CHAPTER 1

CHAPTER 1. INTRODUCTION

1.1. THE ROLE OF STREPTOCOCCUS GORDONII IN HEALTH AND DISEASE

Dental plaque consists of a diverse community of microorganisms including bacteria, fungi and protozoa (Burne, 1998; Chandra et al., 2001; Lyons et al., 1983; Webb et al., 1995). In these biofilm communities different microorganisms co-adhere to one another within a polysaccharide matrix (Kolenbrander, 1993), and cooperate in degrading and utilizing host and dietary nutrients such as glycoproteins (Bradshaw et al., 1994). Studies of oral ecology have led to an appreciation of the complexity of the interactions that oral microorganisms have with the host both in health and disease (Bowden and Li, 1997; Quirynen and Bollen, 1995; Rose, 2000). Despite this, diseases such as dental caries and periodontal diseases are still worldwide human aliments resulting in a high level of morbidity and economic burden to society. Proteomics offers a novel approach to understanding the holistic changes occurring to oral microorganisms as environmental changes impinge on their habitats. Streptococcus gordonii was originally classified among the taxonomically diverse species Streptococcus sanguis2 until its description as a new species in 1989 (Kilian et al., 1989). S. gordonii cells are Gram-positive cocci that grow in short chains in serum broth. They are nonmotile, aerobic and faculatively anaerobic, fermentative, catalase negative, show α-hemolysis on horse blood agar and pronounced greening on chocolate agar. The cell wall contains glycerol teichoic acid and rhamnose, and the peptidoglycan type is Lys-Ala1-3. The guanine-plus-cytosine

2 The currently accepted nomenclature of S. sanguis is “S. sanguinis”, and of S. parasanguis is “S. parasanguinis”. However, a compelling argument has been made for the conservation of the original names (Kilian, 2001). Kilian stated that “The changing of well established names of species of Streptococcus and other genera is not desirable as it will cause unnecessary confusion and frustration for those who use these names in their daily work”. In order to avoid confusion, throughout this thesis, the name stated for Streptococcus species is that used in the associated literature.

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(G + C) content of the DNA is 40 to 43 mol%. S. gordonii is differentiated from S. sanguis by several biochemical characteristics, including a lack of an IgA1 protease, and by a significantly different G + C content (Kilian et al., 1989). S. gordonii is one of the viridans group streptococci (VS), which are part of the normal microbial flora in dental plaque (Whiley and Beighton, 1998). Viridis is Latin for green, and viridans refers to the green colour of the halo that surrounds α-haemolytic streptococci on chocolate agar, which is thought to comprise the metabolic degradation products of haem. While most VS are commensals, an exception is Streptococcus mutans, which is associated with dental caries (Bowden, 1997). Although most are not considered pathogens in the oral cavity, many VS species are associated with infective endocarditis (Douglas et al., 1993). In a study examining the identities of 47 strains of oral streptococci collected from 42 confirmed cases of infective endocarditis, the most common species identified were S. sanguis sensu stricto (31.9%), Streptococcus oralis (29.8%) and S. gordonii (12.7%). Other related species including Streptococcus mitis and Streptococcus parasanguis were less commonly isolated (Douglas et al., 1993). As environmental factors change, organisms either adapt or perish. In the oral cavity, early colonizing Gram-positive bacteria, such as S. gordonii, form biofilms on tooth surfaces and ferment carbohydrate efficiently at pH levels above 6.0 (Loo et al., 2000) and produce antibacterial factors that prevent the overgrowth of dental pathogens such as S. mutans (van der Hoeven and Schaeken, 1995). The initiation of oral biofilms on tooth surfaces by bacteria, such as S. gordonii, depends on the differential expression of genes in response to unique environmental clues. For example, one study observed the involvement of the adc operon and manganese homeostasis in the formation of S. gordonii biofilms (Loo et al., 2003). The conclusion drawn from this study was that AdcR could be a regulator at high levels of extracellular manganese, which is usually found at low levels in its natural and opportunistic habitats, and that the adc operon is not only involved in manganese acquisition and manganese homeostasis in S. gordonii, but appears to modulate sessile growth in this bacterium (Loo et al., 2003). This illustrates how changes in environment can cause a developmental change in a bacterium.

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If the pH of dental plaque drops below pH 6.0, the environment becomes unfavourable, S. gordonii is unable to metabolise efficiently, and aciduric pathogens begin to proliferate and predominate in the oral biofilm, further lowering the pH. However, under certain conditions, including some dental procedures, S. gordonii along with other VS may enter the bloodstream. In the bloodstream the environmental pH is slightly higher (pH 7.3) than the slightly acidic value in dental plaque (pH 6.5). Both the growth rate and the production of extracellular protein of S. gordonii increase as pH increases (Knox et al., 1985). During the resulting transient bacteremia (Vriesema et al., 2000) it is possible to kill the planktonic organisms using antimicrobial treatments while the bacteria are still in the bloodstream (Donlan and Costerton, 2002). However, should a biofilm form, such as that in an infective endocarditis lesion, the condition becomes difficult to treat due to the mass transfer limitations and the inherent resistance of biofilm microorganisms to antibiotic treatment (Donlan and Costerton, 2002; Giebink et al., 1982). In infective endocarditis, the VS adhere to a cardiac vegetation, which is a meshwork of platelets and fibrin present at the site of the endocardial lesions (Durack and Beeson, 1972). A number of surface components such as the fimbrial protein, FimA, of S. parasanguis (Burnette-Curley et al., 1995; Viscount et al., 1997) and the extracellular polysaccharides of various VS species (Dall and Herndon, 1990; Ramirez-Ronda, 1978; Scheld et al., 1978), have been implicated in the adherence of these bacteria to cardiac vegetations. The fibrin in these vegetations is converted from fibrinogen in blood by the enzyme thrombin. Interestingly, thrombin-like activity produced by S. sanguis has been shown to be a potential virulence factor for infective endocarditis (Mayo et al., 1995). Furthermore, this activity is 5-fold higher at pH 7.5 than at pH 5.5, implicating a change in pH following bacteraemia from dental plaque as a trigger for the infectious process (Mayo et al., 1995).

1.2. BIOFILMS AND QUORUM SENSING

Biofilms are formed in diverse environments, from surfboard wax in a marine environment (Dalton et al., 1994), to tooth surfaces in the human mouth (Marsh and

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Bradshaw, 1997). Researchers have estimated that 99% of all bacteria in natural environments exist in biofilms (Costerton et al., 1987). Bacteria in biofilms optimise population survival by differentiation into forms suitable for particular conditions and organising themselves into communal groups exhibiting characteristics not shown by individual planktonic cells (Costerton et al., 1999; Lawrence et al., 1991). In these communities bacterial populations coordinate their activities by quorum sensing. This is accomplished by secreting small diffusible signal molecules (autoinducers), thus allowing a single cell to obtain information about the status of other bacteria from the same (Batchelor et al., 1997; Davies et al., 1998) or even different species (Møller et al., 1998). Autoinduction is commonly activated when the autoinducer reaches a critical density, leading to a fast, population-wide response, and, as such, is believed to be a sensor of population density (Fuqua et al., 1996).

1.2.1. Biofilms and human health

Biofilms have an innate resistance to antibiotics (Donlan and Costerton, 2002; Mah et al., 2003; Stewart, 2002), and present problems of significant economic importance in industry, health and agriculture. Biofilms are often associated with human disease accounting for approximately 60% of human infections and virtually all, chronic, recurrent and implanted device associated infections (Centers for Disease, Control and Prevention, 2000; http://www.cdc.gov/). Cystic fibrosis, native valve endocarditis, otitis media, periodontitis, and chronic prostatitis are all caused by biofilm-associated microorganisms (Donlan, 2002). Biofilms colonize medical devices such as prosthetic heart valves, central venous catheters, urinary catheters, contact lenses, intrauterine devices, and dental unit water lines (Donlan and Costerton, 2002). Medical ventilation systems also support biofilms and fragments of these biofilms can readily detach and colonise the lungs (Costerton et al., 2003).

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1.2.2. The formation of oral biofilms

Previous studies in oral ecology have led to an understanding of many of the mechanisms by which biofilms form on teeth (Bowden and Hamilton, 1987; Quirynen and Bollen, 1995; Rose, 2000), but dental caries and periodontal disease are still widespread human aliments (Hobdell et al., 2003). Researchers are still investigating possible solutions using antiseptics, antibiotics, adhesion agonists, recombinant antibodies and vaccines (Dutton et al., 2000; Hanada, 2000; Jenkinson et al., 1997). The formation of the oral biofilm involves phenotypic changes as planktonic cells become sessile after adhering to a surface, as well as genetic changes involving the increase or decrease of specific gene products, which may be already present in the planktonic state (Loo et al., 2000). Measurement of the differential expression of proteins provides information about these changes Even though the attachment, growth, removal and reattachment of bacteria to the tooth surface is a continuous and dynamic process, the formation of biofilms on tooth surfaces has a particular pattern of development that can be summarised as follows (Marsh and Martin, 1999):

• Absorption of salivary proteins and glycoproteins, together with some bacterial molecules, to the tooth surface to form a conditioning film known as the acquired pellicle. • Long-range (>5Onm), non-specific interaction of microbial cell surfaces with the acquired pellicle via van der Waals attractive forces. • Shorter-range (10-20nm) interactions, in which the interplay of van der Waals attraction forces and electrostatic repulsion produces a weak area of attraction that can result in reversible adhesion to the surface. • Irreversible adhesion can occur if specific inter-molecular interactions take place between adhesins on the cell surface and receptors in the acquired pellicle. • Secondary or late-colonisers attach to primary colonisers (co-adhesion), also by specific inter-molecular interactions.

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• Cell division of the attached cells to produce confluent growth, and a biofilm. The bacteria become embedded in a matrix of extracellular polymers of bacterial and salivary origin • The detachment of cells from the biofilm into the planktonic phase (usually saliva, but in the case of endocarditis the cells initially enter the bloodstream), facilitating colonization of fresh sites. • The diversity of the microflora in the biofilm increases, over time, as the plaque thickens and the environment becomes more anaerobic. • In the climax community there are many types of associations between different bacterial populations, depending on the disease state of the oral cavity (Paster et al., 2001). For example, Streptococcus sanguinis was associated with health and, in order of decreasing cell numbers, Actinomyces gerencseriae, Bifidobacterium, S. mutans, Veillonella, Streptococcus salivarius, Streptococcus constellatus, Streptococcus parasanguinis, and Lactobacillus fermentum were associated with dental caries (Becker et al., 2002; Paster et al., 2001).

Thus, if one considers a tooth surface that has been thoroughly cleaned, it is rapidly coated with a complex mixture of components that include glycoproteins, acidic proline-rich proteins and mucins from saliva, as well as bacterial cell debris and extracellular products, to form the acquired pellicle (Duan et al., 1994; Gibbons et al., 1991; Marsh and Martin, 1999; Scannapieco et al., 1992; Scannapieco et al., 1995). The acquired pellicle becomes the substrate for the primary colonizers, such as S. gordonii, S. sanguis, and S. parasanguis (Nyvad and Kilian, 1987). In the case of S. gordonii, biofilm formation is influenced (in vitro) by a number of environmental parameters, including osmolarity, carbon source, and pH (Loo et al., 2000). For example, S. gordonii biofilm formation is greater in a minimal medium than in a nutritionally rich medium (Loo et al., 2000). This suggests that biofilm formation may be a survival mechanism in nutritionally poor environments, as it has been suggested that one advantage of life in a biofilm is the increased ability to capture nutrients that may be absorbed to surfaces (Wimpenny and Colasanti, 1997). The osmolarity of the growth media also influences biofilm formation.

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Although the growth of S. gordonii increases with increasing osmolarity (0.1M to 0.3M), biofilm formation decreases across the same range (Loo et al., 2000). The pH of the growth media also has an effect. S. gordonii biofilm formation and growth are reduced at pH levels below 6.0. However, biofilm formation of S. gordonii is more sensitive to pH change than growth. For example, biofilm formation is reduced above pH 8.0, whereas growth is only affected above pH 10.5 (Loo et al., 2000).

1.2.3. Quorum sensing signals in Gram-negative and Gram- positive bacteria: Autoinducers HSL and AI-2

A connection has been made between differentiation of cells in biofilms and quorum sensing signals. Homoserine lactone (HSL) quorum sensing is involved in biofilm formation in P. aeruginosa. A P. aeruginosa lasI mutant that was incapable of production of the HSL autoinducer N-(3-oxododecanoyl)-L-homoserine lactone

(3OC12-HSL) has been shown to be defective in biofilm formation. The mutant formed thinner biofilms than the wild type, and it did not form the microcolonies that are characteristic of wild-type biofilms. Exogenous addition of 3OC12-HSL complemented the defect (Davies et al., 1998). As yet, no HSLs have been identified in Gram-positive bacteria, however communication based on autoinducer (AI) 2 is common among both Gram-negative and Gram-positive bacteria (McNab et al., 2003). AI-2 is a furanosyl borate diester. AI-2 is formed chemically from 4,5- dihydroxy-2,3-pentanedione that is generated by the action of LuxS AI synthase on S-ribosylhomocysteine (Chen et al., 2002; Schauder et al., 2001). AI-2 has been found to be produced by the luxS gene in Vibrio harveyi, Escherichia coli and Salmonella typhimurium (Surette et al., 1999). As very highly conserved luxS homologues are widespread in both Gram-negative and Gram-positive bacteria, it is possible that AI-2 signalling could be a universal cell–cell communication system (Surette et al., 1999). Highly conserved luxS homologues have been identified in Streptococcus pneumoniae, Streptococcus pyogenes, and S. mutans (Bassler, 1999). S. gordonii produces an AI-2-like signalling molecule that regulates aspects of carbohydrate metabolism in the organism, it has been found that LuxS-dependent intercellular communication is essential for biofilm formation between non-growing

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cells of Porphyromonas gingivalis and S. gordonii (McNab et al., 2003). Interestingly, no homologues of the receptors for AI-2 have been identified in Gram positive bacteria (or any Gram negative bacteria other than Vibrio species (Bassler, 1999). Thus, the mechanism by which the AI-2 signal is received is unknown.

1.2.4. Other quorum sensing signals in Gram-positive bacteria

Gram-positive bacteria have been shown to communicate using other quorum sensing signals. Many employ post-translationally modified peptides created from larger precursors. These peptides are often secreted by adenosine triphosphate (ATP)-binding cassette (ABC) transporters (Kleerebezem et al., 1997). Proteins in the ABC Transporter family of proteins are found in all domains of life throughout the animal and plant kingdoms, bacteria, and archea (Higgins, 1992; Holland and Blight, 1999; Saurin et al., 1999), forming the largest and most diverse family of membrane-spanning transport proteins. These membrane proteins use the energy of ATP hydrolysis to transport a wide range of molecules across the cytoplasmic membrane (Chang and Roth, 2001; Higgins and Linton, 2001). Many bacteria communicate by secreting and responding to extracellular peptides (pheromones). Some of these peptides act via receptors on the cell surface, such as membrane-bound histidine protein kinases. While other peptides are transported into the cell by oligopeptide permeases and interact with intracellular receptors to modulate gene expression (Lazazzera and Grossman, 1998). These systems are involved in the regulation of processes, such as virulence in S. aureus, competence for DNA-uptake in Bacillus subtilis, S. mutans and S. pneumoniae, sporulation in B. subtilis, conjugal plasmid transfer in Enterococcus faecalis, and bacteriocin production in lactic acid bacteria (Håvarstein et al., 1996; Kleerebezem et al., 1997; Lazazzera and Grossman, 1998; Li et al., 2002b; Novick and Muir, 1999) In one study, real-time Polymerase Chain Reaction (PCR) was used to quantify changes in expression of S. gordonii genes during biofilm formation. Only four genes were found to be up-regulated in the biofilm state, rggD, pbg, comD and comE with a 1.95, 2.29, 4.3, and a 9.40 fold increase respectively (Gilmore et al., 2003). The gene rggD has a helix turn helix motif suggesting a potential DNA-

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binding function and pbg encodes the protein phospho-β-glucosidase which hydrolyzes O-glycosyl compounds (Vriesema et al., 2000). However, of most interest were the comD, and comE genes which encode the histidine kinase and response regulator for the S. gordonii competence pathway responsible for DNA up- take (Håvarstein et al., 1996). Competence for genetic transformation in certain species of streptococci is induced by a secreted protease-sensitive pheromone, referred to as the competence factor, which acts as a quorum-sensing signal to co- ordinate expression of late competence genes. Therefore, of the four genes that were up-regulated in the biofilm phase, comD and comE (which were expressed at greatly increased levels) encode products related to environmental sensing and signalling (Gilmore et al., 2003). The study identified other transcriptional changes that occur with biofilm formation. However, due to the lack of a completed genome, the study became slightly subjective as it focussed only on factors that had been previously identified as being, or thought as being, involved in biofilm formation and adhesion. The authors concluded that eventual release of a completed S. gordonii genome would greatly facilitate differential gene expression studies of this organism (Gilmore et al., 2003).

1.3. THE CHEMOSTAT

“it is virtually meaningless to speak of the chemical composition of a microorganism without at the same time specifying the environmental conditions producing it” (Herbert, 1961).

1.3.1. The use of the chemostat as a model for the oral environment

Bacteria take nutrients from, and expel waste products of metabolism into their environment. In a “closed system” such as batch culture the environment is altered with time as nutrients are used and waste products accumulate. Although bacterial growth initially increases exponentially, at a certain point the environment becomes

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unsuitable for further growth and the culture enters stationary phase where the number of new cells is balanced by the number of dying cells, prior to the viable population going into rapid decline. The cells in these dying cultures are often abnormally distorted, a symptom of unbalanced growth (Zinsser, 1980). A chemostat, on the other hand, is an “open system”, which provides a continuously growing culture where bacterial cells can be maintained in steady state. The chemostats volume is constant as culture medium containing the living cells, cellular debris, and exhausted medium leave the vessel at the same rate that fresh medium is added. Equilibrium is reached when the number of new cells created by division equals the number removed via the overflow. The ratio of the amount of nutrient medium added per hour to the working volume of the culture vessel is known as the dilution rate (D). By ensuring D is constant, the concentration of all the components becomes constant, and the culture is placed in a steady state that may be continued for extended periods. Temperature, pH, stirrer speed and the rate of flow of air can be monitored and adjusted by external devices (Dykhuizen, 1993; Herbert et al., 1965). The bacterium’s growth rate is dependant on the concentration of a growth limiting nutrient, as well as physical parameters such as D, temperature, flow rate, partial pressure, pH, and redox potential (Eh; the measurement of the state of oxidation). Altering any of the parameters can result in structural and functional change to the bacteria (Jacques et al., 1979a; Sevinc et al., 1990; Taylor et al., 1998; Tempest and Woulters, 1981). In the chemostat these growth-associated environmental changes are continuously corrected, and so (theoretically) the culture can be maintained indefinitely. The natural environments of many microorganisms may seem difficult to accurately replicate in vitro, as these environments may be extremely complex, with individual species adapting to fill particular niches. Over time, selective pressures may favour mutations, permanently changing the organism; alternatively, the properties of microbial cells may change reversibly, a process called “phenotypic variation” (Tempest and Woulters, 1981). Bacteria generally live in environments, which often change rapidly requiring them to respond phenotypically. This capacity to adapt to environmental change is conducive to examination using continuous culture experiments. In the mouth, conditions are extremely changeable; nutrient

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availability is unpredictable, saliva and pH can vary, and even temperature and Eh may alter (Macfarlane and Samaranayake, 1989). As in vivo experiments are often impractical, in vitro experiments using continuous cultures in a chemostat provide an appropriate model to investigate bacterial adaptation to specific environmental conditions.

1.3.2. Quantitation of microbial behaviour in chemostat cultures

Many studies have been conducted into quantitative evaluation of microbial behaviour in chemostat cultures; one of the more straightforward parameters to quantify is D. Basically, where growth rate is limited by the concentration of some essential nutrient, for example glucose, the concentration of unconsumed glucose in the culture extracellular fluid is related to the specific growth rate (µ), and therefore to D (Harder and Kuenen, 1977). There is also a relationship between the amount of substrate consumed and the amount of biomass synthesised. In a chemostat the rate at which the population density changes is calculated as the difference between the rate of growth and the rate of “washout”. If µ is greater than D, the population density will increase with time, and visa versa. Population density will remain constant with time only when µ = D, at this stage “steady state” conditions will be attained (Harder and Kuenen, 1977). At “steady state” the following applies:

-1 -1 µ = µmax (s·(ks + s) ) = D = ln2·td

where µmax is the maximum growth rate which is attained, “s” is the concentration of limiting nutrient in the extracellular fluid, ks is a saturation constant (the value of “s” at ½ µmax), and td is the doubling time. If D is greater than µmax the biomass in the chemostat will fall until “washout” occurs, therefore the critical dilution rate is equivalent to µmax. This system is self-balancing as long as the dilution rate is maintained at some value less than the critical dilution rate.

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1.3.3. Streptococcal chemostat studies in the literature

Of 168 chemostat (or continuous culture) studies on Streptococci appearing in the reference database Medline (June 2004; http://medline.cos.com/; key words chemostat or continuous culture, Streptococcus), 93 studies included S. mutans, 20 studies included S. salivarius, 4 studies included S. oralis, 31 studies included S. sanguis (20 of these prior to 1989 when S. gordonii was described as a distinct species from S. sanguis [Kilian et al., 1989]), and five studies included S. gordonii. All of the above chemostat (or continuous culture) studies of S. gordonii appearing in Medline were mixed culture studies. There were 19 single culture studies of S. sanguis (12 of these prior to 1989), whereas, 73% of the S. mutans papers examined S. mutans under single culture conditions (http://medline.cos.com/; key words chemostat or continuous culture, Streptococcus, mutans). Although any Medline search would not be exhaustive, it does clearly indicate that there have not been extensive chemostat studies of S. gordonii. The following sections (Section 1.3.3.1 and Section 1.3.3.2) look at chemostat studies of streptococci, including S. gordonii and S. mutans, examining how they adapt to changes in the environmental pH.

1.3.3.1. Studies on the effect of changes to environmental pH on S. sanguis and S. gordonii

The chemostat, although it does not accurately model physical aspects of a mouth, does allow ecological and physiological (ecophysiological) studies that examine specific interactions among members of the normal oral flora under defined and controllable conditions, where environmental parameters, such as pH, can be altered. In a mixed culture study, involving both Gram-positive and Gram-negative oral bacteria, the nature of the carbohydrate was found to have little influence on the proportions of individual species at neutral pH (Marsh, 1991). However, when the pH was allowed to fall, the relative stability of the microflora was altered. The proportions of S. mutans, Lactobacillus casei and Veillonella increased, becoming

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the predominant species, while levels of Gram-negative bacteria and S. gordonii were reduced. This and similar findings led to the proposal of a modified hypothesis (the "ecological plaque hypothesis") to explain the role of the resident oral microflora in health and dental disease. This hypothesis states that disease follows unfavourable disruption of the dynamic balance between the host and the microbial community at local sites (Marsh, 1994). Inherent in this theory is the concept that potentially cariogenic bacteria can be present in health, but at levels that are not clinically relevant, and that disease could be controlled not only by targeting putative pathogens but also by interfering with the factors responsible for driving the deleterious shifts in the microflora. This is a more holistic approach towards disease control and management strategies (Marsh, 2003). In another study the ecological effects of two antimicrobial agents, triclosan and its phosphorylated derivative triclosan monophosphate were examined (Saunders and Greenman, 2000). Two conditions were simulated at a D of 0.10 h-1, a caries- like state (pH 5.5 with artificial saliva plus glucose as growth medium) and a periodontal disease-like state (pH 7.5 with Brain Heart Infusion (BHI) plus yeast extract, haemin and cysteine as growth medium). The mixed culture included S. oralis, S. gordonii, S. mutans, S. mitis, L. casei, Actinomyces viscosus, Veillonella dispar, Fusobacterium nucleatum, Prevotella nigrescens, P. gingivalis and Neisseria subflava. In the caries-like microcosm, steady state was dominated by streptococci, Lactobacillus and Veillonella sp. with low but detectable levels of Neisseria, Actinomyces and Fusobacterium sp. All species were affected to approximately the same degree following pulses of the antimicrobial agents. In the periodontal disease state, both triclosan and triclosan monophosphate affected the Gram-negative anaerobes to a greater extent than the Gram-positive groups. Although this study used four different streptococcal species in the inoculum, the streptococci were grouped as one taxa for the statistical analysis, not taking into account the ecophysiological differences within the genera (S. mutans being aciduric thrives at pH 5.5, while S. gordonii struggles), thus ignoring the premises of the “ecological plaque hypothesis” noted above. Studies of S. sanguis group bacteria grown in chemostat cultures have indicated that protein profiles are altered when the environmental pH has been

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changed. In a single continuous culture study, S. sanguis (gordonii) G9B was grown at a range of dilution rates (D = 0.10 h-1 to 0.50 h-1) and pH values (pH 5.5 to 7.5) in medium containing either glucose or fructose differing in concentrations of Na+ and K+ (Knox et al., 1985). The study found that the cell density of S. sanguis in the chemostat was generally higher in medium where glucose was limited compared with that where fructose was limited, and that the yield of cells increased when the major ion in the medium was K+ rather than Na+, particularly at pH 6.5 and 7.5 where higher yields were obtained irrespective of the carbohydrate source. The study showed that the cell surface characteristics, extracellular protein profiles, and lipoteichoic acid production, by S. sanguis (gordonii) G9B were altered by changes in growth rate, pH and medium composition, with the total amount of extracellular protein greater at pH 7.5 than at other pH values (Knox et al., 1985). Studies have indicated that arginine plays a key role in the nutrition of S. sanguis (Rogers et al., 1986; Rogers et al., 1987). Arginine can be released from arginine containing peptides to provide energy and release ammonia through its conversion to ornithine via the arginine deiminase pathway (Rogers et al., 1988). To expand on these studies Rogers et al. (1990) examined protease activities of S. sanguis following growth in glucose-limited continuous culture in a chemically defined medium with either free amino acids or casein as the nitrogen source. D was 0.05 h-1 to 0.70 h-1 and pH 7.0 for growth experiments, and D = 0.01 h-1 and a range of pH (pH 6.0 to 8.0) for other experiments. Both culture supernatant and cell- associated endopeptidase and exopeptidase (aminopeptidase and carboxypeptidase) activities were determined. The growth rate had little effect on endopeptidase activity, 75% of which was consistently in culture supernatants; aminopeptidase and carboxypeptidase both decreased as the growth rate was increased and both were predominantly cell-associated (Rogers et al., 1990). At a growth pH of 8.0, endopeptidase activity was substantially increased, while aminopeptidase and carboxypeptidase activities were optimal at pH 7.0. The most prominent nutritional effect occurred under nitrogen limitation (glucose excess) when endopeptidase and aminopeptidase were greatly increased and carboxypeptidase greatly decreased. The study concluded that S. sanguis is able to scavenge its environment for arginine under a wide range of growth conditions (Rogers et al., 1990).

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A study of carbohydrate metabolism by S. sanguis found that the glycolytic activity of cells harvested from the chemostat was affected by the composition of the medium, the pH of the environment, and their original conditions of growth (Marsh et al., 1985). In this study cells were grown at D = 0.05, 0.10, 0.20 and 0.40 h-1. Steady state chemostat cells were harvested and washed with 200mM KCl prior to determining glycolytic activity using a pH-stat, or being assayed for phosphoenolpyruvate (PEP) phosphotransferase system (PTS) activity. Bacterial PTS catalyses the transfer of the phosphoryl group from phosphoenolpyruvate to its sugar substrates (the PTS sugars) and is associated with the translocation of these sugars across the bacterial membrane. The phosphorylation of a given sugar requires four proteins, two general proteins, Enzyme I and HPr, and a pair of sugar-specific proteins designated as the Enzyme II complex. Marsh et al. (1985) found that glucose-limited cells produced more acid than those grown under conditions of glucose excess. At slow growth rates, in particular, greater glycolytic rates were observed with sucrose compared with glucose or fructose. Maximum rates of glycolytic activity were obtained at pH 8.0 (except for cells grown at D = 0.40 h-1 where values were highest at pH 7.0), while slow-growing, amino acid-limited cells could not metabolise at pH 5.0 (Marsh et al., 1985). It was found that changes in growth conditions did not influence the activity of the PTS transport system in S. sanguis as much as they influenced the activity of the PTS transport system S. mutans (Marsh et al., 1985; Slee and Tanzer, 1980), and that the rate of acid production was much slower than S. mutans, with S. sanguis unable to produce acid at low pH values (Harper and Loesche, 1984; Marsh et al., 1985). The results of Marsh’s study indicated that although S. sanguis may contribute to the early stages of dental caries, it is unlikely to be involved in the clinical progression of a lesion due to lack of acid tolerance (Marsh et al., 1985). In another single species continuous culture study, S. sanguis was used to examine several proteases and glycosidases, including a thrombin-like activity that is a potential virulence factor for endocarditis (Section 1.1). Cultures were grown with limiting glucose or galactose in chemostats over a range of dilution rates and pH. At least eight protease or peptidase activities and 11 glycosidase activities were detected in S. sanguis with those with the highest rates of thrombin-like activity, Hageman

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factor-like activity, N-acetyl-β-D-galactosaminidase activity, β-D-glucosidase activity and β-D-galactosidase activity, selected for further investigation. The specific activity of all five enzymes increased, in the glucose-limited chemostat, as the dilution rate decreased from 0.40 h-1 to 0.05 h-1. At a D of 0.10 h -1, specific activities generally were highest in cells grown at pH 6.5 and lower and approximately equal at pH 5.5 and 7.5. The major exception was the thrombin-like activity, for which the specific activity at pH 7.5 was approximately 5-fold higher than at pH 5.5. Hageman factor-like activity was apparently glucose catabolite repressible, as its activity was 3-fold higher in galactose cultures. The measured activities changed as functions of growth conditions and thus were seen to be modulated by the environmental conditions. The authors concluded that the environmental regulation of thrombin-like activity by pH may be more relevant in the relatively constant pH environment of the tissues, than in the fluctuating pH environment of the oral cavity (Mayo et al., 1995).

1.3.3.2. Studies on the effect of changes to environmental pH on mutans streptococci

There has been considerable research using chemostats as model systems to study streptococcal carbohydrate metabolism, particularly the biochemical and physiological adaptations that allow streptococci to survive at low pH in the oral cavity. Streptococcus rattus, a serotype b strain of mutans streptococci, was grown in an anaerobic glucose-limited chemostat, (D = 0.10 h-1 and D = 0.09 h-1) at a range of values between pH 4.8 and 7.0. A steady state was established at each pH between pH 7.0 and 5.0, but could not be attained at pH 4.8. Glucose also became detectable in the culture at pH 4.8, so it was no longer glucose-limited (Miyagi et al.,

1994). An F1F0-type proton-translocating ATPase was isolated from the membrane fraction prepared from cells grown under acidic conditions, but no detectable level of the enzyme was found in cells grown at neutral pH (Miyagi et al., 1994). Proton- translocating ATPases serve two important physiological functions in bacteria. One function is to generate ATP by utilizing the energy provided by an electrochemical gradient of protons across the cellular membrane. A second function is to counteract

16 CHAPTER 1

a loss of the transmembrane ion gradient by pumping protons at the expense of ATP hydrolysis (Singleton and Sainsbury, 1996). The results of Miyagi’s study indicated that the organism responded and adapted to environmental acidification by sacrificing energy in terms of both the efficiency of glucose utilization to generate ATP and the extra maintenance required to continue proficient biomass production (Miyagi et al., 1994). In another study on glycolysis in S. mutans, observations were made at a range of pH (pH 5.0 to 7.0). The results indicated that pH 7.0 was the intracellular pH optimum for glycolysis and that glycolytic activity decreased to zero as the intracellular pH was lowered from 7.0 to 5.0. In contrast, the extracellular pH optimum for glycolysis was 6.0. The relative insensitivity of glycolysis to the lowering of extracellular pH was attributed to the ability of S. mutans to maintain a transmembrane pH gradient at low extracellular pH (Dashper and Reynolds, 1992). Other studies have looked at the effect of acid tolerance on S. mutans survival (Jayaraman et al., 1997; Li et al., 2001a; McNeill and Hamilton, 2003; Svensäter et al., 2000; Welin et al., 2003). Many of these studies only subjected the bacterium to two pH conditions, while others used a range of pH. For instance, in one study, where S. mutans was grown in a chemostat at a range of dilution rates and pH, the effects of these parameters on two wall polysaccharides, the serotype d antigen, a galactose-glucose polymer, and the rhamnose-glucose polysaccharide (RGP) antigen, a rhamnose-glucose polymer, were determined (Linzer et al., 1984). The results indicated that both of the antigens were present in all growth conditions, although significantly less RGP antigen was present in the pH 7.5 culture (Linzer et al., 1984). Although chemostats have been used as a model for the oral environment, bacterial cells growing in biofilms are physiologically distinct from planktonic cells, such as those in a chemostat. For example, a peptide pheromone quorum-signaling system in S. mutans, involved in the induction of genetic competence, has been shown to function more efficiently in biofilms (Li et al., 2001b; Li et al., 2002b). A solution to this problem is the biofilm-chemostat, where sterile platforms are immersed into steady state cultures and then removed at set times after biofilms have formed on their surfaces. Studies utilising biofilm chemostats have examined the effect of acid stress on the physiology of biofilm cells of S. mutans. One study indicated that planktonic and dispersed biofilm cells were very acid-sensitive,

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however, older biofilms showed a strong acid tolerance that enhanced survival (McNeill and Hamilton, 2003). A second study also showed that biofilm cells, under conditions of acid stress, were more physiologically “fit” than the associated planktonic cells in the same chemostat. The biofilm cells had an enhanced capacity to maintain cellular pH homeostasis (McNeill and Hamilton, 2004). Another study of S. mutans examined acid tolerance (the organism is allowed to adapt to low pH) and acid-shock (the pH is suddenly lowered for a short period) responses in both planktonic and biofilm cells. This study demonstrated that both cell density and biofilm growth mode (high-cell-density biofilm or planktonic cells) altered acid adaptation in S. mutans, suggesting that optimal development of acid adaptation in this organism involves both low pH induction and cell-cell communication (Li et al., 2001a). In this study, mutants, defective in the comC, comD, or comE genes, which encode a quorum sensing system essential for cell density-dependent induction of genetic competence, had a diminished log-phase acid tolerance response. The authors suggested that biofilms might provide bacterial cells with a unique environment to fully express their adaptive survival mechanisms. The three dimensional structures, high cell density, and diffusion barriers, result in bacterial cells at different locations within a biofilm not sensing the same degree of pH stress simultaneously. The cells that first sense a pH stress may rapidly process the information and pass their secondary signal to the other members of the population through cell-cell signalling, coordinating a protective response. Unlike planktonic cells, which are required to reach a critical concentration of signal molecules and cell density for quorum sensing to occur, biofilms can allow signal molecules to accumulate rapidly in the local environment (Li et al., 2001a). These results confirmed previous findings that a peptide pheromone system controls genetic competence in S. mutans and that the system functions optimally when the cells are living in actively growing biofilms (Li et al., 2001b). However, there is a major drawback in using biofilm-chemostats in proteomic studies, as a relatively small amount of cells can be collected and processed when compared with the planktonic cells that can be harvested from a chemostat culture.

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1.4. PROTEOMICS

The word "proteome" originated in 1994 when Marc Wilkins coined the term to describe the set of all PROTEins expressed by a genOME (Wilkins et al., 1996). While genomics and transcriptomics provide basic information on DNA sequences, regulatory elements, and gene expression, proteomics provides quantitative information on the total protein profile of a cell, tissue, or organism. Proteomics allows the level of protein expression to be evaluated and can be used to determine the presence of protein isoforms and post-translational modifications or to examine protein-protein interactions. Eventually proteomics may, using a combination of different proteomic techniques, enable a complete description of cellular function (Tyers and Mann, 2003).

1.4.1. Proteomic methods

1.4.1.1. Two-dimensional electrophoresis (2-DE)

Several different methods are available for evaluation of a cell's proteome. As far as oral microorganisms are concerned, only one method of proteome analysis, two- dimensional electrophoresis (2-DE), has been used to any extent. 2-DE allows for the visualization of hundreds of proteins at a time (Rabilloud, 2002). It relies on isoelectric focusing (IEF) in the 1st dimension to separate proteins according to their isoelectric point (pI), followed by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) in the 2nd dimension to separate proteins according to their relative molecular weight (Mr). By using mass spectrometry to identify the proteins, one can produce reference maps that document the functional proteome, similar to the one recently published for S. mutans (Jacques et al., 2002; Len et al., 2003). Although 95% of a bacterial genome is expressed as protein products (Humphery-Smith, 1999), the theoretical resolving power of 2-DE is still estimated to be approximately 75% of the proteome (Cordwell et al., 2000). Proteins with extreme pI values, especially very basic proteins, as well as very low and high Mr proteins, are not readily resolved by current 2-DE technology (Harry et al., 2000). In

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practice, many other proteins are poorly represented, including low-abundance proteins and hydrophobic proteins, particularly integral cytoplasmic membrane proteins, which, although representing 30% of the proteome of a bacterium, are either not detected or represent less than 1.0% of the proteins displayed on 2-DE gels, even in membrane enriched fractions (Jacques et al., 2002; Len et al., 2003; Santoni et al., 2000). This is due to their poor solubility and inherent hydrophobicity. This should be contrasted with membrane-enriched fractions from Gram-negative bacteria which allow the detection of lipoproteins and outer membrane proteins by 2-DE (Cullen et al., 2002; Hommais et al., 2002; Langen et al., 2000; Molloy et al., 2000; Molloy et al., 2001; Nilsson et al., 2000; Nouwens et al., 2000; Phadke et al., 2001; Qi et al., 1996). Whereas the transmembrane domains of outer membrane proteins possess β-pleated sheets formed from alternating polar and non-polar amino acids, integral cytoplasmic membrane proteins generally possess hydrophobic α-helical transmembrane domains consisting of non-polar amino acids. Integral membrane proteins therefore, maintain an overall hydropathy that is hydrophobic even when denatured (Santoni et al., 2000). Unfortunately, solubilization of denatured integral cytoplasmic membrane proteins requires the use of surfactants such as sodium dodecyl sulfate (SDS), which are incompatible with immobilised pH gradient (IPG) strips. Despite recent advances in protein solubilization, separation of hydrophobic integral membrane proteins by 2-DE remains a problem for all cell types (Eucarya, Bacteria, Archea). Clearly, different methods of solubilization and/or new methods for selecting or enriching for peptides and proteins associated with cytoplasmic membranes are required for any comprehensive 2-DE display irrespective of cell type (Santoni et al., 2000).

1.4.1.1.1. Protein detection on 2-DE gels, staining, radiolabelling and immunodetection

Quantitative visualization of proteins on 2-DE gels is paramount for differential or comparative proteome analyses. Silver staining offers high sensitivity but poor linear dynamic range, while Coomassie Blue staining lacks sensitivity. Many researchers overcome this problem by first using a fluorescent stain, such as

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SYPRO Ruby, that has a linear dynamic range almost 700 times greater than that of silver stain and allows for accurate visualization and quantification of proteins with the use of a variety of analytical software (Lopez et al., 2000). Recently a new fluorescent stain, Deep Purple, which is more sensitive than SYPRO Ruby, has been developed and became commercially available in late 2003 (Mackintosh et al., 2003). After imaging the fluorescent stained gel, a second stain, such as Coomassie Blue, can be used to reveal proteins for excision in visible light prior to analysis (Cordwell et al., 2002). Although protein detection is mainly achieved using staining, other methods, such as immunodetection and radiolabelling are commonly used. Immunodetection of specific proteins can be achieved on 2-DE gels by the use of Western blotting. First the proteins are transferred onto a nitrocellulose membrane, which is exposed to a specific antibody that has been coupled to a radioactive isotope, an easily detectable enzyme, or to a fluorescent dye (Alberts et al., 1994). Another method for detecting proteins on 2-DE gels is radiolabelling. In this technique 3H, 14C, 35S, 32P, 33P, or 125I are incorporated into proteins (Patton, 2002). After 2-DE, the signal is detected using film, by direct autoradiography for the γ-emitting isotopes or by fluorography for the β-emitting isotopes. Coomassie and silver staining methods are often used in conjunction with raiolabelling to simultaneously access total protein expression levels along with the protein synthesis rates, obtained from the use of the radioisotopes. Visual staining also shows landmarks for low abundant proteins on the 2-DE gels (Patton, 2002).

1.4.1.2. Other proteomic methods: MUlti-Dimensional Protein Identification Technology (MUDPIT), Isotope Coded Affinity Tag (ICAT) and Tryptophan Residue Labelling

Another method of proteome analysis is now also commonly used. This method, MUlti-Dimensional Protein Identification Technology (MUDPIT), uses liquid chromatography to separate peptides derived from proteins rather than first separating the proteins themselves (Aebersold and Cravatt, 2002; Link et al., 1999; Link, 2002; Rabilloud et al., 1999). In this approach the total protein mixture is

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digested and the peptides loaded onto a strong cation exchange column. Peptides from step-gradient elutions are subsequently bound on a reverse phase column. The reverse phase column is then eluted with a solvent gradient and the eluate passed onto an electrospray ionization (ESI) tandem mass spectrometer (Section 1.5.2 below). This allows identification of the eluting peptides by uninterrupted tandem mass spectrometry (MS/MS) search. The process is repeated until all of the peptides have been eluted and analysed. The downside of this technique is that it only gives a raw list of proteins present in the sample, without accurate quantification. Lack of quantification, however, can be overcome by the use of an Isotope Coded Affinity Tag (ICAT) approach. Unlike MUDPIT technology, with which it shares the same basic methods of separating and detecting peptides, ICAT technology allows the relative levels of peptides to be determined (Flory et al., 2002). This technique selects for cysteine-containing peptides by covalently binding an ICAT probe composed of a biotin tag, a linker and an iodoacetamide handle. The linker can be made in a light or isotopically labelled heavy version containing eight 2H or 9 13C-atoms. The two probes are indistinguishable chemically, but possess mass differences of 8 or 9Da, respectively. When comparative analysis of two extracts is made, proteins from one extract are labelled with the light probe and the other with the heavy probe. The two extracts are pooled and digested with proteinase, prior to loading onto an avidin affinity column that retains the labelled cysteine-containing peptides. After elution of the retained peptides onto a reverse phase column, the peptides are detected by ESI MS/MS in a similar manner to the MUDPIT technique, except that in this instance it is possible to determine the mass signal intensity ratio of the two forms of the co-eluted peptide and hence the ratio between the two peptides in the pooled samples and thus the proteins from which they were derived. Mass spectral comparison of the differentially labelled peptides using the software program, ProICAT (Applied Biosystems, Applied Biosystems, Forester City, CA, USA) provides a ratio of the concentration of the proteins in the samples. The method allows the identification and quantification of many membrane, acidic, basic and low-abundance proteins not likely to be readily detected by 2-DE proteome methods. It does this by reducing sample complexity by selecting for cysteine-containing peptides. This would be a major limitation when working

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with streptococci, as 36% of S. mutans proteins, including 28% of all integral membrane proteins, have been found to be cysteine free. A recent development in ICAT technology is the availability of an acid- cleavable linker that allows removal of the biotin affinity tag before MS or MS/MS analysis. Cleavable ICAT reagents improve MS/MS performance by reducing fragmentation of the tag and thereby significantly increasing the number of proteins that can be identified and quantified with confidence. Incorporation of 13C rather than 2H into the ICAT heavy reagent molecule promotes co-elution of the heavy and light isotopes in reverse phase chromatography, again increasing the accuracy of mass spectral measurements and thus overcoming one of the limitations of the original 2H-labelled ICAT reagents. By using 13C-labelling instead of 2H, the mass difference between the heavy and light reagents changes from 8 to 9 Da, and so avoids possible confusion between oxidized methionine and two cysteines labelled with ICAT reagents in liquid chromatography electrospray ionisation (LC ESI) mass spectral mode (Section 2.7.4). The drawback to this technique is that the cysteine content differs widely from one protein to another, so that some proteins will be assigned by several peptides while others will rely on a single peptide. Since several proteins are cysteine-free, these will not be analysed by this technique. Also, these technologies do not allow for the detection of post-translational modifications, as does 2-DE, however application of MUDPIT and ICAT techniques does enable many integral membrane proteins to be analysed (Han et al., 2001). Another method for quantification and sequence identification of individual proteins in complex mixtures is based on labelling with the chemical reagent 2- nitrobenzenesulfenyl chloride (NBSCl) in conjunction with MS/MS. In this method, there is selective introduction of the 2-nitrobenzenesulfenyl (NBS) moiety onto 13 tryptophan residues, and a 6 Da mass differential is generated using C6-labelled 13 12 12 NBSCl (NBSCl- C6) and C6-labelled NBSCl (NBSCl- C6). The 6 Da mass 12 13 differential between the NBS- C6-labelled and the NBS- C6-labelled peptides assigns a mass signature to all tryptophan-containing peptides in any pool of proteolytic digests for protein identification through peptide mass mapping (Kuyama et al., 2003). Although this method appears to be an effective and simplified approach to proteome analysis its drawback is it cannot target tryptophan-free

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proteins. This method would be difficult to use with proteins from bacteria such as S. mutans, which are 24% tryptophan free (in-house analysis by G. Browne), unless used in conjunction with other methods.

1.4.2. High throughput integrated proteomics platforms

By integrating separation technologies with robotics, it is possible to create high throughput proteome platforms. These systems are prohibitively expensive for small research laboratories and are not currently available in Australia. One such example is the integrated 2-DE proteomics platform, ProteomeIQ, developed by Proteome Systems (Proteome Systems, Sydney, Australia; http://www.proteomesystems.com). The platform covers all areas of proteome analysis from sample preparation, protein image acquisition and analysis, protein identification, data management and system control. Proteome Systems, in partnership with Shimadzu Biotech, has also developed a Chemical Inkjet Printer, the ChIP (Proteome Systems, Sydney, Australia; Shimadzu Biotech, Kyoto, Japan; www.proteomesystems.com), which processes protein samples electroblotted from 2-DE gels onto membranes. The piezoelectric printing technology allows multiple enzymes to be used to digest proteins in situ on the membrane or to undertake alterative chemistries on a single protein spot such as hydrolysis of carbohydrates in glycoproteins. The membrane is then placed directly into the mass spectrometer for analysis. The blotted membrane can also be archived for future use, allowing for further analysis of the same protein spot at a later date should this be required. The whole process is controlled by a web-based bioinformatics package that serves both as a laboratory management system tracking samples at each stage of analysis and as a set of tools for protein identification and data visualisation. Another integrated system is the fluorescence two-dimensional difference gel electrophoresis system (DIGE; Amersham Biosciences, Bucks, UK3), which allows for high throughput proteome analysis using 2-DE technology. This technology was not available in Australian research laboratories until 2003. DIGE overcomes some

3 GE Healthcare acquired Amersham Biosciences in April 2004.

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of the main shortcomings of 2-DE, particularly the low reproducibility and difficulties in quantitatively comparing multiple gels (Lilley et al., 2002), by combining conventional 2-DE with fluorescent labelling making sample multiplexing on a single gel possible. Although this system was first described in 1997 (Ünlü et al., 1997), it has only recently become commercially available (Figure 1.1). Using this technique, proteins in each sample are labelled with one of three spectrally distinct fluorescent dyes, prior to electrophoresis, Cyanine-2, Cyanine-3, or Cyanine-5. The labelled samples are then mixed and separated within the same gel using 2-DE, and the differently coloured collections of co-resolved, fluorescent labelled proteins are viewed individually by scanning the gel at different wavelengths. The platform has three key components; cyanine dye chemistry for fluorescent labelling; the Typhoon™ variable-mode gel imager for quantification and identification; and the DeCyder™ software suite for data and image analysis.

Figure 1.1 Workflow for Ettan™ DIGE system. The figure shows co-separation of three fluorescent-labelled samples separated on a single 2-D gel. One sample is an internal standard. Reproduced in accordance to the copyright requirements of Amersham Biosciences Limited.

25 CHAPTER 1

The DIGE system allows for quantitative gel-to-gel comparisons. When comparing samples resolved within different gels, one of the three fluors is reserved for labelling an internal standard. This standard is generated by pooling an equal amount of all samples being compared, and is included on all gels within an experimental series. Thus, each gel can accommodate two experimental samples plus the internal standard. Normalization of the internal standard across gels allows for a direct comparison of the ratio of the relative expression of the same protein within the different gels, thereby distinguishing gel-to-gel variation from biological variation. Amersham Biosciences states that the system can pinpoint protein expression levels that vary by as little as 10% between samples with greater than 95% confidence (http://www1.amershambiosciences.com/). This and similarly integrated systems under development or recently available, allow for high throughput proteomic analysis using 2-DE technology. However, as the abovementioned are examples of technologies that were unavailable prior to 2003 it was not possible to incorporate them into the current study, as the experiments were designed and executed from 2000 to 2002. Unfortunately as mentioned such technology also comes at a price, which puts it well beyond the means of small laboratories.

1.5. MASS SPECTROMETRY

Mass spectrometry is by far the most common technique used in proteome analysis for the identification of an unknown protein, although alternatives such as N-terminal amino acid analysis or Western blotting can also be used (Section 1.4.1.1.1). Several different mass spectrometers are commercially available with various levels of sensitivity (Aebersold and Cravatt, 2002). The mass spectrometer is used to measure the Mr of a sample. Proteins can be measured with accuracy, allowing for minor changes, such as post-transitional modifications to be detected (Wilkins et al., 1999). Matrix-assisted laser desorption/ionisation (MALDI) time – of – flight (TOF) mass spectrometers can detect peptides with a Mr between 400 and 350,000, and are very sensitive, allowing the detection of low (10-15 – 10-18 mole) quantities of sample with an accuracy of 0.10 – 0.01% (http://www.nwi.unige.it/pdf/nanobiof.pdf). Tandem

26 CHAPTER 1

mass spectrometers also allow peptide sequences to be determined. In these spectrometers, a peptide ion is selected by mass/charge and broken down further into fragments that allow the peptide to be sequenced. Sequence plus mass is more informative than mass alone, and for small amounts of sample, or a mixture of a few peptides, sequencing is essential if a protein is to be identified with confidence. Mass spectrometers basically consist of three main parts, the ionisation source, the analyser and the detector (Chapman, 1996). The ionisation source depends on the mass spectrometer and sample. Ionisation methods include; Atomic Pressure Chemical Ionisation (APCI), Electron Ionisation (EI), ESI, Fast Atom Bombardment (FAB), MALDI, and Thermospray Ionisation (TSP), amongst others. The analyser’s main function is to resolve the ions formed in the ionisation source of the mass spectrometer according to the mass-to-charge (m/z) ratio. Types of mass analysers include quadrupole, TOF (Section 1.5.1) analysers, magnetic sector analysers, ion-trap analysers, and Fourier transform-ion cyclotron resonance analysers. MS/MS mass spectrometers have more than one analyser and are used for structural and sequencing studies. The detector’s purpose is to monitor the ion current, amplify it and transmit it to the data system, where it is recorded as mass spectra. The detector plots the m/z values of the ions against their intensities, showing the number of components in the sample, the Mr of each component, and the relative abundance of the components in the sample. MALDI, TOF, ESI, quadrupole and MS/MS are discussed in greater detail below (Sections 1.5.1 and 1.5.2) due to their relevance to the current study. In the mass spectrometer, the sample is placed inside the ionisation source and the sample molecules ionised. The sample is either inserted directly or can be separated into a series of components by chromatography (Section 1.4.1.2), before entering the mass spectrometer sequentially for individual analysis. The ions are extracted into the analyser where they are separated into their m/z ratios. The separated ions are then detected. The information is incorporated into a data system, where the m/z and relative abundance are stored in the format of an m/z spectrum (Chapman, 1996). Three mass spectrometers were used for protein analysis in this current study:

27 CHAPTER 1

MALDI-TOF mass spectrometry was performed on a Voyager Biospectrometry Workstation (Voyager DE-STR; Applied Biosystems, Forester City, CA, USA; Section 2.7.2), which uses a MALDI ionisation method and TOF analyser. LC ESI MS/MS was performed on a Thermo Finnigan triple quadrupole ion-trap mass spectrometer (TSQ 7000 QQQ MS, Thermo Electron Corporation, Woburn, MA, USA; Section 2.7.4), which uses quadrupole and ion-trap analysers, and an API QSTAR Pulsar i hybrid tandem mass spectrometer (Applied Biosystems, Forester City, CA, USA; Section 2.7.4), which uses quadrupole and TOF analysers. The following, Section (Section 1.5.1), describes the workings of the MALDI-TOF instrument, while Section 1.5.2 describes ESI mass spectrometry providing descriptions of the quadrupole (Section 1.5.2.1) and ion-trap (Section 1.5.2.2) analysers.

