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

vi

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

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 culturevii

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

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

ix

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 (N-

domain) 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 non-

covalent 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 life-

threatening 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 85oC or higher (Dunbar et al,

1990).

5

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

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

6

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

blocks neurotransmitter release by zinc-dependent cleavage of proteins required by the

neuroexocytosis apparatus (Lacy et al, 1998; Wictome & Shone, 1998).

7

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

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).

8

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).

9

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.

10

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.

11

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

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.

12

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

13

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

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.

14

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,

1992; Mitchell, 2001) is also advantageous, as the process can be engineered to promote a

biological state to increase product yields.

15

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.

16

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.

17

Trial

Variables (χ)χ1 χ2 χ3 χ4 χ5 χ6 χ7 χ8 χ9 χ10 χ11

1 + + - + + + - - - + -2 - + + - + + + - - - +3 + - + + - + + + - - -4 - + - + + - + + + - -5 - - + - + + - + + + -6 - - - + - + + - + + +7 + - - - + - + + - + +8 + + - - - + - + + - +9 + + + - - - + - + + -

10 - + + + - - - + - + +11 + - + + + - - - + - +12 - - - - - - - - - - -

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.

18

1.5.4 Exploiting Biomarkers of Product Biosynthesis

19

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

20

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

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

21

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.

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).

22

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-1-

phosphate; 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.

23

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 &

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

24

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

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.

25

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 non-

pathogenic 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

26

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.

27

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

28

(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).

29

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.

30

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).

31

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 time-

independent 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

32

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.

33

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.

34

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.

35

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 37oC for

24 hours in an anaerobic jar. Anaerobic conditions were achieved using AnaeroGenTM

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 37oC 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.

36

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.

37

Strain number Organism Allocated strain reference number

NCIMB532 Clostridium sporogenes 1NCIMB8053 Clostridium sporogenes 2NCIMB8243 Clostridium sporogenes 3NCIMB9381 Clostridium sporogenes 4NCIMB9382 Clostridium sporogenes 5NCIMB9383 Clostridium sporogenes 6

NCIMB10196 Clostridium sporogenes 7NCIMB10696 Clostridium sporogenes 8NCIMB12148 Clostridium sporogenes 9NCIMB12343 Clostridium sporogenes 10

NCIMB700933 Clostridium sporogenes 11NCIMB701789 Clostridium sporogenes 12NCIMB701791 Clostridium sporogenes 13NCIMB701792 Clostridium sporogenes 14NCIMB701793 Clostridium sporogenes 15

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.

38

Basal Defined Medium:

Table 3: Basal Defined Medium (BDM) composition from Karasawa et al, 1995

Amino Acid

HistidineTryptophanGlycineTyrosineArgininePhenylalanineMethionineThreonineAlanineLysineSerineValineIsoleucineAspartic acidLeucineCysteineProlineGlutamic acid

Variable nutrients (see below)GlucoseNa,HPO4

KH2PO4

(NH4)2S04

Concentration (mg/L)

100100100100200200200200200300300300300300400500600900

2000150030040

Vitamin

ThiamineCalcium-D-pantothenateNicotinamideRiboflavinPyridoxinep-Aminobenzoic acidFolic acidBiotinB12

MineralsNaClCaCl,2H2OMgCl, 6H2OMnCl, 4H2OFeSO4 7H2OCoCl2 6H2ODistilled water (ml)NaHCO3

Concentration (mg/L)

111110.050.01250.01250.005

9002620104110005000

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.

39

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.

40

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 100oC 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.

41

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.

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.

42

2 4 6 8 100

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

R² = 0.994837636168925

PHB Standard Curve

PHB (µg/ml)

OD23

5

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 100oC 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 SDS-

PAGE (Biorad Laboratories Ltd., UK) at room temperature and 200V constant voltage for

approximately 30 minutes. A protein marker reagent ranging from 20 to 200kDa (Sigma-

Aldrich 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

43

sample was then calculated based on a calibration of band densities obtained from samples

of known protein quantities present on the gel.

44

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 (Sigma-

Aldrich Co., USA); 1L Milli-Q water.

