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Investigating the Clostridium botulinum neurotoxin production process using a genome-scale metabolic network enhanced surrogate system Daniel Christopher Griffin Thesis submitted August 2015 to the University of Surrey for the degree of Doctor of Philosophy University of Surrey, Microbial Sciences, Guildford, Surrey, United Kingdom © Daniel Griffin, 2015 “Strength without determination means nothing, and determination without strength is equally useless” - Godo Kisaragi Acknowledgements First and foremost, I would like to thank my principal supervisor Professor Michael Bushell for his guidance, mentoring, expertise and friendship throughout the entirety of this research project and I wish him a long and happy retirement. I am very grateful to Dr Noel Wardell, Dr Jane Newcombe, Dr Douglas Hodgson, Dr Claudio Avignone-Rossa and Jonathan Ridgeon for their ever willingness to offer technical advice, support, training and valuable scientific opinion. My appreciations to Sonal Dahale for constructing the genome-scale metabolic networks utilised in my research. Thank you to the project supervisors at Ipsen, Dr Martin Mewies and Dr Mike Burns, for your professional input and support. I am also grateful to Ipsen for funding this PhD research project. Lastly, a massive thank you to my beautiful partner Jessica, for her encouragement and support throughout. i Abstract Clostridium botulinum (C. botulinum) produces a neurotoxin which can be used in a clinical environment to treat diseases and disorders characterised by muscle hypertension or spasm. However, previous research has mostly focused on the biochemical mode of action of the toxin and the disease it manifests. In order to increase our understanding of the process further, this study aimed to investigate the metabolism of various biomarkers, thought to be correlated with neurotoxin biosynthesis. The objective was to increase our understanding of the metabolism which drives the production of C. botulinum toxin using a Genome-Scale Metabolic Network (GSMN) enhanced surrogate system. A linear correlation was established between the accumulation of intracellular Poly-β-hydroxybutyrate (PHB) and neurotoxin in silico (R2 = 0.988). This correlation was confirmed by chemostat experiments in C. sporogenes demonstrating that increased supply of gaseous carbon dioxide (CO2) to the culture results in increased accumulation of PHB and in silico neurotoxin in C. botulinum. Experiments revealed the correlation is a result of modulation of carbon flux partitioning between glycolysis and the TCA cycle, ultimately increasing the availability of carbon for storage as PHB. Phosphate limitation and supplementation with Homoserine and other oxaloacetate derived amino acids, gave rise to increased PHB, owing to reduced activity and/or demand of the TCA cycle increasing the availability of acetyl CoA, the energy storage polymer’s precursor. Altering the growth medium to decrease TCA activity also resulted in decreased flagellin biosynthesis. The results of this study can be used to design a C. botulinum production process based on experimentally proven correlations and pathway analysis to yield a process which promotes neurotoxin biosynthesis over competing pathways, such as flagellin biosynthesis. ii Abbreviations BDM – Basal defined medium CMM – Cooked meat medium EMP - Embden-Meyerhoff-Parnas pathway FBA – Flux balance analysis FVA – Flux variance analysis G6PD – Glucose-6-Phospate Dehydrogenase GMP – Good Manufacturing Practice GSMN – Genome-Scale Metabolic Network HPLC - High-Performance Liquid Chromatography MSC – Microbial Safety Cabinet NCBI - National Centre for Biotechnology Information PBD – Plackett-Burman Design PBS – Phosphate Buffer Solution PEPc – Phosphoenolpyruvate Carboxylase PHB - Poly-β-hydroxybutyrate SNAP - Synaptosomal-associated protein TCA – tricarboxylic acid cycle iii Table of Contents Prologue Acknowledgments i Abstract ii Abbreviations iii Table of Contents iv Chapter 1: Introduction 1.1 The significance of Clostridium 1 1.1.1 Clinical significance 1 1.2 Clostridium botulinum and disease 4 1.3 Botulinum Toxin: Structure & Mechanism of Action 6 1.4 Botulinum Toxin: Commercial Production 10 1.5 Methodologies in Process Development & Optimisation 12 1.5.1 Availability of Metabolites in the Bacterial Growth Medium 12 1.5.2 Statistical Methods for Process Optimisation: Plackett-Burman Experimental Design 15 1.5.3 Strain Selection & Bioengineering 17 1.5.4 Exploiting Biomarkers of Product Biosynthesis 18 1.5.4.1 Sporulation 18 1.5.4.2 Poly-β-hydroxybutyrate Metabolism 19 1.5.4.3 Flagellin Biosynthesis 21 1.5.5 Surrogate Research Approach: Clostridium sporogenes 23 1.6 Research Tools: Genome-Scale Metabolic Networks 25 1.6.1 Flux Balance Analysis 27 1.6.2 Flux Variability Analysis 28 1.7 Research Tools: Using Chemostat Culture in Physiological Investigations 29 iv 1.8 Impact, Aims & Objectives 31 Chapter 2: Methods & Materials 2.1 Clostridium sporogenes strains and working stock preparation 33 2.2 Culture Medium 35 2.3 Determination of Culture Growth by Optical Density 37 2.4 Determination of Biomass by Dry Cell Weight Measurement 37 2.5 Determination of Intracellular PHB Accumulation 38 2.6 Determination of Supernatant Flagellin 40 2.6.1 Bis/Polyacrylamide Gels & SDS-PAGE Reagents 42 2.7 Determination of Sporulation 43 2.8 Plackett-Burman Design 44 2.10 Determination of Nutrient Concentration 46 2.10.1 Glucose Assay 46 2.10.2 Ammonium Assay 46 2.10.3 Phosphate Assay 47 2.11 Determination of Enzyme Activity 48 2.11.1 Glucose-6-Phosphate Dehydrogenase Activity Assay 48 2.11.2 Citrate Synthase Activity Assay 49 2.11.3 Phosphoenolpyruvate Carboxylase Activity Assay 50 2.12 Chemostat Culture 52 2.13 Determination of Protein Concentration 54 2.14 RNA Assay 54 2.15 Amino Acid Determination 56 2.16 Genome-Scale Metabolic Network Analysis 57 2.16.1 Construction of C. sporogenes & C. botulinum GSMN 57 2.16.2 Flux Balance Analysis 57 2.16.3 Flux Variability Analysis 58 v Chapter 3: Validation of the surrogate system – Investigation of metabolism and biomarkers of neurotoxin biosynthesis in Clostridium sporogenes. 3.1 Strain Selection 59 3.2 Bioinformatic Comparison of C. sporogenes & C. botulinum 65 3.3 The Effect of Carbon, Nitrogen and Phosphate limitation on Growth & Biomarker Metabolism in Cultures of C. Sporogenes 67 3.3.1 The Effect of Phosphate Concentration on the Growth of C. sporogenes 69 3.3.2 The Effect of Nitrogen Concentration on the Growth of C. sporogenes 73 3.3.3 The Effect of Carbon Concentration on Growth of C. sporogenes 76 3.3.4 The Effects of Nutrient Limitation on Sporulation, PHB and Flagellin Production in Cultures of C. sporogenes 80 3.3.4.1 The Effect of Phosphate Concentration on Biomarker Production in Cultures of C. sporogenes 81 3.3.4.2 The Effect of Nitrogen Concentration on Biomarker Production in Cultures of C. sporogenes 85 3.3.4.3 The Effect of Carbon Concentration on Biomarker Production in Cultures of C. sporogenes 89 3.3.5 Summary of the Effects of Nutrient Limitation on Sporulation, PHB and Flagellin Production in Cultures of C. sporogenes 94 3.4 Plackett-Burman Design Experimental Approach to Test the Effects of Amino Acid Metabolism on Flagellin Production, PHB Accumulation and Sporulation in Cultures of C. sporogenes 97 3.5 In silico Analysis Investigating the Effects of Nitrogen, Glucose and Phosphate Concentration on Neurotoxin Production by C. botulinum 109 3.6 In silico Analysis of the Correlation between PHB accumulation and Neurotoxin Production by C. botulinum 114 3.7 Chapter Conclusions 120 vi Chapter 4: Assimilating Computational and Experimental Research Tools to Investigate the Correlation between Poly-β-hydroxybutyrate and Botulinum Neurotoxin 4.1 Extrapolating PBD results using Flux Variability Analysis 121 4.2 Increasing PHB yields using a targeted amino acid supplementation approach 127 4.2.1 The relationship between TCA cycle - derived amino acids and PHB accumulation 135 4.3 Investigating the Relationship between PHB Accumulation and Pathways of Central Metabolism using Enzymatic Assay 138 4.4 Plackett-Burman Design Experimental Approach to Test the Effects of Amino Acid Metabolism on Enzymatic Activity in relationship to PHB Accumulation in Cultures of C. sporogenes 147 4.5 Investigating Anaplerotic Reactions as a Process Development Target using Genome Scale Metabolic Modelling 155 4.6 Chapter Conclusions 159 Chapter 5: Investigating the Correlation between Carbon Dioxide Uptake, PHB Accumulation and Neurotoxin Biosynthesis Using Chemostat culture 5.1 The effect of Growth Rate & Increased Carbon Dioxide Concentration on PHB Accumulation in Continuous Cultures of C. sporogenes 162 5.2 Validation of Bicarbonate as an Alternative Supplementation Approach to Altering Carbon Dioxide Concentration 171 5.3 The Effect of Growth Rate & Increased Carbon Dioxide Concentration on Flagellin Biosynthesis in Continuous Cultures of C. sporogenes 173 5.4 The Effect of Growth Rate & Increased Carbon Dioxide Concentration on Sporulation in Continuous Cultures of C. sporogenes 181 vii 5.5 The Effect of Increased Carbon Dioxide Concentration on Nutrient Metabolism in Continuous Cultures of C. sporogenes 185 5.6 Validation of Genome-Scale Metabolic Network using Flux Data obtained by Chemostat Culture 191 5.7 Chapter Conclusions 193 Chapter 6: Conclusions 6.1 Project Conclusions & Achievements 195 6.2 Research Impact 198 6.3 Process Recommendations for the Botulinum Neurotoxin Production Process 198 6.4 Recommendations for Future Studies 200 Appendix 202 Bibliography 203 viii Chapter 1: Introduction 1.1 The Significance of Clostridium Clostridium is a genus of bacteria whose products support a diverse range of industries including medicine, biofuels, synthetic chemicals and cosmetics (Arnon, 2002; Hallett, 1999). Clostridial products have attained significance in disease and even biological warfare; driving scientific research of the bacteria and their related biosynthetic products. The complex metabolism, toxins and products synthesised by the genus, combined with our ability to utilise and wield Clostridia as an industrial tool has yielded a genus of bacteria which is both harmful and beneficial to modern man. 1.1.1 Clinical Significance The clinical significance of Clostridia is far greater than is generally recognised. Whilst Clostridium botulinum (C. botulinum) and Clostridium tetani (C. tetani) are widely known, due to the lethality of their respective neurotoxins, clostridia related disease is both more diverse among the species and clinically widespread. Clostridium perfringens (C. perfringens) is a ubiquitous member of the species with natural habitats including marine sediment, soil, vegetation and the commensal population of humans and other vertebrates (Hendrix et al, 2011). Despite only ~5% of C. perfringens strains being considered pathogenic to humans (Decker & Hall, 1966), the species is one of the most significant causes of foodborne illness in the developed world with an estimated one million cases every year in the United States and is the third most common causative agent of foodborne illness in the United Kingdom (Grass et al, 2013; Scallan et al, 2011). 1 In common with other disease-causing Clostridium species, C. perfringens produces several toxins (over 20 exotoxins in certain strains) which are responsible for illness in humans. The most clinically significant of C. perfringens toxins, α-toxin, is the most common cause of gas gangrene; a life-threatening disease characterised by fever, edema, myonecrosis, pain and gas production (Sakurai et al, 2004). C. perfringens thrives in the anaerobic environment of necropsied tissue (Hendrix et al, 2011). The α-toxin protein biosynthesised during fermentation consists of two domains; a binding domain (C-domain) and action domain (Ndomain) which requires zinc for activation, similar to Botulinum neurotoxin (Lacy et al, 1998; Sakurai et al 2004). The toxin’s action on cells results in the cleavage of Phosphatidylcholine (major component of membrane phospholipids), disrupting the plasma membrane which ultimately results in cellular death and thus the tissue degradation which characterises infection (Ochi et al, 2002). C. perfringens, among other members of the clostridia species, is also a major cause of septic abortion (Rello et al, 2007). Clostridium difficile (C. difficile) is another member of the genus which has attained clinical importance. Despite being a very common gut commensal in humans, the opportunistic pathogen is the most common cause of antibiotic associated diarrhoea (Noren, 2010). In common with many Clostridia, C. difficile is abundant in soils and water and asymptomatic colonisation is frequent. Following antibiotic treatment and disruption of the normal gut microflora, C. difficile can proliferate considerably owing to diminished microbial competition (Novogrudsky & Plaut, 2003). Two primary toxins (Type A & B), produced at significant quantities to cause disease by the increased C. difficile population, target the Ras superfamily of small GTPases for modification via glycosylation, inducing irreversible 2 modification of the colon cells which results in the symptoms of disease (Voth & Ballard, 2005). The family of GTPases is also the target of bacterial toxins produced by Clostridium sordelli and Clostridium novyi; categorised with C. difficile type A & B toxins amongst the largest toxins discovered to date (~300kDa) (Voth & Ballard, 2005). C. difficile toxins have also been correlated with pseudomembranous colitis and paralytic ileus disease (George et al, 1982). C. tetani is the causative agent of tetanus; a disease which has been documented throughout history with records dating back to early Greek physicians over 2000 years ago (Humeau et al, 2000). The characteristics of the disease are very recognisable; severe and debilitating spastic paralysis as a result of motor neuron disinhibition (Curtis & Groat, 1968). Despite the disease manifesting opposite symptoms to the flaccid paralysis induced by C. botulinum, both conditions are the result of the remarkably similar (in terms of structure and function) neurotoxins produced by the respective organisms (Humeau et al, 2000). Tetanospasmin is produced and enters the body via a wound which is colonised by C. tetani. The neurotoxin is produced as a single 150kDa protein which is proteolytically cleaved into a light chain (50kDa) and a heavy chain (100kDa) linked by both a disulphide bridge and noncovalent interactions. During intoxication process, the interchain bridge is reduced; a necessary prerequisite for the intracellular action of the toxin (Hilger et al, 1989; Humeau et al, 2000). Tetanospasmin targets synaptobrevin, a protein also targeted by Botulinum toxin produced by serotypes B, D, F & G (Turton et al, 2002; Wictome & Shone, 1998). However, whilst botulinum toxin affects the neuromuscular junction resulting in inhibition of neurotransmitter release, tetanospasmin acts on the spinal inhibitory interneurons 3 (Montecucco & Schiavo, 1994). This blocking of central nervous system neurons interferes with the release of inhibitory neurotransmitter release and therefore results in the spastic paralysis characteristic of the disease (Bhum et al, 2012). In more recent years, the heavy chain of tetanospasmin has become an interesting medicinal target in the treatment of psychological disorders, such as Alzheimer’s and Parkinson’s disease, owing to its ability to penetrate the central nervous system (Toivonan et al, 2010). As the protein is proteolytically cleaved from the light chain which is responsible for the neurotoxic affects resulting in disease, safe neuronal targeting using the heavy chain of the tetanus toxin shows promise (Toivonan et al, 2010). However, it is Botulinum toxin which has become established as a clostridial medicine of substantial clinical importance (Hallett, 1999; Sifton 2003). 1.2 Clostridium botulinum and Disease Clostridium botulinum (C. botulinum) is a Gram positive, spore-forming, anaerobic bacterium which has attained its significance in the field of microbiology due to its ability to produce Botulinum neurotoxin; the causative agent of botulism and the most potent neurotoxin discovered to date. C. botulinum has been subject to medical research owing to the severity of botulism disease, but it is the mode of action of botulinum toxin which has also led to interest and research in utilising the neurotoxin for clinical medicine, cosmesis and even biological warfare (Arnon, 2002; Hallett, 1999). 4 Records of botulism disease date back to the late 1700s and details of the bacterial agent and toxicological mechanism of action responsible for botulism disease were published by Emile van Ermengem at the end of the 19th century (van Ermengem, 1897) after thorough investigation of an outbreak of botulism in Ellezelles, Belgium. Today, the disease is categorised into 5 clinical forms; foodborne botulism, wound botulism, infant botulism, hidden botulism and inadvertent botulism (Smith, 1979), all of which are serious lifethreatening diseases. The ability of C. botulinum to produce resilient spores is a major factor contributing to the organism’s virulence. The spores are widespread throughout the world, inhabiting soil as well as both fresh and salt water. The spores are capable of surviving for up to 2 hours at temperatures of 100oC. However, the neurotoxin responsible for disease is heat labile and is deactivated by holding for 5 minutes at 85 oC or higher (Dunbar et al, 1990). C. botulinum is phylogenetically categorised based on the distinct antigens of the neurotoxins it produces. Seven original serotypes; A, B, C-α, D, E, F, G have been described, as well as a more recent 8th serotype, C-β. However, this serotype produces C2-toxin which is neither a neurotoxin nor a causative agent of botulism (Hatheway, 1998). As well as being categorised on the distinct neurotoxin antigens, the serotypes of C. botulinum can be grouped by genetic differences. Toxin types A, B and F are coded for by genes located on clostridial chromosome material. The genes responsible for production of neurotoxins C, D and E are carried by bacteriophages and the gene which codes for the production of type G toxin is present on a plasmid (Hatheway, 1998). The majority (98%) of human botulism cases are caused by serotypes A, B and E (Dolman & Lida, 1963; Popoff, 1995). Types C, D and F are predominantly associated with botulism in animals (especially birds), although rare 5 cases have been described in humans (Green et al, 1983; Moller & Scheibel, 1960; Prevot et al, 1955). It is still unclear whether type G toxin is able to cause disease in humans due to a lack of evidence, although it has been reported previously in a case which caused the sudden death of 5 humans (Sonnabend et al, 1981). In addition to the focus on the significant pathology of botulinum toxin, modern medicine is increasingly making positive use of botulinum toxin for treatment of disorders characterised by muscle hyperactivity and/or spasm, including the treatment of blepharospasm, strabismus, cervical dystonia, glabellar lines, spastic dysphonia, limb spasticity associated with underlying neurological problems, tremors, hyperhidrosis and many others currently under development (Hallett, 1999; Sifton 2003). It is the neurotoxic mechanism of botulinum toxin which enables it to be utilised for such clinical applications. 1.3 Botulinum Toxin: Structure & Mechanism of Action Regardless of serotype, the structure of botulinum toxin is a di-chain peptide molecule with a molecular mass of approximately 150kDa (Crane et al, 1999). The neurotoxin is produced and secreted (Wang et al, 2010) as a single inactive polypeptide which is proteolytically cleaved by a clostridial trypsin protease forming the di-chain peptide consisting of a heavy chain (100kDa) and a light chain (50kDa) linked by a disulphide bond (Figure 1). The heavy chain is responsible for binding target neural tissue and the light chain is directly responsible for the neurotoxic effects of the toxin (Lacy et al, 1998; Wictome & Shone, 1998). Botulinum toxin is a metalloproteinase, a class of proteases for which the catalytic mechanism of the enzyme requires a metal ion. Remarkably similar in terms of structure and function to tetanospasmin, the toxin produced by C. tetani, botulinum toxin enters nerve cells and 6 blocks neurotransmitter release by zinc-dependent cleavage of proteins required by the neuroexocytosis apparatus (Lacy et al, 1998; Wictome & Shone, 1998). Figure 1: The structure of Botulinum neurotoxin. Botulinum neurotoxin is composed of a heavy chain (Hc) attached to a light chain (Lc) by a non-covalent disulfide bridge. The heavy chain is divided in an amino region (Hn) and a carboxyl region (Hc). Hc is the binding domain, Hn is the translocation domain and the Lc is the catalytic domain responsible for the neurotoxin effects of the protein (Image adapted from Zhongxing et al, 2012) The target protein affected differs between the different serotypes of toxins. Botulinum toxin type A and E target the synaptosomal-associated protein 25 (SNAP-25), a t-SNARE protein which is associated with the fusion of the synaptic vesicle to the plasma membrane of the neuron. Toxin types B, D, F and G target the vesicle associated membrane protein (VAMP) synaptobrevin, another SNARE protein involved in neuronal exocytosis. Toxin type C targets both SNAP-25 and syntaxin, a membrane protein which forms the core SNARE 7 complex together with SNAP-25 and synaptobrevin (Wictome & Shone, 1998). Regardless of target protein, toxin associated cleavage significantly disrupts the release of the neurotransmitter acetylcholine (Figure 2). Figure 2: The action of botulinum toxin at the axon terminal resulting in the prevention of acetylcholine release. Following heavy chain (Hc) mediated absorption of the neurotoxin, the light chain (Lc) targets neuronal proteins Synaptobrevin/VAMP, SNAP-25 and Syntaxin, dependant on the serotype of toxin. Independent of the target protein, the assembly of the SNARE complex required for acetylcholine exocytosis is disrupted (Image adapted from Turton et al, 2002). 8 Botulinum toxin that has entered the body (most frequently via the gastrointestinal tract after ingestion) is able to travel passively in the bloodstream owing to the biophysical properties of the toxin (Hatheway, 1998). In its native state, botulinum toxin is bound to chaperone proteins which greatly enhance the stability of the toxin, allowing it to reach target tissues without being denatured (Hatheway, 1998). Although not fully characterised, it is likely the heavy chain of the toxin functions as a chaperone to the light chain during translocation (Brunger et al, 2007). Toxin is carried around the body via the bloodstream binds to nerve-ending receptors, becomes internalised within the neuron, and causes an irreversible blockade of cholinergic transmission of ganglionic synapses, post ganglionic parasympathetic synapses and neuromuscular junctions; resulting in widespread flaccid paralysis and potentially fatal autonomic nervous system disruptions (Maselli, 1998; Smith, 1979). It is the ability to exercise control of these symptoms, by controlled dosage and administration, which has led to the neurotoxin becoming the interesting and valuable clinical medicine it has become today. 9 1.4 Botulinum Toxin: Commercial Production Alan Scott, a researcher at the Smith-Kettlewell Institute in California, first had the idea to inject small doses of botulinum toxin into overactive muscles as a means of treating patients with strabismus; a common condition characterised by a lack of coordination between the extraocular muscles (Scott, 1980). The treatment proved successful and investigators began testing the neurotoxin’s ability to treat other diseases and disorders characterised by overactive contraction of muscle tissue. Early results were excellent for the treatment of blepharospasm and hemifacial spasm, and the U.S Food and Drug administration (FDA) approved botulinum toxin for treatment of the three conditions in 1989. Since its introduction into the clinical market, the toxin is now approved for use in over 60 countries and is used to treat over 50 medical conditions; as well as being widely used for cosmetic purposes (Hallett, 1999; Münchau & Bhatia, 2000). This increase in demand led to the requirement for industrial scale production of botulinum toxin. Industrial production of botulinum toxin generally consists of a fermentation process in which C. botulinum is cultured in a nutrient rich growth medium followed by various protein harvesting and purification processes to ensure toxin samples attain the stringent quality standards required by international regulatory agencies. The commercially produced neurotoxin which is utilised in medical practices is actually a very dilute suspension of toxin, containing only 0.44-0.73ng (depending on the product supplier/brand) of toxin per 100 dose vial (Frevert, 2010). Due to such low quantities of neurotoxin being effective for clinical applications, there is currently no overbearing demand to increase production within the botulinum toxin industry, however, due to the potency and lethality of the toxin, as well as 10 the fact the product must meet strict clinical specifications, the production process must be predictable, reproducible, well characterised and comply with strict international good manufacturing practice (GMP) standards. Current published processes for the production of neurotoxin from C. botulinum rely on empirically developed media and procedures, rather than an a priori appreciation of the underlying physiology of regulation of biosynthesis. Several biomarkers have been identified as being associated with botulinum toxin production which could provide valuable insight and increase our understanding of both C. botulinum and the industrial production process. Research has also been published on the effects of various metabolites on the production of botulinum toxin, however metabolically defined biosynthesis physiology of its production remains incomplete and much remains to be gained from process development focused research regarding industrial scale neurotoxin production. 11 1.5 Methodologies in Process Development & Optimisation Commercial process development and optimisation of microbial product synthesis can encompass a wide spectrum of approaches, ranging from simple alterations in culture conditions (such as the effect of pH) to complex production strain bioengineering, offering potential improvements with regard to yield, process robustness, reduced costs and other factors. 1.5.1 Availability of Metabolites in the Bacterial Growth Medium Controlling the availability of metabolites by supplementing the growth medium of the bacterial culture is a common process development approach. Adjustment of medium formulation, and therefore the microbial environment, can have significant impacts on both the quantity and diversity of secondary metabolites produced in culture (Van der Molen et al, 2013). In the commercial environment, establishing and optimising growth conditions can significantly increase the production of the compound of interest (Van der Molen et al, 2013). One of most researched groups of microbial products are the antibiotics; a group of compounds which are typically produced in nature as a method to gain an advantage over other organisms competing for the same environmental nutrient sources (Chater, 2006). Secondary metabolism is triggered by nutrient limitations which activate biochemical pathways resulting in the biosynthesis of secondary metabolites such as antibiotics and toxins (Chater & Horinouchi, 2003). Therefore, the determination and subsequent replication of the conditions which trigger these events in nature by control of the available nutrients in the growth medium can be an effective method of microbial product process 12 development and optimisation. In Streptomyces, a bacterial genus which is responsible for the production of almost two thirds of known antibiotics (Butler et al, 2002), specific macronutrient limitations have been correlated with gene regulation resulting in product biosynthesis, with limitations in carbon, phosphate or nitrogen being the most common controls (Bibb, 2005). Such understanding can offer an invaluable advantage in terms of optimising product yield to an industrial process. Other components of the growth medium, such as amino acids and vitamins, can also have a considerable effect on a process. A substantial fraction of the energy budget of bacteria is devoted to biosynthesis of amino acids (Akashi & Gojobori, 2002). The fuelling reactions of central metabolism provide precursor metabolites for synthesis of the 20 amino acids incorporated into proteins. Thus, synthesis of an amino acid entails a dual cost; energy is lost by diverting chemical intermediates from fuelling reactions and additional energy is required to convert precursor metabolites to amino acids. The range in amino acid biosynthesis cost varies from 11 ATP equivalents per molecule of Glycine, Alanine, and Serine to over 70 ATP per molecule of Tryptophan (Akashi & Gojobori, 2002). Simultaneously, metabolism of amino acids is likely to effect the biosynthetic pathways of microbial products and in many bacteria, including Clostridia, are essential for growth (Karasawa et al, 1995). Therefore, the concentration and diversity of amino acids in the growth medium can have a significant effect on the growth kinetics of a microbial process, which in turn affects the biosynthesis of metabolism dependant compounds. Establishing events which are associated with secondary metabolite production, such as the correlation between sporulation and toxin production in C. perfringens and C. difficile (Kamiya et al, 13 1992; Mitchell, 2001) is also advantageous, as the process can be engineered to promote a biological state to increase product yields. 14 1.5.2 Statistical Methods for Process Optimisation: Plackett-Burman Experimental Design Multifactorial experiments are an effective method to assess the effects of numerous influential factors on a production process, offering a broad-range approach to target factors for more intricate extrapolation and optimisation. The Plackett-Burman design (PBD) fractional experimental approach can prove an effective tool in industrial microbial process optimisation. Owing to is design, PBD allows the investigation of a number of influential factors with a smaller number of trials compared to full factorial design experiments (Plackett & Burman, 1946). For example, to test 10 variables on two levels (210), a full factorial design would require 1024 trials. Using PBD, investigating 10 variables would require only 11 trials (n + 1) (Cordenunsi et al, 1985) (Table 1). Another advantage of PBD over full factorial design is it allows the consideration of possible interactions between factors (Kalil et al, 2000). When investigating the effect of 11 variables on a production process, 12 trials with each variable tested at a low and a high value and at least three dummy variables, also at low and high values, should be employed to estimate the experimental error. The experimental error is generated by calculating the square root of the variance of effect (See equation below) and a t test is used to determine the significance of the data (Greasham & Inamine, 1986). Calculating the variance of effect: Veff = ∑ (Ed)2 / n Veff = variance of effect, Ed = the effect determined for the dummy variables, n = number of dummy variables. 15 Trial χ1 χ2 χ3 χ4 Variables (χ) χ5 χ6 χ7 χ8 1 2 3 4 5 6 7 8 9 10 11 12 + + + + + + - + + + + + + - + + + + + + - + + + + + + - + + + + + + - + + + + + + - + + + + + + - + + + + + + - χ9 χ10 χ11 + + + + + + - + + + + + + - + + + + + + - Table 1: PBD for 11 variables. Number of trials = n +1 (n = number of variables). + and – represent input variables at a high (+) and low (-) value. The results are tested against at least three dummy variables (theoretical data calculated from the predicted effects of variables at a combination not actually used in the experiment), also input at a high and low value, to test the experimental error. Statistical significance of the data can be tested using a t-test (Greasham & Inamine, 1986). An example of the PBD in practice, with regards to microbial process development, is testing the observable effects of altering various medium components on microbial metabolism and products. For example, the effects of 11 different amino acids on protein biosynthesis or sporulation could be trialled using the design detailed in table 1, where ‘+’ would represent the supplementation of a particular amino acid into the growth medium and ‘-‘ would represent no addition of the particular amino acid. As depicted in table 1, trial number 12 would act as a negative control and contain no additional amino acids in this example. 16 1.5.3 Strain Selection & Bioengineering Genetic variation between the strains of a bacterial species can have diverse implications on a microbial process. Members of the clostridia species, beyond the use of C. botulinum in the pharmaceutical industry, are increasingly being utilised for a number of industrial applications. Clostridium acetobutyricum (C. acetobutyricum) can synthesise ethanol, acetone and butanol; all of which are commercially useful products (Lee et al, 2012). A typical wildtype strain of C. acetobutyricum produces butanol, acetone and ethanol at a ratio of 6:3:1 (Jones & Woods, 1986). Although fermentation of all three compounds is feasible (ABE fermentation), ethanol can be produced more efficiently from yeast fermentation (Pfromm et al, 2010), and if either butanol or acetone is the desired microbial product, concurrent synthesis of the other two ultimately lowers the respective yield of desired product per unit of mass substrate utilised. This caveat has become the basis of acetogenic Clostridia bioengineering in more recent years in order to maximise the yield of butanol or acetone (Papoutsakis, 2008). Classical strain development has typically relied on random mutagenesis and subsequent screening to yield improved strains (Parekh et al, 2000), but modern bioengineering approaches can utilise targeted gene and pathway knock outs, such as disrupting the acetate biosynthesis pathway in C. acetobutyricum to create an optimal butanol producing strain (Lehmann et al, 2012). Different strains of a bacterial species can also exhibit random metabolic variation without mutation or strain engineering, resulting in different growth rates, tolerance and product yields (Aucamp et al, 2014). It is therefore a vital element of process development to select a production strain which lends its physiology to a processes advantage, in terms of optimal growth rate and desired product yields for example. 