1.5.1. MALDI-TOF

MALDI-TOF instruments measure peptide mass only. These instruments, while not highly sensitive, can generally analyse excised proteins from Coomassie-Blue- stained 2-DE gels (Lauber et al., 2001). In the MALDI-TOF process, proteins are first digested with proteinases such as trypsin and the peptides mixed with a large excess of UV absorbing matrix (usually a cinnamic acid derivative) and allowed to dry to a small spot on a MALDI plate (Figure 1.2). A pulsed laser illuminates the spot in a vacuum and the matrix absorbs photon energy from the laser and transfers it into excitation energy. The matrix itself serves as a solvent for the peptide, so that the intermolecular forces are reduced and aggregation of the peptide molecules is minimised. The peptides are vaporized and ionised in the resulting gas plume (Figure 1.3), and extracted by an electric field into a TOF mass analyser (Chapman, 1996). MALDI-TOF instruments can be used in either linear or reflectron mode. In linear mode the ions move along a linear flight path. Their m/z is calculated according to the time it takes to reach the detector (TOF). The theory is that ions with larger mass have lower velocities, and therefore take longer to reach the

28 CHAPTER 1

detector. In reflectron mode the ions are reflected by an "ion-mirror” at a slight angle to a detector. The “ion mirror” is a series of electrical potentials of the same polarity as the ions. This process extends the length of the flight path, increasing resolution, giving higher mass accuracy, and enabling limited mass spectromic sequencing via post source decay (Chapman, 1996).

(a) The protein spot of interest on a 2-DE gel (indicated by arrow) is excised either by hand or by a robotic sampler.

(b) Tryptic digestion of protein spot and transfer of peptides to a MALDI-TOF target plate.

1243

(c) Ions are generated by the firing of a laser at a target plate and a fingerprint spectrum of the 1244 relative masses of the peptide 1270 ions calculated based on the time difference between the 1223 laser firing and the arrival of the ions at the detector.

Mass (m/z)

(d) The protein is identified by access to a database containing a theoretical digest of proteins from either the organism of interest or one that is closely related. The In the example the spot theoretical masses are compared with the in (a) is identified by experimentally observed masses, with accurate protein identification dependent MALDI-TOF to be a on the total percentage of the sequence glyceraldehyde-3- covered by the matching peptides and the phosphate size of the original protein. dehydrogenase fragment from Streptococcus gordonii.

Figure 1.2 Protocol for protein identification with the use of mass spectral data from a MALDI- TOF mass spectrometer. Modified from Macarthur and Jacques (2003).

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x LASER Sample plate

_ _ _ _ _ + + + + _ + + AH+ + _ + +

+ 20 kV Variable Ground grid grid

Figure 1.3 Matrix assisted laser desorption-ionization (MALDI). Sample (A; pink dots) is mixed with matrix (M; blue dots) and dried on a MALDI plate. A laser flash ionises the matrix molecules. Sample molecules are ionised by proton transfer from the matrix: MH+ + A → M + AH+. The sample plate is supplied with 0 to 25 kV to accelerate the ions into the flight tube. The variable-voltage grid supplies additional voltage to fine-tune ion acceleration. The ground grid is the ground surface for formation of the potential gradient.

With MALDI-TOF, the identity of the unknown protein is determined from a table of the experimentally derived peptide masses, which together are known as the peptide-mass fingerprint (PMF). The PMF is a signature of a protein and can be compared with the PMF theoretically generated with the same proteinase for each and every protein present in a non-redundant database. In practice, a comprehensive proteome analysis requires the PMF to be compared with an annotated protein database constructed from the genome of the organism under investigation. If the

30 CHAPTER 1

genome has not been sequenced, the PMF can be compared with very closely related species from the same genera. For example, PMFs from 2-DE protein spots obtained from S. oralis and S. mutans have been used to search the annotated genomic database of S. pneumoniae and S. pyogenes (Wilkins et al., 2002). Unfortunately, successful protein identification relies on a high degree of peptide sequence identity between species. Any alteration in peptide sequences and peptide masses will result in a failure of the protein to be identified by MALDI-TOF techniques. As noted, MALDI-TOF is not that sensitive, and hence many proteins detected on 2-DE gels with the use of fluorescent stains or radiolabelling are of insufficient concentration for analysis by this technique (Len et al., 2003). One of the major limits to the practical sensitivity of MALDI mass spectrometry is caused by the background noise rather than the inherent sensitivity of the mass spectrometer. Mass spectra obtained on a MALDI-TOF mass spectrometer show background ion peaks at essentially every m/z value (Krutchinsky et al., 2000). This has been named the “chemical noise background” (Krutchinsky and Chait, 2002). As the amount of material of interest subjected to MALDI analysis is reduced, the signal decreases to the point where it can no longer be differentiated from the chemical noise due to the generation of a low signal-to-noise ratio (Figure 1.4; Krutchinsky and Chait, 2002).

1.5.2. Electrospray ionisation mass spectrometry

Where peptides already exist, such as those eluted by MUDPIT (Section 1.4.1.2), they can be analysed online by mass spectrometers equipped with ESI injectors rather than MALDI plates. In these instruments, a voltage is applied to a fine needle containing a dilute solution of the peptides. This results in a spray of droplets typically containing just a few molecules (Figure 1.5). Repeated break-up of the droplets due to evaporation eventually leads to release of intact peptide ions into the gas phase, whence they are sampled into the mass spectrometer and analysed. Since these instruments work online and at atmospheric pressure, continuous uninterrupted analysis of peptides is possible.

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Spec #1[BP = 1454.8, 5871] 1454.8429 100 5871.0

90 (a) A “good spectrum” identified

80 as DTDP-4-keto-6- 80.3324 BP% deoxyglucose-3,5-epimerase. 70

60 y nsit

e 50 % Int 40 1786.0726 1064.6128 2228.3412 30 1192.7264 1468.8522 1724.8352 854.3347 2165.9021 2530.5457 1077.7461 1260.8883 1708.0275 1938.2525 2349.2442 2761.7177 3047.9519 3318.8592 20

10

0 0 835.0 1368.2 1901.4 2434.6 2967.8 3501.0 Mass (m/z) Spec #1[BP = 1454.8, 5871] 1454.8429 100 5871.0 1455.8442 90 (b) A close up view of the spectrum in (a) above. There is 80.3324 BP% 80 a clear monoisotopic peak at

70 1454.8 Da. The PMF data produced 48.8% peptide 60 y coverage for this protein. it 1456.8494 ns

e 50 Int % 40 1457.8234 30 1456.3074 1468.8522 1446.8325 1451.8190 1459.9092 1463.8781 1474.8412 1478.8910 1482.8413 1489.4615 20

10

0 0 1444.0 1453.8 1463.6 1473.4 1483.2 1493.0 Mass (m/z) Spec #1[BP = 886.3, 2205] 886.3508 100 2205.0

90

80 842.5883

70 918.9985 1097.2187 1424.8964 1735.2993 1170.6155 1473.2760 1972.5889 2157.6289 2459.2912 60 2665.4992 2910.0219 3152.6090 2255.9493

50 Intensity % 40 (c) A “bad spectrum” of a spot, which remained unidentified. When the

30 magnitude of the signal from peptide ions becomes comparable to that of background ions, the ion peaks of interest begin to merge with the noise 20 and can no longer be distinguished due to a high signal-to-noise ratio. 10

0 0 835.0 1368.2 1901.4 2434.6 2967.8 3501.0 Mass (m/z)

Figure 1.4 Mass spectra of two protein spots from this current study.

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Capillary, 3- 4 kV

Droplet evaporating + _ + + _ __ + _ + ++ + _ + + + _+ + + ++ __+ +_ _ + _ + + + _ _ + + Droplet containing ions Ions evaporating from the surface of the droplets

Figure 1.5 Electrospray ionisation (ESI). A voltage is applied to a fine needle containing a dilute solution of the peptides. This results in a spray of droplets typically containing just a few molecules. Repeated break-up of the droplets due to evaporation eventually leads to release of intact peptide ions into the gas phase, whence they are sampled into the mass spectrometer and analysed.

1.5.2.1. Tandem mass spectrometry (MS/MS) triple-quadrupole instrument (used for peptide sequencing)

Triple quadrupole mass spectrometers (Figure 1.6) are used to analyse enzymatic digests of proteins and MS/MS can be performed to elucidate amino acid sequence information for peptides through the use of collision-aided dissociation. The presence of posttranslational modifications can also be directly determined. Although data interpretation has historically limited the throughput of this mass spectrometry approach, algorithms have been developed to automatically interpret tandem mass spectra. The tandem mass spectra are used to search the database using database searching software, such as SEQUEST, thus allowing the identification of proteins present in the mixture from their amino acid sequences (Yates, 1998).

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Figure 1.6 Triple quadrupole mass analyser. Image courtesy of Thermo Finnegan (http://www.thermo.com/). In the first quadrupole,Q0, the ions are scanned by varying the DC/Rf quadrupole voltages. Only ions with the selected mass to charge ratio will have the correct oscillatory pathway in the Rf field. The second quadrupole, Q1, is the first measuring quadrupole. In Q2 fragmentation occurs due to a collision chamber filled with a gas, such as nitrogen or helium. The fragment ions are separated in Q3, the second measuring quadrupole, and then move on to the detector (Chapman, 1996).

The triple-quadrupole instrument uses electric fields to separate ions according to their m/z values. They filter out ions, except those with a predetermined mass, by passing the ions along a line of symmetry between four parallel cylindrical rods (the quadrupole). A quadrupole consists of four parallel rods or poles through which the ions being separated are passed. Each rod is applied with a direct current (DC) and a radio frequency (Rf) voltage (Figure 1.7). Depending on the produced electric field, only ions of a particular m/z will be focused on the detector, all the other ions will be deflected into the rods. By varying the strengths and frequencies of electric fields, different ions will be detected thus making the mass spectrum.

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Figure 1.7 The quadrupole. Image courtesy of Thermo Finnegan (http://www.thermo.com/).

1.5.2.2. Ion trap

The ion trap is a mass analyser made up of two endcap electrodes (entrance and exit) and a ring electrode (Figure 1.8). An ion trap mass spectrometer separates ions based on m/z. Once ions are introduced into the ion trap mass spectrometer, the radio frequency amplitude is increased so that ions are sequentially ejected (by increasing mass) and detected. A quadrupole ion trap is a sensitive mass spectrometer (Jonscher and Yates, 1997). The mass range of commercial LC-traps is well matched to the range of m/z values typically generated from the electrospray ionization process and the unit resolution provided throughout the mass range affords charge state identification of multiple-charged peptide ions. Quadrupole ion trap mass spectrometers can analyse peptides from a tryptic digest present at the 20-100 fmol level. Perhaps the greatest strength of the ion trap technique lies in the ability to perform multiple stages of mass spectrometry, unlike a triple quadrupole instrument or a standard TOF mass spectrometer. Up to 12 stages of MS/MS have

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been performed using an ion trap, greatly increasing the amount of structural information obtainable for a given molecule (Jonscher and Yates, 1997).

Figure 1.8 Ion-trap analyser. Image courtesy of Thermo Finnegan (http://www.thermo.com/).

1.6. PROTEOMICS AND ORAL PATHOGENS

While the characterization of proteins observed under a range of in vitro growth conditions is essential for complete definition of the proteome, differential or comparative proteomics is more frequently used to establish changes in protein expression when cells are grown under different physiological conditions. Recently reported studies with oral microorganisms invariably use this approach. As can be seen from the examples that follow, little application of proteomic approaches have been made to the study of oral pathogens.

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1.6.1. Proteomics and periodontal Diseases

Periodontal diseases are a group of disorders in which the supporting tissues of the teeth are infected. In disease, the gingival crevice becomes a pocket, and the Eh falls, becoming highly anaerobic. Many of the bacteria associated with periodontal diseases are proteolytic, and consequently the pocket, in the disease state, becomes slightly alkaline (pH 7.4 – 7.8). The alkaline environment enhances the growth and enzyme activities of some periodontopathogens, such as P. gingivalis (Marsh and Martin, 1999). The flow of gingival crevicular fluid removes microorganisms that are not firmly attached to a surface. The cementum surface of the tooth is mainly colonised by the Gram-positive genera Streptococcus and Actinomyces naeslundii, while periodontopathogens, such as Prevotella, Porphyromonas, and Fusobacterium, can attach to this biofilm by the process of co-adhesion. Black-pigmented anaerobes and Peptostreptococcus micros can also be found in the pocket, due to their ability to adhere to crevicular epithelial cells (Marsh and Martin, 1999). P. gingivalis is associated with chronic periodontitis (Socransky and Haffajee, 1992). This bacterium is a secondary colonizer of human teeth and co- adheres to primary colonizers such as S. gordonii and Actinomyces spp. that are present in dental plaque (Lamont et al., 1993; Yamaguchi et al., 1998). A number of factors influencing the adhesion of P. gingivalis have been studied, including fimbriae (Goulbourne and Ellen, 1991), lipopolysaccharides (Okuda et al., 1991), a 40 kDa outer membrane protein (Saito et al., 1997), proteinases (Ellen et al., 1992), and an A. naeslundii aggregation factor (AnAF) that mediates coaggregation with P. gingivalis (Yamaguchi et al., 1998). As these factors are associated with the outer surface of this Gram-negative anaerobe, 2-DE has been applied to study the outer membrane sub-proteome of this bacterium with the view to determining other possible pathogenic traits (Veith et al., 2002). Of the 39 outer membrane proteins identified, several displayed pI heterogeneity that was observed as a train of horizontal protein spots on 2-DE gels. For the proteins Omp40 and Omp41, conformational equilibria resulting from incomplete denaturation were shown to account for this phenomenon (Veith et al., 2001). Other charged isoforms were not investigated, so it is not known whether this phenomenon can account for all of these

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observations or whether the isoforms are due to post-translational modifications or some other artefact. However, analysis of the multiple Mr forms of the cysteine proteinases, RgpA and Kgp, and the putative hemagglutinin, HagA, indicated that they resulted from a series of specific C-terminal truncations due to the RgpA and Kgp proteinases themselves. It was suggested that the observed vertical streaking of RgpA was due to the covalent binding of lipopolysaccharide to the C-terminus of the proteinase, suggesting a possible unique mode of attachment of the enzyme to the outer membrane of P. gingivalis. Treponema denticola adheres to P. gingivalis in matured subgingival plaque. In a study of the interaction of T. denticola with P. gingivalis, the P. gingivalis fimbria-binding protein of T. denticola was identified by 2-DE and blotting with a polyvinylidene difluoride membrane, followed by a ligand overlay assay with P. gingivalis fimbriae (Hashimoto et al., 2003). This established that the fimbria- binding protein of T. denticola was dentilisin, a major extracellular protease and virulence factor of T. denticola (Hashimoto et al., 2003; Ishihara et al., 1998). The binding was further demonstrated with a ligand overlay assay using an isolated glutathione S-transferase fusion dentilisin construct. The results suggest that P. gingivalis fimbriae and T. denticola dentilisin are implicated in the coaggregation of these bacteria.

1.6.2. Proteomics and dental Caries

The mutans-group streptococci, including S. mutans and Streptococcus sobrinus, are generally associated with the initial phase of human dental caries, since their acidogenic and aciduric properties allow them to create a low-pH environment in dental plaque following the ingestion of sugars. It is not surprising, therefore, that several 2-DE proteome studies have concentrated on the physiological adaptations associated with S. mutans survival in the oral cavity. For example, 2-DE protein analyses of 14C -labelled cellular proteins of S. mutans have been used to characterize changes in protein expression following the imposition of pH, temperature, salt, and oxidative and starvation stresses (Svensäter et al., 2000). S. mutans responded to

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these adverse environmental conditions by a complex and diverse alteration in protein synthesis. For instance, the protein profile of cells shocked from pH 7.5 to pH 5.0 revealed 64 proteins that were up-regulated (25 of them acid-specific) and 49 that were down-regulated. In a similar study, 78 14C -labelled cellular proteins were diminished and 57 enhanced out of a total of 694 analysed, when S. mutans underwent a transition from the planktonic to the biofilm state (Svensäter et al., 2001). S. mutans also expressed 13 unique proteins in the biofilm state, while nine others present in planktonic cells could not be detected. Mass spectral analysis resulted in the identification of 41 proteins, 21 of which were enhanced in biofilm cells and the remainder reduced. In general, glycolytic enzymes involved in acid formation were repressed in biofilm cells, while proteins associated with protein synthesis, protein folding, and replication were enhanced. More recently, a study of acid tolerance in S. mutans biofilm cells used a 2-DE proteomic approach and found that while the number of changes in protein expression in biofilm cells was within the same range as for planktonic cells, the magnitude of their change was significantly less in biofilm cells. This supports the observation that acidification of biofilm cells induce a negligible acid tolerance response (Welin et al., 2003) In another 2-DE study, 18 proteins were up-regulated and 12 down-regulated when S. mutans was grown at pH 5.2 compared with cells grown at pH 7.0. These proteins were involved in energy metabolism, cell division, translation, and transport (Wilkins et al., 2002). Although the general conclusions of previous studies were confirmed, anomalies were observed. For instance, the protein DnaK was down- regulated when S. mutans was grown at low pH, while transcriptional studies had shown that dnaK gene expression was up-regulated under similar conditions (Jayaraman et al., 1997). The failure of other stress-related proteins to be detected emphasizes an important aspect of current proteomics involving incomplete 2-DE displays, since any undetected changes may be of equal importance in an understanding of the true nature of phenotypic change. The role of stress-related proteins, however, has been examined in S. mutans lacking the Clp ATPase, ClpP. S. mutans lacking ClpP was impaired in its ability to grow at low pH and had a reduced capacity to form biofilms (Lemos and Burne, 2002). Comparison of silver-stained 2-DE gels revealed at least 28 proteins with

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altered levels of expression in the clpP mutant compared with the parent. In particular, evidence was presented that the molecular chaperones DnaK, GroEL, and GroES were elevated in the mutant. The loss of ClpP appeared to induce a stress response in the cells, possibly due to the accumulation of denatured proteins that are normally targeted by the ClpP proteinase. Following a similar mutagenic approach, a two-component regulator system, defined by the genes hk11 and rr11, was also shown to be involved in biofilm formation and acid resistance in S. mutans (Li et al., 2002a). Deletion of the hk11 gene resulted in a mutant that formed biofilms with reduced biomass and possessed greatly reduced resistance to low pH. Autoradiograms of 2-DE gels revealed 594 cellular proteins, 19 of which were acid-regulated in the parent, but only 15 in the mutant. Two of the four missing proteins were putatively identified as an exopolyphosphatase previously linked to biofilm formation, and the histidine kinase HK11 itself. The presence of fluoride in the environment represents another stress factor for many cariogenic bacteria. While fluoride is known to alter and protect tooth enamel from bacterial acids by forming fluorapatite in the outer layers of enamel, the effects of fluoride on dental caries causing bacteria in vivo have not been fully explored. Fluoride, however, is known to inhibit a number of bacterial metabolic processes resulting in a reduced acidification of dental plaque (Bradshaw et al., 2002). 2-DE analysis of 35S methionine labelled S. sobrinus grown in the presence of fluoride revealed that the expression of several proteins was influenced by the presence of the fluoride (Cox et al., 1999). Although these proteins were not identified, it was observed that in the presence of fluoride the chain length of S. sobrinus cells was reduced. The authors suggested that, as cells with high autolytic activity tend not to form long chains of bacteria, fluoride might enhance the activities of autolysins (Cox et al., 1999). A more comprehensive proteomic analysis of S. mutans has recently been completed (Len et al., 2003). The analysis was streamlined due to the publishing of contigs of the S. mutans genome on a public database (http://www.genome.ou.edu/smutans.html). High-resolution proteomic maps were produced after identifying proteins from cells grown in continuous culture in a

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chemostat under conditions that mimic those in the dental plaque of a healthy individual, thus establishing a base line for protein expression (Len et al., 2003). The authors of the proteomic map of S. mutans (Len et al., 2003) identified a total of 421 (314 cellular and 107 extracellular) of the 502 protein spots analysed by MALDI-TOF mass spectrometry or ESI MS/MS. This S. mutans proteomic map was used as a baseline to study alterations in the stress-response proteome of S. mutans, following adaptation and tolerance to growth at low pH (Len et al., 2004a; Len et al., 2004b). Differential 2-DE analysis resolved 199 cellular and extracellular protein spots with altered expression, 167 of which were identified by MALDI-TOF mass spectrometry (Len et al., 2004a; Len et al., 2004b). Analysis of the data indicated that changes in the expression of metabolic proteins were limited to three biochemical pathways; glycolysis, alternate acid production and branched chain amino acid biosynthesis (Len et al., 2004a). The relative expression of the protein spots representing all the enzymes associated with the Embden–Meyerhof–Parnas pathway, and all but one of the enzymes involved in the major alternative acid fermentation pathways of S. mutans, was measured (Len et al., 2004a). Proteome data, in conjunction with end-product and cell-yield analyses, were consistent with a phenotypic change that allowed S. mutans to proliferate at low pH by expending energy to extrude excess H+ from the cell, while minimizing the detrimental effects that result from the uncoupling of carbon flux from catabolism and the consequent imbalance in NADH and pyruvate production. The changes in enzyme levels were consistent with a reduction in the formation of the strongest acid, formic acid, as consequence of the diversion of pyruvate to both lactate and branched-chain amino acid production when S. mutans was cultivated in an acidic environment (Len et al., 2004a). Sixty-one of the 167 differentially expressed protein spots identified following acid tolerant growth of S. mutans at pH 5.0 were associated with stress- responsive pathways involved in DNA replication, transcription, translation, protein folding and proteolysis (Len et al., 2004b). The 61 protein spots represented isoforms or cleavage products of 30 different proteins, 25 of which were either up-regulated or uniquely expressed during acid-tolerant growth at pH 5.0. Among the unique and up-regulated proteins were five that have not previously been identified as being

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associated with acid tolerance in S. mutans and/or have not been studied in any detail in oral streptococci. These were the single-stranded DNA-binding protein, Ssb, which is involved in the SOS response, in which the expression of certain genes is induced in response to damage to DNA or inhibition of DNA replication, this response can result in increased capacity for DNA repair and inhibition of cell division; the transcription elongation factor GreA, that is required for fidelity and cleavage of mRNA following halted or blocked transcription; the RNA exonuclease, polyribonucleotide nucleotidyltransferase, associated with RNA decay; and two proteinases, the ATP-binding subunit, ClpL, of the Clp family of proteinases that is uniquely encoded by Gram-positive bacteria, and a proteinase encoded by the pep gene family with properties similar to the dipeptidase, PepD, of Lactobacillus helveticus. The identification of these and other differentially expressed proteins associated with an acid-tolerant growth phenotype provided new information on targets for future mutagenic studies that will allow the assessment of their physiological significance in the survival and proliferation of S. mutans in low pH environments (Len et al., 2004b).

1.6.3. Infective Endocarditis

S. gordonii and S. oralis are among the earliest colonizers of the primary dentition and are part of the healthy microbial flora in dental plaque (Whiley and Beighton, 1998; Section 1.1 above). Both S. gordonii and S. oralis, however, are associated with community-acquired infective endocarditis (Section 1.1), should they gain access to the vascular system (Douglas et al., 1993). Oral bacteria in a healthy mouth are exposed to slightly acidic conditions (pH 6.0 to 6.5), but when they gain access to the bloodstream, there is an immediate rise to pH 7.3. This may be a stimulus for a change in protein expression and the subsequent ability of the bacteria to colonize a damaged heart valve (Vriesema et al., 2000). Glyceraldehyde-3-phosphate dehydrogenase (GPDH) is implicated as a virulence determinant aiding S. pyogenes invasion of tissues and is considered to act as a possible defence against the immune system in S. gordonii (Nelson et al., 2001).

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Interestingly, when S. oralis grown at either pH 5.2 or pH 7.0 was analysed by 2-DE, 28 cellular proteins, including GPDH, were down-regulated at pH 7.0 (Wilkins et al., 2001). At first sight, this observation seems incompatible with a role for the protein as a virulence determinant in infective endocarditis. However, in S. gordonii, the GPDH becomes the major extracellular protein as the pH rises to pH 7.5 (Nelson et al., 2001). Since the amount of extracellular GPDH was not determined when S. oralis was grown at different pHs, it is not clear whether the reduction in the amount of cellular protein is due to down-regulation of expression or simply secretion into the extracellular milieu as the pH rises. Antibiotic resistance is a major problem for the control of infective endocarditis, requiring an understanding of the changes in the proteome of the resistant phenotype. To this end, the mechanism of penicillin tolerance has been examined in S. gordonii by 2-DE (Caldelari et al., 2000). A tolerant mutant contained two proteins with increased intensity. Comparison of the sequences of their N-terminal amino acids with known proteins showed that they were homologous to the N-termini of arginine deiminase and ornithine carbamoyl transferase of the arginine deiminase (arc) operon. Although the penicillin-tolerance mutation mapped at a physically distinct location on the chromosome from the arc operon, genetic transformation of tolerance always conferred arc deregulation. It was concluded that the tolerance mutant affected a global regulatory mechanism that was important for survival in the presence of penicillin. The correlation between tolerance and arc deregulation has allowed a reporter system to be developed that will aid in determining the central mechanism of penicillin tolerance underlying this clinically important phenotype. S. oralis, a member of the mitis group of oral streptococci, is also implicated in the pathogenesis of infective endocarditis (Marsh and Martin, 1999). Soluble cellular proteins were extracted from S. oralis grown in batch culture at pH 5.2 or 7.0 and were analysed by 2-DE. Thirty-nine proteins that had altered expression at low pH were analysed by MALDI mass spectrometry. The resulting PMF were compared with the genomic database for S. pneumoniae, an organism that is phylogenetically closely related to S. oralis, and putative functions for the majority of these proteins were determined on the basis of functional homology. Twenty-

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eight proteins were up-regulated following growth at pH 5.2. These included the glycolytic proteins GPDH and lactate dehydrogenase, the polypeptide chains comprising ATP synthase, and general stress response proteins including the 60-kDa chaperone, Hsp33, superoxide dismutase, and three distinct ABC transporters. This was the first study to identify gene products that may be important in the survival and proliferation of non-mutans aciduric S. oralis under conditions of low pH that are likely to be encountered by this organism in vivo (Wilkins et al., 2001). In a more recent study, the same researchers examined the most abundant surface-associated proteins of S. oralis and investigated changes in protein expression when the organism was grown under acidic culture conditions in batch culture. A total of 27 proteins were identified including a lipoprotein, a ribosome recycling factor, and the glycolytic enzymes, phosphoglycerate kinase, fructose bisphosphate aldolase, GPDH, and enolase. The most abundant protein, phosphocarrier protein HPr, was present as three isoforms. The role of these surface-associated proteins in the survival and pathogenicity of S. oralis are currently unknown (Wilkins et al., 2003). Surface related proteins play an important part in the virulence of streptococci associated with infective endocarditis, interacting with host components, such as fibronectin and plasminogen, or interacting directly with the host’s cells, triggering signal transduction and enabling the pathogens to invade the host tissue. Five glycolytic enzymes, sequential enzymes in the second half of the Embden-Meyerhof- Parnas glycolytic pathway (GPDH, enolase, phosphoglycerate kinase, phosphoglycerate mutase and trioephosphate isomerase), have been reported to be present on the surface of streptococci (Pancholi and Chhatwal, 2003). As well as these five, three other glycolytic enzymes (6-phosphofructokinase, aldolase and pyruvate kinase) have been found to be present in the culture supernatant of S. pyogenes (Lei et al., 2000), although it is unclear whether these three proteins are actively secreted or passively released from cells undergoing autolysis. It is clear that the activity of surface related protein of S. gordonii warrants further investigation.

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1.7. SPECIFIC AIMS OF THIS THESIS

It is known that oral biofilms are host to more than 700 bacterial taxa (Kazor et al., 2003), and that this estimate will increase as the latest techniques are used to detect new phenotypes (Byun et al., 2004). This level of complexity clearly prevents meaningful concurrent phenotypic analysis using current proteome technologies. In order to unravel environmental effects, it is therefore necessary to determine phenotypic responses in individual species. Only by thorough and comprehensive investigation of the phenotypic changes brought about by environmental constraints can a clear insight into the multitude of observed changes be understood. A subsequent reductionist approach in light of this new knowledge will then allow appropriate susceptible biochemical events to be targeted to eliminate disease. The hypothesis of this study is that changes in the environmental pH of S. gordonii should be reflected phenotypically in changes to the proteome of S. gordonii. The primary goal of this thesis was to map the proteome of S. gordonii and to observe differences in protein expression of S. gordonii at different pH, in order to help elucidate the survival mechanisms S. gordonii uses when exposed to a range of environmental pH conditions in the human body, both in the oral environment and systemically as a pathogen in infective endocarditis. In order to achieve this end several key problems were addressed:

• The development of proteomic methods to produce reproducible proteomic maps. • The identification of proteins by mass spectrometry. • Image analysis for the identification of differentially expressed proteins during changes in the pH of the environment..

By using planktonic cells grown in a chemostat, a baseline model for future biofilm research was established by comprehensively studying the phenotypic changes in S. gordonii using a proteomics approach.

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CHAPTER 2. MATERIALS AND METHODS

2.1. BIOCHEMICALS, REAGENTS AND ASSOCIATED PRODUCTS

All chemicals were of analytical reagent grade except where indicated, and were obtained from the following suppliers:

Amersham Biosciences, Uppsala, Sweden: immobilised pH gradient buffer, immobilised pH gradient strips (4.0 – 5.0, 4.5 – 5.5, 5.5 – 6.7, 6.0 – 11.0).

APS Chemicals, Seven Hills, NSW, Australia: glycerol.

BDH Laboratory Supplies, Poole, England: acetic acid.

Bio-Rad, Hercules, CA, USA: carrier ampholytes (3-10), colloidal Coomassie Brilliant Blue G-250, 3-[(3-cholamidopropyl)dimethylammonio]propanesulfonic acid (CHAPS), immobilised pH gradient strips (4.0-7.0, 5.0-8.0), urea.

Boehringer Mannheim, Sydney, Australia: deoxyribonuclease, ribonuclease.

CHEM-SUPPLY, Gillman, SA, Australia: ethanol, methanol.

Crown Scientific, Moorebank, NSW, Australia: 10 kDa dialysis membrane.

ICN Biomedicals, Irvine, CA, USA: phosphate buffered saline (tablets).

LC-Packings, San Francisco, CA, USA: C18 RP silica.

Mallinckrodt, Chesterfield, MO, USA: CH3CN, NH4HCO3.

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Nalge Nunc International, NY, USA, 0.2 µm Nalgene filter unit.

Michron Bioresourses, Auburn, CA, USA: Micro C18 pre column.

® Millipore, Billerica, MA, USA: ZipTip µ-C18

Molecular Probes, Eugene, OR, USA: SYPRO Ruby.

Oxoid, Basingstoke, Hampshire, England: agarose, bacteriological agar, brain heart infusion, tryptone, yeast extract.

Applied Biosystems, Forester City, CA, USA: Sequazyme Peptide Mass Standards Kit.

Pierce, Rockford, IL, USA: Coomassie®Plus Protein Assay Reagent.

Promega, Madison, WI, USA: sequencing grade modified trypsin.

Sigma-Aldrich, St Lois, MO, USA: 2-mercaptoethanol, α-cyano-4-hydroxycinnamic acid, acetone, acrylamide/bis-acrylamide 40% solution, acrylamide, adenine, adenosine 5'-triphosphate, alanine, ammonium persulphate (APS), arginine HCl, asparagine, aspartic acid, biotin, bis-acrylamide, bromophenol blue, CaCl2.2H20, calcium pantothenate, choline chloride, creatinine HCl, cyanocobalamin, cysteine HCl, endonuclease (Serratia marcescens), folic acid, formic acid, glucose, glutamic acid, glutamine, glycine, guanine, HCl, heptafluorobutyric acid, histidine, inositol, isoleucine, Isopropanol (2-propanol), K2HPO4 3H2O, K2HPO4, KH2PO4, KH2PO4,

KOH, L-ascorbic acid, leucine, lysine HCl, menadione, methionine, Mg Cl2,

MgCl2.6H2O, mutanolysin, NaCl, NaHCO3, NaOH, NH4Cl, (NH4)2SO4, niacin, p- aminobenzoic acid, piperazine diacrylamide (PDA), phenylalanine, phosphoric acid, proline, Protease Inhibitor Cocktail, pyridoxine HCl, riboflavin, caprylyl sulfobetaine, serine, sodium citrate, sodium dodecyl sulfate, taurine, tributyl phosphine, N,N,N’,N’-tetramethylethylenediamine (TEMED), trifluoroacetic acid

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(TFA), thiamine HCl, thiamine, thiourea, threonine, Trizma base (Tris), tryptophan, tyrosine, uracil, uric acid, valine.

2.2. BACTERIAL STRAIN AND STORAGE CONDITIONS

The bacterial strain used in this study was S. gordonii DL1 (Challis) from the Institute of Dental Research culture collection (Kilian et al., 1989). The strain was kindly provided by Professor Howard Jenkinson, University of Bristol, UK. S. gordonii was maintained by weekly transfer on agar plates containing BHI medium (Appendix I) and stored at 4°C. For long-term storage bacteria were prepared as follows: S. gordonii was scrapped from the surface of a BHI agar plate into 10 mL BHI medium in a sterile tube, and an equal volume of 80% (v/v) glycerol added. This was then vortexed to evenly distribute the glycerol, and 2 mL aliquots of the glycerinated culture dispensed into sterile tubes and stored at −80°C.

2.3. BACTERIOLOGICAL MEDIUM

The chemostat bacterial medium used in this study was glucose-limited defined medium mucin, (DMM), containing 25 mM glucose, ions, a basal mixture of amino acids, vitamins and other growth factors. DMM has been described previously (Sissons et al., 1991), but was modified for use in this study by deleting the porcine mucin, and by the inclusion of adenine, guanine, uracil and NaHCO3 (Appendix I).

Although adenine, guanine, uracil and NaHCO3 are only essential medium components for the culture of S. mutans LT11 (Len et al., 2003), they were included in this media to provide consistency for future comparisons of co-cultures of S. gordonii and S. mutans. Porcine mucin was excluded, as it is intrinsically variable. It also undergoes a dramatic increase in viscosity as the pH of the medium is lowered, due to the formation of large aggregates (Bhaskar et al., 1991). Thus, by excluding the mucin a totally defined medium was produced.

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2.4. THE CHEMOSTAT

2.4.1. Chemostat growth conditions

Continuous culture was carried out in a Bio-Flo chemostat (model C30, New Brunswick Scientific Co., Edison, NJ, USA; Figure 2.1) under anaerobic conditions -1 (95% N2 / 5% CO2, 100 mL min ). The chemostat was maintained at a working volume of 365 mL, a stir rate of 200 rpm, dilution rate, D = 0.100 ± 0.001 h-1, and a temperature of 37.0 ± 0.1°C.

Heating element Alkali inlet port

Thermostat Media inlet

Inoculation port Thermometer

Baffle Baffle

Sampler pH electrode

Gas inlet Media vessel

Alkali pump Chemostat vessel Media pump Culture outlet (the pH controller Magnetic stirrer and is located under Teflon disc the bench)

Figure 2.1 The chemostat. Continuous culture was carried out in a chemostat under anaerobic conditions (95% N2 / 5% CO2, 100 mL min-1) with 25 mM glucose as the limiting nutrient and at a D = 0.1 h-1. The continuous flow of media through the media inlet, combined with a stir rate of 200 rpm ensured the chemostat was maintained at a working volume of 365 mL. The pH electrode and controller monitored and facilitated the adjustment of the pH through the addition of alkali. The temperature was maintained at 37.0°C, by way of a thermostat and heating element. A thermometer allowed the temperature to be manually monitored.

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The pH was maintained to within ± 0.05 pH units by automatic addition of a mixture of 1 M KOH and 1 M NaOH. For the pH conditions below pH 7.0 (pH 6.0 and pH 5.5), the initial pH of the medium was allowed to fall in stages to the desired value by the natural production of acid by the bacteria. Steady state conditions were assumed when the alkali addition and the A600 value of the culture were constant over 50 h or at least 10 mean generations (Burne and Chen, 1998; Thevenot et al., 1995). Aliquots were removed daily from the chemostat in order to check for contaminants.

2.4.2. Harvesting of cells

A 200 mL aliquot of the culture was collected after 50 h (minimum) of steady state growth via the overflow port into a 500 mL centrifuge tube on ice containing 500 µL of prepared Protease Inhibitor Cocktail (Appendix VI), which inhibits endogenous proteases and phosphatases (Aoyagi and Umezawa, 1981; Aoyagi et al., 1984; Mumford et al., 1981; Umezawa, 1982). The samples were harvested by centrifugation (10000 g, 4°C, 15 min), the culture supernatant immediately filtered (0.2 µm; Nalgene filter unit) and stored at –80°C until required. The cells were washed thrice, twice by re-suspending in 200 mL phosphate buffered saline (PBS) containing 500 µL of prepared Protease Inhibitor Cocktail, and once by re- suspending the pellet in 120 mL PBS containing 500 µL of prepared Protease Inhibitor Cocktail (10000 g, 15 min, 4°C). For the final wash the cells were split equally into 4 × 50 mL aliquots. After harvesting, the four bacterial pellets were frozen at −80 °C and lyophilised (Virtis ® model 10-1030, Gardiner, NY) overnight (16 h). After lyophilising, the four pellets were combined and stored at −80 °C. Cells were harvested from cultures grown at pH 5.0, 5.5, 6.0, 6.5, 7.0 and 7.5. A total of three replicate independent cultures for each pH condition were processed, except for pH 5.0 where only two were processed due to inherent difficulties in maintaining S. gordonii in steady state at this pH (Section 1.1).

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2.4.3. Measurement of protein concentration

The protein concentration of the filtered culture supernatant was determined using the Coomassie® Plus Reagent according to the manufacturers instructions (Pierce, Rockford, IL, USA) by the “Micro Test Tube Protocol” with a working range of 1-25 µg mL-1. A standard curve was prepared using bovine serum albumin (BSA) as the standard.

2.5. TWO-DIMENSIONAL ELECTROPHORESIS (2-DE)

2.5.1. Preparation of proteins

2.5.1.1. Preparation of cellular proteins

Lysis of Gram-positive cells requires enzymatic and physical treatment (Cash et al., 1999). In order to lyse S. gordonii cells, 1 mL of a solution containing 10 mM

K2PO4, 1 mM CaCl2 and 10 mM MgCl2, 50 µL of prepared Protease Inhibitor Cocktail and 500 U of mutanolysin 4 (Herzberg et al., 1990; Jenkinson et al., 1993; Kawata et al., 1983; McNab and Jenkinson, 1998; Yokogawa et al., 1975) were added to 12 mg dry wt of cells from each pH condition and sonicated on ice for 30 s using a cup horn (Branson Sonifer 450; Branson Ultrasonics, Danbury, CT, USA) at a power setting of 4, on a constant duty cycle. The suspension was then incubated for 20 min at 37°C followed by 30 min at 60°C, before being sonicated again on ice for 1 min using a cup horn. The cell lysate was exhaustively dialysed against 18MΩ

H2O at 4°C for 4 h using 10 kDa dialysis tubing. The dialyzed cell lysate was lyophilised (Virtis ® model 10-1030, Gardiner, NY, USA) overnight (16 h), before being resuspended in 500 µL of 50 mM Tris pH 7.2 containing 50 µL of prepared Protease Inhibitor Cocktail, and sonicated on ice for 1 min using a cup horn as described. A 3 µL aliquot of both RNase (5 µg mL-1) and DNase (5 µg mL-1) were

4 Mutanolysin is a bacterial muramidase that can hydrolyse specific bacterial cell wall peptidoglycan into muropeptides and has been used in the preparation of protoplasts from S. gordonii.

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added to the sample and the cell lysate incubated at room temperature (20-22°C) for 30 min. The RNase and DNase were used to degrade any RNA or DNA present in the sample, as the presence of nucleic acids can have a damaging effect on the separation of proteins by IEF. For example, under denaturing conditions, such as used in the sample preparation, DNA complexes are dissociated and cause an increase in the viscosity of the solution, inhibiting protein entry into IPG strips, thus slowing protein migration. DNA also binds to proteins and causes artifactual migration and streaking. In order to characterize specific proteins in a complex protein mixture by 2- DE, proteins must be completely solubilized under the electrophoresis conditions. In order to achieve this, dry solubilizing chemicals (Appendix II) were added to each cellular protein sample and sonicated in a cup horn as described. The dry solubilizing chemicals were; CHAPS, which solubilizes membrane proteins and breaks protein-protein interaction (Fialka et al., 1997), caprylyl sulfobetaine which also aids in the solubilization of proteins (Schulz et al., 1989), thiourea which increases the solubility of membrane proteins (Rabilloud, 1998), and urea, which denatures proteins and acts as a mild solubilization agent for both insoluble or denatured proteins (Hochstrasser et al., 1988). After sonication, 0.2% (v/v) Bio-Lyte carrier ampholytes (pH 3-10), 2 mM tributyl phosphine (TBP) and 0.002% (v/v) bromophenol blue were added to the solution, which was vortexed for 30 s. TBP is a reducing agent, which improves protein solubility during IEF (Herbert et al., 1998), and carrier ampholytes are low molecular weight molecules (zwitterionic ) that assist in the separation of proteins since they carry a net charge and migrate in the electric field between the electrodes until they reach the position of corresponding pI (Stastná and Šlais, 2003). Following the addition of solubilizing chemicals, reducing agent and carrier ampholyte, the samples were incubated at room temperature (20-22°C) for 1 h, centrifuged (13800 g, 10 min, 20°C) and the supernatant, containing the extracted cellular proteins, divided into four aliquots each derived from an original 3 mg dry wt of cells.

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2.5.1.2. Preparation of extracellular proteins

The detection of extracellular proteins produced by S. gordonii in the chemostat is difficult as they are constantly diluted by the medium as it flows through the culture vessel (Section 1.3). Extracellular proteins were therefore precipitated by the addition of 10% (w/v) trichloroacetic acid (TCA) to 200 mL of the filtered culture supernatant and incubated overnight (16 h) at 4°C. The precipitate was collected by centrifugation (10400g, 4°C, 1 h), and then resuspended in 200 mL of a −20°C acetic acid/EtOH mixture (0.25% (v/v) glacial acetic acid in EtOH) by sonication on ice for 30 s using a cup horn (Section 2.5.1.1) and incubation at −20°C for 1 h with vortexing every 15 min. The solution was centrifuged (13800 g, 4°C, 15 min), and the protein pellet resuspended in 200 mL cold (−20°C) acetone by sonication on ice for 30 s using a cup horn (as described), followed by incubation at −20°C for 1 h. with vortexing every 15 min. After centrifugation (13800 g, 4°C, 15 min), the protein pellet was retained and dried in air. The pellet was then dissolved in 1.2 mL of solubilizing solution (Appendix II) and then divided into four aliquots representing extracellular proteins derived from an original 50 mL of culture.

2.5.2. First dimension isoelectric focussing (IEF)

2.5.2.1. Rehydration of IPG strips

Following 2-DE methods development (CHAPTER 3), 18 cm IPG strips in the pH ranges 4.0 – 5.0, 4.5 – 5.5, 5.5 – 6.7, 6.0 – 11.0, were routinely rehydrated overnight (16 h) in 380 µL of solubilizing solution (Appendix II).

2.5.2.2. Electrophoretic conditions for the first dimension IEF

Just prior to IEF, a volume of 3 µL of IPG buffer corresponding to the pH of the IPG strip was added to each aliquot of sample (e.g. for the IPG strip with a pH range 4.5 –

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5.5, the IPG Buffer pH 4.5 – 5.5 was used). IPG Buffers are ampholyte-containing buffer concentrates specifically formulated for use with Immobiline™ DryStrip gels (Amersham Biosciences, Uppsala, Sweden), that generate uniform conductivity along the IPG strip during focusing. IPG Buffers also eliminate high background staining (http://www5.amershambiosciences.com). Following hydration, the IPG gel strips were placed gel side up on the IPG aligner sheet, on the IPG tray, on the cooling plate of the Multiphore ΙΙ electrophoresis unit (Amersham Biosciences, Uppsala, Sweden). Tapered paper wicks (2 cm) were placed at the anode end of each IPG strip, with the tapered end overlapping the gel by 0.5 cm. The protein solutions, derived from an original 3 mg dry wt cells (Section 2.5.1.1) or aliquots representing extracellular proteins derived from an original 50 mL of culture (Section 2.5.1.2) were pipetted directly onto the paper wicks. Moistened 2 cm paper wicks were then placed at each end of the IPG overlapping the wick at the anode end and the gel at the cathode end by 0.5 cm. The electrodes were placed on top of the remaining 1.5 cm that protruded longitudinally from the end of the IPG strips. Paraffin oil was poured into the IPG tray so that the IPG strips were well covered and would not dry out. The rehydrated IPG gel strips were focussed for 80800 Vh (100 V for 2 h, 300 V for 2 h, 1,000 V for 2 h, 2,500 V for 2 h, 3,500 V for 18 h, and 5,000 V for 2 h) on the Multiphore ΙΙ electrophoresis unit. The focussing temperature was maintained at 20°C using a MultiTemp ΙΙΙ thermostatic circulator (Amersham Biosciences, Uppsala, Sweden).

2.5.2.3. Equilibration of IPG strips prior to SDS-PAGE

To solubilize the focused proteins and to allow SDS binding in preparation for the second dimension electrophoretic separation, the focussed IPG strips were first equilibrated in 2.5 mL of equilibration solution (Appendix II) for 20 min.

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2.5.3. Second dimension SDS-PAGE

2.5.3.1. Preparation of gels

Pilot experiments (Section 3.2.2) were run on 8-18%T gradient gels, however these produced a protein profile occupying the lower two-third of the gel. The final gels were 12-18%T (Appendix II), with the proteins more evenly separated across the whole gel. Once poured, the gels were left to set overnight (16 h). An agarose embedding solution (Appendix II) containing a bromophenol blue marker was boiled and then poured directly on top of the 12-18%T gradient SDS- PAGE gel. The focussed IPG strip was inserted through the agarose, which was allowed to set for 5 min. The focussed proteins used for mass-spectral analyses were resolved in the second dimension on an Ettan Daltsix Electrophoresis System (Amersham Biosciences, Uppsala, Sweden) at 4°C at 75 mA for approximately 18 h, until the bromophenol blue marker ran off the gel. Pilot experiments (Section 3.2.2), however made use of a Protean ΙΙ electrophoresis apparatus (Bio-Rad Laboratories, Hercules, CA, USA). SDS-PAGE was conducted in two steps consisting of 3 mA per gel for 10 h and 18 mA per gel for approximately 12 h, until the bromophenol blue marker ran off the gel.

2.5.4. Staining with fluorescent stain

Following the SDS-PAGE, the gels were fixed for 1 h in a mixture of 20% (v/v) MeOH and 10% (v/v) acetic acid prior to staining with SYPRO Ruby. After staining, the gels were washed for 1 h in a mixture of 10% (v/v) MeOH and 7% (v/v) acetic acid, to decrease background fluorescence and speckling.

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2.5.5. Imaging of 2-DE gels

All stained 2-DE gels from pilot experiments (Section 3.2.2) were scanned with a BioRad FX scanner (BioRad, Hercules, CA, USA), fitted with an external laser. The settings for the BioRad FX were as stated by the manufacture for scans of SYPRO Ruby stained gels (BioRad, Hercules, CA, USA; http://www.bio-rad.com/) i.e. a 532 nm laser, emission filter A set at Blank; emission filter B being a 605 nm BP filter, and excitation filter C being set at 1064 nm blocking. Prior to the main experiments another molecular imager became available, a Typhoon 8600 System (Amersham Biosciences, Uppsala, Sweden). For this scanner the excitation wavelength was set at 532 nm, the emission filter used a 610BP30 filter and the photomultiplier tube (PMT) voltage was set at 600 V. As the scans from the Typhoon were superior to those from the BioRad FX, with more protein spots visualised using the Typhoon (Figure 2.2), it was used for all the main experiments.

Figure 2.2 Comparison of the scanned images from the BioRad FX and the Amersham Typhoon 8600. (a) A scan from the BioRad FX scanner. (b) A scan of the same gel (scanned a week later) using the Typhoon. The scans from the Typhoon were superior to those from the BioRad FX, as more spots were visualised using the Typhoon (note the greater number of spots resolved in the encircled area).

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Once scanned, the gels were counter stained for 24 h with colloidal Coomassie Brilliant Blue G-250 (CBB G-250, Modified Neuhoff Stain) to enable manual protein spot excision in natural light (Appendix II). After staining in CBB G-250, protein detection was enhanced by placing the gels into 1% (v/v) acetic acid overnight (16 h). The gels were then sealed in plastic bags, with 20 mL of the 1% (v/v) acetic acid solution until required.