Stacking Gel Buffer: 60.55g 0.5M Tris-HCL; 0.4% (v/v) Sodium dodecyl Sulphate (Sigma-

Aldrich 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.

45

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.

46

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).

47

Trial

Variables (χ)χ1 χ2 χ3 χ4 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).

48

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

49

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.

50

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 OD 450

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)

51

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 30 oC 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, Sigma-

Aldrich 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); Venz 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).

52

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 30 oC 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).53

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.

54

0 10 20 30 40 500

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

R² = 0.948203754285253

Standard Curve of Phosphoenolpyruvate Carboxylase Activity

Phosphoenolpyruvate (U/ml)

Tota

l Abs

orba

nce D

ecre

ase

(OD3

40)

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 121 oC for 30

minutes. The inoculum was prepared by emptying 1ml frozen stock culture into Erlenmeyer

flasks containing 100ml BDM medium and incubating at 37oC 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 &

55

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.

56

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 4oC. The upper

aqueous phase of the sample was transferred to a new centrifuge tube containing 200µl of

57

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.

58

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.

59

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

60

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).

61

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 37 oC in an

anaerobic cabinet.

62

Figure

5: The growth (OD560) of fifteen different strains of C. sporogenes grown in CMM for 24

hours in an anaerobic cabinet maintained at 37oC. 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.

63

1 2 3 4 5 6 7 8 9 10 11 12 13 14 150

0.5

1

1.5

2

2.5

3

Growth of fifteen strains of C. sporogenes in CMM for 24h

Strain number

Grow

th (O

D560

)

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;

64

1 2 3 4 5 6 7 8 9 10 11 12 13 14 150

0.5

1

1.5

2

2.5

3

Growth of fifteen strains of C. sporogenes in CMM for 72h

Strain number

Grow

th (O

D560

)

Bonventre and Kempe, 1960; Cooksley et al, 2010; Emeruwa & Hawirko, 1974). Sufficient

growth after 24 hours incubation was therefore a necessary requirement when 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).

65

Figure 7: PHB accumulation in fifteen different strains of C. sporogenes grown in CMM for 24

hours in an anaerobic cabinet maintained at 37oC. 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

66

1 2 3 4 5 6 7 8 9 10 11 12 13 14 150

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

PHB accumulation in fifteen strains of C. sporogenes following 24h incubation in CMM

Strain number

PhB

(mg/

g bio

mas

s)

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

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.

67

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.

68

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.

69

Genomic comparison of C. botulinum and C.sporogenes

Identical Genes Genetic Difference

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

70

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.

71

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).

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 37oC 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.

72

0 3 6 9 12 18 240

0.1

0.2

0.3

0.4

0.5

0.6

0.7

The effect of phosphate concentration on growth of C. sporogenes in flask culture

1500mg/L1000mg/L500mg/L250mg/L125mg/L

Time (h)

Grow

th (O

D560

)

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.

73

125mg 250mg 500mg 1000mg 1500mg0

0.1

0.2

0.3

0.4

0.5

0.6

R² = 0.935603898021459

Effect of phosphate concentration on growth at early stationary phase

Na2PO4/L

OD56

0

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 74

125mg 250mg 500mg 1000mg 1500mg0.85

0.9

0.95

1

1.05

1.1

The effect of phosphate concentration on growth rate (y = ln x) of C.sporogenes

Na2PO4/L

Grow

th ra

te (s

lope g

radie

nt/y

= ln

x)

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.

75

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.

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.

76

0 3 6 9 12 18 240

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

The effect of Ammonium Sulphate concentration on growth of C.sporogenes in flask culture

1000mg/L750mg/L500mg/L250mg/L0mg/L

Time (h)

Grow

th (O

D560

)

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.

77

0mg 250mg 500mg 750mg 1000mg0.37

0.38

0.39

0.4

0.41

0.42

0.43

0.44

0.45

Effect of ammonium concentration on growth at early stationary phase

(NH4)2SO4/L

Grow

th (O

D560

)

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.