17 1.5.4 Exploiting Biomarkers of Product Biosynthesis A biomarker is an analyte which is indicative of a specific biological state or process and is often measured to assess the metabolic state or pathological changes in an organism which lead to the ability to cause disease (Aerts et al, 2011). Therefore identifying, assessing and characterising the biomarkers which correlate with toxin production in C. botulinum is a means of understanding the physiology of neurotoxin biosynthesis and offers potential for targeted development of the industrial production process. 1.5.4.1 Sporulation Clostridium species possess the ability to produce heat labile endospores (Dunbar et al, 1990). Spores are involved in host colonisation and persistence of bacteria in the environment. In all spore forming bacteria, the first major morphological manifestation of sporulation is an asymmetric division, which divides the sporulating cell into a larger mother cell and a smaller forespore, which will eventually become the mature spore. Following septum formation, the mother cell engulfs the forespore and a layer of peptidoglycan (the spore cortex) is deposited between the inner and outer forespore membranes. This layer is then encased by a protective proteinaceous coat followed by the release of the mature spore (Stephenson & Lewis, 2005). The initiation of sporulation is dependent on the phosphorylation of the transcription factor Spo0A; the master regulator of sporulation. Phosphorylation of Spo0A occurs in response to environmental and physiological signals such as nutrient deficiency, temperature and other stressors such as the presence of oxygen in anaerobic bacteria (Barketi-Klai et al, 2004). Such environmental and physiological factors 18 are often associated with the production of secondary metabolites and a correlation has been established between sporulation and toxin production in other members of the Clostridial species, including C. perfringens and C. difficile (Kamiya et al, 1992; Mitchell, 2001). Previous studies indicate a possible correlation exists between sporulation and neurotoxin excretion in C. botulinum. Early work (Bonventre and Kempe, 1960) suggests that C. botulinum initiates autolysis as a mechanism to liberate toxin harbored inside the cells, but also demonstrated toxin is excreted prior to autolysis or sporulation of the cells. This is supported by more recent research (Artin et al, 2010), who reported the expression of genes associated with neurotoxin production before sporulation-associated genes were expressed in batch cultures of C. botulinum. Other studies (Cooksley et al, 2010) have shown that C. botulinum and Clostridium sporogenes (C. sporogenes), considered the non-toxigenic equivalent of C. botulinum, possess two distinct agr gene loci, encoding putative proteins similar to those of the well-studied Staphylococcus aureus agr quorum sensing system. The studies of Cooksley et al, 2010 (Cooksley et al, 2010) demonstrated that modulation of agr gene expression drastically reduced both sporulation and neurotoxin production in C. botulinum cultures. To summarise, the correlation between neurotoxin production and sporulation in C. botulinum currently remains unclear. Experimentally establishing a link that sporulation is a biomarker of toxin production would broaden our understanding of the metabolism of C. botulinum and neurotoxin production. 1.5.4.2 Poly-β-hydroxybutyrate Metabolism Poly-β-hydroxybutyrate (PHB) is another metabolite of interest concerning toxin production in cultures of C. botulinum. PHB is utilised as an energy storage molecule by many bacteria. 19 Glucose is metabolised through the Embden-Meyerhoff-Parnas (EMP) pathway, yielding pyruvate and acetate as the main products. Once glucose is exhausted, the acids are oxidised via the tricarboxylic acid (TCA) cycle. Exhaustion of glucose triggers the events that lead to sporulation in many organisms (Mignone & Avignone-Rossa, 1996). Biosynthesis of PHB occurs via a three-step pathway by which acetyl CoA is initially condensed to form acetoacetyl CoA, which is then reduced to D-3-hydroxybutyryl CoA at the expense of NADPH and finally polymerised (Anderson & Dawes, 1990; Steinbüchel, 1991). Studies have shown that in Bacillus species and C. botulinum, amongst other Clostridia, PHB is utilised during sporulation (Benoit et al, 1990; Emeruwa & Hawirko, 1973). In well characterised organisms, the accumulation of PHB and other energy storage compounds is promoted in response to physico-chemical stress and nutrient limitations, and it is therefore often associated with secondary metabolites, including antibiotics and certain toxins. Furthermore, a direct correlation between PHB and toxin biosynthesis has been established in Bacillus thuringiensis (Navarro et al, 2006). In Streptomyces species, glycogen accumulates in response to excess carbon when nitrogen or phosphate is limited (Lillie & Pringle, 1980). Glycogen is metabolised into glucose-1phosphate; a precursor of deoxysugar antibiotics, such as the antibiotic and secondary metabolite, avilamycin (Salas & Mendez, 2005). Glycogen is also utilised during sporulation in Streptomyces species, (Preiss & Romeo, 1989), suggesting PHB may act in an analogous manner to glycogen regarding toxin production in C. botulinum. Ralstonia eutropha (R. eutropha) is perhaps the most well studied PHB producing organism. Studies in R. eutropha have shown that PHB accumulation can be maximised in cultures subject to nitrogen and/or phosphate limited conditions, with carbon in excess (Lillo & 20 Rodriguez-Valera, 1990; Raberg et al, 2008; Ryu et al, 2007; Shang et al, 2003). By limiting nitrogen sources in the production media, the metabolic flux through the tricarboxylic acid (TCA) cycle is limited. This in turn limits growth rate which results in a reduced demand for acetyl CoA by the TCA cycle. Meanwhile, excess glucose is metabolised via the EMP pathway yielding acetyl CoA and pyruvate. The increase in glycolysis, driven by excess carbon availability, combined with the limitation of the TCA cycle, owing to the limited availability of nitrogen, ultimately results in excess acetyl CoA; the precursor for PHB. Similarly, limiting phosphate in the production medium results in less available inorganic phosphate to bond with ADP to form ATP in the TCA cycle and subsequent electron transport chains. This limits the progression of the TCA cycle, which subsequently effects available acetyl CoA; similar to the effects of nitrogen limitation. Raberg et al (Raberg et al, 2008) has also shown that maximising PHB production via nitrogen limitation in cultures of R. eutropha decreases the production of the protein flagellin, which may compete with toxin biosynthesis for metabolite availability in C. botulinum (Peplinski et al, 2010; Raberg et al, 2008). 1.5.4.3 Flagellin Biosynthesis Flagellin is a protein which aligns itself in a hollow cylinder to form the filament of bacterial flagella; the most widely characterised bacterial motility structure (Bardy et al, 2003). Most flagella are composed of over 20 distinct structural proteins that assemble to form the flagellar basal body, hook, and filament, with the filament comprising around 20,000 subunits of the flagellin protein (Paul et al, 2007). In Clostridia spp., flagella synthesis and assembly is regulated by ~35 genes. Genetic variation of Clostridial flagellin is high and can be used as a method for identification between species serotypes, including distinguishing 21 between different toxin producing serotypes of C. botulinum (Paul et al, 2007; Woudstra et al, 2013). Despite being predominantly associated with motility, more recent studies have revealed numerous other functions of bacterial flagella which suggest the structures secondary role as a virulence factor. In some pathogenic bacteria, including C. difficile, the major subunit of flagella, flagellin, has been reported to function as an adhesin (Haiko & Westerlund, 2013). Furthermore, FliC in shiga-toxigenic E. coli has been associated with cellular invasion (Claret et al, 2007). Although a potential but not yet characterised virulence factor, for a currently unknown purpose, cultures of C. botulinum and other genera such as Salmonella typhimurium (S. typhimurium) produce flagellin excessively, beyond the amount necessary for flagella assembly (Homma & Iino, 1985). As flagellin has been established as a virulence factor in many pathogenic bacteria, it is possible that a correlation between flagellin overproduction and neurotoxin biosynthesis could exist in C. botulinum. Comparative studies have revealed many similarities in the amino acid composition of flagellin and botulinum toxin (Appendix Figure 1) and it is thought producing flagellin in such excess is likely to result in competition for metabolites which may result in decreased toxin biosynthesis. Therefore a detailed study of the biosynthesis of flagellin in C. botulinum may not only demonstrate flagellin to be a biomarker for neurotoxin production but may also lead to the ability to control its production and maximise available metabolites for toxin biosynthesis. 22 1.5.5 Surrogate Research Approach: Clostridium sporogenes In the context of microbiology, a surrogate is an organism used to study the physiology of a different but closely related species (Sinclair et al, 2012). Both pathogenic and nonpathogenic organisms are used as surrogates for a variety of purposes, including behaviour, physiology, method development and conditional biomarker research (Sinclair et al, 2012). The greatest benefit of the research approach is often the element of safety provided when studying pathogenic organisms and therefore is commonly practiced when researching C. botulinum; producer of the most lethal toxin discovered to date (Bradbury et al, 2012). C. sporogenes is widely used as a surrogate organism for testing the metabolism of C. botulinum (Bradbury et al, 2012), owing to the fact C. sporogenes is believed to have originated from a non-toxigenic species of C. botulinum and therefore exhibits the same metabolic and behavioural properties, without the hazards associated with C. botulinum (Brown et al, 2012; Cooksley et al, 2010). C. sporogenes has successfully been utilised as a surrogate to C. botulinum for many avenues of research, including food safety (Brown et al, 2012), gene expression (Cooksley et al, 2010) and metabolic studies (Taylor et al, 2013). Owing to the extreme pathogenicity of C. botulinum, the organism can only be cultivated in specialised facilities, under stringent conditions of microbiological and institutional security (which are not present at the University of Surrey). C. sporogenes was therefore, employed as a surrogate species throughout this study to in order to examine the relationship between flagellin production, PHB accumulation and sporulation. This led to the overall objective of this study: to develop C. sporogenes as a “safe” surrogate system to investigate the implied physiology of toxin production in C. botulinum. Because neurotoxin production cannot be assessed in C. sporogenes, genetic annotation software (RAST prokaryotic 23 genome annotation server, the SEED) was used to create a Genome-Scale Metabolic Network (GSMN) of C. botulinum (type A) and C. sporogenes (ATCC15579), using genome sequence data obtained from the National Centre for Biotechnology Information (NCBI) FTP (Bao et al, 2011). The primary requirement of the models was to assess neurotoxin production by C. botulinum in silico using conditions and experimental data obtained in C. sporogenes, in order to evaluate the capabilities of the surrogate system. 24 1.6 Research Tools: Genome-Scale Metabolic Networks Simplified, genome-scale metabolic networks (GSMN) represent a network of chemical reactions which can be utilised to simulate an organism’s metabolism. The information provided by the genome of an organism can be used to obtain information on the metabolic functioning and pathways utilised by the organism (Price et al, 2003). Obtaining the required genetic information can be achieved by screening the genome for open reading frames that code for enzymes utilised in metabolic processes. Identifying the genes coding for metabolic enzymes results in a genome-scale metabolic network which can be used to obtain a better understanding of cellular metabolism as well as aid design of media and experimental processes (Teusink et al, 2005; Xie et al, 1994), analyse culture data and develop metabolic engineering strategies (Hua et al, 2006; Fong et al, 2005; Smid et al, 2005). Although screening opening reading frames will offer an understanding of metabolic enzymes, such a network may still contain gaps due to the incomplete or incorrect annotation of the genome. Biochemical literature, transcriptome data or direct experimental testing can be used to extend the knowledge of the metabolic network, determining the presence of missing enzymatic reactions and metabolic pathways. In the case of a ‘complete’ metabolic model, there are still underdetermined parts due to presence of parallel or cyclic pathways, and experimental data may not agree with the model owing to regulation of gene expression. This means that for certain parts of the genome-scale network, flux values cannot be determined. However, constraints can be set on certain enzymatic reactions on the basis of biochemical or thermodynamic information, found in the literature or determined by experimentation, in order to reduce the possible solutions of metabolic network. The most populated model to date, the iJO1366 metabolic model of Eschericia coli 25 (E. coli) K-12MG1655, has tested 97% of the genomes open reading frames (ORFs) through experimentation (Orth et al, 2011). Such models are invaluable as a tool to aid biotechnological process design, including accurate analysis of substrate metabolism and uptake, growth rates, primary and secondary metabolite biosynthesis, and testing the effects of pathway knockouts or mutations (Feist & Palsson, 2008). 26 1.6.1 Flux Balance Analysis Flux balance analysis (FBA) is a widely used technique to simulate the capabilities of a genome-scale metabolic network (Durot et al, 2009). Mathematically modelling an organism’s metabolism relies on a steady-state assumption, in which all metabolites are produced and consumed at the same rate (Durot et al, 2009). This makes the technique less intensive in terms of input data into the GSMN compared to other methods of modelling, whilst still predicting cellular growth rate accurately (Edwards et al, 2001). Flux through the network is enabled by exchange reactions, such as uptake of nutrients and production of biomass. Stoichiometric constraints are often the only constraints affecting the matrix of reactions and optimal production of biomass can be computed by solving a linear program (Muller & Bockmayr, 2013). However, although an effective and efficient method of biomass generation simulation, FBA only computes one such solution, despite there being more than one optimal flux distribution that achieves optimal biomass production. Techniques which analyse the entire flux network such as elementary flux modes can prove exponentially complex as the number of reactions in a network increases (Zhangellini et al, 2013), which is often unnecessary for metabolic networking in which the effect of fluxes is more valuable than the specific flux data (Driouch et al, 2012). One limitation of FBA, however, is that the array of fluxes calculated is but one of many possible solutions to the balance objective, meaning the individual enzyme flux values may have no physiological significance. Such analyses can only calculate unique values for each individual enzyme reaction if Flux Variability Analysis (FVA) is computed. For each reaction, the range of fluxes which satisfy the constraints allowing optimisation of the objective function, are calculated. 27 1.6.2 Flux Variability Analysis FVA determines the maximum and minimum values of all the fluxes that will satisfy the constraints and allow for the same optimal objective value (Muller & Bockmayr, 2013). Whilst classical FBA can predict optimal fluxes, FVA can be utilised to predict ranges of flux through particular pathways and reactions as well as analyse the reactions which contribute to the observed ranges of flux. Variations of FVA can also be used to determine blocked or unessential reactions (Burgard et al, 2004). This dynamic method of analysis is in most cases more realistic than the steady-state assumption relied on by FBA and flux dynamics can be assimilated with gene expression data to model the ranges of fluxes observed (Bilu et al, 2006). Therefore the benefits offered by this computational technique are particularly advantageous for modelling the biosynthesis of products which have many factors contributing to optimisation, such as secondary metabolites including antibiotics and toxins (Bushell et al, 2006). Utilising FVA with an objective of determining pathways affecting the biosynthesis of a specific microbial product can be analysed to produce a secondary metabolite generating network; a powerful microbial process development tool which has proven successful in optimising antibiotic yields in Streptomyces spp. (Bushell et al, 2006). 28 1.7 Research Tools: Using Chemostat Culture in Physiological Investigations Despite genomic analysis offering valuable insight into the constraints of a microbial process, the kinetics and physiology of microbial growth is fundamental to every discipline of microbiology (Bull, 2010; Hoskisson & Hobbs, 2005). Microbial culture exists in three basic platforms; batch culture, fed-batch culture and continuous culture. Continuous cultures (open systems) receive a constant supply of fresh nutrients whilst spent medium, biomass and microbial products are removed at the same rate (Bull, 2010). This enables the dissection of microbial physiology and growth kinetics independent from the effects of the physiochemical environment (Hoskisson & Hobbs, 2005), including the decreased availability of nutrients and increased accumulation of by-products over time. Many variations of continuous cultures exist (Bull, 2010; Drake & Brogden, 2002), but by far the most widely used is the chemostat culture; cited in around 90% of recent publications on continuous cultures (Bull, 2010). This technique of bacterial culture was developed simultaneously by Monod and Novick & Szilard in 1950 (Monod, 1950; Novick & Szilard, 1950). The unique characteristic of chemostat culture which makes the technique a powerful research method to analyse microbial physiology is the ability to establish a timeindependent steady state. In steady state, growth rate and all parameters affecting the culture including the availability of nutrients, cell density, microbial product concentration, pH and culture volume remain invariant (Herbert et al, 1956; Hoskisson & Hobbs, 2005). Under such conditions, the specific growth rate of an organism is dependent on the rate of supply of a growth limiting substrate present in the growth medium. Therefore in steady state, specific growth rate is equal to dilution rate, allowing external control of the cultures 29 specific growth rate (Bull, 2010; Herbert et al, 1956; Hoskisson & Hobbs, 2005) (See Chapter 2: Materials & Methods – 2.7: Chemostat Culture). As a method to investigate microbial physiology, chemostat culture offers a unique method to analyse supposed constants that are subject to environmental factors and changes in microbial population, which can provide reproducible conditions for global regulation (Bull 2010; Ferenci, 2006). The technique offers a substantial advantage when investigating the effects of a single parameter, for example, on the biosynthesis of a microbial product, which would otherwise be affected by growth kinetics and environmental factors (Aon & Cortassa, 2001; Avignone-Rossa et al, 2002). One limitation of genome-scale flux analysis is the requirement to assume steady state conditions (Durot et al, 2009). Metabolic flux analysis of organisms grown in batch culture can prove unreliable owing to transient growth effects which alter gene regulation and metabolic pathway fluxes (Hoskisson & Hobbs, 2005). Applying metabolic flux analysis to chemostat-grown cultures therefore indirectly eliminates metabolic constraints between the two research tools. Genome-scale flux analysis and chemostat culture in combination therefore complement one another, encompassing techniques from different eras of microbiology research to provide an effective process development and optimisation tool. 30 1.8 Impact, Aims & Objectives Despite C. botulinum fermentation for the production of its neurotoxin being practiced for decades, little research about the underlying physiology of the process has been published. Current methods of commercial botulinum toxin production predominantly rely on legacy production processes which can be inflexible in terms of process development and can prove difficult to rectify specific production issues such as reproducibility and yields. Furthermore, the introduction of modifications into an already licensed and in-manufacture process poses constraints; with large overhauls of the process such as strain changes or mutations requiring costly and timely relicensing. It is therefore an emphasis of this research project to increase our understanding of the botulinum toxin production process and provide insight into potential process development approaches. This research project will utilise a genome-scale metabolic network enhanced surrogate system to assess the physiology, metabolism and behaviour of C. botulinum with regards to neurotoxin biosynthesis and its industrial production process. By using a research approach which combines experimental studies in C. sporogenes with in silico analysis of C. botulinum, the primary aims and objectives of this project were proposed; Seek to establish and elaborate on potential correlations with sporulation, flagellin biosynthesis and PHB metabolism in C. sporogenes cultures. Investigate the potential metabolic factors affecting neurotoxin biosynthesis in silico, particularly in relation to the biomarkers investigated. 31 Build upon established correlations and utilise data obtained from a combination of the process development methodologies covered in sections 1.5, 1.6 & 1.7 to design a physiologically and biochemically defined process, with an outlook of optimising neurotoxin production over other metabolites which may compete for bioflux. Increase knowledge of the botulinum toxin production process, offering potential avenues for process improvements which could benefit the project sponsor. Further validate the suitability of C. sporogenes as a surrogate organism for studies in C. botulinum. Yield metabolic data which may offer insight into the correlations of biomarkers with microbial products in other members of the Clostridium genus, potentially impacting the approach taken for process development in closely related bacteria exploited for industrial processes, such as the acetogenic Clostridia. 32 Chapter 2: Methods & Materials 2.1 Clostridium sporogenes strains and working stock preparation Fifteen different strains of C. sporogenes were obtained from NCIMB (NCIMB Ltd, Aberdeen, UK). The strains were assigned a working reference number (Table 2) before revival on to glucose broth agar and in cooked meat medium (CMM). The cultures were grown at 37 oC for 24 hours in an anaerobic jar. Anaerobic conditions were achieved using AnaeroGen TM anaerobic gas generating sachets (Oxoid Ltd, United Kingdom). Cultures grown in CMM were plated onto glucose broth agar plates (Oxoid Ltd, United Kingdom) and slopes to ensure the cultures were free from contaminants. Isolated colonies were used to inoculate Erlenmeyer flasks containing 100ml of sterile CMM which were incubated at 37 oC in an anaerobic workstation (Don Whitley Scientific Ltd, United Kingdom) for 24h. These cultures were then used to prepare frozen seed stocks by dispensing 600µl of culture into a cryotube containing 400µl sterile glycerol to attain a 40% glycerol stock culture (v/v) and were subsequently frozen at -80oC. The frozen stocks were used as inocula to generate seed cultures for experimentation throughout the study. 33 Strain number Organism NCIMB532 NCIMB8053 NCIMB8243 NCIMB9381 NCIMB9382 NCIMB9383 NCIMB10196 NCIMB10696 NCIMB12148 NCIMB12343 NCIMB700933 NCIMB701789 NCIMB701791 NCIMB701792 NCIMB701793 Clostridium sporogenes Clostridium sporogenes Clostridium sporogenes Clostridium sporogenes Clostridium sporogenes Clostridium sporogenes Clostridium sporogenes Clostridium sporogenes Clostridium sporogenes Clostridium sporogenes Clostridium sporogenes Clostridium sporogenes Clostridium sporogenes Clostridium sporogenes Clostridium sporogenes Allocated strain reference number 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Table 2: Fifteen NCIMB strains of C. sporogenes and their allocated working reference numbers. Lyophilised cultures sealed in glass vials were revived under sterile and anaerobic conditions provided by an anaerobic workstation (Don Whitley Scientific Ltd, United Kingdom). Cultures recovered from sealed glass vials were suspended in glucose broth liquid medium and used to inoculate flasks of CMM and glucose broth agar plates as described in 2.1 above. 34 2.2 Culture Medium Cooked-meat medium: 10g of dry cooked meat medium (Oxoid ltd, United Kingdom) was added to 100ml of Milli-Q water. The medium was sterilised using an autoclave (121oC for 15 minutes). After sterilisation, the liquid was removed from the cooked-meat and replaced with the same volume (100ml) of sterile glucose broth liquid medium (See below) aseptically in a Class II microbial safety cabinet (MSC). Glucose Broth Liquid Medium: 2.5g glucose and 8.25g nutrient broth no.2 (Oxoid Ltd, United Kingdom) added to 250ml of Milli-Q water sterilised by autoclaving (121oC for 15 minutes). Glucose Broth Agar: Glucose Broth Liquid Medium + 1.2% Technical Agar (Oxoid Ltd, United Kingdom) sterilised by autoclaving (121oC for 15 minutes). Liquid agar medium was poured into petri-dishes whilst still molten post-autoclave in an MSC as above. Glucose broth agar which had solidified was warmed in a thermostatic water bath (Fisher scientific Ltd, United Kingdom) maintained at 50oC until molten and then poured as described above. Rich Clostridium Growth Medium (USA2): Proteose peptone No.3 20g/L; Bacto yeast extract 10g/L; NZ Amine A 10g/L; Sodium mercaptoacetate 0.5g/L; Glucose 20ml/L (50%w/v) added post-autoclave to avoid heat decomposition (121oC for 15 minutes) in 1L Milli-Q water. 35 Basal Defined Medium: Amino Acid Concentration (mg/L) Vitamin Concentration (mg/L) Histidine Tryptophan Glycine Tyrosine Arginine Phenylalanine Methionine Threonine Alanine Lysine Serine Valine Isoleucine Aspartic acid Leucine Cysteine Proline Glutamic acid 100 100 100 100 200 200 200 200 200 300 300 300 300 300 400 500 600 900 Thiamine Calcium-D-pantothenate Nicotinamide Riboflavin Pyridoxine p-Aminobenzoic acid Folic acid Biotin B12 1 1 1 1 1 0.05 0.0125 0.0125 0.005 Minerals NaCl CaCl,2H2O MgCl, 6H2O MnCl, 4H2O FeSO4 7H2O CoCl2 6H2O Distilled water (ml) NaHCO3 900 26 20 10 4 1 1000 5000 Variable nutrients (see below) Glucose Na,HPO4 KH2PO4 (NH4)2S04 2000 1500 300 40 Table 3: Basal Defined Medium (BDM) composition from Karasawa et al, 1995 Carbon-limited media: BDM (Table 3) with glucose altered to 200mg/L, 500mg/L, 1000mg/L, 2000mg/L and 4000mg/L respectively. Nitrogen-limited media: BDM (Table 3) with ammonium sulphate altered to 0mg/L, 250mg/L, 500mg/L, 750mg/L and 1000mg/L respectively. Phosphate-limited media: BDM (Table 3) with sodium phosphate/potassium phosphate altered to 1500mg/300mg, 1000mg/200mg, 750mg/150mg, 500mg/100mg and 250mg/50mg respectively. 36 2.3 Determination of Culture Growth by Optical Density Culture growth was determined by optical density. Samples were mixed using a vortex mixer (Fisher scientific Ltd, United Kingdom), 1ml transferred into a silicon cuvette and the absorbance measured at a wavelength of 560nm (Karasawa et al, 1995) against a blank of sterile growth medium using an Ultaspec2000 spectrophotometer (Pharmacia Biotech, UK). 2.4 Determination of Biomass by Dry Cell Weight Measurement Biomass was determined by dry weight measurement following lyophilisation. A 1 ml volume of well-mixed sample was transferred into a pre-weighed micro centrifuge tube and the biomass pelleted using a bench-top centrifuge (Eppendorf, United Kingdom) set at 8000rpm for 10 minutes. The supernatant was carefully removed and the cells washed by re-suspending in Milli-Q and then pelleting again. After two repeats of the wash-spin cycle (described above) to wash the media components from the cells, the pelleted cells were frozen at -80oC for 1 hour before being transferred to a freeze-dryer (Edwards high vacuum International, United Kingdom). Lyophilisation was performed overnight (approximately 12h, -40oC) to remove water from the sample and the micro centrifuge tube, containing the lyophilised cells, was then re-weighed to determine biomass. 37 2.5 Determination of Intracellular PHB Accumulation Whole cells, isolated from culture samples, were lyophilised and weighed to determine cell biomass as described in section 2.4. A 1ml of commercial sodium hypochlorite solution (15% v/v) was added to the lyophilised cells and incubated at 37oC for 1 hour to allow cell lysis to occur. After incubation, 4ml of Milli-Q water was added, the suspension well mixed using a vortex (Fisher scientific Ltd, United Kingdom) and centrifuged at 13000rpm for 10 minutes. The supernatant was removed and the pelleted cells were washed with 5ml of acetone, followed by 5ml of absolute ethanol using the spin and centrifuge profile described above. The sedimented lipid granules generated were then extracted using 3ml of chloroform by submersing submerging the capped tube for 2 minutes in a boiling water bath (Fisher scientific Ltd, United Kingdom). The tubes were removed, cooled on ice, centrifuged at 13000rpm for 10 minutes and the extract decanted into a graduated tube using a pipette. This extraction process was repeated twice and the pooled extracts made-up to 10ml with chloroform. A 1ml volume of the extract was then transferred into a boiling tube and immersed in heating block (Fisher scientific Ltd, United Kingdom) set at 100 oC inside a fume cabinet until the chloroform had fully evaporated. 10ml of concentrated (>95%) sulphuric acid was then added, the tube capped with a glass marble, and heated for a further 10 minutes. After cooling, the sample was transferred into a quartz crystal cuvette and the absorbance measured at 235nm against a blank of the sulphuric acid used in the assay using an Ultaspec2000 spectrophotometer (Pharmacia Biotech, UK). Heating PHB in concentrated sulphuric acid to temperatures of 85-100oC results in the conversion of PHB into crotonic acid, which, according to Law and Slepecky (1961), has a molecular extinction coefficient identical to PHB (ε = 1.56 x 104) when measured at 235nm in concentrated sulphuric acid. 38 This step is necessary because PHB does not absorb light, making it difficult to assay without conversion into crotonic acid. To confirm the correlation and ensure accuracy of the assay, a standard curve of PHB was constructed (Figure 3). This was prepared by adding measured pure PHB (Sigma-Aldrich Co., USA) to chloroform and repeating the method detailed above. PHB Standard Curve 1.8 1.6 R² = 0.9948 1.4 OD235 1.2 1 0.8 0.6 0.4 0.2 0 2 4 6 8 10 PHB (µg/ml) Figure 3: Standard curve of PHB achieved by adding measured pure PHB (Sigma-Aldrich Co., USA) to chloroform and repeating the assay detailed in section 2.5. Data shown are averages of triplicate biological and triplicate technical repeats. Error bars represent standard error of the mean. 39 2.6 Determination of Supernatant Flagellin Supernatant flagellin content was determined following isolation by sodium dodecyl polyacrylamide gel electrophoresis (SDS-PAGE) and subsequent band densitometry analysis (Twine et al, 2009). Samples taken from C. sporogenes cultures were mixed using a vortex mixer (Fisher scientific Ltd, United Kingdom) and centrifuged at 13000rpm for 10 minutes using a bench-top centrifuge (Eppendorf, United Kingdom). The supernatant was transferred to a sterile micro centrifuge tube, frozen at -80oC for >1 hour and lyophilised as described in section 2.5. Following lyophilisation, the samples were resuspended in 100µl of Milli-Q water. Samples were then mixed using a vortex (Fisher scientific Ltd, United Kingdom) with 50µl of Bromophenol Blue loading dye and heated at 100 oC for at least 5 minutes. After cooling, samples were mixed using a vortex mixer (Fisher scientific Ltd, United Kingdom), loaded into 12% w/v bis/polyacrlyamide gels and separated using SDSPAGE (Biorad Laboratories Ltd., UK) at room temperature and 200V constant voltage for approximately 30 minutes. A protein marker reagent ranging from 20 to 200kDa (SigmaAldrich Co., USA) was also added to gels and samples of bovine serum albumin (BSA) of known concentration were added to calculate protein mass from band densities. Gels were then stained with Coomassie-blue overnight (detailed section 2.6.1) and de-stained using a solution containing 30% methanol (v/v) and 10% (v/v) acetic acid in Milli-Q water (detailed section 2.6.1). Flagellin was identified using in-gel tryptic digests of candidate protein bands, analysed by nano-LC-ESI-MS/MS using a hybrid quadrapole/time of flight mass spectrometer. The density of flagellin bands were then determined using a multi-image light cabinet fitted with a high resolution camera (Alpha Innotech, South Africa) combined with Flurochem QTM image analysis software (ProteinSimple, USA). The protein content of the 40 sample was then calculated based on a calibration of band densities obtained from samples of known protein quantities present on the gel. 41 2.6.1 Bis/Polyacrylamide Gels & SDS-PAGE Reagents 12% w/v Bis/polyacrylamide gels: 14.4ml 30% w/v Bis/acrylamide; 12 ml Resolving Gel Buffer; 21.6ml Milli-Q Water; 300µl 10% (w/v) Ammonium Persulphate (Sigma-Aldrich Co., USA); 20µl TEMED-30 (Sigma-Aldrich Co., USA) added before pouring. Bis/polyacrylamide stacking gel (Used for loading section of gel): 4ml 30% w/v Bis/acrylamide; 6ml Stacking Gel Buffer; 14ml Milli-Q Water; 200µl 10% (w/v) Ammonium Persulphate; 20µl TEMED-30 added before pouring. Resolving Gel Buffer: 181.65g 1.5M Tris-HCL; 0.4% (v/v) Sodium dodecyl Sulphate (SigmaAldrich Co., USA); 1L Milli-Q water. Stacking Gel Buffer: 60.55g 0.5M Tris-HCL; 0.4% (v/v) Sodium dodecyl Sulphate (SigmaAldrich Co., USA); 1L Milli-Q water. Tank Buffer: 60.55g 0.5M Tris-HCL; 41.28g Glycine; 1% (v/v) Sodium dodecyl Sulphate (Sigma-Aldrich Co., USA); 1L Milli-Q Water. Coomassie Blue (gel stain): 0.12g Coomasie Blue; 50ml Methanol; 20ml Glacial Acetic Acid; 50ml Milli-Q water. Bromophenol Blue Loading Dye: 1ml Stacking Gel Buffer; 25% (v/v) Sodium dodecyl Sulphate; 0.5ml β-mercaptoethanol; 1ml Glycerol; 0.1g Bromophenol Blue. 42 2.7 Determination of Sporulation The number of spores present in cultures was determined using a 0.1mm grid, laser etched haemocytometer (Hawksley Ltd., UK). Samples were vortex-mixed (Fisher scientific Ltd, United Kingdom) and 50µl transferred onto a haemocytometer and covered with a glass microscope slide cover to create a chamber of known volume. Samples were then examined using phase-contrast light microscopy (x1000 magnification with immersion oil) and spores counted against the laser etched grids of the haemocytometer to calculate spores/ml (Burns et al, 2010). Samples too numerous in spores for accurate determination were diluted in series using Milli-Q water. 