2.6. IMAGE ANALYSIS BY Z3

Z3 (Compugen, Tel-Aviv, Israel) is a software system for 2-DE gel image analysis. Image analysis in Z3 involves comparing pairs of 2-DE gel images by aligning them as precisely as possible, and then detecting and matching similar protein spots on the compared images. Z3 measures the relative expression (RE) of each pair of matching protein spots and is reported in parts per million (ppm). RE is the spot expression level calculated based on all of the differential expression values related to that spot. RE is more accurate and reliable than spot quantity because RE is based on the full analysis of two corresponding areas on two separate gel images, rather than an individual spot quantity derived from a single gel image. Moreover, relative expression is not affected by background estimation, determination of a sensitive boundary shape or saturated regions of a gel. In the current study the “Multiple Gel Analysis wizard” of Z3 was used to compare multiple images automatically, eliminating the need to compare pairs of images manually. The gel from condition pH 7.0 was chosen as the reference image, and Z3 batch-processed all other images (comparative images) to compare them to the reference image. Automatic registration and protein spot detection were achieved, initially using the default parameters (differential expression, 2.0; minimum area, 50; minimum contrast, 25), however these parameter were later altered by changing the minimum contrast to 10, which allowed the inclusion many “lighter” spots resulting in the inclusion of more spots in the data analysis. Minimal spot editing of gel artefacts was performed prior to matching, such as the deletion of “spots” along the edges of 2-DE gel images. The matching data were viewed in the

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“Interactive Spots Table”, from which the RE data was exported to an excel file for statistical analysis.

2.6.1. Predicting pI and mass

One of the functions included in the “Interactive Spots Table” in Z3 is the prediction of the pI and mass of the proteins on the gel image. By assigning at least five pI and mass annotations on the reference gels, Z3 was able to predict the pI and mass of all other spots on those gels.

2.6.2. Statistical analysis of differential proteins

Means were calculated using RE ppm values for the Z3 spot data. Single factor analysis of variance (ANOVA) was performed with Excel (Microsoft, Redmond, WA, USA). ANOVA is a statistical test that compares all sample means in a single test. It tests the null hypothesis (H0) that all means (µ) are the same, i.e. H0: µ1 = µ2 = ...µx =

µy. ANOVA utilizes the F test to test if samples drawn from different populations have the same standard deviation. The F-test statistic is calculated as the ratio of the

“variation between groups” and the “variation within groups”, whereas Fcrit is the critical value as extracted from the f-distribution in statistical tables given the degrees of freedom (df). If F > Fcrit, H0 is rejected, as the means of one or more groups are different (Underwood, 1997). However ANOVA does not indicate where the difference/differences lie (Underwood, 1997). The Student-Newman-Keuls (SNK) test (Zar, 1999) is a procedure for sequentially comparing the ANOVA means, highlighting which alternative to H0 is the most likely to apply. In the SNK test the ANOVA means are arranged in ascending order and compared sequentially, using the test statistic q. The test statistic q is calculated as the ratio of the gap between a larger and a smaller value and the range (the largest value minus the smallest value). The calculated q (qcalc) is

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compared with the tabulated value of q (qcrit) at a chosen probability (p< 0.05; Zar,

1999). If qcalc > qcrit H0 is rejected, i.e. µ1 ≠ µ2, indicating statistical significance. The following are the relevant formulas for the SNK test:

SE = √(mean square within samples [MSW] / number of replicates [n])

qcalc = (mean of sample a − mean of sample b) / SE

The following is a worked example of the statistical methods used to analyse the differences in relative expression between pH conditions. In this example differences in the RE of phosphoprotein phosphatase are examined. All statistical data for the differential proteins appears in Appendix III.

• The RE of the protein from three replicates and five pH conditions

(groups) was analysed using Excel. In this case the value of Fcalc

(9.42) is greater than the tabulated value of Fcrit (3.48) indicating that there is a significant differences between one or more of the groups, however this test does not indicate where any differences lie.

Replicate RE pH 5.5 pH 6.0 pH 6.5 pH 7.0 pH 7.5 Chemostat 1 3516 4963 5286 6320 5103 Chemostat 2 3712 3845 6296 7135 3821 Chemostat 3 4472 3779 4399 6962 4409

Summary Group n Sum Mean pH 5.5 3 11700 3900.00 pH 6.0 3 12587 4195.67 pH 6.5 3 15981 5327.00 pH 7.0 3 20417 6805.67 pH 7.5 3 13333 4444.33

ANOVA Source of Variation Sum of squares (SS) df Mean square (MS) Fcalc Probability (p) Fcrit Between Groups 16534947.73 4 4133736.93 9.42 0.0020 3.48

Within Groups 4389596.00 10 438959.605 Total 20924543.73 14

5 This figure (438959.60) is the mean square within groups (MSW).

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• To ascertain where the differences lay an SNK was performed. For this test the means of the RE of the groups/samples are ranked highest to lowest and then compared. • SE was calculated

SE = √(mean square within samples [MSW] / number of replicates [n]) SE = √(438959.60/3 = 382.52

• The q value was calculated

6 qcalc = (mean of sample a − mean of sample b) / SE

qcalc = (6805.67 - 3900.00) / 382.52 = 7.60

7 • The critical q value was ascertained as qcrit = 4.33 from tables using two values for df (4, 10), the results are statistically significant when

qcalc is greater than qcrit.

SNK test

Compare qcalc pH 7.0 pH 5.5 7.60 * p < 0.05 pH 7.0 pH 6.0 6.82 * p < 0.05 pH 7.0 pH 7.5 6.17 * p < 0.05 pH 7.0 pH 6.5 3.87 pH 6.5 pH 5.5 3.73 pH 6.5 pH 6.0 2.96 pH 6.5 pH 7.5 2.31 pH 7.5 pH 5.5 1.42 pH 7.5 pH 6.0 0.65 pH 6.0 pH 5.5 0.77

• In this example the conclusion can be drawn that the RE of phosphoprotein phosphatase at pH 7.0 is significantly greater than the RE at pH 5.5, 6.0 and 7.5. This is represented graphically in the following figure (Figure 2.3).

6 In this example the mean of sample “a” was the mean RE value at pH 7.0 (6805.67). 7 In this case the statistical tables were from Zar (1999) Biostatistical Analysis, Prentice Hall, NJ.

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8000 7000 6000

) 5000 M P

P 4000 (

RE 3000 Chemostat 1 2000 Chemostat 2 1000 Chemostat 3 0 pH 5.5 pH 6.0 pH 6.5 pH 7.0 pH 7.5 pH

Figure 2.3 The RE of phosphoprotein phosphatase from three replicates and five pH conditions. The RE at pH 7.0 was seen to be significantly greater than the RE at pH 5.5, 6.0 and 7.5 (See the example in Section 2.6.2).

2.7. MASS SPECTROMETRY

2.7.1. In-gel digestion

The protein spots were manually excised from the gel using a modified pipette tip, utilizing the SYPRO Ruby stained scanned images as a template to confirm the location of protein spots that were unclear on the CBB G-250 stained gels. The gel plugs containing individual protein spots from the 2-DE gels were washed in 15 µL of 30 mM NH4HCO3 pH 8.0 containing 40% (v/v) CH3CN for 1 h and then dried for 20 min under vacuum (Speed Vac® Plus, Savant Instruments, Hicksville, NY, USA). The gel plugs were rehydrated at 4°C for 45 min in 5 µL trypsin buffer solution (Appendix IV), incubated at 4°C for 45 min and then incubated overnight (16 h) at 37°C. Peptides were extracted from gel plugs by a 20 min wash in 15 µL 20 mM NH4HCO3 pH 8.0, followed by centrifugation (10000 g, 4°C, 5 min). Peptides were further extracted from the gel plugs by 3 × 20 min washes with 15 µL 5% (v/v) formic acid in 50% (v/v) CH3CN. The supernatants containing the peptides were then pooled and dried under vacuum as described above. The dried peptides were stored at -20º C until required.

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2.7.2. MALDI-TOF mass spectrometry

MALDI-TOF mass spectrometry was performed on a Voyager Biospectrometry Workstation (Voyager DE-STR; Applied Biosystems, Forester City, CA, USA) using the associated Voyager software (Applied Biosystems, Forester City, CA, USA).

Peptides extracted from gel plugs were resuspended in 5 µL 70% CH3CN. A 0.5 µL aliquot of the concentrated protein solution together with 0.5 µL of matrix solution (Appendix IV) were mixed together on a MALDI target plate and allowed to dry. The spectra were obtained in reflectron mode (Section 1.5) using a 256 laser shot average, an accelerating voltage of 20 kV, a 70% grid voltage, 90 ns delay time and a low mass gate of 830 m/z. MALDI-TOF mass spectrometry peptide spectra were calibrated manually, with Data Explorer™ software (Applied Biosystems, Forester City, CA, USA), using the peptide standards calibration mixture 1 from the Sequazyme peptide mass standards kit (Applied Biosystems, Forester City, CA, USA). This peptide mass standards kit contained arg1-brandykinin (monoisotopic mass 904.4681), angiotensin 1 (monoisotopic mass 1296.6853), glu1-fibrinopeptide B (monoisotopic mass 1570.6774), and neurotensin (monoisotopic mass 1673.96). Clearly resolved monoisotopic peaks were selected to generate peak lists for database searches. The resulting mass spectrum of peptides, the PMF, was compared with the masses of all theoretical tryptic peptides generated in silico by the search program used. Proteins were identified from the peptide peak lists using either PeptIdent software (http://www.expasy.ch/tools/peptident.html), or the General Protein Mass Analysis for Windows (GPMAW) program (Peri et al., 2001). Where protein identification proved difficult some data was removed from the peak lists. Small masses provided little discrimination for identification, while very large masses were probably partial cleavage products. Consequently, a mass range of approximately 1000-3000 Da provided reasonable discrimination for protein searches. Trypsin autolysis peaks (842.51, 1045.56 and 2211.10 m/z) dominated some digests; the masses of which were removed from the peak lists.

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2.7.3. Concentrating and purifying protein samples

Samples destined for ESI mass spectral analysis were first concentrated and purified ® using a ZipTip µ-C18, providing improved mass spectrometry data quality ® (http://www.millipore.com/). ZipTip µ-C18 is a 10 µL pipette tip with 0.2 µL of C18 resin fixed at its end such that there is no dead volume. The ZipTip is placed on a pipette and the sample aspirated and dispensed through the resin to bind and wash peptides or small proteins. In this study, the dried peptides were resuspended in 0.75% TFA. A ZipTip was attached to a 10 µL pipette, which was set to its maximum setting, the plunger was depressed to a dead stop and wetting solution

(50% [v/v] CH3CN containing 0.1% [v/v] TFA) was aspirated into the tip, dispensed to waste, and the cycle repeated with a fresh solution. The tip was equilibrated for binding by washing with 0.1% (v/v) TFA, which was dispensed to waste, and the cycle repeated with a fresh solution. The peptides were bound to the equilibrated ZipTip by 10 cycles of aspirating and dispensing the prepared samples. The ZipTip was then washed with 0.1% (v/v) TFA using 3 cycles of aspirating and dispensing to waste. To elute the peptides, 5 µL of elution solution (80% [v/v] CH3CN containing 0.1% [v/v] acetic acid) was added to a clean vial and the elutant carefully aspirated and dispensed, without introducing air.

2.7.4. Liquid chromatography electrospray ionisation tandem mass spectrometry (LC ESI MS/MS)

LC ESI MS/MS was performed on a Thermo Finnigan triple quadrupole ion-trap mass spectrometer8 (TSQ 7000 QQQ MS, Thermo Electron Corporation, Woburn, MA, USA). Samples were concentrated in a 5 cm, 75 µm capillary column packed with silica (made in-house, by Dr Valerie Wassinger, Bioanalytical Mass Spectrometry Facility, University of NSW, Sydney, Australia) with a 20 Å pore in -1 C18 phase and 5 µm particle size. The flow rate was controlled at 50 nl min using a

8 Dr Valerie Wassinger, University of NSW, supervised the running of the Thermo Finnegan MS/MS.

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precolumn splitter. The tryptic peptides were separated with a 30 min linear gradient of 0 to 100% solvent (80% [v/v] CH3CN, 0.4% [v/v] CH3COOH, 0.005% [v/v] heptafluorbutyric acid). Mass spectra were collected automatically during the 30 min LC MS runs (Figure 2.4), using Xcalibur software (Thermo Electron Corporation, Woburn, MA, USA). The MS/MS spectra (Section 2.8.3) were then subjected to SEQUEST database searches (Ducret et al., 1998).

Figure 2.4 MS/MS spectra resulting from analysis of a single spot on a 2-DE gel. The Thermo Finnegan triple quadrupole ion-trap mass spectrometer collected mass spectra automatically during the 30 min LC MS runs. The upper panel in this figure shows the time course of the LC MS run, while the lower panel indicates the abundance and m/z of fragments of amino acids. The location and identity of amino acid in these fragments can be determined by the spacing of these fragments of mass as they relate to those amino acids (de novo sequencing). The computer program Sequest was utilized to match MS/MS spectra to amino acid sequences by database searching.

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In some instances a second ESI mass spectrometer was utilized. This was an API QSTAR Pulsar i hybrid tandem mass spectrometer (Applied Biosystems, Forester City, CA, USA). Dr Mark Raftery, University of NSW, performed the analyses on this mass spectrometer. In this instance, digested peptides were separated by nano-LC using an Ultimate HPLC and Famos autosampler system (LC- Packings, Netherlands). Samples (5 µL) were concentrated and desalted onto a micro C18 precolumn (500 µm x 2 mm; Michron Bioresources, USA) with 2% (v/v) -1 CH3CN containing 0.1% (v/v) formic acid flowing at 25 µL min . After a 4 min wash, the pre-column was switched (Switchos , LC Packings, Sunnyvale, CA,

USA) into line with a 75 µm x 15 cm analytical column containing C18 RP silica (PEPMAP, LC Packings, Sunnyvale, CA, USA). Peptides were eluted over a 30 min period using a linear gradient of 5% CH3CN containing 0.1% formic acid to -1 60% CH3CN containing 0.1% formic acid at a flow rate of 200 nl min . The column was connected via a fused silica capillary to a low volume Tee (Upchurch Scientific, USA) where high voltage (2300 V) was applied and a nano electrospray needle (New Objective, Inc., USA) was positioned approximately 1 cm from the orifice of the mass spectrometer. Positive ions were generated by electrospray and the QSTAR operated in information dependent acquisition mode. A TOF mass spectral survey scan was acquired (m/z 350-1700, 0.5 s) and the 2 largest multiply charged ions (counts > 10) sequentially selected with Q1 (Section 1.5.2) for MS/MS analysis. Nitrogen was used as the collision gas and an optimum collision energy chosen (based on charge state and mass). Tandem mass spectra were accumulated for 2.5 s, m/z 50-2000 (Figure 2.5) and the resulting data submitted to Mascot (Matrix Science; Section 2.8.3) for database searches (Perkins et al., 1999).

2.8. PROTEIN IDENTIFICATION

Protein identification by peptide mass mapping (PMM) is usually accomplished by accessing databases containing theoretical proteinase digests of the proteins. These theoretical masses are compared with experimentally observed masses, and a score assigned to the matches. Protein identification is optimal if the genome of the

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Figure 2.5 Mass spectra from the API QSTAR Pulsar i hybrid tandem mass spectrometer. Tandem mass spectra were accumulated for 2.5 s (m/z 50-2000).

organism of interest has been completely sequenced. However, many proteins may be identified on the basis of their high degree of identity with those from closely related species for which genomic sequences are available. Approximately 50% of the S. gordonii genome has been annotated, in-house, however S. pneumoniae, a phylogenetically closely related bacterium, with 97.24% homology (Figure 2.6; Kawamura et al., 1995), has been completely annotated. (The Institute for Genomic Research [TIGR]; http://www.tigr.org). The open reading frames (ORFs) from the DNA sequences available were individually translated to proteins, using the GPMAW software (Section 2.8.1 below), to produce two protein databases. All PMM data from the Voyager mass spectrometer was initially compared with both of the S. gordonii and S. pneumoniae translated protein databases using this program (http://welcome.to/gpmaw/). The Voyager mass spectrometer data was also processed using the SWISS-PROT and

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Mitis group

S. gordonii 100.00 S. mitis 97.63 S. pneumoniae 97.24 S. oralis 98.16 Salivarius group Anginosus group S. sanguis 97.01 S. parasanguis 96.25 S. sa li vari us 95.86 S. a ngi nosus 94.64 S. thermophilus 95.48 S. constellatus 95.48 S. vestibularis 95.70 S. Intermedius 96.17

Bovis group Pyogenic group S. bovis 95.10 S. pyogenes 94.87 S. equi nus 95.18 S. agalactiae 94.03 S. alactolyticus 95.25 S. canis 94.87 S. dysgalactiae 94.10 S. equi 93.95 S. iniae 94.18 Mutans group S. porcinus 93.64 S. uberis 94.26 S. mutans 94.64 S. parauberis 93.41 S. rattus 94.10 S. hyoi ntestinali s 94.87 S. cricetus 92.73 S. downeii 93.64 S. sobrinus 94.18 S. ma ca ca e 91.49

Not grouped

S. acidominimus 94.72 S. suis 94.64 S. pleomorphus 82.30

Figure 2.6 Sequence homology of S. gordonii to other streptococci.

TrEMBL databases through the Expert Protein Analysis System (ExPASy) Molecular Biology Server and the associated PeptIdent software (http://au.expasy.org/: Section 2.8.2 below) to optimise the possibility of accurate identification, and to locate proteins that were not identified using the available

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translated protein databases from S. gordonii and S. pneumoniae. The criterion to accept the identification of a protein was coverage greater than 19% or, where the coverage was less than 19%, if the observed pI and Mr matched theoretical values and there where at least five matching peptides. The criteria were quite broad as the protein databases are open to revision. Several proteins that were not identified by MALDI-TOF were reprocessed and MS/MS data collected using either the QSTAR or the Thermo Finnigan LC ESI mass spectrometer. These data were analysed using Mascot and SEQUEST respectively (Section 2.8.3).

2.8.1. GPMAW

All PMM data from the Voyager mass spectrometer were initially compared with both of the S. gordonii and S. pneumoniae translated protein databases using the program GPMAW (http://welcome.to/gpmaw/). The GPMAW program is a tool for mass spectrometric analysis of proteins and peptides (Peri et al., 2001). However, a number of other bioinformatics tools have been included. Sequences can be imported in a number of different formats with direct database search in Entrez (the text-based search and retrieval system used at NCBI for the major databases, including PubMed, Nucleotide and Protein Sequences, Protein Structures, Complete Genomes, Taxonomy, and others) and in local databases. Sequences were saved in local files (databases) for future reference. Sequences were exported in FastA format, (either singly or all sequences at once) for easy transfer to other programs. FastA format is where the sequence begins with a single-line description, followed by lines of sequence data. Basically, sequences in FastA format were represented in the standard IUB/IUPAC amino acid and nucleic acid codes. Proteins in GPMAW were cleaved by automatic methods (e.g. a flexible nomenclature for defining enzyme actions) or manually. The peptides are displayed with a number of parameters including mass value and pI. The GPMAW program was used to produce protein databases using the available in-house S. gordonii DNA sequence (both annotated and unannotated) as well as the complete S. pneumoniae genome sequence. Many of the proteins on the S. gordonii database on GPMAW had not been annotated, and for many others the identification was still tentative, so for

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consistency the sequences from all GPMAW identified proteins were run through a blast program to confirm their putative identity. Sequences were blasted against Basic BLAST (http://www.ch.embnet.org/software/bBLAST) using the blastp program and the UniProt (SwissProt/TrEMBL/TrEMBL_NEW) database, which is a combination of SwissProt and TrEMBL (updated weekly).

2.8.2. ExPASy

The PMM data from the Voyager mass spectrometer was also processed using the SWISS-PROT and TrEMBL databases through ExPASy Molecular Biology Server and the associated PeptIdent software (http://au.expasy.org/) to optimise the possibility of accurate identification, and to locate proteins that were not identified using the available translated protein databases from S. gordonii and S. pneumoniae. ExPASy is a World Wide Web server, which provides access to a variety of databases and analytical tools dedicated to proteomics (http://www.expasy.ch). Databases on ExPASy include SWISS-PROT and TrEMBL. SWISS-PROT is a curated protein sequence database, which provides high quality annotations (such as the description of the function of a protein, its domains structure, post-translational modifications, variants, etc.), a minimal level of redundancy and a high level of integration with other databases. SWISS-PROT is supplemented by TrEMBL, which contains computer-annotated entries for all sequences not yet integrated in SWISS- PROT. PeptIdent is a tool on the ExPASy server that allows the identification of proteins using pI, Mr and peptide mass fingerprinting data. Experimentally measured, user-specified peptide masses are compared with the theoretical peptides calculated for all proteins in the SWISS-PROT/TrEMBL databases. A species (or group of species) can also be specified for the search. PeptIdent uses a general algorithm that does not incorporate any knowledge about protein properties, it uses a training set of protein mass spectra to “evolve” the search parameters to find values that give the best results. This approach is different from that of other algorithms, such as those employed in Mascot and SEQUEST search engines (Section 2.8.3 below), which are based on knowledge of the properties of individual proteins.

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2.8.3. Mascot and SEQUEST

Several proteins that were not identified by MALDI-TOF were re-processed and MS/MS data collected using either the QSTAR or the Thermo Finnigan LC ESI mass spectrometer. These data were analysed using Mascot and SEQUEST respectively. Mascot is a search engine that uses mass spectrometry data to identify proteins from primary sequence databases. Mascot incorporates code from MOWSE, which was an acronym of Molecular Weight Search (Pappin et al., 1993). The MOWSE databases are fully indexed so as to allow very rapid searching and retrieval of sequence data. The MOWSE algorithm (Pappin et al., 1993) has a higher selectivity and sensitivity than many other algorithms, which simply count the number of peptide matches. MOWSE takes into account the relative abundances of the peptides of a given mass in the database and also compensates for protein size. Mascot is based on the MOWSE algorithm but also calculates an approximate probability that the observed match between experimental data and a protein sequence is a random event. Mascot can use information from both peptide maps and tandem mass spectra to identify proteins. The program SEQUEST (Thermo Electron Corporation, Woburn, MA, USA) is a method for performing protein identification and peptide sequencing by utilizing mass spectrometry fragmentation patterns to search protein and nucleotide databases. SEQUEST converts the character-based representation of amino acid sequences in a protein database to fragmentation patterns, which are compared against the MS/MS spectrum generated on the target peptide. The algorithm initially identifies amino acid sequences in the database that match the measured mass of the peptide, compares fragment ions against the MS/MS spectrum, and generates a preliminary score for each amino acid sequence. A cross correlation analysis is then performed on the top 500 preliminary scoring peptides by correlating theoretical, reconstructed spectra against the experimental spectrum. Output results are displayed accordingly. In short, SEQUEST performs automated peptide/protein sequencing via database searching of MS/MS spectra without the need for any manual sequence interpretation, though it can make use of interpreted sequence information if

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available. However, the search is time consuming, with computers often performing batch searches overnight.

2.9. CATEGORIZATION OF PUTATIVE TRANSMEMBRANE PROTEINS

Putative transmembrane proteins were located using the PEDANT genome database (http://pedant.gsf.de; Frishman et al., 2003). The Transmembrane option displays a table of all ORFs of the data set that have at least one predicted transmembrane region. Data sets for Streptococcus agalactiae NEM316, S. mutans UA159 (ATCC 700610), S. pneumoniae TIGR4 (type 4), S. pneumoniae R6, S. pyogenes SSI-1, S. pyogenes M1 GAS (SF370, ATCC 700294), S. pyogenes MGAS315, S. pyogenes MGAS8232 were searched.

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CHAPTER 3. 2-DE METHOD DEVELOPMENT

3.1. INTRODUCTION

At the time this project commenced there was little proteome information concerning Gram-positive bacteria, and there were no proteome studies on oral streptococci. This chapter deals with resolving the problem of the preparation of cellular and extracellular proteins from S. gordonii for 2-DE protein display. Although Chapter 2 contains detailed descriptions of the final protocols used in this study, the data presented in this chapter provides an insight into their systematic development.

3.2. METHODS

3.2.1. Bacterial processing and sample preparation

3.2.1.1. Batch culture growth conditions and collections of cells

Batch cultures of S. gordonii were used in the development of methods for 2-DE display of proteins because of the simplicity of the procedure. In these pilot studies the cells were collected at mid-exponential phase. A 50 mL sample of an overnight (16 h) culture of S. gordonii grown anaerobically on BHI medium, was inoculated into 450 mL DMM, and maintained anaerobically at 37°C. At 15 – 30 min intervals 1 mL of culture was removed and the A600 recorded. At mid-exponential phase (A600 = 0.600), 200 mL of the culture was transferred to a 250 mL Schott bottle containing 500 µL Protease Inhibitor Cocktail and the solution gently swirled to mix, then aliquotted in 10 mL lots into 15 mL Sarstedt tubes The sample was frozen immediately in liquid N2, and then stored at −80°C. When required, 30 mL of the frozen cells were thawed at room temperature (20-22°C), and harvested by centrifugation (10000 g, 4°C, 15 min). The culture supernatant was filtered (0.2 µm Nalgene filter unit; Nalge Nunc International, NY,

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USA) and then stored at –80°C. Cells were washed three times by resuspending them in the same volume of PBS (10000 g, 15 min, 4°C). Cells were frozen at -80° C and then lyophilised (Virtis ® model 10-1030, Gardiner, NY) overnight (16 h).

3.2.1.2. IEF protocol used in 2-DE methods development

IPG strips (17 cm) with a linear pH range of 4.0-7.0 were rehydrated overnight (16 h) with 460 µL of sample, while those IPG strips (18 cm) with a linear pH range of 6.0 – 11.0 were rehydrated overnight (16 h) in 460 µL of solubilization solution (Appendix II) before 120 µL of sample was cup loaded onto the IPG strips. However, in the final protocol (Section 2.5.2.2) all the protein samples, regardless of pH range, were wick loaded as other researchers at the Institute of Dental Research found that wick loading produced better recoveries. In the final experiments narrow range gels (pH 4.0 – 5.0, 4.5 – 5.5, 5.5 – 6.7) were utilized instead of the broad range pH 4.0 –7.0 (Section 2.5.2).

3.2.2. Two-dimensional gel electrophoresis

The following protocols were tested for the preparation of cellular and extracellular proteins for 2-DE display.

3.2.2.1. Protocol 1 for the preparation of cellular protein (solubilization solution only)

Samples were prepared for IEF by adding 460 µL of solubilization solution (Appendix II) to 1 mg lyophilised cells. The cells were sonicated using a probe sonicator (Branson Sonifer 450; Branson Ultrasonics, Danbury, CT, USA) for 10 × 30 s, allowing for 10 s cooling periods on ice between bursts, prior to the addition of 75 U (Serratia marcescens) endonuclease, and the samples left at room temperature

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(20-22°C) for 45 min. Samples were centrifuged (12200 g, 8 min, 18°C), and the supernatant retained as the source of cellular protein.

3.2.2.2. Protocol 2 for the preparation of cellular protein (mutanolysin treatment and solubilization solution)

A 1 mL aliquot of mutanolysin solution (10 mM K2PO4, 1 mM CaCl2 and 10 mM

Mg Cl2, containing 500 U of mutanolysin) was added to 10 mg dry wt cells and incubated for 40 min at 60°C. The cell lysate was exhaustively dialysed against

18MΩ H2O at 4°C for 4 h using a 10 kDa dialysis membrane. The dialysed cell lysate was vortexed, divided into 10 equal aliquots, and then lyophilised (Virtis ® model 10-1030, Gardiner, NY) overnight (16 h). Samples were prepared for IEF using Protocol 1 as described in Section 3.2.2.1 above.

3.2.2.3. Protocol 3 for the preparation of cellular protein (mutanolysin treatment, TCA precipitation and solubilization solution)

TCA precipitation of cellular proteins was achieved by adding a cold (-18° C) 1ml solution containing 10% (w/v) TCA and 0.07% % (v/v) 2-mercaptoethanol in acetone (Granier, 1988) to the lyophilised lysates prepared according to Protocol 2 (Section 3.2.2.2) and incubating at -18° C for 1 h. The precipitated protein solution was centrifuged (12200 g, 30 min, 4°C), and the supernatant removed. A 1 mL aliquot of 0.07% (v/v) 2-mercaptoethanol in cold (−20°C) acetone was added to the pellet, and the suspension stored at -18° C for 1 h with vortexing every 15 min. The precipitated proteins were harvested (12200 g, 30 min, 4°C), the supernatant removed and the pellet dried for 1 h under vacuum (Speed Vac® Plus, Savant Instruments, Hicksville, NY, USA).

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Samples were prepared for IEF using Protocol 1 as described in Section 3.2.2.1 above.

3.2.2.4. Protocol 4 for the preparation of cellular protein (solubilization solution and TCA precipitation)

Cellular proteins prepared according to Protocol 1 (Section 3.2.2.1) were precipitated with TCA as described in Protocol 3 (Section 3.2.2.3) before being again solubilized according to Protocol 1 (Section 3.2.2.1) in preparation for IEF.

3.2.2.5. Protocol 1 for the preparation of extracellular protein (TCA precipitation, MeOH wash)

Extracellular proteins were precipitated by the addition of 10% (w/v) TCA to the filtered culture supernatant (Section 3.2.1.1), which was then kept on ice for 30 min. The precipitate was collected by centrifugation (15000g, 4°C, 30 min) and the pellet washed 3 times in cold MeOH (15000g, 4°C, 30 min). The pellet was dried (Speed Vac® Plus, Savant Instruments, Hicksville, NY, USA) thus evaporating the remaining MeOH. Samples were prepared for IEF using Protocol 1 as described in Section 3.2.2.1 above, except that 460 µL of solubilization solution (Appendix II) was added to the dried pellet, as apposed to lyophilised cells.

3.2.2.6. Protocol 2 for the preparation of extracellular protein (TCA precipitation, acetic acid/ethanol and acetone wash)

Extracellular proteins were precipitated by the addition of 10% (w/v) TCA to the filtered culture supernatant (Section 3.2.1.1) and stored overnight (16 h) at 4°C. The precipitate was collected by centrifugation (15000 g, 4°C, 30 min), and resuspended in cold acetic acid/EtOH (0.25% acetic acid in EtOH) using a probe sonicator for 10

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× 30 s, allowing for 10 s cooling periods on ice between bursts, and stored at −20°C for 1 h with vortexing every 15 min. The solution was centrifuged (15000 g, 4°C, 30 min), the protein pellet resuspended in cold acetone and re-sonicated for 10 × 30 s, allowing for 10 s cooling periods on ice between bursts, and then stored at −20°C for 1 h with vortexing every 15 min. The precipitate was collected by centrifugation (15000g, 4°C, 30 min), dried (Speed Vac® Plus, Savant Instruments, Hicksville, NY, USA) to evaporate the remaining acetone. Samples were prepared for IEF using Protocol 1 (Section 3.2.2.1), except that 460 µL of solubilization solution (Appendix II) was added to the dried pellet, as apposed to lyophilised cells.

3.3. RESULTS AND DISCUSSION

It should be noted that the protocols for the first and second dimensions for the pilot experiments were slightly different from the final protocols used in this study due to changes in equipment and the implementation of improvements that were determined from the preliminary experiments described below (Section 2.5).

3.3.1. 2-DE display of cellular proteins

Several methods were considered for the preparation of cellular proteins for 2-DE display. In the initial pilot study (Section 3.2.2.1) a solubilizing solution alone was used to solubilize the proteins (Figure 3.1). Although this treatment may have been effective for Gram-negative bacteria, it did not take into account the thicker peptidoglycan cell wall of the Gram-positive bacteria (Cash et al., 1999). The resultant gel displayed minimal protein spots (104) and had heavy horizontal streaking.

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4.0 pI 7.0

Figure 3.1 Protocol 1for the preparation of cellular protein (solubilization solution only). There were minimal protein spots (104 protein spots) and heavy horizontal streaking using this protocol.

A mutanolysin treatment was then included in the protocol (Section 3.2.2.2). Mutanolysin is a bacterial muramidase (Kawata et al., 1983; Yokogawa et al., 1975), that can hydrolyse specific bacterial cell wall peptidoglycans into muropeptides and has been routinely used in the preparation of spheroplasts and protoplasts from S. sanguis and S. gordonii (Herzberg et al., 1990; Jacques and Wittenberger, 1981; Jenkinson et al., 1993; McNab and Jenkinson, 1998). Although more protein spots (126) were resolved using this treatment there was still considerable streaking (Figure 3.2).

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4.0 pI 7.0

Figure 3.2 Protocol 2 for the preparation of cellular protein (mutanolysin treatment and solubilization solution). Using this protocol more spots were resolved than by using Protocol 1. There were 126 protein spots resolved, compared to the 104 protein spots resolved using Protocol 1. However, considerable horizontal streaking remained.

A treatment with TCA (Section 3.2.2.3) was included after the solubilization solution and mutanolysin treatments to try and reduce the horizontal streaking, which was considered to be due to the presence of polysaccharide contaminants (Granier, 1988). This procedure resulted in a 2-DE protein display consisting of more protein spots (218) and a reduction in streaking (Figure 3.3).

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4.0 pI 7.0

Figure 3.3 Protocol 3 for the preparation of cellular protein (mutanolysin treatment, TCA precipitation and solubilization solution). Using this protocol more spots were resolved than using Protocols 1 or 2 (218 protein spots) and the horizontal streaking was not as prominent.

Another experiment paired the solubilization solution treatment with the TCA treatment to ascertain the necessity of the mutanolysin treatment (Section 3.2.2.4). This protocol produced minimal horizontal streaking. A similar number of protein spots (221) were resolved to Protocol 3 (Figure 3.3), however the protein load appeared to have decreased as the spots were of a much smaller volume (Figure 3.4).

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4.0 pI 7.0

Figure 3.4 Protocol 4 for the preparation of cellular protein (solubilization solution and TCA precipitation). This protocol produced minimal horizontal streaking. A similar number of protein spots were resolved to Protocol 3 (221 spots compared to 218 resolved using Protocol 3), however the protein load appeared to have decreased as the spots were of a much smaller volume. N.B. The black dots on this image are an artifact.

During the period when the 2-DE method development was proceeding for S. gordonii, tandem 2-DE method development was also underway at Institute of Dental Research with S. mutans. The protocol finally developed for producing optimum results includes aspects from developmental experiments from both of these streptococci, and is described in Section 2.5.1.1. The final protocol for the extraction of cellular proteins had several steps, a mutanolysin treatment, dialysis, sonication, the addition of dry solubilization chemicals, and finally the addition of carrier ampholytes and TBP. This method eliminated a step in the protocol requiring mercaptoethanol, which had been shown to be associated with the formation of artefacts in 2-DE gels (Beis and Lazou, 1990). The lysis of S. gordonii cells was achieved by enzymatic treatment using mutanolysin and physical treatment using

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sonication (Herzberg et al., 1990; Jenkinson et al., 1993; Kawata et al., 1983; McNab and Jenkinson, 1998; Yokogawa et al., 1975). The dialysis step facilitated the removal of unwanted compounds, such as degraded cell wall peptidoglycans and extracellular polysaccharides, from the proteins in solution by selective diffusion through a semi-permeable membrane. The proteins were then solubilized using the solubilizing chemicals CHAPS, caprylyl sulfobetaine, thiourea and urea (Fialka et al., 1997; Hochstrasser et al., 1988; Rabilloud, 1998; Schulz et al., 1989). TBP, a reducing agent, was used to improve protein solubility during IEF (Herbert et al., 1998), and carrier ampholytes were added to assist in the separation of proteins (Stastná and Šlais, 2003), This protocol produced a gel with virtually no streaking and maximum spot resolution (512 protein spots; Figure 3.5).

4.0 pI 7.0

Figure 3.5 Final protocol for the preparation of cellular protein. The protocol finally developed for producing optimum results is described in Section 2.5.1.1. This method combined the mutanolysin treatment, dialysis, the addition of dry solubilizing chemicals followed by the addition of carrier ampholytes, TBP and bromophenol blue, to produce a gel with virtually no streaking and maximum spot resolution (512 spots).

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3.3.2. 2-DE display of extracellular proteins

Two protocols were considered for the preparation of the extracellular proteins for 2- DE. The first was a standard TCA precipitation (allowing the direct precipitation of total proteins) where the resultant proteins are washed twice in MeOH (Figure 3.6). There were 64 protein spots resolved using this protocol.

4.0 pI 7.0

Figure 3.6 Protocol 1 for the preparation of extracellular protein (TCA precipitation, MeOH wash). There were 64 protein spots resolved using this protocol.

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The second protocol, and final protocol (Section 2.5.1.2) for the preparation of extracellular proteins (Section 3.2.2.6) was a variation of a standard TCA-acetone precipitation (Granier, 1988), where the resultant proteins were first washed in acetic acid/EtOH (0.25% acetic acid in EtOH) before being washed in acetone (Figure 3.7). There were 106 protein spots resolved, compared to the 64 protein spots resolved using Protocol 1. As there was good spot resolution of the extracellular proteins this protocol was used for all subsequent experiments (Section 2.5.1.2).

4.0 pI 7.0

Figure 3.7 Protocol 2 for the preparation of extracellular protein (TCA precipitation, acetic acid/ethanol and acetone wash). There were 106 protein spots resolved, compared to the 64 protein spots resolved using Protocol 1.

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3.3.3. The benefits of a narrow range gel

Broad range gels (pI 4.0 –7.0) were used in all pilot experiments as they gave an overall view of protein expression. However, for the final experiments three narrow range gels (pI 4.0 – 5.0, 4.5 – 5.5, 5.5 – 6.7; Section 2.5.2) were utilized to cover the same range as they provided a better resolution of proteins. This is illustrated with a specific example in Figure 3.8, where protein spots on the narrow range pI 5.5 – 6.7 2-DE gel are better defined, compared with those on the pI 6.0 – 11.0 2-DE gel which were more condensed. In some cases where different proteins have a similar pI and Mr it can be difficult to distinguish individual protein spots on broad range 2- DE gels, even when using image analysis programs such as Z3. This example highlights one of the benefits of the use of narrow-range gels to increase the resolution of 2-DE displays.

3.3.4. Conclusion

The final protocol for preparation of extracellular proteins was a standard TCA precipitation (allowing the direct precipitation of total proteins) followed by washing in acid ethanol followed by acetone, which effectively removed contaminants allowing better resolution in the first dimension. The problem of prominent streaking that was seen on the cellular protein 2- DE gel using Protocol 1 could have been due to a number of reasons, including poor protein solubilization, insufficient solubility of the proteins during IEF, or presence of polysaccharide contaminants (Görg et al., 2000; Granier, 1988). Furthermore, only a small number of protein spots were resolved by using the simpler protocols. The number of protein spots increased as both chemical and/physical lysis methods were added to the protocol. Both of these problems, streaking and poor protein spot number, were eventually solved in the final protocol after implementing improvements in bacterial lysis conditions, extensive early dialysis of the released proteins, and the appropriated application of protease inhibitors, detergents, carrier ampholytes and reducing agents (Section 2.5.1).

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pI 5.5 – 6.7 pI 6.0 – 11.0

x x

pH 5.5 pH 5.5

pH 6.0 pH 6.0

pH 6.5 pH 6.5

pH 7.0 pH 7.0

pH 7.5 x pH 7.5

Figure 3.8 A corresponding protein spot on narrow and broad range gels. It can be seen that the protein spot (indicated by arrows) is better defined on the narrow range( pI 5.5 – 6.7) 2-DE gel, whereas the protein spots on the broad range ( 6.0 – 11.0) gel are more condensed, making it more difficult to distinguish the appropriate spot.

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CHAPTER 4. PROTEOMIC MAP OF S. GORDONII

4.1. INTRODUCTION

The proteome is the expressed protein complement of a genome, and proteomics is functional genomics at the protein level. Proteomics enables the study of differences in protein expression due to changes in environmental conditions. However, to do this effectively, it is essential to have an annotated reference map. By using mass spectrometry to identify the proteins, reference maps can be created that document the functional proteome. When constructing a reference map it is important to have reproducible protein patterns. This was achieved in the current study by growing S. gordonii in a chemostat, using totally defined and reproducible environmental conditions, and then by processing the cells using clearly defined and reproducible protocols. Although 95% of a bacterial genome is expressed as protein products (Humphery-Smith, 1999), the theoretical resolving power of 2-DE is still estimated to be approximately 75% of the proteome (Cordwell et al., 2000). Proteins with extreme pI values, especially very basic proteins, as well as very low and high Mr proteins, are not readily resolved by current 2-DE technology (Harry et al., 2000). Mycoplasma genitalium has a very small genome (0.58 Mb as compared with 2.27 Mb for S. gordonii, http://www.tigr.org), with the potential to express 480 genes. In a comprehensive 2DE study of M. genitalium, 427 protein spots were resolved, and 201 proteins spots analysed. Of these 201 protein spots, 158 were identified using mass spectrometry. Those 158 protein spots represented the expression of 112 gene products. Extrapolation of observed and expected results gave rise to a proteome of at least 586 gene products, of which 73% were resolved. This study remains the most complete bacterial proteome observed (Wasinger et al., 2000). A proteomic analysis of S. mutans was recently completed (Len et al., 2003). The analysis was streamlined due to the publishing of contigs of the S. mutans genome on a public database (http://www.genome.ou.edu/smutans.html). High- resolution proteomic maps were produced after identifying proteins from cells grown

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in continuous culture under conditions that mimic those in the dental plaque of a healthy individual, thus establishing a baseline for protein expression (Len et al., 2003). This map was used as a baseline for a latter study, a comprehensive comparative proteomic assessment of changes in protein expression by S. mutans following growth in a chemostat at pH 7.0 or pH 5.0 (Len et al., 2004a; Len et al., 2004b). The proteomic map of S. mutans (Len et al., 2003) identified a total of 421 (314 cellular and 107 extracellular) of the 502 protein spots analysed by MALDI- TOF mass spectrometer or ESI tandem mass spectrometer. Of the 172 different proteins identified from the cellular fraction, 26 (15%) were hypothetical proteins (Len et al., 2003), with the majority of the identified proteins being associated with house keeping functions such as energy metabolism, amino biosynthesis, translation and transport. The proteomic map of S. mutans identified 64 proteins, cellular and extracellular, which showed isoelectric heterogeneity (charged isoforms) and/or varied in mass (Len et al., 2003). Isoelectric heterogeneity may be due to post- translational modification of proteins, such as phosphorylation, glycosylation or acylation, or often as the result of an artifactual modification, including urea carbamylation or deamidation (Benz and Schmidt, 2002; Klumpp and Krieglstein, 2002; Kussak and Weintraub, 2002; Volkin et al., 1997; Wilkins et al., 1999). However, the nature of the modifications of the S. mutans proteins has not been established. The study also showed that 52 of the 421 protein spots possessed more than one Mr variant. The number of protein spots representing lower Mr forms accounted for 13% of the total number identified. The authors suggested that the most likely explanation for this observation is that the variants are the by-products of the natural protein turnover process of the cell resulting from the long generation times and partial synchronization of the cell cycle that is an inherent feature of the continuous culture conditions used (Len et al., 2003). A number of protein spots in this study were also present as higher Mr forms, with enolase of particular note. This enzyme was identified with Mrs up to three times that predicted from the gene sequence (Len et al., 2003). This phenomenon has been observed in other bacterial studies (Cordwell et al., 2002; Kilian, 2001; Tonella et al., 1998), but its biological significance, if any, has yet to be established.

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The S. mutans map identified only three wall-associated proteins, WapA, GbpA and SpaP. Despite membrane proteins constituting 32% of total proteins of S. mutans, only a single integral membrane protein, a homologue of the amino acid transporter, LivJ was detected. Of the 107 proteins identified in the extracellular fraction, 91 are usually thought to be cytoplasmic. This study of S. mutans produced the first “comprehensive” proteome reference map of the cytosolic and extracellular proteins of S. mutans and is one of the most extensive bacterial proteome maps currently available. While protein identification using MALDI-TOF mass spectrometry is optimal if an organism’s genome is known and fully annotated, as in the case of S. mutans, many proteins can still be identified by this technique if the genes from which they are transcribed and translated are highly homologous to a closely related species for which the genome sequence is available. Fortunately, this was the case with S. gordonii, as it shares 97.24% DNA sequence homology with S. pneumoniae (Kawamura et al., 1995), the genome of which was annotated in 2001 (Tettelin et al., 2001). The S. pneumoniae genome sequence, along with approximately 50% of the genome of S. gordonii that had been annotated in-house by early 2003, allowed many of the S. gordonii proteins detected on 2-DE gels to be identified 9. The availability and use of both these genome databases formed the basis of the mass spectral proteome analyses presented in this chapter, which documents the production of a proteomic map by the systematic identification of proteins of S. gordonii grown at a neutral pH under clearly defined environmental conditions. By identifying proteins from cells grown in continuous culture under conditions that mimic those in the dental plaque of a healthy individual (Jacques et al., 1979b), a base line for protein expression was established. This map was then used as a reference for the identification of differences in protein expression when S. gordonii was subjected to changes in environmental pH (CHAPTER 5).

9 Attempts to identify more proteins ceased in December 2003.

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4.2. METHODS

Details of the methods used are given in CHAPTER 2. In summary, S. gordonii was grown in a chemostat at pH 7.0 at D = 0.10 h-1. Solubilized cellular and extracellular proteins were separated by 2-DE (Section 2.5), and stained with SYPRO Ruby (Section 2.5.4), and scanned (Section 2.5.5). After re-staining with CBB G-250, the protein spots were manually excised, digested with trypsin (Section 2.7.1) and analysed by MALDI (Section 2.7.2) or ESI (Section 2.7.4) mass spectrometry prior to identification using relevant analysis software. Mass spectrometry data was from single gels as gels were highly reproducible across replicates and could be readily superimposed using Z3 analysis. Z3 analysis was used to predict the pI and mass of the protein spots on the gel (Section 2.6.1). Proteins were chosen as landmarks based on their relatively strong intensity and their wide distribution over the gel. The criterion to accept the identification of a protein was coverage greater than 19% or, when the coverage was less than 19%, at least five matching peptides if the observed pI and Mr and those predicted from the gene sequence corresponded. The criteria were quite broad as the proteome database was open to revision (Section 4.1 above). The bioinformatics resource, Kyoto Encyclopedia of Genes and Genomes (KEGG; http://www.genome.jp/kegg/; Kanehisa Laboratory, Kyoto University Bioinformatics Center, Kyoto, Japan), was used to search for characterised enzymes, for which an Enzyme Commission (EC) number was assigned, and to produce appropriate diagrams (Kanehisa, 1997; Kanehisa and Goto, 2000). Potential transmembrane proteins were identified using the PEDANT genome database (Section 2.9).

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4.3. RESULTS AND DISCUSSION

4.3.1. Application of identification programs for analysis of protein spots from 2-DE gels

Approximately 50% of the translated genome of S. gordonii that had been annotated in-house, as well as that of the complete genome of S. pneumoniae TIGR4 (Tettelin et al., 2001) formed the basis for mass spectral proteome analyses, and allowed many of the protein spots detected on the 2-DE gels to be identified. Earlier in 2003, however, with only approximately 25% of the S. gordonii genome sequenced and annotated, a preliminary identification of a number of protein spots was undertaken (Table 4.1). As the mass spectral data for greater than 1000 protein spots has been collected and archived, this database remains available to enhance the 2-DE proteomic map when sequencing of S. gordonii genome is eventually completed.

Table 4.1 Comparison of the number of protein spots putatively identified, between the 2003 and 2004 analyses, using different analytical programs.

2003 analysis 2004 analysis ExPASy 221 46 GPMAW in-house S. gordonii 116 303 GPMAW TIGR S. pneumoniae 136 120 ESI 9 7 Total 482 476

Six more protein spots were identified in the initial analysis compared with those protein spots identified in the final analysis (Table 4.1). Many of the protein spots were tentatively identified in 2003 using ExPASy, based on very low percentage peptide coverage. There was greater confidence with the 2004 analysis, as many proteins were identified with far broader coverage using the S. gordonii in-

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house database (Figure 4.1), although most retained their original ExPASy identification. There were nine ESI identifications in the earlier analysis, but only seven in the final, as the proteins corresponding to two of these protein spots were identified from the in-house S. gordonii database using PMF data (protein spot number 2 from the cellular protein 2-DE gel pI 4.0 – 5.0 and protein spot number 14 from the cellular protein 2-DE gel pI 5.5 – 6.7). This change highlights the benefits of having a fully sequenced and appropriately annotated genome.

120

d 100 e i f i

t 80 2003 analysis en

d 60 2004 analysis s i t 40 o p

S 20 0

0 5 0 5 0 5 0 5 0 0 -1 -1 -2 -2 -3 -3 -4 -4 -5 0 0 1 6 1 6 1 6 1 6 -1 1 1 2 2 3 3 4 4 1 5 Coverage (%)

Figure 4.1 Comparison of the proteins, identified using ExPASy and GPMAW, in the 2003 and 2004 analyses.

As stated above, the criterion to accept the identification of a protein was coverage greater than 19% or, when the coverage was less than 19%, at least five matching peptides if the observed pI and Mr and predicted values corresponded. Without stringent selection criteria, protein identification would be somewhat subjective, but even with them, protein identification can sometimes be a grey area. For example, Cordwell et al. (1995) used a combination of amino-acid composition using N-terminal protein microsequencing and PMF by MALDI mass spectrometry

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utilizing protein sequences of different species to facilitate protein identification of nine proteins from the 2-DE protein map of Spiroplasma melliferum. Although the N-terminal protein microsequencing was more accurate than PMF, a combination of both techniques was optimal for protein identification (Cordwell et al., 1995).

