate dehydrogenase

(NAD-GDH), butanoate: acetoacetate coenzyme A transferase α-subunit (BAT) and glutamate formiminotransferase (Table 4-1) also signifying a change in metabolic activities under the imposed stress conditions.

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5.2 Aims

The initial aim of the experiments described in this chapter was to validate the expression of stress proteins that were shown to be differentially regulated using proteomic methods. The expression of RecA, GroEL and DnaK was examined using the

Western blotting technique. The transcriptional regulation of these proteins was further investigated using quantitative real time polymerase chain reaction (qRT-PCR).

The second aim was to determine differences between the metabolic activities of planktonic and biofilm cells that relate to the altered expression of LDH, NAD-GDH and BAT. In particular, intracellular polyglucose (IP) and fermentation end products of planktonic and biofilm cultures were determined to establish the effects of regulation of key enzymes on cellular metabolism.

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5.3 Validation studies of stress proteins

In order to confirm the changes in the expression of stress proteins as suggested by the proteomic results, Western blotting and quantitative real qRT-PCR were employed to study their regulation in cells.

5.3.1 Materials and Methods 5.3.1.1 Western blotting analysis of GroEL, DnaK and RecA

5.3.1.1.1 Bacterial growth and cell lysate preparation

Planktonic and biofilm cells were cultured as outlined in Section 3.3.1.1. Cells harvested from five consecutive days were pooled and cell lysates were prepared using the same protocols described in Section 3.3.1.2. Protein quantitation was performed using Coomassie Plus Protein Assay Kit (Thermo Scientific).

5.3.1.1.2 SDS-PAGE and Western blotting analysis

In Western blotting assays, a loading control is recommended when comparing expression levels of a protein in different samples. The commonly used loading controls include β-actin and glyceraldehyde 3-phosphate dehydrogenase (GAPDH) (Aldridge et al., 2008). Of the recommended loading controls, only GAPDH is expressed in bacterial cells. One may question the validity of using a glycolytic enzyme as it was demonstrated that glucose metabolism was probably influenced by the environmental pH (Section 4.5.2.1). Therefore, a second protein assay (Coomassie Plus Protein Assay

Kit) was performed prior to SDS-PAGE to ensure equivalent protein load for each sample.

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Cell lysates of planktonic and biofilm cultures, equivalent to 15 µg of cellular protein (Appendix 7.7), were diluted 1:1 in 2x SDS buffer (pH 6.8) and separated on

12% polyacrylamide gels using a Hoefer SE 250 Mighty Small II electrophoresis unit

(GE Healthcare Life Sciences). Electrophoresis was carried out at 100 volts for 1.5 hours. The proteins were then electro-transferred to an Immuno-Blot PVDF membrane

(Bio-Rad Laboratories) using Mini Trans-Blot Cell (Bio-Rad Laboratories) in transfer buffer containing 25 mM Tris, 192 mM glycine, 10% (v/v) methanol (pH 8.3) at 250 mA for 2 hours. The membrane was blocked with 5% (w/v) of ECL Blocking Agent

(GE Healthcare Life Sciences) in 0.1% (v/v) Tween 20 - TBS (20 mM Tris/HCl pH 7.6,

137 mM NaCl) (TBS-T) buffer for 1 hour at room temperature. The washed membrane was then treated with mouse anti-E. coli RecA monoclonal antibody (mAb) diluted at

1:1000 in TBS-T containing 2% (w/v) BSA (BSA-TBS-T) for 24 hours. The membrane was washed and probed with anti-mouse alkaline phosphate conjugate secondary antibody (GE Healthcare Life Sciences) (1 mAB: 5000 BSA-TBS-T) for 1 hour at room temperature. The target protein was detected using ECF substrate (GE Healthcare Life

Sciences) and scanned using a Typhoon Scanner (GE Healthcare Life Sciences), and the expression of the protein was analysed using ImageQuant TL (GE Healthcare Life

Sciences). Using the same protocol, Western blotting was repeated to detect GroEL and

DnaK using the primary antibodies listed in Table 5-1. E. coli DH5α was used as the positive control.

5.3.1.2 Quantitative real time PCR analysis of groEL, dnaK and recA

One of the frequently used methods in comparative gene expression studies is qRT-PCR (Schwartz et al., 2007; Wong & Medrano, 2005). In contrast to the one step qRT-PCR reaction where the entire reaction from cDNA synthesis to PCR amplification

128 is performed in a single tube, two-step reaction qRT-PCR, which separates the cDNA synthesis and PCR amplification, was employed in this study. The reaction efficiency of reverse transcription is highly variable; however, quantitation of cDNA prior to PCR amplification standardizes the amount of cDNA template used in the two-step qRT-PCR

(Wong & Medrano, 2005).

5.3.1.2.1 Primer design

Gene sequences were retrieved from the Oralgen Databases

(http://www.oralgen.lanl.gov) and primers were designed using the web-based tool

Primer 3 Plus (http://www.bioinformatics.nl/cgi-bin/primer3plus/primer3plus.cgi). The designed primers were between 18 and 22 base pairs in length with a melting temperature (Tm) between 52 and 58ºC. In order to avoid primer secondary structures that can lead to either poor or no yield of the PCR product, only primers without self- and cross- dimers were used. The designed primers were then blasted against the bacterial gene and genome sequence using NetPrimer tool

(http://www.premierbiosoft.com/netprimer/index.html) to make sure the primers did not bind unspecifically. Designed reference gene 16S rRNA primers were included in this study (Table 5-2).

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5.3.1.2.2 RNA isolation and reverse transcription

Biofilm and planktonic cultures were harvested over five consecutive days as described in Section 3.3.1.1 and were processed immediately following each sampling.

Total RNA was isolated from 2 mL of bacterial sample using the RiboPure-Bacteria Kit

(Ambion, Austin, TX USA) following manufacturer‟s instructions. Any genomic DNA was eliminated using DNA-freeTM reagents included with the kit. The RNA content in each sample was determined based on the ratio of absorbance at 260 and 280 nm

(A260:A280) using a NanoDrop spectrophotometer (Thermo Scientific). Only samples that had an A260:A280 between 1.8 and 2.1 (indicating intact mRNA) were used for the subsequent analyses. Bacterial cDNA templates were generated from 1 μg RNA through reverse transcription using a SuperScript® ViloTM cDNA Synthesis Kit (Invitrogen).

qRT-PCR was performed using a Corbett Rotor-Gene RG-3000 Thermal Cycler

(Qiagen, Hilden, Germany) using a standard curve method (Wong & Medrano, 2005).

Each PCR run consisted of a standard curve and five samples for each growth pH. All standards and samples were assessed in triplicate. The total reaction volume of 20 µL consisted of 2 µL of each forward and reverse primer (Table 5-2), 10 µL of Platinum

SYBR Green qPCR SuperMix-UDG (Taq DNA polymerase, SYBR Green I dye, Tris-

HCl, KCl, 6 mM MgCl2, 400 µM dGTP, 400 µM dCTP, 800 µM dUT, UGG and stabilizers) (Invitrogen), 5 µL dH2O and 1 µL of diluted cDNA. The conditions for amplification cycles were as follows: 40 cycles consisting of denaturation at 95˚C for 15 seconds, annealing at 60˚C for 60 seconds, and extension at 72˚C for 30 seconds.