78

0mg 250mg 500mg 750mg 1000mg0.5

0.55

0.6

0.65

0.7

0.75

0.8

The effect of ammonium concentration on growth rate (y = ln x) of C.sporogenes

(NH4)2SO4/L

Grow

th ra

te (s

lope

grad

ient

/y =

ln x)

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.

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 37oC 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.

79

0 3 6 9 12 18 24-0.1

-8.32667268468867E-17

0.0999999999999999

0.2

0.3

0.4

0.5

0.6

The effect of Glucose concentration on growth of C.sporogenes in flask culture

4000mg/L2000mg/L1000mg/L500mg/L200mg/L

Time (h)

Grow

th (O

D560

)

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.

80

200mg 500mg 1000mg 2000mg 4000mg0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5R² = 0.818858953601075

Effect of glucose concentration on growth at early stationary phase

Glucose/L

Grow

th (O

D560

)

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

concentration of environmental glucose leading to a switch in uptake mechanism, resulting

81

200mg 500mg 1000mg 2000mg 4000mg0.5

0.55

0.6

0.65

0.7

0.75

0.8

The effect of Glucose concentration on growth rate (y = ln x) of C. sporogenes

Glucose/L

Grow

th ra

te (s

lope

grad

ient/

y = l

n x)

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.

82

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).

83

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).

84

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.

85

0 3 6 9 12 18 240

2

4

6

8

10

12

14

16

18

20

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Growth, phosphate metabolism and biomarker production over time 1500mg/L Phosphate

FlagellinPhBSporesPhosphateGrowth

Time (h)

Flage

llin (µ

g/m

l-2);

PhB

(% ce

ll wei

ght);

Sp

ores

(spo

res/

µl);

Pho

spha

te (m

g x 1

02/L

)

Grow

th (O

D560

)

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.

86

0 3 6 9 12 18 240

4

8

12

16

20

24

0

0.1

0.2

0.3

0.4

0.5

Growth, phosphate metabolism and biomarker production over time 125mg/L Phosphate

FlagellinPhBSporesPhosphateGrowth

Time (h)

Flag

ellin

(µg/

ml-2

); Ph

B (%

cell

wei

ght)

; Sp

ores

(spo

res/

ml x

3);

Pho

spha

te (m

gx10

/L)

Grow

th (O

D560

)

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).

87

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.

88

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.

89

0 3 6 9 12 18 2402468

101214161820

00.050.10.150.20.250.30.350.40.450.5

Growth, ammonium metabolism and biomarker production over time 1000mg/L Ammonium Sulphate

FlagellinPhBSporesGrowth

Time (h)

Flage

llin (µ

g/m

l-2);

PhB

(% ce

ll wei

ght);

Sp

ores

(spo

res/

ml x

3)

Grow

th (O

D560

); A

mm

oniu

m (g

/L)

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.

90

0 3 6 9 12 18 240

2

4

6

8

10

12

14

16

18

00.050.10.150.20.250.30.350.40.450.5

Growth, Ammonia metabolism and Biomarker Production over Time 0mg/L Ammonium Sulphate

FlagellinPhBSporesGrowth

Time (h)

Flage

llin (µ

g/m

l-2);

PhB

(% ce

ll weig

ht);

Spor

es (s

pore

sx10

3:dc

w)

Grow

th (O

D560

)

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.

91

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.

92

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.

93

0 3 6 9 12 18 240

2

4

6

8

10

12

14

16

0

0.1

0.2

0.3

0.4

0.5

0.6

Growth, Glucose metabolism and Biomarker Production over Time 4000mg/L Glucose

FlagellinPhBSporesGrowth

Time (h)

Flage

llin (µ

g/m

l-2);

PhB (

% ce

ll weig

ht);

Spor

es (s

pore

s/m

l x 4)

Grow

th (O

D560

); G

lucos

e (m

g x 10

4/L)

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.

94

0 3 6 9 12 18 240

2

4

6

8

10

12

14

16

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

Growth, Glucose metabolism and Biomarker Production over Time 500mg/L Glucose

FlagellinPhBSporesGrowthGlucose

Time (h)

Flage

llin (µ

g/m

l-2);

PhB (

% ce

ll weig

ht);

Spor

es (s

pore

s/m

l x 3)

Grow

th (O

D560

); G

lucos

e (m

g x 10

4/L)

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.