Cordwell et al. (1995) deemed that although Mr and pI can be used to support protein identification, they should not be used to eliminate a potential match when using protein matching from different species as a means of identification. Another study which evaluated protein identification using a set of E. coli and human protein sequences from the SWISS-PROT protein database (Wilkins and Williams, 1997) found that there is a greater conservation of the amino-acid composition and Mr between organisms than for either the PMF or pI, with both PMF and pI showing lower conservation as the genome sequence diversity increases. Much of the mass spectral data collected during the study (on individual unidentified proteins) is available for reanalysis when sequencing of the S. gordonii genome is eventually completed. Additional proteins may then be identified, and/or those tentatively identified by low percentage peptide coverage, identified with greater confidence. This is apparent from the number of proteins identified with high percentage peptide coverage in the first compared with the final analyses (Figure 4.1). For example, the two protein spots referred to above, spot number 2 from the cellular protein pI 4.0 – 5.0 2-DE gel and spot number 14 from the cellular protein pI 5.5 – 6.7 2-DE gel, that were initially unidentified after MALDI-TOF mass spectrometry, were subsequently identified as hypothetical proteins in the 2003 analysis using ESI MS/MS. However when the archived mass spectral data was reanalysed, these protein spots were confidently identified using the original MALDI-TOF mass spectrometer PMF data, even though they remained hypothetical proteins (spot number 2, pI 4.18, Mr 10706, 36% coverage, seven matched peptides; spot number 14, pI 6.35, Mr 11200, 40% coverage, seven matched peptides). Analysis of protein spot number 132 from the cellular protein pI 4.0 – 5.0 2- DE gel, represents another example of revised protein identification. This protein spot, a proton-translocating ATPase β-subunit, was initially identified in 2003 using the S. pneumoniae database (nine peptide matches and 25% coverage). In the 2004 analysis this identity was confirmed using the S. gordonii in-house database (14

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peptide matches and 41% coverage). Enolase (spot number 161 from the cellular protein pI 4.0 – 5.0 2-DE gel) was also initially identified in 2003 using the S. pneumoniae database (six peptide matches and 18% coverage). In the 2004 analysis, its identity was confirmed using the S. gordonii in-house database (eight peptide matches and 28% coverage). Similarly, the identification of glucose-1-phosphate thymidylytransferase (spot number 59 from the cellular protein 2-DE gel pI 4.5 – 5.5) although fitting within the criteria using ExPASy in the 2003 analysis (23% coverage, seven matching peptides) was confirmed using the in-house database (61% coverage, 14 matching peptides). Identification of truncated proteins represents another problem. For example, truncated GPDH protein fragments (spot numbers 15 and 16 from the cellular protein 2-DE gel pI 5.5 – 6.7) were initially unidentified in the 2003 analysis, but only tentatively identified in the 2004 analysis using the GPMAW S. pneumoniae database. Although these spots had five and eight peptide matches respectively, they had quite low coverage, both less than 15%. However, additional analysis by ESI mass spectrometry confirmed their identification. The examples above not only highlight the benefits of having a fully sequenced and annotated genome for the species being analysed, but also reinforce the notion that in the absence of a fully sequenced and annotated genome prudence should be shown when eliminating potential protein matches during the identification process.

4.3.2. 2-DE Proteome map of S. gordonii

Cellular and extracellular proteins from continuous cultures grown at D = 0.10 h-1 and pH 7.0 were resolved by 2-DE on gels with ranges of pI 4.0 – 5.0 (Figures 4.2 and 4.3), 4.5 – 5.5 (Figures 4.4 and 4.5), 5.5 – 6.7 (Figures 4.6 and 4.7), 6.0 – 11.0 (Figures 4.8 and 4.9) and visualized following staining with SYPRO Ruby. The gels were prepared for peptide mass fingerprinting by re-staining with CBB G-250 (Section 2.5.5), which is much less sensitive than other methods, but is compatible with mass-spectrometry (Lauber et al., 2001). Following the manual excision of 1022 protein spots, a total of 476 protein spots were identified by PMF analysis

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(Table 4.2), 365 (196 different proteins) from the cellular fraction and 111 (68 different proteins) from the extracellular milieu. The 476 identified protein spots corresponded to a total of 250 expressed ORFs. Fourteen proteins were common to both compartments. Of the 54 proteins that were unique to the extracellular milieu, almost half could be considered cytoplasmic. The cellular location of a further 12 proteins found in the extracellular milieu was unknown. There are several reasons why 544 protein spots remain unidentified. Firstly, a number of the protein spots were manually excised from the 2-DE gels utilizing the SYPRO Ruby stained scanned images as a template to locate protein spots that were ill defined on the CBB G-250 stained gels. Many of these protein spots were at the limit of sensitivity for MALDI-TOF analysis and produced spectra with poor signal to noise ratios. Secondly, numerous excised protein spots were of low mass, producing relatively few tryptic peptides within the mass range covered by MALDI- TOF mass spectrometry. Some of these low mass proteins were, however, identified using ESI MS/MS. Thirdly, as previously noted, only 50% of the S. gordonii genome had been sequenced and annotated at the time of analysis.

Table 4.2 Excised cellular and extracellular proteins.

pI range of 2- Compartment Number of Number of spots DE gel spots excised identified 4.0 – 5.0 Cellular 226 119 Extracellular 128 36 4.5 – 5.5 Cellular 232 123 Extracellular 100 54 5.5 – 6.7 Cellular 166 55 Extracellular 20 8 6.0 – 11.0 Cellular 112 68 Extracellular 38 13

94 4.0 pI 5.0

Mr 181 180 182 183

83500 184 179 187 178 188 177 185 191 174 151 137 190 189 171 172 155 186 173 176 156 160 150 64500 175 157 200 168 159 170 158 154 149 148 144 164 153 136 192 166 167 152 161 169 165 146143 135 47500 140 224 201 147 134 163 212 126 202 162 145 142 132 141 133 196 197 198 207 211 204 138 139 131 203 214 130 208 216 127 205 215 129 128 100 125 194 199 209 213 124 206 121 123 28500 101 105 118120 210 195 106 122 98 89 90 104 119 221 97 99 24 193 102 103 40 107 115116 96 83 88 108 112 86 27 31 117 222 82 87 23 26 30 94 85 32 109 110 84 80 111 95 223 22 39 114 78 77 218 93 75 79 35 113 81 36 219 74 76 21 25 92 28 29 91 72 68 220 217 71 70 38 73 69 62 37 55 63 17500 41 43 46 34 44 45 67 66 65 64 16500 33 42 47 56 48 57 51 53 3 10 61 52 4 49 58 50 5 6 54 59 1 60 13 18 19 225 226 2 14 15 17 20 8 7 11 12 9 16

Figure 4.2 Cellular protein 2-DE gel pI 4.0 – 5.0. A total of 226 spots were excised for analysis, and 119 identified by mass spectrometry. 4.0 pI 5.0

Mr 108 109 110 64 102 103 101 63 89500 60 104 107 62 70 105 106 96 69 124 58 95 66 65 93 68 67 59 128 127 56 97 121 55 99 57 61 92 98 122 54 53 94 52 57000 126 91 100 123 51

42500 115 47 116 43 44 45 48 50 49 117 46 111 118 119 78 112 41 89 90 21 79 113 88 30 25 42 120 125 72 26 114 86 26000 82 87 81 85 73 27 84 22 74 71 39 83 23 75 28 40 24 76 29 36 38 32 19 33

77 34 35 37 31 80 11 20 14 16 18 12 13 15 10000 17 10 1 2 4 5 8 6 3 9 7

Figure 4.3 Extracellular protein 2-DE gel pI 4.0 – 5.0. A total of 128 protein spots were excised for analysis, and 36 identified by mass spectrometry. 4.5 pI 5.5

Mr

178

179 181 177 182 183 184 180 85500 176 174 175 225 224 226 227 228 232 185 166 70000 167 169 168 170 171 172 173 64500 162 161 165 164 223 192 191 163 210 212 213 214 220 222 229 217 221 230 190 193 188 156 151 199 159158157 154152 219 218 231 189 187 122 209 216 196 215 194 186 143 155 150 123149 211 81 92 160 153 206 198 195 82 93 91 141 121 197 98 148 147 100 39500 142 144 97 87 125 145 118 140 139 137 126 124 207 146 200 9996 94 90 136 117 83 89 86 138 119 84 95 127 128 116 204 205 66 65 88 85 132 133 135 201 113 76 77 47 134 120 115 114 46 59 78 203 67 61 131 57 34000 129 58 109 80 68 63 62 45 56 40 55 108 112 69 42 130 50 5253 107 110 79 70 39 26000 43 54 111 75 48 71 64 44 106 105 74 38 41 49 51 104 103 60 73 202 102 72 208 37 34 20 101 36 4 18 28 19 25 27 17 35 26 10 16 11 29 15500 3 5 9 14 15 30 2 21 31 22 8 23 33 1 7 13 6 12 24 32

Figure 4.4 Cellular protein 2-DE gel pI 4.5 – 5.5. A total of 232 spots were excised for analysis, and 123 identified by mass spectrometry. 4.5 pI 5.5

Mr

80 81 100 78 79 74500 88 85 86 87 66 82 77 84 89 67 68 83 75 76 65 74 90 64 63 69 73 62 71 61 70 66000 46 72 45 59 44 47 49 53 57 50 58 91 60 51 56 48 52 92 54 21 43 22 39 99 55 50000 41 42 23 38 37 24 97 25 26 98 36 95 2 94 35 34 40 20 3 27 1 32 33 43000 93 30 28 29 31 19 96 4 7 18 5 21500 6 13 14 10 15 8 12 16 11 17

9

Figure 4.5 Extracellular protein 2-DE gel pI 4.5 – 5.5. A total of 100 spots were excised for analysis, and 54 identified by mass spectrometry. 5.5 pI 6.7

Mr

163 155 156 158 159 152 161 162 150 154 55000 164 151 157 131 165 143 133 132 160 144147 153 140 130 116 118 106 110 124 149 166 123 102 107 141 145 129 109 148 142 127 117 103 115 146 125126 122 119 35500 139 128 121 89 104 135 81 88 108 137 138 134 71 120 82 97 113 136 69 94 105 112 75 87 92 111 66 70 93 72 90 26500 68 74 98 114 50 67 53 80 83 95 99 73 79 51 54 77 78 84 86 100 20500 76 61 85 91 55 56 63 96 48 49 57 35 62 64 65 47 52 101 36 34 58 59 45 26 32 37 60 44 31 38 28 29 33 3 46 1 42 25 4 39 40 30 5 41 43 16 20 17 2 24 6 8 27 13500 9 15 21 7 18 13 12 19 22 10 14 23 11

Figure 4.6 Cellular protein 2-DE gel pI 5.5 – 6.7. A total of 166 spots were excised for analysis, and 55 identified by mass spectrometry. 5.5 pI 6.7

Mr

137000

15 16 17 18 19

20

14 74500 12 13

38000 11

35000 6 9

7 10 8 21500 5 4

3

2

1

Figure 4.7 Extracellular protein 2-DE gel pI 5.5 – 6.7. A total of 20 protein spots were excised for analysis, and 8 identified by mass spectrometry. 6.0 pI 11.0

Mr

88 89 84 83 85 56000 87 82 86 90 91 81 72 74 80 71 51 68 37500 79 73 54 67 75 78 66 76 43 77 52 41 55 39 40 69 53 42 105 50 70 56 24500 65 49 47 48 38 106 63 60 46 108 64 59 45 37 19500 57 44 110 109 33 104 62 36 61 58 35 17500 32 10 34111 1 97 26 28 29 25 93 92 95 14 99 6 11 103 7 9 12 27 30 31 2 98 112 11000 15 24 100 4 5 8 13 107 94 96 22 3 17 102 18 23 16 101 19 21 20

Figure 4.8 Cellular protein 2-DE gel pI 6.0 – 11.0. A total of 112 spots were excised for analysis, and 68 identified by mass spectrometry. 6.0 pI 11.0

Mr

37

34 83000 18

19 20 73500 17

13

14

29 38

43000 35 10 28 11 12 30 8 27 26 32 15 35500 9 36 16 7 31 6

21 23 22 24 20000 1 25 5 33 3 2

4

Figure 4.9 Extracellular protein 2-DE gel pI 6.0 – 11.0. A total of 38 proteins were excised for analysis, and 13 identified by mass spectrometry. CHAPTER 4

4.3.3. Functional classification of the identified S. gordonii proteins

Each identified protein was placed in a functional category (Table 4.3)10, as defined by Riley (1993) based on the functions of gene products in E. coli (Riley, 1993), as modified by Bolotin and colleagues (1999) following sequencing of the Lactococcus lactis genome. These classification schemes are arbitrary as some proteins play several different metabolic roles (Riley, 1993) and there may be some ambiguity in the function of others. For example, GPDH is not only necessary for glycolysis (Poolman et al., 1987), it is also associated with cell wall adhesion (Nelson et al., 2001), and signal transduction (Pancholi and Fischetti, 1993). However, arbitrary classification schemes are a suitable way of providing an overview of a large number of proteins. The majority of protein spots identified in the current study of S. gordonii were classified in the functional category of “energy metabolism” with 111 protein spots identified. Of these, 87 were classified as glycolytic enzymes, representing 12 different proteins (Table 4.3 and Table 4.4). A total of 94 protein spots were identified as being associated with translation. Of these, 21 ribosomal proteins (36 protein spots) were identified, although others with high pI may not have been identified as very basic proteins are not readily resolved using standard 2-DE technology (Harry et al., 2000; Len et al., 2004b). There were a further six proteins (33 protein spots) classified as translation factors. Thirty-nine proteins (68 protein spots) were identified as “transport and binding proteins”. The functional group “cellular processes”, with 15 proteins (34 protein spots) contained cell adhesion, cell division, peptidoglycan catabolism, protein and peptide secretion, and transformation proteins, including a range of chaperones (DnaK, GroEL, GroES, and GrpE). “Cell envelope” proteins are underrepresented in 2-DE studies (Cordwell et al., 2001; Cordwell et al., 2002; Len et al., 2003), as was the case in this study as only 12 proteins (22 protein spots) were classified as “cell envelope” proteins. The expression of 38 proteins (44 protein spots) previously considered to be hypothetical protein was confirmed. This was the largest specific group of proteins identified.

10 Due to length of Table 4.3, it has been moved to the end of the chapter (pages 129 – 158).

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Table 4.4 Summary of the number of protein spots identified in each functional group and sub- group.

FUNCTIONAL sub-group Spots per Spots per GROUP sub-group group AMINO-ACID Aromatic amino-acid family 2 BIOSYNTHESIS Aspartate family 2 Glutamate family 1 Histidine family 1 Serine family 1 7 BIOSYNTHESIS OF Folic acid 1 COFACTORS, General 4 PROSTHETIC Heme and porphyrin 1 GROUPS, AND CARRIERS 6 CELL ENVELOPE Membrane, lipoproteins, and porins 3 Murein sacculus and peptidoglycan 3 Other 3 Surface polysaccharides, lipopolysaccharides and antigens 3 Surface structures 10 22 CELLULAR Cell Adhesion 3 PROCESSES Cell division 6 Chaperones 13 Peptidoglycan catabolism 5 Protein and peptide secretion 5 Transformation 2 34 CENTRAL Amino sugars 2 INTERMEDIARY Phosphorus compounds 3 METABOLISM 5 ENERGY Aerobic 4 METABOLISM Amino acids and amines 1 ATP-proton motive force interconversion 6 Electron transport 2 Fermentation 1 Glycogen metabolism 2 Glycolysis 87 Pentose phosphate pathway 1 Sugars 6 TCA cycle 1 111 FATTY ACID AND General 5 PHOSPHOLIPID METABOLISM 5 HYPOTHETICAL General 44 44

Table 4.4 is continued on page 105.

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Table 4.4 continued

FUNCTIONAL sub-group Spots per Spots per GROUP sub-group group OTHER CATEGORIES Adaptations to atypical conditions 1 Phage related functions and prophages 4 Transposon related functions 1 6 PURINES, Purine ribonucleotide PYRIMIDINES, biosynthesis 7 NUCLEOSIDES AND Salvage of nucleosides and NUCLEOTIDES nucleotides 3 10 REGULATORY General 8 FUNCTIONS GntR-family regulators 3 GTP-binding proteins 3 LacI-family regulators 1 MarR-family regulators 2 Protein interactions 10 Two-component systems 10 37 REPLICATION DNA replication, restriction, modification, recombination, and repair 7 7 TRANSCRIPTION Degradation of RNA 3 RNA synthesis, modification, and DNA transcription 7 10 TRANSLATION Amino acid tRNA synthetases 11 Degradation of proteins, peptides, and glycopeptides 11 Nucleoproteins 2 Protein modification 1 Ribosomal proteins: synthesis and modification 36 Translation factors 33 94 TRANSPORT AND Amino acids, peptides and BINDING PROTEINS amines 48 Cations 15 PTS system 5 68 UNKNOWN Unclassified 10 10

4.3.4. Modified proteins of S. gordonii

The discrepancy between the number of protein spots (476) and the number of proteins identified (250) in this study is due to some degree to the presence of charged isoforms (Section 4.1 above), probably representing post-translationally modified versions of these proteins. Isoforms were present in 12 out of the 16

105 CHAPTER 4

functional groups described. A total of 63 of the identified proteins showed isoelectric heterogeneity and/or varied mass, which is a similar number to the 64 identified for the S. mutans proteome map (Len et al., 2003). The protein represented by the largest number of spots was the glycolytic enzyme, enolase, existing as a group of nine charged isoforms, as well as a group of 10 charged isoforms exhibiting higher Mr forms on pI 4.0 – 5.0 cellular protein 2-DE gel. Among other glycolytic proteins appearing as charged isoforms were, 6- phosphofructokinase, phosphoglycerate kinase, GPDH, fructose-1,6-biphosphate aldolase, L-lactate dehydrogenase, phosphoglycerate kinase, and pyruvate kinase, which had all been observed as charged isoforms on the S. mutans 2-DE proteomic map (Len et al., 2003). Fructose stimulated pyruvate kinase I, existed as a group of four charged isoforms, as well as two charged isoforms with higher Mr. A regulatory protein involved in protein interactions, protein kinase, was also highly modified, with ten protein spots existing as charged isoforms on the pI 4.5 – 5.5 extracellular protein 2-DE gel. There was also pI heterogeneity in the proteins involved in translation, with seven ribosomal proteins existing as isoforms. Many of the translation factor proteins identified also existed as charged isoforms, with elongation factor TU the most modified, with 9 protein spots on the pI 4.0 – 5.0 cellular protein 2-DE gel. Some of the charged isoforms observed may have evolved as a result of posttranslational modifications. Phosphorylation and dephosphorylation of proteins by the action of kinases and phosphatases, respectively, are a key mechanism by which functional activity can be regulated. Consequently, charged isoforms may be important when differential expression occurs due to environmental change. The nature of the modifications that give rise to the different isoforms in the current study of S. gordonii are unknown, as they are for S. mutans (Len et al., 2003) and their biological relevance has yet to be clarified. However, given the relatively small differences in mass between the isoforms, it is probable that these do not occur solely as a result of processing by endogenous bacterial proteinases.

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4.3.5. Metabolic potential of S. gordonii

The survival of S. gordonii depends on it ability to obtain nutrients and grow in its environment. In the oral environment nutrients mainly come from the metabolism of endogenous substrates present in the saliva and the gingival crevicular fluid (Marsh and Martin, 1999). There are also exogenous nutrients arriving intermittently via dietary intake, most notably carbohydrates and proteins (Marsh et al., 1985; Wong and Sissons, 2001). The concentration of the nutrients and changes in pH resulting from their metabolism, affect the growth rate and physiology of S. gordonii (Gilmore et al., 2003; Marsh et al., 1985). For a bacterium to survive in this changing environment it needs to be able to adapt both phenotypically and biochemically, with the bacterium’s metabolism determining whether it has a pathogenic or commensal relationship with the host (Marsh, 2003). S. gordonii has the potential to produce approximately 2000 proteins, however in the current study only 250 proteins were identified, representing 12.5% of the total proteome, which is comparable with the 11% coverage observed with S. mutans (Len et al., 2003). Even though only 12.5% of the proteome was characterised, several key metabolic pathways were well represented. For instance many of the proteins from the Embden–Meyerhof–Parnas glycolytic pathway and proteins in the pentose phosphate pathway were identified (Figure 4.10; http://www.genome.jp/kegg/). Twenty 30S and 50S ribosomal proteins were identified, although many of the others may have been absent due to their high isoelectric points (Cordwell et al., 2002). Other vital components of protein translation (Table 4.5) were also identified, including; the initiation factor IF-2, which play an ancillary role during initiation of translation; a full set of elongation factors (EF-Ts, EF-Tu, EF-G and EF-P), which play an ancillary role in the elongation step of transcription or translation; one of the three release factors (RF-1), which plays an ancillary role during termination of translation; and ribosome recycling factor (RRF), which is responsible for disassociating the ribosome subunits after translation has terminated. Many proteins associated with protein folding were also identified, including 10 kDa and 60 kDa chaperonins; DnaK, GrpE, ClpE, ClpX and trigger factor, some of which will be discussed further in Sections 4.3.8.6 and 4.3.8.8.

107 CHAPTER 4

x Nucleotide sugars metabolism

Pentose and glucuronate interconversions

Starch and sucrose P metabolism e-1 os luc x D-G α-

P 5.4.2.2 e-6 se os e co uc os r) lu Gl uc ula -G D- Gl ell α-D α- D- ac xtr 2.7.1.2 2.7.1.69 (e

x 5.1.3.3 5.3.1.9

2.7.1.2 β- β-D D- -G Fru luc cto os 2.7.1.11 se e -6P

β- D- Fru cto se 4.1.2.13 -6P Pentose phosphate 4.1.2.4 x G 2-D pathway lyc eo er xy ald -D eh -ri yd bo 1.2.1.12 e- se 3P -5P

G lyc er ate -1, 2.7.2.3 3P 2

G lyc era te- 5.4.2.1 3P

G lyc era te- 4.2.1.11 2P

ate ruv lpy no oe 2.7.1.40 ph os Ph 1.1.1.27 te L-L va ac ru at Py ate

Figure 4.10 Glycolysis and pentose phosphate pathways. The enzymes shown above are from the Embden–Meyerhof–Parnas glycolytic and pentose phosphate pathways from S. pneumoniae (http://www.genome.jp/kegg/). Except for EC: 5.1.3.3 and 5.3.1.9 (highlighted in green), all were identified in S. gordonii.

Key: EC: 1.1.1.27, L-lactate dehydrogenase; EC: 1.2.1.12, glyceraldehyde-3-phosphate dehydrogenase; EC: 2.7.1.11, 6-phosphofructokinase; EC: 2.7.1.2, glucokinase; EC: 2.7.1.40, pyruvate kinase; EC: 2.7.1.69, phosphotransferase system, EIIAB; EC: 2.7.2.3, phosphoglycerate kinase; EC: 4.1.2.13, fructose-bisphosphate aldolase; EC: 4.1.2.4, deoxyribose-phosphate aldolase; EC: 4.2.1.11, enolase; EC: 5.1.3.3, aldose 1-epimerase; EC: 5.3.1.9, glucose-6-phosphate isomerase; EC: 5.4.2.1, phosphoglycerate mutase; EC: 5.4.2.2, phosphoglucose mutase.

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Table 4.5 The main function of translation factors.

Translation Name Gene Main Function in Translation factor Initiation IF-1 infA Occludes ribosomal A site, thus factor prevents premature access of elongator tRNAs. Stimulates IF-2 and IF-3 functions. IF-2 infB GTPase ; Binds the initiator tRNA to ribosomal P site. IF-3 infC Prevents ribosome association; monitors correct initiator tRNA/ initiation codon interaction. Elongation EF-Tu tuf GTPase; Forms ternary complex with factor aa-tRNA and GTP; Binds aatRNA to ribosomal A site. EF-Ts tsf Promotes guanine nucleotide exchange on EF-Tu. EF-G fusA GTPase; Promotes translocation reaction. EF-P efp Stimulates peptidyl transferase reaction for certain amino acids. Release factor RF-1 prfA Promotes termination at UAA, UAG. RF-2 prfB Promotes termination at UAA, UGA. RF-3 prfC GTPase; Promotes action of RF-1and RF-2. Ribosome RRF frr Dissociates ribosomes from mRNA recycling after termination of translation (needs factor EF-G or RF-3).

Many transporters and binding proteins were also identified in the current study. These proteins are involved in the active transport of solutes across the cytoplasmic membrane. ABC transporters belong to the ABC superfamily, which utilize the hydrolysis of ATP to energize diverse biological import and export systems (Holland and Blight, 1999; Saurin et al., 1999). ABC transporters are minimally constituted of two conserved regions: a highly conserved ABC and a less conserved transmembrane domain (Section 1.2.4). In Gram-positive bacteria, which are surrounded by a single membrane and therefore have no periplasmic region, solute binding proteins are bound to the membrane via an N-terminal lipid anchor (Tam and Saier, 1993). These proteins do not play a primary role in the transport process, but probably serve as receptors to trigger or initiate translocation of the solute through the membrane by binding to

109 CHAPTER 4

external sites of the integral membrane proteins. Some solute-binding proteins also function in the initiation of sensory transduction pathways (Tam and Saier, 1993). The survival of bacteria in an acid environment depends on the ability of the cell to maintain intracellular homeostasis. S. mutans uses various mechanisms to achieve homeostasis including proton extrusion via a membrane-translocating H+- ATPase, ensuring that the intracellular pH remains higher than that of the external environment when the pH falls as a result of acidic end products of fermentation (Dashper and Reynolds, 1992; Len et al., 2004a). A proton-translocating ATPase β subunit was identified in this study. It existed as two charged isoforms on the cellular and a single protein spot on the extracellular 4.0 – 5.0 2-DE gels. Although H+-ATPases are found in S. gordonii, acid tolerant bacteria such as S. mutans have higher levels of ATPase activity, while the pH optima for activity is lower than for less tolerant species, such as S. gordonii. (Kuhnert and Quivey Jr, 2003). Such altered pH optima make S. gordonii more susceptible to low pH environment.

4.3.6. Probable integral membrane proteins of S. gordonii.

An integral membrane protein is a protein that either spans the biological membrane with which it is associated, or which is sufficiently embedded in the membrane to remain with it during the initial steps of biochemical purification. Integral membrane proteins can be transmembrane, spanning the membrane with sections exposed inside and outside the membrane, or non-transmembrane, only solvent exposed on one side of the membrane. These proteins are tightly bound to lipid bilayers through hydrophobic interactions and although they comprise approximately 20-30% of the proteins encoded by a genome (Krogh et al., 2001), are rarely isolated by 2-DE due to their poor solubility and inherent hydrophobicity (Cordwell et al., 2001; Santoni et al., 2000). This should be contrasted with membrane-enriched fractions from Gram- negative bacteria which allow the detection of lipoproteins and outer membrane proteins by 2-DE (Cullen et al., 2002; Hommais et al., 2002; Langen et al., 2000; Molloy et al., 2000; Molloy et al., 2001; Nilsson et al., 2000; Nouwens et al., 2000; Phadke et al., 2001; Qi et al., 1996). Whereas the transmembrane domains of outer membrane proteins possess β-pleated sheets formed from alternating polar and non-

110 CHAPTER 4

polar amino acids, integral cytoplasmic membrane proteins generally possess hydrophobic α-helical transmembrane domains consisting of non-polar amino acids. Integral membrane proteins therefore, maintain an overall hydropathy and are hydrophobic even when denatured (Santoni et al., 2000). Examples of the functions that different integral membrane proteins serve include the identification of the cell for recognition by other cells, adhesion of one cell to another or to a surface, the initiation of intracellular responses to external stimuli and the transportation of molecules across the membrane. A total of 25 proteins identified in this study, were predicted to have transmembrane regions or to be integral membrane proteins (Table 4.6). Although this may appear a small amount, compared with the 587 transmembrane proteins (approximately 25% of the genome), that have been predicted for the S. pneumoniae TIGR4 genome (http://pedant.gsf.de), this data compares favourably with other studies (Len et al., 2003), and corresponds to 10% of the total proteins identified.

Table 4.6 Proteins of S. gordonii with a predicted transmembrane region.

FUNCTIONAL sub-group Description Genea Genomeb GROUP AMINO-ACID Aromatic amino- Prephenate dehydrogenase tyrA S. pneumoniae BIOSYNTHESIS acid family TIGR4 Aspartate family Homoserine dehydrogenase hom S. pneumoniae TIGR4 General Phosphomevalonate kinase mvaK2 S. pneumoniae TIGR4 CELL ENVELOPE Murein, sacculus UDP-N-acetylmuramoylalanyl-D- murE S. pneumoniae and peptidoglycan glutamate--2,6-diaminopimelate R6 ligase Other Pneumococcal surface protein A pspA S. pneumoniae TIGR4 Surface LicD1 paralog licD1 S. pneumoniae polysaccharides, TIGR4 lipopolysaccharides, and antigens

Table 4.6 is continued on page 112.

111 CHAPTER 4

Table 4.6 continued.

FUNCTIONAL sub-group Description Genea Genomeb GROUP CELLULAR Cell adhesion Choline binding protein J cbpJ S. pneumoniae PROCESSES TIGR4 Cell division Cell division protein FtsZ ftsZ S. pneumoniae TIGR4 Transformation Competence protein celA S. pneumoniae TIGR4 ATP-proton motive ATP synthase B chain atpF S. pyogenes force MGAS8232 interconversion Proton-translocating ATPase beta uncD S. pyogenes subunit MGAS8232 Glycogen Alpha-glycerophosphate oxidase glpO S. pyogenes metabolism MGAS8232 Sugars Maltodextrin phosphorylase malP S. pyogenes MGAS8232 PURINES, Purine Adenylosuccinate lyase purB S. pyogenes PYRIMIDINES, ribonucleotide MGAS8232 NUCLEOSIDES biosynthesis AND NUCLEOTIDES REGULATORY Two-component Histidine kinase hk02 S. pneumoniae FUNCTIONS systems TIGR4 Sensor histidine kinase SPO155 S. pneumoniae TIGR4 TRANSCRIPTION Degredation of tRNA isopentenylpyrophosphate miaA S. pneumoniae RNA transferase TIGR4 TRANSPORT AND Amino acids, ABC transporter substrate-binding aliA S. pneumoniae BINDING peptides and amines protein-oligopeptide transport TIGR4 PROTEINS Choline transporter proV S. pneumoniae TIGR4 Oligopeptide transport protein amiE S. pneumoniae TIGR4 Oligopeptide transport system amiC S. pneumoniae permease protein TIGR4 Oligopeptide-binding protein aliB S. pneumoniae TIGR4 Cations V-type sodium ATP synthase, SP1322 S. pneumoniae subunit I TIGR4 General Metal-binding permease adcA S. pneumoniae TIGR4 Transmembrane protein Vexp1 SP0599 S. pneumoniae TIGR4 a Gene: refers to the gene or locus name as it appears in Swiss-Prot/TrEMBL. b Genome: refers to the genome from the PEDANT database from which the potential transmembrane proteins were identified.

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4.3.7. Streptococcal virulence proteins found in S. gordonii.

Surface proteins, secreted proteins and polysaccharides are important due to their possible roles in virulence and pathogenesis, and thus have a potential for use as antigens for vaccine development. The cell wall of Gram-positive bacteria is an attachment site for proteins that interact with the bacterial environment, and not surprisingly, many surface proteins have been found to be major virulence factors (Navarre and Schneewind, 1999; Overweg et al., 2000). Secreted proteins are also potential virulence factors as their export enables them to interact and potentially modify the host environment to enable survival. Thus, exported proteins of bacterial pathogens, and their protein export pathways contribute to virulence and pathogenesis. In the case of S. gordonii, 22 proteins were identified that may have a potential role in virulence (Table 4.7). A discussion of these and other pertinent putative virulence proteins is included in Section 4.3.8 below.

4.3.8. Some important S. gordonii proteins revealed by 2-DE and mass spectrometry

4.3.8.1. GPDH and enolase

Approximately 40% of proteins in the extracellular fraction were cellular in origin, which suggests they are products formed after cell lysis. However, many proteins classically described as cytoplasmic are also associated with the cell surface or secreted into the external environment (Kolberg and Sletten, 1996; Nelson et al., 2001). GPDH is implicated as a virulence determinant aiding S. pyogenes invasion of tissues and is considered to act as a possible defence against the immune system in S. gordonii (Nelson et al., 2001). The capture of plasminogen can be used to facilitate pneumococcal penetration through biological membranes by plasminogen activation during the invasive infection process (Bergmann et al., 2001; Pancholi and Fischetti, 1998). Surface GPDH aids in the virulence of S. pyogenes by binding streptokinase-activated plasmin(ogen) to cell surfaces, whereas S. gordonii lacks streptokinase, and, so although its role is currently unknown, GPDH may be involved in the defence of S. gordonii against the host’s immune system (Nelson et al., 2001).

113 Table 4.7 Putative virulence proteins a

Functional Sub-group Description Gene b Species c Reference group CELL ENVELOPE Other Pneumococcal surface pspA S. pneumoniae (Briles et al. , 1988; Jedrzejas et al. , 2001) protein A Surface polysaccharides, LicD1 paralog licD1 S. pneumoniae (Zhang et al. , 1999) lipopolysacch-arides and Tyrosine-protein kinase cpsD S. pneumoniae (Bender and Yother, 2001; Bender et al. , 2003) antigens CpsD Surface structures M protein emm S. pyogenes (Bisno et al. , 1987; Fischetti, 1989; Navarre and Schneewind, 1999) Streptococcal surface sspA S. gordonii DL1 (Demuth et al. , 1996) protein A Challis CELLULAR Peptidoglycan catabolism Peptidoglycan mur1.2 S. sanguis (Horne and Tomasz, 1985; Majcherczyk et al. , 1999) PROCESSES hydrolase S. pneumoniae Protein and peptide SecA2 secA2 S. gordonii M99 (Takamatsu et al. , 2004) secretion ENERGY Glycolysis Enolase eno S. pneumoniae (Bergmann et al. , 2001; Ge et al. , 2004; Wilkins et al. , METABOLISM S. mutans 2002) Glyceraldehyde-3- None S. gordonii FSS2 (Nelson et al. , 2001) phosphate dehydrogenase REGULATORY General Metalloregulator scaR S. gordonii DL1 (Jakubovics et al. , 2000; Oetjen et al. , 2002) FUNCTIONS Challis S. parasanguis Two-component systems DNA-binding response micA S. pyogenes (Graham et al. , 2002; Kadioglu et al. , 2003) regulator S. pneumoniae Kinase SCO1217 S. pyogenes (Graham et al. , 2002) Response regulator silA S. pyogenes (Graham et al. , 2002) Sensor histidine kinase SPO155 S. pyogenes (Graham et al. , 2002; Lange et al. , 1999; Li et al. , 2002a) S. pneumoniae S. mutans Table 4.7 is continued on page 115. Table 4.7 Putative virulence proteins continued.

Functional Sub-group Description Gene Species Reference group REGULATORY Two-component systems Transcriptional CTC01421 S. pyogenes (Graham et al. , 2002) FUNCTIONS continued regulatory component continued of sensory transduction system DNA-binding response vncR S. pyogenes (Graham et al. , 2002) regulator REPLICATION DNA replication, recA protein recA S. pneumoniae ( Mortier-Barriére et al. , 1998) restriction, modification, recombination, and repair TRANSLATION Degradation of proteins, ATP-dependent clp clpE S. pneumoniae (Kwon et al. , 2003; Robertson et al. , 2002) peptides, and protease ATP-binding glycopeptides subunit clpE ATP-dependent Clp clpX S. pneumoniae (Kwon et al. , 2003; Robertson et al. , 2002) protease ATP-binding subunit clpX ATP-dependent Clp SP2194 S. pneumoniae (Kwon et al. , 2003; Robertson et al. , 2002) protease, ATP-binding subunit DegP degP S. pyogenes (Jones et al. , 2001) TRANSPORT General Chromosome cshA S. gordonii DL1 (Christie et al. , 2002; McNab and Jenkinson, 1992; McNab AND BINDING segregation helicase Challis et al. , 1994; McNab et al. , 1995; McNab et al. , 1996; PROTEINS McNab and Jenkinson, 1998; McNab et al. , 1999) a Proteins identified from S. gordonii that have been specifically cited as playing a role in streptococcal virulence. b Gene: refers to the gene or locus name as it appears in Swiss-Prot/TrEMBL. c Streptococcal species in which the protein has been associated with virulence. CHAPTER 4

Interestingly, when S. oralis grown at either pH 5.2 or pH 7.0 was analysed by 2-DE, 28 cellular proteins, including GPDH, were down-regulated at pH 7.0 (Wilkins et al., 2001). At first sight, this observation seems incompatible with a role for the protein as a virulence determinant in infective endocarditis. However, in S. gordonii, the GPDH becomes the major extracellular protein as the pH rises to pH 7.5 (Nelson et al., 2001). Since the amount of extracellular GPDH was not determined when S. oralis was grown at different pH, it is not clear whether the reduction in the amount of cellular protein is due to down-regulation of expression or simply secretion into the extracellular milieu as the pH rises. GPDH was found to be expressed in both the cellular fraction and the extracellular milieu of S. gordonii and will be discussed further in Section 5.3.3.3 below. Enolase is another glycolytic enzyme that has also previously been associated with virulence in streptococci. All the protein spots identified as enolase in S. gordonii were from the cellular fraction, implying they were cytosolic, or associated/attached to the cell wall. The detection of enolase in the culture supernatant would have indicated either active secretion or the presence of lysed S. gordonii cells. Enolase is known to be a plasmin(ogen) binding factor in S. pneumoniae (Bergmann et al., 2001; Sha et al., 2003; Wilkins et al., 2002). Furthermore, a recent study has identified enolase as a cell surface component of S. mutans that binds to human plasminogen and salivary mucin (Ge et al., 2004). The authors suggested that, in S. mutans, enolase might play a role in attachment, clearance, or breach of the bloodstream barrier (Ge et al., 2004). If enolase has a similar function in S. gordonii it might have serious implications for the pathogenesis of S. gordonii in acquired endocarditis.

4.3.8.2. Antigen I/II

The cell wall of Gram-positive bacteria has many functions that are crucial for the viability of the cell. Its main function is to provide a rigid exoskeleton as protection against both mechanical and osmotic lysis. However it also is an attachment site for proteins that interact with the environment (Navarre and Schneewind, 1999). These proteins are often involved in direct interactions with host tissues or in concealing the

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bacterial surface from the host defence mechanisms (Jedrzejas, 2001). Several proteins from the cell envelope were identified in S. gordonii, two of which were the antigen I/II polypeptides, SspA and SspB, products of tandem chromosomal genes (Demuth et al., 1996). Surface proteins of the antigen I/II family contain alanine- rich repeats (which adopt an α-helical coiled-coil structure), proline rich repeats and a carboxy-terminal region that includes the Gram-positive cell wall anchor motif LPXTG (Jenkinson and Lamont, 1997; Navarre and Schneewind, 1999). In addition to salivary glycoprotein binding activity, SspA is associated with the coaggregation and coadhesion of S. gordonii with Actinomyces (Demuth et al., 1996), a process involved in dental plaque formation (Kolenbrander et al., 1990; Kolenbrander, 1993; Kolenbrander et al., 1993; Kolenbrander and London, 1993; Kolenbrander and Andersen, 1990; Kolenbrander et al., 1994; Kolenbrander et al., 1998; Kolenbrander et al., 1999).

4.3.8.3. M protein

It has been shown that surface proteins of pathogenic bacteria play an important role during the infectious process by mediating interactions between the pathogen and the host cells and/or help evasion of host defences. One such molecule is the surface- bound M protein produced by S. pyogenes (Robinson and Kehoe, 1992). M protein is a major virulence factor of S. pyogenes, which appears as hair- like projections on the cell surface. One of the major roles of M protein in the virulence of S. pyogenes is to protect the bacteria from phagocytosis, but it is also required for attachment of the bacterium to keratinocytes and is likely to play a significant role in infections initiated at the skin surface (Fischetti, 1989). M protein also causes S. pyogenes to aggregate when they attach to tonsillar epithelial cells. The N-terminal region of M protein extends into the environment and due to its variability is largely responsible for the antigenic variation in S. pyogenes. In contrast, the C-terminus is highly conserved and contains the LPXTG motif that anchors the protein to the peptidoglycan in the cell wall (Section 4.3.8.2 above; Fischetti, 1989).

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There is little likelihood that the proteins identified in this study as M proteins are in fact M proteins, even though they fitted within the selection criteria. M proteins have only been isolated from group A streptococci (S. pyogenes). However, proteins homologous to M protein or M-like proteins have been reported in group B, C, E and G streptococci (Bessen and Fischetti, 1992; Bisno et al., 1987; Fischetti, 1989; Maxted and Potter, 1967; Schnitzler et al., 1995; Woolcock, 1974). The PMF of all proteins (from the current study) were blasted against the GMPAW and ExPASy databases. In the case of the putative M proteins there were no matches fitting within the selection criteria in the GPMAW searches, however ExPASy searches matched the PMFs to N-terminal fragments of M proteins, with coverage of 20 – 28% of those fragments. These data are available for reanalysis once the S. gordonii genome sequence is completed.

4.3.8.4. Autolytic activity due to peptidoglycan hydrolase in S. gordonii

A putative peptidoglycan hydrolase proved to be very interesting as it was only isolated from the extracellular milieu and was prominent on the 4.0 – 5.0 culture supernatant 2-DE gel (Figure 4.11). Peptidoglycan hydrolases, are enzymes that hydrolyse either the glycan or the peptide moieties of peptidoglycan, and are involved in many important biological processes occurring during cell growth and division, including cell wall synthesis, daughter cell separation, and peptidoglycan turnover and recycling (Fournier and Hooper, 2000). Peptidoglycan hydrolases of Gram-positive bacteria are known to trigger cytokine release from peripheral blood mononuclear cells causing a peptidoglycan-induced inflammation (Majcherczyk et al., 1999). Autolytic activity due to peptidoglycan hydrolase was previously observed in S. gordonii (Fussenegger et al., 1996; Horne and Tomasz, 1985) and is associated with virulence in S. pneumoniae. However, a role for this enzyme in the virulence of S. gordonii in infective endocarditis or even in dental plaque formation is as yet unknown.

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M 4.0 pI 5.0 Mr 89500

57000 42500

26000

10000

Figure 4.11 Protein spots identified as peptidoglycan hydrolase (circled).

4.3.8.5. SecA2

The highly conserved components of the Sec system are associated with the translocation of proteins across the bacterial cell membrane. Most bacterial species have a single copy of the genes encoding SecA and SecY, which are essential for viability (Bensing and Sullam, 2002). However, S. gordonii also encodes the SecA and SecY homologues, SecA2 and SecY2, which are not required for viability, but instead are required for the export of the large surface glycoprotein GspB (Bensing and Sullam, 2002), which is necessary for the binding of S. gordonii to human platelets (Bensing et al., 2004; Takamatsu et al., 2004). The relevance of the expression of SecA2 in the current chemostat study of S. gordonii is yet to be determined, but may eventually prove to be important, as the binding of microorganisms to human platelets is thought to be a major virulence determinant in the pathogenesis of infective endocarditis (Sullam, 1994).

4.3.8.6. Heat shock proteins

In response to a sudden elevated temperature or to other types of stress such as low pH, both prokaryotic and eukaryotic cells increase the expression of a small family

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of molecular chaperones, the heat-shock proteins (GrpE, DnaK, DnaJ and GroELS; Singleton and Sainsbury, 1996). Heat-shock proteins were named as such because they were synthesised when bacteria were briefly exposed to high temperatures, but they are also expressed in response to other environmental stress, such as acid shock (Jayaraman and Burne, 1995). Heat-shock proteins are inducible repair proteins that work to reverse protein denaturation caused by heat exposure (Ang and Georgopoulos, 1989; Craig, 1989; Pelham, 1988; Pelham, 1986). However, it has also been shown that molecular chaperones are not only up-regulated in response to heat, but they are constantly being made and are involved in many cellular processes. For example, heat-shock proteins are associated with protein synthesis, as the growing polypeptide that emerges from the ribosome interacts with molecular chaperones (Pelham, 1986). They also interact with proteins and facilitate their transport across membranes (Wickner, 1994). Furthermore, it is known that some heat shock proteins, such as DnaK and GrpE, are essential for bacterial growth and cell viability (Ang and Georgopoulos, 1989; Craig, 1989; Pelham, 1988; Pelham, 1986). Some of these heat shock proteins are also involved in the SOS response, in order to overcome damage to DNA or inhibition of DNA replication. This response can result in increased capacity for DNA repair and inhibition of cell division. In response to a sudden shock, heat shock proteins are usually produced at high levels, but only transiently, (Schulz and Schumann, 1996; Singleton and Sainsbury, 1996). At other times heat shock proteins are continually expressed, representing some of the most abundantly expressed proteins on 2-DE gels (Wasinger et al., 2000). In S. gordonii 15 proteins (34 spots) were concerned with cellular processes. There were four molecular chaperones (GroES, GroEL, DnaK and GrpE; 13 spots) with DnaK and GrpE existing as charged isoforms (Section 4.3.4). The signal for the synchronized induction of DnaK and GroEL is the buildup of unfolded proteins in the cytosol, with the phosphorylation of DnaK and GroEL thought to promote binding to unfolded (denatured) proteins (Sherman and Goldberg, 1994; Sherman and Goldberg, 1993). The isoforms of DnaK and GrpE observed in S. gordonii may be linked to multiple phosphorylation states, and although this may support the role of phosphorylation for the sensing and regulation of stress responses in S. gordonii, the

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presence of shock proteins in the absence of a recognisable specific induction signal is more likely to indicate a constant level of expression, as these proteins are essential for growth irrespective of the presence or absence of imposed stress conditions (Wasinger et al., 2000). Recent studies, however, have found the role that some heat shock proteins play in streptococcal genetic competence and biofilm formation (Li et al., 2002b). This role is discussed in Section 4.3.8.8 below.

4.3.8.7. Two-component systems

Several two-component regulatory system proteins were tentatively identified in the current study that were homologous to genes in S. pneumoniae, DNA-binding response regulator (gene: MicA), histidine kinase (gene: hk02), sensor kinase, (ORF: 2SCG58.17c), response regulator (gene: SilA), sensor histidine kinase (gene: ComD), transcriptional regulatory component of sensory transduction system (ordered locus: CTC01421) and DNA-binding response regulator (gene: VncR). Two-component gene regulatory systems are composed of a membrane-bound sensor (histidine kinase) and cytoplasmic response regulator and are important signalling systems used by bacteria to respond to environmental stimuli (Stock et al., 2000). Many two- component systems have been associated with virulence and pathogenicity in Gram- positive bacteria (Graham et al., 2002; Lange et al., 1999), making two-component signal transduction a potential multicomponent target for intervention by drugs (Throup et al., 2000). Histidine kinases similar to the ones identified in the current study, are regulated by environmental stimuli to autophosphorylate at a histidine residue, creating a high-energy phosphoryl group that is subsequently transferred to an aspartate residue on an associated response regulator. Both prokaryotic and eukaryotic histidine kinases contain the same basic signalling components, namely a diverse sensing domain and a highly conserved kinase core that has a unique fold, distinct from that of the serine, threonine, and tyrosine kinases (Stock et al., 2000). Research has shown that a histidine kinase also plays a role in quorum sensing and competence in S. gordonii (Gilmore et al., 2003; Håvarstein et al., 1996), as discussed in the following section.

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4.3.8.8. Competence proteins of S. gordonii

S. gordonii Challis is naturally competent (Lunsford and London, 1996). The ability of S. gordonii to take up DNA from the environment and to integrate it into its genome has been examined in several studies and a number of competence specific operons have been recognized (Håvarstein et al., 1996; Horne and Tomasz, 1985; Lunsford and London, 1996; Lunsford et al., 1996; Mercer et al., 1999; Raina and Macrina, 1982). It is known that competence occurs in early growth phase in batch culture and the genetic transformation in naturally competent streptococci, such as S. gordonii and S. pneumoniae, is partly controlled by a quorum-sensing system, which is mediated by a peptide pheromone called the competence-stimulating peptide (CSP; Håvarstein et al., 1996; Peterson et al., 2004). A recent study identified competence pheromone responsive genes in S. pneumoniae (Peterson et al., 2004). These genes were grouped according to four temporally distinct expression profiles: “early”, “late” and “delayed” gene induction, and gene repression, which were observed during 20 min after CSP addition (Peterson et al., 2004). The expression pattern of genes in the “early” category consisted of a low basal signal which increased almost immediately upon CSP exposure, reached a maximum between 7.5 min and 10 min, and then declined toward basal levels. In a comX mutant background (deficient in competence for genetic transformation), these genes were similarly induced, but the sharp decline in mRNA levels was strongly alleviated. ComX encodes a unique sigma factor σx that is responsible for competence-stimulating activity in S. gordonii (Lunsford and London, 1996) as well as other streptococci (Lee and Morrison, 1999). The expression pattern of “late” genes was generally similar to that of “early” genes, but was distinguished by a lag of approximately 5 min before induction and a delay of about 5 min in reaching a maximum. “Late” genes were depended on comX for induction of expression. In “delayed” gene induction, mRNA levels increased continually during the first 15 to 17 min after CSP treatment; the increase was generally small, in the order of four to eightfold overall, and was similar in the comX background. Several genes were transiently “repressed” during the response to CSP;

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for these, RNA levels dropped during competence induction, usually two to fourfold, and recovered to pre-treatment levels after 20 min (Peterson et al., 2004). Several proteins associated with “early”, “late”, “delayed” and “repressed” responses to CSP were identified in the current study (Table 4.8). Many “early” CSP genes are involved in competence regulation. For example, ComX (identified in the current study and discussed above) is responsible for competence-stimulating activity in S. gordonii (Lunsford and London, 1996) as well as other streptococci (Lee and Morrison, 1999). In naturally competent streptococci, such as S. gordonii the expression of genes involved in binding, uptake, and integration of extracellular DNA is regulated by the gene products of the competence regulation operon. This is made up of three genes encoding the CSP precursor ComC, ComD (a histidine kinase identified in the current study), and its cognate response regulator ComE. The CSP is secreted and processed by a secretion apparatus consisting of ComA and ComB. Competence is induced when the concentration of CSP in the medium reaches a critical level (Håvarstein et al., 1997). Once the critical level is reached the signal is relayed by the membrane-bound CSP receptor, the histidine kinase, ComD, to activate the response regulator ComE (Håvarstein et al., 1997). Both of these “early” genes are strongly induced in response to CSP and have important functions in competence regulation (Alloing et al., 1998). Quorum sensing systems regulate many physiological activities, including genetic competence and biofilm formation in S. gordonii (Gilmore et al., 2003) and S. mutans (Li et al., 2002b). “Late” competence genes are dependant on ComX. Many of these “late” proteins have important roles in the binding and up-take events involved in transformation (Londono-Vallejo and Dubnau, 1993; Peterson et al., 2004). The “late” competence proteins identified in the current study included ComF, CelA and RecA. ComF is an ATP-dependent helicase required for DNA import, CelA is an integral membrane protein essential for both binding and transport for DNA uptake in S. pneumoniae (Pestova and Morrison, 1998; Provvedi and Dubnau, 1999), whereas, the recombination protein, RecA, regulates bacterial DNA repair and recombination in response to environmental stress. RecA has been found to promote

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Table 4.8 Competence proteins in S. gordonii.

Genea Productsb CPSc comD Putative sensor histidine kinase Early SP2006/SP0014 Transcriptional regulator ComX1/ComX2 (σx) clpEd ATP-dependent clp protease ATP-binding subunit celA Competence protein

SP2207 Competence protein ComF, putative Late SP1090 Hypothetical protein recA recA protein

SP2206 Ribosomal subunit interface protein groeL 60 kDa chaperonin dnaK Chaperone protein Delayed grpE Heat shock protein hrcA Heat-inducible transcription repressor rpsC 30S ribosomal protein S3 rplO 50S ribosomal protein L15 rl16 50S ribosomal protein L16

SP0214 50S ribosomal protein L22 Repressed rplW 50S ribosomal protein L23 rplE 50S ribosomal protein L5

SP0290 Dihydrofolate synthetase manL Phosphotransferase system, mannose-specific EIIAB a Gene: refers to the gene or locus name in S. pneumoniae. b Products refers to gene products. c CPS: refers to the temporal state of the CSP-stimulated competence genes in S. pneumoniae (Peterson et al., 2004), being either “early”, “late”, “delayed”, or “repressed” genes. d All CPS-stimulated genes were identified by Peterson et al. (2004) except for clpE, which was observed in the study of Dagkessamanskaia et al. (2004).