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Table 5-1. List of commercially available antibodies used

Antibody Clone Source of Antibody Dilution* number Mouse Anti-human Hsp60 LK-2 Stressgen 1:2000 Monoclonal Antibody (SPA-807) Biotechnologies (British Columbia, Canada) Mouse anti- E. coli DnaK 8E2/2 MBL International (Des 1:1000 monoclonal antibody (SR-880E) Plaines, IL, USA)

Mouse anti-E. coli RecA ARM321 MBL International (Des 1:1000 monoclonal antibody (MD-02+3) Plaines, IL, USA)

*Antibody was diluted accordingly using 2% (w/v) BSA-TBS-T buffer

Table 5-2. Designed primers used for qRT-PCR

+ Gene Primer sequence Primer GC Tm Product length (bp) %* (ºC)^ size (bp) recA F (5‟-3‟) TGGCAGCGATTACAAAAGG 19 47 57 269 R(3‟-5‟) CATAAACTGGGTCAAGAGC 21 43 55 AT groEL F (5‟-3‟) ATTGACCCAGCAAAAGTTAC 20 40 52 137 R(3‟-5‟) GGCATCATTCCACCAGCA 18 56 58 dnaK F (5‟-3‟) GTATCCCTGCTGCTCCAA 18 56 54 184 R(3‟-5‟) GTGCTTCTGCTTCCTTAGTC 20 50 52 16S F (5‟-3‟) GCTCGTGTCGTGAGATGTT 19 53 53 153 rRNA R(3‟-5‟) CCAGCGTATAAGGGGCA 17 59 55 +F: forward primer; R: reverse primer ^ Tm: melting temperature, the temperature at which one half of the DNA duplex dissociates to become single stranded and indicates the duplex stability. Primers with melting temperatures in the range of 52-58oC generally produce the best results (Burpo, 2001). * GC% gives an indication of the Tm with an ideal GC% is between 40 and 60%

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5.3.2 Results

5.3.2.1 Western blotting analysis of GroEL, DnaK and RecA

Using the anti-DnaK monoclonal antibody, no reaction between F. nucleatum

DnaK and antibody was observed (Figure 5-1). F. nucleatum samples did, however, react with the anti-E. coli RecA (Figure 5-2) and anti-human Hsp60 (Figure 5-3) antibodies. The expression of RecA remained stable between planktonic and biofilm cells (Table 5-3). There was a 1.4 fold up-regulation of GroEL in biofilm cells compared with their planktonic counterparts (Table 5-4).

Figure 5-1. Western blotting analysis of DnaK Lanes a to c: planktonic cell lysates; lanes d to f: biofilm cell lysates. Cell lysate of E. coli was used as positive control.

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Figure 5-2. Western blotting analysis of RecA Lanes a to c: planktonic cell lysates; lanes d to f: biofilm cell lysates. Theoretical MW of F. nucleatum RecA is 40.6 kDa and 38 kDa for E. coli RecA.

Figure 5-3. Western blotting analysis of GroEL Lanes a to c: planktonic cell lysates; lanes d to f: biofilm cell lysates.

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Table 5-3. Expression of RecA in planktonic and biofilm cell lysates determined by Western blotting

Cell Average protein band intensity (pixels) Planktonic 193,036 ± 10,690

Biofilm 201,250 ± 1,910

Ratio 1.04 (biofilm/planktonic cell lysate)

The expression of RecA in lysates from planktonic and biofilm cell lysates was analysed and compared using ImageQuant TL software.

Table 5-4. Expression of GroEL in planktonic and biofilm cell lysates determined by Western blotting

Cell Average protein band intensity (pixels) Planktonic 7,445 ± 2,533

Biofilm 10,653 ± 854

Ratio 1.43 (biofilm/planktonic cell lysate)

The expression of Hsp 60 protein in planktonic and biofilm cell lysates was analysed and compared using ImageQuant TL software.

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5.3.2.2 Quantitative real-time PCR analysis of groEL, dnaK and recA

The relative transcripts of reference gene 16S rRNA and target genes, dnaK, groEL and recA, were calculated and ratio changes are shown in Table 5-5. The abundance of 16S rRNA was not constant in samples. In biofilm cell samples, the 16S rRNA transcripts were approximately 40% higher compared to those from planktonic cells.

After normalization using 16S rRNA gene transcripts, transcripts of all three genes, dnaK, groEL and recA were found to be down-regulated in the biofilm cells when compared to planktonic cells (Figure 5-4). In cells grown at pH 7.4, the expression of dnaK was elevated significantly (p =0.0007, Student‟s t-test), by 2.9-fold.

Similarly, the expression of the groEL gene was down-regulated significantly (p =

0.001, Student‟s t-test), by three-fold, in cells growing in biofilms. The expression of recA was found to be modestly down-regulated, by 1.9-fold, in the biofilm cells (p =

0.0001, Student‟s t-test).

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Table 5-5. Transcript levels of housekeeping gene 16S rRNA and target genes dnaK, groEL and recA in planktonic and biofilm cells

Gene Planktonic cells# Biofilm cells# Ratio* p- Avg Ct Calc. Conc Avg Ct Calc. Conc value^ (ng/rxn) (ng/rxn) dnaK 13.3±0.9 1.37±0.78 14.5±1.2 0.67±0.52 1.0:2.1 0.02 groEL 16.7±0.8 5.14±2.55 17.9±0.7 2.38±1.10 1.0:2.2 0.00 recA 21.8±0.4 1.44±0.47 22.2±0.4 1.08±0.30 1.0:1.3 0.03

16S rRNA 15.0±0.7 0.05±0.02 14.4±0.5 0.07±0.02 1.4:1.0 0.02

Ct: cycle threshold. #Based on samples collected from five consecutive days. Results expressed as means. cDNA concentration based on three replicates of sample from each day. * Ratio based on the calculated concentration of transcript of interest in biofilm cells against planktonic cells ^p value was determined using Student‟s t-test based on the calculated concentration of cDNA in both sample sets. p<0.05 represents significant changes in transcript levels

3.5 2.9 3 3

2.5 1.9 2

1.5 1 1 1

Expression ratio 1

0.5

0 dnaK groEL recA Biofilm cells Planktonic cells

Figure 5-4. Differential expression of target genes in biofilm and planktonic cells following normalization

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5.4 Confirmatory studies of metabolic activity

In order to confirm the changes to the metabolic pathways indicated by the altered expression of key enzymes, a number of enzyme assays were developed to measure their activity in cells. In addition, end-product analysis and IP determination was performed.

5.4.1 Materials and Methods

5.4.1.1 Enzyme assays

5.4.1.1.1 Sample preparation

Planktonic and biofilm cells were cultured and harvested as stated previously in

Section 3.3.1.1. Bacterial cells were collected by centrifugation at 8,000 x g at 4ºC for

15 minutes. The resulting supernatant was filtered using a 0.2 µm filter membrane

(Milipore) and used for end-product analysis (Section 5.4.1.2). Following centrifugation, cell pellets were washed in 50 mM Tris/HCl (pH 7.5) and lysed by sonication on ice (Section 3.3.1.2). Following sonication, intact cells were removed by centrifugation at 2,500 x g at 4ºC for 15 minutes and their protein concentration was determined using a Coomassie Plus Protein Assay kit (Thermo Scientific). The crude cell lysate was used to assay for BAT activity as the reaction catalyzed by this enzyme is not affected by any background reaction as observed in NAD-GDH and LDH assays

(Appendix 7.9).

The cell lysates used in the NAD-GDH and D-LDH assays required additional purification due to the presence of cofactors (NAD+ and NADH), and potential substrates (pyruvate, glutamate and α-ketoglutarate) that resulted in a drift in the absorbance prior to the addition of substrates (Appendices 7.10 and 7.11). Proteins in

137 the samples were precipitated by diluting 5 mL of cell lysate in 10 mL of 80% ammonium sulphate (Ajax Chemicals, Cheltenham, Vic, Australia) for 2 hours at 4°C with constant stirring. The precipitated proteins were resuspended in 50 mM Tris/HCl buffer (pH 7.5) and dialysed against MiliQ water at 4ºC overnight prior to use.