95

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 (OD560) 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).

96

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,

97

presents a requirement to experimentally assess the effects of amino acid metabolism with

regard to the primary objectives of the research project.

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.

98

Biomarker Nitrogen Limitation

Carbon Limitation

Phosphate 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

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.

99

NitrogenCarbon

Phosphate

0

20

40

60

80

100

120

140

160

Flagellin

PhB

Spores

The effect of carbon, nitrogen and phosphate limitation on biomarker production in C. sporogenes

FlagellinPhBSpores

Flage

llin (m

g/g x

106)

PhB (

mg/

g)Sp

ores

(spo

res/

500m

g x 10

3)

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.

100

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.

101

TrialVariables (Amino acids)

HIS SER PHE ALA ASP ASN ILEU VAL LEU GLU ARG

1 + + - + + + - - - + -2 - + + - + + + - - - +3 + - + + - + + + - - -4 - + - + + - + + + - -5 - - + - + + - + + + -6 - - - + - + + - + + +7 + - - - + - + + - + +8 + + - - - + - + + - +9 + + + - - - + - + + -

10 - + + + - - - + - + +11 + - + + + - - - + - +12 - - - - - - - - - - -

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.

102

PHE ARG LEU GLU ALA HIS SER VAL ILEU ASP ASN

-0.6

-0.4

-0.2

1.11022302462516E-16

0.2

0.4

0.6

Effect of amino acid supplementation on growth (OD560) of C.s-porogenes cultures assessed by Plackett-Burman Design

Signi

fican

ce o

f effe

ct (t

-valu

e)

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.

103

ILEU ASN ASP ALA VAL HIS GLU ARG SER LEU PHE

-0.6

-0.4

-0.2

1.11022302462516E-16

0.2

0.4

0.6

Effect of amino acid supplementation on PHB Accumulation (mg/g) of C.sporogenes cultures assessed by Plackett-Burman Design

Signifi

canc

e of

effe

ct (t

-value

)

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.

104

ALA HIS ASN GLU ASP ILEU SER VAL ARG PHE LEU

-0.6

-0.4

-0.2

1.11022302462516E-16

0.2

0.4

0.6

Effect of amino acid supplementation on Supernatant Flagellin (µg/ml) of C.sporogenes cultures assessed by Plackett-Burman De-

sign

Signi

fican

ce o

f effe

ct (t

-valu

e)

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.

105

ASP SER GLU ASN HIS ALA ARG PHE VAL ILEU LEU

-0.6

-0.4

-0.2

1.11022302462516E-16

0.2

0.4

0.6

Effect of amino acid supplementation on Sporulation(spores/ml) of C.sporogenes cultures assessed by Plackett-Burman Design

Signi

fican

ce o

f effe

ct (t

-valu

e)

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

106

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.

107

Figure 29a: Generic pathways of fuelling reactions and amino acid biosynthesis pathways

(blue arrows). Image adapted from Akashi & Gojobori, 2002 (Akashi & Gojobori, 2002).

108

Figure 29b: Central metabolic reactions and amino acid biosynthesis in C. botulinum, assuming anaerobic respiration is occurring over

fermentation. (KEGG: Kanehisa Laboratories, 2012).

109

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.

110

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.

111

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.

112

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).

113

0 500 1000 1500 2000 2500 30000

2

4

6

8

10

12

14

Effect of glucose flux on C. botulinum neurotoxin production (in silico)

Glucose Flux (arb. units)

Toxin

Flux

(arb

. uni

ts)

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).

114

3000 4000 5000 6000 7000 8000 9000 100000

5

10

15

20

25

30

35

Effect of Ammonium flux on C. botulinum neurotoxin production (in silico)

Ammonium Flux (arb. units)

Toxin

Flux

(arb

. uni

ts)

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,

115

0 1000 2000 3000 4000 5000 6000 7000 8000 90000

5

10

15

20

25

30

35

Effect of Phosphate flux on C. botulinum neurotoxin production (in silico)

Phosphate Flux (arb. units)

Toxin

Flux

(arb

. uni

ts)

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.