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in vivo recombination in S. gordonii (Raina and Macrina, 1982; Vickerman et al., 1993). Transcriptional up-regulation of recA occurs during competence, implying that RecA is a virulence factor, due to the regulatory link between competence and virulence (Mortier-Barriére et al., 1998). As with RecA, it has been suggested that other proteins involved in competence are potential virulence factors, also due to their regulatory link between competence and virulence (Hava and Camilli, 2002; Mortier-Barriére et al., 1998; Peterson et al., 2004). In one study (Håvarstein et al., 1997) a number of new pherotypes (of the competence pheromone) were identified by sequencing the genes encoding the CSP and its receptor from different streptococcal species, including S. gordonii (Håvarstein et al., 1997). It was found that naturally competent streptococci occasionally switch pherotypes when they take up and incorporate DNA from streptococci with a different pherotype. The associated genes have a mosaic structure, which arises as a result of recombination between two distinct allelic variants. The observed mosaic blocks encompass the region encoding the CSP and the CSP-binding domain of ComD. As a result, the recombination events have led to switches in pherotype for the strains involved. This would imply that for the adaptation of naturally competent streptococci to new environmental conditions recombinational exchanges, rather than spontaneous mutation may be a major evolutionary force. In other words, naturally competent streptococci occasionally switch pherotypes when they take up and incorporate DNA from streptococci with a different pherotype. The expression of “delayed” genes appear to be stress related. For example, those identified in S. gordonii were dnaK, groeL, grpE and hrcA. These genes are different from the “early” and “late” patterns as they are largely unaffected by inactivation of comX. The role of stress related proteins in competence is probably complicated, as they are known to preform many functions (discussed previously in Section 4.3.8.6), however their role may be related to the gross shift in protein synthesis associated with competence development (Peterson et al., 2004). Most of the genes repressed during the response to added CSP are ribosomal. Even though the levels of ribosomal proteins only drop slightly (two to four-fold), it

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is believed that this indicates an overall reduction in protein synthesis during competence (Rimini et al., 2000). Several of these “repressed” proteins were identified in S. gordonii. These included the 30S ribosomal protein S3, and the 50S ribosomal proteins L15, L16, L22, L23, and L5, dihydrofolate synthetase, and PEP- PTS mannose-specific EIIAB (Peterson et al., 2004). A possible consequence of the capacity of S. gordonii to take up DNA is that its genome may contain genes that were derived from other bacteria. It is interesting to speculate that some of the unexpected proteins tentatively identified in the current study may have owed their origins to other bacteria, both within or transiting the oral cavity. However, the presence of competence proteins in the current study may either imply that S. gordonii is competent in the chemostat and/or that these proteins are being constitutively expressed in S. gordonii Challis. Also, no conclusion can be drawn as to the expression of genes that are “repressed” during competence, as there is no standard to determine if these proteins were repressed in the chemostat. Proposed experiments (See Chapter 6 Discussion) would determine if S. gordoni is indeed competent in the chemostat under the steady state growth conditions used.

4.3.8.9. Proteases

Several proteases were found in S. gordonii, including CLpX, ClpE, and DegP (Table 4.3). Proteases are essential for virulence and for survival under stress conditions in several pathogenic bacteria (Section 1.6). ClpX contributes to virulence by controlling the activity of major virulence factors in S. aureus, ClpE is involved in cell division and virulence of Listeria monocytogenes (Nair et al., 1999), while ClpE and ClpL are induced during competence in S. pneumoniae (Section 4.3.8.8; Dagkessamanskaia et al., 2004; Peterson et al., 2004). In S. mutans, Clp ATPases are implicated in the tolerance and regulation of the response to stresses because of their protein reactivation and remodeling activities and their capacity to target misfolded proteins for degradation by the ClpP peptidase (Lemos and Burne, 2002). The DegP protease is required for thermal and oxidative tolerance and full virulence in S. pyogenes (Section 4.3.7; Jones et al., 2001). Although S. gordonii is not as tolerant of environmental stress in the oral cavity as other more cariogenic

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streptococci (Section 5.2 in the next chapter), it is likely that proteases are essential for its virulence in infective endocarditis.

4.4. CONCLUSION

This study has produced the first comprehensive proteome reference map of S. gordonii. The map is based on a standardized continuous culture condition, allowing for future comparative analyses with other S. gordonii proteomes derived from modeling environmental conditions relevant to the pathogenesis and survival of this organism. By identifying approximately 12.5% of the S. gordonii proteome, a significant contribution has been made towards complementing the genomic sequencing initiative of this oral bacterium. The current study confirmed the expression of 38 proteins previously designated as “hypothetical” or with no known function. Although the S. gordonii genomic DNA sequence is incomplete, the number of proteins identified (476 protein spots corresponding to 250 proteins) compared well with the proteomic mapping of S. mutans (416 protein spots corresponding to 200 proteins; Len et al., 2003), whose genome is fully sequenced and annotated (Ajdic et al., 2002). Many metabolic pathways were well represented. For example, most of the glycolytic proteins were identified, although there was no evidence of enzymes associated with alternate acid production. In this analysis, as with other studies, “Cell Envelope” proteins were underrepresented, indicating that more research into the isolation of S. gordonii membrane fractions will be required in the future. However, a number of cell wall and membrane-associated proteins were detected. Many potential virulence proteins were identified, most with confidence, although the identification of M protein is doubtful, even though it fitted within the selection criteria. Proteins associated with competence, which is known to occur in early growth phase in batch cultures of S. gordonii, were of particular interest especially if S. gordonii is actually competent in the chemostat. The notion is supported by the presence of the products of late competence genes required for incorporation of exogenous DNA. This finding, therefore warrants further future investigation. However, although some competence proteins were identified, others

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(that would be expected to be expressed during competence) were not. This possibly reflects some of the current limitations of the technology used. For example ComA is an integral membrane protein and ComC is a very small (5000 Da) protein, which means that they are unlikely to be identified in 2-DE (Harry et al., 2000; Jacques et al., 2002; Len et al., 2003; Santoni et al., 2000). In the current study, mass spectral data was collected and archived for over 1000 protein spots. Thus this mapping exercise remains a “work-in-progress”, as the map can be updated when the annotation of the S. gordonii genome is completed, making the proteomic map even more robust. In the next chapter the results obtained from the proteome map of S. gordonii are used to identify differentially expressed proteins during a change in environmental pH, in order to obtain some insight as to why S. gordonii does not survive at low pH and what changes in phenotype occur during the process endocarditis.

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Table 4.3 Proteins identified from S. gordonii categorized into putative functional groups and sub-groups. a Gel: refers to the pH range and cellular component of the reference gel (Figures 4.2 – 4.9); 4.0 (pH 4.0 – 5.0), 4.5 (pH 4.5 – 5.5), 5.5 (pH 5.5 – 6.7), 6.0 (pH 6.0 – 11.0); C, cellular; E, extracellular. b Spot #: refers to the assigned number of the protein spot on the reference gel (Figures 4.2 – 4.9). c Description: refers to the name of the best matching protein, from Swiss-Prot/TrEMBL. d Gene name: refers to the gene or locus name as it appears in Swiss-Prot/TrEMBL. e Acc #: refers to the accession number from Swiss-Prot/TrEMBL. f Species: refers to the species with homologous protein from Swiss-Prot/TrEMBL. g Cover: refers to the percentage of the protein sequence covered by the matching peptide. h Peptides: refers to the number of peptides in PMF that matched those from the database entry. i Theoretical pI, Mr: refers to the theoretical isoelectric point and relative molecular weight of the matching protein as calculated from the corresponding translated gene. j Observed pI, Mr: refers to the observed isoelectric point and observed molecular weight of the matching protein, determined by the spots migration on the gel (Section 1.4.11). k Analysis: refers to the analysis program used to identify the protein. In the cases where GPMAW was the analysis program, GPMAW SG indicates the use of the S. gordonii in-house database and GPMAW SP indicates the use of the S. pneumoniae database (Section 2.8.1). l The sections highlighted in yellow indicate the Mr of the observed protein is at least 25% less than the theoretical protein. m The reference proteins used for the Z3 calculation of pI and Mr (Section 2.6.1). n The sections highlighted in green indicate the Mr of the observed protein is at least 25% greater than the theoretical protein. Functional group Sub-group Gel a Spot # Description c Gene d Acc # e Species f Cover g Pept- Analysis k i j b ides h Theoretical Observed pIMr pIMr AMINO-ACID Aromatic amino-acid Chorismate mutase-like BIOSYNTHESIS family protein, prephenate 5.5C 4 dehydratase SPR1174 Q8DPG5 S. pneumoniae 38.6 5 5.3 10186 5.9 15500n GPMAW SG

4.0C 18 Prephenate dehydrogenase tyrA Q8DPD2 S. pneumoniae 27.0 8 4.9 41002 4.7 11500 l GPMAW SP Aspartate family Homoserine 4.0C 131 dehydrogenase hom Q97Q69 S. pneumoniae 27.6 9 4.7 46154 4.9 42000 GPMAW SG

2,3,4,5-tetrahydropyridine- 2-carboxylate N- 4.5C 54 succinyltransferase SP2097 Q97NE6 S. pneumoniae 25.9 7 4.9 24270 4.9 28000 GPMAW SG Glutamate family Gamma-glutamyl 4.5C 82 phosphate reductase gpr Q97R94 S. pneumoniae 23.8 7 5.5 45376 5.3 40000 GPMAW SG Histidine family 4.0C 212 Histidinol dehydrogenase hdh Q8DTQ7 S. mutans 29.8 11 4.6 46917 4.7 43000 GPMAW SG Serine family 4.5C 99 Cysteine synthase SP2210 Q97N57 S. pneumoniae 47.2 11 5.1 32521 5.2 35500 GPMAW SG BIOSYNTHESIS OF Folic acid COFACTORS, PROSTHETIC GROUPS, AND CARRIERS 4.5C 153 Dihydrofolate synthetase SP0290 Q97SN7 S. pneumoniae 13.7 6 4.9 49647 4.7 52000 GPMAW SG General 4.0C 43 Dephospho-CoA kinase coaE Q97R60 S. gordonii 29.3 5 4.5 22599 4.3 18000 GPMAW SG Phosphomevalonate 4.5C 146 kinase mvaK2 Q8DR49 S. pneumoniae 31.5 5 4.9 37126 4.9 45500 GPMAW SP Phosphomevalonate 4.5C 204 kinase mvaK2 Q8DR49 S. pneumoniae 25.6 8 5.4 39078 5.2 34500 GPMAW SG Phosphomevalonate 4.5E 55 kinase mvaK2 Q8DR49 S. pneumoniae 26.3 5 4.8 37027 5.0 49000 GPMAW SP Heme and porphyrin Coproporphyrinogen III 4.5C 194 oxidase hemN Q8DPA6 S. pneumoniae 32.7 7 5.4 43023 5.3 35000 GPMAW SG CELL ENVELOPE Membrane, lipoproteins, and porins Phosphopantetheine 5.5C 40 adenylyltransferase coaD Q8DNE6 S. pneumoniae 37.8 5 6.0 18958 6.0 16500 GPMAW SG

5.5C 142 Lipoprotein SP0149 Q97T11 S. pneumoniae 27.5 6 5.3 31177 5.6 39500 GPMAW SP

6.0C 57 Lipoprotein SP0149 Q97T11 S. pneumoniae 27.2 6 6.3 31849 6.8 22000 l GPMAW SG Functional group Sub-group Gel a Spot # Description c Gene d Acc # e Species f Cover g Pept- Analysis k i j b ides h Theoretical Observed pIMr pIMr CELL ENVELOPE Murein sacculus and D-alanyl-D-alanine continued peptidoglycan carboxypeptidase 4.5C 30 (fragment) SP0872 Q97RF0 S. pneumoniae 29.6 5 4.8 16662 5.2 15000 GPMAW SG

UDP-N-acetylglucosamine 5.5C 150 pyrophosphorylase SP0988 Q97R46 S. pneumoniae 19.6 8 5.7 49569 5.6 53000 GPMAW SG UDP-N- acetylmuramoylalanyl-D- glutamate--2,6- 5.5C 148 diaminopimelate ligase murE Q97PS1 S. pneumoniae 16.2 6 5.6 53384 5.7 42500 GPMAW SG Other Peptidoglycan bound protein, LPXTG motif, Listeria 6.0E 17 putative LM00159 Q8YAG7 monocytogenes 12.2 6 6.2 76590 6.0 73500 GPMAW SG Pneumococcal surface 4.0C 140 protein A [Fragment] pspA Q9LAX6 S. pneumoniae 19.7 9 4.7 51564 4.9 44000 GPMAW SP

4.0C 226 Secreted protein, putative SAG0675 Q8E0Q1 S. agalactiae 1 9.2 18498 4.2 13000 l MASCOT Surface polysaccharides, lipopolysaccharides and antigens 6.0C 5 Tyrosine-protein kinase cpsD Q9AFI1 S. agalactiae 22.3 5 9.4 25064 6.6 11500 l ExPASy

6.0C 66 LicD1 paralog licD1 Q8DPD7 S. pneumoniae 26.3 6 5.9 32661 6.0 26500 GPMAW SP DTDP-4-keto-6- deoxyglucose-3,5- 4.0C 71 epimerase rmlC Q83YT5 S. gordonii 48.8 10 5.0 23172 4.8 21500 GPMAW SG Surface structures EMML15=M-like immunoglobulin-binding 4.5E 35 protein (Fragment) None Q9R5X5 S. pyogenes 20.7 5 6.0 25472 5.3 33000 ExPASy 4.5C 3 M protein (Fragment) emm Q8GL85 S. pyogenes 20.2 5 5.5 15921 4.4 15000 ExPASy 4.5C 9 M protein (fragment) emm Q937W8 S. pyogenes 25.0 4 4.9 14716 4.7 16000 ExPASy 4.5C 10 M protein (fragment) emm Q937W8 S. pyogenes 20.1 4 4.9 14716 4.7 15500 ExPASy 4.5C 14 M protein (Fragment) emm P72564 S. pyogenes 19.7 4 4.9 11190 4.9 14500 ExPASy M protein precursor 6.0C 3 (Fragment) emm Q9L761 S. pyogenes 28.4 7 7.8 23959 6.7 9500 l ExPASy Functional group Sub-group Gel a Spot # Description c Gene d Acc # e Species f Cover g Pept- Analysis k i j b ides h Theoretical Observed pIMr pIMr CELL ENVELOPE Surface structures Streptococcal surface continued continued 4.0C 156 protein A [Precursor] sspA Q54185 S. gordonii 13.3 7 4.9 57003 4.8 58000 GPMAW SG Streptococcal surface 4.0C 157 protein A [Precursor] sspA Q54185 S. gordonii 14.1 8 4.9 57003 4.8 60500 GPMAW SG Streptococcal surface 4.0E 53 protein A [Precursor] sspA Q54185 S. gordonii 24.9 8 4.9 57002 m 4.9 57000 GPMAW SG Streptococcal surface 4.5C 221 protein A [Precursor] sspA Q54185 S. gordonii 23.2 8 4.9 57003 5.0 15000 l GPMAW SG CELLULAR Cell Adhesion PROCESSES 4.0E 42 Choline binding protein J cbpJ Q9KGY7 S. pneumoniae 37.3 12 5.0 36620 4.7 31000 GPMAW SG

4.0E 43 Glucan-binding protein B none Q938V3 S. mutans 23.4 6 4.9 42581 4.8 42500 GPMAW SG

4.0E 44 Glucan-binding protein B none Q938V3 S. mutans 30.3 7 4.9 42581 m 4.9 42500 GPMAW SG Cell division 4.0C 102 Cell division protein divIVA Q9ZHB4 S. pneumoniae 24.0 5 4.4 28382 4.3 28500 GPMAW SG

4.0C 104 Cell division protein divIVA Q9ZHB4 S. pneumoniae 28.0 6 4.4 28382 m 4.4 28500 GPMAW SG

4.0C 193 Cell division protein divIVA Q9ZHB4 S. gordonii 28.0 6 4.4 28382 4.3 29500 GPMAW SG

4.0C 166 Cell division protein ftsZ Q8DNV9 S. pneumoniae 29.0 11 4.7 45426 4.7 46000 GPMAW SG

4.5C 156 Cell division protein ftsZ Q8DNV9 S. gordonii 33.0 13 4.7 45426 4.6 53500 GPMAW SG

6.0C 69 Methyltransferase gigB Q97QD4 S. pneumoniae 30.4 5 6.6 27092 6.3 27000 GPMAW SG Chaperones Clostridium 4.0E 10 10 kDa chaperonin groES P48223 thermocellum 34.0 5 5.2 10156 m 5.0 10000 ExPASy

4.0C 168 60 kDa chaperonin groEL Q8VT58 S. pneumoniae 23.0 10 4.5 56754 4.5 56500 GPMAW SG

4.0C 169 60 kDa chaperonin groEL Q8VT58 S. pneumoniae 26.1 9 4.5 56754 4.6 56500 GPMAW SG

4.0C 172 60 kDa chaperonin groEL Q8VT58 S. pneumoniae 14.4 5 4.5 56770 4.6 63000 GPMAW SG

4.5C 164 60 kDa chaperonin groEL Q8VT58 S. gordonii 21.1 7 4.5 56754 4.5 58500 GPMAW SG

4.5C 165 60 kDa chaperonin groEL Q8VT58 S. gordonii 34.6 14 4.5 56754 4.5 59000 GPMAW SG Functional group Sub-group Gel a Spot # Description c Gene d Acc # e Species f Cover g Pept- Analysis k i j b ides h Theoretical Observed pIMr pIMr CELLULAR Chaperones continued PROCESSES continued 4.0C 171 Chaperone protein dnaK Q8CWT3 S. pneumoniae 15.3 7 4.5 64724 m 4.5 64500 GPMAW SG

4.0C 189 Chaperone protein dnaK Q8CWT3 S. pneumoniae 12.0 5 4.5 64724 4.5 63000 GPMAW SP

4.0E 94 Chaperone protein dnaK Q8CWT3 S. pneumoniae 15.0 5 4.5 64724 4.7 65500 GPMAW SG

4.5C 161 Chaperone protein dnaK Q8CWT3 S. pneumoniae 11.0 5 4.5 64724 4.6 62500 GPMAW SG

4.5C 162 Chaperone protein dnaK Q8CWT3 S. pneumoniae 6.1 5 4.5 64724 4.6 62000 GPMAW SG

4.5C 166 Chaperone protein dnaK Q8CWT3 S. pneumoniae 28.8 13 4.5 64724 m 4.5 64500 GPMAW SG

4.0C 84 Heat shock protein grpE Q8CWT4 S. pneumoniae 16.9 5 4.7 20278 4.7 24000 GPMAW SG Peptidoglycan Peptidoglycan hydrolase, catabolism 4.0E 21 putative mur1.2 Q9A0A8 S. pyogenes 37.2 5 4.4 25936 4.3 26000 GPMAW SG Peptidoglycan hydrolase, 4.0E 22 putative mur1.2 Q9A0A8 S. pyogenes 28.1 6 4.4 25936 4.3 22500 GPMAW SG Peptidoglycan hydrolase, 4.0E 26 putative mur1.2 Q9A0A8 S. pyogenes 40.7 6 4.4 25936 m 4.4 26000 GPMAW SG Peptidoglycan hydrolase, 4.0E 27 putative mur1.2 Q9A0A8 S. pyogenes 39.1 5 4.4 25936 4.4 23000 GPMAW SG Peptidoglycan hydrolase, 4.0E 32 putative mur1.2 Q9A0A8 S. pyogenes 27.7 6 4.4 25936 4.6 23000 GPMAW SG Protein and peptide secretion 4.5C 182 SecA secA Q9AET4 S. gordonii 10.0 7 5.1 94968 5.2 85500 GPMAW SP

4.5C 183 SecA-like protein secA2 Q9AET6 S. gordonii 8.6 6 6.2 93649 5.3 85000 GPMAW SP

4.5C 184 SecA-like protein secA2 Q9AET6 S. gordonii 10.3 6 6.2 93649 5.4 82500 GPMAW SP

4.0C 192 Trigger factor ropA Q8DR29 S. pneumoniae 40.5 14 4.4 47242 4.4 47000 GPMAW SG

4.0C 200 Trigger factor ropA Q8DR29 S. pneumoniae 28.3 10 4.4 47242 4.4 48000 GPMAW SG Transformation 5.5C 70 Competence protein celA O85197 S. pneumoniae 31.5 4 5.6 23244 5.8 29000 GPMAW SP Competence protein 6.0C 60 ComF, putative SP2207 Q97N60 S. pneumoniae 50.7 6 8.4 25895 6.5 21500 GPMAW SG Functional group Sub-group Gel a Spot # Description c Gene d Acc # e Species f Cover g Pept- Analysis k i j b ides h Theoretical Observed pIMr pIMr CENTRAL Amino sugars INTERMEDIARY N-acetyl-glucosamine METABOLISM 4.0C 110 matabolism nagD Q8DPA7 S. pneumoniae 33.6 6 4.4 28265 4.4 25000 GPMAW SG N-acetylmannosamine-6- phosphate 2-epimerase 2, 4.0E 16 putative nanE2 Q8DNU6 S. pneumoniae 32.6 5 4.9 25358 4.8 11000 l GPMAW SG Phosphorus compounds Probable manganese- dependent inorganic 4.0C 196 pyrophosphatase ppaC P95765 S. gordonii 29.6 5 4.5 33541 4.3 34000 GPMAW SG Probable manganese- dependent inorganic 4.0C 197 pyrophosphatase ppaC P95765 S. gordonii 43.1 8 4.5 33541 4.3 33500 GPMAW SG Probable manganese- dependent inorganic 4.0C 198 pyrophosphatase ppaC P95765 S. gordonii 25.1 6 4.5 33541 4.4 34500 GPMAW SG ENERGY Aerobic Acetoin reductase, SPYM3_0 METABOLISM 4.5C 69 putative 458 Q8K853 S. pyogenes 35.7 7 5.7 28807 5.2 27500 GPMAW SG Acetoin reductase, SPYM3_0 4.5C 76 putative 458 Q8K853 S. pyogenes 53.7 9 5.7 28807 5.2 30500 GPMAW SG Acetoin reductase, SPYM3_0 4.5C 77 putative 458 Q8K853 S. pyogenes 47.1 10 5.7 28807 5.3 31000 GPMAW SG Acetoin reductase, SPYM3 6.0C 63 putative 0458 Q8K853 S. pyogenes 39.3 8 5.7 28807 6.0 22000 GPMAW SG Amino acids and amines 5.5C 140 Acetate kinase ackA Q97NI3 S. pneumoniae 19.2 5 5.5 43687 5.5 42000 GPMAW SG ATP-proton motive force interconversion 4.0C 63 ATP synthase B chain atpF Q59952 S. pneumoniae 47.6 4 5.2 18008 5.0 17000 GPMAW SP

4.5C 18 ATP synthase B chain atpF Q59952 S. gordonii 43.9 4 5.2 17987 4.8 18500 GPMAW SP

4.5E 14 ATP synthase B chain atpF Q59952 S. pneumoniae 36.0 6 5.2 17987 5.0 25000 n GPMAW SP Proton-translocating 4.0C 132 ATPase beta subunit undC Q9ZJ01 S. sanguis 41.2 14 4.7 50751 4.9 44000 GPMAW SG Proton-translocating 4.0C 142 ATPase beta subunit undC Q9ZJ01 S. sanguis 36.1 12 4.7 50751 4.8 44500 GPMAW SG Proton-translocating 4.5C 152 ATPase beta subunit undC Q9ZJ01 S. sanguis 27.1 8 4.7 50751 4.7 52000 GPMAW SG Functional group Sub-group Gel a Spot # Description c Gene d Acc # e Species f Cover g Pept- Analysis k i j b ides h Theoretical Observed pIMr pIMr ENERGY Electon transport METABOLISM Arsenate reductase, SMU.1142 continued 4.0E 8 putative C Q8DU17 S. mutans 31.4 4 5.5 15968 4.8 9500 l ExPASy

4.0C 118 Thioredoxin reductase trxB Q97PY2 S. pneumoniae 28.4 7 4.6 35256 4.5 27000 GPMAW SG Fermentation Pyruvate formate-lyase- 5.5C 63 activating enzyme SP1976 Q97NP4 S. pneumoniae 32.7 5 6.3 30868 6.1 23000 l GPMAW SG Glycogen metabolism Alpha-glycerophosphate 4.0C 154 oxidase glpO P35596 S. pneumoniae 19.7 9 4.7 66928 4.8 56000 GPMAW SG

Alpha-glycerophosphate 4.0E 69 oxidase glpO P35596 S. pneumoniae 21.5 8 4.7 66928 4.8 74000 GPMAW SG Glycolysis 5.5C 134 6-phosphofructokinase pfkA Q97RC6 S. gordonii 19.6 6 5.6 35346 5.7 36000 GPMAW SG

5.5C 135 6-phosphofructokinase pfkA Q97RC6 S. gordonii 27.1 10 5.6 35346 5.7 35500 GPMAW SG

5.5C 137 6-phosphofructokinase pfkA Q97RC6 S. gordonii 23.8 10 5.6 35346 m 5.6 35500 GPMAW SG

5.5C 138 6-phosphofructokinase pfkA Q97RC6 S. gordonii 37.5 12 5.6 35346 5.6 35500 GPMAW SG

5.5E 5 6-phosphofructokinase pfkA Q97RC6 S. gordonii 20.2 5 5.6 35346 5.7 21500 l GPMAW SG

5.5E 6 6-phosphofructokinase pfkA Q97RC6 S. gordonii 13.4 5 5.6 35346 5.5 35000 GPMAW SG

4.0C 141 Enolase eno Q97QS2 S. pneumoniae 30.8 9 4.5 43310 4.8 42500 GPMAW SG

4.0C 146 Enolase eno Q97QS2 S. pneumoniae 31.6 9 4.5 43310 4.8 48000 GPMAW SG

4.0C 161 Enolase eno Q97QS2 S. pneumoniae 27.6 8 4.5 43310 4.5 46000 GPMAW SG

4.0C 162 Enolase eno Q97QS2 S. pneumoniae 20.6 7 4.5 43310 4.6 48000 GPMAW SG

4.0C 163 Enolase eno Q97QS2 S. pneumoniae 35.6 9 4.5 43310 4.6 47500 GPMAW SG

4.0C 164 Enolase eno Q97QS2 S. pneumoniae 37.1 11 4.5 43310 4.6 47500 GPMAW SG

4.0C 165 Enolase eno Q97QS2 S. pneumoniae 31.6 9 4.5 43310 4.7 47500 GPMAW SG Functional group Sub-group Gel a Spot # Description c Gene d Acc # e Species f Cover g Pept- Analysis k i j b ides h Theoretical Observed pIMr pIMr ENERGY Glycolysis continued METABOLISM continued 4.0C 167 Enolase eno Q97QS2 S. pneumoniae 37.3 12 4.5 43310 4.7 47500 GPMAW SG

4.0C 175 Enolase eno Q97QS2 S. pneumoniae 16.7 5 4.5 43310 4.7 60500 n GPMAW SG

4.0C 176 Enolase eno Q97QS2 S. pneumoniae 35.3 11 4.5 43310 4.7 66000 n GPMAW SG

4.0C 177 Enolase eno Q97QS2 S. pneumoniae 35.6 9 4.5 43310 4.7 74500 n GPMAW SG

4.0C 178 Enolase eno Q97QS2 S. pneumoniae 27.1 9 4.5 43310 4.7 75500 n GPMAW SG

4.0C 179 Enolase eno Q97QS2 S. pneumoniae 30.1 9 4.5 43310 4.7 76500 n GPMAW SG

4.0C 180 Enolase eno Q97QS2 S. pneumoniae 36.8 12 4.5 43310 4.7 85000 n GPMAW SG

4.0C 181 Enolase eno Q97QS2 S. pneumoniae 33.3 10 4.5 43310 4.7 85500 n GPMAW SG

4.0C 182 Enolase eno Q97QS2 S. pneumoniae 27.6 8 4.5 43310 4.7 85000 n GPMAW SG

4.0C 183 Enolase eno Q97QS2 S. pneumoniae 36.6 7 4.5 43310 4.8 83500 n GPMAW SG

4.0C 184 Enolase eno Q97QS2 S. pneumoniae 19.7 5 4.5 43310 4.8 82000 n GPMAW SG

4.0C 214 Enolase eno Q97QS2 S. pneumoniae 38.8 5 4.5 43310 4.7 42000 GPMAW SG

4.5C 141 Enolase eno Q97QS2 S. pneumoniae 43.0 11 4.5 43310 4.6 48000 GPMAW SG

4.5C 144 Enolase eno Q97QS2 S. pneumoniae 43.0 11 4.5 43310 4.6 47500 GPMAW SG

4.5C 154 Enolase eno Q97QS2 S. pneumoniae 28.6 7 4.5 43310 4.7 53500 GPMAW SG

4.5C 155 Enolase eno Q97QS2 S. pneumoniae 32.1 10 4.5 43310 4.7 52500 GPMAW SG

4.5C 157 Enolase eno Q97QS2 S. pneumoniae 43.3 14 4.5 43310 4.6 52500 GPMAW SG

4.5C 159 Enolase eno Q97QS2 S. pneumoniae 36.6 11 4.5 43310 4.6 52500 GPMAW SG

4.5C 160 Enolase eno Q97QS2 S. pneumoniae 37.3 12 4.5 43310 4.6 52000 GPMAW SG Functional group Sub-group Gel a Spot # Description c Gene d Acc # e Species f Cover g Pept- Analysis k i j b ides h Theoretical Observed pIMr pIMr ENERGY Glycolysis continued METABOLISM continued 4.5C 176 Enolase eno Q97QS2 S. pneumoniae 25.9 7 4.5 43310 4.7 96500 n GPMAW SG

4.5C 177 Enolase eno Q97QS2 S. pneumoniae 38.8 11 4.5 43310 4.7 105500 n GPMAW SG

4.5C 178 Enolase eno Q97QS2 S. pneumoniae 36.1 14 4.5 43310 4.7 126500 n GPMAW SG Fructose-1,6-biphosphate 4.0C 87 aldolase fbaA Q8DWG0 S. mutans 29.4 9 4.6 31394 4.9 26000 GPMAW SG Fructose-1,6-biphosphate 4.5C 108 aldolase fbaA Q8DWG0 S. mutans 45.4 8 4.9 31394 4.9 31500 GPMAW SG Fructose-1,6-biphosphate 4.5C 109 aldolase fbaA Q8DWG0 S. mutans 38.6 9 4.9 31394 4.9 32000 GPMAW SG Fructose-1,6-biphosphate 4.5C 110 aldolase fbaA Q8DWG0 S. mutans 41.0 9 4.9 31394 5.0 31500 GPMAW SG Fructose-bisphosphate 4.5E 28 aldolase [Fragment] fbaA Q9ZES9 S. sanguis 24.9 6 4.7 18950 5.1 34000 n GPMAW SP

4.0C 82 Glucose kinase glcK Q8DQN3 S. pneumoniae 22.8 4 4.8 34185 4.8 25000 l GPMAW SP

4.0C 83 Glucokinase gki Q97RW8 S. pneumoniae 38.2 13 4.7 33827 4.8 26000 GPMAW SG

4.5C 57 Glucokinase gki Q97RW8 S. pneumoniae 15.0 5 4.7 33827 m 4.7 34000 GPMAW SG

Glyceraldehyde 3- 4.0C 78 phosphate dehydrogenase SP2012 Q97NL1 S. pneumoniae 24.8 6 5.3 35856 4.9 24000 l ExPASy

Glyceraldehyde 3- 4.5C 87 phosphate dehydrogenase None Q9L5X6 S. gordonii 9.7 5 5.0 33968 5.5 38000 GPMAW SP

Glyceraldehyde 3- 4.5C 91 phosphate dehydrogenase None Q9L5X6 S. gordonii 16.9 6 5.0 33968 5.4 38500 GPMAW SP

Glyceraldehyde 3- 4.5C 92 phosphate dehydrogenase None Q9L5X6 S. gordonii 20.9 9 5.0 33968 5.4 39000 GPMAW SP

Glyceraldehyde 3- 4.5C 93 phosphate dehydrogenase None Q9L5X6 S. gordonii 12.2 5 5.0 33968 5.3 40000 GPMAW SP Functional group Sub-group Gel a Spot # Description c Gene d Acc # e Species f Cover g Pept- Analysis k i j b ides h Theoretical Observed pIMr pIMr ENERGY Glycolysis continued METABOLISM Glyceraldehyde 3- continued 4.5C 97 phosphate dehydrogenase None Q9L5X6 S. gordonii 25.9 10 5.0 33968 5.2 39500 GPMAW SP

Glyceraldehyde 3- 4.5C 100 phosphate dehydrogenase None Q9L5X6 S. gordonii 9.1 5 5.0 33968 5.1 42500 GPMAW SP

Glyceraldehyde 3- 4.5E 92 phosphate dehydrogenase SP2012 Q97NL1 S. pneumoniae 22.4 6 5.2 35856 5.2 40500 GPMAW SP

Glyceraldehyde 3- 5.5C 15 phosphate dehydrogenase None Q9L5X6 S. gordonii 12.2 5 5.0 33968 6.3 13500 l GPMAW SP

Glyceraldehyde 3- 5.5C 16 phosphate dehydrogenase None Q9L5X6 S. gordonii 14.7 8 5.0 33968 6.3 13500 l GPMAW SP Glycosyltransferase, 5.5C 133 putative None Q83YS4 S. gordonii 24.2 4 6.2 37126 5.8 45000 GPMAW SG

4.5C 86 L-lactate dehydrogenase ldh O33734 S. pneumoniae 33.5 8 5.1 35267 5.5 35000 GPMAW SG

4.5C 89 L-lactate dehydrogenase ldh O33734 S. pneumoniae 21.6 5 5.1 35267 5.4 36000 GPMAW SG

4.5C 96 L-lactate dehydrogenase ldh O33734 S. pneumoniae 33.5 8 5.1 35267 5.3 36500 GPMAW SG

4.0C 106 Phosphoglycerate kinase pgk Q8DQX8 S. pneumoniae 22.1 6 4.9 41939 4.4 26500 l ExPASy

4.0C 107 Phosphoglycerate kinase pgk Q8DQX8 S. pneumoniae 15.1 5 4.9 41939 4.4 26000 l ExPASy

4.0C 108 Phosphoglycerate kinase pgk Q8DQX8 S. pneumoniae 13.6 5 4.9 41939 4.4 25500 l ExPASy

4.0C 125 Phosphoglycerate kinase pgk Q8VVB6 S. pneumoniae 21.6 6 4.7 42089 5.0 38000 GPMAW SG

4.0C 126 Phosphoglycerate kinase pgk Q8VVB6 S. pneumoniae 35.8 10 4.7 42089 5.0 39500 GPMAW SG

4.0C 127 Phosphoglycerate kinase pgk Q8VVB6 S. pneumoniae 43.4 14 4.7 42089 4.9 39500 GPMAW SG

4.0C 130 Phosphoglycerate kinase pgk Q8VVB6 S. pneumoniae 43.1 13 4.7 42089 4.9 40000 GPMAW SG

4.0C 138 Phosphoglycerate kinase pgk Q8DQX8 S. pneumoniae 21.4 6 4.8 41939 4.8 40000 GPMAW SP Functional group Sub-group Gel a Spot # Description c Gene d Acc # e Species f Cover g Pept- Analysis k i j b ides h Theoretical Observed pIMr pIMr ENERGY Glycolysis continued METABOLISM continued 4.0C 139 Phosphoglycerate kinase pgk Q8DQX8 S. gordonii 46.1 15 4.7 42089 4.8 40500 GPMAW SG

4.0C 206 Phosphoglycerate kinase pgk Q8DQX8 S. pneumoniae 31.1 7 4.7 42089 4.5 34500 GPMAW SG

4.0E 50 Phosphoglycerate kinase pgk Q8DQX8 S. pneumoniae 43.6 14 4.7 42089 5.0 47000 GPMAW SG

4.5C 121 Phosphoglycerate kinase pgk Q8DQX8 S. pneumoniae 32.3 11 4.7 42089 4.7 49000 GPMAW SG

4.5C 123 Phosphoglycerate kinase pgk Q8DQX8 S. pneumoniae 50.6 17 4.7 42089 4.8 48500 GPMAW SG

4.5C 147 Phosphoglycerate kinase pgk Q8DQX8 S. pneumoniae 22.3 7 4.7 42089 4.9 46000 GPMAW SG

4.5C 148 Phosphoglycerate kinase pgk Q8DQX8 S. pneumoniae 27.9 8 4.7 42089 4.8 48500 GPMAW SG

4.5E 53 Phosphoglycerate kinase pgk Q8DQX8 S. pneumoniae 24.9 8 4.8 41939 5.0 62000 n GPMAW SP

4.5C 63 Phosphoglycero-mutase gpmA Q97PG5 S. pneumoniae 32.6 7 5.3 26044 m 5.3 26000 GPMAW SG

4.0C 117 Pyruvate kinase pyk Q97RC5 S. pneumoniae 22.0 7 4.9 54755 4.5 27000 l GPMAW SP

4.5E 41 Pyruvate kinase pyk Q97RC5 S. pneumoniae 8.0 5 4.9 54755 4.8 59000 GPMAW SP

4.5E 44 Pyruvate kinase pyk Q97RC5 S. pneumoniae 10.2 7 4.9 54755 4.9 70500 GPMAW SP

4.5E 46 Pyruvate kinase pyk Q97RC5 S. pneumoniae 12.0 7 4.9 54755 4.9 74000 n GPMAW SP

4.5E 52 Pyruvate kinase pyk Q97RC5 S. pneumoniae 10.2 5 4.9 54755 5.0 61500 GPMAW SP

4.5E 61 Pyruvate kinase pyk Q97RC5 S. pneumoniae 17.2 5 4.9 54755 4.8 80000 n GPMAW SP

4.5E 65 Pyruvate kinase pyk Q97RC5 S. pneumoniae 7.0 5 4.9 54755 4.8 97500 n GPMAW SP Pyruvate kinase I, fructose- 4.5C 126 stimulated pykF Q8DQ84 S. pneumoniae 27.9 7 4.8 54799 4.7 46000 GPMAW SG Pyruvate kinase I, fructose- 4.5C 129 stimulated pykF Q8DQ84 S. pneumoniae 26.7 9 4.8 54799 4.5 32500 l GPMAW SG Pyruvate kinase I, fructose- 4.5C 139 stimulated pykF Q8DQ84 S. pneumoniae 21.6 7 4.8 54799 4.6 46500 GPMAW SG Functional group Sub-group Gel a Spot # Description c Gene d Acc # e Species f Cover g Pept- Analysis k i j b ides h Theoretical Observed pIMr pIMr ENERGY Glycolysis continued METABOLISM Pyruvate kinase I, fructose- continued 4.5C 140 stimulated pykF Q8DQ84 S. pneumoniae 21.2 4 4.8 54799 4.5 46500 GPMAW SG Pyruvate kinase I, fructose- 4.5C 179 stimulated pykF Q8DQ84 S. pneumoniae 30.3 13 4.8 54799 4.9 115500 n GPMAW SG Pyruvate kinase I, fructose- 4.5C 181 stimulated pykF Q8DQ84 S. pneumoniae 12.8 6 4.8 54799 4.9 115500 n GPMAW SG Pentose phosphate 6-phosphogluconate pathway 4.0C 38 dehydrogenase gntZ Q9CDN4 L. lactis 28.8 7 4.8 33185 4.4 21000 l ExPASy Sugars Beta-N- acetylhexosaminidase 4.5C 185 [Precursor] strH P49610 S. pneumoniae 23.6 8 5.4 69761 m 5.4 70000 GPMAW SG Maltodextrin 4.5E 87 phosphorylase malP Q8DN49 S. pneumoniae 14.4 8 5.0 74641 5.1 69000 GPMAW SG Glucose-1-phosphate 4.0C 88 thymidylytransferase rmlA Q83YT6 S. gordonii 34.0 8 5.0 34578 4.9 26000 GPMAW SG Glucose-1-phosphate 4.5C 59 thymidylytransferase rmlA Q83YT6 S. gordonii 60.8 14 5.0 34578 4.8 33500 GPMAW SG Glucose-1-phosphate 4.5C 72 thymidylytransferase rmlA Q83YT6 S. gordonii 24.6 6 5.0 34578 5.1 22500 l GPMAW SG Glucose-1-phosphate 4.5C 120 thymidylytransferase rmlA Q83YT6 S. gordonii 28.8 7 5.0 34578 4.9 33000 GPMAW SG TCA cycle 4.0C 159 Malolactic enzyme mleS Q8DWC7 S. mutans 19.0 10 4.6 59839 4.7 56500 GPMAW SG FATTY ACID AND General PHOSPHOLIPID 3-oxoacyl-acyl-carrier- METABOLISM protein reductase / 3- ketoacyl-acyl carrier 4.5C 70 protein reductase, putative fabG Q8DSN5 S. mutans 23.8 7 5.1 25938 5.2 25500 GPMAW SG

Acetyl-CoA carboxylase, carboxyl transferase, alpha 6.0C 71 subunit accA Q9FBB7 S. pneumoniae 28.2 5 6.0 28248 6.0 36000 GPMAW SP Acetyl-CoA:acetoacetyl- CoA transferase A 4.5E 19 subunit, putative atoD.1 Q8K6E4 S. pyogenes 31.9 6 5.7 20259 5.4 24000 ExPASy Functional group Sub-group Gel a Spot # Description c Gene d Acc # e Species f Cover g Pept- Analysis k i j b ides h Theoretical Observed pIMr pIMr FATTY ACID AND General continued PHOSPHOLIPID METABOLISM Acetyl-coenzyme A continued carboxylase carboxyl 5.5C 53 transferase subunit alpha accA Q8DR13 S. pneumoniae 25.4 5 5.7 28433 5.8 26500 GPMAW SG Enoyl-acyl carrier 5.5C 125 protein(ACP) reductase fabK Q8DR17 S. pneumoniae 34.9 9 5.8 34268 m 5.8 34500 GPMAW SG HYPOTHETICAL General Conserved hypothetical SMU.1789 4.0E 20 protein C Q8DSJ4 S. mutans 17.2 5 4.5 25741 4.7 16000 l ExPASy Conserved hypothetical SMU.1789 4.5E 5 protein C Q8DSJ4 S. mutans 31.1 5 4.5 25741 4.7 10000 l ExPASy Conserved hypothetical 5.5C 89 protein SMU.354 Q8DVV8 S. mutans 23.8 6 5.4 48253 6.1 32500 l ExPASy Conserved hypothetical 6.0C 29 protein SMU.566C Q8CMD5 S. mutans 31.6 6 9.5 18243 9.9 14000 GPMAW SG Conserved hypothetical 4.0C 60 protein SP1691 Q54723 S. pneumoniae 42.0 4 4.6 16844 5.0 12000 l GPMAW SP Conserved hypothetical 4.5E 58 protein SPR0009 Q8DRQ0 S. pneumoniae 29.0 11 5.0 48044 5.1 51000 GPMAW SG Conserved hypothetical 4.0C 2 protein GBS1074 Q8E5F8 S. agalactiae 35.8 7 4.2 10706 4.0 14000 GPMAW SG Conserved hypothetical 4.0E 80 protein SMU.1254 Q8DTS3 S. mutans 44.5 5 5.0 24837 4.5 18000 l GPMAW SG Conserved hypothetical 6.0C 51 protein SPR0991 Q8DPV2 S. pneumoniae 30.8 12 9.0 48092 8.2 44500 GPMAW SG Conserved hypothetical 4.0C 61 protein SPR1116 Q8DPK9 S. pneumoniae 44.8 5 4.8 19141 5.0 15500 GPMAW SG Conserved hypothetical 6.0E 10 protein SPR1236 Q8DPD0 S. pneumoniae 25.3 7 7.4 44186 7.4 43000 GPMAW SG Conserved hypothetical 6.0E 11 protein SPR1236 Q8DPD0 S. pneumoniae 25.3 7 7.4 44186 8.0 40000 GPMAW SG Conserved hypothetical 4.0C 31 protein SPR1760 Q8DNF9 S. pneumoniae 24.1 5 4.4 19770 4.3 23500 GPMAW SG Conserved hypothetical protein, possible GTP- 5.5C 96 pyrophosphokinase SMU.926 Q8DUK1 S. mutans 41.3 6 6.5 24575 6.2 24500 GPMAW SG

4.5C 218 Hypothetical protein None S. gordonii 27.1 10 4.9 38624 4.9 54000 n GPMAW SG Hypothetical 21.4 kDa 4.5E 6 protein None Q9EUR1 S. mitis 27.0 5 4.7 21440 m 4.7 21500 ExPASy Functional group Sub-group Gel a Spot # Description c Gene d Acc # e Species f Cover g Pept- Analysis k i j b ides h Theoretical Observed pIMr pIMr HYPOTHETICAL General continued continued Hypothetical ABC transporter ATP-binding 6.0C 44 protein in SCAA 5'region None P42360 S. gordonii 42.6 10 8.9 27038 8.5 23000 GPMAW SG 5.5C 51 Hypothetical protein dnaI Q8E3T8 S. agalactiae 31.7 6 6.3 34679 5.7 25000 l ExPASy 4.5C 37 Hypothetical protein GBS0373 Q8CMA8 S. agalactiae 65.4 4 5.2 16406 5.5 20000 ExPASy 5.5C 120 Hypothetical protein GBS1112 Q8E5C0 S. agalactiae 22.4 5 6.6 35101 6.0 30000 ExPASy 4.5C 6 Hypothetical protein GBS1336 Q8E4R5 S. agalactiae 36.9 5 5.0 15129 4.6 12500 ExPASy

4.5C 102 Hypothetical protein None Q83YS2 S. gordonii 45.5 4 5.2 18564 4.9 24500 n GPMAW SG

6.0E 13 Hypothetical protein None Q93K29 L. lactis 33.5 5 9.2 24592 8.5 60000 n GPMAW SP

4.0E 99 Hypothetical protein None Q9AET9 S. gordonii 26.2 7 4.7 62423 4.8 49000 GPMAW SP

6.0C 7 Hypothetical protein SMU.326 Q93D85 S. mutans 41.3 2 8.1 17311 7.3 13500 GPMAW SG

6.0C 107 Hypothetical protein SP0564 Q97S49 S. pneumoniae 46.6 5 10.6 14295 10.5 10500 l GPMAW SP 4.0E 40 Hypothetical protein SPR0959 Q8CYS8 S. pneumoniae 32.3 6 5.3 23640 4.7 17000 l ExPASy

4.5C 31 Hypothetical protein GBS0793 Q8E634 S. agalactiae 25.4 5 5.1 14051 5.3 13500 GPMAW SG

6.0C 33 Hypothetical protein SPR0488 Q8CZ40 S. pneumoniae 33.6 6 10.5 14355 9.7 17500 GPMAW SP Deinococcus 4.0E 31 Hypothetical protein DR2358 Q9RRX4 radiodurans 28.2 4 4.4 23843 4.5 17500 l GPMAW SG

6.0C 98 Hypothetical protein LMO1116 Q8Y803 L. monocytogenes 46.2 4 8.4 18287 9.0 13500 l GPMAW SG

6.0C 36 Hypothetical protein SP0385 Q97SH7 S. pneumoniae 37.1 6 9.0 27192 8.8 21000 GPMAW SP

4.0C 190 Hypothetical protein SP0443 Q97SE1 S. pneumoniae 9.0 5 4.3 60037 4.4 57500 GPMAW SG

4.0C 191 Hypothetical protein SP0443 Q97SE1 S. pneumoniae 13.2 8 4.3 60037 4.3 56000 GPMAW SG

5.5C 14 Hypothetical protein SP0816 Q97RJ6 S. pneumoniae 40.4 7 6.4 11201 6.3 11000 GPMAW SG

6.0C 109 Hypothetical protein SP1090 Q97QV8 S. pneumoniae 32.3 6 9.8 26433 9.6 25000 GPMAW SG

6.0E 27 Hypothetical protein SP1090 Q97QV8 S. pneumoniae 34.1 4 9.8 26433 8.7 40000 n GPMAW SG Functional group Sub-group Gel a Spot # Description c Gene d Acc # e Species f Cover g Pept- Analysis k i j b ides h Theoretical Observed pIMr pIMr HYPOTHETICAL General continued continued 4.5E 31 Hypothetical protein SP1410 Q97Q21 S. pneumoniae 27.2 5 4.8 16468 5.2 30500 n GPMAW SG

4.5C 66 Hypothetical protein SP1757 Q97P85 S. pneumoniae 30.2 7 4.7 25982 5.3 32500 GPMAW SG SPYM18_ 4.5C 5 Hypothetical protein 1929 Q8NZH1 S. pyogenes 36.4 5 5.1 20060 4.6 16000 ExPASy 6.0E 2 Hypothetical protein ywdG Q9CDQ5 L. lactis 47.9 6 6.0 21172 6.5 20000 ExPASy

Hypothetical zinc metalloproteinase in scaA 4.0C 160 5'region [Fragment] None P42359 S. gordonii 14.8 9 4.6 71540 4.6 60500 GPMAW SG

Hypothetical zinc metalloproteinase in scaA 4.0C 173 5'region [Fragment] None P42359 S. gordonii 27.6 15 4.6 71540 4.6 60500 GPMAW SG

Hypothetical zinc metalloproteinase in scaA 4.0E 70 5'region [Fragment] None P42359 S. pneumoniae 15.2 15 4.6 71540 5.0 46500 l GPMAW SP OTHER Adaptations to atypical Universal stress protein CATEGORIES conditions 4.0C 56 family uspA Q97NM4 S. pneumoniae 55.3 13 5.0 16476 m 5.0 16500 GPMAW SG Phage related functions and prophages Hypothetical phage SPYM3 6.0C 20 associated protein 1232 Q8K6P8 S. pyogenes 28.0 4 10.2 17397 9.9 9000 l GPMAW SP Hypothetical phage SPYM18_ 4.5C 68 protein 1806 Q8NZP2 S. pyogenes 30.9 9 5.7 48682 5.3 27500 l GPMAW SG

4.5C 11 Orf18 protein ORF18 Q38004 Bacteriophage Cp-1 38.8 5 4.9 26008 4.8 15500 l GPMAW SP 6.0E 4 Prophage pi1 protein 28 PI128 Q9CIA3 L. lactis 51.2 6 8.7 14810 9.4 18000 ExPASy Transposon related functions 6.0C 19 Tranposase ISI381 Q8DN68 S. pneumoniae 66.7 6 10.9 14772 9.5 9000 l GPMAW SP PURINES, Purine ribonucleotide PYRIMIDINES, biosynthesis NUCLEOSIDES AND NUCLEOTIDES 5.5C 149 Adenylosuccinate lyase purB P72478 S. mutans 29.6 8 5.5 49377 5.7 45500 ExPASy Orotate phosphoribosyl- 4.5C 73 transferase pyrE Q97RT8 S. pneumoniae 27.3 5 5.1 22851 5.1 23500 GPMAW SG Functional group Sub-group Gel a Spot # Description c Gene d Acc # e Species f Cover g Pept- Analysis k i j b ides h Theoretical Observed pIMr pIMr PURINES, Purine ribonucleotide PYRIMIDINES, biosynthesis continued NUCLEOSIDES AND NUCLEOTIDES continued 5.5C 72 Pur operon repressor purR Q97NP1 S. pneumoniae 36.8 7 6.3 32166 5.8 28000 GPMAW SG