5.4.1.1.2 Butanoate: acetoacetate coenzyme A transferase assay

BAT may be considered as a virulence factor of F. nucleatum (Karpathy et al.,

2007) as butonoate has been shown to inhibit fibroblast proliferation and slows healing by host cells during periodontal disease (Bartold et al., 1991). The enzyme catalyses the reaction below,

Butanoate + acetyl-CoA ↔ butanoyl-CoA + acetoacetate

Two isoforms of BAT α subunits were found to be down-regulated in biofilm cells (Table 4-1). The BAT activity in lysates derived from planktonic and biofilm cells was determined using a method based on that employed by Clark and co-workers

(1989) with slight modifications. The reaction mixture (1 mL) contained final concentrations of 0.1 mM acetyl-CoA, 110 mM Tris/HCl (pH 7.5), 5% (v/v) glycerol,

20 mM MgCl2, and 50 μL of cell lysate (Clark et al., 1989). The reaction was initiated by the addition of 30 µL of 50% (v/v) butanoic acid to the reaction mixture. The assay monitored changes in the absorbance in the sample at 310 nm, resulting from the hydrolysis of acetyl-CoA and the transfer of CoA to butanoate.

138

The change in absorbance at 310 nm over 2 minutes was recorded and BAT activity calculated using the formula,

ilution)

1.0 = Total volume (mL) of the assay 8.0 = Extinction coefficient of acetyl-CoA at 310 nm (in mM-1 cm-1) 0.03 = Volume of enzyme solution (mL)

5.4.1.1.3 Lactate dehydrogenase assay

Previous studies in our laboratory showed that lactate was a common end- product of glucose metabolism by F. nucleatum (Rogers et al., 1991; Zilm et al., 2002;

Zilm et al., 2003). However, the enzyme responsible for the synthesis of lactate, LDH, was only detected in biofilm cells using proteomic methodology (Table 4-1). The enzyme catalyses the reaction below,

Pyruvate + β-NADH  D-lactate + β-NAD+

The enzyme assay protocol used was obtained from Sigma Aldrich

(http://www.sigmaaldrich.com/etc/medialib/docs/Sigma/Enzyme_Assay/l3888enz.Par.0

001.File.tmp/l3888enz.pdf). The amount of LDH was determined by measuring the oxidation of NADH to NAD+ spectrophotometrically, which resulted in a decrease in

UV absorbance at 340 nm. The rate of reaction was dependent on amount of LDH present in the sample.

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The final concentrations of components in the reaction mixture were 94 mM potassium phosphate, 0.2 mM β-nicotinamide adenine dinucleotide (β-NADH) (Sigma

Aldrich), 0.74 mM pyruvate (Sigma Aldrich) and cell lysate. Bovine serum albumin

(BSA) (Sigma Aldirch) was used as the negative control and purified D-lactic dehydrogenase from Lactobacillus species (Sigma Aldrich) was used to construct a standard curve.

5.4.1.1.4 NAD-specific glutamate dehydrogenase assay

Six proteins were significantly regulated and identified as NAD-GDH; four of them were down-regulated while two were up-regulated in biofilm cells (Table 4-1).

NAD-GDH catalyses the oxidative deamination of glutamate to α-ketoglutarate, an intermediate of the 2-oxoglutarate pathway.

+ + + L-glutamate + NAD + H2O  α-ketoglutarate + NADH + NH4 + H

The method used was based on the protocol proposed by Irwin and colleagues

(2001) with slight modifications. The amount of enzyme in the samples was determined by measuring the rate of conversion of NAD+ to NADH that results in an increase in absorbance at 340nm. Reaction mixtures were prepared containing final concentrations of 1 mM NAD+, 4 mM L-glutamate, 50 mM sodium pyrophosphate buffer (pH 8.8) and cell lysate (Irwin et al., 2001). GDH from bovine liver (Sigma Aldrich) was used to construct a standard curve.

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5.4.1.2 End-product and glucose estimation using high performance liquid chromatography

Glucose and acidic end-product analysis was performed by high performance liquid chromatography (HPLC), using a method based on that of Guerrant et al. (1982).

Cell free culture filtrates were prepared as in Section 5.4.1.1 and 20 µl aliquots were injected onto an Aminex HPX-87H cation exchange column (Bio-Rad Laboratories).

The components were separated isocratically using a mobile phase of 7 mM H2SO4 at a flow rate of 0.5 mL/minute and identified using a refraction index detector. All instruments used in this analysis are listed in Appendix 7.8. A standard reference solution containing 5 mM glucose, 10 mM lactate, 20 mM formate, 20 mM acetate and

20 mM butanoate was used to determine peak retention times and concentrations.

5.4.1.3 Intracellular polyglucose assay

The IP concentrations in planktonic and biofilm cells were determined using the method of Hamilton and colleagues (Hamilton et al., 1979). Cell cultures were harvested as described in Section 5.4.1.1.1 with the addition of the glycolytic inhibitor

NaF at a final concentration of 5 mM. Bacterial pellets were resuspended in water and

50% (v/v) KOH was added to give a final concentration of 0.5% (v/v). Cell suspensions were then boiled for 30 minutes and the protein concentration was determined using a

Coomassie Plus Protein Assay Kit (Thermo Scientific). Planktonic and biofilm cell preparations equivalent to 1.1 g of cellular protein were used for the IP assay. Briefly, cell suspensions were acidified to pH 5.0 using 2 M acetic acid and incubated at 37ºC following the addition of 2 units of amyloglucosidase per milliliter of cell suspension.

Glycogen (Sigma Aldrich) was used as a positive control. After 2 hours incubation, the reaction mixture was adjusted to pH 8.0 using 2 N KOH and the glucose concentration

141 determined using a glucose oxidase assay (Sigma Aldrich) in accordance with manufacturer‟s instructions.

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5.5 Results 5.5.1 Butanoate:acetoacetate coenzyme A transferase assay

Results from two independent experiments showed that there were approximately 30 units of BAT per µg of planktonic cell protein compared to 65 units of BAT per µg of biofilm cell protein (Table 5-6). These results did not confirm those arising from the proteomic analysis that showed two BAT α-subunits in planktonic cells only.

Table 5-6. Butanoate: acetoacetate coA transferase concentration present in biofilm and planktonic cells

Culture BAT Mean BAT (unit/µg P)* (unit /µg P) Experiment 1 Experiment 2 Planktonic 31.5±0.0 27.2±0.0 29.35±0.01

Biofilm 61.6±0.0 68.0±0.0 64.80±0.02

Ratio^ 1.96 2.50 2.23

* BAT unit per µg of cell protein. The results represent the mean of three replicates. ^Ratio of the BAT in biofilm: planktonic cells

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5.5.2 Lactate dehydrogenase assay

A LDH standard curve was constructed using LDH from Lactobacillus species and showed a strong coefficient of determination (R2=0.999) (Appendix 7.10). The incubation time used for the assay of samples was 5 minutes, as a linear decrease in absorbance occured over this period, after which the reaction rate decreased. An initial background reaction was eliminated by partial purification using protein precipitation and dialysis (Appendix 7.10). Only slight changes in A340nm were observed in both biofilm and planktonic lysates over time (Table 5-7). Doubling the protein concentration of cell lysates did not result in a significant change in the rate of the reaction (data not shown).

5.5.3 NAD-specific glutamate dehydrogenase assay

The activity of GDH in planktonic and biofilm cells was measured spectrophotometrically and determined by comparison with a GDH standard curve

(Appendix 7.11). Biofilm cell lysates contained approximately 18 units of GDH per mg of cell protein in comparison with 8 units of the enzyme per mg of planktonic cell protein (Figure 5-5), thus suggesting a two-fold increase in GDH activity in biofilm cells.

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Table 5-7. Lactate dehydrogenase activity in biofilm and planktonic cells

Sample Δ A340nm LDH*

Planktonic cells 0.017±0.005

Biofilm cells 0.007±0.002

* Readings based on the changes in A340nm of three replicates. Slight changes in A340nm occurred over 5 minutes were observed indicating that LDH was only present in a small quantity in both planktonic and biofilm cells.