43 2.8 Plackett-Burman Design The Plackett-Burman design (PBD) experimental approach was used to assess the effects of amino acid supplementation on growth, sporulation, PHB accumulation, flagellin biosynthesis (Section 3.4) and enzyme activity (Section 4.4) in C. sporogenes. A series of Erlenmeyer flasks containing BDM (Karasawa et al, 1995) were supplemented with various amino acids following the PBD approach and principles (Plackett & Burman, 1946). All flasks were inoculated using the same seed culture of C. sporogenes, generated using the methods detailed in section 2.1. Inoculated flasks were incubated at 37oC in an anaerobic workstation (Don Whitley Scientific Ltd, United Kingdom) for 24h. Dry cell weight and sporulation was determined immediately following sampling using the methods detailed in section 2.4 & 2.7 respectively and the remaining culture frozen at -20oC for assay dependant pre-treatments. Table 4 displays the schematic of supplementation used to assess the effects of, and interaction between (Kalil et al, 2000), 11 variables. Variables tested using PBD in this study were amino acids added to the growth medium (Sections 3.4 & 4.4). The experimental error was generated by calculating the square root of the variance of effect (Section 1.5.2) and a t test is used to determine the significance of the data (Greasham & Inamine, 1986). 44 Trial χ1 χ2 χ3 χ4 Variables (χ) χ5 χ6 χ7 χ8 χ9 χ10 χ11 1 2 3 4 5 6 7 8 9 10 11 12 + + + + + + - + + + + + + - + + + + + + - + + + + + + - + + + + + + - + + + + + + - + + + + + + - + + + + + + - + + + + + + - + + + + + + - + + + + + + - Table 4: PBD for 11 variables. Number of trials = n +1 (n = number of variables). + and – represent variables at two different values, a high value (+) and low value (-). The results were tested against at least three dummy variables (theoretical data calculated from the predicted effects of variables at a combination not actually used in the experiment), also at high and low values, to generate the experimental error. Statistical significance of the data was tested using a t test (Greasham & Inamine, 1986). 45 2.10 Determination of Nutrient Concentration 2.10.1 Glucose Assay Glucose present in the medium of C. sporogenes cultures was assayed using a Reflectoquant® dipstick glucose assay (Merck & Co., Germany), read using a RQflex2 reflectometer (Merck & Co., Germany). Culture supernatant was separated by centrifugation (Eppendorf, United Kingdom) for 10 minutes at 8000rpm. The test strip was submersed in the supernatant for 15 seconds and immediately scanned using the reflectometer which displayed a value of mg/L glucose. The reaction strip contains glucose oxidase which converts the glucose present in the supernatant into δ-gluconolactone. Hydrogen peroxide generated by the reaction reacts with an organic redox indicator, developing colour that is measured reflectometrically. Samples’ concentrations were determined from a calibration curve prepared using standards of glucose diluted in Milli-Q water. Samples which were out of the range of the assay specifications detailed in the assay kit (Merck & Co., Germany) were diluted in Milli-Q water to obtain a validated glucose concentration. 2.10.2 Ammonium Assay Ammonium present in the medium of C. sporogenes cultures was assayed using a Reflectoquant® dipstick ammonium assay (Merck & Co., Germany), read using a RQflex2 reflectometer (Merck & Co., Germany). Culture supernatant was separated by centrifugation (Eppendorf, United Kingdom) for 10 minutes at 8000rpm. The test strip was submersed in the supernatant for 2 seconds and immediately scanned using the reflectometer which displayed a value of mg/L ammonium. Sample concentrations were 46 determined from a calibration curve prepared using standards of ammonium sulphate diluted in Milli-Q water. Samples which were out of the range of the assay specifications detailed in the assay kit (Merck & Co., Germany) were diluted in Milli-Q water to obtain a validated ammonium concentration. 2.10.3 Phosphate Assay Phosphate present in the medium of C. sporogenes cultures was assayed using a Reflectoquant® dipstick phosphate assay (Merck & Co., Germany), read using a RQflex2 reflectometer (Merck & Co., Germany). Culture supernatant was separated by centrifugation (Eppendorf, United Kingdom) for 10 minutes at 8000rpm. 8ml Milli-Q water was added to 500µl of supernatant, 500µl of PO4-1 reagent (Dipstick Phosphate Assay) and the required dose of PO4-2 reagent (Dipstick Phosphate Assay). The mixture was then well mixed and measured spectrophotometrically at 690nm. Samples were tested against a calibration curve prepared using standards of sodium phosphate diluted in Milli-Q water. The reagents of the assay kit react with phosphate ions in the sample to form molybdophosphoric acid, which is subsequently reduced to phosphomolybdenum blue providing the photometric change. Samples which were out of the range of the assay specifications detailed in the assay kit (Merck & Co., Germany) were diluted in Milli-Q water to obtain a validated phosphate concentration. 47 2.11 Determination of Enzyme Activity 2.11.1 Glucose-6-Phosphate Dehydrogenase Activity Assay C. sporogenes cells were pelleted using a bench-top centrifuge (Eppendorf, United Kingdom) at 8000rpm for 10 minutes. The supernatant was carefully removed and the cell pellet washed with phosphate buffer solution (PBS), remixed and the centrifugation repeated. After two repeats to wash media components from the cells, 500µl CellLytic Cell lysis reagent (Sigma-Aldrich Co., USA) was added and the cells incubated at 30oC on a platform shaker for 15 minutes. The lysed cells were then centrifuged at 13000rpm for 20 minutes to pellet cellular debris. 50µl of the enzyme-containing supernatant was added to a 96 well plate; 50µl of the ’reaction mix’ was then added to the wells and immediately measured spectrophotometrically at 450nm. The ‘reaction mix’ was a mixture of reagents supplied in the Glucose-6-phosphate dehydrogenase (G6PD) kit, containing 46µl Assay buffer (G6PD Kit Sigma-Aldrich Co., USA), 2µl Substrate mix (G6PD Kit Sigma-Aldrich Co., USA) and 2µl Developer (G6PD Kit Sigma-Aldrich Co., USA) per 50µl. Following the first OD450 measurement, plates were incubated at 37oC for 30 minutes and measured spectrophotometrically at 450nm for a second time. The results were compared against a standard curve prepared using a 1.25mM NADH standard (G6PD Kit Sigma-Aldrich Co., USA) to determine G6PD activity in mU/ml (enzyme activity/ml) using the following equation: G6PD activity = (B / (T2-T1) x V) x sample dilution = nmol/min/ml = mU/ml B = NADH generated between T1 & T2 (compare to standard curve); T1 = time at first reading (min); T2 = time at second reading (min); V = Volume of sample added to well (ml) 48 2.11.2 Citrate Synthase Activity Assay Whole cells of C. sporogenes were pelleted as previously using a bench top centrifuge (Eppendorf, United Kingdom). The supernatant was carefully removed and the cells washed with PBS. After two repeats to wash media components from the cells, 500µl CellLytic Cell lysis reagent (Sigma-Aldrich Co., USA) was added and the cells incubated at 30oC on a platform shaker for at least 15 minutes. The lysed cells were then centrifuged at 13000rpm for 20 minutes to pellet cellular debris. 8µl of the enzyme-containing supernatant was added to a 96 well plate in wells containing 186µl assay buffer (Citrate Synthase Assay Kit, Sigma-Aldrich Co., USA), 3µl 30mM Acetyl CoA solution (Citrate Synthase Assay Kit, SigmaAldrich Co., USA) and 10mM DTNB solution (Citrate Synthase Assay Kit, Sigma-Aldrich Co., USA). 10µl 10mM Oxaloacetate solution was added immediately before reading spectrophotometrically at 412nm wavelength. The sample was measured every 10 seconds for a duration of 90 seconds using a spectrophotometric plate reader (MultiSkan-FC, Thermofisher scientific Ltd, United Kingdom). Citrate synthase (Citrate Synthase Assay Kit, Sigma-Aldrich Co., USA) was added to separate wells in place of sample as a positive control. Citrate synthase activity was then calculated using the equation below: Citrate Synthase Activity = Units (µmole/ml/min) = (ΔA412/min x V(ml) x dil) / ɛmM x L(cm) x Venz(ml) dil = dilution factor of original sample; V(ml) reaction volume (ml); V enz Sample volume (ml); ɛmM = extinction coefficient of 5-thio-2-nitrobenzoic acid (412nm = 13.6); L(cm) = pathway length of absorbance measurement (96 well plate reader = 0.552cm). 49 2.11.3 Phosphoenolpyruvate Carboxylase Activity Assay Whole cells of C. sporogenes were pelleted as previously using a bench top centrifuge (Eppendorf, United Kingdom). The supernatant was carefully removed and the cells washed with PBS. After two repeats to wash media components from the cells, 500µl CellLytic Cell lysis reagent (Sigma-Aldrich Co., USA) was added and the cells incubated at 30oC on a platform shaker for at least 15 minutes. The lysed cells were then centrifuged at 13000rpm for 20 minutes to pellet cellular debris. 10 µl of the enzyme-containing supernatant was added to a 1ml cuvette containing 0.9ml 110mM Tris Sulfate Buffer (pH 8.5 at 25°C), 50µl 300mM Magnesium Sulphate, 50µl 6mM ß-Nicotinamide Adenine Dinucleotide, 300µl 100mM sodium bicarbonate, 300µl Dioxane, 100µl 300mM Dithioerythritol solution and 1µl Malic dehydrogenase enzyme solution (6x103 mU/ml). A blank was prepared by substituting 10µl of 5mM MgSO4 in place of the enzyme containing supernatant. The mixture was mixed by inversion and 100µl of 30mM Phosphoenolpyruvate solution added immediately before measuring spectrophotometrically at 340nm. The decrease in OD340 was measured for a period of 5 minutes to achieve a value of ΔOD340/min. Phosphoenolpyruvate carboxylase activity was then calculated using the following equation (Wohl & Markus, 1972): Phosphoenolpyruvate carboxylase activity = Units/mg enzyme = (ΔOD340/min sample - ΔOD340/min blank) / (6.22) (mg enzyme/ml RM) 6.22 = Millimolar extinction coefficient of ß-NADH at 340nm; RM = Reaction Mix The results were compared with a standard curve prepared by substituting the sample with standards of known Phosphoenolpyruvate carboxylase concentration (Figure 4). 50 Standard Curve of Phosphoenolpyruvate Carboxylase Activity Total Absorbance Decrease (OD340) 0.6 0.5 R² = 0.9482 0.4 0.3 0.2 0.1 0 0 10 20 30 Phosphoenolpyruvate (U/ml) 40 50 Figure 4: Standard curve of Phosphoenolpyruvate Carboxylase (PEPc) Activity achieved by testing prepared samples of known PEPc concentration. Data shown are averages of triplicate biological and triplicate technical repeats. Error bars represent standard error of the mean. 51 2.12 Chemostat Culture A 2 litre fermentation vessel (Adaptive Biosciences, USA) equipped with a temperature probe, pH probe, dissolved oxygen probe, heating element and sampling line was used for chemostat culture of C. sporogenes. Flow-rate of BDM medium into the fermentation vessel was externally controlled using pump calibrated for accurate media flow-rate control. pH was controlled using computer software (BioDirector v1.2, Adaptive Biosciences, USA) attuned to the pH probe which introduced 1mM HCl or 1mM NaOH to maintain culture pH. Filtered (0.22 micron) input gas (varied depending on conditions, see below) was controlled using a flow meter and sparged through the culture and was released from the top of the fermentation vessel before passing through a desiccant cylinder packed with silica gel. A weir overflow, set to maintain a working culture-volume of 1.5L facilitated the removal of spent medium and fermentation products into a pre-sterilised waste bottle. All tubing connected to the fermentation vessel was oxygen-impermeable neoprene to maintain anaerobic conditions. The fermentation vessel was sterilised by autoclaving at 121oC for 30 minutes. The inoculum was prepared by emptying 1ml frozen stock culture into Erlenmeyer flasks containing 100ml BDM medium and incubating at 37 oC for 24h in an anaerobic workstation (Don Whitley Scientific Ltd, United Kingdom). Cultures were well mixed before being transferred into the fermentation vessel using a sterile 50ml syringe. Feed media flow rate was set to 0ml/min to allow initial growth in batch culture for 24h (timing based on previous experiments in study). Following batch growth, the contents of the fermentation vessel were tested to ensure culture of axenic C. sporogenes cultures. Growth-rate was then controlled by altering the flow-rate of growth medium into the fermentation vessel (ml/h) to give dilution rates of 500, 750 and 1L per hour in a working volume of 1.5L (0.33, 0.5 & 52 0.66 dilution rate). At least four volume changes were required before sampling following culture parameter changes, such as gas composition or growth rate, to ensure true representation of steady-state culture conditions. Biological repeats were sampled at least one volume change apart (working volume (ml) / dilution rate (ml/h) = volume change (h)). Experiments designed to test the effects of growth rate on C. sporogenes were supplied with 100% nitrogen gas (oxygen-free) in order to maintain strict anaerobic conditions in the culture vessel. Experiments testing the effects of CO2 were supplied with gas mixtures containing 10%, 25% or 50% CO2 and balance nitrogen at a controlled rate of 0.2L/h-1. Samples were collected when required in 30ml Universals sterilised by autoclaving. Sample turbidity (OD560) and biomass (Dry cell weight) were determined immediately following sampling and further sample was stored at -20oC for assay dependant pre-treatments. Every precaution was taken to ensure axenic cultures were maintained throughout experimentation, in particular when sampling and changing medium supply or waste containers. Following sampling and experimental condition changes, the contents of the fermentation vessel were tested to ensure culture of axenic C. sporogenes cultures. 53 2.13 Determination of Protein Concentration Supernatant protein concentration was assayed using the Bradford protein assay (Ernst & Zor, 2010). Samples were diluted with 0.15M NaCl to a final volume of 100µl to achieve a protein content within the range of the standard curve. The standard curve was generated by assaying samples of known protein content, achieved by preparing samples containing 0, 50, 100, 200 & 300mg/L of Bovine Serum Albumin (Sigma-Aldrich Co., USA). 5ml of Bradford reagent (Sigma-Aldrich Co., USA) was then added, vortex-mixed (Fisher scientific Ltd, United Kingdom) and incubated at room temperature for 30 minutes. The optical density was then measured spectrophotometrically at 595nm and compared to the standard curve to determine protein content (mg/L). 2.14 RNA Assay Whole cells of C. sporogenes were pelleted using a bench-top centrifuge (Eppendorf, United Kingdom) at 8000rpm for 10 minutes. The supernatant was carefully removed and the cells washed with PBS, resuspended and the process repeated. The washed pellet was then transferred to a 1ml ‘Fastprep’ tube containing 1ml of sodium phosphate/MT Buffer (RNApro fast direct kit, MPbio Llc, United Kingdom). The tube was then processed to release intracellular content using a Fastprep instrument (MP Bio Llc, United Kingdom) for 40 seconds at setting 6.0. The tube was then centrifuged at 14,000rpm for 5 minutes and the liquid transferred to a new microcentrifuge tube. 750µl of Phenol:Chloroform solution (MP Bio Llc, United Kingdom) was added and the sample vortexed (Fisher scientific Ltd, United Kingdom) for 10 seconds before being incubated at room temperature for 5 minutes. The sample was then centrifuged at 14,000rpm for another 5 minutes at 4 oC. The upper aqueous phase of the sample was transferred to a new centrifuge tube containing 200µl of 54 inhibitor removal solution (MP Bio Llc, United Kingdom) and inverted 5 times to mix thoroughly. The sample was then centrifuged at 14,000rpm for another 5 minutes at room temperature and the supernatant transferred to a new microcentrifuge tube containing 660µl of cold 100% isopropanol. The sample was inverted 5 times to mix thoroughly and centrifuged at 14,000rpm 15 minutes at 4oC. The supernatant was then discarded and the RNA containing pellet washed gently with 500µl of cold 70% ethanol (made with DEPC-H20 (MP Bio Llc, United Kingdom). The ethanol was then carefully removed and the pellet allowed to air dry for 5 minutes at room temperature. The pellet was resuspended in 200µl DEPC-H20 (MP Bio Llc, United Kingdom) and 600µl of RNAMATRIX Binding Solution (MP Bio Llc, United Kingdom) and 10µl RNAMATRIX Slurry (MP Bio Llc, United Kingdom) was added before being incubated at room temperature on a shaker table for 5 minutes. The sample was pelleted using a microcentrifuge (pulse spin) for 10 seconds and the supernatant removed carefully. The pellet was then washed in 500µl of RNAMATRIX wash solution (MP Bio Llc, United Kingdom) and allowed to air dry for 5 minutes at room temperature before being resuspended in 50µl DEPC-H H20 (MP Bio Llc, United Kingdom). RNA content was then analysed using a NanoDrop Spectrophotometer (Fisher scientific Ltd, United Kingdom) to provide RNA content in ng/ml. 55 2.15 Amino Acid Determination Whole cells of C. sporogenes were pelleted using a bench top centrifuge (Eppendorf, United Kingdom) set at 8000rpm for 10 minutes. The supernatant was carefully removed and the cells washed with PBS, remixed and repeated. The resulting sample supernatants were submitted to an external laboratory for their amino acid content (Alta Biosciencess Ltd, Uk). In brief, the supernatants were assayed for amino acid content by ion exchange chromatography using a series of sodium citrate or lithium citrate buffers. After separation via chromatography, the amino acids were reacted post column with a stream of Ninhydrin for photometric detection. Amino acid content of chemostat culture samples were compared with the analysis of sterile BDM medium (medium used for chemostat culture before inoculation) at the different growth rates detailed in the chemostat experiment methods to evaluate amino acid consumption by C. sporogenes cultures. 56 2.16 Genome-Scale Metabolic Network Analysis 2.16.1 Construction of C. sporogenes & C. botulinum GSMN The GSMN employed in this study was constructed by submitting the genomes of both C. botulinum (type A) and C. sporogenes (ATCC15579) to the RAST (Rapid Annotation using Subsystem Technology) server of the SEED. The genome sequence data of both organisms was obtained from the National Centre for Biotechnology Information (NCBI) FTP (Bao et al, 2011). RAST prokaryotic genome annotation server is a fully automated service for annotating bacterial genomes using genome sequence data (Aziz et al, 2008). The metabolic models were downloaded from ‘The Model SEED’ in SBML format and imported into Jymet2 for metabolic network based analysis. Jymet (and Jymet2) are graphics interface software written in Python programming language. The software allows models to be presented in a spreadsheet based format and provides the necessary data format for GSMN analysis. 2.16.2 Flux Balance Analysis Flux balance analysis (FBA) was utilised as a tool to achieve in silico analysis of C. botulinum metabolism throughout this study. FBA values were obtained by testing optimal biomass, PHB and toxin fluxes as individual objective functions using the GSMN of C. botulinum type A imported into Jymet2. Available metabolites were input into the GSMN representative of the available nutrients in BDM growth medium used throughout experimentation, including glucose, phosphate, ammonium and various amino acids and vitamins. Data on the effect of glucose, nitrogen, phosphate and amino acid fluxes (Section 3.6) on both PHB and Toxin flux 57 was achieved by FBA testing optimal PHB and toxin flux as individual objective functions at varied maximum nutrient fluxes. 2.16.3 Flux Variability Analysis Flux variability analysis (FVA) was achieved by simulating optimal PHB flux as an objective function using the GSMN of C. botulinum type A imported into Jymet2. The analysis was then repeated with sub-optimal PHB flux (50% of the maximum flux of PHB used to achieve optimal flux) as an objective function. The values of the 1114 individual reactions which calculated maximum PHB flux were arranged for both analysis and compared to evaluate which reactions demonstrated the greatest flux between the analysis achieved at optimal and sub-optimal PHB flux. This highlighted which reactions in the GSMN exhibited the greatest differences between achieving optimal and sub-optimal PHB flux and therefore which reactions were most important for achieving optimising of the objective function, PHB. The analysis was repeated with neurotoxin flux as an objective function and the results of both FVA arranged into a network of reactions fundamental to both PHB and neurotoxin (Section 4. 1). 58 Chapter 3: Validation of the surrogate system – Investigation of metabolism and biomarkers of neurotoxin biosynthesis in Clostridium sporogenes. 3.1 Strain Selection Selecting a strain of bacteria which lends its physiology to a processes advantage is an important consideration in industrial microbiology (Aucamp et al, 2014). Fifteen strains of C. sporogenes were obtained (See Chapter 2, Table 2) and evaluated in terms of cell growth rate and biomarker expression in order to assess their suitability to fulfil the primary objectives of the research project. Maximum cell density was obtained from incubation and turbidity measurement in CMM medium (Figures 5 & 6) for 24 & 72 hours at 37oC in an anaerobic cabinet. 59 Growth of fifteen strains of C. sporogenes in CMM for 24h 3 Growth (OD560) 2.5 2 1.5 1 0.5 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Strain number Figure 5: The growth (OD560) of fifteen different strains of C. sporogenes grown in CMM for 24 hours in an anaerobic cabinet maintained at 37 oC. As shown by the data, many strains had grown significantly in comparison with others, emphasising the metabolic variation between strains. Results displayed are averages of triplicate biological and technical repeats. Error bars are representative of the standard error of the mean. 60 Growth of fifteen strains of C. sporogenes in CMM for 72h 3 Growth (OD560) 2.5 2 1.5 1 0.5 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Strain number Figure 6: The growth (OD560) of fifteen different strains of C. sporogenes grown in CMM for 72 hours in an anaerobic cabinet maintained at 37oC. Growth at 72 hours was fairly analogous among the strains when compared with the data obtained at 24 hours, suggesting strain differences in lag phase and growth rates. Results displayed are averages of triplicate biological and technical repeats. Error bars are representative of the standard error of the mean. Selecting a strain of C. sporogenes which exhibited a significant growth rate was an important consideration owing to the time constraints of the research project. Furthermore, commercially used research strains of both C. sporogenes and C. botulinum typically complete a growth cycle within 24 hours (Bonventre and Kempe, 1960; Emeruwa & Hawirko, 1974) and PHB accumulation, flagellin biosynthesis and the initiation of sporulation are events observed in the first 24 hours of growth (Artin et al, 2010; Benoit et al, 1990; Bonventre and Kempe, 1960; Cooksley et al, 2010; Emeruwa & Hawirko, 1974). Sufficient growth after 24 hours incubation was therefore a necessary requirement when 61 selecting a strain suitable for the research project. The vast majority of physiological events concerning this research project occur within the first 24 hours of growth in both C. sporogenes and C. botulinum (Artin et al, 2010; Benoit et al, 1990; Bonventre and Kempe, 1960; Cooksley et al, 2010; Emeruwa & Hawirko, 1974), therefore cultures were grown for 72 hours (Figure 6) to confirm which strains had reached stationary phase by 24 hours incubation. The difference between cell density at 24 & 72 hours was then considered during strain selection (see equation below). The fifteen strains of C. sporogenes were also tested following 24 hours incubation for the accumulation of the energy storage polymer, PHB (Figure 7); a hypothesised biomarker of neurotoxin biosynthesis that has been directly correlated with toxin production in other species (Navarro et al, 2006). The presence of spores in the cultures was confirmed by microscopic observation in a haemocytometer and the presence of the protein flagellin in the culture supernatant was determined by SDS-PAGE. The fifteen strains were also tested on their ability to grow in a typical neurotoxin production medium and a defined clostridium growth medium (Karasawa et al, 1995). 62 PHB accumulation in fifteen strains of C. sporogenes following 24h incubation in CMM 1.8 PhB (mg/g biomass) 1.6 1.4 1.2 1 0.8 0.6 0.4 0.2 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Strain number Figure 7: PHB accumulation in fifteen different strains of C. sporogenes grown in CMM for 24 hours in an anaerobic cabinet maintained at 37 oC. PHB accumulation in the cells ranged from 0.61-1.29 which was less diverse than the growth observed at 24 hours between strains. As the data is expressed per gram of biomass, total PHB is dependent on biomass. Results displayed are averages of triplicate biological and technical repeats. Error bars are representative of the standard error of the mean. Many studies have investigated and confirmed the diversity of strains within a species (Davis & Nixon, 1992; Fernández-Espinar et al, 2001). The ranges of cell densities and PHB accumulation between the strains of C. sporogenes tested were indicative of the variation between phenotypes in a single species and emphasised the importance of strain selection in microbial processes. An algorithm (shown below) was applied to the data displayed in figure 5, 6 and 7 to mathematically select the strain which best lends its physiology to the research project. Supernatant flagellin, sporulation and growth in defined and selective 63 medium were satisfactory in all strains and therefore were not deciding factors in strain selection. Determining optimal strain selection = ∑(a/b-a) × c a = Growth (OD560) at 24h, b = growth (OD560) at 72h, c = PHB accumulation (mg/g biomass) Using the equation above, Strain 10 (NCIMB12343) was determined as the most suitable strain to experimentally achieve the primary objectives of the research project in comparison to the other strains tested. 64 3.2 Bioinformatic Comparison of C. sporogenes & C. botulinum C. sporogenes is widely used as a surrogate for studies in C. botulinum owing to the genetic similarity between the two species and the safety concerns associated with C. botulinum (Bradbury et al, 2012; Brown et al, 2012; Cooksley et al, 2010). Published comparisons have identified a gene homology of >85% between C. sporogenes and C. botulinum (Bradbury et al, 2012), with genome nucleotide similarities between 99-100% (Bradbury et al, 2012). In comparison, genetic homology is ~90% between different serotypes of C. botulinum (Virginia et al, 2014) and with the influence of the genetic variation between different strains of the same species, genetic taxonomy between C. sporogenes and C. botulinum can prove untenable (Kalia et al, 2011). Genetic annotation software (RAST prokaryotic genome annotation server, the SEED) was used to compare the genomes of C. botulinum (type A) and C. sporogenes (ATCC15579), which were obtained from NCBI FTP and used to develop the GSMNs employed in this study. A dot plot (Figure 8) was obtained of the ‘bidirectional hits’ arising from a comparison of the genomes. The plot represents genetic matches or regions of strong similarities (identical genes or function) between the C. sporogenes and C. botulinum genomes tested. The majority of genes compared were identical (>86%), confirming that C. sporogenes is genetically a suitable surrogate organism to study the metabolism and behaviour of C. botulinum. 65 Genomic comparison of C. botulinum and C.sporogenes Identical Genes Genetic Difference Figure 8: Genomic comparison of C. botulinum (type A) and C. sporogenes (ATCC15579) using RAST - Prokaryotic genome annotation server (the SEED). The chart represents the ‘bidirectional hits’ between the genomes which is representative of the ‘identical genes’. The comparison confirmed a strong genomic homology between the species reinforcing the use of C. sporogenes as a surrogate organism for studies of C. botulinum. 66 3.3 The Effect of Carbon, Nitrogen and Phosphate limitation on Growth & Biomarker Metabolism in Cultures of C. Sporogenes Secondary metabolism is triggered by nutrient limitations which activate biochemical pathways, resulting in the biosynthesis of secondary metabolites such as antibiotics and toxins (Chater & Horinouchi, 2003). Therefore alterations in culture growth medium, and the microbial environment, can have impact on both the quantity and diversity of microbial products produced in culture (VanderMolen et al, 2013). The biosynthesis of many different types of antibiotics and other secondary metabolites is regulated by phosphate (Martin, 2004). Production of these microbial products, including streptomycin, oxytetracycline, clavulanic acid, tylosin, echinomycin, cephalosporin, cephamycin C and thienamycin, among many other valuable compounds, occurs only under phosphate-limiting conditions (Liras et al, 1990; Martin, 1989; Masuma et al, 1986). The quality and quantity of the nitrogen source in the microbial environment has also been correlated with the biosynthesis of many secondary metabolites (Tudzynski, 2014). These include sterigmatocystin, aflatoxin, fusaric acid, patulin, gibberellin and penicillin (Calvo et al, 2002; Ehrlich and Cotty, 2002; Niehaus et al, 2014; Wiemann et al, 2009). Many secondary metabolites have been associated with carbon limitation. Glucose is the preferred microbial carbon source, and as a consequence, excess glucose availability can interfere with the production of many secondary metabolites (Demain, 1989; Ruiz et al, 2010). Carbon regulation is essential to the growth of most organisms and given the association between secondary metabolites and lower levels of growth, altering the carbon source and concentration is an effective method to control secondary metabolite biosynthesis (Demain, 1989; Ruiz et al, 2010). Important secondary metabolites including 67 streptomycin, kanamycin, istamycin, neomycin, gentamicin and β-lactam antibiotics are all suppressed by the presence of a sufficient carbon source (Demain, 1989; Ruiz et al, 2010; Piepersberg & Distler, 1997). A series of experiments were designed to explore the effects of carbon, nitrogen and phosphate limitations on both the growth and the production of the various biomarkers of interest with regard to neurotoxin biosynthesis in cultures of C. sporogenes (strain NCIMB12343). Cell density, dry cell weight, sporulation, PHB production and supernatant flagellin were experimentally assessed. The basal defined medium (BDM) detailed by Karasawa et al, 1995 (Section 2.2) (Karasawa et al, 1995) was adapted to meet the demands of the experiment, altering the concentration of glucose as a carbon source, ammonium sulphate as a nitrogen source and both sodium phosphate and potassium phosphate simultaneously as phosphate sources. A series of flasks containing a range of nutrient concentrations were prepared to test the effects of different growth - limiting nutrient concentrations on the metabolic behaviour of C. sporogenes (Section 2.2). All cultures were incubated in an anaerobic cabinet (Don Whitley Scientific, UK) at 37oC and inoculated with the same seed culture of C. sporogenes which was prepared from 24 hours growth at 37oC on Sheep blood agar in an anaerobic jar. Harvested cells were washed, resuspended in sterile Milli-Q water and measured in 100µl aliquots to inoculate the flasks. Biological triplicates were prepared for all flasks and strict axenic culture conditions were maintained throughout experimentation. 