5.5C 58 PyrR bifunctional protein pyrR Q97QE1 S. pneumoniae 36.4 5 6.1 19659 6.0 20000 GPMAW SG

5.5C 59 PyrR bifunctional protein pyrR Q97QE1 S. pneumoniae 36.4 5 6.1 19659 6.0 20000 GPMAW SG

6.0C 61 PyrR bifunctional protein pyrR Q97QE1 S. pneumoniae 38.7 7 6.1 19659 m 6.1 19500 GPMAW SG Ribose-phosphate 5.5C 141 pyrophosphokinase 1 prs1 Q97TB4 S. pneumoniae 32.6 5 5.6 35451 5.6 41500 GPMAW SP Salvage of nucleosides Deoxyribose-phosphate and nucleotides 4.0C 72 aldolase deoC Q97RH2 S. pneumoniae 37.9 6 4.9 24012 4.8 22000 GPMAW SG

5.5C 50 Uridine kinase udk Q97QJ7 S. pneumoniae 28.8 5 5.5 24466 5.7 26000 GPMAW SP Xanthine phosphoribosyltransferase 6.0C 16 [Fragment] xpt Q9L2W2 S. oralis 51.9 6 5.8 14059 8.7 9000 l GPMAW SP REGULATORY General GTP-sensing FUNCTIONS transcriptional pleiotropic 4.5C 65 repressor codY Q8DP01 S. gordonii 33.2 7 5.2 29801 5.3 32500 GPMAW SG Heat-inducible 5.5C 143 transcription repressor hrcA Q8DQW9 S. pneumoniae 15.1 6 5.6 40563 5.6 44500 GPMAW SG

4.5C 95 Metalloregulator scaR Q9RFN3 S. gordonii 20.0 5 5.5 24796 5.2 35000 n GPMAW SG

4.0C 211 Transcriptional regulator nadR Q9CE60 L. lactis 24.4 6 4.9 40781 4.7 42000 GPMAW SG

Transcriptional regulator SP2006 6.0C 37 ComX1/ComX2 SP0014 Q97CV2 S. pneumoniae 38.1 5 9.6 19951 9.1 20500 GPMAW SG Transcriptional regulator 5.5C 126 PlcR, putative SP1057 Q97QY5 S. pneumoniae 17.4 6 5.3 34230 5.8 33500 ExPASy Transcriptional regulator, 5.5C 41 putative SMU.136C Q8DWC8 S. mutans 49.6 4 5.8 14291 6.0 16000 GPMAW SG Transcriptional regulator, 4.5C 150 putative SMU.61 Q8DWI6 S. mutans 35.9 6 4.6 36055 4.8 53000 n GPMAW SG Functional group Sub-group Gel a Spot # Description c Gene d Acc # e Species f Cover g Pept- Analysis k i j b ides h Theoretical Observed pIMr pIMr REGULATORY GntR-family regulators FUNCTIONS Transcriptional regulator, continued 6.0C 46 GntR family SP1446 Q97PZ2 S. pneumoniae 37.5 5 9.3 26921 7.9 20000 l GPMAW SG Transcriptional regulator, 6.0C 58 GntR family SP1446 Q97PZ2 S. pneumoniae 47.8 4 9.3 26921 6.7 19500 l GPMAW SG Transcriptional regulator, 6.0C 108 GntR family SP1446 Q97PZ2 S. pneumoniae 40.0 5 9.3 27211 9.7 23000 GPMAW SP GTP-binding proteins 5.5C 102 GTP-binding protein SP1749 Q97P92 S. pneumoniae 29.3 6 6.3 40806 6.2 40000 GPMAW SG GTP-binding protein era 5.5C 108 homolog era Q9XDG9 S. pneumoniae 6.4 34010 6.5 33500 SEQUEST

4.0C 153 GTP-Binding protein lepA lepA Q97QK5 S. pneumoniae 15.6 5 4.7 55337 4.8 53500 GPMAW SG LacI-family regulators Catabolite control protein 5.5C 103 A ccpA Q8DNC3 S. pneumoniae 37.7 10 6.2 37022 6.2 36000 GPMAW SG MarR-family regulators Transcriptional regulator, 6.0C 8 MarR family SP1920 Q97NU2 S. pneumoniae 38.4 6 8.8 18981 7.2 11500 l GPMAW SG Transcriptional regulator, 6.0C 14 MarR family SP1920 Q97NU2 S. pneumoniae 34.2 5 9.7 17438 9.0 13000 l GPMAW SG Protein interactions 4.5E 42 Protein kinase, putative SP1061 Q97QY1 S. pneumoniae 9.2 5 5.5 53477 4.8 57500 GPMAW SP

4.5E 45 Protein kinase, putative SP1061 Q97QY1 S. pneumoniae 11.8 8 5.5 53477 4.8 70500 GPMAW SP

4.5E 47 Protein kinase, putative SP1061 Q97QY1 S. pneumoniae 17.2 8 5.5 53477 4.9 68500 GPMAW SP

4.5E 48 Protein kinase, putative SP1061 Q97QY1 S. pneumoniae 17.2 8 5.5 53477 4.9 66000 GPMAW SP

4.5E 49 Protein kinase, putative SP1061 Q97QY1 S. pneumoniae 16.7 7 5.5 53477 4.9 67500 GPMAW SP

4.5E 50 Protein kinase, putative SP1061 Q97QY1 S. pneumoniae 12.3 8 5.5 53477 4.9 62000 GPMAW SP

4.5E 51 Protein kinase, putative SP1061 Q97QY1 S. pneumoniae 12.5 8 5.5 53477 4.9 62000 GPMAW SP

4.5E 60 Protein kinase, putative SP1061 Q97QY1 S. pneumoniae 16.1 8 5.5 53477 4.8 74000 GPMAW SP

4.5E 64 Protein kinase, putative SP1061 Q97QY1 S. pneumoniae 17.4 8 5.5 53477 4.8 102000 GPMAW SP

4.5E 91 Protein kinase, putative SP1061 Q97QY1 S. pneumoniae 12.9 7 5.5 53477 5.2 46500 GPMAW SP Functional group Sub-group Gel a Spot # Description c Gene d Acc # e Species f Cover g Pept- Analysis k i j b ides h Theoretical Observed pIMr pIMr REGULATORY Two-component systems FUNCTIONS DNA-binding response continued 4.0C 30 regulator micA Q9S1K0 S. pneumoniae 33.5 4 4.8 26818 4.3 23500 GPMAW SG DNA-binding response 4.0C 75 regulator micA Q9S1K0 S. pneumoniae 40.8 5 4.8 26818 4.7 22000 GPMAW SG DNA-binding response 4.5C 107 regulator micA Q9S1K0 S. pneumoniae 38.6 7 4.8 26818 4.8 31500 GPMAW SG DNA-binding response 4.5C 133 regulator micA Q9S1K0 S. pneumoniae 37.8 6 4.8 26818 4.4 35500 GPMAW SG

4.5C 180 Histidine kinase hk02 Q8DPL8 S. pneumoniae 19.4 6 5.1 51698 4.9 100100 n GPMAW SP 2SCG58. Streptomyces 6.0C 75 Sensor kinase, putative 17c Q9FC96 coelicolor 42.7 6 6.3 43652 6.4 33000 GPMAW SP Response regulator, 5.5C 122 putative silA Q8P262 S. pyogenes 33.7 5 5.9 29142 5.9 37000 GPMAW SG Sensor histidine kinase, 4.5C 197 putative comD Q97T05 S. pneumoniae 27.7 7 5.0 63295 5.0 50000 GPMAW SP

Transcriptional regulatory component of sensory 4.0C 79 transduction system CTC01421 Q894W3 Clostridium tetani 20.0 7 5.0 26552 4.9 24500 ExPASy VNCR (DNA-binding 4.5E 1 response regulator vncR Q9XCJ7 S. pneumoniae 28.4 5 4.7 24967 5.1 52000 n ExPASy REPLICATION DNA replication, restriction, modification, recombination, and repair ATP-dependent DNA 4.5E 88 helicase pcrA Q8DPU8 S. pneumoniae 24.4 9 5.3 85975 5.2 74500 GPMAW SP

ATP-dependent DNA 4.5E 89 helicase pcrA Q8DPU8 S. pneumoniae 10.5 9 5.4 87031 5.2 72500 GPMAW SP

Chromosomal replication 4.5E 73 initiator protein dnaA dnaA Q8DRQ4 S. pneumoniae 24.2 7 5.3 51270 5.1 61500 GPMAW SG DNA polymerase III, delta 4.5C 128 subunit holA Q8DQG8 S. pneumoniae 33.0 5 4.7 39454 m 4.7 39500 GPMAW SG DNA polymerase III, 5.5E 8 delta' subunit holB Q8DQ57 S. pneumoniae 23.6 7 6.5 34382 6.0 20000 l GPMAW SP Functional group Sub-group Gel a Spot # Description c Gene d Acc # e Species f Cover g Pept- Analysis k i j b ides h Theoretical Observed pIMr pIMr REPLICATION DNA replication, continued restriction, modification, m recombination, and 4.0E 49 recA protein recA P30759 S. pneumoniae 23.2 6 5.0 41950 5.0 42000 GPMAW SP repair continued Single-strand binding 4.5C 36 protein ssb Q8DSD8 S. gordonii 39.3 6 5.3 18153 5.5 19000 GPMAW SG TRANSCRIPTION Degradation of RNA Anticodon nuclease, 4.5C 213 putative SMU.893 Q8DUM5 S. mutans 25.5 7 5.1 44688 4.9 57500 ExPASy

4.5C 61 Ribonuclease III rnc Q97QG6 S. pneumoniae 47.8 5 5.6 26154 5.5 29500 GPMAW SP tRNA isopentenylpyro- 4.5C 101 phosphate transferase miaA Q8CWS7 S. pneumoniae 28.9 6 5.1 33295 4.9 21000 l GPMAW SG RNA synthesis, modification, and DNA DNA-dependent RNA transcription 5.5E 15 polymerase, beta' subunit SP1960 Q97NQ8 S. pneumoniae 11.0 13 6.7 135511 5.6 137000 GPMAW SG

DNA-dependent RNA 5.5E 16 polymerase, beta' subunit SP1960 Q97NQ8 S. pneumoniae 4.7 6 6.7 135511 5.7 136500 GPMAW SG

DNA-dependent RNA 5.5E 17 polymerase, beta' subunit SP1960 Q97NQ8 S. pneumoniae 8.2 9 6.7 135511 5.7 135000 GPMAW SG

DNA-dependent RNA 5.5E 18 polymerase, beta' subunit SP1960 Q97NQ8 S. pneumoniae 10.5 12 6.7 135511 5.9 133500 GPMAW SG DNA-directed RNA 4.0E 88 polymerase alpha chain rpoA Q97ST7 S. pneumoniae 30.4 8 4.6 34502 4.9 25500 l GPMAW SG Transcription elongation 4.0C 64 factor greA Q97PT2 S. gordonii 25.6 6 4.7 17526 m 4.7 17500 GPMAW SG Transcription elongation 4.0C 65 factor greA Q97PT2 S. pneumoniae 40.0 6 4.7 17526 4.7 17500 GPMAW SG TRANSLATION Amino acyl tRNA Aspartyl-tRNA synthetase, synthetases 4.5E 70 putative gatA Q97R20 S. mutans 20.8 5.1 66062 m 5.1 66000 GPMAW SG Glutamyl-tRNA (Gln) amidotransferase subunit 4.5E 57 B, putative gatB Q8DSG6 S. mutans 21.4 6 4.9 53279 5.1 53500 GPMAW SG Glutamyl-tRNA 4.0C 185 synthetase gltX Q97NG1 S. pneumoniae 16.3 8 4.8 55961 4.9 67000 GPMAW SP Functional group Sub-group Gel a Spot # Description c Gene d Acc # e Species f Cover g Pept- Analysis k i j b ides h Theoretical Observed pIMr pIMr TRANSLATION Amino acyl tRNA Glutamyl-tRNA continued synthetases continued 4.0C 186 synthetase gltX Q97NG1 S. pneumoniae 16.3 8 4.8 55961 4.9 67000 GPMAW SP Glycyl-tRNA synthetase 4.0C 174 beta chain glyS Q8DP66 S. gordonii 20.8 11 4.6 75680 4.6 66000 GPMAW SG

4.5E 74 Threonyl-tRNA synthetase thrS Q97PI4 S. pneumoniae 16.5 8 5.2 74662 5.0 75500 GPMAW SP

4.5E 75 Threonyl-tRNA synthetase thrS Q97PI4 S. gordonii 8.7 7 5.2 74662 5.0 74500 GPMAW SP

4.5E 76 Threonyl-tRNA synthetase thrS Q97PI4 S. pneumoniae 13.6 12 5.2 74662 5.1 73000 GPMAW SP

4.5E 78 Threonyl-tRNA synthetase thrS Q97PI4 S. pneumoniae 13.9 8 5.2 74662 5.0 98000 GPMAW SG Threonyl-tRNA 4.5E 79 synthetase, putative syt1 Q8DT12 S. mutans 10.8 12 5.3 74758 5.1 98000 ExPASy

4.5E 100 Valyl-tRNA synthetase SP0568 Q97S45 S. pneumoniae 9.3 10 4.8 100924 4.9 105500 GPMAW SG Degradation of proteins, peptides, and Amylase-binding protein glycopeptides 4.0C 4 A abpA O52309 S. gordonii 6.8 20506 4.1 15500 SEQUEST Amylase-binding protein 4.5C 191 B abpB Q93TK2 S. gordonii 24.1 9 5.5 72752 5.3 56000 GPMAW SG ATP-dependent clp protease ATP-binding 5.5C 156 subunit clpE P35594 S. pneumoniae 6.8 7 5.4 83814 5.8 56500 l GPMAW SP ATP-dependent clp protease ATP-binding 5.5C 160 subunit clpE P35594 S. pneumoniae 12.2 9 5.5 83814 5.8 55500 l GPMAW SP ATP-dependent Clp protease ATP-binding 4.5E 71 subunit clpX Q97PN4 S. pneumoniae 23.5 7 4.6 45650 5.1 64000 n GPMAW SG ATP-dependent Clp protease, ATP-binding 5.5C 159 subunit SP2194 Q97N72 S. pneumoniae 19.9 10 6.2 90164 5.8 55500 l GPMAW SG ATP-dependent Clp protease, ATP-binding 5.5C 162 subunit SP2194 Q97N72 S. pneumoniae 8.8 6 6.2 90164 5.9 55000 l GPMAW SG Functional group Sub-group Gel a Spot # Description c Gene d Acc # e Species f Cover g Pept- Analysis k i j b ides h Theoretical Observed pIMr pIMr TRANSLATION Degradation of proteins, continued peptides, and ATP-dependent Clp glycopeptides continued protease, ATP-binding 5.5C 165 subunit SP2194 Q97N72 S. pneumoniae 13.0 10 6.2 90164 5.5 45000 l GPMAW SP ATP-dependent Clp protease, ATP-binding 5.5C 166 subunit SP2194 Q97N72 S. pneumoniae 12.0 8 6.2 90164 5.7 45000 l GPMAW SG

4.0E 46 DegP degP Q93F87 S. gordonii 29.7 9 5.1 42184 4.9 41000 GPMAW SP 4.5E 43 DegP degP Q93F87 S. gordonii 15.6 5 5.2 42197 4.8 61000 ExPASy Nucleoproteins 6.0C 100 DNA-binding protein HU hup Q9XB20 S. gordonii 53.8 5 9.9 9634 9.2 11000 ExPASy

6.0C 101 DNA-binding protein HU hup Q9XB20 S. gordonii 45.1 4 9.9 9634 10.7 9000 ExPASy Protein modification Phosphoprotein 4.0C 39 phosphatase pppL Q8DNR9 S. pneumoniae 37.4 8 4.5 26870 4.4 23500 GPMAW SG Ribosomal proteins: synthesis and modification 4.0C 143 30S ribosomal protein S1 rpsA Q8CWR9 S. pneumoniae 33.4 12 5.4 36839 4.9 45500 GPMAW SG

4.0C 147 30S ribosomal protein S1 rpsA Q8CWR9 S. pneumoniae 32.5 12 5.4 36839 4.8 46500 GPMAW SG

4.5C 114 30S ribosomal protein S2 rpsB Q97N56 S. pneumoniae 21.9 5 4.9 29143 5.0 34500 GPMAW SG

6.0C 40 30S ribosomal protein S3 rpsC Q9WW37 S. pneumoniae 58.1 10 10.1 24065 10.0 24000 GPMAW SG

6.0C 104 30S ribosomal protein S4 rpsD Q97T69 S. pneumoniae 37.4 10 10.4 23101 10.5 23000 GPMAW SG

6.0C 105 30S ribosomal protein S4 rpsD Q97T69 S. pneumoniae 24.1 7 10.4 23101 10.6 25000 GPMAW SG

6.0C 106 30S ribosomal protein S4 rpsD Q97T69 S. pneumoniae 37.4 11 10.4 23101 10.6 23000 GPMAW SG

6.0C 28 30S ribosomal protein S5 rpsE Q97SU5 S. pneumoniae 29.9 6 10.3 17090 9.8 14000 GPMAW SG

4.0C 19 30S ribosomal protein S6 rpsF Q97PR1 S. pneumoniae 51.5 8 5.6 15079 5.0 11000 l GPMAW SG

4.5C 12 30S ribosomal protein S6 rpsF Q97PR1 S. pneumoniae 43.8 5 5.0 11153 4.8 12500 GPMAW SP

6.0C 24 30S ribosomal protein S8 rpsH Q97SU8 S. pneumoniae 59.8 11 9.9 14762 9.5 12500 GPMAW SG Functional group Sub-group Gel a Spot # Description c Gene d Acc # e Species f Cover g Pept- Analysis k i j b ides h Theoretical Observed pIMr pIMr TRANSLATION Ribosomal proteins: continued synthesis and modification continued 6.0C 38 50S ribosomal protein L1 rplA Q97RZ5 S. pneumoniae 31.0 6 9.7 24414 9.0 21500 GPMAW SG

6.0C 39 50S ribosomal protein L1 rplA Q97RZ5 S. pneumoniae 31.0 6 9.2 24495 m 9.2 24500 GPMAW SP

6.0C 41 50S ribosomal protein L1 rplA Q97RZ5 S. pneumoniae 33.2 6 9.7 24414 9.3 25000 GPMAW SG

6.0C 42 50S ribosomal protein L1 rplA Q97RZ5 S. pneumoniae 28.8 6 9.7 24414 9.1 24500 GPMAW SG

6.0C 34 50S ribosomal protein L5 rplE Q8E7S9 S. agalactiae 24.4 5 9.3 19814 9.2 18000 ExPASy

6.0C 110 50S ribosomal protein L5 rplE Q8CWV4 S. pneumoniae 39.4 5 9.7 19859 92.0 18000 GPMAW SG

6.0C 111 50S ribosomal protein L5 rplE Q8CWV4 S. pneumoniae 45.6 7 9.7 19859 9.3 18500 GPMAW SG 50S ribosomal protein 4.0C 6 L7/L12 rplL P80714 S. pneumoniae 66.9 15 4.2 13046 4.2 13000 GPMAW SP 50S ribosomal protein 4.0C 8 L7/L12 rplL P80714 S. pneumoniae 5 4.2 13046 4.2 11500 MASCOT 50S ribosomal protein 4.0C 225 L7/L12 rplL P80714 S. pneumoniae 5 4.4 12311 4.1 12500 MASCOT

6.0C 26 50S ribosomal protein L9 rplI Q97N63 S. pneumoniae 46.0 7 9.8 16403 9.8 13500 GPMAW SG

4.0C 53 50S ribosomal protein L10 rplJ Q97Q73 S. gordonii 26.4 9 4.8 16986 5.0 14000 GPMAW SG

4.0C 62 50S ribosomal protein L10 rplJ Q97Q73 S. pneumoniae 40.9 8 4.8 16986 5.1 17000 GPMAW SG

6.0C 32 50S ribosomal protein L13 rplM Q8K5U3 S. pyogenes 37.8 5 10.2 16162 10.1 15000 GPMAW SG

6.0C 21 50S ribosomal protein L15 rplO Q8CWV0 S. pneumoniae 38.4 5 11.0 15415 10.0 11000 l GPMAW SG

6.0C 27 50S ribosomal protein L16 rl16 Q8DS20 S. mutans 24.1 5 11.1 15502 9.8 12500 GPMAW SP

6.0C 30 50S ribosomal protein L17 rplQ Q8CWU8 S. pneumoniae 37.5 5 10.2 14520 10.3 13500 GPMAW SG

6.0C 31 50S ribosomal protein L17 rplQ Q8CWU8 S. pneumoniae 50.0 6 10.2 14520 10.5 13000 GPMAW SG Functional group Sub-group Gel a Spot # Description c Gene d Acc # e Species f Cover g Pept- Analysis k i j b ides h Theoretical Observed pIMr pIMr TRANSLATION Ribosomal proteins: continued synthesis and modification continued 6.0C 103 50S ribosomal protein L20 rplT Q97R68 S. pneumoniae 33.6 5 11.3 13693 10.5 11500 GPMAW SP

6.0C 102 50S ribosomal protein L21 rplU Q8CWR5 S. pneumoniae 56.7 4 10.3 11196 m 10.3 11000 GPMAW SG

6.0E 24 50S ribosomal protein L21 rplU Q8CWR5 S. pneumoniae 44.2 4 10.3 11196 8.4 19000 n GPMAW SG

6.0C 18 50S ribosomal protein L22 None Q8GL54 S. pneumoniae 34.2 6 11.5 12880 9.3 10000 GPMAW SP

6.0C 22 50S ribosomal protein L23 rplW Q8CWV6 S. pneumoniae 50.0 5 10.0 10772 m 10.0 11000 GPMAW SG

4.0C 210 Ribosomal protein S1 SP0862 Q97RF9 S. pneumoniae 21.0 7 4.9 43882 4.7 32500 l GPMAW SP S-adenosyl- 6.0C 68 methyltransferase mraW Q97SK1 S. pneumoniae 25.9 6 6.4 35914 7.0 39000 GPMAW SG Translation factors 4.0C 137 Elongation factor G fusA Q97SQ3 S. pneumoniae 16.3 8 4.7 76831 5.0 59000 GPMAW SP

4.0C 151 Elongation factor G fusA Q97SQ3 S. pneumoniae 20.6 10 4.7 76831 4.9 60500 GPMAW SP

4.5C 174 Elongation factor G fusA Q97SQ3 S. pneumoniae 15.3 8 4.7 76831 4.8 77000 GPMAW SP

4.5C 175 Elongation factor G fusA Q97SQ3 S. pneumoniae 14.4 9 4.7 76831 4.8 76000 GPMAW SP

4.0C 94 Elongation factor P efp Q97SE8 S. pneumoniae 29.0 5 4.7 20600 4.6 24500 GPMAW SP

4.0C 128 Elongation factor Ts tsf P80715 S. pneumoniae 21.1 7 4.7 37362 4.8 37000 GPMAW SP

4.0C 129 Elongation factor Ts tsf P80715 S. pneumoniae 18.5 7 4.7 37362 4.8 38000 GPMAW SP

4.0C 68 Elongation factor Tu tuf Q97PV3 S. pneumoniae 17.6 7 4.7 44011 5.0 22500 l GPMAW SG

4.0C 93 Elongation factor Tu tuf Q93QB3 S. gordonii 40.3 14 4.4 30556 4.6 22500 l GPMAW SP

4.0C 95 Elongation factor Tu tuf Q93QB3 S. gordonii 40.3 17 4.4 30556 4.6 25000 GPMAW SP

4.0C 122 Elongation factor Tu tuf Q97PV3 S. pneumoniae 31.4 9 4.7 44011 4.8 30500 l GPMAW SG Functional group Sub-group Gel a Spot # Description c Gene d Acc # e Species f Cover g Pept- Analysis k i j b ides h Theoretical Observed pIMr pIMr TRANSLATION Translation factors continued continued 4.0C 134 Elongation factor Tu tuf Q97PV3 S. pneumoniae 27.4 9 4.7 44011 5.0 45000 GPMAW SG

4.0C 135 Elongation factor Tu tuf Q97PV3 S. pneumoniae 39.7 13 4.7 44011 4.9 48000 GPMAW SG

4.0C 144 Elongation factor Tu tuf Q97PV3 S. pneumoniae 32.9 10 4.7 44011 4.9 48500 GPMAW SG

4.0C 148 Elongation factor Tu tuf Q97PV3 S. pneumoniae 43.2 12 4.7 44011 4.9 49500 GPMAW SG

4.0C 152 Elongation factor Tu tuf Q97PV3 S. pneumoniae 27.1 12 4.7 43971 4.8 51500 GPMAW SG

4.5C 64 Elongation factor Tu tuf Q97PV3 S. pneumoniae 31.9 10 4.7 44011 5.3 24000 l GPMAW SG

4.5C 75 Elongation factor Tu tuf Q97PV3 S. pneumoniae 21.9 8 4.7 44011 5.1 24500 l GPMAW SG 4.5C 208 Elongation factor Tu tuf Q93QB3 S. gordonii 3 4.5 30556 5.2 22000 l MASCOT

4.5C 209 Elongation factor Tu tuf Q97PV3 S. pneumoniae 19.3 8 4.7 44011 4.8 57500 GPMAW SG

4.5C 210 Elongation factor Tu tuf Q97PV3 S. pneumoniae 36.4 10 4.7 44011 4.8 60500 n GPMAW SG

4.5C 212 Elongation factor Tu tuf Q97PV3 S. pneumoniae 16.1 5 4.7 44011 4.9 56000 GPMAW SG

6.0E 18 Initiation factor IF2 infB Q8DQV2 S. pneumoniae 11.8 6 9.5 105035 8.2 83000 GPMAW SG

6.0E 19 Initiation factor IF2 infB Q8DQV2 S. pneumoniae 10.5 5 9.5 105035 8.3 83000 GPMAW SG

6.0E 20 Initiation factor IF2 infB Q8DQV2 S. pneumoniae 10.2 7 9.5 105035 8.5 82500 GPMAW SG

6.0E 34 Initiation factor IF2 infB Q8DQV2 S. pneumoniae 15.4 10 9.5 105035 9.2 76500 l GPMAW SG Peptide chain release 4.5E 62 factor 1 (RF-1) prfA Q8DPZ4 S. pneumoniae 28.1 8 4.7 40701 4.8 100000 n GPMAW SP Peptide chain release 4.5E 63 factor 1 (RF-1) prfA Q8DPZ4 S. gordonii 28.1 8 4.7 40701 4.8 100000 n GPMAW SG Ribosomal subunit 4.5C 103 interface protein SP2206 Q97N61 S. pneumoniae 5.1 21099 4.9 25000 SEQUEST

4.5E 22 Ribosome recycling factor rrf Q8DQ49 S. pneumoniae 33.5 5 5.7 20643 5.2 47000 n GPMAW SP

4.5E 36 Ribosome recycling factor rrf Q8DQ49 S. gordonii 25.9 6 5.9 20653 5.3 34000 GPMAW SG Functional group Sub-group Gel a Spot # Description c Gene d Acc # e Species f Cover g Pept- Analysis k i j b ides h Theoretical Observed pIMr pIMr TRANSLATION Translation factors continued continued 5.5C 57 Ribosome recycling factor rrf Q97R82 S.pneumoniae 38.9 7 5.9 20653 m 5.9 20500 GPMAW SG

6.0C 62 Ribosome recycling factor rrf Q97R82 S.pneumoniae 51.4 7 5.9 20653 6.0 20500 GPMAW SG TRANSPORT AND Amino acids, peptides BINDING PROTEINS and amines 5.5E 11 ABC transporter ALR007 Q8Z0L4 Anabaena sp. 38.5 6 6.5 25546 5.8 38000 n GPMAW SG

ABC transporter (ATP- 4.0E 48 binding protein), putative SPS1842 Q877W5 S. pyogenes 20.9 5 5.1 33214 4.9 42500 ExPASy ABC transporter ATP binding protein-unknown 4.5C 60 substrate ABC-NBD Q8DNB9 S. pneumoniae 17.2 6 5.1 32992 5.0 23500 l GPMAW SG ABC transporter ATP- 4.5E 2 binding protein ysfB Q9CEP3 L. lactis 27.0 6 4.8 28425 4.8 48500 n ExPASy ABC transporter ATP- 4.5E 4 binding protein ysfB Q9CEP3 L. lactis 27.0 5 4.8 28425 5.5 53500 n ExPASy ABC transporter ATP- binding protein- aspartate/glutamate 5.5C 62 transport ABC-NDB Q8DP77 S. pneumoniae 36.0 4 5.4 27611 6.0 23000 GPMAW SG

ABC transporter ATP- binding protein-branched 5.5C 109 chain amino acid transport livF Q8DQH7 S. pneumoniae 27.1 8 7.7 25655 6.5 37000 n GPMAW SP ABC transporter ATP- binding protein-unknown 4.0C 92 substrate ABC-NBD Q8DQA4 S. pneumoniae 23.8 6 4.6 28406 4.5 22500 GPMAW SG ABC transporter ATP- binding protein-unknown 4.5C 215 substrate ABC-NBD Q8DPT2 S. pneumoniae 19.4 13 4.9 72040 4.9 54500 ExPASy ABC transporter solute- binding protein-unknown 4.5C 67 substrate ABC-SBP Q8DRG1 S. pneumoniae 42.3 6 5.3 31147 5.3 29000 GPMAW SP

ABC transporter substrate- binding protein- 4.5C 172 oligopeptide transport aliA Q8DR61 S. pneumoniae 11.7 5 4.9 73003 4.9 67000 GPMAW SP Functional group Sub-group Gel a Spot # Description c Gene d Acc # e Species f Cover g Pept- Analysis k i j b ides h Theoretical Observed pIMr pIMr TRANSPORT AND Amino acids, peptides BINDING PROTEINS and amines continued ABC transporter substrate- continued binding protein- 4.5C 224 oligopeptide transport aliA Q8DR61 S. pneumoniae 7.3 5 4.9 73211 4.9 73500 GPMAW SP

ABC transporter substrate- binding protein- 4.5C 226 oligopeptide transport aliA Q8DR61 S. pneumoniae 15.3 6 4.9 73211 5.0 73500 GPMAW SP

ABC transporter substrate- binding protein- 4.5C 227 oligopeptide transport aliA Q8DR61 S. pneumoniae 15.1 7 4.9 73211 5.0 72000 GPMAW SP

ABC transporter substrate- binding protein- 4.5C 173 oligopeptide transport appD Q8DPF5 S. pneumoniae 7.3 5 5.8 75406 4.9 66500 GPMAW SP

ABC transporter substrate- binding protein-unknown 6.0C 54 substrate ABC-SBP Q8DPW4 S. pneumoniae 25.6 5 8.7 36440 7.9 29500 GPMAW SP ABC transporter, ATP- 4.5C 170 binding protein SP1114 Q97QT5 S. pneumoniae 11.6 6 4.6 71491 4.8 68000 GPMAW SP ABC transporter, ATP- 4.5C 222 binding protein SP1114 Q97QT5 S. pneumoniae 7.6 6 4.7 72040 5.0 66000 GPMAW SP ABC transporter, ATP- 4.5C 228 binding protein SP1114 Q97QT5 S. pneumoniae 7.6 6 4.6 71491 5.1 71500 GPMAW SP ABC transporter, ATP- 4.5C 171 binding protein SP1435 Q97PZ9 S. pneumoniae 12.6 7 5.7 65667 4.8 68000 GPMAW SP ABC transporter, ATP- 6.0E 25 binding protein SP0151 Q97T09 S. pneumoniae 23.7 5 7.7 38780 8.5 15000 l GPMAW SG

ABC transporter, ATP- 5.5C 77 binding protein, putative SMU.1194 Q8DTX3 S. mutans 29.9 7 6.1 26341 5.9 25500 GPMAW SG

ABC transporter, ATP- 5.5C 84 binding protein, putative SMU.1194 Q8DTX3 S. mutans 38.9 8 6.1 26341 m 6.1 26500 GPMAW SG

ABC transporter, ATP- 5.5C 92 binding protein, putative SMU.1194 Q8DTX3 S. mutans 47.0 11 6.1 26341 6.2 26000 GPMAW SG Functional group Sub-group Gel a Spot # Description c Gene d Acc # e Species f Cover g Pept- Analysis k i j b ides h Theoretical Observed pIMr pIMr TRANSPORT AND Amino acids, peptides BINDING PROTEINS and amines continued continued ABC transporter, ATP- 6.0C 64 binding protein, putative SMU.1194 Q8DTX3 S. mutans 37.2 12 6.1 26341 6.2 24000 GPMAW SG

ABC transporter, ATP- 6.0C 65 binding protein, putative SMU.1194 Q8DTX3 S. mutans 44.4 9 6.1 26341 6.0 24000 GPMAW SG

ABC transporter, ATP- 6.0C 70 binding protein, putative SMU.1194 Q8DTX3 S. mutans 34.2 7 6.1 26341 6.5 24500 GPMAW SG Amino acid ABC transporter, ATP-binding 4.5C 62 protein SP0824 Q97RJ0 S. pneumoniae 31.6 4 5.0 26876 5.3 26000 GPMAW SP

4.5C 106 Choline transporter proV Q9XBN6 S. pneumoniae 34.3 6 5.0 27061 4.9 28000 GPMAW SP

4.0C 145 Dipeptidase SP0623 Q97S02 S. pneumoniae 24.2 10 4.7 53928 4.8 45000 GPMAW SG Oligopeptide transport 5.5C 105 ATP-binding protein amiF P18766 S. pneumoniae 31.9 9 6.3 34922 6.2 32000 GPMAW SG Oligopeptide transport 6.0C 55 ATP-binding protein amiF P18766 S. pneumoniae 31.9 9 6.3 34922 7.2 27000 GPMAW SG Oligopeptide transport 6.0C 72 ATP-binding protein amiF P18766 S. pneumoniae 45.6 12 6.3 34922 6.0 36000 GPMAW SG Oligopeptide transport 6.0C 76 ATP-binding protein amiF P18766 S. pneumoniae 45.6 12 6.3 34922 6.5 32000 GPMAW SG Oligopeptide transport 4.0C 100 protein amiE P18765 S. pneumoniae 23.1 8 5.0 39546 4.8 30000 GPMAW SP

Oligopeptide transport 5.5C 110 system permease protein amiC P18793 S. pneumoniae 37.0 7 9.6 26485 6.5 40500 n GPMAW SG Oligopeptide-binding 4.0E 57 lipoprotein (Fragment) hppH Q54234 S. gordonii 9.0 6 5.0 54384 5.0 67000 GPMAW SG Oligopeptide-binding 4.0E 58 lipoprotein (Fragment) hppH Q54234 S. gordonii 20.1 11 5.0 54384 5.0 72781 n GPMAW SG Oligopeptide-binding 4.0E 59 lipoprotein (Fragment) hppH Q54234 S. gordonii 18.6 11 5.0 54384 5.0 75000 n GPMAW SG Oligopeptide-binding 4.5C 135 lipoprotein [Precursor] hppA Q54232 S. gordonii 34.9 8 5.1 39673 4.6 36500 GPMAW SG Functional group Sub-group Gel a Spot # Description c Gene d Acc # e Species f Cover g Pept- Analysis k i j b ides h Theoretical Observed pIMr pIMr TRANSPORT AND Amino acids, peptides BINDING PROTEINS and amines continued continued Oligopeptide-binding 4.5C 136 lipoprotein [Precursor] hppA Q54232 S. gordonii 34.9 8 5.1 39673 4.7 41000 GPMAW SG Oligopeptide-binding 4.0E 54 protein [Precursor] aliA P35592 S. pneumoniae 14.5 8 4.9 73080 4.9 63500 GPMAW SP Oligopeptide-binding 4.0E 55 protein [Precursor] aliA P35592 S. pneumoniae 5.6 6 4.9 73080 5.0 63000 GPMAW SP Oligopeptide-binding 4.0E 60 protein [Precursor] aliA P35592 S. pneumoniae 14.2 8 4.8 73067 5.0 90500 GPMAW SP Oligopeptide-binding 4.0E 104 protein [Precursor] aliA P35592 S. gordonii 14.4 6 4.9 73080 4.9 80000 GPMAW SG Oligopeptide-binding 4.0E 61 protein [Precursor] aliB Q51933 S. pneumoniae 17.9 7 5.1 72562 5.0 67000 GPMAW SP Oligopeptide-binding 4.5E 86 protein [Precursor] aliB Q51933 S. pneumoniae 15.2 9 5.1 72562 5.1 71000 GPMAW SP Parasanguis adhesin 6.0C 95 (fimA) None Q56005 S. parasanguis 28.7 5 6.5 26603 7.2 11500 l ExPASy Cations Ferrous ion transport 4.5E 21 protein A, putative feoA Q8DVC4 S. mutans 19.0 5 5.8 17776 5.1 46500 n ExPASy V-type Na+-ATPase alpha 4.5E 67 subunit, putative ntpA Q9A1Q3 S. pyogenes 13.3 6 4.7 65670 4.9 93500 n GPMAW SG V-type Na+-ATPase alpha 4.5E 68 subunit, putative ntpA Q9A1Q3 S. pyogenes 16.9 6 4.7 65670 4.9 93000 n GPMAW SG V-type Na+-ATPase 4.5C 80 subunit D, putative ntpD Q9A1Q1 S. pyogenes 30.0 5 5.6 24096 5.2 29500 GPMAW SG V-type sodium ATP 4.0C 150 synthase, subunit A SP1317 Q97QA8 S. pneumoniae 15.8 9 4.7 65670 4.9 55000 GPMAW SG V-type sodium ATP 4.5E 77 synthase, subunit I SP1322 Q97QA3 S. pneumoniae 12.6 6 5.1 72223 5.0 83500 GPMAW SG General Chromosome condensation and 4.0E 62 segregation protein smc Q8DPJ9 S. pneumoniae 16.3 12 4.8 133888 5.0 89500 l GPMAW SP Chromosome segregation 5.5C 130 helicase cshA Q8DNP1 S. pneumoniae 15.4 5 6.0 47375 5.9 44500 GPMAW SG Chromosome segregation 5.5C 132 helicase cshA Q8DNP1 S. pneumoniae 22.9 6 6.0 47375 5.8 45500 GPMAW SG Functional group Sub-group Gel a Spot # Description c Gene d Acc # e Species f Cover g Pept- Analysis k i j b ides h Theoretical Observed pIMr pIMr TRANSPORT AND General continued BINDING PROTEINS D-alanine-- continued poly(phosphoribitol) 4.0C 170 ligase subunit 1 dltA Q97N82 S. pneumoniae 17.7 7 4.6 55720 4.6 53000 GPMAW SG

4.0E 63 Metal-binding permease adcA Q842E9 S. gordonii 22.6 7 5.1 56251 5.0 93000 n GPMAW SG Phosphate import ATP- 5.5C 52 binding protein pstB2 Q97Q34 S. pneumoniae 37.1 5 6.2 30395 5.8 22000 l GPMAW SG Sugar ABC transporter, 6.0C 83 ATP-binding protein SP0846 Q97RG9 S. pneumoniae 26.6 15 6.0 55767 m 6.0 56000 GPMAW SG Sugar ABC transporter, 6.0C 85 ATP-binding protein SP0846 Q97RG9 S. pneumoniae 20.4 9 6.0 55767 6.0 56000 GPMAW SG Transmembrane protein 5.5C 147 Vexp1 SP0599 Q97S18 S. pneumoniae 26.9 8 5.1 42205 5.7 44000 GPMAW SP PTS system Phosphoenolpyruvate- protein phosphotransferase (Phosphotransferase 4.0E 102 system, enzyme I) ptsI P45595 S. mutans 18.4 5 4.6 63398 4.7 95500 n ExPASy Phosphotransferase system sugar-specific EIIB 4.5C 4 component pts-EIIB Q8DRC1 S. pneumoniae 24.5 4 4.5 10759 4.5 21500 n ExPASy Phosphotransferase system, mannose-specific 4.0C 20 EIIAB manL Q8DR97 S. pneumoniae 20.8 7 4.8 35719 5.0 11500 l GPMAW SG Phosphotransferase system, mannose-specific 4.5C 119 EIIAB manL Q8DR97 S. pneumoniae 24.7 8 4.8 35719 4.9 37500 GPMAW SG Phosphotransferase system, mannose-specific 4.5C 201 EIIAB manL Q8DR97 S. pneumoniae 24.7 8 4.8 35719 4.8 38500 GPMAW SG Functional group Sub-group Gel a Spot # Description c Gene d Acc # e Species f Cover g Pept- Analysis k i j b ides h Theoretical Observed pIMr pIMr UNKNOWN Unclassified Acetyltransferase, GNAT 4.5C 7 family SP1516 Q97PT3 S. pneumoniae 35.4 6 4.7 16845 4.6 13000 GPMAW SG

Acetyltransferase, GNAT 6.0C 2 family rimL Q97QW5 S. gordonii 52.5 4 5.9 21804 5.8 14200 l GPMAW SG Acetyltransferase, GNAT 6.0C 23 family rimL Q97QW5 S. pneumoniae 21.9 6 8.6 21836 9.5 12500 l GPMAW SP

4.5C 203 Carbamate kinase arcC Q8GND3 S. gordonii 37.2 6 4.6 34348 5.2 33500 GPMAW SG Holliday junction 5.5C 17 resolvase, putative SPR0176 Q8DRE2 S. pneumoniae 53.2 5 5.4 15655 6.4 13500 GPMAW SP Maltose O- 5.5C 86 acetyltransferase maa Q9CF01 L. lactis 39.4 7 5.1 22684 6.2 26000 ExPASy N utilization substance 4.0C 158 protein A SP0553 Q97S60 S. pneumoniae 25.9 13 4.6 46483 4.7 54500 GPMAW SG

N utilization substance 4.5E 93 protein A SP0553 Q97S60 S. pneumoniae 21.7 7 4.7 42797 m 4.7 43000 GPMAW SP Oxidoreductase, pyridine nucleotide-disulfide, class 4.5C 143 I SP1588 Q97PL8 S. pneumoniae 30.8 7 4.7 47161 4.6 49500 GPMAW SP

6.0C 67 YLME ylmE Q9ZHB8 S. pneumoniae 26.0 5 6.2 25366 6.0 26500 GPMAW SG Chapter 5

CHAPTER 5. THE EFFECT OF pH ON THE PROTEIN PHENOTYPE OF S. GORDONII

5.1. INTRODUCTION

In the oral cavity, early colonizing Gram-positive bacteria, such as S. gordonii, form biofilms on tooth surfaces and ferment carbohydrate efficiently at pH levels above 6.0 (Loo et al., 2000). If the pH of dental plaque drops below pH 6.0, S. gordonii is unable to metabolise efficiently and aciduric pathogens begin to proliferate and predominate in the oral biofilm, forcing the pH even lower. However, under certain conditions, including some dental procedures, S. gordonii along with other VS may enter the bloodstream. Once in the bloodstream, an immediate rise in environmental pH to a value of 7.3 is encountered (Vriesema et al., 2000) which is above the resting pH in dental plaque (approximately pH 6.3 - 6.4; Sissons, 1997: Sissons et al., 1998; Sissons et al., 1991). The transition from oral cavity to bloodstream may be a stimulus for a change in protein expression and the subsequent ability of the bacteria to colonize a damaged heart valve (Vriesema et al., 2000). S. gordonii is also an early coloniser in periodontal disease to which P. gingivalis co-adheres (Lamont et al., 1993). In periodontal disease the environmental pH also becomes slightly alkaline at a value of 7.4 - 7.8 (Socransky and Haffajee, 1992). The effects of acidification on growth and glycolysis of S. sanguis and S. mutans have been examined in an enzyme study (Takahashi et al., 1997). At pH 4.0 and 4.2, both growth and glycolytic activities were suppressed in these streptococci. Prolonged acidification (for 60 min at pH 4.0) not only suppressed growth and glycolytic activities in S. sanguis but also impaired the cells by inactivating the glycolytic enzymes, hexokinase, phosphofructokinase, glyceraldehyde-phosphate dehydrogenase and enolase. These impaired functions recovered following incubation of the cells at pH 7.0 for 80-90 min, due to reactivation of the enzymes. These impairments were not observed in S. mutans exposed to prolonged acidification. These results indicate that the low pH frequently occurring in dental

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plaque may transiently impair streptococcal glycolysis and growth and that S. mutans is more resistant to the acidification than S. sanguis (Takahashi et al., 1997). These types of studies are useful when examining particular enzymes. However, by examining the proteome, it is possible to catalogue (quantitatively and qualitatively) a range of proteins produced by cells under these different pH conditions. In the oral microbial field, several 2-DE proteome studies have concentrated on the physiological adaptations associated with the survival of the oral pathogens, S. mutans and S. oralis in the oral cavity. For example, 2-DE protein analyses of 14C - labelled cellular proteins of S. mutans have been used to characterize changes in protein expression following the imposition of pH, temperature, salt, and oxidative and starvation stresses (Svensäter et al., 2000). S. mutans responds to these adverse environmental conditions by a complex and diverse alteration in protein synthesis. For instance, the protein profile of cells shocked from pH 7.5 to pH 5.0 revealed 64 proteins that were up-regulated (25 of them acid-specific) and 49 that were down- regulated. In another 2-DE study, 18 proteins were up-regulated and 12 down- regulated when S. mutans was grown at pH 5.2 compared with cells grown at pH 7.0. These proteins were involved in energy metabolism, cell division, translation, and transport (Wilkins et al., 2002). Although the general conclusions of previous studies were confirmed, anomalies were observed. For instance, the protein DnaK was down-regulated when S. mutans was grown at low pH, while transcriptional studies had shown that dnaK expression was up-regulated under similar conditions (Jayaraman et al., 1997). S. oralis, on the other hand is the predominant aciduric non-mutans-group streptococcus in dental plaque (Marsh and Martin, 1999). S. oralis is implicated in the pathogenesis of infective endocarditis and shows 98.16% 16S rRNA sequence homology to S. gordonii (Kawamura et al., 1995) Proteome analysis of S. oralis grown in batch culture at pH 5.2 or pH 7.0 resulted in 39 proteins being identified as having altered expression at low pH (Wilkins et al., 2001). It should be noted, however, that only 18 of the differentially expressed protein spots were found to be significantly differentially expressed (p < 0.05) as calculated by the SNK test (Section 2.6.2). As the genome of S. oralis has not been sequenced, the identification of these proteins was achieved by comparing the PMFs to those of the

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translated genome of S. pneumoniae, which is also closely related to S. oralis. Of the 39 proteins showing differential expression following growth at pH 5.2, 28 were up- regulated. The up-regulated proteins included enzymes of the glycolytic pathway, GPDH and lactate dehydrogenase, the polypeptide chains comprising ATP synthase, and general stress response proteins, including the 60-kDa chaperone, Hsp33, and superoxide dismutase, as well as three distinct ABC transporters. This was the first study to identify gene products that may be important in the survival and proliferation of non-mutans aciduric S. oralis under conditions of low pH (Wilkins et al., 2001). In a more recent study, the same researchers examined the most abundant surface-associated proteins of S. oralis and investigated changes in protein expression when the organism was grown under acidic culture conditions in batch culture (Wilkins et al., 2003). A total of 27 proteins were identified, including a lipoprotein, a ribosome recycling factor, and the glycolytic enzymes, phosphoglycerate kinase, fructose bisphosphate aldolase, GPDH, and enolase. Surprisingly, the most abundant protein was a homologue of the phosphocarrier protein, HPr, belonging to the bacterial phosphoenol-pyruvate sugar phosphotransferase system, which is normally associated with the cytoplasm. HPr was present as three different charged isoforms at the surface of the cells (Wilkins et al., 2003). In most of the studies mentioned above the bacterium under study was grown in batch cultures (Svensäter et al., 2000; Takahashi et al., 1997; Wilkins et al., 2001; Wilkins et al., 2002; Wilkins et al., 2003). In such a “closed system”, the environment is altered with time as nutrients are used and waste products accumulate (Zinsser, 1980). A chemostat, on the other hand, is an “open system”, which provides a continuously growing culture where bacterial cells can be maintained in steady state (Section 1.3). The chemostat’s volume is constant as culture medium containing the living cells, cellular debris, and exhausted medium leave the vessel at the same rate that fresh media is added. The results from batch-culture experiments are not as consistent as those from chemostats. For instance, Wilkins et al. (2001) stated that “growth rates were not identical, and the pH of the media during growth (in batch culture) was not constant, parameters which may be controlled by conducting experiments under continuous culture conditions”.

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It seems apparent that there are many proteins differentially expressed due to environmental pH change in streptococci. However, this may not always be the case. One study compared the protein profiles of acid-shocked and control cells of nine bacteria (Hamilton and Svensäter, 1998). Cultures in exponential phase were rapidly acidified from pH 7.5 to 5.5 in the presence of [14C]-amino acids for up to 2 h, the protein extracted, and then subjected to one-dimensional SDS. A total of 36 acid- regulated proteins were produced by S. mutans, 25 of these appeared in the first 30 min of acid shock, whereas S. gordonii produced only eight significant proteins of 75, 67, 64, 59, 48, 43, 39 and 27 kDa, with all but two proteins (67 and 59 kDa) appearing after 60 min of acid shock (Hamilton and Svensäter, 1998). The authors speculated that the survival of oral bacteria at very low pH is related to the formation of key proteins that function to augment normal homeostasis, and not to the total number of acid-regulated proteins induced. A comprehensive comparative proteomic study (Len et al., 2004a; Len et al., 2004b) also assessed changes in protein expression by S. mutans following growth in a chemostat at pH 7.0 or pH 5.0 under steady-state conditions. It was found that changes in the expression of metabolic proteins were limited to three biochemical pathways; glycolysis, alternate acid production and branched chain amino acid biosynthesis (Len et al., 2004a). Of the 167 differentially expressed proteins identified following acid tolerant growth of S. mutans at pH 5.0, 61 were associated with stress-responsive pathways involved in DNA replication, transcription, translation, protein folding and proteolysis (Len et al., 2004b). The 61 protein spots represented isoforms or cleavage products of 30 different proteins, 25 of which were either up-regulated or uniquely expressed during acid-tolerant growth at pH 5.0. The identification of differentially expressed proteins associated with the acid-tolerant growth phenotype provided new information on targets for future mutagenic studies that will allow the assessment of their physiological significance in the survival and proliferation of S. mutans in low pH environments (Len et al., 2004b). In this chapter, the results of growing S. gordonii at steady state and at various pH, and the resultant phenotypic changes that were observed, are discussed in detail.