20 17.73 18 16 14 12 8.87 10 8 6 4 GDH (unit/mgGDH protein) cell 2 0 Planktonic cells Biofilm cells

Figure 5-5. NAD-specific glutamate dehydrogenase activity measured in cells grown at different environmental pH

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5.5.4 Intracellular polyglucose determination and acidic end-product profiles

The results for glucose utilisation, IP concentration and end-products in planktonic and biofilm cultures are summarized in Table 5-8. Glucose utilisation varied with environmental pH. At its optimal growth pH of 7.4, F. nucleatum cells utilised

23.1 mmoles of glucose per gram of cell protein, in contrast to biofilm cells in which the glucose utilisation increased three-fold to 65.9 mmoles glucose per gram of cell protein. A similar three-fold increase in the amount of IP in biofilm cells suggested that glucose uptake and IP synthesis in F. nucleatum were correlated.

F. nucleatum produced a mixture of lactate, formate, acetate and butanoate via the fermentation of glucose and amino acids (Table 5-8). The production of lactate was relatively low for both pH conditions tested. However, at alkaline growth pH the concentration of lactate increased three-fold when compared to that for cells grown at pH 7.4. This result supported the proteomic finding that showed LDH expression was enhanced in biofilm cells.

When comparing the amounts of acetate and lactate produced per gram of cell protein under both growth conditions, a dramatic change in the acetate (two-fold increase) and lactate (three-fold increase) in biofilm cultures was observed (Table 5-8).

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Table 5-8. Glucose consumption and metabolic end-products by planktonic and biofilm cultures

Bacterial Glucose IP2 Acid end-products3 culture Utilisation1

Lactate Formate Acetate Butanoate

Planktonic 23.1 2.39±0.12 5.7 92.4 59.4 63.0

Biofilm 65.9 7.62±0.71 18.3 131.2 115.3 99.6

Ratio 2.9 3.1 3.2 1.4 1.9 1.3

1 Glucose utilisation expressed as mmoles of glucose/g of cell protein 2 Intracellular polyglucose expressed as µg glucose/mg of cell protein 3 Acidic end-products expressed in mmol/g of cell protein

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5.6 Discussion 5.6.1 Expression of key stress proteins

Molecular chaperones including GroEL and DnaK are frequently reported to be regulated by environmental cues in bacterial cells (Goulhen et al., 2003; Lemos et al.,

2007; Skår et al., 2003). Bacterial chaperone proteins GroEL and DnaK are immunodominant antigens in bacterial pathogens such as Neisseria meningitidis, A. actinomycetemcomitans, H. pylori and mycobacteria (Skår et al., 2003). The expression of GroEL was of interest as this protein has been reported to be implicated in human periodontal diseases (Ando et al., 1995). In addition, previous studies showed elevated expression of anti-human Hsp60 antibodies in patients with adult periodontitis (Ando et al., 1995; Tabeta et al., 2000). The proteomic results of the current study suggested an altered expression of two HSPs, the 60 kDa chaperonin GroEL and the 70 kDa chaperone protein DnaK, by bacterial cells when grown under imposed alkaline stress

(Table 4-2).

An attempt to validate the pH-dependent regulation of these proteins was performed using Western blotting and qRT-PCR technique. Due to time constraints and laboratory limitations, commercially available antibodies against GroEL, DnaK and

RecA proteins were used in this study. The expression of HSPs by a variety of strains of

F. nucleatum (ATCC 10953, ATCC 25586, clinical strains Fev1, F1 and F3) were studied by Skår and Bakken (2001). The result in the current study agrees with the finding of Skår and Bakken that F. nucleatum GroEL cross reacts with human Hsp60 antibody. Unfortunately, the DnaK antibody used by these researchers was no longer available and the monoclonal anti-DnaK E. coli antibody (clone 8E2/2) used in our study failed to show reactivity to F. nucleatum DnaK. It is possible that the antibody

148 may target an epitope on E. coli DnaK that is not present in the F. nucleatum protein.

The regulation of dnaK was subsequently investigated using qRT- PCR and the results obtained agreed with the proteomic finding that showed down-regulation of DnaK under alkaline conditions. It was noted, however, that the quantitative change in dnaK mRNA expression was not as great as the change in protein expression (4.6-fold and

13.8-fold decrease in DnaK isoforms by biofilm cells, Table 4-2).

In contrast, PCR results indicated that the transcripts of recA and groEL were down-regulated in the biofilm cells and yet the proteomic analysis found their products to be up-regulated. It should be noted it is not unusual for mRNA levels to differ considerably from the amount of protein expressed (Celis et al., 2000; Pradet-Balade et al., 2001). Using Western blotting, the expression of GroEL was found to be up- regulated in the biofilm cells. The Western blotting and proteomic results indicate that post-transcriptional regulation may have occurred leading to the discrepancy between the qRT-PCR and Western blotting/proteomic results. The production of GroEL by F. nucleatum may induce antibodies that cross-react with human Hsp60, triggering an auto-immune response that exacerbates inflammatory reactions during the progression of periodontal disease.

The results indicate that mRNA levels alone are not reliable indicators of their corresponding protein‟s abundance, mainly due to the control mechanisms that operate in addition to transcription. Gene expression is regulated at several levels and influenced by factors such as the stability of mRNA, translation rates, additional post- translational modifications and rates of protein degradation (Appelbe & Sedgley, 2007;

Mata et al., 2005; Pradet-Balade et al., 2001). However, qRT-PCR is a sensitive method

149 as it is able to produce accurate quantitative data over a dynamic range of seven to eight orders of magnitude (Wong & Medrano, 2005). In contrast, staining solutions used in

2DE offer a dynamic range of two (eg. Coomassie Brilliant Blue R-250 Stain) or three

(eg. Bio-Rad Flamingo Fluorescent Stain) orders of magnitude depending on the stain used.

During the time frame of this research, no study on the gene expression of F. nucleatum ATCC 10953 using qRT-PCR was published. 16S rRNA was chosen as a reference gene because of the many reports of the reliability of this gene in other bacterial species, under different growth conditions (Carrillo-Casas et al., 2008; Savli et al., 2003; Schwartz et al., 2007; Tasara & Stephan, 2007). The results in the current study showed that 16S rRNA was not expressed constantly under different growth pH conditions, supporting the view of Richardson and colleagues that 16S rRNA expression is a dynamic process that adapts to different growth conditions (Richardson et al., 2005). The choice of reference gene in qRT-PCR is challenging, as to date there is no universal housekeeping gene expressed to equivalent levels in both eukaryotic and prokaryotic cells (Tasara & Stephan, 2007). As discussed previously, in the current investigation RecA was found to be regulated in biofilm cells. However, recA gene has been used as a reference gene by others as it showed constant expression levels during the stationary phase and nutrient starvation in Lactobacillus plantarum (Marco &

Kleerebezem, 2008).

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5.6.2 Confirmatory studies of bacterial metabolic activity

In Chapter 4, various metabolic enzymes, including BAT, LDH and NAD-GDH, were found to be regulated by growth pH and their activities were examined.

BAT catalyses the last step in the butanoate pathway, the transfer of coenzyme

A from butanoyl-CoA to produce acetyl-CoA and butanoate. As reported in Section 5.5,

BAT activity was elevated two-fold in biofilm cells compared to those growing in planktonic cells (Table 5-6), a result not consistent with those arising from the proteomic analysis.

As we were unable to obtain butanoyl-CoA, the enzyme assay employed in this study examined the reverse reaction. The finding that biofilm cells had increased activity for this reaction was supported by the end-product analysis of culture supernatants that showed a shift away from butanoate and towards acetate (Table 5-8).

Furthermore, it was noted that the regulation was confined to the α-subunit of the enzyme (Table 4-1). The literature shows that Co-A in bacterial cells are comprised of α and β subunits, and require both subunits to be functional for activity

(Corthésy-Theulaz et al., 1997). In the present study, the expression of the β subunit of

BAT was unaltered. Therefore, up-regulation of the two BAT α-subunit isoforms in isolation may not necessarily result in increased cellular BAT activity.

LDH activity in cells was not detectable by enzyme assay (Table 5-7) and proteomic analysis indicated that this enzyme was produced only by biofilm cells and at low levels (Table 4-1). This was confirmed as lactate was produced at very low rates in culture filtrates as detected by HPLC (Table 5-8) but showed that biofilm cells produced

151 more lactate per gram protein compared to planktonic cells. This result supports the proteomic findings.