68 3.3.1 The Effect of Phosphate Concentration on the Growth of C. sporogenes Cultures grown in BDM containing a range of phosphate concentrations (125mg/L – 1500mg/L) were prepared to identify the concentration of phosphate which limited the growth of C. sporogenes. Sodium phosphate and potassium phosphate were altered simultaneously to maintain the original ratio of the nutrients detailed in the BDM medium (Karasawa et al, 1995) (Appendix 8.4). The effect of phosphate concentration on growth of C. sporogenes in flask culture 0.7 0.6 Growth (OD560) 0.5 1500mg/L 1000mg/L 0.4 500mg/L 0.3 250mg/L 125mg/L 0.2 0.1 0 0 3 6 9 12 18 24 Time (h) Figure 9: The effect of phosphate concentration on the growth of C. sporogenes in flask culture. All flasks were inoculated from the same seed culture and incubated at 37 oC in an anaerobic cabinet for 24h. Data displayed are averages of triplicate biological samples and triplicate technical repeats. Error bars represent the standard error of the mean. 69 Effect of phosphate concentration on growth at early stationary phase 0.6 R² = 0.9356 0.5 OD560 0.4 0.3 0.2 0.1 0 125mg 250mg 500mg 1000mg 1500mg Na2PO4/L Figure 10: The effect of phosphate concentration on the growth of C. sporogenes in flask culture. Data shown are cell density values at early stationary phase of the culture (12h). Phosphate concentration had a linear effect on growth (R2 = 0.9356) up to a concentration of 1000mg/L, demonstrated by no significant difference in growth being observed between cultures grown in 1000mg/L & 1500mg/L (P = 0.3877). Data displayed are averages of triplicate biological samples and triplicate technical repeats. Error bars represent the standard error of the mean. 70 The effect of phosphate concentration on growth rate (y = ln x) of C.sporogenes Growth rate (slope gradient/y = ln x) 1.1 1.05 1 0.95 0.9 0.85 125mg 250mg 500mg 1000mg 1500mg Na2PO4/L Figure 11: The effect of phosphate concentration on growth rate of C. sporogenes cultures. Values displayed are the slope gradient of the exponential phase of growth (calculated using the equation; y = ln x. Applies to all growth rates in section). Data used to calculate values were averages of triplicate biological samples and triplicate technical repeats. Final cell density (Figures 9 and 10) and growth rate (Figure 11) were affected by phosphate concentration. The concentration of phosphate present in the medium had a linear effect on growth (R2= 0.9356). This indicates that phosphate was limiting growth in cultures containing less than 1000mg/L. No statistical difference was found between the growth of C. sporogenes in flasks containing phosphate concentrations of 1000mg/L and 1500mg/L (P = 0.3877) using Student’s t-test. Despite demonstrating both reduced cell density and growth rate, cultures grown in medium containing 125mg/L phosphate were able to complete a full growth cycle, reaching stationary phase by 24h, several hours later than cultures provided with sufficient phosphate. According to literature, conditions in which growth is limited by the availability of phosphate in the growth medium encourage the biosynthesis of 71 secondary metabolites (Liras et al, 1990; Martin, 1989; Masuma et al, 1986). The significant effect on growth observed in these cultures (Figure 9, 10 & 11) advocated the requirement to analyse the metabolic effects of limiting phosphate on the potential biomarkers of neurotoxin biosynthesis. 72 3.3.2 The Effect of Nitrogen Concentration on the Growth of C. sporogenes The effect of ammonium sulphate concentration on the growth of C. sporogenes was assessed at five different concentrations, including flasks containing BDM with the exclusion of ammonium sulphate. Owing to the fact C. sporogenes requires amino acids for growth (Karasawa et al, 1995), ammonium sulphate was not the sole nitrogen source in the growth medium and therefore this experiment aimed to assess the effect of additional nitrogen on the growth of C. sporogenes cultures. The effect of Ammonium Sulphate concentration on growth of C.sporogenes in flask culture 0.6 Growth (OD560) 0.5 0.4 1000mg/L 750mg/L 0.3 500mg/L 250mg/L 0.2 0mg/L 0.1 0 0 3 6 9 12 18 24 Time (h) Figure 12: The effect of ammonium sulphate concentration on the growth of C. sporogenes in flask culture. All flasks were inoculated from the same seed culture and incubated at 37 oC in an anaerobic cabinet for 24h. Data displayed are averages of triplicate biological samples and triplicate technical repeats. Error bars represent the standard error of the mean. 73 Effect of ammonium concentration on growth at early stationary phase 0.6 Growth (OD560) 0.5 0.4 0.3 0.2 0.1 0 0mg 250mg 500mg 750mg 1000mg (NH4)2SO4/L Figure 13: The effect of ammonium concentration on the growth of C. sporogenes in flask culture. Data shown are cell density values at early stationary phase of the culture. Ammonium concentration had no significant effect on cell density at any of the concentrations tested. Data displayed are averages of triplicate biological samples and triplicate technical repeats. Error bars represent the standard error of the mean. 74 Growth rate (slope gradient/y = ln x) The effect of ammonium concentration on growth rate (y = ln x) of C.sporogenes 0.8 0.75 0.7 0.65 0.6 0.55 0.5 0mg 250mg 500mg 750mg 1000mg (NH4)2SO4/L Figure 14: The effect of ammonium concentration on growth rate of C. sporogenes cultures. Values displayed are the slope gradient of the exponential phase of growth (y = ln x). Data used to calculate values were averages of triplicate biological samples and triplicate technical repeats. Ammonium levels in the medium did not have a significant effect on the growth yield or growth rate (Figures 12, 13 and 14) implying that the presence of amino acids in the medium is confounding effects due to varying ammonium concentration. This suggests another nutrient is governing growth rate and that the amino acids present are the preferred nitrogen source in the media; meaning the cultures tested were never nitrogen limited. Despite this finding, it is possible that ammonia is being metabolised without having significant effects on growth, which may lead to differences in biomarker production between cultures. Therefore cultures containing 1000mg/L and 0mg/L (the highest and lowest concentrations tested) were used for further experimentation. 75 3.3.3 The Effect of Carbon Concentration on Growth of C. sporogenes Glucose is the preferred carbon source of many organisms, and as a consequence of this, glucose availability can interfere with the production of many secondary metabolites (Demain, 1989; Ruiz et al, 2010). In order to investigate the effects of altering the available carbon in cultures of C. sporogenes, glucose concentration was altered in flasks of BDM medium (Karasawa et al, 1995). Five different concentrations ranging from 200mg/L to 4000mg/L of glucose were tested. The effect of Glucose concentration on growth of C.sporogenes in flask culture 0.6 Growth (OD560) 0.5 0.4 4000mg/L 2000mg/L 0.3 1000mg/L 0.2 500mg/L 200mg/L 0.1 0 0 3 6 9 12 18 24 Time (h) Figure 15: The effect of glucose concentration on the growth of C. sporogenes in flask culture. All flasks were inoculated from the same seed culture and incubated at 37 oC in an anaerobic cabinet for 24h. Data displayed are averages of triplicate biological samples and triplicate technical repeats. Error bars represent the standard error of the mean. 76 Effect of glucose concentration on growth at early stationary phase 0.6 Growth (OD560) 0.5 R² = 0.8189 0.4 0.3 0.2 0.1 0 200mg 500mg 1000mg 2000mg 4000mg Glucose/L Figure 16: The effect of glucose concentration on the growth of C. sporogenes in flask culture. Data shown are cell density values at early stationary phase of the culture. Glucose concentration had a linear effect on growth from 200mg to 2000mg (R2 = 0.9256). No significant difference was observed between cultures grown in glucose concentrations of 2000mg/L & 4000mg/L (P = 0.7114), proving glucose was no longer limiting growth at concentrations of 2000mg/L and above. This affected the linearity of the correlation when cultures grown in 4000mg/L glucose were included (R2 = 0.8189). Data displayed are averages of triplicate biological samples and triplicate technical repeats. Error bars represent the standard error of the mean. 77 The effect of Glucose concentration on growth rate (y = ln x) of C. sporogenes Growth rate (slope gradient/ y = ln x) 0.8 0.75 0.7 0.65 0.6 0.55 0.5 200mg 500mg 1000mg 2000mg 4000mg Glucose/L Figure 17: The effect of glucose concentration on growth rate of C. sporogenes cultures. Values displayed are the slope gradient of the exponential phase of growth (y = ln x). Data used to calculate values were averages of triplicate biological samples and triplicate technical repeats. The data shows that glucose concentration is limiting growth in cultures grown in media containing less than 2000mg/L. No significant difference in growth was calculated in cultures grown in media containing 2000mg/L and 4000mg/L using a student’s t test (P = 0.7114). The effect of glucose concentration in the medium was linear below 2000mg/L (R2 = 0.9256). Growth rate was noticeably decreased in cultures containing 200mg/L and 500mg/L, however the highest growth rate was observed in cultures containing 1000mg/L, despite these cultures demonstrating lower total growth. This could be due to the lower 78 concentration of environmental glucose leading to a switch in uptake mechanism, resulting in a higher affinity for glucose, meaning it is metabolised faster during the exponential phase of growth. If this is the case, the glucose in the production medium will therefore be fully utilised earlier than in cultures with a higher concentration, leading to the lower total growth but higher growth rate observed in these cultures. 79 3.3.4 The Effects of Nutrient Limitation on Sporulation, PHB and Flagellin Production in Cultures of C. sporogenes Cultures grown at various concentrations of nitrogen, carbon and phosphate were selected to assay the production of the biomarkers significant to the research project; PHB, supernatant flagellin and sporulation. Samples taken from cultures grown in previously determined concentrations (Sections 3.3.1, 3.3.2 & 3.3.3) of nitrogen, carbon and phosphate were used to represent the effect of high concentrations of the nutrients on biomarker production. Cultures grown in the lowest concentration of nitrogen, carbon and phosphate which demonstrated sufficient growth were selected to represented nutrient limited conditions and examine the effects that these conditions had on the potential biomarkers of neurotoxin biosynthesis. Sporulation was assayed using a haemocytometer and phase contrast microscopy (Valdez & Piccolo, 2006); flagellin was assayed by SDS-PAGE with band densitometry (Twine et al, 2009) and PHB production by UV spectrophotometry following conversion into crotonic acid (Law & Slepecky, 1961). 80 3.3.4.1 The Effect of Phosphate Concentration on Biomarker Production in Cultures of C. sporogenes. The effects of phosphate concentration on bacterial metabolism and subsequent biosynthesis of microbial products has been demonstrated extensively (Liras et al, 1990; Martin, 1989; Martin, 2004; Masuma et al, 1986). Using concentrations known to establish metabolic differences in cultures of C. sporogenes (Figures 9, 10 & 11), flasks containing BDM (Karasawa et al, 1995) were prepared with sodium phosphate concentrations of 1500mg/L and 125mg/L. The experiment was used to test the effects of phosphate concentration on the potential biomarkers of neurotoxin biosynthesis, which were identified from reviewing the literature regarding this project in respective to the objectives of the research. Growth was assessed by both optical density and dry cell weight measurement; phosphate metabolism was analysed using reflectometry; sporulation was assayed using a haemocytometer and phase contrast microscopy (Valdez & Piccolo, 2006); flagellin was assayed by SDS-PAGE with band densitometry (Twine et al, 2009) and PHB production by UV spectrophotometry following conversion into crotonic acid (Law & Slepecky, 1961). 81 Growth, phosphate metabolism and biomarker production over time 1500mg/L Phosphate 20 0.7 18 0.6 Flagellin 0.5 14 12 0.4 10 0.3 8 6 0.2 4 Growth (OD560) Flagellin (µg/ml-2); PhB (% cell weight); Spores (spores/µl); Phosphate (mg x 102/L) 16 PhB Spores Phosphate Growth 0.1 2 0 0 0 3 6 9 12 18 24 Time (h) Figure 18: Growth, phosphate metabolism, sporulation, flagellin production and PHB accumulation over time in cultures of C. sporogenes grown in BDM medium (Karasawa et al, 1995) supplemented with 1500mg/L sodium phosphate (data shown represents phosphate concentration from sodium salt). This experiment was designed to analyse cultures grown with sufficient levels of phosphate for comparison with phosphate limited cultures. Phosphate concentrations of 1500mg/L were deemed sufficient by previous experiments which observed no significant effect on biomass or growth rate when decreasing phosphate to 1000mg/L (P = 0.3877). PHB and flagellin are displayed from 9h onwards as cellular density was too low for accurate determination before this stage of the culture. Data displayed are averages of triplicate biological samples and triplicate technical repeats. Error bars represent the standard error of the mean. 82 Growth, phosphate metabolism and biomarker production over time 125mg/L Phosphate 24 0.5 20 Flagellin 16 0.3 12 0.2 8 Growth (OD560) Flagellin (µg/ml-2); PhB (% cell weight); Spores (spores/ml x 3); Phosphate (mgx10/L) 0.4 PhB Spores Phosphate Growth 0.1 4 0 0 0 3 6 9 12 18 24 Time (h) Figure 19: Growth, phosphate metabolism, sporulation, flagellin production and PHB accumulation over time in cultures of C. sporogenes grown in BDM medium (Karasawa et al, 1995) supplemented with 125mg/L sodium phosphate. This experiment was designed to analyse cultures grown under phosphate limited conditions. Limitation was confirmed by previous experiments which demonstrated a significant effect on culture growth when phosphate concentration was 125mg/L (Figures 9, 10 & 11). PHB and flagellin are displayed from 9h onwards as cellular density was too low for accurate determination before this stage of the culture. Data displayed are averages of triplicate biological samples and triplicate technical repeats. Error bars represent the standard error of the mean. 83 It is well established that reducing phosphate in the growth medium leads to an increase in the accumulation of PHB (Lillo & Rodriguez-Valera, 1990; Ryu et al, 2007; Shang et al, 2003). In cultures containing 125mg/L sodium phosphate, PHB was increased, demonstrating the highest observed PHB accumulation of all conditions tested. Sporulation was also increased showing the highest number of spores in all flasks tested by 24 hours. Contrary to previous findings however, flagellin present in the supernatant was also increased in cultures limited by phosphate in the growth medium when compared with those grown in 1500mg/L sodium phosphate. These findings may require refining of the hypothesis to include the possibility that flagellin production and PHB accumulation are not directly correlated, but are being independently effected by the nutrient limitations imposed. On the other hand, these findings could be explained by the fact cultures grown in phosphate limited conditions demonstrated a delayed stationary phase/extended exponential phase. Transciptome analysis of R. eutropha in previous studies have shown that the gene fliC (H16_B2360) which encodes flagellin (Macnab, 2003) and flgE (H16_B0264) which encodes the flagellar hook protein, were significantly repressed in the stationary growth phase (Peplinski et al, 2010). 84 3.3.4.2 The Effect of Nitrogen Concentration on Biomarker Production in Cultures of C. sporogenes. Previous analysis demonstrated that altering the ammonium concentration of the growth medium had no effect on the growth rate or cell density of C. sporogenes cultures (Figures 12, 13 & 14). This suggests the amino acids present are most likely governing growth rate and are the preferred nitrogen source in the media. Despite this finding, it is possible that ammonium is being metabolised without having observable effects on growth rate or biomass, which may affect other areas of metabolism and lead to differences in biomarker production. Therefore cultures containing 1000mg/L and 0mg/L (the highest and lowest concentrations tested in previous experiments; section 3.3.2) were tested to assess the effects of ammonia concentration on the potential biomarkers of neurotoxin biosynthesis. Growth was assessed by both optical density and dry cell weight, ammonia metabolism was analysed using reflectometry, sporulation was assayed by haemocytometry using phase contrast microscopy, flagellin was isolated by SDS-PAGE and quantified using band densitometry and PHB accumulation was assayed by UV spectrophotometry following conversion into crotonic acid. 85 20 0.5 18 0.45 16 0.4 14 0.35 12 0.3 10 0.25 8 0.2 6 0.15 4 0.1 2 0.05 0 Growth (OD560); Ammonium (g/L) Flagellin (µg/ml-2); PhB (% cell weight); Spores (spores/ml x 3) Growth, ammonium metabolism and biomarker production over time 1000mg/L Ammonium Sulphate Flagellin PhB Spores Growth Ammonium 0 0 3 6 9 12 18 24 Time (h) Figure 20: Growth, ammonium metabolism, sporulation, flagellin production and PHB accumulation over time in cultures of C. sporogenes grown in BDM medium (Karasawa et al, 1995) supplemented with 1000mg/L of ammonium sulphate. This experiment was designed to analyse cultures grown with the addition of ammonium for comparison with cultures grown without supplementation (0mg/L ammonium). PHB and flagellin are displayed from 9h onwards as cellular density was too low for accurate determination before this stage of the culture. Data displayed are averages of triplicate biological samples and triplicate technical repeats. Error bars represent the standard error of the mean. 86 18 0.5 16 0.45 14 0.4 0.35 12 0.3 10 0.25 8 0.2 6 0.15 4 0.1 2 0.05 0 Growth (OD560) Flagellin (µg/ml-2); PhB (% cell weight); Spores (sporesx103:dcw) Growth, Ammonia metabolism and Biomarker Production over Time 0mg/L Ammonium Sulphate Flagellin PhB Spores Growth Ammonium 0 0 3 6 9 12 18 24 Time (h) Figure 21: Growth, ammonium metabolism, sporulation, flagellin production and PHB accumulation over time in cultures of C. sporogenes grown in BDM medium (Karasawa et al, 1995) with no ammonium sulphate (0mg/L). Despite previous experiments demonstrating the absence of ammonium from the growth medium did not affect growth rate or yield, this experiment was designed to investigate any metabolic effects regarding sporulation, flagellin production or PHB accumulation influenced by differences in available ammonium. PHB and flagellin are displayed from 9h onwards as cellular density was too low for accurate determination before this stage of the culture. Data displayed are averages of triplicate biological samples and triplicate technical repeats. Error bars represent the standard error of the mean. 87 As shown in Figure 20, cultures metabolised the majority of the ammonium present in the growth medium during the first 6 hours in flasks containing 1000mg/L ammonium sulphate, but was never fully utilised and remained at low concentrations in later stages of the culture. Despite this, these cultures demonstrated little difference in terms of both growth and sporulation when compared with cultures grown in the absence of ammonium (0mg/L; Figure 21). Interestingly however, both PHB and flagellin production were affected by the nutritional differences despite growth remaining unaffected, suggesting the ammonium was having metabolic effects on C. sporogenes but also that PHB and flagellin production are not directly correlated with growth. Flagellin present in the supernatant was increased (56%) in cultures containing 1000mg/L ammonium sulphate compared with those containing 0mg/L. Furthermore, PHB in the cultures with 0mg/L was increased compared to those grown in 1000mg/L ammonium sulphate. This finding agrees with the work of Raberg et al (Raberg et al, 2008) which demonstrated that limiting nitrogen increased cellular PHB and limited flagellin in R. eutropha. The correlation was also demonstrated by Peplinski et al (Peplinski et al, 2010). Whether the excess nitrogen supplied by the ammonium supplementing the growth medium is affecting PHB and flagellin as separate metabolites or whether the differences in flagellin production observed is directly correlated with the accumulation of PHB is difficult to define. Nevertheless, the prospect that flagellin production and PHB accumulation may be closely related, combined with the metabolic information on PHB accumulation from both the literature and demonstrated by this research, may prove to be a powerful tool in understanding flagellin as a by-product. 88 3.3.4.3 The Effect of Carbon Concentration on Biomarker Production in Cultures of C. sporogenes. Glucose is the preferred carbon source of many organisms, and as a consequence of this, glucose availability can interfere with the production of many secondary metabolites (Demain, 1989; Ruiz et al, 2010). The effect of lowering glucose concentration, resulting in the growth of C. sporogenes cultures being limited by the carbon source’s availability, is demonstrated in Figures 15, 16 and 17 (Section 3.3.3). Following analysis of the results, cultures grown in BDM (Karasawa et al, 1995) containing 4000mg/L and 500mg/L of Glucose were assessed to investigate the effect of carbon limitation on the metabolism of C. sporogenes and the subsequent influence on the potential biomarkers of neurotoxin biosynthesis. Growth was assessed by both optical density and dry cell weight, glucose metabolism was analysed using reflectometry, sporulation was assayed by haemocytometry using phase contrast microscopy, flagellin was isolated by SDS-PAGE and quantified using band densitometry and PHB accumulation was assayed by UV spectrophotometry following conversion into crotonic acid. 89 Growth, Glucose metabolism and Biomarker Production over Time 4000mg/L Glucose 0.6 14 0.5 Flagellin (µg/ml-2); PhB (% cell weight); Spores (spores/ml x 4) 12 0.4 10 8 0.3 6 0.2 4 0.1 2 0 Growth (OD560); Glucose (mg x 104/L) 16 Flagellin PhB Spores Growth Glucose 0 0 3 6 9 12 18 24 Time (h) Figure 22: Growth, glucose metabolism, sporulation, flagellin production and PHB accumulation over time in cultures of C. sporogenes grown in BDM medium (Karasawa et al, 1995) supplemented with 4000mg/L of glucose. This experiment was designed to analyse cultures of C. sporogenes grown with sufficient glucose for optimal growth. Concentrations of glucose which yielded optimal growth and those which resulted in carbon limited cultures were assessed in section 3.3.3 (Figures 15, 16 & 17). PHB and flagellin are displayed from 9h onwards as cellular density was too low for accurate determination before this stage of the culture. Data displayed are averages of triplicate biological samples and triplicate technical repeats. Error bars represent the standard error of the mean. 90 16 0.45 14 0.4 0.35 Flagellin (µg/ml-2); PhB (% cell weight); Spores (spores/ml x 3) 12 0.3 10 0.25 8 0.2 6 0.15 4 0.1 2 Growth (OD560); Glucose (mg x 104/L) Growth, Glucose metabolism and Biomarker Production over Time 500mg/L Glucose Flagellin PhB Spores Growth Glucose 0.05 0 0 0 3 6 9 12 18 24 Time (h) Figure 23: Growth, glucose metabolism, sporulation, flagellin production and PHB accumulation over time in cultures of C. sporogenes grown in BDM medium (Karasawa et al, 1995) supplemented with 500mg/L of glucose. This experiment was designed to analyse cultures of C. sporogenes limited by the available glucose in the growth environment. Confirmation that cultures of C. sporogenes grown in 500mg/L glucose are carbon limited was demonstrated by previous experiments (Figures 15, 16 & 17). PHB and flagellin are displayed from 9h onwards as cellular density was too low for accurate determination before this stage of the culture. Data displayed are averages of triplicate biological samples and triplicate technical repeats. Error bars represent the standard error of the mean. 91 The biomass yields in cultures containing 4000mg/L and 500mg/L reflected the two different glucose concentrations, confirming that cultures containing 500mg/L were glucose limited. In both cultures, the majority of glucose was metabolised in the first 6h of the culture, driving an exponential growth phase between 6 and 12h. Interestingly, the cultures grown in 4000mg/L of glucose never fully exhausted the glucose in the media, and it remained present throughout the growth cycle at low levels, suggesting one of the other nutrients present in the growth medium had become limiting before the end of the culture. In the flasks which originally contained 500mg/L glucose however, glucose is fully exhausted by 12h. It is during the time point between 9 and 12h when glucose is exhausted, that changes in the levels of biomarkers are observed. Perhaps the most interesting finding is the level of PHB present in the cells. Prior to glucose exhaustion, PHB accumulations to a relatively low concentration, which is expected in carbon limited cultures owing to the biochemistry of PHB production (Anderson & Dawes, 1990; Mignone & Avignone-Rossa, 1996; Steinbuchel, 1991). However, at the same time that glucose is exhausted in the cultures, an increase in PHB accumulation is being driven. This PHB increase is not observed in cultures grown in 4000mg/L glucose, in which glucose is never fully exhausted throughout the culture. Based on the data that glucose is fully exhausted at this time in cultures grown in 500mg/L glucose (Figure 23), it is likely that C. sporogenes is switching from metabolising glucose as a primary carbon source to relying on the carbon in the amino acids present within the production medium. Furthermore, flagellin in the supernatant was also observed to increase following glucose exhaustion, although whether these events are linked are not is less easy to define owing to the fact flagellin was observed to increase over time in other cultures. Nevertheless, this spurs the hypothesis to whether the metabolism of different amino acids lead to differences in observed biomarker production. 92 The data also showed lower levels of sporulation present in cultures grown in 500mg/L glucose, despite the optical density suggesting the cultures had begun death phase as early as 24h. It is already established that carbon availability (predominantly stored PHB) is necessary to drive sporulation (Emeruwa & Hawirko, 1973), and it is possible that the level of sporulation was decreased due to less available carbon in the production medium. There is a possibility that the growth rate was slowed due to C. sporogenes beginning to metabolise amino acids as a primary carbon source, and this slower growth rate led to less cells developing into spores by 24h, despite the decline in growth values (OD 560) observed. Another interesting observation was the flagellin present in the supernatant of the cultures grown in medium containing 4000mg/L glucose. The high concentration of glucose appeared to delay the production/excretion of flagellin into the growth medium. This also resulted in the lowest overall concentration of flagellin across all the conditions tested. This finding is similar to the findings of Raberg et al (Raberg et al, 2008) in R. eutropha, which demonstrated that flagella assembly was decreased in a rich complex medium. Although the phenomenon as to why flagellin is produced in excess remains unclear, the fact that providing the cultures with excess energy resources reduces its production advocates the possibility that excess flagellin production is a reaction to nutrient limitation and may be affected by secondary metabolism. One possible explanation for this mechanism may be the result of the organism attempting to express motility as a nutritional stress response, therefore increasing the organism’s ability to locate a stable nutrient supply. This finding may also be related to a pathogenic mechanism; coinciding the production of toxin with the pathogenic properties of flagellin (Claret et al, 2007; Haiko & Westerlund, 2013). 93 3.3.5 Summary of the Effects of Nutrient Limitation on Sporulation, PHB and Flagellin Production in Cultures of C. sporogenes The results of the experiments covered in section 3.3 demonstrate that the availability of nitrogen, carbon and phosphate present in the growth medium is affecting the production of flagellin, accumulation of PHB and sporulation in C. sporogenes; the potential biomarkers of neurotoxin biosynthesis which were identified following review of the literature regarding this research project. Phosphate limitation resulted in the greatest increase in all three biomarkers tested: Flagellin, PHB and spores (Table 2 & Figure 24). Although the effects of phosphate on bacterial metabolism are well established (Liras et al, 1990; Martin, 1989; Martin, 2004; Masuma et al, 1986), the data obtained investigating the effects of carbon and nitrogen concentration has highlighted the fact that the metabolism of C. sporogenes is also being controlled by the concentration of different amino acids present in the growth medium. Several members the of Clostridia genus are able to ferment amino acids in a mode of metabolism termed Stickland reactions (Bouillaut et al, 2013; Stickland, 1934; Stickland, 1935). Stickland reactions couple metabolism of pairs of amino acids in which one amino acid, acting as an electron donor, is oxidatively deaminated or decarboxylated and a second amino acid, acting as an electron acceptor, is reduced or reductively deaminated (Bouillaut et al, 2013; Stickland, 1934; Stickland, 1935). This allows certain species of bacteria, such as C. sporogenes and C. botulinum, to utilise amino acids as a primary carbon and/or nitrogen source (Bouillaut et al, 2013). This fact, coupled with our observation led hypothesis that amino acids present in the growth medium are affecting the metabolism of C. sporogenes which in turn influences the biosynthesis of the biomarkers of neurotoxin production, 94 presents a requirement to experimentally assess the effects of amino acid metabolism with regard to the primary objectives of the research project. Biomarker Nitrogen Limitation Carbon Phosphate Limitation Limitation Flagellin (mg/g) 11.9 10.0 17.2 PHB (mg/g) 91.7 97.3 131.7 Spores (spores x103/g) 133.3 48.5 273.8 Table 3: Maximum values of flagellin production, PHB accumulation and sporulation in cultures of C. sporogenes grown in low concentrations of nitrogen, carbon and phosphate. Phosphate limitation resulted in the highest values obtained in the experiment for flagellin production, PHB accumulation and sporulation. Low nitrogen and carbon availability in the growth medium also resulted in differences in observed biomarker metabolism, albeit less dramatic than the effects of phosphate limitation. Values shown are relative of culture biomass to express the effect of the nutrient limitation when compared with other cultures. Data displayed are averages of triplicate biological samples and triplicate technical repeats. 95 The effect of carbon, nitrogen and phosphate limitation on biomarker production in C. sporogenes 160 Flagellin (mg/g x 106) PhB (mg/g) Spores (spores/500mg x 103) 140 120 Flagellin 100 PhB 80 Spores 60 40 Spores 20 PhB 0 Nitrogen Flagellin Carbon Phosphate Figure 24: A perspective presentation summarising the effects of limited availability of phosphate, carbon and nitrogen in the growth medium on flagellin, PHB and spore production in cultures of C. sporogenes. Values shown are relative of culture biomass to express the effect of the nutrient limitation when compared with other cultures. Units of biomarkers have been adjusted to for improved comparison of data. Data shown are maximum values obtained from cultures grown for 24h. Data displayed are averages of triplicate biological samples and triplicate technical repeats. 96 3.4 Plackett-Burman Design Experimental Approach to Test the Effects of Amino Acid Metabolism on Flagellin Production, PHB Accumulation and Sporulation in Cultures of C. sporogenes The findings of the experiments detailed in section 3.3, together with the published physiology of the Clostridium amino acid production process (Bouillaut et al, 2013; Stickland, 1934; Stickland, 1935), led to the conclusion that amino acids are being utilised as a carbon and/or nitrogen source by C. sporogenes. This finding presented a requirement to metabolically assess the effects of amino acid consumption with regards to the objectives of this research project. The PBD experimental approach and principles (Section 1.5.2) were used to test the metabolic effects of supplementing the growth medium with amino acids in cultures of C. sporogenes (Table 4). The experiment was designed to assess the effect of individual amino acids on growth, PHB accumulation, flagellin production and sporulation in cultures of C. sporogenes incubated at 37oC for 24h in an anaerobic cabinet. Values displayed in the results are t-values calculated using the PBD experiment (Plackett & Burman, 1946) and are representative of the level of variance from flasks containing no additional amino acids and therefore represent the calculated observed effect of the trialled amino acid. The calculation used to determine the t-values for each variable (amino acid) also considered the interaction between the variables (Kalil et al, 2000). Data entered into the calculation were averages from triplicate biological and technical repeats. 97 Variables (Amino acids) Trial HIS SER PHE ALA ASP ASN ILEU VAL LEU GLU ARG 1 2 3 4 5 6 7 8 9 10 11 12 + + + + + + - + + + + + + - + + + + + + - + + + + + + - + + + + + + - + + + + + + - + + + + + + - + + + + + + - + + + + + + - + + + + + + - + + + + + + - Table 4: PBD experiment to test the effects of amino acid supplemented growth media on growth, PHB accumulation, flagellin production and sporulation in cultures of C. sporogenes. ‘+’ represents a supplementation of 1mmol of the corresponding amino acid to BDM growth medium (Karasawa et al). All flasks contained the same total volume and were inoculated from the same seed culture of C. sporogenes. 98 Effect of amino acid supplementation on growth (OD560) of C.sporogenes cultures assessed by Plackett-Burman Design 0.6 Significance of effect (t-value) 0.4 0.2 0 PHE ARG LEU GLU ALA HIS SER VAL ILEU ASP ASN -0.2 -0.4 -0.6 Figure 25: T-values of the variance of effect calculated following PBD to assess the effect of amino acid supplementation on the growth of C. sporogenes cultures incubated for 24h. Data shown is calculated from averages of triplicate biological and technical repeats. Calculated values were subject to testing against three ‘dummy variables’ which decreases the experimental error of the calculated t-values displayed. 