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.

116

Biomarker Low Nitrogen

LowCarbon

Low Phosphate

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

Toxin (in silico, FBA) - + +

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.

117

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).

118

0 300 600 900 1200 1500 1800 2100 2400 2700 3000 3300 3600 3900 4200 4500 480062006400660068007000720074007600780080008200

Effect of glucose flux on PHB production in C. botulinum (in silico)

Glucose Flux (arb. units)

PhB

Flux (

arb.

uni

ts)

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).

119

0 600 1200180024003000360042004800540060006600720078008400900096000

1000

2000

3000

4000

5000

6000

7000

8000

9000

Effect of Ammonium flux on PHB production in C. botulinum (in silico)

Ammonium Flux (arb. units)

PhB

Flux (

arb.

uni

ts)

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).

120

0 550 1100 1650 2200 2750 3300 3850 4400 4950 5500 6050 6600 7150 7700 82500

1000

2000

3000

4000

5000

6000

7000

8000

9000

Effect of Phosphate flux on PHB production in C. botulinum (in silico)

Phosphate Flux (arb. units)

PhB

Flux (

arb.

uni

ts)

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).

121

0 500 1000 1500 20000

10

20

30

40

0

1000

2000

3000

4000

5000

6000

7000

8000

9000 [2]

Toxin PHBPhenylalanine Flux (arb. units)

Toxi

n Flux

(arb

. unit

s)

PhB F

lux (a

rb. u

nits)

0 500 1000 1500 2000 2500 3000 350030

40

50

60

70

80

7000

7500

8000

8500

9000

9500

10000 [1]

Toxin PhBAlanine Flux (arb. units)

Toxin

Flux

(arb

. unit

s)

PhB

Flux (

arb.

units

)

30 35 40 45 50 55 60 65 70 75 807500

8000

8500

9000

9500

10000

10500

R² = 0.98802666357947

[3]

Toxin Flux (arb. units)

PhB F

lux (a

rb. u

nits)

0 5 10 15 20 25 304500

5500

6500

7500

8500

R² = 0.901064033337803

[4]

Toxin Flux (arb. units)

PhB F

lux (a

rb. u

nits)

The results generated by the GSMN suggest a strong linear correlation (R2 = 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.

122

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.

123

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.

124

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.

125

126

Figure 37: Network of high flux reactions - Poly-β-hydroxybutyrate (FVA)

- Significant reactions (2000-10000FVA) - Highly significant reactions (FVA >10000)

Coenzyme A

Alanine

O-Succinyl-L-Homoserine

L-Aspartate4-Semialdehyde

Succinate

acetoacetic acid

Succinyl-CoA

3-Acetoacetyl-CoA

Poly-β- hydroxybutyrate(S)-3-Hydroxybutyryl-CoA

HomoserineO-phospho-L-homoserine

Acetaldehyde

Glycerol

Threonine

L-4-Aspartyl PhosphateAspartic Acid

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).

127

128

IminoaspartateDihydroxy-acetone-phosphate

L-Glycerol-1-phosphate

Figure 38: Network of High Flux Reactions - Botulinum Toxin (FVA)

- Significant reactions (<1000FVA) - Highly significant reactions (FVA >1000)

Coenzyme A

Alanine

O-Succinyl-L-homoserine

L-Aspartate4-Semialdehyde

Succinate

acetoacetic acid

Succinyl-CoA

3-Acetoacetyl-CoA

Poly-β- hydroxybutyrate(S)-3-Hydroxybutyryl-CoA

HomoserineO-phospho-L- Homoserine

Acetaldehyde

GlycerolThreonine

L-4-Aspartyl PhosphateAspartic Acid

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.

129

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 37oC 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.

130

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.

131

BDM Threonine 7.5mM

Threonine 15mM

Homoserine 7.5mM

Homoserine 15mM

Aspartic Acid 7.5mM

Aspartic Acid 15mM

80

90

100

110

120

130

140

150

160

170

Effect of amino acid supplementation on PHB accumulation in cultures of C.sporogenes

PHB

(% of

BDM

value

)

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).