Proteomic findings showed that two isoforms of NAD-GDH (Spots 2a and 2b,

Figure 4-3) were up-regulated and four were down-regulated (Spots 2c to 2f, Figure 4-

3) in the biofilm cells. The theoretical pI and molecular weight of this enzyme are 6.1 and 46.6 kDa, respectively. The observed pI and molecular weight of spots 2a and 2b

(Table 4-1) closely matched with these predicted values. However, spots 2c to 2f appeared to have observed pIs between 8 and 9.5 and molecular weights significantly lower, in a range between 23 and 35 kDa. The NAD-GDH assay demonstrated a two- fold increase in the enzyme activity in biofilm cells compared to their planktonic counterparts (Figure 5-5). Therefore, it is likely that proteins identified as GDH (Spots

2c to 2f) identified in the basic pI range (pI 8 to 9.5) may arise from either premature termination of translation or intracellular digestion and may not be functional.

F. nucleatum ferments glutamate, glutamine and histidine predominantly by the

2-oxoglutarate pathway to produce a mixture of acetate, butanoate, ammonia, carbon dioxide and hydrogen. Glucose fermentation occurs by the Embden-Meyerhof-Parnas pathway yielding pyruvate, hydrogen and acetyl-CoA. Pyruvate can be metabolised to lactate via LDH or to acetate and formate by a pyruvate-formate system while acetyl-CoA can be converted to acetate and butanoate (Zilm et al., 2002). The end- product profiles demonstrated increased lactate, formate, acetate and butanoate levels per cell in the biofilms indicating an increased requirement for energy under the imposed stress conditions.

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Growing at an optimal pH of 7.4, F. nucleatum planktonic cultures utilised 23.1 mmol glucose per mg of cell protein and IP content was found to be 2.39 µg of glucose per mg of cell protein. When the growth pH became alkaline, cell numbers decreased and the resultant biofilm cells increased the utilisation of glucose to 65.9 mmol glucose per mg of cell protein and stored three-fold more IP (7.62 µg glucose per mg of cell protein). As discussed in Section 4.5.2.1, glucose uptake is amino acid-dependent as energy generated from the their fermentation is used to transport glucose into cells

(Robrish et al., 1987). The increased glucose uptake in biofilm cells was associated with an increased production of acidic end products; in particular, lactate and acetate (Table

5-8). The production of the more oxidized product acetate yields more ATP per mole than butanoate and lactate (Zilm et al., 2002).

As a summary of the proteomic findings and confirmatory studies, the possible metabolic pathways of planktonic cells are shown in Figure 5-6 while Figure 5-7 explains the possible metabolic patterns of biofilm cells.

At pH 7.4, F. nucleatum grows as planktonic culture. Under such conditions, histidine was deaminated to glutamate by enzymes including glutamate formiminotransferase (EC 2.1.2.5) that later fed into the 2-oxoglutarate pathway to produce NH3, CO2, H2, acetate and butanoate. The up-regulations of butyryl-CoA dehydrogenase, EC 1.3.99.2 and BAT, EC 2.8.3.9, showed that more energy-producing substrates were channelled into butanoate production. Energy derived from the amino acid fermentation can be used to transport glucose into cells. Glucose can be either metabolised to pyruvate or stored as IP. In planktonic cells, pyruvate is then metabolised to lactate via LDH or to acetate and formate by a pyruvate-formate lyase

153 system while acetyl-CoA can be converted to either acetate or butanoate. Interestingly, pyruvate synthase (EC 1.2.7.1) was found to be up-regulated in planktonic cells indicating that cells synthesise IP through the conversion of acetyl-CoA to pyruvate from the energy generated from amino acid metabolism.

At pH 8.2, key metabolic enzyme activities in F. nucleatum cells seemed to increase with one exception. It was noted that the conversion of histidine to glutamate by the enzyme glutamate formiminotransferase (EC 2.1.2.5) was down-regulated.

However, the expression of NAD-GDH (EC 1.4.1.2) was elevated. The 2-oxoglutarate pathway was induced by the up-regulation of another enzyme, glutaconyl-CoA decarboxylase (EC 4.1.1.70) which catalyses the conversion of glutanoyl-CoA to crotonyl-CoA, which can be metabolised ultimately either to butanoate or acetate and this step may be a crucial control point in the butanoate pathway.

In biofilm cells, the level of acetate produced per cell was elevated two-fold compared to planktonic cells while butanoate was only 30% higher than that for planktonic cells (Table 5-8). It is possible that more crotonyl-CoA was channelled into acetate production as more ATP per mole of glutamate can be generated via acetate production (Zilm et al., 2002). In addition, the increased formate (1.6-fold) and lactate

(3-fold) in biofilm cells along with the down-regulation of pyruvate synthase (EC

1.2.7.1) facilitated the metabolism of glucose to acidic end products. In biofilm cells, the increased amount of IP storage per cell was also reflected by increasing the glucose utilisation by these cells compared to the planktonic cells.

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Figure 5-6. Possible metabolic pathways of planktonic cells grown at pH 7.4

Figure 5-7. Possible metabolic pathways of biofilm cells grown at pH 8.2

*Red arrows indicate enzymes that were found to be up-regulated using proteomic approach. Green arrows indicate the shift in metabolic activities in bacterial cells.

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5.7 Summary

The differential expression of a number of proteins identified by proteomic methods was validated and confirmed in the present study. The levels of potential virulence factors GroEL, DnaK and RecA were investigated using Western blotting and qRT-PCR. Using proteomic analysis, GroEL was found to be up-regulated in biofilm cells and this finding supported the Western blotting but not the transcript analysis.

Also, the transcription of dnaK was also down-regulated in biofilm cells and this finding was in line with the proteomic finding in which DnaK was down-regulated under increased pH. Interestingly, both Western blotting and qRT-PCR results failed to show significant differences in the expression of RecA.

Confirmatory studies on bacterial metabolic activities provide clues in the regulation of metabolic pathways of F. nucleatum biofilms grown under stress conditions. As expected in such conditions, the total cell yield decreased. However, the metabolic end products increased per cell and the cells stored significantly more IP.

In conclusion, protein expression is a highly complex process and the choice of validation methods for proteomic studies may not necessarily reflect their results as proteins are products of transcription, translation, post-translational modification and turnover. In this study, Western blotting examined the protein expression using antibody but it is a semi-quantitative method. Quantitative RT-PCR is an alternative validation method that examines the gene transcript. While it is thought that the combination of

2DE and MS analysis remains the most powerful tool in the study of overall physiology of cells under different conditions, validation studies are required to provide more meaningful results.

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6 Summary discussion

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The gingival sulci undergo significant changes in conditions during the progression of periodontal diseases. These ecological changes cause a shift in the balance of the microbial community that exists within healthy dental plaque towards a biofilm more favourable to anaerobic Gram negative bacteria (Marsh, 1994). The microorganisms that exist within plaque from diseased periodontal pockets must be tolerant of these environmental changes to survive and flourish. F. nucleatum is such a species, able to successfully compete with others and possessing cellular mechanisms that enable it to increase its number and proportion. When grown under controlled conditions in the laboratory, this organism formed biofilms when exposed to an environmental pH of 8.2, a condition that it is likely to encounter in diseased periodontal pockets. It is proposed that this switch to biofilm development, triggered by the change in environmental condition in vitro, reflects a range of cellular protective mechanisms that occur under alkaline stress in vivo.

6.1 Biofilm growth and physiology in alkaline environments

Dental plaque is a biofilm and it is now widely recognised that the formation of biofilms is strongly influenced by both genetic and environmental conditions (Hall-

Stoodley et al., 2004). As discussed in Section 1.3.2, bacteria form biofilms in response to change, in particular significant shifts in, redox potential, nutrient availability, osmolarity and pH. In this study, the changes in F. nucleatum ATCC 10953 protein expression, associated with the biofilm formation induced by an alkaline pH environment, was investigated.