99 Effect of amino acid supplementation on PHB Accumulation (mg/g) of C.sporogenes cultures assessed by Plackett-Burman Design 0.6 Significance of effect (t-value) 0.4 0.2 0 ILEU ASN ASP ALA VAL HIS GLU ARG SER LEU PHE -0.2 -0.4 -0.6 Figure 26: T-values of the variance of effect calculated following PBD to assess the effect of amino acid supplementation on PHB accumulation in C. sporogenes cultures incubated for 24h. Data shown is calculated from averages of triplicate biological and technical repeats. Calculated values were subject to testing against three ‘dummy variables’ which decreases the experimental error of the calculated t-values displayed. 100 Effect of amino acid supplementation on Supernatant Flagellin (µg/ml) of C.sporogenes cultures assessed by Plackett-Burman Design Significance of effect (t-value) 0.6 0.4 0.2 0 ALA HIS ASN GLU ASP ILEU SER VAL ARG PHE LEU -0.2 -0.4 -0.6 Figure 27: T-values of the variance of effect calculated following PBD to assess the effect of amino acid supplementation on flagellin production by C. sporogenes cultures incubated for 24h. Data shown is calculated from averages of triplicate biological and technical repeats. Calculated values were subject to testing against three ‘dummy variables’ which decreases the experimental error of the calculated t-values displayed. 101 Effect of amino acid supplementation on Sporulation(spores/ml) of C.sporogenes cultures assessed by Plackett-Burman Design 0.6 Significance of effect (t-value) 0.4 0.2 0 ASP SER GLU ASN HIS ALA ARG PHE VAL ILEU LEU -0.2 -0.4 -0.6 Figure 28: T-values of the variance of effect calculated following PBD to assess the effect of amino acid supplementation on sporulation in C. sporogenes cultures incubated for 24h. Data shown is calculated from averages of triplicate biological and technical repeats. Calculated values were subject to testing against three ‘dummy variables’ which decreases the experimental error of the calculated t-values displayed. 102 In terms of increased growth, phenylalanine supplementation had the largest observed effect (Figure 25). The data from the PBD experiment also shows that arginine is the second highest ranking amino acid in terms of increasing growth. If the assumption is made that C. sporogenes and C. botulinum share these aspects of metabolic physiology, this finding corresponds with the various sources in the literature which suggest growth and toxin production are largely effected by arginine in C. botulinum (Patterson-Curtis & Johnson, 1992). Asparagine had the largest detrimental effect on the growth of the organism and was the only amino acid tested which was not included in the original BDM formula; although no indication to why asparagine is excluded from the BDM is mentioned by Karasawa et al (Karasawa et al, 1995). Supplementation with a number of other amino acids, namely valine, isoleucine and aspartate resulted in detrimental effects on growth. Interestingly, the biosynthesis pathways for these three amino acids are all metabolites of pyruvate (Figure 29). Supplementation of these amino acids may have lowered the demand for metabolites involved in the TCA cycle, indicating that their biosynthesis may be rate limiting factors in cell biomass synthesis. Phenylalanine, the amino acid which resulted in the greatest increase in growth observed, has a biosynthesis pathway early in glycolysis (Figure 29). Therefore supplementation with phenylalanine may result in more available carbon and nitrogen for the production of metabolites in the TCA cycle by lowering the demand for phenylalanine, which is synthesised from precursors of glycolysis, leading to the conclusion that C. sporogenes grown in BDM is rate-limited by metabolites that feed directly into the TCA cycle. Moreover, the differences observed on growth, both positive and negative, may be due to the metabolic cost associated with the metabolism of amino acids. A substantial fraction of 103 the energy budget of bacteria is devoted to biosynthesis of amino acids (Akashi & Gojobori, 2002). The fuelling reactions of central metabolism provide precursor metabolites for synthesis of the 20 amino acids incorporated into proteins. Thus, synthesis of an amino acid entails a dual cost; energy is lost by diverting chemical intermediates from fuelling reactions and additional energy is required to convert precursor metabolites to amino acids. The range in amino acid biosynthesis cost varies from 11 ATP equivalents per molecule of Glycine, Alanine, and Serine to over 70 ATP per molecule of Tryptophan (Akashi & Gojobori, 2002). Observing decreased growth due to the supplementation of a particular amino acid, may be due to the metabolic demands caused by the supplementation. Many enzymes are inhibited by products and other metabolites in a feedback cycle (Engasser & Horvath, 1974) and certain amino acids are precursors for other amino acids (Figures 29a and 29b). Therefore, if a particular amino acid is added to the growth medium, this may result in a decreased affinty of the enzymes responsible for the production of other amino acids. Using Figure 29 and the results (Figure 25) of the experiment as an example, supplementation with Asparagine may be in turn lowering the the demand for Aspartate, its precursor. Owing to the fact Aspartate is also the precursor for Methoinine, this may result in the methonine limitation, which has a negative effect on growth. On the other hand, excess Asparagine may result in an increase in the biosynthesis of Methonine from Aspartate, which may carry a greater metabolic cost and thus carries energy expenditure which would otherwise be utilised in fueling growth dependant reactions. 104 Figure 29a: Generic pathways of fuelling reactions and amino acid biosynthesis pathways (blue arrows). Image adapted from Akashi & Gojobori, 2002 (Akashi & Gojobori, 2002). 105 Figure 29b: Central metabolic reactions and amino acid biosynthesis in C. botulinum, assuming anaerobic respiration is occurring over fermentation. (KEGG: Kanehisa Laboratories, 2012). 106 The PBD experiment showed that supplementation with Isoleucine, Asparagine and Aspartate and to a lesser degree Valine and Alanine, increased the cellular accumulation of PHB in cultures of C. sporogenes. Examination of the amino acid biosynthesis pathways (Figure 29) for these amino acids shows that Isoleucine, Asparagine and Aspartate are all synthesised from oxaloacetate; a key metabolite of the TCA cycle which reacts with acetyl CoA to form citrate (Pentyala & Benjamin, 1995). Therefore, it is possible that supplementation with either of these amino acids ultimately lowers the demand for oxaloacetate or citrate, leading to more available acetyl CoA; the precursor of PHB. Supplementation with Alanine and Valine also increased PHB accumulation, both of which are biosynthesised from pyruvate as a precursor. The increased PHB accumulation observed may therefore be due to the amino acid supplementation resulting in either more available pyruvate or a lessened demand for pyruvate, which would also result in more available acetyl CoA for PHB biosynthesis. The majority of amino acids used to supplement the growth medium resulted in an increase in flagellin production (Figure 27). This observation agreed with the results obtained in nitrogen limited cultures (Figure 21) which demonstrated that increasing available nitrogen in the growth medium increased flagellin production. Interestingly, the only amino acids which decreased flagellin when added to the growth medium were Arginine, Phenylalanine and Leucine; all three of which increased maximum growth. This could be due to changes in growth rate and time spent in exponential phase, owing to the fact it has been demonstrated that the genes responsible for flagellin production are down regulated during stationary phase (Peplinski et al, 2010). On the other hand, the presence of the amino acids could be having a metabolic effect on the production of flagellin precursors. 107 Supplementation with Aspartate, Asparagine and Serine all increased sporulation in the cultures significantly (Figure 28), as well as decreased growth, suggesting that supplementation with the amino acids was limiting a key element of central metabolism. Interestingly, Glutamate increased both growth and sporulation, suggesting the possibility that glutamate had increased growth rate, resulting in cell death and sporulation faster than cultures supplemented with other amino acids. 108 3.5 In silico Analysis Investigating the Effects of Nitrogen, Glucose and Phosphate Concentration on Neurotoxin Production by C. botulinum The results from the experiments detailed in sections 3.3 & 3.4 demonstrated that alteration of the growth medium composition is an effective method to alter the metabolic influences affecting the biomarkers hypothesised to correlate with neurotoxin biosynthesis in C. botulinum. In an effort to compare the results obtained in C. sporogenes on growth, PHB accumulation, flagellin production and sporulation with neurotoxin biosynthesis, we developed a GSMN of C. botulinum, created by Bioinformatics Research Scientist Sonal Dahale, University of Surrey. The GSMN was used to assess the effects of nitrogen (represented by ammonium for comparison with previous experiments in C. sporogenes), glucose and phosphate concentration on botulinum toxin production in silico. The data presented displays the FBA values generated when testing nitrogen, glucose or phosphate flux over a minimum to maximum range with optimal neurotoxin production (flux) as an objective function. The aim of this analysis was to generate data representative of the effects of supplementing the growth medium of C. botulinum cultures with these nutrients at a range of concentrations and the subsequent effects on neurotoxin production for comparison with the data obtained experimentally in C. sporogenes. 109 Effect of glucose flux on C. botulinum neurotoxin production (in silico) 14 Toxin Flux (arb. units) 12 10 8 6 4 2 0 0 500 1000 1500 2000 2500 3000 Glucose Flux (arb. units) Figure 30: The effect of glucose uptake on C. botulinum neurotoxin production (in silico) generated using FBA to test minimum to maximum range glucose flux with optimal toxin yield as an objective. GSMN used in analysis developed from C. botulinum type A genome data obtained from NCBI FTP (Bao et al, 2011). 110 Effect of Ammonium flux on C. botulinum neurotoxin production (in silico) 35 Toxin Flux (arb. units) 30 25 20 15 10 5 0 0 2000 4000 6000 8000 10000 Ammonium Flux (arb. units) Figure 31: The effect of ammonium uptake on C. botulinum neurotoxin production (in silico) generated using FBA to test minimum to maximum range ammonium flux with optimal toxin yield as an objective. GSMN used in analysis developed from C. botulinum type A genome data obtained from NCBI FTP (Bao et al, 2011). 111 Effect of Phosphate flux on C. botulinum neurotoxin production (in silico) 35 Toxin Flux (arb. units) 30 25 20 15 10 5 0 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 Phosphate Flux (arb. units) Figure 32: The effect of phosphate uptake on C. botulinum neurotoxin production (in silico) generated using FBA to test minimum to maximum range phosphate flux with optimal toxin yield as an objective. GSMN used in analysis developed from C. botulinum type A genome data obtained from NCBI FTP (Bao et al, 2011). The in silico data generated by the GSMN (summarised in Table 5) suggests that high ammonia, low glucose and low phosphate concentrations are optimal for neurotoxin production in cultures of C. botulinum. However, owing to the restraints of the model and FBA, growth of the organism is not accounted for, which is a crucial variable in toxin production (Artin et al, 2010; Bonventre and Kempe, 1960). Although the FBA generated by the model demonstrates that minimal glucose is optimal for toxin production, minimal glucose in the growth medium would experimentally result in drastically reduced growth, 112 demonstrated by the experiments testing glucose limitation in C. sporogenes (Figure 15 & 16). Therefore it is more realistic to assume excess ammonia and limited phosphate are conditions which would increase toxin production. These findings correlate with increased sporulation and flagellin production, which were both observed in cultures containing a high concentration of ammonium and limited phosphate in the data collected from cultures of C. sporogenes (Figures 19 & 20). This correlation, although yet to be tested experimentally, is an insight into the possible correlation between these biomarkers and the production of botulinum neurotoxin; a finding which offers potential progression towards the primary objectives of the research project. Biomarker Low Nitrogen Low Carbon Low Phosphate Flagellin (mg/g) 11.9 10.0 17.2 91.7 97.3 131.7 Spores (spores x103/g) 133.3 48.5 273.8 Toxin (in silico, FBA) - + + PHB (mg/g) Table 5: The effects of nitrogen, carbon and phosphate limitation on C. botulinum neurotoxin production (in silico) compared with the experimental results obtained in C. sporogenes demonstrating the effects of nutrient limitation on biomarker production. ‘+’ represents an increased production and ‘-‘ represents a decreased flux in botulinum toxin as a result of the tested nutrient at high or low values, generated by FBA. 113 3.6 In silico Analysis of the Correlation between PHB accumulation and Neurotoxin Production by C. botulinum The GSMN was used to test both the suitability of C. sporogenes as a surrogate for the studies of C. botulinum and whether in silico analysis would reveal any correlation between the production of PHB and toxin; a correlation which has been established in Bacillus thuringiensis (Navarro et al, 2006). The effects of glucose, ammonium, phosphate and amino acid supplementation tested experimentally in cultures of C. sporogenes (Sections 3.3 & 3.4) were compared with data generated using the GSMN of C. botulinum obtained from testing the flux of the nutrient supplementation on both PHB production and toxin production as an objective. The results not only confirmed the observed PHB accumulation in C. sporogenes to be a representation of C. botulinum metabolism, but also highlighted a linear correlation between PHB accumulation and neurotoxin production; validating the use of C. sporogenes as a surrogate organism for studies of C. botulinum and PHB as a potential biomarker of neurotoxin production. 114 Effect of glucose flux on PHB production in C. botulinum (in silico) 8200 PhB Flux (arb. units) 8000 7800 7600 7400 7200 7000 6800 6600 6400 6200 0 2000 4000 Glucose Flux (arb. units) Figure 33: The effect of glucose uptake on PHB production in C. botulinum (in silico) generated using FBA to test minimum to maximum range glucose flux with optimal PHB yield as an objective. GSMN used in analysis developed from C. botulinum type A genome data obtained from NCBI FTP (Bao et al, 2011). 115 Effect of Ammonium flux on PHB production in C. botulinum (in silico) 10000 PhB Flux (arb. units) 9000 8000 7000 6000 5000 4000 3000 2000 1000 0 0 2000 4000 6000 Ammonium Flux (arb. units) 8000 10000 Figure 34: The effect of ammonium uptake on PHB production in C. botulinum (in silico) generated using FBA to test minimum to maximum range ammonium flux with optimal PHB yield as an objective. GSMN used in analysis developed from C. botulinum type A genome data obtained from NCBI FTP (Bao et al, 2011). 116 Effect of Phosphate flux on PHB production in C. botulinum (in silico) 9000 PhB Flux (arb. units) 8000 7000 6000 5000 4000 3000 2000 1000 0 0 2000 4000 6000 8000 Phosphate Flux (arb. units) Figure 35: The effect of phosphate uptake on PHB production in C. botulinum (in silico) generated using FBA to test minimum to maximum range phosphate flux with optimal PHB yield as an objective. GSMN used in analysis developed from C. botulinum type A genome data obtained from NCBI FTP (Bao et al, 2011). 117 10000 40 [1] 9000 60 8500 50 8000 40 7500 30 7000 2000 Alanine Flux (arb. units) 6000 5000 20 4000 3000 10 2000 1000 0 0 0 Toxin 1000 2000 Phenylalanine Flux (arb. units) PhB Toxin PHB [3] 10500 PhB Flux (arb. units) 7000 30 [4] 9500 10000 9500 9000 R² = 0.988 8500 8000 7500 PhB Flux (arb. units) 0 Toxin Flux (arb. units) 9500 PhB Flux (arb. units) Toxin Flux (arb. units) 8000 70 [2] 9000 PhB Flux (arb. units) 80 8500 R² = 0.9011 7500 6500 5500 4500 30 50 Toxin Flux (arb. units) 70 0 10 20 Toxin Flux (arb. units) 30 Figure 36: The correlation between PHB and neurotoxin production in C. botulinum (in silico). Increased Alanine uptake resulted in both increased PHB flux and Toxin flux [1]. Increased Phenylalanine uptake resulted in both decreased PHB flux and Toxin flux [2]. Comparison of the data demonstrated a linear correlation between PHB and neurotoxin production (R2 = 0.988 & R2 = 0.9011) in relation to both Alanine uptake [3] and Phenylalanine uptake [4]. Data generated using FBA. GSMN used in analysis developed from C. botulinum type A genome data obtained from NCBI FTP (Bao et al, 2011). 118 The results generated by the GSMN suggest a strong linear correlation (R 2 = 0.988 & 0.901) between PHB accumulation and neurotoxin production in C. botulinum, which was demonstrated by plotting the results obtained from minimum to maximum flux of alanine and phenylalanine when testing PHB and toxin production as individual objectives (Figure 36). Alanine flux increased both PHB and toxin production (Figure 36). This finding was consistent with experimental data obtained in C. sporogenes which demonstrated alanine supplementation increased the accumulation of PHB (Figure 26). Phenylalanine flux decreased both PHB and toxin production (Figure 36), which was also consistent with the data on phenylalanine supplementation in cultures of C. sporogenes (Figure 26). In silico analysis of the effects of glucose, ammonium and phosphate uptake also demonstrated the correlation between PHB (Figures 33, 34 & 35) and neurotoxin production (Figures 31, 32 & 33) and agreed with experimental findings in C. sporogenes (section 3.3). These results provide confidence in the use of C. sporogenes as a surrogate organism and PHB as a biomarker of neurotoxin production in C. botulinum. 119 3.7 Chapter Conclusions Phosphate limitation in cultures of C. sporogenes gave rise to increased PHB accumulation (Figure 19), presumably due to the increased availability of the storage compounds precursor; acetyl CoA, when the progression of the TCA cycle is limited by the availability of inorganic phosphate for optimal ATP biosynthesis (Lillo & Rodriguez-Valera, 1990; Raberg et al, 2008; Ryu et al, 2007; Shang et al, 2003). The PBD experiment examining the effects of amino acids present in the culture medium (Section 3.4), yielded results which were consistent with the hypothesis that PHB biosynthesis competes with the metabolites of the TCA cycle for carbon flux and confirmed the hypothesis that amino acid metabolism effects biomarker production. Extracellular flagellin production and sporulation were also increased in cultures subject to limited phosphate (Figure 19). High concentrations of glucose and the addition of phenylalanine, leucine and arginine individually, resulted in decreased flagellin production (Figures 22 & 27) and addition of several amino acids into the growth medium resulted in increased sporulation (Figure 28). In silico analysis using a GSMN of C. botulinum and subsequent comparison with experimental data obtained in cultures of C. sporogenes has vindicated the use of C. sporogenes as a surrogate organism for studies of C. botulinum. Furthermore, in silico analysis has demonstrated a linear correlation (R2 = 0.988) between PHB and neurotoxin production in C. botulinum; a correlation to be explored further in pursuit of achieving the primary objectives of this research project. 120 Chapter 4: Assimilating Computational and Experimental Research Tools to Investigate the Correlation between Poly-β-hydroxybutyrate and Botulinum Neurotoxin The results of the experiments described in Chapter 3 revealed a potential correlation between neurotoxin biosynthesis and PHB accumulation (R2 = 0.988) (Section 3.6). PHB has been directly correlated with toxin production in other species previously (Navarro et al, 2002). Furthermore, the biochemistry of the carbon storage polymers metabolism lends evidence to support the hypothesis that the relationship may prove analogous to glycogen and antibiotic production in Streptomyces spp. (Anderson & Dawes, 1990; Lillie & Pringle, 1980; Salas & Mendez, 2005; Steinbuchel, 1991). The data therefore presented an opportunity to investigate and potentially exploit the correlation in order to achieve the primary objectives of the project, driving our subsequent research in an effort to increase our knowledge on the correlation between neurotoxin and PHB. 4.1 Extrapolating PBD results using Flux Variability Analysis The major challenge implicit in this research project is the inability to culture the production organism outside of facilities with elevated levels of microbiological and controlled access containment. The extreme pathogenicity of C. botulinum (Brown et al, 2012; Cooksley et al, 2010), therefore, results in an inability to assay neurotoxin production directly in these investigations. However, C. sporogenes is widely used as a surrogate organism for testing the metabolism of C. botulinum (Brown et al, 2012; Cooksley et al, 2010) and in this study, genome-scale metabolic modelling has been used as a tool to validate the surrogate system and relate findings to the production organism, including calculating toxin yield in silico. 121 In Chapter 3, PBD was utilised yielding results which demonstrated amino acid supplementation affects the production of the energy storage polymer PHB in cells of C. sporogenes (Figure 26). Genome-scale metabolic modelling suggested that this relationship also exists in C. botulinum and demonstrated a correlation between PHB and toxin production in C. botulinum (Figures 32-36). In an effort to explore the biochemical pathways and reactions which effect the correlation between PHB and neurotoxin in C. botulinum, FVA was utilised together with the results obtained in Chapter 3. Despite the correlation between PHB and C. botulinum neurotoxin being established using FBA, FVA can be utilised to predict ranges of flux through particular pathways and reactions as well as analyse the reactions which contribute to the observed ranges of flux, providing a more realistic analysis of products which have many factors contributing to maximum optimisation (Bushell et al, 2006), such as PHB and neurotoxin in this study. FVA was utilised with an objective of obtaining potential PHB increasing supplementation targets based on the reactions which demonstrated the greatest fluxes when PHB was tested as an objective using FBA. The analysis resulted in several reactions which effect the accumulation of PHB. The reactions were organised into a metabolite-increasing network, which has been used successfully to optimise antibiotic yields previously (Bushell et al, 2006). This analysis highlights potential supplementation targets, which based on the biochemistry of the reactions in the network, should theoretically increase PHB accumulation when availability is increased. 122 Figure 37: Network of high flux reactions - Poly-β-hydroxybutyrate (FVA) Alanine Aspartic Acid L-4-Aspartyl Phosphate L-Aspartate4-Semialdehyde Glycerol Threonine Acetaldehyde acetoacetic acid O-phospho-L-homoserine Homoserine Coenzyme A Succinyl-CoA 3-Acetoacetyl-CoA Succinate (S)-3-Hydroxybutyryl-CoA - Highly significant reactions (FVA >10000) O-Succinyl-L-Homoserine Poly-β- hydroxybutyrate - Significant reactions (2000-10000FVA) 123 The network of reactions displayed in Figure 37 was generated using FVA, comparing the results of optimal PHB with suboptimal PHB as an objective function. This determines the reactions which have the greatest effect on PHB yield and therefore highlight reactions which can be targeted with an objective of increasing PHB experimentally. The analysis was repeated with optimal and suboptimal neurotoxin yield as an objective for comparison with the results obtained on PHB metabolism and to further our knowledge on the biosynthetic pathways which result in the in silico correlation calculated (Figures 32-36). 124 Figure 38: Network of High Flux Reactions - Botulinum Toxin (FVA) Alanine Dihydroxy-acetone-phosphate L-Glycerol-1-phosphate Aspartic Acid L-4-Aspartyl Phosphate L-Aspartate4-Semialdehyde Iminoaspartate Glycerol Threonine Acetaldehyde acetoacetic acid O-phospho-L- Homoserine Homoserine Coenzyme A Succinyl-CoA 3-Acetoacetyl-CoA Succinate (S)-3-Hydroxybutyryl-CoA - Highly significant reactions (FVA >1000) O-Succinyl-L-homoserine Poly-β- hydroxybutyrate - Significant reactions (<1000FVA) 125 The results of the FVA and generated networks displayed in Figures 37 & 38 provide confidence to the correlation between PHB accumulation and neurotoxin biosynthesis in C. botulinum, demonstrating multiple pathways which are significant to both metabolites and the importance of PHB biosynthesis to neurotoxin flux. Interpreting the analysis to assume increasing flux of the significant reactions to both PHB and toxin will increase yield, the results of the FVA highlighted three potential PHB and neurotoxin increasing supplements; Aspartic acid, Threonine and Homoserine. To experimentally test the hypothesised PHB and neurotoxin increasing supplements, and simultaneously the accuracy of the GSMN, flasks containing a defined medium (BDM) (Karasawa et al, 1995) were supplemented individually to assess the differences observed in PHB accumulation and compared with data obtained from cells grown without supplementation. 126 4.2 Increasing PHB yields using a targeted amino acid supplementation approach Aspartic acid, Threonine and Homoserine were added to BDM (Karasawa et al, 1995) at concentrations of 7.5mM and 15mM individually to test the hypothesis that providing cultures of C. sporogenes with increased availability of the amino acids will increase PHB accumulation, as demonstrated by the results of the FVA (Figure 37). All flasks were inoculated from the same seed culture and incubated at 37 oC in an anaerobic cabinet for 24h. The results were compared with cultures grown in BDM under the same conditions with no additional amino acids as a negative control. 127 Effect of amino acid supplementation on PHB accumulation in cultures of C.sporogenes 180 170 PHB (% of BDM value) 160 150 140 130 120 110 100 90 80 BDM Threonine 7.5mM Threonine 15mM Homoserine Homoserine Aspartic Acid Aspartic Acid 7.5mM 15mM 7.5mM 15mM Figure 39: The Effect of Aspartic acid, Threonine and Homoserine on PHB accumulation in cultures of C. sporogenes. Supplements were added to BDM (Karasawa et al, 1995) at concentrations of 7.5mM and 15mM individually. Data shown are values obtained following 24h incubation. Data displayed are averages of triplicate biological samples and triplicate technical repeats. Error bars represent the standard error of the mean. 128 Effect of amino acid supplementation on PHB accumulation in cultures of C. sporogenes PHB (% increase from BDM value) 50 *** 45 40 35 30 25 20 15 10 * 5 0 Aspartic acid Threonine Homoserine Figure 40: The Effect of Aspartic acid, Threonine and Homoserine on PHB accumulation in cultures of C. sporogenes. Data shown are averages of supplementation at 7.5mM & 15mM. Data shown are values obtained following 24h incubation. Data displayed are averages of triplicate biological samples and triplicate technical repeats. Error bars represent the standard error of the mean. Statistical significance of the observed PHB increase of cultures supplemented with Threonine (P = 0.045) Homoserine (P = 0.001) and Aspartic acid (P = 0.106) were calculated using a student’s t test. ‘*’ represents a statistical significance of P = ≤0.05 and ‘***’ represents a statistical significance of P = ≤0.001 (applies to all data). 129 Cells grown in medium supplemented with Aspartic acid contained on average 10% more PHB (Figure 40). Higher concentrations of Aspartic acid (15mM) resulted in 21% more PHB when compared with cultures grown with no additional amino acids (Figure 39), leading to the conclusion that Aspartic acid is required at higher concentrations to observe an appreciable effect on the accumulation of PHB. This is possibly due to the plethora of cellular functions governed by aspartic acid, which may possibly take priority over PHB biosynthesis with regard to aspartic acid consumption. The addition of Threonine to the growth medium on average resulted in a 6% increase in total PHB in cells of C. sporogenes (Figure 40), which was calculated as statistically significant using a student’s t test (P = 0.045). PHB was only increased in cultures supplemented with 15mM Threonine however (Figure 39), with no observed effect when the growth medium was supplemented with lower concentrations of the amino acid (7.5mM). The most interesting result of the experiment was observed in cultures supplemented with Homoserine, which resulted in a 43% increase in cellular PHB (Figure 40) following incubation for 24h (P = 0.001). In contrast to the results obtained from cultures supplemented with Threonine and Aspartic acid, those grown in concentrations of 15mM Homoserine accumulated less PHB than those supplemented with a lower concentration of 7.5mM, although both concentrations demonstrated a significant increase in PHB when compared with cultures grown without additional amino acids (Figure 39) (P = 0.001). This is possible explained by the effect of high concentration of Homoserine (15mM) on the growth of C. sporogenes, which demonstrated an extended lag phase (Figure 41). Owing to the fact PHB accumulation is largely affected by the growth cycle (Anderson & Dawes, 1990; Mignone & Avignone-Rossa, 1996; Steinbuchel, 1991) (Section 3.3.4), this is likely to have 130 affected the yield of PHB observed. The inhibitory effect of Homoserine on growth has been demonstrated previously in bacteria including E. coli (Kotrea et al, 1973; Sritharan et al, 1987). The most plausible explanation of this effect is the inhibitory effect of Homoserine on glutamate uptake rate at high concentrations, observed at 15mM Homoserine by Sritharan et al (Sritharan et al, 1987). The effect of Homoserine supplemtation on the growth of C. sporogenes 0.45 0.4 Growth (OD560) 0.35 0.3 BDM 0.25 Homoserine 7.5mM Homoserine 15mM 0.2 0.15 0.1 0.05 0 0 3 6 9 12 15 18 21 24 Time (h) Figure 41: The effect of Homoserine concentration on the growth of C. sporogenes cultures. Increasing Homoserine concentration led to an extended lag phase, which may have affected the total PHB accumulated by 24h. Data displayed are averages of triplicate biological samples and triplicate technical repeats. Error bars represent the standard error of the mean. 131 These findings identify Homoserine as a primary nutrient supplement capable of increasing the accumulation of PHB in cultures of C. sporogenes. In addition, based on the results of the in silico analysis (Figures 37 & 38), increasing Homoserine concentration in the growth medium results in increased neurotoxin biosynthesis in C. botulinum. The correlation between the experimental and in silico data added further confidence to the viability of C. sporogenes as a suitable surrogate organism for this study and the confirmed the reliability of the GSMN utilised for analysis of C. botulinum metabolism in this research project. Furthermore, the findings support the hypothesis proposed in chapter 3; that PHB accumulation is strongly correlated with neurotoxin biosynthesis in C. botulinum. To investigate the correlation further, the biosynthetic pathways of both PHB and neurotoxin were examined. Figure 42 shows the metabolic network of reactions significant to neurotoxin biosynthesis in C. botulinum (Kanehisa laboratories, 2013). Interestingly, the reactions include acetyl-CoA, acetaldehyde and glycerol as metabolites; all three of which were identified as important metabolites with regard to toxin flux using FVA (Figure 38). Furthermore, acetaldehyde and acetyl-CoA are both break down products of PHB, highlighting a possible biosynthetic link for the in silico correlation. Figure 43 displays the possible network of reactions correlating PHB and neurotoxin production based on this hypothesis. Although it is difficult to prove this hypothesis, it has highlighted metabolites which are associated with both PHB and neurotoxin, suggesting the possibility of a directly or indirectly linked correlation. 132 Figure 42: Metabolic reactions essential for neurotoxin biosynthesis in C. botulinum (Kanehisa laboratories, 2013). 133 Dihydroxy-acetone-phosphate L-Glycerol-1-phosphate Iminoaspartate Glycerol Aspartic Acid Alanine Threonine Acetaldehyde Acetoacetic acid Glycerol-3-phosphate O-phospho-L- homoserine Coenzyme A Succinyl-CoA 3-Acetoacetyl-CoA Succinate (S)-3-Hydroxybutyryl-CoA Poly-β- hydroxybutyrate Acetaldehyde Neurotoxin Biosynthesis Acetyl-CoA Figure 43: Network of the correlation reactions between PHB and neurotoxin biosynthesis. Network generated using a combination of FVA and genomic pathway examination of C. botulinum using KEGG (Kanehisa laboratories, 2013). 134 4.2.1 The relationship between TCA cycle - derived amino acids and PHB accumulation Examination of the amino acid biosynthetic pathways of the Threonine, Aspartic Acid and Homoserine (Akashi & Gojobori, 2002) revealed an interesting association; all three supplements tested have biosynthetic pathways originating from Oxaloacetate (OAA) (Figure 44). OAA is a TCA cycle intermediate which reacts with acetyl-CoA, a precursor of PHB, to form citrate (Anderson & Dawes, 1990; Steinbuchel, 1991). The finding that increasing the availability of OAA derived amino acids results in an increase in PHB accumulation, therefore agrees with studies on PHB which have concluded limiting the carbon requirement of the TCA cycle results in an increased accumulation of the energy storage polymer (Lillo & Rodriguez-Valera, 1990; Raberg et al, 2008; Ryu et al, 2007; Shang et al, 2003). Figure 43a: The biosynthetic pathways of amino acids fuelled via central metabolism. Supplementation with Aspartic acid, Threonine and Homoserine gave rise to increased cellular PHB. As displayed, the amino acids have biosynthetic pathways originating from the same metabolite; Oxaloacetate. Image adapted from Akashi & Gojobori, 2002 (Akashi & Gojobori, 2002). 