132

Aspartic acid Threonine Homoserine0

5

10

15

20

25

30

35

40

45

50

Effect of amino acid supplementation on PHB accumulation in cultures of C. sporogenes

PHB (

% inc

reas

e fro

m BD

M va

lue)

***

*

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

133

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).

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.

134

0 3 6 9 12 15 18 21 240

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

The effect of Homoserine supplemtation on the growth of C. sporogenes

BDM Homoserine 7.5mMHomoserine 15mM

Time (h)

Grow

th (O

D560

)

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.

135

Figure 42: Metabolic reactions essential for neurotoxin biosynthesis in C. botulinum (Kanehisa laboratories, 2013).

136

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).

137

IminoaspartateDihydroxy-acetone-phosphate

L-Glycerol-1-phosphate

Coenzyme A

Alanine

Succinate

Acetoacetic acid

Succinyl-CoA

3-Acetoacetyl-CoA

Poly-β- hydroxybutyrate(S)-3-Hydroxybutyryl-CoA

O-phospho-L- homoserine

Acetaldehyde

GlycerolThreonine

Aspartic Acid

Neurotoxin BiosynthesisAcetyl-CoA

Acetaldehyde

Glycerol-3-phosphate

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).

139

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.

140

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.

141

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).

142

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 & Rodriguez-

Valera, 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.

143

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.

144

BDM Threonine 7.5mM

Threonine 15mM

Homoserine 7.5mM

Homoserine 15mM

Aspartic Acid 7.5mM

Aspartic Acid 15mM

0

5

10

15

20

25

30

35

40

45

50

Glucose-6-phosphate dehydrogenase activity in cultures of C.s-porogenes supplemented with PHB increasing Amino Acids

G6PD

∆ (µ

mol

/mín

/mg)

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.

145

BDM Threonine 7.5mM

Threonine 15mM

Homoserine 7.5mM

Homoserine 15mM

Aspartic Acid 7.5mM

Aspartic Acid 15mM

0

2

4

6

8

10

12

14

Citrate Synthase activity in cultures of C.sporogenes supplemented with PHB increasing Amino Acids

Citra

te sy

ntha

se ∆

(µm

ol/m

ín/m

g)

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.

146

BDM Threonine 7.5mM

Threonine 15mM

Homoserine 7.5mM

Homoserine 15mM

Aspartic Acid 7.5mM

Aspartic Acid 15mM

0

2

4

6

8

10

12

14

16

18

20

Phosphoenolpyruvate carboxylase activity in cultures of C.sporogenes supplemented with PHB increasing Amino Acids

PEP

carb

oxyla

se ∆

(µm

ol/m

ín/m

g)

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.

147

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

148

suggest PHB biosynthesis, and consequentially neurotoxin biosynthesis, may be affected by

the ratio of fermentation and anaerobic respiration exhibited by the growing bacterial

culture.

149

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.

150

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.

151

TrialVariables (Amino acids)

PRO TYR PHE ALA ASP THE ILEU LYS LEU GLY ARG

1 + + - + + + - - - + -2 - + + - + + + - - - +3 + - + + - + + + - - -4 - + - + + - + + + - -5 - - + - + + - + + + -6 - - - + - + + - + + +7 + - - - + - + + - + +8 + + - - - + - + + - +9 + + + - - - + - + + -

10 - + + + - - - + - + +11 + - + + + - - - + - +12 - - - - - - - - - - -

THE ASP ILEU PRO LYS LEU ARG ALA PHE TYR GLY

-0.6

-0.4

-0.2

1.11022302462516E-16

0.2

0.4

0.6

Effect of amino acid supplementation on PHB Accumulation (mg/g) of C.sporogenes cultures assessed by Plackett-Burman Design

Signi

fican

ce o

f effe

ct (t

-valu

e)

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.