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As mentioned in Section 1.3.3, bacterial cells growing in biofilms are frequently found to regulate components of the cell envelope, such as adhesins, transport proteins, and peptidoglycan synthesis, in addition to a variety of metabolic activities. The results from this study support these findings, as cell envelope proteins, involved in cell binding and transport, stress proteins and key enzymes, involved in the metabolism of carbohydrates and amino acids, were actively modulated by F. nucleatum cells in the transition from planktonic to biofilm growth.

In chapter 3, changes in the expression of cell envelope proteins were investigated. The regulation of OMPs was of particular interest, as a number of these proteins contribute to the adhesive properties of F. nucleatum and are involved in coaggregation with other plaque bacterial species and the binding to host cells (Section

1.2.1). Of the three F. nucleatum adhesins discussed previously, FomA, RadD and

FadA, only FomA was significantly up-regulated in biofilm cells. The predicted isoelectric points (pI) and molecular weights (MW) of these proteins revealed that

RadD has a MW of 360 kDa (Oralgen Databases) and will not be detected on the 12% polyacrylamide gels, used in this study, that resolved proteins within 10 to 80 kDa. In contrast, FadA would be detected in the present study as it has a pI of 4.88 and MW of

14.4 kDa (Oralgen Databases), however, it was not differentially expressed suggesting that it is not involved in biofilm development.

In this study, FomA was the most significantly up-regulated protein in F. nucleatum cells (Tables 3-3, 4-1 and 4-2). FomA is a porin that allows the passage of ions into the periplasm. It is a trimeric protein, consisting of 16 amphipathic antiparallel

β-strands that exhibit a weak ion selectivity, that allow the passive diffusion of solutes

159 with a defined limit of approximately 600 Da (Kleivdal et al., 1999). The significant over-expression of FomA by the F. nucleatum biofilm cells may directly contribute to the development of the mono-culture biofilms observed when placed under alkaline conditions, due to its widely reported adhesive properties (Kaufman & DiRienzo, 1989;

Kinder & Holt, 1993; Liu et al., 2010; Nakagaki et al., 2010). It is interesting to note, that a previous study conducted in our laboratory examining differential expression of cell envelope proteins of F. nucleatum grown at pH 7.2 and 7.8, did not observe a significant increase in expression of FomA (Zilm et al., 2010). It would seem that the increased expression of FomA coincided with the observed switch to biofilm growth, indicating that this OMP is involved in biofilm formation.

The elevated expression of FomA, in a pH environment similar to that of periodontal pockets, may facilitate the coaggregation of increased numbers of the more strongly proteolytic species P. gingivalis. This interaction is likely to be beneficial to the weakly proteolytic F. nucleatum as the expression of gingipains by P. gingivalis will provide a supply of amino acids and peptides for both species. Recently, Liu and co- workers recognised the importance of FomA when examining the potential targets for vaccination, suggesting that inactivation of this adhesin inhibited coaggregation with P. gingivalis, effectively controlling periodontal infection and halitosis in mouse models

(Liu et al., 2010).

As seen in Chapter 2, under the imposed alkaline environment, F. nucleatum biofilm cells experienced altered morphology. The cells became highly filamentous in comparison with planktonic cells growing at pH 7.4. As noted in Section 1.3.3, bacterial cells present in biofilms frequently exhibit altered phenotype. Kolenbrander and

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Andersen reported that one F. nucleatum cell could bind up to 80 P. gingivalis cells

(Kolenbrander & Andersen, 1989). The increased cell length and significant up- regulation of FomA in biofilm cells may provide significantly more attachment sites for the binding of late colonisers, leading to the increased numbers of these pathogenic species observed in plaque associated with the transition from health to disease.

The increased expression of phosphorylated FomA isoforms, in the envelopes of biofilm cells, may also be an adaptation by the cell to the alkaline environment. The attached phosphate groups may capture and slow the loss of H+ from the periplasm, providing H+ for the Na+:H+ antiporters that assist the maintenance of a neutral intracellular pH. Other studies have shown that FomA exhibits a weak preference for cations and thus may facilitate the passage of H+ into the periplasm (Kleivdal et al.,

1995; Kleivdal et al., 1999).

As discussed previously, biofilm cell morphology changed significantly indicating the cells were not dividing as rapidly as planktonic cells and may have decreased growth rates. This change in cell length was associated with the down- regulation of elongation factors EF-Ts and EF-Tu (Tables 3-3 and 4-2) and the ribosomal protein S2 (Table 3-3) that reflects decreased protein synthesis in biofilm cells.

In previous studies, F. nucleatum, when growing as a planktonic culture at pH

7.8, increased its energy generation by elevation of the 2-oxoglutarate and glycolytic pathways and synthesized significant amount of intracellular polyglucose (IP) (Zilm et al., 2003; Zilm et al., 2007). In this study, similar metabolic patterns were observed in

161 biofilm cells grown at pH 8.2, proteomic analysis and biochemical assays revealing that

F. nucleatum biofilm cells increased ATP generation primarily through amino acid catabolism via the 2-oxoglutarate pathway and increased the uptake of glucose to produce IP. The altered metabolic patterns were associated with differential regulation of substrate specific transporter proteins and key enzymes in catabolic pathways. The down-regulation of ABC binding proteins and up-regulation of ATP-independent TRAP transporters by biofilm cells are postulated to conserve energy consumption under stress conditions (Section 3.5.2). The changes in metabolic activities by biofilm cells in the present study were in agreement with altered energy and metabolic functions described by other studies of bacteria in biofilms (Beloin et al., 2004; Resch et al., 2005; Resch et al., 2006; Sauer, 2003; Schembri et al., 2003; Trémoulet et al., 2002).

The investigation of metabolic end-products showed a shift towards increased acetate and lactate production by cells grown at pH 8.2 (Figure 5-7). In addition to the more efficient channelling of available energy producing substrates, the shift may assist in the maintenance of neutral intracellular pH as the dissociation constants, for these organic acids, particularly lactate are lower than that for butanoate resulting in increased intracellular H+.

The present study did not detect any differentially expressed transcriptional activators, such as, sigma factors (σ32 and σE of heat shock response; σs of general stress response) of bacterial stress response systems (Section 1.3.2.1). Small et al. (1994) showed that an E. coli rpoS mutant was more sensitive to acidic (pH 5) and basic (pH

10) conditions compared to wild type E. coli. The importance of rpoS in the adaptation of bacterial cells was demonstrated by Leaphart et al. (2006) as the rpoS expression of

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Shewanella oneidensis, (a Gram negative anaerobic proteobacterium), was up-regulated when cultured under acidic conditions (Leaphart et al., 2006). Sigma factors have been reported to be highly unstable (Ron, 2006) and may explain why they were not detected by the methods employed in this study.

The proteomic analysis suggested that the formation of biofilm in F. nucleatum was closely linked to the regulation of GroEL and DnaK expression. Studies on the physiology of S. mutans and S. oralis cells, cultured in an acidic growth medium (pH 5), reported that proteins involved in the glycolytic pathway, heat shock response (GroEL,

DnaK) and protein translation were influenced by the acidic pH (Wilkins et al., 2001;

Lens et al., 2004). Results from the current investigation showed that the expression of

GroEL was enhanced while DnaK was suppressed in the biofilm cells. Previous studies in our laboratory showed that the expression of DnaK decreased with increasing growth pH to 7.8 (Zilm et al., 2007) and the results of this study are in agreement, as DnaK was down-regulated by biofilm cells grown at pH 8.2. As discussed previously, DnaK binds nascent peptide chains and assists with polypeptide folding (Lemos et al., 2007). It was proposed that at optimal growth pH, the expression was induced in response to the higher rate of polypeptide synthesis compared to that in stressful environments. In this study, the up-regulation of GroEL by biofilm cells was validated using Western blotting. The increased expression of GroEL, in a pH environment mimicking that of the periodontal pocket was in accord with reports of elevated expression of anti-Hsp60 antibody in patients with adult periodontitis (Ando et al., 1995; Tabeta et al., 2000).