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5.2. METHODS

Details of the methods used are given in CHAPTER 2. In essence, S. gordonii was grown in a chemostat at pH 5.0, 5.5, 6.0, 6.5, 7.0 and 7.5 at D = 0.10 h-1 as a model of conditions encountered in the human oral cavity and in the blood stream. As stated previously (Section 1.1), S. gordonii is unable to metabolise efficiently below pH 6.0, and at these lower pH aciduric pathogens proliferate in the native environment of dental plaque. As a result of its acid intolerance, to the best of our knowledge, S. gordonii has never been successfully grown to steady state in a chemostat at levels below pH 5.5 (Bowden and Hamilton, 1987). In this study, six attempts were made to grow S. gordonii at pH 5.0 resulting in a “washout” in all but two cases. For each of the chemostat conditions, solubilized cellular and extracellular proteins were separated by 2-DE (Section 2.5), and stained with SYPRO Ruby (Section 2.5.4), scanned (Section 2.5.5) and analysed using Z3 software (Section 2.6). After re-staining with CBB G-250, the protein spots were manually excised, digested with trypsin (Section 2.7.1) and analysed by MALDI (Section 2.7.2) or ESI (Section 2.7.4) mass spectrometry prior to identification using relevant analysis software. Statistical analysis of the Z3 data was used to identify those proteins that were differentially expressed (Section 5.2.1).

5.2.1. Image analysis and statistical analyses

Image analysis was performed to detect differences in protein production between pH conditions 5.5, 6.0, 6.5, 7.0, and 7.5, using the pH 7.0 2-DE gel as the reference image. Z3 was used to batch-process all other images to compare them with the reference image (Section 2.6). Each protein spot was assigned a value in ppm (Section 2.6) that corresponded to the single protein spot volume amongst the total protein spot volume of all spots in the protein gel. The assigned values were used to determine n-fold changes in protein abundance between each of the pH conditions

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that were to be analysed. Protein gel spots that varied in RE (Section 2.6) between pH conditions for all three replicates were selected for further analysis. ANOVA (Section 2.6.2) was performed on images from conditions pH 5.5, 6.0, 6.5, 7.0, 7.5, with three replicates for each condition (Section 2.4.2). The SNK test (Zar, 1999) was applied to the ANOVAs that had significant results. This is a Post Hoc test that determines which pair(s) among the groups under study has (have) expression means that are statistically different by sequentially comparing the ANOVA means (Section 2.6.2). Sixty 2-DE gels were analysed using Z3. The 60 images analysed were from triplicate chemostats from four IPG ranges (pI 4.0 – 5.0, 4.5 – 5.5, 5.5 – 6.7 and 6.0 – 11.0) and five pH conditions (pH 5.5, 6.0, 6.5, 7.0 and 7.5). The advantage of using a range of pH conditions (instead of just two) is that one is more likely not only to identify when differences occur but also confirm that the observed differences are “real”. The following hypothetical example will illustrate this point. If the expression of protein spot, X, is not influenced by pH but is randomly expressed in, say, one of two states (e.g. up-regulated), the probability of X being in one of these states on two 2-DE gels, by chance, is one in four, while the probability of the same event occurring on all five 2-DE gels is one in 64. If the three replicates are considered (remember the expression of this protein is random and pH does not effect its expression) there would be a one in 64 chance of X occurring in the same state in two pH conditions (six gels), and a one in 32768 chance of X occurring in the same state in five pH conditions (fifteen gels). Although, this is a theoretical example and is somewhat simplistic, it demonstrates the confidence achieved when statistically significant events occur across five conditions.

5.2.2. In silico analysis

The BLASTP program (http://www.ncbi.nlm.nih.gov/) was used to carry out homology searches in the GenBank database. Amino acid sequences were aligned using CLUSTAL W software (Thompson et al., 1997).

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5.3. RESULTS AND DISCUSSION

5.3.1. General observations from growing S. gordonii in the chemostat

Phenotypic changes to S. gordonii were observed as the environmental pH of the chemostat was altered. The optical density of the culture decreased as pH decreased, which was reflected in the dry wt of cells produced per 100 mL of culture, which also decreased as the pH decreased (Figure 5.1). Difficulty was experienced in growing S. gordonii below pH 5.5, where a “washout” in all but two chemostats occurred despite six attempts to grow cells below this pH. As far as one is aware, this is the first record of S. gordonii being grown in a chemostat at steady state at pH 5.0. Others have reported that S. sanguis is unable to adapt to pH levels below pH 5.5 (Bowden and Hamilton, 1987), whereas earlier studies noted the lack of acid tolerance or adaptation of this species (McDermid et al., 1986). These reports for S. sanguis are relevant, as S. gordonii was only described as a new species in 1989 being originally classified among the taxonomic diverse species S. sanguis (Kilian et al., 1989). The chain length of the cells was observed to increase as the pH decreased. For example, at pH 7.0, there were mainly small chains less than five cells in length as well as many single cells, whereas at pH 5.0 there were mainly long chains of at least 10 to 20 cells. Autolytic activity in L. lactis is involved in cell separation, and is critical in determining chain length in both L. lactis, E. faecalis and S. pyogenes (Buist et al., 1995; Waters et al., 2003). It has been shown that autolytic activity in L. lactis subsp. cremoris has maximal autolytic activity during exponential growth in media with a neutral pH (Mou et al., 1976; Niskassaari, 1989). Furthermore, in the case of E. faecalis the secreted zinc-metalloprotease, gelatinase, which has been implicated as a virulence factor by both epidemiological data and animal model studies, has been shown to reduce chain length (Waters et al., 2003). Gelatinase has been shown to activate a muramiadase-1 autolysin from E. faecalis in vitro, and active gelatinase producing mutants have been shown to increase autolysis. The activation of the muramidase-1 autolysin leading to de-chaining is believed to be a

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mechanism to increase dissemination in high bacterial densities (Waters et al., 2003). In E. faecalis inactivation by another form of autolysin, the muramidase-2 autolysin, also results in an increased chain length (Qin et al., 1998). In S. pneumoniae, a murein hydrolase, LytB, is also essential for cell separation. In no case has a gene for the formation of long chains been identified.

0.09 )

-1 0.08 l

m 0.07 0 0.06 10

g 0.05 ( Chemostat 1 s

l 0.04 l 0.03 Chemostat 2 ce t 0.02 Chemostat 3 y w

r 0.01 d 0.00 5.0 5.5 6.0 6.5 7.0 7.5 Grow th pH

Figure 5.1 Alteration in cell dry weight with growth pH. As a small amount of the pellet of Chemostat 3 grown at pH 5.5 was lost prior to weighing, this would account for this data point being lower than those of Chemostats 1 and 2. The data for the two successful chemostat runs at pH 5.0 have been included to indicate the continuing trend of decline in cell mass with decline in pH.

S. gordonii growing in medium containing minimal concentrations of free amino acids also produces an extracellular gelatinase/type IV collagenase optimally in cultures grown at pH 6.5 and 7.0, with negligible gelatinase activity at pH 5.5 (Juarez and Stinson, 1999). The extracellular enzyme is a potential virulence factor in the amino acid-stringent, thrombotic, valvular lesions of bacterial endocarditis (Juarez and Stinson, 1999). It has been suggested that evolutionary pressures in vivo

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select for autolytic activity, as the breaking up of chains could improve the chances of survival against phagocytic cells (Garcia et al., 1999). Although a gelatinase/type IV collagenase was not found in this study of S. gordonii, peptidoglycan hydrolase, a muramidase, was the dominant protein detected in the extracellular milieu on pI 4.0 - 5.0 2-DE pH 7.0 gel maps (Section 4.3.2). The biological significance of this protein produced by S. gordonii is yet to be determined. However, the formation of long chains at low pH may indicate impairment in cell septation and autolysis, as it has been shown that S. mutans growing under stress-inducing conditions forms long chains, aggregates in culture, has reduced genetic transformation efficiencies, and has reduced capacity to form biofilms (Lemos and Burne, 2002). The reduction in chain length as the growing environment approaches neutrality may therefore be perceived as a mechanism to increase dissemination while the bacteria is under favourable ecological conditions.

5.3.2. Protein expression in the extracellular milieu

Extracellular protein expression was examined. Although all of the parameters, except the variable pH, remained constant during the growth of S. gordonii, as reported above (Section 5.3.1), the higher the pH, the greater the density of cells. This trend was reflected in the amount of extracellular protein (Figure 5.2), consistent with an earlier report that both the cell density and the production of extracellular protein of S. sanguis (gordonii) G9B increase as pH increases, reaching a maximum at pH 7.5 (Knox et al., 1985). The differential expression of extracellular protein was not examined in this study. Unlike the comparison of cellular proteins where protein from a stated weight of cells is compared, the expression of protein in the extracellular milieu is a function of cell density, so the total amount of extracellular protein per given volume changes with pH. No safe conclusion about the differential expression of protein could be obtained from this method, as cell density would be a confounding influence.

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90 2.00 80 1.80 ) -1

1.60 )

70 -1 l

0 ml 1.40 60 m 0 10 0

g 1.20 50 m g 1 (

1.00 m s l

l 40

0.80 n ( i ce e

t 30 dry wt cells protein 0.60 ot w

y 20 pr r 0.40 d 10 0.20 0 0.00 5.5 6.0 6.5 7.0 7.5 pH

Figure 5.2 The expression of protein in the extracellular milieu as a function of cell density. A comparison of dry wt of cells and amount of protein extracted from the extracellular milieu indicates that as the pH is increased, cell density increases, as does the extracellular protein concentration (single experiment only).

5.3.3. Differential expression of cellular proteins

There were only sixteen cellular proteins differentially expressed between the different pH growth conditions (Table 5.1), whereas a recent S. mutans study identified 167 proteins with differential expression following growth in a chemostat at pH 7.0 or pH 5.0 under steady state conditions (Len et al., 2004a; Len et al., 2004b). Of the sixteen differentially expressed proteins, ten were identified using the set criteria (Section 2.8; Table 5.1). These were 2,3,4,5-tetrahydropyridine-2- carboxylate N-succinyltransferase, manganese-dependent inorganic pyrophosphatase, GPDH (N-terminal fragment; Figure 5.3), fructose-1,6-biphosphate aldolase,

168 Table 5.1 Differentially expressed cellular proteins.

Functional group Sub-group Description Gene Coveragea Peptidesb Theoreticalc Observedd Gele Spot # f Charged g name pIMr pIMr isoform AMINO-ACID Aspartate family 2,3,4,5-tetrahydropyridine-2- SP2097 25.9 7 4.9 24270 4.9 28000 4.5C 54 BIOSYNTHESIS carboxylate N-succinyltransferase CENTRAL Phosphorus Manganese-dependent inorganic INTERMEDIARY compounds ppaC 43.1 8 4.5 33541 4.3 34000 4.0 C 197 196, 198 METABOLISM pyrophosphatase

ENERGY METABOLISM Glycolysis Glyceraldehyde-3-phosphate None 14.7 8 5.0 33968 6.3 13500 5.5C 16 15 dehydrogenaseh Fructose-1,6-biphosphate aldolase fbaA 38.6 9 4.9 31394 4.9 32000 4.5C 109 108, 110 Sugars Glucose-1-phosphate rmlA 28.8 7 5.0 34578 4.9 33000 4.5C 120 59 thymidylytransferase REGULATORY General Heat-inducible transcription repressor hrcA 15.1 6 5.6 40563 5.6 44500 5.5C 143 FUNCTIONS TRANSLATION Protein modification Phosphoprotein phosphatase pppL 37.4 8 4.5 26870 4.4 23500 4.0C 39 Translation factors Elongation factor P efp 29.0 5 4.7 20600 4.6 24500 4.0C 94

Ribosome recycling factor rrf 38.9 7 5.9 20653 5.9 20500 5.5C 57 UNKNOWN Unclassified Maltose O-acetyltransferase maa 39.4 7 5.1 22684 6.2 26000 5.5C 86 UNIDENTIFIED 4.9 13000 4.5C 13 4.9 17500 4.5C 17 5.1 16500 4.5C 26 4.8 33000 4.5C 58 5.9 13000 5.5C 6 6.7 13500 5.5C 27 a Coverage: refers to the percentage of the protein sequence covered by the matching peptides. b Peptides: refers to the number of peptides in PMF that matched those from the database entry. c Theoretical pI , M r: refers to the theoretical isoelectric point and relative molecular weight of the matching protein as calculated from the translated gene sequence. d Observed pI , M r: refers to the observed isoelectric point and observed molecular weight of the matching protein, determined by the spot's migration on the gel. e Gel: refers to the pH range and cellular component of the reference gel (Figures 4.2 – 4.9); 4.0 (pH 4.0 – 5.0), 4.5 (pH 4.5 – 5.5), 5.5 (pH 5.5 – 6.7), 6.0 (pH 6.0 – 11.0); C, cellular fraction, E, extracellular fraction. f Spot #: refers to the assigned number of the protein spot on the reference gel (Figures 4.2 – 4.9). g Charged isoform: refers to the other protein spots where the differentially expressed protein also exists as a charged isoform. hGlyceraldehyde-3-phosphate dehydrogenase N -terminal fragment (see Figure 5.3). Chapter 5

Glyceraldehyde-3-phosphate dehydrogenase (Fragment). Streptococcus gordonii

Considered sequence fragment: 1 KVGINGFGRI GRLAFRRIQN VEGVEVTRIN DLTDPVMLAH LLKYDTTQGR FDGTVEVKEG 60 61 GFEVNGKFVK VSAERDPENI DWANDGVEIV LEATGFFATK AAAEKHLHAG GAKKVVITAP 120 121 GGSDVKTVVF

Theoretical Mr: 14026 Theoretical pI: 6.3

Observed Mr: 13500 Observed pI: 6.3

Figure 5.3 Computation of theoretical pI and Mr of spot 16 on pI 5.5 – 6.7 2-DE gel. ExPASy was used to determine the position of a sequence fragment of GPDH. The sequence fragment occupied position 1 to position 130 in a sequence of 320 residues. The peptides obtained from mass spectrometry are highlighted in yellow. The theoretical pI and Mr (calculated using ExPASy) and the observed pI and Mr (calculated from the spots position on the gel using Z3) correspond.

glucose-1-phosphate thymidylytransferase, heat-inducible transcription repressor, phosphoprotein phosphatase, elongation factor P, ribosome recycling factor and maltose O-acetyltransferase. Statistical analyses determined the pH conditions that produced statistically significant results (Table 5.2 and Figure 5.4). Some of the possible biological implications of the differential expression are discussed in the sections below (Sections 5.3.3.1 to 5.3.3.7). It should be noted that the unidentified differentially expressed protein spot 58 on the pI 4.5 - 5.5 2-DE gel may possibly be an isoform of fructose-1, 6-bisphosphate, based on its position on the pI 4.5 - 5.5 and 5.5 - 6.7 gels. Another possible identity is protein spot number 27 on the pI 5.5.6.7 2-DE gel, which corresponds to the position of protein spot number 5, tyrosine-protein kinase (CpsD), on the pI 6.0 – 11.0 2-DE gel. In the following, the section headings refer to the functional classification as in Table 4.3.

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Table 5.2 Ratios of average protein expression between pH conditions.

Tentative protein Differences in average protein expression between pH pH conditions with identification condition relative to pH 7.0 significant differencesa pH 5.5 pH 6.0 pH 6.5 pH 7.0 pH 7.5 2,3,4,5-tetrahydropyridine-2- carboxylate N- 0.83 0.43 0.68 1.00 1.21 pH 7.5 > pH 6.0 succinyltransferase Elongation factor P 0.51 0.52 0.76 1.00 0.74 pH 7.0 > pH 5.5, 6.0

Fructose-1,6-biphosphate pH 7.5 > pH 5.5, 6.0, 0.85 0.60 1.05 1.00 1.59 aldolase 7.0 Glucose-1-phosphate thymidylytransferase 0.65 0.69 0.86 1.00 1.40 pH 7.5 > pH 5.5, 6.0

Glyceraldehyde-3-phosphate pH 7.5 > pH 5.5; pH 0.49 0.72 1.17 1.00 1.30 dehydrogenase 6.5 > pH 5.5 Heat-inducible transcription repressor 0.32 0.49 0.52 1.00 1.70 pH 7.5 > 5.5, 6.0, 6.5 Maltose O-acetyltransferase 0.70 0.60 0.67 1.00 1.33 pH 7.5 > pH 6.0, 6.5

pH 7.0 > pH 5.5; Manganese-dependent 0.45 0.80 0.99 1.00 0.67 inorganic pyrophosphatase pH 6.5 > pH 5.5 Phosphoprotein phosphatase pH 7.0 > pH 5.5, 6.0, 0.57 0.62 0.78 1.00 0.65 7.5 Ribosome recycling factor 0.85 0.62 0.60 1.00 1.16 pH 7.5 > pH 6.5

b Unknown spot 13 pH 7.5 > pH 5.5, 6.0, 0.61 0.74 0.93 1.00 1.27 6.5; pH 7.0 > pH 5.5 Unnown spot 17b 0.85 0.66 0.90 1.00 1.19 pH 7.5 > pH 6.0

Unnown spot 26b 0.42 0.58 1.01 1.00 1.11 pH 7.5 > 5.5

Unnown spot 27b 0.70 0.59 0.80 1.00 1.40 pH 7.5 > 5.5, 6.0

Unnown spot 58b pH 7.5 > pH 5.5, 6.0; 0.57 0.57 0.89 1.00 1.12 pH 7.0 > pH 5.5, 6.0 Unknown spot 6c 0.40 0.55 0.53 1.00 1.43 pH 7.5 > pH 5.5

a Significant differences in protein expression as determined by the SNK test (Section 2.6.2) b pI4.5 – 5.5 2-DE gel c pI 5.5 – 6.7 2-DE gel

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pH 5.5 pH 6.0 pH 6.5 pH 7.0 pH 7.5 2,3,4,5-tetrahydropyridine-2-carboxylate N-succinyltransferase pH 7.5 >pH 6.0

Elongation factor P pH 7.0 > pH 5.5, 6.0

Fructose-1,6-biphosphate aldolase pH 7.5 > pH 5.5, 6.0, 7.0

Glucose-1-phosphate thymidylytransferase pH 7.5 > pH 5.5, 6.0

Glyceraldehyde-3-phosphate dehydrogenase pH 7.5 > pH 5.5; pH 6.5 > pH 5.5

Heat-inducible transcription repressor pH 7.5 > 5.5, 6.0, 6.5

Maltose O-acetyltransferase pH 7.5 > pH 6.0, 6.5

Manganese-dependent inorganic pyrophosphatase pH 7.0 > pH 5.5; pH 6.5 > pH 5.5

Phosphoprotein phosphatase pH 7.0 > pH 5.5, 6.0, 7.5

Ribosome recycling factor pH 7.5 > pH 6.5

Figure 5.4 Protein spots showing significant differences in expression. The pH conditions with significant differences in protein expression that were determined by the SNK test are highlighted in red (Section 2.6.2). Figure 5.4 is continued on page 173.

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pH 5.5 pH 6.0 pH 6.5 pH 7.0 pH 7.5

Unknown spot 6a pH 7.5 > pH 5.5

Unknown spot 13b pH 7.5 > pH 5.5, 6.0, 6.5; pH 7.0 > pH 5.5

Unknown spot 17b pH 7.5 > pH 6.0

Unknown spot 26b pH 7.5 > 5.5

Unknown spot 27b pH 7.5 > 5.5, 6.0

Unknown spot 58b pH 7.5 > pH 5.5, 6.0; pH 7.0 > pH 5.5, 6.0

Figure 5.4 (continued) Protein spots showing significant differences in expression. The pH conditions with significant differences in protein expression that were determined by the SNK test are highlighted in red (Section 2.6.2).

apI 5.5 - 6.7 2-DE gel bpI 4.5 - 5.5 2-DE gel

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5.3.3.1. Amino-acid biosynthesis: Aspartate family: 2,3,4,5- tetrahydropyridine-2-carboxylate N-succinyltransferase and Maltose O-acetyltransferase (Unknown function: Unclassified)

The biosynthesis of lysine in bacteria, blue-green algae, and higher plants proceeds via the diaminopimelate-lysine pathway (Singleton and Sainsbury, 1996). The reaction catalysed by 2,3,4,5-tetrahydropyridine-2-carboxylate N-succinyltransferase is in the central portion of this pathway and represents the step at which cyclic precursors are converted to acyclic intermediates. A putative 2,3,4,5- tetrahydropyridine-2-carboxylate N-succinyltransferase was differentially expressed in this study (Table 5.1), with the expression of the protein 2.8-fold greater at pH 7.5 than at pH 6.0 (Table 5.2). As no other proteins were identified in the pathway for the biosynthesis of lysine it is difficult to speculate as to the effect of the differential expression of this protein. However, although not differentially expressed in this study, the murein hydrolase, MurE (EC: 6.3.2.13), would be affected by the up or down-regulation of 2,3,4,5-tetrahydropyridine-2-carboxylate N-succinyltransferase. MurE is found in a side branch off the Lysine Biosynthesis Pathway, the Peptidoglycan Biosynthesis pathway. A decrease in the acyclic intermediates would eventually result in a decrease in the ability to produce peptidoglycan. Maltose O-acetyltransferase was also differentially expressed with expression significantly higher at pH 7.5 (Table 5.2) than at pH 6.0 (2.0-fold) and pH 6.5 (2.0- fold). The function of maltose O-acetyltransferase is currently unknown (http://www.genome.jp/). However, on the basis of sequence similarity, a number of transferases (including maltose O-acetyltransferase) have been proposed to belong to a single family named hexapep transferases (Downie, 1989; Parent and Roy, 1992; Vaara, 1992; Vuorio et al., 1994). These proteins serve a multitude of functions. These include: • Bacterial 2,3,4,5-tetrahydropyridine-2-carboxylate N-succinyltransferase, identified in S. gordonii and discussed above that catalyses the fourth step in the biosynthesis of diaminopimelate and lysine from aspartate semialdehyde (Table 4.3);

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• Bacterial maltose O-acetyltransferase, also identified in S. gordonii (Table 4.3); • Serine O-acetyltransferase, an enzyme involved in cysteine biosynthesis; • Azotobacter chroococcum nitrogen fixation proteins, most probably involved in the optimisation of nitrogenase activity; • E. coli thiogalactoside acetyltransferase, an enzyme involved in the biosynthesis of lactose; • UDP-N-acetylglucosamine, an enzyme involved in the biosynthesis of lipid A in Gram-negative bacteria, a phosphorylated glycolipid that anchors the lipopolysaccharide to the outer membrane of Gram-negative cells and therefore not relevant to Gram-positive bacteria such as S. gordonii; • UDP-3-O-[3-hydroxymyristoyl] glucosamine N-acyltransferase, which is also involved in the biosynthesis of lipid A and therefore again not relevant to Gram-positive bacteria such as S. gordonii; • Chloramphenicol O-acetyltransferase from Agrobacterium tumefaciens, Bacillus sphaericus, E. coli plasmid IncFII NR79, Pseudomonas aeruginosa, Staphylococcus aureus plasmid pIP630; • Rhizobium nodulation protein, which is an acetyltransferase involved in the O-acetylation of Nod factors; • Bacterial N-acetylglucosamine-1-phosphate uridyltransferase, an enzyme involved in peptidoglycan and lipopolysaccharide biosynthesis; • S. aureus protein capG, which is involved in biosynthesis of type 1 capsular polysaccharide; • Yeast hypothetical protein YJL218w, which is highly similar to E. coli lacA; • Fission yeast hypothetical protein SpAC18B11.09c; Methanococcus jannaschii hypothetical protein MJ1064.

These proteins have been shown to contain a repeat structure composed of tandem repeats of a hexapeptide (Vaara, 1992; Vuorio et al., 1994), which in the tertiary structure of the acetyltransferase LpxA (Raetz and Roderick, 1995), has been shown to form a left-handed parallel β helix.

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Although the function of maltose O-acetyltransferase is unknown it may have a similar function to the putative 2,3,4,5-tetrahydropyridine-2-carboxylate N- succinyltransferase due to its sequence similarity. Interestingly, there was a similarity in the pattern of expression of these proteins across the pH growth conditions (Figure 5.5).

1.40 2,3,4,5- 1.20 tetrahydropyridine-2- n o 1.00 carboxylate N- succinyltransferase,

ressi 0.80 putative 0.60 Maltose O- exp d l acetyltransferase

o 0.40 f -

n 0.20 0.00 pH 5.5 pH 6.0 pH 6.5 pH 7.0 pH 7.5 pH

Figure 5.5 Comparison in n-fold expression of 2,3,4,5-tetrahydropyridine-2-carboxylate N- succinyltransferase and maltose O-acetyltransferase. These two proteins, which possibly belong to the same family, have a similar pattern of n-fold expression.

5.3.3.2. Central intermediary metabolism: Phosphorus compounds: Manganese-dependent inorganic pyrophosphatase

Soluble inorganic pyrophosphatase is a ubiquitous enzyme that hydrolyzes inorganic pyrophosphate to inorganic phosphate, thereby providing a thermodynamic pull for many biochemical processes that yield inorganic pyrophosphate (PPi) as a by product, such as carbohydrate metabolism, ATP hydrolysis, amino acid, and nucleotide biosynthesis to orthophosphate (Zyryanov et al., 2004). It has been shown that dephosphorylation of the phosphocarrier protein Ser(P)-HPr in B. subtilis

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liberates pyrophosphate as a byproduct, suggesting additional mechanisms for PPi synthesis within the cell (Mijakovic et al., 2002). Soluble pyrophosphatases comprise two families, I and II. Family II is found only in bacteria and archea, whereas family I is found in all types of organisms. Manganese-dependent inorganic pyrophosphatase (PpaC) is the predominant form of family II pyrophosphatases in S. gordonii as bacteria containing family II pyrophosphatases (e.g., S. mutans) accumulate Mn2+ (Martin et al., 1986). The relative expression of the PpaC was less at pH 5.5 (2.2-fold), 6.0 (1.3-fold) and 7.5 (1.5-fold) than at pH 7.0, but there was no significant difference between the expression at pH 6.5 and 7.0 (Table 5.2). The expression of this protein was highest at around pH 6.5 to 7.0, the pH of a healthy oral cavity. As intracellular PPi concentrations are regulated by pyrophosphatase activity in the cell, changes in pyrophosphatase activity has been described to have global effects on cell metabolism, growth, and division of bacteria (Lahti, 1983). The PpaC of S. gordonii regulates expression of adhesins required for coaggregation (Whittaker et al., 1996). In the current study, PpaC was up-regulated at the pH encountered in a healthy oral cavity. This may be a survival mechanism for S. gordonii as the coaggregation and coadhesion with other bacteria in the formation of biofilms ultimately protects it as the environment changes (Section 1.2.2). Therefore, PpaC may play an important part in the process involved in dental plaque formation.

5.3.3.3. Energy metabolism: Glycolysis: Fructose-1,6- biphosphate aldolase and glyceraldehyde-3-phosphate dehydrogenase (fragment)

In oral streptococci, glucose enters the cell, either by the phosphoenolpyruvate- glucose phosphotransferase system or the glucose permease/glucokinase pathway. It then enters the Embden–Meyerhof–Parnas glycolytic pathway as glucose 6- phosphate and is converted, via a series of reactions, to pyruvate (Figure 4.10). Two glycolytic enzymes, fructose-1,6-biphosphate aldolase (FbaA) and a fragment of GPDH, were found to be differentially expressed in S. gordonii (Table 5.1), in both

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cases the amount of protein being significantly less at pH 5.5 than at pH 7.5 (Table 5.2). The relative expression of FbaA was lower at pH 5.5 (1.9-fold), 6.0 (2.7-fold) and 7.0 (1.6-fold) than at pH 7.5. An unidentified protein spot (Section 5.3.3) representing a possible charged isoform of FbaA also had a relatively lower level of expression at pH 5.5 (2.2-fold) and 6.0 (1.9-fold) than at pH 7.5. FbaA was also found to be down-regulated (1.4-fold) in S. oralis following growth in batch-cultures at pH 5.0 (Kawamura et al., 1995; Wilkins et al., 2001). Previous analysis of metabolic intermediates formed in the first half of the Embden–Meyerhof–Parnas pathway in S. mutans indicated that the level of glucose 6-phosphate is slightly elevated when S. mutans is grown in continuous culture at pH 5.5 and D = 0.15 h-1, while the levels of all other intermediates were essentially the same, including dihydroxyacetone phosphate and glyceraldehyde 3-phosphate (Iwami et al., 1992). A 2.0 fold increase in the level of glucose-6-phosphate isomerase and an overall 1.5 fold increase in the level of the three isoforms of fructose-1,6-bisphosphate aldolase, was also found in S. mutans grown at D = 0.10 h-1 in a chemostat at pH 5.0 compared with that at pH 7.0 (Len et al., 2004b). These results are similar to those observed for S. mutans in batch culture (Wilkins et al., 2002), possibly reflecting a need to overcome the effect of cytoplasmic acidification on enzyme activity at pH 5.0. In contrast to S. mutans, the levels of fructose-1,6-biphosphate aldolase would appear to decrease in S. gordonii and S. oralis when grown at low pH. The reason for this singular change in just one of the enzymes in the Embden–Meyerhof–Parnas glycolytic pathway at low pH is not intuitively apparent, but may represent a method of redirecting carbon into alternative pathways such as intracellular polysaccharide storage, or cell wall polypeptide synthesis (Section 5.3.3.4), to prevent the further formation of acid end products. Such a possibility is worthy of future examination. There was also a decrease in expression of one of two fragments of GPDH at pH 5.5 compared with growth at pH 7.5 (2.7 fold) and pH 6.5 (1.8-fold). The other fragment, protein spot 15 on the cellular 4.0 – 5.0 2-DE gel, although not statistically significant, appeared to have greater expression at the lower pH. A similar situation was seen with S. mutans (Len et al., 2004b), where GPDH was differentially expressed in eight charged isoforms, half of which were up-regulated at low pH,

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while the other half down-regulated. The up and down-regulation of the GPDH charged isoforms in the cellular fraction may have biological significance or they may somehow cancel each other out. Whatever the case, the reason, if any, is currently unknown.

5.3.3.4. Energy metabolism: Sugars: Glucose-1-phosphate thymidylytransferase

Glucose 1-phosphate is the initial substrate for four proteins RmlA, RfbB, RfbC, RfbD associated with the biosynthesis of deoxythymidine diphosphate (dTDP)-L- rhamnose, a sugar component of S. gordonii cell wall polysaccharide (Reddy et al., 1994). The organization of the four genes for dTDP-L-rhamnose biosynthesis in S. gordonii resembles that seen in S. mutans (Tsukioka et al., 1997a; Tsukioka et al., 1997b), which show a high degree of amino acid sequence identity with the rfbA gene products of Gram-negative bacteria. dTDP-L-rhamnose is the immediate precursor of the rhamnose component in the O antigen of lipopolysaccharide in Gram-negative bacteria (Tsukioka et al., 1997b). As in S. mutans (Zhang et al., 1993), rmlD of S. gordonii is followed by rgpA and additional genes of a putative rhamnose-glucose polysaccharide (Xu et al., 2003). However, the production of this polysaccharide has not yet been demonstrated in S. gordonii (Xu et al., 2003). Glucose-1-phosphate thymidylytransferase (RmlA) is the first step in dTDP-L- rhamnose biosynthesis. It was identified as two charged isoforms in the current study. One of these isoforms had significantly lower expression at pH 5.5 (2.1-fold) and 6.0 (2.0-fold) than at pH 7.5, while the other was unchanged by pH (Table 5.2). This is similar to the results obtained when S. mutans was grown under acid- tolerance, where glucose-1-phosphate thymidylytransferase existed as two isoforms at pH 7.0, however at pH 5.0 only one isoform could be detected which was at a 2.4- fold lower level than at pH 7.0 (Len et al., 2004a). In order for planktonic cells to induce endocarditis they must colonise the thrombotic vegetation comprising deposited platelets and fibrin. The changes in surface properties of S. gordonii cells with the possible production of rhamnose surface antigens at pH 7.5 could affect the cells adherence to heart tissues aiding

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colonisation in infective endocarditis. Such a possibility warrants future investigation. However, as no other enzymes in this pathway were identified it was not possible to determine if changes in expression of RmlA was of consequence or were the result of metabolite flux.

5.3.3.5. Regulatory functions: General: Heat-inducible transcription repressor

Heat shock proteins were discussed previously (Section 4.3.8.6). The heat-inducible transcription repressor (HrcA) negatively regulates the transcription of heat shock genes (Roberts et al., 1996; Schulz and Schumann, 1996). HrcA was expressed at significantly lower levels at pH 5.5 (5.3-fold), 6.0 (3.5-fold) and 6.5 (3.3-fold) than at pH 7.5 in S. gordonii (Table 5.2). Despite this, no change in expression of heat shock proteins due to a lowering of the environmental pH was observed in S. gordonii. One explanation for this is that heat shock proteins are usually produced at high levels, but only transiently, in response to a sudden shock (Schulz and Schumann, 1996; Singleton and Sainsbury, 1996), and as S. gordonii was permitted to reach a steady state, after a gradual change in environmental pH, prior to harvesting there was not a sudden trigger for this response. However, the situation is different with S. mutans where three isoforms of GroEL and three isoforms of DnaK were up-regulated by 6.9-fold, 7.0-fold and 20.0-fold, and 4.6-fold, 5.0-fold and 10.9-fold respectively, after acid adaptation at pH 5.0 (Len et al., 2004b). Acid- tolerance and acid-adaptation in S. mutans is a dynamic phenomenon, which requires elevated levels of DnaK to repair acid-induced damage to proteins (Jayaraman et al., 1997). S. gordonii does not appear to have these mechanisms for adaptation to low pH, for even though heat shock proteins are expressed, there is no differential expression as the pH decreases. The bacterium appears to survive a drop in pH until it cannot function anymore, and then it dies. This does not explain why HrcA, which negatively regulates the transcription of heat shock genes, was up-regulated at pH. 7.5. In B. subtilis, HrcA negatively regulates transcription of class I stress genes by binding to a DNA element called CIRCE (Controlling Inverted Repeat of Chaperone Expression) located in the

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regulatory regions of stress genes (Hecker et al., 1996; Zuber and Schumann, 1994) by way of a helix-loop helix motif, in the C-terminus of the protein (Kim et al., 2001). It also has been found that GroEL modulates the activity of the HrcA repressor (Mogk et al., 1997), stabilizing and preventing aggregation of HrcA, which allows HrcA to function (Mogk et al., 1997). The putative CIRCE element of groESL has been found to be perfectly conserved in S. gordonii as in other Gram- positive bacteria (Mogk et al., 1997; Teng et al., 2002). Alignments of the amino acid sequence of HrcA (using the BLASTP program Section 5.2.2) for S. gordonii and S. pneumoniae (82% identity, 92% similarity), S. gordonii and S. mutans (62% identity, 77% similarity), and S. pneumoniae and S. mutans (60% identity, 76% similarity), indicates a strong similarity between S. gordonii and S. pneumoniae (Tatusova and Madden, 1999). A multialignment of the amino acid sequence of HrcA of S. gordonii, S. pneumoniae and S. mutans (using CLUSTAL W software Section 5.2.2) shows 58 % identity, 86 % similarity (Figure 5.6). It may be that, although the level of expression of HrcA alters as expected for a role as a negative repressor in regulating heat-shock proteins, as the pH falls, it is unable to regulate. Whether it has become non-functional remains to be determined.

5.3.3.6. Translation: Translation factors: Elongation factor P and ribosome recycling factor

The translation factor, elongation factor P (Efp), is responsible for the release of ribosomes from messenger RNA at the termination of protein biosynthesis. It stimulates the peptidyltransferase activity of fully assembled 70 S prokaryotic ribosomes and enhances the synthesis of certain dipeptides initiated by N- formylmethionine. This reaction is highly conserved throughout species and is promoted in eukaryotic cells by a homologous protein, eIF5A (Aoki et al., 1997), which has been found to be essential for growth in Saccharomyces cerevisiae (Kang and Hershey, 1994). Elongation factor P has also been found to be required for cell viability and protein synthesis in E. coli (Aoki et al., 1997). However, a recent study has found this may not to be the case with A. tumefaciens where elongation factor P was found to increase the efficiency of formation of peptide bonds involving

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Figure 5.6 Alignment of the amino acid sequence of HrcA. HrcA sequence: SG, S. gordonii sequence from in-house database; SP, S. pneumoniae TIGR sequence from ExPASy database; SM, S. mutans sequence from ExPASy database. Colour alignment: Red, AVFPMILW, small (small+ hydrophobic (including aromatic - Y); Blue, DE, acidic; Magenta, RHK, basic; Green, STYHCNGQ, hydroxyl +amine + basic - Q. ‘*’ indicates positions which have a single, fully conserved residue ‘:’ indicates that one of the following “strong” groups is fully conserved:- STA, NEQK, NHQK, NDEQ, QHRK, MILV, MILF, HY, FYW ‘.’ indicates that one of the following “weaker” groups is fully conserved:- CSA, ATV, SAG, STNK, STPA, SGND, SNDEQK, NDEQHK, NEQHRK, FVLIM, HFY

aminoacyl acceptors that bind poorly to the ribosome in its absence (Peng et al., 2001). A. tumefaciens has a single copy of chvH (Elongation factor P), which can be disrupted without loss of viability, although the cells grow more slowly and are no longer virulent. On complex medium, a chvH deletion mutant grows slower than the wild-type strain, indicating that elongation factor P is important but not essential for the growth of Agrobacterium (Peng et al., 2001). In S. gordonii elongation factor P was found at significantly lower levels at pH 5.5 (2.0-fold) and 6.0 (1.9-fold) than at pH 7.0, while there was no significant difference in the levels at pH 6.5, 7.0 and 7.5 (Table 5.2). There is a possibility that elongation factor P may selectively positively regulate the synthesis of certain essential proteins involved in control of growth in S. gordonii. This is apparent with

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S. gordonii growing in a chemostat where both the cell mass and extracellular protein expression were reduced following growth at lower pH. Another translation factor, ribosome recycling factor (Rrf), dissociates the ribosome from the mRNA after termination of translation, thus ribosomes are "recycled" and ready for another round of protein synthesis. Ribosome recycling factor has also been found to be essential for bacterial growth in E. coli (Janosi et al., 1994). In the current study, ribosome recycling factor was significantly less at pH 6.5 (1.9-fold) than at pH 7.5, while there was no significant difference between the other pH growth conditions (Table 5.2). At present no conclusion can be drawn on the repression of ribosome recycling factor at pH 6.5 in S. gordonii.

5.3.3.7. Translation: Protein modification: Phosphoprotein phosphatase

Phosphorylation and dephosphorylation of proteins by the action of kinases and phosphatases, respectively, is a key mechanism by which functional activity may be regulated, and a number of such modifications that were originally thought to occur exclusively in eukaryotic systems have now been discovered in prokaryotes (Mukhopadhyay et al., 1999). In the current study the relative expression of phosphoprotein phosphatase was greater at pH 7.0 than at pH 5.5 (1.8-fold), 6.0 (1.6- fold) and 7.5 (1.5-fold), and there was no significant difference between the expression at pH 6.5 and 7.0 (Table 5.2; Section 2.6.2; Figure 2.3). Wilkins et al. (2001) also observed a decrease (4-fold) in phosphoprotein phosphatase levels at low growth pH (pH 5.2). Posttranslational modifications may result in the formation of isoforms, but although it is possible that phosphoprotein phosphatase may play a role in the formation of some of the charged isoforms, the change in the expression of this protein did not effect the formation of isoforms in the current study.

5.3.3.8. Unidentified proteins

The unidentified protein spots, all showed significantly greater expression at pH 7.5 than at pH 5.5 (Table 5.2), and three possessed low Mrs around 13000. Low Mr

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proteins can be difficult to identify (Harry et al., 2000). Two of these unidentified proteins on the pI 4.5 – 5.5 2-DE gel, spot 26 and spot 27, are very likely charged isoforms. Until these proteins are identified no further comment can be made regarding their role in S. gordonii during changes in environmental pH.

5.3.3.9. Comparison of proteins expressed by S. gordonii to unique proteins expressed during acid tolerance in S. mutans

An exhaustive search for unique proteins expressed by S. gordonii either during growth at pH 7.0 or pH 5.5 failed to locate any such proteins. Len et al. (2004b) identified several proteins uniquely expressed following growth at pH 7.0 or pH 5.0. A stress-related protein, DNA-directed DNA polymerase I (PolA), was uniquely expressed at pH 7.0, while the stress-related proteins single stranded binding protein (Ssb), elongation factor TU (Tuf; one of nine forms), and ATP-dependent Clp protease (ClpL) were uniquely expressed at pH 5.0. PolA was not found in the current study, although Ssb, Tuf, and a Clp protease were identified, but showed no change in their level of expression with environmental pH. In S. mutans (Len et al., 2004b) one of the two single-stranded binding proteins coded in the S. mutans genome, Ssb (Ajdic et al., 2002), was uniquely expressed by acid-tolerant growth of S. mutans at pH 5.0. Ssb, along with another protein (RecA), is essential for recombinational rescue and DNA repair of chromosomal replication during the SOS response (Katz and Bryant, 2003; Steffen et al., 2002). However, in S. gordonii although Ssb was present (Table 4.3), similarity of expression was noted across all five pH growth conditions. From the different roles for recombinational repair and the SOS response (Section 1.6.2) currently recognized in Gram-negative and Gram-positive bacteria, and the lack of information available on streptococci, the different responses of S. gordonii and S. mutans warrant further research in order to understand these processes, particularly during acid-tolerant growth. In translation, three elongation factors, elongation factor Tu, elongation factor Ts and elongation factor G, are responsible for guiding aminoacyl tRNAs to the

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ribosome and for translocation of the ribosome along the mRNA (Cooper, 2000). In S. mutans (Len et al., 2004b) an elongation factor TU isoform that was present at pH 5.0, was not present at pH 7.0, while at pH 5.0 there was a fourfold increase in the expression of four other charged isoforms (all with a greater Mr than predicted from the gene sequence). In contrast, there was no significant difference in expression of elongation factor TU in S. gordonii between the five pH growth conditions, either with the charged isoforms or those isoforms with a higher than predicted Mr. In S. mutans (Len et al., 2004b), one protein from the Clp family of proteases, ATP-binding subunit, ClpL, was found to be expressed solely by growth at pH 5.0. ClpL homologues may be specific to Gram-positive bacteria, as they have not been found in Gram-negative bacteria (Kwon et al., 2003; Lemos and Burne, 2002). A study of the heat-shock response in S. pneumoniae found that mutations in the clpL and clpP genes modulated virulence-gene expression, and purified recombinant ClpL was shown to possess molecular chaperone properties (Kwon et al., 2003). The S. mutans data indicated that ClpL is up-regulated and maintained under low pH conditions, and may play a vital role in pH tolerance in S. mutans. Although ClpL was not identified in the current study, three other Clp family proteins, ClpE, ClpX and a homologue to the S. pneumoniae protein, SP2194, were identified. However, as with the majority of S. gordonii proteins, none were significantly differentially expressed between the five pH growth conditions.

5.4. CONCLUSION

This study aimed to identify proteins that are differentially expressed in S. gordonii after a change in environmental pH, as changes in pH are relevant to caries initiation and the vascular environment in endocarditis. Many 2-DE and proteome studies have focussed on streptococci, such as S. mutans, and their relation to low pH and dental caries. The current proteome literature relating to acid adaptation and tolerance in oral streptococci contains many incongruent findings. For instance, a proteome analysis of S. mutans during acid adaptation in batch-culture (without pH control) by Wilkins et al. (2002) identified only eight of the 28 differentially expressed proteins that were found in the chemostat study by Len et al. (2004). In

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the Wilkins study only a 60-kDa chaperonin was up-regulated to a similar extent to that observed with GroEL in the study by Len et al. (2004b). Len et al. (2004b) suggested that since an arbitrary ratio of the levels of expression had been evaluated in the Wilkins et al. (2002) study in batch culture, the control bacteria are adapting to a fall in the extracellular pH by initially over expressing stress-related proteins. This would contrast with the steady state levels measured in a chemostat, once S. mutans had adapted to the prevailing pH conditions. Changes in protein expression in S. gordonii with changes in pH were found to be minimal compared with those observed with S. mutans, confirming a previous report of a similar finding (Hamilton and Svensäter, 1998). Hamilton and Svensäter (1998) speculated that the survival of oral bacteria at very low pH is related to the formation of key proteins that function to augment normal homeostasis, and not to the total number of acid-regulated proteins induced. The effects of acidification on growth and glycolysis of S. sanguis and S. mutans has been previously compared (Takahashi et al., 1997). Acidification (60 min at pH 4.0) was found to repress both growth and glycolytic activities in S. sanguis cells with associated inactivation of the glycolytic enzymes, hexokinase, phosphofructokinase, glyceraldehydephosphate dehydrogenase and enolase. The impaired abilities of glycolysis and growth recovered following incubation at pH 7.0, and this was accompanied by reactivation of the glycolytic enzymes. On the other hand, these impairments were not observed in S. mutans cells exposed to prolonged acidification. The low pH frequently occurring in dental plaque may transiently impair glycolysis and growth in the sanguis group of streptococci, including S. gordonii. By adapting to pH conditions by altering its phenotype and ensuring a supply of ATP through glycolysis, S. mutans can maintain an intracellular pH gradient through proton exclusion by its H+-ATPase activity (Bender et al., 1986) and is therefore more resilient to the acidification than S. sanguis (Takahashi et al., 1997). S. mutans also changes its phenotype in other ways including an increase in glycolytic activity, a decrease in PTS sugar transport, and the synthesis of stress response proteins (Len et al., 2004a; Len et al., 2004b). In S. mutans, the latter response is regulated by HrcA which alters in its level of expression during acid tolerance (Lemos et al., 2001). While, the level of expression of HrcA in S.

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gordonii, is consistent with its role as a negative repressor as the pH falls. However it is unable to regulate heat-shock gene expression. An acid tolerant organism such as S. mutans has higher levels of H+-ATPase activity, and the pH optimum for activity is lower than for less tolerant species such as S. sanguis (Marsh and Martin, 1999). The reason S. mutans can out compete S. gordonii at low pH may therefore be simply due to its ability to manipulate its proteome in a complex manner for survival and persistence at low pH, unlike S. gordonii. This may imply some prevailing level of genetic regulation that is missing in S. gordonii, including the possibility that HrcA is ineffective (Section 5.3.3.5).