152

LEU PRO ARG THE ALA LYS ASP GLY TYR PHE ILEU

-0.6

-0.4

-0.2

1.11022302462516E-16

0.2

0.4

0.6

Effect of amino acid supplementation on G6PD activity (µmol/mín/mg)of C.sporogenes cultures assessed by Plackett-Burman Design

Signifi

canc

e of e

ffect

(t-va

lue)

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.

153

LEU LYS ARG PHE PRO TYR ALA GLY ILEU ASP THE

-0.6

-0.4

-0.2

1.11022302462516E-16

0.2

0.4

0.6

Effect of amino acid supplementation on Citrate Synthase activity (µmol/mín/mg)

of C.sporogenes cultures assessed by Plackett-Burman Design

Signifi

canc

e of e

ffect

(t-va

lue)

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.

154

ALA PHE PRO LYS ARG ASP LEU TYR GLY ILEU THE

-0.6

-0.4

-0.2

1.11022302462516E-16

0.2

0.4

0.6

Effect of amino acid supplementation on PEPc activity (µmol/mín/mg)of C.sporogenes cultures assessed by Plackett-Burman Design

Signifi

canc

e of e

ffect

(t-va

lue)

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.

155

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.

156

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.

157

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.

158

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.

159

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,

160

0 1200 2400 3600 4800 6000 7200 8400 9600 10800 12000 13200 14400 156003000

4000

5000

6000

7000

8000

9000

R² = 0.995012335618929

FBA assessing the effect of CO2 flux on PHB accumulation in C.botulinum (in silico)

CO2 flux (arb. units)

PhB F

lux (a

rb. u

nits)

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.

161

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.

162

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

163

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.

164

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).

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.

165

0.25 0.5 0.7550

60

70

80

90

100

110

120

130

Intracellular PHB Accumulation at specific growth rates in continuous cultures of C. sporogenes

Dilution (Growth) Rate (h-1)

PHB (

mg/g

)

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).

166

**

10% CO2 25% CO2 50% CO260

80

100

120

140

160

180

The effect of CO2 concentration on PHB accumulation in continuous cultures of C. sporogenes

PHB (

mg/g

)

10 25 500

20

40

60

80

100

120

140

160

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8R² = 0.974963048662975R² = 0.98425782644921

The effects of increased CO2 concentration on Biomass and PHB accumulation in chemostat culture of C. sporogenes

PhBLinear (PhB)Biomass

Fermentation Vessel CO2 Concentration (%)

PHB

(mg/

g)

Biom

ass (

g/L)

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.

167

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 490

1000

2000

3000

4000

5000

6000

7000

8000

0

500

1000

1500

2000

2500

3000

3500

R² = 0.975333932169653R² = 0.999999999731069

The effects of increased CO2 flux on Biomass and PHB accumulation in C. botulinum (in silico)

PhBLinear (PhB)Biomass

CO2 flux

PhB

Flux

Biom

ass fl

ux

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.

168

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

169

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.

170

10 25/35 50/700

100

200

300

400

500

600

60

80

100

120

140

160

180

200

220

The effect of CO2 on PHB and Botulinum neurotoxin in C.botulinum (Artin et al, 2008)

BoNT

PhB

CO2 gas composition (%)

Botu

linum

Neu

roto

xin (n

g/g)

PHB (

mg/

g)

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% CO2 compared

with 0% CO2 (100% oxygen-free nitrogen) with regards to either PHB or neurotoxin. R 2

(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.

171

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).

172

0 3 6 9 1215182124273033363942454851545760636669727578818487909396991500

1550

1600

1650

1700

1750

1800

Optimal CO2 concentration (%) to achieve maximum PHB & Neur-otoxin yield in a given culture volume

CO2 Flux (% composition)

Max

imum

PHB

(Bio

mas

s x P

HB fl

ux)

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 CO 2 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.

173

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 CO2

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+ + CO3

2-

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.

174

BDM 5g/L Bicarbonate 10g/L Bicarbonate 10mM Homoserine40

60

80

100

120

140

160

The effect of bicarbonate on PHB accumulation in continuous cultures of C. sporogenes

PHB (

mg/

g)

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).

175

*****

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.

176

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.