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6.2 Biofilm formation of F. nucleatum subspecies nucleatum

As suggested by many researchers, including Fux and Costerton (2005), no clinical strain or laboratory reference strain truly represents a bacterial species. The phenotypic heterogeneity amongst the F. nucleatum subspecies is well known. Only

62% of the F. nucleatum subspecies polymorphum (FNP) opening reading frames

(ORFs) are found in both F. nucleatum subspecies nucleatum ATCC 25586 (FNN) and subspecies vincentii ATCC 49256 (Karpathy et al., 2007). Thus, the effect of alkaline pH on the growth of the subspecies FNN was also investigated in preliminary experiments. Interestingly, FNN also formed mono-culture biofilms, in the chemostat, in response to imposed alkaline stress, at a similar pH to that for FNP (Appendix 7.12).

6.3 Limitations and future studies

The current investigation was carefully designed so that it reflected a pH environment likely to exist in periodontally diseased sites. The cultures were maintained anaerobically at 37ºC and the generation time of culture was set at 10 hours, as bacteria growing in plaque have been reported to have a generation times of 7-12 hours

(Socransky et al., 1977). Although the growth parameters, where possible, replicated the growth parameters of healthy and diseased environments, the mono-culture biofilm model was not able to fully explore the potential interactions of F. nucleatum with other colonisers during the development of dental plaque. In the future, the development of a sophisticated multi-species biofilm system may be used to increase knowledge of the protein expression by its members. F. nucleatum is widely regarded as a bridging organism in the formation of mature plaque and a better understanding in its interactions with oral bacteria at a molecular level will likely provide insight into the progression of

164 periodontal diseases. A recent study assessed the protein expression of P. gingivalis growing in a three-species system also containing the pioneer plaque species S. gordonii and F. nucleatum (Kuboniwa et al., 2009). The results obtained highlighted the protective mechanisms that may exist within multi-species communities, as P. gingivalis exhibited increased protein synthesis and decreased stress, when grown in the presence of the other species, compared to mono-culture P. gingivalis.

A different approach in the study of protein expression in bacterial biofilms was conducted by An Nandakumar and colleagues in 2009. The authors employed a

„bottom-up‟ approach to study the secreted protein profile of bacteria isolated from infected root canals (Nandakumar et al., 2009). This method involved a proteolytic digestion of all the proteins present in the sample and analysis of the resulting peptides.

The study successfully identified proteins including adhesins, autolysins, proteases, virulence factors and antibiotic-resistance proteins, allowing for characterisation of virulence-associated proteins present in endodontic infections. This method eliminated the need for resolving proteins using 2DE and overcoming the limitations associated with that method, outlined in Sections 1.4.1.1 and 4.5.1. The same approach may be applicable in the study of periodontal infections.

The combination of 2DE and MS employed in this study successfully identified differentially expressed proteins in F. nucleatum biofilm cells. The Western blotting was performed to examine the expression of stress proteins DnaK, GroEL and RecA.

This method is a direct investigation of protein expression, however, it is a semi- quantitative assay (Aldridge et al., 2008). Western blotting requires a loading control to ensure the difference in protein expression is not due to variations in protein load. As

165 the glucose metabolism of F. nucleatum cells was influenced by the environmental growth pH, the commonly used loading control, glyceraldehyde 3-phosphate dehydrogenase, was not ideal for the present study. Therefore, standardization of the protein load was required prior to the SDS-PAGE step of Western blotting.

Quantitative RT-PCR (qRT-PCR) is an alternative methodology employed to verify proteomic results. This technique is rapid and sensitive in comparison to Western blotting. As with Western blotting, qRT-PCR requires the normalisation of results using the expression of “housekeeping” genes. Primers for 16S rRNA were designed and the gene expressions determined. Although these genes were suggested to be good candidates as reference genes in the studies of qRT-PCR, 16S rRNA transcript level varied between planktonic and biofilm cells (Table 5-5). Therefore, the identification of a reliable F. nucleatum reference gene for qRT-PCR may be beneficial in the future studies employing this method.

In conclusion, this study builds on the work of others, providing added evidence that reinforces the long held belief that F. nucleatum is well suited to play a key role in the establishment of mature dental plaque.

166

7 Appendices

167

7.1 Composition of Chemically Defined Medium (CDM)

Refer to Section 2.3.1.2.

Amino acids g/L

DL-alanine 0.05 D-arginine 0.1 Na-aspartate 0.1 L-glutamic acid 1.32 L-glycine 0.075 L-histidine 1.39 L-hydroxyproline 0.2 L-isoleucine 0.1 L-leucine 0.1 L-lysine 1.64 L-methionine 0.1 L-phenylalanine 0.1 L-proline 0.2 L-serine 0.95 L-tryptophan 0.2 L-threonine 0.1 L-valine 0.1 L-tyrosine 200

Vitamins mg/L p-aminobenzoic acid 2 biotin 0.1 folic acid 0.5 inositol 2 nicotin amide 2 ca-panthothenate 2 pyridoxal. HCl 1 thiamine. HCl 2

168

Salts concentration mg/L

(NH4)2SO4 600 NaCl 10 CaCl2.2H2O 20 MnSO4 10 MgSO4 200 Na citrate 225 FeSO4 10 NaMoO4.2H2O 0.15

Nucleotides and Tyrosine mg/L

Adenine 10 Guanine 27 Uracil 30

Buffers and other g/L

K2HPO4 1.54 NaHPO4 1.03 K2CO3 1.0 Riboflavin 0.002 Tween 80 0.21 Cysteine 0.2 Glucose 3.6 Thioglycollic acid 0.5 mL/L

169

7.2 Matchset of 2DE gels used for outer membrane protein (pI 4-7) expression analysis

Refer to Section 3.3.1.4 and 3.3.2.1.

Master gel image Planktonic culture1 Planktonic culture2

Planktonic culture3 Planktonic culture4 Biofilm 1

Biofilm 2 Biofilm 3 Biofilm 4

170

7.3 Matchset of 2DE gels used for outer membrane protein (pI 7-10) expression analysis

Refer to Section 3.3.1.4 and 3.3.2.1.

Master gel image Biofilm 1 Biofilm 2 Biofilm 3

Biofilm 4 Planktonic culture1 Planktonic culture2 Planktonic culture3

Planktonic culture4 Planktonic culture5

171

7.4 Optimisation of cytoplasmic protein (pI 7-10) separation

Refer to Section 4.4.1.2.

I. A comparison of protein load, IPG strip length and gel staining method

2DE gels of cytoplasmic proteins (pI 7-10) separated and stained with Coomassie Brilliant Blue R-250 (a) and Silver Plus Stain (b)

(a) 1.2 mg of cytoplasmic proteins were cup-loaded on a 17 cm pH 7-10 IPG strip, IEF was achieved at 60, 000 volt-hour and gel was stained using Coomassie Brilliant Blue R-250. (b) The same gel was destained and stained using a more sensitive stain, Silver Plus Stain (Bio- Rad Laboratories). The number of proteins observed increased indicating the presence of low abundance proteins that were not stained using Coomassie Brilliant Blue R-250 stain.

A total of 1-3 mg of protein load is recommended to be loaded on a 17 cm IPG strips while 0.1-1 mg of proteins were required for a 11 cm IPG strip. It was then decided to use 11 cm IPG strips for cytoplasmic proteins with a pI between 7 and 10.

172

II. Reduction and alkylation of proteins prior to gel-based IEF

Horizontal streaking is commonly observed on 2DE gels that separates protein between pI 7 and 10 due to the formation of inter- and intramolecular disulphide bonds. The use of the alkylation-reduction kit (ReadyPrep Reduction-Alkylation Kit, Bio-Rad Laboratories) and anodic cup-loading method for these proteins separation is recommended.