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CHAPTER 6. DISCUSSION

It is known that oral biofilms are host to more than 700 bacterial taxa (Kazor et al., 2003), and that this estimate will increase as the latest techniques are used to detect new phenotypes (Byun et al., 2004). This level of complexity clearly prevents meaningful concurrent phenotypic analysis using current proteome technologies. In order to unravel environmental effects, it is therefore necessary to determine phenotypic responses in individual species. Only by thorough and comprehensive investigation of the phenotypic changes brought about by environmental constraints can a clear insight into the multitude of observed changes be understood. A subsequent reductionist approach in light of this new knowledge will then allow appropriate susceptible biochemical events to be targeted to eliminate disease. It is for this reason that studies on the phenotype of S. gordonii were undertaken, as this bacterium along with other members of the sanguis-group, are considered to be associated with a “healthy” dental plaque. As previously noted, S. gordonii was originally classified among the taxonomic diverse species S. sanguis until its description as a new species in 1989 (Kilian et al., 1989). S. gordonii is one of VS, which are part of the normal microbial flora in dental plaque (Whiley and Beighton, 1998). While most VS are commensals, an exception is S. mutans, which is associated with dental caries (Bowden, 1997). Although most are not considered pathogens in the oral cavity, many VS species are associated with infective endocarditis (Douglas et al., 1993). Gram-positive bacteria, such as S. gordonii, form biofilms on tooth surfaces and ferment carbohydrate efficiently at pH levels above 6.0 (Loo et al., 2000), while producing antibacterial factors that prevent the overgrowth of dental pathogens such as S. mutans (van der Hoeven and Schaeken, 1995). If the pH of dental plaque drops below pH 6.0, S. gordonii is unable to metabolise efficiently and aciduric pathogens begin to proliferate and predominate in the oral biofilm, forcing the pH even lower. However, under certain conditions, including some dental procedures, S. gordonii along with other VS may enter the bloodstream, causing a transient bacteremia, where it encounters an increase in environmental pH from a slightly acidic resting

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value of 6.5 in dental plaque to a value of 7.3 (Vriesema et al., 2000). In the current study, proteomics was used to both map the protein phenotype of S. gordonii by 2- DE, and to determine any changes that occurred over a range of pH relevant to pH values encountered by the bacterium. When constructing a reference map it is important to have reproducible protein patterns. This was achieved by growing S. gordonii in a chemostat at steady state, using totally defined and reproducible environmental conditions, and then by processing the cells using clearly defined and reproducible protocols. At the time this project commenced there was very little proteomic information concerning Gram-positive bacteria, and there were no proteome studies on oral streptococci. Problems, associated with cell preparation and gel loading for 2-DE protein display were overcome before any analysis could take place, requiring a systematic development of appropriate protocols. By identifying proteins from cells grown in continuous culture under conditions that mimic those in the dental plaque of a healthy individual (Jacques et al., 1979b), a base line for protein expression was established. The 2-DE map was then used as a reference for the identification of differences in protein expression when S. gordonii was subjected to changes in environmental pH and is already being used in our laboratory as a control for changes occurring when S. gordonii is grown in biofilms under a variety of environmental conditions. This study confirmed the expression of 38 proteins previously designated as “hypothetical” or with no known function. Although the S. gordonii genome sequence is incomplete, the number of proteins identified in this study, 476 protein spots corresponding to 250 proteins, was comparable with the proteome map of S. mutans, 416 protein spots corresponding to 200 proteins (Len et al., 2003), whose genome is fully sequenced and annotated (Ajdic et al., 2002). In the current analysis of S. gordonii, as with other 2-DE proteome studies, integral membrane proteins were underrepresented with only 25 proteins predicted to possess transmembrane regions. Although this may appear a small number, compared with the 587 transmembrane proteins (approximately 25% of the genome), that have been predicted for the closely related species, S. pneumoniae (http://pedant.gsf.de), this data compares favourably with other studies (Len et al., 2003), and corresponds to

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10% of the total proteins identified. The lack of representation of membrane- embedded proteins on 2-DE gels is most likely due to their poor solubility and inherent hydrophobicity (Section 1.4.1.1). New technologies have recently become available that would be suitable for targeting membrane proteins. These include MUDPIT and ICAT (Section 1.4.1.2), and more recently iTRAQ, which was released in late 2004 (http://docs.appliedbiosystems.com/pebiodocs/04350831.pdf, Applied Biosystems, Forester City, CA, USA). Two of these technologies have major drawbacks when it comes to quantitative, bacterial proteome studies. MUDPIT does not allow quantification of proteins, and ICAT, focuses specifically on quantifying proteins that contain the amino acid cysteine, and does not allow examination of post transitional modifications. However, iTRAQ Reagents are based on an isobaric peptide tagging system that enables labelling of all primary amines, regardless of peptide class, thus allowing for precise quantification. This leads to broader protein and proteome coverage with the ability to detect post-translational modifications, and to compare four samples in the same experiment. Therefore, iTRAQ technology would also be suitable for identifying the source of post transitional modifications such as phosphorylation and glycosylation (Section 4.3.4). Phosphorylation and dephosphorylation of proteins by the action of kinases and phosphatases, respectively, are a key mechanism by which functional activity can be regulated. Consequently, charged isoforms may be important when differential expression occurs due to environmental change. With 63 out of 250 identified proteins existing as isoforms it would be enlightening to discover the nature of the modifications that gave rise to the different isoforms in S. gordonii. There were 23 proteins identified in this study that have been associated with virulence in bacteria. Of particular interest was a peptidoglycan hydrolase (Section 4.3.8.4). Peptidoglycan hydrolases are involved in key biological processes (Fournier and Hooper, 2000), as well as being major virulence proteins in streptococci (Jedrzejas, 2001). Peptidoglycan hydrolases of Gram-positive bacteria are known to trigger cytokine release from peripheral blood mononuclear cells causing a peptidoglycan-induced inflammation (Majcherczyk et al., 1999). It is not yet known what role, if any, this protein plays in the virulence of S. gordonii, however future

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research involving the chromosomal inactivation of the mur1.2 gene, comparing normal S. gordonii and a mur1.2 mutant, would be required to ascertain if this peptidoglycan hydrolase plays an important part in the biology of S. gordonii. An interesting possibility raised by this research is that planktonic S. gordonii cells may remain competent at a steady state in a chemostat. It is known that competence occurs in early growth phase in batch culture and the genetic transformation in naturally competent streptococci, such as S. gordonii, is partly controlled by a quorum-sensing system, which is mediated by CSP (Håvarstein et al., 1996; Peterson et al., 2004). Several proteins that have been associated with competence were identified in the current study (Section 4.3.8.8). However, the presence of competence proteins may either imply that S. gordonii is competent in the chemostat and/or that these proteins are being constitutively expressed in S. gordonii Challis. Future research will determine which of these hypotheses is the case as these experiments were beyond the scope of the current study. Either plasmid or chromosomal DNA could be used to assay natural genetic transformation of S. gordonii in a chemostat. Basically, S. gordonii would be grown in a chemostat until steady state (Section 2.4.1), and then DNA (plasmid or chromosomal) containing an antibiotic resistant gene added to the chemostat at regular intervals. Bacterial samples would then be plated on nutrient agar containing the appropriate antibiotic, and the number of transformants determined. The transformation efficiency from samples would be compared to samples taken from a control chemostat (identical conditions except that no DNA would be added), as well as from early growth phase in batch culture (Section 4.3.8.8). The pH increase accompanying the transition from the oral cavity to the bloodstream, is thought to be a stimulus for a change in protein expression and the subsequent ability of the bacteria to colonize a damaged heart valve (Vriesema et al., 2000). Of the 19 differentially expressed spots found in this study, 13 showed significantly higher expression at pH 7.5, eight being positively identified. Of particular interest, in relation to endocarditis, is glucose-1-phosphate thymidylytransferase (RmlA). In order for planktonic cells to contribute to endocarditis they must colonise the thrombotic vegetation comprising deposited platelets and fibrin. The changes in surface properties of S. gordonii cells resulting

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from the possible production of rhamnose surface antigens at pH 7.5 could affect the cells adherence to heart tissues aiding colonisation in infective endocarditis. However, as no other enzyme in this pathway was identified it was not possible to determine the relevance of the changes in expression of RmlA. A future experiment could produce an rmlA knockout mutant, generated by insertion of a non-polar antibiotic resistant cassette followed by gene replacement into S. gordonii. It would be appropriate to compare S. gordonii and the rmlA mutant in biofilm studies to ascertain its function in biofilm formation in an endocarditis model. S. mutans has a fully functional repressor protein (HrcA) with heat-shock proteins being expressed as required. In contrast, although HrcA in S. gordonii was up or down-regulated by pH in a predictable manner, no changes were seen in the heat-shock proteins it is supposed to regulate. The reason S. mutans can out compete S. gordonii at low pH may therefore be simply due to its ability to manipulate its proteome in a complex manner for survival and persistence at low pH, unlike S. gordonii. This may imply some prevailing level of genetic regulation that is missing in S. gordonii. A multialignment (Section 5.3.3.5) showed no obvious change in HrcA between streptococci (S. gordonii, S. pneumoniae and S. mutans). Even so, there still is a possibility that the sensitivity of S. gordonii to low environmental pH is due to an aberrant form of the HrcA protein. If this protein fails to de-repress, and leave the CIRCE element, then the heat shock proteins cannot be up-regulated in response to shock, which would explain the sensitivity of S. gordonii to low pH when compared with other strains such as S. mutans. To test this hypothesis two protocols could be utilised. In the first protocol hrcA from S. mutans (which is known to have a functional protein) could be placed into S. gordonii to see if the presence of functional HrcA effects survival of S. gordonii at low pH. The gene sequence of S. mutans hrcA would be selectively amplified using PCR, and modified with primers containing desirable restriction endonuclease sites for directional cloning. After digestion, the purified PCR product would be ligated into a plasmid containing a S. gordonii origin of replication and an upstream constitutive S. gordonii promoter. Suitable selectable markers would be necessary for the recombinant plasmid and the subsequent selection of the transformed S. gordonii. Once transformed into S. gordonii the recombinant strain could be tested for low pH

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tolerance using the original S. gordonii as a control. Increased resistance of the transformed S. gordonii would indicate that the presence of the S. mutans HrcA increases the tolerance of S. gordonii to low pH and would confirm that HrcA plays a role in the survival of S. gordonii under acid conditions. However, this method does not assess if HrcA is functional in S. gordonii. If the S. mutans HrcA is shown to improve the acid tolerance of S. gordonii a second experiment could be performed to confirm that HrcA is the non-functioning component of that pathway. A S. mutans hrcA- knock-out mutant could be prepared using a deletion cassette, and a plasmid containing a S. gordonii hrcA insertion could be transformed into S. mutans. It would be necessary to check for the presence of S. gordonii HrcA in the transformed S. mutans strain (to rule out the possibility of a false negative). Transcriptional activity of the gene on the plasmid could be confirmed using Northern analysis, while the presence of S. gordonii HrcA could be confirmed by Western analysis, and finally the binding of the S. gordonii HrcA to the S. mutans CIRCE elements could be measured using Footprint or South-Western analysis. Quantitative comparison of the acid-tolerance of the various strains (from both the above protocols) would provide evidence as to the function (or non-function) of HrcA in S. gordonii. Several other conclusions can be drawn from the current study of S. gordonii. For instance, from the general observations of the chemostat it was apparent that the cell density in the culture decreased dramatically when the pH dropped below pH 6.0, and that when the pH was at 5.0 the organism was unable to metabolise effectively. This resulted in a “washout” on a number of occasions despite S. gordonii growing at the relatively long generation time of approximately seven hours. The use of integral increases in pH, however, showed a gradient pattern of gain or loss of proteins as the pH rose, emphasizing the validity of the protein changes occurring, and ensuring that unusual changes, such as protein expression peaking, say at pH 6.5, were not missed. Statistically significant changes in protein expression with alternating pH were, however, minimal compared with those observed with S. mutans in keeping with a previous report of a similar finding (Hamilton and Svensäter, 1998). Hamilton and Svensäter speculated that the survival of oral bacteria at very low pH is related to the formation of key proteins that function to augment normal homeostasis, and not to the total number of acid-

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regulated proteins induced. This may be a valid conclusion over the short duration of batch culture experiments. However, it may not be the case when the bacterium is grown in a chemostat taking 50 h to reach steady state. The research covered in this thesis was aimed at approaching old but unresolved questions with the aim of shedding new light onto microbial responses to the onset and progression of oral diseases. The studies completed to date offer a tantalizing insight into the hitherto ignored complexity of protein regulation and expression. Unfortunately, with proteomics being a new science, a number of technical problems still remain. The problem of not being able to visualize the total proteome of a cell using a 2-DE approach, particularly proteins with the extremes of Mr and/or pI, is rarely if ever commented upon. This raises the very real possibility that crucial changes may be overlooked. In the current study protein spots were visualized following staining with SYPRO Ruby and then prepared for peptide mass fingerprinting by re-staining with CBB G-250 (Section 2.5.5). A total of 1022 protein spots were manually excised from the CBB G-250 stained gels. However only those proteins that could be detected with the naked eye were excised, with the positions of some very faint protein spots verified using the initial scanned image of the SYPRO Ruby stained gel. As the CBB G-250 stain is much less sensitive than the SYPRO Ruby (Lauber et al., 2001), more protein spots were visualised than were analysed. However, as noted previously (Section 1.5.1), MALDI-TOF is not that sensitive, and hence many proteins detected on the 2-DE gels with the use of fluorescent stains were of insufficient concentration for analysis by this technique. Also, PMF of several of the very light CBB G-250 stained spots produced poor spectra. This was due to the background noise rather than the inherent sensitivity of the mass spectrometer. So, in practical terms an excess of spots were processed compared with the number that could be identified using the available technology. A range of other technical problems still remain, some of which have already been discussed. These include high abundance proteins swamping low abundance proteins, co-migration of protein spots and the inability to detect integral cytoplasmic membrane proteins. While visualization of membrane proteins on 2-DE gels remains an inherent problem for all proteomic analyses regardless of cell type, most of the others problems can be approached by pre-fractionation of the proteome either by

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cellular compartment (eg separation of cytoplasmic, membrane and wall fractions in bacteria), Mr or pI or preferably all three. Proteome Systems (http://www.proteomesystems. com) is currently developing dissolvable IPG strips for use in the 1st dimension isoelectric focusing that should allow the visualization of high Mr proteins in the 2nd dimension. They and others have also developed equipment that allows prefractionation of proteins by pI so that proteins are only loaded onto IPG strips with matching pI ranges, preventing overloading and allowing for low abundance proteins to be visualised. Since the inception of the research reported in this thesis there has been a significant growth of information related to proteomics on the Internet. Sites are emerging containing 2-DE images, databases of sequenced proteins, and software for gel comparison and protein analysis. One example is the SWISS-2DPAGE Two- dimensional polyacrylamide gel electrophoresis database, which contains data on proteins, which have been identified on various 2-DE reference maps. The proteins can be located on the 2-DE map, or alternatively, the region of a 2-DE map, where one might expect to find a protein from Swiss-Prot, can be displayed (http://tw.expasy.org/ch2d/). In the area of dental research, “Toothprint” (http://toothprint.otago.ac.nz) is another example (Hubbard et al., 2001). “Toothprint” is based on developing rat enamel and provides functionally relevant data and 2-DE protein identification maps. As this database is expanded it will provide an important bioinformatic resource for dental research. The emergence of oral microbial databases of 2-DE gels produced under defined environmental conditions using standardized protocols will ultimately enable researchers to identify and focus on proteins of interest. Despite this, this study has produced the first comprehensive proteome reference map of S. gordonii and has provided tantalizing insights into the phenotypic changes in S. gordonii when subjected to a range of environmental pH conditions. The map is based on a standardized continuous culture condition, allowing for future comparative analyses with other S. gordonii proteomes derived from modeling environmental conditions relevant to the pathogenesis and survival of this organism. By identifying approximately 12.5% of the S. gordonii proteome, a significant contribution has been made towards complementing the genomic sequencing

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initiative of this oral bacterium. The study also confirmed the expression of 38 proteins previously designated as “hypothetical” or with no known function. Mass spectral data was produced and archived for over 1000 protein spots. Consequently, this mapping project remains a “work-in-progress”, as the map can be updated when the annotation of the S. gordonii genome is completed, making the proteomic map even more robust. Most importantly however, are the results from this study that support the hypothesis that S. gordonii does not adapt phenotypically to acid stress, whereas it is able to thrive at pH 7.5, thus partially explaining its role as a pathogen in infective endocarditis.

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225 APPENDIX I

APPENDIX Ι Media preparation

Preparation of BHI agar plates

Reagent Amount Final concentration Bacteriological agar 15 g 1.5% (w/v) BHI 37 g 3.7% (w/v) DH2O Make up the solution to 1 L

The above ingredients are mixed and the resulting solution autoclaved 15 lbs in-2 for 15 min. The melted medium is poured into sterile, plastic, petri dishes.

Preparation of DMM

The following medium is based on DMM, developed by Wong and Sissons (2001). DMM is an artificial-saliva formulation modified to facilitate large-scale biofilm culture. The final concentrations of the components of DMM are listed in a table below. It should be noted that this version of DMM was modified for use in this study by deleting the mucin, because of its intrinsic variability (Section 2.3), and including adenine, guanine, uracil and NaHCO3. Although adenine, guanine, uracil and NaHCO3 are essential medium components for the culture of S. mutans LT11, they were included in this medium to provide consistency to enable future comparisons of S. gordonii and S. mutans. The components of DMM are ions; a mixture of amino acids based on the concentration of free amino acids measured in saliva supernatant; vitamins and other growth factors; and “protein:peptide” equivalent amino acids to model the salivary proteins. Several vitamins are at higher concentrations than reported for saliva, to allow for some degradation during the experimental period of up to several weeks. The bacterial growth factors, p- aminobenzoic acid and inositol, are also included. As the inclusion of natural human or animal salivary proteins is impractical for large-scale plaque modeling, a salivary amino acid mixture is used instead; its composition is based on the known amino acid sequence for casein (Wong and Sissons, 2001). The inclusion of the salivary amino acids necessitated the “doubling-up” of some proteins in the following recipe. Glucose was the limiting nutrient.

226 APPENDIX I

Preparation of DMM

Components prior to autoclaving: A 1 L stock solution of amino acids was prepared by combining the following and heating until soluble; alanine, 1.74 g; arginine HCl, 2.74 g; aspartic acid, 2.02 g; glutamic acid, 7.96 g; glycine, 1.46 g; histidine, 1.68 g; isoleucine, 3.12 g; leucine, 4.83 g; lysine HCl, 5.53 g; methionine, 1.62 g; phenylalanine, 2.86 g; proline, 4.24 g; serine, 3.64 g; threonine, 1.29 g; tyrosine, 3.92 g; and valine, 2.79 g. The stock solution was stored at −20°C until required. When preparing the media the 1l stock amino acid solution was heated in 5 L deionised water (DH2O), in a 10 L media vessel, until solubilized. The following individual stock solutions were also prepared; adenine and guanine, 2 g L-1 (using HCl to effect solution); uracil, 20 g L-1 (using NaOH to effect solution); choline chloride, 13.96 gL-1; sodium citrate, 14.7 g L-1; inositol, 1.80 g L-1; and haemin, 6.52 g L-1. To the basal amino acid solution was added 100 mL of the adenine and guanine stock solution, and 10 mL each of uracil, choline chloride, sodium citrate, and inositol. To this solution the following solids were added; MgCl2

6H2O, 0.41 g; KH2PO4, 20.50 g; K2HPO4 3H2O, 34 g; NaCl, 5.84 g; NH4Cl, 1.07 g; and uric acid, 0.84 g. The volume was made up to 9 L and the pH adjusted to 6.8 by the addition of NaOH, just prior to the pH attaining 6.8, a stock solution of haemin,

10 mL, was added, and the volume made up to 9.95 L with DH2O (if the haemin is added earlier, prior to pH adjustment, it can precipitate). This solution was then autoclaved, 15 lbs in-2 for 30 min. Components after autoclaving: Salivary amino acids were prepared as a stock solution; alanine, 4.45 g L-1; arginine HCl, 10.53 g L-1; asparagine, 3.75 g L-1; aspartic acid, 3.33 g L-1; cysteine HCl, 7.88 g L-1; glutamic acid, 3.68 g L-1; glutamine, 3.65 g L-1; glycine, 7.10 g L-1; histidine, 1.55 g L-1; isoleucine, 3.28 g L-1; leucine, 3.28 g g L-1; lysine HCl, 9.13 g L-1; methionine, 1.49 g L-1; phenylalanine, 4.13 g L-1; proline, 11.51 g L-1; serine, 2.63 g L-1; taurine, 9.38 g L-1; threonine, 2.98 g L-1; tryptophan, 2.04 g L-1; tyrosine, 2.72 g L-1; and valine, 2.93 g L-1. A 1000-fold vitamin stock solution was also prepared; biotin, 24 mg L-1; creatinine HCl, 149 mg L-1; cyanocobalamin, 68 mg L-1; folic acid, 11 mg L-1; menadione, 1381 mg L-1; niacin, 616 mg L-1; p-aminobenzoic acid, 137 mg L-1; calcium pantothenate, 239 mg

227 APPENDIX I

L-1; pyridoxine HCl, 822 mg L-1; riboflavin, 113 mg L-1; and thiamine HCl, 337 mg L-1. To prepare the “components after autoclaving”, 0.88 g tryptophan and 10 mL salivary amino acid stock solution, were added to 20 mL DH2O and heated into solution. Once cooled the following components were added; ascorbic acid 8.81 mg; urea, 0.6 g; asparagine, 2.6 g; cysteine HCl, 0.079 g; glutamine, 4.43 g; NaHCO3, 4.2 g; glucose, 45 g; and 1000-fold vitamin stock solution, 10 mL. The volume of this solution was made up to 50 mL and filter sterilized. The filter sterilized “components after autoclaving” and a sterile stock -1 solution of 10 mL CaCl2 (147.0 g L ), were then added to the autoclaved media component. It was important that the media was stirred during this process, as the

CaCl2 precipitates.

Final concentrations of the components of modified DMM

Total components of Concentration (mM) DMM CaCl2.2H20 1 mM MgCl2.6H2O 0.2 mM

KH2PO4 15 mM K2HPO4 3H2O 15 mM NaCl 10 mM KCl 15 mM NaHCO3 5 mM NH4Cl 2 mM Urea 1 mM Amino acids See table below Salivary amino acids See table below Vitamins/growth See table below factors Glucose 25 mM

Amino acids Concentration (mM) Adenine 0.15 Alanine 1.95 Arginine HCl 1.30 Asparagine 1.97 Aspartic acid 1.52 Cysteine HCl 0.05 Glutamic acid 5.41 Glutamine 3.03 Glycine 1.94 Guanine 0.13 Histidine 1.08 Isoleucine 2.38 Leucine 3.68 Lysine HCl 3.03

228 APPENDIX I

Methionine 1.09 Phenylalanine 1.73 Proline 3.68 Serine 3.46 Threonine 1.08 Tryptophan 0.43 Tyrosine 2.16 Uracil 0.18 Valine 2.38

Salivary amino Concentration (µM) acids Alanine 50 Arginine HCl 50 Asparagine 25 Aspartic acid 25 Cysteine HCl 50 Glutamic acid 25 Glutamine 25 Glycine 100 Histidine 10 Isoleucine 25 Leucine 25 Lysine 50 Methionine 10 Phenylalanine 25 Proline 100 Serine 25 Taurine 75 Threonine 25 Tryptophan 10 Tyrosine 15 Valine 25

Vitamins/growth Concentration (µM) factors Choline chloride 100 Sodium citrate 50 Uric acid 50 Haemin 10 Inositol 10 Ascorbic acid 5 Menadione 5 Niacin 5 Pyridoxine HCl 4 Creatinine HCl 1 p-aminobenzoic 1 acid Calcium 1 pantothenate Thiamine 1 Riboflavin 0.3 Biotin 0.1 Cyanocobalamin 0.05 Folic acid 0.025

229 APPENDIX II

APPENDIX ΙΙ 2-DE methods

200 mM TBP solution

Reagent Amount Final concentration TBP 1 mL 200 mM Isopropanol (2-propanol) Make the solution up to 20 mL

Combine the reagents in a fume cabinet. Flush the concentrated TBP and the 200 mM stock solution with oxygen free nitrogen, and then store in the dark at 4°C. The 200 mM stock solution can be stored up to two weeks. N.B. Warning TBP reacts violently with organic matter.

5 × Tris/HCl buffer

Reagent Amount Final concentration Tris 227 g 1.875 M 18MΩ H2O Make the solution up to 1 L

Dissolve the tris in 800 mL of 18MΩ H2O and degas for 5 min by sonication. Adjust to pH 8.8, using concentrated HCl. Filter the solution.

Acrylamide stock solution

Reagent Amount Final concentration Acrylamide 780 g 39% (w/v) PDA 20 g 1% (w/v) 18MΩ H2O Make the solution up to 2 L

Combine the ingredients and then filter the solution.

Electrophoresis running buffer

Reagent Amount Final concentration Glycine 288 g 192 mM SDS 40 g 0.2%(w/v) Tris 60.5 g 25 mM DH2O Make the solution up to 20 L

Combine the reagents and stir until dissolved.

230 APPENDIX II

Agarose embedding solution

Reagent Amount Final concentration Agarose 0.25 g 0.5% (w/v) Bromophenol blue 0.5 mL of 0.1% solution 0.001% (v/v) Electrophoresis running buffer Make the solution up to 50 mL

Combine ingredients and autoclave 15 lbs in-2 for 15 min. Boil in microwave oven when required.

CBB G-250, Modified Neuhoff Stain

Reagent Amount Final concentration CBB G-250 1 g 0.1% (w/v) MeOH 340 mL 34% (v/v) (NH4)2SO4 170 g 17% (w/v) Phosphoric acid 36 mL of an 85% solution 3% (v/v) 18MΩ H2O Make the solution up to 1 L

Place 170 g (NH4)2SO4 into a beaker, add 340 mL of methanol, followed by 36 mL of phosphoric acid, then make up the volume with 18MΩ H2O. Mix completely with heating and sonication. Finally add 1 g CBB G-250, and stir until dissolved.

Dry solubilizing chemicals

Reagent Amount Final concentration CHAPS 20 mg 4% (w/v) sulfobetaine 3-10 20 mg 4% (w/v) Thiourea 150 mg 30%(w/v) Urea 300 mg 60% (w/v)

Add the regents to each 500 µL sample and sonicate for 1 min.

Equilibration solution

Reagent Amount Final concentration 25% Acrylamide solution 10 mL 2.5 % (v/v) 50% Glycerol 40 mL 20 % (v/v) SDS 2 g 2 % (w/v) 200 mM TBP 2.5 mL 5 mM 5 × Tris/HCl gel buffer (pH 8.8) 20 mL 1 × Urea 36 g 6 M

231 APPENDIX II

Combine all the reagents, except TBP, in a conical flask, sonicate until dissolved. Then add the TBP.

Gradient gels (12-18%T)

%T 5 × Tris/HCl acrylamide/bis- glycerol (50% 18MΩ H2O buffer acrylamide 40% solution) solution 12 40 mL 60 mL nil 100 mL 18 40 mL 90 mL 70 mL nil

The 12-18%T gradient gels are made using a gradient maker (Amersham Biosciences, Uppsala, Sweden) by mixing 2 separate 200 mL solutions together, a heavy (18 %T) solution and a lighter (12 %T) solution. After combining the ingredients, the solutions are degassed in a vacuum. The initiators TEMED (33 µL) and APS (330 µL of a 10% solution), that start the polymerisation process, are then added immediately prior to pouring into the gradient maker.

Solubilizing solution

Reagent Amount Final concentration Bromophenol blue 10 µL of a 0.1% stock 0.002% (v/v) Carrier ampholytes 3-10 (40%) 25 µL 0.2% (v/v) CHAPS 100 mg 2% (w/v) Caprylyl sulfobetaine 100 mg 2% (w/v TBP 50 µL of 200 mM TBP stock 2 mM Thiourea 760 mg 5 M Tris 24.2 mg 40 mM Urea 1.5 g 5 M 18MΩ H2O Make the solution up to 5 mL

Combine the reagents.

232 APPENDIX III

APPENDIX III ANOVA and SNK tests

2, 3, 4, 5 tetrahydropyridine-2 carboxylate N succinyltransferase

(pI 4.5 - 5.5 spot 54)

Replicate RE pH 5.5 pH 6.0 pH 6.5 pH 7.0 pH 7.5 Chemostat 1 2445 1915 2274 2773 4297 Chemostat 2 2375 1282 1632 3078 3319 Chemostat 3 3938 1363 3271 4757 5172

SUMMARY Group n Sum Mean pH 5.5 3 8758 2919.33 pH 6.0 3 4560 1520.00 pH 6.5 3 7177 2392.33 pH 7.0 3 10608 3536.00 pH 7.5 3 12788 4262.67

ANOVA Source of Variation SS df MS Fcalc p Fcrit Between Groups 13261374.93 4 3315343.73 4.63 0.0225 3.48 Within Groups 7161802.00 10 716180.20 Total 20423176.93 14

SNK test qcrit = 4.33 SE = 488.60 6000 Compare qcalc pH 7.5 pH 6.0 5.61 * 5000 pH 7.5 pH 6.5 3.83

) 4000

pH 7.5 pH 5.5 2.75 M P

P 3000

pH 7.5 pH 7.0 1.49 (

pH 7.0 pH 6.0 4.13 RE 2000 pH 7.0 pH 6.5 2.34 Chemostat 1 1000 Chemostat 2 pH 7.0 pH 5.5 1.26 Chemostat 3 pH 5.5 pH 6.0 2.86 0 pH 5.5 pH 6.5 1.08 pH 5.5 pH 6.0 pH 6.5 pH 7.0 pH 7.5 pH 7.0 pH 6.0 4.13 pH

* p < 0.05

233 APPENDIX III

Elongation factor P (pI 4.0 - 5.0 spot 94)

Replicate RE pH 5.5 pH 6.0 pH 6.5 pH 7.0 pH 7.5 Chemostat 1 4573 5761 5954 8239 7243 Chemostat 2 4640 4201 9688 11023 5902 Chemostat 3 6027 5746 7654 10783 9204

SUMMARY Group n Sum Mean pH 5.5 3 15240 5080.00 pH 6.0 3 15708 5236.00 pH 6.5 3 23296 7765.33 pH 7.0 3 30045 10015.00 pH 7.5 3 22349 7449.67

ANOVA Source of Variation SS df MS Fcalc p Fcrit Between Groups 49849899.07 4 12462474.77 6.16 0.0091 3.48 Within Groups 20220111.33 10 2022011.13 Total 70070010.40 14

SNK test qcrit = 4.33 SE = 820.98 12000 Compare qcalc pH 7.0 pH 5.5 6.01 * 10000 pH 7.0 pH 6.0 5.82 * ) 8000

pH 7.0 pH 7.5 3.12 M P

P 6000

pH 7.0 pH 6.5 2.74 (

pH 6.5 pH 5.5 3.27 RE 4000 Chemostat 1 pH 6.5 pH 6.0 3.08 2000 Chemostat 2 pH 6.5 pH 7.5 0.38 Chemostat 3 pH 7.5 pH 5.5 2.89 0 pH 7.5 pH 6.0 2.70 pH 5.5 pH 6.0 pH 6.5 pH 7.0 pH 7.5 pH pH 6.0 pH 5.5 0.19

* p < 0.05

234 APPENDIX III

Fructose-1-6 bisphosphate adolase (pI 4.5 - 5.5 spot 109)

Replicate RE pH 5.5 pH 6.0 pH 6.5 pH 7.0 pH 7.5 Chemostat 1 3553 2347 4629 3568 7329 Chemostat 2 4347 2864 5810 3371 7744 Chemostat 3 3798 3030 4015 6777 6619

SUMMARY Group n Sum Mean pH 5.5 3 11698 3899.33 pH 6.0 3 8241 2747.00 pH 6.5 3 14454 4818.00 pH 7.0 3 13716 4572.00 pH 7.5 3 21692 7230.67

ANOVA Source of Variation SS df MS Fcalc p Fcrit Between Groups 32637000.27 4 8159250.07 7.99 0.0037 3.48 Within Groups 10208751.33 10 1020875.13 Total 42845751.60 14

SNK test qcrit = 4.33 SE = 583.35 Compare q 9000 calc Chemostat 1 8000 pH 7.5 pH 6.0 7.69 * Chemos tat 2 7000 Chemostat 3 pH 7.5 pH 5.5 5.71 * ) 6000 pH 7.5 pH 7.0 4.56 * M P 5000 P

pH 7.5 pH 6.5 4.14 ( 4000

pH 6.5 pH 6.0 3.55 RE 3000 pH 6.5 pH 5.5 1.57 2000 pH 6.5 pH 7.0 0.42 1000 0 pH 7.0 pH 6.0 3.13 pH 5.5 pH 6.0 pH 6.5 pH 7.0 pH 7.5 pH 7.0 pH 5.5 1.15 pH pH 5.5 pH 6.0 1.98

* p < 0.05

235 APPENDIX III

Glucose 1 phosphate thymidylytransferase (pI 4.5 - 5.5 spot 120)

Replicate RE pH 5.5 pH 6.0 pH 6.5 pH 7.0 pH 7.5 Chemostat 1 577 991 1410 1390 2636 Chemostat 2 1412 970 1305 2465 3004 Chemostat 3 1740 1981 2196 1857 2402

SUMMARY Group n Sum Mean pH 5.5 3 3729 1243.00 pH 6.0 3 3942 1314.00 pH 6.5 3 4911 1637.00 pH 7.0 3 5712 1904.00 pH 7.5 3 8042 2680.67

ANOVA Source of Variation SS df MS Fcalc p Fcrit Between Groups 4048818.27 4 1012204.57 3.85 0.0380 3.48 Within Groups 2626234.67 10 262623.47 Total 6675052.93 14

SNK test qcrit = 4.33 SE = 295.87 3500 Compare qcalc Chem ostat 1 3000 pH 7.5 pH 5.5 4.86 * Chem ostat 2 Chemostat 3 pH 7.5 pH 6.0 4.62 * 2500 )

pH 7.5 pH 6.5 3.53 M

P 2000 P pH 7.5 pH 7.0 2.62 ( 1500

pH 7.0 pH 5.5 2.23 RE 1000 pH 7.0 pH 6.0 1.99 pH 7.0 pH 6.5 0.90 500 pH 6.5 pH 5.5 1.33 0 pH 6.5 pH 6.0 1.09 pH 5.5 pH 6.0 pH 6.5 pH 7.0 pH 7.5 pH pH 6.0 pH 5.5 0.24

* p < 0.05

236 APPENDIX III

GPDH (pI 5.5 - 6.7 spot 16)

Replicate RE pH 5.5 pH 6.0 pH 6.5 pH 7.0 pH 7.5 Chemostat 1 6476 16601 26545 16900 22149 Chemostat 2 9111 8892 25325 21730 26676 Chemostat 3 11724 14003 12850 16587 21470

SUMMARY Group n Sum Mean pH 5.5 3 27311 9103.67 pH 6.0 3 39496 13165.33 pH 6.5 3 64720 21573.33 pH 7.0 3 55217 18405.67 pH 7.5 3 70295 23431.67

ANOVA Source of Variation SS df MS Fcalc p Fcrit Between Groups 423665915.60 4 105916478.90 5.51 0.0131 3.48 Within Groups 192071559.33 10 19207155.93 Total 615737474.93 14

SNK test qcrit = 4.33 SE = 2530.29

Compare qcalc 30000 pH 7.5 pH 5.5 5.66 * 25000 pH 7.5 pH 6.0 4.06

pH 7.5 pH 7.0 1.99 ) 20000 M P

pH 7.5 pH 6.5 0.73 P 15000 ( pH 6.5 pH 5.5 4.93 * RE 10000 pH 6.5 pH 6.0 3.32 Ch emos tat 1 Chemos tat 2 pH 6.5 pH 7.0 1.25 5000 Chemos tat 3 pH 7.0 pH 5.5 3.68 0 pH 7.0 pH 6.0 2.07 pH 5.5 pH 6.0 pH 6.5 pH 7. 0 pH 7.5 pH 6.0 pH 5.5 1.61 pH

* p < 0.05

237 APPENDIX III

Heat inducible transcription repressor (pI 5.5 - 6.7 spot 143)

Replicate RE pH 5.5 pH 6.0 pH 6.5 pH 7.0 pH 7.5 Chemostat 1 1105 2498 3744 3976 5057 Chemostat 2 2157 2949 3284 4720 10584 Chemostat 3 2466 3209 2258 9033 14503

SUMMARY Group n Sum Mean pH 5.5 3 5728 1909.33 pH 6.0 3 8656 2885.33 pH 6.5 3 9286 3095.33 pH 7.0 3 17729 5909.67 pH 7.5 3 30144 10048.00

ANOVA Source of Variation SS df MS Fcalc p Fcrit Between Groups 131088041.07 4 32772010.27 5.25 0.0153 3.48 Within Groups 62388486.67 10 6238848.67 Total 193476527.73 14

SNK test qcrit = 4.33 SE = 1442.09

Compare qcalc 16000 Chemostat 1 pH 7.5 pH 5.5 5.64 * 14000 Chemostat 2 pH 7.5 pH 6.0 4.97 * 12000 Chemos tat 3

pH 7.5 pH 6.5 4.82 * ) 10000 M

pH 7.5 pH 7.0 2.87 P P 8000 ( pH 7.0 pH 5.5 2.77

RE 6000 pH 7.0 pH 6.0 2.10 4000 pH 7.0 pH 6.5 1.95 2000 pH 6.5 pH 5.5 0.82 0 pH 6.5 pH 6.0 0.15 pH 5.5 pH 6. 0 pH 6.5 pH 7.0 pH 7.5 pH 6.0 pH 5.5 0.68 pH

* p < 0.05

238 APPENDIX III

Maltose O-acetyltransferase (pI 5.5 - 6.7 spot 86)

Replicate RE pH 5.5 pH 6.0 pH 6.5 pH 7.0 pH 7.5 Chemostat 1 5786 4807 6351 8385 7739 Chemostat 2 8380 5993 6322 7922 15185 Chemostat 3 6215 6527 6892 12805 15759

SUMMARY Group n Sum Mean pH 5.5 3 20381 6793.67 pH 6.0 3 17327 5775.67 pH 6.5 3 19565 6521.67 pH 7.0 3 29112 9704.00 pH 7.5 3 38683 12894.33

ANOVA Source of Variation SS df MS Fcalc p Fcrit Between Groups 104627141.07 4 26156785.27 4.35 0.0271 3.48 Within Groups 60185308.67 10 6018530.87 Total 164812449.73 14

SNK test qcrit = 4.33 SE = 1416.40 Compare q 18000 calc Chemostat 1 16000 pH 7.5 pH 6.0 5.03 * Chem ostat 2 14000 pH 7.5 pH 6.5 4.50 * Chem ostat 3

) 12000

pH 7.5 pH 5.5 4.31 M

P 10000 pH 7.5 pH 7.0 2.25 P ( 8000 pH 7.0 pH 6.0 2.77 RE 6000 pH 7.0 pH 6.5 2.25 4000 pH 7.0 pH 5.5 2.05 2000 pH 5.5 pH 6.0 0.72 0 pH 5.5 pH 6.5 0.19 pH 5.5 pH 6. 0 pH 6.5 pH 7.0 pH 7.5 pH pH 6.5 pH 6.0 0.53

* p < 0.05

239 APPENDIX III

Manganese-dependent inorganic pyrophosphatase (pI 4.0 - 5.0 spot 197)

Replicate RE pH 5.5 pH 6.0 pH 6.5 pH 7.0 pH 7.5 Chemostat 1 2874 7259 9168 8866 6363 Chemostat 2 3816 7778 10460 9852 4997 Chemostat 3 4941 5513 5740 6913 5821

SUMMARY Group n Sum Mean pH 5.5 3 11631 3877.00 pH 6.0 3 20550 6850.00 pH 6.5 3 25368 8456.00 pH 7.0 3 25631 8543.67 pH 7.5 3 17181 5727.00

ANOVA Source of Variation SS df MS F P-value F crit Between Groups 46262314.27 4 11565578.57 5.19 0.0159 3.48 Within Groups 22278416.67 10 2227841.67 Total 68540730.93 14

SNK test qcrit = 4.33 SE = 861.75

Compare qcalc 12000 pH 7.0 pH 5.5 5.42 * 10000 pH 7.0 pH 7.5 3.27

) 8000

pH 7.0 pH 6.0 1.97 M P

pH 7.0 pH 6.5 0.10 P 6000 (

pH 6.5 pH 5.5 5.31 * RE 4000 pH 6.5 pH 7.5 3.17 C hemostat 1 pH 6.5 pH 6.0 1.86 2000 C hemostat 2 Chemostat 3 pH 6.0 pH 5.5 3.45 0 pH 6.0 pH 7.5 1.30 pH 5.5 pH 6.0 pH 6.5 pH 7.0 pH 7.5 pH 7.5 pH 5.5 2.15 pH

* p < 0.05

240 APPENDIX III

Phosphoprotein phosphatase (pI 4.0 - 5.0 spot 39)

Replicate RE pH 5.5 pH 6.0 pH 6.5 pH 7.0 pH 7.5 Chemostat 1 3516 4963 5286 6320 5103 Chemostat 2 3712 3845 6296 7135 3821 Chemostat 3 4472 3779 4399 6962 4409

Summary Group n Sum Mean pH 5.5 3 11700 3900.00 pH 6.0 3 12587 4195.67 pH 6.5 3 15981 5327.00 pH 7.0 3 20417 6805.67 pH 7.5 3 13333 4444.33

ANOVA Source of Variation SS df MS Fcalc p Fcrit Between Groups 16534947.73 4 4133736.93 9.42 0.0020 3.48 Within Groups 4389596.00 10 438959.60 Total 20924543.73 14

SNK test qcrit = 4.33 SE = 382.52 8000 Compare qcalc pH 7.0 pH 5.5 7.60 * 7000 pH 7.0 pH 6.0 6.82 * 6000 )

pH 7.0 pH 7.5 6.17 * M 5000 P

pH 7.0 pH 6.5 3.87 P 4000 ( 3000

pH 6.5 pH 5.5 3.73 RE Chemostat 1 pH 6.5 pH 6.0 2.96 2000 Chemostat 2 pH 6.5 pH 7.5 2.31 1000 Chemostat 3 pH 7.5 pH 5.5 1.42 0 pH 7.5 pH 6.0 0.65 pH 5.5 pH 6.0 pH 6.5 pH 7.0 pH 7.5 pH 6.0 pH 5.5 0.77 pH

* p < 0.05

241 APPENDIX III

Ribosome recycling factor (pI 5.5 - 6.7 spot 57)

Replicate RE pH 5.5 pH 6.0 pH 6.5 pH 7.0 pH 7.5 Chemostat 1 19171 11798 12172 20429 20234 Chemostat 2 27366 15037 14463 23564 30800 Chemostat 3 17625 20096 18477 31728 36961

SUMMARY Group n Sum Mean pH 5.5 3 64162 21387.33 pH 6.0 3 46931 15643.67 pH 6.5 3 45112 15037.33 pH 7.0 3 75721 25240.33 pH 7.5 3 87995 29331.67

ANOVA Source of Variation SS df MS Fcalc p Fcrit Between Groups 453758055.60 4 113439513.90 3.53 0.0481 3.48 Within Groups 321342619.33 10 32134261.93 Total 775100674.93 14

SNK test qcrit = 4.33 SE = 3272.83

Compare qcalc 40000 pH 7.5 pH 6.5 4.37 * 35000 pH 7.5 pH 6.0 4.18 30000

pH 7.5 pH 5.5 2.43 )

M 25000

pH 7.5 pH 7.0 1.25 P P 20000 pH 7.0 pH 6.5 3.12 ( 15000 pH 7.0 pH 6.0 2.93 RE 10000 Chemostat 1 pH 7.0 pH 5.5 1.18 5000 Chemostat 2 pH 5.5 pH 6.5 1.94 C hemostat 3 0 pH 5.5 pH 6.0 1.75 pH 5.5 pH 6.0 pH 6.5 pH 7.0 pH 7.5 pH 6.0 pH 6.5 0.19 pH

* p < 0.05

242 APPENDIX III

Unidentified protein (pI 4.5 - 5.5 spot 13)

Replicate RE pH 5.5 pH 6.0 pH 6.5 pH 7.0 pH 7.5 Chemostat 1 2677 4192 5841 4447 6912 Chemostat 2 2576 3175 4134 5372 6164 Chemostat 3 4140 3993 4235 5474 6408

SUMMARY Group n Sum Mean pH 5.5 3 9393 3131.00 pH 6.0 3 11360 3786.67 pH 6.5 3 14210 4736.67 pH 7.0 3 15293 5097.67 pH 7.5 3 19484 6494.67

ANOVA Source of Variation SS df MS Fcalc p Fcrit Between Groups 19990244.67 4 4997561.17 10.24 0.0015 3.48 Within Groups 4878926.67 10 487892.67 Total 24869171.33 14

SNK test qcrit = 4.33 SE = 403.28 8000 Compare qcalc 7000 pH 7.5 pH 5.5 8.34 * 6000 pH 7.5 pH 6.0 6.72 * ) 5000 pH 7.5 pH 6.5 4.36 * M P

P 4000

pH 7.5 pH 7.0 3.46 (

pH 7.0 pH 5.5 4.88 * RE 3000 Chemostat 1 pH 7.0 pH 6.0 3.25 2000 Chemostat 2 pH 7.0 pH 6.5 0.90 1000 Chemostat 3 pH 6.5 pH 5.5 3.98 0 pH 6.5 pH 6.0 2.36 pH 5.5 pH 6.0 pH 6.5 pH 7.0 pH 7.5 pH 6.0 pH 5.5 1.63 pH

* p < 0.05

243 APPENDIX III

Unidentified protein (pI 4.5 - 5.5 spot 17)

Replicate RE pH 5.5 pH 6.0 pH 6.5 pH 7.0 pH 7.5 Chemostat 1 5779 5216 7532 6806 9648 Chemostat 2 5155 4017 5314 6831 8495 Chemostat 3 8737 6041 7933 9468 9385

SUMMARY Group n Sum Mean pH 5.5 3 19671 6557.00 pH 6.0 3 15274 5091.33 pH 6.5 3 20779 6926.33 pH 7.0 3 23105 7701.67 pH 7.5 3 27528 9176.00

ANOVA Source of Variation SS df MS Fcalc p Fcrit Between Groups 27093225.73 4 6773306.43 3.61 0.0455 3.48 Within Groups 18785156.00 10 1878515.60 Total 45878381.73 14

SNK test qcrit = 4.33 SE = 791.31

Compare qcalc 12000 pH 7.5 pH 6.0 5.16 * 10000 pH 7.5 pH 5.5 3.31

) 8000

pH 7.5 pH 6.5 2.84 M P

pH 7.5 pH 7.0 1.86 P 6000 ( pH 7.0 pH 6.0 3.30 RE 4000 pH 7.0 pH 5.5 1.45 Chemostat 1 pH 7.0 pH 6.5 0.98 2000 Chemostat 2 Chemostat 3 pH 6.5 pH 6.0 0.47 0 pH 6.5 pH 5.5 0.47 pH 5.5 pH 6. 0 pH 6.5 pH 7.0 pH 7.5 pH 5.5 pH 6.0 1.85 pH

* p < 0.05

244 APPENDIX III

Unidentified protein (pI 4.5 - 5.5 spot 26)

Replicate RE pH 5.5 pH 6.0 pH 6.5 pH 7.0 pH 7.5 Chemostat 1 1736 3630 8795 5131 7247 Chemostat 2 1516 3011 3530 6683 5576 Chemostat 3 4271 3819 6010 6281 7218

SUMMARY Group n Sum Mean pH 5.5 3 7523 2507.67 pH 6.0 3 10460 3486.67 pH 6.5 3 18335 6111.67 pH 7.0 3 18095 6031.67 pH 7.5 3 20041 6680.33

ANOVA Source of Variation SS df MS Fcalc p Fcrit Between Groups 40856812.27 4 10214203.07 4.63 0.0225 3.48 Within Groups 22048433.33 10 2204843.33 Total 62905245.60 14

SNK test qcrit = 4.33 SE = 857.29 10000 Compare qcalc 9000 pH 7.5 pH 5.5 4.87 * 8000 pH 7.5 pH 6.0 3.73 7000 )

pH 7.5 pH 7.0 0.76 M 6000 P

pH 7.5 pH 6.5 0.66 P 5000 ( pH 6.5 pH 5.5 4.20 4000 RE 3000 pH 6.5 pH 6.0 3.06 Chemostat 1 2000 pH 6.5 pH 7.0 0.09 Chemostat 2 1000 Chemostat 3 pH 7.0 pH 5.5 4.11 0 pH 7.0 pH 6.0 2.97 pH 5.5 pH 6.0 pH 6.5 pH 7.0 pH 7.5 pH 6.0 pH 5.5 1.14 pH

* p < 0.05

245 APPENDIX III

Unidentified protein (pI 4.5 - 5.5 spot 58)

Replicate RE pH 5.5 pH 6.0 pH 6.5 pH 7.0 pH 7.5 Chemostat 1 1677 2358 3117 2739 3962 Chemostat 2 1883 2076 3776 4037 5034 Chemostat 3 2820 1967 2977 4373 3468

SUMMARY Group n Sum Mean pH 5.5 3 6380 2126.67 pH 6.0 3 6401 2133.67 pH 6.5 3 9870 3290.00 pH 7.0 3 11149 3716.33 pH 7.5 3 12464 4154.67

ANOVA Source of Variation SS df MS Fcalc p Fcrit Between Groups 10224686.27 4 2556171.57 6.46 0.0078 3.48 Within Groups 3958884.67 10 395888.47 Total 14183570.93 14

SNK test qcrit = 4.33 SE = 363.27

Compare qcalc 6000 pH 7.5 pH 5.5 5.58 * 5000 pH 7.5 pH 6.0 5.56 * pH 7.5 pH 6.5 2.38 ) 4000 M P

pH 7.5 pH 7.0 1.21 P 3000 ( pH 7.0 pH 5.5 4.38 * RE 2000 Chemostat 1 pH 7.0 pH 6.0 4.36 * 1000 Chemostat 2 pH 7.0 pH 6.5 1.17 Chemostat 3 pH 6.5 pH 5.5 3.20 0 pH 6.5 pH 6.0 3.18 pH 5.5 pH 6.0 pH 6.5 pH 7.0 pH 7.5 pH 6.0 pH 5.5 3.20 pH

* p < 0.05

246 APPENDIX III

Unidentified protein (pI 5.5 - 6.7 spot 6)

Replicate RE pH 5.5 pH 6.0 pH 6.5 pH 7.0 pH 7.5 Chemostat 1 4553 2897 3540 7811 10325 Chemostat 2 4825 2922 3170 5856 13926 Chemostat 3 2346 10131 8559 15375 17342

SUMMARY Group n Sum Mean pH 5.5 3 11724 3908.00 pH 6.0 3 15950 5316.67 pH 6.5 3 15269 5089.67 pH 7.0 3 29042 9680.67 pH 7.5 3 41593 13864.33

ANOVA Source of Variation SS df MS Fcalc p Fcrit Between Groups 206140011.07 4 51535002.77 3.91 0.0365 3.48 Within Groups 131760648.67 10 13176064.87 Total 337900659.73 14

SNK test qcrit = 4.33 SE = 2095.72 20000 Compare qcalc Chemos tat 1 pH 7.5 pH 5.5 4.75 * Chemos tat 2 Chemostat 3 pH 7.5 pH 6.5 4.19 15000 )

pH 7.5 pH 6.0 4.08 M P

pH 7.5 pH 7.0 2.00 P 10000 (

pH 7.0 pH 5.5 2.75 RE pH 7.0 pH 6.5 2.19 5000 pH 7.0 pH 6.0 2.08 pH 6.0 pH 5.5 0.67 0 pH 6.0 pH 6.5 0.11 pH 5.5 pH 6. 0 pH 6.5 pH 7.0 pH 7.5 pH 6.5 pH 5.5 0.56 pH

* p < 0.05

247 APPENDIX III

Unidentified protein (pI 5.5 - 6.7 spot 27)

Replicate RE pH 5.5 pH 6.0 pH 6.5 pH 7.0 pH 7.5 Chemostat 1 6047 5012 9831 13645 14478 Chemostat 2 11916 6559 11234 8410 21204 Chemostat 3 9920 10675 10721 17800 20058

SUMMARY Group n Sum Mean pH 5.5 3 27883 9294.33 pH 6.0 3 22246 7415.33 pH 6.5 3 31786 10595.33 pH 7.0 3 39855 13285.00 pH 7.5 3 55740 18580.00

ANOVA Source of Variation SS df MS Fcalc p Fcrit Between Groups 225368202.00 4 56342050.50 5.31 0.0148 3.48 Within Groups 106129120.00 10 10612912.00 Total 331497322.00 14

SNK test qcrit = 4.33 SE = 1880.86 25000 Compare qcalc Chemos tat 1 pH 7.5 pH 6.0 5.94 * 20000 Chemos tat 2 pH 7.5 pH 5.5 4.94 * Chemos tat 3 )

M 15000

pH 7.5 pH 6.5 4.25 P P

pH 7.5 pH 7.0 2.82 ( 10000 pH 7.0 pH 6.0 3.12 RE pH 7.0 pH 5.5 2.12 5000 pH 7.0 pH 6.5 1.43 0 pH 6.5 pH 6.0 1.69 pH 5.5 pH 6.0 pH 6.5 pH 7.0 pH 7.5 pH 6.5 pH 5.5 0.69 pH pH 5.5 pH 6.0 1.00

* p < 0.05

248 APPENDIX VI

APPENDIX IV Mass spectrometry methods

Matrix solution

Reagent Amount Final concentration α-cyano-4-hydroxy cinnamic 20 mg 1 % (w/v) acid 80% CH3CN 2 mL 80% (v/v) TFA 2 µL 0.1% (v/v)

Combine the reagents.

Trypsin solution

Reagent Amount Final concentration 50 mM Acetic acid 20 µL 50 mM Trypsin 20 µg 0.1% (w/v)

Combine the reagents.

Trypsin buffer solution

Reagent Amount Final concentration Trypsin solution (0.1% trypsin 2 µL 0.0013% (v/v) solution) 50 mM NH4HCO3 (pH 8.0) 150 µL 50 mM

Combine the reagents.

249 APPENDIX VI

APPENDIX V Protease Inhibitor Cocktail

Product description Protease Inhibitor Cocktail (25 mL) is a mixture of protease inhibitors with a broad specificity for the inhibition of serine, cysteine, aspartic acid and metallo-proteases, and aminopeptidases.

The product consists of one vial of a white lyophilized powder and one vial of dimethyl sulfoxide. To reconstitute the product to 25 mL add 5 mL dimethyl sulfoxide to the lyophilized powder and vortexed for 1 min, then add 20 mL DH2O. The resulting solution should be clear.

Concentration of inhibitors in the reconstituted product

Inhibitor Concentration 4-(2-aminoethyl)benzenesulfonyl fluoride 23 mM Bestatin 2 mM Pepstatin A 0.3 mM Sodium EDTA 100 mM Trans-epoxysuccinyl-L-leucylamido (4-guanidino)butane 0.3 mM

250