177

0.25 0.5 0.750

20

40

60

80

100

120

Flagellin biosynthesis at specific growth rates in continuous cultures of C. sporogenes

Dilution (Growth) Rate (h-1)

Flage

llin (µ

g/ml

)

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 non-

linear x-axis depicted.

178

***

10% 25% 50%0

20

40

60

80

100

120

R² = 0.969560621094145

The effect of CO2 concentration on flagellin biosynthesis in con-tinuous cultures of C. sporogenes

CO2 Concentration

Flage

llin (µ

g/m

l)

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.

179

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.

180

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.

181

0 3 6 9 12 18 240

2

4

6

8

10

12

14

16

0

0.1

0.2

0.3

0.4

0.5

0.6

Growth, Glucose metabolism and Flagellin biosynthesis over Time 4000mg/L Glucose

FlagellinGrowthGlu-cose

Time (h)

Flage

llin (µ

g/ml

-2)

Grow

th (O

D560

); Glu

cose

(mg x

104/

L)

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.

182

0 3 6 9 12 18 240

2

4

6

8

10

12

14

16

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

Growth, Glucose metabolism and Flagellin biosynthesis over Time 500mg/L Glucose

FlagellinGrowth

Time (h)

Flage

llin (m

g/m

l-2)

Grow

th (O

D560

); Glu

cose

(mg x

104/

L)

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.

183

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.

184

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.

185

0.25 0.5 0.750

200

400

600

800

1000

1200

Sporulation at specific growth rates in continuous cultures of C.sporogenes

Dilution (Growth) Rate (h-1)

Spor

ulatio

n (No

. spor

es/m

l)

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.

186

10% CO2 25% CO2 50% CO20

200

400

600

800

1000

1200

1400

1600

1800

2000

The effect of CO2 concentration on sporulation in continuous cultures of C. sporogenes

CO2 Concentration (%)

Sporu

lation

(No.

spores

/ml)

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.

187

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.

188

10 25 500

50

100

150

200

250

Effect of CO2 on amino acid consumption - aspartate group (oxaloacetate biosythetic pathways)

Aspartic acid

Isoleucine

Threonine

CO2 concentration (%)

Cons

umpti

on ra

te (m

g/L)

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 CO2

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.

189

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.

190

10 25 500

500

1000

1500

2000

2500

3000

3500

The effect of CO2 on total amino acid consumption in continuous culutres of C. sporogenes

CO2 concentration (%)

Amino

acid co

nsuptio

n (mg/L

)

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.

191

10 25 500

500

1000

1500

2000

2500

3000

The effect of CO2 on glucose and phosphate consumption in continuous cul-tures of C.sporogenes

Glucose consumption

Phosphate consumption

CO2 concentration (%)

Nutrie

nt Co

nsump

tion (

µg/g)

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 CO2 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% CO2, 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

192

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.

193

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.

194

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.

195

Biomass PhB Toxin0

1000

2000

3000

4000

5000

6000

7000

8000

9000

Flux comparison calculated by C. botulinum GSMN vs chemostat enhanced GSMN

Original C. botulinum Model

Chemostat data enhanced model

Objective Function

Flux (

MoleM

etab

olite-

1gBio

mass-

1)

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 CO2

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 CO2 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

196

(Koo et al, 2004) support the conclusion that both PHB and neurotoxin are largely affected

by the primary respiration mode of the growing culture.

197

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 CO2 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

198

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).

199

Sporulation increased linearly with increased CO2 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 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

200

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.

201

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).

202

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

203

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.

204

Appendix

Appendix I: Amino acid composition comparison of Botulinum toxin and flagellin

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).

205

Aspartic a

cid

Isoleu

cine

Glutamic a

cidLeu

cineSer

ine

Tyrosin

e

Threonine

Lysine

Valine

Phenylalan

ineGlyc

ine

Alanine

Proline

Arginine

Tryptophan

Methionine

Histidine

cystiene

02468

101214161820

Amino acid composition comparison of Botulinum toxin and flagellin

ToxinFlagellin

Amin

o ac

id co

mpo

sition

(%)

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