136 This suggests that by increasing the availability of threonine, aspartic acid and Homoserine, less OAA is required for the amino acids biosynthesis and as a consequence of this, availability of carbon for the TCA cycle is increased, requiring less activity to generate amino acids and demanding less carbon from glycolysis in the form of acetyl-CoA; the precursor of PHB (Anderson & Dawes, 1990; Steinbuchel, 1991). Isoleucine biosynthesis provides a bridge between the metabolites pyruvate and oxaloacetate owing to the fact it can be biosynthesised from pyruvate or the supplementation targets examined; aspartic acid, threonine and Homoserine. Supplementation with isoleucine also provided the largest increase in PHB in the PBD experiments (Figure 26), agreeing with this hypothesis. Furthermore, anaplerotic metabolism provides a direct link between phosphoenolpyruvate (PEP), the intermediate of pyruvate, and oxaloacetate, therefore the increase in PHB observed may be a result of increased available pyruvate, decreased requirement of oxaloacetate, or a combination of both. 137 4.3 Investigating the Relationship between PHB Accumulation and Pathways of Central Metabolism using Enzymatic Assay In an effort to increase our understanding of the effects observed in previous experiments with regards to the effects of supplements on PHB accumulation and to test the hypothesis that amino acid supplementation affects central metabolic pathway fluxes, enzymatic assays were used to test the activity of Glucose-6-phosphate dehydrogenase (G6PD), Citrate synthase and Phosphoenolpyruvate carboxylase (PEPc). This experiment was designed to analyse the activity of particular areas of central metabolism under conditions permissive for increased PHB accumulation, to analyse the metabolic differences which contribute to PHB yield in C. sporogenes. G6PD catalyses the rate limiting step of the pentose phosphate pathway (Tian et al, 1998); a series of reactions which provides the majority of cellular NADPH. NADPH is the principle cellular reductant and plays a vital role in the regulation of energy generating redox reactions (Tian et al, 1998). As an energy storage polymer, reactions effecting energy generation and growth kinetics are likely to affect the metabolism of PHB. Pentose phosphate pathway activity was therefore assessed by assaying G6PD activity in relation to PHB accumulation. 138 Figure 43b: The biosynthetic pathways of amino acids fuelled via central metabolism and the reactions catalysed by Glusose-6-Phosphate dehydrogenase (G6PD), Citrate Synthase and Phosphoenolpyruvate Carboxylase (PEPc). Image adapted from Akashi & Gojobori, 2002 (Akashi & Gojobori, 2002). 139 Citrate synthase is a TCA cycle enzyme which catalyses the reaction between acetyl-CoA and OAA to form citrate (Wigand & Remington, 1986). Acetyl-CoA is the precursor of PHB (Anderson & Dawes, 1990; Steinbuchel, 1991) and OAA is a metabolite of the biosynthetic pathways of amino acids found to increase PHB accumulation by this studies previous experiments (Figures 40 & 43) (Akashi & Gojobori, 2002). Assessing activity of this reaction was therefore deemed vital to understanding the relationship between OAA, acetyl-CoA and PHB accumulation, and simultaneously understanding the findings regarding amino acids which increase PHB biosynthesis when availability is increased. Anaplerotic reactions are responsible for replenishing intermediates of the TCA cycle using metabolites from glycolysis and vice versa (Owen et al, 2002). PEPc is an enzyme which reacts with the glycolysis and pyruvate intermediate phosphoenolpyruvate (PEP), to refuel OAA providing carbon to the TCA cycle (Kai et al, 2003; Owen et al, 2002). With the findings on the effect of OAA derived amino acids on PHB accumulation (Figures 40 & 43) and the literature on PHB biosynthesis which has demonstrated that limiting the carbon requirement of the TCA cycle results in an increased accumulation of PHB (Lillo & RodriguezValera, 1990; Raberg et al, 2008; Ryu et al, 2007; Shang et al, 2003), PEPc activity was assayed to investigate the relationship between TCA cycle anaplerotic reactions and PHB biosynthesis. Cultures grown in a defined growth medium (Karasawa et al, 1995) supplemented with Aspartic acid, Threonine and Homoserine at concentrations of 7.5mM and 15mM individually were tested for G6PD, Citrate synthase and PEPc activity for comparison with PHB accumulation. The results were compared with cultures grown in BDM under the same conditions with no additional amino acids as a negative control. 140 Glucose-6-phosphate dehydrogenase activity in cultures of C.sporogenes supplemented with PHB increasing Amino Acids 50 45 G6PD ∆ (µmol/mín/mg) 40 35 30 25 20 15 10 5 0 BDM Threonine 7.5mM Threonine Homoserine Homoserine Aspartic Acid Aspartic Acid 15mM 7.5mM 15mM 7.5mM 15mM Figure 44: The Effect of Aspartic acid, Threonine and Homoserine on G6PD activity in cultures of C. sporogenes. Supplements were added to BDM (Karasawa et al, 1995) at concentrations of 7.5mM and 15mM individually. Data shown are values obtained following 24h incubation. Data displayed are averages of triplicate biological samples and triplicate technical repeats. Error bars represent the standard error of the mean. 141 Citrate Synthase activity in cultures of C.sporogenes supplemented with PHB increasing Amino Acids Citrate synthase ∆ (µmol/mín/mg) 14 12 10 8 6 4 2 0 BDM Threonine 7.5mM Threonine Homoserine Homoserine Aspartic Acid Aspartic Acid 15mM 7.5mM 15mM 7.5mM 15mM Figure 45: The Effect of Aspartic acid, Threonine and Homoserine on Citrate Synthase activity in cultures of C. sporogenes. Supplements were added to BDM (Karasawa et al, 1995) at concentrations of 7.5mM and 15mM individually. Data shown are values obtained following 24h incubation. Data displayed are averages of triplicate biological samples and triplicate technical repeats. Error bars represent the standard error of the mean. 142 Phosphoenolpyruvate carboxylase activity in cultures of C.sporogenes supplemented with PHB increasing Amino Acids PEP carboxylase ∆ (µmol/mín/mg) 25 20 15 10 5 0 BDM Threonine 7.5mM Threonine 15mM Homoserine Homoserine Aspartic Acid Aspartic Acid 7.5mM 15mM 7.5mM 15mM Figure 46: The Effect of Aspartic acid, Threonine and Homoserine on PEPc activity in cultures of C. sporogenes. Supplements were added to BDM (Karasawa et al, 1995) at concentrations of 7.5mM and 15mM individually. Data shown are values obtained following 24h incubation. Data displayed are averages of triplicate biological samples and triplicate technical repeats. Error bars represent the standard error of the mean. 143 Citrate synthase activity was reduced in cells which had accumulated more PHB (Figure 45), agreeing with the hypothesis that PHB biosynthesis competes with the TCA cycle for carbon flux. Lower activity of Citrate synthase is most likely an indication of less TCA cycle activity in general, owing to the decreased demand for biosynthesis of TCA derived amino acids. This results in a lowered requirement of acetyl-CoA from glycolysis to react with OAA to form citrate, consequentially increasing the available acetyl-CoA available for PHB biosynthesis. No obvious trends were demonstrated with regards to G6PD activity and increased PHB (Figure 44), although increasing the availability of aspartic acid in the growth medium resulted in increased G6P activity. The highest observed G6PD activity was demonstrated in cultures of C. sporogenes grown with an additional 15mM aspartic acid. These cultures also demonstrated a 23% increase in intracellular PHB when compared with those grown without supplementation (Figure 39). This suggests PHB is controlled by 2 key effects; reduction of competition for carbon from the TCA cycle and increased glycolysis, both leading to more of PHB’s precursor, acetyl CoA. These findings also agree with the fact high aspartic acid stimulates synthesis of PEP and pyruvate (Alexander et al, 2000). The PBD experiments covered in chapter 3 (Section 3.4) also yielded results supporting this hypothesis. Supplementation with Alanine and Valine increased PHB accumulation, both of which have biosynthesis pathways from pyruvate (Figure 43). In the previous experiments (Section 3.3.4.2) which investigated the effects of ammonium on cultures of C. sporogenes, we observed an increase in PHB when ammonium was absent from the culture media. Ammonium inhibits enzymes required for desynthesis of Valine and Leucine back to pyruvate (Zhu et al, 2007), which explains this observation and agrees with the hypothesis that increasing available pyruvate results in increased PHB biosynthesis. 144 The most interesting results of the analysis were demonstrated by testing PEPc activity in cultures of C. sporogenes permissive for increased PHB biosynthesis (Figure 46). PEPc activity was decreased in cultures which accumulated more PHB (Figures 39 & 46) and demonstrated the strongest correlation with PHB accumulation of the enzyme activities tested (Figure 47) (R2 = 0.745). Supplementing the growth medium with amino acids which have biosynthetic pathways originating from OAA, resulted in a decrease in the reaction which refuels the TCA cycle metabolite, presumably owing to a decreased demand of OAA for the biosynthesis of amino acids which are already available. This results in a decreased demand for carbon by the TCA cycle, increasing the available carbon for storage in the form of PHB. Owing to the fact the reaction catalyses by PEPc is responsible for refuelling the TCA cycle, this correlation agrees with previous data which suggests PHB is increased as a result of increased available acetyl-CoA, either as a result of decreased activity and/or demand for carbon by the TCA cycle or increased availability from glycolysis in respect to the requirement acetyl-CoA by the TCA cycle. This data suggests the flux of PHB biosynthesis reactions is dictated by the reaction balancing between glycolysis and the TCA cycle; affecting available pyruvate and acetyl-CoA and affecting the demand for acetyl-CoA by the TCA cycle. In C. sporogenes and C. botulinum, bacterial metabolism exists as either fermentation, a mode of metabolism which relies on the reactions of glycolysis, or anaerobic metabolism, which utilises the reactions of the TCA cycle (although incomplete in many Clostridia) (Amador-Noguez et al, 2010; Hasan & Hall, 1974). The physiology of Clostridial metabolism (Amador-Noguez et al, 2010; Hasan & Hall, 1974), the biochemistry of PHB synthesis (Anderson & Dawes, 1990; Steinbuchel, 1991) and the data from these experiments (Figures 39, 44, 45 & 46) therefore 145 suggest PHB biosynthesis, and consequentially neurotoxin biosynthesis, may be affected by the ratio of fermentation and anaerobic respiration exhibited by the growing bacterial culture. 146 4.4 Plackett-Burman Design Experimental Approach to Test the Effects of Amino Acid Metabolism on Enzymatic Activity in relationship to PHB Accumulation in Cultures of C. sporogenes The enzyme assay experiments investigating the effect of amino acid supplementation on central metabolic pathway fluxes yielded results confirming PHB is increased as a result of decreased demand for carbon and/or activity of the TCA cycle and to a lesser degree, when acetyl-CoA availability is increased owing to increased glycolysis activity. However, this correlation was established in cultures permissive for increased PHB biosynthesis, by targeting PHB increasing pathways identified using FVA (Figure 37). The PBD experimental approach was used successfully to assess the effects of and interactions between amino acids when added to the growth medium of C. sporogenes cultures in previous experiments (Section 3.4). In an effort to increase our understanding of the relationship between PHB biosynthesis, amino acid metabolism and central metabolic pathway flux, the PBD approach and principles (Section 1.5.2) were used to investigate PHB accumulation, G6PD, Citrate synthase and PEPc activity in cultures of C. sporogenes supplemented with amino acids (Table 6) which were not selected to target increased PHB accumulation, allowing us to test this correlation further. Values displayed in the results are t-values calculated using the PBD experiment (Plackett & Burman, 1946) and are representative of the level of variance from flasks containing no additional amino acids and therefore represent the calculated observed effect of the trialled amino acid. The calculation used to determine the t-values for each variable (amino acid) also consider the interaction between the variables (Kalil et al, 2000). Data entered into the calculation were averages from triplicate biological and technical repeats. 147 Variables (Amino acids) Trial PRO TYR PHE ALA ASP THE ILEU LYS LEU GLY ARG 1 2 3 4 5 6 7 8 9 10 11 12 + + + + + + - + + + + + + - + + + + + + - + + + + + + - + + + + + + - + + + + + + - + + + + + + - + + + + + + - + + + + + + - + + + + + + - + + + + + + - Table 6: PBD experiment to test the effects of amino acid supplemented growth media on PHB accumulation, G6PD, Citrate synthase and PEPc activity in cultures of C. sporogenes. ‘+’ represents a supplementation of 1mmol of the corresponding amino acid to BDM growth medium (Karasawa et al, 1995). All flasks contained the same total volume and were inoculated from the same seed culture of C. sporogenes. 148 Effect of amino acid supplementation on PHB Accumulation (mg/g) of C.sporogenes cultures assessed by Plackett-Burman Design 0.6 Significance of effect (t-value) 0.4 0.2 0 THE ASP ILEU PRO LYS LEU ARG ALA PHE TYR GLY -0.2 -0.4 -0.6 Figure 47: T-values of the variance of effect calculated following PBD to assess the effect of amino acid supplementation on PHB accumulation in C. sporogenes cultures incubated for 24h. Data shown is calculated from averages of triplicate biological and technical repeats. Calculated values were subject to testing against three ‘dummy variables’ which decreases the experimental error of the calculated t-values displayed. 149 Effect of amino acid supplementation on G6PD activity (µmol/mín/mg) of C.sporogenes cultures assessed by Plackett-Burman Design 0.6 Significance of effect (t-value) 0.4 0.2 0 LEU PRO ARG THE ALA LYS ASP GLY TYR PHE ILEU -0.2 -0.4 -0.6 Figure 48: T-values of the variance of effect calculated following PBD to assess the effect of amino acid supplementation on G6PD activity in C. sporogenes cultures incubated for 24h. Data shown is calculated from averages of triplicate biological and technical repeats. Calculated values were subject to testing against three ‘dummy variables’ which decreases the experimental error of the calculated t-values displayed. 150 Effect of amino acid supplementation on Citrate Synthase activity (µmol/mín/mg) of C.sporogenes cultures assessed by Plackett-Burman Design 0.6 Significance of effect (t-value) 0.4 0.2 0 LEU LYS ARG PHE PRO TYR ALA GLY ILEU ASP THE -0.2 -0.4 -0.6 Figure 49: T-values of the variance of effect calculated following PBD to assess the effect of amino acid supplementation on Citrate Synthase activity in C. sporogenes cultures incubated for 24h. Data shown is calculated from averages of triplicate biological and technical repeats. Calculated values were subject to testing against three ‘dummy variables’ which decreases the experimental error of the calculated t-values displayed. 151 Effect of amino acid supplementation on PEPc activity (µmol/mín/mg) of C.sporogenes cultures assessed by Plackett-Burman Design 0.6 Significance of effect (t-value) 0.4 0.2 0 ALA PHE PRO LYS ARG ASP LEU TYR GLY ILEU THE -0.2 -0.4 -0.6 Figure 50: T-values of the variance of effect calculated following PBD to assess the effect of amino acid supplementation on PEPc activity in C. sporogenes cultures incubated for 24h. Data shown is calculated from averages of triplicate biological and technical repeats. Calculated values were subject to testing against three ‘dummy variables’ which decreases the experimental error of the calculated t-values displayed. 152 The results of the PBD investigating the effects of increasing amino acid availability in the growth medium on PHB accumulation, G6PD, Citrate synthase and PEPc activity in cultures of C. sporogenes were consistent with our previous findings. Conditions permissive for increased PHB accumulation resulted in decreased activity of PEPc (Figures 47 & 50). Threonine, Isoleucine and aspartic acid supplementation resulted in the largest increase in PHB accumulation (Figure 47), agreeing with our hypothesis that increasing the availability of amino acids with biosynthetic pathways originating from OAA results in increased PHB accumulation. Both citrate synthase and PEPc activity were decreased in cultures which demonstrated increased PHB accumulation (Figures 47, 49 & 50), agreeing with our hypothesis that the increased PHB accumulation observed is a result of decreased demand for carbon by and/or activity of the TCA cycle. Supplementation with Phenylalanine and Alanine increased PEPc activity (Figure 50). These amino acids have biosynthetic pathways originating from Phosphoenolpyruvate and pyruvate (Figure 43), two key metabolites of Glycolysis. Together with the results obtained on OAA derived amino acid supplementation and PHB accumulation, this highlights the effect of increasing the availability of amino acids on the demand of for carbon compounds of central metabolism. Supplementation of the growth medium with an amino acid results in a decreased biosynthesis requirement to fuel bacterial metabolism; resulting in an increased availability of the amino acid’s precursor. The finding that this correlation can be exploited to increase pathway fluxes resulting in increased PHB accumulation confirms our hypothesis that the observed effects on intracellular PHB is owing to carbon demand and availability between glycolysis and the TCA cycle. 153 The data also highlights the importance of the reaction catalysed by PEPc with regard to PHB accumulation, which is responsible for carbon balance/refuelling between glycolysis and the TCA cycle (Kai et al, 2003; Owen et al, 2002), and presents the reaction as an interesting process development target, allowing control of PHB accumulation and, presuming our correlation is proved accurate, neurotoxin production. 154 4.5 Investigating Anaplerotic Reactions as a Process Development Target using Genome-Scale Metabolic Modelling Our results confirmed that increasing the availability of amino acids with biosynthetic pathways originating from OAA resulted in increased PHB accumulation in cells of C. sporogenes (Figures 39 & 47) and suggested this would result in increased PHB and neurotoxin biosynthesis in C. botulinum (Figures 37 & 38). The experimentation also highlighted the importance of the anaplerotic reaction catalysed by PEPc, demonstrating a correlation between decreased PEPc activity and increased PHB accumulation (Figure 47) (R2 = 0.745). OAA is refuelled by the reaction catalysed by PEPc (Kai et al, 2003; Owen et al, 2002) which incorporates the carbon from carbon dioxide (CO2) into the TCA cycle (Figure 51). With the data from the previous experiments suggesting PHB accumulation and neurotoxin biosynthesis are effected by carbon balancing and availability of glycolysis and the TCA cycle, together with the finding that increasing available OAA results in increased PHB biosynthesis, increasing the introduction of carbon into central metabolism via anaplerotic reaction incorporating CO2 , attained potential as a process development target. 155 Figure 51: Anaplerotic reactions of central metabolism. CO2 is incorporated during the reaction catalysed by Phosphoenolpyruvate carboxylase (PEPc) in which Phosphoenolpyruvate is metabolised to oxaloacetate (OAA) refuelling the TCA cycle. Image from Eggeling & Bott, 2005 (Eggeling & Bott, 2005). It has been established that increasing atmospheric CO2 increases CO2 fixation by anaplerotic metabolism and increases PHB accumulation in other PHB producing organisms (Miyake et al, 2000; Tanaka et al, 2011), presumably owing the decreased demand of OAA for amino acid biosynthesis. In an effort to assess whether this correlation exists in C. botulinum, FBA was used to assess the effect of CO2 flux on PHB yield as an objective. 156 FBA assessing the effect of CO2 flux on PHB accumulation in C.botulinum (in silico) 9000 PhB Flux (arb. units) 8000 R² = 0.995 7000 6000 5000 4000 0 700 1400 2100 2800 3500 4200 4900 5600 6300 7000 7700 8400 9100 9800 10500 11200 11900 12600 13300 14000 14700 15400 16100 3000 CO2 flux (arb. units) Figure 52: The effect of CO2 flux on PHB biosynthesis in C. botulinum (in silico) generated using FBA to test minimum to maximum range CO2 flux with optimal PHB yield as an objective. PHB flux appears to fall beyond 14000 arbitrary units CO2 flux owing to the polynomial trend line used to increase graph resolution. GSMN used in analysis developed from C. botulinum type A genome data obtained from NCBI FTP (Bao et al, 2011), constructed by Sonal Dahale, University of Surrey. The results of the FBA to investigate the correlation between CO2 uptake and PHB biosynthesis demonstrated a linear correlation (Figure 52) (R2 = 0.995), agreeing with studies in other PHB producing organisms which demonstrated an increase in atmospheric CO2 during culture resulted in increased PHB accumulation (Miyake et al, 2000; Tanaka et al, 157 2011). It has been reported that increasing the concentration of atmospheric CO2 in the culture vessel of C. botulinum cultures results in increased neurotoxin biosynthesis (Artin et al, 2008). The work of Artin et al, demonstrated a two-fold increase in neurotoxin yield when 70% CO2 was supplied to the fermenter (Artin et al, 2008). This relationship has also been established in other toxin producing organisms, including Bacillus anthracis, Staphylococcus aureus and Vibrio cholerae (Drysdale et al, 2004; Ross & Ouderdonk, 2000; Shimamura et al, 1985). This finding, together with the relevant literature (Artin et al, 2008; Drysdale et al, 2004; Miyake et al, 2000; Ross & Ouderdonk, 2000; Shimamura et al, 1985; Tanaka et al, 2011), adds merit to the hypothesised correlation between PHB accumulation and neurotoxin biosynthesis in C. botulinum and suggests increasing atmospheric CO2 as a process development approach, offering the ability to control PHB and therefore neurotoxin yield; presenting an opportunity to achieve the primary goals of this research project. 158 4.6 Chapter Conclusions FVA provided further evidence to the correlation hypothesised in Chapter 3; that increasing PHB accumulation results in increased neurotoxin biosynthesis is C. botulinum (Figures 36 & 38). The analysis also revealed several metabolic pathways significant to the biosynthesis of both neurotoxin and PHB (Figures 37 & 38). Increasing the flux of the pathways, by increasing availability of the metabolites Threonine, Aspartic acid and Homoserine, resulted in increased PHB accumulation in cultures of C. sporogenes (Figure 40). Enzymatic assay of cultures which demonstrated increased PHB accumulation owing to supplementation with OAA derived amino acids and assessment of the relationship using PBD demonstrated a correlation between increased PHB accumulation and decrease PEPc and Citrate synthase activity (Sections 4.3 & 4.4). Review of the results and the relevant literature led to the conclusion that decreased demand for carbon and/or activity of the TCA cycle resulted in increased availability of Acetyl-CoA and consequentially, increased PHB accumulation (Kai et al, 2003; Lillo & Rodriguez-Valera, 1990; Owen et al, 2002; Raberg et al, 2008; Ryu et al, 2007; Shang et al, 2003). These results, combined with the knowledge that the anaplerotic reaction catalysed by PEPc introduces the carbon from CO2 into the TCA cycle (Figure 51), increased atmospheric CO2 increases PHB accumulation in other organisms (Miyake et al, 2000; Tanaka et al, 2011) and increased CO2 has been positively correlated with neurotoxin biosynthesis in cultures of C. botulinum previously (Artin et al, 2008) presented a valuable hypothesis; that increasing the concentration of CO2 available to cultures of C. sporogenes would result in an increase in cellular PHB accumulation and the effect of CO2 would increase both PHB accumulation and neurotoxin biosynthesis in C. botulinum. 159 Chapter 5: Investigating the Correlation between Carbon Dioxide Uptake, PHB Accumulation and Neurotoxin Biosynthesis Using Chemostat culture In Chapter 4, the hypothesis that increasing the concentration of CO2 supplied to cultures of C. sporogenes would result in an increase in intracellular PHB accumulation and the effect of CO2 would increase both PHB accumulation and neurotoxin biosynthesis in C. botulinum was proposed. This hypothesis was formulated based on the data obtained on the correlation between CO2 and PHB in silico (Figure 52), the literature regarding the correlation between CO2 and neurotoxin in C. botulinum (Artin et al, 2008) and other toxin producing species (Drysdale et al, 2004; Ross & Ouderdonk, 2000; Shimamura et al, 1985) and data obtained during this study on factors effecting the accumulation of PHB, including enzyme activity and amino acid supplementation data which suggests a net reduction in TCA cycle activity/demand for carbon results in increased PHB accumulation (Sections 4.3 & 4.4). Chemostat culture enables the dissection of microbial physiology and growth kinetics independent from the effects of the physiochemical environment (Bull, 2010; Hoskisson & Hobbs, 2005), including the decreased availability of nutrients and increased accumulation of by-products over time. Chemostat culture was therefore deemed an appropriate experimental approach in order to test the hypothesis, eliminating changes in growth rate which are likely to affect PHB accumulation. Chemostat culture also provides a method to analyse the effects of growth rate on biomarker production with an outlook of assessing the 160 proposed hypothesis that altering growth conditions can affect the ratio of fermentation and anaerobic metabolism; ultimately effecting PHB and the biosynthesis of other metabolites. Most importantly, it provides steady state conditions, which are directly comparable to flux model predictions. Experiments designed to test the effects of growth rate on C. sporogenes were sparged with 100% nitrogen gas (oxygen-free) in order to maintain strict anaerobic conditions in the Chemostat vessel and experiments testing the effects of CO2 were sparged with gas mixtures containing 10%, 25% or 50% CO2 and the remaining concentration nitrogen at a controlled rate of 0.2L/h -1. Stirrer speed, temperature, pH, dissolved O2 (ensured zero), working culture volume and axenic conditions were controlled and maintained throughout experimentation. 161 5.1 The effect of Growth Rate & Increased Carbon Dioxide Concentration on PHB Accumulation in Continuous Cultures of C. sporogenes The effect of growth rate and CO2 gas concentration on PHB accumulation were investigated in chemostat cultures of C. sporogenes. Assays were performed and experimental conditions were maintained using the methods described previously (section 2.12). Intracellular PHB Accumulation at specific growth rates in continuous cultures of C. sporogenes 130 120 PHB (mg/g) 110 100 90 80 70 60 50 0.25 0.5 Dilution (Growth) Rate 0.75 (h-1) Figure 53: The effect of growth rate on PHB accumulation in continuous cultures of C. sporogenes. Growth rate was controlled by dilution rate of growth medium into the fermentation vessel (ml/h-1) maintained at a working volume of 1.5L. Data shown are averages of triplicate biological samples taken at least one volume change apart and triplicate technical repeats. Error bars represent standard error of the mean. At least four volume changes were required before sampling following change of culture parameter changes such as dilution rate. 162 The effect of CO2 concentration on PHB accumulation in continuous cultures of C. sporogenes 180 ** 160 PHB (mg/g) 140 120 100 80 60 10% CO2 25% CO2 50% CO2 Figure 54: The effect of CO2 concentration on PHB accumulation. Growth rate was controlled by maintaining a dilution rate of 0.5 h -1 throughout experimentation. Statistical analysis using a student’s t-test showed no significant increase in PHB at 10% & 25% CO2 when compared at the same growth rate supplied with 100% nitrogen (P = 0.3569 & P = 0.2332 respectively), however the increase in PHB observed at 50% was calculated to be highly significant (P = 0.01). Data shown are averages of triplicate biological samples taken at least one volume change apart and triplicate technical repeats. Error bars represent standard error of the mean. ‘**’ represents a statistical significance of P = ≤0.01 (applies to all data). 163 The effects of increased CO2 concentration on Biomass and PHB accumulation in chemostat culture of C. sporogenes 0.8 R² = 0.975 140 0.7 120 0.6 100 0.5 80 0.4 R² = 0.9843 60 0.3 40 0.2 20 0.1 0 Biomass (g/L) PHB (mg/g) 160 PhB Biomass 0 10 25 50 Fermentation Vessel CO2 Concentration (%) Figure 55: The contrasting effects of increased atmospheric CO2 on PHB accumulation and Biomass in continuous cultures of C. sporogenes. Biomass was negatively correlated (R2 = 0.9843) with CO2 and PHB positively correlated (R2 = 0.975). Data shown are averages of triplicate biological samples taken at least one volume change apart and triplicate technical repeats. 164 The effects of increased CO2 flux on Biomass and PHB accumulation in C. botulinum (in silico) 8000 3500 R² = 0.9753 7000 3000 2500 5000 R² = 1 2000 4000 1500 3000 Biomass flux PhB Flux 6000 PhB Biomass 1000 2000 500 1000 0 0 1 50 CO2 flux Figure 56: The contrasting effects of increased CO2 flux on PHB and Biomass flux in C. botulinum (in silico). Data was attained by FBA testing the effects on CO2 flux on PHB and Biomass flux as individual objectives. GSMN used in analysis developed from C. botulinum type A genome data obtained from NCBI FTP (Bao et al, 2011), constructed by Sonal Dahale, University of Surrey. 165 Increasing the concentration of CO2 supplied to the fermentation vessel of C. sporogenes cultures had a positive linear correlation on PHB accumulation (R2 = 0.975) and a negative linear correlation on biomass (R2 = 0.9843). When introduced into the chemostat culture at a concentration of 50%, the difference in PHB observed was highly significant (P = 0.01), confirming the hypothesis that CO2 can be used as a process development approach to increase PHB accumulation in cultures of C. sporogenes. FBA on the effects of CO2 flux (Figure 56) suggested the correlations demonstrated with both PHB and biomass also exist in C. botulinum. Artin et al (Artin et al, 2008) tested the effects of increased CO2 concentration in cultures of C. botulinum, demonstrating increased CO2 resulted in an increase in neurotoxin biosynthesis and a decrease in biomass; agreeing with our findings in C. sporogenes and the proposed correlation between PHB and botulinum neurotoxin. The inhibitory effect of CO2 on the growth of C. botulinum has been described previously (Artin et al, 2008; Gibson et al, 2000; Fernandez et al, 2001; Lo¨venklev et al, 2004) and associated with a decrease in culture growth rate (Artin et al, 2008). This agrees with our findings on the reduction of TCA cycle activity/demand for carbon during conditions permissive for increased PHB biosynthesis and suggests increased CO2 concentration promotes fermentative respiration, which although is less energy efficient and results in lower growth rate, provides more available carbon for storage as PHB. This hypothesis is supported by the work of Dixon et al (Dixon et al, 2008) on chemostat culture of C. sporogenes, who demonstrated increased CO2 resulted in increased yields of acetate; a precursor of PHB. Figure 57 represents a compilation graph displaying a comparison of Artin et al’s (Artin et al, 2008) observations on the effects of CO2 on neurotoxin biosynthesis with our own data obtained in C. sporogenes displaying the effect of CO2 on PHB accumulation (Artin et al, 2008). This data comparison, together with the validation of the GSMN enhanced surrogate 166 organism approach used throughout this research project, confirms the hypothesis that PHB is linearly correlated with neurotoxin production in C. botulinum and validates our work on PHB metabolism as a viable process development approach to control the biosynthesis of botulinum neurotoxin. 167 700 220 600 200 180 500 160 400 140 300 120 200 PHB (mg/g) Botulinum Neurotoxin (ng/g) The effect of CO2 on PHB and Botulinum neurotoxin in C.botulinum (Artin et al, 2008) BoNT PhB 100 100 80 0 60 10 25/35 50/70 CO2 gas composition (%) Figure 57: The effect of increased CO2 concentration on PHB accumulation and botulinum neurotoxin biosynthesis. As displayed, both PHB and neurotoxin are increased as a result of increased CO2 concentration in the culture vessel. Correlation plot of botulinum neurotoxin and PHB was linear (R2 = 0.9637). Botulinum neurotoxin data taken from Artin et al, 2008. CO2 gas compositions tested by Artin et al were 10, 35 and 70%. PHB data shown are averages of triplicate biological samples taken at least one volume change apart and triplicate technical repeats. No significant difference was observed at 10% CO 2 compared with 0% CO2 (100% oxygen-free nitrogen) with regards to either PHB or neurotoxin. R2 (0.9637) was calculated by plotting 0 to 50% CO2 for both PHB and neurotoxin and therefore takes into the consideration the non-linear x-axis depicted. 168 The combination of our data obtained in continuous cultures of C. sporogenes and the work of Artin et al in C. botulinum (Artin et al, 2008) demonstrate increased CO2 results in increased PHB accumulation and toxin biosynthesis at the cost of decreased biomass. Owing to the fact cell population will have a direct impact on the total yield of PHB and neurotoxin, the effect of CO2 on PHB and toxin in a given culture volume is not linear. Figure 58 was generated using a combination of in silico data achieved by FBA in C. sporogenes. The graph represents the effect of CO2 concentration on the PHB:Biomass ratio, demonstrating the point at which the increase in PHB/neurotoxin yield influenced by increased CO2 concentration becomes detrimental to the maximum yield of the culture, owing to the negative effects of CO2 on biomass. The optimal CO2 concentration supplied to the culture vessel, with regards to maximum PHB and/or neurotoxin yield in a given culture volume, was calculated at 36% (Figure 58). 