Reduction-alkylation was performed in accordance to the manufacturer‟s instructions. Protein separation did not show great improvement. Gel image showed severe vertical streaking on the anode end where proteins were loaded, thus, indicating the presence of cytoplasmic proteins that have a pI <7 in the protein sample. Therefore, protein fractionation prior to IEF was performed using a MicroRotofor.

2DE gel of cytoplasmic proteins treated with Alkylation-Reduction Kit prior to IEF

173

III. Protein fractionation prior to gel-based IEF: MicroRotofor Liquid-IEF Cell (Bio- Rad Laboratories)

Protein fractionation prior to gel-based IEF was performed. Cytoplasmic proteins equivalent to 10 mg were diluted to 3 mL using ReadyPrep Extraction Reagent 3 and loaded into a focusing chamber of MicroRotofor Liquid IEF Cell. Liquid IEF was performed at 20ºC at 1watt for 2 hours. Isoelectric focuses proteins were then harvested and pooled to give two fractions of proteins with pI between 4-7 and 7-10. Protein concentration in both fractions was determined.

The results demonstrated that the ratio of acidic (pI 4-7) to basic (pI 7-10) proteins was approximately 4:1. Results showed that sample pre-fractionation prior to gel-based IEF eliminated vertical streaking at the protein loading point.

Ratio of cytoplasmic proteins between pI 4-7 and 7-10 Ratio Cell type pI 4-7* pI 7-10* (pI 4-7:pI 7-10) Planktonic 5.3±0.2 1.0±0.1 5: 1 Biofilm 5.6±0.6 1.4±0.1 4: 1 *Mean protein concentration in mg/ml of culture

2DE map of cytoplasmic proteins fractionated by MicroRotofor Cell prior to gel-based IEF

174

IV. Optimisation of IEF focusing time Although vertical streaking problem was solved, minor horizontal streaking was observed. One of the possible causes of horizontal streaking is under- or over- focused proteins. The optimal focusing time for samples was examined. The optimal IEF focusing time was 20,000 volt-hour.

Effect of IEF focusing time on protein resolution IEF focusing time a) 20,000 volt-hour, b) 25,000 volt-hour, c) 30,000 volt-hour and d) 40,000 volt-hour.

175

V. The use of Destreak wick to improve protein resolution

Although the resolution of proteins was improved, streaking was still apparent near to the pI 8-9 region. DeStreak was used to replace deionised water in the wick at the cathode end. The horizontal streaking issue was solved.

Use of DeStreak wick improves horizontal streaking

176

7.5 Matchset of 2DE gels used for cytoplasmic protein (pI 4-7) expression analysis Refer to Section 4.3.4.

Master gel image Planktonic culture1 Planktonic culture2

Planktonic culture3 Biofilm 1 Biofilm 2

Biofilm 3 Biofilm Biofilm 5

177

7.6 Matchset of 2DE gels used for cytoplasmic protein (pI 7-10) expression analysis

Refer to Section 4.3.4.

Planktonic culture1 Planktonic culture2 Planktonic culture3 Master gel image

Biofilm 1 Biofilm 2 Biofilm 3

178

7.7 Optimisation of protein load for Western blotting

Refer to Section 5.3.1.1.2.

i. Anti-RecA antibody MW (kDa) 50 µg protein 15 µg protein 133 71

37 31

18

50 µg cell protein caused smearing of RecA protein on membrane. A smaller amount of protein load (15 µg cell protein) gave tight and sharp protein bands on membrane blot.

ii. Anti-human Hsp 60 antibody 25 µg protein 15 µg protein

133 71

42 31

18 MW (kDa)

Similar observation was noted in the investigation of GroEL expression. When 25 µg cell protein was loaded onto each well, the protein band merges to form a flat line on the membrane blot. A smaller amount of protein load (15 µg cell protein) gave tight and sharp protein bands on membrane blot.

179

7.8 Determination of end products and glucose using HPLC

Refer to Section 5.4.1.1.1.

Estimation of metabolic end products and glucose was performed using ion-exclusion chromatography based on the method of Guerrant et al. (1982).

Instrumentation

Rheodyne 7125 injector

Organic acid analysis HPLC column (Aminex HPX-87H, Bio-Rad Laboratories)

Waters Model 501 HPLC pump

Waters Model 730 Data module

Waters Model 2259 temperature control module

Mobile phase

This was an isocratic separation using 7 mM H2SO4 at a flow rate of 0.5 ml/min for elution.

180

7.9 Butanoate/acetoacetate CoA transferase Assay

Refer to Section 5.4.1.1.1 and 5.4.1.1.2.

0.19 Assay for BAT activitiy using crude lysates 0.17 0.15

310nm 0.13

OD 0.11 0.09 0.07 0.05 -0.3 0.2 0.7 1.2 1.7 2.2 Time (min)

Background rxn Replicate 1 replicate 2 Replicate 3

The reaction catalysed by BAT was not affected by the background reaction as observed in non-dialysed samples in the NAD-GDH and LDH assays (Appendices 7.10 and 7.11).

181

7.10 Optimisation of lactate dehydrogenase assay

Refer to Section 5.4.1.1.1, 5.4.1.1.3 and 5.5.2.

A LDH standard curve was constructed using LDH from Lactobacillus species and showed a strong coefficient of determination (R 2=0.999).

0.6 LDH standard curve 0.5 y = 0.5017x 0.4 R² = 0.999 0.3 A340nm 0.2

0.1

0 0 0.2 0.4 0.6 0.8 1 1.2 LDH (unit)

A steady decrease in absorbance occured in the first 5 minutes after which the reaction rate decreased. Thus, the incubation time for theassay was 5 minutes.

0.9 Assay for LDH activity 0.8 0.7 0.6 0.5 0.4 A340nm 0.3 0.2 0.1 0 0 2 4 6 8 10 12 Time (min)

182

A background reaction (without the presence of substrate pyruvate) was observed. Protein purification was then performed and background reaction was eliminated.

Assay for LDH activity using crude protein sample 1.1 1 0.9 0.8

A340nm 0.7 0.6 0.5 0 1 2 3 4 5 6 Time (min) LDH activity Background reaction

Assay for LDH activity using dialysed sample

1.1 1 0.9 0.8

A340nm 0.7 0.6 0.5 0 1 2 3 4 5 6 Time (min) Background reaction LDH activity

183

7.11 Optimisation of NAD-specific glutamate dehydrogenase assay

Refer to Section 5.4.1.1.1, 5.4.1.1.4 and 5.5.3.

A steady increase in absorbance occurred in the first 5 minutes after which the reaction rate decreased. Thus, the incubation time for the assay was 5 minutes.

Assay for NAD-GDH activity 1.8 1.6 1.4 1.2 1 0.8

A340nm 0.6 0.4 0.2 0 0 2 4 6 8 10 12 Time (min)

GDH standard curve was constructed using GDH powder from bovine liver (Sigma Aldrich). A strong correlation between the absorbance at 340nm and GDH was established.

0.4 NAD-GDH standard curve 0.35 y = 0.0227x 0.3 R² = 0.9931 0.25 0.2

A340nm 0.15 0.1 0.05 0 0 5 10 15 20 GDH (unit/mg)

184

Assay for GDH activity using crude cell lysates 0.28 0.27 0.26 0.25 0.24

A340nm 0.23 0.22 0.21 0.2 0 20 40 60 80 100 120 140 Time (s) GDH Background

Using biofilm sample as an example, absorbance increases in the first 20 to 30 seconds indicating the conversion of NAD+ to NADH. However, the NADH was converted into NAD+ after 20 seconds and the absorbance goes down over time during the next 100 seconds.

Assay for NAD-GDH activity using dialysed samples 1.2

1

0.8

0.6

0.4 A340nm

0.2

0 0 50 100 150 200 250 300 350 Time (s) R1 R2 Background

Protein precipitation and dialysis eliminated the background reaction.

185

7.12 Biofilm formation by F. nucleatum subspecies nucleatum ATCC 25586

Refer to Section 6.2.

Appearance of bacterial growth at pH 8.1

186

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