169 Optimal CO2 concentration (%) to achieve maximum PHB & Neurotoxin yield in a given culture volume Maximum PHB (Biomass x PHB flux) 1800 1750 1700 1650 1600 1550 1500 1450 0 10 20 30 40 50 60 70 CO2 Flux (% composition) Figure 58: Optimal CO2 concentration (%) with an objective of acquiring maximal PHB/toxin yield in a given culture volume. Data represents Biomass × PHB flux with increasing CO2 flux. As demonstrated, although cellular PHB percentage increases up to at least 50% CO2 concentration, the negative effect on biomass results in a decrease in total yield after 36% CO2. Data used to calculate PHB:Biomass ratio were attained by FBA testing the effects of CO2 flux on PHB and Biomass flux as individual objectives. CO2 flux values were adjusted to represent a CO2 percentage of the atmospheric gas available to the culture. GSMN used in analysis developed from C. botulinum type A genome data obtained from NCBI FTP (Bao et al, 2011), constructed by Sonal Dahale, University of Surrey. 170 5.2 Validation of Bicarbonate as an Alternative Supplementation Approach to Altering Carbon Dioxide Concentration To further our findings on the use of increased CO2 as a process development approach when pursuing increased PHB/neurotoxin yield, a series of experiments were design to investigate the substitute of bicarbonate; offering the advantages of increased CO 2 described previously as an addition to the growth medium, as an alternative to input gas alteration. The addition of bicarbonate to the growth medium provides a direct alternative mechanism to increase available CO2 (Ueda et al, 2008), as shown by the equation below; CO2 + H2O ↔ H2CO3 ↔ H+ + HCO3- ↔ H+ + CO32- However, the alternative is not without limitations, as additional buffering capacity is required to maintain pH owing to the hydrogen ions formed in the reactions. In an effort to investigate the effect of bicarbonate on PHB accumulation, flask cultures of C. sporogenes were grown in USA2 medium (Chapter 2, Section 2) supplemented with bicarbonate at 5 & 10g/L. Flasks of USA2 medium were also prepared with 10mM Homoserine; a supplement demonstrated to increase PHB yield in this studies previous experiments (Chapter 4, Section 2), for comparison with the effect of bicarbonate on PHB accumulation. Intracellular PHB of the C. sporogenes cultures was assed following 24h incubation and compared with the values obtained in USA2 medium without additional supplementation. 171 The effect of bicarbonate on PHB accumulation in continuous cultures of C. sporogenes 160 *** 140 ** PHB (mg/g) 120 100 80 60 40 BDM 5g/L Bicarbonate 10g/L Bicarbonate 10mM Homoserine Figure 59: The effect of bicarbonate on PHB accumulation. Statistical analysis using a student’s t–test calculated no significant difference in PHB accumulation between USA2 media and flasks supplemented with 5g/L bicarbonate (P = 0.07), however when supplemented at 10g/L, bicarbonate resulted in a highly significant increase in PHB (P = 0.001). Homoserine, which increases PHB by reducing the carbon requirement of the TCA cycle, demonstrated the greatest increase in PHB when compared with cultures grown without additional supplementation (P = 0.0001), confirming the supplement is effective with regards to increasing PHB in both defined (BDM) and rich medium (USA2). 172 5.3 The Effect of Growth Rate & Increased Carbon Dioxide Concentration on Flagellin Biosynthesis in Continuous Cultures of C. sporogenes Studies have indicated flagellin biosynthesis is negatively correlated with neurotoxin production in C. botulinum. As a consequence of this, one of the primary objectives of this research project was to develop a process under which flagellin biosynthesis could be reduced; minimising competition with neurotoxin for metabolite bioflux. The data obtained previously (Chapter 3, Section 3) demonstrated that flagellin biosynthesis is affected by nutrient alterations to the growth medium, particularly altering the available carbon (Figures 22 & 23), in cultures of C. sporogenes, suggesting the metabolic changes induced by increased CO2 are likely to have an impact flagellin biosynthesis. 173 Flagellin biosynthesis at specific growth rates in continuous cultures of C. sporogenes 120 Flagellin (µg/ml) 100 80 60 40 20 0 0.25 0.5 0.75 Dilution (Growth) Rate (h-1) Figure 60: The effect of growth rate on flagellin biosynthesis in continuous cultures of C. sporogenes. Growth rate is controlled by dilution rate of growth medium into the fermentation vessel (h-1). Data shown are averages of triplicate biological samples taken at least one volume change apart and triplicate technical repeats. Error bars represent standard error of the mean. At least four volume changes were required before sampling following change of culture parameter changes such as dilution rate. 174 120 The effect of CO2 concentration on flagellin biosynthesis in continuous cultures of C. sporogenes Flagellin (µg/ml) 100 * 80 R² = 0.9696 60 *** 40 20 0 10% 25% 50% CO2 Concentration Figure 61: The effect of CO2 concentration on flagellin biosynthesis in chemostat cultures of C. sporogenes. Flagellin production decreased linearly with increasing CO2 concentration (R2 = 0.9696). There was no significant decrease in flagellin at 10% CO2 (P = 0.296) when compared with cultures grown in 100% Nitrogen (oxygen-free) at the same growth rate (0.5 h-1). The decrease in flagellin biosynthesis at CO2 concentrations of 25 & 50% were calculated as statistically significant using a student’s t-test (P = 0.01 & P = 0.0001 respectively). Data shown are averages of triplicate biological samples taken at least one volume change apart and triplicate technical repeats. Error bars represent standard error of the mean. At least four volume changes were required before sampling following change of culture parameter changes such as CO2 concentration. R2 (0.9696) was calculated by plotting flagellin biosynthesis from 0 to 50% CO2 and therefore takes into the consideration the nonlinear x-axis depicted. 175 Flagellin (30kDa) – Figure 62: The effect of CO2 concentration on flagellin biosynthesis. Image displays flagellin bands separated using SDS-PAGE, stained with Coomassie-Blue and visualised using Flurochem QTM image analysis software. Samples displayed were triplicate samples taken at least one volume change apart at 10, 25 and 50% CO2 concentration. Growth rate was maintained at 0.5 h-1. Increasing the concentration of CO2 supplied to the fermentation vessel of C. sporogenes cultures had a negative linear correlation on flagellin biosynthesis (R2 = 0.9696). The effect of CO2 concentration on flagellin biosynthesis observed can be explained by our conclusion that the effect of lowering the demand for carbon by the TCA cycle ultimately results in C. sporogenes respiring predominantly by fermentation over anaerobic metabolism. Our previous work on nutrient limitation demonstrated that high glucose cultures drastically reduced flagellin biosynthesis (Figure 63 & 64). Greater availability of carbon in relation to other nutrients is likely to result in fermentation being utilised as a primary mode of metabolism. Furthermore, studies have demonstrated flagellin biosynthesis genes are greatly expressed during early stationary phase (Bergara et al, 2003), whilst fermentation predominates during exponential phase when nutrient availability drives rapid growth. 176 Studies in Bacillus subtlis, a close relative of the Clostridia species (Brown et al, 1994), have demonstrated that flagellin gene expression is repressed in nutrient rich environments under the effect of the gene repressor CodY (Bergara et al, 2003). The gene is also present in C. botulinum (Ecogene, NCBI library). Flagellin production and increased motility have been directly correlated to TCA cycle activity under anaerobic respiration in Proteus morabilis (Alteri et al, 2012), adding merit to our findings. 177 Growth, Glucose metabolism and Flagellin biosynthesis over Time 4000mg/L Glucose 0.6 14 0.5 Flagellin (µg/ml-2) 12 0.4 10 8 0.3 6 0.2 4 0.1 2 0 Growth (OD560); Glucose (mg x 104/L) 16 Flagellin Growth Glucose 0 0 3 6 9 12 18 24 Time (h) Figure 63: Growth, glucose metabolism and flagellin production over time in cultures of C. sporogenes (Chapter 3, Section 3). High glucose concentration drastically reduced flagellin biosynthesis in flask cultures of C. sporogenes. Interestingly, flagellin biosynthesis increased following glucose exhaustion, which may suggest a metabolic change from rapid glucose metabolism (fermentation) to amino acid respiration (anaerobic metabolism). PHB and flagellin are displayed from 9h onwards as cellular density was too low for accurate determination before this stage of the culture. Data displayed are averages of triplicate biological samples and triplicate technical repeats. Error bars represent the standard error of the mean. 178 16 0.45 14 0.4 0.35 Flagellin (mg/ml-2) 12 0.3 10 0.25 8 0.2 6 0.15 4 0.1 2 Growth (OD560); Glucose (mg x 104/L) Growth, Glucose metabolism and Flagellin biosynthesis over Time 500mg/L Glucose Flagellin Growth Glucose 0.05 0 0 0 3 6 9 12 18 24 Time (h) Figure 64: Growth, glucose metabolism and flagellin production over time in cultures of C. sporogenes (Chapter 3, Section 3). Cultures of C. sporogenes grown under glucose limited conditions exert a drastically reduced glucose consumption rate and increased flagellin biosynthesis. Interestingly, glucose is not fully exhausted until ~12h, when the culture reaches stationary phase. At this time, a further increase in flagellin biosynthesis occurs, which may be due to a switch to a primarily amino acid based metabolism. PHB and flagellin are displayed from 9h onwards as cellular density was too low for accurate determination before this stage of the culture. Data displayed are averages of triplicate biological samples and triplicate technical repeats. Error bars represent the standard error of the mean. 179 Growth rate also had a notable effect on flagellin biosynthesis, with the lowest and highest dilution rates tested (0.25 h-1 & 0.75 h-1) demonstrating lower flagellin biosynthesis than cultures maintained at a dilution rate of 0.5 h-1. These observations may also be due to fermentation and anaerobic metabolism ratio differences that occur throughout the different phases of the bacterial growth cycle, such as flagellin biosynthesis genes being greatly expressed during early stationary phase, for example (Bergara et al, 2003). These observations suggest controlling growth rate in cultures of C. botulinum, in addition to using increased CO2 concentrations, as potential process development approaches which could be used to limit flagellin biosynthesis; limiting competition with botulinum neurotoxin biosynthesis for metabolite bioflux. 180 5.4 The Effect of Growth Rate & Increased Carbon Dioxide Concentration on Sporulation in Continuous Cultures of C. sporogenes Many studies have demonstrated a correlation between sporulation and toxin biosynthesis (Artin et al, 2010; Kamiya et al, 1992; Mitchell, 2001). Studies in C. botulinum have also demonstrated that the carbon in PHB is utilised to drive sporulation (Benoit et al, 1990; Emeruwa & Hawirko, 1973). Following the observations on the effects of increased CO 2 concentration on both PHB and biomass, assessing the effect of CO2 on sporulation was deemed an important consideration. Owing to the fact chemostat culture requires medium and culture turnover to maintain growth rate, spores would not accumulate (as observed in batch culture) unless the spore production rate were to equal or exceed the culture throughput. All cultures tested in the experimentation had controlled drain rates equal to their dilution rate and therefore spore counts in continuous culture are representative of the rate of sporulation in the cellular population maintained by the culture conditions. 181 Sporulation at specific growth rates in continuous cultures of C.sporogenes 1400 Sporulation (No. spores/ml) 1200 1000 800 600 400 200 0 0.25 0.5 0.75 Dilution (Growth) Rate (h-1) Figure 65: The effect of growth rate on sporulation in continuous cultures of C. sporogenes. Growth rate is controlled by dilution rate of growth medium into the fermentation vessel ( h-1). Data shown are averages of triplicate biological samples taken at least one volume change apart and triplicate technical repeats. Error bars represent standard error of the mean. At least four volume changes were required before sampling following change of culture parameter changes such as dilution rate. 182 The effect of CO2 concentration on sporulation in continuous cultures of C. sporogenes 2000 1800 Sporulation (No. spores/ml) 1600 1400 1200 1000 800 600 400 200 0 10% CO2 25% CO2 50% CO2 CO2 Concentration (%) Figure 66: The effect of CO2 concentration on sporulation in chemostat cultures of C. sporogenes. Sporulation increased with increasing CO2 concentration (R2 = 0.9481). Interestingly, sporulation was increased in cultures grown in 100% Nitrogen (oxygen-free) at the same growth rate (0.5 h-1) when compared with cultures subject to 10% CO2. Data shown are averages of triplicate biological samples taken at least one volume change apart and triplicate technical repeats. Error bars represent standard error of the mean. At least four volume changes were required before sampling following change of culture parameter changes such as CO2 concentration. R2 (0.9481) was calculated by plotting sporulation from 0 to 50% CO2 therefore takes into the consideration the non-linear x-axis depicted. 183 Not surprisingly, owing to the detrimental effects observed on biomass, increased CO2 concentration increased sporulation in cultures of C. sporogenes. The correlation was linear (R2 = 0.9481), although cultures grown in 10% CO2 demonstrated lower sporulation than those grown in the absence of CO2 (Figure 65), possibly owing to low concentrations of CO2 having limited effects on culture biomass whilst refuelling carbon. If our hypothesis is correct, it is possible sporulation is increased due to fermentation operating as the primary mode of respiration in cultures subject to increased CO2. The increase in PHB influenced by increased CO2 concentration may also contribute to the increase in sporulation observed, as the carbon storage compound is utilised during sporulation in C. botulinum and other organisms (Benoit et al, 1990; Emeruwa & Hawirko, 1973). Many studies have demonstrated a correlation between sporulation and toxin biosynthesis (Artin et al, 2010; Kamiya et al, 1992; Mitchell, 2001). This correlates with our work and suggests PHB and fermentation/anaerobic respiration ratio play a part in both sporulation and toxin biosynthesis. 184 5.5 The Effect of Increased Carbon Dioxide Concentration on Nutrient Metabolism in Continuous Cultures of C. sporogenes Our experiments detailed in Chapter 3 demonstrated the effects of both nutrient availability and consumption on biomarker metabolism in cultures of C. sporogenes. Owing to our findings on increasing the availability of amino acids on the growth medium’s effect on PHB and neurotoxin biosynthesis (Chapter 4), assessing the metabolic influences of increased CO2 concentration, and thus refuelling the TCA cycle with carbon, was vital to understanding the demonstrated correlations. Analysis of all steady states and CO2 concentrations tested in chemostat culture were assessed for amino acid content using high-performance liquid chromatography (HPLC). Phosphate and Glucose consumption rates were also analysed. 185 Effect of CO2 on amino acid consumption - aspartate group (oxaloacetate biosythetic pathways) Consumption rate (mg/L) 300 250 200 Aspartic acid 150 Isoleucine 100 Threonine 50 0 10 25 50 CO2 concentration (%) Figure 67: The consumption of amino acids with biosynthetic pathways originating from TCA cycle intermediates (a group which increase PHB when supplemented in the growth medium) are increased under the metabolic changes induced by increased CO 2 concentration. This finding offers evidence to our hypothesis that TCA cycle activity is reduced under conditions permissive for PHB accumulation and therefore a greater quantity of TCA cycle derived amino acids are required from the growth medium. The supplied growth medium contained 300mg/L Aspartic acid, 300mg/L Isoleucine and 200mg/L Threonine. Data shown are averages of triplicate biological samples taken at least one volume change apart and triplicate technical repeats. Error bars represent standard error of the mean. 186 The effect of CO2 on total amino acid consumption in continuous culutres of C. sporogenes 3500 Amino acid consuption (mg/L) 3000 2500 2000 1500 1000 500 0 10 25 50 CO2 concentration (%) Figure 68: The effect of increased CO2 concentration on total amino acid consumption, respective of biomass, in continuous cultures of C. sporogenes. Individual amino acid concentration was analysed in samples and combined to calculate total amino acid consumption when compared with unspent growth medium. The supplied growth medium contained 3800mg/L total amino acids. Data shown are averages of triplicate biological samples taken at least one volume change apart and triplicate technical repeats. Error bars represent standard error of the mean. 187 The effect of CO2 on glucose and phosphate consumption in continuous cultures of C.sporogenes Nutrient Consumption (µg/g) 3500 3000 2500 Glucose consumption 2000 1500 Phosphate consumption 1000 500 0 10 25 50 CO2 concentration (%) Figure 69: The effect of CO2 on glucose and phosphate consumption in continuous cultures of C. sporogenes. Glucose and phosphate consumption are affected by the metabolic changes induced by increased CO2 concentration. The greatest consumption rates of Glucose, Phosphate and Amino acids (Figure 68) were observed in cultures subject to 50% CO2, suggesting a less efficient primary mode of metabolism, which results in excess available carbon for storage at a given biomass. Data shown are averages of triplicate biological samples taken at least one volume change apart and triplicate technical repeats. Error bars represent standard error of the mean. 188 The consumption rate of amino acids biosynthesised from TCA cycle intermediates, which we have previously correlated with increased PHB accumulation when supplemented to the growth medium, increased with CO2 concentration (Figure 67). The observation that total amino acid consumption was not increased under conditions of increased CO 2 concentration (Figure 68) adds merit to the fact only amino acids with biosynthetic pathways from TCA intermediates which compete for carbon flux with PHB are consumed at a greater rate. This data agrees with our hypothesis that under conditions permissive for increased PHB accumulation, influenced by increased CO2 concentration, TCA activity is reduced, as higher consumption of amino acids refuelled via this pathway were required. This is most likely owing to a reduction in the amino acid precursors generated from TCA cycle progression, therefore requiring a greater quantify of amino acids from the growth medium. Another interesting observation is that in cultures subject to 25 & 50% CO 2, amino acid analysis detected appreciable quantities of Norleucine, which is generated by the metabolism of acetyl-CoA; the precursor of PHB (Anderson & Dawes, 1990; Carvalho & Blanchard, 2006; Steinbuchel, 1991). Glucose consumption increased with increasing CO2 concentration (Figure 69) and therefore, owing to our previously established correlations (Figure 54), with increased PHB, adding justification to our hypothesis that the metabolic observations are due to an increase in fermentation rate, which is primarily fuelled by glucose. Overall nutrient consumption rates were increased at higher concentrations of CO2, which provides evidence of a less efficient mode of metabolism, such as fermentation, which produces less ATP generation per mole of glucose compared to anaerobic metabolism (Berg et al, 2002). Phosphate consumption rate remained relatively unchanged with increasing CO2 concentration. Owing 189 to the fact the majority of inorganic phosphate is consumed by the TCA cycle (Berg et al, 2002), this adds merit to our hypothesis that TCA cycle activity or the demand of the TCA cycle is reduced under the conditions influenced by increased CO2 concentration, as glucose consumption rate increases driving fermentation. 190 5.6 Validation of Genome-Scale Metabolic Network using Flux Data obtained by Chemostat Culture Experimental values obtained from chemostat culture samples for total RNA, DNA and Protein were used to enhance the GSMN used throughout the research project. The experimental values were used to replace the theoretical values used in the biomass equation of C. botulinum model. Given that all fluxes determined using the metabolic network must balance with the biomass equation, the use of experimentally determined values lowers the calculation error of the model, resulting in more accurate in silico analysis (Beste et al, 2007). The chemostat-data enhanced model was then compared with the original GSMN to determine any significant difference in fluxes with respect to Biomass, PHB and toxin as objectives (Figure 70). As shown below, no significant difference was calculated using Student’s t-test between the two GSMNs when testing biomass, PHB and toxin flux as individual objective functions, validating and adding merit to the in silico data obtained throughout this research project. 191 Flux comparison calculated by C. botulinum GSMN vs chemostat enhanced GSMN 9000 Flux (MoleMetabolite-1gBiomass-1) 8000 7000 6000 Original C. botulinum Model 5000 Chemostat data enhanced model 4000 3000 2000 1000 0 Biomass PhB Toxin Objective Function Figure 70: Determining the flux differences between the original C. botulinum GSMN constructed at the University of Surrey and used throughout this study and the chemostat data enhanced GSMN, constructed by altering our C. botulinum GSMN with experimentally obtained values. As shown, no significant difference was calculated between the two GSMN’s when testing biomass, PHB and toxin flux as individual objective functions, validating and adding merit to the in silico data obtained throughout this research project. 192 5.7 Chapter Conclusions The results covered in this chapter demonstrate that increased CO2 concentration introduces carbon into the TCA cycle via anaplerotic reactions, thus limiting the demand and/or requirement of the TCA cycle. This results in a linear effect on PHB accumulation (R2 = 0.975) and Botulinum neurotoxin (R2 = 0.9637) (Artin et al, 2008). Our findings suggest the metabolic effects of introducing the carbon in CO2 into central metabolism results in fermentation being utilised as the primary mode of respiration in C. sporogenes and our data comparison with the studies on CO2 in C. botulinum (Artin et al, 2008) suggest this effect is homologous in this species. The net result of decreased TCA activity and demand for carbon provides additional acetyl-CoA; the precursor of PHB, which ultimately results in increased neurotoxin biosynthesis. Sporulation increased linearly with increased CO 2 concentration (R2 = 0.9975), perhaps owing to the negative linear effect on biomass induced by elevated concentrations of CO2 (R2 = 0.9843). Although sporulation has been associated with PHB in C. botulinum and other species previously (Benoit et al, 1990; Emeruwa & Hawirko, 1973), it is difficult to directly correlate increased PHB with sporulation or vice versa, although the effects of increased CO2 concentration affect both linearly. Increased CO2 concentration had a negative linear correlation on flagellin biosynthesis (R2 = 0.9696), offering a strategy to achieve a primary objective of this research project; reducing competition for metabolite bioflux with neurotoxin. The effect is likely owing to flagellin gene downregulation influenced by the metabolic effects of increased CO 2 concentration (Alteri et al, 2012; Bergara et al, 2003; Brown et al, 1994). Comparison with findings in C. perfringens that demonstrated increased acetate-derived product yield during fermentation when compared with anaerobic metabolism (Hasan & Hall, 1974) and studies in E. coli which have revealed anaplerotic metabolism regulated fermentation/respiration switch proteins 193 (Koo et al, 2004) support the conclusion that both PHB and neurotoxin are largely affected by the primary respiration mode of the growing culture. 194 Chapter 6: Conclusions 6.1 Project Conclusions & Achievements For the first time, a linear correlation was established between the energy-storage polymer PHB accumulation and Botulinum neurotoxin (R2 = 0.964). This was established by GSMN analysis including FBA (Figure 36), FVA and subsequent testing of PHB metabolism (Figure 40) and confirmed by direct comparison of the effects of CO 2 on PHB accumulation (Figure 57) and neurotoxin (Artin et al, 2008). The work has provided extensive knowledge on PHB and botulinum neurotoxin metabolism, providing possible approaches for process development. Based on a combination of the literature (Lillo & Rodriguez-Valera, 1990; Raberg et al, 2008; Ryu et al, 2007; Shang et al, 2003) and our research on central metabolic pathway fluxes (Chapter 4, Section 3) which influence PHB accumulation, it can be concluded that limited availability of inorganic phosphate for optimal ATP biosynthesis limits the progression of the TCA cycle. This limitation of TCA cycle activity reduces the requirement of carbon required by the TCA cycle from glycolysis in the form of Acetyl-CoA; the precursor of PHB. A combination of data obtained from multi-factorial experiments (Chapter 3, Section 4 & Chapter 4, Section 4), FVA and experiments to assess PHB increasing metabolism (Chapter 4) yielded results consistent with the hypothesis that PHB competes for carbon flux with the TCA cycle. Reducing the biosynthesis requirement of amino acids originating from oxaloacetate, which reacts with the precursor of PHB, acetyl-CoA, to form citrate, decreased the demand for carbon from glycolysis, resulting in an increased availability for storage as PHB. This was confirmed by analysis demonstrating TCA cycle enzymatic activity is reduced (Figures 45, 46, 49 & 50) and consumption of amino acids 195 refuelled by the TCA cycle is increased (Figure 67) under conditions permissive for increased PHB biosynthesis. Furthermore, the demand for carbon by the TCA cycle, which coincidently results in increased availability of carbon from glycolysis for storage as PHB, was further reduced by increasing the introduction of carbon to the TCA cycle via anaplerotic metabolism. This was concluded by observing increased PEPc activity under conditions which resulted in increased PHB biosynthesis (Figures 46 & 50). It can be concluded that the findings on the effect of increased available CO2 on PHB accumulation, and the effect observed on neurotoxin by Artin et al (Artin et al, 2008), can be attributed to a decreased demand for carbon by the TCA cycle, owing to the refuelling effect supplied of CO 2. This increases the availability of Acetyl-CoA which is metabolised by the PHB forming pathway (Anderson & Dawes, 1990; Steinbuchel, 1991), proven by our analysis of anaplerotic metabolism (Figures 46 & 50) and an increase in Norleucine, a biomarker of acetly-CoA metabolism (Carvalho & Blanchard, 2006), under conditions permissive for increased PHB biosynthesis. Although further study would be required to validate our hypothesis, our findings suggest PHB and neurotoxin biosynthesis are largely affected by the ratio of fermentative and anaerobic respiration of the growing culture; akin to the relationship demonstrated in C. perfringens affecting acetate-derived product yield (Hasan & Hall, 1974). By reviewing our results and the relevant literature we can conclude that decreased demand for carbon and/or activity of the TCA cycle resulted in increased availability of Acetyl-CoA, increasing PHB accumulation and consequently, neurotoxin biosynthesis (Artin et al, 2008; Kai et al, 2003; Lillo & Rodriguez-Valera, 1990; Owen et al, 2002; Raberg et al, 2008; Ryu et al, 2007; Shang et al, 2003). 196 Sporulation increased linearly with increased CO2 concentration (R2 = 0.9975), perhaps owing to the negative linear effect on biomass induced by elevated concentrations of CO 2 (R2 = 0.9843). Although sporulation has been associated with PHB in C. botulinum and other species previously (Benoit et al, 1990; Emeruwa & Hawirko, 1973), it is difficult to directly correlate increased PHB with sporulation or vice versa, although the effects of increased CO2 concentration affect both linearly. Increased CO2 concentration had a negative linear correlation on flagellin biosynthesis (R2 = 0.9696), offering a strategy to achieve a primary objective of this research project; reducing competition for metabolite bioflux with neurotoxin. The effect is likely owing to flagellin gene downregulation influenced by the metabolic effects of increased CO2 concentration (Alteri et al, 2012; Bergara et al, 2003; Brown et al, 1994). High glucose concentration in cultures of C. sporogenes drastically reduced flagellin biosynthesis (Figure 63 & 64). As greater availability of carbon in relation to other nutrients is likely to result in fermentation being utilised as a primary mode of metabolism and previous studies have demonstrated flagellin biosynthesis genes are greatly expressed during early stationary phase (Bergara et al, 2003), our findings suggest the reduction in flagellin biosynthesis observed from high glucose and/or CO2 concentration is owing to a downregulation of flagellin biosynthesis genes, influences by the metabolic effects of these process alterations. By investigating biomarkers of neurotoxin biosynthesis, within the constraints of the surrogate system, this research project has proved successful in establishing a strong correlation with PHB which can provide extensive information to further develop a production orientated process. The findings on flagellin metabolism can also be used to 197 reduce competition for metabolite bioflux with botulinum neurotoxin, achieving both primary objectives of our research. 6.2 Research Impact The findings and results revealed in this research project offer substantial insight into what drives PHB metabolism in C. sporogenes and C. botulinum, which combined with the impact of the linear correlation between PHB and botulinum neurotoxin synthesis established by this study, offers unique insight into potential process development approaches. The findings on the metabolism and conditions which result in increased PHB accumulation can also offer guidance for study of PHB in other PHB producing organisms, including offering potential approaches to increasing PHB yield in industrial PHB production processes. Importantly, this research encourages investigations into the possibility of a correlation between PHB and toxin biosynthesis in other toxin producing members of the Clostridia genus, such as C. perfringens and C. difficile. This research could also form the basis of investigation into the correlation between PHB and other secondary metabolites, such as butanol production in C. Acetobutyricum, which shares biosynthetic pathway metabolites with PHB. Our work has also highlighted the use of CO2 as a supplementation approach to refuel TCA cycle intermediates and offered insight into metabolic manipulation of flagellin biosynthesis and sporulation, which may relate to organisms beyond C. sporogenes and C. botulinum. 198 6.3 Process Recommendations for the Botulinum Neurotoxin Production Process The results of this study demonstrate the process yield benefits attainable by controlling growth medium composition in C. botulinum cultures with respect to the botulinum neurotoxin production process. Altering the growth medium to achieve conditions permissive for PHB, and by association, neurotoxin biosynthesis offer a control strategy with experimentally determined biochemical and metabolic effects, offering potential increased and controlled neurotoxin yield. Based on these findings, supplementation with 10mM Homoserine may be an effective method of increasing PHB and neurotoxin biosynthesis by C. botulinum. Growth medium should also be phosphate limited with respect to carbon content and the culture sparged with 36% CO2 gas (remaining composition oxygen-free nitrogen), limiting the flux of competing pathways such as flagellin biosynthesis and increasing toxin yield. If supplementation to the growth medium is the preferred approach owing to process constraints, 10mM Bicarbonate can be substituted in place of increased CO2 concentration, demonstrated by the experiments covered in chapter 5 (Figure 59). 199 6.4 Recommendations for Future Studies Our findings on the conditions which result in increased PHB and neurotoxin biosynthesis and coincidentally decrease flagellin biosynthesis suggest the ratio of fermentative and anaerobic respiration utilised by the growing cultures plays an important role in influencing metabolism and affecting both biomarker and toxin yield. As this was not directly assessed, this work could be progressed by analysing culture respiration modes for comparison with both PHB and botulinum neurotoxin. Another recommendation to further this research is assessing the interplay between glycogen storage, PHB accumulation and neurotoxin biosynthesis. In Streptomyces spp., glycogen accumulates in response to excess carbon (Lillie & Pringle, 1980) and has been correlated with the biosynthesis of secondary metabolites, such as avilamycin (Salas & Mendez, 2005). Glycogen is stored in a pathway originating from glucose. Therefore comparatively, if excess carbon is available prior to being metabolised via glycolysis, the carbon is stored as glycogen and if excess carbon is available at the interaction between glycolysis and the TCA cycle, the carbon is stored as PHB. Based on our findings, this relationship is likely to affect PHB metabolism and coincidently, neurotoxin biosynthesis. Glycogen metabolism therefore may offer a further strategy to control neurotoxin biosynthesis and a further process development approach with regards to the botulinum neurotoxin production process. Increasing the concentration of CO2 offers a process development approach which increases both PHB and neurotoxin; however sporulation is also linearly increased. Owing to the fact 200 our hypothesis were tested in chemostat culture, it is difficult to estimate the effects of increased sporulation on culture population in batch culture, where spores will accumulate as opposed to being removed at the set dilution rate in continuous culture. It would therefore prove advantageous to assess the effects of increased CO2 on sporulation in batch cultures of C. botulinum. 201 Appendix Appendix I: Amino acid composition comparison of Botulinum toxin and flagellin Amino acid composition comparison of Botulinum toxin and flagellin 15 10 Toxin 5 cystiene Histidine Methionine Tryptophan Arginine Proline Alanine Glycine Phenylalan… Valine Lysine Threonine Tyrosine Serine Leucine Isoleucine Glutamic… Flagellin 0 Aspartic acid Amino acid composition (%) 20 Appendix Figure 1: A comparative display of amino acid composition between botulinum neurotoxin and flagellin protein. Values of botulinum toxin amino acid composition were obtained from Stefanye et al (Stefanye et al, 1967) and values of flagellin amino acid composition were obtained from Chang et al (Chang et al, 1976). 202 Bibliography Aerts JM, Kallemeijn WW, Wegdam W, Joao Ferraz M, van Breemen MJ, Dekker N, Kramer G, Poorthuis BJ, Groener JE, Cox-Brinkman J, Rombach SM, Hollak CE, Linthorst GE, Witte MD, Gold H, van der Marel GA, Overkleeft HS, Boot RGJ, (2011). Biomarkers in the diagnosis of lysosomal storage disorders: proteins, lipids, and inhibodies. Inherit Metab Dis. 34(3):60519. Akashi, H, and T. Gojobori (2002) Metabolic efficiency and amino acid composition in the proteomes of Escherichia coli and Bacillus subtilis. Proc. Natl. Acad. Sci. 99: 3695-3700. Alexander, Bloom, Hopwood, Hull, Iglewski, Laskin, Oliver, Schaechter and Summers (2000). Encyclopedia of Microbiology. Vol 1-4. Amador-Noguez Daniel, Xiao-Jiang Feng, Jing Fan, Nathaniel Roquet, Herschel Rabitz and Joshua D. Rabinowitz (2010). Systems-Level Metabolic Flux Profiling Elucidates a Complete, Bifurcated Tricarboxylic Acid Cycle in Clostridium acetobutylicum. Journal of Bacteriology. 192(7): 4452–4461. Amira Barketi-Klai, Marc Monot, and applications. FEMS Microbiol. Rev. 33: 164–190. Anderson, A. & Dawes, E. A. (1990). Occurrence, metabolism, metabolic role and industrial uses of bacterial polyhydroyalkanoates. Microbiol Rev. 54:450-472. 203 Anne Collignon, Aon, J. C. & Cortassa, S. (2001). Involvement of nitrogen metabolism in the triggering of ethanol fermentation in aerobic chemostat cultures of Saccharomyces cerevisiae. Metab Eng. 3: 250–264. Arnon, S. S., R. Schechte , and T. V. Inglesby et al (2002). Botulinum toxin as a biological weapon. Bioterrorism Guidelines for Medical and Public Health Management. Chicago, Ill: AMA Press. 141– 165. Artin, I., D. R. Mason, C. Pin, J. Schelin, M. W. Peck, E. Holst, P. Radstrom, and A. T. Carter. (2010). Effects of carbon dioxide on growth of proteolytic Clostridium botulinum, its ability to produce neurotoxin, and its transcrip- tome. Appl. Environ. Microbiol. 76:1168–1172. Artin, I., D. R. Mason, C. Pin, J. Schelin, M. W. Peck, E. Holst, P. Radstrom, and A. T. Carter. (2010). Effects of carbon dioxide on growth of proteolytic Clostridium botulinum, its ability to produce neurotoxin, and its transcrip- tome. Appl. Environ. Microbiol. 76:1168–1172. Aucamp Jean Paul, Richard Davies, Damien Hallet, Amanda Weiss, and Nigel J TitchenerHooker (2014). Integration of Host Strain Bioengineering and Bioprocess Development Using Ultra-Scale Down Studies to Select the Optimum Combination: An Antibody Fragment Primary Recovery Case Study. Biotechnol Bioeng. 111(10): 1971–1981. Avignone-Rossa, C., White, J., Kuiper, A., Postma, P. W., Bibb, M. & Teixeira de Mattos, M. J. (2002). Carbon flux distribution in antibiotic-producing chemostat cultures of Streptomyces lividans. Metab Eng. 4: 138–150. 204 Axel T Brunger Bao E, Jiang T, Kaloshian I, Girke T (2011). SEED: Efficient Clustering of Next Generation Sequences. Bioinformatics: epub. Bardy, S. L., S. Y. M. Ng, and K. F. Jarrell (2003). Prokaryotic motility structures. Microbiology. 149: 295–304. Beatriz Ruiz, Adán Chávez, Angela Forero, Yolanda García-Huante, Alba Romero, Mauricio Sánchez, Diana Rocha, Brenda Sánchez, Romina Rodríguez-Sanoja, Sergio Sánchez, and Elizabeth Langley (2010). Production of microbial secondary metabolites: Regulation by the carbon source. Critical Reviews in Microbiology. 36(2): 146–167. Benoit TG, Wilson GR, Baugh CL (1990). Fermentation during growth and sporulation of Bacillus thuringiensis HD-1. Lett Appl Microbiol. 10:15–18. Benoit TG, Wilson GR, Baugh CL (1990). Fermentation during growth and sporulation of Bacillus thuringiensis HD-1. Lett Appl Microbiol. 10:15–18. Beste D, Hooper T, Stewart G, Bonde B, Avignone-Rossa C, Bushell ME, Wheeler P & McFadden J (2007). GSMN-TB: a web-based genome-scale network model of Mycobacterium tuberculosis metabolism. Genome Biol. 8(5): 89. Bettina Tudzynski (2014). Nitrogen regulation of fungal secondary metabolism in fungi. Front. Microbiol. doi: 10.3389/fmicb.2014.00656. 205 Bittner J (1980). The clinical significance, taxonomy and special methodological problems of the pathogenic clostridia. Infection. 8 (2): 117-122. Brown DP, Ganova-Raeva L, Green BD, Wilkinson SR, Young M, Youngman P (1994). Characterization of spo0A homologues in diverse Bacillus and Clostridium species identifies a probable DNA-binding domain. Mol Microbiol. 14(3): 411-26. Bull, AT (2010). The renaissance of continuous culture in the post-genomics age. J Ind Microbiol Biotechnol. 37: 993–1021. Brown JL, Tran-Dinh N, Chapman B, (2012). Clostridium sporogenes PA 3679 and its uses in the derivation of thermal processing schedules for low-acid shelf-stable foods and as a research model for proteolytic Clostridium botulinum. J Food Prot. 75(4):779-92. Brown JL, Tran-Dinh N, Chapman B, (2012). Clostridium sporogenes PA 3679 and its uses in the derivation of thermal processing schedules for low-acid shelf-stable foods and as a research model for proteolytic Clostridium botulinum. J Food Prot. 75(4):779-92. Bruno Dupuy, Bruno Dupuy, Imad Kansau (2014). The Flagellin FliC of Clostridium difficile Is Responsible for Pleiotropic Gene Regulation during In Vivo Infection. May 2014, DOI: 10.1371/journal.pone.0096876 206 Burkovski, A., and R. Kramer. (2002). Bacterial amino acid transport proteins: occurrence, functions, and significance for biotechnological applications. Appl. Microbiol. Biotechnol. 58:265-274. Bushell M, Sequeira S, Khannapho C, Hongjuan Z, Chater K, Butler M, Kierzak A, AvignoneRossa C (2006). The use of genome scale metabolic flux variability analysis for process feed formulation based on an investigation of the effects of the zwf mutation on antibiotic production in Streptomyces coelicolor. Butler MJ, Bruheim P, Jovetic S, Marinelli F, Postma PW, Bibb MJ (2002). Engineering of primary carbon metabolism for improved antibiotic production in Streptomyces lividans. Appl Environ Microbiol. 68(10): 4731-9. Byoung-Mo Koo, Mi-Jeong Yoon, Chang-Ro Lee, Tae-Wook Nam, Young-Jun Choe, Howard Jaffe, Alan Peterkofsky, and Yeong-Jae Seok (2004). A Novel Fermentation/Respiration Switch Protein Regulated by Enzyme IIAGlc in Escherichia coli. The journal of biological chemistry. 279:30. 31613–31621. Calvo, A. M., Wilson, R. A., Bok, J. W., and Keller, N. P. (2002). Relationship between secondary metabolism and fungal development. Microbiol. Mol. Biol. Rev. 66: 447–459. Carvalho LP, Blanchard, JS (2006). "Kinetic and Chemical Mechanism of alphaIsopropylmalate Synthase from Mycobacterium tuberculosis". Biochemistry. 45 (29): 89888999. 207 Catherine J. Paul, Susan M. Twine, Kevin J. Tam, James A. Mullen, John F. Kelly, John W. Austin and Susan M. Logan (2007). Flagellin Diversity in Clostridium botulinum Groups I and II: a New Strategy for Strain Identification. Applied and Environmental Microbiology. 73: 2963–2975. Chang JY, DeLange RJ, Shaper JH, Glazer AN (1976). Amino acid sequence of flagellin of Bacillus subtilis 168. I. Cyanogen bromide peptides. J Biol Chem. 251(3): 695-700. Chater KF, Horinouchi S (2003). Signalling early developmental events in two highly diverged Streptomyces species. Mol. Microbiol. 48: 9–15. Christopher J. Alteri, Stephanie D. Himpsl, Michael D (2012). Anaerobic Respiration Using a Complete Oxidative TCA Cycle Drives Multicellular Swarming in Proteus mirabilis. Mbio. 3(6). 00365-12. Chuan-he Zhu, Fu-ping Lu, Ya-nan He, Zhen-lin Han and Lian-xiang Du (2007). Regulation of avilamycin biosynthesis in Streptomyces viridochromogenes: effects of glucose, ammonium ion and inorganic phosphate. Appl Microbiol Biotechnol. 73:1031–1038. Clare M. Cooksley, Ian J. Davis, Klaus Winzer, Weng C. Chan, Michael W. Peck and Nigel P. Minton (2010). Regulation of Neurotoxin Production and Sporulation by a Putative agrBD Signaling System in Proteolytic Clostridium botulinum. Appl. Environ. Microbiol. 76(13):4448. 208 Claret L., Miquel S., Vieille N., Ryjenkov D.A., Gomelsky M., Darfeuille-Michaud A (2007). The flagellar sigma factor FliA regulates adhesion and invasion of Crohn disease-associated Escherichia coli via a cyclic dimeric GMP-dependent pathway. J. Biol. Chem. 282: 33275– 33283. Cordenunsi, B. R., Da Silva, R. S. F., Srivastava, K. C., Fabre-Sanches, S., & Perre, M. a. (1985). Mathematical model for the alcoholic fermentation in batch culture: Comparison between complete and incomplete factorial (33) designs. Journal of Biotechnology. 2(1):1–12. Crane, J. K (1999). Preformed bacterial toxins: botulism. Clin Lab Med. 19:583–589. D Herbert, R Elsworth Telling RC (1956). "The continuous culture of bacteria; a Theoretical and Experimental study". J. Gen. Microbiol 14 (3): 601–622. D Stefanye, E J Schantz, and L Spero (1967). Amino acid composition of crystalline botulinum toxin, type A. J Bacteriol. 94(1): 277–278. D.R Curtis, W.C De Groat (1968). Tetanus toxin and spinal inhibition. Brain Res. 10: 208–212. David A. Burns, John T. Heap, and Nigel P. Minton (2010). SleC Is Essential for Germination of Clostridium difficile Spores in Nutrient-Rich Medium Supplemented with the Bile Salt Taurocholate. J Bacteriol. 192(3): 657–664. 209 Davis JI, Nixon KC (1992). Populations, genetic variation and the delimitation of phylogenetic species. Syst Biol. 41: 421–435. Decker W H & Hall W (1966). Treatment of abortions infected with Clostridium welchii. Am J Obstet Gynecol. 95: 394–399. Demain AL. (1989). Carbon source regulation of idiolite biosynthesis. In: Regulation of secondary metabolism in Actinomycetes. 127–134. Shapiro S Ed. Boca Raton, FL: CRC Press. Dixonr N.M, .W.Lovitt J, .G. Morris & D. B. Kell (1988). Growth energetics of Clostridium sporogenes NCIB 8053: modulation by C02 Received. Journal of Applied Bacteriology. 65(1): 19-133. Dolman, C. E. and H. Iida (1963). Type E botulism: its epidemiology, prevention and specific treatment. Can J Public Health. 54:293–308. Drake DR, Brogden KA (2002). Continuous-Culture Chemostat Systems and Flowcells as Methods to Investigate Microbial Interactions. In: Brogden KA, Guthmiller JM, editors. Polymicrobial Diseases. Chapter 2. Driouch, H. et al. (2012). Integration of in vivo and in silico metabolic fluxes for improvement of recombinant protein production. Metab. Eng. 14: 4758. 210 Drysdale, M., A. Bourgogne, S. G. Hilsenbeck, and T. M. Koehler (2004). atxA controls Bacillus anthracis capsule synthesis via acpA and a newly discovered regulator, acpB. J. Bacteriol. 186: 307–315. Dunbar, E. M (1990). Editorial: botulism. J Infect. 20:1–3. Durot, M. et al. (2009). Genome-scale models of bacterial metabolism: reconstruction Eggeling, L. and Bott, M. (2005). Handbook of Corynebacterium glutamicum. CRC Press. Boca Raton London New York Singapore. Ehrlich, K. C., and Cotty, P. J. (2002). Variability in nitrogen regulation of aflatoxin production by Aspergillus flavus strains. Appl. Microbiol. Biotechnol. 60: 174–178. Emeruwa, a C., & Hawirko, R. Z. (1973). Poly-beta-hydroxybutyrate metabolism during growth and sporulation of Clostridium botulinum. Journal of bacteriology, 116(2): 989–93. Engasser JM , Csaba Horvath C (1974). Inhibition of bound enzymes. II. Characterization of product inhibition and accumulation. Biochemistry. 13(19):3849–3854. F. Bergara, C. Ibarra, J. Iwamasa, J. C. Patarroyo, R. Aguilera and L. M. Ma´rquez-Magan (2003). CodY Is a Nutritional Repressor of Flagellar Gene Expression in Bacillus subtilis Journal of bacteriology. 3118–3126. 211 Faith C. Blum, Chen Chen, Abby R. Kroken and Joseph T. Barbieri (2012). Tetanus Toxin and Botulinum Toxin A Utilize Unique Mechanisms To Enter Neurons of the Central Nervous System. Infect. Immun. 80(5): 1662-1669. Ferenci T (2006) A cultural divide on the use of chemostats. Microbiology. 152: 1247–1248. Fernandez, P. S., J. Baranyi, and M. W. Peck (2001). A predictive model of growth from spores of non-proteolytic Clostridium botulinum in the presence of different CO2 concentrations as influenced by chill temperature, pH and NaCl. Food Microbiol. 18: 453– 461. Fong SS, Burgard AP, Herring CD, Knight EM, Blattner FR, Maranas CD, Palsson BO (2005). In silico design and adaptive evolutionof Escherichia coli for production of lactic acid. Biotechnol Bioeng. 91:643-648. Frevert J (2010). Content of botulinum neurotoxin in Botox®/Vistabel®, Dysport®/Azzalure®, and Xeomin®/Bocouture®. Drugs R D. 10(2):67-73. G Ahnert-Hilger, U Weller, M.E Dauzenroth, E Habermann, M Gratzl (1989). The tetanus toxin light chain inhibits exocytosis. FEBS Lett., 242: 245–248. George, W. L., R. D. Rolfe, G. K. Harding, R. Klein, C. W. Putnam, and S. M. Finegold (1982). Clostridium difficile and cytotoxin in feces of patients with antimicrobial agent-associated pseudomembranous colitis. Infection. 10: 205-208. 212 Gibson, A. M., R. C. Ellis-Brownlee, M. E. Cahill, E. A. Szabo, G. C. Fletcher, and P. J. Bremer. (2000). The effect of 100% CO2 on the growth of nonproteolytic Clostridium botulinum at chill temperatures. Int. J. Food Microbiol. 54:39–48. Grass, J. E., L. H. Gould, and B. E. Mahon (2013). Epidemiology of Foodborne Disease Outbreaks Caused by Clostridium perfringens, United States, 1998-2010. Foodborne Pathogens and Disease. 10(2): 131-135. Greasham, R., & Inamine, E. (1986). Nutritional improvement of processes. Manual of industrial microbiology and biotechnology. 41–48. Green, J., H. Spear, and R. R. Brinson (1983). Human botulism (type F): a rare type. Am J Med. 75:893–895 Hallett, Mn (1999). One man's poison: clinical applications of botulinum toxin. N Engl J Med. 341:118–120. Hatheway, C. L (1998). Clostridium botulinum. In: Gorbach SL, ed. Infectious Diseases. 2nd ed. Philadelphia, Pa: WB Saunders Co. 1919–1925. Homma, M. & lino, T. (1985). Locations of hook-associated proteins in flagellar structures of Salmonella typhimurium. J Bacteriol. 162:183-1 89. 213 Hua Q, Joyce AR, Fong SS, Palsson BO (2006) Metabolic analysis of adaptive evolution for in silico-designed lactate-producing strains. Biotechnol Bioeng. 95:992-1002. Imad Kansau in Clostridium perfringens. Journal of General Microbiology 87. 120-1 28. Ingrid Artin, Andrew T. Carter, Elisabet Holst, Maria Lövenklev, David R. Mason, Michael W. Peck and Peter Rådström (2008). Effects of Carbon Dioxide on Neurotoxin Gene Expression in Nonproteolytic Clostridium botulinum Type E. Appl. Environ. Microbiol. 74(8):2391. Janne M. Toivonen, Sara Oliván, and Rosario Osta (2010). Tetanus Toxin C-Fragment: The Courier and the Cure? Toxins (Basel). 2(11): 2622–2644. Johanna Haiko and Benita Westerlund-Wikström (2013). The Role of the Bacterial Flagellum in Adhesion and Virulence. Biology (Basel). 2(4): 1242–1267. Jones DT, Woods DR (1986). Acetone-butanol fermentation revisited. Microbiol. Rev. 50: 484 –524. Jorge G. Valdez & Ricardo J. Piccolo (2006). Use of Spectrophotometry as a Tool to Quantify the Sporulation of Penicillium allii in Garlic Lesions. Fitopatologia Brasileira. 31: 595-597. Jose Garcia Lillo and Francisco Rodriguez-Valera (1990). Effects of Culture Conditions on Poly(β-Hydroxybutyric Acid) Production by Haloferax mediterranei. Appl. Environ. Microbiol. 56:8:2517-2521 214 Joungmin Lee, Yu-Sin Jang, Sung Jun Choi, Jung Ae Im, Hyohak Song, Jung Hee Cho, Do Young Seung, E. Terry Papoutsakis, George N. Bennett,e and Sang Yup Lee (2012). Metabolic Engineering of Clostridium acetobutylicum ATCC 824 for Isopropanol-Butanol-Ethanol Fermentation. Applied and Environmental Microbiology. 1: 1416–1423. Juan F. Martín (2004). Phosphate Control of the Biosynthesis of Antibiotics and Other Secondary Metabolites Is Mediated by the PhoR-PhoP System: an Unfinished Story. J Bacteriol. 186(16): 5197–5201. Kai, Yasushi; Matsumura, Hiroyoshi; Izui, Katsura (2003). "Phosphoenolpyruvate carboxylase: three-dimensional structure and molecular mechanisms". Archives of Biochemistry and Biophysics. 414 (2): 170–179. Kalil, S. J., Maugeri, F., & Rodrigues, M. I. (2000). Response surface analysis and simulation as a tool for bioprocess design and optimization. Process Biochemistry. 35(6):539–550. Karasawa T., Sayuri Ikoma, Kiyotaka Yamakawa and Shin ichi Na kamura (1995). A defined growth medium for Clostridium difficile. Microbiology. 141:371-375. Karasawa T., Sayuri Ikoma, Kiyotaka Yamakawa and Shin ichi Na kamura (1995). A defined growth medium for Clostridium difficile. Microbiology. 141:371-375. Karen M VanderMolen, Huzefa A Raja, Tamam El-Elimat and Nicholas H Oberlies (2013). Evaluation of culture media for the production of secondary metabolites in a natural products screening program. Published online 2013. 10.1186/2191-0855-3-71. 215 Kathryn Turton, John A Chaddock, K.Ravi Acharya (2002). Botulinum and tetanus neurotoxins: structure, function and therapeutic utility. Trends in biochemical sciences. 27(11): 552-558. KEGG. Kyoto (JP): Kanehisa Laboratories (2013). Glycerophospholipid metabolism, C. botulinum. Keith F Chater (2006). Streptomyces inside-out: a new perspective on the bacteria that provide us with antibiotics. Philos Trans R Soc Lond B Biol Sci. 361(1469): 761–768. Kenji Tanaka , Kianoush Khosravi-Darani, Zahra-Beigom Mokhtari, Tomohito Amai (2011). Microbial production of poly(hydroxybutyrate) from C(1) carbon sources. Applied Microbiology and Biotechnology. 92:1161-1169. Kenji Tanaka , Kianoush Khosravi-Darani, Zahra-Beigom Mokhtari, Tomohito Amai (2011). Microbial production of poly(hydroxybutyrate) from C(1) carbon sources. Applied Microbiology and Biotechnology. 92:1161-1169. Kenji Ueda, Yudai Tagami, Yuka Kamihara, Hatsumi (2008). Isolation of Bacteria Whose Growth Is Dependent on High Levels of CO2 and Implications of Their Potential Diversity. Applied and environmental microbiology. 74: 14. 4535–4538. Kotrea M., Sullivans J. & Savageamu A. (1973). Metabolic regulation by homoserine in Escherichia coli B/r. Journal of Bacteriology. 116: 663-672. 216 Lacy DB, Tepp W, Cohen AC, DasGupta BR, Stevens RC (1998). Crystal structure of botulinum neuro-toxin type A and implications for toxicity. Nat Struct Biol. 5:898–902. Laurent Bouillaut, William T. Self and Abraham L. Sonenshein (2013). Proline-Dependent Regulation of Clostridium difficile Stickland Metabolism. J. Bacteriol. 195(4): 844-854. Law JH and Slepecky RA (1961). Assay of poly-β-hydroxybutyric acid. J. Bacteriol. 82;1:33-36 Lehmann D, Hönicke D, Ehrenreich A, Schmidt M, Weuster-Botz D, Bahl H, Lütke-Eversloh T (2012). Modifying the product pattern of Clostridium acetobutylicum: physiological effects of disrupting the acetate and acetone formation pathways. Appl Microbiol Biotechnol. 94(3): 743-54. Lillie, S.H. & J.R. Pringle, 1980. Reserve carbohydrate metabolism in Saccharomyces cerevisiae: responses to nutrient limitation. J. Bacteriol. 143: 1384-1394. Liras, P., J. A. Asturias, and J. F. Martín (1990). Phosphate control sequences involved in transcriptional regulation of antibiotic biosynthesis. Trends Biotechnol. 8: 184-189. Lo¨venklev, M., I. Artin, O. Hagberg, E. Borch, E. Holst, and P. Rådstro¨m. (2004). Quantitative interaction effects of carbon dioxide, sodium chloride and sodium nitrite on neurotoxin gene expression in nonproteolytic Clostridium botulinum type B. Appl. Environ. Microbiol. 70: 2928–2934. 217 Macnab, R. M. (2003). How bacteria assemble flagella. Annu Rev Microbiol 57:77–110. Mark A Breidenbach, Rongsheng Jin, Audrey Fischer, Jose S Santos, Mauricio Montal (2007). Botulinum Neurotoxin Heavy Chain Belt as an Intramolecular Chaperone for the Light Chain. Plos pathogens. Mark Bradbury, Paul Greenfield, David Midgley, Dongmei i, Nai Tran-Dinh, Frank Vriesekoop and Janelle L. Brown (2012). Draft Genome Sequence of Clostridium sporogenes PA 3679, the Common Nontoxigenic Surrogate for Proteolytic Clostridium botulinum. J. Bacteriol. 194(6): 1631-1632. Martín, J. F (1989). Molecular mechanism for the control by phosphate of the biosynthesis of antibiotic and secondary metabolites. In S. Shapiro (ed.), Regulation of secondary metabolism in actinomycetes. CRC Press, Inc., Boca Raton, Fla. 213-237. Masato Miyake, Kazuya Takase, Midori Narato, Emir Khatipov, Joerg Schnackenberg, Makoto Shirai, Ryuichiro Kurane, Yasuo Asada (2000). Polyhydroxybutyrate Production from Carbon Dioxide by Cyanobacteria. Applied Biochemistry and Biotechnology. 991-1002. Maselli, R. A (1998). Pathogenesis of human botulism. Ann N Y Acad Sci. 841:122–139. Masuma, R., Y. Tanaka, H. Tanaka, and S. Omura (1986). Production of nanomycin and other antibiotics by phosphate-depressed fermentation using phosphate-trapping agents. J. Antibiot. 39: 1557-1564. 218 Mervyn J Bibb (2005) Regulation of secondary metabolism in streptomycetes. Current Opinion in Microbiology. 8: 208–215. Mignone, C. F.; Avignone-Rossa, C. (1996). Analysis of glucose carbon fluxes in continuous cultures of Bacillus thuringiensis. Applied microbiology and biotechnology 46: 78-84 Mitchell, W. J. (2001). Clostridia. Biotechnology and medical applications. In H. Bahl and P. Durre (ed.), Wiley- VCH, Weinheim, Germany. Biology and physiology. 50–104. Moller, V. and I. Scheibel (1960). Preliminary report on the isolation of an apparently new type of Clostridium botulinum. Acta Pathol Microbiol Scand. 48:80. Monod J (1950) La technique de culture continue´e. Theorie etapplication. Ann Inst Pasteur. 79: 390–410. Montecucco C, Schiavo G (1994). Mechanism of action of tetanus and botulinum neurotoxins. Mol Microbiol. 13(1): 1-8. Münchau A & Bhatia KP (2000). Uses of botulinum toxin injection in medicine today. BMJ. 320(7228): 161–165. Nancy W. Hendrix, M.D, A. Dhanya Mackeen, M.D., M.P.H and Stuart Weiner, M.D (2011). Clostridium perfringens Sepsis and Fetal Demise after Genetic Amniocentesis. AJP Rep. 1(1): 25–28. 219 Navarro AK, Farrera RR, López R, Pérez-Guevara F (2006). Relationship between poly-betahydroxybutyrate production and delta-endotoxin for Bacillus thuringiensis var. kurstaki. Biotechnol Lett. 28(9): 641-4. Niehaus, E. M., von Bargen, K. W., Espino, J. J., Pfannmüller, A., Humpf, H. U., and Tudzynski, B. (2014). Characterization of the fusaric acid gene cluster in Fusarium fujikuroi. Appl. Microbiol. Biotechnol. 98: 1749–1762. Nigel Minton, Novick A, Szilard L (1950) Description of the chemostat. Science. 112: 715– 716. Novogrudsky A and Plaut AG. (2003) The bacterial flora of the healthy gastrointestinal tract: colonization of the human colon. Gastroenterology. 4: 584–585. Ochi S, Miyawaki T, Matsuda H, Oda M, Nagahama M, Sakurai J (2002). Clostridium perfringens alpha-toxin induces rabbit neutrophil adhesion. Microbiology. 148(1): 237-45. Oliver E. Owen, Satish C. Kalhan and Richard W. Hanson (2002). The Key Role of Anaplerosis and Cataplerosis for Citric Acid Cycle Function. Journal of Biological Chemistry. 277: 3040930412. Orna Ernst and Tsaffrir Zor (1918). Linearization of the Bradford Protein Assay. J Vis Exp. (38): 1918. 220 Papoutsakis ET (2008). Engineering solventogenic clostridia. Curr. Opin. Biotechnol. 19: 420-429. Patterson-Curtis, S.I & Johnson E.A (1992). Roles of Arginine in Growth of Clostridium botulinum Okra B. Appl. Environ. Microbiol. 58(7): 2334. Paul A. Hoskisson and Glyn Hobbs (2005). Continuous culture – making a comeback? Microbiology. 151(10): 3153-3159. Pentyala SN, Benjamin WB (1995). Effect of oxaloacetate and phosphorylation on ATPcitrate lyase activity. Biochemistry. 5;34(35):10961-9 Peplinski, K., Ehrenreich, A., Döring, C., Bömeke, M., Reinecke, F., Hutmacher, C., & Steinbüchel, A. (2010). Genome-wide transcriptome analyses of the “Knallgas” bacterium Ralstonia eutropha H16 with regard to polyhydroxyalkanoate metabolism. Microbiology (Reading, England), 156;7:2136–52. Peter F. Bonventre and Lloyd L. Kempe (1960). Growth, Autolysis, and toxin botulinum types a and b i: production by clostridium physiology of toxin. J Bacteriol. 79(1): 18. Pfromm PH, Amanor-Boadu V, Nelson R, Vadlani P, Madl R (2010). Bio-butanol bio-ethanol: a technical and economic assessment for corn and switchgrass fermented by yeast or Clostridium acetobutylicum. Biomass Bioenergy. 34: 515–524. 221 Piepersberg W, Distler J. (1997). Aminoglycosides and sugar components in other secondary metabolites. In: Rehm HJ, Reed G (Geneds.); Kleinkauf H, von Döhren H. (Vol eds Biotechnology. 2(7): 397–488. Plackett, R. L., & Burman, J. P. (1946). The design of optimum multifactorial experiments. Biometrika. 33(4): 305–325. Popoff, M. R (1995). Ecology of neurotoxigenic strains of clostridia. Curr Top Microbiol Immunol. 195:1–29. Preiss J & Romeo T, 1989. Genetic regulation of glycogen biosynthesis in Escherichia coli: in vitro effects of cyclic AMP and guanosine 5'-diphosphate 3'-diphosphate and analysis of in vivo transcripts. J Bacteriol. 171(5): 2773–2782. Prevot, A. R. , J. Terrasse , and J. Daumail et al (1955). Existence en France du botulisme humain de type C. Bull Acad Natl Med Paris. 139:355–358. Price ND, Papin JA, Schilling CH, Palsson BO (2003). Genome-scale microbial in silico models: the constraints-based approach. Trends Biotechnol. 21:162-169. R.H. Taylor, M.L. Dunn, L.V. Ogden, L.K. Jefferies D.L. Eggett, F.M. Steele (2013). Conditions associated with Clostridium sporogenes growth as a surrogate for Clostridium botulinum in nonthermally processed canned butter. Journal of Dairy Science. 96(5): 2754–2764. 222 Raberg, M., Reinecke, F., Reichelt, R., Malkus, U., Ko¨ nig, S., Po¨ tter, M., Fricke, W. F., Pohlmann, A., Voigt, B. & other authors (2008). Ralstonia eutropha H16 flagellation changes according to nutrient supply and state of poly(3-hydroxybutyrate) accumulation. Appl Environ Microbiol. 74:4477–4490. Ramy K Aziz et al. (2008). The RAST Server: Rapid Annotations using Subsystems Technology BMC Genomics. 975: 1471-2164. Rello, J., Kollef, M.H., Díaz, E., Rodríguez, A. (2007). Infectious Diseases in Critical Care. Eds. 1 Ross, R. A., and A. B. Onderdonk (2000). Production of toxic shock syndrome toxin 1 by Staphylococcus aureus requires both oxygen and carbon dioxide. Infect. Immun. 68: 5205– 5209. Ryan G. Sinclair, Joan B. Rose, Syed A. Hashsham, Charles P. Gerba and Charles N. Haas (2012). Criteria for Selection of Surrogates Used To Study the Fate and Control of Pathogens in the Environment. Appl Environ Microbiol. 78(6): 1969–1977. Ryu HW, Hahn SK, Chang YK, Chang HN (1997). Production of poly(3-hydroxybutyrate) by high cell density fed-batch culture of Alcaligenes eutrophus with phosphate limitation. BioProcess Engineering Biotechnol Bioeng. 5;55(1):28-32. S. Kamiya, H. Ogura, X. Q. Meng and S. Nakamura (1992). Correlation between cytotoxin production and sporulation in Clostridium difficile. J. Med. Microbiol. 37: 206-210 223 S. M. Hasan And J. B. Hall (1975). The Physiological Function of Nitrate Reduction in Clostridium perfringens. Journal of General Microbiology 87. 120-1 28. Sakurai J, Nagahama M, Oda M (2004). Clostridium perfringens alpha-toxin: characterization and mode of action. J Biochem. 136(5):569-74. Salas, J. A., and C. Méndez, 2005. Biosynthesis pathways for deoxysugars in antibioticproducing actinomycetes: isolation, characterization and generation of novel glycosylated derivatives. J. Molec. Microbiol. Biotechnol. 9: 77-85. Sandra Hoys, Sandra I. Patterson-Curtis & Eric A. Johnson (1992). Roles of Arginine in Growth of Clostridium botulinum Okra B. Appl. Environ. Microbiol. 58(7): 2334. Sandra I. Patterson-Curtis & Eric A. Johnson (1992). Roles of Arginine in Growth of Clostridium botulinum Okra B. Appl. Environ. Microbiol. 58(7): 2334. Sarah A. Kuehne, Scallan, E., R. M. Hoekstra, F. J. Angulo, R. V. Tauxe, M-A. Widdowson, S. L. Roy, J. L. Jones, and P. M. Griffin (2011). Foodborne Illness Acquired in the United States— Major Pathogens. Emerging Infectious Diseases. 17(1): 7-15 Schilling, C. H., Edwards, J. S., & Palsson, B. O. (1999). Toward metabolic phenomics: analysis of genomic data using flux balances. Biotechnology progress. 15(3):288–95. 224 Scott AB (1980). Botulinum toxin injection into extraocular muscles as an alternative to strabismus surgery. Ophthalmology. 87:1044-1049 Shang L, Jiang M, Chang HN (2003) Poly(3-hydroxybutyrate) synthesis in fed-batch culture of Ralstonia eutropha with phosphate limitation under different glucose concentrations. Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology. Biotechnol Lett. 25(17):1415-9. Shimamura, T., S. Watanabe, and S. Sasaki (1985). Enhancement of enterotoxin production by carbon dioxide in Vibrio cholerae. Infect. Immun. 49: 455–456 Sifton, D. W (2003). ed. Physicians Desk Reference. Montvale, NJ: Thomson PDR. 548–554. Smid EJ, Molenaar D, Hugenholtz J, de Vos WM, Teusink B (2005). Functional ingredient production: application of global metabolic models. Curr Opin Biotechnol. 16:190-197 Smith, LDS (1979). Clostridium botulinum characteristics and occurrence. Rev Inf Dis. 1: 637–639 Sonnabend, O., W. Sonnabend, and R. Heinzle et al (1981). Isolation of Clostridium botulinum type G and identification of type G botulinum toxin in humans: report of five sudden unexpected deaths. J Infect Dis. 143:22–27. SteinbUchel, A. (1991). Polyhydroxyalkanoic acids. Edited by D. Byrom. Basingstoke : Macmillan. In Biomaterials.123-213 225 Stephenson K, Lewis RJ (2005). Molecular insights into the initiation of sporulation in Grampositive bacteria: new technologies for an old phenomenon. FEMS Microbiol Rev 29: 281301. Stickland LH. (1934). Studies in the metabolism of the strict anaerobes (genus Clostridium): the chemical reactions by which Cl. sporogenes obtains its energy. Biochem. J. 28: 1746– 1759. Stickland LH. (1935). Studies in the metabolism of the strict anaerobes (genus Clostridium): the oxidation of alanine by Cl. sporogenes. IV. The reduction of glycine by Cl. sporogenes. Biochem. J. 29 :889–898. Sylvie Lambert Bordes, Tadahiro Karasawa, Sayuri Ikoma, Kiyotaka Yamakawa and Shin ichi Na kamura (1995). A defined growth medium for Clostridium difficile. Microbiol. 141: 371375. Tadahiro Karasawa, Sayuri Ikoma, Kiyotaka Yamakawa and Shin ichi Na kamura (1995). A defined growth medium for Clostridium difficile. Microbiol. 141: 371-375. Teresa Fernández-Espinar, Eladio Barrio and Amparo Querol (2003). Analysis of the genetic variability in the species of the Saccharomyces sensu stricto complex M. Article first published online. 226 Teusink B, van Enckevort FH, Francke C, Wiersma A, Wegkamp A, Smid EJ, Siezen RJ (2005). In silico reconstruction of the metabolic pathways of Lactobacillus plantarum : comparing predictions of nutrient requirements with those from growth experiments. Appl Environ Microbiol. 71:7253-7262. Torbjörn Norén (2010) Clostridium difficile and the Disease It Causes. Methods in Molecular Biology. 646: 9-35. Twine, S. M., Reid, C. W., Aubry, A., McMullin, D. R., Fulton, K. M., Austin, J., & Logan, S. M. (2009). Motility and flagellar glycosylation in Clostridium difficile. Journal of bacteriology, 191(22): 7050–62. van Ermengem, E (1897). Ueber einen neuen anaeroben Bacillus und seine Beziehungen zum Botulismus. Z Hyg Infektionskrankheit. 26:1–56. Varma, A., & Palsson, B. O. (1994). Stoichiometric flux balance models quantitatively predict growth and metabolic by-product secretion in wild-type Escherichia coli W3110. Applied and environmental microbiology. 60(10): 3724–31. Venkataraman Sritharan, Colin Ratledge & Paul R. Wheeler (1987). Effect of Homoserine on Growth of Mycobacterium smegmatis: Inhibition of Glutamate Transport by Homoserine. Journal of General Microbiology. 133: 2781-2785. 227 Vipin Chandra Kalia, Tanmoy Mukherjee, Ashish Bhushan, Jayadev Joshi, Pratap Shankar and Nusrat Huma (2011). Analysis of the unexplored features of rrs (16S rDNA) of the Genus Clostridium. BMC Genomics. 12: 18. Virginia Ng and Wei-Jen Lin (2014). Comparison of Assembled Clostridium botulinum A1 Genomes Revealed Their Evolutionary Relationship. Genomics. 103(1): 94–106. Voth Daniel E and Ballard Jimmy D (2005). Clostridium difficile Toxins: Mechanism of Action and Role in Disease. Clin. Microbiol. Rev. 18(2): 247-263. Wang, H.H,. Riding, S., Lindo., & Singh, B.R (2010). Endopeptidase activities of botulinum neurotoxin type B complex, holotoxin, and light chain. Applied environmental microbiology. 76(19): 6658-63. Wang-Ni Tian, Leigh D. Braunstein, Jiongdong Pang, Karl M. Stuhlmeier, Qiong-Chao Xi, Xiaoni Tian and Robert C. (1998). StantonImportance of Glucose-6-phosphate Dehydrogenase Activity for Cell Growth. The Journal of Biological Chemistry.273: 1060910617. Wictome M, Shone CC (1998). Botulinum neurotoxins: Mode of action and detection. J Appl Microbiol. 84:S87–97. Wiegand G, Remington SJ (1986). Citrate synthase: structure, control, and mechanism. Annu Rev Biophys Biophys Chem. 15: 97-117. 228 Wiemann, P., Straeten, M., Kleigrewe, K., Beyer, M., Humpf, H. U., and Tudzynski, B. (2009). Biosynthesis of the red pigment bikaverin in Fusarium fujikuroi: genes, their function and regulation. Mol. Microbiol. 72: 931–946. Wohl, R.C. and Markus, G. (1972) Phosphoenolpyruvate Carboxylase of Escherichia coli: purification and some properties. Journal of Biological Chemistry 247: 5785. Woudstra C, Lambert D, Anniballi F, De Medici D, Austin J, Fach P (2013). Genetic diversity of the flagellin genes of Clostridium botulinum groups I and II. Appl Environ Microbiol. 79(13): 3926-32. Xie L, Wang DIC (1994). Stoichiometric analysis of animal cell growth and its application in medium design. Biotechnol Bioeng. 43:1164-1174. Yann Humeau, Frédéric Doussau, Nancy J Grant, Bernard Poulain (2000). How botulinum and tetanus neurotoxins block neurotransmitter release. Biochimie. 82(5): 427–446. Zhongxing Peng Chen, J. Glenn Morris Jr., Ramon L. Rodriguez, Aparna Wagle Shukla, John Tapia-Núñez and Michael S. Okun (2012). Emerging Opportunities for Serotypes of Botulinum Neurotoxins. Toxins. 4(11): 1196-1222. 229